blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2 values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46 values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14 values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60 values | src_encoding stringclasses 24 values | language stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 7 9.18M | extension stringclasses 20 values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8f3615f123fe149b8fed8ef4f1ec63a165df5d4e | 34228cca7152d4e9b586d447ad744db7195a6e2f | /plot3.R | 57ac13244c04db83fde045522198932298e24512 | [] | no_license | dborisog/ExData_Plotting1 | 2401f7ea2685487b525ed0fc069164bd0807439d | 30e375441207cd9e0cd2ec3a2e2efee4d6a01f77 | refs/heads/master | 2020-04-05T18:55:51.866294 | 2014-05-11T20:08:27 | 2014-05-11T20:08:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 747 | r | plot3.R | setwd("C:/Users/user/gitrep/ExData_Plotting1")
source("preprocessedData.R")
##################
# make plot
##################
##### input ######
# NA
##################
##### output #####
# NA
makePlot3 <- function() {
dataSet <- importData()
png(filename = "plot3.png", width = 480, height = 480, units = "px")
cols = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
plot(dataSet$DateTime, dataSet$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering")
lines(dataSet$DateTime, dataSet$Sub_metering_2, type="l", col="red")
lines(dataSet$DateTime, dataSet$Sub_metering_3, type="l", col="blue")
legend("topright", lty=1, lwd=1, col=c("black","blue","red"), legend=cols)
dev.off()
}
makePlot3()
|
5313c58527a16ffd0626ef2e41beb424dd26662a | b9b74b11bd45b2a81566406576ebd9bfae9dc755 | /R/plots/f_F_plots.R | 0e448d5623996fd97679d4cae4bbd4d72068c027 | [
"MIT"
] | permissive | zejiang-unsw/nowcasting | de79f9fa90fef1affed01ca0830f1d4756854cfc | fd783618d35b0026f81f3fc7e04c6593d03d7ed0 | refs/heads/master | 2022-11-23T03:56:16.834661 | 2020-05-19T16:02:57 | 2020-05-19T16:02:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,044 | r | f_F_plots.R | ####################################################################
### Load Libraries
####################################################################
library(fields)
library(ncdf4)
library(RNetCDF)
library(xtable)
library(forecast)
library(plot3D)
library(ggplot2)
library(MASS)
library(mvtnorm)
####################################################################
### Souce Functions
####################################################################
# source('/Users/joshuanorth/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/Load_netCDF_Data.R', chdir = TRUE)
# source('/Users/joshuanorth/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/Nowcasting_Function.R', chdir = TRUE)
# source('/Users/joshuanorth/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/Finite_Difference_Function.R', chdir = TRUE)
# source('/Users/joshuanorth/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/Move_Functions.R', chdir = TRUE)
# source('/Users/joshuanorth/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/Error_Function.R', chdir = TRUE)
# source('/Users/joshuanorth/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/Prediction_Functions.R', chdir = TRUE)
source('R/Load_netCDF_Data.R', chdir = TRUE)
source('R/Nowcasting_Function.R', chdir = TRUE)
source('R/Finite_Difference_Function.R', chdir = TRUE)
source('R/Move_Functions.R', chdir = TRUE)
source('R/Error_Function.R', chdir = TRUE)
source('R/Prediction_Functions.R', chdir = TRUE)
####################################################################
### Compile Data
####################################################################
# bigD <- load.netCDF.data(filepath = '~/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/lightning_data')
#
# run <- read.csv('~/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/Movement_Data.csv') # mac
# run <- run[,2:5] # drop first column
#
# move.perc <- read.csv('~/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/data/predict_data.csv', header = TRUE)
# col.prec <- read.csv('~/Dropbox/Nowcasting/FDAWLM_USE_THIS_ONE/Final/data/column_predict.csv', header = TRUE)
bigD <- load.netCDF.data(filepath = 'data')
run <- read.csv('movement_data/Movement_Data.csv')
run <- run[,2:5] # drop first column
move.perc <- read.csv('movement_data/predict_data.csv', header = TRUE)
col.prec <- read.csv('movement_data/column_predict.csv', header = TRUE)
# get nowcasts ----------------------------------------------------------------
# timestep 10
begin = 10
# With Q
q <- bigD[,,begin] - bigD[,,(begin-1)]
q <- q/100
est <- nowcast(bigD = bigD, tolerance = 0, iteration = 10,
number = 10, timestep = begin, num = 10,
nframes = 20, Q = q, r = 0.15)
est10 <- ifelse(est < 0.0, 0, est)
est10 <- ifelse(est10 > 1.6, 1.6, est10)
# timestep 20
begin = 20
# With Q
q <- bigD[,,begin] - bigD[,,(begin-1)]
q <- q/100
est <- nowcast(bigD = bigD, tolerance = 0, iteration = 10,
number = 10, timestep = begin, num = 10,
nframes = 20, Q = q, r = 0.15)
est20 <- ifelse(est < 0.0, 0, est)
est20 <- ifelse(est20 > 1.6, 1.6, est20)
# timestep 30
begin = 30
# With Q
q <- bigD[,,begin] - bigD[,,(begin-1)]
q <- q/100
est <- nowcast(bigD = bigD, tolerance = 0, iteration = 10,
number = 10, timestep = begin, num = 10,
nframes = 20, Q = q, r = 0.15)
est30 <- ifelse(est < 0.0, 0, est)
est30 <- ifelse(est30 > 1.6, 1.6, est30)
# timestep 40
begin = 40
# With Q
q <- bigD[,,begin] - bigD[,,(begin-1)]
q <- q/100
est <- nowcast(bigD = bigD, tolerance = 0, iteration = 10,
number = 10, timestep = begin, num = 10,
nframes = 20, Q = q, r = 0.15)
est40 <- ifelse(est < 0.0, 0, est)
est40 <- ifelse(est40 > 1.6, 1.6, est40)
# timestep 50
begin = 50
# With Q
q <- bigD[,,begin] - bigD[,,(begin-1)]
q <- q/100
est <- nowcast(bigD = bigD, tolerance = 0, iteration = 10,
number = 10, timestep = begin, num = 10,
nframes = 20, Q = q, r = 0.15)
est50 <- ifelse(est < 0.0, 0, est)
est50 <- ifelse(est50 > 1.6, 1.6, est50)
# plot nowcasts one -----------------------------------------------------------
mid_col = 'gray97'
png("/Users/joshuanorth/Desktop/f_vs_F.png", height = 2500, width = 1250)
layout.matrix <- matrix(c(1:10, 11, 11), nrow = 6, ncol = 2, byrow = T)
layout(mat = layout.matrix,
heights = c(rep(1.5, 5), 0.5), # Heights of the two rows
widths = rep(1.5, 2)) # Widths of the two columns
# layout.show(11)
par(mar = c(4,8,6,4),
oma = c(4,1,1,1))
# first row
# 10
# par(mfrow = c(1,2))
image(est10[,,1] - bigD[,,10], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), main = "F(s,t) - f(s,t-1)", ylab = 't = 11',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,11] - bigD[,,10], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), main = "f(s,t) - f(s,t-1)",
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# par(mfrow = c(1,2))
# image.plot(est10[,,1] - bigD[,,10], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# image.plot(bigD[,,10] - bigD[,,9], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# second row
# 20
# par(mfrow = c(1,2))
image(est20[,,1] - bigD[,,20], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 21',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,21] - bigD[,,20], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# par(mfrow = c(1,2))
# image.plot(est20[,,1] - bigD[,,20], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# image.plot(bigD[,,20] - bigD[,,19], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# third row
# 30
# par(mfrow = c(1,2))
image(est30[,,1] - bigD[,,30], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 31',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,31] - bigD[,,30], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# par(mfrow = c(1,2))
# image.plot(est30[,,1] - bigD[,,30], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# image.plot(bigD[,,30] - bigD[,,29], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# fourth row
# 40
# par(mfrow = c(1,2))
image(est40[,,1] - bigD[,,40], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 41',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,41] - bigD[,,40], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# par(mfrow = c(1,2))
# image.plot(est40[,,1] - bigD[,,40], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# image.plot(bigD[,,40] - bigD[,,39], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# fifth row
# 50
# par(mfrow = c(1,2))
image(est50[,,1] - bigD[,,50], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 51',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,51] - bigD[,,50], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# par(mfrow = c(1,2))
# image.plot(est50[,,1] - bigD[,,50], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# image.plot(bigD[,,50] - bigD[,,49], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue'))
# sixth row
image(x = seq(-1.6, 1.6, length.out = 256), z = t(t(seq(-1.6, 1.6, length.out = 256))), col = two.colors(start = 'blue', mid = mid_col), xlab='', axes = F)
axis(1, at = seq(-1.6, 1.6, 0.4), cex.axis = 4, outer = T, col = NA)
dev.off()
# plot nowcasts two -----------------------------------------------------------
mid_col = 'gray97'
png("/Users/joshuanorth/Desktop/f_vs_F_two.png", height = 2500, width = 1250)
layout.matrix <- matrix(c(1:10, 11, 11), nrow = 6, ncol = 2, byrow = T)
layout(mat = layout.matrix,
heights = c(rep(1.5, 5), 0.5), # Heights of the two rows
widths = rep(1.5, 2)) # Widths of the two columns
# layout.show(11)
par(mar = c(4,8,6,4),
oma = c(4,1,1,1))
# first row
# 10
# par(mfrow = c(1,2))
image(est10[,,1] - bigD[,,10], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), main = "F(s,t+1) - f(s,t)", ylab = 't = 10',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,10] - bigD[,,9], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), main = "f(s,t) - f(s,t-1)",
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# second row
# 20
# par(mfrow = c(1,2))
image(est20[,,1] - bigD[,,20], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 20',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,20] - bigD[,,19], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# third row
# 30
# par(mfrow = c(1,2))
image(est30[,,1] - bigD[,,30], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 30',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,30] - bigD[,,29], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# fourth row
# 40
# par(mfrow = c(1,2))
image(est40[,,1] - bigD[,,40], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 40',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,40] - bigD[,,39], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# fifth row
# 50
# par(mfrow = c(1,2))
image(est50[,,1] - bigD[,,50], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 50',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,50] - bigD[,,49], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# sixth row
image(x = seq(-1.6, 1.6, length.out = 256), z = t(t(seq(-1.6, 1.6, length.out = 256))), col = two.colors(start = 'blue', mid = mid_col), xlab='', axes = F)
axis(1, at = seq(-1.6, 1.6, 0.4), cex.axis = 4, outer = T, col = NA)
dev.off()
# plot nowcasts three -----------------------------------------------------------
mid_col = 'gray97'
png("/Users/joshuanorth/Desktop/f_vs_F_three.png", height = 2500, width = 1250)
layout.matrix <- matrix(c(1:10, 11, 11), nrow = 6, ncol = 2, byrow = T)
layout(mat = layout.matrix,
heights = c(rep(1.5, 5), 0.5), # Heights of the two rows
widths = rep(1.5, 2)) # Widths of the two columns
# layout.show(11)
par(mar = c(4,8,6,4),
oma = c(4,1,1,1))
# first row
# 10
# par(mfrow = c(1,2))
image(bigD[,,11] - est10[,,1], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), main = "f(s,t) - F(s,t)", ylab = 't = 11',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,11] - bigD[,,10], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), main = "f(s,t) - f(s,t-1)",
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# second row
# 20
# par(mfrow = c(1,2))
image(bigD[,,21] - est20[,,1], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 21',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,21] - bigD[,,20], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# third row
# 30
# par(mfrow = c(1,2))
image(bigD[,,31] - est30[,,1], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 31',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,31] - bigD[,,30], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# fourth row
# 40
# par(mfrow = c(1,2))
image(bigD[,,41] - est40[,,1], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 41',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,41] - bigD[,,40], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# fifth row
# 50
# par(mfrow = c(1,2))
image(bigD[,,51] - est50[,,1], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col), ylab = 't = 51',
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
image(bigD[,,51] - bigD[,,50], zlim = c(-1.6, 1.6), col = two.colors(start = 'blue', mid = mid_col),
axes = F, cex.lab = 5, cex.main = 5, font.lab = 2)
# sixth row
image(x = seq(-1.6, 1.6, length.out = 256), z = t(t(seq(-1.6, 1.6, length.out = 256))), col = two.colors(start = 'blue', mid = mid_col), xlab='', axes = F)
axis(1, at = seq(-1.6, 1.6, 0.4), cex.axis = 4, outer = T, col = NA)
dev.off()
|
7314afb225b9d474d851b52087debe7f91a85706 | 3da2b08ad47a140738bbb484a1429b523caa54b6 | /Sim2ClusterPS.R | 73487549419de6606011246728dad139326dbb92 | [] | no_license | knickodem/AggCovsForPS | f83d4156562dcbcb89eea53b67ef5e220a4b1178 | 7e97520b95532fedc6af893e529fdf423672352f | refs/heads/master | 2022-10-16T07:56:44.001412 | 2020-06-08T18:21:54 | 2020-06-08T18:21:54 | 245,267,187 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 23,314 | r | Sim2ClusterPS.R | ##################################################################################
# #
# Simulation 2 - Cluster-Level Treatment Exposure and Subject-Level Outcome #
# #
##################################################################################
## Loading Packages
library(simstudy)
library(tictoc)
library(dplyr)
library(tidyr)
library(MatchIt)
library(cobalt)
library(lme4)
##########################################
#### Defining Conditions and Outcomes ####
## Study Conditions
DataGenConds <- crossing(nsub = c(20, 60, 100), # number of subjects per cluster
nclus = c(60, 100, 140), # number of clusters
icc = c(.05, .1, .2), # intraclass correlation [ICC(1)] for outcome, Y, and reflective L2 aggregations
aggvar = c(.1, .3, .5, 1), # error/1-% sampled in observed L2 covariates
psmod = c("Cluster","Subject"), # PS estimation and conditioning at cluster or subject level
mw = c("Matching", "Weighting")) # PS conditioning method
nreps <- 1000 # number of replications
## Simulation dependent variables
DVnames <- c("Prop_Treated", "PS_Converged", "Baseline_True.Agg_L2_Cor", # Proportion in treatment group and Yes/No for PS model convergence
"PS_Bias", "PS_MAE", "PS_RMSE", "Logit_Bias", "Logit_MAE", "Logit_RMSE", # Bias and Error in estimation of PS and logit of PS
"Analytic_Subjects", "Analytic_Clusters", "True.Agg_L2_Cor", # Num subjects & clusters and correlation b/t true and obs L2 in sample used in outcome model
"Y_ICC1", "X_ICC1", "X_ICC2", # Characteristics of sample used in outcome analysis
"Un.ASD", "Un.VR", "Un.ASD_Balanced_Count", "Un.VR_Balanced_Count", # For unadjusted sample - mean Absolute standardized difference & Variance ratio; Count of the 30 covariates balanced (on ASD or VR) at subject level
"Adj.ASD", "Adj.VR", "Adj.ASD_Balanced_Count", "Adj.VR_Balanced_Count", # For adjusted sample - mean ASD & VR; Count of the 30 covariates balanced (on ASD or VR) at subject level
"Y_Converged", "TE_Bias", "TE_MAE", "TE_RMSE", # Yes/No for Outcome model convergence; If yes, Bias, MAE and RMSE for estimation of the treatment effect
"Baseline_Converged", "Baseline_Bias", "Baseline_MAE", "Baseline_RMSE") # TE estimation from full (unadjusted) sample
## Creating blank matrix for summary statistics for each data generation condition
DVSummaryStats <- matrix(-999, ncol = length(DVnames), nrow = nrow(DataGenConds)) # Summary stats for generated datasets
## Variables consistent across all conditions
delta <- .50 # Average Treatment Effect ($\delta$)
marg <- -1.386294 # marginal probability of treatment;intercept of -1.0986 produces a marginal probability of .25; stats::plogis(-1.0986); -1.386294 = .2
L1names <- paste0("x", 1:20) # names of Level 1 covariates
trueL2names <- paste(L1names[1:10], "c", sep = "_") # names of true level 2 covariates
obsL2names <- paste(trueL2names, "o", sep = "_") # names of aggregated level 2 covariates
############################
#############################
#### Running Simulation ####
set.seed(1213611) # Seed for reproducing results
tic("Total")
for(con in 1:nrow(DataGenConds)){
tic(as.character(con)) # Record time to run all replications for each condition
## Defining characteristics of the simulation condition
nsub <- DataGenConds[con,][[1]] # Number of Level 1 units (i.e., subjects)
nclus <- DataGenConds[con,][[2]] # Number of Level 2 units (i.e., clusters)
ntot <- nclus*nsub # Total sample size
tau00 <- DataGenConds[con,][[3]] # between cluster (L2) variance ($\tau_{00}$)
sigma2 <- 1 - tau00 # within cluster (L1) variance ($\sigma^2$)
aggvar <- DataGenConds[con,][[4]] # error for creating observed L2 covariates
psmod <- DataGenConds[con,][[5]] # PS estimation and equating at cluster or subject level
mw <- DataGenConds[con,][[6]] # PS equating method
## Dependent Variable (DV) values for each replication of a condition
DVReplicationStats <- matrix(-999, ncol = length(DVnames), nrow = nreps)
for(r in 1:nreps){
##########################
#### Generating data ####
#### Formative-RE: Generate L1, aggregate true L2, then add random error for observed L2 ####
## Level 1 covariates - 20 continuous covariates from a standard normal distribution with correlation of .2
GenData <- genCorData(n = ntot, mu = rep(0, 20), sigma = sqrt(sigma2), rho = 0.2, corstr = "cs",
cnames = L1names, idname = "sid") %>%
mutate(rij = rnorm(n = ntot, mean = 0, sd = sqrt(sigma2)), # random error for outcome model from normal distribution (i.e, $\mu$ = 0, $\sigma^2$ = 1)
cid = as.character(rep(1:nclus, each = nsub))) %>% # creating cluster ID
group_by(cid) %>%
mutate_at(vars(x1:x20), ~. + rnorm(n = 1, mean = 0, sd = sqrt(tau00))) %>% # Adding cluster variation to maintain ICC(1)
mutate_at(vars(x1:x10), list(c = ~mean(.))) %>% # aggregating L1 to true L2 value
mutate_at(vars(x1_c:x10_c), list(o = ~. + rnorm(n = 1, mean = 0, sd = aggvar))) %>% # Adding random variation to true cluster mean; selects a random number for each cluster for each covariate
mutate(zuj = rlogis(n = 1, location = 0, scale = 1), # random error for PS model (probability of treatment) from logistic distribution with mean = 0 and variance of $\pi^2 / 3$
uj = rnorm(n = 1, mean = 0, sd = sqrt(tau00))) %>% # random error for outcome model from normal distribution (i.e, $\mu$ = 0, $\tau_{00} depends on condition)
ungroup() %>%
mutate(TrueLogit = marg + 5*x1_c + .5*x2_c + .5*x3_c + .5*x4_c + .5*x5_c + # True logit of the probability of treatment exposure (i.e. propensity score)
.5*x6_c + .5*x7_c + .5*x8_c + .5*x9_c + .5*x10_c + zuj,
TruePS = stats::plogis(TrueLogit), # true PS
z = ifelse(TrueLogit > 0, 1, 0), # Determining treatment exposure
Yij = .5*x1 + .5*x2 + .5*x3 + .5*x4 + .5*x5 + .5*x6 + .5*x7 + .5*x8 + .5*x9 + .5*x10 + # Generating outcome values
.5*x11 + .5*x12 + .5*x13 + .5*x14 + .5*x15 + .5*x16 + .5*x17 + .5*x18 + .5*x19 + .5*x20 + rij + # Level 1 covariates
.5*x1_c + .5*x2_c + .5*x3_c + .5*x4_c + .5*x5_c +
.5*x6_c + .5*x7_c + .5*x8_c + .5*x9_c + .5*x10_c + delta*z + uj, # Level 2 covariates
cid = factor(cid)) # converting cluster id from character to factor
#### Estimating PS ####
if(psmod == "Cluster"){
## Model appraises treatment at the cluster-level and only uses L2 covariates
ThePSModel <- paste0("z ~ 1 + ", paste(obsL2names, collapse = " + "))
PS.mod <- glm(as.formula(ThePSModel), data = GenData, family = binomial("logit"))
} else if(psmod == "Subject"){
## Model ignores clusters and uses L1 and L2 covariates, the latter conceptually treated as L1 covariates
ThePSModel <- paste0("z ~ 1 + ", paste(L1names, collapse = " + "), " + ", paste(obsL2names, collapse = " + "))
PS.mod <- glm(as.formula(ThePSModel), data = GenData, family = binomial("logit"))
} else {stop("something went wrong")}
## Did the PS model converge?
PSModConverge <- ifelse(PS.mod$converged == TRUE, 1, 0)
## Adding estimated PS to dataframe
GenData$PS <- fitted(PS.mod)
GenData$Logit <- predict(PS.mod) # observed logit of the PS
GenData$LogitDiff <- GenData$Logit - GenData$TrueLogit # Error in observed (estimated) and true logit of the PS
GenData$PSDiff <- GenData$PS - GenData$TruePS # Error in observed and true PS
## Saving DVs
DVReplicationStats[r,1] <- mean(GenData$z) # proportion with treatment exposure
DVReplicationStats[r,2] <- PSModConverge # PS model convergence
#### Checking Overlap assumption ####
## Removing cases where PS is equal to 1 or 0
AnalyticSample <- GenData %>%
filter(PS > .001 & PS < .999)
if(PSModConverge == 0 | nrow(AnalyticSample) == 0){
## Don't run conditioning and outcome model. Make remaining DV values NA
DVReplicationStats[r,3:31] <- NA
} else {
#### Calculating the pre-conditioning DVs that utilize the full sample ####
DVReplicationStats[r,3] <- purrr::map2_dbl(.x = trueL2names, .y = obsL2names,
~cor(GenData[,.x],GenData[,.y])) %>% # correlation b/t true and observed L2 covariates in full sample
psych::fisherz() %>% ifelse(. > 7.3, 7.3, .) %>% ifelse(. < -7.3, -7.3, .) %>% mean() # z = +- 7.3 is equivalent to r = .9999991; when r = 1, z = Inf which throws of calculation of mean correlation
DVReplicationStats[r,4] <- mean(GenData$PSDiff) # bias of PS
DVReplicationStats[r,5] <- mean(abs(GenData$PSDiff)) # mae of PS
DVReplicationStats[r,6] <- sqrt(mean(GenData$PSDiff^2)) # rmse of PS
DVReplicationStats[r,7] <- mean(GenData$LogitDiff) # bias of logit
DVReplicationStats[r,8] <- mean(abs(GenData$LogitDiff)) # mae of logit
DVReplicationStats[r,9] <- sqrt(mean(GenData$LogitDiff^2)) # rmse of logit
#################################
######################################
#### Analyzing Generated Datasets ####
#### Conditioning on the PS ####
if(mw == "Matching"){
if(psmod == "Cluster"){
## Extracting cluster-level data for matching; i.e., if nclus = 100, nrow(ClusterData) = 100 unless a cluster had a PS == 0|1
ClusterData <- AnalyticSample %>%
select(cid, one_of(obsL2names), z, PS, Logit) %>%
unique()
## Running matching algorithm - 1:1 nearest neighbor w/o replacement with caliper of .2 SD of the logit of the PS
tryCatch(expr = {TheMatches <- matchit(as.formula(ThePSModel), data = ClusterData,
method = "nearest", replace = FALSE, caliper = .2,
distance = ClusterData$Logit)},
error = function(e){TheMatches <<- TRUE; return(TheMatches)})
## Extracting the weights or assigning weights to 0 if no matches
if(class(TheMatches) == "matchit"){
ClusterData$weight <- TheMatches$weights
} else {
ClusterData$weight <- 0
}
## Adding weight to subject-level data
AnalyticSample <- inner_join(AnalyticSample, ClusterData %>% select(cid,weight), by = "cid")
} else if(psmod == "Subject"){
## Running matching algorithm - 1:1 nearest neighbor w/o replacement with caliper of .2 SD of the logit of the PS
# Catch error if one should occur
tryCatch(expr = {TheMatches <- matchit(as.formula(ThePSModel), data = AnalyticSample,
method = "nearest", replace = FALSE, caliper = .2,
distance = AnalyticSample$Logit)},
error = function(e){TheMatches <<- TRUE; return(TheMatches)})
## Extracting the weights or assigning weights to 0 if no matches
if(class(TheMatches) == "matchit"){
AnalyticSample$weight <- TheMatches$weights
} else {
AnalyticSample$weight <- 0
}
} else {stop("something went wrong")}
} else if(mw == "Weighting"){
AnalyticSample$weight <- WeightIt::get_w_from_ps(ps = AnalyticSample$PS, estimand = "ATE",
treat = AnalyticSample$z, treated = 1)
} else {stop("something went wrong")}
## If no matches occured
if(sum(AnalyticSample$weight) == 0){
## Don't run conditioning and outcome model. Make remaining DV values NA
DVReplicationStats[r,2] <- 0 # Change PS convergence code to 0
DVReplicationStats[r,3:31] <- NA # make remaining DV values NA
} else {
## With weighting or when matching was successful
#### Assessing Balance ####
## Balance is only evaluated at the subject-level in accordance with WWC guidelines, but includes all covariates
TheBalance <- bal.tab(formula = as.formula(paste0("z ~ 1 + ", paste(L1names, collapse = " + "), " + ", paste(obsL2names, collapse = " + "))),
data = AnalyticSample,
continuous = "std", binary = "std", s.d.denom = "pooled",
abs = TRUE, un = TRUE, quick = TRUE,
disp.means = FALSE, disp.sd = FALSE, disp.v.ratio = TRUE,
weights = AnalyticSample$weight, method = tolower(mw))
## Calculating ICC(1) and (2) of 10 aggregated covariates
ICC1 <- purrr::map_dbl(paste0("x",1:10), ~ICC::ICCbare(factor(cid), quo_name(.x), AnalyticSample[AnalyticSample$weight != 0,])) %>% # ICC(1) of aggregated covariates
ifelse(. < 0, 0, .) # negatives constrained to 0
ICC2 <- (nsub*ICC1) / (1 + (nsub - 1)*ICC1)
#### Calculating the post-conditioning DVs for each replication ####
DVReplicationStats[r,10] <- nrow(AnalyticSample[AnalyticSample$weight != 0,]) # Number of subjects in analytic sample
DVReplicationStats[r,11] <- unique(AnalyticSample[AnalyticSample$weight != 0,]$cid) %>% length() # Number of clusters in analytic sample
DVReplicationStats[r,12] <- purrr::map2_dbl(.x = trueL2names, .y = obsL2names,
~cor(AnalyticSample[AnalyticSample$weight != 0, .x],
AnalyticSample[AnalyticSample$weight != 0, .y])) %>% # correlation b/t true and observed L2 covariates
psych::fisherz() %>% ifelse(. > 7.3, 7.3, .) %>% ifelse(. < -7.3, -7.3, .) %>% mean() # z = +- 7.3 is equivalent to r = .9999991; when r = 1, z = Inf which throws of calculation of mean correlation
DVReplicationStats[r,13] <- ICC::ICCbare(factor(cid), Yij, AnalyticSample[AnalyticSample$weight != 0,]) %>%
ifelse(. < 0, 0, .) # ICC(1) of outcome Yij (negatives constrained to 0)
DVReplicationStats[r,14] <- mean(ICC1) # mean ICC(1) of L1 X covariates
DVReplicationStats[r,15] <- mean(ICC2) # mean ICC(2) of L2 X covariates
DVReplicationStats[r,16] <- mean(TheBalance[[1]]$Diff.Un) # mean Absolute Standardized Difference (ASD) before conditioning (Unadjusted)
DVReplicationStats[r,17] <- mean(TheBalance[[1]]$V.Ratio.Un) # mean Variance Ration (VR) before conditioning (Unadjusted)
DVReplicationStats[r,18] <- sum(TheBalance[[1]]$Diff.Un < .10) # Unadjusted count of covariates (out of 30) that were balanced at L1 based on ASD
DVReplicationStats[r,19] <- sum(TheBalance[[1]]$V.Ratio.Un < 2) # Unadjusted count of covariates (out of 30) that were balanced at L1 based on VR
DVReplicationStats[r,20] <- mean(TheBalance[[1]]$Diff.Adj) # mean ASD after conditioning (Adjusted)
DVReplicationStats[r,21] <- mean(TheBalance[[1]]$V.Ratio.Adj) # mean VR after conditioning (Adjusted)
DVReplicationStats[r,22] <- sum(TheBalance[[1]]$Diff.Adj < .10) # Adjusted count of covariates (out of 30) that were balanced at L1 based on ASD
DVReplicationStats[r,23] <- sum(TheBalance[[1]]$V.Ratio.Adj < 2) # Adjusted count of covariates (out of 30) that were balanced at L1 based on VR
#### Estimating Treatment Effect ####
## The Outcome Model
TheOutcomeModel <- paste0("Yij ~ 1 + z + ", paste(L1names, collapse = " + "), " + ", paste(obsL2names, collapse = " + "), " + (1|cid)")
# Running model and catching errors if necessary
tryCatch(expr = {Out.mod <- lmer(as.formula(TheOutcomeModel), data = AnalyticSample[AnalyticSample$weight != 0, ], # removes observations where weight = 0 (these were the unmatched observations)
weights = AnalyticSample[AnalyticSample$weight != 0, ]$weight)},
error = function(e){Out.mod <<- TRUE; return(Out.mod)})
if(class(Out.mod) == "lmerMod"){
## Did the Outcome model converge?
OutModConverge <- ifelse(is.null(Out.mod@optinfo$conv$lme4$code), 1, 0)
} else{
## Did the Outcome model converge?
OutModConverge <- 0
}
## Recording Outcome model convergence
DVReplicationStats[r,24] <- OutModConverge
if(is.logical(Out.mod)){
## Don't calculate TE and make remaining DV values NA
DVReplicationStats[r,25:31] <- NA
} else if(OutModConverge == 0 | !("z" %in% attr(fixef(Out.mod), "names"))){
## Don't calculate TE and make remaining DV values NA
DVReplicationStats[r,25:31] <- NA
} else {
## Difference between treatment effect estimate and true delta (i.e. bias)
TEDiff <- fixef(Out.mod)[["z"]] - delta
#### Treatment Effect DVs for each replication from outcome model ####
DVReplicationStats[r,25] <- TEDiff # bias of TE
DVReplicationStats[r,26] <- abs(TEDiff) # mae of TE
DVReplicationStats[r,27] <- TEDiff^2 # rmse of TE
#### Estimating Treatment Effect with full sample ####
## Establishes baseline to which the PS methods can be compared
# (i.e, is the PS even worth it)
Base.mod <- lmer(as.formula(TheOutcomeModel), data = GenData)
## Did the Baseline model converge?
BaseModConverge <- ifelse(is.null(Base.mod@optinfo$conv$lme4$code), 1, 0)
DVReplicationStats[r,28] <- BaseModConverge # Baseline model convergence
if(BaseModConverge == 0 | !("z" %in% attr(fixef(Base.mod), "names"))){
## Don't calculate TE
DVReplicationStats[r,29:31] <- NA
} else {
## Difference between treatment effect estimate from baseline model and the true delta (i.e. bias)
BaseTEDiff <- fixef(Base.mod)[["z"]] - delta
#### Treatment Effect DVs for each replication ####
DVReplicationStats[r,29] <- BaseTEDiff # bias of TE from baseline model
DVReplicationStats[r,30] <- abs(BaseTEDiff) # mae of TE from baseline model
DVReplicationStats[r,31] <- BaseTEDiff^2 # rmse of TE from baseline model
} # ends BaseModConverge evaluation
} # ends OutModConverge evaluation
} # ends TheMatches evaluation (i.e., when no matches, skip remaining analysis)
} # ends PSModConverge evaluation
} # ends replication
## logging time to run condition
toc(quiet = TRUE, log = TRUE)
#### DVs averaged across replications ####
DVSummaryStats[con, ] <- colMeans(DVReplicationStats, na.rm = TRUE)
if(con == 1){
Con1GenDatP <- GenData # saves dataset from the 1000th rep
Con1AnalyticSampleP <- AnalyticSample # saves dataset from the 1000th rep
Con1DVReplicationStatsP <- DVReplicationStats
}
}
toc()
#################################
#### Saving Initial Results ####
save(DVSummaryStats, file = "Sim2_ThousandRep_DVSummaryOnly_Pooled.RData")
colnames(DVSummaryStats) <- DVnames
Sim2DVsbyCondPooled <- data.frame(DVSummaryStats, stringsAsFactors = FALSE) %>%
mutate_all(as.numeric) %>% # Converting from character to numeric
mutate(Logit_RMSE = sqrt(Logit_RMSE),
TE_RMSE = sqrt(TE_RMSE),
Baseline_RMSE = sqrt(Baseline_RMSE),
True.Agg_L2_Cor = psych::fisherz2r(True.Agg_L2_Cor), # Converting z-scores to correlations
Baseline_True.Agg_L2_Cor = psych::fisherz2r(Baseline_True.Agg_L2_Cor)) %>% # Converting z-scores to correlations
tibble::rownames_to_column("Con") %>%
left_join(DataGenConds %>% tibble::rownames_to_column("Con"), by = "Con") %>%
select(-Con)
#### Extracting timing ####
# Character vector
Sim2TimingLog <- tic.log(format = TRUE) %>% unlist()
# Converted to dataframe and cleaned for analysis
Sim2TLogDFPooled <- data.frame(temp = Sim2TimingLog, stringsAsFactors = FALSE) %>%
separate(temp, c("Row", "Time"), sep = ": ") %>%
mutate(Time = as.numeric(gsub(" sec elapsed", "", Time, fixed = TRUE)))
# Total time (in min) to run the simulation
sum(Sim2TLogDFPooled$Time) / 60
# Clearing time log
tic.clearlog()
#### Saving Simulation Results ####
## Intermediate information from condition 432
Con432GenDatP <- GenData # saved sample data from last rep of condition 432
Con432AnalyticSampleP <- AnalyticSample # saved summary statistics for each rep of condition 432
Con432DVReplicationStatsP <- data.frame(DVReplicationStats, stringsAsFactors = FALSE)
save(Con432GenDatP, Con432AnalyticSampleP, Con432DVReplicationStatsP,
Con1GenDatP, Con1AnalyticSampleP, Con1DVReplicationStatsP,
DVSummaryStats, Sim2DVsbyCondPooled, Sim2TLogDFPooled, DVnames,
file = "Sim2_ThousandRep_Pooled.RData")
##################################################
|
4ef3a25c4270b6d0ef49b9ff79f8b0618a48ad4f | b48ea7f06b12d71fe597adefa5da35e81d08caf8 | /inst/examples/20-api/server.R | 16cc51dee182452bb5852c247e33d130b10b7cab | [
"MIT"
] | permissive | shinyTree/shinyTree | c840dd94af787e15cce6b7c0d4b73b2537123b8a | 110b49970d117d0638746d47b074e955287abed0 | refs/heads/master | 2023-08-30T22:50:33.875203 | 2023-08-07T15:53:07 | 2023-08-07T15:53:07 | 22,565,116 | 75 | 42 | NOASSERTION | 2023-02-08T10:37:30 | 2014-08-03T02:44:13 | JavaScript | UTF-8 | R | false | false | 543 | r | server.R | library(shiny)
library(shinyjs)
library(shinyTree)
#' Define custom JS functions to implement jsTree core functionality
#' @author McClelland Legge \email{McClelland.Legge@@gmail.com}
shinyServer(function(input, output, session) {
observeEvent(input$reset, {
js$resetTree("tree")
})
output$tree <- renderTree({
list(
root1 = "123",
root2 = list(
SubListA = list(leaf1 = "", leaf2 = "", leaf3 = ""),
SubListB = structure(list(leafA = "", leafB = ""), stselected = TRUE)
)
)
})
}) |
8d19eb8ca5e94c0fed3d7f9f156b663c3a3f2d2f | e8fdbae58c34c896736babebcf93048a0969d619 | /src/R/experiments/bootstrapping.R | b6865bf64c5bed036d1db196b0cc37549d0e8960 | [] | no_license | emilysturdivant/biomass-espanola | cb8c71433bab50f4a9f447005e5654e680e9add1 | 3cc020614b2bdaf53eddaf3f4eef92ac12e03436 | refs/heads/master | 2021-12-23T23:34:29.977417 | 2021-11-25T19:56:37 | 2021-11-25T19:56:37 | 225,423,106 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,605 | r | bootstrapping.R | # Script where I experiment with cross-validation and bootstrapping to measure the regression fit
library(readr)
library(BIOMASS)
library(tidyverse)
require(boot)
require(MASS)
# Load data
g0.agb <- readRDS("results/R_out/plots_g0agb_dfslim.rds")
# Pairs Bootstrap ---- ###########################################################
# OLS
set.seed(45)
boot.ols.100k <- boot(g0.agb, function(data=g0.agb, index) {
data <- data[index,] # we sample along rows of the data frame
model.boot <- lm(AGB ~ backscatter, data=data)
coef(model.boot)
}, R=100000)
# Results
boot.ols.100k
plot(boot.ols.100k, index=1)
cis <- list()
ci <- boot.ci(boot.ols.100k, conf=0.95, type=c("basic", "bca", "perc"), index=1)
cis[['b']] <- ci$bca[4:5]
ci <- boot.ci(boot.ols.100k, conf=0.95, type=c("basic", "bca", "perc"), index=2)
cis[['m']] <- ci$bca[4:5]
cis <- as.data.frame(cis, row.names = c('lwr', 'upr'))
# Save/Load outputs from before creating AGB raster
boot.ols.100k %>% saveRDS("results/R_out/boot_g0nu_100k.rds")
# Experimentation, old ---- ###########################################################
# Load data - Desktop
# Load data - Mac
g0_AGB <- read_csv("~/GitHub/biomass-espanola/data/plots_g0nu_AGB.csv")
# just get the two columns we care about
g0.agb <- as.data.frame(cbind(g0_AGB$AGB_ha, g0_AGB$'2018mean')) %>%
rename(AGB = V1, backscatter =V2)
#----
# Scatterplot
p <- ggplot(g0.agb, aes(x=backscatter, y=AGB)) + geom_point() +
labs(y = expression(paste("Aboveground biomass (MgC ha"^"-1", ")")),
x = expression(paste("Radar backscatter, ",sigma['HV']^0," (m"^2, "/m"^2, ")")))
p
p2 <- p + geom_smooth(method="lm", se=TRUE, fullrange=TRUE, level=0.95, col='black')
p2 + labs(caption = 'OLS regression')
p2
p1 + geom_smooth(method="rlm", col='red', se=TRUE, fullrange=TRUE, level=0.95) +
labs(caption = 'RLM regression')
p2 + geom_smooth(method="lm", col='red', fill='red', se=TRUE, fullrange=TRUE, level=0.95) +
geom_smooth(method="rlm", col='blue', fill='blue', se=TRUE, fullrange=TRUE, level=0.95)
# Manually construct confidence bands around OLS regression line
mm <- model.matrix(~ backscatter, data = g0.agb)
vars <- mm %*% vcov(ols) %*% t(mm)
sds <- sqrt(diag(vars))
t.val <- qt(1 - (1 - 0.95)/2, ols$df.residual)
t.val
g0.agb$LoCI.man <- ols$fitted.values - t.val * sds
g0.agb$HiCI.man <- ols$fitted.values + t.val * sds
p + geom_ribbon(aes(ymin=g0.agb$LoCI.man, ymax=g0.agb$HiCI.man), linetype=2, alpha=0.1)
ols.ci95 <- predict(ols, newdata = g0.agb, interval = 'confidence')
ols.pi95 <- predict(ols, newdata = g0.agb, interval = 'prediction')
p2 <- p + geom_ribbon(aes(ymin=ols.ci95[,2], ymax=ols.ci95[,3]), linetype=2, alpha=0.1)
p2 + geom_ribbon(aes(ymin=ols.pi95[,2], ymax=ols.pi95[,3]), linetype=2, alpha=0.1)
# plot BCa CI from bootstrapping - not sure if this is appropriate
g0.agb$loCI <- -6.96 + 799*g0.agb$backscatter
g0.agb$hiCI <- 9.63 + 1358*g0.agb$backscatter
p2 + geom_ribbon(aes(ymin=g0.agb$loCI, ymax=g0.agb$hiCI), linetype=2, alpha=0.1)
# plot parameter CI from OLS - not sure if this is appropriate
g0.agb$loCI <- -14.6454 + 713.18*g0.agb$backscatter
g0.agb$hiCI <- 14.64934 + 1349.83*g0.agb$backscatter
p2 + geom_ribbon(aes(ymin=g0.agb$loCI, ymax=g0.agb$hiCI), linetype=2, alpha=0.1)
# plot parameter CI from OLS - not sure if this is appropriate
g0.agb$loCI <- 14.64934 + 713.18*g0.agb$backscatter
g0.agb$hiCI <- -14.6454 + 1349.83*g0.agb$backscatter
p2 + geom_ribbon(aes(ymin=g0.agb$loCI, ymax=g0.agb$hiCI), linetype=2, alpha=0.1)
#
boot.ols.100k$t0
confint(ols)[2,2] - ols$coefficients[2]
ols$model
# Bias
g0.agb$resids <- ols$residuals
p <- ggplot(g0.agb, aes(x=backscatter, y=resids)) + geom_point() +
labs(y = expression(paste("Aboveground biomass (MgC ha"^"-1", ")")),
x = expression(paste("Radar backscatter, ",sigma['HV']^0," (m"^2, "/m"^2, ")")))
p
mean(abs(ols$residuals))
model.10000x5$results
model.10000x5
# Basic OLS regression
ols <- lm(g0.agb$AGB ~ g0.agb$backscatter, x=TRUE, y=TRUE)
summary(ols)
confint(ols)
cov2cor(vcov(ols))
anova(ols)
coef(ols)[2]*10000
coef(ols)[1]*100000000
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(ols, las = 1)
ols$residuals
rmse <- sqrt(var(ols$residuals)) # RMSE
rmse <- sd(ols$residuals)
mse <- mean((residuals(ols))^2)
mse
rss <- sum(residuals(ols)^2)
rss
rse <- sqrt(rss / ols$df.residual)
rse
mean(abs(residuals(ols)))
summary(ols)$adj.r.squared
sigma(ols)
# OLS with intercept=0
ols.0 <- lm(g0.agb$AGB ~ 0+g0.agb$backscatter, x=TRUE, y=TRUE)
abline(ols.0, col='cyan')
summary(ols.0)
confint(ols.0)
cov2cor(vcov(ols))
anova(ols)
coef(ols)
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(ols, las = 1)
#----
# Robust Linear Model regression,
# with parameters set based on optimization performed by cross-validation below
rr <- rlm(AGB ~ backscatter, g0.agb, psi=psi.hampel)
rr$coefficients
abline(rr, col='red')
rr
anova(rr)
summary(rr)
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(rr, las = 1)
View(rr$w)
rr.int0 <- rlm(AGB ~ 0 + backscatter, g0.agb, psi=psi.hampel)
rr.int0$coefficients
abline(rr, col='pink')
anova(rr.int0)
summary(rr.int0)
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(rr.int0, las = 1)
View(rr.int0$w)
#----
# Cross-validation
set.seed(45)
# LOOCV
# Train the model and summarize results
model.loocv <- train(AGB ~ backscatter, data = g0.agb, method = "lm",
trControl = trainControl(method = "LOOCV"))
print(model.loocv)
model.loocv$finalModel
# K-fold
# Train model and summarize results
model.5fold <- train(AGB ~ backscatter, data = g0.agb, method = "lm",
trControl = trainControl(method = "cv", number = 5))
print(model.5fold)
model.5fold$finalModel
# Repeated K-fold
# Define training control, train model, and summarize results
# OLS w/ int=0, 10,000 x 10-fold
fxn.bias <- function(data, lev = NULL, model = NULL) {
resids <- data$pred - data$obs
rss <- sum(resids^2)
n <- length(resids)
df <- n-2
mse <- rss / n
c(RMSE=sqrt(mse),
Rsquared=summary(lm(pred ~ obs, data))$r.squared,
MAE=sum(abs(resids)) / n,
MSE=mse,
B=sum(resids) / n,
RSS=rss,
MSS=rss/df,
RSE=sqrt(rss / df))
}
set.seed(45)
model.10000x10 <- train(AGB ~ backscatter, data = g0.agb, method = "lm",
trControl = trainControl(method = "repeatedcv",
number = 10, repeats = 10000,
summaryFunction = fxn.bias))
model.10000x10$results
head(model.10000x10$resample)
set.seed(3)
model.2x5 <- train(AGB ~ backscatter, data = g0.agb, method = "lm",
trControl = trainControl(method = "repeatedcv",
number = 5, repeats = 2,
summaryFunction = fxn.bias))
model.2x5$results
set.seed(3)
model.2x5.rmse <- train(AGB ~ backscatter, data = g0.agb, method = "lm",
trControl = trainControl(method = "repeatedcv",
number = 5, repeats = 2))
model.2x5.rmse$results
model.10000x10 <- train(AGB ~ backscatter, data = g0.agb, method = "lm",
trControl = trainControl(method = "repeatedcv",
number = 10, repeats = 10000,
returnResamp='all',
trim=FALSE))
print(model.10000x10)
model.10000x10$results
model.10000x10$finalModel
head(model.10000x10$resample)
head(model.10000x10$metric)
# OLS w/ int=0, 10,000 x 10-fold
model.boot100k <- train(AGB ~ backscatter, data = g0.agb, method = "lm",
trControl = trainControl(method = "boot",
number = 100000))
print(model.boot100k)
model.boot100k$results
model.boot100k$finalModel
# 5,000 x10-fold
model.5000x10.int0$results
model.5000x10.int0$finalModel
# Repeated K-fold with Robust Linear Regression
# RLM, 10,000 x 5-fold
model.5000x10.rlmI <- train(AGB ~ backscatter, data = g0.agb, method = "rlm",
tuneGrid = expand.grid(intercept = TRUE, psi = 'psi.hampel'),
trControl = trainControl(method = "repeatedcv",
number = 10, repeats = 5000))
print(model.5000x10.rlmI)
model.5000x10.rlmI$results
model.5000x10.rlmI$finalModel
abline(model.5000x10.rlmI$finalModel, col='red')
model.10000x10.rlmI
#----
# Pairs Bootstrap with the boot library
set.seed(45)
# OLS
boot.ols.100k <- boot(g0.agb, function(data=g0.agb, index) {
data <- data[index,] # we sample along rows of the data frame
model.boot <- lm(AGB ~ backscatter, data=data)
coef(model.boot)
}, R=100000)
# Results
boot.ols.100k
plot(boot.ols.100k, index=1)
boot.ci(boot.ols.100k, conf=0.95, type=c("basic", "bca", "perc"), index=1)
boot.ci(boot.ols.100k, conf=0.95, type=c("basic", "bca", "perc"), index=2)
# OLS with int=0
boot.ols.int0.100k <- boot(g0.agb, function(data=g0.agb, index) {
data <- data[index,] # we sample along rows of the data frame
model.boot <- lm(AGB ~ 0 + backscatter, data=data)
coef(model.boot)
}, R=100000)
# Results
boot.ols.int0.100k
plot(boot.ols.int0.100k, index=1)
boot.ci(boot.ols.int0.100k, conf=0.95, type=c("basic", "bca", "perc"), index=1)
boot.ci(boot.ols.int0.100k, conf=0.95, type=c("basic", "bca", "perc"), index=2)
# Pairs bootstrap with RLM
boot.rlm.int0.100k <- boot(g0.agb, function(data=g0.agb, index) {
data <- data[index,] # we sample along rows of the data frame
model.boot <- rlm(AGB ~ 0 + backscatter, data=data, psi=psi.hampel)
coef(model.boot)
}, R=100000)
# Results
boot.rlm.int0.100k
plot(boot.rlm.int0.100k, index=1)
boot.ci(boot.rlm.int0.100k, conf=0.95, type=c("basic", "bca", "perc"), index=1)
plot(boot.rlm.int0.100k$t[,1], boot.rlm.int0.100k$t[,2],
xlab="t1", ylab="t2", pch=1)
# Pairs bootstrap with RLM w/ intercept
boot.rlm.100k <- boot(g0.agb, function(data=g0.agb, index) {
data <- data[index,] # we sample along rows of the data frame
model.boot <- rlm(AGB ~ backscatter, data=data, psi=psi.hampel)
coef(model.boot)
}, R=100000)
# Results
boot.rlm.100k
plot(boot.rlm.100k, index=1)
boot.ci(boot.rlm.100k, conf=0.95, type=c("basic", "bca", "perc"), index=1)
boot.ci(boot.rlm.100k, conf=0.95, type=c("basic", "bca", "perc"), index=2)
plot(boot.rlm.100k$t[,1], boot.rlm.100k$t[,2],
xlab="t1", ylab="t2", pch=1)
#----
# Save
save(boot.ols.100k, boot.ols.30k, boot.rlm.100k, boot.rlm.int0.100k,
model.10000x10, model.10000x10.rlm, model.10000x10.rlmI, model.10000x10.int0,
model.10000x5, model.10000x5.boot, model.10000x5.rlm, model.10000x5.rlmI,
model.boot100k,
file = "~/PROJECTS/Haiti_biomass/R_out/model_trains.RData")
#----
# Residuals bootstrap
BootstrapFunctionRegression <- function(data=g0.agb, index) {
mod.object <- lm(AGB ~ backscatter, data=data)
resids = mod.object$resid
fittedValues = mod.object$fitted
matr <- model.matrix(mod.object)
# generating new values for each y[i], by adding bootstrapped resids to fitted values.
Y <- fittedValues + resids[index] # we sample along rows of the data frame
# Using model.matrix for the predictors
model.boot <- lm(Y ~ 0 + matr, data=data)
coef(model.boot)
}
bootstrappedModel <- boot(g0.agb, BootstrapFunctionRegression, R=10000)
bootstrappedModel
plot(bootstrappedModel, index=1)
boot.ci(bootstrappedModel, conf=0.95, type=c("basic", "bca", "perc"), index=1)
boot.ci(bootstrappedModel, conf=0.95, type=c("basic", "bca", "perc"), index=2)
# Bootstrap options - manual
# from "Using the non-parametric bootstrap for regression models in R" by Ian Dworkin
# Non-parametric bootstrap: Pairs (Random x) approach
N = 10000 # Perform N bootstrap iterations
BootstrapRandomX <- function(dat=g0.agb, mod.formula=formula(AGB ~ backscatter)){
dat.boot <- dat[sample(x = nrow(dat), size = nrow(dat), replace=T),] # samples along index
boot.lm <- lm(mod.formula, data=dat.boot)
coef(boot.lm)
}
vector.boot <- t(replicate(N, BootstrapRandomX()))
# standard error of the estimates via bootstrap
# (standard deviations of those distributions)
apply(vector.boot, MARGIN = 2, sd)
# Percentile CIs (transposed to compare to simple confints)
t(apply(vector.boot, MARGIN = 2, quantile, probs=c(0.025, 0.975)))
# Histogram of distributions from Pairs bootstrap
par(mfrow=c(1,2))
MultipleHistograms <- function(X=vector.boot){
for (i in 1:ncol(X)){
hist(X[,i], freq=F,
main=colnames(X)[i],
xlab=colnames(X)[i])
}
}
MultipleHistograms()
pairs(vector.boot)
# Use Bias-Corrected (BC) and accelerated (a) non-parametric bootstrap
# confidence intervals (BCa) to adjust for biases in the Percentile Confidence
# Intervals. We can calculate them with boot() in the boot library.
# Non-parametric bootstrap: Residual (Fixed effect / Experimental) approach
# Analogous analysis to Monte Carlo simulations to generate confidence intervals
# 1) fit model as normal and 2) get residuals,
# 3) bootstrap the residuals from the model (r*)
# 4) add r* back onto fitted component of the model (i.e. b*x[i] + r*[i])
resid.model.1 <- resid(linreg)
par(mfrow=c(2,1))
plot(density(resid.model.1, bw=0.5))
plot(density(resid.model.1, bw=1))
par(mfrow=c(1,2))
plot(resid.model.1 ~ g0.agb$AGB)
plot(resid.model.1 ~ g0.agb$backscatter)
BootstrapFromResiduals <- function(mod.object = linreg, dat = g0.agb) {
resids = mod.object$resid
fittedValues = mod.object$fitted
matr <- model.matrix(mod.object)
# generating new values for each y[i], by adding bootstrapped resids to fitted values.
Y <- fittedValues + sample(resids, length(resids), replace=T)
# Using model.matrix for the predictors
model.boot <- lm(Y ~ 0 + matr, data=dat)
coef(model.boot) # Extract coefficients
}
# Run and look at it.
residual.boot.N <- t(replicate(N, BootstrapFromResiduals()))
par(mfrow=c(1,2))
MultipleHistograms(X=residual.boot.N)
pairs(residual.boot.N)
apply(residual.boot.N, MARGIN = 2, sd)
t(apply(residual.boot.N, MARGIN=2, quantile, probs=c(0.025, 0.975)))
# Monte Carlo bootstrap (parametric) - simulating values in the response
SimulationUnderModel <- function(model = linreg) {
# extract design matrix
matr <- model.matrix(model)
rse = summary(model)$sigma
df = model$df
# incorporate uncertainty in RSE
rse.sim <- rse*sqrt(df/rchisq(1, df=df))
# Simulate data (response) conditional on the simulated RSE.
y.sim <- rnorm(n = nrow(matr),
mean=matr%*%coef(model), sd=rse.sim)
# 0 + design matrix (since the intercept is already in the design matrix)
lm.sim <- lm(y.sim ~ 0 + matr) # fit model with simulated response
coef(lm.sim)
}
# Run and look at it
sim.coef <- t(replicate(N, SimulationUnderModel()))
apply(sim.coef, MARGIN = 2, sd)
t(apply(sim.coef, MARGIN=2, quantile, probs=c(0.025, 0.975)))
par(mfrow=c(1,2))
MultipleHistograms(X=sim.coef)
# Compare
par(mfrow=c(2,1))
plot(density(residual.boot.N[,2], bw=10),
main="Comparing bootstrap methods for parameter uncertainty: backscatter",
lwd=2, lty=1)
lines(density(vector.boot[,2], bw=10), col='red', lwd=2, lty=1)
lines(density(sim.coef[,2], bw=10), col='purple', lwd=2, lty=1)
#legend('topright', legend=c("Residual Boot", "Pairs Boot", "Monte Carlo Normal"),
# col=c("black", "red", "purple"), lty=c(1,1,1), lwd=2, bg=NULL)
# Compare
#par(mfrow=c(1,1))
plot(density(residual.boot.N[,1], bw=0.5),
main="Comparing bootstrap methods for parameter uncertainty: AGB",
lwd=2, lty=1)
lines(density(vector.boot[,1], bw=0.5), col='red', lwd=2, lty=1)
lines(density(sim.coef[,1], bw=0.5), col='purple', lwd=2, lty=1)
#legend('topright', legend=c("Residual Boot", "Pairs Boot", "Monte Carlo Normal"),
# col=c("black", "red", "purple"), lty=c(1,1,1), lwd=2, bg=NULL)
# ----
sqrt(var(linreg$residuals))
library(normwhn.test)
DHtest <- normality.test1(cbind(g0_AGB$AGB_ha, g0_AGB$`2018mean`))
normality.test1(cbind(g0_AGB$AGB_ha))
# Use Model II regression
library(lmodel2)
lm2 <- lmodel2(AGB_ha ~ `2018mean`, g0_AGB, range.y='interval', range.x='relative')
summary(lm2)
lm2$regression.results
lm2$confidence.intervals
lm2$rsquare
lm2$H
lm2$r
lm2$P.param
lm2$eigenvalues
# 4 figures arranged in 2 rows and 2 columns
par(mfrow=c(2,2))
plot.lmodel2(lm2, 'OLS')
plot.lmodel2(lm2, 'MA')
plot.lmodel2(lm2, 'SMA')
plot.lmodel2(lm2, 'RMA')
# Get Spearman's rank correlation coefficient
corr <- cor.test(x=g0_AGB$`2018mean`, y=g0_AGB$AGB_ha, method = 'spearman')
corr$estimate |
ee6080c4155c4f9d624c1548f84b5d453aecb4c0 | bcbef153e26ebb86bd78ffbcacdb2e31046e168f | /man/subset-dendrogram.Rd | 7290479329e91ec938f6b2dd34eff85bad0529d3 | [] | no_license | npcooley/SynExtend | 5f40f841a4cf609c47127850f5c583a6819b6f06 | bd899be4365463884096d74d27411a26b0ac6d7a | refs/heads/master | 2023-08-04T04:31:20.644818 | 2023-07-25T13:21:18 | 2023-07-25T13:21:18 | 240,341,045 | 2 | 0 | null | 2023-07-24T15:38:33 | 2020-02-13T19:14:36 | R | UTF-8 | R | false | false | 1,429 | rd | subset-dendrogram.Rd | \name{subset.dendrogram}
\alias{subset.dendrogram}
\title{
Subsetting dendrogram objects
}
\description{
Subsets dendrogram objects based on leaf labels. Subsetting can either be by leaves to keep, or leaves to remove.
NOTE: This man page is specifically for \code{subset.dendogram}, see \code{?base::subset} for the generic \code{subset} function defined for vectors, matrices, and data frames.
}
\usage{
\method{subset}{dendrogram}(x, subset, invert=FALSE, ...)
}
\arguments{
\item{x}{
An object of class \code{'dendogram'}
}
\item{subset}{
A vector of labels to keep (see \code{invert}).
}
\item{invert}{
If set to \code{TRUE}, subset to only the leaves \emph{not} in \code{subset}.
}
\item{...}{
Additional arguments for consistency with generic.
}
}
\value{
An object of class \code{'dendrogram'} corresponding to the subsetted tree.
}
\author{
Aidan Lakshman \email{ahl27@pitt.edu}
}
\note{
If none of the labels specified in the \code{subset} argument appear in the tree (or if all do when \code{invert=TRUE}), a warning is thrown and an empty object of class \code{'dendrogram'} is returned.
}
\seealso{
\code{\link[base]{subset}}
}
\examples{
d <- as.dendrogram(hclust(dist(USArrests), "ave"))
# Show original dendrogram
plot(d)
# Subset to first 10 labels
d1 <- subset(d, labels(d)[1:10])
plot(d1)
# Subset d1 to all except the first 2 labels
d2 <- subset(d1, labels(d1)[1:2], invert=TRUE)
plot(d2)
}
|
d356440f5a68eaf8d2c86070aba1665f8bb54e0c | 8284b1b45303414dc77a233ea644488b91d9f649 | /man/dbListFields-JDBCQueryResult-missing-method.Rd | 99121ca4a8b9ef9e3d8f56b9e2636c8058f79541 | [] | no_license | hoesler/dbj | b243eaa14bd18e493ba06e154f02034296969c47 | c5a4c81624f5212a3e188bd6102ef61d6057af0b | refs/heads/master | 2020-04-06T06:59:06.471743 | 2016-07-14T10:16:04 | 2016-07-14T10:16:04 | 40,183,942 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 597 | rd | dbListFields-JDBCQueryResult-missing-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/JDBCQueryResult.R
\docType{methods}
\name{dbListFields,JDBCQueryResult,missing-method}
\alias{dbListFields,JDBCQueryResult,missing-method}
\title{List fields in specified table.}
\usage{
\S4method{dbListFields}{JDBCQueryResult,missing}(conn, name, ...)
}
\arguments{
\item{conn}{an \code{\linkS4class{JDBCQueryResult}} object.}
\item{name}{Ignored. Needed for compatiblity with generic.}
\item{...}{Ignored. Needed for compatiblity with generic.}
}
\description{
List fields in specified table.
}
\keyword{internal}
|
881a0cffca9335c5bcb95075860dbdf7a31db9de | 33200c34185918b4490a9fb28b0c2ae9c4d47808 | /R/fonctions.R | b6a910ff6f5ea78a11a18492ca1be6f749214322 | [] | no_license | timmimohamed/carteMaroc | 7c4e3ff45f4fdaf2518988fc6a5a2982e2037cf4 | 361de1ca6af45ee8952b8cadfd1868fa3fdfa5a2 | refs/heads/master | 2020-03-28T18:55:50.828041 | 2018-09-22T06:44:42 | 2018-09-22T06:44:42 | 148,927,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,809 | r | fonctions.R | #' uploadSHP
#'
#' Cette fonction permet de charger les fichiers shapefile, ou « fichier de formes » est un format de fichier pour les systèmes d'informations géographiques (SIG) pour les régions du Maroc
#' @param sans parametres
#' @examples
#' uploadSHP()
#' @export
uploadSHP <- function() {
print("Chargement des fichiers shp - regions du Maroc....")
dir.create("data")
download.file("https://raw.githubusercontent.com/timmimohamed/carteMaroc/master/regions/regions.shp", destfile =paste0(getwd(),"/data/","regions.shp"),method="wininet")
download.file("https://raw.githubusercontent.com/timmimohamed/carteMaroc/master/regions/regions.prj", destfile =paste0(getwd(),"/data/","regions.prj"),method="wininet")
download.file("https://raw.githubusercontent.com/timmimohamed/carteMaroc/master/regions/regions.qpj", destfile =paste0(getwd(),"/data/","regions.qpj"),method="wininet")
download.file("https://raw.githubusercontent.com/timmimohamed/carteMaroc/master/regions/regions.dbf", destfile =paste0(getwd(),"/data/","regions.dbf"),method="wininet")
download.file("https://raw.githubusercontent.com/timmimohamed/carteMaroc/master/regions/regions.shx", destfile =paste0(getwd(),"/data/","regions.shx"),method="wininet")
}
#' plotSHP
#'
#' Cette fonction permet de ploter la carte des regions du Maroc
#' @param chemin valeur de chemin : l'eplacement ou ce trouve le fichier shapefile
#' @examples
#' plotSHP("data/shp_nouveau_decoupage_territorial_2015")
#' @export
plotSHP <- function(chemin) {
print("ploting - regions du Maroc....")
shp <- readOGR(dsn=chemin,layer = 'region')
plot(shp)
}
#' dataShpReg
#'
#' Cette fonction permet de récupérer le contenu (data) du fichier shapefile de regions du Maroc
#' @param chemin valeur de chemin : l'eplacement ou ce trouve le fichier shapefile
#' @return data shapefile
#' @examples
#' dataShpReg("data/shp_nouveau_decoupage_territorial_2015")
#' @export
dataShpReg <- function(chemin) {
shp <-readOGR(dsn=chemin,layer = 'region')
return(shp)
}
dataShpBas <- function(chemin) {
shp <-readOGR(dsn=chemin,layer = 'bassins')
return(shp)
}
#' mapShpRegs0
#'
#' Creation d'une carte interactive avec LEAFLET
#' @param shp valeur de data du fichier shapefile
#' @param density valeur de density (nombre d'ahibitants par km)
#' @param lab valeur de label
#' @examples
#' shp <- dataShpReg("data/shp_nouveau_decoupage_territorial_2015")
#' reg <- read.csv("data/regions.csv", stringsAsFactors = FALSE)
#' lab<- sprintf(
#' "<strong>%s</strong><br>%s Hab/Km2",
#' reg$name,reg$density
#' ) %>% lapply(htmltools::HTML)
#' mapShpRegs0(shp,density,lab)
#' @export
mapShpRegs0<- function(shp,density,lab) {
bins <- c(0, 5, 10, 20, 40, 100, 200, 250, 300, Inf)
pall <- colorBin("BuGn", domain = density, bins = bins)
leaflet() %>%addTiles() %>%
setView(lng = -7.092620000000011, lat=31.791702,zoom=6) %>%
addPolygons(
data= spTransform(shp, CRS("+proj=longlat +ellps=GRS80")),
weight = 2,
opacity = 1,
fillColor = ~pall(density),
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlight = highlightOptions(
weight = 3,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = lab,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")
)%>%
addLegend(pal = pall, values = density, opacity = 0.7, title = NULL,
position = "bottomright")
}
#' mapShpReg
#'
#' Cette fonction permet de selectionner la region dont son nom passe au parametre
#' @param shp valeur de data du fichier shapefile
#' @param reg valeur de la region a afficher
#' @param coul valeur de la couleur de la region choisis
#' @examples
#' shp <- dataShpReg("data/shp_nouveau_decoupage_territorial_2015")
#' reg = "Fes-Meknes"
#' mapShpReg(shp,reg,"green")
#' @export
mapShpReg<- function(shp,reg,coul) {
shp <- subset(shp, shp$name %in% c(reg))
leaflet() %>%addTiles() %>%
setView(lng = -7.092620000000011, lat=31.791702,zoom=6) %>%
addPolygons(
data= spTransform(shp, CRS("+proj=longlat +ellps=GRS80")),
weight = 2,
opacity = 1,
color = coul,
dashArray = "3",
fillOpacity = 0.7,
label = reg
)
}
#' mapShpDensPop
#'
#' Cette fonction permet de selectionner la region dont son nom passe au parametre
#' @param shp valeur de data du fichier shapefile
#' @param reg valeur de la region a afficher
#' @param coul valeur de la couleur de la region choisis
#' @examples
#' shp <- dataShpReg("data/shp_nouveau_decoupage_territorial_2015")
#' reg = "Fes-Meknes"
#' mapShpDensPop(shp,reg,"green")
#' @export
mapShpDensPop<- function(shp,reg,coul,dens) {
shp <- subset(shp, shp$name %in% c(reg))
leaflet() %>%addTiles() %>%
setView(lng = -7.092620000000011, lat=31.791702,zoom=6) %>%
addPolygons(
data= spTransform(shp, CRS("+proj=longlat +ellps=GRS80")),
weight = 2,
opacity = 1,
color = coul,
dashArray = "3",
fillOpacity = 0.7,
label = reg
)%>%
addCircles(long,etab$lat, weight = 1,radius = sqrt(etab$nbr_eleve) * 50,
popup = lab, fillOpacity = 0.5)
}
#' mapShpRegs
#'
#' Creation d'une carte interactive avec LEAFLET
#' @param couleur valeur de couleur degradee selon la densite de la region
#' Toutes ces palettes peuvent être utilisées conjointement avec colorRamp () et colorRampPalette ().
#' Voici un affichage de toutes les palettes de couleurs disponibles dans le package RColorBrewer.
#' > library(RColorBrewer)
#' > display.brewer.all()
#' Permet d'afficher une liste de combinaisons de couleurs.
#' @examples
#' mapShpRegs("YlOrRd")
#' @export
mapShpRegs<- function(couleur) {
bins <- c(0, 5, 10, 20, 40, 100, 200, 250, 300, Inf)
pall <- colorBin(couleur, domain = shpMaroc@data$density, bins = bins)
lab<- sprintf(
"<strong>%s</strong><br>%s Hab/Km2",
shpMaroc@data$name,shpMaroc@data$density
) %>% lapply(htmltools::HTML)
leaflet() %>%addTiles() %>%
setView(lng = -7.092620000000011, lat=31.791702,zoom=6) %>%
addPolygons(
data= shpMaroc,
weight = 2,
opacity = 1,
fillColor = ~pall(shpMaroc@data$density),
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlight = highlightOptions(
weight = 3,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = lab,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")
)%>%
addLegend(pal = pall, values = shpMaroc@data$density, opacity = 0.7, title = NULL,
position = "bottomright")
}
#' mapCsvRegs
#'
#' Cette fonction permet de tracer la carte du Maroc - regions
#' @param col valeur de la couleur des frontieres des regions
#' @param we valeur de l'paisseur es frontieres des regions
#' @examples
#' output$regions <- renderLeaflet({
#' mapCsvRegs("red",1.25)
#' })
#' @export
mapCsvRegs<- function(col,we) {
# turn into SpatialLines
split_data = lapply(unique(gpsMaroc$group), function(x) {
df = as.matrix(gpsMaroc[gpsMaroc$group == x, c("Longitude", "Latitude")])
lns = Lines(Line(df), ID = x)
return(lns)
})
data_lines = SpatialLines(split_data)
leaflet(data_lines) %>%
addTiles() %>%
addPolylines(color = col, weight=we)
}
#' mapCsvReg
#'
#' Cette fonction permet de tracer une region choisis du Maroc
#' @param num de la region a afficher
#' @param col valeur de la couleur des frontieres des regions
#' @param we valeur de l'paisseur es frontieres des regions
#' @examples
#' output$regions <- renderLeaflet({
#' mapCsvReg(6,"red",1.25)
#' })
#' @export
mapCsvReg<- function(num,col,we) {
gpsMaroc <- subset(gpsMaroc, gpsMaroc$group %in% c(num))
# turn into SpatialLines
split_data = lapply(unique(gpsMaroc$group), function(x) {
df = as.matrix(gpsMaroc[gpsMaroc$group == x, c("Longitude", "Latitude")])
lns = Lines(Line(df), ID = x)
return(lns)
})
data_lines = SpatialLines(split_data)
leaflet(data_lines) %>%
addTiles() %>%
addPolylines(color = col, weight=we) %>%
addMarkers(lng = -7.092620000000011, lat=31.791702)
}
#' mapCsvRegMark
#'
#' Cette fonction permet de tracer une region choisis du Maroc et d'ajouter des Markers selon le choix d'utilisateur
#' @param num valeur de la region a afficher
#' @param col valeur de la couleur des frontieres des regions
#' @param we valeur de l'paisseur des frontieres des regions
#' @param long valeur d'un vecteur de longitudes
#' @param lat valeur d'un vecteur de latitudes
#' @param pop valeur de popup
#' @examples
#' etab <- read.csv("data/population_Fes_Meknes/etab_meknes.csv", stringsAsFactors = FALSE)
#' mapCsvRegMark(3,"green",1.25,etab$long,etab$lat,etab$nbr_eleve)
#' @export
mapCsvRegMark<- function(num,col,we,long,lat,pop) {
gpsMaroc <- subset(gpsMaroc, gpsMaroc$group %in% c(num))
# turn into SpatialLines
split_data = lapply(unique(gpsMaroc$group), function(x) {
df = as.matrix(gpsMaroc[gpsMaroc$group == x, c("Longitude", "Latitude")])
lns = Lines(Line(df), ID = x)
return(lns)
})
data_lines = SpatialLines(split_data)
leaflet(data_lines) %>%
addTiles() %>%
addPolylines(color = col, weight=we) %>%
addMarkers(lng = long, lat=lat,popup = pop)
}
|
6088ee267e611d865506f6afaf9ac6b0bbf56d61 | da9b15a6d555b3c9540705e69f0c4d7baa39a1b3 | /scripts/alt_run_2_parse_pdfs_ABBYY_outputs/run_6_fix_spelling.R | 704451a4531a09a054a807f540fc27b25d0fba1f | [] | no_license | RohanAlexander/hansard | 0be2c6b43b053a048896d3d8d98fc633dde323fa | 300fac35e8714871dcf0a6225db3e4a1f33754d2 | refs/heads/master | 2022-03-11T00:08:03.183499 | 2019-11-25T10:53:18 | 2019-11-25T10:53:18 | 138,767,582 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,047 | r | run_6_fix_spelling.R | # !diagnostics off
#### Preamble ####
# Purpose: This file takes Australian Hansard CSV files that are all in one column and one row and applies a brutal, replacement-based, custom-dictionary, spell-checker.
# Author: Rohan Alexander
# Email: rohan.alexander@anu.edu.au
# Last updated: 11 October 2018
# Prerequisites: You need to have done all the other parsing steps. For testing purposes there should be some in the /outputs/hansard folder.
# To do:
#### Set up workspace ####
# devtools::install_github("DavisVaughan/furrr")
library(furrr)
library(lubridate)
# library(pdftools)
library(stringi)
library(tidyverse)
library(tictoc)
library(tm)
# update.packages()
# Set up furrr
plan(multiprocess)
# Get the spell checker
fix_wrong_spellings <-
read_csv2("inputs/misc/misspelt_words_with_corrections.csv") %>%
mutate(numberOfCharacters = nchar(original)) %>%
arrange(desc(numberOfCharacters)) %>%
select(-numberOfCharacters)
#### Create lists of CSVs to read ####
# Change the path as required:
# use_this_path_to_get_csvs <- "outputs/hansard/run_5_output"
# use_this_path_to_get_csvs <- "/Volumes/Hansard/parsed/federal/for_zoe/run_5_output"
# use_this_path_to_get_csvs <- "/Volumes/Hansard/parsed/federal/senate/run_5_output"
use_this_path_to_get_csvs <- "/Volumes/Hansard/parsed/federal/hor/run_5_output"
# use_this_path_to_get_csvs <- "/Volumes/Hansard/parsed/federal/hortest"
# Get list of Hansard csvs that have been parsed from PDFs and had front matter removed
file_names <-
list.files(
path = use_this_path_to_get_csvs,
pattern = "*.csv",
recursive = FALSE,
full.names = TRUE
)
file_names <- file_names %>% sample() # Randomise the order
# Just use this to filter if needed
file_tibble <- tibble(filename = file_names)
file_tibble <- file_tibble %>%
mutate(the_year = filename,
the_year = str_replace(the_year, "/Volumes/Hansard/parsed/federal/senate/run_5_output/", ""),
the_year = str_replace(the_year, "/Volumes/Hansard/parsed/federal/hor/run_5_output/", ""),
the_year = str_replace(the_year, ".csv", ""),
the_year = ymd(the_year)) %>%
# filter(year(the_year) < 1981) %>%
filter(year(the_year) == 2015)
file_names <- file_tibble$filename
rm(file_tibble)
# use_this_path_to_save_csvs <- "outputs/hansard/run_6_output"
# use_this_path_to_save_csvs <- "/Volumes/Hansard/parsed/federal/for_zoe/run_6_output"
# use_this_path_to_save_csvs <- "/Volumes/Hansard/parsed/federal/senate/run_6_output"
use_this_path_to_save_csvs <- "/Volumes/Hansard/parsed/federal/hor/run_6_output"
# use_this_path_to_save_csvs <- "/Volumes/Hansard/parsed/federal/hortest"
save_names <- file_names %>%
str_replace(use_this_path_to_get_csvs, use_this_path_to_save_csvs)
#### Create the function that will be applied to the files ####
fix_spelling <-
function(name_of_input_csv_file,
name_of_output_csv_file) {
# Read in the csv, based on the filename list
# name_of_input_csv_file <- "/Volumes/Backup/temp/1971-10-05.csv" # uncomment for testing
csv_to_clean <-
read_csv(name_of_input_csv_file,
trim_ws = FALSE,
col_types = cols())
#Fix the spelling
csv_to_clean$text <-
stri_replace_all_regex(
csv_to_clean$text,
"\\b" %s+% fix_wrong_spellings$original %s+% "\\b",
fix_wrong_spellings$corrected,
vectorize_all = FALSE
)
# Save file
write_csv(csv_to_clean, name_of_output_csv_file)
print(paste0("Done with ", name_of_output_csv_file, " at ", Sys.time()))
}
safely_fix_spelling <- safely(fix_spelling)
#### Walk through the lists and parse the PDFs ####
# Normal walk2
# tic("Normal walk2")
# walk2(file_names,
# save_names,
# ~ safely_fix_spelling(.x, .y)
# )
# toc()
# file_names_d <- file_names[1:3]
# save_names_d <- save_names[1:3]
# Furrr walk2
tic("Furrr walk2")
future_walk2(file_names,
save_names,
~ safely_fix_spelling(.x, .y),
.progress = TRUE)
toc()
|
c3ef4c400cef880ee18959db4cd6b06126f43318 | f6721eb9594039cc2743b83a0737ffdf6e726970 | /res_tol_ad_manual_setup.R | 242222e651c9a1d18f7e6cf42cfdb5e0dbeeb1cf | [] | no_license | morgankain/Stochastic_Virulence_Evolution | a8ff25a7cce60ad56f74b29ba1072c7cb950c57c | 397dc3358e0296b576769b6f2f08261fd7e86611 | refs/heads/master | 2020-05-30T23:45:30.666215 | 2020-03-16T23:21:34 | 2020-03-16T23:21:34 | 190,020,966 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,900 | r | res_tol_ad_manual_setup.R | #########################################################################
## Setup AD runs with similar parameter values to the stochastic model ##
#########################################################################
nt <- 5e5
num_points <- 1500
rptfreq <- max(nt / num_points, 1)
nrpt <- nt %/% rptfreq
num_runs <- 250
deterministic <- FALSE
mut_link_h <- make.link("log")
mut_link_p <- make.link("logit")
#alpha0 <- c(seq(0.01, 0.99, by = 0.01), 0.999)
#tuning <- c(seq(0.01, 0.99, by = 0.01), 0.999)
alpha0 <- c(seq(
mut_link_p$linkfun(0.01), mut_link_p$linkfun(0.99)
, length = 100))
tuning <- c(seq(
mut_link_p$linkfun(0.01), mut_link_p$linkfun(0.99)
, length = 100))
## For the AD model all we really care about is the shpae of the surface, parameters such as
## mu, mut_sd, N don't mean anything
params <- data.frame(
nt = nt
, rptfreq = rptfreq
, nrpt = nrpt
, mut_var = "beta"
, d = 0.01
, mu = 1
, mut_mean = 1
, mut_sd = 1
, tol0 = 1
, res0 = 1
, mut_host_mean_shift = 1
, mut_host_sd_shift = 1
, mut_host_mu_shift = 100000000000
, mut_host_res_bias = 0
, host_dyn_only = FALSE
, power_c = 2
, power_exp = 2
, b_decay = 2.3
, b = 0.5
, N = 1
, balance_birth = FALSE
, stochastic_birth = TRUE
, fill_birth = TRUE
, agg_eff_adjust = TRUE
, parasite_tuning = TRUE
, eff_scale = 3 # rep(c(10, 30, 50), 3)
, R0_init = 2
, determ_length = 1000
, determ_timestep = 2
, lsoda_hini = 1
, Imat_seed1 = 13 # rep(c(13, 87, 13), each = 3)
, Imat_seed2 = 87 # rep(c(87, 13, 13), each = 3)
, numbins = 1000
, deterministic = deterministic)
######
## Run the sims
######
for (i in 1:nrow(params)) {
print(i / nrow(params))
## Gradient Ascent
## Need power c and power exp?
grad_ascent <- with(params
, par_evo_AD(
c = power_c[i]
, curv = power_exp[i]
, eff_scale = eff_scale[i]
, mut_link = mut_link_p
, numbins = numbins[i]
## same parameter as in the RD model, but used a bit differently
, Iseed = c(Imat_seed1[i], Imat_seed2[i]) * (numbins[i] / length(alpha0))
, simul_mut = TRUE
, max_range = FALSE
, debug1 = FALSE
, debug1_val = 759
))
grad_ascent <- transform(
grad_ascent
, param_num = i
, eff_scale = params$eff_scale[i]
, numbins = params$numbins[i]
, Imat_seed1 = params$Imat_seed1[i]
, Imat_seed2 = params$Imat_seed2[i])
## Maximum range in which a postive R0 can be obtained
grad_ascent_sing <- with(params
, par_evo_AD(
c = power_c[i]
, curv = power_exp[i]
, eff_scale = eff_scale[i]
, mut_link = mut_link_p
, numbins = numbins[i]
## same parameter as in the RD model, but used a bit differently
, Iseed = c(Imat_seed1[i], Imat_seed2[i]) * (numbins[i] / length(alpha0))
, simul_mut = FALSE
, max_range = FALSE
, debug1 = FALSE
, debug1_val = 360
))
grad_ascent_sing <- transform(
grad_ascent_sing
, param_num = i
, eff_scale = params$eff_scale[i]
, numbins = params$numbins[i]
, Imat_seed1 = params$Imat_seed1[i]
, Imat_seed2 = params$Imat_seed2[i])
if (i == 1) {
grad_ascent_tot <- grad_ascent
grad_ascent_sing_tot <- grad_ascent_sing
} else {
grad_ascent_tot <- rbind(grad_ascent_tot, grad_ascent)
grad_ascent_sing_tot <- rbind(grad_ascent_sing_tot, grad_ascent_sing)
}
}
saveRDS(grad_ascent_tot, "res_out/res_out_AD/AD_grad_ascent.Rds")
saveRDS(grad_ascent_sing_tot, "res_out/res_out_AD/AD_grad_ascent_sing.Rds")
######
## Stochastic AD version for thesis defense
######
i = 1 ## just for single parameter set
for (j in 1:10) {
print(j / nrow(params))
## Gradient Ascent
## Need power c and power exp?
grad_ascent3 <- with(params
, par_evo_AD_rand(
c = power_c[i]
, curv = power_exp[i]
, eff_scale = eff_scale[i]
, mut_link = mut_link_p
, numbins = numbins[i]
## same parameter as in the RD model, but used a bit differently
, Iseed = c(Imat_seed1[i], Imat_seed2[i]) * (numbins[i] / length(alpha0))
, simul_mut = TRUE
, max_range = FALSE
, debug1 = FALSE
, debug1_val = 759
))
grad_ascent3 <- transform(
grad_ascent3
, param_num = i
, run_num = j
, eff_scale = params$eff_scale[i]
, numbins = params$numbins[i]
, Imat_seed1 = params$Imat_seed1[i]
, Imat_seed2 = params$Imat_seed2[i])
if (j == 1) {
grad_ascent_tot3 <- grad_ascent3
} else {
grad_ascent_tot3 <- rbind(grad_ascent_tot3, grad_ascent3)
}
}
saveRDS(grad_ascent_tot, "res_out/res_out_AD/AD_grad_ascent_stochas.Rds")
|
7dba8d5b7c05b4f6c38128f221ac6ab25e6b29f3 | cf91ca9b31f1a5e6601a4d763175536d425ac66f | /misc/FigS2.R | c7fad0f61f5b6b02a6f7cdd20bbbc82d1e8305e9 | [] | no_license | ViolaYing/correlationAnalyzeR | 1c0fbbe43d70654e9058a01acc2b01feb352d6e2 | a405e878c48d84b8691069c05b142e3435ce3b8d | refs/heads/master | 2023-04-08T07:31:44.890040 | 2021-04-02T19:08:54 | 2021-04-02T19:08:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,102 | r | FigS2.R | library(tidyverse)
library(ggpubr)
library(correlationAnalyzeR)
res <- correlationAnalyzeR::analyzeGenePairs(
genesOfInterest = c("BRCA1", "BRCA2"),
Sample_Type = "all",
runGSEA = FALSE
)
g1 <- res$compared$VST_corrPlot$corrPlot_disease +
labs(title = "All samples")
resNorm <- correlationAnalyzeR::analyzeGenePairs(
genesOfInterest = c("BRCA1", "BRCA2"),
Sample_Type = "normal",
runGSEA = FALSE
)
g2 <- resNorm$compared$VST_corrPlot$corrPlot_disease +
labs(title = "Normal samples")
resCancer <- correlationAnalyzeR::analyzeGenePairs(
genesOfInterest = c("BRCA1", "BRCA2"),
Sample_Type = "cancer",
runGSEA = FALSE
)
g3 <- resCancer$compared$VST_corrPlot$corrPlot_disease +
labs(title = "Cancer samples")
ggarrange(g1, g2, g3, nrow = 1, align = "hv") +
ggsave(filename = "../Manuscript/FinalAssets/FigureS2_raw.png",
height = 5, width = 18)
cd <- correlationAnalyzeR::human_coldata
write_csv(cd, file = "misc/colData.csv")
### IL1B; IL1RN
resRev <- analyzeGenePairs(genesOfInterest = c("IL1B", "IL1RN"),
runGSEA = F)
resRev$compared$VST_corrPlot$corrPlot_tissue
g1 <- res$compared$VST_corrPlot$corrPlot_disease +
labs(title = "All samples")
resNorm <- correlationAnalyzeR::analyzeGenePairs(
genesOfInterest = c("BRCA1", "NQO1"),
Sample_Type = "normal",
runGSEA = FALSE
)
g2 <- resNorm$compared$VST_corrPlot$corrPlot_disease +
labs(title = "Normal samples")
resCancer <- correlationAnalyzeR::analyzeGenePairs(
genesOfInterest = c("BRCA1", "NQO1"),
Sample_Type = "cancer",
runGSEA = FALSE
)
g3 <- resCancer$compared$VST_corrPlot$corrPlot_disease +
labs(title = "Cancer samples")
geneOne <- "BRCA1"
geneTwo <- "NQO1"
titleStr <- "Normal samples"
Rval <- res$compared$VST_corrPlot$Rval
Padj <- res$compared$VST_corrPlot$Padj
resNorm$compared$VST_corrPlot$corrPlot_VST_data %>%
mutate(Condition = ifelse(Group %in% c(
# "Mammary - Normal",
# "Respiratory - Normal",
# "Thyroid - Normal",
# "Esophagus - Normal",
"Pancreas - Normal",
"Prostate - Normal",
"Skin - Normal",
# "Mammary - Normal",
# "Female Reproductive - Normal",
# "Respiratory - Normal",
# "Kidney - Normal",
"Liver - Normal"
# "Cartilage - Normal",
# "Adispoe - Normal",
# "Muscle - Normal",
# "Brain - Normal",
# "Retina - Normal",
# "Endothelial - Normal"
), "Correlated",
ifelse(Group %in% c(
"Prenatal - Normal",
"Male Reproductive - Normal",
"Stem Like - Normal",
"Bone - Normal"
), "Anticorrelated", "Other"
))) %>%
mutate(Condition = factor(Condition, levels = c(
"Correlated",
"Anticorrelated",
"Other"
))) %>%
arrange(desc(Condition)) %>%
ggplot2::ggplot(ggplot2::aes_string(x = geneOne,
y = geneTwo,
group="Group",
text = "samples",
color = "Condition")) +
ggplot2::geom_point(alpha = .8) +
ggplot2::labs(title = titleStr) +
ggplot2::theme_bw(base_size = 16) +
ggplot2::xlab(paste0(geneOne, " Expression (VST)")) +
ggplot2::ylab(paste0(geneTwo, " Expression (VST)"))
|
3aaf4c0b32723797bdf4cd5c30f0d1c54f28cb7a | d164a5c189a89d1b61f03693d70898e6f359fff4 | /src/53_discriptive_3.R | 79e2e94038dc19ecb90e2e7e7b4fe8bbe827fb75 | [] | no_license | nishinoh/jahead_secondary2019 | fed33966620372b5cf000a4be119de58584be91a | f61e5393b443d840edcb6309d6000ed03aa18254 | refs/heads/master | 2020-07-04T12:19:55.135616 | 2020-04-07T09:59:49 | 2020-04-07T09:59:49 | 202,285,264 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,381 | r | 53_discriptive_3.R | library(tidyverse)
load("~/Data/JAHEAD/Process_Files/data_after_41_data_frame.rda")
load("~/Data/JAHEAD/Process_Files/data_after_14.rda")
theme_set(theme_bw(base_size = 18, base_family = "HiraKakuProN-W3") +
theme(axis.text=element_text(colour="black")))
# 1. 基本的な情報の作成 ==================================
N <- nrow(data_complete_cases)
# data_long(回答者のロングデータ)から分析に使われるケースのみ抜き出す
# 次の抜き出しでだけ利用する
tmp <- data_complete_cases %>%
distinct(id_personyear)
data_complete_cases_personyear <- data_long %>%
filter(id_personyear %in% tmp$id_personyear)
rm(tmp)
##### 回答者単位の記述統計 ==============================
# ケース数
data_complete_cases_personyear %>%
count(wave, ques_type)
# アウトカムの分布
data_complete_cases %>%
count(do_care_parents_adl, do_care_parents_iadl, do_care_parents_iadl_only) %>%
mutate(r = n/N * 100)
# ADL・IADLの困難度の分布
p_lim_adl <- data_complete_cases_personyear %>%
count(lim_adl) %>%
ggplot(aes(x=factor(lim_adl), y=n)) +
geom_bar(stat="identity", fill="#009FE1") +
labs(x="ADLの困難度")
quartz(file="~/downloads/fig_lim_adl.pdf", type="pdf", width=4,height=4)
p_lim_adl
dev.off()
p_lim_iadl <- data_complete_cases_personyear %>%
count(lim_iadl) %>%
ggplot(aes(x=factor(lim_iadl), y=n)) +
geom_bar(stat="identity", fill="#009FE1") +
labs(x="IADLの困難度")
quartz(file="~/downloads/fig_lim_iadl.pdf", type="pdf", width=4,height=4)
p_lim_iadl
dev.off()
# デイサービス・ホームヘルプの利用頻度
p_use_dayservice <- data_complete_cases_personyear %>%
count(use_dayservice_n) %>%
ggplot(aes(x=factor(use_dayservice_n), y=n)) +
geom_bar(stat="identity", fill="#009FE1") +
labs(x="デイサービス利用頻度")
quartz(file="~/downloads/fig_use_dayservice.pdf", type="pdf", width=4,height=4)
p_use_dayservice
dev.off()
p_use_homehelp <- data_complete_cases_personyear %>%
count(use_homehelp_n) %>%
ggplot(aes(x=factor(use_homehelp_n), y=n)) +
geom_bar(stat="identity", fill="#009FE1") +
labs(x="ホームヘルプ利用頻度")
quartz(file="~/downloads/fig_use_homehelp.pdf", type="pdf", width=4,height=4)
p_use_homehelp
dev.off()
|
a0c69aa6b9af22a19e8d1e1c0d25fbd9987c031b | f91c8c94d9c374b5d368802a882e8fd9fd9b5932 | /R_come_from_sxjns/R语言小作业/e1.R | f0d0aabaa0c12d8e41da38bb7e0c94da0d2e36dd | [] | no_license | chenw265/R_picking | 7cd6273d5dc6a1838a4759148a7e3b73f9492af5 | bcadbc0b35de5c86176a4bacc277817583a74a07 | refs/heads/master | 2020-05-23T13:56:07.563751 | 2019-05-15T09:22:17 | 2019-05-15T09:22:17 | 186,789,743 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 392 | r | e1.R | rm(list = ls())
options(stringsAsFactors = F)
a=read.table('e1.txt')
head(a)
library(org.Hs.eg.db)
ls("package:org.Hs.eg.db")
g2s=toTable(org.Hs.egSYMBOL);head(g2s)
g2e=toTable(org.Hs.egENSEMBL);head(g2e)
head(g2e)
library(stringr)
a$ensembl_id=unlist(lapply(a$V1,function(x){
strsplit(as.character(x),'[.]')[[1]][1]
})
)
tmp=merge(a,g2e,by='ensembl_id')
tmp=merge(tmp,g2s,by='gene_id')
|
cbe0b963f9604bd7af4b71ca4523701a232d6871 | b0c5f1ab6c4e098219b4732f9b050b2aa7871f56 | /OptumOncologyEHR/R/OptumOncologyTestFramework.r | e37bf71a4a1a3e11c86ff0b97f97b9786bdfad4f | [] | no_license | chrisknoll/CDMTests | ae89f0ec70c829b33a45205d254430b04775f42e | 758fa82381e8c9a5d791890b806f16241df3f2d7 | refs/heads/master | 2021-04-09T10:20:32.349463 | 2016-06-09T13:43:50 | 2016-06-09T14:04:29 | 60,777,314 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 461,040 | r | OptumOncologyTestFramework.r | frameworkContext <- new.env(parent = emptyenv());
initFramework <- function() {
insertSql <- c()
insertSql <- c(insertSql, "TRUNCATE TABLE provider;")
insertSql <- c(insertSql, "TRUNCATE TABLE care_area;")
insertSql <- c(insertSql, "TRUNCATE TABLE patient;")
insertSql <- c(insertSql, "TRUNCATE TABLE encounter_provider;")
insertSql <- c(insertSql, "TRUNCATE TABLE visit;")
insertSql <- c(insertSql, "TRUNCATE TABLE encounter;")
insertSql <- c(insertSql, "TRUNCATE TABLE diagnosis;")
insertSql <- c(insertSql, "TRUNCATE TABLE nlp_sds;")
insertSql <- c(insertSql, "TRUNCATE TABLE nlp_sds_family;")
insertSql <- c(insertSql, "TRUNCATE TABLE [procedure];")
insertSql <- c(insertSql, "TRUNCATE TABLE labs;")
insertSql <- c(insertSql, "TRUNCATE TABLE observations;")
insertSql <- c(insertSql, "TRUNCATE TABLE nlp_measurements;")
insertSql <- c(insertSql, "TRUNCATE TABLE microbiology;")
insertSql <- c(insertSql, "TRUNCATE TABLE nlp_biomarker;")
insertSql <- c(insertSql, "TRUNCATE TABLE medication_administrations;")
insertSql <- c(insertSql, "TRUNCATE TABLE patient_reported_meds;")
insertSql <- c(insertSql, "TRUNCATE TABLE prescriptions_written;")
insertSql <- c(insertSql, "TRUNCATE TABLE nlp_drug_rationale;")
insertSql <- c(insertSql, "TRUNCATE TABLE immunization;")
insertSql <- c(insertSql, "TRUNCATE TABLE insurance;")
frameworkContext$insertSql <- insertSql;
testSql <- c()
testSql <- c(testSql, "IF OBJECT_ID('test_results', 'U') IS NOT NULL")
testSql <- c(testSql, " DROP TABLE test_results;")
testSql <- c(testSql, "")
testSql <- c(testSql, "CREATE TABLE test_results (id INT, description VARCHAR(512), test VARCHAR(256), status VARCHAR(5));")
testSql <- c(testSql, "")
frameworkContext$testSql <- testSql;
frameworkContext$testId = 1;
frameworkContext$testDescription = "";
frameworkContext$defaultValues =new.env(parent = emptyenv());
defaults <- new.env(parent = emptyenv())
defaults$provid <- "List truncated..."
defaults$specialty <- "Unspecified"
defaults$prim_spec_ind <- "1"
frameworkContext$defaultValues$provider = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT095853463"
defaults$encid <- "List truncated..."
defaults$carearea <- "UNKNOWN CARE AREA"
defaults$carearea_time <- "1900-01-01.000000"
frameworkContext$defaultValues$care_area = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "List truncated..."
defaults$birth_yr <- "1930 and Earlier"
defaults$gender <- "Male"
defaults$race <- "Caucasian"
defaults$ethnicity <- "Not Hispanic"
defaults$region <- "South"
defaults$division <- "South Atl/West South Crl"
defaults$avg_hh_income <- "39005.0"
defaults$pct_college_educ <- "26.0"
defaults$deceased_indicator <- "0"
defaults$idn_indicator <- "1"
defaults$first_month_active <- "200601"
defaults$last_month_active <- "201506"
defaults$notes_eligible <- "1"
defaults$has_notes <- "1"
defaults$sourceid <- "S0034"
defaults$source_data_through <- "201506"
frameworkContext$defaultValues$patient = defaults;
defaults <- new.env(parent = emptyenv())
defaults$encid <- "List truncated..."
defaults$provid <- "70371"
defaults$provider_role <- "ATTENDING"
frameworkContext$defaultValues$encounter_provider = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT091048451"
defaults$visitid <- "List truncated..."
defaults$visit_type <- "Inpatient"
defaults$visit_start_date <- "2012-08-13"
defaults$visit_start_time <- "1900-01-01.000000"
defaults$visit_end_date <- "2014-07-25"
defaults$visit_end_time <- "1900-01-01.000000"
defaults$discharge_disposition <- "01 DISCHARGED TO HOME OR SELF CARE"
defaults$admission_source <- "Referred by physician or self referral; non-healthcare facility point of origin"
frameworkContext$defaultValues$visit = defaults;
defaults <- new.env(parent = emptyenv())
defaults$encid <- "List truncated..."
defaults$ptid <- "List truncated..."
defaults$interaction_type <- "Office or clinic patient"
defaults$interaction_date <- "2014-06-16"
defaults$interaction_time <- "1900-01-01.000000"
frameworkContext$defaultValues$encounter = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "List truncated..."
defaults$diagnosis_status <- "Diagnosis of"
defaults$diagnosis_cd_type <- "ICD9"
defaults$diag_date <- "2014-05-02"
defaults$diag_time <- "1900-01-01.000000"
defaults$primary_diagnosis <- "0"
defaults$admitting_diagnosis <- "0"
defaults$discharge_diagnosis <- "0"
defaults$poa <- "0"
defaults$problem_list <- "N"
frameworkContext$defaultValues$diagnosis = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "List truncated..."
defaults$note_date <- "2013-04-23"
defaults$sds_term <- "pain"
frameworkContext$defaultValues$nlp_sds = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT084524827"
defaults$note_date <- "2014-07-23"
defaults$sds_term <- "cancer"
defaults$sds_family_member <- "who=family"
defaults$note_section <- "family medical history"
frameworkContext$defaultValues$nlp_sds_family = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "List truncated..."
defaults$proc_code_type <- "CPT4"
defaults$proc_date <- "2012-10-22"
defaults$proc_time <- "1900-01-01.000000"
frameworkContext$defaultValues$procedure = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT143890838"
defaults$test_name <- "O2 saturation.oximetry"
defaults$result_time <- "1900-01-01.000000"
defaults$test_result <- "Negative"
defaults$result_unit <- "%"
defaults$evaluated_for_range <- "N"
defaults$value_within_range <- "U"
frameworkContext$defaultValues$labs = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT143890838"
defaults$obs_type <- "SBP"
defaults$obs_date <- "2014-06-10"
defaults$obs_time <- "1900-01-01.000000"
defaults$obs_result <- "18"
defaults$obs_unit <- "mm Hg"
defaults$evaluated_for_range <- "N"
defaults$value_within_range <- "U"
frameworkContext$defaultValues$observations = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT130234983"
defaults$note_date <- "2013-04-23"
defaults$measurement_type <- "DBP"
defaults$measurement_value <- "2"
frameworkContext$defaultValues$nlp_measurements = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT117858758"
defaults$order_time <- "1900-01-01.000000"
defaults$collect_time <- "1900-01-01.000000"
defaults$result_date <- "2015-05-22"
defaults$result_time <- "1900-01-01.000000"
defaults$specimen_source <- "Urine"
frameworkContext$defaultValues$microbiology = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT084542965"
defaults$note_date <- "2013-04-23"
defaults$biomarker <- "CD20"
defaults$biomarker_status <- "positive"
frameworkContext$defaultValues$nlp_biomarker = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT093154612"
defaults$drug_name <- "SODIUM CHLORIDE 0.9 %"
defaults$ndc <- "00338004904"
defaults$ndc_source <- "Derived"
defaults$order_date <- "2014-10-07"
defaults$order_time <- "1900-01-01.000000"
defaults$route <- "Intravenous"
defaults$generic_desc <- "SODIUM CHLORIDE"
defaults$drug_class <- "Intravenous nutritional therapy; electrolyte; trace element; metal; vitamin; alone or combinations"
frameworkContext$defaultValues$medication_administrations = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT155237089"
defaults$reported_date <- "2009-03-27"
defaults$ndc <- "49999035930"
defaults$route <- "Oral"
defaults$strength_unit <- "mg"
defaults$dosage_form <- "Tabs"
defaults$drug_class <- "Salicylates"
defaults$ndc_source <- "Direct"
frameworkContext$defaultValues$patient_reported_meds = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT118881881"
defaults$rxdate <- "2014-02-24"
defaults$rxtime <- "1900-01-01.000000"
defaults$ndc <- "00406035705"
defaults$quantity_per_fill <- "30"
defaults$num_refills <- "0.0"
defaults$route <- "Oral"
defaults$strength_unit <- "mg"
defaults$ndc_source <- "Direct"
defaults$drug_class <- "HMG & CoA reductase inhibitors (statins)"
defaults$dosage_form <- "Tabs"
frameworkContext$defaultValues$prescriptions_written = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT191017751"
defaults$note_date <- "2013-04-23"
defaults$note_section <- "MEDICATIONS"
defaults$drug_name <- "ASPIRIN"
defaults$drug_action <- "N/A"
defaults$drug_action_preposition <- "OF"
frameworkContext$defaultValues$nlp_drug_rationale = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT194773862"
defaults$ndc <- "49281001350"
defaults$ndc_source <- "Derived"
defaults$immunization_desc <- "INFLUENZA VIRUS VACCINE; INACTIVATED"
defaults$mapped_name <- "Influenza Inactivated Vaccine"
frameworkContext$defaultValues$immunization = defaults;
defaults <- new.env(parent = emptyenv())
defaults$ptid <- "PT130555350"
defaults$encid <- "List truncated..."
defaults$insurance_date <- "2008-09-15"
defaults$insurance_time <- "1900-01-01.000000"
defaults$ins_type <- "Medicare"
frameworkContext$defaultValues$insurance = defaults;
}
declareTest <- function(id, description) {
frameworkContext$testId = id;
frameworkContext$testDescription = description;
sql <- c("", paste0("-- ", id, ": ", description));
frameworkContext$insertSql = c(frameworkContext$insertSql, sql);
frameworkContext$testSql = c(frameworkContext$testSql, sql);
}
set_defaults_provider <- function(provid, specialty, prim_spec_ind) {
defaults <- frameworkContext$defaultValues$provider;
if (!missing(provid)) {
defaults$provid <- provid
}
if (!missing(specialty)) {
defaults$specialty <- specialty
}
if (!missing(prim_spec_ind)) {
defaults$prim_spec_ind <- prim_spec_ind
}
invisible(defaults)
}
set_defaults_care_area <- function(ptid, encid, carearea, carearea_date, carearea_time) {
defaults <- frameworkContext$defaultValues$care_area;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(carearea)) {
defaults$carearea <- carearea
}
if (!missing(carearea_date)) {
defaults$carearea_date <- carearea_date
}
if (!missing(carearea_time)) {
defaults$carearea_time <- carearea_time
}
invisible(defaults)
}
set_defaults_patient <- function(ptid, birth_yr, gender, race, ethnicity, region, division, avg_hh_income, pct_college_educ, deceased_indicator, date_of_death, provid_pcp, idn_indicator, first_month_active, last_month_active, notes_eligible, has_notes, sourceid, source_data_through) {
defaults <- frameworkContext$defaultValues$patient;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(birth_yr)) {
defaults$birth_yr <- birth_yr
}
if (!missing(gender)) {
defaults$gender <- gender
}
if (!missing(race)) {
defaults$race <- race
}
if (!missing(ethnicity)) {
defaults$ethnicity <- ethnicity
}
if (!missing(region)) {
defaults$region <- region
}
if (!missing(division)) {
defaults$division <- division
}
if (!missing(avg_hh_income)) {
defaults$avg_hh_income <- avg_hh_income
}
if (!missing(pct_college_educ)) {
defaults$pct_college_educ <- pct_college_educ
}
if (!missing(deceased_indicator)) {
defaults$deceased_indicator <- deceased_indicator
}
if (!missing(date_of_death)) {
defaults$date_of_death <- date_of_death
}
if (!missing(provid_pcp)) {
defaults$provid_pcp <- provid_pcp
}
if (!missing(idn_indicator)) {
defaults$idn_indicator <- idn_indicator
}
if (!missing(first_month_active)) {
defaults$first_month_active <- first_month_active
}
if (!missing(last_month_active)) {
defaults$last_month_active <- last_month_active
}
if (!missing(notes_eligible)) {
defaults$notes_eligible <- notes_eligible
}
if (!missing(has_notes)) {
defaults$has_notes <- has_notes
}
if (!missing(sourceid)) {
defaults$sourceid <- sourceid
}
if (!missing(source_data_through)) {
defaults$source_data_through <- source_data_through
}
invisible(defaults)
}
set_defaults_encounter_provider <- function(encid, provid, provider_role) {
defaults <- frameworkContext$defaultValues$encounter_provider;
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(provid)) {
defaults$provid <- provid
}
if (!missing(provider_role)) {
defaults$provider_role <- provider_role
}
invisible(defaults)
}
set_defaults_visit <- function(ptid, visitid, visit_type, visit_start_date, visit_start_time, visit_end_date, visit_end_time, discharge_disposition, admission_source, drg) {
defaults <- frameworkContext$defaultValues$visit;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(visitid)) {
defaults$visitid <- visitid
}
if (!missing(visit_type)) {
defaults$visit_type <- visit_type
}
if (!missing(visit_start_date)) {
defaults$visit_start_date <- visit_start_date
}
if (!missing(visit_start_time)) {
defaults$visit_start_time <- visit_start_time
}
if (!missing(visit_end_date)) {
defaults$visit_end_date <- visit_end_date
}
if (!missing(visit_end_time)) {
defaults$visit_end_time <- visit_end_time
}
if (!missing(discharge_disposition)) {
defaults$discharge_disposition <- discharge_disposition
}
if (!missing(admission_source)) {
defaults$admission_source <- admission_source
}
if (!missing(drg)) {
defaults$drg <- drg
}
invisible(defaults)
}
set_defaults_encounter <- function(encid, ptid, visitid, interaction_type, interaction_date, interaction_time) {
defaults <- frameworkContext$defaultValues$encounter;
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(visitid)) {
defaults$visitid <- visitid
}
if (!missing(interaction_type)) {
defaults$interaction_type <- interaction_type
}
if (!missing(interaction_date)) {
defaults$interaction_date <- interaction_date
}
if (!missing(interaction_time)) {
defaults$interaction_time <- interaction_time
}
invisible(defaults)
}
set_defaults_diagnosis <- function(ptid, diagnosis_status, diagnosis_cd, diagnosis_cd_type, diag_date, diag_time, encid, primary_diagnosis, admitting_diagnosis, discharge_diagnosis, poa, problem_list) {
defaults <- frameworkContext$defaultValues$diagnosis;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(diagnosis_status)) {
defaults$diagnosis_status <- diagnosis_status
}
if (!missing(diagnosis_cd)) {
defaults$diagnosis_cd <- diagnosis_cd
}
if (!missing(diagnosis_cd_type)) {
defaults$diagnosis_cd_type <- diagnosis_cd_type
}
if (!missing(diag_date)) {
defaults$diag_date <- diag_date
}
if (!missing(diag_time)) {
defaults$diag_time <- diag_time
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(primary_diagnosis)) {
defaults$primary_diagnosis <- primary_diagnosis
}
if (!missing(admitting_diagnosis)) {
defaults$admitting_diagnosis <- admitting_diagnosis
}
if (!missing(discharge_diagnosis)) {
defaults$discharge_diagnosis <- discharge_diagnosis
}
if (!missing(poa)) {
defaults$poa <- poa
}
if (!missing(problem_list)) {
defaults$problem_list <- problem_list
}
invisible(defaults)
}
set_defaults_nlp_sds <- function(ptid, encid, note_date, sds_term, sds_location, sds_attribute, sds_sentiment, note_section) {
defaults <- frameworkContext$defaultValues$nlp_sds;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(note_date)) {
defaults$note_date <- note_date
}
if (!missing(sds_term)) {
defaults$sds_term <- sds_term
}
if (!missing(sds_location)) {
defaults$sds_location <- sds_location
}
if (!missing(sds_attribute)) {
defaults$sds_attribute <- sds_attribute
}
if (!missing(sds_sentiment)) {
defaults$sds_sentiment <- sds_sentiment
}
if (!missing(note_section)) {
defaults$note_section <- note_section
}
invisible(defaults)
}
set_defaults_nlp_sds_family <- function(ptid, encid, note_date, sds_term, sds_location, sds_family_member, sds_sentiment, note_section) {
defaults <- frameworkContext$defaultValues$nlp_sds_family;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(note_date)) {
defaults$note_date <- note_date
}
if (!missing(sds_term)) {
defaults$sds_term <- sds_term
}
if (!missing(sds_location)) {
defaults$sds_location <- sds_location
}
if (!missing(sds_family_member)) {
defaults$sds_family_member <- sds_family_member
}
if (!missing(sds_sentiment)) {
defaults$sds_sentiment <- sds_sentiment
}
if (!missing(note_section)) {
defaults$note_section <- note_section
}
invisible(defaults)
}
set_defaults_procedure <- function(ptid, proc_code, proc_code_type, proc_date, proc_time, provid_perform, encid, proc_desc, provid_order, betos_code, betos_desc) {
defaults <- frameworkContext$defaultValues$procedure;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(proc_code)) {
defaults$proc_code <- proc_code
}
if (!missing(proc_code_type)) {
defaults$proc_code_type <- proc_code_type
}
if (!missing(proc_date)) {
defaults$proc_date <- proc_date
}
if (!missing(proc_time)) {
defaults$proc_time <- proc_time
}
if (!missing(provid_perform)) {
defaults$provid_perform <- provid_perform
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(proc_desc)) {
defaults$proc_desc <- proc_desc
}
if (!missing(provid_order)) {
defaults$provid_order <- provid_order
}
if (!missing(betos_code)) {
defaults$betos_code <- betos_code
}
if (!missing(betos_desc)) {
defaults$betos_desc <- betos_desc
}
invisible(defaults)
}
set_defaults_labs <- function(ptid, encid, test_name, order_date, order_time, collected_date, collected_time, result_date, result_time, test_result, relative_indicator, result_unit, normal_range, evaluated_for_range, value_within_range) {
defaults <- frameworkContext$defaultValues$labs;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(test_name)) {
defaults$test_name <- test_name
}
if (!missing(order_date)) {
defaults$order_date <- order_date
}
if (!missing(order_time)) {
defaults$order_time <- order_time
}
if (!missing(collected_date)) {
defaults$collected_date <- collected_date
}
if (!missing(collected_time)) {
defaults$collected_time <- collected_time
}
if (!missing(result_date)) {
defaults$result_date <- result_date
}
if (!missing(result_time)) {
defaults$result_time <- result_time
}
if (!missing(test_result)) {
defaults$test_result <- test_result
}
if (!missing(relative_indicator)) {
defaults$relative_indicator <- relative_indicator
}
if (!missing(result_unit)) {
defaults$result_unit <- result_unit
}
if (!missing(normal_range)) {
defaults$normal_range <- normal_range
}
if (!missing(evaluated_for_range)) {
defaults$evaluated_for_range <- evaluated_for_range
}
if (!missing(value_within_range)) {
defaults$value_within_range <- value_within_range
}
invisible(defaults)
}
set_defaults_observations <- function(ptid, encid, obs_type, obs_date, obs_time, obs_result, obs_unit, evaluated_for_range, value_within_range) {
defaults <- frameworkContext$defaultValues$observations;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(obs_type)) {
defaults$obs_type <- obs_type
}
if (!missing(obs_date)) {
defaults$obs_date <- obs_date
}
if (!missing(obs_time)) {
defaults$obs_time <- obs_time
}
if (!missing(obs_result)) {
defaults$obs_result <- obs_result
}
if (!missing(obs_unit)) {
defaults$obs_unit <- obs_unit
}
if (!missing(evaluated_for_range)) {
defaults$evaluated_for_range <- evaluated_for_range
}
if (!missing(value_within_range)) {
defaults$value_within_range <- value_within_range
}
invisible(defaults)
}
set_defaults_nlp_measurements <- function(ptid, encid, note_date, measurement_type, measurement_value, measurement_detail, note_section, measurement_date) {
defaults <- frameworkContext$defaultValues$nlp_measurements;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(note_date)) {
defaults$note_date <- note_date
}
if (!missing(measurement_type)) {
defaults$measurement_type <- measurement_type
}
if (!missing(measurement_value)) {
defaults$measurement_value <- measurement_value
}
if (!missing(measurement_detail)) {
defaults$measurement_detail <- measurement_detail
}
if (!missing(note_section)) {
defaults$note_section <- note_section
}
if (!missing(measurement_date)) {
defaults$measurement_date <- measurement_date
}
invisible(defaults)
}
set_defaults_microbiology <- function(ptid, encid, order_date, order_time, collect_date, collect_time, receive_date, receive_time, result_date, result_time, result_status, specimen_source, organism, mapped_organism_found, mapped_organism_excluded, culture_growth, culture_value, culture_unit, antibiotic, mapped_antibiotic, sensitivity) {
defaults <- frameworkContext$defaultValues$microbiology;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(order_date)) {
defaults$order_date <- order_date
}
if (!missing(order_time)) {
defaults$order_time <- order_time
}
if (!missing(collect_date)) {
defaults$collect_date <- collect_date
}
if (!missing(collect_time)) {
defaults$collect_time <- collect_time
}
if (!missing(receive_date)) {
defaults$receive_date <- receive_date
}
if (!missing(receive_time)) {
defaults$receive_time <- receive_time
}
if (!missing(result_date)) {
defaults$result_date <- result_date
}
if (!missing(result_time)) {
defaults$result_time <- result_time
}
if (!missing(result_status)) {
defaults$result_status <- result_status
}
if (!missing(specimen_source)) {
defaults$specimen_source <- specimen_source
}
if (!missing(organism)) {
defaults$organism <- organism
}
if (!missing(mapped_organism_found)) {
defaults$mapped_organism_found <- mapped_organism_found
}
if (!missing(mapped_organism_excluded)) {
defaults$mapped_organism_excluded <- mapped_organism_excluded
}
if (!missing(culture_growth)) {
defaults$culture_growth <- culture_growth
}
if (!missing(culture_value)) {
defaults$culture_value <- culture_value
}
if (!missing(culture_unit)) {
defaults$culture_unit <- culture_unit
}
if (!missing(antibiotic)) {
defaults$antibiotic <- antibiotic
}
if (!missing(mapped_antibiotic)) {
defaults$mapped_antibiotic <- mapped_antibiotic
}
if (!missing(sensitivity)) {
defaults$sensitivity <- sensitivity
}
invisible(defaults)
}
set_defaults_nlp_biomarker <- function(ptid, note_date, biomarker, variation_detail, biomarker_status) {
defaults <- frameworkContext$defaultValues$nlp_biomarker;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(note_date)) {
defaults$note_date <- note_date
}
if (!missing(biomarker)) {
defaults$biomarker <- biomarker
}
if (!missing(variation_detail)) {
defaults$variation_detail <- variation_detail
}
if (!missing(biomarker_status)) {
defaults$biomarker_status <- biomarker_status
}
invisible(defaults)
}
set_defaults_medication_administrations <- function(ptid, encid, drug_name, ndc, ndc_source, order_date, order_time, admin_date, admin_time, provid, quantity_of_dose, route, strength, strength_unit, dosage_form, dosefreq, generic_desc, drug_class) {
defaults <- frameworkContext$defaultValues$medication_administrations;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(drug_name)) {
defaults$drug_name <- drug_name
}
if (!missing(ndc)) {
defaults$ndc <- ndc
}
if (!missing(ndc_source)) {
defaults$ndc_source <- ndc_source
}
if (!missing(order_date)) {
defaults$order_date <- order_date
}
if (!missing(order_time)) {
defaults$order_time <- order_time
}
if (!missing(admin_date)) {
defaults$admin_date <- admin_date
}
if (!missing(admin_time)) {
defaults$admin_time <- admin_time
}
if (!missing(provid)) {
defaults$provid <- provid
}
if (!missing(quantity_of_dose)) {
defaults$quantity_of_dose <- quantity_of_dose
}
if (!missing(route)) {
defaults$route <- route
}
if (!missing(strength)) {
defaults$strength <- strength
}
if (!missing(strength_unit)) {
defaults$strength_unit <- strength_unit
}
if (!missing(dosage_form)) {
defaults$dosage_form <- dosage_form
}
if (!missing(dosefreq)) {
defaults$dosefreq <- dosefreq
}
if (!missing(generic_desc)) {
defaults$generic_desc <- generic_desc
}
if (!missing(drug_class)) {
defaults$drug_class <- drug_class
}
invisible(defaults)
}
set_defaults_patient_reported_meds <- function(ptid, reported_date, ndc, provid, route, quantity_of_dose, strength, strength_unit, dosage_form, dosefreq, generic_desc, drug_class, drug_name, ndc_source) {
defaults <- frameworkContext$defaultValues$patient_reported_meds;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(reported_date)) {
defaults$reported_date <- reported_date
}
if (!missing(ndc)) {
defaults$ndc <- ndc
}
if (!missing(provid)) {
defaults$provid <- provid
}
if (!missing(route)) {
defaults$route <- route
}
if (!missing(quantity_of_dose)) {
defaults$quantity_of_dose <- quantity_of_dose
}
if (!missing(strength)) {
defaults$strength <- strength
}
if (!missing(strength_unit)) {
defaults$strength_unit <- strength_unit
}
if (!missing(dosage_form)) {
defaults$dosage_form <- dosage_form
}
if (!missing(dosefreq)) {
defaults$dosefreq <- dosefreq
}
if (!missing(generic_desc)) {
defaults$generic_desc <- generic_desc
}
if (!missing(drug_class)) {
defaults$drug_class <- drug_class
}
if (!missing(drug_name)) {
defaults$drug_name <- drug_name
}
if (!missing(ndc_source)) {
defaults$ndc_source <- ndc_source
}
invisible(defaults)
}
set_defaults_prescriptions_written <- function(ptid, rxdate, rxtime, ndc, quantity_per_fill, num_refills, days_supply, provid, route, quantity_of_dose, strength, strength_unit, generic_desc, ndc_source, drug_class, drug_name, dosefreq, dosage_form) {
defaults <- frameworkContext$defaultValues$prescriptions_written;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(rxdate)) {
defaults$rxdate <- rxdate
}
if (!missing(rxtime)) {
defaults$rxtime <- rxtime
}
if (!missing(ndc)) {
defaults$ndc <- ndc
}
if (!missing(quantity_per_fill)) {
defaults$quantity_per_fill <- quantity_per_fill
}
if (!missing(num_refills)) {
defaults$num_refills <- num_refills
}
if (!missing(days_supply)) {
defaults$days_supply <- days_supply
}
if (!missing(provid)) {
defaults$provid <- provid
}
if (!missing(route)) {
defaults$route <- route
}
if (!missing(quantity_of_dose)) {
defaults$quantity_of_dose <- quantity_of_dose
}
if (!missing(strength)) {
defaults$strength <- strength
}
if (!missing(strength_unit)) {
defaults$strength_unit <- strength_unit
}
if (!missing(generic_desc)) {
defaults$generic_desc <- generic_desc
}
if (!missing(ndc_source)) {
defaults$ndc_source <- ndc_source
}
if (!missing(drug_class)) {
defaults$drug_class <- drug_class
}
if (!missing(drug_name)) {
defaults$drug_name <- drug_name
}
if (!missing(dosefreq)) {
defaults$dosefreq <- dosefreq
}
if (!missing(dosage_form)) {
defaults$dosage_form <- dosage_form
}
invisible(defaults)
}
set_defaults_nlp_drug_rationale <- function(ptid, encid, note_date, note_section, drug_name, drug_action, drug_action_preposition, reason_general, sentiment, sentiment_who) {
defaults <- frameworkContext$defaultValues$nlp_drug_rationale;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(note_date)) {
defaults$note_date <- note_date
}
if (!missing(note_section)) {
defaults$note_section <- note_section
}
if (!missing(drug_name)) {
defaults$drug_name <- drug_name
}
if (!missing(drug_action)) {
defaults$drug_action <- drug_action
}
if (!missing(drug_action_preposition)) {
defaults$drug_action_preposition <- drug_action_preposition
}
if (!missing(reason_general)) {
defaults$reason_general <- reason_general
}
if (!missing(sentiment)) {
defaults$sentiment <- sentiment
}
if (!missing(sentiment_who)) {
defaults$sentiment_who <- sentiment_who
}
invisible(defaults)
}
set_defaults_immunization <- function(ptid, immunization_date, ndc, pt_reported, ndc_source, immunization_desc, mapped_name) {
defaults <- frameworkContext$defaultValues$immunization;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(immunization_date)) {
defaults$immunization_date <- immunization_date
}
if (!missing(ndc)) {
defaults$ndc <- ndc
}
if (!missing(pt_reported)) {
defaults$pt_reported <- pt_reported
}
if (!missing(ndc_source)) {
defaults$ndc_source <- ndc_source
}
if (!missing(immunization_desc)) {
defaults$immunization_desc <- immunization_desc
}
if (!missing(mapped_name)) {
defaults$mapped_name <- mapped_name
}
invisible(defaults)
}
set_defaults_insurance <- function(ptid, encid, insurance_date, insurance_time, ins_type) {
defaults <- frameworkContext$defaultValues$insurance;
if (!missing(ptid)) {
defaults$ptid <- ptid
}
if (!missing(encid)) {
defaults$encid <- encid
}
if (!missing(insurance_date)) {
defaults$insurance_date <- insurance_date
}
if (!missing(insurance_time)) {
defaults$insurance_time <- insurance_time
}
if (!missing(ins_type)) {
defaults$ins_type <- ins_type
}
invisible(defaults)
}
get_defaults_provider <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_care_area <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_patient <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_encounter_provider <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_visit <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_encounter <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_diagnosis <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_nlp_sds <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_nlp_sds_family <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_procedure <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_labs <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_observations <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_nlp_measurements <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_microbiology <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_nlp_biomarker <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_medication_administrations <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_patient_reported_meds <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_prescriptions_written <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_nlp_drug_rationale <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_immunization <- function() {
return(frameworkContext$defaultValues)
}
get_defaults_insurance <- function() {
return(frameworkContext$defaultValues)
}
add_provider <- function(provid, specialty, prim_spec_ind) {
defaults <- frameworkContext$defaultValues$provider;
insertFields <- c()
insertValues <- c()
if (missing(provid)) {
provid <- defaults$provid
}
if (!is.null(provid)) {
insertFields <- c(insertFields, "provid")
insertValues <- c(insertValues, provid)
}
if (missing(specialty)) {
specialty <- defaults$specialty
}
if (!is.null(specialty)) {
insertFields <- c(insertFields, "specialty")
insertValues <- c(insertValues, specialty)
}
if (missing(prim_spec_ind)) {
prim_spec_ind <- defaults$prim_spec_ind
}
if (!is.null(prim_spec_ind)) {
insertFields <- c(insertFields, "prim_spec_ind")
insertValues <- c(insertValues, prim_spec_ind)
}
statement <- paste0("INSERT INTO provider (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_care_area <- function(ptid, encid, carearea, carearea_date, carearea_time) {
defaults <- frameworkContext$defaultValues$care_area;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(carearea)) {
carearea <- defaults$carearea
}
if (!is.null(carearea)) {
insertFields <- c(insertFields, "carearea")
insertValues <- c(insertValues, carearea)
}
if (missing(carearea_date)) {
carearea_date <- defaults$carearea_date
}
if (!is.null(carearea_date)) {
insertFields <- c(insertFields, "carearea_date")
insertValues <- c(insertValues, carearea_date)
}
if (missing(carearea_time)) {
carearea_time <- defaults$carearea_time
}
if (!is.null(carearea_time)) {
insertFields <- c(insertFields, "carearea_time")
insertValues <- c(insertValues, carearea_time)
}
statement <- paste0("INSERT INTO care_area (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_patient <- function(ptid, birth_yr, gender, race, ethnicity, region, division, avg_hh_income, pct_college_educ, deceased_indicator, date_of_death, provid_pcp, idn_indicator, first_month_active, last_month_active, notes_eligible, has_notes, sourceid, source_data_through) {
defaults <- frameworkContext$defaultValues$patient;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(birth_yr)) {
birth_yr <- defaults$birth_yr
}
if (!is.null(birth_yr)) {
insertFields <- c(insertFields, "birth_yr")
insertValues <- c(insertValues, birth_yr)
}
if (missing(gender)) {
gender <- defaults$gender
}
if (!is.null(gender)) {
insertFields <- c(insertFields, "gender")
insertValues <- c(insertValues, gender)
}
if (missing(race)) {
race <- defaults$race
}
if (!is.null(race)) {
insertFields <- c(insertFields, "race")
insertValues <- c(insertValues, race)
}
if (missing(ethnicity)) {
ethnicity <- defaults$ethnicity
}
if (!is.null(ethnicity)) {
insertFields <- c(insertFields, "ethnicity")
insertValues <- c(insertValues, ethnicity)
}
if (missing(region)) {
region <- defaults$region
}
if (!is.null(region)) {
insertFields <- c(insertFields, "region")
insertValues <- c(insertValues, region)
}
if (missing(division)) {
division <- defaults$division
}
if (!is.null(division)) {
insertFields <- c(insertFields, "division")
insertValues <- c(insertValues, division)
}
if (missing(avg_hh_income)) {
avg_hh_income <- defaults$avg_hh_income
}
if (!is.null(avg_hh_income)) {
insertFields <- c(insertFields, "avg_hh_income")
insertValues <- c(insertValues, avg_hh_income)
}
if (missing(pct_college_educ)) {
pct_college_educ <- defaults$pct_college_educ
}
if (!is.null(pct_college_educ)) {
insertFields <- c(insertFields, "pct_college_educ")
insertValues <- c(insertValues, pct_college_educ)
}
if (missing(deceased_indicator)) {
deceased_indicator <- defaults$deceased_indicator
}
if (!is.null(deceased_indicator)) {
insertFields <- c(insertFields, "deceased_indicator")
insertValues <- c(insertValues, deceased_indicator)
}
if (missing(date_of_death)) {
date_of_death <- defaults$date_of_death
}
if (!is.null(date_of_death)) {
insertFields <- c(insertFields, "date_of_death")
insertValues <- c(insertValues, date_of_death)
}
if (missing(provid_pcp)) {
provid_pcp <- defaults$provid_pcp
}
if (!is.null(provid_pcp)) {
insertFields <- c(insertFields, "provid_pcp")
insertValues <- c(insertValues, provid_pcp)
}
if (missing(idn_indicator)) {
idn_indicator <- defaults$idn_indicator
}
if (!is.null(idn_indicator)) {
insertFields <- c(insertFields, "idn_indicator")
insertValues <- c(insertValues, idn_indicator)
}
if (missing(first_month_active)) {
first_month_active <- defaults$first_month_active
}
if (!is.null(first_month_active)) {
insertFields <- c(insertFields, "first_month_active")
insertValues <- c(insertValues, first_month_active)
}
if (missing(last_month_active)) {
last_month_active <- defaults$last_month_active
}
if (!is.null(last_month_active)) {
insertFields <- c(insertFields, "last_month_active")
insertValues <- c(insertValues, last_month_active)
}
if (missing(notes_eligible)) {
notes_eligible <- defaults$notes_eligible
}
if (!is.null(notes_eligible)) {
insertFields <- c(insertFields, "notes_eligible")
insertValues <- c(insertValues, notes_eligible)
}
if (missing(has_notes)) {
has_notes <- defaults$has_notes
}
if (!is.null(has_notes)) {
insertFields <- c(insertFields, "has_notes")
insertValues <- c(insertValues, has_notes)
}
if (missing(sourceid)) {
sourceid <- defaults$sourceid
}
if (!is.null(sourceid)) {
insertFields <- c(insertFields, "sourceid")
insertValues <- c(insertValues, sourceid)
}
if (missing(source_data_through)) {
source_data_through <- defaults$source_data_through
}
if (!is.null(source_data_through)) {
insertFields <- c(insertFields, "source_data_through")
insertValues <- c(insertValues, source_data_through)
}
statement <- paste0("INSERT INTO patient (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_encounter_provider <- function(encid, provid, provider_role) {
defaults <- frameworkContext$defaultValues$encounter_provider;
insertFields <- c()
insertValues <- c()
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(provid)) {
provid <- defaults$provid
}
if (!is.null(provid)) {
insertFields <- c(insertFields, "provid")
insertValues <- c(insertValues, provid)
}
if (missing(provider_role)) {
provider_role <- defaults$provider_role
}
if (!is.null(provider_role)) {
insertFields <- c(insertFields, "provider_role")
insertValues <- c(insertValues, provider_role)
}
statement <- paste0("INSERT INTO encounter_provider (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_visit <- function(ptid, visitid, visit_type, visit_start_date, visit_start_time, visit_end_date, visit_end_time, discharge_disposition, admission_source, drg) {
defaults <- frameworkContext$defaultValues$visit;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(visitid)) {
visitid <- defaults$visitid
}
if (!is.null(visitid)) {
insertFields <- c(insertFields, "visitid")
insertValues <- c(insertValues, visitid)
}
if (missing(visit_type)) {
visit_type <- defaults$visit_type
}
if (!is.null(visit_type)) {
insertFields <- c(insertFields, "visit_type")
insertValues <- c(insertValues, visit_type)
}
if (missing(visit_start_date)) {
visit_start_date <- defaults$visit_start_date
}
if (!is.null(visit_start_date)) {
insertFields <- c(insertFields, "visit_start_date")
insertValues <- c(insertValues, visit_start_date)
}
if (missing(visit_start_time)) {
visit_start_time <- defaults$visit_start_time
}
if (!is.null(visit_start_time)) {
insertFields <- c(insertFields, "visit_start_time")
insertValues <- c(insertValues, visit_start_time)
}
if (missing(visit_end_date)) {
visit_end_date <- defaults$visit_end_date
}
if (!is.null(visit_end_date)) {
insertFields <- c(insertFields, "visit_end_date")
insertValues <- c(insertValues, visit_end_date)
}
if (missing(visit_end_time)) {
visit_end_time <- defaults$visit_end_time
}
if (!is.null(visit_end_time)) {
insertFields <- c(insertFields, "visit_end_time")
insertValues <- c(insertValues, visit_end_time)
}
if (missing(discharge_disposition)) {
discharge_disposition <- defaults$discharge_disposition
}
if (!is.null(discharge_disposition)) {
insertFields <- c(insertFields, "discharge_disposition")
insertValues <- c(insertValues, discharge_disposition)
}
if (missing(admission_source)) {
admission_source <- defaults$admission_source
}
if (!is.null(admission_source)) {
insertFields <- c(insertFields, "admission_source")
insertValues <- c(insertValues, admission_source)
}
if (missing(drg)) {
drg <- defaults$drg
}
if (!is.null(drg)) {
insertFields <- c(insertFields, "drg")
insertValues <- c(insertValues, drg)
}
statement <- paste0("INSERT INTO visit (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_encounter <- function(encid, ptid, visitid, interaction_type, interaction_date, interaction_time) {
defaults <- frameworkContext$defaultValues$encounter;
insertFields <- c()
insertValues <- c()
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(visitid)) {
visitid <- defaults$visitid
}
if (!is.null(visitid)) {
insertFields <- c(insertFields, "visitid")
insertValues <- c(insertValues, visitid)
}
if (missing(interaction_type)) {
interaction_type <- defaults$interaction_type
}
if (!is.null(interaction_type)) {
insertFields <- c(insertFields, "interaction_type")
insertValues <- c(insertValues, interaction_type)
}
if (missing(interaction_date)) {
interaction_date <- defaults$interaction_date
}
if (!is.null(interaction_date)) {
insertFields <- c(insertFields, "interaction_date")
insertValues <- c(insertValues, interaction_date)
}
if (missing(interaction_time)) {
interaction_time <- defaults$interaction_time
}
if (!is.null(interaction_time)) {
insertFields <- c(insertFields, "interaction_time")
insertValues <- c(insertValues, interaction_time)
}
statement <- paste0("INSERT INTO encounter (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_diagnosis <- function(ptid, diagnosis_status, diagnosis_cd, diagnosis_cd_type, diag_date, diag_time, encid, primary_diagnosis, admitting_diagnosis, discharge_diagnosis, poa, problem_list) {
defaults <- frameworkContext$defaultValues$diagnosis;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(diagnosis_status)) {
diagnosis_status <- defaults$diagnosis_status
}
if (!is.null(diagnosis_status)) {
insertFields <- c(insertFields, "diagnosis_status")
insertValues <- c(insertValues, diagnosis_status)
}
if (missing(diagnosis_cd)) {
diagnosis_cd <- defaults$diagnosis_cd
}
if (!is.null(diagnosis_cd)) {
insertFields <- c(insertFields, "diagnosis_cd")
insertValues <- c(insertValues, diagnosis_cd)
}
if (missing(diagnosis_cd_type)) {
diagnosis_cd_type <- defaults$diagnosis_cd_type
}
if (!is.null(diagnosis_cd_type)) {
insertFields <- c(insertFields, "diagnosis_cd_type")
insertValues <- c(insertValues, diagnosis_cd_type)
}
if (missing(diag_date)) {
diag_date <- defaults$diag_date
}
if (!is.null(diag_date)) {
insertFields <- c(insertFields, "diag_date")
insertValues <- c(insertValues, diag_date)
}
if (missing(diag_time)) {
diag_time <- defaults$diag_time
}
if (!is.null(diag_time)) {
insertFields <- c(insertFields, "diag_time")
insertValues <- c(insertValues, diag_time)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(primary_diagnosis)) {
primary_diagnosis <- defaults$primary_diagnosis
}
if (!is.null(primary_diagnosis)) {
insertFields <- c(insertFields, "primary_diagnosis")
insertValues <- c(insertValues, primary_diagnosis)
}
if (missing(admitting_diagnosis)) {
admitting_diagnosis <- defaults$admitting_diagnosis
}
if (!is.null(admitting_diagnosis)) {
insertFields <- c(insertFields, "admitting_diagnosis")
insertValues <- c(insertValues, admitting_diagnosis)
}
if (missing(discharge_diagnosis)) {
discharge_diagnosis <- defaults$discharge_diagnosis
}
if (!is.null(discharge_diagnosis)) {
insertFields <- c(insertFields, "discharge_diagnosis")
insertValues <- c(insertValues, discharge_diagnosis)
}
if (missing(poa)) {
poa <- defaults$poa
}
if (!is.null(poa)) {
insertFields <- c(insertFields, "poa")
insertValues <- c(insertValues, poa)
}
if (missing(problem_list)) {
problem_list <- defaults$problem_list
}
if (!is.null(problem_list)) {
insertFields <- c(insertFields, "problem_list")
insertValues <- c(insertValues, problem_list)
}
statement <- paste0("INSERT INTO diagnosis (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_nlp_sds <- function(ptid, encid, note_date, sds_term, sds_location, sds_attribute, sds_sentiment, note_section) {
defaults <- frameworkContext$defaultValues$nlp_sds;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(note_date)) {
note_date <- defaults$note_date
}
if (!is.null(note_date)) {
insertFields <- c(insertFields, "note_date")
insertValues <- c(insertValues, note_date)
}
if (missing(sds_term)) {
sds_term <- defaults$sds_term
}
if (!is.null(sds_term)) {
insertFields <- c(insertFields, "sds_term")
insertValues <- c(insertValues, sds_term)
}
if (missing(sds_location)) {
sds_location <- defaults$sds_location
}
if (!is.null(sds_location)) {
insertFields <- c(insertFields, "sds_location")
insertValues <- c(insertValues, sds_location)
}
if (missing(sds_attribute)) {
sds_attribute <- defaults$sds_attribute
}
if (!is.null(sds_attribute)) {
insertFields <- c(insertFields, "sds_attribute")
insertValues <- c(insertValues, sds_attribute)
}
if (missing(sds_sentiment)) {
sds_sentiment <- defaults$sds_sentiment
}
if (!is.null(sds_sentiment)) {
insertFields <- c(insertFields, "sds_sentiment")
insertValues <- c(insertValues, sds_sentiment)
}
if (missing(note_section)) {
note_section <- defaults$note_section
}
if (!is.null(note_section)) {
insertFields <- c(insertFields, "note_section")
insertValues <- c(insertValues, note_section)
}
statement <- paste0("INSERT INTO nlp_sds (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_nlp_sds_family <- function(ptid, encid, note_date, sds_term, sds_location, sds_family_member, sds_sentiment, note_section) {
defaults <- frameworkContext$defaultValues$nlp_sds_family;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(note_date)) {
note_date <- defaults$note_date
}
if (!is.null(note_date)) {
insertFields <- c(insertFields, "note_date")
insertValues <- c(insertValues, note_date)
}
if (missing(sds_term)) {
sds_term <- defaults$sds_term
}
if (!is.null(sds_term)) {
insertFields <- c(insertFields, "sds_term")
insertValues <- c(insertValues, sds_term)
}
if (missing(sds_location)) {
sds_location <- defaults$sds_location
}
if (!is.null(sds_location)) {
insertFields <- c(insertFields, "sds_location")
insertValues <- c(insertValues, sds_location)
}
if (missing(sds_family_member)) {
sds_family_member <- defaults$sds_family_member
}
if (!is.null(sds_family_member)) {
insertFields <- c(insertFields, "sds_family_member")
insertValues <- c(insertValues, sds_family_member)
}
if (missing(sds_sentiment)) {
sds_sentiment <- defaults$sds_sentiment
}
if (!is.null(sds_sentiment)) {
insertFields <- c(insertFields, "sds_sentiment")
insertValues <- c(insertValues, sds_sentiment)
}
if (missing(note_section)) {
note_section <- defaults$note_section
}
if (!is.null(note_section)) {
insertFields <- c(insertFields, "note_section")
insertValues <- c(insertValues, note_section)
}
statement <- paste0("INSERT INTO nlp_sds_family (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_procedure <- function(ptid, proc_code, proc_code_type, proc_date, proc_time, provid_perform, encid, proc_desc, provid_order, betos_code, betos_desc) {
defaults <- frameworkContext$defaultValues$procedure;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(proc_code)) {
proc_code <- defaults$proc_code
}
if (!is.null(proc_code)) {
insertFields <- c(insertFields, "proc_code")
insertValues <- c(insertValues, proc_code)
}
if (missing(proc_code_type)) {
proc_code_type <- defaults$proc_code_type
}
if (!is.null(proc_code_type)) {
insertFields <- c(insertFields, "proc_code_type")
insertValues <- c(insertValues, proc_code_type)
}
if (missing(proc_date)) {
proc_date <- defaults$proc_date
}
if (!is.null(proc_date)) {
insertFields <- c(insertFields, "proc_date")
insertValues <- c(insertValues, proc_date)
}
if (missing(proc_time)) {
proc_time <- defaults$proc_time
}
if (!is.null(proc_time)) {
insertFields <- c(insertFields, "proc_time")
insertValues <- c(insertValues, proc_time)
}
if (missing(provid_perform)) {
provid_perform <- defaults$provid_perform
}
if (!is.null(provid_perform)) {
insertFields <- c(insertFields, "provid_perform")
insertValues <- c(insertValues, provid_perform)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(proc_desc)) {
proc_desc <- defaults$proc_desc
}
if (!is.null(proc_desc)) {
insertFields <- c(insertFields, "proc_desc")
insertValues <- c(insertValues, proc_desc)
}
if (missing(provid_order)) {
provid_order <- defaults$provid_order
}
if (!is.null(provid_order)) {
insertFields <- c(insertFields, "provid_order")
insertValues <- c(insertValues, provid_order)
}
if (missing(betos_code)) {
betos_code <- defaults$betos_code
}
if (!is.null(betos_code)) {
insertFields <- c(insertFields, "betos_code")
insertValues <- c(insertValues, betos_code)
}
if (missing(betos_desc)) {
betos_desc <- defaults$betos_desc
}
if (!is.null(betos_desc)) {
insertFields <- c(insertFields, "betos_desc")
insertValues <- c(insertValues, betos_desc)
}
statement <- paste0("INSERT INTO [procedure] (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_labs <- function(ptid, encid, test_name, order_date, order_time, collected_date, collected_time, result_date, result_time, test_result, relative_indicator, result_unit, normal_range, evaluated_for_range, value_within_range) {
defaults <- frameworkContext$defaultValues$labs;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(test_name)) {
test_name <- defaults$test_name
}
if (!is.null(test_name)) {
insertFields <- c(insertFields, "test_name")
insertValues <- c(insertValues, test_name)
}
if (missing(order_date)) {
order_date <- defaults$order_date
}
if (!is.null(order_date)) {
insertFields <- c(insertFields, "order_date")
insertValues <- c(insertValues, order_date)
}
if (missing(order_time)) {
order_time <- defaults$order_time
}
if (!is.null(order_time)) {
insertFields <- c(insertFields, "order_time")
insertValues <- c(insertValues, order_time)
}
if (missing(collected_date)) {
collected_date <- defaults$collected_date
}
if (!is.null(collected_date)) {
insertFields <- c(insertFields, "collected_date")
insertValues <- c(insertValues, collected_date)
}
if (missing(collected_time)) {
collected_time <- defaults$collected_time
}
if (!is.null(collected_time)) {
insertFields <- c(insertFields, "collected_time")
insertValues <- c(insertValues, collected_time)
}
if (missing(result_date)) {
result_date <- defaults$result_date
}
if (!is.null(result_date)) {
insertFields <- c(insertFields, "result_date")
insertValues <- c(insertValues, result_date)
}
if (missing(result_time)) {
result_time <- defaults$result_time
}
if (!is.null(result_time)) {
insertFields <- c(insertFields, "result_time")
insertValues <- c(insertValues, result_time)
}
if (missing(test_result)) {
test_result <- defaults$test_result
}
if (!is.null(test_result)) {
insertFields <- c(insertFields, "test_result")
insertValues <- c(insertValues, test_result)
}
if (missing(relative_indicator)) {
relative_indicator <- defaults$relative_indicator
}
if (!is.null(relative_indicator)) {
insertFields <- c(insertFields, "relative_indicator")
insertValues <- c(insertValues, relative_indicator)
}
if (missing(result_unit)) {
result_unit <- defaults$result_unit
}
if (!is.null(result_unit)) {
insertFields <- c(insertFields, "result_unit")
insertValues <- c(insertValues, result_unit)
}
if (missing(normal_range)) {
normal_range <- defaults$normal_range
}
if (!is.null(normal_range)) {
insertFields <- c(insertFields, "normal_range")
insertValues <- c(insertValues, normal_range)
}
if (missing(evaluated_for_range)) {
evaluated_for_range <- defaults$evaluated_for_range
}
if (!is.null(evaluated_for_range)) {
insertFields <- c(insertFields, "evaluated_for_range")
insertValues <- c(insertValues, evaluated_for_range)
}
if (missing(value_within_range)) {
value_within_range <- defaults$value_within_range
}
if (!is.null(value_within_range)) {
insertFields <- c(insertFields, "value_within_range")
insertValues <- c(insertValues, value_within_range)
}
statement <- paste0("INSERT INTO labs (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_observations <- function(ptid, encid, obs_type, obs_date, obs_time, obs_result, obs_unit, evaluated_for_range, value_within_range) {
defaults <- frameworkContext$defaultValues$observations;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(obs_type)) {
obs_type <- defaults$obs_type
}
if (!is.null(obs_type)) {
insertFields <- c(insertFields, "obs_type")
insertValues <- c(insertValues, obs_type)
}
if (missing(obs_date)) {
obs_date <- defaults$obs_date
}
if (!is.null(obs_date)) {
insertFields <- c(insertFields, "obs_date")
insertValues <- c(insertValues, obs_date)
}
if (missing(obs_time)) {
obs_time <- defaults$obs_time
}
if (!is.null(obs_time)) {
insertFields <- c(insertFields, "obs_time")
insertValues <- c(insertValues, obs_time)
}
if (missing(obs_result)) {
obs_result <- defaults$obs_result
}
if (!is.null(obs_result)) {
insertFields <- c(insertFields, "obs_result")
insertValues <- c(insertValues, obs_result)
}
if (missing(obs_unit)) {
obs_unit <- defaults$obs_unit
}
if (!is.null(obs_unit)) {
insertFields <- c(insertFields, "obs_unit")
insertValues <- c(insertValues, obs_unit)
}
if (missing(evaluated_for_range)) {
evaluated_for_range <- defaults$evaluated_for_range
}
if (!is.null(evaluated_for_range)) {
insertFields <- c(insertFields, "evaluated_for_range")
insertValues <- c(insertValues, evaluated_for_range)
}
if (missing(value_within_range)) {
value_within_range <- defaults$value_within_range
}
if (!is.null(value_within_range)) {
insertFields <- c(insertFields, "value_within_range")
insertValues <- c(insertValues, value_within_range)
}
statement <- paste0("INSERT INTO observations (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_nlp_measurements <- function(ptid, encid, note_date, measurement_type, measurement_value, measurement_detail, note_section, measurement_date) {
defaults <- frameworkContext$defaultValues$nlp_measurements;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(note_date)) {
note_date <- defaults$note_date
}
if (!is.null(note_date)) {
insertFields <- c(insertFields, "note_date")
insertValues <- c(insertValues, note_date)
}
if (missing(measurement_type)) {
measurement_type <- defaults$measurement_type
}
if (!is.null(measurement_type)) {
insertFields <- c(insertFields, "measurement_type")
insertValues <- c(insertValues, measurement_type)
}
if (missing(measurement_value)) {
measurement_value <- defaults$measurement_value
}
if (!is.null(measurement_value)) {
insertFields <- c(insertFields, "measurement_value")
insertValues <- c(insertValues, measurement_value)
}
if (missing(measurement_detail)) {
measurement_detail <- defaults$measurement_detail
}
if (!is.null(measurement_detail)) {
insertFields <- c(insertFields, "measurement_detail")
insertValues <- c(insertValues, measurement_detail)
}
if (missing(note_section)) {
note_section <- defaults$note_section
}
if (!is.null(note_section)) {
insertFields <- c(insertFields, "note_section")
insertValues <- c(insertValues, note_section)
}
if (missing(measurement_date)) {
measurement_date <- defaults$measurement_date
}
if (!is.null(measurement_date)) {
insertFields <- c(insertFields, "measurement_date")
insertValues <- c(insertValues, measurement_date)
}
statement <- paste0("INSERT INTO nlp_measurements (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_microbiology <- function(ptid, encid, order_date, order_time, collect_date, collect_time, receive_date, receive_time, result_date, result_time, result_status, specimen_source, organism, mapped_organism_found, mapped_organism_excluded, culture_growth, culture_value, culture_unit, antibiotic, mapped_antibiotic, sensitivity) {
defaults <- frameworkContext$defaultValues$microbiology;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(order_date)) {
order_date <- defaults$order_date
}
if (!is.null(order_date)) {
insertFields <- c(insertFields, "order_date")
insertValues <- c(insertValues, order_date)
}
if (missing(order_time)) {
order_time <- defaults$order_time
}
if (!is.null(order_time)) {
insertFields <- c(insertFields, "order_time")
insertValues <- c(insertValues, order_time)
}
if (missing(collect_date)) {
collect_date <- defaults$collect_date
}
if (!is.null(collect_date)) {
insertFields <- c(insertFields, "collect_date")
insertValues <- c(insertValues, collect_date)
}
if (missing(collect_time)) {
collect_time <- defaults$collect_time
}
if (!is.null(collect_time)) {
insertFields <- c(insertFields, "collect_time")
insertValues <- c(insertValues, collect_time)
}
if (missing(receive_date)) {
receive_date <- defaults$receive_date
}
if (!is.null(receive_date)) {
insertFields <- c(insertFields, "receive_date")
insertValues <- c(insertValues, receive_date)
}
if (missing(receive_time)) {
receive_time <- defaults$receive_time
}
if (!is.null(receive_time)) {
insertFields <- c(insertFields, "receive_time")
insertValues <- c(insertValues, receive_time)
}
if (missing(result_date)) {
result_date <- defaults$result_date
}
if (!is.null(result_date)) {
insertFields <- c(insertFields, "result_date")
insertValues <- c(insertValues, result_date)
}
if (missing(result_time)) {
result_time <- defaults$result_time
}
if (!is.null(result_time)) {
insertFields <- c(insertFields, "result_time")
insertValues <- c(insertValues, result_time)
}
if (missing(result_status)) {
result_status <- defaults$result_status
}
if (!is.null(result_status)) {
insertFields <- c(insertFields, "result_status")
insertValues <- c(insertValues, result_status)
}
if (missing(specimen_source)) {
specimen_source <- defaults$specimen_source
}
if (!is.null(specimen_source)) {
insertFields <- c(insertFields, "specimen_source")
insertValues <- c(insertValues, specimen_source)
}
if (missing(organism)) {
organism <- defaults$organism
}
if (!is.null(organism)) {
insertFields <- c(insertFields, "organism")
insertValues <- c(insertValues, organism)
}
if (missing(mapped_organism_found)) {
mapped_organism_found <- defaults$mapped_organism_found
}
if (!is.null(mapped_organism_found)) {
insertFields <- c(insertFields, "mapped_organism_found")
insertValues <- c(insertValues, mapped_organism_found)
}
if (missing(mapped_organism_excluded)) {
mapped_organism_excluded <- defaults$mapped_organism_excluded
}
if (!is.null(mapped_organism_excluded)) {
insertFields <- c(insertFields, "mapped_organism_excluded")
insertValues <- c(insertValues, mapped_organism_excluded)
}
if (missing(culture_growth)) {
culture_growth <- defaults$culture_growth
}
if (!is.null(culture_growth)) {
insertFields <- c(insertFields, "culture_growth")
insertValues <- c(insertValues, culture_growth)
}
if (missing(culture_value)) {
culture_value <- defaults$culture_value
}
if (!is.null(culture_value)) {
insertFields <- c(insertFields, "culture_value")
insertValues <- c(insertValues, culture_value)
}
if (missing(culture_unit)) {
culture_unit <- defaults$culture_unit
}
if (!is.null(culture_unit)) {
insertFields <- c(insertFields, "culture_unit")
insertValues <- c(insertValues, culture_unit)
}
if (missing(antibiotic)) {
antibiotic <- defaults$antibiotic
}
if (!is.null(antibiotic)) {
insertFields <- c(insertFields, "antibiotic")
insertValues <- c(insertValues, antibiotic)
}
if (missing(mapped_antibiotic)) {
mapped_antibiotic <- defaults$mapped_antibiotic
}
if (!is.null(mapped_antibiotic)) {
insertFields <- c(insertFields, "mapped_antibiotic")
insertValues <- c(insertValues, mapped_antibiotic)
}
if (missing(sensitivity)) {
sensitivity <- defaults$sensitivity
}
if (!is.null(sensitivity)) {
insertFields <- c(insertFields, "sensitivity")
insertValues <- c(insertValues, sensitivity)
}
statement <- paste0("INSERT INTO microbiology (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_nlp_biomarker <- function(ptid, note_date, biomarker, variation_detail, biomarker_status) {
defaults <- frameworkContext$defaultValues$nlp_biomarker;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(note_date)) {
note_date <- defaults$note_date
}
if (!is.null(note_date)) {
insertFields <- c(insertFields, "note_date")
insertValues <- c(insertValues, note_date)
}
if (missing(biomarker)) {
biomarker <- defaults$biomarker
}
if (!is.null(biomarker)) {
insertFields <- c(insertFields, "biomarker")
insertValues <- c(insertValues, biomarker)
}
if (missing(variation_detail)) {
variation_detail <- defaults$variation_detail
}
if (!is.null(variation_detail)) {
insertFields <- c(insertFields, "variation_detail")
insertValues <- c(insertValues, variation_detail)
}
if (missing(biomarker_status)) {
biomarker_status <- defaults$biomarker_status
}
if (!is.null(biomarker_status)) {
insertFields <- c(insertFields, "biomarker_status")
insertValues <- c(insertValues, biomarker_status)
}
statement <- paste0("INSERT INTO nlp_biomarker (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_medication_administrations <- function(ptid, encid, drug_name, ndc, ndc_source, order_date, order_time, admin_date, admin_time, provid, quantity_of_dose, route, strength, strength_unit, dosage_form, dosefreq, generic_desc, drug_class) {
defaults <- frameworkContext$defaultValues$medication_administrations;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(drug_name)) {
drug_name <- defaults$drug_name
}
if (!is.null(drug_name)) {
insertFields <- c(insertFields, "drug_name")
insertValues <- c(insertValues, drug_name)
}
if (missing(ndc)) {
ndc <- defaults$ndc
}
if (!is.null(ndc)) {
insertFields <- c(insertFields, "ndc")
insertValues <- c(insertValues, ndc)
}
if (missing(ndc_source)) {
ndc_source <- defaults$ndc_source
}
if (!is.null(ndc_source)) {
insertFields <- c(insertFields, "ndc_source")
insertValues <- c(insertValues, ndc_source)
}
if (missing(order_date)) {
order_date <- defaults$order_date
}
if (!is.null(order_date)) {
insertFields <- c(insertFields, "order_date")
insertValues <- c(insertValues, order_date)
}
if (missing(order_time)) {
order_time <- defaults$order_time
}
if (!is.null(order_time)) {
insertFields <- c(insertFields, "order_time")
insertValues <- c(insertValues, order_time)
}
if (missing(admin_date)) {
admin_date <- defaults$admin_date
}
if (!is.null(admin_date)) {
insertFields <- c(insertFields, "admin_date")
insertValues <- c(insertValues, admin_date)
}
if (missing(admin_time)) {
admin_time <- defaults$admin_time
}
if (!is.null(admin_time)) {
insertFields <- c(insertFields, "admin_time")
insertValues <- c(insertValues, admin_time)
}
if (missing(provid)) {
provid <- defaults$provid
}
if (!is.null(provid)) {
insertFields <- c(insertFields, "provid")
insertValues <- c(insertValues, provid)
}
if (missing(quantity_of_dose)) {
quantity_of_dose <- defaults$quantity_of_dose
}
if (!is.null(quantity_of_dose)) {
insertFields <- c(insertFields, "quantity_of_dose")
insertValues <- c(insertValues, quantity_of_dose)
}
if (missing(route)) {
route <- defaults$route
}
if (!is.null(route)) {
insertFields <- c(insertFields, "route")
insertValues <- c(insertValues, route)
}
if (missing(strength)) {
strength <- defaults$strength
}
if (!is.null(strength)) {
insertFields <- c(insertFields, "strength")
insertValues <- c(insertValues, strength)
}
if (missing(strength_unit)) {
strength_unit <- defaults$strength_unit
}
if (!is.null(strength_unit)) {
insertFields <- c(insertFields, "strength_unit")
insertValues <- c(insertValues, strength_unit)
}
if (missing(dosage_form)) {
dosage_form <- defaults$dosage_form
}
if (!is.null(dosage_form)) {
insertFields <- c(insertFields, "dosage_form")
insertValues <- c(insertValues, dosage_form)
}
if (missing(dosefreq)) {
dosefreq <- defaults$dosefreq
}
if (!is.null(dosefreq)) {
insertFields <- c(insertFields, "dosefreq")
insertValues <- c(insertValues, dosefreq)
}
if (missing(generic_desc)) {
generic_desc <- defaults$generic_desc
}
if (!is.null(generic_desc)) {
insertFields <- c(insertFields, "generic_desc")
insertValues <- c(insertValues, generic_desc)
}
if (missing(drug_class)) {
drug_class <- defaults$drug_class
}
if (!is.null(drug_class)) {
insertFields <- c(insertFields, "drug_class")
insertValues <- c(insertValues, drug_class)
}
statement <- paste0("INSERT INTO medication_administrations (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_patient_reported_meds <- function(ptid, reported_date, ndc, provid, route, quantity_of_dose, strength, strength_unit, dosage_form, dosefreq, generic_desc, drug_class, drug_name, ndc_source) {
defaults <- frameworkContext$defaultValues$patient_reported_meds;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(reported_date)) {
reported_date <- defaults$reported_date
}
if (!is.null(reported_date)) {
insertFields <- c(insertFields, "reported_date")
insertValues <- c(insertValues, reported_date)
}
if (missing(ndc)) {
ndc <- defaults$ndc
}
if (!is.null(ndc)) {
insertFields <- c(insertFields, "ndc")
insertValues <- c(insertValues, ndc)
}
if (missing(provid)) {
provid <- defaults$provid
}
if (!is.null(provid)) {
insertFields <- c(insertFields, "provid")
insertValues <- c(insertValues, provid)
}
if (missing(route)) {
route <- defaults$route
}
if (!is.null(route)) {
insertFields <- c(insertFields, "route")
insertValues <- c(insertValues, route)
}
if (missing(quantity_of_dose)) {
quantity_of_dose <- defaults$quantity_of_dose
}
if (!is.null(quantity_of_dose)) {
insertFields <- c(insertFields, "quantity_of_dose")
insertValues <- c(insertValues, quantity_of_dose)
}
if (missing(strength)) {
strength <- defaults$strength
}
if (!is.null(strength)) {
insertFields <- c(insertFields, "strength")
insertValues <- c(insertValues, strength)
}
if (missing(strength_unit)) {
strength_unit <- defaults$strength_unit
}
if (!is.null(strength_unit)) {
insertFields <- c(insertFields, "strength_unit")
insertValues <- c(insertValues, strength_unit)
}
if (missing(dosage_form)) {
dosage_form <- defaults$dosage_form
}
if (!is.null(dosage_form)) {
insertFields <- c(insertFields, "dosage_form")
insertValues <- c(insertValues, dosage_form)
}
if (missing(dosefreq)) {
dosefreq <- defaults$dosefreq
}
if (!is.null(dosefreq)) {
insertFields <- c(insertFields, "dosefreq")
insertValues <- c(insertValues, dosefreq)
}
if (missing(generic_desc)) {
generic_desc <- defaults$generic_desc
}
if (!is.null(generic_desc)) {
insertFields <- c(insertFields, "generic_desc")
insertValues <- c(insertValues, generic_desc)
}
if (missing(drug_class)) {
drug_class <- defaults$drug_class
}
if (!is.null(drug_class)) {
insertFields <- c(insertFields, "drug_class")
insertValues <- c(insertValues, drug_class)
}
if (missing(drug_name)) {
drug_name <- defaults$drug_name
}
if (!is.null(drug_name)) {
insertFields <- c(insertFields, "drug_name")
insertValues <- c(insertValues, drug_name)
}
if (missing(ndc_source)) {
ndc_source <- defaults$ndc_source
}
if (!is.null(ndc_source)) {
insertFields <- c(insertFields, "ndc_source")
insertValues <- c(insertValues, ndc_source)
}
statement <- paste0("INSERT INTO patient_reported_meds (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_prescriptions_written <- function(ptid, rxdate, rxtime, ndc, quantity_per_fill, num_refills, days_supply, provid, route, quantity_of_dose, strength, strength_unit, generic_desc, ndc_source, drug_class, drug_name, dosefreq, dosage_form) {
defaults <- frameworkContext$defaultValues$prescriptions_written;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(rxdate)) {
rxdate <- defaults$rxdate
}
if (!is.null(rxdate)) {
insertFields <- c(insertFields, "rxdate")
insertValues <- c(insertValues, rxdate)
}
if (missing(rxtime)) {
rxtime <- defaults$rxtime
}
if (!is.null(rxtime)) {
insertFields <- c(insertFields, "rxtime")
insertValues <- c(insertValues, rxtime)
}
if (missing(ndc)) {
ndc <- defaults$ndc
}
if (!is.null(ndc)) {
insertFields <- c(insertFields, "ndc")
insertValues <- c(insertValues, ndc)
}
if (missing(quantity_per_fill)) {
quantity_per_fill <- defaults$quantity_per_fill
}
if (!is.null(quantity_per_fill)) {
insertFields <- c(insertFields, "quantity_per_fill")
insertValues <- c(insertValues, quantity_per_fill)
}
if (missing(num_refills)) {
num_refills <- defaults$num_refills
}
if (!is.null(num_refills)) {
insertFields <- c(insertFields, "num_refills")
insertValues <- c(insertValues, num_refills)
}
if (missing(days_supply)) {
days_supply <- defaults$days_supply
}
if (!is.null(days_supply)) {
insertFields <- c(insertFields, "days_supply")
insertValues <- c(insertValues, days_supply)
}
if (missing(provid)) {
provid <- defaults$provid
}
if (!is.null(provid)) {
insertFields <- c(insertFields, "provid")
insertValues <- c(insertValues, provid)
}
if (missing(route)) {
route <- defaults$route
}
if (!is.null(route)) {
insertFields <- c(insertFields, "route")
insertValues <- c(insertValues, route)
}
if (missing(quantity_of_dose)) {
quantity_of_dose <- defaults$quantity_of_dose
}
if (!is.null(quantity_of_dose)) {
insertFields <- c(insertFields, "quantity_of_dose")
insertValues <- c(insertValues, quantity_of_dose)
}
if (missing(strength)) {
strength <- defaults$strength
}
if (!is.null(strength)) {
insertFields <- c(insertFields, "strength")
insertValues <- c(insertValues, strength)
}
if (missing(strength_unit)) {
strength_unit <- defaults$strength_unit
}
if (!is.null(strength_unit)) {
insertFields <- c(insertFields, "strength_unit")
insertValues <- c(insertValues, strength_unit)
}
if (missing(generic_desc)) {
generic_desc <- defaults$generic_desc
}
if (!is.null(generic_desc)) {
insertFields <- c(insertFields, "generic_desc")
insertValues <- c(insertValues, generic_desc)
}
if (missing(ndc_source)) {
ndc_source <- defaults$ndc_source
}
if (!is.null(ndc_source)) {
insertFields <- c(insertFields, "ndc_source")
insertValues <- c(insertValues, ndc_source)
}
if (missing(drug_class)) {
drug_class <- defaults$drug_class
}
if (!is.null(drug_class)) {
insertFields <- c(insertFields, "drug_class")
insertValues <- c(insertValues, drug_class)
}
if (missing(drug_name)) {
drug_name <- defaults$drug_name
}
if (!is.null(drug_name)) {
insertFields <- c(insertFields, "drug_name")
insertValues <- c(insertValues, drug_name)
}
if (missing(dosefreq)) {
dosefreq <- defaults$dosefreq
}
if (!is.null(dosefreq)) {
insertFields <- c(insertFields, "dosefreq")
insertValues <- c(insertValues, dosefreq)
}
if (missing(dosage_form)) {
dosage_form <- defaults$dosage_form
}
if (!is.null(dosage_form)) {
insertFields <- c(insertFields, "dosage_form")
insertValues <- c(insertValues, dosage_form)
}
statement <- paste0("INSERT INTO prescriptions_written (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_nlp_drug_rationale <- function(ptid, encid, note_date, note_section, drug_name, drug_action, drug_action_preposition, reason_general, sentiment, sentiment_who) {
defaults <- frameworkContext$defaultValues$nlp_drug_rationale;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(note_date)) {
note_date <- defaults$note_date
}
if (!is.null(note_date)) {
insertFields <- c(insertFields, "note_date")
insertValues <- c(insertValues, note_date)
}
if (missing(note_section)) {
note_section <- defaults$note_section
}
if (!is.null(note_section)) {
insertFields <- c(insertFields, "note_section")
insertValues <- c(insertValues, note_section)
}
if (missing(drug_name)) {
drug_name <- defaults$drug_name
}
if (!is.null(drug_name)) {
insertFields <- c(insertFields, "drug_name")
insertValues <- c(insertValues, drug_name)
}
if (missing(drug_action)) {
drug_action <- defaults$drug_action
}
if (!is.null(drug_action)) {
insertFields <- c(insertFields, "drug_action")
insertValues <- c(insertValues, drug_action)
}
if (missing(drug_action_preposition)) {
drug_action_preposition <- defaults$drug_action_preposition
}
if (!is.null(drug_action_preposition)) {
insertFields <- c(insertFields, "drug_action_preposition")
insertValues <- c(insertValues, drug_action_preposition)
}
if (missing(reason_general)) {
reason_general <- defaults$reason_general
}
if (!is.null(reason_general)) {
insertFields <- c(insertFields, "reason_general")
insertValues <- c(insertValues, reason_general)
}
if (missing(sentiment)) {
sentiment <- defaults$sentiment
}
if (!is.null(sentiment)) {
insertFields <- c(insertFields, "sentiment")
insertValues <- c(insertValues, sentiment)
}
if (missing(sentiment_who)) {
sentiment_who <- defaults$sentiment_who
}
if (!is.null(sentiment_who)) {
insertFields <- c(insertFields, "sentiment_who")
insertValues <- c(insertValues, sentiment_who)
}
statement <- paste0("INSERT INTO nlp_drug_rationale (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_immunization <- function(ptid, immunization_date, ndc, pt_reported, ndc_source, immunization_desc, mapped_name) {
defaults <- frameworkContext$defaultValues$immunization;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(immunization_date)) {
immunization_date <- defaults$immunization_date
}
if (!is.null(immunization_date)) {
insertFields <- c(insertFields, "immunization_date")
insertValues <- c(insertValues, immunization_date)
}
if (missing(ndc)) {
ndc <- defaults$ndc
}
if (!is.null(ndc)) {
insertFields <- c(insertFields, "ndc")
insertValues <- c(insertValues, ndc)
}
if (missing(pt_reported)) {
pt_reported <- defaults$pt_reported
}
if (!is.null(pt_reported)) {
insertFields <- c(insertFields, "pt_reported")
insertValues <- c(insertValues, pt_reported)
}
if (missing(ndc_source)) {
ndc_source <- defaults$ndc_source
}
if (!is.null(ndc_source)) {
insertFields <- c(insertFields, "ndc_source")
insertValues <- c(insertValues, ndc_source)
}
if (missing(immunization_desc)) {
immunization_desc <- defaults$immunization_desc
}
if (!is.null(immunization_desc)) {
insertFields <- c(insertFields, "immunization_desc")
insertValues <- c(insertValues, immunization_desc)
}
if (missing(mapped_name)) {
mapped_name <- defaults$mapped_name
}
if (!is.null(mapped_name)) {
insertFields <- c(insertFields, "mapped_name")
insertValues <- c(insertValues, mapped_name)
}
statement <- paste0("INSERT INTO immunization (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
add_insurance <- function(ptid, encid, insurance_date, insurance_time, ins_type) {
defaults <- frameworkContext$defaultValues$insurance;
insertFields <- c()
insertValues <- c()
if (missing(ptid)) {
ptid <- defaults$ptid
}
if (!is.null(ptid)) {
insertFields <- c(insertFields, "ptid")
insertValues <- c(insertValues, ptid)
}
if (missing(encid)) {
encid <- defaults$encid
}
if (!is.null(encid)) {
insertFields <- c(insertFields, "encid")
insertValues <- c(insertValues, encid)
}
if (missing(insurance_date)) {
insurance_date <- defaults$insurance_date
}
if (!is.null(insurance_date)) {
insertFields <- c(insertFields, "insurance_date")
insertValues <- c(insertValues, insurance_date)
}
if (missing(insurance_time)) {
insurance_time <- defaults$insurance_time
}
if (!is.null(insurance_time)) {
insertFields <- c(insertFields, "insurance_time")
insertValues <- c(insertValues, insurance_time)
}
if (missing(ins_type)) {
ins_type <- defaults$ins_type
}
if (!is.null(ins_type)) {
insertFields <- c(insertFields, "ins_type")
insertValues <- c(insertValues, ins_type)
}
statement <- paste0("INSERT INTO insurance (", paste(insertFields, collapse = ", "), ") VALUES ('", paste(insertValues, collapse = "', '"), "');")
frameworkContext$insertSql = c(frameworkContext$insertSql, statement);
invisible(statement);
}
expect_provider <- function(provider_id, provider_name, npi, dea, specialty_concept_id, care_site_id, year_of_birth, gender_concept_id, provider_source_value, specialty_source_value, specialty_source_concept_id, gender_source_value, gender_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect provider' AS test, CASE WHEN(SELECT COUNT(*) FROM provider WHERE")
first <- TRUE
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(provider_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_name)) {
statement <- paste0(statement, " provider_name IS NULL")
} else {
statement <- paste0(statement, " provider_name = '", provider_name,"'")
}
}
if (!missing(npi)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(npi)) {
statement <- paste0(statement, " npi IS NULL")
} else {
statement <- paste0(statement, " npi = '", npi,"'")
}
}
if (!missing(dea)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dea)) {
statement <- paste0(statement, " dea IS NULL")
} else {
statement <- paste0(statement, " dea = '", dea,"'")
}
}
if (!missing(specialty_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_concept_id)) {
statement <- paste0(statement, " specialty_concept_id IS NULL")
} else {
statement <- paste0(statement, " specialty_concept_id = '", specialty_concept_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(year_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(year_of_birth)) {
statement <- paste0(statement, " year_of_birth IS NULL")
} else {
statement <- paste0(statement, " year_of_birth = '", year_of_birth,"'")
}
}
if (!missing(gender_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_concept_id)) {
statement <- paste0(statement, " gender_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_concept_id = '", gender_concept_id,"'")
}
}
if (!missing(provider_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_source_value)) {
statement <- paste0(statement, " provider_source_value IS NULL")
} else {
statement <- paste0(statement, " provider_source_value = '", provider_source_value,"'")
}
}
if (!missing(specialty_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_source_value)) {
statement <- paste0(statement, " specialty_source_value IS NULL")
} else {
statement <- paste0(statement, " specialty_source_value = '", specialty_source_value,"'")
}
}
if (!missing(specialty_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_source_concept_id)) {
statement <- paste0(statement, " specialty_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " specialty_source_concept_id = '", specialty_source_concept_id,"'")
}
}
if (!missing(gender_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_value)) {
statement <- paste0(statement, " gender_source_value IS NULL")
} else {
statement <- paste0(statement, " gender_source_value = '", gender_source_value,"'")
}
}
if (!missing(gender_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_concept_id)) {
statement <- paste0(statement, " gender_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_source_concept_id = '", gender_source_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_care_site <- function(care_site_id, care_site_name, place_of_service_concept_id, location_id, care_site_source_value, place_of_service_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect care_site' AS test, CASE WHEN(SELECT COUNT(*) FROM care_site WHERE")
first <- TRUE
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(care_site_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_name)) {
statement <- paste0(statement, " care_site_name IS NULL")
} else {
statement <- paste0(statement, " care_site_name = '", care_site_name,"'")
}
}
if (!missing(place_of_service_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(place_of_service_concept_id)) {
statement <- paste0(statement, " place_of_service_concept_id IS NULL")
} else {
statement <- paste0(statement, " place_of_service_concept_id = '", place_of_service_concept_id,"'")
}
}
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(care_site_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_source_value)) {
statement <- paste0(statement, " care_site_source_value IS NULL")
} else {
statement <- paste0(statement, " care_site_source_value = '", care_site_source_value,"'")
}
}
if (!missing(place_of_service_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(place_of_service_source_value)) {
statement <- paste0(statement, " place_of_service_source_value IS NULL")
} else {
statement <- paste0(statement, " place_of_service_source_value = '", place_of_service_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_location <- function(location_id, address_1, address_2, city, state, zip, county, location_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect location' AS test, CASE WHEN(SELECT COUNT(*) FROM location WHERE")
first <- TRUE
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(address_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(address_1)) {
statement <- paste0(statement, " address_1 IS NULL")
} else {
statement <- paste0(statement, " address_1 = '", address_1,"'")
}
}
if (!missing(address_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(address_2)) {
statement <- paste0(statement, " address_2 IS NULL")
} else {
statement <- paste0(statement, " address_2 = '", address_2,"'")
}
}
if (!missing(city)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(city)) {
statement <- paste0(statement, " city IS NULL")
} else {
statement <- paste0(statement, " city = '", city,"'")
}
}
if (!missing(state)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(state)) {
statement <- paste0(statement, " state IS NULL")
} else {
statement <- paste0(statement, " state = '", state,"'")
}
}
if (!missing(zip)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(zip)) {
statement <- paste0(statement, " zip IS NULL")
} else {
statement <- paste0(statement, " zip = '", zip,"'")
}
}
if (!missing(county)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(county)) {
statement <- paste0(statement, " county IS NULL")
} else {
statement <- paste0(statement, " county = '", county,"'")
}
}
if (!missing(location_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_source_value)) {
statement <- paste0(statement, " location_source_value IS NULL")
} else {
statement <- paste0(statement, " location_source_value = '", location_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_person <- function(person_id, gender_concept_id, year_of_birth, month_of_birth, day_of_birth, time_of_birth, race_concept_id, ethnicity_concept_id, location_id, provider_id, care_site_id, person_source_value, gender_source_value, gender_source_concept_id, race_source_value, race_source_concept_id, ethnicity_source_value, ethnicity_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect person' AS test, CASE WHEN(SELECT COUNT(*) FROM person WHERE")
first <- TRUE
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(gender_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_concept_id)) {
statement <- paste0(statement, " gender_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_concept_id = '", gender_concept_id,"'")
}
}
if (!missing(year_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(year_of_birth)) {
statement <- paste0(statement, " year_of_birth IS NULL")
} else {
statement <- paste0(statement, " year_of_birth = '", year_of_birth,"'")
}
}
if (!missing(month_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(month_of_birth)) {
statement <- paste0(statement, " month_of_birth IS NULL")
} else {
statement <- paste0(statement, " month_of_birth = '", month_of_birth,"'")
}
}
if (!missing(day_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(day_of_birth)) {
statement <- paste0(statement, " day_of_birth IS NULL")
} else {
statement <- paste0(statement, " day_of_birth = '", day_of_birth,"'")
}
}
if (!missing(time_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(time_of_birth)) {
statement <- paste0(statement, " time_of_birth IS NULL")
} else {
statement <- paste0(statement, " time_of_birth = '", time_of_birth,"'")
}
}
if (!missing(race_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_concept_id)) {
statement <- paste0(statement, " race_concept_id IS NULL")
} else {
statement <- paste0(statement, " race_concept_id = '", race_concept_id,"'")
}
}
if (!missing(ethnicity_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_concept_id)) {
statement <- paste0(statement, " ethnicity_concept_id IS NULL")
} else {
statement <- paste0(statement, " ethnicity_concept_id = '", ethnicity_concept_id,"'")
}
}
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(person_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_source_value)) {
statement <- paste0(statement, " person_source_value IS NULL")
} else {
statement <- paste0(statement, " person_source_value = '", person_source_value,"'")
}
}
if (!missing(gender_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_value)) {
statement <- paste0(statement, " gender_source_value IS NULL")
} else {
statement <- paste0(statement, " gender_source_value = '", gender_source_value,"'")
}
}
if (!missing(gender_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_concept_id)) {
statement <- paste0(statement, " gender_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_source_concept_id = '", gender_source_concept_id,"'")
}
}
if (!missing(race_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_source_value)) {
statement <- paste0(statement, " race_source_value IS NULL")
} else {
statement <- paste0(statement, " race_source_value = '", race_source_value,"'")
}
}
if (!missing(race_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_source_concept_id)) {
statement <- paste0(statement, " race_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " race_source_concept_id = '", race_source_concept_id,"'")
}
}
if (!missing(ethnicity_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_source_value)) {
statement <- paste0(statement, " ethnicity_source_value IS NULL")
} else {
statement <- paste0(statement, " ethnicity_source_value = '", ethnicity_source_value,"'")
}
}
if (!missing(ethnicity_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_source_concept_id)) {
statement <- paste0(statement, " ethnicity_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " ethnicity_source_concept_id = '", ethnicity_source_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_observation_period <- function(observation_period_id, person_id, observation_period_start_date, observation_period_end_date, period_type_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect observation_period' AS test, CASE WHEN(SELECT COUNT(*) FROM observation_period WHERE")
first <- TRUE
if (!missing(observation_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_id)) {
statement <- paste0(statement, " observation_period_id IS NULL")
} else {
statement <- paste0(statement, " observation_period_id = '", observation_period_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(observation_period_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_start_date)) {
statement <- paste0(statement, " observation_period_start_date IS NULL")
} else {
statement <- paste0(statement, " observation_period_start_date = '", observation_period_start_date,"'")
}
}
if (!missing(observation_period_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_end_date)) {
statement <- paste0(statement, " observation_period_end_date IS NULL")
} else {
statement <- paste0(statement, " observation_period_end_date = '", observation_period_end_date,"'")
}
}
if (!missing(period_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(period_type_concept_id)) {
statement <- paste0(statement, " period_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " period_type_concept_id = '", period_type_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_death <- function(person_id, death_date, death_type_concept_id, cause_concept_id, cause_source_value, cause_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect death' AS test, CASE WHEN(SELECT COUNT(*) FROM death WHERE")
first <- TRUE
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(death_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(death_date)) {
statement <- paste0(statement, " death_date IS NULL")
} else {
statement <- paste0(statement, " death_date = '", death_date,"'")
}
}
if (!missing(death_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(death_type_concept_id)) {
statement <- paste0(statement, " death_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " death_type_concept_id = '", death_type_concept_id,"'")
}
}
if (!missing(cause_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_concept_id)) {
statement <- paste0(statement, " cause_concept_id IS NULL")
} else {
statement <- paste0(statement, " cause_concept_id = '", cause_concept_id,"'")
}
}
if (!missing(cause_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_source_value)) {
statement <- paste0(statement, " cause_source_value IS NULL")
} else {
statement <- paste0(statement, " cause_source_value = '", cause_source_value,"'")
}
}
if (!missing(cause_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_source_concept_id)) {
statement <- paste0(statement, " cause_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " cause_source_concept_id = '", cause_source_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_visit_occurrence <- function(visit_occurrence_id, person_id, visit_concept_id, visit_start_date, visit_start_time, visit_end_date, visit_end_time, visit_type_concept_id, provider_id, care_site_id, visit_source_value, visit_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect visit_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM visit_occurrence WHERE")
first <- TRUE
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(visit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_concept_id)) {
statement <- paste0(statement, " visit_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_concept_id = '", visit_concept_id,"'")
}
}
if (!missing(visit_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_start_date)) {
statement <- paste0(statement, " visit_start_date IS NULL")
} else {
statement <- paste0(statement, " visit_start_date = '", visit_start_date,"'")
}
}
if (!missing(visit_start_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_start_time)) {
statement <- paste0(statement, " visit_start_time IS NULL")
} else {
statement <- paste0(statement, " visit_start_time = '", visit_start_time,"'")
}
}
if (!missing(visit_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_end_date)) {
statement <- paste0(statement, " visit_end_date IS NULL")
} else {
statement <- paste0(statement, " visit_end_date = '", visit_end_date,"'")
}
}
if (!missing(visit_end_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_end_time)) {
statement <- paste0(statement, " visit_end_time IS NULL")
} else {
statement <- paste0(statement, " visit_end_time = '", visit_end_time,"'")
}
}
if (!missing(visit_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_type_concept_id)) {
statement <- paste0(statement, " visit_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_type_concept_id = '", visit_type_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(visit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_source_value)) {
statement <- paste0(statement, " visit_source_value IS NULL")
} else {
statement <- paste0(statement, " visit_source_value = '", visit_source_value,"'")
}
}
if (!missing(visit_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_source_concept_id)) {
statement <- paste0(statement, " visit_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_source_concept_id = '", visit_source_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_condition_occurrence <- function(condition_occurrence_id, person_id, condition_concept_id, condition_source_concept_id, condition_source_value, condition_start_date, provider_id, visit_occurrence_id, condition_type_concept_id, condition_end_date, stop_reason) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect condition_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM condition_occurrence WHERE")
first <- TRUE
if (!missing(condition_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_occurrence_id)) {
statement <- paste0(statement, " condition_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " condition_occurrence_id = '", condition_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(condition_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_concept_id)) {
statement <- paste0(statement, " condition_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_concept_id = '", condition_concept_id,"'")
}
}
if (!missing(condition_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_source_concept_id)) {
statement <- paste0(statement, " condition_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_source_concept_id = '", condition_source_concept_id,"'")
}
}
if (!missing(condition_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_source_value)) {
statement <- paste0(statement, " condition_source_value IS NULL")
} else {
statement <- paste0(statement, " condition_source_value = '", condition_source_value,"'")
}
}
if (!missing(condition_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_start_date)) {
statement <- paste0(statement, " condition_start_date IS NULL")
} else {
statement <- paste0(statement, " condition_start_date = '", condition_start_date,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(condition_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_type_concept_id)) {
statement <- paste0(statement, " condition_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_type_concept_id = '", condition_type_concept_id,"'")
}
}
if (!missing(condition_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_end_date)) {
statement <- paste0(statement, " condition_end_date IS NULL")
} else {
statement <- paste0(statement, " condition_end_date = '", condition_end_date,"'")
}
}
if (!missing(stop_reason)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(stop_reason)) {
statement <- paste0(statement, " stop_reason IS NULL")
} else {
statement <- paste0(statement, " stop_reason = '", stop_reason,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_device_exposure <- function(device_exposure_id, person_id, device_concept_id, device_exposure_start_date, device_exposure_end_date, device_type_concept_id, unique_device_id, quantity, provider_id, visit_occurrence_id, device_source_value, device_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect device_exposure' AS test, CASE WHEN(SELECT COUNT(*) FROM device_exposure WHERE")
first <- TRUE
if (!missing(device_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_id)) {
statement <- paste0(statement, " device_exposure_id IS NULL")
} else {
statement <- paste0(statement, " device_exposure_id = '", device_exposure_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(device_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_concept_id)) {
statement <- paste0(statement, " device_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_concept_id = '", device_concept_id,"'")
}
}
if (!missing(device_exposure_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_start_date)) {
statement <- paste0(statement, " device_exposure_start_date IS NULL")
} else {
statement <- paste0(statement, " device_exposure_start_date = '", device_exposure_start_date,"'")
}
}
if (!missing(device_exposure_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_end_date)) {
statement <- paste0(statement, " device_exposure_end_date IS NULL")
} else {
statement <- paste0(statement, " device_exposure_end_date = '", device_exposure_end_date,"'")
}
}
if (!missing(device_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_type_concept_id)) {
statement <- paste0(statement, " device_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_type_concept_id = '", device_type_concept_id,"'")
}
}
if (!missing(unique_device_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unique_device_id)) {
statement <- paste0(statement, " unique_device_id IS NULL")
} else {
statement <- paste0(statement, " unique_device_id = '", unique_device_id,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(device_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_source_value)) {
statement <- paste0(statement, " device_source_value IS NULL")
} else {
statement <- paste0(statement, " device_source_value = '", device_source_value,"'")
}
}
if (!missing(device_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_source_concept_id)) {
statement <- paste0(statement, " device_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_source_concept_id = '", device_source_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_observation <- function(observation_id, person_id, observation_concept_id, observation_date, observation_time, observation_type_concept_id, value_as_number, value_as_string, value_as_concept_id, qualifier_concept_id, unit_concept_id, provider_id, visit_occurrence_id, observation_source_value, observation_source_concept_id, unit_source_value, qualifier_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect observation' AS test, CASE WHEN(SELECT COUNT(*) FROM observation WHERE")
first <- TRUE
if (!missing(observation_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_id)) {
statement <- paste0(statement, " observation_id IS NULL")
} else {
statement <- paste0(statement, " observation_id = '", observation_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(observation_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_concept_id)) {
statement <- paste0(statement, " observation_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_concept_id = '", observation_concept_id,"'")
}
}
if (!missing(observation_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_date)) {
statement <- paste0(statement, " observation_date IS NULL")
} else {
statement <- paste0(statement, " observation_date = '", observation_date,"'")
}
}
if (!missing(observation_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_time)) {
statement <- paste0(statement, " observation_time IS NULL")
} else {
statement <- paste0(statement, " observation_time = '", observation_time,"'")
}
}
if (!missing(observation_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_type_concept_id)) {
statement <- paste0(statement, " observation_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_type_concept_id = '", observation_type_concept_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_string)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_string)) {
statement <- paste0(statement, " value_as_string IS NULL")
} else {
statement <- paste0(statement, " value_as_string = '", value_as_string,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
if (!missing(qualifier_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_concept_id)) {
statement <- paste0(statement, " qualifier_concept_id IS NULL")
} else {
statement <- paste0(statement, " qualifier_concept_id = '", qualifier_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(observation_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_source_value)) {
statement <- paste0(statement, " observation_source_value IS NULL")
} else {
statement <- paste0(statement, " observation_source_value = '", observation_source_value,"'")
}
}
if (!missing(observation_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_source_concept_id)) {
statement <- paste0(statement, " observation_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_source_concept_id = '", observation_source_concept_id,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(qualifier_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_source_value)) {
statement <- paste0(statement, " qualifier_source_value IS NULL")
} else {
statement <- paste0(statement, " qualifier_source_value = '", qualifier_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_measurement <- function(measurement_id, person_id, measurement_concept_id, measurement_date, measurement_time, measurement_type_concept_id, operator_concept_id, value_as_number, value_as_concept_id, unit_concept_id, range_low, range_high, provider_id, visit_occurrence_id, measurement_source_value, measurement_source_concept_id, unit_source_value, value_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect measurement' AS test, CASE WHEN(SELECT COUNT(*) FROM measurement WHERE")
first <- TRUE
if (!missing(measurement_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_id)) {
statement <- paste0(statement, " measurement_id IS NULL")
} else {
statement <- paste0(statement, " measurement_id = '", measurement_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(measurement_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_concept_id)) {
statement <- paste0(statement, " measurement_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_concept_id = '", measurement_concept_id,"'")
}
}
if (!missing(measurement_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_date)) {
statement <- paste0(statement, " measurement_date IS NULL")
} else {
statement <- paste0(statement, " measurement_date = '", measurement_date,"'")
}
}
if (!missing(measurement_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_time)) {
statement <- paste0(statement, " measurement_time IS NULL")
} else {
statement <- paste0(statement, " measurement_time = '", measurement_time,"'")
}
}
if (!missing(measurement_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_type_concept_id)) {
statement <- paste0(statement, " measurement_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_type_concept_id = '", measurement_type_concept_id,"'")
}
}
if (!missing(operator_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(operator_concept_id)) {
statement <- paste0(statement, " operator_concept_id IS NULL")
} else {
statement <- paste0(statement, " operator_concept_id = '", operator_concept_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(range_low)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(range_low)) {
statement <- paste0(statement, " range_low IS NULL")
} else {
statement <- paste0(statement, " range_low = '", range_low,"'")
}
}
if (!missing(range_high)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(range_high)) {
statement <- paste0(statement, " range_high IS NULL")
} else {
statement <- paste0(statement, " range_high = '", range_high,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(measurement_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_source_value)) {
statement <- paste0(statement, " measurement_source_value IS NULL")
} else {
statement <- paste0(statement, " measurement_source_value = '", measurement_source_value,"'")
}
}
if (!missing(measurement_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_source_concept_id)) {
statement <- paste0(statement, " measurement_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_source_concept_id = '", measurement_source_concept_id,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(value_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_source_value)) {
statement <- paste0(statement, " value_source_value IS NULL")
} else {
statement <- paste0(statement, " value_source_value = '", value_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_procedure_occurrence <- function(procedure_occurrence_id, person_id, procedure_concept_id, procedure_date, procedure_type_concept_id, modifier_concept_id, quantity, provider_id, visit_occurrence_id, procedure_source_value, procedure_source_concept_id, qualifier_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect procedure_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM procedure_occurrence WHERE")
first <- TRUE
if (!missing(procedure_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_occurrence_id)) {
statement <- paste0(statement, " procedure_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " procedure_occurrence_id = '", procedure_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(procedure_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_concept_id)) {
statement <- paste0(statement, " procedure_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_concept_id = '", procedure_concept_id,"'")
}
}
if (!missing(procedure_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_date)) {
statement <- paste0(statement, " procedure_date IS NULL")
} else {
statement <- paste0(statement, " procedure_date = '", procedure_date,"'")
}
}
if (!missing(procedure_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_type_concept_id)) {
statement <- paste0(statement, " procedure_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_type_concept_id = '", procedure_type_concept_id,"'")
}
}
if (!missing(modifier_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(modifier_concept_id)) {
statement <- paste0(statement, " modifier_concept_id IS NULL")
} else {
statement <- paste0(statement, " modifier_concept_id = '", modifier_concept_id,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(procedure_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_source_value)) {
statement <- paste0(statement, " procedure_source_value IS NULL")
} else {
statement <- paste0(statement, " procedure_source_value = '", procedure_source_value,"'")
}
}
if (!missing(procedure_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_source_concept_id)) {
statement <- paste0(statement, " procedure_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_source_concept_id = '", procedure_source_concept_id,"'")
}
}
if (!missing(qualifier_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_source_value)) {
statement <- paste0(statement, " qualifier_source_value IS NULL")
} else {
statement <- paste0(statement, " qualifier_source_value = '", qualifier_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_drug_exposure <- function(drug_exposure_id, person_id, drug_exposure_start_date, drug_concept_id, drug_source_value, drug_source_concept_id, drug_type_concept_id, provider_id, visit_occurrence_id, route_concept_id, route_source_value, quantity, refills, days_supply, dose_unit_concept_id, dose_unit_source_value, effective_drug_dose, stop_reason, sig, lot_number, drug_exposure_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_exposure' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_exposure WHERE")
first <- TRUE
if (!missing(drug_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_id)) {
statement <- paste0(statement, " drug_exposure_id IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_id = '", drug_exposure_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_exposure_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_start_date)) {
statement <- paste0(statement, " drug_exposure_start_date IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_start_date = '", drug_exposure_start_date,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(drug_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_source_value)) {
statement <- paste0(statement, " drug_source_value IS NULL")
} else {
statement <- paste0(statement, " drug_source_value = '", drug_source_value,"'")
}
}
if (!missing(drug_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_source_concept_id)) {
statement <- paste0(statement, " drug_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_source_concept_id = '", drug_source_concept_id,"'")
}
}
if (!missing(drug_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_type_concept_id)) {
statement <- paste0(statement, " drug_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_type_concept_id = '", drug_type_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(route_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(route_concept_id)) {
statement <- paste0(statement, " route_concept_id IS NULL")
} else {
statement <- paste0(statement, " route_concept_id = '", route_concept_id,"'")
}
}
if (!missing(route_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(route_source_value)) {
statement <- paste0(statement, " route_source_value IS NULL")
} else {
statement <- paste0(statement, " route_source_value = '", route_source_value,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(refills)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(refills)) {
statement <- paste0(statement, " refills IS NULL")
} else {
statement <- paste0(statement, " refills = '", refills,"'")
}
}
if (!missing(days_supply)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(days_supply)) {
statement <- paste0(statement, " days_supply IS NULL")
} else {
statement <- paste0(statement, " days_supply = '", days_supply,"'")
}
}
if (!missing(dose_unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_unit_concept_id)) {
statement <- paste0(statement, " dose_unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " dose_unit_concept_id = '", dose_unit_concept_id,"'")
}
}
if (!missing(dose_unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_unit_source_value)) {
statement <- paste0(statement, " dose_unit_source_value IS NULL")
} else {
statement <- paste0(statement, " dose_unit_source_value = '", dose_unit_source_value,"'")
}
}
if (!missing(effective_drug_dose)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(effective_drug_dose)) {
statement <- paste0(statement, " effective_drug_dose IS NULL")
} else {
statement <- paste0(statement, " effective_drug_dose = '", effective_drug_dose,"'")
}
}
if (!missing(stop_reason)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(stop_reason)) {
statement <- paste0(statement, " stop_reason IS NULL")
} else {
statement <- paste0(statement, " stop_reason = '", stop_reason,"'")
}
}
if (!missing(sig)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(sig)) {
statement <- paste0(statement, " sig IS NULL")
} else {
statement <- paste0(statement, " sig = '", sig,"'")
}
}
if (!missing(lot_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(lot_number)) {
statement <- paste0(statement, " lot_number IS NULL")
} else {
statement <- paste0(statement, " lot_number = '", lot_number,"'")
}
}
if (!missing(drug_exposure_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_end_date)) {
statement <- paste0(statement, " drug_exposure_end_date IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_end_date = '", drug_exposure_end_date,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_fact_relationship <- function(domain_concept_id_1, fact_id_1, domain_concept_id_2, fact_id_2, relationship_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect fact_relationship' AS test, CASE WHEN(SELECT COUNT(*) FROM fact_relationship WHERE")
first <- TRUE
if (!missing(domain_concept_id_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(domain_concept_id_1)) {
statement <- paste0(statement, " domain_concept_id_1 IS NULL")
} else {
statement <- paste0(statement, " domain_concept_id_1 = '", domain_concept_id_1,"'")
}
}
if (!missing(fact_id_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(fact_id_1)) {
statement <- paste0(statement, " fact_id_1 IS NULL")
} else {
statement <- paste0(statement, " fact_id_1 = '", fact_id_1,"'")
}
}
if (!missing(domain_concept_id_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(domain_concept_id_2)) {
statement <- paste0(statement, " domain_concept_id_2 IS NULL")
} else {
statement <- paste0(statement, " domain_concept_id_2 = '", domain_concept_id_2,"'")
}
}
if (!missing(fact_id_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(fact_id_2)) {
statement <- paste0(statement, " fact_id_2 IS NULL")
} else {
statement <- paste0(statement, " fact_id_2 = '", fact_id_2,"'")
}
}
if (!missing(relationship_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(relationship_concept_id)) {
statement <- paste0(statement, " relationship_concept_id IS NULL")
} else {
statement <- paste0(statement, " relationship_concept_id = '", relationship_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_payer_plan_period <- function(payer_plan_period_id, person_id, payer_plan_period_start_date, payer_plan_period_end_date, payer_source_value, plan_source_value, family_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect payer_plan_period' AS test, CASE WHEN(SELECT COUNT(*) FROM payer_plan_period WHERE")
first <- TRUE
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(payer_plan_period_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_start_date)) {
statement <- paste0(statement, " payer_plan_period_start_date IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_start_date = '", payer_plan_period_start_date,"'")
}
}
if (!missing(payer_plan_period_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_end_date)) {
statement <- paste0(statement, " payer_plan_period_end_date IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_end_date = '", payer_plan_period_end_date,"'")
}
}
if (!missing(payer_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_source_value)) {
statement <- paste0(statement, " payer_source_value IS NULL")
} else {
statement <- paste0(statement, " payer_source_value = '", payer_source_value,"'")
}
}
if (!missing(plan_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(plan_source_value)) {
statement <- paste0(statement, " plan_source_value IS NULL")
} else {
statement <- paste0(statement, " plan_source_value = '", plan_source_value,"'")
}
}
if (!missing(family_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(family_source_value)) {
statement <- paste0(statement, " family_source_value IS NULL")
} else {
statement <- paste0(statement, " family_source_value = '", family_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_note <- function(note_id, person_id, note_date, note_time, note_type_concept_id, note_text, provider_id, visit_occurrence_id, note_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect note' AS test, CASE WHEN(SELECT COUNT(*) FROM note WHERE")
first <- TRUE
if (!missing(note_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_id)) {
statement <- paste0(statement, " note_id IS NULL")
} else {
statement <- paste0(statement, " note_id = '", note_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(note_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_date)) {
statement <- paste0(statement, " note_date IS NULL")
} else {
statement <- paste0(statement, " note_date = '", note_date,"'")
}
}
if (!missing(note_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_time)) {
statement <- paste0(statement, " note_time IS NULL")
} else {
statement <- paste0(statement, " note_time = '", note_time,"'")
}
}
if (!missing(note_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_type_concept_id)) {
statement <- paste0(statement, " note_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " note_type_concept_id = '", note_type_concept_id,"'")
}
}
if (!missing(note_text)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_text)) {
statement <- paste0(statement, " note_text IS NULL")
} else {
statement <- paste0(statement, " note_text = '", note_text,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(note_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_source_value)) {
statement <- paste0(statement, " note_source_value IS NULL")
} else {
statement <- paste0(statement, " note_source_value = '", note_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_specimen <- function(specimen_id, person_id, specimen_concept_id, specimen_type_concept_id, specimen_date, specimen_time, quantity, unit_concept_id, anatomic_site_concept_id, disease_status_concept_id, specimen_source_id, specimen_source_value, unit_source_value, anatomic_site_source_value, disease_status_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect specimen' AS test, CASE WHEN(SELECT COUNT(*) FROM specimen WHERE")
first <- TRUE
if (!missing(specimen_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_id)) {
statement <- paste0(statement, " specimen_id IS NULL")
} else {
statement <- paste0(statement, " specimen_id = '", specimen_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(specimen_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_concept_id)) {
statement <- paste0(statement, " specimen_concept_id IS NULL")
} else {
statement <- paste0(statement, " specimen_concept_id = '", specimen_concept_id,"'")
}
}
if (!missing(specimen_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_type_concept_id)) {
statement <- paste0(statement, " specimen_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " specimen_type_concept_id = '", specimen_type_concept_id,"'")
}
}
if (!missing(specimen_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_date)) {
statement <- paste0(statement, " specimen_date IS NULL")
} else {
statement <- paste0(statement, " specimen_date = '", specimen_date,"'")
}
}
if (!missing(specimen_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_time)) {
statement <- paste0(statement, " specimen_time IS NULL")
} else {
statement <- paste0(statement, " specimen_time = '", specimen_time,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(anatomic_site_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(anatomic_site_concept_id)) {
statement <- paste0(statement, " anatomic_site_concept_id IS NULL")
} else {
statement <- paste0(statement, " anatomic_site_concept_id = '", anatomic_site_concept_id,"'")
}
}
if (!missing(disease_status_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(disease_status_concept_id)) {
statement <- paste0(statement, " disease_status_concept_id IS NULL")
} else {
statement <- paste0(statement, " disease_status_concept_id = '", disease_status_concept_id,"'")
}
}
if (!missing(specimen_source_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_source_id)) {
statement <- paste0(statement, " specimen_source_id IS NULL")
} else {
statement <- paste0(statement, " specimen_source_id = '", specimen_source_id,"'")
}
}
if (!missing(specimen_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_source_value)) {
statement <- paste0(statement, " specimen_source_value IS NULL")
} else {
statement <- paste0(statement, " specimen_source_value = '", specimen_source_value,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(anatomic_site_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(anatomic_site_source_value)) {
statement <- paste0(statement, " anatomic_site_source_value IS NULL")
} else {
statement <- paste0(statement, " anatomic_site_source_value = '", anatomic_site_source_value,"'")
}
}
if (!missing(disease_status_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(disease_status_source_value)) {
statement <- paste0(statement, " disease_status_source_value IS NULL")
} else {
statement <- paste0(statement, " disease_status_source_value = '", disease_status_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_procedure_cost <- function(procedure_cost_id, procedure_occurrence_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, revenue_code_concept_id, payer_plan_period_id, revenue_code_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect procedure_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM procedure_cost WHERE")
first <- TRUE
if (!missing(procedure_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_cost_id)) {
statement <- paste0(statement, " procedure_cost_id IS NULL")
} else {
statement <- paste0(statement, " procedure_cost_id = '", procedure_cost_id,"'")
}
}
if (!missing(procedure_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_occurrence_id)) {
statement <- paste0(statement, " procedure_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " procedure_occurrence_id = '", procedure_occurrence_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(revenue_code_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(revenue_code_concept_id)) {
statement <- paste0(statement, " revenue_code_concept_id IS NULL")
} else {
statement <- paste0(statement, " revenue_code_concept_id = '", revenue_code_concept_id,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
if (!missing(revenue_code_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(revenue_code_source_value)) {
statement <- paste0(statement, " revenue_code_source_value IS NULL")
} else {
statement <- paste0(statement, " revenue_code_source_value = '", revenue_code_source_value,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_visit_cost <- function(visit_cost_id, visit_occurrence_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect visit_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM visit_cost WHERE")
first <- TRUE
if (!missing(visit_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_cost_id)) {
statement <- paste0(statement, " visit_cost_id IS NULL")
} else {
statement <- paste0(statement, " visit_cost_id = '", visit_cost_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_drug_cost <- function(drug_cost_id, drug_exposure_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, ingredient_cost, dispensing_fee, average_wholesale_price, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_cost WHERE")
first <- TRUE
if (!missing(drug_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_cost_id)) {
statement <- paste0(statement, " drug_cost_id IS NULL")
} else {
statement <- paste0(statement, " drug_cost_id = '", drug_cost_id,"'")
}
}
if (!missing(drug_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_id)) {
statement <- paste0(statement, " drug_exposure_id IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_id = '", drug_exposure_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(ingredient_cost)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ingredient_cost)) {
statement <- paste0(statement, " ingredient_cost IS NULL")
} else {
statement <- paste0(statement, " ingredient_cost = '", ingredient_cost,"'")
}
}
if (!missing(dispensing_fee)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dispensing_fee)) {
statement <- paste0(statement, " dispensing_fee IS NULL")
} else {
statement <- paste0(statement, " dispensing_fee = '", dispensing_fee,"'")
}
}
if (!missing(average_wholesale_price)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(average_wholesale_price)) {
statement <- paste0(statement, " average_wholesale_price IS NULL")
} else {
statement <- paste0(statement, " average_wholesale_price = '", average_wholesale_price,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_device_cost <- function(device_cost_id, device_exposure_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect device_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM device_cost WHERE")
first <- TRUE
if (!missing(device_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_cost_id)) {
statement <- paste0(statement, " device_cost_id IS NULL")
} else {
statement <- paste0(statement, " device_cost_id = '", device_cost_id,"'")
}
}
if (!missing(device_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_id)) {
statement <- paste0(statement, " device_exposure_id IS NULL")
} else {
statement <- paste0(statement, " device_exposure_id = '", device_exposure_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_drug_era <- function(drug_era_id, person_id, drug_concept_id, drug_era_start_date, drug_era_end_date, drug_exposure_count, gap_days) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_era' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_era WHERE")
first <- TRUE
if (!missing(drug_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_id)) {
statement <- paste0(statement, " drug_era_id IS NULL")
} else {
statement <- paste0(statement, " drug_era_id = '", drug_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(drug_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_start_date)) {
statement <- paste0(statement, " drug_era_start_date IS NULL")
} else {
statement <- paste0(statement, " drug_era_start_date = '", drug_era_start_date,"'")
}
}
if (!missing(drug_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_end_date)) {
statement <- paste0(statement, " drug_era_end_date IS NULL")
} else {
statement <- paste0(statement, " drug_era_end_date = '", drug_era_end_date,"'")
}
}
if (!missing(drug_exposure_count)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_count)) {
statement <- paste0(statement, " drug_exposure_count IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_count = '", drug_exposure_count,"'")
}
}
if (!missing(gap_days)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gap_days)) {
statement <- paste0(statement, " gap_days IS NULL")
} else {
statement <- paste0(statement, " gap_days = '", gap_days,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_dose_era <- function(dose_era_id, person_id, drug_concept_id, unit_concept_id, dose_value, dose_era_start_date, dose_era_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect dose_era' AS test, CASE WHEN(SELECT COUNT(*) FROM dose_era WHERE")
first <- TRUE
if (!missing(dose_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_id)) {
statement <- paste0(statement, " dose_era_id IS NULL")
} else {
statement <- paste0(statement, " dose_era_id = '", dose_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(dose_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_value)) {
statement <- paste0(statement, " dose_value IS NULL")
} else {
statement <- paste0(statement, " dose_value = '", dose_value,"'")
}
}
if (!missing(dose_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_start_date)) {
statement <- paste0(statement, " dose_era_start_date IS NULL")
} else {
statement <- paste0(statement, " dose_era_start_date = '", dose_era_start_date,"'")
}
}
if (!missing(dose_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_end_date)) {
statement <- paste0(statement, " dose_era_end_date IS NULL")
} else {
statement <- paste0(statement, " dose_era_end_date = '", dose_era_end_date,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_condition_era <- function(condition_era_id, person_id, condition_concept_id, condition_era_start_date, condition_era_end_date, condition_occurrence_count) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect condition_era' AS test, CASE WHEN(SELECT COUNT(*) FROM condition_era WHERE")
first <- TRUE
if (!missing(condition_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_id)) {
statement <- paste0(statement, " condition_era_id IS NULL")
} else {
statement <- paste0(statement, " condition_era_id = '", condition_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(condition_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_concept_id)) {
statement <- paste0(statement, " condition_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_concept_id = '", condition_concept_id,"'")
}
}
if (!missing(condition_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_start_date)) {
statement <- paste0(statement, " condition_era_start_date IS NULL")
} else {
statement <- paste0(statement, " condition_era_start_date = '", condition_era_start_date,"'")
}
}
if (!missing(condition_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_end_date)) {
statement <- paste0(statement, " condition_era_end_date IS NULL")
} else {
statement <- paste0(statement, " condition_era_end_date = '", condition_era_end_date,"'")
}
}
if (!missing(condition_occurrence_count)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_occurrence_count)) {
statement <- paste0(statement, " condition_occurrence_count IS NULL")
} else {
statement <- paste0(statement, " condition_occurrence_count = '", condition_occurrence_count,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_cdm_source <- function(cdm_source_name, cdm_source_abbreviation, cdm_holder, source_description, source_documentation_reference, cdm_etl_reference, source_release_date, cdm_release_date, cdm_version, vocabulary_version) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cdm_source' AS test, CASE WHEN(SELECT COUNT(*) FROM cdm_source WHERE")
first <- TRUE
if (!missing(cdm_source_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_source_name)) {
statement <- paste0(statement, " cdm_source_name IS NULL")
} else {
statement <- paste0(statement, " cdm_source_name = '", cdm_source_name,"'")
}
}
if (!missing(cdm_source_abbreviation)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_source_abbreviation)) {
statement <- paste0(statement, " cdm_source_abbreviation IS NULL")
} else {
statement <- paste0(statement, " cdm_source_abbreviation = '", cdm_source_abbreviation,"'")
}
}
if (!missing(cdm_holder)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_holder)) {
statement <- paste0(statement, " cdm_holder IS NULL")
} else {
statement <- paste0(statement, " cdm_holder = '", cdm_holder,"'")
}
}
if (!missing(source_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_description)) {
statement <- paste0(statement, " source_description IS NULL")
} else {
statement <- paste0(statement, " source_description = '", source_description,"'")
}
}
if (!missing(source_documentation_reference)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_documentation_reference)) {
statement <- paste0(statement, " source_documentation_reference IS NULL")
} else {
statement <- paste0(statement, " source_documentation_reference = '", source_documentation_reference,"'")
}
}
if (!missing(cdm_etl_reference)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_etl_reference)) {
statement <- paste0(statement, " cdm_etl_reference IS NULL")
} else {
statement <- paste0(statement, " cdm_etl_reference = '", cdm_etl_reference,"'")
}
}
if (!missing(source_release_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_release_date)) {
statement <- paste0(statement, " source_release_date IS NULL")
} else {
statement <- paste0(statement, " source_release_date = '", source_release_date,"'")
}
}
if (!missing(cdm_release_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_release_date)) {
statement <- paste0(statement, " cdm_release_date IS NULL")
} else {
statement <- paste0(statement, " cdm_release_date = '", cdm_release_date,"'")
}
}
if (!missing(cdm_version)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_version)) {
statement <- paste0(statement, " cdm_version IS NULL")
} else {
statement <- paste0(statement, " cdm_version = '", cdm_version,"'")
}
}
if (!missing(vocabulary_version)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(vocabulary_version)) {
statement <- paste0(statement, " vocabulary_version IS NULL")
} else {
statement <- paste0(statement, " vocabulary_version = '", vocabulary_version,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_cohort <- function(cohort_definition_id, subject_id, cohort_start_date, cohort_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(subject_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_id)) {
statement <- paste0(statement, " subject_id IS NULL")
} else {
statement <- paste0(statement, " subject_id = '", subject_id,"'")
}
}
if (!missing(cohort_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_start_date)) {
statement <- paste0(statement, " cohort_start_date IS NULL")
} else {
statement <- paste0(statement, " cohort_start_date = '", cohort_start_date,"'")
}
}
if (!missing(cohort_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_end_date)) {
statement <- paste0(statement, " cohort_end_date IS NULL")
} else {
statement <- paste0(statement, " cohort_end_date = '", cohort_end_date,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_cohort_definition <- function(cohort_definition_id, cohort_definition_name, cohort_definition_description, definition_type_concept_id, cohort_definition_syntax, subject_concept_id, cohort_instantiation_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort_definition' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort_definition WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(cohort_definition_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_name)) {
statement <- paste0(statement, " cohort_definition_name IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_name = '", cohort_definition_name,"'")
}
}
if (!missing(cohort_definition_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_description)) {
statement <- paste0(statement, " cohort_definition_description IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_description = '", cohort_definition_description,"'")
}
}
if (!missing(definition_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(definition_type_concept_id)) {
statement <- paste0(statement, " definition_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " definition_type_concept_id = '", definition_type_concept_id,"'")
}
}
if (!missing(cohort_definition_syntax)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_syntax)) {
statement <- paste0(statement, " cohort_definition_syntax IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_syntax = '", cohort_definition_syntax,"'")
}
}
if (!missing(subject_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_concept_id)) {
statement <- paste0(statement, " subject_concept_id IS NULL")
} else {
statement <- paste0(statement, " subject_concept_id = '", subject_concept_id,"'")
}
}
if (!missing(cohort_instantiation_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_instantiation_date)) {
statement <- paste0(statement, " cohort_instantiation_date IS NULL")
} else {
statement <- paste0(statement, " cohort_instantiation_date = '", cohort_instantiation_date,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_cohort_attribute <- function(cohort_definition_id, cohort_start_date, cohort_end_date, subject_id, attribute_definition_id, value_as_number, value_as_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort_attribute' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort_attribute WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(cohort_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_start_date)) {
statement <- paste0(statement, " cohort_start_date IS NULL")
} else {
statement <- paste0(statement, " cohort_start_date = '", cohort_start_date,"'")
}
}
if (!missing(cohort_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_end_date)) {
statement <- paste0(statement, " cohort_end_date IS NULL")
} else {
statement <- paste0(statement, " cohort_end_date = '", cohort_end_date,"'")
}
}
if (!missing(subject_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_id)) {
statement <- paste0(statement, " subject_id IS NULL")
} else {
statement <- paste0(statement, " subject_id = '", subject_id,"'")
}
}
if (!missing(attribute_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_definition_id)) {
statement <- paste0(statement, " attribute_definition_id IS NULL")
} else {
statement <- paste0(statement, " attribute_definition_id = '", attribute_definition_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_attribute_definition <- function(attribute_definition_id, attribute_name, attribute_description, attribute_type_concept_id, attribute_syntax) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect attribute_definition' AS test, CASE WHEN(SELECT COUNT(*) FROM attribute_definition WHERE")
first <- TRUE
if (!missing(attribute_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_definition_id)) {
statement <- paste0(statement, " attribute_definition_id IS NULL")
} else {
statement <- paste0(statement, " attribute_definition_id = '", attribute_definition_id,"'")
}
}
if (!missing(attribute_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_name)) {
statement <- paste0(statement, " attribute_name IS NULL")
} else {
statement <- paste0(statement, " attribute_name = '", attribute_name,"'")
}
}
if (!missing(attribute_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_description)) {
statement <- paste0(statement, " attribute_description IS NULL")
} else {
statement <- paste0(statement, " attribute_description = '", attribute_description,"'")
}
}
if (!missing(attribute_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_type_concept_id)) {
statement <- paste0(statement, " attribute_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " attribute_type_concept_id = '", attribute_type_concept_id,"'")
}
}
if (!missing(attribute_syntax)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_syntax)) {
statement <- paste0(statement, " attribute_syntax IS NULL")
} else {
statement <- paste0(statement, " attribute_syntax = '", attribute_syntax,"'")
}
}
statement <- paste0(statement, ") = 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_provider <- function(provider_id, provider_name, npi, dea, specialty_concept_id, care_site_id, year_of_birth, gender_concept_id, provider_source_value, specialty_source_value, specialty_source_concept_id, gender_source_value, gender_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect provider' AS test, CASE WHEN(SELECT COUNT(*) FROM provider WHERE")
first <- TRUE
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(provider_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_name)) {
statement <- paste0(statement, " provider_name IS NULL")
} else {
statement <- paste0(statement, " provider_name = '", provider_name,"'")
}
}
if (!missing(npi)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(npi)) {
statement <- paste0(statement, " npi IS NULL")
} else {
statement <- paste0(statement, " npi = '", npi,"'")
}
}
if (!missing(dea)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dea)) {
statement <- paste0(statement, " dea IS NULL")
} else {
statement <- paste0(statement, " dea = '", dea,"'")
}
}
if (!missing(specialty_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_concept_id)) {
statement <- paste0(statement, " specialty_concept_id IS NULL")
} else {
statement <- paste0(statement, " specialty_concept_id = '", specialty_concept_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(year_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(year_of_birth)) {
statement <- paste0(statement, " year_of_birth IS NULL")
} else {
statement <- paste0(statement, " year_of_birth = '", year_of_birth,"'")
}
}
if (!missing(gender_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_concept_id)) {
statement <- paste0(statement, " gender_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_concept_id = '", gender_concept_id,"'")
}
}
if (!missing(provider_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_source_value)) {
statement <- paste0(statement, " provider_source_value IS NULL")
} else {
statement <- paste0(statement, " provider_source_value = '", provider_source_value,"'")
}
}
if (!missing(specialty_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_source_value)) {
statement <- paste0(statement, " specialty_source_value IS NULL")
} else {
statement <- paste0(statement, " specialty_source_value = '", specialty_source_value,"'")
}
}
if (!missing(specialty_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_source_concept_id)) {
statement <- paste0(statement, " specialty_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " specialty_source_concept_id = '", specialty_source_concept_id,"'")
}
}
if (!missing(gender_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_value)) {
statement <- paste0(statement, " gender_source_value IS NULL")
} else {
statement <- paste0(statement, " gender_source_value = '", gender_source_value,"'")
}
}
if (!missing(gender_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_concept_id)) {
statement <- paste0(statement, " gender_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_source_concept_id = '", gender_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_care_site <- function(care_site_id, care_site_name, place_of_service_concept_id, location_id, care_site_source_value, place_of_service_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect care_site' AS test, CASE WHEN(SELECT COUNT(*) FROM care_site WHERE")
first <- TRUE
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(care_site_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_name)) {
statement <- paste0(statement, " care_site_name IS NULL")
} else {
statement <- paste0(statement, " care_site_name = '", care_site_name,"'")
}
}
if (!missing(place_of_service_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(place_of_service_concept_id)) {
statement <- paste0(statement, " place_of_service_concept_id IS NULL")
} else {
statement <- paste0(statement, " place_of_service_concept_id = '", place_of_service_concept_id,"'")
}
}
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(care_site_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_source_value)) {
statement <- paste0(statement, " care_site_source_value IS NULL")
} else {
statement <- paste0(statement, " care_site_source_value = '", care_site_source_value,"'")
}
}
if (!missing(place_of_service_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(place_of_service_source_value)) {
statement <- paste0(statement, " place_of_service_source_value IS NULL")
} else {
statement <- paste0(statement, " place_of_service_source_value = '", place_of_service_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_location <- function(location_id, address_1, address_2, city, state, zip, county, location_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect location' AS test, CASE WHEN(SELECT COUNT(*) FROM location WHERE")
first <- TRUE
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(address_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(address_1)) {
statement <- paste0(statement, " address_1 IS NULL")
} else {
statement <- paste0(statement, " address_1 = '", address_1,"'")
}
}
if (!missing(address_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(address_2)) {
statement <- paste0(statement, " address_2 IS NULL")
} else {
statement <- paste0(statement, " address_2 = '", address_2,"'")
}
}
if (!missing(city)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(city)) {
statement <- paste0(statement, " city IS NULL")
} else {
statement <- paste0(statement, " city = '", city,"'")
}
}
if (!missing(state)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(state)) {
statement <- paste0(statement, " state IS NULL")
} else {
statement <- paste0(statement, " state = '", state,"'")
}
}
if (!missing(zip)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(zip)) {
statement <- paste0(statement, " zip IS NULL")
} else {
statement <- paste0(statement, " zip = '", zip,"'")
}
}
if (!missing(county)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(county)) {
statement <- paste0(statement, " county IS NULL")
} else {
statement <- paste0(statement, " county = '", county,"'")
}
}
if (!missing(location_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_source_value)) {
statement <- paste0(statement, " location_source_value IS NULL")
} else {
statement <- paste0(statement, " location_source_value = '", location_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_person <- function(person_id, gender_concept_id, year_of_birth, month_of_birth, day_of_birth, time_of_birth, race_concept_id, ethnicity_concept_id, location_id, provider_id, care_site_id, person_source_value, gender_source_value, gender_source_concept_id, race_source_value, race_source_concept_id, ethnicity_source_value, ethnicity_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect person' AS test, CASE WHEN(SELECT COUNT(*) FROM person WHERE")
first <- TRUE
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(gender_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_concept_id)) {
statement <- paste0(statement, " gender_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_concept_id = '", gender_concept_id,"'")
}
}
if (!missing(year_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(year_of_birth)) {
statement <- paste0(statement, " year_of_birth IS NULL")
} else {
statement <- paste0(statement, " year_of_birth = '", year_of_birth,"'")
}
}
if (!missing(month_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(month_of_birth)) {
statement <- paste0(statement, " month_of_birth IS NULL")
} else {
statement <- paste0(statement, " month_of_birth = '", month_of_birth,"'")
}
}
if (!missing(day_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(day_of_birth)) {
statement <- paste0(statement, " day_of_birth IS NULL")
} else {
statement <- paste0(statement, " day_of_birth = '", day_of_birth,"'")
}
}
if (!missing(time_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(time_of_birth)) {
statement <- paste0(statement, " time_of_birth IS NULL")
} else {
statement <- paste0(statement, " time_of_birth = '", time_of_birth,"'")
}
}
if (!missing(race_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_concept_id)) {
statement <- paste0(statement, " race_concept_id IS NULL")
} else {
statement <- paste0(statement, " race_concept_id = '", race_concept_id,"'")
}
}
if (!missing(ethnicity_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_concept_id)) {
statement <- paste0(statement, " ethnicity_concept_id IS NULL")
} else {
statement <- paste0(statement, " ethnicity_concept_id = '", ethnicity_concept_id,"'")
}
}
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(person_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_source_value)) {
statement <- paste0(statement, " person_source_value IS NULL")
} else {
statement <- paste0(statement, " person_source_value = '", person_source_value,"'")
}
}
if (!missing(gender_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_value)) {
statement <- paste0(statement, " gender_source_value IS NULL")
} else {
statement <- paste0(statement, " gender_source_value = '", gender_source_value,"'")
}
}
if (!missing(gender_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_concept_id)) {
statement <- paste0(statement, " gender_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_source_concept_id = '", gender_source_concept_id,"'")
}
}
if (!missing(race_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_source_value)) {
statement <- paste0(statement, " race_source_value IS NULL")
} else {
statement <- paste0(statement, " race_source_value = '", race_source_value,"'")
}
}
if (!missing(race_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_source_concept_id)) {
statement <- paste0(statement, " race_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " race_source_concept_id = '", race_source_concept_id,"'")
}
}
if (!missing(ethnicity_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_source_value)) {
statement <- paste0(statement, " ethnicity_source_value IS NULL")
} else {
statement <- paste0(statement, " ethnicity_source_value = '", ethnicity_source_value,"'")
}
}
if (!missing(ethnicity_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_source_concept_id)) {
statement <- paste0(statement, " ethnicity_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " ethnicity_source_concept_id = '", ethnicity_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_observation_period <- function(observation_period_id, person_id, observation_period_start_date, observation_period_end_date, period_type_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect observation_period' AS test, CASE WHEN(SELECT COUNT(*) FROM observation_period WHERE")
first <- TRUE
if (!missing(observation_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_id)) {
statement <- paste0(statement, " observation_period_id IS NULL")
} else {
statement <- paste0(statement, " observation_period_id = '", observation_period_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(observation_period_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_start_date)) {
statement <- paste0(statement, " observation_period_start_date IS NULL")
} else {
statement <- paste0(statement, " observation_period_start_date = '", observation_period_start_date,"'")
}
}
if (!missing(observation_period_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_end_date)) {
statement <- paste0(statement, " observation_period_end_date IS NULL")
} else {
statement <- paste0(statement, " observation_period_end_date = '", observation_period_end_date,"'")
}
}
if (!missing(period_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(period_type_concept_id)) {
statement <- paste0(statement, " period_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " period_type_concept_id = '", period_type_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_death <- function(person_id, death_date, death_type_concept_id, cause_concept_id, cause_source_value, cause_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect death' AS test, CASE WHEN(SELECT COUNT(*) FROM death WHERE")
first <- TRUE
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(death_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(death_date)) {
statement <- paste0(statement, " death_date IS NULL")
} else {
statement <- paste0(statement, " death_date = '", death_date,"'")
}
}
if (!missing(death_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(death_type_concept_id)) {
statement <- paste0(statement, " death_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " death_type_concept_id = '", death_type_concept_id,"'")
}
}
if (!missing(cause_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_concept_id)) {
statement <- paste0(statement, " cause_concept_id IS NULL")
} else {
statement <- paste0(statement, " cause_concept_id = '", cause_concept_id,"'")
}
}
if (!missing(cause_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_source_value)) {
statement <- paste0(statement, " cause_source_value IS NULL")
} else {
statement <- paste0(statement, " cause_source_value = '", cause_source_value,"'")
}
}
if (!missing(cause_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_source_concept_id)) {
statement <- paste0(statement, " cause_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " cause_source_concept_id = '", cause_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_visit_occurrence <- function(visit_occurrence_id, person_id, visit_concept_id, visit_start_date, visit_start_time, visit_end_date, visit_end_time, visit_type_concept_id, provider_id, care_site_id, visit_source_value, visit_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect visit_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM visit_occurrence WHERE")
first <- TRUE
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(visit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_concept_id)) {
statement <- paste0(statement, " visit_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_concept_id = '", visit_concept_id,"'")
}
}
if (!missing(visit_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_start_date)) {
statement <- paste0(statement, " visit_start_date IS NULL")
} else {
statement <- paste0(statement, " visit_start_date = '", visit_start_date,"'")
}
}
if (!missing(visit_start_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_start_time)) {
statement <- paste0(statement, " visit_start_time IS NULL")
} else {
statement <- paste0(statement, " visit_start_time = '", visit_start_time,"'")
}
}
if (!missing(visit_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_end_date)) {
statement <- paste0(statement, " visit_end_date IS NULL")
} else {
statement <- paste0(statement, " visit_end_date = '", visit_end_date,"'")
}
}
if (!missing(visit_end_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_end_time)) {
statement <- paste0(statement, " visit_end_time IS NULL")
} else {
statement <- paste0(statement, " visit_end_time = '", visit_end_time,"'")
}
}
if (!missing(visit_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_type_concept_id)) {
statement <- paste0(statement, " visit_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_type_concept_id = '", visit_type_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(visit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_source_value)) {
statement <- paste0(statement, " visit_source_value IS NULL")
} else {
statement <- paste0(statement, " visit_source_value = '", visit_source_value,"'")
}
}
if (!missing(visit_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_source_concept_id)) {
statement <- paste0(statement, " visit_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_source_concept_id = '", visit_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_condition_occurrence <- function(condition_occurrence_id, person_id, condition_concept_id, condition_source_concept_id, condition_source_value, condition_start_date, provider_id, visit_occurrence_id, condition_type_concept_id, condition_end_date, stop_reason) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect condition_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM condition_occurrence WHERE")
first <- TRUE
if (!missing(condition_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_occurrence_id)) {
statement <- paste0(statement, " condition_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " condition_occurrence_id = '", condition_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(condition_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_concept_id)) {
statement <- paste0(statement, " condition_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_concept_id = '", condition_concept_id,"'")
}
}
if (!missing(condition_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_source_concept_id)) {
statement <- paste0(statement, " condition_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_source_concept_id = '", condition_source_concept_id,"'")
}
}
if (!missing(condition_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_source_value)) {
statement <- paste0(statement, " condition_source_value IS NULL")
} else {
statement <- paste0(statement, " condition_source_value = '", condition_source_value,"'")
}
}
if (!missing(condition_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_start_date)) {
statement <- paste0(statement, " condition_start_date IS NULL")
} else {
statement <- paste0(statement, " condition_start_date = '", condition_start_date,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(condition_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_type_concept_id)) {
statement <- paste0(statement, " condition_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_type_concept_id = '", condition_type_concept_id,"'")
}
}
if (!missing(condition_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_end_date)) {
statement <- paste0(statement, " condition_end_date IS NULL")
} else {
statement <- paste0(statement, " condition_end_date = '", condition_end_date,"'")
}
}
if (!missing(stop_reason)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(stop_reason)) {
statement <- paste0(statement, " stop_reason IS NULL")
} else {
statement <- paste0(statement, " stop_reason = '", stop_reason,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_device_exposure <- function(device_exposure_id, person_id, device_concept_id, device_exposure_start_date, device_exposure_end_date, device_type_concept_id, unique_device_id, quantity, provider_id, visit_occurrence_id, device_source_value, device_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect device_exposure' AS test, CASE WHEN(SELECT COUNT(*) FROM device_exposure WHERE")
first <- TRUE
if (!missing(device_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_id)) {
statement <- paste0(statement, " device_exposure_id IS NULL")
} else {
statement <- paste0(statement, " device_exposure_id = '", device_exposure_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(device_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_concept_id)) {
statement <- paste0(statement, " device_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_concept_id = '", device_concept_id,"'")
}
}
if (!missing(device_exposure_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_start_date)) {
statement <- paste0(statement, " device_exposure_start_date IS NULL")
} else {
statement <- paste0(statement, " device_exposure_start_date = '", device_exposure_start_date,"'")
}
}
if (!missing(device_exposure_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_end_date)) {
statement <- paste0(statement, " device_exposure_end_date IS NULL")
} else {
statement <- paste0(statement, " device_exposure_end_date = '", device_exposure_end_date,"'")
}
}
if (!missing(device_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_type_concept_id)) {
statement <- paste0(statement, " device_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_type_concept_id = '", device_type_concept_id,"'")
}
}
if (!missing(unique_device_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unique_device_id)) {
statement <- paste0(statement, " unique_device_id IS NULL")
} else {
statement <- paste0(statement, " unique_device_id = '", unique_device_id,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(device_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_source_value)) {
statement <- paste0(statement, " device_source_value IS NULL")
} else {
statement <- paste0(statement, " device_source_value = '", device_source_value,"'")
}
}
if (!missing(device_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_source_concept_id)) {
statement <- paste0(statement, " device_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_source_concept_id = '", device_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_observation <- function(observation_id, person_id, observation_concept_id, observation_date, observation_time, observation_type_concept_id, value_as_number, value_as_string, value_as_concept_id, qualifier_concept_id, unit_concept_id, provider_id, visit_occurrence_id, observation_source_value, observation_source_concept_id, unit_source_value, qualifier_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect observation' AS test, CASE WHEN(SELECT COUNT(*) FROM observation WHERE")
first <- TRUE
if (!missing(observation_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_id)) {
statement <- paste0(statement, " observation_id IS NULL")
} else {
statement <- paste0(statement, " observation_id = '", observation_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(observation_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_concept_id)) {
statement <- paste0(statement, " observation_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_concept_id = '", observation_concept_id,"'")
}
}
if (!missing(observation_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_date)) {
statement <- paste0(statement, " observation_date IS NULL")
} else {
statement <- paste0(statement, " observation_date = '", observation_date,"'")
}
}
if (!missing(observation_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_time)) {
statement <- paste0(statement, " observation_time IS NULL")
} else {
statement <- paste0(statement, " observation_time = '", observation_time,"'")
}
}
if (!missing(observation_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_type_concept_id)) {
statement <- paste0(statement, " observation_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_type_concept_id = '", observation_type_concept_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_string)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_string)) {
statement <- paste0(statement, " value_as_string IS NULL")
} else {
statement <- paste0(statement, " value_as_string = '", value_as_string,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
if (!missing(qualifier_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_concept_id)) {
statement <- paste0(statement, " qualifier_concept_id IS NULL")
} else {
statement <- paste0(statement, " qualifier_concept_id = '", qualifier_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(observation_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_source_value)) {
statement <- paste0(statement, " observation_source_value IS NULL")
} else {
statement <- paste0(statement, " observation_source_value = '", observation_source_value,"'")
}
}
if (!missing(observation_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_source_concept_id)) {
statement <- paste0(statement, " observation_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_source_concept_id = '", observation_source_concept_id,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(qualifier_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_source_value)) {
statement <- paste0(statement, " qualifier_source_value IS NULL")
} else {
statement <- paste0(statement, " qualifier_source_value = '", qualifier_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_measurement <- function(measurement_id, person_id, measurement_concept_id, measurement_date, measurement_time, measurement_type_concept_id, operator_concept_id, value_as_number, value_as_concept_id, unit_concept_id, range_low, range_high, provider_id, visit_occurrence_id, measurement_source_value, measurement_source_concept_id, unit_source_value, value_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect measurement' AS test, CASE WHEN(SELECT COUNT(*) FROM measurement WHERE")
first <- TRUE
if (!missing(measurement_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_id)) {
statement <- paste0(statement, " measurement_id IS NULL")
} else {
statement <- paste0(statement, " measurement_id = '", measurement_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(measurement_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_concept_id)) {
statement <- paste0(statement, " measurement_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_concept_id = '", measurement_concept_id,"'")
}
}
if (!missing(measurement_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_date)) {
statement <- paste0(statement, " measurement_date IS NULL")
} else {
statement <- paste0(statement, " measurement_date = '", measurement_date,"'")
}
}
if (!missing(measurement_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_time)) {
statement <- paste0(statement, " measurement_time IS NULL")
} else {
statement <- paste0(statement, " measurement_time = '", measurement_time,"'")
}
}
if (!missing(measurement_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_type_concept_id)) {
statement <- paste0(statement, " measurement_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_type_concept_id = '", measurement_type_concept_id,"'")
}
}
if (!missing(operator_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(operator_concept_id)) {
statement <- paste0(statement, " operator_concept_id IS NULL")
} else {
statement <- paste0(statement, " operator_concept_id = '", operator_concept_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(range_low)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(range_low)) {
statement <- paste0(statement, " range_low IS NULL")
} else {
statement <- paste0(statement, " range_low = '", range_low,"'")
}
}
if (!missing(range_high)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(range_high)) {
statement <- paste0(statement, " range_high IS NULL")
} else {
statement <- paste0(statement, " range_high = '", range_high,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(measurement_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_source_value)) {
statement <- paste0(statement, " measurement_source_value IS NULL")
} else {
statement <- paste0(statement, " measurement_source_value = '", measurement_source_value,"'")
}
}
if (!missing(measurement_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_source_concept_id)) {
statement <- paste0(statement, " measurement_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_source_concept_id = '", measurement_source_concept_id,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(value_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_source_value)) {
statement <- paste0(statement, " value_source_value IS NULL")
} else {
statement <- paste0(statement, " value_source_value = '", value_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_procedure_occurrence <- function(procedure_occurrence_id, person_id, procedure_concept_id, procedure_date, procedure_type_concept_id, modifier_concept_id, quantity, provider_id, visit_occurrence_id, procedure_source_value, procedure_source_concept_id, qualifier_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect procedure_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM procedure_occurrence WHERE")
first <- TRUE
if (!missing(procedure_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_occurrence_id)) {
statement <- paste0(statement, " procedure_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " procedure_occurrence_id = '", procedure_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(procedure_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_concept_id)) {
statement <- paste0(statement, " procedure_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_concept_id = '", procedure_concept_id,"'")
}
}
if (!missing(procedure_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_date)) {
statement <- paste0(statement, " procedure_date IS NULL")
} else {
statement <- paste0(statement, " procedure_date = '", procedure_date,"'")
}
}
if (!missing(procedure_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_type_concept_id)) {
statement <- paste0(statement, " procedure_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_type_concept_id = '", procedure_type_concept_id,"'")
}
}
if (!missing(modifier_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(modifier_concept_id)) {
statement <- paste0(statement, " modifier_concept_id IS NULL")
} else {
statement <- paste0(statement, " modifier_concept_id = '", modifier_concept_id,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(procedure_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_source_value)) {
statement <- paste0(statement, " procedure_source_value IS NULL")
} else {
statement <- paste0(statement, " procedure_source_value = '", procedure_source_value,"'")
}
}
if (!missing(procedure_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_source_concept_id)) {
statement <- paste0(statement, " procedure_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_source_concept_id = '", procedure_source_concept_id,"'")
}
}
if (!missing(qualifier_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_source_value)) {
statement <- paste0(statement, " qualifier_source_value IS NULL")
} else {
statement <- paste0(statement, " qualifier_source_value = '", qualifier_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_drug_exposure <- function(drug_exposure_id, person_id, drug_exposure_start_date, drug_concept_id, drug_source_value, drug_source_concept_id, drug_type_concept_id, provider_id, visit_occurrence_id, route_concept_id, route_source_value, quantity, refills, days_supply, dose_unit_concept_id, dose_unit_source_value, effective_drug_dose, stop_reason, sig, lot_number, drug_exposure_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_exposure' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_exposure WHERE")
first <- TRUE
if (!missing(drug_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_id)) {
statement <- paste0(statement, " drug_exposure_id IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_id = '", drug_exposure_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_exposure_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_start_date)) {
statement <- paste0(statement, " drug_exposure_start_date IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_start_date = '", drug_exposure_start_date,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(drug_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_source_value)) {
statement <- paste0(statement, " drug_source_value IS NULL")
} else {
statement <- paste0(statement, " drug_source_value = '", drug_source_value,"'")
}
}
if (!missing(drug_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_source_concept_id)) {
statement <- paste0(statement, " drug_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_source_concept_id = '", drug_source_concept_id,"'")
}
}
if (!missing(drug_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_type_concept_id)) {
statement <- paste0(statement, " drug_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_type_concept_id = '", drug_type_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(route_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(route_concept_id)) {
statement <- paste0(statement, " route_concept_id IS NULL")
} else {
statement <- paste0(statement, " route_concept_id = '", route_concept_id,"'")
}
}
if (!missing(route_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(route_source_value)) {
statement <- paste0(statement, " route_source_value IS NULL")
} else {
statement <- paste0(statement, " route_source_value = '", route_source_value,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(refills)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(refills)) {
statement <- paste0(statement, " refills IS NULL")
} else {
statement <- paste0(statement, " refills = '", refills,"'")
}
}
if (!missing(days_supply)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(days_supply)) {
statement <- paste0(statement, " days_supply IS NULL")
} else {
statement <- paste0(statement, " days_supply = '", days_supply,"'")
}
}
if (!missing(dose_unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_unit_concept_id)) {
statement <- paste0(statement, " dose_unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " dose_unit_concept_id = '", dose_unit_concept_id,"'")
}
}
if (!missing(dose_unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_unit_source_value)) {
statement <- paste0(statement, " dose_unit_source_value IS NULL")
} else {
statement <- paste0(statement, " dose_unit_source_value = '", dose_unit_source_value,"'")
}
}
if (!missing(effective_drug_dose)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(effective_drug_dose)) {
statement <- paste0(statement, " effective_drug_dose IS NULL")
} else {
statement <- paste0(statement, " effective_drug_dose = '", effective_drug_dose,"'")
}
}
if (!missing(stop_reason)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(stop_reason)) {
statement <- paste0(statement, " stop_reason IS NULL")
} else {
statement <- paste0(statement, " stop_reason = '", stop_reason,"'")
}
}
if (!missing(sig)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(sig)) {
statement <- paste0(statement, " sig IS NULL")
} else {
statement <- paste0(statement, " sig = '", sig,"'")
}
}
if (!missing(lot_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(lot_number)) {
statement <- paste0(statement, " lot_number IS NULL")
} else {
statement <- paste0(statement, " lot_number = '", lot_number,"'")
}
}
if (!missing(drug_exposure_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_end_date)) {
statement <- paste0(statement, " drug_exposure_end_date IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_end_date = '", drug_exposure_end_date,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_fact_relationship <- function(domain_concept_id_1, fact_id_1, domain_concept_id_2, fact_id_2, relationship_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect fact_relationship' AS test, CASE WHEN(SELECT COUNT(*) FROM fact_relationship WHERE")
first <- TRUE
if (!missing(domain_concept_id_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(domain_concept_id_1)) {
statement <- paste0(statement, " domain_concept_id_1 IS NULL")
} else {
statement <- paste0(statement, " domain_concept_id_1 = '", domain_concept_id_1,"'")
}
}
if (!missing(fact_id_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(fact_id_1)) {
statement <- paste0(statement, " fact_id_1 IS NULL")
} else {
statement <- paste0(statement, " fact_id_1 = '", fact_id_1,"'")
}
}
if (!missing(domain_concept_id_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(domain_concept_id_2)) {
statement <- paste0(statement, " domain_concept_id_2 IS NULL")
} else {
statement <- paste0(statement, " domain_concept_id_2 = '", domain_concept_id_2,"'")
}
}
if (!missing(fact_id_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(fact_id_2)) {
statement <- paste0(statement, " fact_id_2 IS NULL")
} else {
statement <- paste0(statement, " fact_id_2 = '", fact_id_2,"'")
}
}
if (!missing(relationship_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(relationship_concept_id)) {
statement <- paste0(statement, " relationship_concept_id IS NULL")
} else {
statement <- paste0(statement, " relationship_concept_id = '", relationship_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_payer_plan_period <- function(payer_plan_period_id, person_id, payer_plan_period_start_date, payer_plan_period_end_date, payer_source_value, plan_source_value, family_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect payer_plan_period' AS test, CASE WHEN(SELECT COUNT(*) FROM payer_plan_period WHERE")
first <- TRUE
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(payer_plan_period_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_start_date)) {
statement <- paste0(statement, " payer_plan_period_start_date IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_start_date = '", payer_plan_period_start_date,"'")
}
}
if (!missing(payer_plan_period_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_end_date)) {
statement <- paste0(statement, " payer_plan_period_end_date IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_end_date = '", payer_plan_period_end_date,"'")
}
}
if (!missing(payer_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_source_value)) {
statement <- paste0(statement, " payer_source_value IS NULL")
} else {
statement <- paste0(statement, " payer_source_value = '", payer_source_value,"'")
}
}
if (!missing(plan_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(plan_source_value)) {
statement <- paste0(statement, " plan_source_value IS NULL")
} else {
statement <- paste0(statement, " plan_source_value = '", plan_source_value,"'")
}
}
if (!missing(family_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(family_source_value)) {
statement <- paste0(statement, " family_source_value IS NULL")
} else {
statement <- paste0(statement, " family_source_value = '", family_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_note <- function(note_id, person_id, note_date, note_time, note_type_concept_id, note_text, provider_id, visit_occurrence_id, note_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect note' AS test, CASE WHEN(SELECT COUNT(*) FROM note WHERE")
first <- TRUE
if (!missing(note_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_id)) {
statement <- paste0(statement, " note_id IS NULL")
} else {
statement <- paste0(statement, " note_id = '", note_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(note_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_date)) {
statement <- paste0(statement, " note_date IS NULL")
} else {
statement <- paste0(statement, " note_date = '", note_date,"'")
}
}
if (!missing(note_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_time)) {
statement <- paste0(statement, " note_time IS NULL")
} else {
statement <- paste0(statement, " note_time = '", note_time,"'")
}
}
if (!missing(note_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_type_concept_id)) {
statement <- paste0(statement, " note_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " note_type_concept_id = '", note_type_concept_id,"'")
}
}
if (!missing(note_text)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_text)) {
statement <- paste0(statement, " note_text IS NULL")
} else {
statement <- paste0(statement, " note_text = '", note_text,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(note_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_source_value)) {
statement <- paste0(statement, " note_source_value IS NULL")
} else {
statement <- paste0(statement, " note_source_value = '", note_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_specimen <- function(specimen_id, person_id, specimen_concept_id, specimen_type_concept_id, specimen_date, specimen_time, quantity, unit_concept_id, anatomic_site_concept_id, disease_status_concept_id, specimen_source_id, specimen_source_value, unit_source_value, anatomic_site_source_value, disease_status_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect specimen' AS test, CASE WHEN(SELECT COUNT(*) FROM specimen WHERE")
first <- TRUE
if (!missing(specimen_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_id)) {
statement <- paste0(statement, " specimen_id IS NULL")
} else {
statement <- paste0(statement, " specimen_id = '", specimen_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(specimen_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_concept_id)) {
statement <- paste0(statement, " specimen_concept_id IS NULL")
} else {
statement <- paste0(statement, " specimen_concept_id = '", specimen_concept_id,"'")
}
}
if (!missing(specimen_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_type_concept_id)) {
statement <- paste0(statement, " specimen_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " specimen_type_concept_id = '", specimen_type_concept_id,"'")
}
}
if (!missing(specimen_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_date)) {
statement <- paste0(statement, " specimen_date IS NULL")
} else {
statement <- paste0(statement, " specimen_date = '", specimen_date,"'")
}
}
if (!missing(specimen_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_time)) {
statement <- paste0(statement, " specimen_time IS NULL")
} else {
statement <- paste0(statement, " specimen_time = '", specimen_time,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(anatomic_site_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(anatomic_site_concept_id)) {
statement <- paste0(statement, " anatomic_site_concept_id IS NULL")
} else {
statement <- paste0(statement, " anatomic_site_concept_id = '", anatomic_site_concept_id,"'")
}
}
if (!missing(disease_status_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(disease_status_concept_id)) {
statement <- paste0(statement, " disease_status_concept_id IS NULL")
} else {
statement <- paste0(statement, " disease_status_concept_id = '", disease_status_concept_id,"'")
}
}
if (!missing(specimen_source_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_source_id)) {
statement <- paste0(statement, " specimen_source_id IS NULL")
} else {
statement <- paste0(statement, " specimen_source_id = '", specimen_source_id,"'")
}
}
if (!missing(specimen_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_source_value)) {
statement <- paste0(statement, " specimen_source_value IS NULL")
} else {
statement <- paste0(statement, " specimen_source_value = '", specimen_source_value,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(anatomic_site_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(anatomic_site_source_value)) {
statement <- paste0(statement, " anatomic_site_source_value IS NULL")
} else {
statement <- paste0(statement, " anatomic_site_source_value = '", anatomic_site_source_value,"'")
}
}
if (!missing(disease_status_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(disease_status_source_value)) {
statement <- paste0(statement, " disease_status_source_value IS NULL")
} else {
statement <- paste0(statement, " disease_status_source_value = '", disease_status_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_procedure_cost <- function(procedure_cost_id, procedure_occurrence_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, revenue_code_concept_id, payer_plan_period_id, revenue_code_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect procedure_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM procedure_cost WHERE")
first <- TRUE
if (!missing(procedure_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_cost_id)) {
statement <- paste0(statement, " procedure_cost_id IS NULL")
} else {
statement <- paste0(statement, " procedure_cost_id = '", procedure_cost_id,"'")
}
}
if (!missing(procedure_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_occurrence_id)) {
statement <- paste0(statement, " procedure_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " procedure_occurrence_id = '", procedure_occurrence_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(revenue_code_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(revenue_code_concept_id)) {
statement <- paste0(statement, " revenue_code_concept_id IS NULL")
} else {
statement <- paste0(statement, " revenue_code_concept_id = '", revenue_code_concept_id,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
if (!missing(revenue_code_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(revenue_code_source_value)) {
statement <- paste0(statement, " revenue_code_source_value IS NULL")
} else {
statement <- paste0(statement, " revenue_code_source_value = '", revenue_code_source_value,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_visit_cost <- function(visit_cost_id, visit_occurrence_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect visit_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM visit_cost WHERE")
first <- TRUE
if (!missing(visit_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_cost_id)) {
statement <- paste0(statement, " visit_cost_id IS NULL")
} else {
statement <- paste0(statement, " visit_cost_id = '", visit_cost_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_drug_cost <- function(drug_cost_id, drug_exposure_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, ingredient_cost, dispensing_fee, average_wholesale_price, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_cost WHERE")
first <- TRUE
if (!missing(drug_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_cost_id)) {
statement <- paste0(statement, " drug_cost_id IS NULL")
} else {
statement <- paste0(statement, " drug_cost_id = '", drug_cost_id,"'")
}
}
if (!missing(drug_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_id)) {
statement <- paste0(statement, " drug_exposure_id IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_id = '", drug_exposure_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(ingredient_cost)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ingredient_cost)) {
statement <- paste0(statement, " ingredient_cost IS NULL")
} else {
statement <- paste0(statement, " ingredient_cost = '", ingredient_cost,"'")
}
}
if (!missing(dispensing_fee)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dispensing_fee)) {
statement <- paste0(statement, " dispensing_fee IS NULL")
} else {
statement <- paste0(statement, " dispensing_fee = '", dispensing_fee,"'")
}
}
if (!missing(average_wholesale_price)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(average_wholesale_price)) {
statement <- paste0(statement, " average_wholesale_price IS NULL")
} else {
statement <- paste0(statement, " average_wholesale_price = '", average_wholesale_price,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_device_cost <- function(device_cost_id, device_exposure_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect device_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM device_cost WHERE")
first <- TRUE
if (!missing(device_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_cost_id)) {
statement <- paste0(statement, " device_cost_id IS NULL")
} else {
statement <- paste0(statement, " device_cost_id = '", device_cost_id,"'")
}
}
if (!missing(device_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_id)) {
statement <- paste0(statement, " device_exposure_id IS NULL")
} else {
statement <- paste0(statement, " device_exposure_id = '", device_exposure_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_drug_era <- function(drug_era_id, person_id, drug_concept_id, drug_era_start_date, drug_era_end_date, drug_exposure_count, gap_days) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_era' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_era WHERE")
first <- TRUE
if (!missing(drug_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_id)) {
statement <- paste0(statement, " drug_era_id IS NULL")
} else {
statement <- paste0(statement, " drug_era_id = '", drug_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(drug_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_start_date)) {
statement <- paste0(statement, " drug_era_start_date IS NULL")
} else {
statement <- paste0(statement, " drug_era_start_date = '", drug_era_start_date,"'")
}
}
if (!missing(drug_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_end_date)) {
statement <- paste0(statement, " drug_era_end_date IS NULL")
} else {
statement <- paste0(statement, " drug_era_end_date = '", drug_era_end_date,"'")
}
}
if (!missing(drug_exposure_count)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_count)) {
statement <- paste0(statement, " drug_exposure_count IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_count = '", drug_exposure_count,"'")
}
}
if (!missing(gap_days)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gap_days)) {
statement <- paste0(statement, " gap_days IS NULL")
} else {
statement <- paste0(statement, " gap_days = '", gap_days,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_dose_era <- function(dose_era_id, person_id, drug_concept_id, unit_concept_id, dose_value, dose_era_start_date, dose_era_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect dose_era' AS test, CASE WHEN(SELECT COUNT(*) FROM dose_era WHERE")
first <- TRUE
if (!missing(dose_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_id)) {
statement <- paste0(statement, " dose_era_id IS NULL")
} else {
statement <- paste0(statement, " dose_era_id = '", dose_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(dose_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_value)) {
statement <- paste0(statement, " dose_value IS NULL")
} else {
statement <- paste0(statement, " dose_value = '", dose_value,"'")
}
}
if (!missing(dose_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_start_date)) {
statement <- paste0(statement, " dose_era_start_date IS NULL")
} else {
statement <- paste0(statement, " dose_era_start_date = '", dose_era_start_date,"'")
}
}
if (!missing(dose_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_end_date)) {
statement <- paste0(statement, " dose_era_end_date IS NULL")
} else {
statement <- paste0(statement, " dose_era_end_date = '", dose_era_end_date,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_condition_era <- function(condition_era_id, person_id, condition_concept_id, condition_era_start_date, condition_era_end_date, condition_occurrence_count) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect condition_era' AS test, CASE WHEN(SELECT COUNT(*) FROM condition_era WHERE")
first <- TRUE
if (!missing(condition_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_id)) {
statement <- paste0(statement, " condition_era_id IS NULL")
} else {
statement <- paste0(statement, " condition_era_id = '", condition_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(condition_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_concept_id)) {
statement <- paste0(statement, " condition_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_concept_id = '", condition_concept_id,"'")
}
}
if (!missing(condition_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_start_date)) {
statement <- paste0(statement, " condition_era_start_date IS NULL")
} else {
statement <- paste0(statement, " condition_era_start_date = '", condition_era_start_date,"'")
}
}
if (!missing(condition_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_end_date)) {
statement <- paste0(statement, " condition_era_end_date IS NULL")
} else {
statement <- paste0(statement, " condition_era_end_date = '", condition_era_end_date,"'")
}
}
if (!missing(condition_occurrence_count)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_occurrence_count)) {
statement <- paste0(statement, " condition_occurrence_count IS NULL")
} else {
statement <- paste0(statement, " condition_occurrence_count = '", condition_occurrence_count,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_cdm_source <- function(cdm_source_name, cdm_source_abbreviation, cdm_holder, source_description, source_documentation_reference, cdm_etl_reference, source_release_date, cdm_release_date, cdm_version, vocabulary_version) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cdm_source' AS test, CASE WHEN(SELECT COUNT(*) FROM cdm_source WHERE")
first <- TRUE
if (!missing(cdm_source_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_source_name)) {
statement <- paste0(statement, " cdm_source_name IS NULL")
} else {
statement <- paste0(statement, " cdm_source_name = '", cdm_source_name,"'")
}
}
if (!missing(cdm_source_abbreviation)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_source_abbreviation)) {
statement <- paste0(statement, " cdm_source_abbreviation IS NULL")
} else {
statement <- paste0(statement, " cdm_source_abbreviation = '", cdm_source_abbreviation,"'")
}
}
if (!missing(cdm_holder)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_holder)) {
statement <- paste0(statement, " cdm_holder IS NULL")
} else {
statement <- paste0(statement, " cdm_holder = '", cdm_holder,"'")
}
}
if (!missing(source_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_description)) {
statement <- paste0(statement, " source_description IS NULL")
} else {
statement <- paste0(statement, " source_description = '", source_description,"'")
}
}
if (!missing(source_documentation_reference)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_documentation_reference)) {
statement <- paste0(statement, " source_documentation_reference IS NULL")
} else {
statement <- paste0(statement, " source_documentation_reference = '", source_documentation_reference,"'")
}
}
if (!missing(cdm_etl_reference)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_etl_reference)) {
statement <- paste0(statement, " cdm_etl_reference IS NULL")
} else {
statement <- paste0(statement, " cdm_etl_reference = '", cdm_etl_reference,"'")
}
}
if (!missing(source_release_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_release_date)) {
statement <- paste0(statement, " source_release_date IS NULL")
} else {
statement <- paste0(statement, " source_release_date = '", source_release_date,"'")
}
}
if (!missing(cdm_release_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_release_date)) {
statement <- paste0(statement, " cdm_release_date IS NULL")
} else {
statement <- paste0(statement, " cdm_release_date = '", cdm_release_date,"'")
}
}
if (!missing(cdm_version)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_version)) {
statement <- paste0(statement, " cdm_version IS NULL")
} else {
statement <- paste0(statement, " cdm_version = '", cdm_version,"'")
}
}
if (!missing(vocabulary_version)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(vocabulary_version)) {
statement <- paste0(statement, " vocabulary_version IS NULL")
} else {
statement <- paste0(statement, " vocabulary_version = '", vocabulary_version,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_cohort <- function(cohort_definition_id, subject_id, cohort_start_date, cohort_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(subject_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_id)) {
statement <- paste0(statement, " subject_id IS NULL")
} else {
statement <- paste0(statement, " subject_id = '", subject_id,"'")
}
}
if (!missing(cohort_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_start_date)) {
statement <- paste0(statement, " cohort_start_date IS NULL")
} else {
statement <- paste0(statement, " cohort_start_date = '", cohort_start_date,"'")
}
}
if (!missing(cohort_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_end_date)) {
statement <- paste0(statement, " cohort_end_date IS NULL")
} else {
statement <- paste0(statement, " cohort_end_date = '", cohort_end_date,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_cohort_definition <- function(cohort_definition_id, cohort_definition_name, cohort_definition_description, definition_type_concept_id, cohort_definition_syntax, subject_concept_id, cohort_instantiation_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort_definition' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort_definition WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(cohort_definition_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_name)) {
statement <- paste0(statement, " cohort_definition_name IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_name = '", cohort_definition_name,"'")
}
}
if (!missing(cohort_definition_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_description)) {
statement <- paste0(statement, " cohort_definition_description IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_description = '", cohort_definition_description,"'")
}
}
if (!missing(definition_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(definition_type_concept_id)) {
statement <- paste0(statement, " definition_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " definition_type_concept_id = '", definition_type_concept_id,"'")
}
}
if (!missing(cohort_definition_syntax)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_syntax)) {
statement <- paste0(statement, " cohort_definition_syntax IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_syntax = '", cohort_definition_syntax,"'")
}
}
if (!missing(subject_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_concept_id)) {
statement <- paste0(statement, " subject_concept_id IS NULL")
} else {
statement <- paste0(statement, " subject_concept_id = '", subject_concept_id,"'")
}
}
if (!missing(cohort_instantiation_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_instantiation_date)) {
statement <- paste0(statement, " cohort_instantiation_date IS NULL")
} else {
statement <- paste0(statement, " cohort_instantiation_date = '", cohort_instantiation_date,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_cohort_attribute <- function(cohort_definition_id, cohort_start_date, cohort_end_date, subject_id, attribute_definition_id, value_as_number, value_as_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort_attribute' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort_attribute WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(cohort_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_start_date)) {
statement <- paste0(statement, " cohort_start_date IS NULL")
} else {
statement <- paste0(statement, " cohort_start_date = '", cohort_start_date,"'")
}
}
if (!missing(cohort_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_end_date)) {
statement <- paste0(statement, " cohort_end_date IS NULL")
} else {
statement <- paste0(statement, " cohort_end_date = '", cohort_end_date,"'")
}
}
if (!missing(subject_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_id)) {
statement <- paste0(statement, " subject_id IS NULL")
} else {
statement <- paste0(statement, " subject_id = '", subject_id,"'")
}
}
if (!missing(attribute_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_definition_id)) {
statement <- paste0(statement, " attribute_definition_id IS NULL")
} else {
statement <- paste0(statement, " attribute_definition_id = '", attribute_definition_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_no_attribute_definition <- function(attribute_definition_id, attribute_name, attribute_description, attribute_type_concept_id, attribute_syntax) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect attribute_definition' AS test, CASE WHEN(SELECT COUNT(*) FROM attribute_definition WHERE")
first <- TRUE
if (!missing(attribute_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_definition_id)) {
statement <- paste0(statement, " attribute_definition_id IS NULL")
} else {
statement <- paste0(statement, " attribute_definition_id = '", attribute_definition_id,"'")
}
}
if (!missing(attribute_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_name)) {
statement <- paste0(statement, " attribute_name IS NULL")
} else {
statement <- paste0(statement, " attribute_name = '", attribute_name,"'")
}
}
if (!missing(attribute_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_description)) {
statement <- paste0(statement, " attribute_description IS NULL")
} else {
statement <- paste0(statement, " attribute_description = '", attribute_description,"'")
}
}
if (!missing(attribute_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_type_concept_id)) {
statement <- paste0(statement, " attribute_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " attribute_type_concept_id = '", attribute_type_concept_id,"'")
}
}
if (!missing(attribute_syntax)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_syntax)) {
statement <- paste0(statement, " attribute_syntax IS NULL")
} else {
statement <- paste0(statement, " attribute_syntax = '", attribute_syntax,"'")
}
}
statement <- paste0(statement, ") != 0 THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_provider <- function(rowCount, provider_id, provider_name, npi, dea, specialty_concept_id, care_site_id, year_of_birth, gender_concept_id, provider_source_value, specialty_source_value, specialty_source_concept_id, gender_source_value, gender_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect provider' AS test, CASE WHEN(SELECT COUNT(*) FROM provider WHERE")
first <- TRUE
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(provider_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_name)) {
statement <- paste0(statement, " provider_name IS NULL")
} else {
statement <- paste0(statement, " provider_name = '", provider_name,"'")
}
}
if (!missing(npi)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(npi)) {
statement <- paste0(statement, " npi IS NULL")
} else {
statement <- paste0(statement, " npi = '", npi,"'")
}
}
if (!missing(dea)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dea)) {
statement <- paste0(statement, " dea IS NULL")
} else {
statement <- paste0(statement, " dea = '", dea,"'")
}
}
if (!missing(specialty_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_concept_id)) {
statement <- paste0(statement, " specialty_concept_id IS NULL")
} else {
statement <- paste0(statement, " specialty_concept_id = '", specialty_concept_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(year_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(year_of_birth)) {
statement <- paste0(statement, " year_of_birth IS NULL")
} else {
statement <- paste0(statement, " year_of_birth = '", year_of_birth,"'")
}
}
if (!missing(gender_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_concept_id)) {
statement <- paste0(statement, " gender_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_concept_id = '", gender_concept_id,"'")
}
}
if (!missing(provider_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_source_value)) {
statement <- paste0(statement, " provider_source_value IS NULL")
} else {
statement <- paste0(statement, " provider_source_value = '", provider_source_value,"'")
}
}
if (!missing(specialty_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_source_value)) {
statement <- paste0(statement, " specialty_source_value IS NULL")
} else {
statement <- paste0(statement, " specialty_source_value = '", specialty_source_value,"'")
}
}
if (!missing(specialty_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specialty_source_concept_id)) {
statement <- paste0(statement, " specialty_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " specialty_source_concept_id = '", specialty_source_concept_id,"'")
}
}
if (!missing(gender_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_value)) {
statement <- paste0(statement, " gender_source_value IS NULL")
} else {
statement <- paste0(statement, " gender_source_value = '", gender_source_value,"'")
}
}
if (!missing(gender_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_concept_id)) {
statement <- paste0(statement, " gender_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_source_concept_id = '", gender_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_care_site <- function(rowCount, care_site_id, care_site_name, place_of_service_concept_id, location_id, care_site_source_value, place_of_service_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect care_site' AS test, CASE WHEN(SELECT COUNT(*) FROM care_site WHERE")
first <- TRUE
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(care_site_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_name)) {
statement <- paste0(statement, " care_site_name IS NULL")
} else {
statement <- paste0(statement, " care_site_name = '", care_site_name,"'")
}
}
if (!missing(place_of_service_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(place_of_service_concept_id)) {
statement <- paste0(statement, " place_of_service_concept_id IS NULL")
} else {
statement <- paste0(statement, " place_of_service_concept_id = '", place_of_service_concept_id,"'")
}
}
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(care_site_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_source_value)) {
statement <- paste0(statement, " care_site_source_value IS NULL")
} else {
statement <- paste0(statement, " care_site_source_value = '", care_site_source_value,"'")
}
}
if (!missing(place_of_service_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(place_of_service_source_value)) {
statement <- paste0(statement, " place_of_service_source_value IS NULL")
} else {
statement <- paste0(statement, " place_of_service_source_value = '", place_of_service_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_location <- function(rowCount, location_id, address_1, address_2, city, state, zip, county, location_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect location' AS test, CASE WHEN(SELECT COUNT(*) FROM location WHERE")
first <- TRUE
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(address_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(address_1)) {
statement <- paste0(statement, " address_1 IS NULL")
} else {
statement <- paste0(statement, " address_1 = '", address_1,"'")
}
}
if (!missing(address_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(address_2)) {
statement <- paste0(statement, " address_2 IS NULL")
} else {
statement <- paste0(statement, " address_2 = '", address_2,"'")
}
}
if (!missing(city)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(city)) {
statement <- paste0(statement, " city IS NULL")
} else {
statement <- paste0(statement, " city = '", city,"'")
}
}
if (!missing(state)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(state)) {
statement <- paste0(statement, " state IS NULL")
} else {
statement <- paste0(statement, " state = '", state,"'")
}
}
if (!missing(zip)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(zip)) {
statement <- paste0(statement, " zip IS NULL")
} else {
statement <- paste0(statement, " zip = '", zip,"'")
}
}
if (!missing(county)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(county)) {
statement <- paste0(statement, " county IS NULL")
} else {
statement <- paste0(statement, " county = '", county,"'")
}
}
if (!missing(location_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_source_value)) {
statement <- paste0(statement, " location_source_value IS NULL")
} else {
statement <- paste0(statement, " location_source_value = '", location_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_person <- function(rowCount, person_id, gender_concept_id, year_of_birth, month_of_birth, day_of_birth, time_of_birth, race_concept_id, ethnicity_concept_id, location_id, provider_id, care_site_id, person_source_value, gender_source_value, gender_source_concept_id, race_source_value, race_source_concept_id, ethnicity_source_value, ethnicity_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect person' AS test, CASE WHEN(SELECT COUNT(*) FROM person WHERE")
first <- TRUE
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(gender_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_concept_id)) {
statement <- paste0(statement, " gender_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_concept_id = '", gender_concept_id,"'")
}
}
if (!missing(year_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(year_of_birth)) {
statement <- paste0(statement, " year_of_birth IS NULL")
} else {
statement <- paste0(statement, " year_of_birth = '", year_of_birth,"'")
}
}
if (!missing(month_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(month_of_birth)) {
statement <- paste0(statement, " month_of_birth IS NULL")
} else {
statement <- paste0(statement, " month_of_birth = '", month_of_birth,"'")
}
}
if (!missing(day_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(day_of_birth)) {
statement <- paste0(statement, " day_of_birth IS NULL")
} else {
statement <- paste0(statement, " day_of_birth = '", day_of_birth,"'")
}
}
if (!missing(time_of_birth)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(time_of_birth)) {
statement <- paste0(statement, " time_of_birth IS NULL")
} else {
statement <- paste0(statement, " time_of_birth = '", time_of_birth,"'")
}
}
if (!missing(race_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_concept_id)) {
statement <- paste0(statement, " race_concept_id IS NULL")
} else {
statement <- paste0(statement, " race_concept_id = '", race_concept_id,"'")
}
}
if (!missing(ethnicity_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_concept_id)) {
statement <- paste0(statement, " ethnicity_concept_id IS NULL")
} else {
statement <- paste0(statement, " ethnicity_concept_id = '", ethnicity_concept_id,"'")
}
}
if (!missing(location_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(location_id)) {
statement <- paste0(statement, " location_id IS NULL")
} else {
statement <- paste0(statement, " location_id = '", location_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(person_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_source_value)) {
statement <- paste0(statement, " person_source_value IS NULL")
} else {
statement <- paste0(statement, " person_source_value = '", person_source_value,"'")
}
}
if (!missing(gender_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_value)) {
statement <- paste0(statement, " gender_source_value IS NULL")
} else {
statement <- paste0(statement, " gender_source_value = '", gender_source_value,"'")
}
}
if (!missing(gender_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gender_source_concept_id)) {
statement <- paste0(statement, " gender_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " gender_source_concept_id = '", gender_source_concept_id,"'")
}
}
if (!missing(race_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_source_value)) {
statement <- paste0(statement, " race_source_value IS NULL")
} else {
statement <- paste0(statement, " race_source_value = '", race_source_value,"'")
}
}
if (!missing(race_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(race_source_concept_id)) {
statement <- paste0(statement, " race_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " race_source_concept_id = '", race_source_concept_id,"'")
}
}
if (!missing(ethnicity_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_source_value)) {
statement <- paste0(statement, " ethnicity_source_value IS NULL")
} else {
statement <- paste0(statement, " ethnicity_source_value = '", ethnicity_source_value,"'")
}
}
if (!missing(ethnicity_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ethnicity_source_concept_id)) {
statement <- paste0(statement, " ethnicity_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " ethnicity_source_concept_id = '", ethnicity_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_observation_period <- function(rowCount, observation_period_id, person_id, observation_period_start_date, observation_period_end_date, period_type_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect observation_period' AS test, CASE WHEN(SELECT COUNT(*) FROM observation_period WHERE")
first <- TRUE
if (!missing(observation_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_id)) {
statement <- paste0(statement, " observation_period_id IS NULL")
} else {
statement <- paste0(statement, " observation_period_id = '", observation_period_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(observation_period_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_start_date)) {
statement <- paste0(statement, " observation_period_start_date IS NULL")
} else {
statement <- paste0(statement, " observation_period_start_date = '", observation_period_start_date,"'")
}
}
if (!missing(observation_period_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_period_end_date)) {
statement <- paste0(statement, " observation_period_end_date IS NULL")
} else {
statement <- paste0(statement, " observation_period_end_date = '", observation_period_end_date,"'")
}
}
if (!missing(period_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(period_type_concept_id)) {
statement <- paste0(statement, " period_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " period_type_concept_id = '", period_type_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_death <- function(rowCount, person_id, death_date, death_type_concept_id, cause_concept_id, cause_source_value, cause_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect death' AS test, CASE WHEN(SELECT COUNT(*) FROM death WHERE")
first <- TRUE
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(death_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(death_date)) {
statement <- paste0(statement, " death_date IS NULL")
} else {
statement <- paste0(statement, " death_date = '", death_date,"'")
}
}
if (!missing(death_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(death_type_concept_id)) {
statement <- paste0(statement, " death_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " death_type_concept_id = '", death_type_concept_id,"'")
}
}
if (!missing(cause_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_concept_id)) {
statement <- paste0(statement, " cause_concept_id IS NULL")
} else {
statement <- paste0(statement, " cause_concept_id = '", cause_concept_id,"'")
}
}
if (!missing(cause_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_source_value)) {
statement <- paste0(statement, " cause_source_value IS NULL")
} else {
statement <- paste0(statement, " cause_source_value = '", cause_source_value,"'")
}
}
if (!missing(cause_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cause_source_concept_id)) {
statement <- paste0(statement, " cause_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " cause_source_concept_id = '", cause_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_visit_occurrence <- function(rowCount, visit_occurrence_id, person_id, visit_concept_id, visit_start_date, visit_start_time, visit_end_date, visit_end_time, visit_type_concept_id, provider_id, care_site_id, visit_source_value, visit_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect visit_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM visit_occurrence WHERE")
first <- TRUE
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(visit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_concept_id)) {
statement <- paste0(statement, " visit_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_concept_id = '", visit_concept_id,"'")
}
}
if (!missing(visit_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_start_date)) {
statement <- paste0(statement, " visit_start_date IS NULL")
} else {
statement <- paste0(statement, " visit_start_date = '", visit_start_date,"'")
}
}
if (!missing(visit_start_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_start_time)) {
statement <- paste0(statement, " visit_start_time IS NULL")
} else {
statement <- paste0(statement, " visit_start_time = '", visit_start_time,"'")
}
}
if (!missing(visit_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_end_date)) {
statement <- paste0(statement, " visit_end_date IS NULL")
} else {
statement <- paste0(statement, " visit_end_date = '", visit_end_date,"'")
}
}
if (!missing(visit_end_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_end_time)) {
statement <- paste0(statement, " visit_end_time IS NULL")
} else {
statement <- paste0(statement, " visit_end_time = '", visit_end_time,"'")
}
}
if (!missing(visit_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_type_concept_id)) {
statement <- paste0(statement, " visit_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_type_concept_id = '", visit_type_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(care_site_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(care_site_id)) {
statement <- paste0(statement, " care_site_id IS NULL")
} else {
statement <- paste0(statement, " care_site_id = '", care_site_id,"'")
}
}
if (!missing(visit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_source_value)) {
statement <- paste0(statement, " visit_source_value IS NULL")
} else {
statement <- paste0(statement, " visit_source_value = '", visit_source_value,"'")
}
}
if (!missing(visit_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_source_concept_id)) {
statement <- paste0(statement, " visit_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " visit_source_concept_id = '", visit_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_condition_occurrence <- function(rowCount, condition_occurrence_id, person_id, condition_concept_id, condition_source_concept_id, condition_source_value, condition_start_date, provider_id, visit_occurrence_id, condition_type_concept_id, condition_end_date, stop_reason) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect condition_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM condition_occurrence WHERE")
first <- TRUE
if (!missing(condition_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_occurrence_id)) {
statement <- paste0(statement, " condition_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " condition_occurrence_id = '", condition_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(condition_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_concept_id)) {
statement <- paste0(statement, " condition_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_concept_id = '", condition_concept_id,"'")
}
}
if (!missing(condition_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_source_concept_id)) {
statement <- paste0(statement, " condition_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_source_concept_id = '", condition_source_concept_id,"'")
}
}
if (!missing(condition_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_source_value)) {
statement <- paste0(statement, " condition_source_value IS NULL")
} else {
statement <- paste0(statement, " condition_source_value = '", condition_source_value,"'")
}
}
if (!missing(condition_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_start_date)) {
statement <- paste0(statement, " condition_start_date IS NULL")
} else {
statement <- paste0(statement, " condition_start_date = '", condition_start_date,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(condition_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_type_concept_id)) {
statement <- paste0(statement, " condition_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_type_concept_id = '", condition_type_concept_id,"'")
}
}
if (!missing(condition_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_end_date)) {
statement <- paste0(statement, " condition_end_date IS NULL")
} else {
statement <- paste0(statement, " condition_end_date = '", condition_end_date,"'")
}
}
if (!missing(stop_reason)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(stop_reason)) {
statement <- paste0(statement, " stop_reason IS NULL")
} else {
statement <- paste0(statement, " stop_reason = '", stop_reason,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_device_exposure <- function(rowCount, device_exposure_id, person_id, device_concept_id, device_exposure_start_date, device_exposure_end_date, device_type_concept_id, unique_device_id, quantity, provider_id, visit_occurrence_id, device_source_value, device_source_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect device_exposure' AS test, CASE WHEN(SELECT COUNT(*) FROM device_exposure WHERE")
first <- TRUE
if (!missing(device_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_id)) {
statement <- paste0(statement, " device_exposure_id IS NULL")
} else {
statement <- paste0(statement, " device_exposure_id = '", device_exposure_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(device_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_concept_id)) {
statement <- paste0(statement, " device_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_concept_id = '", device_concept_id,"'")
}
}
if (!missing(device_exposure_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_start_date)) {
statement <- paste0(statement, " device_exposure_start_date IS NULL")
} else {
statement <- paste0(statement, " device_exposure_start_date = '", device_exposure_start_date,"'")
}
}
if (!missing(device_exposure_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_end_date)) {
statement <- paste0(statement, " device_exposure_end_date IS NULL")
} else {
statement <- paste0(statement, " device_exposure_end_date = '", device_exposure_end_date,"'")
}
}
if (!missing(device_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_type_concept_id)) {
statement <- paste0(statement, " device_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_type_concept_id = '", device_type_concept_id,"'")
}
}
if (!missing(unique_device_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unique_device_id)) {
statement <- paste0(statement, " unique_device_id IS NULL")
} else {
statement <- paste0(statement, " unique_device_id = '", unique_device_id,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(device_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_source_value)) {
statement <- paste0(statement, " device_source_value IS NULL")
} else {
statement <- paste0(statement, " device_source_value = '", device_source_value,"'")
}
}
if (!missing(device_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_source_concept_id)) {
statement <- paste0(statement, " device_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " device_source_concept_id = '", device_source_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_observation <- function(rowCount, observation_id, person_id, observation_concept_id, observation_date, observation_time, observation_type_concept_id, value_as_number, value_as_string, value_as_concept_id, qualifier_concept_id, unit_concept_id, provider_id, visit_occurrence_id, observation_source_value, observation_source_concept_id, unit_source_value, qualifier_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect observation' AS test, CASE WHEN(SELECT COUNT(*) FROM observation WHERE")
first <- TRUE
if (!missing(observation_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_id)) {
statement <- paste0(statement, " observation_id IS NULL")
} else {
statement <- paste0(statement, " observation_id = '", observation_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(observation_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_concept_id)) {
statement <- paste0(statement, " observation_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_concept_id = '", observation_concept_id,"'")
}
}
if (!missing(observation_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_date)) {
statement <- paste0(statement, " observation_date IS NULL")
} else {
statement <- paste0(statement, " observation_date = '", observation_date,"'")
}
}
if (!missing(observation_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_time)) {
statement <- paste0(statement, " observation_time IS NULL")
} else {
statement <- paste0(statement, " observation_time = '", observation_time,"'")
}
}
if (!missing(observation_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_type_concept_id)) {
statement <- paste0(statement, " observation_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_type_concept_id = '", observation_type_concept_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_string)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_string)) {
statement <- paste0(statement, " value_as_string IS NULL")
} else {
statement <- paste0(statement, " value_as_string = '", value_as_string,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
if (!missing(qualifier_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_concept_id)) {
statement <- paste0(statement, " qualifier_concept_id IS NULL")
} else {
statement <- paste0(statement, " qualifier_concept_id = '", qualifier_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(observation_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_source_value)) {
statement <- paste0(statement, " observation_source_value IS NULL")
} else {
statement <- paste0(statement, " observation_source_value = '", observation_source_value,"'")
}
}
if (!missing(observation_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(observation_source_concept_id)) {
statement <- paste0(statement, " observation_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " observation_source_concept_id = '", observation_source_concept_id,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(qualifier_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_source_value)) {
statement <- paste0(statement, " qualifier_source_value IS NULL")
} else {
statement <- paste0(statement, " qualifier_source_value = '", qualifier_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_measurement <- function(rowCount, measurement_id, person_id, measurement_concept_id, measurement_date, measurement_time, measurement_type_concept_id, operator_concept_id, value_as_number, value_as_concept_id, unit_concept_id, range_low, range_high, provider_id, visit_occurrence_id, measurement_source_value, measurement_source_concept_id, unit_source_value, value_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect measurement' AS test, CASE WHEN(SELECT COUNT(*) FROM measurement WHERE")
first <- TRUE
if (!missing(measurement_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_id)) {
statement <- paste0(statement, " measurement_id IS NULL")
} else {
statement <- paste0(statement, " measurement_id = '", measurement_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(measurement_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_concept_id)) {
statement <- paste0(statement, " measurement_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_concept_id = '", measurement_concept_id,"'")
}
}
if (!missing(measurement_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_date)) {
statement <- paste0(statement, " measurement_date IS NULL")
} else {
statement <- paste0(statement, " measurement_date = '", measurement_date,"'")
}
}
if (!missing(measurement_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_time)) {
statement <- paste0(statement, " measurement_time IS NULL")
} else {
statement <- paste0(statement, " measurement_time = '", measurement_time,"'")
}
}
if (!missing(measurement_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_type_concept_id)) {
statement <- paste0(statement, " measurement_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_type_concept_id = '", measurement_type_concept_id,"'")
}
}
if (!missing(operator_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(operator_concept_id)) {
statement <- paste0(statement, " operator_concept_id IS NULL")
} else {
statement <- paste0(statement, " operator_concept_id = '", operator_concept_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(range_low)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(range_low)) {
statement <- paste0(statement, " range_low IS NULL")
} else {
statement <- paste0(statement, " range_low = '", range_low,"'")
}
}
if (!missing(range_high)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(range_high)) {
statement <- paste0(statement, " range_high IS NULL")
} else {
statement <- paste0(statement, " range_high = '", range_high,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(measurement_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_source_value)) {
statement <- paste0(statement, " measurement_source_value IS NULL")
} else {
statement <- paste0(statement, " measurement_source_value = '", measurement_source_value,"'")
}
}
if (!missing(measurement_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(measurement_source_concept_id)) {
statement <- paste0(statement, " measurement_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " measurement_source_concept_id = '", measurement_source_concept_id,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(value_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_source_value)) {
statement <- paste0(statement, " value_source_value IS NULL")
} else {
statement <- paste0(statement, " value_source_value = '", value_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_procedure_occurrence <- function(rowCount, procedure_occurrence_id, person_id, procedure_concept_id, procedure_date, procedure_type_concept_id, modifier_concept_id, quantity, provider_id, visit_occurrence_id, procedure_source_value, procedure_source_concept_id, qualifier_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect procedure_occurrence' AS test, CASE WHEN(SELECT COUNT(*) FROM procedure_occurrence WHERE")
first <- TRUE
if (!missing(procedure_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_occurrence_id)) {
statement <- paste0(statement, " procedure_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " procedure_occurrence_id = '", procedure_occurrence_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(procedure_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_concept_id)) {
statement <- paste0(statement, " procedure_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_concept_id = '", procedure_concept_id,"'")
}
}
if (!missing(procedure_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_date)) {
statement <- paste0(statement, " procedure_date IS NULL")
} else {
statement <- paste0(statement, " procedure_date = '", procedure_date,"'")
}
}
if (!missing(procedure_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_type_concept_id)) {
statement <- paste0(statement, " procedure_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_type_concept_id = '", procedure_type_concept_id,"'")
}
}
if (!missing(modifier_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(modifier_concept_id)) {
statement <- paste0(statement, " modifier_concept_id IS NULL")
} else {
statement <- paste0(statement, " modifier_concept_id = '", modifier_concept_id,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(procedure_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_source_value)) {
statement <- paste0(statement, " procedure_source_value IS NULL")
} else {
statement <- paste0(statement, " procedure_source_value = '", procedure_source_value,"'")
}
}
if (!missing(procedure_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_source_concept_id)) {
statement <- paste0(statement, " procedure_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " procedure_source_concept_id = '", procedure_source_concept_id,"'")
}
}
if (!missing(qualifier_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(qualifier_source_value)) {
statement <- paste0(statement, " qualifier_source_value IS NULL")
} else {
statement <- paste0(statement, " qualifier_source_value = '", qualifier_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_drug_exposure <- function(rowCount, drug_exposure_id, person_id, drug_exposure_start_date, drug_concept_id, drug_source_value, drug_source_concept_id, drug_type_concept_id, provider_id, visit_occurrence_id, route_concept_id, route_source_value, quantity, refills, days_supply, dose_unit_concept_id, dose_unit_source_value, effective_drug_dose, stop_reason, sig, lot_number, drug_exposure_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_exposure' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_exposure WHERE")
first <- TRUE
if (!missing(drug_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_id)) {
statement <- paste0(statement, " drug_exposure_id IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_id = '", drug_exposure_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_exposure_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_start_date)) {
statement <- paste0(statement, " drug_exposure_start_date IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_start_date = '", drug_exposure_start_date,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(drug_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_source_value)) {
statement <- paste0(statement, " drug_source_value IS NULL")
} else {
statement <- paste0(statement, " drug_source_value = '", drug_source_value,"'")
}
}
if (!missing(drug_source_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_source_concept_id)) {
statement <- paste0(statement, " drug_source_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_source_concept_id = '", drug_source_concept_id,"'")
}
}
if (!missing(drug_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_type_concept_id)) {
statement <- paste0(statement, " drug_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_type_concept_id = '", drug_type_concept_id,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(route_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(route_concept_id)) {
statement <- paste0(statement, " route_concept_id IS NULL")
} else {
statement <- paste0(statement, " route_concept_id = '", route_concept_id,"'")
}
}
if (!missing(route_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(route_source_value)) {
statement <- paste0(statement, " route_source_value IS NULL")
} else {
statement <- paste0(statement, " route_source_value = '", route_source_value,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(refills)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(refills)) {
statement <- paste0(statement, " refills IS NULL")
} else {
statement <- paste0(statement, " refills = '", refills,"'")
}
}
if (!missing(days_supply)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(days_supply)) {
statement <- paste0(statement, " days_supply IS NULL")
} else {
statement <- paste0(statement, " days_supply = '", days_supply,"'")
}
}
if (!missing(dose_unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_unit_concept_id)) {
statement <- paste0(statement, " dose_unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " dose_unit_concept_id = '", dose_unit_concept_id,"'")
}
}
if (!missing(dose_unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_unit_source_value)) {
statement <- paste0(statement, " dose_unit_source_value IS NULL")
} else {
statement <- paste0(statement, " dose_unit_source_value = '", dose_unit_source_value,"'")
}
}
if (!missing(effective_drug_dose)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(effective_drug_dose)) {
statement <- paste0(statement, " effective_drug_dose IS NULL")
} else {
statement <- paste0(statement, " effective_drug_dose = '", effective_drug_dose,"'")
}
}
if (!missing(stop_reason)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(stop_reason)) {
statement <- paste0(statement, " stop_reason IS NULL")
} else {
statement <- paste0(statement, " stop_reason = '", stop_reason,"'")
}
}
if (!missing(sig)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(sig)) {
statement <- paste0(statement, " sig IS NULL")
} else {
statement <- paste0(statement, " sig = '", sig,"'")
}
}
if (!missing(lot_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(lot_number)) {
statement <- paste0(statement, " lot_number IS NULL")
} else {
statement <- paste0(statement, " lot_number = '", lot_number,"'")
}
}
if (!missing(drug_exposure_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_end_date)) {
statement <- paste0(statement, " drug_exposure_end_date IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_end_date = '", drug_exposure_end_date,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_fact_relationship <- function(rowCount, domain_concept_id_1, fact_id_1, domain_concept_id_2, fact_id_2, relationship_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect fact_relationship' AS test, CASE WHEN(SELECT COUNT(*) FROM fact_relationship WHERE")
first <- TRUE
if (!missing(domain_concept_id_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(domain_concept_id_1)) {
statement <- paste0(statement, " domain_concept_id_1 IS NULL")
} else {
statement <- paste0(statement, " domain_concept_id_1 = '", domain_concept_id_1,"'")
}
}
if (!missing(fact_id_1)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(fact_id_1)) {
statement <- paste0(statement, " fact_id_1 IS NULL")
} else {
statement <- paste0(statement, " fact_id_1 = '", fact_id_1,"'")
}
}
if (!missing(domain_concept_id_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(domain_concept_id_2)) {
statement <- paste0(statement, " domain_concept_id_2 IS NULL")
} else {
statement <- paste0(statement, " domain_concept_id_2 = '", domain_concept_id_2,"'")
}
}
if (!missing(fact_id_2)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(fact_id_2)) {
statement <- paste0(statement, " fact_id_2 IS NULL")
} else {
statement <- paste0(statement, " fact_id_2 = '", fact_id_2,"'")
}
}
if (!missing(relationship_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(relationship_concept_id)) {
statement <- paste0(statement, " relationship_concept_id IS NULL")
} else {
statement <- paste0(statement, " relationship_concept_id = '", relationship_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_payer_plan_period <- function(rowCount, payer_plan_period_id, person_id, payer_plan_period_start_date, payer_plan_period_end_date, payer_source_value, plan_source_value, family_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect payer_plan_period' AS test, CASE WHEN(SELECT COUNT(*) FROM payer_plan_period WHERE")
first <- TRUE
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(payer_plan_period_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_start_date)) {
statement <- paste0(statement, " payer_plan_period_start_date IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_start_date = '", payer_plan_period_start_date,"'")
}
}
if (!missing(payer_plan_period_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_end_date)) {
statement <- paste0(statement, " payer_plan_period_end_date IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_end_date = '", payer_plan_period_end_date,"'")
}
}
if (!missing(payer_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_source_value)) {
statement <- paste0(statement, " payer_source_value IS NULL")
} else {
statement <- paste0(statement, " payer_source_value = '", payer_source_value,"'")
}
}
if (!missing(plan_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(plan_source_value)) {
statement <- paste0(statement, " plan_source_value IS NULL")
} else {
statement <- paste0(statement, " plan_source_value = '", plan_source_value,"'")
}
}
if (!missing(family_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(family_source_value)) {
statement <- paste0(statement, " family_source_value IS NULL")
} else {
statement <- paste0(statement, " family_source_value = '", family_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_note <- function(rowCount, note_id, person_id, note_date, note_time, note_type_concept_id, note_text, provider_id, visit_occurrence_id, note_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect note' AS test, CASE WHEN(SELECT COUNT(*) FROM note WHERE")
first <- TRUE
if (!missing(note_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_id)) {
statement <- paste0(statement, " note_id IS NULL")
} else {
statement <- paste0(statement, " note_id = '", note_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(note_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_date)) {
statement <- paste0(statement, " note_date IS NULL")
} else {
statement <- paste0(statement, " note_date = '", note_date,"'")
}
}
if (!missing(note_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_time)) {
statement <- paste0(statement, " note_time IS NULL")
} else {
statement <- paste0(statement, " note_time = '", note_time,"'")
}
}
if (!missing(note_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_type_concept_id)) {
statement <- paste0(statement, " note_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " note_type_concept_id = '", note_type_concept_id,"'")
}
}
if (!missing(note_text)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_text)) {
statement <- paste0(statement, " note_text IS NULL")
} else {
statement <- paste0(statement, " note_text = '", note_text,"'")
}
}
if (!missing(provider_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(provider_id)) {
statement <- paste0(statement, " provider_id IS NULL")
} else {
statement <- paste0(statement, " provider_id = '", provider_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(note_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(note_source_value)) {
statement <- paste0(statement, " note_source_value IS NULL")
} else {
statement <- paste0(statement, " note_source_value = '", note_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_specimen <- function(rowCount, specimen_id, person_id, specimen_concept_id, specimen_type_concept_id, specimen_date, specimen_time, quantity, unit_concept_id, anatomic_site_concept_id, disease_status_concept_id, specimen_source_id, specimen_source_value, unit_source_value, anatomic_site_source_value, disease_status_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect specimen' AS test, CASE WHEN(SELECT COUNT(*) FROM specimen WHERE")
first <- TRUE
if (!missing(specimen_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_id)) {
statement <- paste0(statement, " specimen_id IS NULL")
} else {
statement <- paste0(statement, " specimen_id = '", specimen_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(specimen_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_concept_id)) {
statement <- paste0(statement, " specimen_concept_id IS NULL")
} else {
statement <- paste0(statement, " specimen_concept_id = '", specimen_concept_id,"'")
}
}
if (!missing(specimen_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_type_concept_id)) {
statement <- paste0(statement, " specimen_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " specimen_type_concept_id = '", specimen_type_concept_id,"'")
}
}
if (!missing(specimen_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_date)) {
statement <- paste0(statement, " specimen_date IS NULL")
} else {
statement <- paste0(statement, " specimen_date = '", specimen_date,"'")
}
}
if (!missing(specimen_time)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_time)) {
statement <- paste0(statement, " specimen_time IS NULL")
} else {
statement <- paste0(statement, " specimen_time = '", specimen_time,"'")
}
}
if (!missing(quantity)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(quantity)) {
statement <- paste0(statement, " quantity IS NULL")
} else {
statement <- paste0(statement, " quantity = '", quantity,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(anatomic_site_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(anatomic_site_concept_id)) {
statement <- paste0(statement, " anatomic_site_concept_id IS NULL")
} else {
statement <- paste0(statement, " anatomic_site_concept_id = '", anatomic_site_concept_id,"'")
}
}
if (!missing(disease_status_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(disease_status_concept_id)) {
statement <- paste0(statement, " disease_status_concept_id IS NULL")
} else {
statement <- paste0(statement, " disease_status_concept_id = '", disease_status_concept_id,"'")
}
}
if (!missing(specimen_source_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_source_id)) {
statement <- paste0(statement, " specimen_source_id IS NULL")
} else {
statement <- paste0(statement, " specimen_source_id = '", specimen_source_id,"'")
}
}
if (!missing(specimen_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(specimen_source_value)) {
statement <- paste0(statement, " specimen_source_value IS NULL")
} else {
statement <- paste0(statement, " specimen_source_value = '", specimen_source_value,"'")
}
}
if (!missing(unit_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_source_value)) {
statement <- paste0(statement, " unit_source_value IS NULL")
} else {
statement <- paste0(statement, " unit_source_value = '", unit_source_value,"'")
}
}
if (!missing(anatomic_site_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(anatomic_site_source_value)) {
statement <- paste0(statement, " anatomic_site_source_value IS NULL")
} else {
statement <- paste0(statement, " anatomic_site_source_value = '", anatomic_site_source_value,"'")
}
}
if (!missing(disease_status_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(disease_status_source_value)) {
statement <- paste0(statement, " disease_status_source_value IS NULL")
} else {
statement <- paste0(statement, " disease_status_source_value = '", disease_status_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_procedure_cost <- function(rowCount, procedure_cost_id, procedure_occurrence_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, revenue_code_concept_id, payer_plan_period_id, revenue_code_source_value) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect procedure_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM procedure_cost WHERE")
first <- TRUE
if (!missing(procedure_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_cost_id)) {
statement <- paste0(statement, " procedure_cost_id IS NULL")
} else {
statement <- paste0(statement, " procedure_cost_id = '", procedure_cost_id,"'")
}
}
if (!missing(procedure_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(procedure_occurrence_id)) {
statement <- paste0(statement, " procedure_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " procedure_occurrence_id = '", procedure_occurrence_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(revenue_code_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(revenue_code_concept_id)) {
statement <- paste0(statement, " revenue_code_concept_id IS NULL")
} else {
statement <- paste0(statement, " revenue_code_concept_id = '", revenue_code_concept_id,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
if (!missing(revenue_code_source_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(revenue_code_source_value)) {
statement <- paste0(statement, " revenue_code_source_value IS NULL")
} else {
statement <- paste0(statement, " revenue_code_source_value = '", revenue_code_source_value,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_visit_cost <- function(rowCount, visit_cost_id, visit_occurrence_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect visit_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM visit_cost WHERE")
first <- TRUE
if (!missing(visit_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_cost_id)) {
statement <- paste0(statement, " visit_cost_id IS NULL")
} else {
statement <- paste0(statement, " visit_cost_id = '", visit_cost_id,"'")
}
}
if (!missing(visit_occurrence_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(visit_occurrence_id)) {
statement <- paste0(statement, " visit_occurrence_id IS NULL")
} else {
statement <- paste0(statement, " visit_occurrence_id = '", visit_occurrence_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_drug_cost <- function(rowCount, drug_cost_id, drug_exposure_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, ingredient_cost, dispensing_fee, average_wholesale_price, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_cost WHERE")
first <- TRUE
if (!missing(drug_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_cost_id)) {
statement <- paste0(statement, " drug_cost_id IS NULL")
} else {
statement <- paste0(statement, " drug_cost_id = '", drug_cost_id,"'")
}
}
if (!missing(drug_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_id)) {
statement <- paste0(statement, " drug_exposure_id IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_id = '", drug_exposure_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(ingredient_cost)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(ingredient_cost)) {
statement <- paste0(statement, " ingredient_cost IS NULL")
} else {
statement <- paste0(statement, " ingredient_cost = '", ingredient_cost,"'")
}
}
if (!missing(dispensing_fee)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dispensing_fee)) {
statement <- paste0(statement, " dispensing_fee IS NULL")
} else {
statement <- paste0(statement, " dispensing_fee = '", dispensing_fee,"'")
}
}
if (!missing(average_wholesale_price)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(average_wholesale_price)) {
statement <- paste0(statement, " average_wholesale_price IS NULL")
} else {
statement <- paste0(statement, " average_wholesale_price = '", average_wholesale_price,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_device_cost <- function(rowCount, device_cost_id, device_exposure_id, currency_concept_id, paid_copay, paid_coinsurance, paid_toward_deductible, paid_by_payer, paid_by_coordination_benefits, total_out_of_pocket, total_paid, payer_plan_period_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect device_cost' AS test, CASE WHEN(SELECT COUNT(*) FROM device_cost WHERE")
first <- TRUE
if (!missing(device_cost_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_cost_id)) {
statement <- paste0(statement, " device_cost_id IS NULL")
} else {
statement <- paste0(statement, " device_cost_id = '", device_cost_id,"'")
}
}
if (!missing(device_exposure_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(device_exposure_id)) {
statement <- paste0(statement, " device_exposure_id IS NULL")
} else {
statement <- paste0(statement, " device_exposure_id = '", device_exposure_id,"'")
}
}
if (!missing(currency_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(currency_concept_id)) {
statement <- paste0(statement, " currency_concept_id IS NULL")
} else {
statement <- paste0(statement, " currency_concept_id = '", currency_concept_id,"'")
}
}
if (!missing(paid_copay)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_copay)) {
statement <- paste0(statement, " paid_copay IS NULL")
} else {
statement <- paste0(statement, " paid_copay = '", paid_copay,"'")
}
}
if (!missing(paid_coinsurance)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_coinsurance)) {
statement <- paste0(statement, " paid_coinsurance IS NULL")
} else {
statement <- paste0(statement, " paid_coinsurance = '", paid_coinsurance,"'")
}
}
if (!missing(paid_toward_deductible)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_toward_deductible)) {
statement <- paste0(statement, " paid_toward_deductible IS NULL")
} else {
statement <- paste0(statement, " paid_toward_deductible = '", paid_toward_deductible,"'")
}
}
if (!missing(paid_by_payer)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_payer)) {
statement <- paste0(statement, " paid_by_payer IS NULL")
} else {
statement <- paste0(statement, " paid_by_payer = '", paid_by_payer,"'")
}
}
if (!missing(paid_by_coordination_benefits)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(paid_by_coordination_benefits)) {
statement <- paste0(statement, " paid_by_coordination_benefits IS NULL")
} else {
statement <- paste0(statement, " paid_by_coordination_benefits = '", paid_by_coordination_benefits,"'")
}
}
if (!missing(total_out_of_pocket)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_out_of_pocket)) {
statement <- paste0(statement, " total_out_of_pocket IS NULL")
} else {
statement <- paste0(statement, " total_out_of_pocket = '", total_out_of_pocket,"'")
}
}
if (!missing(total_paid)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(total_paid)) {
statement <- paste0(statement, " total_paid IS NULL")
} else {
statement <- paste0(statement, " total_paid = '", total_paid,"'")
}
}
if (!missing(payer_plan_period_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(payer_plan_period_id)) {
statement <- paste0(statement, " payer_plan_period_id IS NULL")
} else {
statement <- paste0(statement, " payer_plan_period_id = '", payer_plan_period_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_drug_era <- function(rowCount, drug_era_id, person_id, drug_concept_id, drug_era_start_date, drug_era_end_date, drug_exposure_count, gap_days) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect drug_era' AS test, CASE WHEN(SELECT COUNT(*) FROM drug_era WHERE")
first <- TRUE
if (!missing(drug_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_id)) {
statement <- paste0(statement, " drug_era_id IS NULL")
} else {
statement <- paste0(statement, " drug_era_id = '", drug_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(drug_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_start_date)) {
statement <- paste0(statement, " drug_era_start_date IS NULL")
} else {
statement <- paste0(statement, " drug_era_start_date = '", drug_era_start_date,"'")
}
}
if (!missing(drug_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_era_end_date)) {
statement <- paste0(statement, " drug_era_end_date IS NULL")
} else {
statement <- paste0(statement, " drug_era_end_date = '", drug_era_end_date,"'")
}
}
if (!missing(drug_exposure_count)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_exposure_count)) {
statement <- paste0(statement, " drug_exposure_count IS NULL")
} else {
statement <- paste0(statement, " drug_exposure_count = '", drug_exposure_count,"'")
}
}
if (!missing(gap_days)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(gap_days)) {
statement <- paste0(statement, " gap_days IS NULL")
} else {
statement <- paste0(statement, " gap_days = '", gap_days,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_dose_era <- function(rowCount, dose_era_id, person_id, drug_concept_id, unit_concept_id, dose_value, dose_era_start_date, dose_era_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect dose_era' AS test, CASE WHEN(SELECT COUNT(*) FROM dose_era WHERE")
first <- TRUE
if (!missing(dose_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_id)) {
statement <- paste0(statement, " dose_era_id IS NULL")
} else {
statement <- paste0(statement, " dose_era_id = '", dose_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(drug_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(drug_concept_id)) {
statement <- paste0(statement, " drug_concept_id IS NULL")
} else {
statement <- paste0(statement, " drug_concept_id = '", drug_concept_id,"'")
}
}
if (!missing(unit_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(unit_concept_id)) {
statement <- paste0(statement, " unit_concept_id IS NULL")
} else {
statement <- paste0(statement, " unit_concept_id = '", unit_concept_id,"'")
}
}
if (!missing(dose_value)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_value)) {
statement <- paste0(statement, " dose_value IS NULL")
} else {
statement <- paste0(statement, " dose_value = '", dose_value,"'")
}
}
if (!missing(dose_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_start_date)) {
statement <- paste0(statement, " dose_era_start_date IS NULL")
} else {
statement <- paste0(statement, " dose_era_start_date = '", dose_era_start_date,"'")
}
}
if (!missing(dose_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(dose_era_end_date)) {
statement <- paste0(statement, " dose_era_end_date IS NULL")
} else {
statement <- paste0(statement, " dose_era_end_date = '", dose_era_end_date,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_condition_era <- function(rowCount, condition_era_id, person_id, condition_concept_id, condition_era_start_date, condition_era_end_date, condition_occurrence_count) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect condition_era' AS test, CASE WHEN(SELECT COUNT(*) FROM condition_era WHERE")
first <- TRUE
if (!missing(condition_era_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_id)) {
statement <- paste0(statement, " condition_era_id IS NULL")
} else {
statement <- paste0(statement, " condition_era_id = '", condition_era_id,"'")
}
}
if (!missing(person_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(person_id)) {
statement <- paste0(statement, " person_id IS NULL")
} else {
statement <- paste0(statement, " person_id = '", person_id,"'")
}
}
if (!missing(condition_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_concept_id)) {
statement <- paste0(statement, " condition_concept_id IS NULL")
} else {
statement <- paste0(statement, " condition_concept_id = '", condition_concept_id,"'")
}
}
if (!missing(condition_era_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_start_date)) {
statement <- paste0(statement, " condition_era_start_date IS NULL")
} else {
statement <- paste0(statement, " condition_era_start_date = '", condition_era_start_date,"'")
}
}
if (!missing(condition_era_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_era_end_date)) {
statement <- paste0(statement, " condition_era_end_date IS NULL")
} else {
statement <- paste0(statement, " condition_era_end_date = '", condition_era_end_date,"'")
}
}
if (!missing(condition_occurrence_count)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(condition_occurrence_count)) {
statement <- paste0(statement, " condition_occurrence_count IS NULL")
} else {
statement <- paste0(statement, " condition_occurrence_count = '", condition_occurrence_count,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_cdm_source <- function(rowCount, cdm_source_name, cdm_source_abbreviation, cdm_holder, source_description, source_documentation_reference, cdm_etl_reference, source_release_date, cdm_release_date, cdm_version, vocabulary_version) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cdm_source' AS test, CASE WHEN(SELECT COUNT(*) FROM cdm_source WHERE")
first <- TRUE
if (!missing(cdm_source_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_source_name)) {
statement <- paste0(statement, " cdm_source_name IS NULL")
} else {
statement <- paste0(statement, " cdm_source_name = '", cdm_source_name,"'")
}
}
if (!missing(cdm_source_abbreviation)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_source_abbreviation)) {
statement <- paste0(statement, " cdm_source_abbreviation IS NULL")
} else {
statement <- paste0(statement, " cdm_source_abbreviation = '", cdm_source_abbreviation,"'")
}
}
if (!missing(cdm_holder)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_holder)) {
statement <- paste0(statement, " cdm_holder IS NULL")
} else {
statement <- paste0(statement, " cdm_holder = '", cdm_holder,"'")
}
}
if (!missing(source_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_description)) {
statement <- paste0(statement, " source_description IS NULL")
} else {
statement <- paste0(statement, " source_description = '", source_description,"'")
}
}
if (!missing(source_documentation_reference)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_documentation_reference)) {
statement <- paste0(statement, " source_documentation_reference IS NULL")
} else {
statement <- paste0(statement, " source_documentation_reference = '", source_documentation_reference,"'")
}
}
if (!missing(cdm_etl_reference)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_etl_reference)) {
statement <- paste0(statement, " cdm_etl_reference IS NULL")
} else {
statement <- paste0(statement, " cdm_etl_reference = '", cdm_etl_reference,"'")
}
}
if (!missing(source_release_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(source_release_date)) {
statement <- paste0(statement, " source_release_date IS NULL")
} else {
statement <- paste0(statement, " source_release_date = '", source_release_date,"'")
}
}
if (!missing(cdm_release_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_release_date)) {
statement <- paste0(statement, " cdm_release_date IS NULL")
} else {
statement <- paste0(statement, " cdm_release_date = '", cdm_release_date,"'")
}
}
if (!missing(cdm_version)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cdm_version)) {
statement <- paste0(statement, " cdm_version IS NULL")
} else {
statement <- paste0(statement, " cdm_version = '", cdm_version,"'")
}
}
if (!missing(vocabulary_version)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(vocabulary_version)) {
statement <- paste0(statement, " vocabulary_version IS NULL")
} else {
statement <- paste0(statement, " vocabulary_version = '", vocabulary_version,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_cohort <- function(rowCount, cohort_definition_id, subject_id, cohort_start_date, cohort_end_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(subject_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_id)) {
statement <- paste0(statement, " subject_id IS NULL")
} else {
statement <- paste0(statement, " subject_id = '", subject_id,"'")
}
}
if (!missing(cohort_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_start_date)) {
statement <- paste0(statement, " cohort_start_date IS NULL")
} else {
statement <- paste0(statement, " cohort_start_date = '", cohort_start_date,"'")
}
}
if (!missing(cohort_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_end_date)) {
statement <- paste0(statement, " cohort_end_date IS NULL")
} else {
statement <- paste0(statement, " cohort_end_date = '", cohort_end_date,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_cohort_definition <- function(rowCount, cohort_definition_id, cohort_definition_name, cohort_definition_description, definition_type_concept_id, cohort_definition_syntax, subject_concept_id, cohort_instantiation_date) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort_definition' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort_definition WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(cohort_definition_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_name)) {
statement <- paste0(statement, " cohort_definition_name IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_name = '", cohort_definition_name,"'")
}
}
if (!missing(cohort_definition_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_description)) {
statement <- paste0(statement, " cohort_definition_description IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_description = '", cohort_definition_description,"'")
}
}
if (!missing(definition_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(definition_type_concept_id)) {
statement <- paste0(statement, " definition_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " definition_type_concept_id = '", definition_type_concept_id,"'")
}
}
if (!missing(cohort_definition_syntax)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_syntax)) {
statement <- paste0(statement, " cohort_definition_syntax IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_syntax = '", cohort_definition_syntax,"'")
}
}
if (!missing(subject_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_concept_id)) {
statement <- paste0(statement, " subject_concept_id IS NULL")
} else {
statement <- paste0(statement, " subject_concept_id = '", subject_concept_id,"'")
}
}
if (!missing(cohort_instantiation_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_instantiation_date)) {
statement <- paste0(statement, " cohort_instantiation_date IS NULL")
} else {
statement <- paste0(statement, " cohort_instantiation_date = '", cohort_instantiation_date,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_cohort_attribute <- function(rowCount, cohort_definition_id, cohort_start_date, cohort_end_date, subject_id, attribute_definition_id, value_as_number, value_as_concept_id) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect cohort_attribute' AS test, CASE WHEN(SELECT COUNT(*) FROM cohort_attribute WHERE")
first <- TRUE
if (!missing(cohort_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_definition_id)) {
statement <- paste0(statement, " cohort_definition_id IS NULL")
} else {
statement <- paste0(statement, " cohort_definition_id = '", cohort_definition_id,"'")
}
}
if (!missing(cohort_start_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_start_date)) {
statement <- paste0(statement, " cohort_start_date IS NULL")
} else {
statement <- paste0(statement, " cohort_start_date = '", cohort_start_date,"'")
}
}
if (!missing(cohort_end_date)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(cohort_end_date)) {
statement <- paste0(statement, " cohort_end_date IS NULL")
} else {
statement <- paste0(statement, " cohort_end_date = '", cohort_end_date,"'")
}
}
if (!missing(subject_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(subject_id)) {
statement <- paste0(statement, " subject_id IS NULL")
} else {
statement <- paste0(statement, " subject_id = '", subject_id,"'")
}
}
if (!missing(attribute_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_definition_id)) {
statement <- paste0(statement, " attribute_definition_id IS NULL")
} else {
statement <- paste0(statement, " attribute_definition_id = '", attribute_definition_id,"'")
}
}
if (!missing(value_as_number)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_number)) {
statement <- paste0(statement, " value_as_number IS NULL")
} else {
statement <- paste0(statement, " value_as_number = '", value_as_number,"'")
}
}
if (!missing(value_as_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(value_as_concept_id)) {
statement <- paste0(statement, " value_as_concept_id IS NULL")
} else {
statement <- paste0(statement, " value_as_concept_id = '", value_as_concept_id,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
expect_count_attribute_definition <- function(rowCount, attribute_definition_id, attribute_name, attribute_description, attribute_type_concept_id, attribute_syntax) {
statement <- paste0("INSERT INTO test_results SELECT ", frameworkContext$testId, " AS id, '", frameworkContext$testDescription, "' AS description, 'Expect attribute_definition' AS test, CASE WHEN(SELECT COUNT(*) FROM attribute_definition WHERE")
first <- TRUE
if (!missing(attribute_definition_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_definition_id)) {
statement <- paste0(statement, " attribute_definition_id IS NULL")
} else {
statement <- paste0(statement, " attribute_definition_id = '", attribute_definition_id,"'")
}
}
if (!missing(attribute_name)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_name)) {
statement <- paste0(statement, " attribute_name IS NULL")
} else {
statement <- paste0(statement, " attribute_name = '", attribute_name,"'")
}
}
if (!missing(attribute_description)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_description)) {
statement <- paste0(statement, " attribute_description IS NULL")
} else {
statement <- paste0(statement, " attribute_description = '", attribute_description,"'")
}
}
if (!missing(attribute_type_concept_id)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_type_concept_id)) {
statement <- paste0(statement, " attribute_type_concept_id IS NULL")
} else {
statement <- paste0(statement, " attribute_type_concept_id = '", attribute_type_concept_id,"'")
}
}
if (!missing(attribute_syntax)) {
if (first) {
first <- FALSE
} else {
statement <- paste0(statement, " AND")
}
if (is.null(attribute_syntax)) {
statement <- paste0(statement, " attribute_syntax IS NULL")
} else {
statement <- paste0(statement, " attribute_syntax = '", attribute_syntax,"'")
}
}
statement <- paste0(statement, ") != ",rowCount ," THEN 'FAIL' ELSE 'PASS' END AS status;")
frameworkContext$testSql = c(frameworkContext$testSql, statement);
invisible(statement)
}
|
77063e2dfec2a65c4ed7ee1dddd57f971cd91744 | 63083d152778be7c5e30d0ebc3e073d8a645e7eb | /tests/testthat/test-nearest_stations_ogimet.R | 328c15063f8cee7ddcc61e9fe6b6882f685185cf | [] | no_license | cran/climate | 0cf4e64785a87081773626b76e303cb295158b9f | ef0ccd4da3a94488674bd1be34ab1b402086b721 | refs/heads/master | 2022-08-19T10:18:59.929179 | 2022-08-09T15:00:11 | 2022-08-09T15:00:11 | 236,571,855 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 814 | r | test-nearest_stations_ogimet.R | context("meteo_imgw")
test_that("nearest_stations_ogimet works!", {
x <- nearest_stations_ogimet(country = "United+Kingdom", point = c(10, 50), add_map = FALSE, no_of_stations = 10)
x <- nearest_stations_ogimet(country = "United+Kingdom", point = c(10, 50), add_map = TRUE, no_of_stations = 10)
x <- nearest_stations_ogimet(country = "United+Kingdom", point = c(-10, -50), add_map = TRUE, no_of_stations = 10)
x <- nearest_stations_ogimet(country = "Poland", point = c(10, 50), add_map = TRUE, no_of_stations = 10)
# expected error
testthat::expect_error(nearest_stations_ogimet(country = "Pland", point = c(10, 50), add_map = TRUE, no_of_stations = 10))
x <- nearest_stations_ogimet(country = c("United+Kingdom", "Poland"), point = c(0, 0), add_map = TRUE, no_of_stations = 150)
})
|
dd067c6f34bee0dae9f0e98edc8b49e5ced44525 | 6e1e34b7a10ceff84608053a4aaea1eed1ca98ea | /inst/app-visualize/ui/ui_bar_plot_1.R | e85375e72cd869e3709143dbeb36c7ca5968fc2d | [] | no_license | cran/xplorerr | c88cfe0f6ab1162fbc1b51aad949407dcd8352a7 | b3f6db79da83568ad1ae89e657f3ccff94de43de | refs/heads/master | 2021-06-08T06:09:44.106519 | 2021-05-21T03:50:02 | 2021-05-21T03:50:02 | 164,905,699 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,530 | r | ui_bar_plot_1.R | tabPanel('Bar Plot', value = 'tab_bar_plot_1',
fluidPage(
fluidRow(
column(12, align = 'left',
h4('Bar Plot - I')
)
),
hr(),
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('plotly',
fluidRow(
column(2,
selectInput('barly1_select_x', 'Variable: ',
choices = "", selected = ""),
textInput(inputId = "barly1_xlabel", label = "X Axes Label: ",
value = "label"),
textInput(inputId = "barly1_color", label = "Color: ",
value = "blue")
),
column(2,
textInput(inputId = "barly1_title", label = "Title: ",
value = "title"),
textInput(inputId = "barly1_ylabel", label = "Y Axes Label: ",
value = "label"),
textInput(inputId = "barly1_btext", label = "Text: ",
value = "")
),
column(8, align = 'center',
plotly::plotlyOutput('barly1_plot_1', height = '600px')
)
)
),
tabPanel('rbokeh',
fluidRow(
column(2,
selectInput('bobar1_select_x', 'Variable: ',
choices = "", selected = ""),
textInput(inputId = "bobar1_xlabel", label = "X Axes Label: ",
value = "label"),
textInput(inputId = "bobar1_color", label = "Color: ",
value = ""),
selectInput('bobar1_hover', 'Hover: ',
choices = c("TRUE" = TRUE, "FALSE" = FALSE), selected = "TRUE"
),
textInput(inputId = "bobar1_lcolor", label = "Line Color: ",
value = ""),
selectInput('bobar1_xgrid', 'X Axis Grid: ',
choices = c("TRUE" = TRUE, "FALSE" = FALSE), selected = "TRUE"
)
),
column(2,
textInput(inputId = "bobar1_title", label = "Title: ",
value = "title"),
textInput(inputId = "bobar1_ylabel", label = "Y Axes Label: ",
value = "label"),
numericInput(inputId = "bobar1_alpha", label = "Alpha: ",
value = 1, min = 0, max = 1, step = 0.1),
numericInput(inputId = "bobar1_width", label = "Width: ",
value = 0.9, min = 0, step = 0.1),
numericInput(inputId = "bobar1_lalpha", label = "Line Alpha: ",
value = 1, min = 0, max = 1, step = 0.1),
selectInput('bobar1_ygrid', 'Y Axis Grid: ',
choices = c("TRUE" = TRUE, "FALSE" = FALSE), selected = "TRUE"
)
),
column(8, align = 'center',
rbokeh::rbokehOutput('bobar1_plot_1', height = '600px')
)
)
),
tabPanel('highcharts',
fluidRow(
column(2,
selectInput('hibar1_select_x', 'Variable: ',
choices = "", selected = ""),
textInput(inputId = "hibar1_xlabel", label = "X Axes Label: ",
value = "label")
),
column(2,
textInput(inputId = "hibar1_title", label = "Title: ",
value = "title"),
textInput(inputId = "hibar1_ylabel", label = "Y Axes Label: ",
value = "label")
),
column(8, align = 'center',
highcharter::highchartOutput('hibar1_plot_1', height = '600px')
)
)
)
)
)
)
)
) |
158a5dae0f4092d6a95a81327486bf0f82c4a741 | 1d5d7db989e58b33420349b25831a1ed842f72d7 | /12_barplots_pvalues.R | dff6c70250c3f2748a52cecfa2e0f83a3f8e5901 | [
"MIT"
] | permissive | piquelab/murine_scRNA | b5aeae17800e1a2abb3f0467fe2fe664cc280c9e | fd293c575e6a261d7d33aa31be9cfab09ac98ec0 | refs/heads/main | 2023-04-17T19:08:03.768414 | 2022-11-18T17:07:33 | 2022-11-18T17:07:33 | 477,785,328 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,469 | r | 12_barplots_pvalues.R | library(Seurat)
library(Matrix)
library(tidyverse)
library(future)
library(harmony)
#######################################################################
#cell count / DEGs comparison between E.coli and control
#######################################################################
outFolder="12_comparison_DEGs_cellcounts/"
system(paste0("mkdir -p ", outFolder))
future::plan(strategy = 'multicore', workers = 16)
options(future.globals.maxSize = 30 * 1024 ^ 3)
###########################################
# annotation
cell.type.annotation<-read.delim("cell-type.annotation.txt")
clust2Names<-cell.type.annotation$Cell.type
clust2Names<-paste0(cell.type.annotation$Cluster,"_",clust2Names)
names(clust2Names)<-cell.type.annotation$Cluster
clust2Name<-cell.type.annotation$Cell.type
clust2Name<-paste0(c(0:30),"_",clust2Name)
names(clust2Name)<-c(0:30)
# seurat obj
sc <- read_rds("4_harmony_cellClass_soupx_doubletfinder_chrM/sc.NormByLibrary.cellclassify_newfilter-res0.5.2021-06-28.rds")
#######################################
# cell count compariosn E.coli vs pbs
#######################################
aa <- FetchData(sc,c("seurat_clusters","Location","Condition","status","Library"))
aa$seurat_clusters <- clust2Name[aa$seurat_clusters]
aa<-aa %>% mutate(Pregnancy_ID=str_match(Library,"[0-9]+"))
# per location
ccdiff_all<-lapply( unique(aa$Location),function(locationx){
#locationx <-"Myometrium"
aa2<-aa %>% filter(Location==locationx)
cc <- aa2 %>% group_by(seurat_clusters,Condition,Pregnancy_ID) %>%
summarize(n=n()) %>%
group_by(seurat_clusters) %>% mutate(p0=sum(n)/nrow(aa2)) %>%
group_by(Pregnancy_ID) %>%
mutate(nt=sum(n),p=n/nt,z=(p-p0)/sqrt(p0*(1-p0)*(1/nt+1/nrow(aa2))))
cluster_filter<-cc %>% group_by(seurat_clusters)%>% summarise(count_control=count(Condition=="Control"), count_ecoli=count(Condition=="E. coli"))%>% filter (count_control>=2 & count_ecoli>=2) %>% select (seurat_clusters) %>% unlist %>% unique()
ccdiff <- cc %>% filter(seurat_clusters%in% cluster_filter) %>% group_by(seurat_clusters) %>% summarize(wilcox.pval = wilcox.test(p ~ Condition)$p.value,
test.t = t.test(z ~ Condition)$statistic) %>% ungroup() %>% mutate(wilcox.padj = p.adjust(wilcox.pval))
ccdiff_filtered<-ccdiff %>% filter(wilcox.padj<0.1)
#ccdiff$Location<-locationx
# return(ccdiff_filtered)
write.csv(ccdiff,file=paste0(outFolder,"/wilcox_result_cellcount_",locationx,".csv"))
}
)
######################################################################
# up-regulated vs down-regulated
######################################################################
res <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_res0.5/ALL.combined.2021-06-29.tsv")
res<-res %>% filter(padj<0.1)
res <- res %>% separate(cname,c("Location","Cell_type"),sep="_",remove=FALSE)
res$Cell_type<-clust2Names[res$Cell_type]
sc@meta.data$cluster_name <- clust2Names[sc@meta.data$seurat_clusters]
binom.test.locs<-lapply( unique(aa$Location),function(locationx){
res_loc<-res %>% filter (Location==locationx)
resDE<-res_loc %>% filter(padj<0.1 & !is.na(padj)) #single cell fdr 0.1
total<-table(resDE$Cell_type)
res_up<-resDE%>%filter(log2FoldChange>0)
upregulated<-rep(0,length(total))
names(upregulated)<-names(total)
upregulated[names(table(res_up$Cell_type))]<-table(res_up$Cell_type)
binom.test.res<-c()
sc_loc<-subset(sc,Location==locationx)
cell_counts<-table(sc_loc$cluster_name)
for( x in unique(resDE$Cell_type))
{
btest<-binom.test(upregulated[x],total[x],0.5)
if(is.na(upregulated[x]))
upregulated[x]<-0
rb<-as.numeric(c(cell_counts[x],upregulated[x], (total[x]-upregulated[x]),total[x],btest$p.value))
binom.test.res<-rbind(binom.test.res,rb)
print(x)
print(btest$p.value)
}
rownames(binom.test.res)<-unique(resDE$Cell_type)
colnames(binom.test.res)<-c("Cell-counts","Up-regulated","Down-regulated","Total","P-value")
binom.test.res<-as.data.frame(binom.test.res)
binom.test.res$padj<-p.adjust(binom.test.res$`P-value`,"fdr")
binom.test.res<-binom.test.res[order(binom.test.res[,"padj"],decreasing = FALSE),]
binom.test.res$Location<-locationx
write.csv(binom.test.res,file=paste0(outFolder,"binom.test.DEGs.",locationx,".csv"))
return(binom.test.res)
})
binom.test.all<-do.call(rbind,binom.test.locs)
|
f945b97b4b48d1a686241460f0973cc6c3b0b7ff | eedafd67512fc0146ee0d9d2910764978f541802 | /man/count_map.Rd | d149b927d25bc56ccb06cb5d7c6f4aa2fa592a47 | [
"MIT"
] | permissive | MSKCC-Epi-Bio/bstfun | e1e3925278d3f18ab62501bdc5930b4d2b768dd4 | 532465ec4a7097d8cf2e4aea50f0add44f361320 | refs/heads/main | 2023-06-27T13:02:47.872731 | 2023-06-26T18:04:43 | 2023-06-26T18:04:43 | 237,299,694 | 7 | 3 | NOASSERTION | 2023-06-26T18:04:45 | 2020-01-30T20:30:30 | R | UTF-8 | R | false | true | 539 | rd | count_map.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/count_map.R
\name{count_map}
\alias{count_map}
\title{Check variable derivations}
\usage{
count_map(data, ...)
}
\arguments{
\item{data}{data frame}
\item{...}{sets of variables to check. variables that are checked together
are included in the same vector. See example below.}
}
\description{
Function assists in checking the values of new, derived variables against
the raw, source variables.
}
\examples{
count_map(
mtcars,
c(cyl, am), c(gear, carb)
)
}
|
5eb4c2520ca1488fd39727e02e01de3252b7b710 | 967fedcd8b7636103273d344e9df41e48a1cc58d | /day16.R | d78a46f828767f5373c300c3f10ee1c7590eae4e | [] | no_license | HHoofs/AoC2020 | 8ee3205e0075c1284d7a36bead0b7520d084a92a | 369591c8604693380b4eb28a85751d0e911d964c | refs/heads/main | 2023-02-11T19:49:46.399255 | 2021-01-07T15:26:36 | 2021-01-07T15:26:36 | 319,048,608 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,222 | r | day16.R | ### Input --------------------------
inp = readLines('inp/day16_inp.txt')
# Determine where the new entries start
cuts = which(inp == '')
# Retrieve entries
rules = inp[1:(cuts[1]-1)]
ticket = inp[(cuts[1]+2):(cuts[2]-1)]
ticket = as.numeric(strsplit(ticket, ",")[[1]])
nearby_tickets = inp[(cuts[2]+2):(length(inp))]
nearby_tickets = lapply(nearby_tickets, function(x) as.numeric(strsplit(x, ",")[[1]]))
# Parse rules
all_rules = list()
for (rule in rules) {
parsed_rule = strsplit(rule, ": ")[[1]]
rule_range = c()
for(min_max in strsplit(parsed_rule[[2]], " or ")[[1]]) {
min_max_parsed = as.numeric(strsplit(min_max,'-')[[1]])
rule_range = c(rule_range, (min_max_parsed[1]:min_max_parsed[2]))
}
all_rules[[parsed_rule[1]]] = rule_range
}
### Star 1 ------------
cat(paste('Solution to part 1 is:',
sum(unlist(lapply(nearby_tickets, function(x) x[!x %in% unlist(all_rules)]))),
sep='\n'))
cat('\n====\n')
### Start 2 -----------
# Filter out invalid tickets
nearby_tickets = nearby_tickets[which(unlist(lapply(nearby_tickets, function(x) length(x[!x %in% unlist(all_rules)]))) == 0)]
# Retrieve all options for each information point in a ticket
ticket_option = list()
for(nearby_ticket in nearby_tickets){
ticket_option[[length(ticket_option) + 1]] =
matrix(unlist(lapply(nearby_ticket, function(i) lapply(all_rules, function(x) any(x == i)))), ncol=length(all_rules), byrow = TRUE)
}
ticket_option = lapply(ticket_option, function(x) apply(x, 1, function(y) which(y)))
valid_options = list()
for(i in seq(length(all_rules))){
valid_options[[length(valid_options) + 1]] = Reduce(intersect,lapply(ticket_option, function(x) x[[i]]))
}
valid_option = valid_options
while(TRUE){
singles = unlist(lapply(valid_option, function(x) length(x) == 1))
single_values = unlist(valid_option[singles])
valid_option = lapply(valid_option, function(x) {
if(length(x) == 1){
x
} else {
x[!(x %in% single_values)]
}
})
if(all(unlist(lapply(valid_option, function(x) length(x) == 1)))){
break()
}
}
cat(paste('Solution to part 2 is:',
prod(ticket[which(unlist(valid_option) %in% c(1,2,3,4,5,6))]),
sep='\n'))
|
a44d67202abc88176d4870069466a61f3f59b4e6 | a7045f6708f8bd65a54a946a544682325a4ae004 | /R/varRename.R | 1ddffb0ffc81a1a9aa72b314c6fe3f540f4b47b6 | [] | no_license | jjcurtin/lmSupport | a621e2a38211e4aacd4e41597d92df6ac303f97c | 1ba8feed959abd9c01c7041f8457d775cb59bb24 | refs/heads/main | 2023-04-06T01:36:59.490682 | 2021-05-06T02:12:53 | 2021-05-06T02:12:53 | 364,758,953 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 499 | r | varRename.R | varRename <- function(Data, From, To)
#2010-10-11: released, JJC
{
if (length(From) != length(To)) stop("Length of 'From' and 'To' vectors must match")
for (i in 1:length(From) ) {
if(is.element(From[i], names(Data))){
if (!is.element(To[i],names(Data))) names(Data)[names(Data)==From[i]] = To[i]
else stop('Variable name already exists in dataframe')
} else warning (sprintf('Variable name does not exist: %s', From[i]))
}
return(Data)
}
|
bf6d7f9148f03f6d8ca2eeed9c4729c6c0da1398 | dfa07b04688eb0edf2869b26807c1c00e1d71949 | /cursomultivarinble/primera_practica_R.R | c4de1e246a129089b44e0732ec3a62b97caf29c1 | [] | no_license | gitaprendiendoaprogramar/diferentes_trabajos_R_PY | 0964cecc406c368255032d537543d2539a567723 | a0c89eb3f9164092558b0fec9f84987c0ed2bd6d | refs/heads/master | 2023-01-21T11:23:14.289333 | 2020-12-01T14:26:03 | 2020-12-01T14:26:03 | 317,566,051 | 0 | 0 | null | null | null | null | ISO-8859-2 | R | false | false | 5,908 | r | primera_practica_R.R | iris <- data.frame(iris)
write.csv(iris,"iris.csv")
####################
iris <- iris[,2:6]
dim(iris)
ncol_iris <- dim(iris)[2]
nrow_iris <- dim(iris)[1]
####################
######### BOXPLOTS
par(mfrow=c(1,1))
## hace divisiones del lienzo, por filas y columnas, el primero aon filas y el segundo columnas
boxplot(iris[,1:4],main="titulo de lagrafica",xlab="etiqueta",col="blue")
## boxplot solo recive numericos....
# para documentacion
# ? boxplot
#########
colores <- c("blue", "red", "green", "yellow")
cajas <- function(j){
## crear una funcion que me haga un boxplot para cada columna
boxplot(iris[,j],main=colnames(iris)[j],xlab="",col= colores[j])
}
par(mfrow=c(2,2))
# divide el lienzo en dos por dos
sapply(c(1:4),cajas)
# sapply parece un for pero mas sofisticado
# la familia de funciones aplay es muy importante en todo R
######## BOXPLOTS por especie
cajas_especies <- function(j){
boxplot(iris[,j]~iris[,5],main=colnames(iris)[j],xlab="",col=colores[j])
}
# ~ significa pero clasificada por
par(mfrow=c(2,2))
sapply(c(1:4),cajas_especies)# repito funciona como un for, aplica la funcion cajas... a cada variable
######## BAGPLOTS
#intall.packages("aplpack", depedence = T)
#install.packages("aplpack")
library(aplpack)
par(mfrow=c(1,1))
bagplot(iris$Sepal.Length,iris$Sepal.Width,
xlab="Longitud Sépalo",
ylab="Ancho Sépalo",
main="Bagplot")
######## HISTOGRAMAS
par(mfrow=c(1,1))
hist(iris[,2],main = "Ejemplo de histograma",
xlab="",
col="blue",
breaks = "Sturges")# con esto controlo el ancho de las vandas
########
histos <- function(x){
hist(iris[,x],main = colnames(iris)[x],
xlab="",
col="blue",
breaks = "Sturges")
}
par(mfrow=c(2,2))
sapply(1:4,histos)
########
summary(iris$Species)
setosa <- iris[iris$Species == "setosa",]
versicolor <- iris[iris$Species == "versicolor",]
virginica <- iris[iris$Species == "virginica",]
par(mfrow=c(1,3))
hist(setosa[,1],main = "Setosa (sl)", xlab="",col="blue",breaks = "Sturges")
hist(versicolor[,1],main = "Versicolor (sl)", xlab="",col="red",breaks = "Sturges")
hist(virginica[,1],main = "Virginicia (sl)", xlab="",col="green",breaks = "Sturges")
######## DENSIDADES KERNEL
den_ker <- function(x){
plot(density(iris[,x],kernel = "gaussian"),
main = colnames(iris)[x],xlab="",
col="blue",lwd=2)
}
par(mfrow=c(2,2))
sapply(1:4,den_ker)
########
install.packages("sm")
library(sm)
cyl.f <- factor(iris$Species,
labels = unique(iris$Species))
par(mfrow=c(1,1))
sm.density.compare(iris$Sepal.Length,iris$Species)
# sm.density.compare(columna, clasificador)
colfill<-c(2:(2+length(levels(cyl.f)))) # con este codigo se pone la leyenda en el cursos, donde uno quiera
legend(locator(1), levels(cyl.f),fill=colfill)
####### SCATTERPLOT
library(ggplot2)
ggplot(data = iris) +
geom_point(mapping = aes(x=Sepal.Length,y=Petal.Length,color = Species),size=5,alpha=0.5)+
theme_bw()
######## Scatterplot 3d
install.packages("scatterplot3d")
library(scatterplot3d)
# para hacer graficas en 3 D
scatterplot3d(iris$Sepal.Length,iris$Petal.Length,iris$Petal.Width,pch=19,color=c("blue","green","orange")[iris[,5]])
scatterplot3d(iris$Sepal.Length,iris$Petal.Length,iris$Petal.Width,pch=19,color=c("blue","green","orange")[iris[,5]],type="h")
######## scatter matrix
pairs(iris[,1:4],pch=19,col=c("blue","green","orange")[iris[,5]])
####### COORDENADAS PARALELAS
install.packages("MASS")
library(MASS)
parcoord(iris[,1:4],col=c("blue","green","orange")[iris[,5]])
# para controlar el orden de la columnas
parcoord(iris[,c(1,3,2,4)],col=c("blue","green","orange")[iris[,5]], main = "Hola mundo de R")
###############################################
###############################################
######## Medidas descriptivas MEDIDAS DESCRIPTIVAS
###############################################
###############################################
# medidas promedios de las la columnas que le de comer
medias <- colMeans(iris[,1:4], na.rm = TRUE)
medias[3]
# tambien funciona para sumas, colSum()
# para omitir los NA es na.rm = TRUE
summary(iris)
### MEDIANA
install.packages("Gmedian") #
library(Gmedian)
Gmedian(iris[,1:4])
medias
Gmedian(iris[,c(1,2)])
par(mfrow=c(1,1))
plot(iris$Sepal.Length,iris$Sepal.Width,xlab="long sépalo",ylab="ancho sépalo",pch=19,col=c("blue","green","orange")[iris[,5]])
points(Gmedian(iris[,c(1,2)])[1],Gmedian(iris[,c(1,2)])[2],pch=19,col="red",lwd=10)
##############################################
mediana_setosa <- Gmedian(setosa[,c(1,2)])
mediana_versicolor <- Gmedian(versicolor[,c(1,2)])
mediana_virginicia <- Gmedian(virginica[,c(1,2)])
par(mfrow=c(1,1))
plot(iris$Sepal.Length,iris$Sepal.Width,xlab="long sépalo",ylab="ancho sépalo",pch=19,col=c("blue","green","orange")[iris[,5]])
points(Gmedian(iris[,c(1,2)])[1],Gmedian(iris[,c(1,2)])[2],pch=19,col="yellow",lwd=10)
points(Gmedian(setosa[,c(1,2)])[1],Gmedian(setosa[,c(1,2)])[2],pch=19,col="blue",lwd=10)
points(Gmedian(versicolor[,c(1,2)])[1],Gmedian(versicolor[,c(1,2)])[2],pch=19,col="green",lwd=10)
points(Gmedian(virginica[,c(1,2)])[1],Gmedian(virginica[,c(1,2)])[2],pch=19,col="red",lwd=10)
############################################# MATRIZ DE COVARIANZA
Cov_iris <- cov(iris[,1:4])
Corr_iris <- cor(iris[,1:4])
install.packages("reshape2")
library(reshape2)
library(tidyverse)
Corr_iris_arreglada <- melt(Corr_iris) # prepara los datos para que los pueda graficar melt
Cov_iris_arreglada <- melt(Cov_iris)
ggplot(data=Corr_iris_arreglada) +
geom_tile(mapping = aes(x=Var2,y=Var1,fill=value))
ggplot(data=Cov_iris_arreglada) +
geom_tile(mapping = aes(x=Var1,y=Var2,fill=value))
############################################
########## Referencia útil:
## https://rua.ua.es/dspace/bitstream/10045/69767/1/Modulo_4_-_Graficos_avanzados_con_ggplot2.pdf |
726c6e848254e2ca0ae883feaa185fe3bb50f02e | ac727c693ba0899b59859a399f8dc3b6b3a5e01d | /man/Qaux.beta.Rd | cf4163cfb0b2fb02c547834996ac8a9562554039 | [
"MIT"
] | permissive | htso/Hext | 2fb2d3cb74e88fe032a0d6f82b6a1bb5a7f0a42e | 8f4d99d20019a0bdd4522873ee20d758e2cb8187 | refs/heads/master | 2021-01-17T07:30:58.919595 | 2020-01-30T22:08:20 | 2020-01-30T22:08:20 | 83,738,303 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 437 | rd | Qaux.beta.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auxQ_func.R
\name{Qaux.beta}
\alias{Qaux.beta}
\title{Compute EM Auxiliary Q function for beta distribution}
\usage{
Qaux.beta(theta, x, wt)
}
\arguments{
\item{theta}{the shape parameters of beta distribution}
\item{x}{numeric vector to evaluate Aux Q function}
\item{wt}{weight vector}
}
\value{
numeric value of Aux Q function
}
\description{
See ref
}
|
ff61f93638c047129984c46587179c2f84578901 | b58c7bc0a6a611622634e883b17ddf188967695b | /ui.R | ee29f3f1afd9302a60c11f4254937df2d746b8eb | [] | no_license | jjames2/asthmacdc500citiesshinyapp | 4cd5e7322a34c0caafedc8f4a0f696b052b6f2f1 | b7440ba195c566308afc016d0a12ad92070a0637 | refs/heads/master | 2020-04-13T16:37:52.719343 | 2019-02-27T17:48:31 | 2019-02-27T17:48:31 | 163,326,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,407 | r | ui.R | library(leaflet)
choices <- c("Current Asthma", "Current Smoking", "Obesity")
levels <- c("City", "Census Tract")
tabPanel("Interactive map",
div(class="outer",
tags$head(
# Include our custom CSS
includeCSS("styles.css")
),
# If not using custom CSS, set height of leafletOutput to a number instead of percent
leafletOutput("map", width="100%", height="100%"),
absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 60, left = "auto", right = 20, bottom = "auto",
width = 330, height = "auto",
h2("Asthma Data Explorer"),
selectInput("geographicLevel", "Geographic Level", levels, selected = "City"),
selectInput("colorBy", "Color", choices, selected = "Current Asthma"),
selectInput("sizeBy", "Size", choices, selected = "Obesity"),
sliderInput("circlesize", "Circle Size",
min = 0, max = 100,
value = 50),
plotOutput("scatterplot", height = 250)
))) |
7a231f56ce29b26673950d3cac190a20f6c8e619 | 259421358681edd4ec4f9b8db2051a579a0380cf | /R/ocd_divisions.R | 7f0c0d162901b00da095dc63addc1bc424c7c322 | [
"MIT"
] | permissive | willdebras/googlecivic | df5493528dfe0865fc63ba4b2c0836a969c501b9 | 58bbbef3d5545c2f32dd7f2562a30e5aa5acebab | refs/heads/master | 2021-08-16T14:13:00.459536 | 2021-02-10T20:32:55 | 2021-02-10T20:32:55 | 243,639,743 | 3 | 4 | NOASSERTION | 2020-04-19T18:32:24 | 2020-02-27T23:45:09 | R | UTF-8 | R | false | false | 713 | r | ocd_divisions.R | #' Open Civic Data Division Identifiers
#'
#' A data frame of Open Civic Data division identifiers. The IDs are suitable
#' passing to Google Civic Information API's `representativeInfoByDivision`
#' endpoint. Searching for relevant strings in `name` can help locate relevant
#' IDs.
#'
#' @format A data frame with 193,493 row and 13 columns, including
#'
#' - id
#' - name
#' - sameAs
#' - sameAsNote
#' - validThrough
#' - census_geoid
#' - census_geoid_12
#' - census_geoid_14
#' - openstates_district
#' - placeholder_id
#' - sch_dist_stateid
#' - state_id
#' - validFrom
#'
#' @source [OpenCivicData](https://github.com/opencivicdata/ocd-division-ids/blob/master/identifiers/country-us.csv)
"ocd_divisions"
|
a7024e2bc8fbc83c948ecb7ffb8b50e88be4d548 | 4124f2d134ae5dd7c907ae1214b709798db22d20 | /program/pizzaNewton_update.R | 1abf2be22131e3b39257568f482aa96dfe9cf641 | [] | no_license | hsssgddtc/pizzaPred | 2bfb55afc6929e74333c6f79071dac6943feae40 | 579bbdf2a76885d9954dd3989497db05a4bb5f9c | refs/heads/master | 2016-09-06T03:47:21.616516 | 2014-06-26T14:54:26 | 2014-06-26T14:54:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,867 | r | pizzaNewton_update.R | setwd("G:\\Project\\R\\Workspace")
rm(list=ls())
library(matlab)
load_data <- function(){
X_train <<- read.table("data/X.dat",header=F,sep="");
y_train <<- read.table("data/y.dat",header=F,sep="");
}
repmat <- function(X,m,n){
##R equivalent of repmat (matlab)
mx = dim(X)[1]
nx = dim(X)[2]
matrix(t(matrix(X,mx,nx*n)),mx*m,nx*n,byrow=T)
}
lwlr <- function(X,y,x,tau){
m = nrow(X)/nrow(x);
n = ncol(X)/ncol(x);
theta = matrix(0,ncol(X),1);
X = as.matrix(X);
x = as.matrix(x);
#compute weights
#w = as.matrix(exp(as.matrix(-rowSums((X-repmat(x,m,n))^2)) / (2*tau)));
w = as.matrix(exp(as.matrix(-rowSums((X-x)^2)) / (2*tau)));
#perform Newton's method
g = matrix(1,n,1);
while(norm(g) > 1e-6){
h = 1 / (1+exp(-X %*% theta));
g = t(X) %*% (w * as.matrix(y - h)) - 1e-4*theta;
H = -t(X) %*% diag(as.vector(w*h*(1-h))) %*% X - 1e-4*diag(ncol(X));
theta = theta - solve(H) %*% g;
}
#return predicted new y
return(as.double(x%*%theta-10));
}
plot_lwlr <- function(X,y,tau,res){
x = matrix(0,2,1);
pred = matrix(0,res,res);
for(i in 1:res){
for(j in 1:res){
x[1] = 2*(i-1)/(res-1) - 1;
x[2] = 2*(j-1)/(res-1) - 1;
pred[j,i] = lwlr(X,y,x,tau);
}
}
imagesc(pred,main=paste("tau=",tau))
}
par(mfrow=c(2,3))
load_data()
pred <- lwlr(subPizzaReddit_X,subPizzaReddit_y,subPizzaReddit_X,0.01);
pizzaReddit <- cbind(pizzaReddit,pred)
pizzaReddit$pre <- ifelse(pizzaReddit$requester_received_pizza,1,0)
p <- subset(pizzaReddit,pizzaReddit$request_id %in% pizzaReddit_test$request_id ,c("request_id","pred","pre"))
sum(ifelse(p$pre == p$pred,1,0))/nrow(p)
names(p) <- c("request_id","requester_received_pizza","Real Value")
write.csv(p,"G:\\Project\\GitHub\\pizzaPred\\data\\result.csv",row.names=F)
|
8ea1ac8fc6c6f8ad7912ef23dd7adbde016f7f0b | d0bde1b4c9396b13252c0f1f7fbceb49887ff155 | /R/saveOutReports.R | 2128c8d31dcc948779df52e073f42e672dfcbc4c | [] | no_license | bpb824/orcycler | eec3335cc07bea7f3e2f1620145fcdb6d6fe5f73 | 22f571100905c322a291283e33d404509f97d077 | refs/heads/master | 2021-01-01T05:48:22.208599 | 2015-10-18T21:16:05 | 2015-10-18T21:16:05 | 42,019,202 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,173 | r | saveOutReports.R | #' Save Report Summary to Excel
#'
#' @param crashSummary Crash report summary table
#' @param issueSummary Issue report summary table
#' @param notes Note table from SQL database
#' @param saveFolder Relative or Absolute path to output folder
#'
#' @return None
#' @export
saveReportsToExcel=function(crashSummary,issueSummary,notes,saveFolder){
crashSheet = data.frame(matrix(nrow=nrow(crashSummary),ncol = ncol(crashSummary)+3))
colnames(crashSheet)=c("latitude","longitude","googleLink",colnames(crashSummary))
crashSheet$latitude = notes$latitude[notes$id %in% crashSummary$note_id]
crashSheet$longitude = notes$longitude[notes$id %in% crashSummary$note_id]
crashSheet$googleLink = paste0("http://maps.google.com/maps?q=",crashSheet$latitude,",",crashSheet$longitude,"&ll=",crashSheet$latitude,",",crashSheet$longitude,"&z=16")
crashSheet[,colnames(crashSummary)]=crashSummary
issueSheet = data.frame(matrix(nrow=nrow(issueSummary),ncol = ncol(issueSummary)+3))
colnames(issueSheet)=c("latitude","longitude","googleLink",colnames(issueSummary))
issueSheet$latitude = notes$latitude[notes$id %in% issueSummary$note_id]
issueSheet$longitude = notes$longitude[notes$id %in% issueSummary$note_id]
issueSheet$googleLink = paste0("http://maps.google.com/maps?q=",issueSheet$latitude,",",issueSheet$longitude,"&ll=",issueSheet$latitude,",",issueSheet$longitude,"&z=16")
issueSheet[,colnames(issueSummary)]=issueSummary
###Function code taken from http://www.r-bloggers.com/quickly-export-multiple-r-objects-to-an-excel-workbook/
save.xlsx <- function (file, ...)
{
require(xlsx, quietly = TRUE)
objects <- list(...)
fargs <- as.list(match.call(expand.dots = TRUE))
objnames <- as.character(fargs)[-c(1, 2)]
nobjects <- length(objects)
for (i in 1:nobjects) {
if (i == 1)
write.xlsx(objects[[i]], file, sheetName = objnames[i],row.names = FALSE)
else write.xlsx(objects[[i]], file, sheetName = objnames[i],
append = TRUE)
}
print(paste("Workbook", file, "has", nobjects, "worksheets."))
}
save.xlsx(paste0(saveFolder,"/reportSheet.xlsx"),crashSheet,issueSheet)
}
|
7224add7651523fb48a3060a3f9224cf7791c927 | bb8f4d4f7c52259fb58b99a0a956ec3e57e14410 | /Ltac_shiny/app.R | b00e46fa83b098b2b2ff976a24a08970826d9ba8 | [] | no_license | mirjamlaager/Ltac_simulations | ef4d65c75e33e3311cb68d9f3029f1c3a2b9be20 | 6413de6c96eb28ca89e0ce2bc0f22914c78e523f | refs/heads/master | 2021-10-16T15:07:22.950538 | 2019-02-11T19:16:07 | 2019-02-11T19:16:07 | 168,308,713 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,235 | r | app.R | library(shiny)
library(shinythemes)
source("run_simulations_shiny.R")
library(ggplot2)
library(gtable)
ui <- fluidPage(theme = shinytheme("journal"),
title = "Long Time Acute Care Simulations",
titlePanel("Long Time Acute Care Simulations"),
fluidRow(
column(3,
h3("Baseline Scenario"),
p("This is the baseline scenario.")
),
column(3,
h3("Scenario 1"),
p("This is scenario 1.")
)
),
fluidRow(
column(3,
h4("Ward Setup Baseline"),
sliderInput("n_standard_wards_baseline",
"Number of standard wards:",
min = 1,max = 10,value = 7),
sliderInput("beds_per_standard_ward_baseline",
"Number of beds per standard ward:",
min = 1,max = 20,value = 15),
sliderInput("n_ltac_wards_baseline",
"Number of ltac wards:",
min = 0,max = 5,value = 0),
sliderInput("beds_per_ltac_ward_baseline",
"Number of beds per ltac ward:",
min = 1,max = 20,value = 5),
sliderInput("mean_length_of_stay_baseline",
"Mean length of stay:",
min = 1,max = 14,value = 4)
),
column(3,
h4("Ward Setup Scenario 1"),
sliderInput("n_standard_wards_s1",
"Number of standard wards:",
min = 1,max = 10,value = 5),
sliderInput("beds_per_standard_ward_s1",
"Number of beds per standard ward:",
min = 1,max = 20,value = 15),
sliderInput("n_ltac_wards_s1",
"Number of ltac wards:",
min = 0,max = 5,value = 2),
sliderInput("beds_per_ltac_ward_s1",
"Number of beds per ltac ward:",
min = 1,max = 20,value = 15),
sliderInput("mean_length_of_stay_s1",
"Mean length of stay:",
min = 1,max = 14,value = 4)
),
column(6,
plotOutput("distPlot")
)
),
fluidRow(
column(3,
h4("Transmission Setup Baseline"),
sliderInput("within_ward_transmission_rate_baseline",
"within ward transmission rate:",
min = 0,max = 0.2,value = 0.02,step=0.001),
sliderInput("importation_probability_baseline",
"proportion positive on admission:",
min = 0,max = 1,value = 0.1,step=0.01)
),
column(3,
h4("Transmission Setup Scenario 1"),
sliderInput("within_ward_transmission_rate_s1",
"within ward transmission rate:",
min = 0,max = 0.2,value = 0.02,step=0.001),
sliderInput("importation_probability_s1",
"proportion positive on admission:",
min = 0,max = 1,value = 0.1,step=0.01)
)
),
fluidRow(
column(10,
h3("Simulation Setup"),
sliderInput("n_days",
"Number of days:",
min = 1,max = 500,value = 50,step=10),
sliderInput("n_runs",
"Number of simulation runs:",
min = 1,max = 100,value = 20),
actionButton("goButton", "Run Simulations"))
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
output$distPlot <- renderPlot({
if (input$goButton == 0)
return()
input$goButton
isolate({
n_patients_baseline <- rep(0,input$n_runs)
n_patients_short_baseline <- rep(0,input$n_runs)
n_patients_medium_baseline <- rep(0,input$n_runs)
n_patients_long_baseline <- rep(0,input$n_runs)
n_bed_days_standard_ward_baseline <-rep(0,input$n_runs)
n_bed_days_ltac_ward_baseline <- rep(1,input$n_runs)
n_patients_s1 <- rep(0,input$n_runs)
n_patients_short_s1 <- rep(0,input$n_runs)
n_patients_medium_s1 <- rep(0,input$n_runs)
n_patients_long_s1 <- rep(0,input$n_runs)
n_bed_days_standard_ward_s1 <-rep(0,input$n_runs)
n_bed_days_ltac_ward_s1 <- rep(0,input$n_runs)
col_while_in_standard_ward_baseline <- rep(0,input$n_runs)
col_while_in_standard_ward_s1 <- rep(0,input$n_runs)
col_while_in_ltac_ward_baseline <- rep(0,input$n_runs)
col_while_in_ltac_ward_s1 <- rep(0,input$n_runs)
col_and_short_stay_baseline <- rep(0,input$n_runs)
col_and_medium_stay_baseline <- rep(0,input$n_runs)
col_and_long_stay_baseline <- rep(0,input$n_runs)
col_and_short_stay_s1 <- rep(0,input$n_runs)
col_and_medium_stay_s1 <- rep(0,input$n_runs)
col_and_long_stay_s1 <- rep(0,input$n_runs)
withProgress(message = 'Running Baseline', value = 0, {
for (k in 1:input$n_runs){
simulation_output <- run_simulations_shiny(input$n_standard_wards_baseline, input$n_ltac_wards_baseline,
input$beds_per_standard_ward_baseline, input$beds_per_ltac_ward_baseline,
input$n_days, input$mean_length_of_stay_baseline,input$importation_probability_baseline,
input$within_ward_transmission_rate_baseline)
ptLog <- as.data.frame(simulation_output[1])
wardLog <- as.data.frame(simulation_output[2])
n_patients_baseline[k] <- nrow(ptLog)
n_bed_days_standard_ward_baseline[k] <- sum(pmin(ptLog$discharge,ptLog$transfer_to_ltac,na.rm = T) - ptLog$admission +1)
col_while_in_standard_ward_baseline[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge
& (is.na(ptLog$transfer_to_ltac) | ptLog$colonisation <= ptLog$transfer_to_ltac))
n_patients_short_baseline[k] <- sum(ptLog$discharge - ptLog$admission <= 2)
n_patients_medium_baseline[k] <- sum(ptLog$discharge - ptLog$admission > 2 & ptLog$discharge - ptLog$admission <= 7)
n_patients_long_baseline[k] <- sum( ptLog$discharge - ptLog$admission > 7)
col_and_short_stay_baseline[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge & ptLog$discharge - ptLog$admission <=2)
col_and_medium_stay_baseline[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge & ptLog$discharge - ptLog$admission >2 &ptLog$discharge - ptLog$admission <=7 )
col_and_long_stay_baseline[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge & ptLog$discharge - ptLog$admission >7)
incProgress(1/input$n_runs, detail = paste("Simulation", k))
}
})
withProgress(message = 'Running Scenario 1', value = 0, {
for (k in 1:input$n_runs){
simulation_output <- run_simulations_shiny(input$n_standard_wards_s1, input$n_ltac_wards_s1,
input$beds_per_standard_ward_s1, input$beds_per_ltac_ward_s1,
input$n_days, input$mean_length_of_stay_s1,input$importation_probability_s1,
input$within_ward_transmission_rate_s1)
ptLog <- as.data.frame(simulation_output[1])
wardLog <- as.data.frame(simulation_output[2])
n_patients_s1[k] <- nrow(ptLog)
n_bed_days_standard_ward_s1[k] <- sum(pmin(ptLog$discharge,ptLog$transfer_to_ltac,na.rm = T) - ptLog$admission +1)
n_bed_days_ltac_ward_s1[k] <- sum(ptLog$discharge - ptLog$transfer_to_ltac,na.rm = T)
col_while_in_standard_ward_s1[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge
& (is.na(ptLog$transfer_to_ltac) | ptLog$colonisation <= ptLog$transfer_to_ltac))
col_while_in_ltac_ward_s1[k] <- sum(is.na(ptLog$ltac_ward)==FALSE & ptLog$colonisation > ptLog$transfer_to_ltac & +
ptLog$discharge >= ptLog$colonisation)
n_patients_short_s1[k] <- sum(ptLog$discharge - ptLog$admission <= 2)
n_patients_medium_s1[k] <- sum(ptLog$discharge - ptLog$admission > 2 & ptLog$discharge - ptLog$admission <= 7)
n_patients_long_s1[k] <- sum( ptLog$discharge - ptLog$admission > 7)
col_and_short_stay_s1[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge & ptLog$discharge - ptLog$admission <=2)
col_and_medium_stay_s1[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge & ptLog$discharge - ptLog$admission >2 &ptLog$discharge - ptLog$admission <=7 )
col_and_long_stay_s1[k] <- sum(ptLog$colonisation >= ptLog$admission & ptLog$colonisation <= ptLog$discharge & ptLog$discharge - ptLog$admission >7)
incProgress(1/input$n_runs, detail = paste("Simulation", k))
}
})
#plot acquisitions per patient in the different groups
scenarios <- rep(c("baseline","scenario 1"),each=(input$n_runs)*4)
subgroup <- rep(c("0 - 3","4 - 7","8+","total","0 - 3","4 - 7","8+","total"),each=input$n_runs)
proportion <- c(col_and_short_stay_baseline/n_patients_short_baseline,col_and_medium_stay_baseline/n_patients_medium_baseline,
col_and_long_stay_baseline/n_patients_long_baseline,(col_while_in_standard_ward_baseline+col_while_in_ltac_ward_baseline)/n_patients_baseline,
col_and_short_stay_s1/n_patients_short_s1, col_and_medium_stay_s1/n_patients_medium_s1,
col_and_long_stay_s1/n_patients_long_s1, (col_while_in_ltac_ward_s1+col_while_in_standard_ward_s1)/n_patients_s1)
data <- data.frame(scenarios, subgroup, proportion)
ggplot(data, aes(x=scenarios, y=proportion, fill=subgroup)) +
geom_boxplot() + ylab("acquisionts/patients") + ggtitle("acquisitions by length of stay")
})
})
}
# Run the application
shinyApp(ui = ui, server = server)
|
c512530a5544c54d3c88b02a21b5cd6c84fbd3cb | 8865b563ebd8d1d9d045e04c9686ce46970ab3a1 | /notes/reese_code.R | 2141396aa25d2be22eb43dc1088dbcfc25e9447b | [] | no_license | zachmwhite/research | 6ffa3c877b670199aad547dff5ba410a19322375 | e1efc6be8ebdbc89d44a4b417577aac02245b825 | refs/heads/master | 2020-07-19T06:00:58.806225 | 2018-03-11T01:37:39 | 2018-03-11T01:37:39 | 94,335,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,143 | r | reese_code.R | # Basic GP code. Stolen shamelessly form Dr. Reese at BYU. https://madison.byu.edu/bayes/OurGP.R
library(MASS)
####################
nreal<-50
np<-1000
phi10<-0.5
xx<-seq(0,1,length=np)
dd<-dist(xx,upper=T,diag=T)^2
Sig<-exp(-phi10*as.matrix(dd))
gpreal<-mvrnorm(nreal,rep(0,np),Sig)
matplot(t(gpreal),type="l",main=expression(paste(phi,'=',phi10)))
########################
mysimfunc<-function(x){
out<-16*x^2*(1-x)^2
out[x>=0.7]<- out[x>=0.7] +32*(x[x>=0.7]-0.7)^3
return(out)
}
nrep<-1000
ndat<-6
np<-100
mydatx<-runif(ndat)
#mydatx<-seq(0,1,length=ndat)
mydaty<-mysimfunc(mydatx)+rnorm(length(mydatx),0,sqrt(0.01))
phi<-10
xx<-seq(0,1,length=np)
dd<-dist(xx,upper=T,diag=T)
Sig<-exp(-phi*as.matrix(dd^2))
yy<-mvrnorm(20,rep(0,np),Sig)
matplot(t(yy),type="l")
Sigxx<-exp(-phi*as.matrix(dist(mydatx,upper=T,diag=T)^2))
mypostmean<-rep(0,np)
signoise<-0.01
smallk1<-matrix(0,ncol=np,nrow=ndat)
smallk2<-matrix(0,ncol=ndat,nrow=np)
for(i in 1:np){
smallk1[,i]<-exp(-phi*(xx[i]-mydatx)^2)
}
for(i in 1:ndat){
smallk2[,i]<-exp(-phi*(mydatx[i]-xx)^2)
}
covinv<-solve(Sigxx+diag(signoise,ndat))
mumat<-t(smallk1)%*%covinv%*%mydaty
sigmat<-Sig-t(smallk1)%*%covinv%*%t(smallk2)
yfit<-mvrnorm(nrep,mumat,sigmat)
meany<-apply(yfit,2,mean)
plot(mydatx,mydaty,pch=1,cex=2.5,xlim=c(0,1),ylim=c(0,2))
for(i in 1:nrep){
lines(xx,yfit[i,],col="lightgray")
}
lines(xx,meany,col="blue",lwd=3)
points(mydatx,mydaty,pch=1,cex=1.8)
#######################
mysimfunc<-function(x){
out<-16*x^2*(1-x)^2
out[x>=0.7]<- out[x>=0.7] +32*(x[x>=0.7]-0.7)^3
out<-out+rnorm(length(x),0,.01)
return(out)
}
#phi<-10
np<-50
xx<-seq(0,1,length=np)
dd<-dist(xx,upper=T,diag=T)
#Sig<-exp(-phi[i-1]*as.matrix(dd^2))
#yy<-mvrnorm(20,rep(0,np),Sig)
#matplot(t(yy),type="l")
nrep<-1000
ndat<-6
mydatx<-runif(ndat)
mydaty<-mysimfunc(mydatx)
mypostmean<-rep(0,np)
smallk1<-matrix(0,ncol=np,nrow=ndat)
smallk2<-matrix(0,ncol=ndat,nrow=np)
niter<-100
signoise<-rep(0.000001,niter)
phi<-rep(10,niter)
ar.phi<-ar.sig<-0
cs.phi<-0.001
cs.sig<-0.001
for(i in 2:niter){
Sigxx<-exp(-phi[i-1]*as.matrix(dist(mydatx,upper=T,diag=T)^2))
Sig<-exp(-phi[i-1]*as.matrix(dd^2))
for(j in 1:np){
smallk1[,j]<-exp(-phi[i-1]*(xx[j]-mydatx)^2)
}
for(j in 1:ndat){
smallk2[,j]<-exp(-phi[i-1]*(mydatx[j]-xx)^2)
}
covinv<-solve(Sigxx+diag(signoise[i-1],ndat))
mumat<-t(smallk1)%*%covinv%*%mydaty
sigmat<-Sig-t(smallk1)%*%covinv%*%t(smallk2)
yfit<-mvrnorm(1,mumat,sigmat)
#METROPOLIS HASTINGS FOR PHI
phi[i]<-phi[i-1]
llo<--np/2*det(sigmat,log=T)-0.5*t(yfit-mumat)%*%solve(sigmat)%*%(yfit-mumat)+dgamma(phi[i],10,1,log=T)
cand<-rnorm(1,phi[i-1],cs.phi)
Sigxx<-exp(-cand*as.matrix(dist(mydatx,upper=T,diag=T)^2))
Sig<-exp(-cand*as.matrix(dd^2))
for(i in 1:np){
smallk1[,i]<-exp(-cand*(xx[i]-mydatx)^2)
}
for(i in 1:ndat){
smallk2[,i]<-exp(-cand*(mydatx[i]-xx)^2)
}
covinv<-solve(Sigxx+diag(signoise[i-1],ndat))
mumat<-t(smallk1)%*%covinv%*%mydaty
sigmat<-Sig-t(smallk1)%*%covinv%*%t(smallk2)
lln<--np/2*det(sigmat,log=T)-0.5*t(yfit-mumat)%*%solve(sigmat)%*%(yfit-mumat)+dgamma(cand,10,1,log=T)
if((lln-llo)>log(runif(1))){phi[i]<-cand;ar.phi<-ar.phi+1}
#METROPOLIS HASTINGS FOR PHI
signoise[i]<-signoise[i-1]
llo<--np/2*det(sigmat,log=T)-0.5*t(yfit-mumat)%*%solve(sigmat)%*%(yfit-mumat)+dgamma(phi[i],10,1,log=T)
cand<-rnorm(1,signoise[i-1],cs.sig)
Sigxx<-exp(-phi[i]*as.matrix(dist(mydatx,upper=T,diag=T)^2))
Sig<-exp(-phi[i]*as.matrix(dd^2))
for(i in 1:np){
smallk1[,i]<-exp(-phi[i]*(xx[i]-mydatx)^2)
}
for(i in 1:ndat){
smallk2[,i]<-exp(-phi[i]*(mydatx[i]-xx)^2)
}
covinv<-solve(Sigxx+diag(signoise[i],ndat))
mumat<-t(smallk1)%*%covinv%*%mydaty
sigmat<-Sig-t(smallk1)%*%covinv%*%t(smallk2)
lln<--np/2*det(sigmat,log=T)-0.5*t(yfit-mumat)%*%solve(sigmat)%*%(yfit-mumat)+dgamma(cand,10,1,log=T)
if((lln-llo)>log(runif(1))){signoise[i]<-cand;ar.sig<-ar.sig+1}
}
meany<-apply(yfit,2,mean)
plot(mydatx,mydaty,pch=1,cex=2.5,xlim=c(0,1),ylim=c(0,2))
for(i in 1:nrep){
lines(xx,yfit[i,],col="lightgray")
}
lines(xx,meany,col="blue",lwd=3) |
977e757b493abf8e4322f4609113f3d8031d0ecf | 302489278ac5ba1ce25c60a23f22043044dc08e0 | /R/calculate_csi.R | 38be693101367a64687b5f36490d205f5a0ef2c9 | [] | no_license | crsky1023/scFunctions | 8bab4a8643dcaab45ed067d64f9ff6ae8a2658f3 | 07c34da4db10d63d8123b21685d8974f98740948 | refs/heads/master | 2023-05-06T16:38:03.623814 | 2021-05-28T14:42:16 | 2021-05-28T14:42:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,519 | r | calculate_csi.R | #' Calculates CSI values for all regulon pairs
#'
#' @param regulonAUC The AUC values for all regulons as calculated by SCENIC (content of file:3.4_regulonAUC.Rds).
#' @keywords SCENIC, regulons, binary activity, kmeans, thresholds
#' @import SCENIC
#' @import tidyverse
#' @import svMisc
#' @export
#' @examples
#' \donttest{
#' regulon_thresholds <- auc_thresh_kmeans(regulonAUC)
#' }
calculate_csi <- function(regulonAUC,
calc_extended = FALSE,
verbose = FALSE){
compare_pcc <- function(vector_of_pcc,pcc){
pcc_larger <- length(vector_of_pcc[vector_of_pcc > pcc])
if(pcc_larger == length(vector_of_pcc)){
return(0)
}else{
return(length(vector_of_pcc))
}
}
calc_csi <- function(reg,reg2,pearson_cor){
test_cor <- pearson_cor[reg,reg2]
total_n <- ncol(pearson_cor)
pearson_cor_sub <- subset(pearson_cor,rownames(pearson_cor) == reg | rownames(pearson_cor) == reg2)
sums <- apply(pearson_cor_sub,MARGIN = 2, FUN = compare_pcc, pcc = test_cor)
fraction_lower <- length(sums[sums == nrow(pearson_cor_sub)]) / total_n
return(fraction_lower)
}
regulonAUC_sub <- regulonAUC@assays@data@listData$AUC
if(calc_extended == TRUE){
regulonAUC_sub <- subset(regulonAUC_sub,grepl("extended",rownames(regulonAUC_sub)))
} else if (calc_extended == FALSE){
regulonAUC_sub <- subset(regulonAUC_sub,!grepl("extended",rownames(regulonAUC_sub)))
}
regulonAUC_sub <- t(regulonAUC_sub)
pearson_cor <- cor(regulonAUC_sub)
pearson_cor_df <- as.data.frame(pearson_cor)
pearson_cor_df$regulon_1 <- rownames(pearson_cor_df)
pearson_cor_long <- pearson_cor_df %>%
gather(regulon_2,pcc,-regulon_1) %>%
mutate("regulon_pair" = paste(regulon_1,regulon_2,sep="_"))
regulon_names <- unique(colnames(pearson_cor))
num_of_calculations <- length(regulon_names)*length(regulon_names)
csi_regulons <- data.frame(matrix(nrow=num_of_calculations,ncol = 3))
colnames(csi_regulons) <- c("regulon_1",
"regulon_2",
"CSI")
num_regulons <- length(regulon_names)
f <- 0
for(reg in regulon_names){
## Check if user wants to print info
if(verbose == TRUE){
print(reg)
}
for(reg2 in regulon_names){
f <- f + 1
fraction_lower <- calc_csi(reg,reg2,pearson_cor)
csi_regulons[f,] <- c(reg,reg2,fraction_lower)
}
}
csi_regulons$CSI <- as.numeric(csi_regulons$CSI)
return(csi_regulons)
}
|
a8d5639adbe04487024cce1b44eb7645a8a14082 | 216b7cbdcd61f0cdfc5a8f74e8a24d68b56c3057 | /R_scripts/which_grid_cell_function.R | 4da5e3193986c82a185c623deb4e80f90a17e04e | [] | no_license | Preetis17/CapstoneProject | 7e0ebb9e02958ea100cc8153e625b4fe27644997 | c026bc5369b77d9d46ffffb2f5f7c629d62ea4ad | refs/heads/master | 2020-03-23T01:08:46.791202 | 2018-12-13T01:26:08 | 2018-12-13T01:26:08 | 140,902,835 | 0 | 2 | null | 2018-12-12T04:50:34 | 2018-07-14T00:07:43 | Jupyter Notebook | UTF-8 | R | false | false | 2,311 | r | which_grid_cell_function.R |
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# ... file : which_grid_cell_function.R
# ...
# ... find closest grid point (cell centroid) to data value location
# ...
# ... ref : https://stackoverflow.com/questions/21977720/
# ... r-finding-closest-neighboring-point-and-number-of-neighbors-within-a-given-rad
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# ... 21-sep-2018
# ...
# ... patrick.mcdevitt@smu.edu
# ...
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
library(sp)
library(rgeos)
library(geosphere)
library(dplyr)
library(tictoc)
printf <- function(...) invisible(cat(sprintf(...)))
which_grid_cell <- function(grid_centroid, df_to_map)
{
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# ... transform to spatial objects
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
sp.df_to_map <- df_to_map
coordinates(sp.df_to_map) <- ~long+lat
sp.grid_centroid <- grid_centroid
coordinates(sp.grid_centroid) <- ~long+lat
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# ... calculate distance pairs between all point pairs
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
dist_pair <- gDistance(sp.grid_centroid, sp.df_to_map, byid = TRUE)
# ... mid distance is closest point
min_dist <- apply(dist_pair, 1, function(x) order(x, decreasing = FALSE)[1])
# ... define corresponding columns from grid data frame
dist <- dist_pair[min_dist]
cell_id <- grid_centroid[min_dist, 3]
lat_cell <- grid_centroid[min_dist, 4]
long_cell <- grid_centroid[min_dist, 5]
hood <- grid_centroid[min_dist, 9]
hood_id <- grid_centroid[min_dist, 10]
# ... new data frame ... all prior columns + grid centroid identifiers and coords
df_mapped_to_grid <- cbind(df_to_map, dist, cell_id, lat_cell, long_cell, hood, hood_id)
return (df_mapped_to_grid)
}
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# ... end_of_file
# ... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- |
0323fb28fde07c23a37472c2fee26c0ae6c591fb | fe0ee8318230706a087eb33e98b32e2c7e9e3af1 | /plot4.R | 44e2db97eb4bd813e1f702b91193cecb010aa3da | [] | no_license | maxmoro/ExData_Plotting1 | 181965fd26aeaba71be2770db93e6dbed1783327 | e0906916f1c0feae67d4c9cdbddb9f2ad7eea427 | refs/heads/master | 2021-01-18T03:10:15.945974 | 2014-11-08T16:09:12 | 2014-11-08T16:09:12 | 26,354,655 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,304 | r | plot4.R | #Read full file exdata-data-household_power_consumption.zip saved in the workdir
file=unz("exdata-data-household_power_consumption.zip","household_power_consumption.txt")
DTOri=read.csv(file,sep=";",na.string="?")
#subset file and create full_date
DT=subset(DTOri,Date %in% c("1/2/2007","2/2/2007"))
DT$Full_Date=strptime(paste(DT$Date,DT$Time),format="%d/%m/%Y %H:%M:%S")
#open dev and set parameters
png(file="plot4.png",width=480,height=480)
par(ps=12)
par(mfrow = c(2, 2))
#plot1
plot(DT$Full_Date, DT$Global_active_power, type = "l", ylab = "Global Active Power", xlab = '')
#plot2
plot(DT$Full_Date, DT$Voltage, type = "l", xlab = "datetime",ylab = "Voltage")
#plot3
plot(DT$Full_Date, DT$Sub_metering_1, type = "l", col = "black", ylab = "Energy sub metering", xlab = "")
points(DT$Full_Date, DT$Sub_metering_2, type = "l", col = "red" , xlab = "", ylab = "Sub_metering_2")
points(DT$Full_Date, DT$Sub_metering_3, type = "l", col = "blue", xlab = "", ylab = "Sub_metering_3")
legend("topright", lty = 1, col = c("black", "red", "blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), bty = "n",xjust = 1, yjust = 1)
#plot4
plot(DT$Full_Date, DT$Global_reactive_power, type = "l", xlab = "datetime", ylab = "Global_reactive_power", ylim = c(0, 0.5))
#close device
dev.off()
|
0f00d21b65fc7414b65fe4326b9ae2af4870f573 | 5bd4b82811be11bcf9dd855e871ce8a77af7442f | /kinship/inst/tests/test/chtest.R | 0cbe5c76484dd29d9a8b5491050cc5dd07f6f924 | [] | no_license | jinghuazhao/R | a1de5df9edd46e53b9dc90090dec0bd06ee10c52 | 8269532031fd57097674a9539493d418a342907c | refs/heads/master | 2023-08-27T07:14:59.397913 | 2023-08-21T16:35:51 | 2023-08-21T16:35:51 | 61,349,892 | 10 | 8 | null | 2022-11-24T11:25:51 | 2016-06-17T06:11:36 | R | UTF-8 | R | false | false | 1,097 | r | chtest.R | #
# Test out the Cholesky
#
aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
tmat <- bdsmatrix(c(3,2,2,4),
c(22,1,2,21,3,20,19,4,18,17,5,16,15,6,7, 8,14,9,10,13,11,12),
matrix(c(1,0,1,1,0,0,1,1,0,1,0,10,0,
0,1,1,0,1,1,0,1,1,0,1,0,10), ncol=2))
dimnames(tmat) <- list(NULL, letters[1:13])
smat <- as.matrix(tmat)
yy <- c(30,35,42,56,34,45,32,37,78,56,40,52,39)
aeq(diag(tmat), diag(smat))
zz <- seq(1,13,2)
aeq(as.matrix(tmat[zz,zz]), smat[zz,zz])
ch0 <- chol(smat)
ch1 <- gchol(smat)
ch2 <- gchol(tmat)
# The gchol routines use the composition LDL', where L is lower triangular
# with a diagonal of 1's, and D is diagonal. chol() uses U'U where U is
# upper trangular.
# The as.matrix function returns L and the diag function returns D.
# Convert and compare
aeq(diag(ch1), diag(ch2))
temp <- as.matrix(ch2)
aeq(temp, as.matrix(ch1))
temp3 <- temp %*% diag(sqrt(diag(ch2)))
aeq(temp3, t(ch0))
zz0 <- solve(smat, yy)
zz1 <- solve(ch1, yy)
zz2 <- solve(tmat, yy)
aeq(zz1, zz2)
aeq(zz0, zz1)
rm(zz1, zz2, zz, temp, temp3, ch0, ch1, ch2)
|
22f3ab5f437e7f5435a0cbef57d262a29cb2d0d9 | 8fda7334773928c884f4813efbbde9921ef13b53 | /poissonModel4.R | fb956662fad48dacf0cbd82b7ad2b43b4ca03ef6 | [] | no_license | jazose/flows | 606afb322e1312f69420f1f568e5d684b3657584 | 4feb02feabb0cb44385252acbb58776602e301ac | refs/heads/master | 2021-01-19T00:46:14.399732 | 2013-09-04T22:23:43 | 2013-09-04T22:23:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,105 | r | poissonModel4.R | #Run a simulated Poisson hierarchical model.
#Model 4 takes log(lambda) to be a linear combination of log(previous flow + 1), log(pop. at origin), and log(pop. at destination)
library(rjags);library(coda);
###############
#Read in data.#
###############
setup=function(){
if(file.exists("C:/Users/jonazose")){
setwd("C:/Users/jonazose/Dropbox/RA/Code/flows/")
}else if(file.exists("C:/Users/Jon-Everyday")){
setwd("C:/Users/Jon-Everyday/Dropbox/RA/Code/flows/flows_git/")
}else{
setwd("/homes/jonazose/RA/flows_git/flows/")
}
#Read in data
rawDat=scan("./Abel/flows_Decadal.txt")
countryList<<-scan("./Abel/countryList_Decadal.txt",what="",sep=" ")
flowArray<<-array(rawDat[4:length(rawDat)],dim=rawDat[1:3])
rm(rawDat)
rawPopDat=scan("./Abel/popDatMatrix_Decadal.txt");
popDatMatrix<<-matrix(rawPopDat[3:length(rawPopDat)],nrow=rawPopDat[1],ncol=rawPopDat[2])
#Toss out some countries that aren't in the CEPII database.
#Guam (GUM), Mayotte (MYT), and US Virgin Islands (VIR)
tossOutIndices=which(countryList %in% c("GUM","MYT","VIR"))
flowArray<<-flowArray[-tossOutIndices,-tossOutIndices,];
shortCountryList<<-countryList[-tossOutIndices];
popDatMatrix<<-popDatMatrix[-tossOutIndices,]
nm=length(shortCountryList);
#####################
#Read in CEPII stuff#
#####################
if(!file.exists("distanceArray.txt")){#If we didn't already make the distance array
distData=read.csv("dist_cepii.csv",header=TRUE)
modifiedCountryList=shortCountryList;
modifiedCountryList[which(shortCountryList=="COD")]="ZAR"#COD in WorldBank is ZAR in CEPII
modifiedCountryList[which(shortCountryList=="TLS")]="TMP"#TLS in WorldBank is TMP in CEPII
modifiedCountryList[which(shortCountryList=="PSE")]="PAL"#PSE in WorldBank is PAL in CEPII
distanceArray<<-array(0,dim=c(nm,nm,12))
cat("Constructing distance matrix\n")
for(i in 1:nm){
cat(i,"\n")
for(j in 1:nm){
distanceArray[i,j,]=as.numeric(distData[which(distData$iso_o==modifiedCountryList[i]
& distData$iso_d==modifiedCountryList[j]),
3:14])
}
}
write(dim(distanceArray),"distanceArray.txt")
write(distanceArray,"distanceArray.txt",append=TRUE)
}else{#If we did make the distance array already, just read it in.
distanceArrayDat=scan("distanceArray.txt")
distanceArray<<-array(distanceArrayDat[4:length(distanceArrayDat)],
dim=distanceArrayDat[1:3]);
}
#Convert everything to vector form
vectorLength=nm*(nm-1);#Keep track of the length of a single year's worth of data
flowMatrix<<-matrix(0,nrow=dim(flowArray)[3],ncol=vectorLength);#Construct a matrix
#where each row is a single year's data
for(i in 1:dim(flowArray)[3]){
M=flowArray[,,i];
flowMatrix[i,]=M[row(M)!=col(M)]
}
flowMatrix<<-flowMatrix;
originMatrix<<-matrix(rep(shortCountryList,nm),nrow=nm,byrow=FALSE);
originVector<<-originMatrix[row(originMatrix)!=col(originMatrix)];
destMatrix<<-t(originMatrix);
destVector<<-destMatrix[row(destMatrix)!=col(destMatrix)];
distanceMatrix<<-matrix(0,nrow=12,ncol=vectorLength);
for(i in 1:12){
M=distanceArray[,,i];
distanceMatrix[i,]=M[row(M)!=col(M)];
}
distanceMatrix<<-distanceMatrix;
}
setup();
#Use for annual data
#y=as.vector(flowMatrix[c(15,25,35),])
#x=as.vector(flowMatrix[c(5,15,25),])
#Use for decadal data
y=as.vector(flowMatrix[c(2,3,4),]);
x=as.vector(flowMatrix[c(1,2,3),]);
#Construct vectors of origin and destination populations at beginning of the decade
oVec=numeric(0);
dVec=numeric(0);
for(j in 1:3){
temp=rep(0,length(originVector));
for(i in 1:length(originVector)){
countryIndex=which(shortCountryList==originVector[i])
temp[i]=popDatMatrix[countryIndex,j]
}
oVec=c(oVec,temp);
temp=rep(0,length(destVector));
for(i in 1:length(destVector)){
countryIndex=which(shortCountryList==destVector[i])
temp[i]=popDatMatrix[countryIndex,j]
}
dVec=c(dVec,temp);
}
#Choose a small sample from the y and x vectors
sampleFraction=1 #Use all of the data for fitting
compressedDataSize=floor(sampleFraction*length(y))
compressedDataIndices=sort(sample.int(n=length(y),size=compressedDataSize))
smallY=y[compressedDataIndices];
smallX=x[compressedDataIndices];
##################
#Run through JAGS#
##################
#The compressed version
jags <- jags.model('model4.bug.R',
data=list('n' = compressedDataSize,
'hist' = smallX,
'f' = smallY,
'o' = oVec,
'd' = dVec),
n.chains = 4,
n.adapt = 100)
update(jags, 500)
samples=coda.samples(jags,
c('alpha1','alpha2','alpha3','beta'),
1000,
thin=5)
sampleData=as.matrix(rbind(samples[[1]],samples[[2]],samples[[3]],samples[[4]]))
write(sampleData,"./Output/model4output.txt") |
b938fbce4e1808cdb6c88a2004b0cfe885eec6be | acf095499a8283654b2cacf546124b04febb350e | /classify/man/gpcm.bug.Rd | 6327830c038963d7cb36e7d1653d95ebf5eb1433 | [] | no_license | cbwheadon/classify | 6831fbd214cb69127b219d58385d7bb6e2701fa4 | 72c4c1b0e744e6b15353135e9c5315f30156b6d0 | refs/heads/master | 2016-09-08T05:09:13.811832 | 2014-08-17T09:42:38 | 2014-08-17T09:42:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 769 | rd | gpcm.bug.Rd | \name{gpcm.bug}
\alias{gpcm.bug}
\title{
Extract IRT Model Parameters from Bugs Models
}
\description{
Internal function which draws heavily on Curtis, S.M.(2010).
}
\usage{
gpcm.bug(v, cats, mdl, gibbs=c("bugs","jags"))
}
\arguments{
\item{v}{Bugs sims matrix}
\item{cats}{Numeric vector of item categories}
\item{mdl}{Bugs file: Partial Credit Model - "pcm.bug" or Generalised Partial Credit Model - "gpcm.bug" or Rasch model - "rasch.bug" or 2pl model "tpl.bug"}
\item{gibbs}{Gibbs sampler: "bugs" or "jags"}
}
\details{Extracts IRT Model Parameters from Bugs Models}
\value{
List with theta and beta parameters
}
\references{
Curtis, S.M.(2010) BUGS Code for Item Response Theory, \emph{Journal of Statistical Software}, Code Snippets, \bold{36(1)},1--34
} |
e6c4b20a16d204b489b4d216bf9c1905cff560d1 | 6dbb5242837e7210e9e1abb8497706e29c252098 | /plot2.R | 148d4bbb0a22a5be82603e9ac374a60c0dfe002a | [] | no_license | jasminchia/ExData_Plotting1 | 54feb064c54d2bdea395ed6ebf84eef04bb105fe | 4443cdc52a7846759bf916a435d3cf80d4054e2e | refs/heads/master | 2021-01-16T22:28:31.566652 | 2014-11-09T16:52:35 | 2014-11-09T16:52:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 813 | r | plot2.R | #read all data from file
full_data <- read.csv("./household_power_consumption.txt", header=T, sep=';', na.string="?", nrows=2075259,
check.names=F, stringsAsFactors=F, comment.char="")
#reformat the date into standard format
full_data$Date <- as.Date(full_data$Date, format="%d/%m/%Y")
#subset data from 2007-02-01 to 2007-02-02
chart2_data <- subset(full_data, Date >= "2007-02-01" & Date <= "2007-02-02")
#remove all data to release memory
rm(full_data)
#re-format the datetime
datetime <- paste(as.Date(chart2_data$Date), chart2_data$Time)
chart2_data$Datetime <- as.POSIXct(datetime)
## Plot 2
plot(chart2_data$Global_active_power~chart2_data$Datetime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
dev.copy(png, file="plot2.png", height=480, width=480)
dev.off()
|
6f033bf57cb958c74135c58e23f2d5c1754f441f | bd5f1f295766b7a22a51273650855c592a1a037e | /R/deprecated/compare_miseq_clones.R | a4c3355517a670ace376076296b4935b618cc197 | [] | no_license | tebbej/ArGa_MHC_DQB_R | bbdc1598f1dfeadf1c3c1b2a8653e41b3f5ea983 | 191feb360e4364edeb224743dc217c9ea77c3726 | refs/heads/main | 2023-04-11T10:32:17.169374 | 2022-10-06T12:55:10 | 2022-10-06T12:55:10 | 474,030,764 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,305 | r | compare_miseq_clones.R | ## NGS AND CLONING DATA COMPARISON (HEATMAP?) ##
miseq_data <- readDNAStringSet("data/ArGa-DQB-NGS_Artemis_20210301.fas")
miseq_data <- miseq_data[-length(miseq_data)]
miseq_info <- read.table(file = "data/ngs/Acacia_data_artemis.txt", header = T)[1:21,]
clone_genotypes <- readDNAStringSet("data/ArGa-DQB_clone-alleles_20210430.fas") #order by occurence frequency in cloning seqs
# in case allele ordering is desired to be after actual genotypes:
# clone_genotypes <- readDNAStringSet("data/allele_order_from_genotypes.fas")
#assign frequency values from plot to correct alleles (unsorted ngs alleles)
freq_info <- data.frame(ngs_alleles = names(miseq_data),
freq = miseq_info$freq[1:21]/100)
# freq = c(.15,.08,.10,.07,.11,.08,.07,.05,.06,.04,.02,
# .03,.02,.03,.01,.02,.02,.02,.00,.01,.01))
# along sequences in miseq_data, compare the sequences in miseq_data with
# sequences in clone_genotypes and create a matrix with 1's indicating correspondence
# of sequences and 0's non-correspondence
miseq_in_genotypes <- as.array(sapply(seq_along(miseq_data),
function(x) vcountPattern(as.vector(miseq_data[x]), clone_genotypes)))
# adjust col and row names according to their corresponding data.frame of origin
colnames(miseq_in_genotypes) <- names(miseq_data)
rownames(miseq_in_genotypes) <- names(clone_genotypes)
miseq_in_genotypes <- miseq_in_genotypes %>% # convert matrix to long data frame for ggplot heatmap format
as.data.frame() %>%
rownames_to_column("clone_alleles") %>%
pivot_longer(-c(clone_alleles), names_to = "ngs_alleles", values_to = "counts") %>%
mutate(., clone_alleles = factor(clone_alleles, levels = str_sort(unique(clone_alleles), numeric = T)),
ngs_alleles = factor(ngs_alleles, levels = rev(str_sort(unique(ngs_alleles), numeric = T)))) %>%
arrange(., desc(ngs_alleles)) %>% #sort both alleles for tidy plotting
arrange(., clone_alleles) %>%
# create column with information whether one or both of the listed allele is a putative artefact or not
mutate(., artefact_ngs = ifelse((ngs_alleles %in% paste0("ArGa-DQB*", c(15,20,21))) == T, 1, 0)) %>%
mutate(., artefact_clone = ifelse((clone_alleles %in% paste0("ArGa-DQB*", 20:30)) == T, 1,0))
shared_alleles <- miseq_in_genotypes[-which(miseq_in_genotypes$counts != 1),]
shared_alleles <- miseq_in_genotypes[-which(miseq_in_genotypes$counts != 1),1:2] %>%
mutate(sequence = as.vector(
miseq_data[na.omit(
match(shared_alleles$ngs_alleles, names(miseq_data)))]))
# double check whether sequences now coincide
iter_bin <- vector(length=dim(shared_alleles)[1])
for (i in seq_along(shared_alleles[[3]])) {
iter_bin[i] <- str_detect(as.character(clone_genotypes[i]), shared_alleles[[3]][[i]])
}
if (any(iter_bin != T)) {
warning("Allele matches share erroneous sequences")
} else {
message("Allele sequences coincide")
}
# update frequency of occurences of MiSeq alleles and overwrite names in clone allele manner
# (miseq alleles, are named according to their matching clone allele sequence)
miseq_alleles <- shared_alleles %>%
mutate(
freq = miseq_info$freq[na.omit(match(shared_alleles$ngs_alleles, miseq_info$allele))]/100,
variant_count = miseq_info$count[na.omit(match(shared_alleles$ngs_alleles, miseq_info$allele))]
) %>%
mutate(ngs_alleles = clone_alleles) %>%
select(!(clone_alleles))
miseq_alleles <- as.data.frame(miseq_alleles)
miseq_alleles_variant_counter <- unlist(sapply(miseq_alleles$variant_count, function(x) 1:x))
miseq_alleles_expand <- as.data.frame(lapply(miseq_alleles, rep, miseq_alleles$variant_count)) %>%
mutate(., variant_counter = miseq_alleles_variant_counter)
miseq_alleles_expand <- miseq_alleles_expand %>%
mutate(mutate(.,variant_no = sapply(
miseq_alleles_expand$ngs_alleles,
function(x) {
stringr::str_split(x, "\\*")[[1]][2] %>%
paste0(., collapse = "\\*") %>%
as.numeric()
})))
figures[[7]] <- ggplot(miseq_alleles_expand,
aes(x = variant_no,
group = desc(variant_counter),
fill = variant_counter)) +
geom_bar(aes(y = stat(count) / sum(count))) +
scale_y_continuous(labels = scales::label_percent(accuracy = 1),
limits = c(0,0.2), #swapped x & y values for reversing!
expand = c(0,0)) +
scale_fill_viridis_c(option = "cividis",
begin = 0,
end = 1) +
ylab("Frequency\n'MiSeq'\n") +
labs(fill = "Allele\ncounts") +
scale_x_continuous(breaks = seq_along(unique(miseq_alleles$ngs_alleles)),
labels = str_sort(unique(miseq_alleles$ngs_alleles), numeric = T),
expand = c(0, 0.3)) +
theme_minimal() +
theme(panel.grid = element_line(color = "white"),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.line = element_line(color = "black"),
axis.text = element_text(color = "black"),
axis.title = element_text(color = "black",
margin = margin(10,10,20,10)),
axis.ticks = element_line(color = "black",
size = 0.2),
axis.line.x = element_line(color = "black"),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x.bottom = element_text(angle = 45,
vjust = 1,
hjust = 1,
size = 11),
axis.line.y = element_line(color = "black"),
axis.title.y = element_text(size = 15.5),
axis.ticks.y = element_blank(),
axis.ticks.length = unit(.15,"cm"),
plot.background = element_rect(color = "white",
fill = "white"),
legend.position = "none",
plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm")
)
figures[[7]]
ggsave(filename = "graphics/allele_frequency_ngs_artemis_preliminary_scale.png",
figures[[7]],
dpi = 400)
freq_compare <- data.frame(clone_freq = alleles$frequency[1:19]/100,
ngs_freq =miseq_alleles$freq)
ngs_clone_correlation <- ggplot(freq_compare, aes(x=ngs_freq, y=clone_freq)) +
geom_point(shape = 15,
size = 3) +
geom_smooth(method = "lm",
se = T,
colour = "orange") +
scale_y_continuous(name = "Allele frequency\ncloning data\n",
limits = c(0,0.2),
expand = c(0,0)) +
scale_x_continuous(name = "\nAllele frequency\nMiSeq data",
limits = c(0,0.2),
expand = c(0,0)) +
annotate(geom = "text", x = 0.181, y = 0.01,
label = "italic(R²)==0.84", parse = T) +
theme_bw() +
theme(
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
plot.margin = unit(c(0.5,0.5,0.5,0,5), "cm"),
axis.title = element_text(color = "black",
margin = margin(10,10,20,10))
)
ngs_clone_correlation
ggsave("graphics/freq_correlation.png",
ngs_clone_correlation,
dpi = 400)
corr <- lm(ngs_freq~clone_freq, data = freq_compare)
summary(corr)
|
225877e2ddaa0f868f9de9af0bcf9277b7241ad6 | dc3642ea21337063e725441e3a6a719aa9906484 | /DevInit/R/experiments/logins.R | 0485d7eed8af09916f546a35e7417507151905f8 | [] | no_license | akmiller01/alexm-util | 9bbcf613384fe9eefd49e26b0c841819b6c0e1a5 | 440198b9811dcc62c3eb531db95abef8dbd2cbc7 | refs/heads/master | 2021-01-18T01:51:53.120742 | 2020-09-03T15:55:13 | 2020-09-03T15:55:13 | 23,363,946 | 0 | 7 | null | null | null | null | UTF-8 | R | false | false | 103 | r | logins.R | wd <- "C:/Users/alexm/Documents"
setwd(wd)
#Read
dat <- read.csv("logins.txt",header=FALSE,as.is=TRUE) |
32d14e89856d957d02b2e8ffe6be0110914c4da0 | 7c6deaad20a9507e809a70c694975bb31da5d5f0 | /Breast_cancer.R | 6c579ab5ac75b22923ce559b747ea24d3f55e090 | [] | no_license | Zeev17/Median-knn_imputation | 771ae10fbb6b6afccb7732512acbef9ece58c86e | 0a3e55838682946095774a97a022a898819498db | refs/heads/master | 2020-03-29T22:03:39.977914 | 2018-09-26T09:24:39 | 2018-09-26T09:24:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,117 | r | Breast_cancer.R | #setwd("C:/Users/Install/Desktop/CASE_R_september")
load("BreastCancer.RData")
#This dataset is interesting because many of the predictors contain missing values
#and most rows of the dataset have at least one missing value
library(caret)
library(RANN)
myControl<- trainControl(method = "cv", number = 10, verboseIter = TRUE)
####################################################################
#Median Uputation
####################################################################
# Apply median imputation: model
model <- train(
x = breast_cancer_x, y = breast_cancer_y,
method = "glm",
trControl = myControl,
preProcess = "medianImpute"
)
# Print model to console
model
####################################################################
#KNN imputatin
####################################################################
# Apply KNN imputation: model2
model2 <- train(
x = breast_cancer_x, y = breast_cancer_y,
method = "glm",
trControl = myControl,
preProcess = "knnImpute"
)
# Print model to console
model2
####################################################################
#Compare KNN and median imputation
####################################################################
median_model <- model
knn_model <- model2
resamples <- resamples(x = list(median_model = median_model, knn_model = knn_model))
#Plot to see
dotplot(resamples, metric = "Accuracy")
#knn model is slightly better.
####################################################################
#Combining preprocessing methods
####################################################################
# Fit glm with median imputation: model1
model1 <- train(
x = breast_cancer_x, y = breast_cancer_y,
method = "glm",
trControl = myControl,
preProcess = "medianImpute"
)
# Print model1
model1
# Fit glm with median imputation and standardization: model2
model2 <- train(
x = breast_cancer_x, y = breast_cancer_y,
method = "glm",
trControl = myControl,
preProcess = c("medianImpute", "center", "scale")
)
# Print model2
model2
|
348600ea63ed50a2d3b41793fcd8f0fa1ac6d612 | d1452f430d7f5b28380ca6d5a68bf2d3b51e7443 | /hhold_cons.R | 11967fb073dcea7e080379d9e7d867a2b2b73203 | [] | no_license | julianflowers/ex-data | c8a6909e911d9e174bf88e7c636739ae39e2d630 | f20c7b124113c4b950eff06cdbf426f26ab34b72 | refs/heads/master | 2021-01-20T13:49:02.756602 | 2015-09-20T07:54:58 | 2015-09-20T07:54:58 | 42,403,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 814 | r | hhold_cons.R | library(lubridate)
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(url, destfile = "hh.zip")
unzip("hh.zip")
hh <- read.csv("household_power_consumption.txt", header = TRUE, sep = ";", stringsAsFactors = FALSE) ## turns out that file is ; separated
hh$datetime <- with(hh, paste(Date,"",Time)) ##create a new datetime variable
hh$datetime <- dmy_hms(hh$datetime) ## convert to date time from character to POSIXct format
hh_s <- subset(hh, dmy(Date) == "2007-02-01" | dmy(Date) == "2007-02-02") ## subset the data set for 1st and 2nd Feb and store to a new variable
hh_s1 <-apply(hh_s[, 3:8], 2, function(x) as.numeric(x)) ## convert character data back to numeric
hh_s1 <- cbind(as.data.frame(hh_s1), hh_s[,c(9:10)]) ## recombine back into data frame |
b651342a0ad348df57d178e11a915a19063977bc | 9594d219126402ec4f5a58d32c8a57c82cab1e25 | /man/BandLevelFlatSpectrum.Rd | 0624e39eb14a9e7fbbc9901b04b4433c39d6a624 | [] | no_license | ctdClub/sonar | 037ade64e0c4efab690a1bc3c0ec1bd598be25b0 | c054c140714acb507a7e9d9d481149c9b3c38143 | refs/heads/master | 2022-02-01T17:43:37.739473 | 2016-09-15T15:46:37 | 2016-09-15T15:46:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 592 | rd | BandLevelFlatSpectrum.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/sonar.R
\name{BandLevelFlatSpectrum}
\alias{BandLevelFlatSpectrum}
\title{band level (BL) for flat spectrum}
\usage{
BandLevelFlatSpectrum(SpL, deltaf)
}
\arguments{
\item{SpL}{spectrum level}
\item{deltaf}{band frequency}
}
\value{
band level (BL)
}
\description{
Returns the total intensity of the sound in a band for flat spectrum
}
\examples{
BandLevelFlatSpectrum( 3, 2 )
}
\author{
Jose Gama
}
\references{
Waite A. D., 2002
Sonar for Practising Engineers, 3rd Edition
Chichester: Wiley. pp. 10.
}
|
4d8ffae32749941f0006a2abbec7808aacd7950e | 5434a6fc0d011064b575b321e93a3519db5e786a | /man/checkRunningRemote.Rd | c8e7c91c694ae889c640b74ac501d3ef5a5644e6 | [
"MIT"
] | permissive | cytoscape/RCy3 | 4813de06aacbaa9a3f0269c0ab8824a6e276bad9 | 18d5fac035e1f0701e870150c55231c75309bdb7 | refs/heads/devel | 2023-09-01T18:23:28.246389 | 2023-08-23T07:57:19 | 2023-08-23T07:57:19 | 118,533,442 | 47 | 22 | MIT | 2023-04-03T17:52:34 | 2018-01-23T00:21:43 | R | UTF-8 | R | false | true | 769 | rd | checkRunningRemote.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RCy3-notebook.R
\name{checkRunningRemote}
\alias{checkRunningRemote}
\title{checkRunningRemote}
\usage{
checkRunningRemote()
}
\value{
None
}
\description{
Determine whether we're running locally or on a remote server. If locally (either via raw R or via a
locally installed Notebook), we prefer to connect to Cytoscape over a local socket. If remote, we have to
connect over Jupyter-Bridge. Either way, we can determine which by whether Cytoscape answers to a version
check. If Cytoscape doesn't answer, we have no information ... and we have to wait until Cytoscape is
started and becomes reachable before we can determine local vs remote.
}
\examples{
\donttest{
checkRunningRemote()
}
}
|
9a0a5ddfbf955edf0a9948079de4c4bc17c3988c | 10c62818f4b83b7338e29799869aa3dc051fd6b4 | /src/svm/dual_svm.R | 2daa23c46adbb59ac17d273a9357330967d0cfc5 | [
"Apache-2.0"
] | permissive | chianwei/MachineLearning-memo | e27426d3121dfe3f81e80928af301fa5071e76cd | 29284ca24041969eeb59851a43ab6c28c685fae5 | refs/heads/master | 2020-03-16T19:27:01.105127 | 2017-01-18T07:50:09 | 2017-01-18T07:50:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,029 | r | dual_svm.R | #############
# dual SVM
n <- 5
Y <- c(-1,-1,-1,1,1)
data <- matrix(c(-2, 1, 0, 0, 2, 2, 2, 0, 3, 0),2,n)
#Dmat <- Y %o% Y * t(data) %*% data
m <- matrix(0, n, n)
for(i in 1:n){
for(j in 1:n){
m[i,j] <- Y[i]*Y[j]*t(data[, i]) %*% data[, j]
}
}
Dmat <- m
dvec <- rep(-1, n)
bvec <- rep(0, n)
#https://stat.ethz.ch/pipermail/r-sig-finance/attachments/20080901/637ba8c6/attachment.pl
#Amat <- matrix(0, n, n)
#diag(Amat) <- Y
#A=matrix(1:1,1, n)
#for(i in 1:3){
# A[,i] <- -1
#}
A <- matrix(Y, 1)
#http://d.hatena.ne.jp/repose/20080917/1221580572
library(kernlab)
res <- ipop(c = dvec, H = Dmat, A=A, b=0, l=rep(0, n),u=rep(10000,length=n), r=0, sigf=7)
sol <- res@primal
SV <- sol > 0.1 #simulate = 0
w <- apply(sol[SV] * Y[SV] * data[, SV], MARGIN = 1, sum)
b <- Y[SV] - t(w) %*% data[,SV]
b <- b[1,1]
f <- function(x) { return(-(b + w[1]*x)/w[2]) }
plot(-5:5, f(-5:5), type="l")
points(c(-2,0,2),c(1,0,2), pch = "x", col="red")
points(c(2,3),c(0,0), pch = "o", col="blue")
|
ebc4504d1e1e95ee080b623fc4364ad9ed7722b3 | f26fd7932132c77d1f0ced4339f5eac3c974ebae | /R/plot-cpue.R | f69946e1973c331c29fb70e602d3196c8a15202a | [] | no_license | szuwalski/gmr | 687f86e8e125b5dca96b6ff677eedf3212b24cc4 | 92abdaffccbd6d39ea7466529af9e6ea79b891c4 | refs/heads/main | 2023-01-09T13:07:24.481235 | 2020-11-05T20:00:14 | 2020-11-05T20:00:14 | 306,703,007 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,346 | r | plot-cpue.R | #' Get cpue or other indices
#'
#' @param M List object created by read_admb function
#' @return dataframe of observed and predicted indices and residuals
#' @author SJD Martell, D'Arcy N. Webber
#' @export
#'
.get_cpue_df <- function(M)
{
n <- length(M)
mdf <- NULL
for (i in 1:n)
{
A <- M[[i]]
df <- data.frame(Model = names(M)[i], as.data.frame(A$dSurveyData))
colnames(df) <- c("Model","Index","year","seas","fleet","sex","mature","cpue","cv","units")
df$sex <- .SEX[df$sex+1]
df$fleet <- .FLEET[df$fleet]
sd <- sqrt(log(1 + df$cv^2))
df$lb <- exp(log(df$cpue) - 1.96*sd)
df$ub <- exp(log(df$cpue) + 1.96*sd)
df$cvest <- na.exclude(as.vector(t(A$cpue_cv_add)))
sde <- sqrt(log(1 + df$cvest^2))
df$lbe <- exp(log(df$cpue) - 1.96*sde)
df$ube <- exp(log(df$cpue) + 1.96*sde)
df$pred <- na.exclude(as.vector(t(A$pre_cpue)))
df$resd <- na.exclude(as.vector(t(A$res_cpue)))
mdf <- rbind(mdf, df)
}
return(mdf)
}
#' Plot cpue or other indices
#'
#' @param M list object created by read_admb function
#' @param subsetby the fleet to subset the data to
#' @param xlab the x-axis label for the plot
#' @param ylab the y-axis label for the plot
#' @param ShowEstErr Shows errorbars from estimated CVs as well
#' @param logy Plot the CPUE in log-space
#' @param slab the sex label for the plot that appears above the key
#' @return plot of all observed and predicted incices
#' @export
#'
plot_cpue <- function(M, subsetby = "", xlab = "Year", ylab = "CPUE", slab = "Sex", ShowEstErr = FALSE, logy = FALSE)
{
mdf <- .get_cpue_df(M)
if (subsetby != "") mdf <- subset(mdf, fleet == subsetby)
if (logy) {
mdf$cpue <- log(mdf$cpue)
mdf$lb <- log(mdf$lb)
mdf$ub <- log(mdf$ub)
mdf$lbe <- log(mdf$lbe)
mdf$ube <- log(mdf$ube)
mdf$pred <- log(mdf$pred)
ylab <- paste0("log(", ylab, ")")
}
xlab <- paste0("\n", xlab)
ylab <- paste0(ylab, "\n")
p <- ggplot(mdf, aes(year, cpue)) +
expand_limits(y = 0) +
geom_pointrange(aes(year, cpue, ymax = ub, ymin = lb), col = "black")
if (ShowEstErr) {
if (length(M) == 1 && length(unique(mdf$sex)) == 1) {
p <- p + geom_pointrange(aes(year, cpue, ymax = ube, ymin = lbe), color = "red", shape = 1, linetype = "dotted", position = position_dodge(width = 1))
} else if (length(M) != 1 && length(unique(mdf$sex)) == 1) {
p <- p + geom_pointrange(aes(year, cpue, ymax = ube, ymin = lbe, col = Model), shape = 1, linetype = "dotted", position = position_dodge(width = 1))
} else if (length(M) == 1 && length(unique(mdf$sex)) != 1) {
p <- p + geom_pointrange(aes(year, cpue, ymax = ube, ymin = lbe, col = sex), shape = 1, linetype = "dotted", position = position_dodge(width = 1))
} else {
p <- p + geom_pointrange(aes(year, cpue, ymax = ube, ymin = lbe, col = Model), shape = 1, linetype = "dotted", position = position_dodge(width = 1))
}
}
if (.OVERLAY) {
if (length(M) == 1 && length(unique(mdf$sex)) == 1) {
p <- p + geom_line(data = mdf, aes(year, pred)) +
facet_wrap(~fleet, scales = "free_y",ncol=1)
} else if (length(M) != 1 && length(unique(mdf$sex)) == 1) {
p <- p + geom_line(data = mdf, aes(year, pred, color = Model, linetype = Model)) +
facet_wrap(~fleet, scales = "free_y",ncol=1)
} else if (length(M) == 1 && length(unique(mdf$sex)) != 1) {
p <- p + geom_line(data = mdf, aes(year, pred, color = sex)) + labs(col = slab) +
facet_wrap(~fleet + sex, scales = "free_y",ncol=1)
} else {
p <- p + geom_line(data = mdf, aes(year, pred, color = Model, linetype = Model)) +
facet_wrap(~fleet + sex, scales = "free_y",ncol=1)
}
} else {
p <- p + geom_line(data = mdf, aes(year, pred))
p <- p + facet_wrap(~fleet + sex + Model, scales = "free_y",ncol=1)
}
p <- p + labs(x = xlab, y = ylab)
print(p + .THEME + theme(legend.position=c(.7,.85)))
}
#' Plot residuals of cpue or other indices
#'
#' @param M List object created by read_admb function
#' @param subsetby the fleet or fleets to plot
#' @param xlab the x-axis label for the plot
#' @param ylab the y-axis label for the plot
#' @param slab the sex label for the plot that appears above the key
#' @return plot of fit indices residuals
#' @export
#'
plot_cpue_res <- function(M, subsetby = "", xlab = "Year", ylab = "Residual", slab = "Sex")
{
xlab <- paste0("\n", xlab)
ylab <- paste0(ylab, "\n")
mdf <- .get_cpue_df(M)
if (subsetby != "") mdf <- subset(mdf, fleet == subsetby)
p <- ggplot(data = mdf, aes(year, resd)) +
geom_hline(aes(yintercept = 0))
if (length(M) == 1 && length(unique(mdf$sex)) == 1)
{
p <- p + geom_point(data = mdf, aes(year, resd), position = position_dodge(0.5)) +
geom_linerange(aes(ymin = 0, ymax = resd), position = position_dodge(0.5)) +
facet_wrap(~fleet, scales = "free_y")
} else if (length(M) != 1 && length(unique(mdf$sex)) == 1) {
p <- p + geom_point(data = mdf, aes(year, resd, color = Model, shape = Model), position = position_dodge(0.5)) +
geom_linerange(aes(ymin = 0, ymax = resd, color = Model), position = position_dodge(0.5)) +
facet_wrap(~fleet, scales = "free_y")
} else if (length(M) == 1 && length(unique(mdf$sex)) != 1) {
p <- p + geom_point(data = mdf, aes(year, resd, color = sex, shape = sex), position = position_dodge(0.5)) + labs(col = slab) +
geom_linerange(aes(ymin = 0, ymax = resd, color = sex), position = position_dodge(0.5)) +
facet_wrap(~fleet + sex, scales = "free_y")
} else {
p <- p + geom_point(data = mdf, aes(year, resd, color = Model, shape = Model), position = position_dodge(0.5)) +
geom_linerange(aes(ymin = 0, ymax = resd, color = Model), position = position_dodge(0.5)) +
facet_wrap(~fleet + sex, scales = "free_y")
}
p <- p + labs(x = xlab, y = ylab, fill = slab)
print(p + .THEME)
}
|
a773b21d214c22c1f7c454c8cd308479c07cf3bd | 3ae7ca784edd942e246357a7a8a55c07dbfe3f38 | /src/clean.R | 3dcd00f21e0e280d3c0adc052d749621fdafde48 | [
"MIT"
] | permissive | HFAnalyticsLab/HES_pipeline | a4acbdf6dce34ba7cf18af74ae627828509791e8 | cab20a88e584d42a0c3c3d89898bfef89e102344 | refs/heads/master | 2023-04-16T10:20:56.933701 | 2023-03-22T09:48:09 | 2023-03-22T09:48:09 | 206,582,339 | 31 | 6 | MIT | 2021-01-25T09:51:19 | 2019-09-05T14:21:15 | R | UTF-8 | R | false | false | 7,280 | r | clean.R |
# If a column is present in dataframe apply provided function.
# Warnings generated by 'one_of' when col not found are supressed.
# Requires a dataframe, a column name as a string and a function to
# recursively apply to the column if present.
# Returns dataframe with modifed column.
mutate_if_present <- function(data, cols, fn) {
return(suppressWarnings(data %>% mutate_at(vars(one_of(cols)), fn)))
}
# Parse columns (or single column) converting a specifc value to NA
# Requires a dataframe, a vector of column names as strings and a value to convert
# to NA.
# Returns a modified dataframe.
convert_to_NA <- function(data, cols, v) {
return(data %>% mutate_if_present(cols, ~na_if(., v)))
}
# Parse columns (or single column) converting strings to date format,
# e.g. 2010-31-01.
# Requires a dataframe, a vector of column names as strings and a date format e.g. "%Y%m%d.
# Returns a modified dataframe.
convert_date <- function(data, cols) {
return(data %>% mutate_if_present(cols, as.Date))
}
# Parse columns (or single column) converting values to integers.
# Requires a dataframe, and a vector of column names as strings.
# Returns a modified dataframe.
convert_to_int <- function(data, cols) {
return(mutate_if_present(data, cols, as.integer))
}
# Parse columns (or single column) converting a set of values (or single
# value) to a new set of values (or single value).
# Requires a dataframe, a vector of column names as strings, a vector of values to be replaced
# and a vector of values to replace with.
# Returns a modified dataframe.
convert_vals <- function(data, cols, old_vals, new_vals) {
return(data %>% mutate_if_present(cols, ~plyr::mapvalues(., old_vals, new_vals)))
}
# Generates a vector of headers corresponding to the provided string with numbers 01 to
# n appended, n times
# Requires a string and a maximum number
# Returns a character vector
generate_numbered_headers <- function(string, n) {
return(c(str_c(string, "0", 1:9), str_c(string, 10:n)))
}
# Parse columns, where present, into required data formats.
# Requires a dataframe.
# Returns a modifed dataframe.
parse_HES <- function(data) {
return(data %>%
convert_to_NA(c("ADMINCAT", "ADMINCATST", "ADMISORC", "DISDEST", "RTTPERSTAT", "EPIORDER"), 98) %>%
convert_to_NA(c("ADMINCAT", "ADMINCATST", "ADMISORC" ,"CARERSI", "DISDEST",
"EPIORDER", "LOCCLASS", "REFSOURC", "RTTPERSTAT", "STAFFTYP", "AEATTENDDISP",
"AEDEPTTYPE", "AEINCLOCTYPE", "AEPATGROUP", "AEREFSOURCE", "ccapcrel"), 99) %>%
convert_to_NA(c("ATENTYPE"), 13) %>%
convert_to_NA(c("AEARRIVALMODE", "AEATTENDCAT", "CLASSPAT", "DISMETH", "INTMANIG",
"NEOCARE", "OPERSTAT", "ATTENDED", "OUTCOME", "PRIORITY", "SERVTYPE",
"STAFFTYP"), 9) %>%
convert_to_NA(c("CLASSPAT", "INTMANIG", "NEOCARE", "OPERSTAT", "STAFFTYP"), 8) %>%
convert_to_NA(c("SEX"), 0) %>%
convert_to_NA(c("ARRIVALTIME", "CONCLTIME", "DEPTIME", "INITTIME", "TRETTIME"), 3000) %>%
convert_to_NA(c("ARRIVALTIME", "CONCLTIME", "DEPTIME", "INITTIME", "TRETTIME"), 4000) %>%
convert_to_NA(c("FIRSTATT"), "9") %>%
convert_to_NA(c("ADMIMETH"), "98") %>%
convert_to_NA(c("ADMIMETH", "ETHNOS"), "99") %>%
convert_to_NA(c("PROCODE", "SITETRET"), "89999") %>%
convert_to_NA(c("PROCODE", "SITETRET"), "89997") %>%
convert_to_NA(c("AEKEY"), "0") %>%
convert_to_NA(c("ETHNOS", "FIRSTATT"), "X") %>%
convert_to_NA(c("ETHNOS"), "Z") %>%
convert_to_NA(generate_numbered_headers("OPERTN_", n = 24), "-") %>%
convert_to_NA(c(c("ARRIVALDATE", "ADMIDATE", "DISDATE", "DISREADYDATE",
"ELECDATE", "EPIEND", "EPISTART", "RTTPEREND", "RTTPERSTART",
"SUBDATE", "APPTDATE", "DNADATE", "REQDATE", "DOD", "DOR",
"DISDATE", "ccdisdate", "ccdisrdydate", "ccstartdate"),
generate_numbered_headers("OPDATE_", n = 24)),
"1800-01-01") %>%
convert_to_NA(c(c("ARRIVALDATE", "ADMIDATE", "DISDATE", "DISREADYDATE",
"ELECDATE", "EPIEND", "EPISTART", "RTTPEREND", "RTTPERSTART",
"SUBDATE", "APPTDATE", "DNADATE", "REQDATE", "DOD", "DOR",
"DISDATE", "ccdisdate", "ccdisrdydate", "ccstartdate"),
generate_numbered_headers("OPDATE_", n = 24)),
"1801-01-01") %>%
convert_to_NA(c(c("ARRIVALDATE", "ADMIDATE", "DISDATE", "DISREADYDATE",
"ELECDATE", "EPIEND", "EPISTART", "RTTPEREND", "RTTPERSTART",
"SUBDATE", "APPTDATE", "DNADATE", "REQDATE", "DOD", "DOR",
"DISDATE", "ccdisdate", "ccdisrdydate", "ccstartdate"),
generate_numbered_headers("OPDATE_", n = 24)),
"1600-01-01") %>%
convert_to_NA(c(c("ARRIVALDATE", "ADMIDATE", "DISDATE", "DISREADYDATE",
"ELECDATE", "EPIEND", "EPISTART", "RTTPEREND", "RTTPERSTART",
"SUBDATE", "APPTDATE", "DNADATE", "REQDATE", "DOD", "DOR",
"DISDATE", "ccdisdate", "ccdisrdydate", "ccstartdate"),
generate_numbered_headers("OPDATE_", n = 24)),
"1582-10-15") %>%
convert_to_NA(c("APPTDATE", "ARRIVALDATE"), "18000101") %>%
convert_to_NA(c("APPTDATE", "ARRIVALDATE"), "18010101") %>%
convert_to_NA(c(c("DOMPROC", "GPPRAC", "MAINSPEF", "TRETSPEF"),
generate_numbered_headers("OPERTN_", n = 24)), "&") %>%
convert_to_NA(generate_numbered_headers("OPERTN_", n = 24), "X999") %>%
convert_to_NA(generate_numbered_headers("OPERTN_", n = 24), "X998") %>%
convert_to_NA(generate_numbered_headers("OPERTN_", n = 24), "X997") %>%
convert_to_NA(c("REFERORG"), "X99998") %>%
convert_to_NA(c("REFERORG"), "X99999") %>%
convert_to_NA(c("GPPRAC"), "V81998") %>%
convert_to_NA(c("GPPRAC"), "V81997") %>%
convert_to_NA(c("GPPRAC"), "V81999") %>%
convert_to_NA(c("LSOA11"), "Z99999999") %>%
convert_to_NA(generate_numbered_headers("DIAG_", n = 20), "R69X") %>%
convert_to_NA(generate_numbered_headers("DIAG_", n = 20), "R69X6") %>%
convert_to_NA(generate_numbered_headers("DIAG_", n = 20), "R69X8") %>%
convert_to_NA(generate_numbered_headers("DIAG_", n = 20), "R69X3") %>%
convert_vals(c("ADMIMETH"), old_vals = c("2A", "2B", "2C", "2D"), new_vals = c("66", "67", "68", "69")) %>%
convert_vals(c("DOMPROC"), old_vals = c("-"), new_vals = c("None")) %>%
convert_vals(c("SPELEND"), old_vals = c("N", "Y"), new_vals = c(0, 1)) %>%
convert_vals(c("STARTAGE", "APPTAGE", "ARRIVALAGE"), old_vals = 7001:7007,
new_vals = seq(0, 0, length.out = (7007-7000))) %>%
convert_to_int(c("ADMIMETH", "FIRSTATT"))
)
}
|
824eb3f66e1e371dc0960b997ba2cc243687a946 | 8dc93e785685daea5eee3bc467b5ccadc0b7db69 | /mkt_share_est.R | 84e7c0bd2648ef3d54e43bd71ddfdb6a24434dfe | [] | no_license | probmetrics/BLP_R_demo | 1ae7edd58f27fac16d74771aa905ce9c914f4a23 | ebea55986f633145bcd7548310c19137d63bf7c4 | refs/heads/master | 2020-06-01T02:46:11.857110 | 2019-06-06T15:26:24 | 2019-06-06T15:26:24 | 190,603,119 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,848 | r | mkt_share_est.R | ##
## Functions for estimating market share in BLP model
##
## Shu Xu, <May, 2019>
##
share_per_mkt <- function(delta, alpha, sigma, p, X, y, vshk) {
#
# ** Estimating market share in a single market **
#
# Inputs:
# delta, -- J vector
# X, -- J x K matrix
# y, -- S vector of income y_i
# p, -- J vector of price p_j
# vshk, -- K x S matrix
# alpha, -- scalar, coef of price/income
# sigma, -- K vector
#
# Output:
# J x 1 vector of estimated market share
#
require(matrixStats)
# ubar <- delta - alpha * outer(p, y, "/") + X %*% sigma %*% vshk
ubar <- delta - alpha * outer(p, y, "/") + X %*% (sigma * vshk) #<-- J by S matrix
ubars <- cbind(0, t(ubar)) #<-- S x (J + 1) matrix
prob <- exp(ubars - rowLogSumExps(ubars))[, -1]
share <- colMeans(prob)
return(share)
}
mkt_share_est <- function(mkt_idx, delta, alpha, sigma, price, X, yshk, vshk){
#
# ** Estimating market share for all markets **
#
# Inputs:
# mkt_idx -- index indicating diff. markets
# delta, -- J x M vector
# X, -- JM x K matrix
# yshk -- M x S matrix of income y_i
# p, -- JM vector of price p_j
# vshk, -- (K x S x M) 3D array
# alpha, -- scalar, coef of price/income
# sigma, -- K vector
#
# Output:
# JM x 1 vector of estimated market shares
#
mid <- unique(mkt_idx)
mshr_fun <- function(i) {
midx <- mkt_idx == mid[i]
mshr <- share_per_mkt(delta[midx], alpha, sigma, price[midx], X[midx, ], yshk[i, ], vshk[, , i])
return(mshr)
}
share_list <- sapply(1:length(mid), mshr_fun)
mkt_share <- unlist(share_list)
return(mkt_share)
}
|
420d61cf313570a39a95209f276dc23d986e9c92 | d62d96ab238bbd3fde1554b9b3a933333a8ff068 | /script.R | 6138f9f468a224eba3ae2dbd8e903b150f10902d | [] | no_license | rfael0cm/AchimTalk | 9748855e4a57dcd14a6b1bf67ce9f62716c5d052 | 5eec71c955784901f531f83ac14c9423297245ca | refs/heads/master | 2020-03-15T13:56:25.840120 | 2018-05-04T19:06:17 | 2018-05-04T19:06:17 | 132,178,823 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,221 | r | script.R | ## requires: gplots, MASS, Gviz
## work with vignette es
### Course in bdHMM
## 1. load a bdHMM object and see its characteristics
## 2. Generate 10^4 time points from the model
## 3. Fit a standar HMM model to the generated Data
## 4. See some characteristics
## 5. Fit a bdHMM
## 6. Compare it with the HMM and bdHMM
# 1. LOAD A bdHMM OBJECT AND SEE ITS CHARACTERISTICS
library(STAN)
library(gplots)
load('/home/campos/Desktop/Achim_talk/bdhmm_fitted_class.RData')
bdhmm_fitted # This bdHMM model has been fitted to a set of 10 different tracks, 2 of them directional.
bdhmm_fitted$loglik # We can see the last 10 iteration during Baum-Welch Method
bdhmm_fitted$stateLabel # Name of the states, this model was fitted for 12 states 5 directional (F/R 1:5) and 2 undirectional (U1, U2)
bdhmm_fitted$hmm@directedObs # Directionality of the Data we have 8 tracks undirectional (TF, histone modification,...) coded by 0 and 2 directional (strand specific RNA-seq data) coded by 1
bdhmm_fitted$hmm@initProb # Initial Probabilities
bdhmm_fitted$hmm@transMat # transition Matrix between states
source('/home/campos/Desktop/Achim_talk/MeanHeatMap_function.R')
MeanHeatMap(bdhmm_fitted, bdhmm_yeast)
heat=c('dark grey','steelblue', 'yellow','gold', 'orange', 'dark orange',rep('red',10),rep('white',200))
colfct = colorRampPalette(heat)
colpal = colfct(200)
heatmap.2(bdhmm_fitted$hmm@transMat, col=colpal,trace="none", cexCol=0.9, cexRow=0.9, notecol="black", dendrogram="none",
Rowv=F, Colv=FALSE, notecex=0.9)
## GENERATE 10^3 OBSERVATIONS
source('/home/campos/Desktop/CreateData/Function/GenerateData_function.R')
Data_G<-GenerateData(1299, bdhmm_fitted)
colnames(Data_G$observation)<-names(bdhmm_yeast@emission@parameters$mean[[1]])
table(Data_G$viterbi)
ChrMatrix<-list()
ChrMatrix$chr<-Data_G$observation
## GENERATE HMM MODEL
nStates=7
myMat = ChrMatrix$chr[apply(ChrMatrix$chr, 1, function(x) all(! is.na(x))),]
myMat[, c("YPDexprW", "YPDexprC")] = t(apply(myMat[, c("YPDexprW", "YPDexprC")], 1,
sort, decreasing=TRUE))
km = kmeans(myMat, centers=7, iter.max=1000, nstart=100)$centers
Mean_hmm <- lapply(1:nrow(km), function(x)km[x,])
Covs_hmm<-cov(myMat[complete.cases(myMat),])
Covs_hmm<-lapply(1:nStates, function(x)Covs_hmm)
gaussEmission = HMMEmission(type="Gaussian",
parameters=list(mean=Mean_hmm, cov=new_Colapsed$hmm@emission@parameters$cov),
nStates=nStates)
transMat = matrix(1/nStates, nrow=nStates, ncol=nStates)
initProb = rep(1/nStates, nStates)
hmm = HMM(initProb=initProb, transMat=transMat,
emission=gaussEmission, nStates=nStates,
status="initial")
hmm_fitted<-fitHMM(ChrMatrix, hmm)
hmm_viterbi<-getViterbi(hmm_fitted$hmm,ChrMatrix )
table(hmm_viterbi$chr, Data_G$viterbi)
names(hmm@emission@parameters$mean[[1]])<-colnames(Data_G$observation)
MeanHeatMap(hmm_fitted, hmm)
heat=c('dark grey','steelblue', 'yellow','gold', 'orange', 'dark orange',rep('red',10),rep('white',200))
colfct = colorRampPalette(heat)
colpal = colfct(200)
heatmap.2(hmm_fitted$hmm@transMat, col=colpal,trace="none", cexCol=0.9, cexRow=0.9, notecol="black", dendrogram="none",
Rowv=F, Colv=FALSE, notecex=0.9)
## GENERATE bdHMM MODEL
nStates<- 11
stateLabel<-bdhmm_fitted$hmm@stateLabel
myMat = ChrMatrix$chr[apply(ChrMatrix$chr, 1, function(x) all(! is.na(x))),]
myMat[, c("YPDexprW", "YPDexprC")] = t(apply(myMat[, c("YPDexprW", "YPDexprC")], 1,
sort, decreasing=TRUE))
km = kmeans(myMat, centers=7, iter.max=1000, nstart=100)$centers
km<-rbind(km, km[c(1:4),])
km<-km[c(1:4,8:11,5,6,7),]
Mean_bdhmm <- lapply(1:nrow(km), function(x)km[x,])
for (i in 5:8){
Mean_bdhmm[[i]]<-Mean_bdhmm[[i]][c(1:8,10,9)]
names(Mean_bdhmm[[i]])<-names(Mean_bdhmm[[1]])
}
Covs_hmm<-cov(myMat[complete.cases(myMat),])
Covs_hmm<-lapply(1:nStates, function(x)Covs_hmm)
gaussEmission <- HMMEmission(type="Gaussian",
parameters=list(mean=bdhmm_fitted$hmm@emission@parameters$mean, cov=bdhmm_fitted$hmm@emission@parameters$cov),
nStates=nStates)
dirobs = as.integer(c(rep(0,8), 1, 1))
transMat <- bdhmm_fitted$hmm@transMat
initProb <-bdhmm_fitted$hmm@initProb
bdhmm_GD = bdHMM(initProb=initProb, transMat=transMat,
emission=gaussEmission, nStates=nStates,
status="initial", stateLabel=stateLabel,
transitionsOptim="analytical", directedObs=dirobs)
bdhmm_fittedGD = fitHMM(ChrMatrix, bdhmm_yeast, maxIters=100, verbose=FALSE)
viterbiGD = getViterbi(bdhmm_fittedGD$hmm, ChrMatrix)
table(viterbiGD$chr,Data_G$viterbi )
MeanHeatMap(bdhmm_fittedGD, bdhmm_GD)
## PLOT WITH TRACKS
library(Gviz)
ucscChromosomeNames=FALSE
gtrack<-GenomeAxisTrack()
names(faccols) = colnames(Data_G$observation)
chr = "chrIV"
gen = "sacCer3"
gtrack <- GenomeAxisTrack()
faccols = hcl(h = seq(15, 375 - 360/dim(ChrMatrix$chr)[2],
length = dim(ChrMatrix$chr)[2])%%360, c = 100, l = 65)
names(faccols) = colnames(ChrMatrix$chr)
dlist=list()
for(n in colnames(ChrMatrix$chr)) {
dlist[[n]] = DataTrack(data = ChrMatrix$chr[,n],
start = yeastTF_probeAnno_ex$chr04,
end = yeastTF_probeAnno_ex$chr04+8,
chromosome = "chrIV", genome=gen,
name = n, type="h", col=faccols[n])
}
library(GenomicRanges)
library(IRanges)
myViterbiDirs = list(F=c("F1", "F2", "F3", "F4"), U=c("U1", "U2","U3"),
R=c("R1", "R2", "R3", "R4"))
myViterbiPanels = list()
cols = rainbow(7)
cols = cols[c(1:5,1:5,6:7)]
names(cols) = stateLabel
myHiddenStates = list()
for(dir in c("F", "U", "R")) {
myPos = yeastTF_probeAnno_ex$chr04 >= 1217060 & yeastTF_probeAnno_ex$chr04 <= 1225000
myRle = Rle(viterbiGD$chr[myPos])
currItems = which(myRle@values %in% myViterbiDirs[[dir]])
start = yeastTF_probeAnno_ex$chr04[myPos][start(myRle)][currItems]
width = myRle@lengths[currItems]
ids = as.character(myRle@values[currItems])
values = as.character(myRle@values[currItems])
myViterbiPanels[[dir]] = AnnotationTrack(range=GRanges(seqnames=rep("chrIV",
length(currItems)), ranges=IRanges(start=start, width=width*8, names=values)),
genome=gen, chromosome=chr, name=paste("Viterbi\n", "(", dir, ")", sep=""),
id=ids[order(start)], shape="box",fill=cols[values[order(start)]], col="black",
stacking="dense")
}
for(dir in c("F", "U", "R")) {
myPos = yeastTF_probeAnno_ex$chr04 >= 1217060 & yeastTF_probeAnno_ex$chr04 <= 1225000
myRle = Rle(Data_G$viterbi[myPos])
currItems = which(myRle@values %in% myViterbiDirs[[dir]])
start = yeastTF_probeAnno_ex$chr04[myPos][start(myRle)][currItems]
width = myRle@lengths[currItems]
ids = as.character(myRle@values[currItems])
values = as.character(myRle@values[currItems])
myHiddenStates[[dir]] = AnnotationTrack(range=GRanges(seqnames=rep("chrIV",
length(currItems)), ranges=IRanges(start=start, width=width*8, names=values)),
genome=gen, chromosome=chr, name=paste("Hidden States\n", "(", dir, ")", sep=""),
id=ids[order(start)], shape="box",fill=cols[values[order(start)]], col="black",
stacking="dense")
}
sizes = rep(1,16)
sizes[12:16] = 0.7
plotTracks(c( dlist, myViterbiPanels, myHiddenStates),
from=1217060, to=1225000, sizes=sizes, showFeatureId=TRUE, featureAnnotation="id",
fontcolor.feature="black", cex.feature=0.7, background.title="darkgrey", showId=TRUE)
|
5bc2519aa9ef2cd799cce6da18bfeda20270103a | 035229c811d57d91a11e3d43bc4b71dc58222f3b | /analysis/revision/tcell_klk3_exp.R | 73be02070e64f82b22507218214970fb5713983c | [] | no_license | Limour-dev/scRNA | d40580541e7ceef1a4ff875628a1a619e2912db7 | 834ec5c0c4323b521c96ca86db973b170831f2da | refs/heads/master | 2023-06-15T13:40:41.843619 | 2021-07-17T14:44:22 | 2021-07-17T14:44:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,678 | r | tcell_klk3_exp.R | library(BoutrosLab.plotting.general);
library(Seurat);
library(reshape);
library(plyr);
setwd('/cluster/projects/hansengroup/sujunc/scRNA/primary/scran/revision/tcell');
seurat.all <- readRDS('/cluster/projects/hansengroup/sujunc/scRNA/primary/scran/normalize_data/objects/2019-07-25_seurat_manual_all.rds');
name <- 'all';
seurat.all@meta.data$type <- gsub('Macrophage|Myeloid', 'Monolytic', seurat.all@meta.data$type);
seurat.all@meta.data$type <- gsub('Myofibroblast', 'Fibroblast', seurat.all@meta.data$type);
seurat.all <- SetAllIdent(seurat.all, id = 'type');
myexp <- data.frame(exp = seurat.all@data['KLK3', ]);
myexp$type <- seurat.all@meta.data$type;
myexp <- myexp[myexp$exp>0, ]
myexp$group <- 'epithelia';
myexp[myexp$type%in%c('Endothelia', 'Fibroblast'), ]$group <- 'stroma';
myexp[!myexp$type%in%c('Endothelia', 'Fibroblast', 'Basal/intermediate', 'Luminal'), ]$group <- 'immune';
myexp$group <- factor(myexp$group);
#myexp <- ddply(myexp, 'group', numcolwise(mean));
#myexp$pos <- seq(3)
pval1 <- scientific.notation(wilcox.test(myexp[myexp$group=='epithelia', ]$exp, myexp[myexp$group=='immune', ]$exp)$p.value);
pval1 <- ifelse(pval1==0, expression('< 2.2'%*%10^-16, ));
pval2 <- scientific.notation(wilcox.test(myexp[myexp$group=='stroma', ]$exp, myexp[myexp$group=='immune', ]$exp)$p.value)
create.boxplot(
formula = exp~group,
data = myexp,
#add.stripplot = TRUE,
xlab.label = 'Type',
ylab.label = 'KLK3 abundance',
ylimits = c(0, 8),
add.text = TRUE,
text.x = c(1.5, 2.5),
text.y = 7.8,
text.labels = c(pval1, pval2),
text.cex = 1.5,
xaxis.cex = 2,
yaxis.cex =2,
filename = generate.filename('exp_klk3p', 'type_all', 'pdf'),
style = 'Nature'
);
###
to.plot <- data.frame(exp = seurat.all@data['KLK3', ], type = seurat.all@meta.data$type,
samp = 'sample13');
to.plot <- to.plot[to.plot$type!='Epithelia', ]
to.plot.m <- data.frame(reshape::cast(to.plot, type~samp, mean, value = 'exp', fill = 0));
to.plot.f <- data.frame(reshape::cast(to.plot[to.plot$exp>0, ], type~samp, length, value = 'exp', fill = 0));
to.plot.n <- data.frame(reshape::cast(to.plot, type~samp, length, value = 'exp', fill = 0));
#
to.plot.n$group <- 'all';
to.plot.f$group <- 'pos';
to.plot <- rbind(to.plot.f, to.plot.n);
to.plot.f[, 2] <- round(100*to.plot.f[, 2]/to.plot.n[, 2]);
to.plot.n$group <- 'all';
to.plot.f$group <- 'pos';
to.plot <- rbind(to.plot.f, to.plot.n);
to.plot.f[, 2:4] <- sapply(seq(3), function(x) 100*to.plot.f[, (x+1)]/to.plot.n[, (x+1)]);
to.plot.f[, 2:4] <- round(to.plot.f[, 2:4])
to.plot.f[6, 3] <- 'NA';
create.barplot(
data = to.plot[, c('type', 'S5.LEFT.2', 'group')],
formula = S5.LEFT.2~type,
groups = to.plot$group,
col = default.colours(2),
style = 'Nature',
#xaxis.lab = c('R', 'L', 'T'),
xlab.label = '',
ylab.labe = '# cells',
xaxis.cex = 2,
yaxis.cex = 2,
xaxis.rot = 45,
ylimits = c(0, 9000),
height = 4,
width = 8,
add.text = TRUE,
text.x = seq(10),
text.y = to.plot[11:20, ]$S5.LEFT.2 + 500,
text.labels = paste0(to.plot.f$S5.LEFT.2, '%'),
text.cex = 1.5,
text.fontface = 'plain',
key = list(
text = list(
lab = c('All', 'KLK3 positive'),
col = default.colours(2)
),
points = list(
pch = 22,
fill = default.colours(2),
col = 'black'
),
x = 0.02,
y = 0.9
),
filename = generate.filename('number_cells', 'samp3p1_left', 'pdf')
);
###
iseurat <- readRDS('/cluster/projects/hansengroup/sujunc/scRNA/primary/scran/samp3p1/objects/AllSample_GraphClust.seuset.rds');
mytype <- read.table('/cluster/projects/hansengroup/sujunc/scRNA/primary/scran//samp3p1/objects/CellType_Rename.txt', header = TRUE);
iseurat@meta.data$type <- mytype[match(rownames(iseurat@meta.data), mytype$Cell), ]$Cluster;
iseurat@meta.data$type <- gsub('Cell$', '', iseurat@meta.data$type);
iseurat@meta.data$type <- gsub('Macrophage|^DC', 'Monocytic', iseurat@meta.data$type);
imyexp <- data.frame(exp = iseurat@data['KLK3', ]);
imyexp$type <- iseurat@meta.data$type;
imyexp <- imyexp[grepl('LEFT', rownames(imyexp))&imyexp$exp>0, ];
imyexp$group <- 'epithelia';
imyexp[imyexp$type%in%c('Endothelia', 'Fibroblast'), ]$group <- 'stroma';
imyexp[!imyexp$type%in%c('Endothelia', 'Fibroblast', 'Epithelia'), ]$group <- 'immune';
#imyexp <- ddply(imyexp, 'group', numcolwise(mean));
pval1 <- scientific.notation(wilcox.test(imyexp[imyexp$group=='epithelia', ]$exp, imyexp[imyexp$group=='immune', ]$exp)$p.value);
pval1 <- ifelse(pval1==0, expression('< 2.2'%*%10^-16, ));
pval2 <- scientific.notation(wilcox.test(imyexp[imyexp$group=='stroma', ]$exp, imyexp[imyexp$group=='immune', ]$exp)$p.value)
create.boxplot(
formula = exp~group,
data = imyexp,
#add.stripplot = TRUE,
xlab.label = 'Type',
ylab.label = 'KLK3 abundance',
add.text = TRUE,
text.x = c(1.5, 2.5),
text.y = 6.5,
text.labels = c(pval1, pval2),
text.cex = 1.5,
xaxis.cex = 2,
yaxis.cex =2,
filename = generate.filename('exp_klk3p', 'type_left', 'pdf'),
style = 'Nature'
);
####
####
imyexp <- data.frame(exp = iseurat@data['KLK3', ]);
imyexp$type <- iseurat@meta.data$type;
imyexp <- imyexp[grepl('TUMOR', rownames(imyexp))&imyexp$exp>0, ];
imyexp$group <- 'epithelia';
imyexp[imyexp$type%in%c('Endothelia', 'Fibroblast'), ]$group <- 'stroma';
imyexp[!imyexp$type%in%c('Endothelia', 'Fibroblast', 'Epithelia'), ]$group <- 'immune';
#imyexp <- ddply(imyexp, 'group', numcolwise(mean));
pval1 <- scientific.notation(wilcox.test(imyexp[imyexp$group=='epithelia', ]$exp, imyexp[imyexp$group=='immune', ]$exp)$p.value);
pval1 <- ifelse(pval1==0, expression('< 2.2'%*%10^-16, ));
pval2 <- scientific.notation(wilcox.test(imyexp[imyexp$group=='stroma', ]$exp, imyexp[imyexp$group=='immune', ]$exp)$p.value)
create.boxplot(
formula = exp~group,
data = imyexp,
#add.stripplot = TRUE,
xlab.label = 'Type',
ylab.label = 'KLK3 abundance',
add.text = TRUE,
text.x = c(1.5, 2.5),
text.y = 6.2,
text.labels = c(pval1, pval2),
text.cex = 1.5,
xaxis.cex = 2,
yaxis.cex =2,
filename = generate.filename('exp_klk3p', 'type_tumor', 'pdf'),
style = 'Nature',
#width = 4.5,
#height = 4.5
);
###
summary(data.frame(t(iseurat@data[c('TP63', 'KRT14', 'KRT5'), iseurat@meta.data$orig.ident=='S6.TUMOR.M'&iseurat@meta.data$type=='Epithelia'])))
imyexp <- data.frame(t(as.matrix(iseurat@data[c('TP63', 'KRT14', 'KRT5'), ])));
imyexp$type <- iseurat@meta.data$type;
imyexp$orig.ident <- iseurat@meta.data$orig.ident;
imyexp <- imyexp[imyexp$type=='Epithelia', ];
to.plot <- data.frame(exp = iseurat@data['KLK3', ], type = iseurat@meta.data$type,
samp = iseurat@meta.data$orig.ident);
to.plot <- to.plot[to.plot$type!='Epithelia', ]
to.plot.m <- data.frame(reshape::cast(to.plot, type~samp, mean, value = 'exp', fill = 0));
to.plot.f <- data.frame(reshape::cast(to.plot[to.plot$exp>0, ], type~samp, length, value = 'exp', fill = 0));
to.plot.n <- data.frame(reshape::cast(to.plot, type~samp, length, value = 'exp', fill = 0));
#
to.plot.n$group <- 'all';
to.plot.f$group <- 'pos';
to.plot <- rbind(to.plot.f, to.plot.n);
to.plot.f[, 2:4] <- sapply(seq(3), function(x) 100*to.plot.f[, (x+1)]/to.plot.n[, (x+1)]);
to.plot.f[, 2:4] <- round(to.plot.f[, 2:4])
to.plot.f[6, 3] <- 'NA';
create.barplot(
data = to.plot[, c('type', 'S5.LEFT.2', 'group')],
formula = S5.LEFT.2~type,
groups = to.plot$group,
col = default.colours(2),
style = 'Nature',
#xaxis.lab = c('R', 'L', 'T'),
xlab.label = '',
ylab.labe = '# cells',
xaxis.cex = 2,
yaxis.cex = 2,
xaxis.rot = 45,
ylimits = c(0, 9000),
height = 4,
width = 8,
add.text = TRUE,
text.x = seq(10),
text.y = to.plot[11:20, ]$S5.LEFT.2 + 500,
text.labels = paste0(to.plot.f$S5.LEFT.2, '%'),
text.cex = 1.5,
text.fontface = 'plain',
key = list(
text = list(
lab = c('All', 'KLK3 positive'),
col = default.colours(2)
),
points = list(
pch = 22,
fill = default.colours(2),
col = 'black'
),
x = 0.02,
y = 0.9
),
filename = generate.filename('number_cells', 'samp3p1_left', 'pdf')
);
create.barplot(
data = to.plot[, c('type', 'S6.TUMOR.M', 'group')],
formula = S6.TUMOR.M~type,
groups = to.plot$group,
col = default.colours(2),
style = 'Nature',
#xaxis.lab = c('R', 'L', 'T'),
xlab.label = '',
ylab.labe = '# cells',
xaxis.cex = 2,
yaxis.cex = 2,
xaxis.rot = 45,
ylimits = c(0, 9000),
height = 4,
width = 8,
add.text = TRUE,
text.x = seq(10),
text.y = to.plot[11:20, ]$S6.TUMOR.M + 500,
text.labels = paste0(to.plot.f$S6.TUMOR.M, '%'),
text.cex = 1.5,
text.fontface = 'plain',
key = list(
text = list(
lab = c('All', 'KLK3 positive'),
col = default.colours(2)
),
points = list(
pch = 22,
fill = default.colours(2),
col = 'black'
),
x = 0.02,
y = 0.9
),
filename = generate.filename('number_cells', 'samp3p1_tumor', 'pdf')
);
|
e5c34f14716186523bc2effda07f395659f1eda5 | 7afbb148ec11b3105aaead6bdd900f847e49eb18 | /tests/testthat/test-hyperbolic.R | 86c0dbb9a0ca2f72efe6bbe8ca438f35a2e1b3d2 | [
"MIT"
] | permissive | tidymodels/recipes | 88135cc131b4ff538a670d956cf6622fa8440639 | eb12d1818397ad8780fdfd13ea14d0839fbb44bd | refs/heads/main | 2023-08-15T18:12:46.038289 | 2023-08-11T12:32:05 | 2023-08-11T12:32:05 | 76,614,863 | 383 | 123 | NOASSERTION | 2023-08-26T13:43:51 | 2016-12-16T02:40:24 | R | UTF-8 | R | false | false | 3,055 | r | test-hyperbolic.R | library(testthat)
library(recipes)
n <- 20
set.seed(1)
ex_dat <- data.frame(
x1 = runif(n, min = -1, max = 1),
x2 = runif(n, min = -1, max = 1)
)
set.seed(2)
ex_dat1 <- data.frame(
x1 = runif(n, min = 1, max = 5),
x2 = runif(n, min = 1, max = 5)
)
get_exp <- function(x, f) {
as_tibble(lapply(x, f))
}
test_that("simple hyperbolic trans", {
for (func in c("sinh", "cosh", "tanh")) {
rec <- recipe(~., data = ex_dat) %>%
step_hyperbolic(x1, x2, func = func, inverse = FALSE)
rec_trained <- prep(rec, training = ex_dat, verbose = FALSE)
rec_trans <- bake(rec_trained, new_data = ex_dat)
foo <- get(func)
exp_res <- get_exp(ex_dat, foo)
expect_equal(rec_trans, exp_res)
}
for (func in c("sinh", "tanh")) {
rec <- recipe(~., data = ex_dat) %>%
step_hyperbolic(x1, x2, func = func, inverse = TRUE)
rec_trained <- prep(rec, training = ex_dat, verbose = FALSE)
rec_trans <- bake(rec_trained, new_data = ex_dat)
foo <- get(paste0("a", func))
exp_res <- get_exp(ex_dat, foo)
expect_equal(rec_trans, exp_res)
}
rec <- recipe(~., data = ex_dat1) %>%
step_hyperbolic(x1, x2, func = "cosh", inverse = TRUE)
rec_trained <- prep(rec, training = ex_dat1, verbose = FALSE)
rec_trans <- bake(rec_trained, new_data = ex_dat1)
exp_res <- get_exp(ex_dat1, "acosh")
expect_equal(rec_trans, exp_res)
})
test_that("wrong function", {
rec <- recipe(mpg ~ ., mtcars)
expect_snapshot_error(step_hyperbolic(rec, func = "cos"))
})
# Infrastructure ---------------------------------------------------------------
test_that("bake method errors when needed non-standard role columns are missing", {
rec <- recipe(~., data = ex_dat) %>%
step_hyperbolic(x1, x2, func = "sinh", inverse = FALSE) %>%
update_role(x1, x2, new_role = "potato") %>%
update_role_requirements(role = "potato", bake = FALSE)
rec_trained <- prep(rec, training = ex_dat, verbose = FALSE)
expect_error(bake(rec_trained, new_data = ex_dat[, 2, drop = FALSE]),
class = "new_data_missing_column")
})
test_that("empty printing", {
rec <- recipe(mpg ~ ., mtcars)
rec <- step_hyperbolic(rec)
expect_snapshot(rec)
rec <- prep(rec, mtcars)
expect_snapshot(rec)
})
test_that("empty selection prep/bake is a no-op", {
rec1 <- recipe(mpg ~ ., mtcars)
rec2 <- step_hyperbolic(rec1)
rec1 <- prep(rec1, mtcars)
rec2 <- prep(rec2, mtcars)
baked1 <- bake(rec1, mtcars)
baked2 <- bake(rec2, mtcars)
expect_identical(baked1, baked2)
})
test_that("empty selection tidy method works", {
rec <- recipe(mpg ~ ., mtcars)
rec <- step_hyperbolic(rec)
expect <- tibble(
terms = character(),
inverse = logical(),
func = character(),
id = character()
)
expect_identical(tidy(rec, number = 1), expect)
rec <- prep(rec, mtcars)
expect_identical(tidy(rec, number = 1), expect)
})
test_that("printing", {
rec <- recipe(~., data = ex_dat) %>%
step_hyperbolic(x1, x2)
expect_snapshot(print(rec))
expect_snapshot(prep(rec))
})
|
f551506676629711ac1e0057678e73fa10cb60e7 | 0711ce0882fcbeb16113f4bdda073372c94ea4f6 | /projects/Alpha101/R/alpha101_12.R | 2a29a1db6d348197458c00eb62188b08707f1316 | [] | no_license | maxclchen/myStrat.bk | 4bc960a04d40fdecbfa7cb0b34c5901a47810c8e | 78a8fe055c40d4b9e462ebcb2f8bc7caadfc6d3a | refs/heads/master | 2023-03-16T01:53:54.995233 | 2017-10-17T07:53:02 | 2017-10-17T07:53:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,682 | r | alpha101_12.R | # ==============================================================================
# Ref: "Alpha 101"
# Alpha#12:
# sign[ delta(volume,1) ] * {-1 * [ delta(close,1) ]}
# |------ 1 ----| |------ 1 ----|
# |--------- 2 ---------| |---------- 2 ---------|
# 算法
# 1.分别计算 Volume 和 close 的价差
# 2.
#
# 含义
# 如果出现成交量上升，但是收盘价却下跌，则发出买入信号
#
# 关联：
#
# ==============================================================================
# ------------------------------------------------------------------------------
## Alpha Name
alphaName <- 'alpha101_12'
## 需要去除的日期
truncatedTD <- 1
# ------------------------------------------------------------------------------
# ==========: 提取dtX ===============================================================================
# ------------------------------------------------------------------------------
# dtX
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
dtX <- inSample[, .SD[.N > truncatedTD]
, by = 'InstrumentID'] %>%
.[,":="(
signal1 = sign(volume - shift(volume,1L,type='lag')),
signal2 = close - shift(close,1L,type='lag')
)
,by='InstrumentID'] %>%
.[!is.na(signal1) & !is.na(signal2)] %>%
.[,mySignal := signal1 * signal2]
# ==================================================================================================
|
155c4183b8e4b3b47fac14b463e98189f762bd97 | 7ace7c36aed23ab7f8b42e890d76fab81fb4787f | /shiny-app/ui.R | 06ac010d4d526d3ef519175a60715875819fdc35 | [
"MIT"
] | permissive | kngan/us-baby-names | 5f284485b29e9287f3de29938f47f8433883ed3e | 7ae55b51d933992d18678da50d75276ff0b431a4 | refs/heads/master | 2021-01-10T08:32:46.436233 | 2016-02-01T02:17:14 | 2016-02-01T02:17:14 | 50,361,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 510 | r | ui.R | library(shiny)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
# Application title
titlePanel("Prevalence of Baby Names"),
# Sidebar with a slider input for the number of bins
sidebarLayout(
sidebarPanel(
sliderInput("picked_year",
"Year:",
min = 1910,
max = 2014,
value = 1)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("mapPlot")
)
)
)) |
10ad40fa702fc0b53a68901eb8b82f91b39cfe31 | 92e40da6cc8f28c6af06b13c633b5eb43784ec26 | /amto/matrix/htmlpdfr/fs_mat_generate.R | 4ad76faba0ad698e1f3f7e32a37917c67c92de10 | [
"MIT"
] | permissive | JesusRQP96/R4Econ | 4845e4aa94d833cc829a4507606e418d4c873fa9 | 7275585a7fb3436af9b0fd6727f847c9ca39da42 | refs/heads/master | 2023-05-22T15:02:04.090757 | 2021-06-14T01:49:15 | 2021-06-14T01:49:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,050 | r | fs_mat_generate.R | ## ----global_options, include = FALSE----------------------------------------------------------------------------------
try(source("../../.Rprofile"))
## ----fixed matrix-----------------------------------------------------------------------------------------------------
ar_row_one <- c(-1,+1)
ar_row_two <- c(-3,-2)
ar_row_three <- c(0.35,0.75)
mt_n_by_2 <- rbind(ar_row_one, ar_row_two, ar_row_three)
kable(mt_n_by_2) %>%
kable_styling_fc()
## ---------------------------------------------------------------------------------------------------------------------
# An empty matrix with Logical NA
mt_named <- matrix(data=NA, nrow=2, ncol=2)
colnames(mt_named) <- paste0('c', seq(1,2))
rownames(mt_named) <- paste0('r', seq(1,2))
mt_named
## ---------------------------------------------------------------------------------------------------------------------
# An empty matrix with Logical NA
mt_na <- matrix(data=NA, nrow=2, ncol=2)
str(mt_na)
# An empty matrix with numerica NA
mt_fl_na <- matrix(data=NA_real_, nrow=2, ncol=2)
mt_it_na <- matrix(data=NA_integer_, nrow=2, ncol=2)
str(mt_fl_na)
str(mt_fl_na)
## ----random matrix----------------------------------------------------------------------------------------------------
# Generate 15 random normal, put in 5 rows, and 3 columns
mt_rnorm <- matrix(rnorm(15,mean=0,sd=1), nrow=5, ncol=3)
# Generate 15 random normal, put in 5 rows, and 3 columns
mt_runif <- matrix(runif(15,min=0,max=1), nrow=5, ncol=5)
# Combine
mt_rnorm_runif <- cbind(mt_rnorm, mt_runif)
# Display
kable(mt_rnorm_runif) %>% kable_styling_fc_wide()
## ---------------------------------------------------------------------------------------------------------------------
fl_new_first_col_val <- 111
fl_new_last_col_val <- 999
mt_with_more_columns <- cbind(rep(fl_new_first_col_val, dim(mt_rnorm_runif)[1]),
mt_rnorm_runif,
rep(fl_new_last_col_val, dim(mt_rnorm_runif)[1]))
# Display
kable(mt_with_more_columns) %>% kable_styling_fc_wide()
|
df90d083301aa18fd7f9ebe1251d7894d86e489f | 3686f5b321e9e0c550ae56c3a005b6d81a30d34c | /R/vis.R | 159acc6bcb94ea2e4b075964e30260f416ef24f0 | [
"MIT"
] | permissive | jenslaufer/artventure-site | fd9848f5c035f5a092b01470230c9a2228c3c133 | 20f2eb80d7b08d172ce70ea295c68a44784f629c | refs/heads/master | 2023-03-28T14:09:44.716264 | 2021-03-23T13:14:36 | 2021-03-23T13:14:36 | 339,119,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,324 | r | vis.R | library(tidyverse)
library(glue)
library(bbplot)
library(ggthemes)
library(aws.s3)
library(knitr)
library(scales)
library(gridExtra)
library(emojifont)
valueBoxes <- function(data) {
data %>%
ggplot(aes(x,
y,
height = h,
width = w)) +
bbc_style() +
geom_tile(aes(fill = color)) +
geom_text(
color = "white",
fontface = "bold",
size = 17,
aes(
label = value,
x = x - 2.5,
y = y + 1
),
hjust = 0
) +
geom_text(
color = "white",
fontface = "bold",
size = 10,
aes(
label = info,
x = x - 2.5,
y = y - 1
),
hjust = 0
) +
geom_text(
size = 22,
aes(
label = shape,
family = font_family,
x = x + 1,
y = y + 1
),
alpha = 0.25
) +
scale_fill_brewer(type = "qual", palette = "Dark2") +
coord_fixed() +
#theme_void() +
guides(fill = FALSE)
}
plotQual <- function(data, var) {
varData <- data %>%
mutate(total = n()) %>%
group_by(!!sym(var), total) %>%
summarise(n = n()) %>%
mutate(pct = n / total) %>%
arrange(n) %>%
#drop_na() %>%
ungroup()
plot1 <- varData %>%
mutate(pct = round(pct * 100, 1)) %>%
ggplot(aes(!!sym(var), n)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
geom_text(aes(label = "{pct}%" %>% glue()),
hjust = -.05) +
scale_x_discrete(limit = varData %>% pull(!!sym(var))) +
labs(title = 'Number of Art Pieces for Variable "{var}"' %>% glue()) +
bbc_style()
plot1 %>%
finalise_plot(
source = "Source: https://artventure.me, Data Source: https://expressobeans.com",
width_pixels = 800,
height_pixels = 500,
save_filepath = "{var}.jpg" %>% glue()
)
}
plotQuant <- function(data, var) {
plot <- data %>%
ggplot(aes(!!sym(var))) +
geom_freqpoly() +
geom_vline(aes(xintercept = median(!!sym(var), na.rm = T)), color =
"steelblue") +
bbc_style()
plot %>%
finalise_plot(
source = "Source: https://artventure.me, Data Source: https://expressobeans.com",
width_pixels = 800,
height_pixels = 500,
save_filepath = "{var}.jpg" %>% glue()
)
}
|
8f0395c29af5bd20ed85923ef68f2840ecd1865e | 7bbc2f83e7ca8aca63d0610a876780649b3fce95 | /Assignment 1.R | 5fa178834e0fbf4fd483b49392a28be12ab55a47 | [] | no_license | jeunbeen/Introduction-to-Statistics | 0c514546e8c81a5bde628d1e635c7faef60b3db6 | 8d1411a3cd78b3d0ab30eaac80c135bc4e9b74a3 | refs/heads/main | 2023-07-15T02:08:34.389880 | 2021-08-29T17:52:11 | 2021-08-29T17:52:11 | 401,099,501 | 0 | 0 | null | 2021-08-29T17:37:23 | 2021-08-29T17:17:36 | null | UTF-8 | R | false | false | 110 | r | Assignment 1.R | a=c(1:150)
b=c(11:160)
c=a+b
a+b
a*b
a/b
a-b
stem(c)
hist(c)
boxplot(c)
?stem
?hist
?boxplot |
9a171f91b67d54e584b4c88b907540783996ac5e | 03c99906a94c70e9a13e7714aad996f461f339c1 | /R/FPdivparam.R | 7d355a8973a117580cabac4bfda2b2f8686e3aa6 | [] | no_license | cran/adiv | 6a111f6a1ef39fe302a2f882b9a9d04e7d652c04 | d65d6e0301e4611a94a91933299bff1fdc06d96b | refs/heads/master | 2022-10-28T08:07:33.352817 | 2022-10-06T12:40:04 | 2022-10-06T12:40:04 | 97,764,074 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,439 | r | FPdivparam.R | FPdivparam <- function (comm, disORtree, method = c("KY", "KstarI"), palpha = 2, equivalent = FALSE, option = c("asymmetric", "symmetric"), dmax = NULL, tol = 1e-8)
{
option <- option[1]
method <- method[1]
if (!method %in% c("KY", "KstarI"))
stop("unconvenient method")
if (!inherits(comm, "data.frame") & !inherits(comm,
"matrix"))
stop("comm must be a data frame or a matrix")
comm <- as.matrix(comm)
if (any(comm < 0))
stop("Negative value in comm")
nsp <- ncol(comm)
if (!is.null(disORtree)) {
if(inherits(disORtree, "phylo") | inherits(disORtree, "phylo4") | inherits(disORtree, "hclust")){
arg.phyl <- .checkphyloarg(disORtree)
phyl.phylo <- arg.phyl$phyl.phylo
tre4 <- arg.phyl$phyl
if (!hasEdgeLength(tre4))
phyl.phylo <- compute.brlen(phyl.phylo, 1)
if(is.ultrametric(phyl.phylo) | option == c("symmetric"))
dis <- cophenetic.phylo(phyl.phylo)/2
else
dis <- matrix(rep(diag(vcv.phylo(phyl.phylo))), nsp, nsp)-vcv.phylo(phyl.phylo)
}
else if(inherits(disORtree, "matrix") | inherits(disORtree, "dist") )
dis <- as.matrix(disORtree)
else stop("unconvenient definition of disORtree")
if(any(!colnames(comm)%in%colnames(dis)))
stop("Some species names in comm are missing in disORtree")
dis <- dis[colnames(comm), colnames(comm)]
dis <- as.dist(dis)
}
if (is.null(disORtree)) {
dis <- as.dist( matrix(1, ncol(comm), ncol(comm))
- diag(rep(1, ncol(comm))) )
}
if(method == "KY") {
if(max(dis) > 1) {
if(!is.null(dmax) && (dmax-max(dis)) < tol) dis <- dis/dmax
else dis <- dis/max(dis)
}
sim <- 1 - as.matrix(dis)
FREQ <- sweep(comm, 1, rowSums(comm), "/")
FUN <- function(aaa) {
divv <- rep(0, nrow(comm))
for (i in 1:nrow(comm)) {
if (sum(comm[i, ]) < 1e-16)
divv[i] <- 0
else{
FFF <- FREQ[i, FREQ[i, ]> tol]
simFFF <- sim[FREQ[i, ]> tol, FREQ[i, ]> tol]
if(abs(aaa-1) > tol){
if(equivalent)
divv[i] <- (t(FFF) %*% t(((FFF%*% simFFF)^(aaa-1)) ))^(1/(1-aaa))
else
divv[i] <- t(FFF) %*% t((1-(FFF%*% simFFF)^(aaa-1)) )/(aaa-1)
}
else{
divv[i] <- -t(FFF) %*% t(log(FFF%*% simFFF))
if(equivalent) divv[i] <- exp(divv[i])
}
}
}
return(divv)
}
if(length(palpha)==1){
div <- cbind.data.frame(FUN(palpha))
colnames(div) <- "K"
rownames(div) <- rownames(comm)
class(div) <- c("FPdivparam", "data.frame")
}
else{
div <- sapply(palpha, FUN)
colnames(div) <- paste("K", 1:length(palpha), sep="_")
rownames(div) <- rownames(comm)
div <- list(palpha = palpha, div = div)
class(div) <- c("FPdivparam", "list")
}
return(div)
}
else{
commgardees <- (1:nrow(comm))[rowSums(comm) >= tol]
commdiv <- comm[commgardees, ]
FREQ <- sweep(commdiv, 1, rowSums(comm), "/")
PA <- commdiv
PA[PA>0] <- 1
FUN <- function(aaa) {
ORIval <- distinctAb(comm = commdiv, disORtree = dis, method = "KstarI", palpha = aaa)[[1]]
ORIval[is.na(ORIval)] <- 0
divv <- rowSums(ORIval)
if(equivalent){
if(is.null(dmax)) dmax <- max(dis)
if((dmax-max(dis)) >= tol) dmax <- max(dis)
if(abs(aaa-1) > tol) divv <- (( 1-(aaa-1)*divv/dmax ))^(1/(1-aaa))
else divv <- exp(divv/dmax)
}
return(divv)
}
if(length(palpha)==1){
div <- FUN(palpha)
names(div) <- rownames(commdiv)
DIV <- rep(0, nrow(comm))
names(DIV) <- rownames(comm)
DIV[names(div)] <- div
class(div) <- c("FPdivparam", "vector")
}
else{
div <- sapply(palpha, FUN)
rownames(div) <- rownames(commdiv)
DIV <- matrix(0, nrow(comm), length(palpha))
rownames(DIV) <- rownames(comm)
colnames(DIV) <- paste("KstarI", 1:length(palpha), sep="_")
DIV[rownames(div), ] <- div
div <- DIV
div <- list(palpha = palpha, div = DIV)
class(div) <- c("FPdivparam", "list")
}
return(div)
}
}
|
094d7fcfdc1ee25443f4492328e55e8f3acbca30 | fa6bf9f5bee1627b6158a5e693c8c15969c019ee | /Simulation & Risk/HW 2/Ellie_SR_HW2.R | d1b52ff469d9ae6157ab406d23e3689bbaff8c7d | [] | no_license | gclark422/spring1-orange5 | 59eb61880226000b9f9a6244a1e653319f0d3e3a | 13586f04939ac66f5edf0db4cf34e66be2a81634 | refs/heads/master | 2020-12-06T23:02:53.183034 | 2020-02-22T03:27:17 | 2020-02-22T03:27:17 | 232,574,947 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,916 | r | Ellie_SR_HW2.R | ###############################
# #
# Simulation and Risk: #
# Homework 2 #
# #
# Ellie Caldwell #
# #
###############################
#Objective: Simulate 1) cost of a single dry well 2)NPV of single wet well
########### LIBRARIES ###########
#Load in needed libraries
library(readxl)
library(metRology)
library(graphics)
library(ks)
library(dplyr)
library(car)
library(stats)
########### DATA ###########
#Load in 100,000 2020 predictions from last phase
drilling_costs <- read.csv('C:/Users/Ellie Caldwell/Documents/Simulation and Risk/2020 Predictions.csv')
drilling_costs <- drilling_costs[,2]
drilling_costs <- as.vector(drilling_costs)
drilling_costs <- 1000*drilling_costs
#Load in Price Projection data
proj <- read_excel("C:/Users/Ellie Caldwell/Documents/Simulation and Risk/Homework2_SR/Analysis_Data.xlsx", sheet = "Price Projections")
#Function to set first row to be headers
header.true <- function(df) {
names(df) <- as.character(unlist(df[1,]))
df[-1,]
}
proj <- proj[-1,]
proj <- header.true(proj)
proj_2020_2035 <- proj[1:16,]
proj_2020_2035 <- as.data.frame(proj_2020_2035)
proj_2020_2035 <- as.numeric(proj_2020_2035)
########### DRY WELL ###########
#Drilling costs + Lease costs + Seismic Costs + Professional Overhead
#Lease costs
set.seed(12345)
r <- rnorm(n=100000, mean=600, sd=50)
Pt_lease <- 960*r
#Seismic Costs
set.seed(12345)
r <- rnorm(n=100000, mean=3, sd=0.35)
Pt_seis <- 43000*r
#Professional Overhead
set.seed(12345)
r <- rtri(n=100000, min=172000, max=279500, mode=215000)
Pt_prof <- r
dry_well = drilling_costs + Pt_lease + Pt_seis + Pt_prof
mean(dry_well)
sd(dry_well)
min(dry_well)
max(dry_well)
median(dry_well)
quantile(dry_well, c(0.05, 0.25, 0.75, 0.95))
hist(dry_well, breaks=50)
########### WET WELL ###########
#Drilling costs
#Lease costs
set.seed(123456)
r <- rnorm(n=100000, mean=600, sd=50)
Pt_lease <- 960*r
#Seismic Costs
set.seed(123456)
r <- rnorm(n=100000, mean=3, sd=0.35)
Pt_seis <- 43000*r
#Professional Overhead
set.seed(123456)
r <- rtri(n=100000, min=172000, max=279500, mode=215000)
Pt_prof <- r
#Completetion Costs
set.seed(123456)
r <- rnorm(n=100000, mean=390000, sd=50000)
Pt_comp <- r
#IP
set.seed(12345)
r <- rnorm(n=100000, mean=6, sd=0.28)
Pt_IP <- exp(r)
#Decline rate
set.seed(12345)
r <- runif(n=100000, min=0.15, max=0.32)
Pt_decline <- r
#Need Cholesky Decomposition
R <- matrix(data=cbind(1,0.64, 0.64, 1), nrow=2)
U <- t(chol(R))
standardize <- function(x){
x.std = (x - mean(x))/sd(x)
return(x.std)
}
destandardize <- function(x.std, x){
x.old = (x.std * sd(x)) + mean(x)
return(x.old)
}
Both.r <- cbind(standardize(Pt_decline), standardize(Pt_IP))
dip.r <- U %*% t(Both.r)
dip.r <- t(dip.r)
final.dip.r <- cbind(destandardize(dip.r[,1], Pt_decline), destandardize(dip.r[,2], Pt_IP))
nri <- rep(0, 100000)
#Calculate revenue
initial_costs <- drilling_costs + Pt_lease + Pt_seis + Pt_comp + Pt_prof
proj_2020_2035[,2] <- as.numeric(proj_2020_2035[,2])
proj_2020_2035[,3] <- as.numeric(proj_2020_2035[,3])
proj_2020_2035[,4] <- as.numeric(proj_2020_2035[,4])
npv <- rep(0,100000)
set.seed(12345)
for(j in 1:100000) {
r_ov <- rep(0,15)
r_ye1 <- rep(0,15)
r_ye <- rep(0,15)
rev <- rep(0,15)
op_cos <- rep(0,15)
nri <- rep(0,15)
royal_pay <- rep(0,15)
sev_tax <- rep(0,15)
prof_over <- rep(0,15)
net_sales <- rep(0,100000)
r_ye[1] <- final.dip.r[j,2]
r_ye1[1] = final.dip.r[j,2]
r_ye[1] = (1- final.dip.r[j,1])*r_ye[1]
r_ov[1] = 365 * (0.5 * (r_ye1[1] + r_ye[1]))
price <- rtri(n=1, min=proj_2020_2035[1, 3], max=proj_2020_2035[1, 2], mode = proj_2020_2035[1, 4])
rev[1] <- r_ov[1] * price
op_cos[1] <- (rnorm(n=1, mean=2.25, sd=0.3)) * r_ov[1]
nri[1] <- rnorm(n=1, mean=0.75, sd=0.02)
prof_over[1] <- rtri(n=1, min=172000, max=279500, mode=215000)
royal_pay[1] <- rev[1] * nri[1]
sev_tax[1] <- royal_pay[1] * 0.046
net_sales[1] <- ((rev[1] - prof_over[1] - op_cos[1] - sev_tax[1])/((1+0.1)^1))
for(i in 2:15){
r_ye[i] <- final.dip.r[j,2]
r_ye1[i] = r_ye[i-1]
r_ye[i] = (1- final.dip.r[j,1])*r_ye[i-1]
r_ov[i] = 365 * (0.5 * (r_ye1[i] + r_ye[i]))
price <- rtri(n=1, min=proj_2020_2035[i, 3], max=proj_2020_2035[i, 2], mode = proj_2020_2035[i, 4])
rev[i] = r_ov[i] * price
op_cos[i] <- (rnorm(n=1, mean=2.25, sd=0.3)) * r_ov[i]
nri[i] <- nri[1]
prof_over[i] <- prof_over[1]
royal_pay[i] <- rev[i] * nri[i]
sev_tax[i] <- royal_pay[i] * 0.046
net_sales[i] <- ((rev[i] - prof_over[i] - op_cos[i] - sev_tax[i])/((1+0.1)^i))
}
npv[j] = (-1*initial_costs) + sum(net_sales)
}
mean(npv)
sd(npv)
median(npv)
quantile(npv, c(0, 0.05, 0.25, 0.75, 0.95, 1))
hist(npv, breaks=50, xlim=c(1000000, 105000000))
|
3d813b00cabc592f067ec0eb555fd5d022e14a97 | 7a173aaf44b4c01ba9c9dd907862a5fe65e079b3 | /R/txt.to.words.R | 79cf9e01fe6f5dccf1bd8b35864928d71befe734 | [] | no_license | Quares/stylo | 221904d14aa516f97b4fe6402d394fc568c084fa | eb42d0de8a3f5cbbcac70c10a33ede40fee8f670 | refs/heads/master | 2020-04-05T23:23:57.101344 | 2015-08-23T16:17:59 | 2015-08-23T16:17:59 | 41,256,678 | 0 | 0 | null | 2015-08-23T16:18:00 | 2015-08-23T16:10:34 | R | UTF-8 | R | false | false | 2,556 | r | txt.to.words.R |
# #################################################
# The generic function for splitting a given input text into
# single words (chains of characters delimited with
# spaces or punctuation marks). Alternatively,
# you can replace it with another rule.
# Required argument: name of the text (string) to be split.
# ATTENTION: this is (almost) the only piece of coding in this script
# that dependens on the operating system used
# #################################################
txt.to.words <-
function(input.text, splitting.rule = NULL, preserve.case = FALSE) {
# converting characters to lowercase if necessary
if (!(preserve.case)){
input.text = tryCatch(tolower(input.text),
error=function(e) NULL)
if(is.null(input.text) == TRUE) {
input.text = "empty"
cat("turning into lowercase failed!\n")
}
}
# if no custom splitting rule was detected...
if(length(splitting.rule) == 0 ) {
# splitting into units specified by regular expression; here,
# all sequences between non-letter characters are assumed to be words:
splitting.rule = paste("[^A-Za-z",
# Latin supplement (Western):
"\U00C0-\U00FF",
# Latin supplement (Eastern):
"\U0100-\U01BF",
# Latin extended (phonetic):
"\U01C4-\U02AF",
# modern Greek:
"\U0386\U0388-\U03FF",
# Cyrillic:
"\U0400-\U0481\U048A-\U0527",
# Hebrew:
"\U05D0-\U05EA\U05F0-\U05F4",
# Arabic:
"\U0620-\U065F\U066E-\U06D3\U06D5\U06DC",
# extended Latin:
"\U1E00-\U1EFF",
# ancient Greek:
"\U1F00-\U1FBC\U1FC2-\U1FCC\U1FD0-\U1FDB\U1FE0-\U1FEC\U1FF2-\U1FFC",
# Coptic:
"\U03E2-\U03EF\U2C80-\U2CF3",
# Georgian:
"\U10A0-\U10FF",
"]+",
sep="")
tokenized.text = c(unlist(strsplit(input.text, splitting.rule)))
# if custom splitting rule was indicated:
} else {
# sanity check
if(length(splitting.rule) == 1) {
# just in case, convert to characters
splitting.rule = as.character(splitting.rule)
# splitting into units specified by custom regular expression
tokenized.text = c(unlist(strsplit(input.text, splitting.rule)))
} else {
stop("Wrong splitting regexp")
}
}
# getting rid of emtpy strings
tokenized.text = tokenized.text[nchar(tokenized.text) > 0]
# outputting the results
return(tokenized.text)
}
|
a8cdc4efdd0eb7fa7f3c9b021225c3c8e773df6c | edd10cb0ebe95cee716b04778d454ff64039ebe3 | /Older Scripts /gradient.R | e28ee86d1904ae0ba0f7baf3a8c0792e75731eaa | [] | no_license | sidAvad/ZIBB-model | 7c9d009b50b7b0265c643bcfcb775619cd13ce5c | f244260d34326940778530d7432ac1d8cfdc7cc7 | refs/heads/master | 2023-02-07T15:18:44.266459 | 2016-08-30T01:01:59 | 2016-08-30T01:01:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,091 | r | gradient.R | ds$grad.log.density <- function(theta,y){
## Evaluates the gradient of the likelihood function of ZIBB
## given theta and the data (y)
y <-r
lastcol <- ncol(y)
y1 <- y[,lastcol]
X <- y[,1:ncol(y)-1]
ones <- rep(1,nrow(y))
X <- cbind(ones,X)
n <- 10
rho <- theta[length(theta)]
phi <- (1-rho)/rho
B <- theta[c(1:length(theta)-1)]
Alpha <- B[c(1,2,3)]
Gamma <- B[c(4,5,6)]
Beta <- Gamma + exp(Alpha)
pi <- sigmoid(X%*%Beta)
mupi <- sigmoid(X%*%Gamma)
mu <- mupi/pi
gradb <- vector()
gradt <- vector()
P2 <- gamma(n+phi)
P1 <- gamma(phi)
der <- matrix(nrow=length(y1),ncol=7)
for (i in 1:length(y1)){
cb <- (mupi[i]/(pi[i])^2)*(1-pi[i])*(pi[i])
ct <- (1/pi[i])*(mupi[i])*(1-mupi[i])
gradb <- cb*X[i,] ## Derivatives of mu w.r.t beta(gradb) and theta(gradt)
gradt <- ct*X[i,]
grad <- append(gradb,gradt)
if(y1[i] != 0){
a <- phi*mu[i]
b <- phi*(1-mu[i])
## derivative w.r.t thetas ( all params except phi)
T2mu <- digamma(y1[i]+a)*(phi*grad) + digamma(n-y1[i]+b)*(-phi*grad) -
digamma(a)*(phi*grad) - digamma(b)*(-phi*grad)
## derivative w.r.t phi
T2phi <- digamma(y1[i]+a)*mu[i] + digamma(n-y1[i]+b)*(1-mu[i]) - digamma(n+phi) +
digamma(phi) - digamma(a)*mu[i] - digamma(b)*(1-mu[i])
app <- append(T2mu,T2phi)
der[i,] <- app
}else{
R <- beta(y[i]+mu[i]*phi,n-y[i]+phi*(1-mu[i]))/beta(phi*mu[i],phi*(1-mu[i]))
A1 <- pi[i]*choose(n,y1[i])/(1-pi[i] + pi[i]*choose(n,y1[i])*R)
a <- phi*mu[i]
b <- phi*(1-mu[i])
D1 <- gamma(a)
D2 <- gamma(b)
D3 <- gamma(y1[i]+ a)
D4 <- gamma(n-y1[i]+ b)
C <- A1*gamma(phi)/gamma(n+phi)
## derivative w.r.t thetas ( all params except phi)
T1mu <- (C/(gamma(a)*gamma(b))^2)*( D1*D2*(D3*D4*digamma(n-y1[i]+b)*(-phi*grad) +
D4*D3*digamma(y1[i]+a)*(phi*grad)) -
D3*D4*(D1*D2*digamma(b)*(-phi*grad) +
D2*D1*digamma(a)*(phi*grad))
)
## derivative w.r.t phi
T1phi <- (A1/(gamma(n+phi)*gamma(a)*gamma(b))^2)*(P2*D1*D2*(P1*D3*D4*digamma(n-y1[i]+b)*(1-mu[i]) +
P1*D4*D3*digamma(y1[i]+a)*(mu[i]) + D3*D4*P1*digamma(phi)) -
P1*D3*D4*(P2*D1*D2*digamma(b)*(1-mu[i]) +
P2*D2*D1*digamma(a)*(mu[i]) + D1*D2*P2*digamma(n+phi))
)
der[i,] <- append(T1mu,T1phi)
}
}
derfinal <- colSums(der)
derfinal1 <- -derfinal
}
|
10d263818c6348b283da45721496bd4e165e2c21 | bbd9ca286fd2289c9576aaea736b5519dab035f4 | /Tester3.R | 0819c96af394ab36ac34452e9dfdd00e311caafa | [] | no_license | KevLeeKL/Tester3-import-local-pj | 791c94803f91959e6dbd7cd2f5bdce8e754905ef | d0d3a716bf19507dd553b6af12a6cb02f93567fe | refs/heads/master | 2022-08-05T19:13:42.393428 | 2020-05-25T19:16:21 | 2020-05-25T19:16:21 | 266,860,539 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 237 | r | Tester3.R | mean(mtcars$mpg)
library(help='base')
head(iris)
summary(iris$Species)
install.packages('devtools')
library(devtools)
help(package = 'ggplot2')
help(package = 'devtools')
sessionInfo()
install.packages('KernSmooth')
library(KernSmooth)
|
e33f688d6393fa10ad4ad5c60396151408d44759 | 696e530b91e3c64a7ffae95f227b31b16c43e9a2 | /R/copykat_no_heatmap.R | 13ef57f9cea8bcb8002a1d3078749c5e7557b477 | [
"MIT"
] | permissive | abelson-lab/scATOMIC | c56520ce594000737ddb242d473a81129c5c8110 | bc7adc8486d70b980af149283e85c84e5f7c9abd | refs/heads/main | 2023-08-03T08:10:04.592709 | 2023-07-19T13:18:50 | 2023-07-19T13:18:50 | 418,635,282 | 31 | 3 | null | null | null | null | UTF-8 | R | false | false | 13,410 | r | copykat_no_heatmap.R | #' copy_kat_no_heatmap
#'
#' @param rawmat matrix of counts
#' @param id.type set S if gene names
#' @param cell.line set to no for tissues
#' @param ngene.chr minimum nchrm, for my tool i use 0 as i want every cell included
#' @param LOW.DR minimal population fractions of genes for smoothing.
#' @param UP.DR minimal population fractions of genes for segmentation.
#' @param win.size minimal window sizes for segmentation.
#' @param norm.cell.names vector of normal cells use the blood and stromal from the classifier
#' @param KS.cut segmentation parameters, input 0 to 1; larger looser criteria.
#' @param sam.name sample name.
#' @param distance default eucledian
#' @param n.cores for parallel computing
#'
#' @return returns a CNV matrix for use in create_summary_matrix
#' @export
#function is code modified from Gao R. 2021 https://github.com/navinlabcode/copykat
copy_kat_no_heatmap <- function (rawmat = rawdata,summary_matrix, id.type = "S", cell.line = "no",
ngene.chr = 5, LOW.DR = 0.05, UP.DR = 0.1, win.size = 25, KS.cut = 0.1, sam.name = "", distance = "euclidean",
n.cores = (detectCores() - 1))
{
if(.Platform$OS.type == "windows"){
mc.cores = 1
n.cores = 1
}
start_time <- Sys.time()
if(length(which(duplicated(row.names(rawmat))))> 0){
rawmat <- rawmat[-which(duplicated(row.names(rawmat))),]
}
norm.cell.names = row.names(summary_matrix)[which(summary_matrix$layer_1 == "Blood_Cell" |
summary_matrix$layer_2 %in% c("Endothelial Cells", "Stromal Cell", "Oligodendrocytes"))]
set.seed(1)
sample.name <- paste(sam.name, "_copykat_", sep = "")
print("step1: read and filter data ...")
print(paste(nrow(rawmat), " genes, ", ncol(rawmat), " cells in raw data",
sep = ""))
genes.raw <- apply(rawmat, 2, function(x) (sum(x > 0)))
if (sum(genes.raw > 200) == 0)
stop("none cells have more than 200 genes")
if (sum(genes.raw < 100) > 1) {
rawmat <- rawmat[, -which(genes.raw < 200)]
print(paste("filtered out ", sum(genes.raw <= 200), " cells with less than 200 genes; remaining ",
ncol(rawmat), " cells", sep = ""))
}
der <- apply(rawmat, 1, function(x) (sum(x > 0)))/ncol(rawmat)
if (sum(der > LOW.DR) >= 1) {
rawmat <- rawmat[which(der > LOW.DR), ]
print(paste(nrow(rawmat), " genes past LOW.DR filtering",
sep = ""))
}
WNS1 <- "data quality is ok"
if (nrow(rawmat) < 7000) {
WNS1 <- "low data quality"
UP.DR <- LOW.DR
print("WARNING: low data quality; assigned LOW.DR to UP.DR...")
}
print("step 2: annotations gene coordinates ...")
anno.mat <- annotateGenes.hg20(mat = rawmat, ID.type = id.type)
anno.mat <- anno.mat[order(anno.mat$abspos, decreasing = FALSE),
]
HLAs <- anno.mat$hgnc_symbol[grep("^HLA-", anno.mat$hgnc_symbol)]
toRev <- which(anno.mat$hgnc_symbol %in% c(as.vector(cyclegenes[[1]]),
HLAs))
if (length(toRev) > 0) {
anno.mat <- anno.mat[-toRev, ]
}
ToRemov2 <- NULL
for (i in 8:ncol(anno.mat)) {
cell <- cbind(anno.mat$chromosome_name, anno.mat[, i])
cell <- cell[cell[, 2] != 0, ]
if (length(as.numeric(cell)) < 5) {
rm <- colnames(anno.mat)[i]
ToRemov2 <- c(ToRemov2, rm)
}
else if (length(rle(cell[, 1])$length) < 23 | min(rle(cell[,
1])$length) < ngene.chr) {
rm <- colnames(anno.mat)[i]
ToRemov2 <- c(ToRemov2, rm)
}
i <- i + 1
}
if (length(ToRemov2) == (ncol(anno.mat) - 7))
stop("all cells are filtered")
if (length(ToRemov2) > 0) {
anno.mat <- anno.mat[, -which(colnames(anno.mat) %in%
ToRemov2)]
}
rawmat3 <- data.matrix(anno.mat[, 8:ncol(anno.mat)])
norm.mat <- log(sqrt(rawmat3) + sqrt(rawmat3 + 1))
norm.mat <- apply(norm.mat, 2, function(x) (x <- x - mean(x)))
colnames(norm.mat) <- colnames(rawmat3)
print("step 3: smoothing data with dlm ...")
dlm.sm <- function(c) {
model <- dlm::dlmModPoly(order = 1, dV = 0.16, dW = 0.001)
x <- dlm::dlmSmooth(norm.mat[, c], model)$s
x <- x[2:length(x)]
x <- x - mean(x)
}
test.mc <- parallel::mclapply(1:ncol(norm.mat), dlm.sm, mc.cores = n.cores)
norm.mat.smooth <- matrix(unlist(test.mc), ncol = ncol(norm.mat),
byrow = FALSE)
colnames(norm.mat.smooth) <- colnames(norm.mat)
print("step 4: measuring baselines ...")
if (cell.line == "yes") {
print("running pure cell line mode")
relt <- baseline.synthetic(norm.mat = norm.mat.smooth,
min.cells = 10, n.cores = n.cores)
norm.mat.relat <- relt$expr.relat
CL <- relt$cl
WNS <- "run with cell line mode"
preN <- NULL
}
else if (length(norm.cell.names) > 1) {
NNN <- length(colnames(norm.mat.smooth)[which(colnames(norm.mat.smooth) %in%
norm.cell.names)])
print(paste(NNN, " known normal cells found in dataset",
sep = ""))
if (NNN == 0)
stop("known normal cells provided; however none existing in testing dataset")
print("run with known normal...")
basel <- apply(norm.mat.smooth[, which(colnames(norm.mat.smooth) %in%
norm.cell.names)], 1, median)
print("baseline is from known input")
d <- parallelDist::parDist(t(norm.mat.smooth), threads = n.cores,
method = "euclidean")
km <- 6
fit <- hclust(d, method = "ward.D2")
CL <- cutree(fit, km)
while (!all(table(CL) > 5)) {
km <- km - 1
CL <- cutree(fit, k = km)
if (km == 2) {
break
}
}
WNS <- "run with known normal"
preN <- norm.cell.names
norm.mat.relat <- norm.mat.smooth - basel
}
else {
basa <- baseline.norm.cl(norm.mat.smooth = norm.mat.smooth,
min.cells = 5, n.cores = n.cores)
basel <- basa$basel
WNS <- basa$WNS
preN <- basa$preN
CL <- basa$cl
if (WNS == "unclassified.prediction") {
Tc <- colnames(rawmat)[which(as.numeric(apply(rawmat[which(rownames(rawmat) %in%
c("PTPRC", "LYZ", "PECAM1")), ], 2, mean)) >
1)]
length(Tc)
preN <- intersect(Tc, colnames(norm.mat.smooth))
if (length(preN) > 5) {
print("start manual mode")
WNS <- paste("copykat failed in locating normal cells; manual adjust performed with ",
length(preN), " immune cells", sep = "")
print(WNS)
basel <- apply(norm.mat.smooth[, which(colnames(norm.mat.smooth) %in%
preN)], 1, mean)
}
else {
basa <- baseline.GMM(CNA.mat = norm.mat.smooth,
max.normal = 5, mu.cut = 0.05, Nfraq.cut = 0.99,
RE.before = basa, n.cores = n.cores)
basel <- basa$basel
WNS <- basa$WNS
preN <- basa$preN
}
}
norm.mat.relat <- norm.mat.smooth - basel
}
DR2 <- apply(rawmat3, 1, function(x) (sum(x > 0)))/ncol(rawmat3)
norm.mat.relat <- norm.mat.relat[which(DR2 >= UP.DR), ]
anno.mat2 <- anno.mat[which(DR2 >= UP.DR), ]
ToRemov3 <- NULL
for (i in 8:ncol(anno.mat2)) {
cell <- cbind(anno.mat2$chromosome_name, anno.mat2[,
i])
cell <- cell[cell[, 2] != 0, ]
if (length(as.numeric(cell)) < 5) {
rm <- colnames(anno.mat2)[i]
ToRemov3 <- c(ToRemov3, rm)
}
else if (length(rle(cell[, 1])$length) < 23 | min(rle(cell[,
1])$length) < ngene.chr) {
rm <- colnames(anno.mat2)[i]
ToRemov3 <- c(ToRemov3, rm)
}
i <- i + 1
}
if (length(ToRemov3) == ncol(norm.mat.relat))
stop("all cells are filtered")
if (length(ToRemov3) > 0) {
norm.mat.relat <- norm.mat.relat[, -which(colnames(norm.mat.relat) %in%
ToRemov3)]
}
CL <- CL[which(names(CL) %in% colnames(norm.mat.relat))]
CL <- CL[order(match(names(CL), colnames(norm.mat.relat)))]
print("step 5: segmentation...")
results <- CNA.MCMC(clu = CL, fttmat = norm.mat.relat, bins = win.size,
cut.cor = KS.cut, n.cores = n.cores)
if (length(results$breaks) < 25) {
print("too few breakpoints detected; decreased KS.cut to 50%")
results <- CNA.MCMC(clu = CL, fttmat = norm.mat.relat,
bins = win.size, cut.cor = 0.5 * KS.cut, n.cores = n.cores)
}
if (length(results$breaks) < 25) {
print("too few breakpoints detected; decreased KS.cut to 75%")
results <- CNA.MCMC(clu = CL, fttmat = norm.mat.relat,
bins = win.size, cut.cor = 0.5 * 0.5 * KS.cut, n.cores = n.cores)
}
if (length(results$breaks) < 25)
stop("too few segments; try to decrease KS.cut; or improve data")
colnames(results$logCNA) <- colnames(norm.mat.relat)
results.com <- apply(results$logCNA, 2, function(x) (x <- x -
mean(x)))
RNA.copycat <- cbind(anno.mat2[, 1:7], results.com)
print("step 6: convert to genomic bins...")
Aj <- convert.all.bins.hg20(DNA.mat = DNA.hg20, RNA.mat = RNA.copycat,
n.cores = n.cores)
uber.mat.adj <- data.matrix(Aj$RNA.adj[, 4:ncol(Aj$RNA.adj)])
print("step 7: adjust baseline ...")
if (cell.line == "yes") {
mat.adj <- data.matrix(Aj$RNA.adj[, 4:ncol(Aj$RNA.adj)])
if (distance == "euclidean") {
hcc <- hclust(parallelDist::parDist(t(mat.adj), threads = n.cores,
method = distance), method = "ward.D")
}
else {
hcc <- hclust(as.dist(1 - cor(mat.adj, method = distance)),
method = "ward.D")
}
end_time <- Sys.time()
print(end_time - start_time)
reslts <- list(cbind(Aj$RNA.adj[, 1:3], mat.adj), hcc)
names(reslts) <- c("CNAmat", "hclustering")
return(reslts)
}
else {
if (distance == "euclidean") {
hcc <- hclust(parallelDist::parDist(t(uber.mat.adj),
threads = n.cores, method = distance), method = "ward.D")
}
else {
hcc <- hclust(as.dist(1 - cor(uber.mat.adj, method = distance)),
method = "ward.D")
}
hc.umap <- cutree(hcc, 2)
names(hc.umap) <- colnames(results.com)
cl.ID <- NULL
for (i in 1:max(hc.umap)) {
cli <- names(hc.umap)[which(hc.umap == i)]
pid <- length(intersect(cli, preN))/length(cli)
cl.ID <- c(cl.ID, pid)
i <- i + 1
}
com.pred <- names(hc.umap)
com.pred[which(hc.umap == which(cl.ID == max(cl.ID)))] <- "diploid"
com.pred[which(hc.umap == which(cl.ID == min(cl.ID)))] <- "nondiploid"
names(com.pred) <- names(hc.umap)
results.com.rat <- uber.mat.adj - apply(uber.mat.adj[,
which(com.pred == "diploid")], 1, mean)
results.com.rat <- apply(results.com.rat, 2, function(x) (x <- x -
mean(x)))
results.com.rat.norm <- results.com.rat[, which(com.pred ==
"diploid")]
dim(results.com.rat.norm)
cf.h <- apply(results.com.rat.norm, 1, sd)
base <- apply(results.com.rat.norm, 1, mean)
adjN <- function(j) {
a <- results.com.rat[, j]
a[abs(a - base) <= 0.25 * cf.h] <- mean(a)
a
}
mc.adjN <- parallel::mclapply(1:ncol(results.com.rat),
adjN, mc.cores = n.cores)
adj.results <- matrix(unlist(mc.adjN), ncol = ncol(results.com.rat),
byrow = FALSE)
colnames(adj.results) <- colnames(results.com.rat)
rang <- 0.5 * (max(adj.results) - min(adj.results))
mat.adj <- adj.results/rang
print("step 8: final prediction ...")
if (distance == "euclidean") {
hcc <- hclust(parallelDist::parDist(t(mat.adj), threads = n.cores,
method = distance), method = "ward.D")
}
else {
hcc <- hclust(as.dist(1 - cor(mat.adj, method = distance)),
method = "ward.D")
}
hc.umap <- cutree(hcc, 2)
names(hc.umap) <- colnames(results.com)
cl.ID <- NULL
for (i in 1:max(hc.umap)) {
cli <- names(hc.umap)[which(hc.umap == i)]
pid <- length(intersect(cli, preN))/length(cli)
cl.ID <- c(cl.ID, pid)
i <- i + 1
}
com.preN <- names(hc.umap)
com.preN[which(hc.umap == which(cl.ID == max(cl.ID)))] <- "diploid"
com.preN[which(hc.umap == which(cl.ID == min(cl.ID)))] <- "aneuploid"
names(com.preN) <- names(hc.umap)
if (WNS == "unclassified.prediction") {
com.preN[which(com.preN == "diploid")] <- "c1:diploid:low.conf"
com.preN[which(com.preN == "nondiploid")] <- "c2:aneuploid:low.conf"
}
print("step 9: saving results...")
res <- cbind(names(com.preN), com.preN)
colnames(res) <- c("cell.names", "copykat.pred")
end_time <- Sys.time()
print(end_time - start_time)
return(res)
}
}
|
f41573b7731bce5e3292d4d23c0e5760e86f7e7f | e89658aa8eae0c384c0b9ae0c47045cdad5d7341 | /grizz def reb rate.R | 350086dc8ed601c3aaa9c026f3dc048e593986bd | [] | no_license | ramirobentes/NBA-in-R | 0d036a4cfe49c5ccf5eadec6c642b3c295321351 | 3aa845788ac447f4ccf8127e595adebf5e6ad4e1 | refs/heads/master | 2023-08-30T13:48:25.595147 | 2023-08-17T18:26:29 | 2023-08-17T18:26:29 | 230,468,326 | 31 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,060 | r | grizz def reb rate.R | library(tidyverse)
library(nbastatR)
final_poss_pack <- read_csv("https://github.com/ramirobentes/NBA-in-R/releases/download/final-poss-pack-42862e5/data.csv",
col_types = c(timeQuarter = "c",
start_poss = "c")) %>%
mutate(across(starts_with("description"), ~ coalesce(., ""))) # url above might change daily
team_logs <- game_logs(seasons = 2022, result_types = "team")
# put every missed shot and rebound in order
shots_rebs <- final_poss_pack %>%
filter(numberEventMessageType %in% c(2, 4) | numberEventMessageType == 3 & shotPtsHome + shotPtsAway == 0) %>%
mutate(slugTeamPlayer1 = case_when(is.na(slugTeamPlayer1) & descriptionPlayHome == "" ~ slugTeamAway,
is.na(slugTeamPlayer1) & descriptionPlayVisitor == "" ~ slugTeamHome,
TRUE ~ slugTeamPlayer1)) %>%
group_by(idGame, numberPeriod, shot_reb = ifelse(numberEventMessageType == 4, "rebound", "shot")) %>%
mutate(sequence_num = row_number()) %>%
ungroup() %>%
arrange(idGame, numberPeriod, sequence_num) %>%
mutate(desc_type = case_when(numberEventMessageType == 4 & slugTeamPlayer1 == lag(slugTeamPlayer1) ~ "off reb",
numberEventMessageType == 4 & slugTeamPlayer1 != lag(slugTeamPlayer1) ~ "def reb",
TRUE ~ "missed shot")) %>%
filter(!(numberEventMessageType == 3 & numberEventActionType %in% c(11, 13, 14, 16, 18:22, 25, 27:29))) %>%
filter(!(desc_type == "def reb" & numberEventMessageType == 4 & numberEventActionType == 1)) %>%
mutate(lineup_team = ifelse(slugTeamPlayer1 == slugTeamHome, lineupHome, lineupAway),
lineup_opp = ifelse(slugTeamPlayer1 == slugTeamHome, lineupAway, lineupHome)) %>%
select(idGame, numberPeriod, timeQuarter, numberEventMessageType, numberEventActionType, slugTeam = slugTeamPlayer1,
descriptionPlayHome, descriptionPlayVisitor, desc_type, lineup_team, lineup_opp) %>%
left_join(team_logs %>%
distinct(idGame, slugTeam, slugOpponent, dateGame)) %>%
mutate(slugTeam = case_when(slugTeam == "MEM" & str_detect(lineup_team, "Steven Adams") ~ "MEM with Adams",
slugTeam == "MEM" & !str_detect(lineup_team, "Steven Adams") ~ "MEM without Adams",
TRUE ~ slugTeam),
slugOpponent = case_when(slugOpponent == "MEM" & str_detect(lineup_opp, "Steven Adams") ~ "MEM with Adams",
slugOpponent == "MEM" & !str_detect(lineup_opp, "Steven Adams") ~ "MEM without Adams",
TRUE ~ slugOpponent))
shots_rebs %>%
filter(desc_type == "def reb") %>%
count(slugTeam, name = "def_reb") %>%
left_join(shots_rebs %>%
filter(desc_type == "missed shot") %>%
count(slugTeam = slugOpponent, name = "def_reb_chances")) %>%
mutate(def_reb_pct = round(def_reb / def_reb_chances, 4)) %>%
arrange(-def_reb_pct) |
0ab35ddfa07e1cdb51cd70283523f416124d6ad0 | 64257a0e57cf928b0ae7676a108a3688001181bd | /R/data.R | bb7971de8b45e2cabdb2a9552db7a7c532270ce4 | [
"BSD-3-Clause"
] | permissive | marcpaterno/artsupport | 9842a678c8070468dd93a258810b84067fe22f32 | 803310561741c4aa54bdd44e393da9ae8551bfa0 | refs/heads/master | 2020-06-30T06:49:04.780687 | 2020-04-20T23:14:48 | 2020-04-20T23:14:48 | 74,387,093 | 0 | 1 | NOASSERTION | 2019-02-07T07:11:33 | 2016-11-21T17:15:33 | R | UTF-8 | R | false | false | 1,309 | r | data.R | #' art event timing data for 240 events.
#'
#' Several datasets containing the data recorded by the *art* framework's Timing service.
#'
#' The event-by-event timing data written into the `TimeEvent` table, which contains one record per event.
#' @format A tibble with 240 observatoins of 5 variables:
#' \describe{
#' \item{Run}{the run number}
#' \item{SubRUn}{the subrun number}
#' \item{Event}{the event number}
#' \item{Time}{event processing time in seconds}
#' \item{sample}{sample id, an integer unique for each event}
#' }
"events"
#' The module-by-module timing data written into the `TimeModule` table, which contains one record
#' per module for each event. The recorded art process used 3 modules on just 1 path. There is also
#' a record for the TriggerResults 'module'.
#' @format A tibble with 960 observatios of 8 variables:
#' \describe{
#' \item{Run}{the run number}
#' \item{SubRun}{the subrun number}
#' \item{Event}{the event number}
#' \item{Path}{the path on which the module ran; note that one module can appear on multiple paths}
#' \item{ModuleLabel}{the label of the module}
#' \item{ModuleType}{the C++ class of the module}
#' \item{Time}{module executon time in seconds}
#' \item{sample}{sample id, and integer unique for each event}
#' }
"modules"
|
3e4d29718e940554cd27816c40e056a7f6a4934c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/colorpatch/examples/DistColorFun.Rd.R | 5075a3db7d6a822773bc199072eba4f9550de615 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 264 | r | DistColorFun.Rd.R | library(colorpatch)
### Name: DistColorFun
### Title: Creates a color distance function
### Aliases: DistColorFun
### ** Examples
library(colorspace)
library(colorpatch)
fn <- DistColorFun("LUV")
a <- sRGB(1,0,0)
b <- sRGB(0.8,0.1,0)
my.distance <- fn(a,b)
|
5b06821d5b3578f4b4296defe623614bcb460bec | fc36112ec2687ee3a56086fc121a8e8101c5d62c | /man/validate_table_quote_character.Rd | ae658c27fd65e31dfce0c497f74ff269fc1ed7bf | [
"MIT"
] | permissive | EDIorg/EMLassemblyline | ade696d59147699ffd6c151770943a697056e7c2 | 994f7efdcaacd641bbf626f70f0d7a52477c12ed | refs/heads/main | 2023-05-24T01:52:01.251503 | 2022-11-01T01:20:31 | 2022-11-01T01:20:31 | 84,467,795 | 36 | 17 | MIT | 2023-01-10T01:20:56 | 2017-03-09T17:04:28 | R | UTF-8 | R | false | true | 528 | rd | validate_table_quote_character.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/validate_arguments.R
\name{validate_table_quote_character}
\alias{validate_table_quote_character}
\title{Validate data table quote characters}
\usage{
validate_table_quote_character(fun.args)
}
\arguments{
\item{fun.args}{(named list) Function arguments and their values.}
}
\value{
\item{issues}{Description of issues}
\item{fun.args}{Updated list of function arguments}
}
\description{
Validate data table quote characters
}
\keyword{internal}
|
5ab619cdad7b676531f913346b8b9c4b43ccc7bb | 26c2b1e98c34d865b51202188551239de8f1b500 | /man/l.small.Rd | 71701897de3be86836a562b3e3a407ecfcc53930 | [] | no_license | cran/LogicOpt | a88799844bd3b3c60e73dad2dae2e9acd0381beb | cad5a27882670bf7c4c3680a0e0e3550359ad33a | refs/heads/master | 2020-12-22T00:57:09.661699 | 2016-05-07T01:27:50 | 2016-05-07T01:27:50 | 236,621,369 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 555 | rd | l.small.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/l.small-data.R
\docType{data}
\name{l.small}
\alias{l.small}
\title{Espresso truth table with 4 inputs and 3 outputs}
\format{R data frame table}
\usage{
data(l.small)
}
\description{
Espresso compatible truth table generated
from espresso format file small.esp.
}
\examples{
\dontrun{
# steps to recreate
inpath <- system.file("extdata/espresso/small.esp", package="LogicOpt")
l.small <- logicopt(esp_file=inpath,mode="echo")[1]
}
}
\keyword{Espresso}
\keyword{truth-table}
|
1f327402f03d4c659433c5c84f0b38693edece7b | a4c6dcffc21de8138403a3d0348bf4efe8736f1e | /MyFirstShinyApplication/server.R | 05b49b13b34a673c3035990fa4f3e940406dddf2 | [] | no_license | ricardorac/ddp_final | c576258b9b015bb7fd7109e6fe428766cb9082ba | 9fbc5984b2b49b12b6e43d3c4880408ee5a89708 | refs/heads/master | 2022-09-20T05:32:38.773047 | 2020-06-05T12:29:09 | 2020-06-05T12:29:09 | 265,584,403 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,779 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(lgrdata)
library(ggplot2)
library(RColorBrewer)
library(caret)
data(anthropometry)
set.seed(1979)
anthropometry <- anthropometry[complete.cases(anthropometry), ]
inTrain = createDataPartition(anthropometry$height, p = 0.8)[[1]]
testing = anthropometry[-inTrain, ]
training = anthropometry[inTrain, ]
trainCtrl <-
trainControl(method = "cv",
savePredictions = "none",
number = 3)
RMSE <- function(m, o) {
sqrt(mean((m - o) ^ 2))
}
pal <- "Set1"
shinyServer(function(input, output) {
formula <- ""
model <- reactive({
if (input$buildModelButton) {
selMethod <- input$modelInput
selPred <- c()
if (input$addFootLength) {
selPred <- c(selPred, "foot_length")
}
if (input$addAge) {
selPred <- c(selPred, "age")
}
if (input$addGender) {
selPred <- c(selPred, "gender")
}
if (length(selPred) == 0) {
return(NULL)
}
selFormula <-
as.formula(paste("height", paste(selPred, collapse = " + "), sep = " ~ "))
modFit <-
train(
selFormula,
method = selMethod,
data = training,
trControl = trainCtrl,
model = FALSE
)
modFit
} else {
return(NULL)
}
})
modelFootLength <- reactive({
brushed_data <-
brushedPoints(
anthropometry,
input$brushHeightFootLength,
xvar = "foot_length",
yvar = "height"
)
if (nrow(brushed_data) < 2) {
return(NULL)
}
lm(height ~ foot_length, data = brushed_data)
})
output$dataSummaryOut <- renderPrint(
summary(anthropometry)
)
output$rmseOnTesting <- reactive({
if (input$buildModelButton) {
modFit <- model()
if (is.null(modFit)) {
return("You have to build a model first.")
} else {
pred <- predict(modFit, testing)
return(paste(
"RMSE on testing samples: ",
RMSE(pred, testing$height),
" (expected out-of-sample RMSE)"
))
}
}
})
output$rmseOnTraining <- reactive({
if (input$buildModelButton) {
modFit <- model()
if (is.null(modFit)) {
return("You have to build a model first.")
} else {
pred <- predict(modFit, training)
return(paste(
"RMSE on training samples: ",
RMSE(pred, training$height)
))
}
}
})
output$textPrediction <- reactive({
if (input$predictButton) {
modFit <- model()
if (is.null(modFit)) {
return("You have to build a model first.")
} else {
toPred <-
data.frame(
age = as.numeric(input$ageValue),
foot_length = as.numeric(input$footLengthValue),
gender = input$genderValue
)
pred <- predict(modFit, toPred)
return(paste("The predicted Height (in cm) is ", pred[[1]]))
}
}
})
output$modelOutput <- renderPrint({
modFit <- model()
if (!is.null(modFit)) {
return(summary(modFit$finalModel))
}
})
output$plotModel <- renderPlot({
modFit <- model()
if (!is.null(modFit)) {
predicted <- predict(modFit, training)
predictedDF <-
data.frame(pred_height = predicted, age = training$age)
g <-
ggplot(training, aes(y = height)) + scale_color_brewer(palette = pal)
g <-
g + geom_point(aes(x = age, colour = gender)) + ggtitle("Predicted Height") + xlab("Age") + ylab("Height")
g + geom_smooth(
color = 'green',
data = predictedDF,
aes(x = age, y = pred_height),
size = 2
)
}
})
output$plotHeightFootLength <- renderPlot({
anthropometry <- anthropometry[complete.cases(anthropometry), ]
g <-
ggplot(anthropometry, aes(y = height)) + scale_color_brewer(palette = pal)
g <-
g + geom_point(aes(x = foot_length, colour = gender)) + ggtitle("Height by Foot Length") + xlab("Foot length") + ylab("Height")
if (!is.null(modelFootLength())) {
intrcpt <- coef(modelFootLength())["(Intercept)"]
slp <- coef(modelFootLength())["foot_length"]
g <-
g + geom_abline(
slope = slp,
intercept = ,
color = "green",
size = 2
)
}
g
})
output$slopeOut <- reactive({
if (!is.null(modelFootLength())) {
return(paste(coef(modelFootLength())["foot_length"]))
}
})
output$interceptOut <- reactive({
if (!is.null(modelFootLength())) {
return(paste(coef(modelFootLength())["(Intercept)"]))
}
})
output$plot1 <- renderPlot({
anthropometry <- anthropometry[complete.cases(anthropometry), ]
g <-
ggplot(anthropometry, aes(y = height)) + scale_color_brewer(palette = pal)
g + geom_point(aes(x = age, colour = gender)) + ggtitle("Height by Age") + xlab("Age") + ylab("Height")
})
output$plot2 <- renderPlot({
anthropometry <- anthropometry[complete.cases(anthropometry), ]
g <-
ggplot(anthropometry, aes(y = height)) + scale_color_brewer(palette = pal)
g + geom_point(aes(x = foot_length, colour = gender)) + ggtitle("Height by Foot length") + xlab("Foot length") + ylab("Height")
})
output$plot3 <- renderPlot({
anthropometry <- anthropometry[complete.cases(anthropometry), ]
g <-
ggplot(anthropometry, aes(y = height)) + scale_fill_brewer(palette = pal)
g + geom_boxplot(aes(x = gender, fill = gender)) + ggtitle("Height by Gender") + xlab("Gender") + ylab("Height")
})
}) |
62a8e5b0ac0a92c89b121cc2df577a0a21a206e1 | 7538c1f30b9c46d3901cae4c47e20f9a6e57f80e | /L01B2.isimip_nobias_global_processing.R | 70a402ecb720201d3f8138b7c78a55a82b19248c | [] | no_license | JGCRI/frontEnd_grandExp | 2e804320fc98d1f39a4389abf035652ff08cbbb8 | bc09667e9aff9f7fe6aa6d12d145b3b4523f6b81 | refs/heads/master | 2020-03-28T21:32:13.983743 | 2018-10-12T17:05:44 | 2018-10-12T17:05:44 | 149,162,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,548 | r | L01B2.isimip_nobias_global_processing.R |
# Purpose: Because the bias corrected ISIMIP netcdfs only includes values over land and fldgen
# requires a global mean average this script processes the no bias corrected monthly
# output to a annual global average.
# Users should check that all the directories defined in section 0 exists and should check
# out section 1 for decisions about what isimip files to search for.
# TODO may be we want to include some code that will check to see if we have all the global
# averages we need to train the fldgen emulator? Might have to update the script so that
# the output includes a column name and a years column
# 0. Set Up -----------------------------------------------------------------------------
showMessages <- TRUE # a T/F to suppress or show script messages
if(showMessages) message('0. Set Up')
library(dplyr)
library(tidyr)
library(tibble)
library(purrr)
BASE <- "/pic/projects/GCAM/Dorheim/frontEnd_grandExp" # The project location on pic
CDO_DIR <- "/share/apps/netcdf/4.3.2/gcc/4.4.7/bin/cdo" # Define the cdo directory
ISIMIP_DIR <- "/pic/projects/GCAM/leng569/CMIP5" # The location of the isimip files on pic
INTER_DIR <- '/pic/scratch/dorh012' # The intermediate dir to write things out to
OUTPUT_DIR <- file.path(BASE, "output-L1", "annual_isimip"); dir.create(OUTPUT_DIR, showWarnings = FALSE, recursive = TRUE)
# 1. User Decisions -----------------------------------------------------------------------------------------------------
# Select the ismip variables, experiment, model, and so on to process. The strings listed in each of the
# vectors will be used in a search pattern to identify the files to process from the ISIMIP_DIR. We are interested
# in the biased correct files and they have the following file pattern. If the vector is set to NULL then the
# pattern will search for all options.
# variable_bced_1960_1999_model_experiment_startYr-endYr.nc4
if(showMessages) message('1. User Decisions')
# isimip search vectors
VARIABLES <- c("tas") # isimip variables to process
EXPERIMENTS <- c("rcp45", "rcp26", "rcp85", "rcp60") # isimip experiments to process
MODELS <- "IPSL-CM5A-LR" # isimip models to process
ENSEMBLES <- "r1i1p1" # the ensemble number
# 2. Find the isimip files to process -----------------------------------------------------------------------------------------------------
# Create the search netcdf search pattern and then search the isimip directory.
if(showMessages) message('2. Find the isimip files to process')
# pattern_gen is a function that converts the isimip search vectors from section
# into strings that will be used to create the regex search pattern. If the
# input vector is set to NULL then the function will return a search all pattern.
pattern_gen <- function(vector){
if(is.null(vector)){
"[a-zA-Z0-9-]+"
} else {
paste(vector, collapse = "|")
}
}
varpattern <- pattern_gen(VARIABLES)
modelpattern <- pattern_gen(MODELS)
experimentpattern <- pattern_gen(EXPERIMENTS)
ensemblepattern <- pattern_gen(ENSEMBLES)
# Create the pattern for the isimip file names for the netcdfs to search for.
isimip_search_pattern <- paste("(regridded)_(1deg)_(",
varpattern,
")_(Amon)_(",
modelpattern, ")_(",
experimentpattern, ")_(",
ensemblepattern, ')_',
"([0-9]{6})-([0-9]{6}", # time, set up to search for any 6 digit date
").nc$", sep = "")
# Sanity check
if(showMessages) message("isimip file search pattern: ", isimip_search_pattern, "\n")
# Search for the files
file_list <- list.files(ISIMIP_DIR, pattern = isimip_search_pattern, full.names = TRUE, recursive = TRUE)
# Sanity check
if(length(file_list) < 1) stop('Could not find any isimip files files matching ', isimip_search_pattern)
# Parse out the run meta information from the file_list
tibble(path = file_list) %>%
separate(path, into = c('A', 'B', 'variable', 'C', 'model', 'experiment', 'ensemble', 'yrs'), remove = FALSE, sep = '_' ) ->
to_process
# 4. Process to global annual average -------------------------------------------------------------------
# Because we need a global annual average but only have access to monthly gridded nobias corrected data
# we will use CDO to process to the correct data.
if(showMessages) message('4. Process to global annual average')
# Create a mapping file to use to name the output so that it is compatible with the isismip
# bias corrected files.
tibble(experiment = c('rcp26', 'rcp45', 'rcp60', 'rcp85'),
new_experiment = c('rcp2p6', 'rcp4p5', 'rcp6p0', 'rcp8p5')) %>%
mutate(model = rep("IPSL-CM5A-LR", nrow(.)),
new_model = rep("ipsl-cm5a-lr", nrow(.))) ->
mapping_names
# The fldgen training function will want txt files with the format
# variable_annual_model_experiment_ensemble_start-end.ncglobalAvg.txt
# So this function will use a mapping file to rename models / experiments to
# match the isimip bias corrected nomenclature.
annual_globalAvg_func <- function(df, cdo_dir, inter_dir, output_dir, mapping = mapping_names,
showMessages = FALSE, removeIntermediate = TRUE){
# This function depends on CDO (https://code.zmaw.de/projects/cdo) being installed
stopifnot(file.exists(cdo_dir))
stopifnot(dir.exists(inter_dir))
stopifnot(dir.exists(output_dir))
# Based on the isimip file information create the output file name.
interOut1_nc <- file.path(inter_dir, paste0('inter-annual_avg-', basename(df[['path']])))
interOut2_nc <- file.path(inter_dir, paste0('inter-global_avg-', basename(df[['path']])))
# Get the global annual average.
# TODO in theory this could be accomplished by a single
if(showMessages) message("Convert to absolute time and annual average ", df[['path']])
system2(cdo_dir, args = c("-a", "yearmean", df[['path']], interOut1_nc), stdout = TRUE, stderr = TRUE)
if(showMessages) message("Calculate global average ", df[['path']])
system2(cdo_dir, args = c("fldmean", interOut1_nc, interOut2_nc), stdout = TRUE, stderr = TRUE)
# Extract the data from the global annual average nc to save as the txt file.
# TODO it looks like the isimip data only includes values to 2099, we will want to
# subset the results to match the number of years in the bias corrected data.
nc <- nc_open(interOut2_nc)
nc_time <- ncvar_get(nc, 'time')
nc_data <- ncvar_get(nc, df[['variable']])
nc_units <- ncatt_get(nc, df[['variable']], 'units')[['value']]
# The bias corrected data temp is in K so if we run into an issue where the
# tas data is in C convert it to K
if( df[['variable']] == 'tas' & nc_units == 'C'){
if(showMessages) message('converting from deg C to K')
# Convert from C to K
nc_data <- nc_data + 273.15
}
# TODO make this not hard coded
# Subset the data so that it matches the time step of the output
# generated by L01B1.
start <- 2006
end <- 2099
time <- as.integer(substr(nc_time, 1, 4))
keep <- which(start <= time & time <= end)
final_data <- nc_data[keep]
# Now that we have the final data with the correct units we need to name
# the output file using the new names mapping file.
df_mapping <- left_join(x = df, y = mapping, by = c("model", "experiment"))
out_name <- paste0(df_mapping[['variable']], '_annual_', df_mapping[['new_model']], "_",
df_mapping[['new_experiment']], '_xxx_', start, '-', end, '.ncGlobalAvg.txt')
out_file <- file.path(output_dir, out_name)
if(showMessages) message('saving ', out_file)
write.table(final_data, file = out_file, row.names = FALSE, col.names = FALSE)
if(removeIntermediate){
file.remove(interOut1_nc, interOut2_nc)
}
}
# Use lapply to apply the annual_globalAvg_func to process all of the files in the to process data frame.
lapply(X = split(to_process, to_process$path),
FUN = annual_globalAvg_func, cdo_dir = CDO_DIR, inter_dir = INTER_DIR, output_dir = OUTPUT_DIR, mapping = mapping_names,
showMessages = showMessages, removeIntermediate = TRUE)
# Finish script
if(showMessages) message('script complete')
|
cab9d981d771361f602857a1d10e3ba6908e185e | c4547314bb5e40b6386968ef995b1a4149c1de8c | /R/colorSpec.read.R | f8ad8dd268af678575681fb8eec590f1441a6d8e | [] | no_license | cran/colorSpec | a23ea51692949e43fce61e7ead9ba10b39668c58 | 7b6120a30cad781b413e6145a7d5b73c10991a64 | refs/heads/master | 2022-05-10T19:23:30.384707 | 2022-05-04T01:40:02 | 2022-05-04T01:40:02 | 58,973,975 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 30,611 | r | colorSpec.read.R |
# pathvec a vector of paths to files
# ... optional arguments to pass to resample.colorSpec
#
# value:
# a single colorSpec object, or NULL in case of error
#
readSpectra <- function( pathvec, ... )
{
if( is.null(pathvec) || length(pathvec)==0 ) return(NULL)
if( 1 < length(pathvec) )
{
# call coroutine
return( readAllSpectra( pathvec, ... ) )
}
# only a single path now
path = pathvec
ftype = spectralFileType( path )
if( is.na(ftype) )
{
log.string( ERROR, "Cannot determine type of file '%s'.", path )
return(NULL)
}
if( ftype == "Control" || ftype == "CGATS" )
{
# These 2 types are special because they return a *list* of objects.
if( ftype == "Control" )
out = readSpectraControl( path )
else
out = readSpectraCGATS( path )
if( is.null(out) ) return(NULL)
if( 0 < length( list(...) ) )
{
# resample all of them
for( k in 1:length(out) )
out[[k]] = resample( out[[k]], ... )
log.string( INFO, "Resampled %d spectra from file '%s' to new wavelengths.", length(out), path )
}
if( length(out) == 1 )
{
# only 1 object, so just return i
return( out[[1]] )
}
if( 0 )
{
if( ! areSpectraBindable(out) )
{
log.string( ERROR, "File '%s' has %d distinct spectra that cannot be combined. Try assigning a new wavelength sequence for resampling.",
path, length(out) )
return(NULL)
}
}
out.bound = bindSpectra( out )
return( out.bound )
}
if( ftype == "scope" )
{
# the simplest type
out = readSpectrumScope( path )
}
else if( ftype == "XYY" )
{
# the simplest type
out = readSpectraXYY( path )
}
else if( ftype == "spreadsheet" )
{
# this means tab-delimited text spreadsheet, e.g. the IT8 target files from Wolf-Faust
out = readSpectraSpreadsheet( path )
}
else if( ftype == "Excel" )
{
# this means a real Excel spreadsheet
out = NULL # readSpectraExcel( path )
}
else
{
return(NULL)
}
if( is.null(out) ) return(NULL)
# strip the extension off of .path, for nicer display
# attr( out, "path" ) = sub( "[.][a-zA-Z]+$", "", .path )
#if( is.null(attr( out, "path" )) ) attr( out, "path" ) = .path
#if( is.null(attr( out, "date" )) ) attr( out, "date" ) = file.info(.path)$mtime
if( 0 < length( list(...) ) )
{
out = resample( out, ... )
}
return( out )
}
# pathvec vector of paths, always 2 or more
# ... optional arguments to pass to resample.colorSpec
#
# value:
# a colorSpec with organization 'df.row', or NULL in case of ERROR
readAllSpectra <- function( pathvec, ... )
{
# read the 1st one
out = readSpectra( pathvec[1], ... )
if( is.null(out) ) return(NULL)
organization(out) = 'df.row'
if( length(pathvec) == 1 ) return(out)
# specnames = specnames(out)
path = rep( pathvec[1], numSpectra(out) )
for( k in 2:length(pathvec) )
{
obj = readSpectra( pathvec[k], ... )
if( is.null(obj) ) return(NULL)
# organization(obj) = 'df.row'
out = bind( out, obj )
if( is.null(out) ) return(NULL)
# specnames = c( specnames, specnames(obj) )
path = c( path, rep( pathvec[k], numSpectra(obj) ) )
}
extradata(out) = cbind( data.frame( path=path, stringsAsFactors=F ), extradata(out) )
return(out)
}
# readSpectraControl()
#
# unlike most of these functions, this one returns a *list* of colorSpecs
# Each colorSpec in the list may have a different wavelength sequence !
readSpectraControl <- function( path)
{
line_vec = readLines( path)
n = length(line_vec)
if( n == 0 ) return( NULL )
# comments = line_vec[ grepl( "^[ \t]*#", line_vec ) ]
# look for [Control]
idx = which( grepl( "^\\[Control\\]", line_vec, ignore.case=TRUE ) )
if( length(idx) != 1 )
{
log.string( ERROR, "Cannot find [Control] section in '%s'.", path)
return( NULL )
}
header = line_vec[ 1:(idx-1) ] # every line above [Control]
# look for constant increment
increment = 0
pattern = "^#[ \t]+increment[ \t=]+([0-9.]+).*$"
inc = which( grepl( pattern, line_vec ) )
if( 0 < length(inc) )
{
increment = sub( pattern, "\\1", line_vec[inc[1]] )
increment = as.numeric( increment )
# print( increment )
}
x_px_vec = numeric(0)
y_px_vec = numeric(0)
wavelength_vec = numeric(0)
response_vec = numeric(0)
for( k in (idx+1):n )
{
word = strsplit( line_vec[k], "[ \t]" )[[1]]
#print(word)
if( length(word) != 4 ) break
vec = suppressWarnings( as.numeric(word) )
#print( vec )
if( all( ! is.na(vec) ) )
{
x_px_vec = c( x_px_vec, vec[1] )
y_px_vec = c( y_px_vec, vec[2] )
wavelength_vec = c( wavelength_vec, vec[3] )
response_vec = c( response_vec, vec[4] )
}
}
#print( x_px_vec )
#print( y_px_vec )
#print( wavelength_vec )
#print( response_vec )
lm.x = lm( wavelength_vec ~ x_px_vec + y_px_vec )
lm.y = lm( response_vec ~ y_px_vec + x_px_vec )
# print( summary( lm.x ) )
# print( summary( lm.y ) )
out = list()
# look for the data
idx = grep( "^\\[[-._a-zA-Z0-9 ]+\\]", line_vec, value=F )
# print(idx)
if( length(idx) <= 1 )
{
log.string( ERROR, "Cannot find any [data] sections, only a [Control] section in '%s'.", path)
return( NULL )
}
# rgb_vec = c( "Red", "Green", "Blue" )
base = sub( "[.][a-zA-Z]+$", "", basename( path) ) # take away the extension
for( i in idx )
{
cname = gsub( "(\\[)|(\\])", "", line_vec[i] )
# print( cname )
if( cname == "Control" ) next # not a channel
log.string( DEBUG, "Found [%s] channel on line %d.", cname, i )
x_px_vec = numeric(0)
y_px_vec = numeric(0)
for( k in (i+1):n )
{
word = strsplit( line_vec[k], "[ \t]" )[[1]]
#print(word)
if( length(word) != 2 ) break
vec = suppressWarnings( as.numeric(word) )
#print( vec )
if( all( ! is.na(vec) ) )
{
x_px_vec = c( x_px_vec, vec[1] )
y_px_vec = c( y_px_vec, vec[2] )
}
}
if( length(x_px_vec) < 2 )
{
log.string( WARN, "Found only %d points in [%s] channel", length(x_px_vec), cname )
}
data_new = data.frame( x_px_vec=x_px_vec, y_px_vec=y_px_vec ) # ; print( data_new )
y = predict(lm.y,data_new)
cextra = paste( base, cname, sep='.', collapse=NULL )
x_new = predict(lm.x,data_new)
if( 0 < increment )
{
# round to nearest multiple of increment
x_new = increment * round( x_new / increment )
}
# attr( y, "specname" ) = cname
quant = guessSpectrumQuantity( cname, header )
if( is.na(quant) )
{
quant = 'energy'
log.string( WARN, "Cannot guess quantity from from contents of '%s', so assigning quantity='%s'.",
basename(path), quant )
}
spec = colorSpec( y, x_new, quantity=quant )
specnames( spec ) = cname
metadata( spec ) = list( path=path, header=header )
out[[ cname ]] = spec
}
if( length(out) == 0 ) return(NULL)
return( out )
}
readSpectraXYY <- function( path )
{
if( ! file.exists( path) )
{
log.string( ERROR, "File '%s' does not exist !", path)
return(NULL)
}
# peek to see whether tabs are used as separator
header = readLines( path, 100 )
if( length(header) == 0 ) return(NULL)
pattern = "^(wave|wv?l)"
idx = which( grepl( pattern, header , ignore.case=T ) )
if( length(idx) == 0 )
{
log.string( ERROR, "Cannot find Wavelength column in '%s' !\n", path)
return(NULL)
}
# comments = header[ grepl( "^[ \t]*#", header ) ]
# print( comments )
line = header[ idx[1] ]
if( grepl( "\t", line) )
sep = '\t'
else if( grepl( ",", line) )
sep = ','
else
sep = ''
df = read.table( path, header=T, sep=sep, skip=idx[1]-1, stringsAsFactors=F ) #; print( str(df) )
df[ is.na(df) ] = 0
# print( 2:ncol(df) )
data = as.matrix( df[ , 2:ncol(df) ] ) #; print( str(data) )
colnames(data) = colnames(df)[ 2:ncol(df) ]
m = ncol(data)
if( sep == '' ) sep = ' '
if( TRUE && m == 1 )
{
base = sub( "[.][a-zA-Z]+$", "", basename( path) ) # take away the extension
cname = colnames(data)
colnames(data) = paste( base, cname, sep='.', collapse=NULL )
}
header = header[ 1:(idx[1]-1) ] #; log.object( DEBUG, header )
# theType = guessSpectrumType( colnames(data), header ) #; print( theType )
theQuantity = guessSpectrumQuantity( colnames(data), header )
if( is.na(theQuantity) )
{
theQuantity = 'energy'
log.string( WARN, "Cannot guess quantity from from contents of '%s', so assigning quantity='%s'.",
basename(path), theQuantity )
}
out = colorSpec( data, df[[1]], quantity=theQuantity, organization='df.col' )
metadata( out ) = list( path=path)
if( 0 < length(header) )
metadata( out, add=TRUE ) = list(header=header)
return(out)
}
# Spreadsheet here means one of:
# the Wolf Faust files: E131102.txt etc.
readSpectraSpreadsheet <- function( path )
{
if( ! file.exists( path) )
{
log.string( ERROR, "File '%s' does not exist !", path)
return(NULL)
}
# peek to find first line with data
line = readLines( path, 60 )
if( length(line) == 0 ) return(NULL)
idx_start = which( grepl( "^(ID|SAMPLE|Time)", line , ignore.case=F ) )
if( length(idx_start) == 0 )
{
log.string( ERROR, "Cannot find header line in '%s' !\n", path)
return(NULL)
}
skip = idx_start[1] - 1
header = line[ 1:skip ] #; print( header )
df = read.table( path, skip=skip, sep='\t', header=T, quote='', stringsAsFactors=F )
# print( str(df) )
pattern = "^[A-Z]+([0-9.]+)nm$"
mask_nm = grepl( pattern, colnames(df) )
count = sum(mask_nm)
log.string( INFO, "Found %d wavelengths in '%s'\n", count, path)
if( count == 0 )
{
return(NULL)
}
# print( colnames(df)[mask_nm] )
# convert wavelength from text to numeric
wavelength = as.numeric( sub( pattern, "\\1", colnames(df)[mask_nm] ) )
# print( wavelength )
data = t( as.matrix( df[ , mask_nm ] ) )
# print( str(val) )
if( ! is.null( df$Name ) )
{
# from Wolf Faust CD
cnames = sub( "[ ]+$", '', df$Name ) # trim trailing space
# cnames = paste( cnames, ".Transmittance", sep='' )
}
else if( ! is.null( df$Sample ) )
{
if( is.null( df$Sequence ) )
cnames = df$Sample
else
cnames = paste( df$Sample, df$Sequence, sep='.' )
# cnames = paste( cnames, ".Power", sep='' ) # ; print( cnames )
}
colnames(data) = cnames
# type = 'material'
quantity = guessSpectrumQuantity( cnames, header )
if( is.na(quantity) )
{
quantity = 'reflectance'
log.string( WARN, "Cannot guess quantity from from contents of '%s', so assigning quantity='%s'.",
basename(path), quantity )
}
out = colorSpec( data, wavelength, quantity=quantity, organization="df.row" )
metadata( out ) = list( path=path, header=header )
part_left = df[ , ! mask_nm, drop=F ]
if( 0 < ncol(part_left) )
extradata(out) = part_left
for( w in c("date","originator","serial","white.point") )
attr( out, "metadata" )[[w]] = extractFieldFromHeader( header, w )
return( out )
}
# .path to a CGATS file, e.g. .sp or .cal .cgt or .txt, or the Rosco files
#
# returns a *list* of colorSpec objects, each with organization 'df.row'.
# WARNING: does not return a single object.
readSpectraCGATS <- function( path )
{
theData = readCGATS( path )
if( is.null(theData) ) return(NULL)
n = length(theData) # n is the number of data.frames
base = sub( "[.][a-zA-Z]+$", "", basename( path ) )
preamble = attr(theData,'preamble')
# attr_name = c( "Substrate", "RefractiveIndex", "Thickness" )
# iterate in reverse order, so we can drop the tables with non-spectral data
for( i in n:1 )
{
obj = colorSpecFromDF( theData[[i]], path, preamble, i )
if( is.null(obj) )
# this is an error
return(NULL)
if( ! is.colorSpec(obj) )
{
# table i does not have spectral data, so delete it
theData = theData[-i]
next
}
# overwrite previous data.frame with new colorSpec object
theData[[i]] = obj
}
if( length(theData) == 0 )
{
log.string( ERROR, "Found no tables with spectral data in '%s'.", path )
return(NULL)
}
attr( theData, 'path' ) = path
return( theData )
}
# df data.frame, from a table in a CGATS file
# path the file
# preamble from path. might be NULL
# idxtable index of the table in path. 1-based
#
# returns a colorSpec object
# in case of ERROR, returns NULL
# if there is no spectral data, returns as.logical(NA)
colorSpecFromDF <- function( df, path, preamble, idxtable )
{
base = sub( "[.][a-zA-Z]+$", "", basename( path ) )
colspec = spectralColumns( df, path, idxtable ) #; print( colspec )
if( is.null(colspec) )
# this is an error
return(NULL)
if( ! is.list(colspec) )
{
# table i does not have spectral data, so return any non-trivial thing
return( as.logical(NA) )
}
idx_val = colspec$idx_val
idx_extra = colspec$idx_extra
wavelength = colspec$wavelength
# idx = which( mask )
mat = as.matrix.data.frame( df[ ,idx_val, drop=F] ) / colspec$divisor
mat = t( mat ) #; print( dim(mat) )
rownames( mat ) = as.character( wavelength ) #; print( str(mat) ) # colnames(df)[mask]
# print( mat )
# specnames = paste( base, as.character( 1:nrow(df)), sep='.' ) # just the defaults
tableid = sprintf( "%s-%d", base, idxtable )
if( nrow(df) == 1 )
specnames = tableid
else
{
#specnames = paste( tableid, as.character( 1:nrow(df)), sep='.' )
specnames = sprintf( "%s.%d", tableid, 1:nrow(df) )
}
if( 0 < length(idx_extra) )
{
candidate = c("SAMPLE_NAME","SAMPLE_ID","SampleID","Name")
idx = which( candidate %in% colnames(df) )
if( 1 <= length(idx) )
specnames = df[[ candidate[idx[1]] ]]
}
# print(specnames)
colnames(mat) = specnames
header = attr( df, "header" )
# look for SPECTRAL_NORM, and if present divide by it
pattern = '^SPECTRAL_NORM[ \t]*"?([.0-9]+)"?'
vec = sub( pattern, "\\1", header, ignore.case=T )
idx = which( nchar(vec) < nchar(header) )
if( 0 < length(idx) )
{
# found 1 or lines matching, only look at the first one
value = as.numeric( vec[idx[1]] )
if( is.numeric(value) && is.finite(value) && 0 < value && value != 1 )
{
log.string( INFO, "Dividing spectral values by SPECTRAL_NORM=%g.", value )
mat = mat / value
}
}
theQuantity = guessSpectrumQuantity( specnames, c(preamble,header) )
if( is.na(theQuantity) )
{
theQuantity = 'energy'
log.string( WARN, "Cannot guess quantity from from contents of '%s', so assigning quantity='%s'.",
basename(path), theQuantity )
}
out = colorSpec( mat, wavelength, quantity=theQuantity, organization="df.row" )
# ColorMunki wavelengths in hires mode are equally spaced at 3.333 nm, but rounded to the nearest integer
# check for this and fix it
wavereg = regularizeWavelength(wavelength)
discrep = max( abs(wavereg - wavelength) )
if( 0 < discrep && discrep < 0.5 )
{
step.wl = wavereg[2] - wavereg[1]
log.string( WARN, "Perturbed wavelengths in '%s' to have equal increments of %g nm [%g to %g nm]",
path, step.wl, wavereg[1], wavereg[ length(wavereg) ] )
wavelength(out) = wavereg
}
if( 0 < length(idx_extra) )
{
extra = df[ , idx_extra, drop=F ] # ;print( part_left )
extradata(out) = extra
}
metadata( out ) = list( header=header,
date=attr(df,"date"),
descriptor=attr(df,"descriptor") )
return( out )
}
# df data.frame, from a table in a CGATS file
# path to the file
# idxtable index of the table in path
#
# returns a list with items
# idx_val columns that correspond to a spectral values (but not to wavelength values)
# idx_extra columns that correspond to extra data, maybe none
# wavelength vector of wavelengths, the same length as idx_val
# divisor for the values
# in case of ERROR, returns NULL
# if there is no spectral data, returns as.logical(NA)
spectralColumns <- function( df, path, idxtable )
{
#cat( "spectralColumns()\n" )
out = list()
cname = colnames(df) #; print(cname)
# search for standard convention for spectral data
mask_dec = ("SPECTRAL_DEC" == cname) # grepl( "^SPECTRAL_DEC", cname )
mask_pct = ("SPECTRAL_PCT" == cname) # grepl( "^SPECTRAL_PCT", cname )
if( any( mask_dec | mask_pct ) )
{
# the standard convention
log.string( INFO, "In file '%s' and table %d, using standard convention for spectral data.", path, idxtable )
#cat( "Standard convention\n" )
idx_dec = which( mask_dec )
idx_pct = which( mask_pct )
# pattern = "^SPECTRAL_NM"
mask_wl = ("SPECTRAL_NM" == cname) # grepl( pattern, cname )
idx_wl = which( mask_wl )
n = length(idx_wl) # number of wavelengths
if( n == 0 )
{
log.string( ERROR, "In file '%s' and table %d, there are %d spectral value fields, but there are no wavelengths.",
path, idxtable, length(idx_dec)+length(idx_pct) )
return(NULL)
}
divisor = 0
if( length(idx_dec)==n && all( idx_dec == idx_wl+1 ) )
divisor = 1 # got a match
else if( length(idx_pct)==n && all( idx_pct == idx_wl+1 ) )
divisor = 100 # got a match
if( divisor == 0 )
{
log.string( ERROR, "In file '%s' and table %d, there are %d wavelength columns, but spectral value columns do not match up.",
path, idxtable, n )
return(NULL)
}
wavelength = lapply( df[ , idx_wl ], unique ) #; print(wavelength)
waveunique = sapply( wavelength, length ) #; print(wavelength)
idx_max = which.max( waveunique )
if( 1 < waveunique[ idx_max ] )
{
# not unique
log.object( ERROR, wavelength[[idx_max]] )
log.string( ERROR, "In file '%s', wavelength columns in table %d exist, but they are not unique.",
path, idxtable )
return(NULL)
}
out$idx_val = idx_wl + 1
out$idx_extra = which( ! (mask_wl | mask_dec | mask_pct) )
out$wavelength = unlist( wavelength, use.names=FALSE ) #; print( out$wavelength )
out$divisor = divisor
# print( out )
}
else
{
# try again with non-standard pattern
pattern = "^(nm|SPEC_|SPECTRAL_)[A-Z_]*([0-9.]+)$"
mask_val = grepl( pattern, cname ) # ; print( idx )
idx_val = which( mask_val ) # ; print(idx_wl)
n = length(idx_val) # number of wavelengths
if( n == 0 )
# no spectral data found
return( as.logical(NA) )
log.string( INFO, "In file '%s' and table %d, using non-standard convention for spectral data.", path, idxtable )
contig = all( diff(idx_val) == 1 )
if( ! contig )
{
log.string( ERROR, "In file '%s' and table %d, there are %d spectral columns, but they are not contiguous.",
path, idxtable, n )
# log.object( WARN, idx_val )
return(NULL)
}
wavelength = sub( pattern, "\\2", cname[idx_val] ) #; print( wavelength )
out$wavelength = type.convert( wavelength, as.is=TRUE ) #; print( wavelength )
out$idx_val = idx_val
out$idx_extra = which( ! mask_val )
out$divisor = 1
}
# verify that wavelength is valid
ok = is.numeric(out$wavelength) && all( is.finite(out$wavelength) )
if( ! ok )
{
log.string( ERROR, "In file '%s' and table %d, the %d wavelength values are not all numeric and finite.",
path, idxtable, length(wavelength) )
return(NULL)
}
# verify that spectral values are valid
ok = all( sapply( df[out$idx_val], function(y) { is.numeric(y) && all( is.finite(y) ) } ) )
if( ! ok )
{
log.string( ERROR, "In file '%s' and table %d, the %dx%d spectral values are not all numeric and finite.",
path, idxtable, nrow(df), length(idx_val) )
return(NULL)
}
return(out)
}
# read Ocean Optics format
# we already know this is a light source of with quantity 'energy'
#
# TODO: extract integration time from header and organize as df.row
readSpectrumScope <- function( path)
{
linevec = readLines( path)
ok = (0 < length(linevec)) ; # assert( ok )
if( ! ok ) { return(NULL) }
header = NULL
idx = which( grepl( "^>+Begin", linevec[1:30] ) )
if( length(idx) == 1 )
header = linevec[ 1:(idx-1) ]
# make a list of character vectors, tab-delimited
line_split = strsplit( linevec, '\t', fixed=T )
# form subset of those lines that have data - 2 tab-delimted words
theData = base::subset( line_split, sapply(line_split,length) == 2 )
# unlist() is faster than sapply()
theData = as.double( unlist(theData) ) ;
dim(theData) = c(2,length(theData)/2)
y = theData[ 2, ]
out = colorSpec( y, wavelength=theData[1, ], quantity="energy", organization="vector" )
# there is only 1 spectrum here, so use the path as the name
specnames(out) = stripExtension( basename(path) )
metadata( out ) = list( path=path, header=header )
return( out )
}
# plain text means:
# the usual characters
# TAB through CR
# © and µ
isPlainText <- function( .stringvec )
{
n = length(.stringvec)
out = logical(n)
for( i in 1:n )
{
byte = charToRaw( .stringvec[i] ) # utf8ToInt( .stringvec[i] )
out[i] = all( (32 <= byte & byte <= 126) | (9 <= byte & byte <= 13) | byte==169 | byte==181 )
}
return( out )
}
is.UTF8 <- function( .stringvec )
{
n = length(.stringvec)
out = logical(n)
for( i in 1:n )
{
ivec = try( utf8ToInt( .stringvec[i] ), silent=TRUE )
out[i] = ! inherits(ivec,"try-error") #class(ivec) != "try-error"
}
return( out )
}
# does not look at file extension, except for .XLS(X)
spectralFileType <- function( .path )
{
out = as.character(NA)
if( is.null(.path) )
{
log.string( ERROR, "File argument is NULL !" )
return(out)
}
if( ! file.exists( .path) )
{
log.string( ERROR, "File '%s' does not exist !", .path)
return(out)
}
# read a few lines at the beginning of file
line = readLines( .path, 32, warn=F )
# ignore comment lines
line = line[ ! grepl( "^#", line ) ]
if( length(line) == 0 ) return(out)
# crude check for a text file
if( ! all( is.UTF8(line) ) )
{
# a binary file
if( FALSE && grepl( "[.]xlsx?$", basename(.path), ignore.case=T ) )
return( "Excel" )
else
{
log.string( WARN, "File type of '%s' unnknown. It appears to be binary. !", .path )
return(out)
}
}
pattern = c( "^CGATS|^ISO28178|^NUMBER_OF_FIELDS|^KEYWORD" , "^\\[Control\\]", "^(wave|wv?l)", "^>+Begin", "^ID\tName", "^Time" )
type = c( "CGATS", "Control", "XYY", "scope", "spreadsheet", "spreadsheet" )
for( i in 1:length(pattern) )
{
# print( type )
# print( pattern[type] )
if( any( grepl( pattern[i] , line , ignore.case=T ) ) ) return( type[i] )
}
return( out )
}
stripExtension <- function( .path )
{
pattern = "[.][a-z0-9]+$"
return( sub(pattern,'',.path,ignore.case=T) )
}
################## semi-deadwood below ###############################
if( 0 )
{
# Spreadsheet here means a real Excel spreadsheet, e.g. ASTMG173.xls, sheet SMARTS2
#
# path full path to file
# worksheet name of the worksheet. If NULL then search for the 1st one with data.
#
readSpectraExcel <- function( path, worksheet=NULL )
{
for( p in c('rJava','xlsxjars','xlsx') )
{
ok = requireNamespace( p, quietly=TRUE )
if( ! ok )
{
log.string( ERROR, "Package '%s' is required. Please install it.", p )
return(NULL)
}
}
if( ! file.exists( path) )
{
log.string( ERROR, "File '%s' does not exist !\n", path)
return(NULL)
}
wb = xlsx::loadWorkbook( path)
sheetlist = xlsx::getSheets(wb)
# print( str(sheetlist) )
idx_sheet = 0
if( is.null(worksheet) )
{
# search for the first worksheet that has rows
for( i in 1:length(sheetlist) )
{
# print( str(getRows( sheetlist[[i]] ) ) )
rowcount = length( xlsx::getRows( sheetlist[[i]] ) )
if( 0 < rowcount )
{
log.string( INFO, "Found sheet '%s' rows=%d.",
names(sheetlist)[i], rowcount )
idx_sheet = i
break
}
}
if( idx_sheet == 0 )
{
log.string( ERROR, "Cannot find a suitable worksheet, with rows, in '%s'.", path)
return(NULL)
}
}
else
{
# search for 1st worksheet with matching name
idx_sheet = which( worksheet == names(sheetlist) )
if( length(idx_sheet) != 1 )
{
log.string( ERROR, "Cannot find worksheet '%s' in '%s'.",
worksheet, path )
return(NULL)
}
}
theSheet = sheetlist[[ idx_sheet ]]
# look for Lambda in 1st column
col1 = xlsx::readColumns(theSheet, 1, 1, 1, 16, header=F, as.data.frame=F )[[1]]
# print( str(col1) )
iname = sub( "^([A-Za-z0-9]+).*$", "\\1", col1[1] )
pattern = "^lambda|nm$"
idx = which( grepl( pattern, col1, ignore.case=T ) )
# print( idx )
if( length(idx) != 1 )
{
log.string( ERROR, "Cannot find '%s' in worksheet %d in '%s'.",
pattern, idx_sheet, path)
return(NULL)
}
row1 = xlsx::readRows( theSheet, 1, 1, 1 )
# print( row1 )
spectra = length(row1)-1
mat = xlsx::readColumns( theSheet, 1, spectra+1, idx+1, header=F, colClasses='numeric' ) #; print( str(mat) )
wave = as.numeric( mat[ ,1] )
mat = as.matrix( mat[ ,2:ncol(mat)] )
colnames(mat) = sprintf( "%s%02d.Energy", iname, 1:spectra )
out = colorSpec( mat, wave, quantity='energy' )
metadata(out) = list( path=path)
return( out )
}
} |
c724f33d11b30ba23402f16640c97b836291c861 | 92c22064740e2ebfc09bcbcad308b0420dbda343 | /man/estat_index.Rd | ed355fe654ca60c334f10707cf5b8537120bdd1a | [] | no_license | Tungurahua/reurostat | 9643b6486ed324d3091da660130e78c0ed365bf1 | 982944f0e19659c0a6685ed974aec045ebcfe715 | refs/heads/master | 2020-05-17T18:00:22.922657 | 2014-07-17T21:56:10 | 2014-07-17T21:56:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 665 | rd | estat_index.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{estat_index}
\alias{estat_index}
\title{Loads the index of available datasets from Eurostat.}
\usage{
estat_index(lang = c("en", "de", "fr"),
url = "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/dataflow/ESTAT/all/latest")
}
\arguments{
\item{lang}{language used to display name of dataflow. \code{en} for english (default),
\code{de} for german or \code{fr} for french.}
\item{url}{address of the dataflow list (REST)}
}
\value{
dataframe with two variables ID and name
}
\description{
This function downloads the current index of available datasets from Eurostat
using the websites REST service.
}
|
a8c59ba802d4323666d92ff984decc8d10f3be45 | 24c6064c55846266c96dc3ddbbd944e156c90e03 | /man/betafunction.Rd | ffe57559e96c576510f18fa803029845493ee3e1 | [] | no_license | liujin07/StartComp18065 | 78f04ffec16b20b3a99f9bff138d93cd999246e5 | 6d5cd6eea6b9c8298396676962a1d16130e954b0 | refs/heads/master | 2020-04-16T03:19:34.634420 | 2019-01-11T14:42:50 | 2019-01-11T14:42:50 | 164,424,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 690 | rd | betafunction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/acc_reBeta.R
\name{betafunction}
\alias{betafunction}
\title{The function that generates random Numbers with the accept-rejection method}
\usage{
betafunction(n, a, b)
}
\arguments{
\item{n}{The number of random Numbers generated}
\item{a}{Parameter of beta distribution}
\item{b}{Parameter of beta distribution}
}
\value{
a random sample of size \code{n}
}
\description{
a function to generate a random sample of size n from the Beta(a,b) distribution by the acceptance-rejection method.
}
\examples{
\dontrun{
sample_beta <- betafunction(1e3,3,2)#record the sample
head(sample_beta,20)
hist(sample_beta)
}
}
|
b91e4f42cd4a5d90eb6901b0c5a204a498f50977 | fd76bfc240e097bf6040819061fe9c36d79a36de | /R/cross_validation.R | 0f7b4726c909313c1bd9a25254b808529ad778e1 | [] | no_license | lierscn789/interflex | b2409521f325e3b2bf83a3c1d2d353e46dcc9951 | 9abec7d1acae7c3f0cd904996726442bcef6b6ad | refs/heads/master | 2022-12-23T12:20:43.573283 | 2020-09-28T04:56:25 | 2020-09-28T04:56:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,569 | r | cross_validation.R | crossvalidate.new <- function(data, Y, D, X, CV.method='simple', FE=NULL, treat.type, Z = NULL, weights = NULL, cl = NULL, base=NULL,
X.eval, grid, kfold = 10, parallel = TRUE, seed=NULL, bw.adaptive = FALSE,
metric = "MSPE"){
bw <- NULL
if(is.null(seed)==FALSE){
set.seed(seed)
}
requireNamespace('lfe')
cat("Cross-validating bandwidth ... \n")
if(TRUE){ ## Sample
## generate K random folds
## if CV.method=="cluster" then fold by cluster
## if CV.method=="stratify" then conduct stratified cross-validation
## if CV.method=="simple" then randomly sample
n<-dim(data)[1]
fold<-rep(0,n)
if(CV.method=='simple'){
kfold <- min(n,kfold)
cat("#folds =",kfold)
cat("\n")
fold <- c(0:(n-1))%%kfold + 1
fold<-sample(fold, n, replace = FALSE)
}
if(CV.method=='cluster' & is.null(cl)==TRUE){
stop("\"cl\" is not specified when using cluster cross-validation.")
}
if(CV.method=='cluster' & is.null(cl)==FALSE){
clusters<-unique(data[,cl])
m <- length(clusters)
kfold <- min(m,kfold)
cat("Use clustered cross-validation.\n")
cat("#folds =",kfold)
cat("\n")
id.list<-split(1:n,data[,cl])
cl.fold <- c(0:(m-1))%%kfold + 1
cl.fold <- sample(cl.fold, m, replace = FALSE)
for (i in 1:m) {
id.list[[i]] <- rep(cl.fold[i],length(id.list[[i]]))
}
fold <- unlist(id.list)
}
if(CV.method=='stratify'){
requireNamespace("caret")
fold <- createFolds(factor(data[,D]), k = kfold, list = FALSE)
cat("Use stratified cross-validation.\n")
cat("#folds =",kfold)
cat("\n")
}
}
if(TRUE){ ## TREAT.TYPE
if(treat.type=='discrete') {
all.treat <- unique(data[,D])
if(is.null(base)==TRUE){
base <- all.treat[1]
}
other.treat <- all.treat[which(all.treat!=base)]
}
}
##FUNCTION: calculate errors in testing set
getError.new <- function(train, test, bw, Y, X, D, Z, FE, weights,
X.eval, treat.type){
if(is.null(weights)==T){
w <- rep(1,dim(train)[1])
w2 <- rep(1,dim(test)[1])
}
else{
w <- train[,weights]
w2 <- test[,weights]
}
# FIXED EFFECTS
## demean outcome(y) and estimate fixed effects
## save fixed effects in add_FE
## add fixed effects to predicted values
add_FE <- rep(0,dim(test)[1])
if(is.null(FE)==FALSE){
train_y <- as.matrix(train[,Y])
train_FE <- as.matrix(train[,FE])
invisible(
capture.output(
fastplm_res <- fastplm(data = train_y,FE = train_FE,weight = w, FEcoefs = 1L),type='message'
)
)
FEvalues <- fastplm_res$FEvalues
FEnumbers <- dim(fastplm_res$FEvalues)[1]
FE_coef <- matrix(FEvalues[,3],nrow = FEnumbers, ncol = 1)
rowname <- c()
for(i in 1:FEnumbers){
rowname <- c(rowname,paste0(FE[FEvalues[i,1]+1],'.',FEvalues[i,2]))
}
rownames(FE_coef) <- rowname
train[,Y] <- fastplm_res$residuals
#addictive FE
add_FE <- matrix(0,nrow = dim(test)[1],ncol = length(FE))
colnames(add_FE) <- FE
for(fe in FE){
add_FE[,fe] <- 0
fe_name <- paste0(fe,".",test[,fe])
add_FE[which(fe_name %in% rownames(FE_coef)),fe] <- FE_coef[fe_name[(which(fe_name %in% rownames(FE_coef)))],]
}
add_FE <- rowSums(add_FE)
add_FE <- add_FE + fastplm_res$mu #intercept
}
if(treat.type=='discrete'){
#generate dummy variable
test_d <- test[,c(Y,X)]
for (char in other.treat) {
test_d[,paste0("D.",char)] <- as.numeric(test[,D]==char)
}
#get coef
coef<-coefs.new(data=train,bw=bw,Y=Y,X=X,D=D,Z=Z,base=base,treat.type = 'discrete',
weights = weights, X.eval= X.eval,bw.adaptive = bw.adaptive)
coef[is.na(coef)] <- 0
num.Z<-length(Z)
num.treat <- length(other.treat)
esCoef<-function(x){ ##obtain the coefficients for x[i]
Xnew<-abs(X.eval-x)
d1<-min(Xnew) ## distance between x[i] and the first nearest x in training set
label1<-which.min(Xnew)
Xnew[label1]<-Inf
d2<-min(Xnew) ## distance between x[i] and the second nearest x in training set
label2<-which.min(Xnew)
if(d1==0){
if(is.null(Z)==T){
func <- coef[label1,c(2:(2+num.treat))] # X.eval (1), intercept (2), d (3), xx (4), d:xx (5), z
}
else{
func <- coef[label1,c(c(2:(2+num.treat)),c((4+2*num.treat):(3+2*num.treat+num.Z)))] # X.eval (1), intercept (2), d (3), xx (4), d:xx (5), z
}
}
else if(d2==0){
if(is.null(Z)==T){
func <- coef[label2,c(2:(2+num.treat))]
}
else{
func <- coef[label2,c(c(2:(2+num.treat)),c((4+2*num.treat):(3+2*num.treat+num.Z)))]
}
}
else{ ## weighted average
if(is.null(Z)==T){
func1 <- coef[label1,c(2:(2+num.treat))]
func2 <- coef[label2,c(2:(2+num.treat))]
}
else{
func1 <- coef[label1,c(c(2:(2+num.treat)),c((4+2*num.treat):(3+2*num.treat+num.Z)))]
func2 <- coef[label2,c(c(2:(2+num.treat)),c((4+2*num.treat):(3+2*num.treat+num.Z)))]
}
func <- (func1 * d2 + func2 * d1)/(d1 + d2)
}
return(func)
}
Knn<-t(sapply(test[,X],esCoef)) ## coefficients for test class==matrix
## predicting
test.Y <- test[,Y]
test.X <- as.data.frame(rep(1,dim(test)[1]))
for (char in other.treat) {
test.X[,paste0("D.",char)] <- test_d[,paste0("D.",char)]
}
test.X <- cbind(test.X,as.data.frame(test[,Z]))
test.X <- as.matrix(test.X)
sumOfEst<-matrix(lapply(1:length(test.X), function(i){test.X[i]*Knn[[i]]}),
nrow=nrow(test.X), ncol=ncol(test.X))
error <- test.Y - rowSums(matrix(unlist(sumOfEst),length(test.Y))) - add_FE
## weights
error <- error*w2/mean(w2)
return(c(mean(abs(error)),mean(error^2)))
}
if(treat.type=='continuous'){
coef<-coefs.new(data=train,bw=bw,Y=Y,X=X,D=D,Z=Z,treat.type = 'continuous',bw.adaptive = bw.adaptive,
weights = weights, X.eval= X.eval)
coef[is.na(coef)] <- 0
n2<-length(Z)
esCoef<-function(x){ ##obtain the coefficients for x[i]
Xnew<-abs(X.eval-x)
d1<-min(Xnew) ## distance between x[i] and the first nearest x in training set
label1<-which.min(Xnew)
Xnew[label1]<-Inf
d2<-min(Xnew) ## distance between x[i] and the second nearest x in training set
label2<-which.min(Xnew)
if(d1==0){
func <- coef[label1,-c(1,4,5)] # X.eval (1), intercept (2), d (3), xx (4), d:xx (5), z
} else if(d2==0){
func <- coef[label2,-c(1,4,5)]
} else{ ## weighted average
func <- (coef[label1,-c(1,4,5)] * d2 + coef[label2,-c(1,4,5)] * d1)/(d1 + d2)
}
return(func)
}
Knn<-t(sapply(test[,X],esCoef)) ## coefficients for test class==matrix
## predicting
test.Y <- test[,Y]
test.X <- as.matrix(cbind(rep(1,dim(test)[1]),test[,c(D,Z)]))
sumOfEst<-matrix(lapply(1:length(test.X), function(i){test.X[i]*Knn[[i]]}),
nrow=nrow(test.X), ncol=ncol(test.X))
error<-test.Y - rowSums(matrix(unlist(sumOfEst),length(test.Y)))-add_FE
## weights
error <- error*w2/mean(w2)
return(c(mean(abs(error)),mean(error^2)))
}
}
##FUNCTION: calculate MSE
## grid search and k fold cross-validation
cv.new<-function(bw){
mse<-matrix(NA,kfold,2)
for(j in 1:kfold){ # K-fold CV
testid <- which(fold==j)
train <- data[-testid,]
test <- data[testid,]
mse[j,] <- getError.new(train= train, test = test, treat.type = treat.type,FE=FE,
bw = bw, Y=Y, X=X, D=D, Z=Z, weights = weights, X.eval= X.eval)
}
return(c(bw, apply(mse,2,mean)))
}
## Parallel Computing or Not
if (parallel == TRUE) {
Error<-suppressWarnings(foreach(bw = grid, .combine = rbind,
.export = c("coefs.new","getError.new"),
.inorder = FALSE) %dopar% {cv.new(bw)})
}
else {
Error <- matrix(NA, length(grid), 3)
for (i in 1:length(grid)) {
Error[i, ] <- cv.new(grid[i])
cat(".")
}
}
colnames(Error) <- c("bandwidth","MAPE","MSPE")
rownames(Error) <- NULL
if (metric=="MAPE") {
opt.bw <- grid[which.min(Error[,2])]
}
else {
opt.bw <- grid[which.min(Error[,3])]
}
output <- list(CV.out = round(Error,3),
opt.bw = opt.bw)
cat(paste("Bandwidth =", round(output$opt.bw,3),"\n"))
return(output)
}
|
e31e81ca02a167ce0a9d6b38883e2cd3b319400c | 856fc05ab99ef6c205d61be0468cac347184269a | /RSWMM_Autocalibration_Nasrin Alamdari.R | 5acae0fcd853c9f7692bc93606a1473da747cfcc | [] | no_license | nasrinalam/RSWMM-CostAutomation | 3101f5e17eeaf4ec7dafb04d49d503eaca7851dd | 6a3f6b92919ce2d456002ce10b155ccda9fc7000 | refs/heads/master | 2020-03-18T19:57:41.793200 | 2018-05-28T17:00:01 | 2018-05-28T17:00:01 | 135,187,942 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,235 | r | RSWMM_Autocalibration_Nasrin Alamdari.R | SWMMExe <-function(swmm,Input,Report,Output,iType,vIndex){
swmm=paste('"',swmm,'"',sep="")
Input=paste('"', Input,'"',sep="")
Report=paste('"', Report,'"',sep="")
Output=paste('"', Output,'"',sep="")
# Running executable file
system(paste(swmm,Input,Report,Output,sep=" "),show.output.on.console=T)
return(Output)
}
GetObjectsSWMM<-function(Output){
BinaryFile = file(Output,"rb")
Status ={}
seek(BinaryFile,1*4,"start")
#the version number of the engine (currently 51000)
Status$VersionNum = readBin(BinaryFile, integer(), n = 1, size = 4)
#a code number for the flow units that are in effect where
#0 = CFS
#1 = GPM
#2 = MGD
#3 = CMS
#4 = LPS
#5 = LPD
Status$UnitCode = readBin(BinaryFile, integer(), n = 1, size = 4)
#the number of subcatchments in the project reported on
Status$SubCatchNum = readBin(BinaryFile, integer(), n = 1, size = 4)
#the number of nodes in the project reported on
Status$NodesNum = readBin(BinaryFile, integer(), n = 1, size = 4)
#the number of links in the project reported on
Status$LinksNum = readBin(BinaryFile, integer(), n = 1, size = 4)
#the number of pollutants in the project
Status$PollutantsNum = readBin(BinaryFile, integer(), n = 1, size = 4)
seek(BinaryFile,-2*4,"end")
#Check the error status (0 or 1)
Status$Err= readBin(BinaryFile, integer(), n = 1, size = 4)
if (Status$Err == 0){
print ("There is no error in the file")
}else{
print("find the error in the file")
}
seek(BinaryFile,-6*4,"end")
Status$ObjectID = readBin(BinaryFile, integer(), n = 1, size = 4)
#Getting Object ID
seek(BinaryFile,Status$ObjectID,"start")
#subcatchment ID names
Status$SubCatchName = {}
if (Status$SubCatchNum>0){
for (i in 1:Status$SubCatchNum){
Status$SubCatchName[i]= readChar(BinaryFile,readBin(BinaryFile, integer(), n = 1, size = 4),useBytes = FALSE)
}
}
else {
print ("No Subcatchment")
}
#node ID names
Status$NodesName = {}
if (Status$NodesNum>0){
for (i in 1:Status$NodesNum){
Status$NodesName[i]= readChar(BinaryFile,readBin(BinaryFile, integer(), n = 1, size = 4),useBytes = FALSE)
}
}
else {
print ("No Node")
}
#link ID names
Status$LinksName = {}
if (Status$LinksNum>0){
for (i in 1:Status$LinksNum){
Status$LinksName[i]= readChar(BinaryFile,readBin(BinaryFile, integer(), n = 1, size = 4),useBytes = FALSE)
}
}
else {
print ("No Link")
}
#Pollutant names
Status$PollutantsName = {}
if (Status$PollutantsNum>0){
for (i in 1:Status$PollutantsNum){
Status$PollutantsName[i]= readChar(BinaryFile,readBin(BinaryFile, integer(), n = 1, size = 4),useBytes = FALSE)
}
}
else {
print ("No Pollutant")
}
#pollutant concentration units codes
#0 for mg/L
#1 for ug/L
#2 for counts/L.
Status$PollutantsUnitCode = {}
for (i in 1:Status$PollutantsNum){
Status$PollutantsUnitCode[i]= readBin(BinaryFile, integer(), n = 1, size = 4)
}
#Getting Object Properties
seek(BinaryFile,-5*4,"end")
Status$ObjectProperties = readBin(BinaryFile, integer(), n = 1, size = 4)
seek(BinaryFile,Status$ObjectProperties,"start")
#Number of subcatchment properties saved (Currently equal to 1)
Status$NumofSubCatchSaved = readBin(BinaryFile, integer(), n = 1, size = 4)
#Code number of each subcatchment property saved (Currently equal to 1 for subcatchment area)
Status$CodeNumSubCatchSaved = readBin(BinaryFile, integer(), n = 1, size = 4)
#Value of each property for each subcatchment (Subcatchment area (ac or ha) for each subcatchment)
Status$SubCatchArea=readBin(BinaryFile,what="double",n=Status$SubCatchNum,size=4);
#Number of node properties saved (Currently equal to 3)
Status$NumofNodesSaved = readBin(BinaryFile, integer(), n = 1, size = 4)
#Code number of each node property saved
#0 (node type code)
#2 (node invert elevation)
#3 (node max. depth)
Status$CodeNumNodesSaved = readBin(BinaryFile, integer(), n = Status$NumofNodesSaved, size = 4)
Status$TypeofNodes = {}
if(Status$NodesNum>0){
NodeType=readBin(BinaryFile,what="double",n=Status$NumofNodesSaved*Status$NodesNum,size=4)
Status$CodeNumNodesSaved=NodeType[seq(from=1,by=3,to=length(NodeType))]
#0 = Junction
#1 = Outfall
#2 = Storage
#3 = Divider
for (i in 1 :length(Status$CodeNumNodesSaved)){
if (Status$CodeNumNodesSaved[i]==0){
Status$TypeofNodes[i]="Junction"}
else if (Status$CodeNumNodesSaved[i]==1){
Status$TypeofNodes[i]="Outfall"}
else if (Status$CodeNumNodesSaved[i]==2){
Status$TypeofNodes[i]="Storage"}
else if (Status$CodeNumNodesSaved[i]==3){
Status$TypeofNodes[i]="Divider"}
}
Status$InvertElevation=NodeType[seq(from=2,by=3,to=length(NodeType))]
Status$MaximumDepth=NodeType[seq(from=3,by=3,to=length(NodeType))]
}
#Number of node properties saved (Currently equal to 5)
Status$NumofLinksSaved = readBin(BinaryFile, integer(), n = 1, size = 4)
#Code number of each link property saved
#0 (link type code)
#4 (upstream invert offset)
#4 (downstream invert offset)
#3 (link max. depth)
#5 (link length)
Status$CodeNumLinkSaved = readBin(BinaryFile, integer(), n = Status$NumofLinksSaved, size = 4)
Status$TypeofLinks = {}
if(Status$LinksNum>0){
LinkType=readBin(BinaryFile,what="double",n=Status$NumofLinksSaved*Status$LinksNum,size=4)
Status$CodeNumLinksSaved=LinkType[seq(from=1,by=5,to=length(LinkType))]
#0 = Conduit
#1 = Pump
#2 = Orifice
#3 = Weir
#4 = Outlet
for (i in 1 :length(Status$CodeNumLinksSaved)){
if (Status$CodeNumLinksSaved[i]==0){
Status$TypeofLinks[i]="Conduit"}
else if (Status$CodeNumLinksSaved[i]==1){
Status$TypeofLinks[i]="Pump"}
else if (Status$CodeNumLinksSaved[i]==2){
Status$TypeofLinks[i]="Orifice"}
else if (Status$CodeNumLinksSaved[i]==3){
Status$TypeofLinks[i]="Weir"}
else if (Status$CodeNumLinksSaved[i]==4){
Status$TypeofLinks[i]="Outlet"}
#4 (upstream invert offset)
Status$UpstreamInvertOffset=LinkType[seq(from=2,by=5,to=length(LinkType))]
#4 (downstream invert offset)
Status$DownstreamInvertOffset=LinkType[seq(from=3,by=5,to=length(LinkType))]
#3 (link max. depth)
Status$MaximumDepth=LinkType[seq(from=4,by=5,to=length(LinkType))]
#5 (link length)
Status$LinkLength=LinkType[seq(from=5,by=5,to=length(LinkType))]
}
}
#Getting Reporting Variables
#Number of subcatchment variables (currently 8 + number of pollutants).
Status$NumofSubCatchVariables = readBin(BinaryFile, integer(), n = 1, size = 4)
#Code number of each subcatchment variable
#0 for rainfall (in/hr or mm/hr),
#1 for snow depth (in or mm),
#2 for evaporation loss (in/day or mm/day),
#3 for infiltration losses (in/hr or mm/hr),
#4 for runoff rate (flow units),
#5 for groundwater outflow rate (flow units),
#6 for groundwater water table elevation (ft or m),
#7 for unsaturated zone moisture content (fraction)
#8 for runoff concentration of first pollutant,
#...
#7 + N for runoff concentration of N-th pollutant.
Status$CodeNumSubCatch = readBin(BinaryFile, integer(), n = Status$NumofSubCatchVariables, size = 4)
#Number of node variables (currently 6 + number of pollutants)
Status$NumNodesVariables = readBin(BinaryFile, integer(), n = 1, size = 4)
#Code number of each node variable
#0 for depth of water above invert (ft or m),
#1 for hydraulic head (ft or m),
#2 for volume of stored + ponded water (ft3 or m3),
#3 for lateral inflow (flow units),
#4 for total inflow (lateral + upstream) (flow units),
#5 for flow lost to flooding (flow units),
#6 for concentration of first pollutant,
#...
#5 + N for concentration of N-th pollutant.
Status$CodeNumNode = readBin(BinaryFile, integer(), n = Status$NumNodesVariables, size = 4)
#Number of link variables (currently 5 + number of pollutants)
Status$NumLinksVariables = readBin(BinaryFile, integer(), n = 1, size = 4)
#Code number of each link variable:
#0 for flow rate (flow units),
#1 for flow depth (ft or m),
#2 for flow velocity (ft/s or m/s),
#3 for flow volume (ft3 or m3)
#4 for fraction of conduit's area filled or setting for non-conduits
#5 for concentration of first pollutant,
#...
#4 + N for concentration of N-th pollutant.
Status$CodeNumLink = readBin(BinaryFile, integer(), n = Status$NumLinksVariables, size = 4)
#Number of system-wide variables (currently 14)
Status$NumSystemVariables = readBin(BinaryFile, integer(), n = 1, size = 4)
#Code number of each system-wide variable:
#0 for air temperature (deg. F or deg. C),
#1 for rainfall (in/hr or mm/hr),
#2 for snow depth (in or mm),
#3 for evaporation + infiltration loss rate (in/hr or mm/hr),
#4 for runoff flow (flow units),
#5 for dry weather inflow (flow units),
#6 for groundwater inflow (flow units),
#7 for RDII inflow (flow units),
#8 for user supplied direct inflow (flow units),
#9 for total lateral inflow (sum of variables 4 to 8) (flow units),
#10 for flow lost to flooding (flow units),
#11 for flow leaving through outfalls (flow units),
#12 for volume of stored water (ft3 or m3),
#13 for evaporation rate (in/day or mm/day)
Status$CodeNumSystems = readBin(BinaryFile, integer(), n = Status$NumSystemVariables, size = 4)
#Getting Reporting Interval
Status$BytesPerPeriod= 2*4 +(Status$SubCatchNum*(Status$NumofSubCatchVariables) +
Status$NodesNum*(Status$NumNodesVariables) +
Status$LinksNum*(Status$NumLinksVariables) + Status$NumSystemVariables)*4;
seek(BinaryFile,-3*4,"end")
Status$ReportingPeriods= readBin(BinaryFile, integer(), n = 1, size = 4)
Status$BinaryFile = BinaryFile
seek(BinaryFile,-4*4,"end")
Status$ComputedResults= readBin(BinaryFile, integer(), n = 1, size = 4)
#Writing function to get table of computed results
#ObjectType is the type of the Object that you want to see the result (i.e., Subcatchment, node, conduit, or system)
#Object ID is the name of object to see the result (i.e., S1 or J2)
#Index is the position of one object among other objects
#Codenum is the code number of each variable from abover list
#Time period
return(Status)
}
getSWMMResult<-function(headObj,iType,iIndex,vIndex,period){
SUBCATCH=0
NODE = 1;
LINK = 2;
SYS = 3;
f=headObj$BinaryFile
StartPos=headObj$ComputedResults
off = StartPos + period*(headObj$BytesPerPeriod) + 2*4;
if ( iType == SUBCATCH )
{
off = off+ 4*(iIndex*(headObj$NumofSubCatchVariables) + vIndex);
}
else if (iType == NODE)
{
off = off+ 4*(headObj$SubCatchNum*(headObj$NumofSubCatchVariables) +
iIndex*(headObj$NumNodesVariables) + vIndex);
}
else if (iType == LINK)
{
off = off+ 4*(headObj$SubCatchNum*(headObj$NumofSubCatchVariables) +
headObj$NodesNum*(headObj$NumNodesVariables) +
iIndex*(headObj$NumLinksVariables) + vIndex);
}
else if (iType == SYS)
{
off = off+ 4*(headObj$SubCatchNum*(headObj$NumofSubCatchVariables) +
headObj$NodesNum*(headObj$NumNodesVariables) +
headObj$LinksNum*(headObj$NumLinksVariables) + vIndex);
}
seek(f,off,"start")
Status=readBin(f,what="double",size=4,n=1)
return(Status)
}
getSWMMTimes<-function(headObj){
#gets the time stamps of the SWMM results in binary file
f=headObj$BinaryFile
seek(f,headObj$ComputedResults,"start")
headObj$SWMMTimes<-array(NaN,headObj$ReportingPeriods)
if(headObj$ReportingPeriods>0){
for(i in 1:headObj$ReportingPeriods){
headObj$SWMMTimes[i]<-readBin(f,what="double",size=8,n=1)
# if(i<100){
# print(headObj$SWMMTimes[i])
# }
seek(f,headObj$BytesPerPeriod-8,"current")
}
}else{
stop("No time steps listed in SWMM output file.")
}
#Convert SWMM times to R POSIXlt datetimes
headObj$SWMMTimes<-headObj$SWMMTimes*86400.0+as.POSIXct(strptime("12/30/1899", format="%m/%d/%Y",tz="GMT"))#edit 2/10/2012 to force GMT time zone rather than locale specific
return(headObj)
}
getSWMMTimeSeriesData<-function(headObj,iType,nameInOutputFile,vIndex){
if(iType==0){
iIndex=(0:(headObj$SubCatchNum-1))[headObj$SubCatchName==nameInOutputFile]
}else if(iType==1){
iIndex=(0:(headObj$NodesNum-1))[headObj$NodesName==nameInOutputFile]
}else if(iType==2){
iIndex=(0:(headObj$LinksNum-1))[headObj$LinksName==nameInOutputFile]
}else if(iType==3){
iIndex=0
}
Status=array(NaN,headObj$ReportingPeriods)
for(period in 0:(-1+headObj$ReportingPeriods)){
#browser()
Status[period+1]=getSWMMResult(headObj=headObj,iType=iType,iIndex=iIndex,vIndex=vIndex,period=period)
}
return(Status)
}
TimeSeries<-function(Q, headObj){
f= data.frame(Q)
f$hours = c(1:headObj$ReportingPeriods)
require (ggplot2)
p <- ggplot(f, aes(x = hours, y = Q)) + geom_line(color="red", size=1.5)+ scale_x_log10()+ scale_y_log10()
p2<-p + ggtitle("Timeseries")+labs(x="Time",y="Flow(cfs)")
print(p)
return(p)
}
Exceedence1<-function(Q,headObj){
y=data.frame(Q)
row_sub = apply(y, 1, function(row) all(row !=0 ))
h = y[row_sub,]
rank <- rank(h, ties.method="min")
exceedtime <- 100 * (rank / (length(h)+1))
m=data.frame(exceedtime)
m$Probability= sort(exceedtime, decreasing = T)
m$Flow= sort(h)
require (ggplot2)
p2 <- ggplot(m, aes(x = Probability, y = Flow)) + geom_line(color="blue", size=1.5)+ scale_x_log10()+ scale_y_log10()
p3<-p2 + ggtitle("Duration Curve for Simulated")+labs(x="Probability(%)",y="Flow(cfs)")
print(p3)
return(p3)
}
Exceedence2<-function(CalibrationData,headObj){
#wkObs = loadWorkbook("C:\\Users\\Alamdari\\Research\\R-SWMM Test Files\\Observed.xlsx")
CalibrationData = readWorksheet(wkObs, sheet="Observed")
x=data.frame(CalibrationData)
rank <- rank(x$Observed, ties.method="min")
exceedtime <- 100 * (rank / (nrow(x)+1))
m=data.frame(exceedtime)
m$Probability= sort(exceedtime, decreasing = T)
m$Flow= sort(x$Observed)
require (ggplot2)
p2 <- ggplot(m, aes(x = Probability, y = Flow)) + geom_line(color="red", size=1.5)+ scale_x_log10()+ scale_y_log10()
p3<-p2 + ggtitle("Duration Curve for Observed")+labs(x="Probability(%)",y="Flow(cfs)")
print(p3)
return(p3)
}
# SimulatedData<-function(Q,headObj){
# Date = headObj$SWMMTimes
# SimulatedData<-{}
# SimulatedData$Times= data.frame(Date)
# SimulatedData$Flow = data.frame(Q)
# #write.xlsx(SimulatedData,"C:\\Users\\Alamdari\\Research\\R-SWMM Test Files\\Simulated.xlsx",sheet="TestSheet")
# return(SimulatedData)
# }
# Calculate Runoff Volume
# RunoffVolume<-function(Average){
# library(readxl)
# Rainfall = read_excel("C:\\Users\\david\\Research\\DifficultRun_18Subs\\Washington_Hourly_2013.xlsx", sheet="Rainfall_Raw")# load XLConnect package
# SimObsDate = data.frame(Average)
# A = as.character.Date(Rainfall[,1])
# m = str_sub(A,12,19)
# RainfallDate = as.POSIXct(paste(Rainfall[,1], m), format="%Y-%m-%d %H:%M:%S",tz="GMT")
# Num1 = as.integer(RainfallDate)
# left = str_sub(Num1,9,10)
# left = as.numeric(left)
# for (i in 1:length(left)){
# if(left[i]==99){
# Num1[i] = Num1[i]+ 1}
# }
# Num2 = as.integer(Average$Date)
# Events = {}
# Events$Date = SimObsDate$Date
# k=1
# for (i in 1:nrow(SimObsDate)){
# Events$Sim[k]= 0
# Events$Obs[k]=0
# Events$Rain[k] = 0
#
# k=k+1
# }
#
# k=1
# j=1
#
# for (i in 1:nrow(SimObsDate)){
# for (j in 1:nrow(Rainfall)){
# if (Num2[i]-Num1[j]==0){
# Events$Rain[i]= Rainfall$Rainfall[j]
# Events$Sim[i]= SimObsDate$Sim[i]
# Events$Obs[i]= SimObsDate$Obs[i]
# Events$Date[i] = SimObsDate$Date[i]
# }
#
# }
# }
#
# J=Events$Rain
# H = Events$Sim
# I = Events$Obs
# X= Events$Date
# ReadEventsFile = data.frame(Events)
#
# max=0
# count=0
# set=0
# for (i in 1:nrow(ReadEventsFile)){
# if(J[i]>=0.1){
# count=count+1
# if (count>max){
# max=count
# }
# if (J[i+1]<0.1){
# set=set+1
# }
# }
# else{
# count=0
# }
# }
# mm <- matrix(0, max, set)
# nn <-matrix(0, max, set)
# rr <-matrix(0, max+8, set)
# xx<-matrix(0,max,set)
# k=1
# m=1
# j=1
#
# while (m!=(set+1)){
# if (J[k]>=0.1){
# mm[j,m]=J[k]
# nn[j,m]=H[k]
# rr[j,m]=I[k]
# xx[j,m]=X[k]
# j=j+1
# if (J[k+1]<0.1){
# m=m+1
# j=1
# }
# k=k+1
# }
#
# else{
# k=k+1
# }
# }
# Sum = {}
# Sum$Sim = colSums(nn)
# Sum$Obs = colSums(rr)
# Format= as.POSIXct(xx, origin = "1970-01-01", tz= "GMT")
# Date = as.numeric(Format)
# a = {}
# b= {}
# k=1
# m=1
# for (i in 1:set){
# a[k] = xx[1,i]
# k=k+1
# }
# m=2
# k=1
# for (i in 1:set){
# for (m in 2:(max)){
# if ((xx[m,i] == 0) & (xx[m-1,i]!=0)) {
# b[k]=xx[m-1,i]
#
# }
# if ((xx[m,i]!=0)){
# b[k]=xx[m,i]
# }
#
# }
# k=k+1
# }
#
# RunoffVolume = {}
# RunoffVolume$Sim = Sum$Sim
# RunoffVolume$Obs = Sum$Obs
# return (RunoffVolume)
# }
#Hourly Calibration
Aggregate1<-function(Q){
#define wkObs
Sim <- aggregate(Q,
list(hour=cut(as.POSIXct(headObj$SWMMTimes, format="%Y-%m-%d %H:%M:%S",tz="GMT"), "hour")),
mean)
#wkObs = loadWorkbook("2016_obs_watershed_outflow.xlsx")
library(readxl)
CalibrationData = read_excel("Observed.xlsx")
#Creat Observed Data in the same format of Simulated
Obs <- aggregate(CalibrationData$Flow,
list(hour=cut(as.POSIXct(CalibrationData$Date, format="%Y-%m-%d %H:%M:%S",tz="GMT"), "hour")),
mean)
Average = {}
Average$Date = Obs$hour
Average$Sim = Sim$x
Average$Obs = Obs$x
return(Average)
}
#Create your rainfall data date same as writesimulatedObservedData File
#Calibration for the events
CalculateEvent<-function(Average){
library(stringi)
library(stringr)
library(lubridate)
Rainfall = read_excel("Rainfall_Raw.xlsx")
#wkSimObsDate = loadWorkbook("C:\\Users\\Alamdari\\Research\\R-SWMM Test Files\\WriteSimulatedObserved.xlsx")
SimObsDate = data.frame(Average)
A = as.character.Date(Rainfall[,1])
RainfallDate = as.POSIXct(A, format="%Y-%m-%d %H:%M:%S",tz="GMT")
t <- strftime(RainfallDate, format="%H:%M:%S")
a <- hms(as.character(t))
minute = minute(a)
for (i in 1: length(RainfallDate)){
if(minute[i]==59){
RainfallDate[i]= RainfallDate[i]+1
}
}
Average$Date = as.POSIXct(Average$Date, format="%Y-%m-%d %H:%M:%S",tz="GMT")
Num1 = as.integer(RainfallDate)
Num2 = as.integer(Average$Date)
Events = {}
Events$Date = SimObsDate$Date
k=1
for (i in 1:nrow(SimObsDate)){
Events$Sim[k]= 0
Events$Obs[k]=0
Events$Rain[k] = 0
k=k+1
}
k=1
j=1
for (j in 1:nrow(Rainfall)){
for (i in 1:nrow(SimObsDate)){
if (Num2[i]-Num1[j]==0){
Events$Rain[i]= Rainfall$Rainfall[j]
Events$Sim[i]= SimObsDate$Sim[i]
Events$Obs[i]= SimObsDate$Obs[i]
k=k+1
}
}
}
J=Events$Rain
H = Events$Sim
I = Events$Obs
X= Events$Date
ReadEventsFile = data.frame(Events)
max=0
count=0
set=0
for (i in 1:nrow(ReadEventsFile)){
if(J[i]>=0.1){
count=count+1
if (count>max){
max=count
}
if (J[i+1]<0.1){
set=set+1
}
}
else{
count=0
}
}
mm <- matrix(0, max, set)
nn <-matrix(0, max, set)
rr <-matrix(0, max, set)
xx<-matrix(0,max,set)
k=1
m=1
j=1
while (m!=(set+1)){
if (J[k]>=0.1){
mm[j,m]=J[k]
nn[j,m]=H[k]
rr[j,m]=I[k]
xx[j,m]=X[k]
j=j+1
if (J[k+1]<0.1){
m=m+1
j=1
}
k=k+1
}
else{
k=k+1
}
}
Maxmm <- matrix(0, 1,set)
Maxnn <-matrix(0, 1,set)
Maxrr <-matrix(0, 1,set)
Maxxx <-matrix(0, 1,set)
k=1
for (i in 1:set){
Maxmm[k,i]= max(apply(mm[,(i),drop=FALSE] ,2,max))
Maxnn[k,i]= max(apply(nn[,(i),drop=FALSE] ,2,max))
Maxrr[k,i]= max(apply(rr[,(i),drop=FALSE] ,2,max))
Maxxx[k,i]= xx[1,i]
i=i+1
}
FormatDate= as.POSIXct(Maxxx, origin = "1970-01-01", tz= "GMT")
#
# Char = as.character.Date(FormatDate)
# #Months = str_sub(Char,1,7)
# Days = str_sub(Char,1,10)
# #NumDays = as.integer(Days)
# Hours = str_sub(Char,12,13)
# NumHours = as.integer(Hours)
#
# set2= 1
# for (j in 1:set){
# if (identical(Days[j+1],Days[j], num.eq = TRUE, single.NA = TRUE, attrib.as.set = TRUE,
# ignore.bytecode = TRUE, ignore.environment = FALSE)==TRUE & (NumHours[j+1]- NumHours[j])<=(6+max)){ # use str_sub
# set2=set2
# }
# else{
# set2=set2+1
# }
# }
# Maxr = matrix(0, set+set,set2+set2)
# Maxn = matrix(0, set+set,set2+set2)
# Maxm = matrix(0, set+set,set2+set2)
# k=1
# m=1
# j=1
# for (j in 1:1){
# if (identical(Days[j+1],Days[j], num.eq = TRUE, single.NA = TRUE, attrib.as.set = TRUE,
# ignore.bytecode = TRUE, ignore.environment = FALSE)==TRUE & (NumHours[j+1]- NumHours[j])<=(6+max)){ # use str_sub
# Maxm[k,m] = max(Maxmm[(j):(j+1)])
# Maxn[k,m] = max(Maxnn[(j):(j+1)])
# Maxr[k,m] = max(Maxrr[(j):(j+1)])
# k=k+1
# }
# else{
# Maxm[k,m]= Maxmm[j]
# Maxn[k,m]= Maxnn[j]
# Maxr[k,m]= Maxrr[j]
# m=m+1
# k=k+1
# }
# }
#
# for (j in 2:(set)){
# if (identical(Days[j+1],Days[j], num.eq = TRUE, single.NA = TRUE, attrib.as.set = TRUE,
# ignore.bytecode = TRUE, ignore.environment = FALSE)==TRUE & (NumHours[j+1]- NumHours[j])<=(6+max)){ # use str_sub
#
#
# Maxm[k,m] = max(Maxmm[(j):(j+1)])
# Maxn[k,m] = max(Maxnn[(j):(j+1)])
# Maxr[k,m] = max(Maxrr[(j):(j+1)])
# k=k+1
#
#
# }
#
# else{
# if (j==2){
# m=m+1
# #Maxm[k]= max(Maxmm[i])
# # Maxn[k]= max(Maxnn[i])
# #Maxr[k]= max(Maxrr[i])
# #Max[k]= Maxxx[i]
# Maxm[k,m]= Maxmm[j]
# Maxn[k,m]= Maxnn[j]
# Maxr[k,m]= Maxrr[j]
# k=k+1
# m=m+1
# }
# else{
# m=m+1
# #Maxm[k]= max(Maxmm[i])
# # Maxn[k]= max(Maxnn[i])
# #Maxr[k]= max(Maxrr[i])
# #Max[k]= Maxxx[i]
# Maxm[k,m]= Maxmm[j+1]
# Maxn[k,m]= Maxnn[j+1]
# Maxr[k,m]= Maxrr[j+1]
# k=k+1
# m=m+1
# }
#
# }
#
#
# #mydf <- data.frame(mm[i+1],mm[i])
# #sdf <- stack(mydf)
# #uni <- unique(sdf[, "values"])
# }
# Maxm[is.na(Maxm)] <- 0
# Maxn[is.na(Maxn)] <- 0
# Maxr[is.na(Maxr)] <- 0
#
#
# MaxmNew = matrix(0, 1,(set2+set2))
# MaxnNew = matrix(0, 1,(set2+set2))
# MaxrNew = matrix(0, 1,(set2+set2))
#
# k=1
# for (i in 1:(set2+set2)){
# MaxmNew[k,i]= max(apply(Maxm[,(i),drop=FALSE] ,2,max))
# MaxnNew[k,i]= max(apply(Maxn[,(i),drop=FALSE] ,2,max))
# MaxrNew[k,i]= max(apply(Maxr[,(i),drop=FALSE] ,2,max))
# i=i+1
# }
MaxmNew = t(Maxmm)
MaxnNew = t(Maxnn)
MaxrNew = t(Maxrr)
MaxmNew = data.frame(MaxmNew)
MaxnNew = data.frame(MaxnNew)
MaxrNew = data.frame(MaxrNew)
EventsNew = {}
EventsNew$Rain = MaxmNew$MaxmNew
EventsNew$Sim = MaxnNew$MaxnNew
EventsNew$Obs = MaxrNew$MaxrNew
Dataframe = data.frame(EventsNew)
EventsNew = Dataframe[apply(Dataframe[,-1], 1, function(x) !all(x==0)),]
return(EventsNew)
}
#Calculating summary statistics
PerfomrmStatistic2<-function(EventsNew){
require(hydroGOF)
sim2 = EventsNew$Sim
obs2 = EventsNew$Obs
MeanError_2 = me(sim2,obs2)
MeanSquaredError_2 = mse(sim2,obs2)
IndexAgreement = d(sim2,obs2)
IndexAgreementTimesMinus1_2 = -1*IndexAgreement
Nashsutcliffe2 = NSE(sim2, obs2)
NashsutcliffeTimesMinus1_2 = -1*Nashsutcliffe2
PercentBias2 = pbias(sim2,obs2)
linearCorrelation2 =cor(sim2,obs2)
linearCorrelationTimesMinus1_2 = -1*linearCorrelation2
output2=data.frame(MeanError_2,MeanSquaredError_2,IndexAgreementTimesMinus1_2,NashsutcliffeTimesMinus1_2,PercentBias2,linearCorrelationTimesMinus1_2)
return(output2)
}
# Creating input files with uncertain parameters and name it as $1$ $2$ $3$
ReadSWMMOptFile<-function(SWMMOptFile){
SWMMOpt=readLines(con = SWMMOptFile, n = -1L, ok = TRUE, warn = TRUE,
encoding = "unknown")
return(SWMMOpt)
}
#Replace Optimization parameters to the Input File
OptimizationFile<-function(Optimization){
Optimization = read.csv(file=Optimization, header = TRUE, sep = ",", quote="\"", dec=".",
fill = TRUE, comment.char="",stringsAsFactors = FALSE)
parameters = Optimization[,2]
replacementCodes = Optimization[,1]
return (Optimization)
}
replaceCodesInTemplateFile<-function(SWMMOpt,parameters, replacementCodes,File){
#Optimization = read.csv(file=Optimization, header = TRUE, sep = ",", quote="\"", dec=".",
#fill = TRUE, comment.char="",stringsAsFactors = FALSE)
#parameters = Optimization[,2]
#replacementCodes = Optimization[,1]
for(i in 1:length(parameters)){
SWMMOpt=sub(replacementCodes[i], parameters[i], SWMMOpt,fixed=TRUE)
}
writeLines(SWMMOpt, con = File, sep = "\n", useBytes = FALSE)
return(SWMMOpt)
}
#Defining Minim, initial, and Maximum Vallues for uncertain parameters
ParametersBound<-function(ParametersFile){
Bounds = read.csv(file=ParametersFile, header = TRUE, sep = ",", quote="\"", dec=".",
fill = TRUE, comment.char="",stringsAsFactors = FALSE)
#initial = Bounds$Initial
#Minimum = Bounds$Minimum
#Maximum = Bounds$Maximum
return(Bounds)
}
Objectivefunction<-function(SWMMOptFile,x,OutFile,swmm,Timeseries,StatParameters){
SWMMOpt= ReadSWMMOptFile(SWMMOptFile)
Input=paste(OutFile,iteration,'.inp',sep="")
ReplaceCodes<<- replaceCodesInTemplateFile(SWMMOpt,x,as.matrix(Bounds["Code"]),Input)
Report=paste(OutFile,iteration,'.rpt',sep="")
Output=paste(OutFile,iteration,'.out',sep="")
swmm= "C:\\Program Files (x86)\\EPA SWMM 5.1\\swmm5.exe"
SWMMExe(swmm,Input,Report,Output)
headObj = GetObjectsSWMM (Output)
headObj = getSWMMTimes (headObj)
Q = getSWMMTimeSeriesData(headObj,iType = 1,nameInOutputFile = "J11510.66",vIndex = 4)
#TimeSeries = TimeSeries(Q, headObj)
#Exceedence = Exceedence(Q, headObj)
# SimulatedData = SimulatedData(Q, headObj)
Average = Aggregate1(Q)
# Average = read.zoo(data.frame(Average))
EventsNew = CalculateEvent(Average)
require(hydroGOF)
sim2 = EventsNew$Sim
obs2 = EventsNew$Obs
MeanError_2 = me(sim2,obs2)
MeanSquaredError_2 = mse(sim2,obs2)
IndexAgreement = d(sim2,obs2)
IndexAgreementTimesMinus1_2 = -1*IndexAgreement
Nashsutcliffe2 = NSE(sim2, obs2)
NashsutcliffeTimesMinus1_2 = -1*Nashsutcliffe2
PercentBias2 = pbias(sim2,obs2)
linearCorrelation2 =cor(sim2,obs2)
linearCorrelationTimesMinus1_2 = -1*linearCorrelation2
library(hydroGOF)
ggof(EventsNew$Sim, EventsNew$Obs, na.rm = TRUE, pt.style = "ts", ftype = "o", stype="default",
gof.leg = TRUE, digits=2,
gofs=c("me","RMSE","PBIAS","NSE","d","R2"),xlab = "Time", ylab=c("Q[cfs]"))
output1=data.frame(NashsutcliffeTimesMinus1_2,PercentBias2,linearCorrelationTimesMinus1_2)
library(xlsx)
for (i in iteration){
write.xlsx(output1,paste("Combinations",i,".xlsx",sep=""))}
SummaryStatistics1 = PerfomrmStatistic2(EventsNew)
perfStatsToUse=as.numeric(SummaryStatistics1[StatParameters])
summaryRow=unlist(c(iteration,x,perfStatsToUse))
#write.xlsx(summaryRow[3:5],"summaryRow.xlsx",sheetName = paste("iteration",iteration),append = TRUE)
names(summaryRow)= c("iteration",paste("Parameter",t(Bounds["Code"]),sep=""),StatParameters)
iteration<<-iteration+1
print(summaryRow)
return (summaryRow)
}
OptimizationFunction<-function(SWMMOptFile,OutFile,swmm,Timeseries,StatParameters,initial,lower,upper){
optimOpt={}
optimOpt$OutFile = OutFile
optimOpt$SWMMOptFile = SWMMOptFile
optimOpt$swmm = swmm
optimOpt$Timeseries = 'getSWMMTimeSeriesData(headObj=headObj,iType = 1,nameInOutputFile = "J11510.66",vIndex = 4)'
optimOpt$StatParameters = StatParameters
optimOpt$lower = lower
optimOpt$upper = upper
library(mco)
out= nsga2(Objectivefunction,
idim=length(optimOpt$lower),
odim=length(optimOpt$StatParameters),
OutFile= optimOpt$OutFile,
SWMMOptFile = optimOpt$SWMMOptFile,
swmm = optimOpt$swmm,
Timeseries = optimOpt$Timeseries,
StatParameters = optimOpt$StatParameters,
generations=200,
lower.bounds=as.double(optimOpt$lower),
upper.bounds=as.double(optimOpt$upper),
constraints=NULL,popsize = 1000)
}
|
828c6c974c7e6f508e4890b199cd8f6acb454ca5 | 32ca0dd0dafbe713e2b7473dc036be1af9ed8b54 | /run_analysis.R | e650a8ec2d3c28c0a25bd8447f306da7e2457cfc | [] | no_license | pyiapa/get-data004-project | f89f0f11e69ae40937f10edcc734a09a7b8e9800 | 9b4fdeae7a5811a2bbfd65c7d167b406d28b6c16 | refs/heads/master | 2021-01-13T01:55:39.299580 | 2014-06-22T23:20:59 | 2014-06-22T23:20:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,470 | r | run_analysis.R | # Load the test and train data sets
test_data = read.table("UCI HAR Dataset/test/X_test.txt")
train_data = read.table("UCI HAR Dataset/train/X_train.txt")
# Load the activity labels into a data frame
activity_labels = read.table("UCI HAR Dataset/activity_labels.txt")
colnames(activity_labels) <- c("id", "activity")
# Get the id's that correspond to each activity for the subjects
test_id_data = read.table("UCI HAR Dataset/test/y_test.txt")
activity_id <- test_id_data$V1
# Create a vector with activity labels that correspond to the
# activity id's.
activities <- character()
for(i in 1:length(activity_id)){
temp <- activity_labels[activity_id[i],]
activities <- c(activities,as.character(temp$activity))
}
# Attach the column with activity labels to the test data set
test_data <- cbind(activities, test_data)
# Attach the column with activity id's to the test data set
test_data <- cbind(activity_id, test_data)
# Load the subject id's
subject_test_data = read.table("UCI HAR Dataset/test/subject_test.txt")
subject_id <- subject_test_data$V1
# Attach the column with subject id's to the test data set
test_data <- cbind(subject_id, test_data)
# Collect train data
# Get the id's that correspond to each activity for the subjects
train_id_data = read.table("UCI HAR Dataset/train/y_train.txt")
activity_id <- train_id_data$V1
# Create a vector with activity labels that correspond to the
# activity id's.
activities <- character()
for(i in 1:length(activity_id)){
temp <- activity_labels[activity_id[i],]
activities <- c(activities,as.character(temp$activity))
}
# Attach the columns with activity labels and activity ids to the test data set
train_data <- cbind(activities, train_data)
train_data <- cbind(activity_id, train_data)
# Load the subject id's
subject_train_data = read.table("UCI HAR Dataset/train/subject_train.txt")
subject_id <- subject_train_data$V1
# Attach the column with subject id's to the train data set
train_data <- cbind(subject_id, train_data)
# Merge the two data sets together
mergedData = merge(test_data, train_data, all=TRUE)
# Name the columns with proper labels from the features file
features_data = read.table("UCI HAR Dataset/features.txt")
column_names <- features_data$V2
column_names <- c("SubjectID", "ActivityID","Activity", as.character(column_names))
colnames(mergedData) <- column_names
# Extract mean and standard deviation related columns
col_names <- colnames(mergedData)
mean_std_vector <- character()
for(col in col_names){
if(grepl("std",col) | grepl("mean",col) | grepl("Mean",col)){
mean_std_vector <- c(mean_std_vector, col)
}
}
mean_std_vector <- c("SubjectID", "ActivityID","Activity",mean_std_vector)
mean_std_data <- mergedData[,mean_std_vector]
# Find the averages and put them in a tidy file
library(reshape2)
# Get approproate column labels
column_labels <- colnames(mean_std_data)
column_labels <- column_labels[4:length(column_labels)]
# Calculate avarages based on Acticity and Subject ID
meltData <- melt(mean_std_data, id=c("Activity", "SubjectID"), measure.vars=column_labels)
tidy_data <- dcast(meltData, SubjectID + Activity ~ column_labels, mean)
# Tidy up the column labels
column_labels <- lapply(column_labels, FUN= function(x) paste("AVG-", x, sep=""))
column_labels <- c("SubjectID", "Activity", column_labels)
colnames(tidy_data) <- column_labels
# Write the tidy talbe back to a txt file
write.table(tidy_data, file="tidy_set.txt")
|
194d63d12e101df09088495a198f0af378f3a915 | 0e1cd4250b7661a885b49ba3bbfd88c775d3869f | /searching_cited_books.R | 2e8f81ed422aa8ce3f17e18471c7fe9044dd0918 | [] | no_license | silviaegt/wiki-cite-colmex | c7439d89baf11c3442252add420a250074518960 | 848594fda469192ef23122fa14b947721fb8047b | refs/heads/master | 2021-07-16T06:52:47.280466 | 2020-05-22T18:58:33 | 2020-05-22T18:58:33 | 149,190,161 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,504 | r | searching_cited_books.R | getwd()
#install.packages("XML")
require(XML)
library("methods")
library("dplyr")
library("tidyr")
# set working directory
# here you add were your data is (remember to change \ from Windows paths)
setwd("yourpath")
#1. Read your tsv with all citations with identifiers extracted from the most recent version of your language Wikipedia
#Available here: https://figshare.com/articles/Wikipedia_Scholarly_Article_Citations/1299540/10
# (in this example we look at Spanish Wiki)
eswiki <- read.table(file = 'eswiki.tsv', sep = '\t', header = TRUE, fill=TRUE, encoding = "UTF-8")
#2. To get a glimpse of the first rows and thus of how data is ordered:
#head(eswiki)
#3. To get a summary of your data
#sumeswiki <- summary(eswiki)
#here we get, for instance, that the pages with the biggest number of references are:
#Evolución bilógica(461), Infiriendo transferencia genética horizontal (252), and Historia social de los virus (229)
############Let's start digging into our data#################
#1. Retrieve all types of ids
types <- sort(table(eswiki$type),decreasing=T)
View(types)
barplot(types)
#Compare with: https://medium.com/freely-sharing-the-sum-of-all-knowledge/what-are-the-ten-most-cited-sources-on-wikipedia-lets-ask-the-data-34071478785a
#note how isbns are the more dominant!
#2. Filter rows that have "isbn"; thus all book-citations
libros <- eswiki[ which(eswiki$type=='isbn'), ]
#2.1 Convert "libros" into a dataframe
library("tibble")
libros_df <- as_data_frame(libros)
#View(libros)
#summary(libros)
#2.2 Create a subset with all the books that cite one book you know about as a test
#Here you can see my test with "Historia Mínima de México" el El Colegio de México
historia_minima = c(9681211391,9789681211394,9788415832010, 9789681211387, 9789680101672)
hm_citations <- libros_df[which(libros_df$id %in% historia_minima),]
View(hm_citations)
#Notes: only one out of the 5 different isbns of the same book has been used: "9681211391" (2004 edition)
#######################################################
####3. Extract all ISBNs from your University Press####
#######################################################
# A little pre-processing was necessary for my data (did it with openrefine)
#registroscolmex <- separate(registroscolmexraw, ISBN, into = c("ISBN", "Notas"), sep = "\\s")
library(readr)
registroscolmex <- read_csv("delColmex_limpio.csv",
col_types = cols(ISBN = col_character()))
View(registroscolmex)
## This is to subset where my real information is
registroscolmexisbn <- subset(registroscolmex, (!is.na(registroscolmex$ISBN)))
colmexissn <- subset(registroscolmex, (!is.na(registroscolmex$ISSN)))
#This is a list of registers without ISBN, ISSN
registroscolmexsinisbn <- subset(registroscolmex, (is.na(registroscolmex$ISBN)) & (is.na(registroscolmex$ISSN)))
titulos_sin_isbn <- sort(table(registroscolmexsinisbn$title), decreasing = T)
write.csv(registroscolmexsinisbn, file = "registroscolmex_sin-isbn-issn.csv", row.names=FALSE)
length(registroscolmexisbn$ISBN)
isbncolmexfreq <- as.data.frame(sort(table(registroscolmex$ISBN), decreasing = T))
colnames(isbncolmexfreq) <- c("ISBN", "Frecuencia")
write.csv(isbncolmexfreq, file = "isbncolmexfreq.csv", row.names=FALSE)
############################################################
###4. Compare your ISBNs with the ones cited in Wikipedia###
###########################################################
#Now that I have my list of unique ISBNs
isbncolmex <- isbncolmexfreq$ISBN
#I will see if they are in the df of cited books in Wikipedia
colmexcite_df <- as_data_frame(libros_df[which(libros_df$id %in% isbncolmex),])
View(colmexcite_df)
#Number of unique different websites that cite our books
length(unique(colmexcite_df$page_title)) #63
#Number of unique different books cited in Wikipedia
length(unique(colmexcite_df$id)) #38
wikipages_list <- as.character(colmexcite_df$page_title)
wikipagesfreq <- sort(table(wikipages_list), decreasing=T)
View(wikipagesfreq)
citedisbn_list <- as.character(colmexcite_df$id)
citedisbnfreq <- as.data.frame(sort(table(citedisbn_list), decreasing=T))
registroscolmex_sin_repetir <- registroscolmexisbn[!duplicated(registroscolmexisbn$ISBN),]
citedisbn_joined <- inner_join(citedisbnfreq , registroscolmex_sin_repetir, by=c("citedisbn_list"="ISBN"))
citedisbn_joined[is.na(citedisbn_joined)] = 0
length(citedisbn_joined$freq)
citedisbn <- data.frame(citedisbn_joined$citedisbn_list, citedisbn_joined$title, citedisbn_joined$Autor, citedisbn_joined$Freq)
names(citedisbn) <- c("ISBN", "Título", "Autor", "No. de Citas")
sort(table(citedisbn$Autor), decreasing = T)
write.csv(citedisbn, file = "citedisbn.csv", row.names=FALSE)
#############################################################################
# Get a filtered version of the books that do have citations on Wikipedia
cited_books <- registroscolmex[which(registroscolmex$ISBN %in% citedisbn_list),]
citedcenter <- table(cited_books$DEPARTAMENTO)
citedyear <- table(cited_books$A.d1.O.EDICI.d3.N)
View(citedyear)
#3.1 Export different results into a csv
#write.csv(libros, file = "eslibros.csv", row.names=FALSE)
#write.csv(hm_citations, file = "hm_citations.csv", row.names=FALSE)
#write.csv(colmexcite_df, file = "colmexcitations.csv", row.names=FALSE)
write.csv(cited_books, file = "cited_books.csv", row.names=FALSE)
pagwiki_citations <- table(colmex_citations$page_title)
pagwiki_citations
#4. Retrieve the most frequent isbns and get titles from OCLC (work in prgress)
freqbooks <- sort(table(libros$id),decreasing=T)
View(freqbooks)
twenty <- data.frame(freqbooks[1:20])
class(data.frame(twenty))
twenty[1,1]
dim(twenty)
length(twenty$Var1)
for (i in 1:3){
isbn = twenty[i,1]
url = sprintf("http://classify.oclc.org/classify2/Classify?isbn=%s&summary=true", isbn)
data <- xmlTreeParse(url)
xml_data <- xmlToList(data)
works = xml_data[[8]]
print(works[[1]])
}
library(rvest)
work <- read_html("http://classify.oclc.org/classify2/Classify?isbn=3822847445&summary=true")
#data <- xmlParse()
# load packages
library("XML")
data <- xmlTreeParse(url)
# convertimos a lista
xml_data <- xmlToList(data)
# hay 8 elementos en la lista
length(xml_data)
works = xml_data[[8]]
# y de ahí quieres: author, holding, hyr y title
# author
works[[1]]
# holding
works[[4]]
#hyr
works[[5]]
#title
works[[10]]
#http://classify.oclc.org/classify2/Classify?isbn=0940228475&summary=truev
barplot(freqbooks)
|
d91611d89f68cb769bf6c07a65c2cf5809d221a6 | 93356416c6941126c3627edccabb08696d10dd05 | /phospho_network/regression/generate/regression_multi_kinase_match_sub_BI.R | bcdb54817ca1b49a794965ad9b4f5833f67e9dc5 | [] | no_license | ding-lab/phosphoproteomics | 22c9a69127e7397c54dddba044d4588b495f21c5 | 00538a56143be08c0fffae8df6dd54f2bfdd4734 | refs/heads/master | 2021-03-19T06:18:15.490109 | 2019-04-25T21:36:45 | 2019-04-25T21:36:45 | 90,892,479 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,127 | r | regression_multi_kinase_match_sub_BI.R | # Yige Wu @ WashU 2017 Feb
# adopted by Kuan Huang @ Washu 2017 Apr
# look at correlations of kinase and downstream substrates phosphorylation status
# pho_sub~pro_sub+pho_kin(callapsed,multiple)
# choose kinase/phosphotase, significance level, outlier threshold and least sample number-------------------------
least_samples <- 10# least number of samples with complete data for each model
protein <- "kinase"
# protein <- "phosphotase"
# library -----------------------------------------------------------------
library(stringr)
library(ggplot2)
library(reshape)
library(grid)
require(plyr)
# for working on Kuan's mac
baseD = "/Users/khuang/Box\ Sync/PhD/proteogenomics/CPTAC_pan3Cancer/"
# # for working on Yige's mac
# baseD = "/Users/yigewu/Box\ Sync/"
setwd(paste(baseD,"pan3can_analysis/phospho_network",sep=""))
source("../pan3can_aes.R") # aes for general purposes; it should be one directory out of the working directory
# input k_s_table according to kinase or phosphotase-------------------------------------------------------------------
if ( protein == "kinase" ) {
### read in the kinase/substrate table/ phosphorylation data ###
k_s_table_phosphosite = read.delim(paste(baseD,"pan3can_shared_data/Phospho_databases/PhosphositePlus/data/Kinase_Substrate_Dataset_human_final_hugoified.txt",sep=""))
k_s_table_network = read.delim(paste(baseD,"pan3can_shared_data/Phospho_databases/PhosphoNetworks/comKSI.csv",sep=""))
k_s_table_phosphosite_sum = k_s_table_phosphosite[,c("GENE","SUB_GENE")]
colnames(k_s_table_phosphosite_sum) = c("Kinase","Substrate")
k_s_table_phosphosite_sum$Score=0
k_s_table = rbind(k_s_table_phosphosite_sum,k_s_table_network)
}
if ( protein == "phosphotase" ) {
### read in the phosphotase/substrate table/ phosphorylation data ###
k_s_table <- read.csv(paste(baseD,"pan3can_shared_data/Phospho_databases/DEPOD/DEPOD_201612_human_phosphatase-protein_substrate_to_Kuan-lin.csv",sep = ""))
colnames(k_s_table) <- c("Phosphatase_UniProtAC_human","GENE","Substrate_UniProtAC_ref","SUB_GENE","Substrate_Type","DephosphoSite","BioassayType", "PubMed_ID_rev")
}
substrate_trans <- as.vector(unique(k_s_table$Substrate[as.vector(k_s_table$Kinase)!=as.vector(k_s_table$Substrate)]))
kinase_trans <- as.vector(unique(k_s_table$Kinase[as.vector(k_s_table$Kinase)!=as.vector(k_s_table$Substrate)]))
table_single <- read.delim(paste(baseD,"pan3can_shared_data/analysis_results/tables/BRCA_HUMAN_",protein,"_substrate_regression_trans.txt", sep=""))
# looping cancer -----------------------------------------------------------
for (cancer in c("HUMAN","PDX")) {
# input according to cancer type-------------------------------------------------------------------
if (cancer == "HUMAN") {
# HUMAN
HUMAN_pro_f = paste(baseD,"pan3can_shared_data/BRCA/BRCA77_unimodal_proteome-ratio-norm_exp_collapsed.txt",sep="")
pro_data <- read.delim(HUMAN_pro_f)
HUMAN_pho_f = paste(baseD,"pan3can_shared_data/BRCA/BRCA77_unimodal_phosphoproteome-ratio-norm_wGpos_cleaned.txt",sep="")
pho_data = read.delim(HUMAN_pho_f)
## read in grouped phosphorylation data!
HUMAN_pho_g = paste(baseD,"pan3can_shared_data/BRCA/BRCA77_unimodal_phosphoproteome-ratio-norm_collapsed.txt",sep="")
pho_gdata = read.delim(HUMAN_pho_g)
}
if ( cancer == "PDX" ) {
PDX_pho_f = paste(baseD,"pan3can_shared_data/BRCA/whim_phosphoproteome-ratio-norm_exp_filtered.txt",sep="")
pho_data = read.delim(PDX_pho_f)
colnames(pho_data)[2:3] = c("Gene","Gene.site")
PDX_pho_g = paste(baseD,"pan3can_shared_data/BRCA/whim_phosphoproteome-ratio-norm_exp_filtered_collapsed.txt",sep="")
pho_gdata = read.delim(PDX_pho_g)
PDX_pro_f = paste(baseD,"pan3can_shared_data/BRCA/whim_proteome-ratio-norm_exp_v2_filtered_collapsed.txt",sep="")
pro_data <- read.delim(PDX_pro_f)
}
# remove the phosphoproteins that have identical levels
#pho_gdata = pho_gdata[pho_gdata$X != "CSNK2A2",]# identical entry as CSNK2A1
pho_gdata = pho_gdata[!duplicated(pho_gdata[,colnames(pho_gdata) != "X"]),]
#split the SUBSTRATE and SUB_MOD_RSD in the first column
pho_rsd_split <- data.frame(str_split_fixed(pho_data$Gene.site, ":", 3))
#covert the SUB_MOD_RSD from lowercase to uppercase
pho_rsd_split[,3] <- toupper(pho_rsd_split[,3])
colnames(pho_rsd_split) <- c("SUBSTRATE","transcript","SUB_MOD_RSD")
# remove duplicate and identical phosphoyrlation levels for different transcript
# ps: they're not important genes
dup_pho <- data.frame(table(paste(pho_rsd_split$SUBSTRATE,pho_rsd_split$SUB_MOD_RSD,sep = ":")))
dup_pho <- dup_pho[dup_pho$Freq>1,]
dup_pho <- data.frame(str_split_fixed(dup_pho$Var1, ":", 2))
dup_pro <- as.vector(dup_pho$X1); dup_rsd <- as.vector(dup_pho$X2)
remove_rows <- c()
for (i in 1:nrow(dup_pho)) {
dup_rows <- which(pho_rsd_split$SUBSTRATE==dup_pro[i] & pho_rsd_split$SUB_MOD_RSD==dup_rsd[i])
remove_rows <- c(remove_rows,dup_rows[-1])
}
pho_rsd_split <- pho_rsd_split[-remove_rows,]
pho_data <- pho_data[-remove_rows,]
transcripts <- as.vector(pho_rsd_split$transcript)
colx <- which(colnames(pro_data)=="X")
# initiate ----------------------------------------------------------------
# calculate the length of trans table
substrate_reg <- intersect(intersect(substrate_trans, pro_data$X),pho_rsd_split$SUBSTRATE)
# use pro data and phosphosite data to narrow down the substrates to be examined(2119 to 1364)
ntrans <- 0
for (gene in kinase_trans) {
subs <- k_s_table$Substrate[k_s_table$Kinase==gene & k_s_table$Substrate!=gene]
for ( sub in unique(subs)) {
ntrans <- ntrans + length(which(pho_rsd_split$SUBSTRATE==sub))
}
}
phosphosites <- unique(table_single[table_single$SELF=="trans",c("SUBSTRATE","SUB_MOD_RSD","transcript")])
substrates <- as.vector(phosphosites$SUBSTRATE)
rsds <- as.vector(phosphosites$SUB_MOD_RSD)
for (i in 1:nrow(phosphosites)) {
phosphosites$pho_size[i] <- length(which(!is.na(pho_data[pho_rsd_split$SUBSTRATE==substrates[i] & pho_rsd_split$SUB_MOD_RSD==rsds[i],-colx])))
phosphosites$num_k[i] <- nrow(table_single[table_single$SELF=="trans" & table_single$SUBSTRATE==substrates[i] & table_single$SUB_MOD_RSD==rsds[i],])
}
phosphosites_multi <- phosphosites[phosphosites$num_k > 1,]
substrates <- as.vector(phosphosites_multi$SUBSTRATE)
rsds <- as.vector(phosphosites_multi$SUB_MOD_RSD)
# looping over phosphosites for trans pairs -----------------------------------------------------------------
# initiating the table for trans
vec_char <- vector(mode = "character", length = ntrans)
vec_num <- vector(mode = "numeric", length = ntrans) + NA
KINASE <- vec_char;SUBSTRATE <- vec_char; SUB_MOD_RSD <- vec_char;
FDR_pro_kin <- vec_num;FDR_pro_sub <- vec_num;FDR_pho_kin <- vec_num;
coef_pro_kin <- vec_num;coef_pro_sub <- vec_num;coef_pho_kin <- vec_num;
Cancer <- vec_char;transcript <- vec_char;model <- vec_char;
Size <- vec_num;num_k <- vec_num;P_pro_kin <- vec_num;P_pro_sub <- vec_num;P_pho_kin <- vec_num;
count <- 0
examine1 <- vector(mode = "logical", length = nrow(phosphosites_multi))
for (i in 1:nrow(phosphosites_multi)) {
substrate <- substrates[i]
rsd <- rsds[i]
pho_row <- which(pho_rsd_split$SUBSTRATE==substrate & pho_rsd_split$SUB_MOD_RSD==rsd)
tscp <- transcripts[pho_row]
#pho_sub <- t(pho_data[pho_row,-colx])
pho_sub <- t(pho_data[pho_row,-c(1:3)])
pro_sub <- t(pro_data[pro_data$X == substrate,-colx])
sub_k <- as.vector(unique(table_single$KINASE[table_single$SELF=="trans" & table_single$SUBSTRATE==substrate & table_single$SUB_MOD_RSD==rsd]))
num_k <- sum(pho_gdata$X %in% sub_k)
# rows <- c()
# for (k in sub_k) {
# temp <- which(pho_gdata$X==k)
# rows <- c(rows,temp)
# }
# pho_kins <- t(pho_gdata[rows,-colx])
pho_kins <- t(pho_gdata[pho_gdata$X %in% sub_k,-colx])
data1 <- data.frame(pro_sub,pho_kins)
colnames(data1)[1] <- "pro_sub"
data2 <- data.frame(pho_sub,data1)
colnames(data2)[1] <- "pho_sub"
size <- nrow(data2[complete.cases(data2),])
if(size > least_samples & size >= num_k + 15){
fit2 <- lm(pho_sub ~ ., data = data2)
pvalues <- coef(summary(fit2))
coefs <- fit2$coefficients
examine1[i] <- TRUE
for (j in 1:num_k) {
count <- count + 1
KINASE[count] <- sub_k[j]
SUBSTRATE[count] <- substrate
SUB_MOD_RSD[count] <- rsd
transcript[count] <- transcripts[i]
P_pro_sub[count] <- pvalues[2,4]
coef_pro_sub[count] <- fit2$coefficients[2]
P_pho_kin[count] <- pvalues[j+2,4]
coef_pho_kin[count] <- coefs[j+2]
Size[count] <- size
}
}
cat(i,'processed \n')
}
table_trans <- data.frame(KINASE,SUBSTRATE,SUB_MOD_RSD,
FDR_pro_kin,FDR_pro_sub,FDR_pho_kin,
coef_pro_kin,coef_pro_sub,coef_pho_kin,
Cancer,transcript,model,Size,
P_pro_kin,P_pro_sub,P_pho_kin)
phosphosites_multi$examine1 <- examine1
# integrate table from all the models(need to repetite again for another cancer dataset) --------------------------------------------------------
table_trans$model <- "pho_sub~pro_sub+pho_kins"
tabletrans <- table_trans[!is.na(table_trans$P_pro_sub),]
name = c("pro_kin","pro_sub","pho_kin")
## adjust p-values to FDR
for(coln in name) {#adjust pvalues for each variable
tabletrans[,paste("FDR_",coln,sep = "")] <-p.adjust(tabletrans[,paste("P_",coln,sep = "")],method = "fdr")
}
tabletrans$pair <- paste(tabletrans$KINASE,tabletrans$SUBSTRATE,tabletrans$SUB_MOD_RSD,sep = ":")
tabletrans = tabletrans[order(tabletrans$P_pho_kin),]
# write out tables --------------------------------------------------------
tn = paste(baseD,"pan3can_shared_data/analysis_results/tables/BRCA_",cancer, "_", protein,"_substrate_regression_multi.txt", sep="")
write.table(tabletrans, file=tn, quote=F, sep = '\t', row.names = FALSE)
} |
8fe00d4350d195ba4f971ae1e326b7125916a683 | bcb5995a65f0d1764d1eb56816016f1aab9195ef | /workspace.r | ea5cb7e458b167eb373f3bd14de077f12de3b221 | [] | no_license | pamgene/combatapp_nosync | c99c476edae5618c16c1e05722da7b62cbb78e56 | 77fcd3476cc1c979a6f25a598c4c6b0cfdd5d32b | refs/heads/master | 2023-04-25T04:35:58.209522 | 2020-07-23T07:53:58 | 2020-07-23T07:53:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 556 | r | workspace.r | library(bnutil)
library(shiny)
library(reshape2)
library(pgBatch)
library(combatapp)
library(ggplot2)
library(dplyr)
getdata = function() {
aData = AnnotatedData$new(data = combat_testdf, metadata =combat_testmetadf)
}
setResult = function(annotatedResult){
print(annotatedResult)
result = annotatedResult$data
}
bnMessageHandler = bnshiny::BNMessageHandler$new()
bnMessageHandler$getDataHandler = getdata
bnMessageHandler$setResultHandler = setResult
bnshiny::startBNTestShiny('combatapp', sessionType='show', bnMessageHandler=bnMessageHandler)
|
1a63ac51ff1cc1bb7367afa4becf32bd4d4b2bcb | 67d49ab26430e7bfb5ca8889d2250309d1e97b9a | /xgboost_cv.R | 27c64d3121e2690f191320f96e4df94670594ea2 | [] | no_license | arpang87/XGBOOST_CROSSVALIDATION_IN_R | ff192ad391619d20bb682fbc8a9a2b67f2134a72 | 7cddc72bb3394571c45fb1962eb91cc4a54909d7 | refs/heads/master | 2022-12-23T14:03:08.760919 | 2020-09-30T12:07:34 | 2020-09-30T12:07:34 | 299,905,455 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,988 | r | xgboost_cv.R | #title: "xgboost with crossvalidation in R"
#author: "Arpan"
#date: "January 4, 2017"
#output: pdf_document
Setting the seed so that we get the same results each time we run the model
set.seed(123)
Importing the library mlbench for sonar dataset
library(mlbench)
library(caret)
Storing the data set named "Sonar" into DataFrame named "DataFrame"
data("Sonar")
DataFrame <- Sonar
Type help("Sonar") to know about the data set
help("Sonar")
Check the dimension of this data frame
dim(DataFrame)
Check first 3 rows
head(DataFrame,3)
Check the summary of data
summary(DataFrame)
Lets check the data set again
str(DataFrame)
Lets create the train and test data set.Target variable is Class
library(caTools)
library(caret)
ind = createDataPartition(DataFrame$Class, p = 2/3, list = FALSE)
trainDF<-DataFrame[ind,]
testDF<-DataFrame[-ind,]
We will be using the caret package for crossvalidation.Function named train in caret package is used for crossvalidation.
Let's choose the paramters for the train function in caret
number = 5(It means we are using 5 fold cross-validation)
method="cv"(Means we are using cross-validation.You can also choose other like LOOCV or repeated CV,etc.)
classProbs=TRUE(It will give the probabilities for each class.Not just the class labels)
```{r}
ControlParamteres <- trainControl(method = "cv",
number = 5,
savePredictions = TRUE,
classProbs = TRUE
)
We will put the above paramter in the model below in trControl argument
Following are the Tuning parameters which one can tune for xgboost model in caret:
1. nrounds (# Boosting Iterations)
It is the number of iterations the model runs before it stops.With higher value of nrounds model will take more time and vice-versa.
2. max_depth (Max Tree Depth)
Higher value of max_depth will create more deeper trees or we can say it will create more complex model.Higher value of max_depth may create overfitting and lower value of max_depth may create underfitting.All depends on data in hand.Default value is 6.
range: [1,infinity]
3. eta (Shrinkage)
It is learning rate which is step size shrinkage which actually shrinks the feature weights. With high value of eta,model will run fast and vice versa.With higher eta and lesser nrounds,model will take lesser time to run.With lower eta and higher nrounds model will take more time.
range: [0,1]
4. gamma (Minimum Loss Reduction)
It is minimum loss reduction required to make a further partition on a leaf node of the tree. The larger value will create more conservative model.
One can play with this parameter also but mostly other parameters are used for model tuning.
range: [0,infinity]
5. colsample_bytree (Subsample Ratio of Columns)
Randomly choosing the number of columns out of all columns or variables at a time while tree building process.You can think of mtry paramter in random forest to begin understanding more about this.Higher value may create overfitting and lower value may create underfitting.One needs to play with this value.
range: (0,1]
6. min_child_weight (Minimum Sum of Instance Weight)
You can try to begin with thinking of min bucket size in decision tree( rpart).It is like number of observations a terminal node.If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node
range: [0,infinity]
Why do we need model tuning?
As we have already seen there are lot of parameters in xgboost model like eta,colsample_bytree,etc.You do not know which values of each parameters would give you the best predictive model.So you need to create a grid of several combinations of parameters which you think that can deliver best results.You can start by your intuition and later on keep on modifying the paramters till you are satisfied with the results.
Here,for demonstration purpose I'm only choosing two values of colsample_bytree and two values of max_depth.For rest of the parameters single value is taken.
parametersGrid <- expand.grid(eta = 0.1,
colsample_bytree=c(0.5,0.7),
max_depth=c(3,6),
nrounds=100,
gamma=1,
min_child_weight=2
)
To check how this grid looks like type as below.It gives four combinations of parameters.You can choose more combinations if you need or want.
parametersGrid
Let's now do the 5-fold crossvalidation for the xboost the model with the chosen parameters grid using train function.We will put the parametersGrid in the tuneGrid argument and controlParameters in trControl argument of train function.
To know more about the train function type and run ?train in the console
modelxgboost <- train(Class~.,
data = trainDF,
method = "xgbTree",
trControl = ControlParamteres,
tuneGrid=parametersGrid)
Let's check the crossvalidation results for parameters tuning for xgboost model.We can easily see that there are four rows with each having the combination and their corresponding accuracy and kappa,etc.For max_depth=3 and colsample_bytree=0.5( rest values are fixed as we choose),the value of accuracy and kappa is 0.8203 and 0.6375(approx) respectively.
As the max_depth =3 and colsample_bytree=0.7 gives the best accuracy,the final model is choosen for [nrounds = 100, max_depth = 3, eta =
0.1, gamma = 1, colsample_bytree = 0.7 and min_child_weight = 2].You can choose any customized metric other than accuracy.You have to put that in trainControl function.
modelxgboost
Let's check the predictions on the test data set
predictions<-predict(modelxgboost,testDF)
Let's check the confusion matrix
t<-table(predictions=predictions,actual=testDF$Class)
t
|
c81af4afb98574503fdeae7396a71beddb32ceaf | d1e8ac310e5ab7ad7e7e8641951cc0217efd10fa | /man/setDreamerr_dev.mode.Rd | a0bb6ee89b0ff06466f89668addb38a102c9367a | [] | no_license | cran/dreamerr | ed38bbc4838c2e86ed6567564eadd236c174a8e4 | 57b2abc755ed9663f95f770bb865dd53d75bd6b9 | refs/heads/master | 2023-08-31T02:45:12.600138 | 2023-08-23T21:30:02 | 2023-08-23T22:30:49 | 255,887,390 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,884 | rd | setDreamerr_dev.mode.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc_funs.R
\name{setDreamerr_dev.mode}
\alias{setDreamerr_dev.mode}
\title{Sets the developer mode to help form check_arg calls}
\usage{
setDreamerr_dev.mode(dev.mode = FALSE)
}
\arguments{
\item{dev.mode}{A logical, default is \code{FALSE}.}
}
\description{
Turns on/off a full fledged checking of calls to \code{\link[dreamerr]{check_arg}}. If on, it enables the developer mode which checks extensively calls to check_arg, allowing to find any problem. If a problem is found, it is pinpointed and the associated help is referred to.
}
\details{
Since this mode ensures a detailed cheking of all \code{\link[dreamerr]{check_arg}} calls, it is thus a strain on performance and should be always turned off otherwise needed.
}
\examples{
# If you're new to check_arg, given the many types available,
# it's very common to make mistakes when creating check_arg calls.
# The developer mode ensures that any problematic call is spotted
# and the problem is clearly stated
#
# Note that since this mode ensures a detailed cheking of the call
# it is thus a strain on performance and should be always turned off
# otherwise needed.
#
# Setting the developer mode on:
setDreamerr_dev.mode(TRUE)
# Creating some 'wrong' calls => the problem is pinpointed
test = function(x) check_arg(x, "integer scalar", "numeric vector")
try(test())
test = function(...) check_arg("numeric vector", ...)
try(test())
test = function(x) check_arg(x$a, "numeric vector")
try(test())
test = function(x) check_arg(x, "numeric vector integer")
try(test())
test = function(x) check_arg(x, "vector len(,)")
try(test())
# etc...
# Setting the developer mode off:
setDreamerr_dev.mode(FALSE)
}
\seealso{
\code{\link[dreamerr]{check_arg}}
}
\author{
Laurent Berge
}
|
8670b12256578f07d708cf7e08ef0f953743314f | 9e96145ecbea0530c967c14d17c35f1635f3c6f6 | /FC_script.R | ebde99a8da5b2262aad9a2618e710cb8d1b147df | [] | no_license | colina83/Case_Study_Data_Science_for_Business | ceda63f67d9997c9163edd959ba44e3ad48a997a | 7e72f02db17946b0686347342c9007032b88240a | refs/heads/main | 2023-03-14T04:41:24.529452 | 2021-03-02T18:45:04 | 2021-03-02T18:45:04 | 341,239,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,244 | r | FC_script.R | ###############################################
## Tasks to do Prior Starting the project
#Import Libraries
library(Hmisc)
library(Rmisc)
library(jtools)
library(UsingR)
library(tidyverse)
library(lmtest)
library(sandwich)
library(mctest)
library(ggplot2)
library(expss)
#Uploading Data to R
d <- ThyssenKrupp_PPL_Data_Final_20150513 # Excel File
e <- ThyssenKrupp_Data # RDS File from
all(e == d) # JUst checking that all values are the same in both datasets
#Arrange the names of variables
names(d) <- gsub(" ", "_", names(d))
#Information regarding size and Variables ##
dim(d) # Number of Variables -> Rows (Observations ) & Variables
names(d) # Variable Names
table(d$shift_type)
#Cleaning & Preparing Data
## identify variables that needs to be converted to factors (Day & Night Shift)
## Cleanup the date variable, we only need the date
d$shift <- str_remove(d$shift,"22:00:00")
d$shift <- str_remove(d$shift,"06:00:00")
d$shift <- str_remove(d$shift,"14:00:00")
#Exploratory Data Analysis and Plots
#######################################################
#PART A
#Answering Questions:
#1.Perform a univariate analysis and answer the following questions:
#a.- What is the average number of strips per shift?
d$Total_Strips <- (d$thickness_1+d$thickness_2+d$thickness_3)
Average_strips <- mean(d$Total_Strips)
t1 <- sum(d$thickness_1)
summary(d$MPT)
#1.b- Which is the most common and least common
a = sum(d$thickness_1)/sum(d$Total_Strips)*100
b = sum(d$thickness_2)/sum(d$Total_Strips)*100
c = sum(d$thickness_3)/sum(d$Total_Strips)*100
## Thickness 2 is the most common and thickness 3 is the least common
## FC to create graph
jpeg('1b.jpg')
Bar_Plot_Q1b <- barplot(c(a,b,c), b,col=c("darkblue","lightblue","gray"), main = "Thickness Values", xlab = "Thickness", legend = c("Thickness 1", "Thickness 2", "Thickness 3"))
dev.off()
#1.C Values of delta
par(mfrow =c(1,2))
boxplot(summary(d$run_time_ratio))
boxplot(summary(d$delta_throughput))
## Add a summary table, and make the plot prettier
Delta_Throughput_Summary <- summary(d$delta_throughput)
Run_Time_Ratio <- summary(d$run_time_ratio)
rbind(table_DT,table_TR)
#####################################################
table(d$grade_1 == 100)
table(d$grade_2 == 100)
table(d$grade_3 == 100)
table(d$grade_4 == 100)
table(d$grade_5 == 100)
table(d$grade_rest == 100)
##grade 1, 4, 5 and rest have 100 %
## 2.- Can the RTR Theory explain deviations
valcol <- (d$MPT + abs(min(d$MPT)))/max(d$MPT + abs(min(d$MPT)))
# This variable is to identify the low MPT values in light blue and the high MPT values in darker color
plot(d$delta_throughput ~ d$run_time_ratio, xlab="RTR", ylab="Delta Throughput", main="Scatterplot Delta Througput", col = rgb(0, 0, valcol))
abline(h= 0,col ="black")
lm_dt <- lm(d$delta_throughput ~ d$run_time_ratio)
abline(lm_dt,col = "red")
abline(h= 0,col ="black") # Points below zero
summary(lm_dt)
#3.- MPT
plot(d$delta_throughput ~ d$MPT, xlab = "MPT", ylab = "Delta Throughput", main = "Scatterplot")
lm_mpt <- lm(d$delta_throughput ~ d$MPT)
abline(lm_mpt,col = "red")
plot(d$delta_throughput ~ d$Total_Strips, xlab="Number of Strips per Shift", ylab="Delta Throughput", main="Scatterplot Delta Througput")
# 4.- Regression model to predict delta throughput
# The question alludes that all independent variables should be based on the characteristic of the strips
# We did not include MPT (meters per ton), a dimension indicator, but a theoretical factor calculated by the engineers
# Schulze did not account for MPT, so we based our models on the characteristics of the material
# We have left thickness 1 to 3 and Width and Width3 , as this complements each other to account to a total number of strips
# Grade is significant, and we have included 3 variables out of 6
# Test - heteroskedasticity - Removal of Grade 3
# Test Pass - Model with Grade 1 and Grade 5 (Final)
lm_final <- lm(delta_throughput ~ thickness_1+ thickness_2 + thickness_3 + width_1 + width_3 + grade_1 + grade_5 + run_time_ratio, data = d)
summary(lm_final)
# Plotting to see the dispersion between the fitted values (estimations) and the residuals (errors)
plot(fitted(lm_new),residuals(lm_new))
#Breusch-Pagan Test, small p-value, we can assume, we have to reject H:0
bptest(lm_final)
# We are testing heteroskedasticity,heteroscedasticity is the absence of homoscedasticity.
coeftest(lm_final, vcov = vcovHC(lm_new,"HC1"))
# Testing Multi-Collinearity
imcdiag(lm_final) # Low VIF's no correlation
##########
#5.- Confidence Interval (90%)
# Coefficient for run_time_ratio = 4.7 to 6.01
# This means that for each increase in percentage of RTR (Efficiency), we se an increase in the delta throughput of 5.40
confint(lm_final, level = 0.90)
#############################
#6.- Change in delta throughput
delta_1_3 <- 15.8708 + 3.4007 - 6.9680 + 6.4173
delta_1_3
## 7.- Production Forecast - Delta Throughput (tons) Estimation per shift for the month of May
## The estimate for change delta throughput due to change in thickness and width (from 1 to 3) is 18.72
# Creating a dataframe for each individual value of X (independent variables)
new <- data.frame(thickness_1 = 996/84, thickness_2 = 1884/84,thickness_3 = 434/84,width_1 = 1242/84,width_3 = 881/84,grade_1 = 109/3314,grade_5 =121/3314,run_time_ratio = 86)
## The prediction is -14.66 tons average delta through per shift for the month of May
predict(lm_final, newdata = new, interval="prediction")
## Prediction Plot delta-Throughput for Schuze's prediction (86%)
pred.int <- predict(lm_final, interval = "prediction")
hist(d$delta_throughput)
abline(v= -14.7,col ="red")
###############
##8.- Provide 90% confidence interval for the average delta throughput for 90% interval
predict(lm_new, newdata = new, interval="confidence", level = 0.9)
# Boundary level between -24.79 and -4.54
####################################################################
# Appendix Statistics ###
par(mfrow = c(2,2))
hist(main = "Run Time Ratio", xlab = "Run Time Ratio", d$run_time_ratio)
hist(main = "MPT", xlab = "MPT", d$MPT)
hist(main = "Total Strips", xlab = "Total Strips", d$Total_Strips)
hist(main = "Throughput", xlab = "Throughput", d$throughput)
|
84fdbbf49c862a0e7d0c36fabb3c8f3f42a10858 | 536dca63b85c687da2d179b16d062519f6af4e9d | /PilotRetention.R | 69ec68fe005406c428e9c51bb076a9b02509556e | [] | no_license | madikas/R-Client_Retention | 567be9aee64707f3783b8c62b0c02900a8cc8e12 | ff740348bbdd4ce2a3ac232f8ed498f8bdf6fb58 | refs/heads/main | 2023-01-30T04:20:15.756558 | 2020-12-10T18:53:38 | 2020-12-10T18:53:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,700 | r | PilotRetention.R | library(tidyverse)
library(caret)
library(rpart)
library(rpart.plot)
library(ranger)
# Data import
Pilot_data <- read.csv("~/Documents/HEC/Statistical_Learning/Client Retention/train_student.csv")
Realdata <-read.csv("~/Documents/HEC/Statistical_Learning/Client Retention/score_student_withID.csv")
str(Pilot_data)
Pilot_data$unlimited_text <- NULL
Pilot_data$active <- NULL
Realdata$active <- NULL
Realdata$unlimited_text <- NULL
#Period id into categorical variable
CreateGrp <- function(period){
if (period >= 0 & period <= 12){
return('0-12')
}else if(period > 12 & period <= 24){
return('12-24')
}else if (period > 24 & period <= 48){
return('24-48')
}else if (period > 48 & period <=60){
return('48-60')
}else if (period > 60){
return('> 60')
}
}
#Factorize the customer by period of months
Pilot_data$GrpTenure = sapply(Pilot_data$period_id, CreateGrp)
Pilot_data$GrpTenure = as.factor(Pilot_data$GrpTenure)
Realdata$GrpTenure = sapply(Realdata$period_id, CreateGrp)
Realdata$GrpTenure = as.factor(Realdata$GrpTenure)
# Target variable as 0 or 1 , I tried as.numeric but it gives back 1 and 2...
Pilot_data$churn_in_12 <- as.numeric(as.logical(toupper(Pilot_data$churn_in_12)))
summary(Pilot_data)
set.seed(123456)
#Remove useless columns
Pilot_data$voice_minutes <- NULL
Pilot_data$time_since_overage <- NULL
Pilot_data$time_since_data_overage <- NULL
Pilot_data$time_since_voice_overage <- NULL
Realdata$voice_minutes <- NULL
Realdata$time_since_overage <- NULL
Realdata$time_since_data_overage <- NULL
Realdata$time_since_voice_overage <- NULL
#Split trainset into train and test set
index_train <- sample(1:nrow(Pilot_data), 4/5 * nrow(Pilot_data))
train_set <- Pilot_data[index_train, ]
test_set <- Pilot_data[-index_train, ]
summary(train_set)
#Impute trainset median for phone price, time_since_technical_problems and time_since_complaints
phone_price_median <- 737.54
train_set$phone_price <- ifelse(is.na(train_set$phone_price), phone_price_median, train_set$phone_price)
test_set$phone_price <- ifelse(is.na(test_set$phone_price), phone_price_median, test_set$phone_price)
Realdata$phone_price <- ifelse(is.na(Realdata$phone_price), phone_price_median, Realdata$phone_price)
tech_problem_median <- 3
train_set$time_since_technical_problems <- ifelse(is.na(train_set$time_since_technical_problems), tech_problem_median, train_set$time_since_technical_problems)
test_set$time_since_technical_problems <- ifelse(is.na(test_set$time_since_technical_problems), tech_problem_median, test_set$time_since_technical_problems)
Realdata$time_since_technical_problems <- ifelse(is.na(Realdata$time_since_technical_problems), tech_problem_median, Realdata$time_since_technical_problems)
complaint_median <- 3
train_set$time_since_complaints <- ifelse(is.na(train_set$time_since_complaints), complaint_median, train_set$time_since_complaints)
test_set$time_since_complaints <- ifelse(is.na(test_set$time_since_complaints), complaint_median, test_set$time_since_complaints)
Realdata$time_since_complaints <- ifelse(is.na(Realdata$time_since_complaints), complaint_median, Realdata$time_since_complaints)
summary(train_set)
summary(test_set)
# random forest
case_weights <- ifelse(train_set$churn_in_12 == 1 , 5 , 1)
RF <- ranger(formula = churn_in_12~ .-unique_id -id -family_id -total_overage_fees, data = train_set ,
case.weights = case_weights , classification = TRUE , verbose = TRUE , num.trees = 300,
mtry = 6 , importance = "impurity", write.forest = TRUE , probability = TRUE ,
min.node.size = 1000 , max.depth = 15 , splitrule = "gini", )
PredTest <- predict(RF , data = test_set, num.trees = 300 , type = "response" , verbose= T )
PredTest <- PredTest$predictions
PredTest <- PredTest[,2]
source("~/Documents/HEC/Statistical_Learning/Week5-PredBinaries/PredictingBinaries.R")
confusion(test_set$churn_in_12,as.numeric(PredTest>0.5))
roc(test_set$churn_in_12,PredTest,col="blue")$AUC
importance(RF)
#Realdataset prediction for churn probability
RF_Prediction <- predict(RF , data = Realdata , num.trees = 300 , type = "response" , verbose= T )
RF_Prediction <- RF_Prediction$predictions
RF_Prediction<- RF_Prediction[,2]
#Best cut off was 0.5023 trees = 300
Cutoff_up <- 0.51
#Result
Realdata$INVITATION <- ifelse(RF_Prediction> Cutoff_up, 1, 0)
Realdata$INVITATION = factor(Realdata$INVITATION)
Result <- data.frame(Realdata$unique_family , Realdata$INVITATION)
Index_NoChurn <- which(Result$Realdata.INVITATION==0)
Churn <- Result[-Index_NoChurn,]
nrow(Churn)
write.csv(Churn$Realdata.unique_family , "~/Documents/HEC/Statistical_Learning/Client Retention/Result1.csv",row.names = FALSE)
|
e9a74b8178dadb26b535ef912343a89be4875939 | c9e938b2098f8d2b4a7a1f524247523acd3327f2 | /cachematrix.R | 4d1dd8210d5e4d02339750b696d8d33174e8de6f | [] | no_license | cherish2019/ProgrammingAssignment2 | 23affb7ff7728801c049fff270278aa991c45441 | 6985abed3fe7b390917cec722223b1c77a38bace | refs/heads/master | 2020-04-20T18:25:33.128702 | 2019-02-04T03:15:53 | 2019-02-04T03:15:53 | 169,021,078 | 0 | 0 | null | 2019-02-04T03:04:32 | 2019-02-04T03:04:31 | null | UTF-8 | R | false | false | 803 | r | cachematrix.R | ## This function creates the invserse mayrix of the original one
## The inverse of a matrix can be extracted by function cacheSolve
makeCacheMatrix <- function(x = matrix()) {
inverse <- NULL
set <- function(y) {
x <<- y
inverse <<- NULL
}
get <- function() x
setInverse <- function(inverse) inv <<- inverse
getInverse <- function() inv
list(set = set,
get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## create "matrix" that cache the inverse of the matrix
cacheSolve <- function(x, ...) {
inverse <- x$getInverse()
if (!is.null(inverse)) {
message("getting cached data")
return(inverse)
}
mat <- x$get()
inverse <- solve(mat, ...)
x$setInverse(inverse)
inverse ## Return a matrix that is the inverse of 'x'
}
|
475dbab196d47bb4e8756a677d566de0a7c81d64 | 11b126a360963aee9042c4b94a95854b9f6e0348 | /Microarray/Filtering_absent_genes_in_microarray.R | 48f0eba2296699f4fca1ac0131780d01bac08911 | [] | no_license | Cosmoboss/CRC-biomarkers | 82ebe9fa39767a41ae1c3e2a48f99c09114a6fe7 | a607c0a9144098315bb0a9d35dd50fdd8040e7dd | refs/heads/main | 2023-05-24T10:38:59.429686 | 2021-06-09T17:02:10 | 2021-06-09T17:02:10 | 368,797,688 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,142 | r | Filtering_absent_genes_in_microarray.R | #Load the libraries that will be used.
library(matrixStats)
#Read in the merged microarray datasets
hgu133plus2 <- read.table("HGU133Plus2_after_ComBat_df.xls", sep="\t", header=T)
hgu133a <- read.table("HGU133A_after_ComBat_df.xls", sep="\t", header=T)
#Read in the RNA-Seq studies
gse74369 <- read.table("gse74369_processed.xls", header=T)
gse146009 <- read.table("gse146009_processed.xls", sep = "\t", header=T)
#Remove any gene that is an NA
gse74369 <- gse74369[!is.na(gse74369$external_gene_name),]
gse146009 <- gse146009[!is.na(gse146009$external_gene_name),]
#Identify all unique genes in RNA-Seq
rna_seq_genes <- unique(c(gse74369$external_gene_name, gse146009$external_gene_name))
`%notin%` <- Negate(`%in%`) # Create a negate of %in% operator
hgu133plus2_genes <- unique(hgu133plus2$Gene) #All unique genes in HGU133Plus2
hgu133a_genes <- unique(hgu133a$Gene) #All unique genes in HGU133A
#Find all HGU133's genes not present in RNA-Seq genes
hgu133a_specific <- hgu133a_genes[hgu133a_genes %notin% rna_seq_genes]
hgu133plus2_specific <- hgu133plus2_genes[hgu133plus2_genes %notin% rna_seq_genes]
#Overlap both HGU133 to find microarray specific genes
microarray_specific <- hgu133a_specific[hgu133a_specific %in% hgu133plus2_specific]
#find all genes specific in HGU133plus2 only
hgu133plus2_specific <- hgu133plus2_specific[hgu133plus2_specific %notin% microarray_specific]
#List of the Housekeeping genes
HKG_genes <- c("GPI","PSMB2","PSMB4","SNRPD3","RAB7A","REEP5","VCP","CHMP2A","C1orf43","VPS29","EMC7","CSTB","GUSB","HADHA","HPRT1",
"SGSH","PGK1","TCOF1","CYB5R3","GM2A","SOD1","GNAS","COMT","GUK1","IMPDH2","P4HB","POLR2A","RPL5","RPL8","RPL11","RPL19",
"RPL17","RPL27","RPL32","RPL34","RPL36AL","RPS5","RPS9","RPS11","RPS13","RPS15","RPS16","RPS24","RPS25","ADAR","ADD1",
"AP1B1","AES","ANXA6","ATP6AP1","BTF3","ENTPD6","GAPDH","CHD4","TBCB","CSNK2B","CTBP1","DAD1","DAXX","DDT","EIF4G2",
"ENO1","FBL","EXTL3","XRCC6","BLOC1S1","GDI1","GDI2","PRMT1","HSBP1","APLP2","ARAF","ARF1","ARF4","ARF5","RHOA","ATF4",
"ATP5D","ATP5G3","ATP6V0C","ATP6V1E1","ATP5O","BSG","CANX","CAPNS1","SEPT7","CENPB","CLTA","CLTB","COX4I1","COX5B",
"COX6B1","COX7A2","COX7C","CTNNB1","CTSD","CYC1","E2F4","EEF2","FAU","FTH1","GAPDH","GOT2","GPX4","HMGB1","HNRNPD",
"HNRNPK","ID3","JAK1","M6PR","MAP4","MAZ","MIF","MAP3K11","MPG","MTX1","NDUFA2","NDUFC1","NME2","ODC1","PRDX1","PFDN1",
"PFDN5","SLC25A3","PHF1","PIM1","PPP1R10","PRKAG1","PRKCSH","PSMA7","PSMB1","PSMB7","PSMD2","PSMD3","PSMD8","PSMD11",
"PSME2","PTBP1","RING1","RNH1","RPA2","RPL15","RPN1","RPS27A","SAFB","SRSF2","SGTA","SNRNP70","SNRPB","SNRPG","SRM",
"SRP14","SSR2","TAPBP","TMBIM6","TSTA3","TTC1","TUFM","TXN","UBA1","UBE2D2","UBE2I","UQCRC1","YWHAB","YWHAZ","ZNF91",
"HIST1H2BC","SLC25A11","STK24","YARS","EIF3D","EIF3F","EIF3G","EIF3I","BECN1","B4GALT3","SNX3","GPAA1","ADAM15","BANF1",
"ARHGEF7","MCM3AP","BUD31","CPNE1","RPS6KB2","UBE2M","RPL14","ATP5A1","ATP6V0B","AP2M1","AP2S1","COX8A","NDUFB7","RAB1A",
"SDHA","SREBF1","ATP6V1F","ARHGAP1","ARHGDIA","PTTG1IP","CD81","SEPT2","ENSA","ERH","HDGF","HNRNPAB","ILF2","ILK",
"NDUFA1","NDUFS5","SNRPA","SNRPD2","DDX39B","PABPN1","C21orf33","MTA1","HGS","COX7A2L","MAPKAPK2","VAMP3","ATP6V1G1",
"ATP5J2","SPAG7","H2AFY","C14orf2","CLOCK","ACTN4","CAPZB","FUS","NDUFA7","PFN1","RBM8A","WDR1","ABL1","ATP5G1",
"BMI1","DDOST","GNB2","HINT1","HSPA5","JUND","NCL","MLF2","CFL1","HNRNPH1","KARS","LAMP1","LDHA","RPS14","SCAMP3",
"ARPC4","ARPC3","TSFM","ARPC2","BCAP31","TRIM28","EIF1","SRRM1","SAP18","ACAT2","SLC25A1","VPS72","CCT3","UQCRFS1",
"UQCRH","RPL10","AKR1A1","TUBA1B","HAX1","NEDD8","PIN1","DPF2","VARS","RAN","C1D","ZNHIT1","TIMM44","TADA3","ATP5H",
"NXF1","CREB3","SYNCRIP","HYOU1","ANP32B","AGPAT1","RABAC1","CCT7","DRAP1","PRPF8","HEXIM1","TRIM27","SARS","RRAGA",
"API5","HSPA8","TUBGCP2","JTB","NUDT3","RNPS1","TALDO1","ZFPL1","AFG3L2","KDELR1","SEC61B","TMED2","YWHAQ","UQCR11",
"COPS6","CALM1","IDH3B","RAC1","SUMO3","RTN4","KAT7","ATP5I","NDUFV1","RPL10A","TCEB2","RPL35","ATXN2L","LYPLA2",
"PARK7","COPE","GABARAP","GABARAPL2","ABL1","HSP90AB1","CASC3","NONO","CD3EAP","DNPEP","ARL2BP","AHSA1","CIZ1","AATF",
"FBXO7","PICK1","H2AFV","RPL13A","PDCD6","EIF3K","PRRC2B","PPP2R1A","CNPY2","PUF60","SEC61G","SND1","UQCRQ","ZNF592",
"MLEC","PTDSS1","IST1","EFCAB14","MFN2","PDAP1","LMTK2","TCF25","XPO7","ESYT1","CTDNEP1","BRMS1","NELFB","RAP1B","ANAPC5",
"TRAP1","INPP5K","PTOV1","TMED9","OTUB1","BTBD2","COMMD4","UBB","TERF2IP","TOMM7","GSK3A","SAR1A","STARD7","SDR39U1",
"UBC","NDUFV2","MRPS12","POLR2L","MRPL23","PPP1R11","MCL1","POLR2F","RELA","TUT1","CDK11A","KXD1","TMEM109","C9orf16",
"MAP2K2","MRPL9","TMEM147","MYL12B","ZNF384","TEX261")
#put in the microarray platform that you want to filter
a1 <- hgu133plus2
#Remove any genes that are labelled as NA
a1 <- a1[!is.na(a1$Gene),]
#Calculate the average expression of the genes in all samples
a1$AveExpr <- rowMeans(a1[,1:(length(a1)-1)])
#Add this if you're curious about the hgu133plus2 specific genes
sel_3381 <- a1[a1$Gene %in% hgu133plus2_specific,]
#Safe the expression levels of the microarray specific genes
sel_1504 <- a1[a1$Gene %in% microarray_specific,]
columns_sel <- c("Gene","AveExpr") #Save these columns for later
#get the selected columns from hgu133plus2 specific genes if you're processing it
sel_3381_sel <- sel_3381[,columns_sel]
#Safe the gene names and average expression of the microarray specific genes
sel_1504_sel <- sel_1504[,columns_sel]
#Get all HKG genes from all the genes
sel_hkg <- a1 [a1$Gene %in% HKG_genes,]
#Safe the gene names and average expression from the HKG
sel_hkg_sel <- sel_hkg[,columns_sel]
#Plot the average expression of all genes
hist(a1$AveExpr, col="skyblue", ylim = c(0, 2500),border=F) #All genes in microarray
hist(sel_3381$AveExpr, col="gold",add=TRUE,border=F) #hgu133plus2 specific genes
hist(sel_1504$AveExpr, col="red",add=TRUE,border=F) #microarray specific genes
hist(sel_hkg$AveExpr, col="black",add=TRUE,border=F) #Housekeeping genes
#To calculate Standard Deviation (SD)
aa1 <- transform(as.matrix(a1[, 1:(length(a1)-2)]), SD=apply(as.matrix(a1[, 1:(length(a1)-2)]),1, sd, na.rm = TRUE))
A1_Sel <- a1[,columns_sel]
A1_Sel$SD <- aa1$SD
#Add the SD for each GENE type
sel_3381_sel$SD <- A1_Sel[which(row.names(sel_3381_sel) %in% row.names(A1_Sel)),]$SD
sel_1504_sel$SD <- A1_Sel[which(row.names(sel_1504_sel) %in% row.names(A1_Sel)),]$SD
sel_hkg_sel$SD <- A1_Sel[which(row.names(sel_hkg_sel) %in% row.names(A1_Sel)),]$SD
#Calculate the average SD and average expression to identify the cut-off
AveExpr <- mean(apply(sel_1504[,1:(length(sel_1504)-2)], 1, mean))
AveSD <- mean(apply(sel_1504[,1:(length(sel_1504)-2)], 1, sd))
#Test the filtering criteria of AveExpr + SD times 1,2, or 3
#SD less than 1
FilteredGenes_V1 <- A1_Sel[which(A1_Sel$AveExpr < AveExpr + (AveSD * 1)),]
SelectedGenes_V1 <- A1_Sel[- which(A1_Sel$AveExpr < AveExpr + (AveSD * 1)),]
filtered_1504_V1 <- sel_1504_sel[which(row.names(sel_1504_sel) %in% row.names(FilteredGenes_V1)),]
filtered_HKG_V1 <- sel_hkg_sel[which(row.names(sel_hkg_sel) %in% row.names(FilteredGenes_V1)),]
print(c(dim(filtered_1504_V1), dim(filtered_HKG_V1), dim(SelectedGenes_V1), (AveExpr+AveSD*1)))
#SD less than 2
FilteredGenes_V2 <- A1_Sel[which(A1_Sel$AveExpr < AveExpr + (AveSD * 2)),]
SelectedGenes_V2 <- A1_Sel[- which(A1_Sel$AveExpr < AveExpr + (AveSD * 2)),]
filtered_1504_V2 <- sel_1504_sel[which(row.names(sel_1504_sel) %in% row.names(FilteredGenes_V2)),]
filtered_HKG_V2 <- sel_hkg_sel[which(row.names(sel_hkg_sel) %in% row.names(FilteredGenes_V2)),]
print(c(dim(filtered_1504_V2), dim(filtered_HKG_V2), dim(SelectedGenes_V2), (AveExpr+AveSD*2)))
#SD less than 3
FilteredGenes_V3 <- A1_Sel[which(A1_Sel$AveExpr < AveExpr + (AveSD * 3)),]
SelectedGenes_V3 <- A1_Sel[- which(A1_Sel$AveExpr < AveExpr + (AveSD * 3)),]
filtered_1504_V3 <- sel_1504_sel[which(row.names(sel_1504_sel) %in% row.names(FilteredGenes_V3)),]
filtered_HKG_V3 <- sel_hkg_sel[which(row.names(sel_hkg_sel) %in% row.names(FilteredGenes_V3)),]
print(c(dim(filtered_1504_V3), dim(filtered_HKG_V3), dim(SelectedGenes_V3), (AveExpr+AveSD*3)))
#Safe the criteria where the least amount of HKG were filtered whilst filtering
#as much absent/low expressed genes (SD times 1 in my case)
safe_filtered <- a1[which(row.names(a1) %in% row.names(FilteredGenes_V1)),]
write.table(safe_filtered, "HGU133Plus2_filtered.xls", sep="\t", quote=F)
safe_selected <- a1[which(row.names(a1) %in% row.names(SelectedGenes_V1)),]
write.table(safe_selected, "HGU133Plus2_after_lowExpression_filter.xls", sep="\t", quote=F)
|
1ae919c984adac4cccd24587a9ee9bbe5752357c | 1b605394809312ea6c0a1a6d69a1a24f9cdd8982 | /inst/examples/server.R | 30e0f2c276012c50cfb0502ea90e8f3dfe61d3b5 | [] | no_license | AparicioJohan/GS | 7994604c10904927ceefe29289fbeeaec0d32ba2 | d81b725ff79726f182e148732e92d997348de6c4 | refs/heads/master | 2021-03-30T05:58:42.203100 | 2020-10-07T13:54:53 | 2020-10-07T13:54:53 | 248,023,382 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,810 | r | server.R | library(shiny)
library(ggplot2)
library(plotly)
library(SpATS)
library(ggsci)
library(ggpubr)
library(shinyjs)
library(shinytoastr)
library(GS)
library(sommer)
# source("https://raw.githubusercontent.com/AparicioJohan/GPrediction/master/crossGP.R")
options(shiny.maxRequestSize = 70*1024^2)
shinyServer(function(input,output,session)({
# Update variable traits --------------------------------------------------
observe({
phen <- input$file3$datapath
validate(need(input$file3, "missing file 3"))
phen <- read.csv(phen)
updatePickerInput(session, "Id094", choices=names(phen[,-1]))
})
observe({
updatePickerInput(session, "Id095", choices=input$Id094)
})
# Read data and run the models --------------------------------------------
df <- eventReactive(input$action,{
geno <- input$file1$datapath
samp <- input$file2$datapath
phen <- input$file3$datapath
if(is.null(input$file1)|is.null(input$file2)|is.null(input$file2))
toastr_error( "Missing files..." ,position = "bottom-right")
validate(need(input$file1, "missing file 1"))
validate(need(input$file2, "missing file 2"))
validate(need(input$file3, "missing file 3"))
prior <- input$checkGroup
# prior[prior=="1"] <- "ASReml"
validate(need(input$checkGroup, "Select one model"))
validate(need(input$Id094, "Select the traits"))
withCallingHandlers({
results <- crossGP(geno,samp,phen,prior,niter = input$iter, testporc = input$porcent, traits = input$Id094)
},
message = function(m) {
shinyjs::html("console", m$message, TRUE)
}
)
results
})
# Output data -------------------------------------------------------------
output$Rawdata <- DT::renderDataTable({
req(nrow(df()$data)>=1)
df()$data %>%
DT::datatable(
extensions = 'Buttons', filter = 'top', selection="multiple",
options = list(dom = 'lfrtipB', scrollX = TRUE, pageLength = 10,lengthMenu = c(2, nrow(df()$data)),
buttons = c('excel', "csv")))
})
# Plot correlations -------------------------------------------------------
output$plot <- renderPlot({
if (input$action==0) {return()}
else {
validate(need(input$file1, "missing file 1"))
validate(need(input$file2, "missing file 2"))
validate(need(input$file3, "missing file 3"))
if(length(input$checkGroup)>=1){
g1 <- df()$data %>%
ggplot(aes(x=trait,y=corr,fill=prior))+geom_boxplot()+theme_bw(base_size = 13)+
theme(axis.text.x = element_text(angle = 70,hjust = 1),legend.title = element_blank())+
ylab("Prediction Ability")+xlab("")+ scale_fill_simpsons()
if(input$iter==1||input$porcent==0){
g1 <- df()$data %>%
ggplot(aes(x=trait,y=corr,color=prior))+geom_point()+theme_bw(base_size = 13)+
theme(axis.text.x = element_text(angle = 70,hjust = 1),legend.title = element_blank())+
ylab("Prediction Ability")+xlab("")+ scale_fill_simpsons()
}
} else {
g1 <- df()$data %>%
ggplot(aes(x=trait,y=corr,fill=trait))+geom_boxplot()+theme_bw(base_size = 13)+
theme(axis.text.x = element_text(angle = 70,hjust = 1),legend.title = element_blank())+
ylab("Prediction Ability")+xlab("")+ scale_fill_simpsons()
if(input$iter==1||input$porcent==0){
g1 <- df()$data %>%
ggplot(aes(x=trait,y=corr,color=trait))+geom_point()+theme_bw(base_size = 13)+
theme(axis.text.x = element_text(angle = 70,hjust = 1),legend.title = element_blank())+
ylab("Prediction Ability")+xlab("")+ scale_fill_simpsons()
}
}
isolate(g1)
}
})
# Marker information ------------------------------------------------------
output$mark <- renderPlot({
if (input$action==0) {return()}
else {
validate(need(input$file1, "missing file 1"))
validate(need(input$file2, "missing file 2"))
validate(need(input$file3, "missing file 3"))
req(input$Id095)
validate(need(input$porcent==0, "Percentage of test population should be zero"))
validate(need(input$method%in%input$checkGroup, paste(input$method, "is not include in the fitted models")))
out_table <- df()
bHat <- out_table$models[[input$Id095,input$method]]$ETA[[1]]$b
MarK <- data.frame(Marker=1:length(bHat),bHat = bHat^2)
g1 <- MarK %>%
ggplot(aes(x=Marker, y=bHat)) +
geom_point(size=1.5,color="black") +
geom_segment(aes(x=Marker,
xend=Marker,
y=0,
yend=bHat), color="red") +
labs(title="Marker Effects",
subtitle=paste(input$method, "model",sep = " "),
caption="generated by: Mr.Gen", y= 'Estimated Squared-Marker Effect') +
theme(axis.text.x = element_text(angle=65, vjust=0.6))+theme_bw(base_size = 15)
isolate(g1)
}
})
output$pred <- renderPlot({
if (input$action==0) {return()}
else {
validate(need(input$file1, "missing file 1"))
validate(need(input$file2, "missing file 2"))
validate(need(input$file3, "missing file 3"))
req(input$Id095)
validate(need(input$porcent==0, " "))
req(input$method%in%input$checkGroup)
out_table <- df()
bHat <- out_table$models[[input$Id095,input$method]]$ETA[[1]]$b
MarK <- data.frame(Marker=1:length(bHat),bHat = bHat^2)
Pred <- data.frame( yHat=out_table$models[[input$Id095,input$method]]$yHat,
y=out_table$models[[input$Id095,input$method]]$y)
g1 <-
Pred %>%
ggplot(aes(x=yHat,y=y)) +geom_point(alpha=0.2,size=3)+
geom_abline(slope = 1,intercept = 0,linetype=2, color="red",size=1)+
geom_smooth(method = "lm",formula = y~x, se = F)+
labs(y="Phenotype",x='Predicted genomic value', title="Predicted genomic values vs phenotypes")+
stat_cor()+theme_bw(base_size = 15)
isolate(g1)
}
})
# update number iterations ------------------------------------------------
observe({
if (input$porcent != 0) {
shinyjs::show("nonIter",animType = "fade",anim = TRUE)
# enable("nonIter")
} else {
shinyjs::hide("nonIter",animType = "fade",anim = TRUE)
# disable("nonIter")
toastr_info("Percentage of test population equal to zero: Only one iteration selected",position = "bottom-right")
}
})
observeEvent(input$run,{
toastr_info(paste(input$Id094,collapse = " "))
})
}))
|
c600bea64280bcbe8eabc2ef721ffc0774a25aeb | 07cbeb7cffaa00d21734c486c793e4ed4fe698db | /plot1.R | 078e0038dd7fda725b8f75a8b52d88d2c289fd51 | [] | no_license | ssuresh8/ExData_Plotting1 | 0897bd40a96cb1769c270a640e196496fa28f558 | 376b0d95156015f90de680d346d831aac8a8233a | refs/heads/master | 2020-05-29T11:45:56.200047 | 2015-12-13T22:19:51 | 2015-12-13T22:19:51 | 47,864,087 | 0 | 0 | null | 2015-12-12T04:25:19 | 2015-12-12T04:25:19 | null | UTF-8 | R | false | false | 893 | r | plot1.R | ## read in the text file
## since it is a semicolon delimited file use sep as semi colon and headings are in first row
dat <- read.table("household_power_consumption.txt", header = T, sep= ";")
#convert the date and time columns in to the date and time classes in R
dat$Date <-as.Date(dat$Date, format = "%d/%m/%Y")
##index the data frame for the required dates of 2007-02-01 and 2007-02-02
dat2 <- dat[(dat$Date=="2007-2-1" | dat$Date=="2007-2-2" ), ]
##convert global active power column to numeric
dat2$Global_active_power <- as.numeric(as.character(dat2$Global_active_power))
#send the plot to PNG
png('plot1.png', width=480, height=480)
##plot the histogram
#Title:Global Active Power
#x-ais Global Activer Power (kilowatts)
# column color red
hist(dat2$Global_active_power,main = "Global Active Power", xlab = "Global Active Power (kilowatts)",col ='red')
##complete plot
dev.off()
|
912bf5673e5d3c7f162f0ab57a5c7bf080e84c44 | c3ab6a0f29d068ff5f25dae32cbcefea14645fef | /man/JustATestPlot.Rd | 6e552c12fb05dda8893291d3276abcebd23aeef7 | [] | no_license | 1587causalai/gong-pkg | acac9de28c0e81bce531dc79591a03a8a108616d | fe53d136b828170ee0d4d6cb0c8dbb4da5bcfae8 | refs/heads/master | 2023-02-06T20:01:21.233805 | 2017-10-06T01:13:03 | 2017-10-06T01:13:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 240 | rd | JustATestPlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hello.R
\name{JustATestPlot}
\alias{JustATestPlot}
\title{A function for plot a picture with ggplot2}
\usage{
JustATestPlot()
}
\description{
This function ...
}
|
d4df0c02fe5c70be41a473769041135260cfb3c2 | 2bce49ab57997fe6121efcfba8544e8ede5f6376 | /tests/testthat.R | afcde10bc7bf8d5b6654f8246735af8035ef102c | [] | no_license | hfjn/nmmso_benchmark | 7e0309244529226f1ab2b0bd7091772392ceba38 | bf13085ce7ce6d597c45095d30d2e0909467eb56 | refs/heads/master | 2021-05-30T02:31:26.276871 | 2016-01-09T16:24:01 | 2016-01-09T16:24:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 72 | r | testthat.R | library(testthat)
library(nmmsoBenchmark)
test_check("nmmsoBenchmark")
|
542666acf64a00a63a4e8c505be066d56c4730af | 8d2fab7f71a20ebbc5d9de832cf16a62ba6ca0b4 | /src/SEER_Summary.R | b8cacdd2eda7603a12c0b6199bf6be2305c7b14f | [
"MIT"
] | permissive | phildwalker/local_cancer_prev_MrP | e89986afa4059f71d449b705cc706e41b6bf4d10 | a269935cc001fdeda71e5f71821b7af574ee17c4 | refs/heads/main | 2023-03-09T05:59:51.330416 | 2021-02-18T16:20:27 | 2021-02-18T16:20:27 | 338,878,514 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,235 | r | SEER_Summary.R | # https://seer.cancer.gov/explorer/application.html?site=55&data_type=1&graph_type=3&compareBy=race&chk_race_1=1&chk_race_5=5&chk_race_4=4&chk_race_3=3&chk_race_6=6&chk_race_8=8&chk_race_2=2&sex=3&rate_type=2&advopt_precision=1&advopt_display=2
SEER <-
readxl::read_excel(here::here("data-raw", "Summary_Cancer_Age_Rates.xlsx"), sheet = "All_Cancer") %>%
pivot_longer(cols = 3:9, names_to = "Races", values_to = "RatePer") %>%
left_join(.,
readxl::read_excel(here::here("data-raw", "Summary_Cancer_Age_Rates.xlsx"), sheet = "lookup"),
by = c("Races" = "ColName"))
SEER_All_Long <-
SEER %>%
filter(Short == "All_Race") %>%
mutate(RatePer = ifelse(is.na(RatePer), 0,RatePer)) %>%
group_by(AgeGroup, Short) %>%
summarise(RateAvg = mean(RatePer)) %>%
ungroup() %>%
mutate(NonCan = 100000-RateAvg) %>%
pivot_longer(cols = 3:4, names_to = "CancerFlag", values_to = "EstIncid") %>%
mutate(CancerFlag = ifelse(CancerFlag == "RateAvg", 1,0),
EstCount = round(EstIncid)) %>%
uncount(weights = EstCount)
library(lme4)
fit <- glmer(CancerFlag ~ 1 + (1|AgeGroup) , data = SEER_All_Long, family = binomial)
summary(fit)
save(fit, file = here::here("data", "fit.rda"))
|
81c291d37aed3f7dafc2323d609051c042f5461d | b5555aca84eba3fdad763c8922e5b856718ff1d9 | /Image Restoration.R | 2640c9c5db3f3accbc0753edd7b8ee60ae06dbb9 | [] | no_license | resobyte/ImageRestorationR | 242edbac317133e1846fa1f5cf0acef320811335 | 31d89048e368431dc2ca10352457c7b6c1682c65 | refs/heads/master | 2020-03-18T14:36:29.973462 | 2018-05-25T13:29:45 | 2018-05-25T13:29:45 | 134,856,833 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,716 | r | Image Restoration.R | temp <- (matrix(1,nrow=3,ncol=3) %x% matrix(c(2,1,1,2),2))[1:5,1:5]
M1 <- temp %x% matrix(1,nrow=10,ncol=10)
M2 <- matrix(2,nrow=50, ncol=50)
M2[21:30,1:10] <- M2[21:30,41:50] <- 1
M2[11:40,11:20] <- M2[11:40,31:40] <- 1
M2[1:50,21:30] <- 1
M3 <- matrix(2,nrow=50, ncol=50)
M3[11:20,] <- M3[31:40,] <- 1
M3[1:10,21:30] <- M3[41:50,21:30] <- 1
M4 <- 3 - M2
X.ori <- cbind(rbind(M1,M4),rbind(M2,M3))
image(X.ori, axes=FALSE, frame.plot=FALSE, col = grey(c(0, 0.8)))
p <- 0.2
set.seed(12345)
N <- dim(X.ori)[1]
Y <- X.ori
Y1 <- Y[2:(N-1),2:(N-1)]
i1 <- which(runif(((N-2)^2)) < p)
Y1[i1] <- 3-Y1[i1]
Y[2:(N-1),2:(N-1)] <- Y1
image(Y, axes=FALSE, frame.plot=FALSE, col=grey(c(0, 0.8)))
alpha <- 0.03
beta <- 1
nmcmc <- 50
N <- 100
set.seed(12345)
X <- matrix(runif(N^2)<0.5,N)+1
for (k in (1:nmcmc)) {
for (i in sample(2:(N-1))) {
for (j in sample(2:(N-1))) {
Vij <- c(X[i-1,j], X[i+1,j], X[i,j-1], X[i,j+1])
u1 <- beta*sum(Vij!=1) + alpha
u2 <- beta*sum(Vij!=2)
p1 <- 1/(1+exp(-u2+u1))
r <- runif(1)
if (r < p1)
X[i,j] <- 1
else
X[i,j] <- 2
}
}
}
image(X, axes=FALSE, frame.plot=FALSE, col = grey(c(0, 0.8)))
alpha <- 0
beta <- 1.5
lambda <- log((1-p)/p)
nmcmc <- 50
set.seed(12345)
X <- Y
for (k in (1:nmcmc)) {
for (i in sample(2:(N-1))) {
for (j in sample(2:(N-1))) {
Vij <- c(X[i-1,j], X[i+1,j], X[i,j-1], X[i,j+1])
u1 <- beta*sum(Vij!=1) + alpha + lambda*(Y[i,j]!=1)
u2 <- beta*sum(Vij!=2) + lambda*(Y[i,j]!=2)
p1 <- 1/(1+exp(-u2+u1))
r <- runif(1)
if (r < p1)
X[i,j] <- 1
else
X[i,j] <- 2
}
}
}
image(X, axes=FALSE, frame.plot=FALSE, col = grey(c(0, 0.8))) |
ea927be4e0ecff9bd5bd1dadb6562a72e82238d8 | 655f8fe46c2c6143cf3889df5d3191f07ce09563 | /man/hngm.Rd | 26aac35299c4a8086378ddb7da638b1a7770241b | [] | no_license | jongguri80/NGM | 374b9909e9094ad84f14fcc9e407a1414fa9a418 | 192b4543d641ef0d58158d52b06cfe9e9441aa97 | refs/heads/master | 2020-06-24T23:13:12.783303 | 2017-07-12T00:59:03 | 2017-07-12T00:59:03 | 96,938,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,445 | rd | hngm.Rd | \name{SSVM_OU}
\alias{SSVM_OU}
\docType{package}
\title{
Bayesian Semi-parametric Stochastic Model with Ornstein-Uhlenbeck process prior (B-SSVM-OU)
}
\description{
To fit B-SSVM-OU with longitudial data to examine the association between growth acceleration and an exposure.
}
\usage{
SSVM_OU(Y_list, X_logrho, X_nu_bar, del_list, mu0, C0,
smooth_fix = FALSE, lambda = 1,
int_list = list(sigma2_eps = 0.5, sigma2_xi = 0.1, rho = rep(5, N),
nu_bar = rep(-0.5, N), sigma2_logrho = .1, sigma2_nu_bar = .1),
prior_list = list(a_eps = .001, b_eps = .001, eps_lb = 0, eps_ub = 10,
a_xi = .1, b_xi = .1, xi_lb = 0, xi_ub = 200, a0 = .01, b0 = .01),
n_iter = 1500, burn_in=500, thin=1, per_print=500)
}
\examples{
\dontrun{
data(sparse_dat) ## visit for each subject J_i = 8 ##
## same for data(moderate_dat): visit for each subject J_i = 30 ##
head(sparse_dat)
dat = dat_list(indata=sparse_dat, subj_id="subj", time_var="year", Y_var="Y")
del_list = dat$del_list
Y_list = dat$Y_list
N = length(Y_list)
X_dat = unique(data.frame(subj=sparse_dat$subj, X=sparse_dat$X))
X_logrho = model.matrix(~ X, data=X_dat)
X_nu_bar = model.matrix(~ 1, data=X_dat)
#################################### MCMC simulation #####################################
n_iter = 2500; burn_in = 500; thin=1; per_print=500
## prior for initial distribution of trajectory and velocity (should be set as a list format)
mu0 = C0 = as.list(NULL)
for(i in 1:N){
mu0[[i]] = c(13.1, 31) # c(m01, m02), where m01 and m02 are prior means, respectively, for trajectory and velocity at the initial time.
C0[[i]] = diag(c(10, 20)) # c(c01, c02), where m01 and m02 are prior variances, respectively, for trajectory and velocity at the initial time.
}
## initial values for MCMC iterations.
int_list = list(sigma2_eps = 0.5, sigma2_xi = 0.5, rho = rep(5, N), nu_bar = rep(-0.5, N),
sigma2_logrho = .1, sigma2_nu_bar = .1)
## values for hyper-parameters.
## sigma2_eps ~ IG(a_eps, b_eps)I(eps_lb, eps_ub)
## sigma2_xi ~ IG(a_xi, b_xi)I(xi_lb, xi_ub)
## sigma2_logrho, sigma2_gamma ~ IG(a0, b0)I(0, inf).
prior_list = list(a_eps = .01, b_eps = .01, eps_lb = 0, eps_ub = 10000,
a_xi = .1, b_xi = .1, xi_lb = 0, xi_ub = 10000,
a0 = .01, b0 = .01)
SSVM = SSVM_OU(Y_list, X_logrho, X_nu_bar, del_list, mu0=mu0, C0=C0,
prior_list = prior_list, int_list = int_list,
n_iter = n_iter, burn_in=burn_in, thin=thin, per_print=per_print)
}
}
|
ba1fd6f304090a80d20a52e49abbd805cf0d2d7d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/circlize/examples/circos.yaxis.rd.R | e7066f961af84f02c08ebf3e671d1103fbb1ec3d | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 561 | r | circos.yaxis.rd.R | library(circlize)
### Name: circos.yaxis
### Title: Draw y-axis
### Aliases: circos.yaxis
### ** Examples
op = par(no.readonly = TRUE)
factors = letters[1:8]
circos.par(points.overflow.warning = FALSE)
circos.par(gap.degree = 8)
circos.initialize(factors = factors, xlim = c(0, 10))
circos.trackPlotRegion(factors = factors, ylim = c(0, 10), track.height = 0.5)
par(cex = 0.8)
for(a in letters[2:4]) {
circos.yaxis(side = "left", sector.index = a)
}
for(a in letters[5:7]) {
circos.yaxis(side = "right", sector.index = a)
}
circos.clear()
par(op)
|
450765baf45213b58f8bdfed371bdf28bfd52500 | ce2bed3a57df5f6c25141b017bac624f3915ac7e | /week 2/week 2 programming assessment.R | 3809f4cf405e07d61061470027686f73fc24d5de | [] | no_license | Doublefacez/datasciencecoursera | e0b72d40dc0d0e245d4f85e2b411b29716d4f64f | e9d1b9ecf28fabbe1e5ed5c76ba3439fea9ad4d9 | refs/heads/master | 2022-11-12T10:43:22.984794 | 2020-06-23T16:53:35 | 2020-06-23T16:53:35 | 271,297,858 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,233 | r | week 2 programming assessment.R | ####################### Week 2 Programming Assignmane #####################
## Part 1 Write a function "pollutantmean" to calculate the mean
pollutantmean <- function(directory, pollutant, id = 1:332){
locate_files <- list.files(directory, full.names = TRUE) #Locating the files in the directoroy
data <- data.frame() #Creating an empty data frame for storing the values
for (i in id) {
data <- rbind(data, read.csv(locate_files[i])) #subseting the data in all files and put it into the data frame
}
mean <- mean(data[, pollutant], na.rm = TRUE) #calculating the mean of the pollutant
return(mean)
}
pollutantmean("specdata","sulfate", 1:10)
pollutantmean("specdata", "nitrate", 70:72)
pollutantmean("specdata", "sulfate", 34)
pollutantmean("specdata", "nitrate")
####Part 2 Write a function that reads a directory full of files
####and reports the number of completely observed cases in each data file.
complete <- function(directory, id= 1:332){
locate_files <- list.files(directory, full.names = TRUE)
data <- data.frame()
for(i in id){
read_each_file <- read.csv(locate_files[i]) #Read the specified files
nobs <- sum(complete.cases(read_each_file)) #calculate the total number of complete rows in each file
store_data <- data.frame(i, nobs) #Store the file name and the values into a data frame
data <- rbind(data, store_data)
}
colnames(data) <- c("id", "nobs") #provide a names to each column in the data frame
return(data)
}
complete("specdata", 1:32)
#Question 5
cc <- complete("specdata", c(6, 10, 20, 34, 100, 200, 310))
print(cc$nobs)
#Questopm 6
cc <- complete("specdata", 54)
print(cc$nobs)
#Question 7
RNGversion("3.5.1")
set.seed(42)
cc <- complete("specdata", 332:1)
use <- sample(332, 10)
print(cc[use, "nobs"])
#### Part 3 Write a function that takes a directory of data files
#and a threshold for complete cases and calculates the correlation
#between sulfate and nitrate for monitor locations where the
#number of completely observed cases (on all variables) is greater
#than the threshold.
corr <- function(directory, threshold = 0){
files <- list.files(directory, full.names = TRUE)
correlation <- c()
id <- 1:332
for (i in id){
data_each_file <- read.csv(files[i])
cvalue <- if(sum(complete.cases(data_each_file))> threshold){
cor(data_each_file$sulfate, data_each_file$nitrate, use ="pairwise.complete.obs")
}
correlation <- c(correlation, cvalue)
}
return(correlation)
}
cr <- corr("specdata", 150)
head(cr)
#Question 8
cr <- corr("specdata")
cr <- sort(cr)
RNGversion("3.5.1")
set.seed(868)
out <- round(cr[sample(length(cr), 5)], 4)
print(out)
#Question 9
cr <- corr("specdata", 129)
cr <- sort(cr)
n <- length(cr)
RNGversion("3.5.1")
set.seed(197)
out <- c(n, round(cr[sample(n, 5)], 4))
print(out)
#Question 10
cr <- corr("specdata", 2000)
n <- length(cr)
cr <- corr("specdata", 1000)
cr <- sort(cr)
print(c(n, round(cr, 4)))
|
020b61b8d8ba5e56260d430c5007354289c5362d | 55d7f8bf639aa3f5b00597acb4017021d7f9ed1e | /tests/testthat.R | e8427cba2a0bfa7511ba2c09c7b66fbf8607a0e1 | [
"MIT"
] | permissive | nemochina2008/mgcvtools | b6b131fb6fba0c1ea5535930a6e2730c4d068747 | 3bc04bda15065ea6d4d6a6c632bb88cbdd998b34 | refs/heads/master | 2021-06-14T07:24:45.465596 | 2016-12-22T09:40:26 | 2016-12-22T09:40:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 62 | r | testthat.R | library(testthat)
library(mgcvtools)
test_check("mgcvtools")
|
8126ef880101c365f43b53e0cfc2da75075972de | b2f46956d55e81c4abe581d90b059675678b611a | /Clustering.r | 1f0ec08662b8dfa8633bd63c981931085281588e | [] | no_license | cosmoschen94/BigHero | 568a4063633d7f52edf50953532f59dc60fa1034 | db54ffe1b7771ae89f07435dba6cf77f384b7729 | refs/heads/master | 2021-01-10T13:18:23.218299 | 2016-12-08T17:32:57 | 2016-12-08T17:32:57 | 46,522,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,791 | r | Clustering.r | library("rjson")
library(fpc)
library(cluster)
library("party")
json_file <- "data.json"
json_data <- fromJSON(file=json_file)
nrow <- length(json_data)
ncol <- length(json_data[[1]])
data_attribute <- names(json_data[[1]])
data_mat <- matrix(data = unlist(json_data), nrow = nrow, ncol = ncol, byrow = TRUE)
colnames(data_mat) <- data_attribute
data_frame <- data.frame(data_mat)
data_frame_no_price <- data_frame
data_frame_no_price$Rent.Price <- NULL
#visualize
pie(table(data_frame$Number.of.Bedrooms))
pie(table(data_frame$Rent.Price))
#dbscan
ds <- dbscan(data_frame_no_price, eps = 0.5, MinPts = 2)
plot(ds, data_frame)
plotcluster(data_frame_no_price, ds$cluster)
#k-mean
set.seed(8953)
(kmeans.result <- kmeans(data_frame,2))
plot(data_frame[c("Latitude", "Longitude","Number.of.Bedrooms","Rent.Price")], col = kmeans.result$cluster)
#pam, k = 2
pam.result <- pam(data_frame,2)
plot(pam.result)
#pam, k = 3
pam.result <- pam(data_frame,3)
plot(pam.result)
#pam, k = 4
pam.result <- pam(data_frame,4)
plot(pam.result)
#pam, k = 5
pam.result <- pam(data_frame,5)
plot(pam.result)
cluster_result <- pam(data_frame,2)$clustering
data_frame$Cluster <- cluster_result
data_frame_no_price$Cluster <- cluster_result
cluster1_mean<-mean(data_frame$Rent.Price[data_frame$Cluster == 1])
cluster2_mean<-mean(data_frame$Rent.Price[data_frame$Cluster == 2])
cluster1_min<-min(data_frame$Rent.Price[data_frame$Cluster == 1])
cluster2_min<-min(data_frame$Rent.Price[data_frame$Cluster == 2])
cluster1_max<-max(data_frame$Rent.Price[data_frame$Cluster == 1])
cluster2_max<-max(data_frame$Rent.Price[data_frame$Cluster == 2])
#decision tree
my_ctree <- ctree(Cluster ~ Number.of.Bedrooms + Latitude + Longitude, data=data_frame_no_price)
print(my_ctree)
plot(my_ctree, type="simple")
|
885536b9415e8053828e2c2db859a763d842e487 | 57d9c9c9603f66cfadbaf99c62366d0bf02cadc5 | /man/runCubeShiny.Rd | 79a977e8ac456a25433f56913f5a16e31e5dcd2c | [] | no_license | jlwaddell/soloDrafting | ad5393afd14d3089aa9f1b8fd80f983da141f3fc | 472316034854ec691a1d1234d789801aab49ae46 | refs/heads/main | 2023-04-05T15:59:02.920252 | 2021-04-18T13:20:19 | 2021-04-18T13:20:19 | 346,645,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 527 | rd | runCubeShiny.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/runCubeShiny.R
\name{runCubeShiny}
\alias{runCubeShiny}
\title{Run the shiny application}
\usage{
runCubeShiny(installDependencies = FALSE, ...)
}
\arguments{
\item{installDependencies}{boolean, whether to first install packages listed
in the Suggests field of DESCRIPTION; default value is FALSE}
\item{...}{further arguments that can be passed to \code{\link[shiny]{runApp}}}
}
\value{
no return value
}
\description{
Run the shiny application
}
|
241e47ad1c6e1040ec004c18e7749d6755419993 | f0ba683353c4e3faf242e56c84defda4972686e1 | /man/pmid2doi.Rd | 90d9db92512d92191215d82c71504b44d3126d38 | [
"MIT"
] | permissive | epongpipat/eepR | bf567c666eef0417b0dece4088ec95697f02cdba | 970c4699db1e005cabd282e903706239033c7b02 | refs/heads/main | 2023-04-01T22:44:23.247733 | 2023-03-28T17:42:07 | 2023-03-28T17:42:07 | 205,262,495 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 258 | rd | pmid2doi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pmid2funcs.R
\name{pmid2doi}
\alias{pmid2doi}
\title{pmid2doi}
\usage{
pmid2doi(pmid)
}
\arguments{
\item{pmid}{}
}
\value{
}
\description{
pmid2doi
}
\concept{references_pubmed}
|
fafada14acf96875e037a1216d1fb8899e405265 | d7beab3a9f1aeaa2db5978582f7ae4c3d3f265d7 | /STORING_USER_EDUCATION.R | 5605ef2db25ae7c3b1334b4351dc439c1ea76cbb | [] | no_license | raunaqrameshporwal/Facebook-Influencer-Identification | 6f22e0ca2e0592888338bda539dc981d7f09d2c9 | 44e3f8d188cfb77ce6a4c5700bd88303dc025291 | refs/heads/master | 2022-11-09T04:40:54.483055 | 2020-07-02T23:40:34 | 2020-07-02T23:40:34 | 276,765,312 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,438 | r | STORING_USER_EDUCATION.R | library(RMySQL)
library(magrittr)
library(httr)
library(jsonlite)
library(RCurl)
library(Rfacebook)
load("my_db_connection")
load("FB_ID_FILE")
load("fb_oauth_auto")
load("fb_oauth_manual")
options(stringsAsFactors = FALSE)
#EDUCATION TABLE IS CREATED WITH THE NAME -> education_details
#IN CASE OF " ' EXTRACT CODE FROM extra_education.r
#FORMING HTTP QUERY
q1 <- "https://graph.facebook.com/v2.12/"
q2 <- "?fields=education%2Cname&access_token="
httpquery <- paste(q1,fb_user_id,q2,fb_oauth_manual,sep = "")
#CALLINT THE API
result <- GET(httpquery) %>% stop_for_status()
#PARSING THE ABOVE API's REPLY
parsed_content <- parse_content(result)
names(parsed_content$education$school$name)
parsed_content$education$school$name[2]
#CONVERTING THE PARSED CONTENT INTO DATA FRAME
#df <- as.data.frame(parsed_content)
#FORMING AN INSERT FUNCTION
insert_education <- function (df)
{
q1 <- "INSERT INTO education_details VALUES("
q2 <-paste(q1,df$id,",","'",df$name,"',",sep = "")
i<-1
while(i<=3)
{
if(i<3)
{
temp <-paste("'",df$education$type[i],"','",df$education$school$name[i],"',",sep = "")
q2 <- paste(q2,temp,sep = "")
}
if(i==3)
{
temp <-paste("'",df$education$type[i],"','",df$education$school$name[i],"')",sep = "")
q2 <- paste(q2,temp,sep = "")
}
i <-i+1
}
dbSendQuery(mydb,q2)
}
#Calling the above function
insert_education(parsed_content)
|
1e609c2063e3dd3b03401668ce7de759a601dd7a | 1fd484b29193d33a9ecffb964540b9fe2dbcb134 | /server.R | 5393c8de1ecff9565da4f164bc236aae9ad11aba | [] | no_license | alanct/shiny | 5d976d67574ea02ba4f04bd156a7c01e8eecfd39 | cd8ec6d167d0b51302e8c77cd6761b5e24bad687 | refs/heads/master | 2020-05-10T00:20:17.379141 | 2014-09-20T02:21:03 | 2014-09-20T02:21:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,246 | r | server.R | library(shiny)
library(kernlab); data(spam)
library(MASS)
mydata <- spam
attach(mydata)
type <- factor(type, labels = c("spam", "not spam"))
shinyServer(function(input, output) {
formulaText <- reactive({
paste(input$variable, "~ type")
})
formulaTextPoint <- reactive({
paste("type ~", "as.integer(", input$variable, ")")
})
formulaTextPoint1 <- reactive({
paste("fit()$fitted ~", "as.integer(", input$variable, ")")
})
fit <- reactive({
glm(as.formula(formulaTextPoint()), family=binomial(logit), data=mydata)
# fit = glm(type ~ make, family=binomial(logit), data=mydata)
})
output$caption <- renderText({
formulaText()
})
output$typeBoxPlot <- renderPlot({
boxplot(as.formula(formulaText()),
data = mydata,
outline = input$outliers)
})
output$fit <- renderPrint({
summary(fit())
})
output$typePlot <- renderPlot({
with(mydata, {
#plot(input$variable, fit()$fitted, type="l", col="red")
# plot(as.integer(input$variable), fit()$fitted, type="l", col="red")
plot(as.formula(formulaTextPoint1()))
#abline(fit(), col=2)
title(main="Spam Data with Fitted Logistic Regression Line")
})
})
})
|
6005104be7799dc343f5514ec161f73b25f133ac | e2b1baef26e62a7de8ff2b916fe2afd737f1b378 | /scripts/machine_learning_algorithms/knn.R | 3312ed166015abe88a943c7f6b7f89388101f41a | [] | no_license | MuhammadYasirAliKhan786/SDMGISR | 9ecb7ae1996c7714d27295fbbc172b871168a6db | 03087bee61a7da470895a89eb8181cc9f2c8af11 | refs/heads/master | 2022-01-15T14:26:28.400240 | 2019-07-14T22:10:45 | 2019-07-14T22:10:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,310 | r | knn.R | #######################################################################
############### kNN
head(pa)
set.seed(1) #pseudo-repeatability
trainIndex = createDataPartition(pa$pb, p = .75,
list = FALSE,
times = 1) #y as basis of splitting
training = pa[ trainIndex,] #75% data for model training
testing= pa[-trainIndex,] #25% for model testing
set.seed(825)
pb=as.factor(training$pb) #1 stands for presence and 0 for absence
land=as.factor(training$land) #land use categories are categorical
## caret
# define training control--> 10fold cv
train_control = trainControl(method="cv", number=10)
mod_fit=train(pb~.,data=training,trControl=train_control,method="knn")
summary(mod_fit)
## importance of the different predictors
varImp(mod_fit)
## test the model
p1=predict(mod_fit, newdata=testing) #predict on the test data
#test model fit-auc
library(pROC)
roc.glmModel = pROC::roc(testing[,"pb"], p1) #compare testing data
#with predicted responses
auc= pROC::auc(roc.glmModel)
auc
plot(roc.glmModel)
text(0.5,0.5,paste("AUC = ",format(auc, digits=5, scientific=FALSE)))
p1 = predict(stck, mod_fit) #use predict to implement the MARS model stored
#in mod_fit on the raster stack of our predictors
plot(p1,main="kNN Predictive Map")
|
4245a3a4e2b55c9c0222b9e91d66d1f3016bd4e5 | 05407980f99cf6f828ce411e728c3a140093b970 | /beaver(july19).R | e76e6839c0459dd69cfe36ec4d29edf04781b3a7 | [] | no_license | BeliveINkevin/R-project-July-19- | 4edcb47e16f54093218942d7d9ba73f460251e62 | b4cbdf6c8050651fd92f5c6b0da234e5261e9eb6 | refs/heads/master | 2020-03-23T13:59:43.323407 | 2018-07-20T01:45:00 | 2018-07-20T01:45:00 | 141,649,456 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,175 | r | beaver(july19).R | library(readr)
beaver <- read_csv("C:/Documents/My Excel/beaver.csv")
View(beaver)
install.packages("dplyr")
install.packages("ggplot2")
library(dplyr)
library(ggplot2)
head(beaver)
tail(beaver)
nrow(beaver)
#Note that 307 corresponds to day 1 while 308 corresponds to day 2
day1<-dplyr::filter(beaver,day=='307')
day1day2<-dplyr::filter(beaver,day=='308')
#this code filters out the bevers that were and weren't active. Since all beavers in day 2 were active
#then it's not necessary to write any code
day1active<-dplyr::filter(day1,activ=='1')
day1NOTactive<-dplyr::filter(day1,activ=='0')
summary(day1)
summary(day2)
summary(day1active)
summary(day1NOTactive)
var(day1active$temp)
var(day1NOTctive$temp)
var(day2$temp)
f<-ggplot(data=day1active,aes(time,temp))
f+geom_point()+ggtitle("Temperature vs time for day1 active ")
g<-ggplot(data=day1NOTactive,aes(time,temp))
g+geom_point()+ggtitle("Temperature vs time for day1 inactive ")
h<-ggplot(data=day2,aes(time,temp))
h+geom_point()+ggtitle("Temperature vs time for day2 active ")
i<-ggplot(data=day1,aes(time,temp))
i+geom_point()+ggtitle("Temperature vs time for day1 all ")
|
775af18cd5b85c67ad601b2f4ced17bb037bb8c1 | 468d9f58ec5449bb5db4decdb757c406a295a081 | /MyWork/plot4.R | 73b62a99703e7d49388cb7826d36cdef05298b6c | [] | no_license | JamesZDonline/ExData_Plotting1 | f07b0325404199b874218043daa13be604fec6b1 | 08b0d8e1061d8f18d3acf73cb022f7cabe3ea6bf | refs/heads/master | 2021-01-14T11:25:11.698262 | 2015-01-11T00:52:54 | 2015-01-11T00:52:54 | 29,076,238 | 0 | 0 | null | 2015-01-11T00:26:02 | 2015-01-11T00:26:01 | null | UTF-8 | R | false | false | 1,918 | r | plot4.R | #Exploratory Data Analysis First Project Plot 4 R Script
#Download Data and unzip file
URLname<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(URLname,"PowerConsumptionData.zip",method="curl")
unzip("PowerConsumptionData.zip")
#Read Data in
filename="household_power_consumption.txt"
data<-read.delim(filename,header=TRUE,sep=";",na.strings="?")
#Combine Date and time into a single POSIXct column and save it in DateTime
#Also Put Dates and Times in Date and time classes
data$DateTime<-as.POSIXct(paste(data$Date,data$Time,sep=" "), format="%d/%m/%Y %H:%M:%S")
data$Date<-as.Date(data$Date, format="%d/%m/%Y")
data$Time<-strptime(data$Time, format="%H:%M:%S")
#Subset data to first two days in February of 2007
ourData<-data[data$Date==as.Date("2007/02/01")|data$Date==as.Date("2007/02/02"),]
#Create png device
png(filename="plot1.png",width=480, height=480,units="px")
# Plotting ----------------------------------------------------------------
#create four places to put graphs
par(mfrow=c(2,2))
#Plot the first graph against global power
plot(ourData$DateTime, ourData$Global_active_power,type="l", xlab="", ylab="Global Active Power")
#Plot the second graph agains voltage
plot(ourData$DateTime, ourData$Voltage,type="l", ylab="Voltage",xlab="datetime")
#Plot the third with all of the submetering as in plot3.R
plot(ourData$DateTime, ourData$Sub_metering_1,type="l", xlab="", ylab="Energy sub metering")
lines(ourData$DateTime,ourData$Sub_metering_2,col="red")
lines(ourData$DateTime,ourData$Sub_metering_3,col="blue")
legendText<-c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
legend(x="topright",legend=legendText, col=c("black","red","blue"),lty=1)
#Plot the final against reactive power
plot(ourData$DateTime, ourData$Global_reactive_power,type="l", ylab="Global_reactive_power",xlab="datetime")
#Close png device
dev.off()
|
5a669fbbf53702e09377f10133c405bd85514689 | 449714efa83eb3071200b90bd5d584e0e3e5103f | /cachematrix.R | cbc9599621ba4c6cac8d10690cab433f9c5ed0d8 | [] | no_license | LindaS321/ProgrammingAssignment2 | 237977455fb188e5b3d898ad05660dd0a55b2a1f | f56590f71d9ea8ab82e1a90b10e2a8c340ab1b6a | refs/heads/master | 2021-01-15T21:07:56.739556 | 2015-03-20T23:15:19 | 2015-03-20T23:15:19 | 32,351,537 | 0 | 0 | null | 2015-03-16T20:37:25 | 2015-03-16T20:37:23 | null | UTF-8 | R | false | false | 1,783 | r | cachematrix.R | ## A pair of functions that cache the inverse of a matrix
##
## creates a special "matrix" object that can cache its inverse
makeCacheMatrix <- function(x = matrix()) {
inv = NULL
## Set the value of the matrix to value passed as y
setMatrix = function(y) {
## use `<<-` to assign a value to an object in an environment
## different from the current environment. (create a cache)
## Set the value of the matrix to value passed as y
x <<- y
## set the variable that holds the inverse to NULL
inv <<- NULL
}
getMatrix <- function() x
## save the inverse of the matrix to inv
setInverse <- function(solve) inv <<- solve
## retrieve the inverse of the matrix
getInverse <- function() inv
## create a list which holds get and set inverse functions
list(setMatrix = setMatrix, getMatrix = getMatrix,
setInverse = setInverse,
getInverse = getInverse)
}
## computes the inverse of the special "matrix" returned by makeCacheMatrix
## If the inverse has already been calculated and the matrix has not changed
## then the cachesolve will retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
## Determine if the inverse has already been calculated and the matrix has not changed
i <- x$getInverse()
## If the inverse has already been calculated returned cached value
if(!is.null(i)){
message("getting cached data")
return(i)
}
## if the inverse has not been calculated, then do it with solve
## get the data
data <- x$getMatrix()
## Use the solve function to return the inverse of the matrix
i <- solve(data, ...)
## Use the set function to set new value of matrix
x$setInverse(i)
## return the matrix's inverse
i
}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.