pierreqi/r_code_50k · Datasets at Hugging Face

fd5d5f2d24d08721a96c0580f87e70166dfb03cd

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/ggfortify/examples/autoplot.glmnet.Rd.R

2f8a89a1bbffdcb6bc19bac10aaf2c1916eb8b70

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

208

r

autoplot.glmnet.Rd.R

library(ggfortify) ### Name: autoplot.glmnet ### Title: Autoplot 'glmnet::glmnet' ### Aliases: autoplot.glmnet ### ** Examples autoplot(glmnet::glmnet(data.matrix(Orange[-3]), data.matrix(Orange[3])))

df8445dadb197fdf55dc19a52da905974aeb4542

05435fbdd6a6f58d2ebd4092129f4219132b707f

/code/Tross_Sebens_ER_model5_time.R

8d8859d94a05aa3f37a419ba74b0fd057739af4a

[]

no_license

earobert/BE_2019_01_25

760587a5ec2ad6ba367c232bb1b311442213ea59

e15f1e74c14328ba708cbe9ceb98e82c892cce28

refs/heads/master

2020-03-21T08:39:29.381960

2019-02-16T00:36:42

null

0

null

UTF-8

R

false

16,383

r

Tross_Sebens_ER_model5_time.R

#_____________________________________________________________________---- # This is the main working model right now. Now I'm adding time as an aspect in... #===============================================# # Model 5 - there is no gamma, and no cost of threads # MLE estimation of cost of byssus given growth and thread production#### # With separate estimates of a and the cost of thread production # BUT including a baseline value # -use Ken's suggestion of having their be a baseline... Tried that here. Doesn't change anything. # I could assume that 0 thread cutting was for that baseline value. #===============================================# mod.1 <- function(params, season) { food_scalar <- params[1] cost_per_thread <- params[2] #baseline_byssus_multiplier <- 0.08 sigma <- params[3] season <- season # conversion factors conversion_gWW_per_gDW <- 3.918 # From Summer 2015 collection, shape coeff estimation shape_coeff <- .304 # From Summer 2015 collection, shape coeff estimation mass_ww_per_dw <- 3.9 #coverts from mass_DW (dry weight) to mass_WW (wet weight) # exponents #### d <- 0.67 # intake; the model is very sensitive to this. (hope this is an exponent for g WW not mg WW) e <- 1 # the model is very sensitive to this (hope this is an exponent for g WW not mg WW) # calculation of b respiration_reference_J_per_day <- 0.07*4.75*4.184*24 # Units: J / (day) from 0.07mlO2/hr, Fly and Hilbish 2013 respiration_reference_gDW <- 0.24 # Fly and Hilbish 2013 respiration_reference_gWW <- respiration_reference_gDW * 3.9 # Using wet:dry conversion b_permass <- respiration_reference_J_per_day / (respiration_reference_gWW)^e # Note that e is just 1 b_J_per_g_WW_per_day <- b_permass # Units: J / (gWW * day) # Temp response, other #### Tmult_cost <- 1 Tmult_int <- 1 reprod_multiplier <- 1 # reproduction multiplier, brachi was 1, but if k is about half and half then this should be more like .5 opt_size_reducer <- 1 # lets reproduction equal surplus early # other parameters #### en_density_g_p_J <- .002 #energy_density size_maturity <- 10 # mg, size at maturity, 3000mg from tross del_M calc worksheet, somatic<full tissue weight # calculated scalers b <- b_J_per_g_WW_per_day*Tmult_cost #Wait the byssus multiplier is earlier here than I thought Wopt_measured_gDW <- 0.8 #gDW from sample collection Wopt_measured_gWW <- Wopt_measured_gDW * conversion_gWW_per_gDW a_fromWopt <- (b*e)/((Wopt_measured_gWW)^(d-e)*d) # backwards calculation of a a_J_per_day_per_food_scalar <- a_fromWopt a <- a_J_per_day_per_food_scalar*food_scalar*Tmult_int # import data #### setwd("~/BE/BE/Datasets") df <- read.csv(file="Spring_Fall.csv", stringsAsFactors = FALSE) df$treatment <- as.factor(df$treatment) df$season <- as.factor(df$season) df$treatment <- ordered(df$treatment, levels = c("never", "weekly","daily")) df <- df[df$season==season,] df.never <- df[df$treatment=="never",] # all data df <- df[!is.na(df$len_init_QC)&!is.na(df$len_final_QC),] len_init <- df[df$season==season,]$len_init_QC/10 #converted from mm to cm len_final <- df[df$season==season,]$len_final_QC/10# converted from mm to cm thread_num <- df[df$season==season,]$thread_count_QC mass_ww_init <- (len_init*.304)^3 mass_ww_final <- (len_final*.304)^3 mass_ww <- mass_ww_init growth_shell <- len_final-len_init growth_tissue <- mass_ww_final-mass_ww_init gonad_wt_dry <- df[df$season==season,]$gonad_wt_dry total_wt_dry <- df[df$season==season,]$total_wt_dry gonad_proportion <- gonad_wt_dry / total_wt_dry # model#### intake <- a*(mass_ww^d) #good, this is already in mass_ww... hope this is g not mg cost <- b*(mass_ww^e) #*(1-baseline_byssus_multiplier) # byssus_baseline <- b*(mass_ww^e)*baseline_byssus_multiplier byssus_induced <- thread_num*cost_per_thread reproduction <- 0 model.predG_J <- intake-cost-byssus_induced-reproduction #predicts mussel growth in J model.predG_g <- model.predG_J*en_density_g_p_J #predicts mussel growth in g DW or WW??? out <- data.frame( growth_tissue = growth_tissue, model.predG_g = model.predG_g ) return(out) } #For troubleshooting: params <- c(food = 3, cost_induced_byssus = 0.01, sigma = 100) season <- "Autumn" out <- mod.1(params,season) growth_tissue <- out$growth_tissue model.predG_g <- out$model.predG_g #plot(growth_tissue,model.predG_g) # a.est.NLL1 <- function(params, season) { out <- mod.1(params,season) growth_tissue <- out$growth_tissue model.predG_g <- out$model.predG_g sigma <- params[3] NLL <- -sum(dnorm(x=growth_tissue, mean=model.predG_g, sd=sigma, log=TRUE)) return(NLL) } Autumn.est <- optim(fn=a.est.NLL1, par=c(food = 3, cost_induced_byssus = 0.01, sigma = 100), season = "Autumn") # par are the starting values Spring.est <- optim(fn=a.est.NLL1, par=c(food = 3, cost_induced_byssus = 0.01, sigma = 100), season = "Spring") # par are the starting values #===================== #plot#### #===================== a.plot.NLL1 <- function(params, season) { out <- mod.1(params,season) growth_tissue <- out$growth_tissue model.predG_g <- out$model.predG_g # import data #### setwd("~/BE/BE/Datasets") df <- read.csv(file="Spring_Fall.csv") df <- df[df$season==season,] df <- df[!is.na(df$len_init_QC)&!is.na(df$len_final_QC),] p.1 <- lm(growth_tissue~ df$thread_count_QC) plot(x = df$thread_count_QC, y = growth_tissue, col = as.numeric(df$treatment)+26, pch = 19, xlab = "Thread production (#)", ylab = "Observed growth (gWW)" ) abline(p.1) p.2 <- lm(model.predG_g~growth_tissue) plot(x = growth_tissue, y = model.predG_g, col = as.numeric(df$treatment)+26, pch = 19, ylim = c(0,.3), xlim = c(0,.3), xlab = "Observed growth (gWW)", ylab = "Predicted growth (gWW)" ) abline(p.2) x <- seq(from = -.4, to=.4, by=.1) lines(x,x, lty = 2) } dev.off() par(mfrow = c(2,2),mar = c(5,4,4,2)+0.1) a.plot.NLL1(par=c(food = Autumn.est$par[1], cost_per_byssus = Autumn.est$par[2], sigma = Autumn.est$par[3]), season = "Autumn") a.plot.NLL1(par=c(food = Spring.est$par[1], cost_per_byssus = Spring.est$par[2], sigma = Spring.est$par[3]), season = "Spring") # plot cost using estimated costs. #Spring#### season <- "Spring" # import data #### setwd("~/BE/BE/Datasets") df <- read.csv(file="Spring_Fall.csv", stringsAsFactors = FALSE) df$treatment <- as.factor(df$treatment) df$season <- as.factor(df$season) df$treatment <- ordered(df$treatment, levels = c("never", "weekly","daily")) df <- df[df$season==season,] df <- df[!is.na(df$len_init_QC)&!is.na(df$len_final_QC),] df.never <- df[df$treatment=="never",] plot(df$treatment, df$thread_count_QC*Spring.est$par[2], ylab = "Cost (J)") params <- Spring.est$par food_scalar <- params[1] cost_per_thread <- params[2] #baseline_byssus_multiplier <- 0.08 sigma <- params[3] season <- season # conversion factors conversion_gWW_per_gDW <- 3.918 # From Summer 2015 collection, shape coeff estimation shape_coeff <- .304 # From Summer 2015 collection, shape coeff estimation mass_ww_per_dw <- 3.9 #coverts from mass_DW (dry weight) to mass_WW (wet weight) # exponents #### d <- 0.67 # intake; the model is very sensitive to this. (hope this is an exponent for g WW not mg WW) e <- 1 # the model is very sensitive to this (hope this is an exponent for g WW not mg WW) # calculation of b respiration_reference_J_per_day <- 0.07*4.75*4.184*24 # Units: J / (day) from 0.07mlO2/hr, Fly and Hilbish 2013 respiration_reference_gDW <- 0.24 # Fly and Hilbish 2013 respiration_reference_gWW <- respiration_reference_gDW * 3.9 # Using wet:dry conversion b_permass <- respiration_reference_J_per_day / (respiration_reference_gWW)^e # Note that e is just 1 b_J_per_g_WW_per_day <- b_permass # Units: J / (gWW * day) # Temp response, other #### Tmult_cost <- 1 Tmult_int <- 1 reprod_multiplier <- 1 # reproduction multiplier, brachi was 1, but if k is about half and half then this should be more like .5 opt_size_reducer <- 1 # lets reproduction equal surplus early # other parameters #### en_density_g_p_J <- .002 *29 #energy_density size_maturity <- 10 # mg, size at maturity, 3000mg from tross del_M calc worksheet, somatic<full tissue weight # calculated scalers b <- b_J_per_g_WW_per_day*Tmult_cost #Wait the byssus multiplier is earlier here than I thought Wopt_measured_gDW <- 0.8 #gDW from sample collection Wopt_measured_gWW <- Wopt_measured_gDW * conversion_gWW_per_gDW a_fromWopt <- (b*e)/((Wopt_measured_gWW)^(d-e)*d) # backwards calculation of a a_J_per_day_per_food_scalar <- a_fromWopt a <- a_J_per_day_per_food_scalar*food_scalar*Tmult_int # import data #### setwd("~/BE/BE/Datasets") df <- read.csv(file="Spring_Fall.csv", stringsAsFactors = FALSE) df$treatment <- as.factor(df$treatment) df$season <- as.factor(df$season) df$treatment <- ordered(df$treatment, levels = c("never", "weekly","daily")) df <- df[df$season==season,] df.never <- df[df$treatment=="never",] # all data df <- df[!is.na(df$len_init_QC)&!is.na(df$len_final_QC),] len_init <- df[df$season==season,]$len_init_QC/10 #converted from mm to cm len_final <- df[df$season==season,]$len_final_QC/10# converted from mm to cm thread_num <- df[df$season==season,]$thread_count_QC mass_ww_init <- (len_init*.304)^3 mass_ww_final <- (len_final*.304)^3 mass_ww <- mass_ww_init growth_shell <- len_final-len_init growth_tissue <- mass_ww_final-mass_ww_init gonad_wt_dry <- df[df$season==season,]$gonad_wt_dry total_wt_dry <- df[df$season==season,]$total_wt_dry gonad_proportion <- gonad_wt_dry / total_wt_dry # model#### intake <- a*(mass_ww^d) #good, this is already in mass_ww... hope this is g not mg cost <- b*(mass_ww^e) #*(1-baseline_byssus_multiplier) # byssus_baseline <- b*(mass_ww^e)*baseline_byssus_multiplier byssus_induced <- thread_num*cost_per_thread reproduction <- 0 model.predG_J <- intake-cost-byssus_induced-reproduction #predicts mussel growth in J model.predG_g <- model.predG_J*en_density_g_p_J #predicts mussel growth in g DW or WW??? length(df$treatment) length(model.predG_J) new <- data.frame(treat = df$treatment, pred_J =model.predG_J) new$treat <- as.factor(new$treat) dev.off() par(mfrow = c(2,2)) plot(new$treat, intake, ylim = c(0,120), ylab = "intake (J)") plot(new$treat,byssus_induced, ylim = c(0,120), ylab = "cost byssus (J)") plot(new$treat,cost, ylim = c(0,120), ylab = "cost non-byssus (J)") plot(new$treat,new$pred_J, ylim = c(0,120), ylab = "surplus (J)") dev.off() par(mfrow = c(2,2)) prop_byss <- byssus_induced / (intake) plot(new$treat, prop_byss*100, ylim = c(0,40), ylab = "% energy to byssus") prop_predG_J <- new$pred_J/ intake plot(new$treat,prop_predG_J*100, ylim = c(0,80), ylab = "% energy to growth") prop_byss <- cost / (intake) plot(new$treat, prop_byss*100, ylim = c(0,100), ylab = "% energy to non-byssus costs") plot(df$treatment, gonad_proportion*100*prop_byss, ylim = c(0,100), ylab = "% energy to reproduction") # plot cost using estimated costs. #Autumn#### season <- "Autumn" # import data #### setwd("~/BE/BE/Datasets") df <- read.csv(file="Spring_Fall.csv", stringsAsFactors = FALSE) df$treatment <- as.factor(df$treatment) df$season <- as.factor(df$season) df$treatment <- ordered(df$treatment, levels = c("never", "weekly","daily")) df <- df[df$season==season,] df <- df[!is.na(df$len_init_QC)&!is.na(df$len_final_QC),] df.never <- df[df$treatment=="never",] plot(df$treatment, df$thread_count_QC*Autumn.est$par[2], ylab = "Cost (J)") params <- Autumn.est$par food_scalar <- params[1] cost_per_thread <- params[2] #baseline_byssus_multiplier <- 0.08 sigma <- params[3] season <- season # conversion factors conversion_gWW_per_gDW <- 3.918 # From Summer 2015 collection, shape coeff estimation shape_coeff <- .304 # From Summer 2015 collection, shape coeff estimation mass_ww_per_dw <- 3.9 #coverts from mass_DW (dry weight) to mass_WW (wet weight) # exponents #### d <- 0.67 # intake; the model is very sensitive to this. (hope this is an exponent for g WW not mg WW) e <- 1 # the model is very sensitive to this (hope this is an exponent for g WW not mg WW) # calculation of b respiration_reference_J_per_day <- 0.07*4.75*4.184*24 # Units: J / (day) from 0.07mlO2/hr, Fly and Hilbish 2013 respiration_reference_gDW <- 0.24 # Fly and Hilbish 2013 respiration_reference_gWW <- respiration_reference_gDW * 3.9 # Using wet:dry conversion b_permass <- respiration_reference_J_per_day / (respiration_reference_gWW)^e # Note that e is just 1 b_J_per_g_WW_per_day <- b_permass # Units: J / (gWW * day) # Temp response, other #### Tmult_cost <- 1 Tmult_int <- 1 reprod_multiplier <- 1 # reproduction multiplier, brachi was 1, but if k is about half and half then this should be more like .5 opt_size_reducer <- 1 # lets reproduction equal surplus early # other parameters #### en_density_g_p_J <- .002 #energy_density size_maturity <- 10 # mg, size at maturity, 3000mg from tross del_M calc worksheet, somatic<full tissue weight # calculated scalers b <- b_J_per_g_WW_per_day*Tmult_cost #Wait the byssus multiplier is earlier here than I thought Wopt_measured_gDW <- 0.8 #gDW from sample collection Wopt_measured_gWW <- Wopt_measured_gDW * conversion_gWW_per_gDW a_fromWopt <- (b*e)/((Wopt_measured_gWW)^(d-e)*d) # backwards calculation of a a_J_per_day_per_food_scalar <- a_fromWopt a <- a_J_per_day_per_food_scalar*food_scalar*Tmult_int # import data #### setwd("~/BE/BE/Datasets") df <- read.csv(file="Spring_Fall.csv", stringsAsFactors = FALSE) df$treatment <- as.factor(df$treatment) df$season <- as.factor(df$season) df$treatment <- ordered(df$treatment, levels = c("never", "weekly","daily")) df <- df[df$season==season,] df.never <- df[df$treatment=="never",] # all data df <- df[!is.na(df$len_init_QC)&!is.na(df$len_final_QC),] len_init <- df[df$season==season,]$len_init_QC/10 #converted from mm to cm len_final <- df[df$season==season,]$len_final_QC/10# converted from mm to cm thread_num <- df[df$season==season,]$thread_count_QC mass_ww_init <- (len_init*.304)^3 mass_ww_final <- (len_final*.304)^3 mass_ww <- mass_ww_init growth_shell <- len_final-len_init growth_tissue <- mass_ww_final-mass_ww_init gonad_wt_dry <- df[df$season==season,]$gonad_wt_dry total_wt_dry <- df[df$season==season,]$total_wt_dry gonad_proportion <- gonad_wt_dry / total_wt_dry # model#### intake <- a*(mass_ww^d) #good, this is already in mass_ww... hope this is g not mg cost <- b*(mass_ww^e) #*(1-baseline_byssus_multiplier) # byssus_baseline <- b*(mass_ww^e)*baseline_byssus_multiplier byssus_induced <- thread_num*cost_per_thread reproduction <- 0 model.predG_J <- intake-cost-byssus_induced-reproduction #predicts mussel growth in J model.predG_g <- model.predG_J*en_density_g_p_J #predicts mussel growth in g DW or WW??? length(df$treatment) length(model.predG_J) new <- data.frame(treat = df$treatment, pred_J =model.predG_J) new$treat <- as.factor(new$treat) dev.off() par(mfrow = c(2,2)) plot(new$treat, intake, ylim = c(0,120), ylab = "intake (J)") plot(new$treat,byssus_induced, ylim = c(0,120), ylab = "cost byssus (J)") plot(new$treat,cost, ylim = c(0,120), ylab = "cost non-byssus (J)") plot(new$treat,new$pred_J, ylim = c(0,120), ylab = "surplus (J)") dev.off() par(mfrow = c(2,2)) prop_byss <- byssus_induced / (intake) plot(new$treat, prop_byss*100, ylim = c(0,40), ylab = "% energy to byssus") prop_predG_J <- new$pred_J/ intake plot(new$treat,prop_predG_J*100, ylim = c(0,80), ylab = "% energy to growth") prop_byss <- cost / (intake) plot(new$treat, prop_byss*100, ylim = c(0,100), ylab = "% energy to non-byssus costs") plot(df$treatment, gonad_proportion*100*prop_byss, ylim = c(0,100), ylab = "% energy to reproduction") # Some things to try... # -use frequency of manipulation rather than number of threads (.3per week, 1per week, 7per week) ... # -use Ken's suggestion of having their be a baseline... Tried that here. # -calculate with same cost of threads # -perform a monte carlo on a and threads # Some things to try... # -use frequency of manipulation rather than number of threads (.3per week, 1per week, 7per week) ... # -use Ken's suggestion of having their be a baseline... Tried that here. # -calculate with same cost of threads # -perform a monte carlo on a and threads

fa2ff59477effa8323d8a6a1fc3146dc30f02ef9

9638273b355612ca5b366eb79927129ac51fa6d9

/scripts/DBI_connection.R

d59cf11727dff2212141c4370ab0419284ed5a08

[]

no_license

RyanFarquharson/ACPMD

f8984344205a7faff890df8b75186dee929dfdcf

c7dc779096754ac330c505d8dcd7571321699647

refs/heads/master

2020-04-07T22:49:57.484592

2019-02-01T05:45:37

158,787,059

0

null

UTF-8

R

false

2,670

r

DBI_connection.R

# connected to Access database using Connections tab in Rstudio # help available at https://db.rstudio.com/odbc/ library(DBI) con <- dbConnect(odbc::odbc(), "ABS198296") # Look at what is in the database dbListTables(con) dbListFields(con, "Ag1983") dbListFields(con, "Item_list") dbListFields(con, "ASGC96") # Create a list of tables Aglist <- dbListTables(con, table_name = "Ag%") # iterate through list to import into R as dataframes and use inner join to match up item info and save as csv Item_listR <- dbReadTable(con, "Item_list") ASGC96R <- dbReadTable(con, "ASGC96") for (item in Aglist) { tablename <- paste0(item,"R") assign(tablename, dbReadTable(con, item) %>% inner_join(Item_listR) %>% inner_join(ASGC96R) %>% select("Area_id", "Name", "Item_id", "Item_Name", "Units", "Value")%>% write_csv(path = paste0("./data/198296/",tablename)) ) } # Add a column for the census year in each table first then bind rows. Yearlist <- seq(1982,1996,1) ABS <- data.frame(Area_id = integer(), Name = character(), Item_id = integer(), Item_Name = character(), Units = character(), Value = double(), Year = integer()) counter <- 1 for (f in list.files("./data/198296/")) { new_table <- read_csv(paste0("./data/198296/",f)) new_table$Year <- Yearlist[counter] ABS <- bind_rows(ABS, new_table) counter <- counter + 1 } # concord to SA2s # import ABS correspondence file SLA_1996_SA2_2011 <- head(read_excel("./data/raw_data/concordance/CG_SLA_1996_SA2_2011.xls", sheet = "Table 3", skip = 5), n = -3) # use an inner join to match up SLA names with the correspondence data ABS_SA2 <- inner_join(ABS, SLA_1996_SA2_2011, by = c("Name" = "SLA_NAME_1996")) # Use the concordance data to calculate new "Estimate" values by SA2 ABS_SA2$Estimate_SA2 <- ABS_SA2$Value * ABS_SA2$RATIO ------------- # Use a group_by and summarise to sum Estimates for each commodity by SA2 commodities_198296_SA2 <- ABS_SA2 %>% select(`SA2_MAINCODE_2011`, SA2_NAME_2011, `Item_Name`, Estimate_SA2) %>% group_by(`SA2_MAINCODE_2011`, SA2_NAME_2011, `Item_Name`) %>% summarise(SA2Est = sum(Estimate_SA2)) # rename columns to be consistent across all epochs commodities_198296_SA2 <- rename(commodities_198296_SA2, ASGS_code = "SA2_MAINCODE_2011", ASGS_label = "SA2_NAME_2011", Commodity = "Item_Name", Estimate = "SA2Est" ) write_csv(commodities_198296_SA2, "./data/commodities_198296_SA2.csv")

5ced8e7a4cd043df9ee6c270a65b84d8dd56041e

8c1daa6967fd693652dd1eac38a9f666fc65c8ee

/man/get.varitas.options.Rd

18f9928ed177db607d5afa9f2f761493dd22ce10

[]

no_license

cran/varitas

ae90d05e61f5004a07d09ec5861724218215fdcd

603e4ec1d6d90678eb54486f7a0faf6a76a14114

refs/heads/master

2021-01-13T21:42:36.802682

2020-11-13T23:30:03

242,504,094

0

null

UTF-8

R

false

true

731

rd

get.varitas.options.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get.varitas.options.R \name{get.varitas.options} \alias{get.varitas.options} \title{Return VariTAS settings} \usage{ get.varitas.options(option.name = NULL, nesting.character = "\\\\.") } \arguments{ \item{option.name}{Optional name of option. If no name is supplied, the full list of VariTAS options will be provided.} \item{nesting.character}{String giving Regex pattern of nesting indication string. Defaults to '\\.'} } \value{ varitas.options list specifying VariTAS options } \description{ Return VariTAS settings } \examples{ reference.build <- get.varitas.options('reference_build'); mutect.filters <- get.varitas.options('filters.mutect'); }

d04fd40113bfe51e877ae4ed1812e479dc40d88c

645ff6a53c2093037c7154cdd87714942385ffd4

/R/collection_rebalanceleaders.R

a6cdc72c0f0929770187e35750b6be6ec63b578a

[ "MIT" ]

permissive

1havran/solrium

04c6754d14509e0e46e50d39f074d17b190eb050

a30015c1d1a28fc7293d67854c12d8f3fc99fad0

refs/heads/master

2021-01-01T06:09:13.350637

2017-02-01T19:44:27

97,371,390

0

null

UTF-8

R

false

2,075

r

collection_rebalanceleaders.R

#' @title Rebalance leaders #' #' @description Reassign leaders in a collection according to the preferredLeader #' property across active nodes #' #' @export #' @param name (character) Required. The name of the collection rebalance preferredLeaders on. #' @param maxAtOnce (integer) The maximum number of reassignments to have queue up at once. #' Values <=0 are use the default value Integer.MAX_VALUE. When this number is reached, the #' process waits for one or more leaders to be successfully assigned before adding more #' to the queue. #' @param maxWaitSeconds (integer) Timeout value when waiting for leaders to be reassigned. #' NOTE: if maxAtOnce is less than the number of reassignments that will take place, #' this is the maximum interval that any single wait for at least one reassignment. #' For example, if 10 reassignments are to take place and maxAtOnce is 1 and maxWaitSeconds #' is 60, the upper bound on the time that the command may wait is 10 minutes. Default: 60 #' @param raw (logical) If \code{TRUE}, returns raw data #' @param ... curl options passed on to \code{\link[httr]{GET}} #' @examples \dontrun{ #' solr_connect() #' #' # create collection #' collection_create(name = "mycollection2") # bin/solr create -c mycollection2 #' #' # balance preferredLeader property #' collection_balanceshardunique("mycollection2", property = "preferredLeader") #' #' # balance preferredLeader property #' collection_rebalanceleaders("mycollection2") #' #' # examine cluster status #' collection_clusterstatus()$cluster$collections$mycollection2 #' } collection_rebalanceleaders <- function(name, maxAtOnce = NULL, maxWaitSeconds = NULL, raw = FALSE, ...) { conn <- solr_settings() check_conn(conn) args <- sc(list(action = 'REBALANCELEADERS', collection = name, maxAtOnce = maxAtOnce, maxWaitSeconds = maxWaitSeconds, wt = 'json')) res <- solr_GET(file.path(conn$url, 'solr/admin/collections'), args, conn$proxy, ...) if (raw) { return(res) } else { jsonlite::fromJSON(res) } }

9af3d143f5b1634500e606df0d89c5a3cac2c874

8748271d8301a95a15c9b2effb121e8bae0a418a

/Rcodes/Eq1ProdFun1/explanatory.R

15688660b938e2e5223b8e6a4ed88b2c2ffe5200

[]

no_license

MonikaNovackova/FoodSystemGitH

2a8c9606451cd5b68e387b0f460a50140059da80

849480432be11062d16812830d894e6f3f7fd8c5

refs/heads/master

2021-10-10T21:55:14.555493

2019-01-17T16:05:13

157,595,866

0

null

UTF-8

R

false

1,495

r

explanatory.R

rm(list=ls()) WDuni<-c("/home/m/mn/mn301/foodSystems/dataFS") # uni WDhome<-c("/home/trennion/foodSystems/dataFS") # doma setwd(WDuni) setwd(WDhome) #wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww #wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww # load and prepare Maize data Crops<-read.csv( "New31Jan2018/KE_Ag_Stats_2014.csv",header=TRUE, na.strings = c("NA","NC","NC.","-","#DIV/0!"),colClasses=c("factor","factor","factor","factor","factor","factor","factor","factor","numeric","numeric","numeric")) #numeric,numeric,numeric)) CrMaize<-subset(Crops, Crop=="Maize" & !Admin2=="") CrMaize2<-subset(Crops, Crop=="Maize" & !Admin2=="" & Season=='') #wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww sapply(unique(PercZ1$Year), function (x) summary(CrMaize2$Yield[CrMaize2$Year==x])) sapply(seq(1970,2014),function (x) summary(CrMaize2$Yield[CrMaize2$Year==x])) sapply(unique(PercZ1$Year),function (x) summary(CrMaize2$Yield[CrMaize2$Year==x])) sapply(unique(PercZ1$Year),function (x) sum(is.na(CrMaize2$Yield[CrMaize2$Year==x]))) is.na(CrMaize2$Yield[CrMaize2$Year==2015]) summary(CrMaize2$Yield[CrMaize2$Year==2010])

9182d5fd934bd0a1726664a81c36a50006d94416

1181197b6995d81a5982597db18e9a2bf4d3346e

/normalising/TPM_normalised_look.R

68f9539e1c053e92a28db0b30b79cdc95e96e3ff

[]

no_license

oknox/Research-project

011a6054a43b3cb11a1688227d05d1439934487d

c9e19cd5952f64f5e41336dd039c2254605860c0

refs/heads/master

2020-03-26T11:41:12.994576

2018-08-15T13:21:16

144,854,214

0

null

UTF-8

R

false

476

r

TPM_normalised_look.R

#Looking at TPM normalised counts setwd("Library/Mobile Documents/com~apple~CloudDocs/CAMBRIDGE/Research project/") library(data.table) norm_counts <- fread("Transcripts/Nonlogged_tpm_normalised_counts.csv", data.table = F) jcounts <- norm_counts[,2:ncol(norm_counts)] jcounts <- as.matrix(jcounts) #Make histogram of logged 10 normalised counts hist(log10(jcounts), breaks = 100, xlab="log10 TPM normalised counts", main = "Distribution of log10 TPM normalised counts")

5b102647cd68cab5c83a9aad65c4d5a68ee2fcdf

d86913a4c99d7666bf457f81be2581ad0241d86e

/ui.R

bb9d891c8100efa5a3b30133fec6a36b61a05f22

[]

no_license

cgtyoder/DevDataProdWk4

5de37ad6aa3874194b71fb4acd0eaf5e7c92e760

84108a3718623e980ec6fdfbbdd5b52bf2da4639

refs/heads/master

2021-09-08T09:14:46.761295

2018-03-09T02:46:54

124,477,513

0

null

UTF-8

R

false

1,011

r

ui.R

library(shiny) shinyUI(fluidPage( # Application title titlePanel("mtcars Data"), # Sidebar with 3 selectors to narrow data sidebarLayout( sidebarPanel( h3("Instructions:"), h5("Change the values of the selectors below to plot the observations from the mtcars data set which match the selected critera."), br(), selectInput("cyls", "Cylinders:", c("4" = 4, "6" = 6, "8" = 8)), selectInput("automan", "Tranmission:", c("Automatic" = 0, "Manual" = 1)), selectInput("gears", "Gears:", c("3" = 3, "4" = 4, "5" = 5)), br(), h5("Please see https://github.com/cgtyoder/DevDataProdWk4 for the server.R and ui.R files") ), # Show a plot of the generated distribution mainPanel( plotOutput("distPlot") ) ) ))

381e6f51618909cb67e1f89ab481291411ec779d

0b46530108be813ad07dddc3bf3cd10b01f8c10c

/man/temperature_graph.Rd

b372752b8441c878deb14f0427064dc5a64cde9c

[]

no_license

jcorain/NewBoRn

86a76d0a596eefeb4096e87baa3411df016fc1f1

62f7764fe5514c416f941e0180ef97f38012cdb4

refs/heads/main

2023-02-27T07:33:16.558981

2021-01-28T17:05:53

329,053,524

0

null

UTF-8

R

false

true

723

rd

temperature_graph.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/graph.R \name{temperature_graph} \alias{temperature_graph} \title{temperature_graph} \usage{ temperature_graph(dataframe = NULL, birthdate = NULL) } \arguments{ \item{dataframe}{The dataframe you want to analyze} \item{birthdate}{The birthdate of the child. If NULL we assume that this is the first day of the dataframe. Defaut is NULL} } \value{ plotly graphic object } \description{ Function to plot the temperature versus time } \examples{ dummy_data <- utils::read.csv(file.path(system.file("extdata", package = "NewBoRn"), "dummy_data.csv")) dummy_data <- dplyr::select(.data = dummy_data, -X) temperature_graph(dataframe = dummy_data) }

142a41e2f4cca2e2f5ad056fdea3998cb5063e53

f1dd4979186d90cc479c48d7673f4ce4c633cf35

/psf/astro/zone099.r

e63d92b44589045e112d34d6cbeafc5b9c3f6dc2

[]

no_license

flaviasobreira/DESWL

9d93abd3849f28217ae19c41d9b270b9b1bd5909

6ba2a7f33196041aa7f34d956535f7a0076ae1f2

refs/heads/master

2020-03-26T05:50:05.292689

2018-08-12T21:43:34

null

0

null

UTF-8

R

false

441

r

zone099.r

350947 350957 350960 350962 369473 369490 369508 369512 369514 377872 377875 377881 377883 379013 379021 379024 379027 379031 379033 381899 381907 391292 398748 398750 400382 400386 400388 400395 401153 401155 401158 401163 402248 402256 402258 402265 474402 474417 474422 483199 483200 484949 484951 484972 484974 486435 486833 486834 489938 490354 490763 490765 490767 490772 491146 492768 493523 494333 495022 495025 508068 510091 510478

7001ce8b84707ae51617654bd508a64963e11e84

fac8df50cfc58cc485cfba09115866d7650afc39

/testfiles/r/input/functions/functions0.r

38158127ecd3c7c029ac8d0d8bd2da793406e5fe

[ "MIT" ]

permissive

jorconnor/senior-design-rpl

5be9b69fc646976334413de881e0b5751f2d15d2

10afb254e3591c6fc367a95f42df43fbee51fd26

refs/heads/master

2021-01-09T05:51:17.992817

2017-05-09T18:50:46

80,846,535

0

1

null

2017-04-19T14:57:40

2017-02-03T16:24:40

Java

UTF-8

R

false

519

r

functions0.r

# Read file statesInfo <- read.csv('stateData.csv') # subset data by region if region is 1 subset(statesInfo, state.region == 1) stateSubset <- statesInfo[statesInfo$illiteracy == 0.5, ] library(ggplot2) library(plyr) library(twitteR) # Attributes and dimensions of data dim(stateSubset) str(stateSubset) source(file.path(codeDir, 'dependencies.r')) stateSubset # print out stateSubset square <- function(x) { sq <- x * x return(square) } sum <- function(a, b) { return(a + b) } square(4) sum(1, 2)

4ab4091dfadf985dcc908888b2a94726f35adf10

229be3eec8eda763405e9147de5279d0c783b4ac

/xmlR/mergedCatalog.R

da580f47973f53459f577239f9f9027391f08ca2

[]

no_license

gvravi/healapp

98ed06127651361048d7f5e43add08f24c9d01bb

5ca7d0774f313f137bf4308706dc4d8fbf8d7976

refs/heads/master

2020-12-24T06:08:17.933512

2016-11-08T11:35:20

49,939,162

0

null

UTF-8

R

false

1,593

r

mergedCatalog.R

# Chapter 3 section 3.2 setwd("~/R/xmlR") library(XML) #Read XML document into R with xmlParse doc1 = xmlParse("merged_catalog.xml") #Identify root element of xml class root = xmlRoot(doc1) #Name of root xmlName(root) #No of children in Root xmlSize(root) #Accessing Nodes in the DOM(Document Object Model) #The [[ and [ operators allows us to treat an XML node as a list of its child nodes. #Similar to subsetting lists, we can use [ and [[ to access child nodes by positions, #names, a logical vector, or exclusion. When we subset by "name",we use the node's #element name ## Note: We can see that root[[event]] give first element #but root[element] gives all elements in event1 = root[["event"]] event1 names(event1) xmlName(event1) #We can retrieve, say, the tenth child of the first <event> with event1[[10]] #get the first seven <event> nodes with root[1:7] #get all but the first seven children of the root node with root[ -(1:7) ] #we subset by name using the single square bracket to extract all <event> nodes #from the <merge> node. evs = root["event"] evs # The evs object is of class XMLInternalNodeList, which is essentially a list of # XMLInternalElementNode objects. This means that we can apply the methods xmlName(), # xmlValue(), etc. to the elements in evs, e.g., we find that the first <event> node #has 18 children with xmlSize(evs[[1]]) #Alternately root[ names(root) == "event" ] #then we extract all <event> nodes from the <merge> node. #A call to length() confirms this: length(evs)

62c1f2b58277ec39562f1a7718a3b5b272bfaea4

2db27775c676d46d2f28f239b148463aa9288371

/man/eptplot.Rd

fba15d76362a47964329a176a3e25d019d697cde

[]

no_license

cran/EPT

f2af2930cc2791fa75a91a42a78c0d69f59b1408

a86526fab7d443983e0fa72ab21523f8d9c1ed77

refs/heads/master

2022-01-21T09:38:38.437692

2022-01-05T00:10:02

236,593,183

0

null

UTF-8

R

false

1,811

rd

eptplot.Rd

\name{eptplot} \alias{eptplot} \title{Plot of Components by Ensemble Patch Transform of a Signal} \description{ This function plots ensemble patch transform of a signal for a sequence of size parameters tau's. } \usage{ eptplot(eptransf, taus = eptransf$parameters$tau) } \arguments{ \item{eptransf}{R object of ensemble patch transform by \code{eptransf()} or \code{meptransf()}.} \item{taus}{specifies size parameters for which ensemble patch transform of a signal is displayed.} } \details{ This function plots ensemble patch transform of a signal for a sequence of size parameters \code{taus}. } \value{ plot } \seealso{ \code{\link{eptransf}}, \code{\link{meptransf}}, \code{\link{eptmap}}. } \examples{ n <- 500 set.seed(1) x <- c(rnorm(n), arima.sim(list(order = c(1,0,0), ar = 0.9), n = n, sd=sqrt(1-0.9^2))) taus <- seq(10, 100, by=10) # eptr1 : Multiscale EPT by average patch transform and average ensemble transform eptr1 <- meptransf(tindex=1:(2*n), signal=x, taus=taus, process=c("average", "average"), boundary="none") names(eptr1) op <- par(mfcol=c(4,1), mar=c(4,2,2,0.1)) plot(x, xlab="", type="l", main="signal") eptplot(eptr1) eptplot(eptr1, taus=20) eptplot(eptr1, taus=c(20, 30)) lines(eptr1$Epstat[, 2], col="blue") lines(eptr1$Epstat[, 3], col="red") # eptr2 : Multiscale EPT by envelope patch transform and average ensemble transform eptr2 <- meptransf(tindex=1:(2*n), signal=x, type="oval", taus=taus, process=c("envelope", "average"), pquantile=c(0,1), gamma=0.06, boundary="none") names(eptr2) plot(x, xlab="", type="l") eptplot(eptr2) eptplot(eptr2, taus=20) eptplot(eptr2, taus=c(20, 30)) lines(eptr2$EpM[, 2], col="blue") lines(eptr2$EpM[, 3], col="red") par(op) } \keyword{nonparametric}

36fa6520452a9ad646304b1bc9c41f4c05fffde2

63f247fa699153303a6481f86d98764a7d88529d

/2/2.2/3.R

ae457f167939d3dda903c701d720388e40cdb550

[]

no_license

Alex1472/Statistic-on-R.-Part-1

86ec8c2e1024de0aec3c3922b863f156cb57b200

ce071d7c93cedf395c452f61a48fbafd4eaf99d1

refs/heads/master

2020-03-27T21:10:15.314847

2018-09-09T01:05:29

147,120,542

0

null

UTF-8

R

false

86

r

3.R

df = read.table("dataset_11504_15.txt") bartlett.test(V1 ~ V2, df) t.test(V1 ~ V2, df)

084ac597a2037909c759ec1ee47db45f8818c7c0

508fa9bfaae7fab2b5662b93f5dd858cae7767ff

/R/geneModel.R

8391b8cda83916b5c3eb4b99766252a2a2060d75

[]

no_license

cran/refGenome

2ec5e1be73d89ec48a1506d0323bf2ebb0c305f2

5f92690b4099770bedf3ae1d8cef5a28ac6250a9

refs/heads/master

2021-01-17T10:10:20.769482

2019-05-22T16:10:09

17,699,105

1

0

null

UTF-8

R

false

22,619

r

geneModel.R

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Declaration of generics for geneModel.r # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # setGeneric("getTranscript", function(object, i) standardGeneric("getTranscript")) setGeneric("getExonData", function(object) standardGeneric("getExonData")) setGeneric("getCdsData", function(object) standardGeneric("getCdsData")) setGeneric("geneModel", function(object, gene_id, interior=TRUE) standardGeneric("geneModel")) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Generate consecutive identifer from two data.frame columns # Only for internal use (not exported) # Used in geneModel - function. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # uid <- function(dfr, f, s) { # - - - - - - - - - - - - - - # # dfr : data.frame # f : name of first column # s : name of second column # - - - - - - - - - - - - - - # f <- f[1] s <- s[1] dfr <- dfr[order(dfr[, f], dfr[, s]), ] fn <- c(dfr[-1, f], dfr[nrow(dfr), f]) sn <- c(dfr[-1, s], dfr[nrow(dfr), s]) fne <- dfr[, f] == fn fse <- dfr[, s] == sn inc <- as.numeric(!(fne & fse)) # Leading 1 means that uid starts with 1 cinc <- c(1, inc[-length(inc)]) uid <- cumsum(cinc) dfr$uid <- cumsum(cinc) return(invisible(dfr)) } # + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # # CLASS transcriptModel # + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # # ident : transcript_id, transcript_name, gene_id, gene_name, seq_name # coords : start, end .transcriptModel <- setClass("transcriptModel", slots=c( id="character", # transcript_id name="character", # transcript_name gene_id="character", gene_name="character", seq_name="character", strand="character", biotype="character", coords="integer", # start, end of transcript exons="data.frame", # start, end of exons cds="data.frame", # start, end of CDS stcodon="integer", # start_codon, stop_codon prime_utr="integer", # five, three #introns="data.frame", # begin <(?) end (Position of 1st nucleotde) version="integer" ) ) setMethod("initialize", "transcriptModel", function(.Object) { .Object@id <- "" .Object@name <- "" # .Object@coords <- rep(0L, 2) names(.Object@coords) <- c("start", "end") # .Object@exons <- data.frame(start=0L, end=0L) .Object@cds <- data.frame(start=0L, end=0L) # .Object@stcodon <- rep(0L, 2) names(.Object@stcodon) <- c("start", "stop") # .Object@prime_utr <- rep(0L, 2) names(.Object@prime_utr) <- c("five", "three") # return(.Object) }) setMethod("show", "transcriptModel", function(object){ bm<-Sys.localeconv()[7] cat("An object of class '", class(object), "'.\n", sep="") cat("ID : ", object@id, "\n") cat("Name : ", object@name, "\n") cat("Gene ID : ", object@gene_id, "\n") cat("Gene Name : ", object@gene_name, "\n") cat("Start : ", format(object@coords[1], big.mark=bm), "\t\t") cat("End : ", format(object@coords[2], big.mark=bm), "\n") cat("Start codon : ", format(object@stcodon[1], big.mark=bm), "\t\t") cat("Stop codon : ", format(object@stcodon[2], big.mark=bm), "\n") cat("5' prime utr: ", format(object@prime_utr[1], big.mark=bm), "\t\t") cat("3' prime utr: ", format(object@prime_utr[2], big.mark=bm), "\n") cat("Seq Name : ", object@seq_name, "\n") cat("Strand : ", object@strand, "\n") }) plot.transcriptModel <- function(x, ylim, col, lwd=2, ...) { # adjustcolor: Package grDevices # cols=border, background = alpha(border, 0.5) for(i in 1:nrow(x@exons)) rect( x@exons$start[i], ylim[1], x@exons$end[i], ylim[2], col=adjustcolor(col[1], alpha.f=0.5), border=col[1], lwd=lwd, ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Extract TranscriptModel Object from GTF table # For internal use only (not exported) # Used in geneModel function # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # getTranscriptFromGtf <- function(gtf, transcript_id) { transcript_id <- as.character(transcript_id[1]) gtf <- gtf[gtf$transcript_id==transcript_id, ] if(nrow(gtf)==0) stop("Transcript_id '", transcript_id, "' not found", sep="") res <- new("transcriptModel") # .transcriptModel() res@id <- transcript_id res@name <- as.character(gtf$transcript_name[1]) res@gene_id <- as.character(gtf$gene_id[1]) res@gene_name <- as.character(gtf$gene_name[1]) res@strand <- as.character(gtf$strand[1]) res@seq_name <- as.character(gtf$seqid[1]) res@coords[1] <- min(gtf$start) res@coords[2] <- max(gtf$end) res@version <- as.integer(gtf$transcript_version[1]) wc <- which(gtf$feature=="start_codon") res@stcodon[1] <- ifelse(length(wc)>0, gtf$start[wc[1]], NA) wc <- which(gtf$feature=="stop_codon") res@stcodon[2] <- ifelse(length(wc)>0, gtf$start[wc[1]], NA) wc <- which(gtf$feature=="five_prime_utr") res@prime_utr[1] <- ifelse(length(wc)>0, gtf$start[wc[1]], NA) wc <- which(gtf$feature=="three_prime_utr") res@prime_utr[2] <- ifelse(length(wc)>0, gtf$start[wc[1]], NA) # - - - - - - - - - - - - - - - - - - - - - - - - - - # # Exon data.frame # - - - - - - - - - - - - - - - - - - - - - - - - - - # exons <- gtf[gtf$feature=="exon", ] if(nrow(exons) > 0) { # Missing exon if(is.na(match("exon_number", names(exons)))) { # + strand: increasing, - strand: decreasing exons <- exons[order(exons$start), ] if(exons$strand[1]=="+"){ exons$exon_number <- 1:nrow(exons) }else{ exons$exon_number <- nrow(exons):1 } }else{ exons$exon_number <- as.numeric(exons$exon_number) } exn <- c("start", "end", "exon_id", "exon_number", "exon_version", "seqid", "strand") # Missing columns are removed from column names mtc <- match(exn, names(exons)) exn <- exn[!is.na(mtc)] res@exons <- exons[order(exons$exon_number), exn] rownames(res@exons) <- as.character(res@exons$exon_number) }else{ cat("No exons found for transcript '", transcript_id, "'\n", sep="") } # - - - - - - - - - - - - - - - - - - - - - - - - - - # # CDS data.frame # - - - - - - - - - - - - - - - - - - - - - - - - - - # cds <- gtf[gtf$feature=="CDS", ] res@cds <- cds return(res) } setMethod("getExonData", "transcriptModel", function(object){ return(object@exons) }) setMethod("getCdsData", "transcriptModel", function(object){ return(object@exons) }) # + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # # CLASS geneModel # + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # .geneModel <- setClass("geneModel", slots=c(gene_id="character", gene_name="character", seq_name="character", strand="character", transcripts="character", coords="integer"), contains="refGenome") setMethod("initialize", "geneModel", function(.Object) { # ev needs to be done explicitly assigned here # because otherwise obscure copies appear .Object@ev <- new.env() .Object@gene_id <- "" .Object@gene_name <- "" .Object@seq_name <- "" .Object@strand <- "*" .Object@transcripts <- "" .Object@coords <- rep(0L, 2) return(.Object) }) setGeneric("geneName", function(object) standardGeneric("geneName")) setMethod("geneName", "geneModel", function(object) { return(object@gene_name) }) setGeneric("geneName<-", function(object, value) standardGeneric("geneName<-")) setReplaceMethod("geneName", c("geneModel", "character"), function(object, value){ object@gene_name <- value[1] return(object) }) setReplaceMethod("geneName", c("geneModel", "factor"), function(object, value){ geneName(object) <- as.character(value[1]) return(object) }) setGeneric("geneId", function(object) standardGeneric("geneId")) setMethod("geneId", "geneModel", function(object) { return(object@gene_name) }) setGeneric("geneId<-", function(object, value) standardGeneric("geneId<-")) setReplaceMethod("geneId", c("geneModel", "character"), function(object, value){ object@gene_name <- value[1] return(object) }) setReplaceMethod("geneId", c("geneModel", "factor"), function(object, value){ geneId(object) <- as.character(value[1]) return(object) }) setMethod("show", "geneModel", function(object) { bm<-Sys.localeconv()[7] cat("Object of class '", class(object), "'\n", sep="") cat("Gene id : ", object@gene_id[1] , "\n") cat("Gene name : ", object@gene_name[1], "\n") cat("Seqid : ", object@seq_name[1], "\n") cat("Strand : ", object@strand[1], "\n") cat("Start : ", format(object@coords[1], big.mark=bm), "\n") cat("End : ", format(object@coords[2], big.mark=bm), "\n") cat("Transcripts : ", length(object@transcripts), "\n") if(!exists("exons", where=object@ev, inherits=FALSE)) cat("(No exon table present)\n") else { n<-min(nrow(object@ev$exons), 6L) cat("Exon Nr : ", format(nrow(object@ev$exons), big.mark = bm), "\n") print(head(object@transcripts)) } }) plot.geneModel <- function(x, cols=c("firebrick2", "gray40"), ...) { gene_text <- paste("Gene id :", x@gene_id, " Seqid : " , x@seq_name, " Strand: " , x@strand) ylim=c(0,10) op <- par(mar=c(5,8,4,2) + 0.1) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Do main plot # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # plot(x@coords, ylim, type="n", bty="n", yaxt="n", main=paste("Gene model :", x@gene_name), xlab=paste("Position on seqid", x@seq_name), ylab="", ...) #bm<-Sys.localeconv()[7] mtext(gene_text, side=3, line=0) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Strand arrow # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # if(x@strand == "+"){ arrows(x0=x@coords[1], y0=ylim[2], x1=x@coords[2], y1=ylim[2], code=2, length=0.1) } else { arrows(x0=x@coords[1], y0=ylim[2], x1=x@coords[2], y1=ylim[2], code=1, length=0.1) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Draw "All exons" line # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # if(!exists("exons", envir=x@ev)) { cat("[plot.geneModel] No exon data found!\n") }else{ # Draw all exon boxes for gene using second color value # adjustcolor: Package grDevices # cols=border, background = alpha(border, 0.5) exons <- x@ev$exons for(i in 1:nrow(exons)) rect(exons$start[i], 0.2, exons$end[i], 0.8, col=adjustcolor(cols[2], alpha.f=0.5), border=cols[2]) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Draw exon boxes for transcripts # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # if(!exists("transcripts", envir=x@ev)) { cat("[plot.geneModel] No transcript data found!\n") }else{ trans <- x@ev$transcripts ntrans <- length(trans) ylim_t <- c(1, 8) ywid <- ylim_t[2] - ylim_t[1] allrects <- ywid * 2/3 allgaps <- ywid * 1/3 rectwid <- allrects / ntrans # vertical number of transcript boxes gapwid <- allgaps / (ntrans - 1) # number of vertical gaps ylolim <- (0:(ntrans-1) * (rectwid + gapwid)) + ylim_t[1] for(i in 1:ntrans) plot(trans[[i]], ylim=c(ylolim[i], ylolim[i] + rectwid), col=cols[1]) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Draw labels on y axis # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # axis(side=2, at=c(0.5, ylolim + rectwid/2), tick=FALSE, line=NA, labels=c("All exons",names(x@transcripts)), las=1, cex.axis=0.6) par(op) } setMethod("getTranscript", c("geneModel", "numeric"), function(object, i) { return(object@ev$transcripts[[i]]) }) setMethod("getTranscript", c("geneModel", "character"), function(object, i) { mtc <- match(i[1], names(object@ev$transcripts)) if(!is.na(mtc)) return(object@ev$transcripts[[mtc]]) mtc <- match(i[1], object@transcripts) if(!is.na(mtc)) return(object@ev$transcripts[[mtc]]) stop("No Match for transcript name '", i[1], "'") }) setMethod("geneModel", c("ensemblGenome", "character"), function(object, gene_id, interior=TRUE) { # Object only contains data for one single gene_id gene_id <- as.character(gene_id[1]) gg <- extractByGeneId(object, gene_id) if(!exists("genes", envir=object@ev)) stop("genes table missing in ensemblGenome object") gtb <- gg@ev$gtf # shortcut # Create output object res <- .geneModel() res@gene_id <- gene_id res@gene_name <- as.character(gg@ev$gtf$gene_name[1]) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Extract gene data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # mtc <- match(gene_id, gg@ev$genes$gene_id) assign("genes", gg@ev$genes[mtc, ], envir=res@ev) genes<-res@ev$genes res@seq_name <- as.character(genes$seqid[1]) res@strand <- as.character(genes$strand[1]) res@coords <- c(genes$start[1], genes$end[1]) names(res@coords) <- c("start", "end") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Exon and transcript data eventually is skipped # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # if(!interior) return(res) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Extract exon data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # exn <- c("seqid", "start", "end", "exon_id", "exon_number", "exon_version", "transcript_version", "transcript_id","transcript_name") # Missing columns are removed from column names mtc <- match(exn, names(gtb)) exn <- exn[which(!is.na(mtc))] exons <- gtb[gtb$feature=="exon", exn] assign("exons", exons, envir=res@ev) # Generate coordinates of unique exons exons <- uid(exons, "start", "end") exid <- sort(unique(exons$uid)) mtc <- match(exid, exons$uid) assign("uexons", exons[mtc, ], envir=res@ev) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Extract transcript data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # tr <- sort(unique(gtb$transcript_name)) mtc <- match(tr, gtb$transcript_name) res@transcripts <- as.character(gtb$transcript_id[mtc]) names(res@transcripts) <- tr l <- lapply(rep("transcriptModel", length(res@transcripts)), new) for(i in 1:length(l)) { tro <- getTranscriptFromGtf(gtb, res@transcripts[i]) etr <- extractTranscript(gg, res@transcripts[i]) if(nrow(etr@ev$gtf) < 2) { # Empty splice table tdf <- data.frame(begin=character(0), end=character(0)) }else{ etj <- getSpliceTable(etr) utj <- unifyJuncs(etj) tdf <- data.frame(begin=utj@ev$gtf$lend + 1, end=utj@ev$gtf$rstart - 1) } #tro@introns <- tdf l[[i]] <- tro } names(l) <- tr assign("transcripts", l, envir=res@ev) return(res) } ) setMethod("geneModel", c("ucscGenome", "character"), function(object, gene_id, interior=TRUE) { # Object only contains data for one single gene_id gene_id <- as.character(gene_id[1]) gg <- extractByGeneId(object, gene_id) if(!exists("genes", envir=object@ev)) stop("genes table missing in ensemblGenome object") gtb <- gg@ev$gtf # shortcut # Create output object res <- .geneModel() res@gene_id <- gene_id res@gene_name <- as.character(gg@ev$gtf$gene_name[1]) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Extract gene data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # mtc <- match(gene_id, gg@ev$genes$gene_id) assign("genes", gg@ev$genes[mtc, ], envir=res@ev) genes<-res@ev$genes res@seq_name <- as.character(genes$seqid[1]) res@strand <- as.character(genes$strand[1]) res@coords <- c(genes$start[1], genes$end[1]) names(res@coords) <- c("start", "end") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Exon and transcript data eventually is skipped # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # if(!interior) return(res) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Extract exon data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # exn <- c("seqid", "start", "end", "exon_id", "exon_number", "exon_version", "transcript_version", "transcript_id","transcript_name") # Missing columns are removed from column names mtc <- match(exn, names(gtb)) exn <- exn[which(!is.na(mtc))] exons <- gtb[gtb$feature=="exon", exn] assign("exons", exons, envir=res@ev) # Generate coordinates of unique exons exons <- uid(exons, "start", "end") exid <- sort(unique(exons$uid)) mtc <- match(exid, exons$uid) assign("uexons", exons[mtc, ], envir=res@ev) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Extract transcript data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # tr <- sort(unique(gtb$transcript_id)) mtc <- match(tr, gtb$transcript_id) res@transcripts <- as.character(gtb$transcript_id[mtc]) names(res@transcripts) <- tr l <- lapply(rep("transcriptModel", length(res@transcripts)), new) for(i in 1:length(l)) { l[[i]] <- getTranscriptFromGtf(gtb, res@transcripts[i]) } names(l) <- tr assign("transcripts", l, envir=res@ev) return(res) } ) # + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # # CLASS geneList # + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # .geneList <- setClass("geneList", slots=c( l="list" ) ) setMethod("initialize", "geneList", function(.Object){ .Object@l <- list() return(.Object) }) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # S3 generics # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # setMethod("length", "geneList", function(x){ return(length(x@l)) }) setMethod("names", "geneList", function(x) { return(names(x@l))}) setMethod("names<-", c("geneList", "character"), function(x, value) { names(x@l) <- value return(x) } ) setMethod("names<-", c("geneList", "numeric"), function(x, value) { names(x@l) <- value return(x) } ) setMethod("show", "geneList", function(object) { bm<-Sys.localeconv()[7] cat("Object of class '", class(object), "'\n", sep="") cat("Length : ", length(object) , "\n") cat("Names:\n") print(names(object)) }) setMethod("[", signature="geneList", function(x, i) { if(length(i) == 1) return(x@l[[i]]) res <- .geneList() res@l <- x@l[i] return(res) }) setMethod("+", signature=c("geneList", "geneList"), function(e1, e2){ res <- .geneList() res@l <- c(e1@l, e2@l) return(res) }) setMethod("+", c("geneModel", "geneModel"), function(e1, e2){ res <- .geneList() res@l <- list(e1, e2) names(res@l) <- c(e1@gene_id, e2@gene_id) return(res) }) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # Creation of geneList objects # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # setGeneric("geneList", function(ref, genes, interior=TRUE) standardGeneric("geneList")) setMethod("geneList", c("ensemblGenome", "character"), function(ref, genes, interior=TRUE) { ng <- length(genes) # convert genl <- split(genes, 1:ng) names(genl) <- genes getGeneModel <- function(x) { return(geneModel(ref, x, interior))} gl <- .geneList() gl@l <- lapply(genl, getGeneModel) return(gl) }) setMethod("geneList", c("ensemblGenome", "factor"), function(ref, genes, interior=TRUE) { return(geneList(ref, as.character(genes), interior)) }) setMethod("geneList", c("ucscGenome", "character"), function(ref, genes, interior=TRUE) { ng <- length(genes) # convert genl <- split(genes, 1:ng) names(genl) <- genes getGeneModel <- function(x) { return(geneModel(ref, x, interior))} gl <- .geneList() gl@l <- lapply(genl, getGeneModel) return(gl) }) setMethod("geneList", c("ucscGenome", "factor"), function(ref, genes, interior=TRUE) { return(geneList(ref, as.character(genes), interior)) })

ec9391c3d35ef2e5491a5e6e425716ec7f643096

5639bad159509b15a8cc6951e22fed8cf8439b1b

/scripts/endo_tss/endo_tss_format.R

cf0764cb70779105aac864252e95142652f292f6

[]

no_license

KosuriLab/ecoli_promoter_mpra

3f9fe47e40a81f9bafd27f672d0e7ece5924789f

484abdb6ba2d398501009ee6eb25ca7f190d3b4c

refs/heads/master

2021-07-09T00:38:46.186513

2020-08-10T17:28:54

179,758,077

0

1

null

UTF-8

R

false

1,910

r

endo_tss_format.R

library(dplyr) library(tidyr) options(stringsAsFactors = F) # args = commandArgs(trailingOnly=TRUE) # # infile <- args[1] # outfile <- args[2] infile <- '../../processed_data/endo_tss/lb/rLP5_Endo2_lb_expression.txt' outfile <- '../../processed_data/endo_tss/lb/rLP5_Endo2_lb_expression_formatted.txt' # infile <- '../../processed_data/endo_tss/alt_landing_pads/fLP3/fLP3_Endo2_lb_expression.txt' # outfile <- '../../processed_data/endo_tss/alt_landing_pads/fLP3/fLP3_Endo2_lb_expression_formatted.txt' # infile <- '../../processed_data/endo_tss/alt_landing_pads/rLP6/rLP6_Endo2_lb_expression.txt' # outfile <- '../../processed_data/endo_tss/alt_landing_pads/rLP6/rLP6_Endo2_lb_expression_formatted.txt' data <- read.table(file = infile, header = T) # parse name data <- data %>% mutate(name = gsub('>', '', orig_name), name = gsub('_rc', '', name)) %>% separate(name, into = c('tss_name', 'tss_position', 'strand'), sep = ',', remove = F) %>% mutate(tss_position = as.numeric(tss_position), start = ifelse(strand == '+', tss_position - 120, tss_position - 30), end = ifelse(strand == '+', tss_position + 30, tss_position + 120)) %>% select(name:end, variant:num_barcodes_integrated, -orig_name) data$category <- "tss" data$category[grep("pos_control", data$name)] <- "pos_control" data$category[grep("neg_control", data$name)] <- "neg_control" # separately format negative controls neg <- filter(data, category == 'neg_control') %>% mutate(strand = '+') %>% separate(name, into = c('dummy1', 'dummy2', 'loc'), sep = '_', remove = F) %>% select(-dummy1, -dummy2) %>% separate(loc, into = c('start', 'end'), sep = ':', convert = T) data <- filter(data, category != 'neg_control') %>% bind_rows(select(neg, name, tss_name:strand, start, end, variant:category)) write.table(data, file = outfile, quote = F, row.names = F)

322da95fb3bb94cc03ba560bfdd7d0a1acd3dd8b

4e4bc3bb5e2186fde35f3b2fcd2d2ea92195d1c5

/Plot3.R

72f06c239f92b9d06016927de0132e451b3b0d3b

[]

no_license

thuangpham/ExData_Plotting1

36a4315c00fd8a2b9455f081cc1504adea226de3

d74834248ff48e68c1b3c37e16a796e3e19023a3

refs/heads/master

2021-01-17T09:59:07.337152

2016-07-16T11:10:22

63,211,217

0

null

2016-07-13T03:28:19

2016-07-13T03:28:17

null

UTF-8

R

false

1,027

r

Plot3.R

#set the working directory setwd("C:/DataScience/Exploratory Data/ExData_Plotting1/ExData_Plotting1") library(dplyr) file<-"Data/household_power_consumption.txt" df <- read.csv(file, sep=";", header=TRUE,stringsAsFactors=FALSE,skip = 66637, nrow = 2880, na.strings="?") name <- sapply(read.table(file, nrow = 1, sep = ";"), as.character) names(df) <- name ##set datetime column by combining Date and Time columns df$DateTime <- strptime(paste(df$Date, df$Time), format="%d/%m/%Y %H:%M:%S") #plot to png png("plot3.png",width=500,height=500) plot(df$DateTime, df$Sub_metering_1,ylab="Energy sub metering",xlab=NA,type="l",col="black") #add lines for sub_metering_2 column using red lines(df$DateTime, df$Sub_metering_2, col="red") #add lines for sub_metering_3 column using blue lines(df$DateTime, df$Sub_metering_3, col="blue") # add legend legend("topright", col = c("black", "red", "blue"), lty=1, legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")) dev.off()

3f48d7612c431bfb7b09ff726f9b86e664848d5c

26ba3082f3586332d3a81c7cd5f71a0bd7eb6889

/R/omim.R

acb89e1735f619fe29f62235fe9a29cee919e278

[]

no_license

ExoLab-UPLCMS/MetaboliteHub

0ec60bcf2b50169b9288077786c206049be99d4d

b5491b63ccec3589a4a3c1d84a1ec544cb303e30

refs/heads/master

2022-04-11T15:45:06.191108

2020-03-31T15:34:51

203,401,595

1

0

null

UTF-8

R

false

663

r

omim.R

#' Retrieves diseases and metabolic pathways alttered present in OMIM database. #' #' For a gene, the functions searches on OMIM database and retrieves what is publicated there, the diseases and metabolic alterations reported. #' #' @param x is the gene of interest. #' @return returns a list of the different alterations described for the gene. #' @import rentrez #' @export omim<-function(x){ omim_id<-entrez_search("omim", x)$ids if (length(omim_id)==0){ summary_omim<-c('No entries found for this gene') } else { summary_omim<-sapply(omim_id, FUN=function(x){ entrez_summary("omim", x)$title }) } return(unname(summary_omim)) }

8f3ee363eeff6c02fe24966486b8af6453f03de5

643a4f814e3696da39814bec6ff21e90c16995f3

/man/getLevels-LagOperator.Rd

58616fcbeccee1570d2c4c037c199d34a9811d32

[]

no_license

LiangCZhang/quantspec

186a66f508155f093fe37dcd9067bfe12db54265

6e7e929893a68bd1af76ac259180584aa28b3813

refs/heads/develop

2021-01-12T08:15:11.620545

2016-03-28T16:17:11

76,523,170

2

1

null

2016-12-15T04:13:16

2016-12-15T04:13:15

null

UTF-8

R

false

true

730

rd

getLevels-LagOperator.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Class-LagOperator.R \docType{methods} \name{getLevels-LagOperator} \alias{getLevels,LagOperator-method} \alias{getLevels-LagOperator} \title{Get attribute \code{levels} from a \code{LagOperator}.} \usage{ \S4method{getLevels}{LagOperator}(object, j) } \arguments{ \item{object}{\code{LagOperator} from which to get the \code{levels}.} \item{j}{Index pointing to a set of levels in the list; optional.} } \value{ Returns levels attribute, as a vector of real numbers. } \description{ If the optional parameter \code{j} is supplied, then the \code{j}th vector of levels will be returned, a list with all vectors otherwise. } \keyword{Access-functions}

5176825fc5a82d5a5efbe88cc96499e99a116c14

3ee04b4129e86c9218a34f402349649727baa646

/man/jtrace_is_installed.Rd

718f9149e3969b0b7fd927eda87eba12f5e2cd9f

[ "MIT" ]

permissive

gongcastro/jtracer

c34233cfcebba4dce8e7c5be72f09b626c3573ec

ed4126d5a6b92034182eb9e77d6c357453af34c5

refs/heads/master

2023-09-04T11:31:43.980588

2021-10-15T15:37:16

365,167,721

0

1

null

UTF-8

R

false

true

556

rd

jtrace_is_installed.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/install.R \name{jtrace_is_installed} \alias{jtrace_is_installed} \title{Check if jTRACE is installed} \usage{ jtrace_is_installed() } \value{ A logical values indicating whether jTRACE has been already installed } \description{ Check if jTRACE is installed } \details{ jTRACe website: \code{https://magnuson.psy.uconn.edu/jtrace/} } \examples{ jtrace_is_installed() } \author{ Gonzalo Garcia-Castro \href{mailto:gonzalo.garciadecastro@upf.edu}{gonzalo.garciadecastro@upf.edu} }

22a07916ff0f0748a8456e393ac5c860d520e09e

4a033f9a65e4dcf36533b6a5ec92620fe229ab75

/cachematrix.R

4ee892721e29593f049e77da669456871c6f1e6e

[]

no_license

JayaCh/ProgrammingAssignment2

bd8ec125361e3ace7d94af9d8d20c05a8f2bd96b

4fac1e45ff804e2ab9289cb9b819d28854abdc1a

refs/heads/master

2020-12-01T09:32:10.031816

2014-11-20T15:36:31

null

0

null

UTF-8

R

false

1,938

r

cachematrix.R

## makeCacheMatrix: Creates and returns the list of getMatrix, setMatrix, getInverse and setInverse functions. ## The superassignment operator is used in the setMatrix and setInverse methods to cache the ## values of input matrix and the inverse matrix. ## cacheSolve: Calculates the inverse matrix of the matrix, that is retrieved by makeCacheMatrix object. ## First checks if the inverse matrix is available and if so gets it from cache and returns it. ## Otherwise calcluates the inverse matrix, cahces it and returns it. # makeCacheMatrix returns the list with the setMatrix, getMatrix, setInverse and getInverse functions. makeCacheMatrix <- function(x = matrix()) { # input x will be a matrix cachedInv <- NULL # Cache the input matrix setMatrix <- function(y) { x <<- y # Input matrix changed, hence the cachedInv needs to be reset to NULL cachedInv <<- NULL } # Retreive the input matrix getMatrix <- function() { x } # Cache the inverse matrix setInverse <- function(inverse) { cachedInv <<- inverse } # Retrieve the inverse matrix getInverse <- function() { cachedInv } # Return the list with the functions list( setMatrix = setMatrix, getMatrix = getMatrix, setInverse= setInverse, getInverse = getInverse) } # cacheSolve returns the inverse matrix of the matrix retrieved by makeCacheMatrix. cacheSolve <- function(x, ...) { # input x will be makeCacheMatrix object # Retrieve the chached inverse matrix inverse <- x$getInverse() # If the cached inverse matrix is not NULL, return it if(!is.null(inverse)) { message("getting cached data") return(inverse) } # Retrieve the input matrix data <- x$getMatrix() # Calculate the inverse matrix inverse <- solve(data) # Cache the inverse matrix x$setInverse(inverse) # Return the calculated matrix inverse }

03ec4a8bd4fd198b69ae2bf3c3bdbb202a063f69

d916b13e8151b66bae458dba011ad02a3a699f1d

/src/ggplot2 separate mean segment.R

6dc074eb7396c51901373d897ccf8233adb57166

[ "MIT" ]

permissive

korkridake/cssejhucovid19

8fdc3906332ef047bab75e42d0f01f49d30574a1

fc13516eeeca87b5345a169587be5776b061f83d

refs/heads/main

2023-05-26T17:36:51.182358

2021-06-16T08:31:31

375,930,293

0

null

UTF-8

R

false

1,627

r

ggplot2 separate mean segment.R

### separate mean segment # install.packages("ggplot2", "dplyr") library(ggplot2) library(dplyr) set.seed(22) d <- data.frame(t = seq(as.Date("2011-1-1"), by = "month", length.out = 48), v = as.integer(rnorm(48, mean=50, sd=10))) ### plain line chart ggplot(d, aes(x=t, y=v)) + geom_line() ### add average line ggplot(d, aes(x=t, y=v)) + geom_line() + geom_hline(yintercept = mean(d$v), color="limegreen") + theme_bw() ### create mean by year d2 <- d %>% group_by(year=format(t,'%Y')) %>% summarise(avg=mean(v), minT=min(t), maxT=max(t)) ### draw only avg lines for each year with geom_segment ggplot() + geom_segment(data=d2, aes(x = minT, y = avg, xend = maxT, yend = avg)) # add color=year to aes(). # Note: year is a character not numeric (continuous variable) ggplot() + geom_segment(data=d2, aes(x = minT, y = avg, xend = maxT, yend = avg, color=year)) + ylim(min(d$v), max(d$v)) ### use geom_segment to draw separate avg line segment for each year # data sources came from 2 data frames: one for geom_line, another for geom_segment ggplot(d, aes(x=t, y=v)) + geom_line(color="grey") + geom_segment(data=d2, aes(x = minT, y = avg, xend = maxT, yend = avg, color=year), size = 1, linetype="solid") + theme_bw() + theme(legend.position="none") + xlab("") + ylab("")

f751cc15b40e1bd6afbbe480b8997989d520a895

e66f8e5ef689a7b6dae10ca5958a4b510dbde13f

/man/write_shinyrates_data.Rd

ddb91b28e24213b89e4182587f610e6a3b0115c7

[]

no_license

delabj/pogoshinyrates

11a5914a7ec017917f378085cf9edf0e5f146c79

98d04f1a704e0bac74cc1c9ecec7ccd193913d35

refs/heads/master

2022-11-12T19:50:17.295599

2020-07-09T21:26:10

264,212,546

1

0

null

UTF-8

R

false

true

565

rd

write_shinyrates_data.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_shinyrates_data.R \name{write_shinyrates_data} \alias{write_shinyrates_data} \title{Write the shinyrates data.} \usage{ write_shinyrates_data(df, name = "shinyrates.csv") } \arguments{ \item{df:}{a data frame to write} \item{name:}{the name of the file (with file type ie name.csv)} } \description{ writes the data from shinyrates.com } \examples{ date <- Sys.Date() df <- scrape_shinyrates_website() df <- format_shinyrates_data(df=df, timestamp=date) write_shinyrates_data(df) }

9f4e557abd5141db60232133b3353c53bac968f7

d64c6c986730b2e989673679796cf4c968474de8

/data_wrangling.R

afa48e082a411443149593ca6961ec576f50aa2c

[]

no_license

han-tun/vizrisk

4e5e1e341706075d7f8458cfb693d192fcceaa09

5219f3cde6b6089024e932c1b5acf45a562c7def

refs/heads/master

2020-12-08T11:40:37.503228

2020-01-10T06:29:47

232,973,137

0

null

2020-01-10T05:34:12

2020-01-10T05:34:11

null

UTF-8

R

false

3,645

r

data_wrangling.R

library(tidyverse) library(geosphere) library(geojsonio) ##landers landers <- read_csv("landers.csv") landers_mainshock <- landers %>% filter(mag == 7.3) landers_rupture_length <- 80 * 2 * 1000 landers_mainshock_time <- landers_mainshock$time landers_dist <- landers %>% mutate(mainshock_lon = landers_mainshock$longitude, mainshock_lat = landers_mainshock$latitude) %>% group_by(id) %>% mutate(dist = distHaversine(p1 = c(mainshock_lon, mainshock_lat), p2 = c(longitude, latitude))) %>% ungroup() landers_triggered <- landers_dist %>% filter(time > landers_mainshock_time) %>% filter(dist > landers_rupture_length) %>% arrange(time) %>% mutate(id_overall = row_number()) %>% mutate(day = lubridate::floor_date(time, unit = "day")) %>% group_by(day) %>% mutate(id_day = row_number()) %>% ungroup() landers_before <- landers_dist %>% filter(time < landers_mainshock_time) %>% filter(dist > landers_rupture_length) %>% arrange(time) %>% mutate(id_overall = row_number()) %>% mutate(day = lubridate::floor_date(time, unit = "day")) %>% group_by(day) %>% mutate(id_day = row_number()) %>% ungroup() landers_aftershocks <- landers_dist %>% filter(time > landers_mainshock_time) %>% filter(dist < landers_rupture_length) %>% arrange(time) %>% mutate(id_overall = row_number()) %>% mutate(day = lubridate::floor_date(time, unit = "day")) %>% group_by(day) %>% mutate(id_day = row_number()) %>% ungroup() #sumatra sumatra <- read_csv("sumatra.csv") sumatra_mainshock <- sumatra %>% filter(mag == 8.6) sumatra_rupture_length <- 500 * 2 * 1000 sumatra_mainshock_time <- sumatra_mainshock$time sumatra_dist <- sumatra %>% mutate(mainshock_lon = sumatra_mainshock$longitude, mainshock_lat = sumatra_mainshock$latitude) %>% group_by(id) %>% mutate(dist = distHaversine(p1 = c(mainshock_lon, mainshock_lat), p2 = c(longitude, latitude))) %>% ungroup() sumatra_triggered <- sumatra_dist %>% filter(time > sumatra_mainshock_time) %>% filter(dist > sumatra_rupture_length) %>% arrange(time) %>% mutate(id_overall = row_number()) %>% mutate(day = lubridate::floor_date(time, unit = "day")) %>% group_by(day) %>% mutate(id_day = row_number()) %>% ungroup() sumatra_before <- sumatra_dist %>% filter(time < sumatra_mainshock_time) %>% filter(dist > sumatra_rupture_length) %>% arrange(time) %>% mutate(id_overall = row_number()) %>% mutate(day = lubridate::floor_date(time, unit = "day")) %>% group_by(day) %>% mutate(id_day = row_number()) %>% ungroup() sumatra_aftershocks <- sumatra_dist %>% filter(time > sumatra_mainshock_time) %>% filter(dist < sumatra_rupture_length) %>% arrange(time) %>% mutate(id_overall = row_number()) %>% mutate(day = lubridate::floor_date(time, unit = "day")) %>% group_by(day) %>% mutate(id_day = row_number()) %>% ungroup() #write data as geojson landers_before %>% select(time, latitude, longitude, mag, id_overall, id_day, day) %>% geojson_write(., lat = "latitude", lon = "longitude", file = "landers_before.geojson") landers_triggered %>% select(time, latitude, longitude, mag, id_overall, id_day, day) %>% geojson_write(., lat = "latitude", lon = "longitude", file = "landers_triggered.geojson") sumatra_before %>% select(time, latitude, longitude, mag, id_overall, id_day, day) %>% geojson_write(., lat = "latitude", lon = "longitude", file = "sumatra_before.geojson") sumatra_triggered %>% select(time, latitude, longitude, mag, id_overall, id_day, day) %>% geojson_write(., lat = "latitude", lon = "longitude", file = "sumatra_triggered.geojson")

81537d634660c74a072f68a35427f0f431c56852

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/ProFit/examples/profitOpenCLEnv.Rd.R

ccb77717548f7f9c5f26af419c1d15333082c72d

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

728

r

profitOpenCLEnv.Rd.R

library(ProFit) ### Name: profitOpenCLEnv ### Title: Create OpenCL Pointer Object ### Aliases: profitOpenCLEnv ### Keywords: GPU OpenCL ### ** Examples modellist = list( sersic = list( xcen = c(180, 60), ycen = c(90, 10), mag = c(15, 13), re = c(14, 5), nser = c(3, 10), ang = c(46, 80), axrat = c(0.4, 0.6), box = c(0.5,-0.5) ), pointsource = list( xcen = c(34,10,150), ycen = c(74,120,130), mag = c(10,13,16) ), sky = list( bg = 3e-12 ) ) magimage(profitMakeModel(modellist=modellist, dim=c(200,200))) ## Not run: ##D tempCL=profitOpenCLEnv() ##D magimage(profitMakeModel(modellist=modellist, dim=c(200,200), openclenv=tempCL)) ## End(Not run)

aee2a95454f0bc35e6460ea0fdcb2359123e57ca

ce94e221e5fd686cfb1218b0a9625decb77ac0c7

/man/mmplot.Rd

a171650f07b6e5f553ab0dd39128931d7248b144

[]

no_license

daniel-gerhard/medrc

bb95f91a63e150dd4a114fbfa409dcddc89195ea

232b2f3887510add1851e6eae21f6ac529b6bf33

refs/heads/master

2020-12-24T08:24:02.948797

2017-12-27T03:39:07

10,939,171

5

0

null

UTF-8

R

false

511

rd

mmplot.Rd

\name{mmplot} \alias{mmplot} \title{Plot multiple medrc objects} \description{Plot multiple predicted dose-response curves based on fixed effect estimates from multiple medrc objects} \usage{ mmplot(x, ..., ndose=25, logx = FALSE) } \arguments{ \item{x}{An object of class medrc} \item{...}{further objects of class medrc} \item{ndose}{Number of points to interpolate the dose response curve} \item{logx}{If TRUE, plot x-axis on a logarithmic scale} } \author{Daniel Gerhard} \keyword{ graphics }

5ed4082e7f679a2ac91b72b38b4b23cb8e729967

3b54cf65d257611c74f23fab637a6c86c05d84c6

/R/sequences.R

758ceb34f8a85153c6619d6072fce0a45072bd98

[]

no_license

epigen/RnBeadsAnnotationCreator

624a02f2ae039a6ee4c3ddb0a497296bddbe9058

62ee92cd76c601966656c3bf134e3e7a9ba794a7

refs/heads/master

2022-07-07T20:30:18.419055

2022-06-22T07:55:34

64,145,268

1

2

null

UTF-8

R

false

5,458

r

sequences.R

######################################################################################################################## ## sequences.R ## created: 2015-12-10 ## creator: Yassen Assenov ## --------------------------------------------------------------------------------------------------------------------- ## Utility functions for working with genomic sequences encoded as character vectors. ######################################################################################################################## ## F U N C T I O N S ################################################################################################### rnb.seq.bisulfite.convert <- function(x.char) { for (i in 1:length(x.char)) { xx <- x.char[[i]] for (j in 1:length(xx)) { if (xx[j] == "C" && (j == length(xx) || xx[j + 1] != "G")) { xx[j] <- "T" } } x.char[[i]] <- xx } x.char } ######################################################################################################################## rnb.seq.reverse.complement <- function(x.char) { lapply(x.char, function(xx) { rev(unname(c("A" = "T", "C" = "G", "G" = "C", "T" = "A", "N" = "N")[xx])) }) } ######################################################################################################################## rnb.seq.replace <- function(x.char, s.old, s.new) { for (i in 1:length(x.char)) { xx <- x.char[[i]] xx[xx == s.old] <- s.new x.char[[i]] <- xx } x.char } ######################################################################################################################## rnb.infinium.probe.coords <- function(loci, pr.design, pr.strand) { if (pr.design == "I") { if (pr.strand == "+") { return(cbind(loci + 0L, loci + 50L)) } return(cbind(loci - 48L, loci + 2L)) } # else pr.design == "II" if (pr.strand == "+") { return(cbind(loci + 1L, loci + 51L)) } return(cbind(loci - 49L, loci + 1L)) } ######################################################################################################################## rnb.seq.get.expected.sequence <- function(chrom.sequence, loci, pr.design, pr.strand) { p.coords <- rnb.infinium.probe.coords(loci, pr.design, pr.strand) dna.seq <- suppressWarnings(Views(chrom.sequence, start = p.coords[, 1], end = p.coords[, 2])) dna.seq <- strsplit(as.character(dna.seq), NULL) if (pr.strand == "-") { dna.seq <- rnb.seq.reverse.complement(dna.seq) } alleles.exp <- rnb.seq.reverse.complement(rnb.seq.bisulfite.convert(dna.seq)) if (pr.design == "I") { alleles.exp <- list("A" = rnb.seq.replace(alleles.exp, "G", "A"), "B" = alleles.exp) } else { # pr.design == "II" alleles.exp <- rnb.seq.replace(alleles.exp, "G", "R") } alleles.exp } ######################################################################################################################## rnb.seq.probe.mismatches <- function(probes.exp, probes.obs) { result <- sapply(probes.exp, length) - sapply(probes.obs, length) result[sapply(probes.exp, function(x) { length(x) == 1 && is.na(x) })] <- as.integer(NA) result[sapply(probes.obs, function(x) { length(x) == 1 && is.na(x) })] <- as.integer(NA) i <- which(result < 0L | result > 1L) result[i] <- NA if (length(i) != 0) { warning("Unexpected differences in probe sequence lengths") } for (i in 1:length(result)) { if (is.na(result[i])) { next } x <- probes.exp[[i]] if (result[i] == 0) { result[i] <- sum(x != probes.obs[[i]]) } else { # result[i] == 1 result[i] <- min(sum(x[-length(x)] != probes.obs[[i]]), sum(x[-1] != probes.obs[[i]])) } } result } ######################################################################################################################## rnb.seq.guess.strand.allele <- function(alleles.exp.pos, alleles.exp.neg, alleles.obs) { mismatches.pos <- rnb.seq.probe.mismatches(alleles.exp.pos, alleles.obs) mismatches.neg <- rnb.seq.probe.mismatches(alleles.exp.neg, alleles.obs) sel.strand <- as.factor(1L + (mismatches.neg < mismatches.pos)) levels(sel.strand) <- c("+", "-", "*") sel.strand[mismatches.pos == mismatches.neg] <- "*" mismatches.sel <- ifelse(sel.strand == "+", mismatches.pos, mismatches.neg) return(data.frame("Strand" = sel.strand, "Mismatches" = mismatches.sel)) } ######################################################################################################################## rnb.seq.guess.strands <- function(chrom.sequence, loci, pr.design, alleles.A, alleles.B = NULL) { alleles.exp.pos <- rnb.seq.get.expected.sequence(chrom.sequence, loci, pr.design, "+") alleles.exp.neg <- rnb.seq.get.expected.sequence(chrom.sequence, loci, pr.design, "-") alleles.char <- strsplit(alleles.A, NULL) if (pr.design == "I") { result <- rnb.seq.guess.strand.allele(alleles.exp.pos$A, alleles.exp.neg$A, alleles.char) alleles.char <- strsplit(alleles.B, NULL) resultB <- rnb.seq.guess.strand.allele(alleles.exp.pos$B, alleles.exp.neg$B, alleles.char) i <- which(result$Strand != resultB$Strand) result[i, "Strand"] <- "*" result <- data.frame( "Guessed Strand" = result$Strand, "Mismatches A" = result$Mismatches, "Mismatches B" = resultB$Mismatches, check.names = FALSE) } else { # pr.design == "II" result <- rnb.seq.guess.strand.allele(alleles.exp.pos, alleles.exp.neg, alleles.char) result <- data.frame( "Guessed Strand" = result$Strand, "Mismatches A" = result$Mismatches, "Mismatches B" = 0L, check.names = FALSE) } result }

c38574489df6112f22a40f81cb60e32b59092048

bc304dc82564cf44b7fe73d8722f1ac6e680dacc

/Elections/BosniaHoR-drive .R

5f72efe209d935b3cb2c392ee8dc2fe5d5062a7b

[]

no_license

rs2903/BiH

7f993a06724e6a72a73810d2f59823c67dba8882

6c2f9b7413634ea0e0bd347cd74756cb989986ae

refs/heads/master

2020-06-19T04:09:30.983391

2019-07-12T09:34:33

196,551,852

0

null

UTF-8

R

false

31,670

r

BosniaHoR-drive .R

# load packages ----------------------------------------------------------- require(ggplot2) #require(plotly) require(stringi) require(tidyr) require(purrr) require(dplyr) #require(rio) require(lubridate) require(readxl) require(stringdist) library(xlsx) library(reldist) library(ggthemes) library(highcharter) #setwd("//fs.univie.ac.at/homedirs/schmidr9/Documents/ro - ceu/R") setwd("C:/Users/Roland/Google Drive/CEU/THESIS/R") #setwd("~/Google Drive/CEU/THESIS/R") #setwd("Z:/Documents/ro - ceu/R") Sys.setlocale("LC_CTYPE", "russian") #allows displaying party names in cyrillic # file 1996 --------------------------------------------------------------- res96 <- read_excel("Elections/Electoral Results Bosnia.xlsx",sheet="HoR 1996") hor96 <- res96 %>% mutate(date = as.Date(date), year = year(date)) %>% select(-(source)) # IMPORT FILE 1998 & 2000 ------------------------------------------------------------- #df <- import("Electoral Results Bosnia.xlsx") res98_00 <- read_excel("Elections/Electoral Results Bosnia.xlsx",sheet="HoR 1998 2000") res98_00$seats98 <- as.numeric(res98_00$seats98) #drop perc since only realte to entity res98_00 <- subset(res98_00, select = -c(perc98,perc00) ) # 1998 -------------------------------------------------------------------- #only parties which ran in 98 hor98 <- res98_00 %>% select(entity,party,type,seats98,abs98) %>% filter(type=="Total:") %>% select(-(type)) %>% filter(abs98>0) %>% rename(seats=seats98, votes=abs98) hor98 <- hor98 %>% mutate(date=as.Date("1998-9-13", format="%Y-%m-%d"), year=year(date)) hor98$seats[is.na(hor98$seats)] <- 0 # 2000 -------------------------------------------------------------------- #only parties which ran in 00 hor00 <- res98_00 %>% select(entity,party,type,seats00,abs00) %>% filter(type=="Total:") %>% select(-(type)) %>% filter(abs00>0) %>% rename(seats=seats00, votes=abs00) hor00 <- hor00 %>% mutate(date=as.Date("2000-11-11", format="%Y-%m-%d"), year=year(date)) hor00$seats[is.na(hor00$seats)] <- 0 # 2002 -------------------------------------------------------------------- res02 <- read_excel("Elections/Electoral Results Bosnia.xlsx",sheet="HoR 2002") hor02 <- res02 %>% select(entity, party, votes, seats, year) %>% mutate(date=as.Date("2002-10-5", format="%Y-%m-%d")) class(hor02$date) # import 2006 - 2014 ------------------------------------------------------ res06_14 <- read_excel("Elections/Electoral Results Bosnia.xlsx",sheet="HoR 2006 - 2014") hor06_14 <- res06_14 %>% select(date.election, entity, party, votes, seats) %>% mutate(date=as.Date(date.election), year=year(date), seats=as.numeric(seats)) %>% select(-(date.election)) hor06_14$seats[is.na(hor06_14$seats)] <- 0 # bind results ----------------------------------------------------------- hor <- bind_rows(hor96, hor98, hor00, hor02, hor06_14) i <- hor %>% group_by(year, entity) %>% summarise(sum.seats=sum(seats)) i # unify names ------------------------------------------------------------- # length(unique(hor$party)) #180 different party names # p <- as.data.frame(unique(hor$party)) # export(p,"partynames.xlsx") # >> import df w with standardized names ---------------------------------- #party.names2 from party.nat project party.names <- read_excel("Elections/Electoral Results Bosnia.xlsx",sheet="party.names2") hor <- hor %>% rename(party.original=party) #hor$party.standard <- party.names$name.unified2[match(gsub("\\s","",tolower(hor$party.original)),gsub("\\s","",tolower(party.names$name.original)))] hor$party.standard <- party.names$name.unified2[amatch(gsub("\\s","",tolower(hor$party.original)),gsub("\\s","",tolower(party.names$name.original)), maxDist = 5)] length(unique(hor$party.original)) length(unique(hor$party.standard)) d<- hor %>% filter(is.na(hor$party.standard)) %>% #non-matched party names filter(seats > 0) non.matched<- unique(d$party.original) length(unique(d$party.original)) #write.xlsx(non.matched, "Elections/NonMatchedNames.xlsx") non.matched.seats <- hor %>% filter(seats>0)%>% filter(is.na(party.standard))%>% select(party.original,party.standard) # Unique standardized party names with seats ------------------------------ unique.standard.seats <- hor %>% filter(seats > 0) %>% select(party.standard) %>% distinct(party.standard) class(unique.standard.seats) unique.standard.seats <- as.data.frame(unique.standard.seats) # write.xlsx2(unique.standard.seats, "Elections/Electoral Results Bosnia.xlsx", # sheetName="UniquePartyNamesSeats", # col.names=TRUE,row.names = FALSE, # append=TRUE, showNA=FALSE) # >> Percentage in votes & seats in Entity and State ------------------------------------------- hor <- hor %>% group_by(year) %>% mutate(votes.perc=round(votes/sum(votes)*100, 2))%>% #wrong: doesn't consider 1 party in RS & FED in same year mutate(seats.perc=round(seats/sum(seats)*100, 2))%>% group_by(year, entity) %>% mutate(votes.perc.ent=round(votes/sum(votes)*100,2)) %>% mutate(seats.perc.ent=round(seats/sum(seats)*100,2)) # FILTER - only parties with at least 1 seat in HoR ----------------------- x <- hor %>% filter(seats>0) party.filter <- unique(x$party.standard) # > percentage of votes & abs seats over year entire state ------------------------- year.sums<- hor %>% select(year, entity, party.original, party.standard, votes, seats) %>% group_by(year, party.standard) %>% summarize(votes.annual=sum(votes), seats.annual=sum(seats)) %>% ungroup() %>% group_by(year) %>% mutate(vote.perc.annual=round(votes.annual/sum(votes.annual)*100, 2)) length(unique(year.sums$party.standard)) # >> add ethnicity and coalition to results ------------------------------- # >>> load ethnicity from file -------------------------------------------- eth.coal <- read_excel("Elections/Electoral Results Bosnia.xlsx",sheet="UniquePartyNamesSeats") year.sums$ethnicity <- eth.coal$ethnicity[match(year.sums$party.standard,eth.coal$party.standard)] year.sums$coalition <- eth.coal$coalition[match(year.sums$party.standard,eth.coal$party.standard)] # Creating Table on Election Results ------------------------------------- # not based on harmonized party names #https://stackoverflow.com/questions/24929954/is-it-possible-to-use-spread-on-multiple-columns-in-tidyr-similar-to-dcast #gather - unite - spread sequence hor.table <- hor %>% filter(seats > 0) %>% select(year, entity, party.original, seats, votes.perc.ent) %>% gather(key, value, -c(1:3)) %>% unite(year.key,year,key)%>% spread(year.key,value) hor.table <- as.data.frame(hor.table) #if not dataframe write.xlsx crashes class(hor.table) # write.xlsx2(hor.table,"Electoral Results Bosnia.xlsx", # sheetName="ResultTable",col.names = TRUE,row.names = FALSE, # append=TRUE, showNA=FALSE) # > plot - annual % and seats --------------------------------------------- # >> seats p.a. ---------------------------------------------------------- seats.plot <- year.sums %>% filter(party.standard %in% party.filter) %>% ggplot(.,aes(year, seats.annual)) + geom_bar(stat="identity",aes(fill=party.standard))+ labs(x="year", y="seats",title="Seats in House of Representatives")+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) + theme_minimal() print(seats.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"seat.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) # highchart bar ------------------------------------------------------- wide <- year.sums %>% filter(party.standard %in% party.filter) %>% select(year,party.standard, seats.annual)%>% spread(.,year,seats.annual) %>% gather("year","n",2:8) #data is spread and gathered; spread creates cells with NA for years without observations #transforming the data back with gather does not result in a loss of rows/years with NA #NAs are needed as element in list; otherwise data is projected to wrong year wide.list <- wide %>% group_by(name=party.standard)%>% do(data=.$n) #creates list series <- list_parse(wide.list) #see http://stackoverflow.com/questions/38093229/multiple-series-in-highcharter-r-stacked-barchart hc <- highchart() %>% hc_chart(type = "column") %>% hc_xAxis(categories=unique(wide$year)) %>% hc_plotOptions(column = list( dataLabels = list(enabled = FALSE), stacking = "normal", enableMouseTracking = TRUE)) %>% hc_add_series_list(series) hc #unique for years since otherwise always frist year # >> votes perc per year --------------------------------------------------- year.sums %>% filter(party.standard %in% party.filter) %>% ggplot(.,aes(year, vote.perc.annual)) + geom_line(aes(color=party.standard))+ xlab("year")+ylab("votes %")+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) #+ theme_minimal()+ # distribution of votes btw entities per party ---------------------------- v <- hor %>% select(year, party.standard, votes, entity,seats) %>% group_by(year, party.standard) %>% mutate(entity.perc=round(votes/sum(votes),2), number=max(row_number(party.standard))) v %>% filter(party.standard %in% party.filter) %>% ggplot(.,aes(year, seats))+ geom_bar(stat="identity", aes(fill=entity, color="red"))+ facet_wrap(~party.standard)+# + xlab("year")+ylab("seats in total")+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) hor1 <- hor %>% filter(party.standard %in% party.filter) nrow(hor1) ggplot(hor1, aes(year, votes.perc.ent))+ # geom_line()+ geom_point(aes(color=entity, shape=entity))+ facet_wrap(~party.standard)+ xlab("year")+ylab("% of votes in entity")+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) d <- hor1 %>% group_by(year, party.standard) %>% mutate(number=max(row_number(party.standard))) # Number of Parties per Ethnic Group -------------------------------------- hor$ethnicity <- eth.coal$ethnicity[match(hor$party.standard,eth.coal$party.standard)] hor$coalition <- eth.coal$coalition[match(hor$party.standard,eth.coal$party.standard)] hor$party.abbrev <- eth.coal$party.abbrev[match(hor$party.standard,eth.coal$party.standard)] hor$ethnicity <- as.factor(hor$ethnicity) levels(hor$ethnicity) hor$ethnicity <- ordered(hor$ethnicity, c("B","C","S","M","nk")) #number of parties per ethnic group with a seat in HoR per year n <- hor %>% filter(seats>0)%>% group_by(year,ethnicity) %>% summarise(n.parties=n_distinct(party.standard), n.coalition=n_distinct(party.standard[coalition=="y"], na.rm=TRUE), name.parties=paste(party.standard, sep=" ",collapse=";")) #reordering levels of ethnic groups n$ethnicity <- as.factor(n$ethnicity) levels(n$ethnicity) n$ethnicity <- ordered(n$ethnicity, c("B","C","S","M","nk")) n.parties.ethnicity.plot <- n %>% filter(ethnicity %in% c("B","C","S","M")) %>% ggplot(.,aes(year,n.parties))+ geom_step(aes(color=ethnicity), size=2)+ labs(x="year",y="seats", title=paste0("Number of Parties/Coalitions per Ethnic Group with Seats in the House of Representatives"), subtitle="1 coalition = 1 party")+ scale_x_continuous(breaks=c(1996,1998,2000,2002,2006,2010,2014)) + scale_y_continuous(limits = c(0,5))+ theme_minimal()+ theme(legend.position="bottom", panel.grid.minor.x = element_blank())+ facet_grid(ethnicity~.) print(n.parties.ethnicity.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"n.parties.ethnicity.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) ### >> differentiation parties/coalitions (STACKED & FACET bar plot) ----------------------------------------------------------- parties.col.facet.ethnicity.plot <- n %>% mutate(n.single.party = n.parties-n.coalition) %>% gather(key,number, n.single.party,n.coalition) %>% ggplot(.,aes(as.factor(year),number,group=ethnicity))+ geom_bar(stat="identity",aes(fill=key))+ labs(x="year", y="seats", title="Number of parties and coalitions with seats in House of Representatives", subtitle="Disaggregated by ethnic affiliation of party/coalition")+ # scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) + scale_fill_manual(values=c("red","darkblue"), labels=c("coalitions","single parties"))+ #scale_fill_discrete(labels=c("coalitions","single parties"), values=c("red","darkblue"))+ theme_minimal()+ theme(legend.position="bottom", legend.title = element_blank(), axis.title.x=element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank())+ #facet_grid(ethnicity~.) facet_grid(.~ethnicity) print(parties.col.facet.ethnicity.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"parties.col.facet.ethnicity.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) ## >> differentiation parties/coalitions (STACKED & NO FACET bar plot) ----------------------------------------------------------- parties.col.facet.yar.plot <- n %>% mutate(n.single.party = n.parties-n.coalition) %>% filter(ethnicity %in% c("B","C","S","M")) %>% gather(key,number, n.single.party,n.coalition) %>% ggplot(.,aes(ethnicity, number))+ geom_bar(stat="identity",aes(fill=key))+ labs(y="seats", title="Number of parties and coalitions with seats in House of Representatives", subtitle="Disaggregated by ethnic affiliation of party/coalition")+ scale_fill_manual(values=c("red","darkblue"), labels=c("coalitions","single parties"))+ # #scale_fill_discrete(labels=c("coalitions","single parties"), values=c("red","darkblue"))+ theme_minimal()+ theme(legend.position="bottom", legend.title = element_blank(), axis.title.x=element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank())+ facet_grid(~year) print(parties.col.facet.yar.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"parties.col.facet.yar.plot .pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) # >> Gini Seats in HOR per Ethic groups --------------------------------------------------- gini.seats <- hor %>% ungroup() %>% filter(seats > 0) %>% select(year, party.standard,seats,votes,ethnicity)%>% group_by(year, ethnicity, party.standard) %>% summarize(seats.year=sum(seats)) %>% group_by(year, ethnicity) %>% summarize(gini.seats=gini(seats.year)) gini.seats.plot <- gini.seats %>% ggplot(.,aes(year,gini.seats))+ geom_point(aes(color=ethnicity), size=3)+ geom_step(aes(color=ethnicity), size=2)+ labs(y="Gini Coefficient", title="Intra-Group Concentration of seats in House of Represenatitves", subtitle="Gini Coefficient on seats per ethnic group")+ # scale_fill_manual(values=c("red","darkblue"), labels=c("coalitions","single parties"))+ # #scale_fill_discrete(labels=c("coalitions","single parties"), values=c("red","darkblue"))+ scale_color_manual(values=c("darkgreen","darkblue","darkred","orange","grey"))+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) + theme_minimal()+ theme(legend.position="bottom", legend.title = element_blank(), axis.title.x=element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank())+ facet_grid(.~ethnicity) print(gini.seats.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"gini.seats.plot .pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) #>> Composition of HoR ------------------------------------------------------ comp.hor.plot <- hor %>% select(entity,votes,year,party.standard,ethnicity,coalition,seats)%>% filter(seats>0) %>% ggplot(.,aes(year,seats))+ geom_col(aes(fill=ethnicity,color=party.standard))+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) + scale_fill_manual(values=c("darkgreen","darkblue","darkred","orange","grey"))+ #scale_color_manual(values = c("blacks"))+ labs(title="Composition of House of Representatives", subtitle="categorized by ethnic affiliation", caption="Federation: 28 seats; RS: 14 seats")+ theme_minimal()+ theme(legend.position="none", legend.title = element_blank(), axis.title.x=element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank())+ geom_text(data=subset(hor, party.standard %in% c("Stranka Demokratske Akcije (SDA)")), aes(label = party.standard), size=2, position = position_stack())+ facet_grid(.~entity, scales="free_y") +coord_flip() print(comp.hor.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"comp.hor.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) # >> Composition of HoR - Geom_Area --------------------------------------- comp.hor.area <- hor %>% filter(seats>0)%>% select(year,seats, party.standard, ethnicity)%>% group_by(year,party.standard,ethnicity)%>% summarise(seats.y=sum(seats))%>% group_by(year,ethnicity)%>% summarise(seats.eth=sum(seats.y)) comp.hor.area$ethnicity <- ordered(comp.hor.area$ethnicity, c("M","B","C","S")) comp.hor.area.plot <- comp.hor.area %>% ggplot(.,aes(year,seats.eth))+ geom_area(aes(fill=ethnicity))+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) + scale_fill_manual(values=c("orange","darkgreen","darkblue","darkred","orange"))+ labs(title="Composition of House of Representatives", subtitle="categorized by ethnic affiliation", caption="comp.hor.area.plot")+ theme_minimal()+ theme(legend.position="bottom", legend.title = element_blank(), axis.title.x=element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) print(comp.hor.area.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"comp.hor.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) print(comp.hor.area.plot) # >> Composition of HoR - Tree map ------------------------------------------- # install.packages("devtools") # library(devtools) # install_github("wilkox/ggfittext") # install_github("wilkox/treemapify") #https://github.com/wilkox/treemapify library(treemapify) tree <- hor %>% ungroup(entity) %>% select(year, party.abbrev, ethnicity, seats, votes.perc, entity)%>% filter(seats>0)%>% group_by(year,ethnicity,party.abbrev)%>% summarise(seats.y=sum(seats))%>% ungroup() tree.plot <- tree %>% ggplot(., aes(area=seats.y, fill=ethnicity, subgroup=ethnicity, label=paste(party.abbrev," (",seats.y,")",sep="")))+ geom_treemap()+ geom_treemap_text( colour = "white", place = "topleft", reflow = T)+ theme_minimal()+ scale_fill_manual(values=c("darkgreen","darkblue","darkred","orange","grey"))+ labs(title="Ethnic segmentation of parties in House of Representative", subtitle="intertemporal changes largely within segments")+ facet_wrap(~year) print(tree.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"tree.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) # >> Election results per Ethnic Group and Entity------------------------------------------------------------------------ require(ggalt) hor.res <- hor %>% select(entity,seats, votes,year, party.standard,coalition,ethnicity, party.abbrev)%>% group_by(year, entity) %>% mutate(votes.perc.ent=round(votes/sum(votes)*100,2)) %>% ungroup()%>% filter(votes.perc.ent>1)%>% filter(seats>0)%>% arrange(year, entity, votes.perc.ent)%>% mutate(order=row_number())%>% ungroup() hor.res.plot <- ggplot(hor.res, aes(as.factor(order),votes.perc.ent))+ geom_lollipop(aes(color=ethnicity), size=1, horizontal = FALSE)+ scale_color_manual(values=c("darkgreen","darkblue","darkred","orange","grey"))+ scale_y_continuous(breaks=c(0,10,20,30,40,50,60)) + scale_x_discrete(breaks = hor.res$order,labels = hor.res$party.abbrev, expand = c(0.1,.1)) + labs(title="Election Results: House of Representatives", subtitle="Categorized by ethnic affiliation and entity")+ theme_minimal()+ theme(legend.position="bottom", legend.title = element_blank(), axis.title=element_blank(), # axis.text.x=element_text(angle=90, hjust=5), #axis.text.y=element_blank(), panel.grid.major.x =element_blank(), panel.grid.minor.y = element_blank(), panel.grid.major.y = element_blank())+ #facet_wrap(year~entity, scales="free",drop=TRUE, space="free")+coord_flip() facet_wrap(year~entity, labeller=label_parsed, scales="free",drop=TRUE, ncol=2)+coord_flip() print(hor.res.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"hor.res.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=21, height=29.7, unit="cm") # >> Number of parties ---------------------- #discrepancy when running on party.original #run on original; standradization of party names leads to NAs with some parties who did not get seats library(dplyr) n.parties <- hor %>% group_by(year) %>% summarise(n.parties.run=n_distinct(party.original), n.parties.seats=n_distinct(party.original[seats>0]))%>% ungroup() # >>> plot number of parties --------------------------------------------------------------------- plot.n.parties <- n.parties %>% gather(parties,number,n.parties.run, n.parties.seats)%>% ggplot(.,aes(year,number))+ geom_line(aes(color=parties),size=2)+ geom_point(aes(color=parties), size=3)+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) + labs(title="Number of Parties" , subtitle="running for or winning seat for House of Representatives", caption="plot.n.parties", x="",y="")+ theme_minimal()+ theme(legend.position="bottom", legend.title = element_blank(), axis.title.x=element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank())+ scale_y_continuous(limits=c(0,40)) print(plot.n.parties) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"plot.n.parties.png" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=21, height=29.7, unit="cm") freq.table <- hor %>% ungroup()%>% select(party.standard, year) %>% distinct()%>% ungroup()%>% count(party.standard)%>% arrange(-n)%>% ungroup() freq.table %<>% group_by(n) %>% summarise(n.parties=n_distinct(party.standard), names=paste(party.standard, sep=";",collapse=";")) # >> Inter-Segment volatility --------------------------------------------- hor <- hor %>% ungroup() # >>> inter-seg volatility based on votes [pending]---------------------------------- vol.seg <- hor %>% ungroup() %>% select(year, ethnicity, votes)%>% group_by(ethnicity, year)%>% summarise(sum.y.ethno=sum(votes))%>% group_by(year)%>% mutate(sum.y=sum(sum.y.ethno))%>% mutate(ethno.share=round(sum.y.ethno/sum.y*100,2))%>% arrange(ethnicity, year) %>% group_by(ethnicity) %>% mutate(diff.ethno=ethno.share-lag(ethno.share))%>% filter(ethnicity %in% c("B","C","S","M")) %>% group_by(year)%>% summarise(vol.y=sum(abs(diff.ethno))/2) # >>> inter-seg volatility based on seats ---------------------------------- vol.seg <- hor %>% ungroup()%>% filter(seats>0)%>% select(year, ethnicity, seats) %>% group_by(year, ethnicity) %>% summarise(y.eth.total=sum(seats))%>% group_by(year) %>% mutate(y.total=sum(y.eth.total))%>% ungroup()%>% mutate(share=round(y.eth.total/y.total,2))%>% group_by(ethnicity)%>% mutate(vol.eth.annual=share-lag(share))%>% group_by(year)%>% summarise(vol.y=sum(abs(vol.eth.annual))/2) # >>> inter-seg volatility plot (seats)---------------------------------- vol.seg.plot<- vol.seg %>% ggplot(.,aes(year,vol.y))+ geom_line()+ geom_point()+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014)) + labs(title="Inter-Segment Volatility", subtitle="volatility of relative size of Bosniak, Croat, Serb, and mulitethnic/civic electoral segment; based on seats in HoR", caption="vol.seg.plot", x="",y="")+ theme_minimal()+ theme(legend.position="none", legend.title = element_blank(), axis.title.x=element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank())+ scale_y_continuous(limits=c(0,1)) print(vol.seg.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"vol.seg.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=21, height=29.7, unit="cm") # >> Intra-Segment Volatility --------------------------------------------- vol.intra <- hor %>% filter(seats>0)%>% group_by(ethnicity, year, party.standard)%>% summarise(votes.y=sum(votes))%>% arrange(year, party.standard) %>% group_by(year, ethnicity)%>% mutate(sum.y=sum(votes.y))%>% mutate(perc=round(votes.y/sum.y*100,2))%>% group_by(party.standard)%>% mutate(diff.perc=perc-lag(perc))%>% group_by(year)%>% summarise(vol.y=sum(abs(diff.perc))/2) # number of parties running & getting seats----------------------------------------------- n.parties.entity <- hor %>% select(party.original, year, entity, seats, votes) %>% group_by(year, entity) %>% summarize(parties.all=n_distinct(party.original), parties.seats=n_distinct(party.original[seats>0]), gini.votes=gini(votes), gini.seats=gini(seats)) n.parties.state <- hor %>% select(party.original, year, entity, seats, votes) %>% group_by(year, party.original) %>% summarize(votes.year=sum(votes), seats.year=sum(seats)) %>% ungroup() %>% group_by(year) %>% summarize(parties.all=n_distinct(party.original), parties.seats=n_distinct(party.original[seats.year>0]), gini.votes=gini(votes.year), gini.seats=gini(seats.year)) n.parties <- bind_rows(n.parties.state,n.parties.entity) n.parties$entity[is.na(n.parties$entity)] <- "total" #number of parties for the state level is calculated not as the #sum of fed + rs since one party may run in two entities; avoid double counting n.parties <- n.parties %>% gather(parties,number, c(parties.all,parties.seats)) n.parties.plot <- n.parties %>% ggplot(.,aes(year, number, group=entity))+ geom_line(aes(color=entity))+ labs(x="year", y="number of parties", title="Number of Parties", subtitle="Coalitions are counted as parties")+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014))+ theme_minimal()+ theme(legend.position="bottom", panel.grid.minor.x = element_blank())+ facet_wrap(.~parties, scales = "free") print(n.parties.plot) folder <-"graphs/draft/" time <- format(Sys.time(),"%Y%m%d-%H%M%S") scriptname <- "BosniaHoR" plotname <-"n.parties.plot.pdf" ggsave(paste(folder,time,scriptname,plotname, sep="-"), width=15, height=7) # vote & seat concentration in entites ------------------------------------------- n.parties %>% select(year, entity, gini.votes, gini.seats) %>% gather(concentration, number, c(gini.votes, gini.seats)) %>% ggplot(.,aes(year, number, group=entity))+ geom_line(aes(color=entity))+ xlab("year")+ylab("Gini Coefficient")+ scale_x_continuous(breaks=c(1996,1998,2000,2002, 2006,2010,2014))+ facet_grid(concentration~.) seats$name.original <- seats$party seats$party <- partynames$name.harmonized[match(seats$party, partynames$name.original)] length(unique(seats$party)) length(unique(seats$name.original)) p.s<- as.data.frame(unique(seats$party)) colnames(p.s) <- "name" setdiff(p.l[,1], p.s[,1]) anti_join(p.l, p.s, by="name") # add Ethnicity to each party --------------------------------------------- # str(partynames) # party.ethnicity <- as.data.frame(unique(partynames$name.harmonized)) # colnames(party.ethnicity) <- c("name.harmonized") # #party.ethnicity<- export(party.ethnicity,"party.ethnicity.xlsx") # class(party.ethnicity$name.harmonized) # # party.ethnicity.list <- read_excel("party.ethnicity1.xlsx") # class(party.ethnicity.list$name.harmonized) # # #check whether new parties are in party.ethnicity which were previously not included # new2 <- anti_join(party.ethnicity, party.ethnicity.list, by="name.harmonized") #graph ggplot(data=seats,aes(x=as.factor(year), y=seats, fill=party, label=party))+ geom_bar(stat="identity") + geom_text(size = 3, position = position_stack(vjust = 0.5))+ #geom_bar(stat = "identity", position = "fill")#+ geom_density(adjust=1.5, position="fill")+ theme(legend.position="none") facet_grid(.~entity) # Number of Parties in HoR ------------------------------------------------ n.parties <- seats %>% group_by(year) %>% summarise(n.parties=length(party)) ggplot(n.parties, aes(x=as.Date(as.character(year),"%Y"), n.parties, group=1))+ geom_point()+ geom_line()+ xlab("year")+ylab("number of parties in HoR")+ expand_limits(y=0) + scale_x_date(date_breaks="years", date_labels="%Y") #scale_x_continuous(breaks=1996:2014) class(n.parties$year) # % Seats SDA, HDZ, SNSD, SDS --------------------------------------------- u <- seats %>% group_by(party, year) %>% summarise(party.seats=sum(seats))%>% filter(party %in% c("SDA", "HDZ", "SDS", "SNSD")) class(u) u <- as.data.frame(u) dput(u) ggplot(u, aes(x=as.Date(as.character(year), "%Y"), party.seats, group=party, color=party))+ geom_point()+ geom_step()+ xlab("year")+ylab("seats in HoR")+ scale_x_date(date_breaks="years", date_labels="%Y") #+ # > joint share of seats -------------------------------------------------------- ggplot(u, aes(x=as.Date(as.character(year), "%Y"), y = party.seats, fill = party)) + geom_area(position = "stack")+ xlab("year")+ylab("seats in HoR")+ scale_x_date(date_breaks="years", date_labels="%Y") #https://www.safaribooksonline.com/library/view/r-graphics-cookbook/9781449363086/ch04.html #http://stackoverflow.com/questions/29807080/geom-area-not-stacking-ggplot #problem: lines run as if seats would in/decrease during legislative period d <- as.data.frame(xtabs(party.seats~year+party, u)) ggplot(d,aes(x=as.Date(as.character(year), "%Y"), y = Freq, fill = party)) + #geom_bar(position="stack")+ geom_area(position = "stack")+ xlab("year")+ylab("seats in HoR")+ scale_x_date(date_breaks="years", date_labels="%Y")

c52460d6ba5253d1840d661a215f9aa9ee8e7ea6

1201b4b111882eef0960608d2e9262349e1bf88c

/R/swing.r

c844d86997b7c4211b9a72df9ac4427d073c4402

[]

no_license

awaardenberg/KinSwingR

c96610e04f340a06072f036bd77ea37e980a859a

6d8e6b4c8f49e50346727c6d7948c06636856ff7

refs/heads/master

2020-03-29T08:05:28.567017

2019-04-30T05:32:15

149,693,793

2

0

null

UTF-8

R

false

11,537

r

swing.r

#' Swing statistic #' #' @description This function integrates the kinase-substrate predictions, #' directionality of phosphopeptide fold change and signficance to assess #' local connectivity (swing) of kinase-substrate networks. The final score #' is a normalised and weighted score of predicted kinase activity. If #' permutations are selected, network node:edges are permutated. P-values will #' be calculated for both ends of the distribution of swing scores (positive and #' negative swing scores). #' @param input_data A data.frame of phoshopeptide data. Must contain 4 columns #' and the following format must be adhered to. Column 1 - Annotation, Column 2 #' - centered peptide sequence, Column 3 - Fold Change [-ve to +ve], Column 4 #' - p-value [0-1]. This must be the same dataframe used in scoreSequences() #' @param pwm_in List of PWMs created using buildPWM() #' @param pwm_scores List of PWM-substrate scores created using #' scoreSequences() #' @param pseudo_count Pseudo-count acts at two levels. 1) It adds a small #' number to the counts to avoid zero divisions, which also 2) avoids log-zero #' transformations. Note that this means that pos, neg and all values in the #' output table include the addition of the pseudo-count. Default: "1" #' @param p_cut_pwm Significance level for determining a significant #' kinase-substrate enrichment. Default: "0.05" #' @param p_cut_fc Significance level for determining a significant level of #' Fold-change in the phosphoproteomics data. Default: "0.05" #' @param permutations Number of permutations to perform. This will shuffle the #' kinase-subtrate edges of the network n times. To not perform permutations and #' only generate the scores, set permutations=1 or permutations=FALSE. Default: #' "1000" #' @param return_network Option to return an interaction network for visualising #' in cystoscape. Default = FALSE #' @param verbose Turn verbosity on/off. To turn on, verbose=TRUE. Options are: #' "TRUE, FALSE". Default=FALSE #' #' @examples #' ## import data #' data(example_phosphoproteome) #' data(phosphositeplus_human) #' #' ## clean up the annotations #' ## sample 100 data points for demonstration #' sample_data <- head(example_phosphoproteome, 100) #' annotated_data <- cleanAnnotation(input_data = sample_data) #' #' ## build the PWM models: #' set.seed(1234) #' sample_pwm <- phosphositeplus_human[sample(nrow(phosphositeplus_human), #' 1000),] #' pwms <- buildPWM(sample_pwm) #' #' ## score the PWM - substrate matches #' ## Using a "random" background, to calculate the p-value of the matches #' ## Using n = 100 for demonstration #' ## set.seed for reproducibility #' set.seed(1234) #' substrate_scores <- scoreSequences(input_data = annotated_data, #' pwm_in = pwms, #' background = "random", #' n = 100) #' #' ## Use substrate_scores and annotated_data data to predict kinase activity. #' ## This will permute the network node and edges 10 times for demonstration. #' ## set.seed for reproducibility #' set.seed(1234) #' swing_output <- swing(input_data = annotated_data, #' pwm_in = pwms, #' pwm_scores = substrate_scores, #' permutations = 10) #' #' @return A data.table of swing scores #' #' @export swing #' @importFrom BiocParallel bplapply #' @importFrom BiocParallel MulticoreParam #' @importFrom stats setNames #' @importFrom stats sd #' @importFrom data.table melt.data.table swing <- function(input_data = NULL, pwm_in = NULL, pwm_scores = NULL, pseudo_count = 1, p_cut_pwm = 0.05, p_cut_fc = 0.05, permutations = 1000, return_network = FALSE, verbose = FALSE) { #---------------------------------------------- #format checks: if (is.null(input_data)) stop("input_data not provided; you must provide an input table") if (!is.data.frame(input_data)) stop("input_data is not a data.frame; you must provide an input table") if (is.null(pwm_in)) stop( "pwm_in not provided; you must provide an input table containing computed position weight matrices using buildPWM()" ) if (!is.list(pwm_in)) stop( "pwm_in is not a list format; something has gone wrong. Make sure you compute the position weight matrices using buildPWM()" ) if (is.null(pwm_scores)) stop( "pwm_scores not provided; you must provide an input table containing computed scores using scoreSequences()" ) if (!is.list(pwm_scores)) stop( "pwm_scores is not a list format; something has gone wrong. Make sure PWM-substrate matches are scored using scoreSequences()" ) if (p_cut_pwm >= 1) stop("p_cut_pwm needs to be less than 1; make sure your p-values are not log transformed") if (p_cut_fc >= 1) stop("p_cut_fc needs to be less than 1; make sure your p-values are not log transformed") if (permutations != FALSE & (!is.numeric(permutations) | permutations < 1)) stop( "permutations needs to be a numerical number. Either 1 or FALSE (for no permutation) or greater than 1 to set the number of pemutations" ) #---------------------------------------------- # 1. binarise p-values and fold change if (verbose) { start_time <- Sys.time() message("Start: ", start_time) message("[Step1/3] : Calculating Swing Scores") } pwm_pval <- pwm_scores$peptide_p # binarise p-values pwm_pval[, 3:ncol(pwm_pval)] <- ifelse(pwm_pval[, 3:ncol(pwm_pval)] > p_cut_pwm, 0, 1) # binarise FC input_data[, 3] <- ifelse(as.numeric(input_data[, 3]) > 0, 1, -1) # binarise p-value input_data[, 4] <- ifelse(as.numeric(input_data[, 4]) > p_cut_fc, 0, 1) # compute Sipk statistic (Sipk - see paper): input_data$Sipk <- as.numeric(input_data[, 3]) * as.numeric(input_data[, 4]) # 2. merge tables, summarise counts and unique input_data$annotation <- paste(input_data$annotation, input_data$peptide, sep = "::") pwm_pval$annotation <- paste(pwm_pval$annotation, pwm_pval$peptide, sep = "::") data_merge <- unique(merge(input_data, pwm_pval, by = "annotation")) # 3. Swing scores of REAL data. swing_out <- swingScore( data_merge = data_merge, pwm_in = pwm_in, permute = FALSE, pseudo_count = pseudo_count ) # 4. permute and calculate p-values if (permutations != FALSE && permutations > 1) { if (verbose) { message("[Step2/3] : Permuting Network") } # obtain permuted column names swing_names <- colnames(data_merge)[7:ncol(data_merge)] n_permute <- lapply(seq_len(permutations), function(i) sample(as.character(swing_names), length(swing_names),replace = FALSE)) names(n_permute) <- paste("rand", seq_len(permutations), sep = "") # calculate swing scores (with permuted names) and return as vector swing_permute <- list(bplapply(seq_len(permutations), function(i) swingScore( data_merge = setNames(data.frame(data_merge), c(colnames(data_merge)[1:6], n_permute[[i]])), pwm_in = pwm_in, permute = TRUE, pseudo_count = pseudo_count, n = i ))) # returns ordered dataframe; order names and merge swing_permute_names <- swing_names[order(swing_names)] swing_permute <- data.frame("kinase" = swing_permute_names, swing_permute) colnames(swing_permute) <- c("kinase", names(n_permute)) if (verbose) { message("[Step3/3] : Calculating p-values") } # obtain p-values, two sided swing_out$p_greater <- unlist(bplapply(seq_len(nrow(swing_out)), function(i) (sum( ifelse( as.numeric(swing_permute[swing_permute$kinase == swing_out$kinase[i], ][2:ncol(swing_permute)]) > as.numeric(swing_out$swing_raw[i]), 1, 0), na.rm = TRUE) + 1) / (as.numeric(permutations) + 1))) swing_out$p_less <- unlist(bplapply(seq_len(nrow(swing_out)), function(i) (sum( ifelse( as.numeric(swing_permute[swing_permute$kinase == swing_out$kinase[i], ][2:ncol(swing_permute)]) < as.numeric(swing_out$swing_raw[i]), 1, 0 ), na.rm = TRUE) + 1) / (as.numeric(permutations) + 1))) # order by p-value swing_out <- swing_out[order(swing_out$p_greater),] } if (verbose) { message("[FINISHED]\n") end_time <- Sys.time() - start_time message("Finish: ", Sys.time()) } # set option to return a network network <- NULL if (return_network == "TRUE"){ network <- data.table::melt(data_merge, id.vars = c("annotation"), measure.vars = colnames(data_merge[7:ncol(data_merge)])) # keep "significant" edges network <- network[network$value == 1, ] network <- data.frame( "source" = as.character(network$variable), "target" = as.character(network$annotation) ) } return(list("scores" = swing_out, "network" = network)) } # describeIn swing This helper function performs the swing score calculation # no verbose in this helper # permute - for setting right table format (using data.table) for merging. swingScore <- function(data_merge, pwm_in, pseudo_count = 1, permute = FALSE, n = 1) { #---------------------------------------------- data_merge <- data_merge$Sipk * data_merge[7:ncol(data_merge)] # propogation of NaN here due to small number division, add psuedo-count # count significant positive p_k <- colSums(data_merge == 1, na.rm = TRUE) + pseudo_count # count significant negative n_k <- colSums(data_merge == -1, na.rm = TRUE) + pseudo_count #all counts (both positive and negative). t_k <- p_k + n_k #---------------------------------------------- # compute swing statistics pk_ratio <- (p_k)/(t_k) nk_ratio <- (n_k)/(t_k) swing_raw <- (pk_ratio) / (nk_ratio) p_n_all <- data.frame( "kinase" = colnames(data_merge), "pos" = p_k, "neg" = n_k, "all" = t_k, "pk" = pk_ratio, "nk" = nk_ratio, "swing_raw" = swing_raw ) # include the count numbers: p_n_all <- merge(p_n_all, pwm_in$kinase, by = "kinase", sort = TRUE) # weighted by number of substrates # not possible to have zero - must have substrates to build model p_n_all$swing_raw <- log2(p_n_all$swing_raw) * log2(p_n_all$n) # weighted by size of kinase-substrate network: p_n_all$swing_raw <- p_n_all$swing_raw * log2(p_n_all$all) # z-score transform p_n_all$swing <- (p_n_all$swing_raw - mean(p_n_all$swing_raw, na.rm = TRUE)) / sd(p_n_all$swing_raw, na.rm = TRUE) if (permute == "TRUE") { p_n_all <- p_n_all$swing_raw } return(p_n_all) }

75c910857c226328ddf684d55f0354897617714e

6b3215ae22fb53df23457105f4249e2a7e56bd2e

/inst/doc/Intro.R

1a61f4303ad9686cdc1ae48f31953d8bf70b4177

[]

no_license

cran/lmvar

26f45be9ebeb93467ae389ec00f034bb8054c239

a40d38d4d227aa3aade534bae73fb4625d4ae96d

refs/heads/master

2021-01-22T14:15:23.807165

2019-05-16T09:10:10

82,301,074

0

null

UTF-8

R

false

4,130

r

Intro.R

## ---- include=FALSE------------------------------------------------------ knitr::opts_chunk$set(fig.width=6, fig.height=6) ## ---- message = FALSE---------------------------------------------------- # As example we use the dataset 'cats' from the library 'MASS'. require(lmvar); require(MASS) # A plot of the heart weight versus the body weight in the data set plot(cats$Bwt, cats$Hwt, xlab = "Body weight", ylab = "Heart weight") ## ------------------------------------------------------------------------ # Create the model matrix. It only contains the body weight. An intercept term will be added by 'lmvar'. X = model.matrix(~ Bwt - 1, cats) # Carry out the fit with the same model matrix for mu (the expected heart weight) and for log sigma (the standard deviation) fit = lmvar(cats$Hwt, X_mu = X, X_sigma = X) ## ------------------------------------------------------------------------ summary(fit) ## ------------------------------------------------------------------------ nobs(fit) logLik(fit) ## ------------------------------------------------------------------------ dfree(fit, sigma = FALSE) ## ------------------------------------------------------------------------ fit = lmvar(cats$Hwt, X_mu = X, X_sigma = X, intercept_mu = FALSE, intercept_sigma = FALSE) ## ------------------------------------------------------------------------ dfree(fit, sigma = FALSE) ## ------------------------------------------------------------------------ summary(fit) ## ------------------------------------------------------------------------ sigma = fitted(fit, mu = FALSE) plot(cats$Bwt, residuals(fit) / sigma, xlab = "Body weight", ylab = "z-score") abline(0, 0, col = "red") ## ------------------------------------------------------------------------ mu = fitted(fit, sigma = FALSE) plot(cats$Bwt, mu, xlab = "Body weight", ylab = "Average heart weight", ylim = c(7, 16)) intervals = fitted(fit, interval = "confidence", level = 0.95) lwr = intervals[, "mu_lwr"] upr = intervals[, "mu_upr"] segments(cats$Bwt, lwr, cats$Bwt, upr) ## ------------------------------------------------------------------------ fit_lm = lm(cats$Hwt ~ Bwt - 1, cats) ## ------------------------------------------------------------------------ AIC(fit); AIC(fit_lm) BIC(fit); BIC(fit_lm) ## ------------------------------------------------------------------------ plot(cats$Bwt, mu, xlab = "Body weight", ylab = "Average heart weight", ylim = c(7, 16)) segments(cats$Bwt, lwr, cats$Bwt, upr) points(cats$Bwt, fitted(fit_lm), col = "red") ## ------------------------------------------------------------------------ plot(cats$Bwt, sigma, xlab = "Body weight", ylab = "St dev of heart weight", ylim = c(1, 2)) lwr = intervals[, "sigma_lwr"] upr = intervals[, "sigma_upr"] segments(cats$Bwt, lwr, cats$Bwt, upr) sigma_lm = summary(fit_lm)$sigma abline(sigma_lm, 0, col = "red") ## ------------------------------------------------------------------------ coef(fit) ## ------------------------------------------------------------------------ coef(fit, mu = FALSE) ## ------------------------------------------------------------------------ vcov(fit) ## ---- eval=FALSE--------------------------------------------------------- # require(statmod) # # # Run the regression including intercept terms # intercept = rep(1, nrow(cats)) # # fit = remlscore( cats$Hwt, cbind(intercept, X), cbind(intercept, X)) ## ---- eval=FALSE--------------------------------------------------------- # require(crch) # # # Run the regression including intercept terms # fit = crch(Hwt ~ Bwt | Bwt , data = cats) ## ---- eval=FALSE--------------------------------------------------------- # require(mgcv) # # # Run the regression including intercept terms # fit = gam(list(Hwt ~ Bwt, ~ Bwt) , data = cats, family = gaulss()) ## ---- eval=FALSE--------------------------------------------------------- # require(gamlss) # # # Run the regression including intercept terms # fit = gamlss(Hwt ~ Bwt, ~ Bwt, data = cats)

82f381c3feb4ef90080b5e5b84d5fec877818c37

d9274644eeb7a0414c7f80a93878681baddc8812

/final_code_check/script.R

d71136596715d77ee75723d92115fc2447841897

[]

no_license

lynnzoo/hfd-research

99979cfc7f4068b455d5ba0ae9e9e134782d7d45

220588b3c09bb34e5777766816d859a121d7a945

refs/heads/master

2022-02-17T07:11:23.303625

2019-08-23T01:41:26

196,340,413

0

null

UTF-8

R

false

9,605

r

script.R

#!/usr/bin/Rscript ####################### ## title: "Running Pipeline for HFD Choropleth Maps" ## author: "Shannon Chen" ####################### ## Automatically detect the file path of this file and set the working directory the to folder where this file is located this_dir <- function(directory) setwd( file.path(getwd(), directory) ) # setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) ## Print out the current working directory getwd() ## Add the names of all the packages that you used in the pipeline to list.of.packages list.of.packages <- c("cartography", "rgdal", "gsubfn", "plyr", "rstudioapi") ## Automatically install any necessary packages that have not already been installed new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] if(length(new.packages)) install.packages(new.packages) ## load all the packages that you used in the pipeline here library("cartography") library("rgdal") library("gsubfn") library("plyr") library("rstudioapi") ####################### ## title: "Creating the Choropleth Maps for HFD" ## author: "Shannon Chen" ####################### ################### Median EMS Response Times ################### yearextract <- function(df, yr) { df_year <- as.data.frame(df[which(df$year == yr),]) return(df_year) } #mutates the dataframe to create intervals used in the choropleth map. breaksplit <- function(df, breaks, idx){ df <- as.data.frame(df) df$fac <- as.numeric(df[,idx]) cutintervals <- cut(df$fac, breaks) ranges <- cbind(as.numeric( sub("\\((.+),.*", "\\1", cutintervals) ), as.numeric( sub("[^,]*,([^]]*)\\]", "\\1", cutintervals) )) df$fac <- ranges[,2] return(df) } ################### Slow EMS ################### slowems <- function(data, time){ slow <- data[which(data$responsetime > time),] return(slow) } slowambulance <- function(data){ data$unit <- as.character(data$unit) slowunits <- data[which(startsWith(data$unit, "A")==TRUE | startsWith(data$unit, "M")==TRUE),] return(slowunits) } slowcounting <- function(slowdf, col){ slowcounted <- count(slowdf, col) return(slowcounted) } ################### Incident Densities ################### incidentyears <- function(data, yr){ data$eventnum <- as.numeric(data$eventnum ) incidentyr <- data[which(data$eventnum >= (yr * 100000000) & data$eventnum < ((yr + 1)*100000000)), ] return(incidentyr) } incidentcounts <- function(data, colname){ #column name in string incidentcount <- count(data, colname) incidentcount[,1] <- as.numeric(unlist(incidentcount[,1])) incidentcount[,2] <- as.numeric(unlist(incidentcount[,2])) incident_grouped <- cbind(incidentcount[,1], incidentcount[,2]) colnames(incident_grouped) <- c("Station", "Count") incident_grouped[1,1] <- 1 #get rid of station 0 because it's useless return(incident_grouped) } ################### Busy Fractions ################### busyfractionfixed <- function(data){ #Formats the busy fraction to fit the shapefile data[which(data$X==23),1] <- 22 return(data) } ################### Choropleth Plotting Function ################### choroplot <- function(shape, data, variable, brk, color, legendtitle, maintitle, png_name){ png(paste0("./figures/",png_name), width = 8, height = 7, units = 'in', res = 300) plot(shape, border = NA, col = NA, bg = "#A6CAE0", xlim=c(2891000, 3353840) , ylim= c(13750000, 14038770), main = maintitle) choroLayer(spdf = shape, df = data, var = variable, breaks = brk, col = color, border = "grey40", legend.pos = "right", lwd = 0.5, legend.title.txt = legendtitle, legend.values.rnd = 2, add = TRUE) dev.off() } ####################### ## title: "Pipeline for HFD Choropleth Maps" ## author: "Shannon Chen" ####################### run_pipeline <- function(shapefile_dsn, shapefile, med_ems_file, ems_file, incidents_file, distress_file, helping_file, chain_file){ #Shapefile of HFD Station Jurisdictions HFD_jurs <- readOGR(dsn=shapefile_dsn, layer=shapefile) HFD_jurs <- HFD_jurs[-1, ] #Removed station 0 for aesthetic reasons HFD_jurs[which(HFD_jurs$D1==23),1] <- 22 #Changed to 22 because R doesn't recognize 22 as having a jurisdiction #Raw files that were uploaded med_ems <- read.csv(med_ems_file) ems <- read.csv(ems_file) incidents <- read.csv(incidents_file) distress <- read.csv(distress_file) helping <- read.csv(helping_file) averagechains <- read.csv(chain_file) ################### Median EMS Maps ################### medems12 <- yearextract(med_ems, 2012) medems16 <- yearextract(med_ems, 2016) medems17 <- yearextract(med_ems, 2017) write.csv(medems12, "./data/median_ems2012.csv") write.csv(medems16, "./data/median_ems2016.csv") write.csv(medems17, "./data/median_ems2017.csv") colsmed <- carto.pal(pal1 = "turquoise.pal", n1 = 5) #Median EMS Color Scheme medems12_fac <-breaksplit(medems12, c(0, 5, 6, 7, 10, 30), 3) medems16_fac <-breaksplit(medems16, c(0, 5, 6, 7, 10, 30), 3) medems17_fac <-breaksplit(medems17, c(0, 5, 6, 7, 10, 30), 3) write.csv(medems12_fac, "./data/medems2012factorized.csv") write.csv(medems16_fac, "./data/medems2016factorized.csv") write.csv(medems17_fac, "./data/medems2017factorized.csv") choroplot(HFD_jurs, medems12_fac, "fac", c(5, 6, 7, 10, 30), colsmed, "Median Minutes", "2012 Response Times", "figure10a.png") choroplot(HFD_jurs, medems16_fac, "fac", c(5, 6, 7, 10, 30), colsmed, "Median Minutes", "2016 Response Times", "figure10b.png") choroplot(HFD_jurs, medems16_fac, "fac", c(5, 6, 7, 10, 30), colsmed, "Median Minutes", "2016 Response Times", "figure11b.png") choroplot(HFD_jurs, medems17_fac, "fac", c(5, 6, 7, 10, 30), colsmed, "Median Minutes", "2017 Response Times", "figure12.png") ################### Slow EMS Map ################### colslow <- carto.pal(pal1 = "sand.pal", n1 = 6) #Slow Ambulances Color Palette slowstuff <- slowems(ems, 10) write.csv(slowstuff, "./data/slowems.csv") slow_amb <- slowambulance(slowstuff) write.csv(slow_amb, "./data/slowunits.csv") slowcount <- slowcounting(slow_amb, "incident_juris") write.csv(slowcount, "./data/slowunitscount.csv") slow_grouped <- breaksplit(slowcount, c(0, 2500, 4500, 7500, 12000, 18000), 2) write.csv(slow_grouped, "./data/slowresponsefactorized.csv") choroplot(HFD_jurs, slow_grouped, "fac", c(2500, 4500, 7500, 12000, 18000), colslow, "EMS Dispatches\n2011-18", "Slow Response Counts (>10 Minutes)", "figure13.png") ################### Incident Density Maps ################### colsincidents <- carto.pal(pal1 = "red.pal", n1 = 6) #Incident Map Color Palette incidents12 <- incidentyears(incidents, 12) incidents16 <- incidentyears(incidents, 16) write.csv(incidents12, "./data/incidents2012.csv") write.csv(incidents16, "./data/incidents2016.csv") incidents12_grouped <- incidentcounts(incidents12, "incident_juris") incidents12split <- breaksplit(incidents12_grouped, c(0, 1000, 2000, 3000, 5000, 7000, 10000), 2) incidents16_grouped <- incidentcounts(incidents16, "incident_juris") incidents16split <- breaksplit(incidents16_grouped, c(0, 1000, 2000, 3000, 5000, 7000, 10000), 2) write.csv(incidents12split, "./data/incidents2012factorized.csv") write.csv(incidents16split, "./data/incidents2016factorized.csv") choroplot(HFD_jurs, incidents12split , "fac", c(1000, 2000, 3000, 5000, 7000, 10000), colsincidents , "Counts", "2012 All Incidents", "figure7a.png") choroplot(HFD_jurs, incidents16split , "fac", c(1000, 2000, 3000, 5000, 7000, 10000), colsincidents , "Counts", "2016 All Incidents", "figure7b.png") choroplot(HFD_jurs, incidents16split , "fac", c(1000, 2000, 3000, 5000, 7000, 10000), colsincidents , "Counts", "2016 All Incidents", "figure11a.png") ################### Busy Fractions Maps ################### colsbusy <- carto.pal(pal1 = "orange.pal", n1 = 5) #Busy Fraction Color Palette distress_grouped <- breaksplit(distress, c(0, 0.2, 0.3, 0.4, 0.6, 0.7), 2) write.csv(distress_grouped, "./data/distressfractiongrouped2018.csv") helpingfixed <- busyfractionfixed(helping) helping_grouped <- breaksplit(helpingfixed, c(0, 0.2, 0.3, 0.4, 0.6, 0.7), 2) write.csv(helping_grouped, "./data/helpingfractiongrouped2018.csv") choroplot(HFD_jurs, distress_grouped, "fac", c(0.2, 0.3, 0.4, 0.6, 0.7), colsbusy, "2018", "Distress Fractions", "distress_fractions2018.png") choroplot(HFD_jurs, helping_grouped, "fac", c(0.2, 0.3, 0.4, 0.6, 0.7), colsbusy, "2018", "Helping Fractions", "figure14.png") ################## Average Chain Length Maps ######################## colschain <- carto.pal(pal1="turquoise.pal", n1=5) #Average Chain Length Color Palette avchain_grouped <- breaksplit(averagechains, c(0, 1.12, 1.20, 1.28, 1.36, 1.44), 2) write.csv(avchain_grouped, "./data/averagechainsfactorized.csv") choroplot(HFD_jurs, avchain_grouped, "fac", c(1.12, 1.20, 1.28, 1.36, 1.44), colschain, "Chain Analaysis", "Average Chain Length", "figure22.png") } ## call the driver function to run the entire automated data wrangling pipeline hfd_maps <- run_pipeline(shapefile_dsn = "data/raw", shapefile="Still_Alarms_012319", med_ems_file = "data/median_by_station.csv", ems_file= "data/data_ems.csv", incidents_file = "data/incident_jurisdictions.csv", distress_file = "data/distress_fractions.csv", helping_file = "data/helping_fractions.csv", chain_file="data/average_chain_lengths.csv")

0c056b5b601014e2c0a55f09f51bb6b757dc5947

6cb339f74f32213420a1ad2b535a7d819231581d

/cbind.fill.R

cf16bb8ded380ab547fe6f3971c4118713389915

[]

no_license

MHS-R/miscHelper

879518d6d3e8229cc84c37f5c34dc38c4c4400da

16cffcd7aa96e5e97db5b2a81b1468a74fe4f65e

refs/heads/master

2021-01-25T06:18:03.605379

2017-06-15T15:31:04

93,549,719

0

null

UTF-8

R

false

574

r

cbind.fill.R

#' Function to draw observations from stratified norm sample #' #' This function creates a stratified random sample, given an arbitrary number of #' factors and levels within those factors. #' #' @param ... x, y, z can be matrices or data.frames #' @references http://stackoverflow.com/questions/7962267/cbind-a-df-with-an-empty-df-cbind-fill #' @export cbind.fill cbind.fill <- function(...){ nm <- list(...) nm <- lapply(nm, as.matrix) n <- max(sapply(nm, nrow)) do.call(cbind, lapply(nm, function (x) rbind(x, matrix(, n-nrow(x), ncol(x))))) }

1b9efc1280bc2a3557f1a26363f416417694fa06

117ee80f5a04d6dd69d665eaa5d5b50c997c7faf

/analysis/dropout/simulate_dropout.R

3a781d1a72b425f538d11cf2cf3ba2c91fb79ea8

[]

no_license

kieranrcampbell/phenopath_revisions

1a60f4e3a8d330dea73af435492cdf4f604af996

2c52ed25cedb0abb49465e6bf71b1b25640fd068

refs/heads/master

2021-03-16T08:28:27.378349

2018-10-11T18:11:06

103,834,518

0

null

UTF-8

R

false

6,145

r

simulate_dropout.R

library(SummarizedExperiment) library(scater) library(dplyr) library(tidyr) library(magrittr) library(ggplot2) library(polyester) library(Biostrings) library(readr) source("analysis/simulations/simulate_funcs.R") set.seed(12345L) get_dropout_relationship <- function() { sce_rds <- "data/dropout/tec_sceset_qc.rds" sce <- readRDS(sce_rds) cc <- assayData(sce)[['counts']] cc <- cc[rowMeans(cc) > 0, ] # genes that are expressed at all pdrop_df <- data.frame(p_dropout = rowMeans(cc == 0), base_expression = rowMeans(cc)) fit <- glm(p_dropout ~ base_expression, data = pdrop_df, family = "binomial") coefs <- coef(fit) pdrop <- function(x) 1 / (1 + exp(-(coefs[1] + coefs[2] * x))) pdrop } tidy_sim_data <- function(mat, pst, x, sim_str) { df <- as_data_frame(mat) names(df) <- paste0(sim_str, "_gene_", seq_len(ncol(mat))) df <- mutate(df, pst, x, sim_str) gather(df, gene, expression, -pst, -x, -sim_str) } #' Simulate the mean function for sigmoidal interactions #' @param N Number of cells #' @param G Number of genes #' @param prop_interaction Proportion of genes exhibiting interaction, in [0,1] simulate_mean_function <- function(N, G, prop_interaction){ pst <- runif(N) x <- sample(c(0, 1), N, replace = TRUE) G_pst <- round(G * (1 - prop_interaction)) G_int <- G - G_pst G_class <- rep(round(G_int / 4), 3) G_class <- c(G_class, G_int - sum(G_class)) k_lower <- 2 k_upper <- 20 sample_k <- function() runif(1, k_lower, k_upper) t0_lower <- 0.2 t0_upper <- 0.8 sample_t0 <- function() runif(1, t0_lower, t0_upper) mu0_lower <- 3 mu0_upper <- 6 sample_mu0 <- function() runif(1, mu0_lower, mu0_upper) y_pst_only <- replicate(G_pst, f_pseudotime_sample(pst, sample_k(), sample_t0(), sample_mu0())) df_pst_only <- tidy_sim_data(y_pst_only, pst, x, "pseudotime_only") y_interaction_1 <- replicate(G_class[1], fx_1(pst, x, sample_k(), sample_t0(), sample_mu0())) df_interaction_1 <- tidy_sim_data(y_interaction_1, pst, x, "int_type_1") y_interaction_2 <- replicate(G_class[2], fx_2(pst, x, sample_k(), sample_t0(), sample_mu0())) df_interaction_2 <- tidy_sim_data(y_interaction_2, pst, x, "int_type_2") y_interaction_3 <- replicate(G_class[3], fx_3(pst, x, sample_k(), sample_t0(), sample_mu0())) df_interaction_3 <- tidy_sim_data(y_interaction_3, pst, x, "int_type_3") y_interaction_4 <- replicate(G_class[4], fx_4(pst, x, sample_k(), sample_t0(), sample_mu0())) df_interaction_4 <- tidy_sim_data(y_interaction_4, pst, x, "int_type_4") bind_rows( df_pst_only, df_interaction_1, df_interaction_2, df_interaction_3, df_interaction_4 ) } mean_to_wide <- function(df) { ydf <- select(df, -sim_str) %>% spread(gene, expression) ymat <- select(ydf, -pst, -x) %>% as.matrix() %>% t() colnames(ymat) <- paste0("cell_", seq_len(ncol(ymat))) is_interaction <- grepl("int", rownames(ymat)) list( exprsmat = ymat, pst = ydf$pst, x = ydf$x, is_interaction = is_interaction ) } #' Simulate the mean function for sigmoidal interactions #' @param N Number of cells #' @param G Number of genes #' @param prop_interaction Proportion of genes exhibiting interaction, in [0,1] #' @param replication The particular replication for saving files #' @param ad Additional dropout (proportion of dropout to add to a dataset) simulate_counts <- function(N, G, prop_interaction, replication, noise, ad) { # The signature string for this run, to be used in output files # So we can always parse what comes back sig_str <- paste0("ad_", ad, "_rep_", replication) df <- simulate_mean_function(N, G, prop_interaction) sim <- mean_to_wide(df) pos_gex <- 2^sim$exprsmat - 1 # Make expression positive count_mat <- sapply(seq_len(nrow(pos_gex)), function(i) { x <- pos_gex[i,] if(noise == "low") { return(NB(x, x / 3 + 1)) } else { return(NB(x, 1)) } }) count_mat <- t(count_mat) - 1 rownames(count_mat) <- rownames(sim$exprsmat) colnames(count_mat) <- colnames(sim$exprsmat) # Time to add in dropout ------- pdrop <- get_dropout_relationship() # how many counts are non-zero? is_zero <- count_mat == 0 original_prop_zero <- mean(is_zero) non_zero_counts <- sum(!is_zero) # of these we want to set proportion ad to 0 num_to_zero <- ad * non_zero_counts potential_zero <- which(!is_zero) # probabilities of being set to zero - p_zero <- pdrop(count_mat[potential_zero]) to_zero <- sample(potential_zero, num_to_zero, prob = p_zero) count_mat[to_zero] <- 0 new_prop_zero <- mean(count_mat == 0) if(any(count_mat < 0)) stop("Negative counts!") output_sce_file <- file.path("data", "dropout", "scesets", paste0("sceset_", sig_str, ".rds")) pdata <- data.frame(pst = sim$pst, x = sim$x, original_prop_zero = original_prop_zero, new_prop_zero = new_prop_zero, proportion_set_to_zero = ad) rownames(pdata) <- colnames(count_mat) fdata <- data.frame(is_interaction = sim$is_interaction) rownames(fdata) <- rownames(count_mat) bioc_version <- tools:::.BioC_version_associated_with_R_version() sce <- NULL if(bioc_version > 3.5) { library(SingleCellExperiment) sce <- SingleCellExperiment(assays = list(counts = count_mat), colData = pdata, rowData = fdata) sce <- normaliseExprs(sce) } else { sce <- newSCESet(countData = count_mat, phenoData = new("AnnotatedDataFrame", pdata), featureData = new("AnnotatedDataFrame", fdata)) } saveRDS(sce, output_sce_file) } ## Make sure these line up with the snakefile N <- 200 G <- 500 # prop_interactions <- c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5) pi <- 0.2 reps <- 40 # noises <- c("low", "high") noise <- "high" additional_dropout <- c(0.05, 0.1, 0.2, 0.5, 0.8, 0.9) for(r in seq_len(reps)) { for(ad in additional_dropout) { simulate_counts(N, G, pi, r, noise, ad) } }

3b8204a5b4baed74df194a9c0c15e8ed476fdea4

6542f5b12f43a4021e9defd0f230e1e3ce719432

/regresionLogistica.R

fe2676f47d5fdf155006b68d119376392165dcdd

[]

no_license

andreamcm/HDT6RegresionLogistica

f9434d49fc7589588c9b7d034aa52462e6c04b01

a720b486bc7f9f77f6d850fd0aaee34e1fdbd061

refs/heads/master

2020-05-02T16:19:35.061664

2019-04-01T07:09:13

178,065,228

0

null

ISO-8859-1

R

false

2,622

r

regresionLogistica.R

#----------------------------------------------------------------------------------------------------------------------------------------------- # Universidad del Valle de Guatemala # Autores: Andrea Maria Cordon Mayen, 16076 # Cristopher Sebastian Recinos RamÃ?rez, 16005 # Fecha: 18/03/2019 # arboles.R #----------------------------------------------------------------------------------------------------------------------------------------------- #----------------------------------------------------------------------------------------------------------------------------------------------- # Librerias a utilizar library(caret) install.packages("fmsb") library(fmsb) install.packages("e1071") library(e1071) install.packages("mlr") library(mlr) install.packages("dummy") library(dummy) #----------------------------------------------------------------------------------------------------------------------------------------------- #----------------------------------------------------------------------------------------------------------------------------------------------- # Set de ambientes y variables generales # -------------------------------------- # Set del working directory de Andrea setwd("~/2019/UVG/Primer Semestre/Minería de Datos/Laboratorios/Laboratorio6/HDT6RegresionLogistica/Datos") # Set del working directory de Sebastian setwd("C:/Users/sebas/Documents/UVG/201901/Mineria/Laboratorio5/HDT5NaiveBayes/Datos") # Se cargan todos los datos datos <- read.csv("train2.csv") str(datos) # Tipos de variables de las columnas de la base de datos #----------------------------------------------------------------------------------------------------------------------------------------------- # Regresión Logistica #----------------------------------------------------------------------------------------------------------------------------------------------- porcentaje<-0.7 set.seed(123) datos<-cbind(datos,dummy(datos,verbose = T)) # Datos de entrenamiento y prueba corte <- sample(nrow(datos), nrow(datos)*porcentaje) train <- datos[corte, ] test <- datos[-corte, ] modelo <- glm(AdoptionSpeed~., data = train[,c(1, 3:18)],family = binomial(), maxit = 10000) modelo<-glm(Species_virginica~., data = train[,c(1:4,8)],family = binomial(), maxit=100) #------------------------------------------------- # Regresión Logistica #------------------------------------------------- ##Modelo con todas las variables pred<-predict(modelo,newdata = test[,1:4], type = "response") prediccion<-ifelse(pred>=0.5,1,0) confusionMatrix(as.factor(test$Species_virginica),as.factor(prediccion))

94cbc57c23a179fc47e805e46d192eeea0146c9a

74453745dd2a15c8e310e8f4446ccada9702435e

/R/index.R

db6236fd865d07d196fc29fd850b2ecabb534e6d

[ "MIT" ]

permissive

rstudio/renv

ffba012525e8b1e42094899c3df9952b54ecb945

8c10553e700cad703ddf4dd086104f9d80178f3a

refs/heads/main

2023-08-29T08:45:28.288471

2023-08-28T22:01:19

159,560,389

958

169

MIT

2023-09-14T00:55:28

2018-11-28T20:25:39

R

UTF-8

R

false

4,622

r

index.R

the$index <- new.env(parent = emptyenv()) index <- function(scope, key = NULL, value = NULL, limit = 3600L) { enabled <- renv_index_enabled(scope, key) if (!enabled) return(value) # resolve the root directory root <- renv_paths_index(scope) # make sure the directory we're indexing exists memoize( key = root, value = ensure_directory(root, umask = "0") ) # make sure the directory is readable / writable # otherwise, attempts to lock will fail # https://github.com/rstudio/renv/issues/1171 if (!renv_index_writable(root)) return(value) # resolve other variables key <- if (!is.null(key)) renv_index_encode(key) now <- as.integer(Sys.time()) # acquire index lock lockfile <- file.path(root, "index.lock") renv_scope_lock(lockfile) # load the index file index <- tryCatch(renv_index_load(root, scope), error = identity) if (inherits(index, "error")) return(value) # return index as-is when key is NULL if (is.null(key)) return(index) # check for an index entry, and return it if it exists item <- renv_index_get(root, scope, index, key, now, limit) if (!is.null(item)) return(item) # otherwise, update the index renv_index_set(root, scope, index, key, value, now, limit) } renv_index_load <- function(root, scope) { filebacked( context = "renv_index_load", path = file.path(root, "index.json"), callback = renv_index_load_impl ) } renv_index_load_impl <- function(path) { json <- tryCatch( withCallingHandlers( renv_json_read(path), warning = function(w) invokeRestart("muffleWarning") ), error = identity ) if (inherits(json, "error")) { unlink(path) return(list()) } json } renv_index_get <- function(root, scope, index, key, now, limit) { # check for index entry entry <- index[[key]] if (is.null(entry)) return(NULL) # see if it's expired if (renv_index_expired(entry, now, limit)) return(NULL) # check for in-memory cached value value <- the$index[[scope]][[key]] if (!is.null(value)) return(value) # otherwise, try to read from disk data <- file.path(root, entry$data) if (!file.exists(data)) return(NULL) # read data from disk value <- readRDS(data) # add to in-memory cache the$index[[scope]] <- the$index[[scope]] %||% new.env(parent = emptyenv()) the$index[[scope]][[key]] <- value # return value value } renv_index_set <- function(root, scope, index, key, value, now, limit) { # force promises force(value) # files being written here should be shared renv_scope_umask("0") # write data into index data <- tempfile("data-", tmpdir = root, fileext = ".rds") ensure_parent_directory(data) saveRDS(value, file = data, version = 2L) # clean up stale entries index <- renv_index_clean(root, scope, index, now, limit) # add index entry index[[key]] <- list(time = now, data = basename(data)) # update index file path <- file.path(root, "index.json") ensure_parent_directory(path) # write to tempfile and then copy to minimize risk of collisions tempfile <- tempfile(".index-", tmpdir = dirname(path), fileext = ".json") renv_json_write(index, file = tempfile) file.rename(tempfile, path) # return value value } renv_index_encode <- function(key) { key <- stringify(key) memoize(key, renv_hash_text(key)) } renv_index_clean <- function(root, scope, index, now, limit) { # figure out what cache entries have expired ok <- enum_lgl( index, renv_index_clean_impl, root = root, scope = scope, index = index, now = now, limit = limit ) # return the existing cache entries index[ok] } renv_index_clean_impl <- function(key, entry, root, scope, index, now, limit) { # check if cache entry has expired expired <- renv_index_expired(entry, now, limit) if (!expired) return(TRUE) # remove from in-memory cache cache <- the$index[[scope]] cache[[key]] <- NULL # remove from disk unlink(file.path(root, entry$data), force = TRUE) FALSE } renv_index_expired <- function(entry, now, limit) { now - entry$time >= limit } renv_index_enabled <- function(scope, key) { getOption("renv.index.enabled", default = TRUE) } renv_index_writable <- function(root) { memoize( key = root, value = unname(file.access(root, 7L) == 0L) ) } # in case of emergency, break glass renv_index_reset <- function(root = NULL) { root <- root %||% renv_paths_index() lockfiles <- list.files(root, pattern = "^index\\.lock$", full.names = TRUE) unlink(lockfiles) }

629966b86f6e6692d3ee7f56c6f4ba59cae033f0

173e8e734ee2d8e3eeeac0a86ce06ab48db15a08

/man/Corner_text.Rd

747af63a09bd4c77d96a1d31d134d78974e696f6

[ "MIT" ]

permissive

aemon-j/gotmtools

b0ba213f83d0f3ccbee9f34b7efc69fc0e0dc86a

4eb90e9e6ad960c36a78cc51e9c77b4d826a6197

refs/heads/main

2023-04-28T07:43:19.483815

2021-01-28T19:17:37

220,067,540

5

MIT

2022-02-04T15:27:59

2019-11-06T18:52:40

R

UTF-8

R

false

true

437

rd

Corner_text.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Corner_text.R \name{Corner_text} \alias{Corner_text} \title{Add text to the corner of a plot} \usage{ Corner_text(text, location = "topleft") } \arguments{ \item{text}{text to be added to the plot as a language object} \item{location}{location within the plot for the text to be placed} } \value{ data } \description{ Add text to a location within a plot. }

86b58266f14fd3c549d28c535ede6d4715134572

813e8ed246baf7ff8d039df4fb92b0b97e6ddad8

/e-commerce tweet.R

d75c3c854eb3bbd90ad0a342d5de155ed4846d21

[]

no_license

iqbalhanif/Twitter-Crawling

94c40d342d6465b63591111aa54ce409d95249ff

deaba7ed0c84035f6f3653d55e60b4db999d2ee9

refs/heads/master

2020-06-02T20:47:17.239429

2020-02-04T02:56:25

191,304,925

0

null

UTF-8

R

false

2,566

r

e-commerce tweet.R

install.packages("rtweet") install.packages("httpuv") install.packages("openssl") install.packages("httpuv") install.packages("TweetR") install.packages("devtools") library(rtweet) library(jsonlite) library(magrittr) library(dplyr) library(stringr) library("httr") library(httpuv) library(openssl) library(devtools) ##set work directory setwd('D:/DATA/Desktop/') ##create token twitter_token <- create_token(app = a, consumer_key = b, consumer_secret = c, access_token = d, access_secret = e) ##note: setiap token hanya mampu mengambil tweet dalam jumlah terbatas, diperlukan token tambahan jika dibutuhkan ##a,b,c,d,e disesuaikan dengan token masing2 ##e-commerce keyword keywords <- c("BLANJA OR blanjacom", "Blibli OR bliblidotcom OR BlibliCare", "Bukalapak OR bukalapak OR BukaBantuan", "Elevenia OR eleveniaID OR eleveniacare", "JD.ID OR JDid OR csjd_id", "Lazada OR LazadaID OR LazadaIDCare", "Matahari Mall OR MatahariMallCom OR MatahariMallCS", "Shopee OR ShopeeID OR ShopeeCare", "Tokopedia OR tokopedia OR TokopediaCare" ) ##create directory for output sapply(c("out_rds", "out_json", "out_csv"), dir.create, showWarnings=FALSE) ##looping for get tweet for (key in keywords){ twitter_token <- twitter_token message(key) #edit untuk tentukan max dan min tweet twit <- search_tweets( key, n = 18000, include_rts = TRUE, token = twitter_token ) #file name output hasil crawling file_name <- key %>% str_replace_all("\\s+", "-") %>% str_to_lower() twit %>% saveRDS(file = file.path("out_rds", paste0(file_name, ".Rds"))) toJSON(rt, auto_unbox = TRUE) %>% cat(file = file.path("out_json", paste0(file_name, ".json"))) #create twit dataframe twitDf <- twit %>% select(status_id:retweet_count, place_full_name) #ambil text dari twit twitDf$text <- twitDf$text %>% str_replace_all("\r|\n", " ") %>% str_replace("\"", "'") #ambil hasshtag dan mention dari twit twitDf$hastags <- sapply(twit$hashtags, paste, collapse = ",") twitDf$mentions_screen_name <- sapply(twit$mentions_screen_name, paste, collapse = ",") #hasil akhir twitDf %>% write.table(file = file.path("out_csv", paste0(file_name, ".csv")), row.names = FALSE, na = "", sep = ",", append = TRUE) }

b72da82118651aef3e311690c27fee9169c226c5

d8763dda7b7d7010df41aed1727346c765332de8

/rl/ssteps6/findWayFinLearn2.R

087b761f75e310e0856660e83fb85200f6943f4e

[]

no_license

shadogray/MA-ICA

9a5aa9e9a5e9810cfd10b0c9a1ffad572b3d6329

6b48b3fd418ed3e187dfd2603e2f062df2996860

refs/heads/master

2020-12-31T11:24:38.442199

2020-02-07T20:49:03

239,016,502

0

null

UTF-8

R

false

24,879

r

findWayFinLearn2.R

library(tidyverse) #library(timeSeries) #library(corrr) library(lubridate) source('finlearn.R') sig.v <- NULL orig.s <- NULL test <- F evalPlot <- T evalPrint <- T local <- F args <- commandArgs(trailingOnly = T) if (length(args) > 0) sig.v <- unlist(strsplit(args[1],',')) if (length(args) > 1) orig.s <- unlist(strsplit(args[2],',')) if (length(args) > 2) { test <- str_detect(args[3],'T') local <- str_detect(args[3],'L') if (str_detect(args[3],'[pP]')) evalPlot <- !str_detect(args[3],'p') if (str_detect(args[3],'[rR]')) evalPrint <- !str_detect(args[3],'r') } node <- Sys.info()['nodename'] nlog = T deblog = F if (deblog) { nlog = T } options(tibble.width = Inf) options(width = 2000) #options(error = function(){traceback(1);print();stop()}) numIter = 100000 deltaSumR = 2*.001 epsilon = .0 alpha = .1 gamma = .5 ctrl.types = factor('Q','SARSA') type = 'SARSA' #args <- commandArgs(trailingOnly = T) #if (length(args) > 0) type = args[1] ## load('test.rdata');res=result;env=result$myEnv;tracks=result$tracks ####### getTrack <- function(env = myEnv) { env$SumR <- 0 result <- findWay(env) b <- tibble(.rows = length(env$B1)) for (n in names(env)[grep('B\\d',names(env))]) b <- bind_cols(b, env[n]) if (!has_name(result$track,'t')) result$track <- result$track %>% rowid_to_column('t') result } printTrack <- function(res, env = res$myEnv, track = res$track) { print(track, n=nrow(track)) print(paste('Type:',type, 'Iterations:',env$Iterations, 'FinalR:', track %>% filter(res.terminal) %>% select(res.R), 'SumR:',toString(env$SumR),'W:',toString(env$W))) } plotResults <- function(res, env = res$myEnv, track = res$track) { print(paste0('processing result: ',env$numSigs,'/',env$numOrig,': plots')) titleSfx <- paste0('ICA',env$numSigs,'/',env$numOrig,': ') fileSfx <- paste0('S_density_ica_',env$numSigs,'_',env$numOrig) b <- as_tibble(as.matrix(res$b)*env$baseX) w.s <- colMeans(env$log %>% tail(200) %>% select(matches('W\\.S.'))) W <- env$W / sum(abs(env$W)) fs <- env$FS %>% rowid_to_column('t') for (n in env$xColNames) fs[paste0(n,'.w')] <- w.s[paste0('W.',n)]*env$X[n] fs$S.w <- rowSums(fs %>% select(matches('^S(\\d|C)\\.w$'))) comb <- gtools::combinations(env$xColNum-1,2) for (i in 1:nrow(comb)) { si <- paste0('SVal.S',comb[i,]) #ggplot(env$S %>% select(si,n,sig.p) %>% group_by_at(c(si,'sig.p')) %>% summarize(n=sum(n))) + # geom_contour(aes_string(si[1],si[2], z='n'))+ # facet_wrap(~sig.p)+theme(strip.text.x = element_text(size = 5)) + #ggplot(env$S %>% select(si,n) %>% group_by_at(si) %>% summarize(n=sum(n))) + # geom_contour(aes_string(si[1],si[2], z='n'))+ # theme(strip.text.x = element_text(size = 5)) + # ggtitle(paste0(titleSfx,'State-Evaluation Density - States ',si[2],'~',si[1])) ggplot(env$log %>% select(si) %>% mutate_all(list(~round(.))), mapping=aes_string(si[1],si[2])) + geom_density_2d() + theme(strip.text.x = element_text(size = 5)) + ggtitle(paste0(titleSfx,'State-Evaluation Density - States ',si[2],'~',si[1])) ggsave(paste0(fileSfx,'_',i,'.png')) } fileSfx <- paste0('Signals_ica_',env$numSigs,'_',env$numOrig) ggplot(bind_cols(b,FS=env$FS$Si) %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+ geom_line(aes(tx,value,color=signal)) + ggtitle(paste0(titleSfx,'Base Timeseries (Bi) and Target Sum (FS)')) + theme(plot.title = element_text(size = 10)) + geom_line(data=fs, mapping=aes(t,Si), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_BaseSignals','.png')) x.x <- env$X %>% select(env$xColNames) ggplot(bind_cols(x.x, FS = env$FS$Si) %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+ geom_line(aes(tx,value,color=signal))+ ggtitle(paste0(titleSfx,'Target Signal (FS), ICA Signals')) + theme(plot.title = element_text(size = 10)) + geom_line(data=fs, mapping=aes(t,Si), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_ICASignals','.png')) ggplot(fs %>% gather(signal,value,matches('^Si$|^S.?\\.w$'),-t))+ geom_line(aes(t,value,color=signal))+ ggtitle(paste0(titleSfx,'Target Signal (FS), Weighted ICA Signals (W*Si)')) + theme(plot.title = element_text(size = 10)) + geom_line(data=fs, mapping=aes(t,Si), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_WeightedICASignals','.png')) ggplot(as_tibble(env$X %>% select(env$xColNames)) %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+ geom_line(aes(tx,value,color=signal)) + ggtitle(paste0(titleSfx,'Raw ICA Signals (Si)')) + theme(plot.title = element_text(size = 10)) + geom_line(data=fs, mapping=aes(t,Si), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_RawSignals','.png')) ggplot(as_tibble(env$X.orig) %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+ geom_line(aes(tx,value,color=signal)) + ggtitle(paste0(titleSfx,'Artificial TimeSeries generated from BaseSignals Bi')) + theme(plot.title = element_text(size = 10)) ggsave(paste0(fileSfx,'_ArtificialSeries','.png')) #ggplot(b %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+geom_line(aes(tx,value,color=signal)) #ggplot(select(env$FS,Si) %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+geom_line(aes(tx,value,color=signal)) #ggplot(select(env$FS,S) %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+geom_line(aes(tx,value,color=signal)) #ggplot(select(env$FS,p) %>% rowid_to_column('tx') %>% gather(signal,value,-tx))+geom_line(aes(tx,value,color=signal)) #ggplot(env$A %>% filter(n>0)) + geom_density_2d(aes(sid,aid)) #ggplot(env$A %>% filter(n>0) %>% group_by(sid,aid) %>% summarise(n = sum(n))) + geom_point(aes(sid,aid,size=n)) fileSfx <- paste0('Tracks_ica_',env$numSigs,'_',env$numOrig) track <- track %>% inner_join(fs, suffix = c('','.FS'), by='t') track$F.Si <- env$FS$Si[1:nrow(track)] track <- track %>% mutate_at(vars(matches(regex('^res\\..*R\\d?$', ignore_case = F))), list(~./env$xColNum)) ggplot(track %>% select(t,matches('^S.i$')) %>% gather(signal,value,-t))+ geom_line(aes(t,value,color=signal)) + ggtitle(paste0(titleSfx,'Resulting State-Track following generated Policy'), ) + theme(plot.title = element_text(size = 10)) + geom_line(data=track, mapping = aes(t,Si), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_StateTrack_Si','.png')) ggplot(track %>% select(t,matches('^res\\.R\\d$')) %>% gather(signal,value,-t))+ geom_line(aes(t,value,color=signal)) + ggtitle(paste0(titleSfx,'Resulting Reward vector following generated Policy')) + theme(plot.title = element_text(size = 10)) ggsave(paste0(fileSfx,'_Reward_Vector','.png')) ggplot(track %>% select(t,matches('^res\\.SumR\\d$')) %>% gather(signal,value,-t))+ geom_line(aes(t,value,color=signal)) + ggtitle(paste0(titleSfx,'Resulting Sum(Reward) vector following generated Policy')) + theme(plot.title = element_text(size = 10)) ggsave(paste0(fileSfx,'_Sum_Reward_Vector','.png')) ggplot(track %>% select(t,matches('^res\\..*R$')) %>% gather(signal,value,-t))+ geom_line(aes(t,value,color=signal)) + ggtitle(paste0(titleSfx,'Resulting Reward following generated Policy')) + theme(plot.title = element_text(size = 10)) ggsave(paste0(fileSfx,'_Reward','.png')) #ggplot(track %>% select(t,matches('S\\di1')) %>% gather(signal,value,-t))+geom_line(aes(t,value,color=signal)) #ggplot(env$S %>% select(matches('S\\d'),P,n) %>% gather(key,value,-P,-n)) + #ggplot(env$S %>% select(matches('S\\d'),n,P)) + geom_contour(aes_string(x='S1',y='S2', z='n'))+facet_wrap(~P) } run_many <- function(env) { for (i in 1:9) { assign(paste0('res',i), getTrack(env)) } res <- gather(res1$track %>% select(matches('S\\di')), key, value) for (i in 2:9) { res <- bind_cols(res, gather(get(paste0('res',i))$track %>% select(matches('S\\di')), key, value) %>% select(value)) } ggplot(res %>% rowid_to_column('t') %>% gather(k,v,-t,-key)) + geom_line(aes(t,v,color=k)) } evalLog <- function(res, env = res$myEnv) { print(paste0('processing result: ',env$numSigs,'/',env$numOrig,': logs')) titleSfx <- paste0('ICA',env$numSigs,'/',env$numOrig,': ') fileSfx <- paste0('eval_',env$numSigs,'_',env$numOrig) idxs <- str_extract(env$xColNames, '.$') fs <- env$FS %>% rowid_to_column('t') l <- env$log %>% inner_join(fs, suffix = c('','.FS'), by='t') l$Qpo <- rowSums(l %>% select(matches('Qpo.S\\d'))) for (s in env$xColNames) { l <- l %>% mutate(!!paste0('dqw.',s) := !!as.name(paste0('dQ.',s)) * !!as.name(paste0('W.',s))) } ggplot(l %>% gather(key,val,matches('^[W]\\.*'),-lid))+geom_smooth(aes(lid,val,color=key)) ggsave(paste0(fileSfx,'_W','.png')) ggplot(tail(l,500) %>% gather(key,val,matches('^[W]\\.*'),-lid))+geom_line(aes(lid,val,color=key)) ggsave(paste0(fileSfx,'_W_500','.png')) l.w <- l %>% select(matches('^SVal\\.S.$|^W\\.S.$|^[ti]$')) %>% mutate(S.w = 0) for (idx in idxs) l.w <- l.w %>% mutate(!!paste0('SVal.S',idx,'.w') := !!as.name(paste0('W.S',idx)) * !!as.name(paste0('SVal.S',idx))) l.w$SVal.S.w <- rowSums(select(l.w, matches('SVal\\.S.\\.w'))) ggplot(l.w %>% select(matches('^SVal\\.S.$|^t$')) %>% gather(signal,value,-t))+ geom_smooth(aes(t,value,color=signal)) + ggtitle(paste0(titleSfx,'Steps SVals')) + theme(plot.title = element_text(size = 10)) ggsave(paste0(fileSfx,'_Steps','.png')) ggplot(l.w %>% select(matches('^SVal\\.S.?.w$|^t$')) %>% gather(signal,value,-t))+ geom_smooth(aes(t,value,color=signal)) + ggtitle(paste0(titleSfx,'W weighted Steps (SVals.i * W)')) + theme(plot.title = element_text(size = 10)) ggsave(paste0(fileSfx,'_WeightedSteps','.png')) #ggplot(l %>% gather(key,val,matches('^[Z]\\.*'),-t,-n))+geom_smooth(aes(n,val,color=key))+ # facet_wrap(~t)+theme(strip.text.x = element_text(size = 5)) #ggsave(paste0(fileSfx,'_Z_t','.png')) #ggplot(l %>% gather(key,val,matches('d[Q]\\..*'),-sid))+geom_smooth(aes(sid,val,color=key)) #ggsave(paste0(fileSfx,'_dQ','.png')) #ggplot(l %>% gather(key,val,matches('d[Q]\\..*'),-n,-t))+geom_smooth(aes(n,val,color=key))+ # facet_wrap(~t)+theme(strip.text.x = element_text(size = 5)) #ggsave(paste0(fileSfx,'_dQ_t','.png')) #ggplot(l %>% gather(key,val,matches('dqw\\..*'),-lid))+geom_smooth(aes(lid,val,color=key)) #ggsave(paste0(fileSfx,'_dqw','.png')) #ggplot(l %>% gather(key,val,matches('dqw\\..*'),-n,-t))+geom_smooth(aes(n,val,color=key))+ # facet_wrap(~t)+theme(strip.text.x = element_text(size = 5)) #ggsave(paste0(fileSfx,'_dqw_t','.png')) #comb <- gtools::combinations(env$xColNum-1,2) #for (i in 1:nrow(comb)) { # si <- paste0('S',comb[i,]) # #ggplot(l %>% select(si,matches('^(sid|Qpo|sig.p)$')) %>% # # group_by_at(c(si,'sig.p')) %>% summarise(Qpo = mean(Qpo)))+ # # geom_contour(aes_string(x=si[1],y=si[2],z='Qpo'))+ # # facet_wrap(~sig.p)+theme(strip.text.x = element_text(size = 5))+ # ggplot(l %>% select(si,matches('^(sid|Qpo)$')) %>% # group_by_at(si) %>% summarise(Qpo = mean(Qpo)))+ # geom_contour(aes_string(x=si[1],y=si[2],z='Qpo'))+ # theme(strip.text.x = element_text(size = 5))+ # ggtitle(paste0(titleSfx,'State-Evaluation Sum(Qpo.i) - States ',si[2],'~',si[1])) # ggsave(paste0(fileSfx,'_SumQ_',i,'.png')) #} } evalTracks <- function(res, env = res$myEnv, tracks = res$tracks) { print(paste0('processing result: ',env$numSigs,'/',env$numOrig,': tracks')) titleSfx <- paste0('ICA',env$numSigs,'/',env$numOrig,': ') fileSfx <- paste0('track_',env$numSigs,'_',env$numOrig) idxs <- str_extract(env$xColNames, '.$') fs <- env$FS %>% rowid_to_column('t') %>% rename(Si.FS=Si) tracks <- tracks %>% mutate_at(vars(matches(regex('^res\\..*R\\d?$', ignore_case = F))), list(~./env$xColNum)) %>% mutate_at(vars(i), as.factor) %>% mutate(t = stid) %>% inner_join(fs, suffix = c('','.FS'), by='t') %>% left_join(env$X %>% set_names(paste0('X.',names(.))), by=c('t'='X.xid')) trs <- select(tracks,matches('^SVal\\.S.$|^S.?i?$|^W\\.S.$|^S.i.w$|^t$|^i$|^X\\.S.$')) %>% rowid_to_column('id') %>% inner_join(fs, suffix = c('','.FS'), by='t') %>% mutate(Si.w = 0, i = as.factor(i)) for (idx in idxs) trs <- mutate(trs, !!as.name(paste0('S',idx,'.w')) := !!as.name(paste0('W.S',idx))*!!as.name(paste0('X.S',idx))) for (idx in idxs) trs <- mutate(trs, Si.w := Si.w + !!as.name(paste0('W.S',idx))*!!as.name(paste0('X.S',idx))) for (idx in idxs) tracks <- mutate(tracks, !!as.name(paste0('S',idx,'i.w')) := !!as.name(paste0('W.S',idx))*!!as.name(paste0('X.S',idx))) tracks$Si.w <- rowSums(tracks %>% select(matches('S.i\\.w'))) tsi <- tracks %>% select(t,i,matches('^SVal\\.S.$')) %>% gather(si,v,-t,-i) sumR <- tracks %>% filter(t==19) %>% rename(SumR = res.SumR) %>% select(i,SumR) topSumR <- sumR %>% arrange(desc(SumR)) %>% head(5) tsi <- tsi %>% left_join(sumR, by='i') topTsi <- tsi %>% filter(i %in% topSumR$i) topTracks <- tracks %>% filter(i %in% topSumR$i) tsi.w <- tracks %>% select(t,i,matches('^S.?i.w$')) %>% gather(si,v,-t,-i) tsi.w <- tsi.w %>% left_join(sumR, by='i') topTsi.w <- tsi.w %>% filter(i %in% topSumR$i) ggplot()+ geom_point(data=trs %>% tail(19*10), mapping=aes(t,Si.w), linetype='dotted', color='black', size=1) + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2) + geom_smooth(data=tracks, mapping=aes(t,Si.w), linetype='dashed', color='green', size=.5, span=.2) ggplot()+ geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed') ggplot(tail(trs,50*(env$tSteps-1)))+geom_line(aes(t,S,color=i))+ geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ #geom_line(aes(t,Si.w), linetype='dotted', color='red', size=2)+ geom_line(aes(t,Si.FS), linetype='dashed')+ theme(legend.position = 'none') ggsave(paste0(fileSfx,'_WeightedTrack_FS','.png')) ggplot(tail(tracks,50*(env$tSteps-1)) %>% gather(k,s,matches('^(S.i\\.w)$'),-t,-i))+ geom_smooth(aes(t,s,color=k), span=.2) #+ #geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2)+ #geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_WeightedTracks_Tail','.png')) ggplot(topTracks %>% gather(k,s,matches('^(S.?i\\.w)|Si\\.FS$'),-t,-i))+ geom_smooth(aes(t,s,color=k), span=.2) + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_WeightedTracks_Top','.png')) ggplot(tsi)+geom_smooth(aes(t,v,color=si)) + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed') ggsave(paste0(fileSfx,'_Tracks_smooth','.png')) ggplot(tsi %>% mutate(ti=as.factor(paste0(si,'_',i)))) + geom_line(aes(t,v,color=ti), position = position_jitter(.3,.3))+ theme(legend.position = 'none') + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed')+ ggtitle(paste0(titleSfx,'Tracks')) ggsave(paste0(fileSfx,'_Tracks','.png')) ggplot(topTsi %>% mutate(ti=as.factor(paste0(si,'_',i)))) + geom_line(aes(t,v,color=ti), position = position_jitter(.3,.3)) + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed')+ ggtitle(paste0(titleSfx,'most successful Tracks')) ggsave(paste0(fileSfx,'_Tracks_Top5','.png')) ggplot(tsi.w)+geom_smooth(aes(t,v,color=si)) + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed')+ ggtitle(paste0(titleSfx,'Weighted Tracks')) ggsave(paste0(fileSfx,'_WeightedTracks_smooth','.png')) ggplot(tsi.w %>% mutate(ti=as.factor(paste0(si,'_',i)))) + geom_line(aes(t,v,color=ti), position = position_jitter(.3,.3))+ theme(legend.position = 'none') + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed')+ ggtitle(paste0(titleSfx,'Weighted Tracks')) ggsave(paste0(fileSfx,'_WeightedTracks','.png')) ggplot(topTsi.w %>% mutate(ti=as.factor(paste0(si,'_',i)))) + geom_line(aes(t,v,color=ti), position = position_jitter(.3,.3)) + geom_smooth(data=trs, mapping=aes(t,Si.w), linetype='dotted', color='red', size=2, span=.2)+ geom_line(data=tracks, mapping=aes(t,Si.FS), show.legend = F, linetype = 'dashed')+ ggtitle(paste0(titleSfx,'most successful Weighted Tracks')) ggsave(paste0(fileSfx,'_WeightedTracks_Top5','.png')) discNum <- round(min(.1*nrow(tracks),200)/(env$tSteps-1))*(env$tSteps-1) tailNum <- 500*(env$tSteps-1) trs <- tail(tracks, -discNum) tr.stat <- tail(tracks, tailNum) %>% filter(t > 4) r.mean <- mean(tail(tracks, tailNum)$act.R) r.center <- tr.stat %>% group_by(t) %>% summarize(R.center = mean(act.R)) r.sd <- inner_join(tr.stat, r.center, by='t', suffix=c('','.c')) %>% group_by(t) %>% summarize(R.sd = sd(act.R-R.center)) r.csd <- inner_join(r.center, r.sd, by='t', suffix=c('','.sd')) ggplot(trs)+geom_smooth(aes(t,act.R), span=.2) + geom_hline(yintercept = r.mean, linetype='dashed') + geom_hline(yintercept = mean(r.center$R.center), linetype='dashed', color='red') + geom_line(data=r.csd, mapping=aes(t,R.center+R.sd), linetype='dotted', size=.5) + geom_line(data=r.csd, mapping=aes(t,R.center-R.sd), linetype='dotted', size=.5) + ggtitle(paste0(titleSfx,'Reward')) ggsave(paste0(fileSfx,'_Reward_smooth','.png')) ggplot(trs %>% filter(stid==19) %>% mutate(i = as.numeric(as.character(i))))+ geom_line(aes(i,res.SumR)) + geom_smooth(aes(i,res.SumR), span=.5) + ggtitle(paste0(titleSfx,'final SumReward')) ggsave(paste0(fileSfx,'_SumReward_Tail','.png')) ggplot(trs %>% filter(stid==19) %>% mutate(i = as.numeric(as.character(i))))+ geom_smooth(aes(i,res.SumR), span=.2)+ ggtitle(paste0(titleSfx,'SumReward')) ggsave(paste0(fileSfx,'_SumReward','.png')) ggplot(trs %>% filter(stid==19) %>% mutate(i = as.numeric(as.character(i))) %>% gather(w,val,matches('W.S.'),-i))+ geom_line(aes(i,val,color=w)) + geom_smooth(aes(i,val,color=w), linetype='dashed', span=.3, size=.5) + ggtitle(paste0(titleSfx,'final W')) ggsave(paste0(fileSfx,'_W_Tail','.png')) } runOptim <- function(xts.fs) { ofs <- xts.fs[5:nrow(xts.fs),] doLag <- function(d.t) { dt<-round(d.t); sum((ofs$Si[1:(nrow(ofs)-dt)]-ofs$S.w[(dt+1):nrow(ofs)])^2, na.rm=T) } o <- optim(c(0), doLag, method = 'Brent', lower=0, upper=5) o$par } printTracks <- function(res, env = res$myEnv, tracks = res$tracks) { titleSfx <- paste0('ICA',env$numSigs,'/',env$numOrig,': ') fileSfx <- paste0('xts_',env$numSigs,'_',env$numOrig) tracks <- tracks %>% mutate(t = stid) %>% mutate_at(vars(matches(regex('^res\\..*R\\d?$', ignore_case = F))), list(~./env$xColNum))%>% left_join(env$X %>% set_names(paste0('X.',names(.))), by=c('t'='X.xid')) tsi <- tracks %>% select(t,i,matches('^SVal\\.S.$')) %>% gather(si,v,-t,-i) sumR <- tracks %>% filter(t==19) %>% rename(SumR = res.SumR) %>% select(i,SumR) w.s <- colMeans(env$log %>% tail(200) %>% select(matches('W\\.S.'))) W <- env$W fs <- env$FS %>% rowid_to_column('t') for (n in env$xColNames) fs[paste0(n,'.w')] <- w.s[paste0('W.',n)]*env$X[n] fs$S.w <- rowSums(fs %>% select(matches('^S(\\d|C)\\.w$'))) tailNum <- 500*(env$tSteps-1) tr.stat <- tail(tracks, tailNum) %>% filter(t > 4) r.mean <- mean(tail(tracks, tailNum)$act.R) r.center <- tr.stat %>% group_by(t) %>% summarize(R.center = mean(act.R)) r.sd <- inner_join(tr.stat, r.center, by='t', suffix=c('','.c')) %>% group_by(t) %>% summarize(R.sd = sd(act.R-R.center)) r.csd <- inner_join(r.center, r.sd, by='t', suffix=c('','.sd')) numVar <- ncol(fs) d <- as_datetime(1000*fs$t) xts.fs <- xts::xts(as.matrix(fs), order.by = d) xts.fs <- xts::merge.xts(xts.fs, as_datetime(1000*seq(1,max(fs$t),1/4)), fill=NA) xts.fs <- zoo::na.spline(xts.fs[,1:numVar]) png(paste0(fileSfx,'_timeline.png')) plot(xts.fs[,c('S.w','Si')]) dev.off() lag <- runOptim(xts.fs) xts.fs.lag <- xts.fs xts.fs.lag$Si <- xts::lag.xts(xts.fs.lag$Si, lag) png(paste0(fileSfx,'_timeline_lag.png')) plot(xts.fs.lag[,c('S.w','Si')]) dev.off() xts.fs.lag.diff <- diff(xts.fs) png(paste0(fileSfx,'_timeline_lag_diff.png')) plot(xts.fs.lag.diff[,c('S.w','Si')]) dev.off() print(paste(titleSfx,'Track: ',titleSfx,', W=',toString(w.s))) print(w.s) print(paste(titleSfx,'Summary of SumR:')) print(summary(sumR$SumR)) print(paste(titleSfx,'Track: Reward=',toString(r.center$R.center))) print(paste(titleSfx,'Track: Reward: Sum=',toString(sum(r.center$R.center)), ', Mean=',toString(mean(r.center$R.center)), ', SD=',toString(sd(r.sd$R.sd)))) print(paste(titleSfx,'Track: Reward Summary=',toString(summary(r.center$R.center)))) print(paste(titleSfx,'Track: Reward Summary:')) print(summary(r.center$R.center)) print(paste(titleSfx,'Summary of FS - Sum(W*Xi)=',toString(summary(fs$Si - fs$S.w)))) print(paste(titleSfx,'Summary of FS - Sum(W*Xi):')) print(summary(fs$Si - fs$S.w)) snames <- na.exclude(names(xts.fs) %>% str_extract('^Si$|^S\\d?\\.w$')) print(paste(titleSfx,'Correlation of Signals:')) print(suppressWarnings(corrr::correlate(fs[5:(nrow(fs)-5),snames]))) print(paste(titleSfx,'Correlation of Signals - Lag =',lag,':')) print(suppressWarnings(corrr::correlate(xts.fs.lag[5:(nrow(fs)-5),snames], quiet = T))) print(paste(titleSfx,'Correlation of Diff-Signals - Lag =',lag,':')) print(suppressWarnings(corrr::correlate(xts.fs.lag.diff[5:(nrow(fs)-5),snames], quiet = T))) #print(sumR %>% arrange(desc(SumR)), n=20) } icns <- c(2,3,4,5) #icns <- c(3) #baseDir <- list('v3sig3'='storage','v3sig4'='storage','v3sig2'='tfrlnx.fruehbeck.at') #baseDir <- list('v4sig2'='tfrlnx.fruehbeck.at','v4sig3'='storage','v4sig4'='storage') #baseDir <- list('v5sig2'='tfrlnx.fruehbeck.at','v5sig3'='storage','v5sig4'='storage') #baseDir <- list('v6sig2'='tfrlnx.fruehbeck.at','v6sig3'='storage','v6sig4'='storage') #baseDir <- list('v6sig2'='storage','v6sig3'='storage') #baseDir <- list('v6sig3'='monster') baseDir <- list('v6sig2'='monster','v6sig3'='monster','v6sig4'='monster') if (!is.null(sig.v)) { #bd <- str_detect(names(baseDir),paste0('v\\dsig',sig.v)) baseDir <- sig.v } if (!is.null(orig.s)) icns <- c(as.numeric(orig.s)) dn <- NA icn <- NA if (evalPlot) { for (dn in baseDir) { currDir <- getwd() if (local) dn <- '.' print(paste('switched to:',getwd())) tryCatch({ host <- baseDir[dn] for (icn in icns) { file <- paste0('finDemo.env_',host,'_ica_',icn,'.rdata') if (!file.exists(file)) host <- node file <- paste0('finDemo.env_',host,'_ica_',icn,'.rdata') print(paste('loading:',file)) try({ load(file) print(paste('loaded:',file)) if (test) next assign(paste0('x',icn),myEnv) #myEnv <- get(paste0('x',icn)) result <- getTrack(myEnv) result$tracks <- tracks if (icn < 5) assign(paste0(dn,'r',icn),result) save(result, file=paste0('finDemo.env_',host,'_ica_',icn,'_Res.rdata')) plotResults(result) evalLog(result) evalTracks(result) }) } }, error = function(err) { print(paste('Error:',dn,'/',icn,':',toString(err))) }, finally = setwd(currDir)) } } if (evalPrint) { for (dn in baseDir) { host <- baseDir[dn] for (icn in icns) { try({ if (local) dn <- '.' file <- paste0(dn,'/','finDemo.env_',host,'_ica_',icn,'_Res.rdata') if (!file.exists(file)) { host <- node file <- paste0(dn,'/','finDemo.env_',host,'_ica_',icn,'_Res.rdata') } print(paste('loading:',file)) load(file) print(paste('loaded:',file)) printTracks(result) }) } } }

382c9f106bb435a4ae0f11d2884b852c07ec79c3

d47f618c48d9be0c052402e82b4787c3397a7f84

/script_packages.R

442c3504921c73f56a62a660aea31806c1d5d2c0

[]

no_license

agenis/citynames-clustering

9b927095991b02b8b8c31166623111c51132ab84

52d8db81b5369dc60a2d77200abf20d9e1425cb4

refs/heads/master

2020-04-15T08:07:28.330775

2019-01-08T00:02:40

164,518,596

1

0

null

UTF-8

R

false

2,849

r

script_packages.R

# packages library(tidyverse) library(tm) library(stringdist) library(maptools) library(cluster) library(dbscan) library(fpc) deps_names= structure(list(dpmt = c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "2A", "2B", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "97" ), dpmt_long = c("Ain", "Aisne", "Allier", "Alpes de Hautes-Provence", "Hautes-Alpes", "Alpes-Maritimes", "Ardèche", "Ardennes", "Ariège", "Aube", "Aude", "Aveyron", "Bouches-du-Rhône", "Calvados", "Cantal", "Charente", "Charente-Maritime", "Cher", "Corrèze", "Corse-du-Sud", "Haute-Corse", "Côte-d'Or", "Côtes d'Armor", "Creuse", "Dordogne", "Doubs", "Drôme", "Eure", "Eure-et-Loir", "Finistère", "Gard", "Haute-Garonne", "Gers", "Gironde", "Hérault", "Ille-et-Vilaine", "Indre", "Indre-et-Loire", "Isère", "Jura", "Landes", "Loir-et-Cher", "Loire", "Haute-Loire", "Loire-Atlantique", "Loiret", "Lot", "Lot-et-Garonne", "Lozère", "Maine-et-Loire", "Manche", "Marne", "Haute-Marne", "Mayenne", "Meurthe-et-Moselle", "Meuse", "Morbihan", "Moselle", "Nièvre", "Nord", "Oise", "Orne", "Pas-de-Calais", "Puy-de-Dôme", "Pyrénées-Atlantiques", "Hautes-Pyrénées", "Pyrénées-Orientales", "Bas-Rhin", "Haut-Rhin", "Rhône", "Haute-Saône", "Saône-et-Loire", "Sarthe", "Savoie", "Haute-Savoie", "Paris", "Seine-Maritime", "Seine-et-Marne", "Yvelines", "Deux-Sèvres", "Somme", "Tarn", "Tarn-et-Garonne", "Var", "Vaucluse", "Vendée", "Vienne", "Haute-Vienne", "Vosges", "Yonne", "Territoire-de-Belfort", "Essonne", "Hauts-de-Seine", "Seine-Saint-Denis", "Val-de-Marne", "Val-d'Oise", "DOMTOM")), class = "data.frame", row.names = c(NA, -97L))

07cfad4d92c2242c0f9ee7ed28715707f21eba7e

94f2d32736ee169853a4969bae5436d4764f1f5f

/R/keyword_relevance.R

073c1da7e14882d27873a8a877d2bee7544b75dd

[ "MIT" ]

permissive

johannesharmse/watsonNLU

55eaf62fba16d0a37494fe34d392682b9cac5ab4

bfd042d9bda0df78002bb49fa6c9a15dbb515e09

refs/heads/master

2020-03-08T06:32:43.344968

2018-11-13T00:33:02

127,974,332

4

3

MIT

2018-04-15T22:39:38

2018-04-03T22:10:37

R

UTF-8

R

false

6,635

r

keyword_relevance.R

#' Watson Natural Language Understanding: Relevance of Keywords #' #' See the \href{https://github.com/johannesharmse/watsonNLU/blob/master/README.md}{sign-up} documentation for step by step instructions to secure your own username and password to enable you to use the Watson NLU API. The \strong{keyword_relevance} function takes a text or URL input, along with the input type. The function then returns a dataframe that contains keywords and their likelihood of being a keyword, from the given input. See the \href{https://github.com/johannesharmse/watsonNLU/blob/master/README.md}{keyword_relevance} documentation for more usage cases. #' #' @param input Either a text string input or website URL. #' Either \code{text} or \code{url} argument has to be specified, #' but not both. #' @param input_type Specify what type of input was entered. #' Either \code{text} or \code{url} argument has to be specified, #' but not both. #' @param limit The number of keywords to return. #' @param version The release date of the API version to use. Default value is \code{version="?version=2018-03-16"} #' #' @return A dataframe containing a list of keywords and their corresponding likelihoods for the given input. #' #' @examples #' #' credentials <- readRDS("../tests/testthat/credentials.rds") #' username <- credentials$username #' password <- credentials$password #' #' # Authenticate using Watson NLU API Credentials #' auth_NLU(username, password) #' # Authenticate using Watson NLU API Credentials #' auth_NLU(username="XXXX", password="XXXX") #' #' # Top 5 keywords from the text input. #' keyword_relevance(input = 'This is a great API wrapper', input_type='text', limit = 5) #' #' # Top 5 keywords from the URL input. #' keyword_relevance(input = 'http://www.nytimes.com/guides/well/how-to-be-happy', input_type='url', limit = 5) #' #' @seealso \code{\link[watsonNLU]{keyword_sentiment}}, \code{\\link[watsonNLU]{text_categories}}, \code{\\link[watsonNLU]{keyword_emotions}}, \code{\\link[watsonNLU]{auth_NLU}} #' #' #' @import httr #' #' @export keyword_relevance <- function(input = NULL, input_type = NULL, limit = NULL, version="?version=2018-03-16"){ # initialization accepted_input_types <- c('text', 'url') # api URL # this is the base of the API call # the variables for the API call will get appended to this url_NLU <- "https://gateway.watsonplatform.net/natural-language-understanding/api" # function feature # no attribute needs to be specified feauture <- "keywords" features_string <- paste0("&features=", feauture) # input argument error checking if (is.null(input)){ stop("Please specify an input to analyze.") }else if(!is.character(input)){ stop("Please specify input text or URL as a character string") } if (is.null(input_type)){ message("Input type not specified. Assuming text input.") input_type <- 'text' } if (!is.character(input_type)){ stop("Input type needs to be specified as a character string('url' or 'text').") }else{ input_type <- tolower(input_type) } if (!input_type %in% accepted_input_types){ stop("Input type should be either 'url' or 'text'.") } if(is.null(limit)){ message("No limit specified. Using default API call limit.") }else if(!is.numeric(limit) || length(limit) > 1){ message("Limit needs to be specified as a numeric integer.") }else{ limit <- paste0("&", feauture, ".limit=", limit) } # check if credential environment exists if(is.null(watson_credentials) || !is.environment(watson_credentials)){ stop("No credentials found. Provide credentials using watsonNLU::auth_NLU") } # get credentials username <- get("username", envir = watson_credentials) password <- get("password", envir = watson_credentials) ### ENCODING ### # format input text/URL # in the case of text input # the text needs to be encoded # in the case of url input # no encoding is necessary if (input_type == 'text'){ input <- URLencode(input) } ### STANDARDISE INPUT ### # assign either text or # url as a general # variable # the pre-text is necessary # for the API call if (input_type == 'text'){ input <- paste0("&text=", input) }else if(input_type == 'url'){ input <- paste0("&url=", input) } ### API CALL ### # GET # the POST call doesn't seem to do anything different # this is the actual API call # version - version of API to be used # input - text or url string # feature_string - string containing all specified features # feature_attr - string containing all specified attributes for specific features # authenticate - used to verify credentials # add_header - fails if not specified response <- GET(url=paste0( url_NLU, "/v1/analyze", version, input, features_string, limit), authenticate(username,password), add_headers("Content-Type"="application/json") ) ### ERROR CHECKING ### # check for successful response # successful response has a code of 200 # all other codes are unsuccessful responses status <- status_code(response) if (status != 200){ message(response) if(status == 401){ stop("Invalid or expired credentials provided. Provide credentials using watsonNLU::auth_NLU") } # include message to give user more insight into why the call was unsuccessful # can be due to query limit, internet connection, authentication fail, etc. stop("Please make sure you have a valid internet connection and provided a valid input. Check the response log above for further details.") } ### API RESPONSE CONTENT ### # get response structured content # this is the list that gets returned response <- content(response) ### CLEAN OUTPUT ### # remove unwanted output # there are generally unwanted list items # that the user isn't interested in # this needs to be removed # this can include things like input text metadata ### ERROR HANDLING ### # account for no results returned # shouldn't ever reach this point # since Watson will catch it if # there isn't enough input text # to identify keywords if (!is.null(response$keywords) && length(response$keywords) > 0){ response <- response$keywords }else{ stop("No results available") } ### OUTPUT ### keywords <- sapply(1:length(response), function(x) response[[x]]$text) relevance <- sapply(1:length(response), function(x) response[[x]]$relevance) response_df <- data.frame('keyword' = keywords, 'relevance' = relevance) # return clean output return(response_df) }

e0787f7c587aa18948811b10276c55d2c3bf89a4

6cb4fbdd76a338d95f8348ef1351ec124aebc39f

/R/utils.R

91442cfea043e9fc1890ff1e93cb0d9e9617d0ec

[]

no_license

kevinykuo/vctrs

f0224bd1b011535935a8bc8d198da181d090b6ef

2992d4737d35ff4dbd6c15b895fc4c2dc6c71066

refs/heads/master

2020-03-25T23:16:53.434722

2018-08-10T00:25:01

null

0

null

UTF-8

R

false

909

r

utils.R

indent <- function(x, n) { if (length(x) == 0) return(character()) pad <- strrep(" ", n) out <- Map(gsub, "\n", paste0("\n", pad), x) unlist(out, use.names = FALSE) } ones <- function(...) { array(1, dim = c(...)) } vec_coerce_bare <- function(x, type) { # Unexported wrapper around Rf_coerceVector() coerce <- env_get(ns_env("rlang"), "vec_coerce") coerce(x, type) } # Matches the semantics of c() - based on experimenting with the output # of c(), not reading the source code. outer_names <- function(x, outer) { has_outer <- !is.null(outer) && !outer %in% c("", NA) if (!has_outer) return(names(x)) has_inner <- !is.null(names(x)) if (has_inner) { paste0(outer, "..", names(x)) } else { if (length(x) == 1) { outer } else { paste0(outer, seq_along(x)) } } } hash <- function(x, length = 5) { substr(digest::digest(x), 1, length) }

e6fd8a594a530be7a7b218537fece680cd895fe4

4bd41b3ea5014a386820af21c3f3039800cdc688

/scropt.R

d0afdb303f325ffe597289c1eeae5c93b0ed50ca

[]

no_license

RichChu1/ma350-demo

650d5e08cb9ba222fc30e6d1066d18ddd886b4c3

8a274e02b9bb512a48553805f474ca0603b9fe11

refs/heads/master

2021-04-18T04:36:24.637506

2020-03-23T18:19:28

249,505,185

0

null

UTF-8

R

false

41

r

scropt.R

plot(rnorm(1000)) # New line of comments

f36ba77fc0a3a94d906906cb08467fea414276c0

5a71730ca0a0088621fc8a2f41a9f42e39acc7f8

/cachematrix.R

d4042ed885550f2e9b7d5cdb623de4308dc7e3fa

[]

no_license

ajtrask/ProgrammingAssignment2

9bd2907d9cc6b321d46d4c5d33889315588f47ec

9684a605e63a4b9b7dc0b16ba3842e9b9a4db3a6

refs/heads/master

2021-01-18T18:43:54.224953

2016-02-06T00:48:24

50,941,865

0

null

2016-02-02T18:25:53

null

UTF-8

R

false

1,419

r

cachematrix.R

## These functions (makeCacheMatrix and cacheSolve) implement ## a matrix "object" with the ability to cache its inverse ## makeCacheMatrix creates a matrix "object" that has funtions to: ## set and get the matrix ## set and get the inverse of the matrix makeCacheMatrix <- function(x = matrix()) { minv <- NULL ## set the matrix and NULL the inverse placeholder set <- function(y) { x <<- y minv <<- NULL } ## get the matrix get <- function() x ## set the inverse cache setinv <- function(solve) minv <<- solve ## get the inverse getinv <- function() minv ## create the list of functions list(set = set, get = get, setinv = setinv, getinv = getinv) } ## cacheSolve calculates the inverse of the matrix "object" ## created in makeCacheMatrix by: ## checking if the inverse has already been calculated ## and getting the inverse from the cache ## or calculating the invese and storing in the cache cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' minv <- x$getinv() ## if the inverse exists, return the cached value if(!is.null(minv)) { message("returning cached value") return(minv) } ## otherwise, compute and store the cache and return the value a <- x$get() ## get the matrix minv <- solve(a, ...) ## compute the inverse x$setinv(minv) ## cache the inverse minv ## return the inverse }

a7e26b792cd63b9920a9fb624ffde57ae0a789dc

c9a9019ebc1e1ab5921dfa63ad07e50f1db523d4

/CourseProjectEx.R

60572fdf1274b259bde38f3c86377afd13e9747a

[]

no_license

burtks/ADEC7430-KBFinal

a715e6d94d9595ca19c5bb24b89506b3fb1be289

ed621cdd2f2063e0f63084ff4c7df7095af72fa8

refs/heads/main

2023-01-04T04:31:55.863843

2020-10-19T00:28:18

304,186,574

0

2

null

2020-10-19T00:28:19

2020-10-15T02:20:14

HTML

UTF-8

R

false

8,210

r

CourseProjectEx.R

# ADEC 7430 Big Data Econometrics # Course Project - Example R Script File # OBJECTIVE: A charitable organization wishes to develop a machine learning # model to improve the cost-effectiveness of their direct marketing campaigns # to previous donors. # 1) Develop a classification model using data from the most recent campaign that # can effectively capture likely donors so that the expected net profit is maximized. # 2) Develop a prediction model to predict donation amounts for donors - the data # for this will consist of the records for donors only. # load the data charity <- read.csv(file.choose()) # load the "charity.csv" file # predictor transformations charity.t <- charity charity.t$avhv <- log(charity.t$avhv) # add further transformations if desired # for example, some statistical methods can struggle when predictors are highly skewed # set up data for analysis data.train <- charity.t[charity$part=="train",] x.train <- data.train[,2:21] c.train <- data.train[,22] # donr n.train.c <- length(c.train) # 3984 y.train <- data.train[c.train==1,23] # damt for observations with donr=1 n.train.y <- length(y.train) # 1995 data.valid <- charity.t[charity$part=="valid",] x.valid <- data.valid[,2:21] c.valid <- data.valid[,22] # donr n.valid.c <- length(c.valid) # 2018 y.valid <- data.valid[c.valid==1,23] # damt for observations with donr=1 n.valid.y <- length(y.valid) # 999 data.test <- charity.t[charity$part=="test",] n.test <- dim(data.test)[1] # 2007 x.test <- data.test[,2:21] x.train.mean <- apply(x.train, 2, mean) x.train.sd <- apply(x.train, 2, sd) x.train.std <- t((t(x.train)-x.train.mean)/x.train.sd) # standardize to have zero mean and unit sd apply(x.train.std, 2, mean) # check zero mean apply(x.train.std, 2, sd) # check unit sd data.train.std.c <- data.frame(x.train.std, donr=c.train) # to classify donr data.train.std.y <- data.frame(x.train.std[c.train==1,], damt=y.train) # to predict damt when donr=1 x.valid.std <- t((t(x.valid)-x.train.mean)/x.train.sd) # standardize using training mean and sd data.valid.std.c <- data.frame(x.valid.std, donr=c.valid) # to classify donr data.valid.std.y <- data.frame(x.valid.std[c.valid==1,], damt=y.valid) # to predict damt when donr=1 x.test.std <- t((t(x.test)-x.train.mean)/x.train.sd) # standardize using training mean and sd data.test.std <- data.frame(x.test.std) ##### CLASSIFICATION MODELING ###### # linear discriminant analysis library(MASS) model.lda1 <- lda(donr ~ reg1 + reg2 + reg3 + reg4 + home + chld + hinc + I(hinc^2) + genf + wrat + avhv + incm + inca + plow + npro + tgif + lgif + rgif + tdon + tlag + agif, data.train.std.c) # include additional terms on the fly using I() # Note: strictly speaking, LDA should not be used with qualitative predictors, # but in practice it often is if the goal is simply to find a good predictive model post.valid.lda1 <- predict(model.lda1, data.valid.std.c)$posterior[,2] # n.valid.c post probs # calculate ordered profit function using average donation = $14.50 and mailing cost = $2 profit.lda1 <- cumsum(14.5*c.valid[order(post.valid.lda1, decreasing=T)]-2) plot(profit.lda1) # see how profits change as more mailings are made n.mail.valid <- which.max(profit.lda1) # number of mailings that maximizes profits c(n.mail.valid, max(profit.lda1)) # report number of mailings and maximum profit # 1329.0 11624.5 cutoff.lda1 <- sort(post.valid.lda1, decreasing=T)[n.mail.valid+1] # set cutoff based on n.mail.valid chat.valid.lda1 <- ifelse(post.valid.lda1>cutoff.lda1, 1, 0) # mail to everyone above the cutoff table(chat.valid.lda1, c.valid) # classification table # c.valid #chat.valid.lda1 0 1 # 0 675 14 # 1 344 985 # check n.mail.valid = 344+985 = 1329 # check profit = 14.5*985-2*1329 = 11624.5 # logistic regression model.log1 <- glm(donr ~ reg1 + reg2 + reg3 + reg4 + home + chld + hinc + I(hinc^2) + genf + wrat + avhv + incm + inca + plow + npro + tgif + lgif + rgif + tdon + tlag + agif, data.train.std.c, family=binomial("logit")) post.valid.log1 <- predict(model.log1, data.valid.std.c, type="response") # n.valid post probs # calculate ordered profit function using average donation = $14.50 and mailing cost = $2 profit.log1 <- cumsum(14.5*c.valid[order(post.valid.log1, decreasing=T)]-2) plot(profit.log1) # see how profits change as more mailings are made n.mail.valid <- which.max(profit.log1) # number of mailings that maximizes profits c(n.mail.valid, max(profit.log1)) # report number of mailings and maximum profit # 1291.0 11642.5 cutoff.log1 <- sort(post.valid.log1, decreasing=T)[n.mail.valid+1] # set cutoff based on n.mail.valid chat.valid.log1 <- ifelse(post.valid.log1>cutoff.log1, 1, 0) # mail to everyone above the cutoff table(chat.valid.log1, c.valid) # classification table # c.valid #chat.valid.log1 0 1 # 0 709 18 # 1 310 981 # check n.mail.valid = 310+981 = 1291 # check profit = 14.5*981-2*1291 = 11642.5 # Results # n.mail Profit Model # 1329 11624.5 LDA1 # 1291 11642.5 Log1 # select model.log1 since it has maximum profit in the validation sample post.test <- predict(model.log1, data.test.std, type="response") # post probs for test data # Oversampling adjustment for calculating number of mailings for test set n.mail.valid <- which.max(profit.log1) tr.rate <- .1 # typical response rate is .1 vr.rate <- .5 # whereas validation response rate is .5 adj.test.1 <- (n.mail.valid/n.valid.c)/(vr.rate/tr.rate) # adjustment for mail yes adj.test.0 <- ((n.valid.c-n.mail.valid)/n.valid.c)/((1-vr.rate)/(1-tr.rate)) # adjustment for mail no adj.test <- adj.test.1/(adj.test.1+adj.test.0) # scale into a proportion n.mail.test <- round(n.test*adj.test, 0) # calculate number of mailings for test set cutoff.test <- sort(post.test, decreasing=T)[n.mail.test+1] # set cutoff based on n.mail.test chat.test <- ifelse(post.test>cutoff.test, 1, 0) # mail to everyone above the cutoff table(chat.test) # 0 1 # 1676 331 # based on this model we'll mail to the 331 highest posterior probabilities # See below for saving chat.test into a file for submission ##### PREDICTION MODELING ###### # Least squares regression model.ls1 <- lm(damt ~ reg1 + reg2 + reg3 + reg4 + home + chld + hinc + genf + wrat + avhv + incm + inca + plow + npro + tgif + lgif + rgif + tdon + tlag + agif, data.train.std.y) pred.valid.ls1 <- predict(model.ls1, newdata = data.valid.std.y) # validation predictions mean((y.valid - pred.valid.ls1)^2) # mean prediction error # 1.867523 sd((y.valid - pred.valid.ls1)^2)/sqrt(n.valid.y) # std error # 0.1696615 # drop wrat for illustrative purposes model.ls2 <- lm(damt ~ reg1 + reg2 + reg3 + reg4 + home + chld + hinc + genf + avhv + incm + inca + plow + npro + tgif + lgif + rgif + tdon + tlag + agif, data.train.std.y) pred.valid.ls2 <- predict(model.ls2, newdata = data.valid.std.y) # validation predictions mean((y.valid - pred.valid.ls2)^2) # mean prediction error # 1.867433 sd((y.valid - pred.valid.ls2)^2)/sqrt(n.valid.y) # std error # 0.1696498 # Results # MPE Model # 1.867523 LS1 # 1.867433 LS2 # select model.ls2 since it has minimum mean prediction error in the validation sample yhat.test <- predict(model.ls2, newdata = data.test.std) # test predictions # FINAL RESULTS # Save final results for both classification and regression length(chat.test) # check length = 2007 length(yhat.test) # check length = 2007 chat.test[1:10] # check this consists of 0s and 1s yhat.test[1:10] # check this consists of plausible predictions of damt ip <- data.frame(chat=chat.test, yhat=yhat.test) # data frame with two variables: chat and yhat write.csv(ip, file="ABC.csv", row.names=FALSE) # use your initials for the file name # submit the csv file in Canvas for evaluation based on actual test donr and damt values

ca9c49e8c535851f3957375de37b274a400962d9

3f41dcde4498fcf47a5f8314de6086ffec3dd082

/man/makeplot.asdsf.Rd

529ac1bf862d260c102a04e40c72b847844846b7

[]

no_license

arborworkflows/RWTY

6f961f76b69776d9c752be0483416dec7448661b

fbf5b695a1c8d16f7532123fb86715152f430b06

refs/heads/master

2020-12-31T02:01:50.609590

2016-08-17T22:16:18

65,751,392

0

1

null

2016-08-15T17:31:51

null

UTF-8

R

false

true

1,229

rd

makeplot.asdsf.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/makeplot.asdsf.R \name{makeplot.asdsf} \alias{makeplot.asdsf} \title{Plot the Standard Deviation of Split Frequencies over the course of an MCMC.} \usage{ makeplot.asdsf(chains, burnin = 0, window.size = 20, min.freq = 0) } \arguments{ \item{chains}{A list of rwty.trees objects.} \item{burnin}{The number of trees to eliminate as burnin. Defaults to zero.} \item{window.size}{The number of trees between each point at which the ASDSFs is calculated (note, specified as a number of sampled trees, not a number of generations)} \item{min.freq}{The minimum frequency for a node to be used for calculating ASDSF.} } \value{ output A cumulative plot of ASDSF across all chains } \description{ This function takes two or more rwty.trees ojects and returns a plot of ASDSF as the run progresses. The solid line with points shows the Average Standard Deviation of Split Frequences at the current generation The grey ribbon shows the upper and lower 95% quantiles of the SDSFs at the current generation } \examples{ data(fungus) p <- makeplot.asdsf(fungus, burnin = 20) p } \keyword{ASDSF,} \keyword{MCMC,} \keyword{cumulative} \keyword{phylogenetics,}

a81b14d9bd610adb9f8346678fdc284a7fb98aa5

318c102d1f9055cac2a790363c8c3f6af024702c

/man/TempTraject.Rd

feb6ada2487e17313f9745cf3f7448e86ca8e22a

[]

no_license

cran/OceanView

342ae464107823ff3324d26d9e28290779e768b3

1272fbf444372dfa4475c255da5be3d764651d3f

refs/heads/master

2021-07-21T02:21:50.718897

2021-07-12T07:00:13

18,805,213

0

null

UTF-8

R

false

4,940

rd

TempTraject.Rd

\name{Profile data set} \docType{data} \alias{TrackProf} \title{ Temperature profiles made along a ship track. } \description{ Profiles of temperature made along a ship track, originally made available by US NOAA NODC. The data were merged from 29 input files named \code{gtspp_103799_xb_111.nc} till \code{gtspp_103827_xb_111.nc}. These data were acquired from the US NOAA National Oceanographic Data Center (NODC) on 9/06/2012 from \url{https://www.nodc.noaa.gov/gtspp/}. } \usage{ data(TrackProf) } \format{ list with \itemize{ \item \code{meta}, a \code{data.frame} with the metadata, containing for each of the 29 profiles the following: \itemize{ \item \code{station}, the number of the station (part of the original filename). \item \code{filename}, the original name of the NetCDF file. \item \code{date}, the date of sampling. \item \code{time}, the time of sampling, a number relative to 1-1-1900 0 hours. \item \code{longitude}, dg E. \item \code{latitutde}, dg N. } \item \code{temp}, the seawater temperature, at the \code{depth} of the measurement in dg C. A matrix of dimension \code{(29, 93)} for the 29 profiles and (at most) 93 depth values; \code{NA} means no measurement. \item \code{depth}, the depth of the measurement in \code{temp}, in metres, positive downward. A matrix of dimension \code{(29, 93)} for the 29 profiles and (at most) 93 depth values; \code{NA} means no measurement. } } \author{ Karline Soetaert <karline.soetaert@nioz.nl> } \examples{ # save plotting parameters pm <- par(mfrow = c(2, 2)) mar <- par("mar") ## ============================================================================= ## show the metadata ## ============================================================================= print(TrackProf$meta) ## ============================================================================= ## display the cruisetrack on the Ocean Bathymetry data ## ============================================================================= # 1. plots the ocean's bathymetry and add sampling positions ImageOcean(xlim = c(-50, 50), ylim = c(-50, 50), main = "cruise track") points(TrackProf$meta$longitude, TrackProf$meta$latitude, pch = "+") # mark starting point points(TrackProf$meta$longitude[1], TrackProf$meta$latitude[1], pch = 18, cex = 2, col = "purple") ## ============================================================================= ## image plots of raw data ## ============================================================================= image2D(z = TrackProf$depth, main = "raw depth values", xlab = "station nr", ylab = "sample nr", clab = "depth") image2D(z = TrackProf$temp, main = "raw temperature values", xlab = "station nr", ylab = "sample nr", clab = "dgC") ## ============================================================================= ## image plots of temperatures at correct depth ## ============================================================================= # water depths to which data set is interpolated depth <- 0 : 809 # map from "sigma" to "depth" coordinates Temp_Depth <- mapsigma (TrackProf$temp, sigma = TrackProf$depth, depth = depth)$var # image with depth increasing downward and increased resolution (resfac) image2D(z = Temp_Depth, main = "Temperature-depth", ylim = c(809, 0), y = depth, NAcol ="black", resfac = 2, xlab = "station nr", ylab = "depth, m", clab = "dgC") ## ============================================================================= ## scatterplot of surface values on ocean bathymetry ## ============================================================================= par(mar = mar + c(0, 0, 0, 2)) par(mfrow = c(1, 1)) # No colors, but add contours ImageOcean(xlim = c(-30, 30), ylim = c(-40, 40), main = "cruise track", col = "white", contour = TRUE) # use data set TrackProf to add measured temperature, with color key with (TrackProf, scatter2D(colvar = temp[,1], x = meta[ ,"longitude"], y = meta[ ,"latitude"], clab = "temp", add = TRUE, pch = 18, cex = 2)) # reset plotting parameters par(mar = mar) par(mfrow = pm) } \references{ \url{https://www.nodc.noaa.gov/gtspp/} U.S. National Oceanographic Data Center: Global Temperature-Salinity Profile Programme. June 2006. U.S. Department of Commerce, National Oceanic and Atmosphere Administration, National Oceanographic Data Center, Silver Spring, Maryland, 20910. Date of Access: 9/06/2012. } \seealso{ \link{image2D} for plotting images, package \code{plot3D}. \link{ImageOcean} for an image of the ocean bathymetry, package \code{plot3D}. \link{scatter2D} for making scatterplots, package \code{plot3D}. \link{Oxsat} for a 3-D data set, package \code{plot3D}. } \keyword{datasets}

288916873461bfcda4a21c6a7f8eb32040ec355d

7329459bb72ddd723bc58358e80b5f0db3db730c

/man/Normal_ct.Rd

773405664d95d210173887d7414ca70a1adfb1d7

[]

no_license

knygren/glmbayes

6f2411da073f3d6bfcb727e8d02d4888cacb8fef

3c25c08c1f4ac71a0e67d47341fb0cf39497d5f8

refs/heads/master

2021-01-17T10:49:54.755257

2020-08-29T21:24:08

18,466,002

2

0

null

UTF-8

R

false

true

2,454

rd

Normal_ct.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnvNorm_ct.R \name{Normal_ct} \alias{Normal_ct} \alias{pnorm_ct} \alias{rnorm_ct} \title{The Central Normal Distribution} \usage{ pnorm_ct(a = -Inf, b = Inf, mu = 0, sigma = 1, log.p = TRUE, Diff = FALSE) rnorm_ct(n, lgrt, lglt, mu = 0, sigma = 1) } \arguments{ \item{a}{lower bound} \item{b}{upper bound} \item{mu}{mean parameter} \item{sigma}{standard deviation} \item{log.p}{Logical argument. If \code{TRUE}, the log probability is provided} \item{Diff}{Logical argument. If \code{TRUE} the second parameter is the difference between the lower and upper bound} \item{n}{number of draws to generate. If \code{length(n) > 1}, the length is taken to be the number required} \item{lgrt}{log of the distribution function between the lower bound and infinity} \item{lglt}{log of the distribution function between negative infinity and the upper bound} } \value{ For \code{pnorm_ct}, vector of length equal to length of \code{a} and for \code{rnorm_ct}, a vector with length determined by \code{n} containing draws from the center of the normal distribution. } \description{ Distribution function and random generation for the center (between a lower and an upper bound) of the normal distribution with mean equal to mu and standard deviation equal to sigma. } \details{ The distribution function pnorm_ct finds the probability of the center of a normal density (the probability of the area between a lower bound a and an upper bound b) while the random number generator rnorm_ct samples from a restricted normal density where lgrt is the log of the distribution between the lower bound and infinity and lglt is the log of the distribution function between negative infinity and the upper bound. The sum of the exponentiated values for the two (exp(lgrt)+exp(lglt)) must sum to more than 1. These functions are mainly used to handle cases where the differences between the upper and lower bounds \code{b-a} are small. In such cases, using \code{pnorm(b)-pnorm(a)} may result in 0 being returned even when the difference is supposed to be positive. } \examples{ pnorm_ct(0.2,0.4) exp(pnorm_ct(0.2,0.4)) pnorm_ct(0.2,0.4,log.p=FALSE) log(pnorm_ct(0.2,0.4,log.p=FALSE)) ## Example where difference between two pnorm calls fail ## but call to pnorm_ct works pnorm(0.5)-pnorm(0.4999999999999999) pnorm_ct(0.4999999999999999,0.5,log.p=FALSE) } \keyword{internal}

75d754748ca6e1b3c918aed44989829dd24c9690

1c531619b82ab3ab8d50f554f9e1707bcd8f3dfc

/class scores.R

7762c90f334134c9291b626998f26c058b5f0aa6

[]

no_license

acehjy97/20180721

0ab5278f8cdd727e1df2d49120970aadecfba8d0

2c4371d08543ff23f622a5a5351591932afabf12

refs/heads/master

2020-03-23T16:15:58.924940

2018-08-04T09:19:09

141,801,193

0

null

UHC

R

false

3,179

r

class scores.R

library(rJava) library(DBI) library(RJDBC) library(XML) library(memoise) library(KoNLP) library(wordcloud) library(dplyr) library(ggplot2) library(ggmap) library(rvest) library(RColorBrewer) library(data.table) library(reshape) read.csv("class_scores.csv") ####################################################### ## 문제 1. class_scores.csv을 데이터프레임으로 전환하시오 # 데이터프레임의 이름은 다음과 같이 하시오. # scores # R에서는 기본적으로 데이터파일안에 들어있는 문자열(Strings)를 # 요인(Factor)로 취급하는 것이 기본이다. 만약 요인으로 설정하지 않고 # 불러오고 싶다면 readCSV()함수내에 stringsAsFactors=FALSE; 를 추가한다. ####################################################### ####################################################### ## 문제 2. scores을 분석하시오 ####################################################### ####################################################### ## 문제 3. scores 의 컬럼명을 한글로 전환하시오 ## 학생아이디 = Stu_ID ## 학년 = grade ## 등급 = class ## 성별 = gender ## 수학 = Math ## 영어 = English ## 과학 = Science ## 마케팅 = Marketing ## 작문 = Writing ####################################################### ####################################################### ## 문제 4. 1학년 학생들을 학번내림차순 정렬 ####################################################### ####################################################### ## 문제 5. 1학년 남학생만 보기 ####################################################### ####################################################### ## 문제 6. 1학년이 아닌 학생들 중 학번이 ## 가장 빠른 번호 3개만 보기 ## !grade == 1 ## top_n(n=3,wt=학생아이디) ## n은 상위 3개, wt 는 기준 ####################################################### ####################################################### ## 문제 7. 1, 2학년 학생들을 학년,학번 순으 ## grade == 1 | grade == 2 ## arrange(학년,학번) ## tail 은 하단부분 6개만 보여줌 ####################################################### ####################################################### ## 문제 8. 수학,영어,과학,마케팅,작문 과목의 ## 평균을 나타내는 컬럼을 추가한 후 ## 평균점수가 80이상인 학생들을 출력하시오. ## 단 평균내림차순, 학번오름차순으로 정렬하시오. ## mutate(평균 = (수학+영어+과학+마케팅+작문) %/% 5) ## 평균 >= 80 ## getter, setter 개념설명 ####################################################### ####################################################### ## 문제 9. 학생들 중 한 과목이라도 100점이 있는 ## 학생만 보기 ## ※ | 를 연속사용하기 ####################################################### ####################################################### ## 문제 10. 학번이 홀수인 학생들 중 남자이면서 ## 수학과 과학이 모두 90점 이상인 ## 학생들을 학번 오름차순으로 정렬 #######################################################

270d1b12289d4b708ce4160e1dd7f07343db3046

af72407b36c1ee3182f3a86c3e73071b31456702

/data-raw/ames.R

4fc5ade4acd17e9fd1b2ac680d3048f0ad46174d

[ "MIT" ]

permissive

bcjaeger/ipa

f89746d499500e0c632b8ca2a03904054dc12065

2e4b80f28931b8ae6334d925ee8bf626b45afe89

refs/heads/master

2021-07-12T20:52:23.778632

2020-04-26T16:44:01

207,016,384

0

null

UTF-8

R

false

662

r

ames.R

## code to prepare `amesHousing` dataset goes here set.seed(329) # library(tidyverse) # library(AmesHousing) ames_complete <- ames_missing <- drop_na(make_ames()) rows <- sample(nrow(ames_complete), 1200) for(i in rows){ cols <- sample(ncol(ames_complete)-1, size = ncol(ames_complete) - 5) ames_missing[i, cols] <- NA } ames <- list( complete = ames_complete, missing = ames_missing ) %>% map(mutate, Sale_Price = log(Sale_Price)) %>% map(mutate, Kitchen_AbvGr = factor(Kitchen_AbvGr)) %>% map(select, Sale_Price, everything(), -Utilities) ames$missing$Sale_Price <- ames$complete$Sale_Price usethis::use_data(ames, overwrite = TRUE)

1193a1c2b1ce70c45e74207be77dab84b98d6f00

718f5c39e5749f259cfa3a64ac9fd7c314dd3408

/Log log.R

4056d246622321d202cfddf162322455830f6b94

[]

no_license

Reinaldodos/Covid19

3be30953d482c18b7c53d320a26cc134728e8d7a

e791b7dd69559fe60070b5480ea7494815029b17

refs/heads/master

2023-03-02T22:33:37.553423

2021-02-18T11:51:01

255,569,854

0

null

UTF-8

R

false

766

r

Log log.R

TEST = TEST %>% group_by(country) %>% mutate(Daily_cases = Number - lag(Number)) %>% ungroup %>% drop_na(Daily_cases) %>% filter(Daily_cases>0) TEST %>% filter(Daily_cases> 200) %>% count(country) %>% top_n(n = 12, wt = n) %>% semi_join(x = TEST, by = "country") %>% # filter(str_detect(string = country, pattern = "China", negate = T)) %>% # mutate_at(.vars = vars(Number, Daily_cases), .funs = ~./Population) %>% ggplot(mapping = aes( x = Number, y = Daily_cases, colour = country, group = country )) + geom_smooth(se = F) + scale_x_log10() + scale_y_log10() + theme(legend.position = "bottom") + xlab(label = "Total confirmed cases") + ylab(label = "New confirmed cases")+ facet_wrap(facets = ~region)

eb2dd23442e249a96bcbc62e2dffeaa7fbcfdec1

b3c5f225ac5fc4e3280591206bac374565c65a86

/R/check_bw.R

bb4753183ef663670b3cb62684020831c6e31049

[]

no_license

ClaudiaRHD/chipAnalyseR

4d5c404b51130ab650383022801bb2c50eba3111

2e41b66048e3945f90d3ac30be22700d4157ba04

refs/heads/master

2021-04-15T10:09:31.490707

2020-06-15T09:41:37

126,216,813

3

2

null

UTF-8

R

false

1,108

r

check_bw.R

#' checks for bwtool binary #' @description Takes inserted path to bwtool and checks if bwtool is already installed. #' @param bw_path The path to directory where bwtool is installed on the computer. Default value is NULL. #' @import ggplot2 #' @import data.table #' @import cowplot #check if bwtool is available on computer check_bw = function(bw_path = NULL){ if(is.null(bw_path)){ bw_path = 'bwtool' if(Sys.which(names = bw_path) == ""){ stop("bwtool could not be found. Please download bwtool: https://github.com/CRG-Barcelona/bwtool/wiki ") } else{ if(file.exists(bw_path)){ message("bwtool found in sys") } else{ stop("bwtool could not be found. Please download bwtool: https://github.com/CRG-Barcelona/bwtool/wiki ") } } }else{ bw_path = paste0(bw_path, 'bwtool') if (file.exists(bw_path )){ message("inserted bwpath is correct. bwtool found") } else { stop(paste0("bwtool could not be found in ", bw_path,". Please download bwtool: https://github.com/CRG-Barcelona/bwtool/wiki")) } } return(bw_path) }

6cf119625a89160c73d08a8738d4297496fe8c10

4aae56c278bde19385a0e52ce75edc8bdd241740

/man/multinomial_metrics.Rd

bc482f5f3762e96041b540b4236f8458708588e8

[ "MIT" ]

permissive

LudvigOlsen/cvms

627d216939203b7e6e1da6b68802d2208138b622

38dc4d5117d67d00c81fb05677b771d3b39a6c28

refs/heads/master

2023-07-05T08:33:44.190664

2023-06-30T04:06:31

71,063,931

38

7

NOASSERTION

2022-09-23T10:57:12

2016-10-16T16:54:21

R

UTF-8

R

false

true

4,697

rd

multinomial_metrics.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/choosing_metrics_functions.R \name{multinomial_metrics} \alias{multinomial_metrics} \title{Select metrics for multinomial evaluation} \usage{ multinomial_metrics( all = NULL, overall_accuracy = NULL, balanced_accuracy = NULL, w_balanced_accuracy = NULL, accuracy = NULL, w_accuracy = NULL, f1 = NULL, w_f1 = NULL, sensitivity = NULL, w_sensitivity = NULL, specificity = NULL, w_specificity = NULL, pos_pred_value = NULL, w_pos_pred_value = NULL, neg_pred_value = NULL, w_neg_pred_value = NULL, auc = NULL, kappa = NULL, w_kappa = NULL, mcc = NULL, detection_rate = NULL, w_detection_rate = NULL, detection_prevalence = NULL, w_detection_prevalence = NULL, prevalence = NULL, w_prevalence = NULL, false_neg_rate = NULL, w_false_neg_rate = NULL, false_pos_rate = NULL, w_false_pos_rate = NULL, false_discovery_rate = NULL, w_false_discovery_rate = NULL, false_omission_rate = NULL, w_false_omission_rate = NULL, threat_score = NULL, w_threat_score = NULL, aic = NULL, aicc = NULL, bic = NULL ) } \arguments{ \item{all}{Enable/disable all arguments at once. (Logical) Specifying other metrics will overwrite this, why you can use (\code{all = FALSE, accuracy = TRUE}) to get only the Accuracy metric.} \item{overall_accuracy}{\code{Overall Accuracy} (Default: TRUE)} \item{balanced_accuracy}{\code{Balanced Accuracy} (Default: TRUE)} \item{w_balanced_accuracy}{\code{Weighted Balanced Accuracy} (Default: FALSE)} \item{accuracy}{\code{Accuracy} (Default: FALSE)} \item{w_accuracy}{\code{Weighted Accuracy} (Default: FALSE)} \item{f1}{\code{F1} (Default: TRUE)} \item{w_f1}{\code{Weighted F1} (Default: FALSE)} \item{sensitivity}{\code{Sensitivity} (Default: TRUE)} \item{w_sensitivity}{\code{Weighted Sensitivity} (Default: FALSE)} \item{specificity}{\code{Specificity} (Default: TRUE)} \item{w_specificity}{\code{Weighted Specificity} (Default: FALSE)} \item{pos_pred_value}{\code{Pos Pred Value} (Default: TRUE)} \item{w_pos_pred_value}{\code{Weighted Pos Pred Value} (Default: FALSE)} \item{neg_pred_value}{\code{Neg Pred Value} (Default: TRUE)} \item{w_neg_pred_value}{\code{Weighted Neg Pred Value} (Default: FALSE)} \item{auc}{\code{AUC} (Default: FALSE)} \item{kappa}{\code{Kappa} (Default: TRUE)} \item{w_kappa}{\code{Weighted Kappa} (Default: FALSE)} \item{mcc}{\code{MCC} (Default: TRUE) Multiclass Matthews Correlation Coefficient.} \item{detection_rate}{\code{Detection Rate} (Default: TRUE)} \item{w_detection_rate}{\code{Weighted Detection Rate} (Default: FALSE)} \item{detection_prevalence}{\code{Detection Prevalence} (Default: TRUE)} \item{w_detection_prevalence}{\code{Weighted Detection Prevalence} (Default: FALSE)} \item{prevalence}{\code{Prevalence} (Default: TRUE)} \item{w_prevalence}{\code{Weighted Prevalence} (Default: FALSE)} \item{false_neg_rate}{\code{False Neg Rate} (Default: FALSE)} \item{w_false_neg_rate}{\code{Weighted False Neg Rate} (Default: FALSE)} \item{false_pos_rate}{\code{False Pos Rate} (Default: FALSE)} \item{w_false_pos_rate}{\code{Weighted False Pos Rate} (Default: FALSE)} \item{false_discovery_rate}{\code{False Discovery Rate} (Default: FALSE)} \item{w_false_discovery_rate}{\code{Weighted False Discovery Rate} (Default: FALSE)} \item{false_omission_rate}{\code{False Omission Rate} (Default: FALSE)} \item{w_false_omission_rate}{\code{Weighted False Omission Rate} (Default: FALSE)} \item{threat_score}{\code{Threat Score} (Default: FALSE)} \item{w_threat_score}{\code{Weighted Threat Score} (Default: FALSE)} \item{aic}{AIC. (Default: FALSE)} \item{aicc}{AICc. (Default: FALSE)} \item{bic}{BIC. (Default: FALSE)} } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("experimental")} Enable/disable metrics for multinomial evaluation. Can be supplied to the \code{`metrics`} argument in many of the \code{cvms} functions. Note: Some functions may have slightly different defaults than the ones supplied here. } \examples{ \donttest{ # Attach packages library(cvms) # Enable only Balanced Accuracy multinomial_metrics(all = FALSE, balanced_accuracy = TRUE) # Enable all but Balanced Accuracy multinomial_metrics(all = TRUE, balanced_accuracy = FALSE) # Disable Balanced Accuracy multinomial_metrics(balanced_accuracy = FALSE) } } \seealso{ Other evaluation functions: \code{\link{binomial_metrics}()}, \code{\link{confusion_matrix}()}, \code{\link{evaluate_residuals}()}, \code{\link{evaluate}()}, \code{\link{gaussian_metrics}()} } \author{ Ludvig Renbo Olsen, \email{r-pkgs@ludvigolsen.dk} } \concept{evaluation functions}

d71649acf3303ef445c0556985631de660bbfa55

5d820c8e7f4b458f6783a35d1022d4f6a8f8dc02

/man/crop.Rd

c4a0a27e470a67fdb93881556b451b3003008c2a

[ "MIT" ]

permissive

UBC-MDS/image-compression-toolkit--R

307a5968f1adda8638b6cfe0552d1b1ac60b9353

d0879412ab8d24e621e8da4754392d4893660ba4

refs/heads/master

2020-04-21T19:25:07.974713

2019-03-06T05:09:25

169,805,189

0

2

NOASSERTION

2019-03-06T05:09:26

2019-02-08T22:06:29

R

UTF-8

R

false

true

616

rd

crop.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/func_crop.R \name{crop} \alias{crop} \title{Crop images} \usage{ crop(img_path, H, W, out_path) } \arguments{ \item{img_path}{---- String , file path of the image .} \item{H}{---- Integer, the desired height of the cropped image} \item{W}{---- Integer, the desired width of the cropped image} \item{out_path}{---- String , desired file path of the cropped image} } \value{ ---String , cropped image path and saves the image . } \description{ Crop images } \examples{ # crop("..data/image.png", 10L, 15L, "..data/cropped_image.png") }

d641a6c0ebbbce9892399b355fc3bc6f82dc09c3

1c9c5d98c6a1d4132762ed058f6b007bb5648afb

/Coursera Statistics Princeton/Stats1.13.HW.02.LAB.R

1538610bcc7ebf78e3c450286b03dcdd50e979a9

[ "MIT" ]

permissive

dmpe/R

4475efefd5921551dde96ef19e971773b33d9451

ba8cb71f54ec969073d2b64a6cfdbc9a6042d178

refs/heads/master

2022-08-14T07:26:06.512865

2021-04-17T11:03:40

8,522,174

685

440

MIT

2021-04-17T11:03:41

2013-03-02T15:04:10

R

UTF-8

R

false

2,449

r

Stats1.13.HW.02.LAB.R

library(psych) setwd("C:/Users/Dima/Documents/R/coursera/") file <- read.table("Stats1.13.HW.02.txt", header=T) names(file) dim(file) # number of rows mean(file$SR) var(file$SR) subfile1 <- subset(file, file$time=="pre") mean(subfile1$SR) subfile2 <- subset(file, file$time=="post") sd(subfile2$SR) median(subfile2$SR) subfile1 subfile2 # Either you do the subsetting or just # describeBy(subfile2, subfile2$condition) # I did the subsetting # POST test1 <- subset(subfile2, subfile2$condition=="WM") mean(test1$SR) test2 <- subset(subfile2, subfile2$condition=="PE") mean(test2$SR) test3 <- subset(subfile2, subfile2$condition=="DS") mean(test3$SR) # PRE test4 <- subset(subfile1, subfile1$condition=="WM") mean(test4$SR) test5 <- subset(subfile1, subfile1$condition=="PE") mean(test5$SR) test6 <- subset(subfile1, subfile1$condition=="DS") mean(test6$SR) par(mfrow= c(2,3)) file1 = subset(subfile1, subfile1$condition=="WM") file2 = subset(subfile2, subfile2$condition=="WM") file3 = subset(subfile1, subfile1$condition=="PE") file4 = subset(subfile2, subfile2$condition=="PE") file5 = subset(subfile1, subfile1$condition=="DS") file6 = subset(subfile2, subfile2$condition=="DS") # http://stackoverflow.com/a/10759521/1607152 hist(file1$SR) hist(file2$SR) hist(file3$SR) hist(file4$SR) hist(file5$SR) hist(file6$SR) # Beware: if you compare density then the more "normal distributed" is Pre PM # But this is wrong. The right one is Post WM par(mfrow = c(2,3)) plot(density(test1[, 4]), xlab = "Total sympton score", main = "") plot(density(test2[, 4]), xlab = "Total sympton score", main = "") plot(density(test3[, 4]), xlab = "Total sympton score", main = "") plot(density(test4[, 4]), xlab = "Total sympton score", main = "") plot(density(test5[, 4]), xlab = "Total sympton score", main = "") plot(density(test6[, 4]), xlab = "Total sympton score", main = "") subfile1.wm = subset(subfile1, subfile1$condition=="WM") subfile2.wm = subset(subfile2, subfile2$condition=="WM") subfile1.pe = subset(subfile1, subfile1$condition=="PE") subfile2.pe = subset(subfile2, subfile2$condition=="PE") subfile1.ds = subset(subfile1, subfile1$condition=="DS") subfile2.ds = subset(subfile2, subfile2$condition=="DS") mean(subfile2.wm$SR)-mean(subfile1.wm$SR) mean(subfile2.pe$SR)-mean(subfile1.pe$SR) mean(subfile2.ds$SR)-mean(subfile1.ds$SR)

93be9995fe7290e933e39ef95010df69ac8cc78c

155cfb7d883ad64a68c77185d41c9501ed8b91d8

/Fig3_Figure_smoothed_LA_data_per_species.r

41aa18613c9947a3cb3fb7472119d76212a0bad2

[ "MIT" ]

permissive

nielsjdewinter/Sr_spiking

7db1ad2602491bbd8603037bcb5a1dc0e3beaffc

9a43c00646bf2e517cb80fe9d24bccf47e0900d1

refs/heads/main

2023-04-14T19:47:50.192420

2023-03-21T08:36:50

559,837,029

0

null

UTF-8

R

false

9,796

r

Fig3_Figure_smoothed_LA_data_per_species.r

# Plot smothed Sr profiles for Figure 1 # Project "LAICPMS_Sr_spiking" require(tidyverse) require(RColorBrewer) require(readxl) require(ggpubr) # Load data - Batch 1 Cedule_G003_1 <- read.csv("Batch1/HR/Cedule_003_1.csv", header = TRUE)[-c(1, 2), ] Cedule_G003_2 <- read.csv("Batch1/HR/Cedule_003_2.csv", header = TRUE) Cedule_G003_3 <- read.csv("Batch1/HR/Cedule_003_3.csv", header = TRUE) Cedule_G511_1 <- read.csv("Batch1/HR/Cedule_511_1.csv", header = TRUE) Cedule_G511_2 <- read.csv("Batch1/HR/Cedule_511_2.csv", header = TRUE) Cedule_G511_3 <- read.csv("Batch1/HR/Cedule_511_3.csv", header = TRUE) Cedule_G600_1 <- read.csv("Batch1/HR/Cedule_600_1.csv", header = TRUE) Cedule_G600_2 <- read.csv("Batch1/HR/Cedule_600_1.csv", header = TRUE) Cedule_G600_3 <- read.csv("Batch1/HR/Cedule_600_3.csv", header = TRUE) # Vectorize dataframe names dfnames_batch1 <- c("Cedule_G003_1", "Cedule_G003_2", "Cedule_G003_3", "Cedule_G511_1", "Cedule_G511_2", "Cedule_G511_3", "Cedule_G600_1", "Cedule_G600_2", "Cedule_G600_3" ) # Rename columns and remove CaCa column for(i in dfnames_batch1){ df <- get(i) df <- df[-c(1, 2), ] df <- as.data.frame(apply(df, 2, as.numeric)) colnames(df) <- c("Time", "Depth", "MgCa24", "MgCa", "CaCa", "CaCa44", "CaCa48", "MnCa", "SrCa87", "SrCa", "BaCa") df$MgCa24 <- df$CaCa <- df$CaCa44 <- df$CaCa48 <- df$SrCa87 <- NULL df$Specimen <- strsplit(i, "_")[[1]][2] df$Species <- strsplit(i, "_")[[1]][1] df$Profile <- strsplit(i, "_")[[1]][3] df$Depth2 <- max(df$Depth) - df$Depth # Invert growth direction assign(i, df) } LA_combined_batch1 <- bind_rows(Cedule_G003_1, Cedule_G003_2, Cedule_G003_3, Cedule_G511_1, Cedule_G511_2, Cedule_G511_3, Cedule_G600_1, Cedule_G600_2, Cedule_G600_3, .id = "Specimen_id" ) # Create profile plots of batch1 data Profile_plot_Sr_offset_batch1 <- ggplot(LA_combined_batch1) + geom_point(aes(Depth2, SrCa + as.numeric(Specimen_id) * 2 - 2, col = Specimen), alpha = 0.1, size = 0.1) + scale_y_continuous("Sr/Ca (mmol/mol)", breaks = seq(0, 23, 5), labels = seq(0, 23, 5)) + scale_x_continuous("Distance from ventral margin [mm]") + coord_cartesian(ylim = c(0, 23)) + ggtitle("Offset (+ 2 mmol/mol) Sr/Ca curves \n Cerastoderma edule batch 1") + theme_bw() # ------------------------------------------------------------------------------ # Load smoothed data filename_smooth <- "Batch2/LA data/01_20211025_SEQ326_NdW_LR__profiles_smooth_0.05.xlsx" sheets_smooth <- excel_sheets(filename_smooth) datalist <- lapply(sheets_smooth, function(X) read_excel(filename_smooth, sheet = X)) names(datalist) <- sheets_smooth Medulis_G177_smooth <- as.data.frame(datalist["G177"]) Medulis_G191_smooth <- as.data.frame(datalist["G191"]) Medulis_G259_smooth <- as.data.frame(datalist["G259"]) Oedulis_G271_smooth <- as.data.frame(datalist["G271"]) Oedulis_G271_chalky_smooth <- as.data.frame(datalist["G271_chalky"]) Oedulis_G282_smooth <- as.data.frame(datalist["G282"]) Oedulis_G282_chalky_smooth <- as.data.frame(datalist["G282_chalky"]) Oedulis_G372_smooth <- as.data.frame(datalist["G372"]) Oedulis_G372_chalky_smooth <- as.data.frame(datalist["G372_chalky"]) Cedule_G457_smooth <- as.data.frame(datalist["G457"]) Cedule_G472_smooth <- as.data.frame(datalist["G472"]) Cedule_G555_smooth <- as.data.frame(datalist["G555"]) # Vectorize dataframe names dfnames_smooth <- c("Medulis_G177_smooth", "Medulis_G191_smooth", "Medulis_G259_smooth", "Oedulis_G271_smooth", "Oedulis_G271_chalky_smooth", "Oedulis_G282_smooth", "Oedulis_G282_chalky_smooth", "Oedulis_G372_smooth", "Oedulis_G372_chalky_smooth", "Cedule_G457_smooth", "Cedule_G472_smooth", "Cedule_G555_smooth" ) # Rename columns and remove CaCa column for(i in dfnames_smooth){ df <- get(i) colnames(df) <- c("Time", "Depth", "Xpos", "Ypos", "NaCa", "MgCa", "CaCa", "MnCa", "SrCa", "BaCa") df$CaCa <- NULL df$Specimen <- strsplit(i, "_")[[1]][2] df$Species <- strsplit(i, "_")[[1]][1] # calculate distance along profile df$Distance <- 0 for(j in 2:length(df$Xpos)){ df$Distance[j] <- df$Distance[j - 1] + sqrt((df$Xpos[j] - df$Xpos[j - 1]) ^ 2 + (df$Ypos[j] - df$Ypos[j - 1]) ^ 2) } assign(i, df) } # Load relative start and end positions of chalky lines in O. edulis chalkypos <- read.csv("Batch2/Oedulis_chalky_positions.csv", header = TRUE) # Update position of chalky data Oedulis_G271_chalky_smooth$Distance <- Oedulis_G271_chalky_smooth$Distance + chalkypos$start[1] * 1000 Oedulis_G282_chalky_smooth$Distance <- Oedulis_G282_chalky_smooth$Distance + chalkypos$start[2] * 1000 Oedulis_G372_chalky_smooth$Distance <- Oedulis_G372_chalky_smooth$Distance + chalkypos$start[3] * 1000 # Combine data LA_combined_smooth <- bind_rows(Medulis_G177_smooth, Medulis_G191_smooth, Medulis_G259_smooth, Oedulis_G271_smooth, Oedulis_G271_chalky_smooth, Oedulis_G282_smooth, Oedulis_G282_chalky_smooth, Oedulis_G372_smooth, Oedulis_G372_chalky_smooth, Cedule_G457_smooth, Cedule_G472_smooth, Cedule_G555_smooth, .id = "Specimen_id" ) #save(LA_combined_smooth, file = "Batch2/LA data/LA_combined_batch2_smooth.Rdata") # Create profile plots Profile_plot_Sr_offset_all <- ggplot(LA_combined_smooth) + geom_point(data = subset(LA_combined_smooth, !(Specimen_id %in% c(5, 7, 9))), aes(Distance, SrCa + as.numeric(Specimen_id) - 1, col = Species), alpha = 0.1, size = 0.1) + scale_y_continuous("Sr/Ca (mmol/mol)", breaks = seq(0, 20, 2), labels = seq(0, 20, 2), limits = c(0, 20)) + scale_x_continuous("Distance from ventral margin [um]") + ggtitle("Offset (+ 1) Sr/Ca curves") + theme_bw() Profile_plot_Sr_offset_all2 <- ggplot(LA_combined_smooth) + geom_point(data = LA_combined_smooth, aes(Distance, SrCa + as.numeric(Specimen_id) - 1, col = Species), alpha = 0.1, size = 0.1) + scale_y_continuous("Sr/Ca (mmol/mol)", breaks = seq(0, 20, 2), labels = seq(0, 20, 2), limits = c(0, 20)) + scale_x_continuous("Distance from ventral margin [um]") + ggtitle("Offset (+ 1) Sr/Ca curves") + theme_bw() # Isolate C. edule data Profile_plot_Sr_Cedule <- ggplot(LA_combined_smooth) + geom_point(data = subset(LA_combined_smooth, Species == "Cedule"), aes(Distance / 1000, SrCa + as.numeric(Specimen_id) * 2 - 20, col = Specimen), alpha = 0.1, size = 0.1) + scale_y_continuous("Sr/Ca (mmol/mol)", breaks = seq(0, 20, 5), labels = seq(0, 20, 5), limits = c(0, 20)) + scale_x_continuous("Distance from ventral margin [mm]") + ggtitle("Offset (+ 2 mmol/mol) Sr/Ca curves \n Cerastoderma edule") + theme_bw() # Isolate O. edulis data Profile_plot_Sr_Oedulis <- ggplot(LA_combined_smooth) + geom_point(data = subset(LA_combined_smooth, Species == "Oedulis" & Specimen_id %in% c(4, 6, 8)), aes(Distance / 1000, SrCa + as.numeric(Specimen_id) - 4, col = Specimen), alpha = 0.1, size = 0.1) + scale_y_continuous("Sr/Ca (mmol/mol)", breaks = seq(0, 10, 2), labels = seq(0, 10, 2), limits = c(0, 8)) + scale_x_continuous("Distance from ventral margin [mm]") + ggtitle("Offset (+ 2 mmol/mol) Sr/Ca curves \n Ostrea edulis") + theme_bw() # Isolate O. edulis data, excluding chalky records Profile_plot_Sr_Oedulis_nochalky <- ggplot(LA_combined_smooth) + geom_point(data = subset(LA_combined_smooth, Species == "Oedulis" & !(Specimen_id %in% c(5, 7, 9))), aes(Distance / 1000, SrCa + as.numeric(Specimen_id) - 4, col = Specimen), alpha = 0.1, size = 0.1) + scale_y_continuous("Sr/Ca (mmol/mol)", breaks = seq(0, 10, 2), labels = seq(0, 10, 2), limits = c(0, 10)) + scale_x_continuous("Distance from ventral margin [mm]") + ggtitle("Offset (+ 2 mmol/mol) Sr/Ca curves \n Ostrea edulis") + theme_bw() # Isolate M. edulis data Profile_plot_Sr_Medulis <- ggplot(LA_combined_smooth) + geom_point(data = subset(LA_combined_smooth, Species == "Medulis"), aes(Distance / 1000, SrCa + as.numeric(Specimen_id) * 2 - 2, col = Specimen), alpha = 0.1, size = 0.1) + scale_y_continuous("Sr/Ca (mmol/mol)", breaks = seq(0, 10, 2), labels = seq(0, 10, 2), limits = c(0, 10)) + scale_x_continuous("Distance from ventral margin [mm]") + ggtitle("Offset (+ 2 mmol/mol) Sr/Ca curves \n Mytilus edulis") + theme_bw() # Combine species plots into one multi-panel plot Species_combined <- ggarrange( Profile_plot_Sr_offset_batch1 + theme(legend.position = "none") + coord_cartesian(ylim = c(0, 23)), Profile_plot_Sr_Cedule + theme(legend.position = "none") + coord_cartesian(ylim = c(0, 23)), Profile_plot_Sr_Medulis + theme(legend.position = "none") + coord_cartesian(ylim = c(0, 10), xlim = c(0, 18)), Profile_plot_Sr_Oedulis + theme(legend.position = "none") + coord_cartesian(ylim = c(0, 10), xlim = c(0, 36)), ncol = 2, nrow = 2, labels = c("A", "B", "C", "D") )

269afecf3cac76201afd2afd239ec12503e13f37

b32192ef5ee4869289ead0cac00702906bb4b488

/src/utsg2021/getSocio.R

2a3831d037c331c26ab62c6b28df5fdab7e48054

[ "MIT" ]

permissive

3rfm-its-davis/covid-19-ldt

10fe23996512a38de01813abdd82f6a77070ded9

11367f772054798b182de49ccdca558b7ca18264

refs/heads/master

2022-12-20T09:40:23.217388

2020-09-20T10:52:26

275,208,352

0

null

UTF-8

R

false

3,344

r

getSocio.R

getSocio <- function(raw, y) { socio <- raw %>% inner_join(y, by = "ResponseId") %>% select(c( "ResponseId", "City", "B02_5", "D01", "D09_2", "C07", "C12_3", "C12_13", "C13_3", "I01_1", "I04_2", "I04_3", "I04_1", "I04_5", "I04_4", "I05", "I09_USD", "I07", "I12" )) %>% mutate(Age = cut(I01_1, c(1920, 1965, 1980, 1995, 2010), labels = c("(1920-1965]", "(1965-1980]", "(1980-1995]", "(1995-2010]") )) %>% mutate(Gen = case_when( I05 == 2 ~ "Male", I05 != 2 ~ "Non-Male" )) %>% mutate(Edc = case_when(I07 > 3 ~ ">=Bachelor", I07 < 4 ~ "<Bachelor")) %>% mutate(Chl = case_when(D09_2 > 0 ~ "Yes", D09_2 == 0 ~ "No")) %>% mutate(Inc = case_when( I09_USD < 3 ~ "<50,000", I09_USD > 4 ~ ">=100,000", T ~ "50,000-99,999" )) %>% mutate(Rem = case_when(C07 == 0 ~ "No", C07 > 0 ~ "Yes")) %>% mutate(Hel = C12_3 - 3) %>% mutate(Vdc = C12_13 - 3) %>% mutate(Fut = case_when( C13_3 < 3 ~ "less", C13_3 > 3 ~ "more", T ~ "same" )) %>% mutate(Raa = case_when(I04_1 == 1 ~ "Yes", I04_1 == 0 ~ "No")) %>% mutate(Rab = case_when(I04_2 == 1 ~ "Yes", I04_2 == 0 ~ "No")) %>% mutate(Ran = case_when(I04_3 == 1 ~ "Yes", I04_3 == 0 ~ "No")) %>% mutate(Raw = case_when(I04_4 == 1 ~ "Yes", I04_4 == 0 ~ "No")) %>% mutate(Rao = case_when(I04_5 == 1 ~ "Yes", I04_5 == 0 ~ "No")) %>% mutate(Ngh = case_when(D01 == 1 ~ "Urban", D01 > 1 ~ "Non-Urban")) %>% mutate(City = case_when( City %in% c( "LA", "SF", "Sacramento", "Seattle", "SD", "Denver", "SLC" ) ~ "West", City %in% c("Kansas", "Detroit", "Chicago") ~ "Midwest", City %in% c("Atlanta", "Tampa", "DC") ~ "South", City %in% c("NYC", "Boston") ~ "Northeast" )) %>% mutate_at(c( "City", "Age", "Inc", "Edc", "Rem", "Gen", "Ngh", "Raa", "Rab", "Ran", "Raw", "Rao" ), as.factor) %>% mutate(Age = factor(Age, levels = c( "(1965-1980]", "(1995-2010]", "(1980-1995]", "(1920-1965]" ))) %>% mutate(Inc = factor(Inc, levels = c( "<50,000", "50,000-99,999", ">=100,000" ))) %>% mutate(Raa = factor(Raa, levels = c("No", "Yes"))) %>% mutate(Rab = factor(Rab, levels = c("No", "Yes"))) %>% mutate(Ran = factor(Ran, levels = c("No", "Yes"))) %>% mutate(Raw = factor(Raw, levels = c("No", "Yes"))) %>% mutate(Rao = factor(Rao, levels = c("No", "Yes"))) %>% mutate(Chl = factor(Chl, levels = c("No", "Yes"))) %>% mutate(Edc = factor(Edc, levels = c("<Bachelor", ">=Bachelor"))) %>% mutate(Fut = factor(Fut, levels = c("less", "same", "more"))) %>% mutate(Ngh = factor(Ngh, levels = c("Urban", "Non-Urban"))) %>% mutate(Rem = factor(Rem, levels = c("No", "Yes"))) %>% mutate(City = factor(City, levels = c( "West", "Midwest", "South", "Northeast" ))) %>% mutate_at(c( "City", "Age", "Inc", "Edc", "Rem", "Gen", "Ngh", "Raa", "Rab", "Ran", "Raw", "Rao", "Chl", "Fut" ), as.numeric) %>% select( ResponseId, City, Age, Inc, Hel, Vdc, Rem, Edc, Chl, Gen, Ngh, Raa, Rab, Ran, Raw, Rao, Fut ) return(socio) }

cef7ad4f0acd315163d6db2ca47f03d907b01c0f

2e27d0ee5455c14be50ac40871cbb9538b29b8d1

/R/sphere.smooth.R

58b63dff88b387a9dfca58816e7cdc3aed64f483

[]

no_license

antiphon/sphere

66975fa754559dc61f4bfa0aa51e4757f22353b2

e6e3b7e5e31741503718da18d102695f277258a3

refs/heads/master

2022-05-06T13:33:35.720737

2022-03-25T05:37:34

29,916,065

0

null

UTF-8

R

false

1,220

r

sphere.smooth.R

#' Smoothed unit sphere values #' #' Krige on a sphere, return a plottable orb. #' #' @param latlon latitude-longitude of data #' @param v values on data points #' @param N refinenement of the icosahedron triangulation #' @param s smoothing sd for gaussian smoothing #' #' @import rgl #' @export sphere.smooth <- function(latlon, v, newlatlon, N=3, s=.25){ # the smoothing locations ico <- icosahedron3d() for(i in 1:N) ico <- subdivision3d(ico) newxyz <- t(ico$vb[1:3,])/ico$vb[4,] newlatlon <- xyz2ll(newxyz) # predict f <- sphere.predict(latlon, v, newlatlon, s=s) # assign to icosahedron vertices ico$vb <- t( t(ico$vb)/ico$vb[4,]) ico$data <- if(missing(v)) latlon else cbind(latlon, v) ico$prediction <- f class(ico) <- c("spheresmooth", class(ico)) ico } #' plot the 3d sphere of predictions #' #' @import rgl #' @export plot.spheresmooth <- function(obj, col=heat.colors, col_zlim, ..., data=FALSE) { co <- values2colors(obj$prediction, col=col, zlim=col_zlim, ...) cols <- co[c(obj$it)] plot3d(obj, col=cols, aspect=FALSE, ...) if(data){ cod <- values2colors(obj$data[,3], col=col, ...) text3d(ll2xyz(obj$data[,1:2]), col=cod, texts=1:nrow(obj$data)) } }

c3d09b90f1dd8913a1d417b2b76f2222de6d0d3a

fb0cbf6db81ee5ff6dfe73b618e2d649251b8b77

/Model1.R

1a19db5090f166db08a2f519145625b356c292e2

[]

no_license

PGC-PTSD-EWAS/PGC-PTSD-Longitudinal-Analysis

6a5841a9a2a33388959851e555a608315611b677

8ff7f5832bccac3343f5fefc3ee1d408885edeee

refs/heads/main

2023-08-12T00:50:22.291579

2021-10-11T14:49:57

416,046,475

0

null

UTF-8

R

false

3,674

r

Model1.R

################################################################################ # Model 1: RE model to evaluate CpGs associated with PTSS in each cohort ################################################################################ library(lme4) library(lmerTest) library(data.table) # Load methylation data beta.norm<-fread(" ",data.table=F) # beta matrix. CpG x Participants rownames(beta.norm)<-beta.norm$V1 beta.norm<-beta.norm[,-1] # Load phenotype file pheno<-read.csv(" ",stringsAsFactors=FALSE,header=TRUE, row.names = 1) # Define Variables ptsdVariable<-" " # 0 -1 scaled covariates<-c("age","CD8T.Epic","CD4T.Epic","NK.Epic","Bcell.Epic","Mono.Epic","Comp.2","Comp.3") # E.g., c("Age", "Gender", "CD8T", ....) idVariable<-"studyid" # Participant ID for the Random Effects. E.g., "IID" studyID<-" " # E.g. "MRS","Prismo" beta.norm<-beta.norm[,names(beta.norm)%in%row.names(pheno)] pheno<-pheno[row.names(pheno)%in%names(beta.norm),] beta.norm<-beta.norm[,order(names(beta.norm))] pheno<-pheno[order(row.names(pheno)),] table(colnames(beta.norm)==rownames(pheno)) range(pheno[,ptsdVariable]) # Remove NAs naindex<-pheno[,c(ptsdVariable,covariates,idVariable)] naindex<-complete.cases(naindex) beta.norm<-beta.norm[,naindex] pheno<-pheno[naindex,] table(rownames(pheno)==colnames(beta.norm)) # Converting to M-values range(beta.norm, na.rm=T) # Changing beta values of 0 to 0.0001 if(min(beta.norm, na.rm=T)==0){ beta.norm[which(beta.norm==0)]<-0.0001 } # Changing beta values of 1 to 0.9999 if(max(beta.norm, na.rm=T)==1.000000e+00){ beta.norm[which(beta.norm==1.000000e+00)]<-0.9999 } range(beta.norm, na.rm=T) sum(is.na(beta.norm)) # Convert to Mvalues using log2 beta.norm<-log2(beta.norm/(1-beta.norm)) # log transforming sum(beta.norm=="-Inf", na.rm=T) # Should be 0 sum(is.na(beta.norm)) # should be same as the number of missing in the beta.norm matrix range(beta.norm, na.rm=T) # Configuring phenotypes pheno$meth<-NA pheno[,idVariable]<-factor(pheno[,idVariable]) # Results objects resultsBeta<-matrix(nrow=nrow(beta.norm), ncol=2+length(covariates)) rownames(resultsBeta)<-rownames(beta.norm) colnames(resultsBeta)<-c("(Intercept)", ptsdVariable, covariates) resultsSE<-resultsT<-resultsP<-resultsDF<-resultsBeta errorProbes<-NULL warningProbes<-NULL assign("last.warning", NULL, envir = baseenv()) formula<-as.formula(paste("meth~",ptsdVariable, "+", paste(covariates, collapse="+"), "+(1|", idVariable, ")", sep="")) # Running analyses start<-proc.time()[3] for(ii in 1:nrow(beta.norm)){ pheno$meth<-t(beta.norm[ii, rownames(pheno)]) fit<-try(lmer(formula, data=pheno), silent=F) if(class(fit)!="try-error" & is.null(warnings())){ res<-coef(summary(fit)) resultsBeta[ii,]<-res[, "Estimate"] resultsSE[ii,]<-res[, "Std. Error"] resultsT[ii,]<-res[, "t value"] resultsP[ii,]<-res[, "Pr(>|t|)"] resultsDF[ii,]<-res[, "df"] } if(class(fit)=="try-error"){ errorProbes<-append(errorProbes, rownames(beta.norm)[ii]) } if(!is.null(warnings())){ warningProbes<-append(warningProbes, rownames(beta.norm)[ii]) assign("last.warning", NULL, envir = baseenv()) } if(ii%%10==0){print(ii)} pheno$meth<-NA } end<-proc.time()[3] end-start rm(res, fit) # For main Pheno final<-as.data.frame(cbind(resultsBeta[,ptsdVariable],resultsSE[,ptsdVariable],resultsT[,ptsdVariable],resultsP[,ptsdVariable],resultsDF[,ptsdVariable])) rownames(final)<-rownames(beta.norm) colnames(final)<-c("BETA","SE","t","pval","df") write.csv(final,file = paste0(studyID,"_RE_PCL01scaled_covar_age_epicCellTypes_methPC.csv"),quote = F,row.names = F)

c265d727f2d15a84fc01b8c9811e4ef91bd6b76d

f697336c25ca6fadd13c8746309f3d1b47d13864

/man/validateIcon.Rd

da76da12c5f002c963b470e63df5c4340ceae6b2

[]

no_license

cran/shiny.pwa

c1116d1086192c064789e3cda570206be92e1268

d15ae361ec8be31278ec92d6b7282a6d06d17e7c

refs/heads/master

2023-05-31T08:19:17.120970

2021-06-19T15:50:02

298,777,337

0

null

UTF-8

R

false

true

637

rd

validateIcon.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/validators.R \name{validateIcon} \alias{validateIcon} \title{Validates the provided icon. If the icon does not exist returns a default one.} \usage{ validateIcon(icon) } \arguments{ \item{icon}{Path location for an icon relative to the project root} } \value{ A valid icon path } \description{ Validates the provided icon. If the icon does not exist returns a default one. } \seealso{ [validateDomain()], [validateLocation()] Other validators: \code{\link{validateDomain}()}, \code{\link{validateLocation}()} } \concept{validators}

0f0586aab28f60679599806ec5e3d55c0bbf8cea

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/iccbeta/examples/Hofmann.Rd.R

403c16c7fc7aa655a2787c484e365afdb2e12e45

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

1,482

r

Hofmann.Rd.R

library(iccbeta) ### Name: Hofmann ### Title: A multilevel dataset from Hofmann, Griffin, and Gavin (2000). ### Aliases: Hofmann ### Keywords: datasets ### ** Examples ## Not run: ##D ##D if(requireNamespace("lme4") && requireNamespace("RLRsim")){ ##D data(Hofmann) ##D library("lme4") ##D ##D # Random-Intercepts Model ##D lmmHofmann0 = lmer(helping ~ (1|id), data = Hofmann) ##D vy_Hofmann = var(Hofmann[,'helping']) ##D ##D # Computing icca ##D VarCorr(lmmHofmann0)$id[1,1]/vy_Hofmann ##D ##D # Estimating Group-Mean Centered Random Slopes Model, no level 2 variables ##D lmmHofmann1 <- lmer(helping ~ mood_grp_cent + (mood_grp_cent |id), ##D data = Hofmann, REML = FALSE) ##D X_Hofmann = model.matrix(lmmHofmann1) ##D P = ncol(X_Hofmann) ##D T1_Hofmann = VarCorr(lmmHofmann1)$id[1:P,1:P] ##D ##D # Computing iccb ##D icc_beta(X_Hofmann, Hofmann[,'id'], T1_Hofmann, vy_Hofmann)$rho_beta ##D ##D # Performing LR test ##D # Need to install 'RLRsim' package ##D library("RLRsim") ##D lmmHofmann1a <- lmer(helping ~ mood_grp_cent + (1 | id), ##D data = Hofmann, REML = FALSE) ##D obs.LRT <- 2*(logLik(lmmHofmann1) - logLik(lmmHofmann1a))[1] ##D X <- getME(lmmHofmann1,"X") ##D Z <- t(as.matrix(getME(lmmHofmann1,"Zt"))) ##D sim.LRT <- LRTSim(X, Z, 0, diag(ncol(Z))) ##D (pval <- mean(sim.LRT > obs.LRT)) ##D } else { ##D stop("Please install packages `RLRsim` and `lme4` to run the above example.") ##D } ## End(Not run)

242d38a87b2456fca802d0bffb11124b8a01c425

db2b9f7e9ae8019b3ad7f9c154ac38389c129bd8

/aargh/dataprocessing.R

f50d7ee20def4d08f26428c20acb54c31bfb8ee5

[]

no_license

borealbirds/foam

bc70fb46efb744d202eddc6804f086e82766b1de

9b30065e500156372767bc66ea5d7c59d86bc5ca

refs/heads/master

2021-09-04T10:10:20.055552

2018-01-17T21:24:27

114,273,850

0

null

UTF-8

R

false

19,624

r

dataprocessing.R

##--- ##title: "Data processing for nationam BAM analyses" ##author: "Peter Solymos" ##date: "Apr 18, 2016" ##output: ## pdf_document: ## toc: true ## toc_depth: 2 ##--- ### Preliminaries start here ## Define root folder where data are stored ROOT <- "c:/bam/May2015" ROOT2 <- "e:/peter/bam/Apr2016" ## Load required packages library(mefa4) library(RODBC) library(maptools) library(QPAD) ## Load functions kept in separate file source("~/repos/bamanalytics/R/dataprocessing_functions.R") ### Pulling in tables ## Define MS Access database connection con <- odbcConnectAccess2007(file.path(ROOT, "BAM_BayneAccess_BAMBBScore.accdb")) con2 <- odbcConnectAccess2007(file.path(ROOT2, "BBS_V4draft_2016_aprilForErin.accdb")) #### Species lookup table TAX <- sqlFetch(con, "dbo_DD_BAM_AOU52_SPECIES_TBL") TAX$SSMA_TimeStamp <- NULL #### SS level stuff for BAM+BBS combined ## This table has time zone, BCR, jurisdiction, XY SS01 <- sqlFetch(con, "dbo_BBSBAM_V4_XYTBL_ATTRIBUTES1") SS01 <- nonDuplicated(SS01, SS, TRUE) SS01$COUNTRY <- ifelse(SS01$JURSALPHA %in% c("AB","BC","MB","NB", "NL","NS","NT","NU","ON","PEI","QC","SK","YK"), "CAN", "USA") save(SS01, file=file.path(ROOT2, "out", "SS-regions-and-xy.Rdata")) ## Tree proportions SS02 <- sqlFetch(con, "dbo_TREE_BBSBAM_V4_tbl") SS02 <- nonDuplicated(SS02, SS, TRUE) SS02 <- SS02[rownames(SS01),] ## TREE update 2016-12-01 tr <- read.csv("e:/peter/bam/Apr2016/tree_update/BAM_BBS_2015_XY_AVHRRTREE_nov30_2016.csv") SS02$TREE_WRONG <- SS02$TREE SS02$TREE <- tr$VCFAVHRR[match(SS02$SS, tr$SS)] SS02$TREE[SS02$TREE > 100] <- NA SS02$TREE[SS02$TREE < 0] <- NA SS02$TREE <- SS02$TREE / 100 SS02$TREE3 <- factor(NA, levels=c("Open", "Sparse", "Dense")) SS02$TREE3[SS02$TREE < 0.25] <- "Open" SS02$TREE3[SS02$TREE >= 0.25 & SS02$TREE < 0.60] <- "Sparse" SS02$TREE3[SS02$TREE >= 0.60] <- "Dense" ## Point level land cover SS03 <- sqlFetch(con, "dbo_BAMBBS_LANDCOVER_PTS") SS03 <- nonDuplicated(SS03, SS, TRUE) SS03 <- SS03[rownames(SS01),] ## comment out all LCC05 and EOSD ## Reclass LCC05 ltlcc <- read.csv("~/repos/bamanalytics/lookup/lcc05.csv") SS03$LCC05 <- SS03$LCC05_PT SS03$LCC05_PT[SS03$LCC05_PT < 1 | SS03$LCC05_PT > 39] <- NA SS03$LCC05_PT[SS01$COUNTRY == "USA"] <- NA SS03$HAB_LCC1 <- ltlcc$BAMLCC05V2_label1[match(SS03$LCC05_PT, ltlcc$lcc05v1_2)] SS03$HAB_LCC2 <- ltlcc$BAMLCC05V2_label2[match(SS03$LCC05_PT, ltlcc$lcc05v1_2)] SS03$HAB_LCC1 <- relevel(SS03$HAB_LCC1, "ConifDense") SS03$HAB_LCC2 <- relevel(SS03$HAB_LCC2, "Conif") if (FALSE) { ## for Nicole SS_Nicole <- data.frame( PCODE=SS01$PCODE, SS=SS01$SS, X=SS01$X_GEONAD83, Y=SS01$Y_GEONAD83, JURS=SS01$JURSALPHA, COUNTRY=SS01$COUNTRY, BCR=as.factor(SS01$BCR), SS03[,c("LCC05","HAB_LCC1","HAB_LCC2")]) ## Reclass EOSD lteosd <- read.csv("~/repos/bamanalytics/lookup/eosd.csv") levels(SS03$EOSD_PT) <- sub(",", "", levels(SS03$EOSD_PT)) SS03$EOSD_PT <- as.integer(as.character(SS03$EOSD_PT)) SS03$EOSD_PT[SS03$EOSD_PT < 1] <- NA SS03$HAB_EOSD1 <- lteosd$Reclass_label1[match(SS03$EOSD_PT, lteosd$Value)] SS03$HAB_EOSD2 <- lteosd$Reclass_label2[match(SS03$EOSD_PT, lteosd$Value)] SS03$HAB_EOSD1 <- relevel(SS03$HAB_EOSD1, "ConifDense") SS03$HAB_EOSD2 <- relevel(SS03$HAB_EOSD2, "Conif") } ## Reclass NALCMS ltnalc <- read.csv("~/repos/bamanalytics/lookup/nalcms.csv") SS03$HAB_NALC2 <- ltnalc$Label[match(SS03$NALCMS_PT, ltnalc$Value)] tmp <- as.character(interaction(SS03$HAB_NALC2, SS02$TREE3, sep="", drop=TRUE)) SS03$HAB_NALC1 <- as.character(SS03$HAB_NALC2) ii <- SS03$HAB_NALC1 %in% c("Conif", "Decid", "Mixed", "Wet") SS03$HAB_NALC1[ii] <- tmp[ii] SS03$HAB_NALC1 <- as.factor(SS03$HAB_NALC1) SS03$HAB_NALC1 <- relevel(SS03$HAB_NALC1, "ConifDense") SS03$HAB_NALC2 <- relevel(SS03$HAB_NALC2, "Conif") ## NALC is used for QPADv3 if (FALSE) { ## LCC for offsets SS03$LCC_OFF1 <- as.factor(ltlcc$qpad_num[match(SS03$LCC05_PT, ltlcc$lcc05v1_2)]) SS03$LCC_OFF2 <- factor(5, 1:5) SS03$LCC_OFF2[is.na(SS03$HAB_NALC1)] <- NA SS03$LCC_OFF2[SS03$HAB_NALC1 %in% c("DecidSparse")] <- "4" SS03$LCC_OFF2[SS03$HAB_NALC1 %in% c("ConifSparse","MixedSparse")] <- "3" SS03$LCC_OFF2[SS03$HAB_NALC1 %in% c("DecidDense")] <- "2" SS03$LCC_OFF2[SS03$HAB_NALC1 %in% c("ConifDense","MixedDense")] <- "1" SS03$LCC_combo <- SS03$LCC_OFF1 SS03$LCC_combo[is.na(SS03$LCC_OFF1)] <- SS03$LCC_OFF2[is.na(SS03$LCC_OFF1)] } ## Grid ID 4x4 km SS_grid <- read.csv(file.path(ROOT, "BAMBBS_Gridcode.csv")) rownames(SS_grid) <- SS_grid$SS compare_sets(rownames(SS01), rownames(SS_grid)) SS_grid <- SS_grid[rownames(SS01),"gridcode",drop=FALSE] levels(SS_grid$gridcode) <- gsub(",", "", levels(SS_grid$gridcode)) ## Road: dist to, class, #lanes, surface SS_road <- sqlFetch(con, "dbo_BAMBBS_2015_NearDistanceRoadJoin1000M") rownames(SS_road) <- SS_road$SS compare_sets(rownames(SS01), rownames(SS_road)) SS_road <- SS_road[rownames(SS01),] SS_road$d2road <- SS_road[["Distance to Road"]] table(SS_road$ROADCLASS, SS_road$d2road > 0, useNA="a") table(SS_road$NBRLANES, SS_road$d2road > 0, useNA="a") table(SS_road$PAVSTATUS, SS_road$d2road > 0, useNA="a") table(SS_road$ROADCLASS, SS_road$NBRLANES, useNA="a") SS_road <- SS_road[,c("d2road","ROADCLASS","NBRLANES","PAVSTATUS")] ## need to exclude # lanes >2 ## Fire: year, size SS_fire <- sqlFetch(con, "dbo_BBSBAM_2015_FIRE") rownames(SS_fire) <- SS_fire$SS compare_sets(rownames(SS01), rownames(SS_fire)) SS_fire <- SS_fire[rownames(SS01),] SS_fire <- SS_fire[,c("Year","SIZE_HA")] colnames(SS_fire) <- c("YearFire","FIRE_HA") ## Terrain: slope, twi, elev SS_terr <- sqlFetch(con, "dbo_BBSBAM_2015_TERRAIN90") rownames(SS_terr) <- SS_terr$SS compare_sets(rownames(SS01), rownames(SS_terr)) SS_terr <- SS_terr[rownames(SS01),] t(table(is.na(SS_terr$cti90), SS01$PCODE)) # mostly affects BBS in some states SS_terr <- SS_terr[,c("slp90","cti90","elv90")] ## Climate variables from DS and NALCMS 4x4 level SS_clim <- sqlFetch(con, "dbo_BBSBAM_2015__CLIMLU") rownames(SS_clim) <- SS_clim$SS compare_sets(rownames(SS01), rownames(SS_clim)) SS_clim <- SS_clim[rownames(SS01),] tmp <- as.matrix(SS_clim[,grepl("NALCMS05_", colnames(SS_clim))]) SS_clim <- SS_clim[,!grepl("NALCMS05_", colnames(SS_clim))] colnames(tmp) <- gsub("NALCMS05_", "", colnames(tmp)) Col <- as.character(ltnalc$Label)[match(colnames(tmp), as.character(ltnalc$Value))] Col[is.na(Col)] <- "Water" ## 4x4 stuff is not done #SS_NALC4x4 <- data.frame(groupSums(tmp, 2, Col, na.rm=TRUE)) #colnames(SS_NALC4x4) <- paste0("GRID4_NALC_", colnames(SS_NALC4x4)) SS_clim$NALCMS05 <- NULL SS_clim$PCODE <- NULL SS_clim$SS <- NULL ## 4x4 stuff is not done if (FALSE) { ## LCC05 4x4 level SS_LCC4x4 <- sqlFetch(con, "dbo_BBSBAM_V4_LCC05CND_4X4SUMM") SS_LCC4x4 <- nonDuplicated(SS_LCC4x4, SS, TRUE) #rownames(SS_LCC4x4) <- SS_LCC4x4$SS compare_sets(rownames(SS01), rownames(SS_LCC4x4)) SS_LCC4x4 <- SS_LCC4x4[rownames(SS01),] SS_LCC4x4$SS <- NULL SS_LCC4x4$gridcode <- NULL SS_LCC4x4$LCCVVSUM <- NULL SS_LCC4x4 <- as.matrix(SS_LCC4x4) colnames(SS_LCC4x4) <- gsub("LCCVV", "", colnames(SS_LCC4x4)) #Col <- as.character(ltlcc$BAMLCC05V2_label1)[match(colnames(SS_LCC4x4), # as.character(ltlcc$lcc05v1_2))] Col <- as.character(ltlcc$BAMLCC05V2_label2)[match(colnames(SS_LCC4x4), as.character(ltlcc$lcc05v1_2))] Col[is.na(Col)] <- "BARREN" SS_LCC4x4 <- data.frame(groupSums(SS_LCC4x4, 2, Col, na.rm=TRUE)) SS_LCC4x4[is.na(SS03$HAB_LCC2),] <- NA colnames(SS_LCC4x4) <- paste0("GRID4_LCC_", colnames(SS_LCC4x4)) ## EOSD 4x4 level SS_EOSD4x4 <- sqlFetch(con, "dbo_BBSBAM_V4_EOSD_4X4SUMM") SS_EOSD4x4$upsize_ts <- NULL rownames(SS_EOSD4x4) <- SS_EOSD4x4$SS compare_sets(rownames(SS01), rownames(SS_EOSD4x4)) SS_EOSD4x4 <- SS_EOSD4x4[match(rownames(SS01), rownames(SS_EOSD4x4)),] rownames(SS_EOSD4x4) <- SS01$SS SS_EOSD4x4 <- as.matrix(SS_EOSD4x4[,grepl("eosdVV", colnames(SS_EOSD4x4))]) colnames(SS_EOSD4x4) <- gsub("eosdVV", "", colnames(SS_EOSD4x4)) #Col <- as.character(lteosd$Reclass_label1)[match(colnames(SS_EOSD4x4), # as.character(lteosd$Value))] Col <- as.character(lteosd$Reclass_label2)[match(colnames(SS_EOSD4x4), as.character(lteosd$Value))] Col[is.na(Col)] <- "BARREN" SS_EOSD4x4 <- data.frame(groupSums(SS_EOSD4x4, 2, Col, na.rm=TRUE)) SS_EOSD4x4[is.na(SS03$HAB_EOSD2),] <- NA colnames(SS_EOSD4x4) <- paste0("GRID4_EOSD_", colnames(SS_EOSD4x4)) } ## HEIGHT (Simard) SS_height <- sqlFetch(con, "dbo_Height") SS_height <- nonDuplicated(SS_height, SS, TRUE) compare_sets(rownames(SS01), rownames(SS_height)) SS_height <- SS_height[rownames(SS01),] SS_height <- SS_height[,"HEIGHTSIMARD",drop=FALSE] if (FALSE) { ## Nature Serve range: 3 spp (Can clipped range used 0/1) SS_nserv <- sqlFetch(con, "dbo_BBSBAM_SARSPPLOCATIONSRange") SS_nserv <- nonDuplicated(SS_nserv, SS, TRUE) compare_sets(rownames(SS01), rownames(SS_nserv)) SS_nserv <- SS_nserv[rownames(SS01),] SS_nserv <- SS_nserv[,c("CAWAINOUT","OSFLINOUT","CONIINOUT")] } ## GFW yearly loss intersections and 1st year of loss #SS_gfw <- sqlFetch(con, "dbo_BAMBBS_GFWLossYear") SS_gfw <- read.csv(file.path(ROOT, "GFWLossYear.csv")) SS_gfw <- nonDuplicated(SS_gfw, SS, TRUE) compare_sets(rownames(SS01), rownames(SS_gfw)) levels(SS_gfw$YearLoss) <- gsub(",", "", levels(SS_gfw$YearLoss)) SS_gfw$YearLoss <- as.integer(as.character(SS_gfw$YearLoss)) SS_gfw <- SS_gfw[rownames(SS01),] SS_gfw <- SS_gfw[,"YearLoss",drop=FALSE] ## Pasher disturbance SS_pash <- read.csv(file.path(ROOT, "bambbs2015beadandpasher.csv")) SS_pash <- nonDuplicated(SS_pash, SS, TRUE) compare_sets(rownames(SS01), rownames(SS_pash)) SS_pash <- SS_pash[rownames(SS01),] SS_pash <- SS_pash[,c("BEADTotalL","BEADtotPol")] ## Local spring SS_sprng <- read.csv("e:/peter/bam/May2015/NRCAN_SG_001_BAMBBS2015_71_13.csv") SS_sprng <- SS_sprng[,c("SS","RASTERVALU")] SS_sprng$SPRNG <- SS_sprng$RASTERVALU levels(SS_sprng$SPRNG) <- gsub(",", "", levels(SS_sprng$SPRNG)) SS_sprng$SPRNG <- as.numeric(as.character(SS_sprng$SPRNG)) SS_sprng$SPRNG[SS_sprng$SPRNG < 0] <- NA rownames(SS_sprng) <- SS_sprng$SS SS_sprng <- SS_sprng[rownames(SS01),] ## Put together the main SS level object SS <- data.frame( PCODE=SS01$PCODE, SS=SS01$SS, X=SS01$X_GEONAD83, Y=SS01$Y_GEONAD83, Xcl=SS01$X_CLCC, Ycl=SS01$Y_CLCC, JURS=SS01$JURSALPHA, COUNTRY=SS01$COUNTRY, TZONE=SS01$TZONE_CODE, BOREALLOC=SS01$BOREALLOC, BCR=as.factor(SS01$BCR), TREE=SS02$TREE, TREE3=SS02$TREE3, SPRNG=SS_sprng$SPRNG, #LCC05_PT=SS03$LCC05_PT, # -- FOR NICOLE SS03[,c("LCC05","HAB_LCC1","HAB_LCC2")], SS03[,c("HAB_NALC2", "HAB_NALC1")], SS_grid, #SS_nserv, SS_road, SS_terr, SS_fire, SS_clim, SS_pash, SS_gfw, SS_height) #SS_NALC4x4, #SS_LCC4x4, #SS_EOSD4x4) #### Project summary table ## This table needed local tweaks to be operable #PCODE <- sqlFetch(con, "dbo_National_Proj_Summary_V4_2015") #PCODE$SSMA_TimeStamp <- NULL PCODE <- read.csv(file.path(ROOT,"proj.csv")) levels(PCODE$Maxdist) <- tolower(levels(PCODE$Maxdist)) levels(PCODE$Maxdist)[levels(PCODE$Maxdist)=="unlimited"] <- "Inf" PCODE$Maxdist[PCODE$Maxdist=="unknown"] <- NA PCODE$Maxdist <- droplevels(PCODE$Maxdist) PCODE$Maxdist <- as.numeric(as.character(PCODE$Maxdist)) PCODE$Maxdur <- pmin(PCODE$MaxDuration, 10) #### Survey level fields ## Pull in PKEY tables pkbam <- sqlFetch(con, "dbo_National_PKEY_V4_2015") pkbam$SSMA_TimeStamp <- NULL #pkbbs <- sqlFetch(con, "dbo_PKEY_BBS_V3_2015") pkbbs <- sqlFetch(con2, "PKEY_BBS_V4_2016") ## What columns to retain PKCOLS <- c("PKEY","SS","PCODE","METHOD","SITE","STN","ROUND", "YEAR","MONTH","DAY","HOUR","MIN","PART","MAXDUR","MAXDIS") colnames(pkbam) <- toupper(colnames(pkbam)) pkbam$MAXDUR <- PCODE$Maxdur[match(pkbam$METHOD, PCODE$Method)] pkbam$MAXDIS <- PCODE$Maxdist[match(pkbam$METHOD, PCODE$Method)] pkbam$PART <- 1L pkbam$MONTH <- pkbam$MM pkbam$DAY <- pkbam$DD pkbam$HOUR <- pkbam$HR pkbam$YEAR <- pkbam$YYYY levels(pkbam$ROUND) <- sub("[[:alpha:]]+$", "", levels(pkbam$ROUND)) pkbam$ROUND <- as.integer(as.character(pkbam$ROUND)) pkbam <- pkbam[,PKCOLS] colnames(pkbbs) <- toupper(colnames(pkbbs)) pkbbs$MAXDUR <- 3 pkbbs$MAXDIS <- Inf pkbbs$PART <- 2L pkbbs$METHOD <- as.factor("BBS:9999") pkbbs$SITE <- as.factor(pkbbs$SITE) pkbbs$YEAR <- pkbbs$YYYY pkbbs$MONTH <- pkbbs$MM pkbbs$DAY <- pkbbs$DD pkbbs$HOUR <- pkbbs$HR pkbbs <- pkbbs[,PKCOLS] PKEY <- rbind(pkbam, pkbbs) #rm(pkbam, pkbbs) #gc() ## Map `METHOD` field from project summary table onto `PKEY$METHOD` ## so that duration and distance method can be carried forward to ## point count table levels(PCODE$Method)[levels(PCODE$Method) == "QCAtlas:118"] <- "QCATLAS:118" compare_sets(PCODE$Method, PKEY$METHOD) setdiff(PKEY$METHOD, PCODE$Method) setdiff(PCODE$Method, PKEY$METHOD) PKEY$DURMETH <- PCODE$DURMETH[match(PKEY$METHOD, PCODE$Method)] PKEY$DISMETH <- PCODE$DISTMETH[match(PKEY$METHOD, PCODE$Method)] ## Identifying roadside surveys PKEY$ROAD <- 0L treat.as.bbs <- c("HOBBS","CF","MNBBA", levels(pkbbs$PCODE)) PKEY$ROAD[PKEY$PCODE %in% treat.as.bbs] <- 1L #### Offset specific variables ## Date/time components PKEY$MIN[is.na(PKEY$MIN)] <- 0 # min that critical, is not -- said Yoda MM <- ifelse(PKEY$MONTH < 10, paste0("0", PKEY$MONTH), as.character(PKEY$MONTH)) HH <- ifelse(PKEY$HOUR < 10, paste0("0", PKEY$HOUR), as.character(PKEY$HOUR)) mm <- ifelse(PKEY$MIN < 10, paste0("0", PKEY$MIN), as.character(PKEY$MIN)) #mm[is.na(mm) & !is.na(HH)] <- "00" DD <- with(PKEY, paste0(YEAR, "-", MM, "-", DAY, " ", HH, ":", mm, ":00")) DD <- strptime(DD, "%Y-%m-%e %H:%M:%S") PKEY$DATE <- DD ## Julian day PKEY$JULIAN <- DD$yday # this is kept as original PKEY$JDAY <- DD$yday / 365 summary(PKEY$JDAY) ## prevent too far extrapolation PKEY$JDAY[PKEY$JDAY < 0.35 | PKEY$JDAY > 0.55] <- NA ## TSSR = time since sunrise Coor <- as.matrix(cbind(as.numeric(SS$X),as.numeric(SS$Y)))[match(PKEY$SS, rownames(SS)),] JL <- as.POSIXct(DD) subset <- rowSums(is.na(Coor))==0 & !is.na(JL) sr <- sunriset(Coor[subset,], JL[subset], direction="sunrise", POSIXct.out=FALSE) * 24 PKEY$srise <- NA PKEY$srise[subset] <- sr PKEY$start_time <- PKEY$HOUR + PKEY$MIN/60 TZ <- SS$TZONE[match(PKEY$SS, rownames(SS))] lttz <- read.csv("~/repos/bamanalytics//lookup/tzone.csv") lttz <- nonDuplicated(lttz, Timezone, TRUE) PKEY$MDT_offset <- lttz$MDT_offset[match(TZ, rownames(lttz))] table(TZ, PKEY$MDT_offset) PKEY$TSSR <- (PKEY$start_time - PKEY$srise + PKEY$MDT_offset) / 24 PKEY$TSSR_orig <- PKEY$TSSR # keep a full copy PKEY$TSSR[PKEY$start_time > 12] <- NA ## after noon summary(PKEY$TSSR) summary(PKEY$start_time) PKEY <- PKEY[PKEY$DURMETH != "J",] # unknown duration PKEY <- PKEY[PKEY$DISMETH != "O",] # unknown distance PKEY <- droplevels(PKEY) ## QC Atlas problem #with(PKEY, table(PCODE, is.na(MAXDUR))) #with(PKEY, table(PCODE, is.na(MAXDIS))) #### Point count tables and methodology ## Some of these tables are also tweaked locally (see below) BEH <- sqlFetch(con, "dbo_DD_DescripBEH") #DISINT <- sqlFetch(con, "dbo_DD_DescripDistance") #DISINT$SSMA_TimeStamp <- NULL #DURINT <- sqlFetch(con, "dbo_DD_DescripPeriod") DISMET <- sqlFetch(con, "dbo_DD_distance_codes_methodology") DURMET <- sqlFetch(con, "dbo_DD_duration_codes_methodology") ## Trailing whitespace removed from factor levels levels(DISMET$DISTANCECODE) <- gsub(" *$", "", levels(DISMET$DISTANCECODE)) levels(DURMET$DURATIONCODE) <- gsub(" *$", "", levels(DURMET$DURATIONCODE)) #### Point count tables pcbam <- sqlFetch(con, "dbo_National_PtCount_V4_2015") pcbam$SSMA_TimeStamp <- NULL pcbbs <- sqlFetch(con2, "POINTCOUNT_BBS_V4_2016") colnames(pcbbs) <- toupper(colnames(pcbbs)) colnames(pcbbs)[colnames(pcbbs) == "SPECIES_ID"] <- "SPECIES" colnames(pcbbs)[colnames(pcbbs) == "PERIOD"] <- "DURATION" pcbbs$PCODE <- "BBS" ## Columns to keep pccols <- c("PCODE","SS","PKEY","DURATION","DISTANCE", "SPECIES","ABUND","BEH") ## Close the database connections close(con) close(con2) ## Duration and distance intervals (locally tweaked) DURINT <- read.csv("~/repos/bamanalytics/lookup/durint.csv") DISINT <- read.csv("~/repos/bamanalytics/lookup/disint.csv") DURINT$dur <- paste0(DURINT$Dur_Start, "-", DURINT$DUR_end) DISINT$dis <- paste0(DISINT$DIST_START, "-", DISINT$DIST_END) rownames(DURINT) <- DURINT[,1] rownames(DISINT) <- DISINT[,1] ## Combined point count table PCTBL <- rbind(pcbam[,pccols], pcbbs[,pccols]) ## Mapping duration and distance intervals PCTBL$dur <- as.factor(DURINT$dur[match(PCTBL$DURATION, rownames(DURINT))]) PCTBL$dis <- as.factor(DISINT$dis[match(PCTBL$DISTANCE, rownames(DISINT))]) ## Methodology PCTBL$DISMETH <- droplevels(PKEY$DISMETH[match(PCTBL$PKEY, PKEY$PKEY)]) PCTBL$DURMETH <- droplevels(PKEY$DURMETH[match(PCTBL$PKEY, PKEY$PKEY)]) ## Filtering surveys (need to exclude PKEY) keeppkey <- rep(TRUE, nrow(PCTBL)) ## 11=0-20 ## 8=unk keeppkey[PCTBL$DURATION %in% c(11,8)] <- FALSE ## Excluding unknown distance bands keeppkey[PCTBL$DISTANCE %in% c(4,5,9)] <- FALSE ## Excluding unknown duration methodology keeppkey[PCTBL$DURMETH == "J"] <- FALSE ## Excluding unknown distance methodology keeppkey[PCTBL$DISMETH == "O"] <- FALSE ## Actual filtering -- but dropping PKEYs PCTBL <- droplevels(PCTBL[keeppkey,]) ## Filtering within survey (do not exclude PKEY) ## Filtering behaviour #sort(100 * table(PCTBL$BEH) / sum(table(PCTBL$BEH))) ## 1=Heard ## 11=no birds observed at station - added 2011 ## 6=seen and heard ## Excluding non-aerial detections table(PCTBL$BEH, PCTBL$PCODE=="BBS") keep <- rep(TRUE, nrow(PCTBL)) keep[!(PCTBL$BEH %in% c("1","6","11"))] <- FALSE ## this is fake, but there is no other option until a fix #keep[is.na(PCTBL$BEH)] <- TRUE # dont know what this in -- FIXED in BBS_V4 ## Excluding >10 min intervals ## 10=10-20 ## 3=before or after ## 9=10-15 keep[PCTBL$DURATION %in% c(10,3,9)] <- FALSE ## Excluding NA values keep[is.na(PCTBL$dur)] <- FALSE keep[is.na(PCTBL$dis)] <- FALSE keep[is.na(PCTBL$ABUND)] <- FALSE ## Actual filtering -- but keeping PKEYs (do not drop levels) #PCTBL$keep <- keep PCTBL <- PCTBL[keep,] ## Excluding/dropping species PCTBL$SPECIES <- droplevels(PCTBL$SPECIES) levels(PCTBL$SPECIES) <- toupper(levels(PCTBL$SPECIES)) compare_sets(PCTBL$SPECIES, TAX$Species_ID) setdiff(PCTBL$SPECIES, TAX$Species_ID) levels(TAX$Species_ID)[levels(TAX$Species_ID) == "YWAR"] <- "YEWA" levels(TAX$Species_ID)[levels(TAX$Species_ID) == "SCJU"] <- "DEJU" # change SCJU to DEJU levels(TAX$Species_ID)[levels(TAX$Species_ID) == "MYWA"] <- "YRWA" # change MYWA to YRWA levels(TAX$Species_ID)[levels(TAX$Species_ID) == "COSN"] <- "WISN" # change COSN to WISN levels(PCTBL$SPECIES)[levels(PCTBL$SPECIES) == "YWAR"] <- "YEWA" levels(PCTBL$SPECIES)[levels(PCTBL$SPECIES) == "SCJU"] <- "DEJU" # change SCJU to DEJU levels(PCTBL$SPECIES)[levels(PCTBL$SPECIES) == "MYWA"] <- "YRWA" # change MYWA to YRWA levels(PCTBL$SPECIES)[levels(PCTBL$SPECIES) == "COSN"] <- "WISN" # change COSN to WISN PCTBL$SPECIES <- droplevels(PCTBL$SPECIES) setdiff(PCTBL$SPECIES, TAX$Species_ID) PCTBL$SPECIES_ALL <- PCTBL$SPECIES sspp <- read.csv("~/repos/bamanalytics/lookup/singing-species.csv") levels(PCTBL$SPECIES)[!(levels(PCTBL$SPECIES) %in% sspp$Species_ID[sspp$Singing_birds])] <- "NONE" ## Excluding columns PCTBL$DURATION <- NULL PCTBL$DISTANCE <- NULL PCTBL$BEH <- NULL PCTBL$dur <- droplevels(PCTBL$dur) PCTBL$dis <- droplevels(PCTBL$dis) compare_sets(SS$SS, PKEY$SS) compare_sets(SS$SS, PCTBL$SS) compare_sets(PKEY$PKEY, PCTBL$PKEY) save(SS, PKEY, PCTBL, TAX, file=file.path(ROOT2, "out", #paste0("data_package_2016-04-18.Rdata"))) # paste0("data_package_2016-07-05.Rdata"))) paste0("data_package_2016-12-01.Rdata")))

7912041bec3dd5b4fc230887ea4aaae6f414b69a

011e1a0d512282aca1397335d1be5db1ae75de5b

/somPlot/plotUMatrix.R

22a7580707e88b9f108292f829330144d1c2911e

[]

no_license

spmunc/TrumpNN

6df78a3bda1e08e5d542b2c236f803d7777caf5c

33b3c3f7ea66d0e478ea10b0509ca834c0c94665

refs/heads/master

2021-01-21T13:14:20.872935

2016-04-23T20:53:18

54,686,068

2

0

null

UTF-8

R

false

2,670

r

plotUMatrix.R

########################################## ##PLOT HEXAGONAL U-MATRIX ##from "kohnonen" library output ## ## ##BY SETH E. SPIELMAN, UNIVERSITY OF COLORADO ## ##NEEDS SOME LOVE. ##RUDIMENTRARY BUT FUNCTIONAL ## ##BORROWS CODE FROM ##http://nbremer.blogspot.nl/2013/11/how-to-create-hexagonal-heatmap-in-r.html ################################################ library(RColorBrewer) #to use brewer.pal library(fields) #to use designer.colors library(network) library(deldir) plotUmat <- function(som_obj, type="Equal Interval"){ if (som_obj$grid$topo != "hexagonal"){ stop("function assumes hexgonal SOM") } #CALCULATE U-MATRIX #Delaunay Triangulation to form network of neurons d <- deldir(x=som_obj$grid$pts[,1], y=aSom$grid$pts[,2]) #Build network n <- network(x=unique(d$delsgs[,5:6]), directed=FALSE, matrix.type="edgelist") #calculate u-matrix: the average eudlidean distance between each vertex and its neighbors neigh.dists <- NA for(vert in network.vertex.names(n)){ neighs <- get.neighborhood(x=n, v=vert) neigh.dists[vert] <- (sum(dist(aSom$codes[c(vert, neighs),][,1]))/length(neighs)) } #Function to create the polygon for each hexagon Hexagon <- function (x, y, unitcell = 1, col = "grey", border=NA) { polygon(c(x, x, x + unitcell/2, x + unitcell, x + unitcell, x + unitcell/2), c(y + unitcell * 0.125, y + unitcell * 0.875, y + unitcell * 1.125, y + unitcell * 0.875, y + unitcell * 0.125, y - unitcell * 0.125), col = col, border=border) } plot(0, 0, type = "n", axes = FALSE, xlim=c(0, som_obj$grid$xdim), ylim=c(0, som_obj$grid$ydim), xlab="", ylab= "", asp=1, main="U-Matrix") ColRamp <- rev(designer.colors(n=9, col=brewer.pal(9, "Spectral"))) #color code for each neuron ColorCode <- rep("#FFFFFF", length(neigh.dists)) #default is all white if(type == "Equal Interval") { #Equal interval bins Bins <- seq(min(neigh.dists), max(neigh.dists), length=length(ColRamp)) } if(type == "Quantile") { #Quantile colorbins Bins <- quantile(x=neigh.dists, probs=cumsum(rep(1/length(ColRamp), length(ColRamp)))) } for (i in 1:length(neigh.dists)) if (!is.na(neigh.dists[i])) ColorCode[i] <- ColRamp[which.min(abs(Bins-neigh.dists[i]))] offset <- 0.5 #offset for the hexagons when moving up a row ind <- 1 for (row in 1:som_obj$grid$ydim) { for (column in 0:(som_obj$grid$xdim - 1)){ Hexagon(column + offset, row - 1, col = ColorCode[ind]) ind <- ind +1} offset <- ifelse(offset, 0, 0.5) } }

2838e0a3b514ff1140270e92d73aba62c5ad793b

78255ea5630895b338fd588752b66a2e8a157702

/R/getters.R

069ec70029886abddc749d8a1276a9e8a7b90cd8

[]

permissive

Illumina/happyR

d2164760d2bb5b30b01c3464f0ecea203baeef3c

97d093a8b00d6f631e76cfac3411c29db0cd7044

refs/heads/master

2021-01-18T09:55:50.592615

2019-07-12T12:02:04

100,359,406

16

2

BSD-3-Clause

2019-07-12T12:02:05

2017-08-15T09:01:57

R

UTF-8

R

false

7,052

r

getters.R

#' Extract hap.py Precision-Recall data #' #' Simpler interface to retrieve a data.frame #' of PR metrics from a happy_result object. #' #' @param happy_result a happy result loaded #' via \code{\link[happyR]{read_happy}} #' @param var_type subset for either insertions #' and deletions \code{"indel"}, SNVs \code{"snv"} #' or keep both #' @param filter include all records (ALL), only #' passing (PASS) or with selective filters applied #' (SEL) #' @param subtype variant subtype of the form \code{[IDC]length_range}, #' e.g. \code{"D6_15"} is deletions of length \eqn{>=5} and \eqn{<=15} #' @param subset when run with stratification regions, the subset is #' the region ID. \code{"*"} for genome-wide PR data. See details. #' @param quietly suppress info messages #' #' @details #' #' \strong{Subsets}: hap.py v0.3.7+ writes subsets \code{TS_contained} and #' \code{TS_boundary} by default, corresponding to truth variants #' well contained or at the boundary of confident regions. In some #' truthsets, those in \code{TS_boundary} will show worse performance #' metrics due to issues with variant representation or a partial #' haplotype description. #' #' \strong{Subtypes}: Insertion subtypes are of the form: \code{[IDC]length_range} #' where the first letter indicates the variant classification: \code{I} insertion; #' \code{D} deletion; and \code{C} complex. Hap.py bins the lengths of these records #' into ranges by ALT allele length in basepairs: \code{1_5}, \code{6_15} and \code{16_PLUS}. #' #' @return a \code{data.frame} of Precision-Recall metrics for the #' selected subset #' #' @examples #' #' # figure out prefix from pkg install location #' happy_input <- system.file("extdata", "happy_demo.summary.csv", package = "happyR") #' happy_prefix <- sub(".summary.csv", "", happy_input) #' #' # load happy result #' hapdata <- read_happy(happy_prefix) #' #' # long deletion PR curve #' del_pr <- pr_data(hapdata, var_type = "indel", subtype = "D16_PLUS") #' #' @export pr_data <- function(happy_result, var_type = c("both", "snv", "indel"), filter = c("ALL", "PASS", "SEL"), subtype = c("*", "C16_PLUS", "C1_5", "C6_15", "D16_PLUS", "D1_5", "D6_15", "I16_PLUS", "I1_5", "I6_15"), subset = "*", quietly = TRUE) { if (class(happy_result) != "happy_result") { stop("Object must be a happy_result loaded via happyR, ", "not ", class(happy_result)) } filter <- match.arg(filter) var_type <- match.arg(var_type) if (!missing(subtype)) { subtype <- match.arg(subtype, several.ok = TRUE) } else { # pick first, i.e. '*' subtype <- match.arg(subtype) } # starting point: smallest possible PR file outdf <- if (filter == "ALL" | var_type == "both") { happy_result$pr_curve$all } else if (filter == "SEL") { if (var_type == "snv") { happy_result$pr_curve$SNP_SEL } else { happy_result$pr_curve$INDEL_SEL } } else { if (var_type == "indel") { happy_result$pr_curve$SNP_PASS } else { happy_result$pr_curve$INDEL_PASS } } if (!quietly){ message(nrow(outdf), " records loaded") } # filter var_type for all if (filter == "ALL" & var_type != "both") { if (var_type == "snv") { outdf <- outdf[outdf$Type != "INDEL",] } else { outdf <- outdf[outdf$Type == "INDEL",] } } outdf <- outdf[outdf$Subset %in% subset,] if (!nrow(outdf) > 0){ warning("No PR data found for subset: ", subset) } outdf <- outdf[outdf$Subtype %in% subtype & outdf$Filter %in% filter,] if (!quietly) { message("subset contains ", nrow(outdf), " records") } outdf } #' Extract tables from hap.py result lists #' #' Extract results tables from multiple hap.py result objects and combine #' into a single \code{data.frame}. Source information from each #' result is added as an additional column (\code{happy_prefix}). #' #' @param happy_result_list A \code{happy_result_list} object. #' @param table Table of data to extract from each result. #' \code{"summary"} or \code{"extended"} get top level tables; #' the \code{pr} options get Precision-Recall tables. #' #' @return a \code{data.frame} of combined tables from list #' #' @examples #' #' \dontrun{ #' samplesheet <- readr::read_csv("group_id,replicate_id,happy_prefix #' PCR-Free,NA12878-I30,NA12878-I30_S1 #' PCR-Free,NA12878-I33,NA12878-I33_S1 #' Nano,NA12878-R1,NA12878-R1_S1 #' Nano,NA12878-R2,NA12878-R2_S1 #' ") #' hap_samplesheet <- read_samplesheet_(samplesheet = samplesheet_df) #' #' # get collapsed summary table of high-level metrics #' summary_df <- extract_results(hap_samplesheet$results, table = "summary") #' unique(summary_df$happy_prefix) #' # [1] "/output/path/prefix" "/different/path/prefix" #' } #' #' @export extract_results <- function(happy_result_list, table = c("summary", "extended", "pr.all", "pr.indel.pass", "pr.indel.sel", "pr.indel.all", "pr.snp.pass", "pr.snp.sel", "pr.snp.all")) { # validate input if (!"happy_result_list" %in% class(happy_result_list)) { stop("Must provide a happy_result_list object.") } table <- match.arg(table) if (grepl("^pr\\.", table)) { if (grepl("all", table)) { if (table == "pr.all") { path <- "all" } else { # pr.indel.all -> INDEL path <- sub(".*?\\.([[:alpha:]]*?)\\..*$", "\\U\\1\\E", table, perl = TRUE) } } else { # reformat + convert to uppercase, e.g.: pr.snp.pass -> "SNP_PASS" path <- sub(".*?\\.([[:alpha:]]*?)\\.([[:alpha:]]*$)", "\\U\\1_\\2\\E", table, perl = TRUE) } item_list <- lapply(happy_result_list, function(d) { if (!exists(path, envir = d$pr_curve, inherits = FALSE)) { warning("missing pr data: ", path, " in R object from: ", attr(d, "happy_prefix"), " - skipping", call. = FALSE) return (NULL) } table_out <- d$pr_curve[[path]] if (is.null(table_out)) { warning("missing pr data: ", path, " in R object from: ", attr(d, "happy_prefix"), " - skipping", call. = FALSE) return (NULL) } table_out$happy_prefix <- attr(d, "happy_prefix") table_out }) } else { # not PR data, e.g. summary / extended item_list <- lapply(happy_result_list, function(d) { if (!table %in% names(d)) { stop("Could not find ", table, " in happy_result_list") } table_out <- d[[table]] table_out$happy_prefix <- attr(d, "happy_prefix") table_out }) } df <- dplyr::bind_rows(item_list) if (nrow(df) == 0) { stop("no results found for extraction") } # set class if (table == "summary") { class(df) <- c("happy_summary", class(df)) } if (table == "extended") { class(df) <- c("happy_extended", class(df)) } df }

17e8e22764312dd6ceafae11394e98a7f738a651

d06f4860f0815281085689b706a923740476b386

/_site/landing/code/newsletter/src/datacamp.R

42a137d349f8d9576f48e59a9c1c308a29ce3eb6

[ "Apache-2.0" ]

permissive

jacobgreen4477/withmakers.github.io

b94d3a9e6247e161328224761047ad3478f4e436

28d3b68572b195f9bc84a44f32d31228dd31a16b

refs/heads/master

2022-01-08T21:31:08.236198

2019-05-15T11:55:35

null

0

null

UTF-8

R

false

1,323

r

datacamp.R

# title : newsletter # author : Hyunseo Kim # depends : rvest, dplyr, stringr, data.table, R2HTML, NLP, openNLP # datacamp blog ---- # Get the time baseHTML <- get_baseHTML("https://www.datacamp.com/community/blog") Sys.sleep(3) # Get the title timeHTML <- get_time(baseHTML, '.jsx-566588255 a .date') # 일자 변수 변환: format에서 %B가 안되기 때문에 숫자로 변환 후 %m 이용 timeHTML <- gsub("st","",timeHTML) timeHTML <- gsub("nd","",timeHTML) timeHTML <- gsub("rd","",timeHTML) timeHTML <- gsub("th","",timeHTML) timeHTML <- Month2Num(timeHTML) timeHTML <- as.Date(timeHTML, format = '%m %d, %Y') # Get the title titleHTML <- get_title(baseHTML, 'h2') # Get the url urlHTML <- get_url(baseHTML, 'h2 a', 'href') url_list <- c() for (i in urlHTML){ tmp <- paste0("https://www.datacamp.com",i) url_list <- c(url_list, tmp) } # make data frame datacamp <- data.frame( site = "datacamp", date = as.Date(timeHTML), headline = as.character(titleHTML), url_address = as.character(url_list)) datacamp <- as.data.table(unique(datacamp)) # Get the valid information datacamp <- datacamp[from < datacamp$date & to >= datacamp$date, ] # Save .csv & .html after collecting the data from valid url(different condition among sites) datacamp <- mksave_data(datacamp, "datacamp", 2, 0) gc()

b5957db01a9f93fa1bbfce5341c0d1b395e3d765

8c26f6153cbaec6957389cd7e659def3c468e10e

/week37-friends.R

a06e735dc5f823fff116de46b815209263efaac9

[]

no_license

TrevorKMDay/TidyTuesday

59be20bf67772617b73b470caed0bdacd7761310

93ff776ba797f1073f11fde26ed8169939a68121

refs/heads/master

2022-12-21T21:05:23.980437

2020-09-22T14:31:44

297,407,134

0

null

UTF-8

R

false

2,356

r

week37-friends.R

library(tidyverse) library(corrplot) library(viridis) # Load data and separate into semantically named dfs friends <- tidytuesdayR::tt_load('2020-09-08') friends_info <- friends[[1]] friends_dirs <- friends[[2]] friends_emot <- friends[[3]] # Join emotion info with who said it lines_emot <- right_join(friends_info, friends_emot) # Number of lines by emotion ggplot(lines_emot, aes(x = emotion)) + geom_histogram(stat = "count") # Identify which characters had more than 50 lines (so we don't get weird # proportions) lines_per_character <- table(lines_emot$speaker) gt_50_lines <- names(lines_per_character[lines_per_character > 50]) # Filter down to those people char_emot <- lines_emot %>% filter(speaker %in% gt_50_lines) # Now tabulate, and remove "#ALL#" ce_table <- table(char_emot$speaker, char_emot$emotion) %>% as.data.frame(col.names = c("speaker", "emotion", "freq")) %>% filter(Var1 != "#ALL#") %>% pivot_wider(id_cols = "Var1", names_from = "Var2", values_from = "Freq", names_prefix = "emotion_") %>% rename(speaker = Var1) # Calculate total lines, removing Neutral because it skews total_lines <- ce_table %>% select(starts_with("emotion_"), -emotion_Neutral) %>% rowSums() # Lines by percent of speakers' total lines with that emotion ce_table_pct <- ce_table %>% mutate_at(vars(starts_with("emotion_")), function(x) x / total_lines) %>% select(-emotion_Neutral) # Scaled so we can see who stands out ce_table_pct_z <- ce_table_pct %>% mutate_at(vars(starts_with("emotion_")), scale) # Pivot longer for plotting ce_pct_long <- ce_table_pct %>% pivot_longer(-speaker) ggplot(ce_pct_long, aes(x = name, y = speaker, fill = value)) + geom_tile() + scale_fill_viridis() + scale_x_discrete(labels = unique(ce_pct_long$name) %>% gsub("emotion_", "", .)) + labs(x = "Emotion", y = "Character", fill = "Proportion", title = "Characters' lines by emotion") ce_z_long <- ce_table_pct_z %>% pivot_longer(-speaker) ggplot(ce_z_long, aes(x = name, y = speaker, fill = value)) + geom_tile() + scale_x_discrete(labels = unique(ce_pct_long$name) %>% gsub("emotion_", "", .)) + scale_fill_viridis(option = "magma") + labs(x = "Emotion", y = "Character", fill = "Z score", title = "Characters' lines by unique emotions")

c721b1bbd304b3289b143693000fdb8e957bf071

1a03b5b0240dae14b110848d33f7da34245903db

/man/ml_predict.Rd

9796128b1f6e2536820a7680d989f30b4308e500

[]

no_license

skranz/mlogitExtras

1c3dfacb04b870243c78d0d5796b3919fdb256bf

62bb14fed7a1302888d699be9ea489ed8ab32746

refs/heads/master

2023-07-08T14:40:47.504079

2023-06-26T06:47:31

269,279,209

0

null

UTF-8

R

false

true

1,179

rd

ml_predict.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predict.R \name{ml_predict} \alias{ml_predict} \title{Alternative predict function for mlogit objects} \usage{ ml_predict(mod, newdata, num.draws = 1000, use.halton = TRUE) } \arguments{ \item{mod}{An estimated mlogit model} \item{newdata}{A data set for the prediction in long format. The data set should have a column alt the indexed the alternative. The data set must be ordered first by choice situation and each choice situation must have the same number of alternatives in the same order. You may simply add a column chid specifying choice situation then arrange by chid, alt.} \item{num.draws}{Number of simulated consumers to compute market shares (relevant for mixed logit models only)} \item{use.halton}{Should Halton sequences instead of pseudo-random numbers be used to simulate consumers (default TRUE)} } \value{ A matrix of predicted choice probabilities with one row per choice situation and one column for each alternative. Each row sums up to 1. } \description{ Currently only works for models without alternative specific constants or alternative specific interaction effects. }

f76418c682f03ea46888f1b2c8b15366708a6cae

06888de22ecff4d48a621c778aa35b18a28c32f1

/R/bedtools.R

b5b61112d854b1b8bc7134f3603e308cd92c25c9

[ "MIT" ]

permissive

joelnitta/baitfindR

fcb9db8bc978a6004e988003a5b1fb4675f84162

de73d9451115ad143da249e5ef2146bb929cd17b

refs/heads/master

2021-07-20T14:16:15.081289

2020-04-29T20:04:47

140,532,881

2

0

null

UTF-8

R

false

12,128

r

bedtools.R

# bedtools functions ------------------------------------------------------ #' Extract regions from a fasta file #' #' Wrapper for bedtools getfasta. #' #' @param bed_file Path to bed file with locations of regions to extract. #' bed file is a tab-separated file with columns for chromosome (e.g., chr1), #' start position (e.g., 1), and end position (e.g., 10), in that order. #' No column headers are used. #' @param fasta_file Path to file in fasta format to extract regions from. #' @param out_fasta_file Path to write extracted regions (in fasta format). #' @param ... Other arguments. Not used by this function, but meant to be used #' by \code{\link[drake]{drake_plan}} for tracking during workflows. #' @return List; output of processx::run(). Externally, a fasta file will be #' written to the path specified by `out_fasta_file`. #' @author Joel H Nitta, \email{joelnitta@@gmail.com} #' @examples #' \dontrun{ #' # First write gene, intron, and exon bed regions out as tsv files #' #' temp_dir <- tempdir() #' #' find_bed_regions( #' gff3_file = system.file("extdata", "Arabidopsis_thaliana_TAIR10_40_small.gff3", #' package = "baitfindR", mustWork = TRUE), #' source_select = "araport11", #' out_type = "write_all", #' out_dir = temp_dir, #' prefix = "arabidopsis" #' ) #' #' # Extract genes. #' extract_regions_from_fasta( #' bed_file = fs::path(temp_dir, "arabidopsis_introns"), #' fasta_file = system.file("extdata", "Arabidopsis_thaliana_TAIR10_40_small.fasta", #' package = "baitfindR", mustWork = TRUE), #' out_fasta_file = fs::path(temp_dir, "arabidopsis_gene_seqs.fasta") #' ) #' } #' @export extract_regions_from_fasta <- function (bed_file, fasta_file, out_fasta_file, ...) { # Check input assertthat::assert_that(assertthat::is.string(bed_file)) bed_file <- fs::path_abs(bed_file) assertthat::assert_that(assertthat::is.readable(bed_file)) assertthat::assert_that(assertthat::is.string(fasta_file)) fasta_file <- fs::path_abs(fasta_file) assertthat::assert_that(assertthat::is.readable(fasta_file)) assertthat::assert_that(assertthat::is.string(out_fasta_file)) out_fasta_file <- fs::path_abs(out_fasta_file) assertthat::assert_that(assertthat::is.dir(fs::path_dir(out_fasta_file))) bed <- readr::read_tsv(bed_file, col_names = c("chr", "start", "end"), col_types = "cdd") checkr::check_data( bed, values = list( chr = "a", start = 1, end = 1 ), order = TRUE) if (!(check_bed_genome_names(fasta_file, bed))) { stop ("Names don't match between bed file and fasta file headers") } # Run bedtools getfasta processx::run( command = "bedtools", args = c( "getfasta", "-fi", fasta_file, "-bed", bed_file, "-fo", out_fasta_file ), echo = TRUE ) } #' Mask regions in a fasta file. #' #' Wrapper for bedtools maskfasta. #' #' All regions of the `fasta_file` specified by the `bed_file` will be #' replaced ("hard-masked") with 'N's. #' #' The bed file is a tab-separated file with columns for chromosome (e.g., chr1), #' start position (e.g., 1), and end position (e.g., 10), in that order. #' No column headers are used. #' #' @param bed_file Path to bed file with locations of regions to mask. #' @param fasta_file Path to unmasked fasta file. #' @param out_fasta_file Path to write masked fasta file. #' @param ... Other arguments. Not used by this function, but meant to be used #' by \code{\link[drake]{drake_plan}} for tracking during workflows. #' @return List; output of processx::run(). Externally, a fasta file will be #' written to the path specified by `out_fasta_file`. #' @author Joel H Nitta, \email{joelnitta@@gmail.com} #' @examples #' \dontrun{ #' # First write genes, introns, and exons out as tsv files #' #' temp_dir <- tempdir() #' find_bed_regions( #' gff3_file = system.file("extdata", "Arabidopsis_thaliana_TAIR10_40_small.gff3", package = "baitfindR", mustWork = TRUE), #' source_select = "araport11", #' out_type = "write_all", #' out_dir = temp_dir, #' prefix = "arabidopsis" #' ) #' #' # Now mask the genome, using the bed file and genome fasta file. #' mask_genome( #' bed_file = "temp_dir/test_introns", #' fasta_file = "data_raw/Arabidopsis_thaliana.TAIR10.dna.toplevel.renamed.fasta", #' out_fasta_file = "temp_dir/test_masked" #' ) #' } #' @export mask_regions_in_fasta <- function (bed_file, fasta_file, out_fasta_file, ...) { # Check input assertthat::assert_that(assertthat::is.string(bed_file)) bed_file <- fs::path_abs(bed_file) assertthat::assert_that(assertthat::is.readable(bed_file)) assertthat::assert_that(assertthat::is.string(fasta_file)) fasta_file <- fs::path_abs(fasta_file) assertthat::assert_that(assertthat::is.readable(fasta_file)) assertthat::assert_that(assertthat::is.string(out_fasta_file)) out_fasta_file <- fs::path_abs(out_fasta_file) assertthat::assert_that(assertthat::is.dir(fs::path_dir(out_fasta_file))) bed <- readr::read_tsv(bed_file, col_names = c("chr", "start", "end"), col_types = "cdd") checkr::check_data( bed, values = list( chr = "a", start = 1, end = 1 ), order = TRUE) if (!(check_bed_genome_names(fasta_file, bed))) { stop ("Names don't match between bed file and fasta file headers") } # Run bedtools maskfasta processx::run( command = "bedtools", args = c( "maskfasta", "-fi", fasta_file, "-bed", bed_file, "-fo", out_fasta_file ), echo = TRUE ) } #' Clean up data from a gff file and #' convert to bed format #' #' Helper function for `find_bed_regions`. Merges overlapping regions and sorts #' regions. #' #' @param region Dataframe; list of gene regions in "bed" format. Must include #' the following columns in order: `chr` ('chromosome', character), `start` #' (start position, numeric), and `end` (end position, numeric). #' @param check.chr Logical; should coordinates be checked for chromosomal #' format with "chr" prefix? #' @param verbose Logical; should `bedr` functions output all messages? #' #' @return Dataframe in "bed" format. #' clean_gff <- function (region, check.chr = FALSE, verbose = FALSE) { region %>% # Check if region is valid dplyr::filter(bedr::is.valid.region(., check.chr = check.chr, verbose = verbose)) %>% # Collapse overlapping regions bedr::bedr.merge.region(check.chr = check.chr, verbose = verbose) %>% # Sort regions bedr::bedr.sort.region(check.chr = check.chr, verbose = verbose) %>% # Convert to bed format bedr::convert2bed(check.chr = check.chr, verbose = verbose) } #' Find genes, exons, and introns in a gff3 file #' #' If tsv files are written out by selecting "write_all" for `out_type`, #' they will overwrite any existing files with the same name in `out_dir`. #' #' @param gff3_file Path to input file in `gff3` format. #' @param source_select Character vector; only use regions from these #' sources. Must match values in `source` column of gff3 file. Optional. #' @param gene_label String; value used to indicate genes in gff3 file. #' Must match at least one value in `type` column of gff3 file. Default "gene". #' @param exon_label String; value used to indicate exons in gff3 file. #' Must match at least one value in `type` column of gff3 file. Default "exon". #' @param verbose Logical; should `bedr` functions output all messages? #' @param out_type Type of output to return: #' "genes": dataframe in "bed" format of genes. #' "introns": dataframe in "bed" format of introns. #' "exons": dataframe in "bed" format of exons. #' "write_all": write tab-separated files for each of `genes`, `introns`, and #' `exons` to `out_dir`. The hash digest of the combined genes, introns, and #' exons will be returned. #' @param prefix String; prefix to attach to tsv files if `out_type` is #' "write_all". #' @param out_dir Directory to write tsv files if `out_type` is "write_all". #' @param ... Other arguments. Not used by this function, but meant to #' be used by \code{\link[drake]{drake_plan}} for tracking during workflows. #' @return Dataframe or character. #' @author Joel H Nitta, \email{joelnitta@@gmail.com} #' @examples #' # Find genes #' #' arabidopsis_gff_file <- system.file("extdata", "Arabidopsis_thaliana_TAIR10_40_small.gff3", package = "baitfindR", mustWork = TRUE) #' #' genes <- find_bed_regions( #' gff3_file = arabidopsis_gff_file, #' source_select = "araport11", #' out_type = "genes" #' ) #' head(genes) #' #' # Find introns #' introns <- find_bed_regions( #' gff3_file = arabidopsis_gff_file, #' source_select = "araport11", #' out_type = "introns" #' ) #' head(introns) #' #' # Find exons #' exons <- find_bed_regions( #' gff3_file = arabidopsis_gff_file, #' source_select = "araport11", #' out_type = "exons" #' ) #' head(exons) #' #' \dontrun{ #' # Write genes, introns, and exons out as tsv files #' temp_dir <- tempdir() #' find_bed_regions( #' gff3_file = arabidopsis_gff_file, #' source_select = "araport11", #' out_type = "write_all", #' out_dir = temp_dir, #' prefix = "arabidopsis" #' ) #' } #' @export find_bed_regions <- function (gff3_file, source_select = NULL, gene_label = "gene", exon_label = "exon", verbose = FALSE, prefix = NULL, out_dir = NULL, out_type = c("genes", "introns", "exons", "write_all"), ...) { # Check input assertthat::assert_that(assertthat::is.readable(gff3_file)) assertthat::assert_that(assertthat::is.string(gene_label)) assertthat::assert_that(assertthat::is.string(exon_label)) assertthat::assert_that(assertthat::is.string(out_type)) assertthat::assert_that(is.logical(verbose)) assertthat::assert_that(out_type %in% c("genes", "introns", "exons", "write_all"), msg = "'out_type' must be one of 'genes', 'introns', 'exons', or 'write_all'") # Read in gff3 file as dataframe gff3 <- ape::read.gff(gff3_file) %>% dplyr::mutate(chr = as.character(seqid)) # Keep only annotations from selected source if (!is.null(source_select)) { assertthat::assert_that(is.character(source_select)) assertthat::assert_that(all(source_select %in% gff3$source)) gff3 <- dplyr::filter(gff3, source %in% source_select) } # Extract and clean up genes genes <- gff3 %>% dplyr::filter(type == gene_label) %>% dplyr::select(chr, start, end) %>% clean_gff(verbose = verbose) # Extract and clean up exons exons <- gff3 %>% dplyr::filter(type == exon_label) %>% dplyr::select(chr, start, end) %>% clean_gff(verbose = verbose) # Introns are genes - exons introns <- bedr::bedr.subtract.region( genes, exons, remove.whole.feature = FALSE, check.chr = FALSE, verbose = verbose) # Write out all regions and return hash of genes + exons + introns if (out_type == "write_all") { out_dir <- fs::path_abs(out_dir) assertthat::assert_that(assertthat::is.writeable(out_dir)) assertthat::assert_that(assertthat::is.string(prefix)) all_regions <- list(genes = genes, exons = exons, introns = introns) all_regions %>% purrr::set_names(fs::path(out_dir, paste0(prefix, "_", names(.)))) %>% purrr::iwalk(readr::write_tsv, col_names = FALSE) return(digest::digest(all_regions)) } # Or, return a particular result type. return(switch( out_type, genes = genes, exons = exons, introns = introns )) } # Check that genome fasta headers and # "chromosome" names in bed file match. # (This must be true for # bedtools maskfasta to work). check_bed_genome_names <- function (fasta_file, bed) { # find all sequence headers in fasta file seq_names <- readr::read_lines(fasta_file) %>% purrr::keep(~ grepl(">", .x)) %>% purrr::map(~ gsub(">", "", .x)) # make sure "chr" (chromosome) names of bed file are all in # fasta sequence headers chr <- unique(bed$chr) all(chr %in% seq_names) }

82c59c5fe2484138e581f45a8368c63f8103bd8d

e508870d7b82ca065aff9b7bf33bc34d5a6c0c1c

/pkg/man/listProduct.Rd

3ebc402520b1b0eae3006db6765831e9d5fdab1f

[]

no_license

Dong-po/SoilR-exp

596be0e6c5d291f00c6e08c348952ee23803e15e

c10d34e035deac8af4912c55382012dfc247eedc

refs/heads/master

2021-09-03T11:12:49.199268

2018-01-08T15:52:17

null

0

null

UTF-8

R

false

566

rd

listProduct.Rd

\name{listProduct} \alias{listProduct} \usage{listProduct(...)} \examples{listProduct(list('a','b'),list(1,2))} \arguments{ \item{...}{lists} } \description{Creates a list of all combinations of the elements of the inputlists (like a "tensor product list " The list elements can be of any class. The function is used in examples and tests to produce all possible combinations of arguments to a function. look at the tests for example usage} \title{tensor product of lists} \value{a list of lists each containing one combinations of the elements of the input lists}

33956c49db6741f7bd71bb226f7c7a8970af0f45

cef4d8774f2eb276a8212ce460fb546de6122a91

/Rplot5.R

8d54ebd70fec3619cc34a2def413b63aa45911b0

[]

no_license

universaljames/PM2.5-in-US

e085406a3bc5a05d0e0e1b9f02cdcfdc5c947d70

ac08cea08cebefde38f1c62a6949b7b21d75cdfc

refs/heads/master

2022-10-26T09:32:48.127894

2020-06-15T05:04:28

272,344,398

0

null

UTF-8

R

false

1,138

r

Rplot5.R

#Data Preparation DataFile <- "NEI_data.zip" DataFileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" download.file(url=DataFileURL,destfile=DataFileFile,method="curl") unzip(DataFile) NEI <- readRDS("summarySCC_PM25.rds") SCC <- readRDS("Source_Classification_Code.rds") colToFactor <- c("year", "type", "Pollutant","SCC","fips") NEI[,colToFactor] <- lapply(NEI[,colToFactor], factor) levels(NEI$fips)[1] = NA NEIdata<-NEI[complete.cases(NEI),] colSums(is.na(NEIdata)) #Question 5 SCCvehicle<-grepl(pattern = "vehicle", SCC$EISector, ignore.case = TRUE) SCCvehicleSCC <- SCC[SCCvehicle,]$SCC NEIvehicleSSC <- NEIdata[NEIdata$SCC %in% SCCvehicleSCC, ] NEIvehicleBaltimore <- subset(NEIvehicleSSC, fips == "24510") NIEvehicleBaltimoreTotEm<-aggregate(Emissions~year, NEIvehicleBaltimore, sum) g<-ggplot(aes(year, Emissions/10^5), data=NIEvehicleBaltimoreTotEm) g+geom_bar(stat="identity",fill="grey",width=0.75) + guides(fill=FALSE) + labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) + labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore from 1999-2008"))

77c9ef64627876f3b9a1edfe3f6b1dd150a0a324

1203cc14b7416390beb8149c3a75c28c9177a681

/scripts/monthly report text 5_18.R

3347b99a56c2f5a34de613fec4257e9478ee6e33

[]

no_license

AZASRS/DB_R_Testing_Environment

7801fc9bcf078be259ed8ef80335729623bdb1dd

bf65706a2244dc67e63cd25382488d2219756b11

refs/heads/master

2020-03-20T06:49:08.414225

2018-07-27T00:31:20

137,261,803

0

1

null

2018-07-16T22:23:26

2018-06-13T19:33:14

R

UTF-8

R

false

11,744

r

monthly report text 5_18.R

#monthly report ## @knitr setup setwd("P:/IMD/Karl/R projects/private investment performance") load('pmedata.rdata') source('../basic financial.r') require(xtable) require(zoo) require(lubridate) # get values for valdate and lastcfdate valdf=read.csv('tfundvals.csv') valdf$dates=as.Date(valdf$dates, format='%m/%d/%Y') tfund.valdate=valdf$vals[which(valdf$dates==valdate)] tfund.cfdate=valdf$vals[which(valdf$dates==lastcfdate)] if (is.null(tfund.valdate)) print(paste( 'need total fund value in tfundvals.csv for', valdate)) if (is.null(tfund.cfdate)) print(paste( 'need total fund value in tfundvals.csv for', lastcfdate)) twrs=read.csv(file='twrs.csv') twrs$Twrdate=as.Date(twrs$Twrdate,format='%m/%d/%Y') twrs=subset(twrs,Twrdate==valdate) p2pirrs=read.csv(file='p2pirrs.csv') portmat=read.csv("portmat.csv") #if working on one portfolio and running portfolios individually (or code line individually) or diagnosing report formatting # skip first line of the function and run the rowpm and beyond. need to initialize port by assigning argument for # portfolio you want to run - e.g. port <- "PE" print.portfolio=function(port) { rowpm=which(portmat$portshort==port) port.longname=as.character(portmat$portlongname[rowpm]) pme.x=subset(pme.df,Portfolio==port) pme.x=pme.x[-grep("Vintage",rownames(pme.x)),] pme.x$longname=rownames(pme.x) #summary page cf.port=y.cf[[paste("Total",port)]] val.valdate=pme.x[paste("Total",port),"val"] val.cfdate=sum(pme.x[ ,"Cash Adj NAV"][pme.x$isfund&(!pme.x$sponsorsum)]) draw.valdate=-sum(cf.port[(time(cf.port)<=valdate)&cf.port<0]) draw.sincevaldate=-sum(cf.port[(time(cf.port)>valdate)&cf.port<0]) dist.valdate=-sum(cf.port[(time(cf.port)<=valdate)&cf.port>0]) dist.sincevaldate=-sum(cf.port[(time(cf.port)>valdate)&cf.port>0]) deltaval.valdate=-dist.valdate+val.valdate-draw.valdate deltaval.sincevaldate=-dist.sincevaldate+val.cfdate-draw.sincevaldate-val.valdate valpct.valdate=100*val.valdate/(tfund.valdate*1000000) valpct.cfdate=100*val.cfdate/(tfund.cfdate*1000000) # portirr.valdate=pme.x[paste("Total",port),"Fund.IRR"] # portirr.cfdate=100*irr.z(mergesum.z(cf.port,zoo(val.cfdate,lastcfdate))) portunf=pme.x[paste("Total",port),"unf"] portexp=val.cfdate+(portunf*1000000)+sum(cf.port[(time(cf.port)>unfunded.date)&cf.port<0]) portexppct=100*portexp/(tfund.cfdate*1000000) roll12draw=sum(cf.port[(time(cf.port)>(lastcfdate-years(1)))&cf.port<0]) roll12dist=sum(cf.port[(time(cf.port)>(lastcfdate-years(1)))&cf.port>0]) roll12net=roll12dist+roll12draw titleframe=data.frame(titles=c("Arizona State Retirement System",paste(port.longname,"Portfolio"), paste("Information compiled through",format(lastcfdate,"%m/%d/%Y")))) title.x=xtable(titleframe) align(title.x)='lc' print(title.x,hline.after=NULL,include.rownames=FALSE,include.colnames=FALSE) column1=c(rep(" ",2),formatwblank(draw.valdate/1000000), formatwblank(dist.valdate/1000000), formatwblank(deltaval.valdate/1000000), formatwblank(val.valdate/1000000)," ", formatwblank(valpct.valdate,2,'%'), rep(" ",9) ) column2=c(" ",formatwblank(val.valdate/1000000), formatwblank(draw.sincevaldate/1000000), formatwblank(dist.sincevaldate/1000000), formatwblank(deltaval.sincevaldate/1000000), formatwblank(val.cfdate/1000000)," ", formatwblank(valpct.cfdate,2,'%')," ", formatwblank(portunf), formatwblank(portexp/1000000), formatwblank(portexppct,2,'%')," "," ", formatwblank(roll12draw/1000000), formatwblank(roll12dist/1000000), formatwblank(roll12net/1000000) ) policy.txt=paste0(" Policy weight equals ",portmat[rowpm,"pol.low"],'%') if (portmat[rowpm,"pol.high"]>portmat[rowpm,"pol.low"]) policy.txt=paste0(policy.txt,' to ', portmat[rowpm,"pol.high"],'%') textcol=c(" ","Beginning NAV","Contributions","Distributions", "Net Increase/(Decrease)","Ending NAV", " ","Percent of Total Fund"," ", paste("Unfunded Commitments as of",format(unfunded.date,"%m/%d/%Y")), "Estimated exposure of ending NAV and net unfunded", "Estimated exposure (NAV+unfunded) as % of TF_NAV", policy.txt, " ", "Rolling 12 month draws","Rolling 12 month distribution", "Rolling 12 month net cash flow") printmat=data.frame(textcol,column1,column2) colnames(printmat)=c(" ", paste("Reported by SS-AIS as of",format(valdate,"%m/%d/%Y")), paste("Estimated as of",format(lastcfdate,"%m/%d/%Y"))) printmat.x=xtable(printmat) align(printmat.x)='l|r|ZZ|' print(printmat.x,tabular.environment='tabularx', width='5.5in',scalebox=.9,include.rownames=FALSE, hline.after=c(-1,0,6,9,13,length(column1))) #return table row.re=which(twrs$Asset==port) row.odce=which(twrs$Asset==as.character(portmat[rowpm,'b1'])) if(port=='PE') row.odce=c(row.odce,row.odce+1) if(port=='POPP') row.odce=row.odce[1] if(port=='OPP') row.odce=row.odce[2] twrs.re=twrs[c(row.re,row.odce),2:6] row.re=which(p2pirrs$X==port.longname) row.odce=which(p2pirrs$X==as.character(portmat[rowpm,'b2'])) if(port=='PE') row.odce=c(row.odce,row.odce+1) if(port=='POPP') row.odce=row.odce[1] if(port=='OPP') row.odce=row.odce[2] p2pirrs.re=p2pirrs[c(row.re,row.odce),2:6] countrows=sum(length(row.re),length(row.odce)) p2pirrs.re=matrix(paste0(format(as.vector(as.matrix(p2pirrs.re))),'%'),ncol=5,nrow=countrows) twrs.re=matrix(paste0(format(as.vector(as.matrix(twrs.re))),'%'),ncol=5,nrow=countrows) rn1=c(paste(port.longname,"IRR"), paste(portmat[rowpm,'b2'],"IRR")) if(port=='PE') rn1=c(rn1,'Burgiss IRR') rn2=c(paste(port.longname,"TWR"), paste(portmat[rowpm,'b2'],"TWR")) if(port=='PE') rn2=c(rn2,'Burgiss TWR') rownames(p2pirrs.re)=rn1 rownames(twrs.re)= rn2 colnames(twrs.re)=colnames(p2pirrs.re)=c( "One Quarter", "One Year","Three Years","Five Years", paste0("Inception (",portmat[rowpm,"inception"],")")) twrs.re.x=xtable(rbind(p2pirrs.re), caption=paste("Summary of IRRs as of",format(valdate,'%m/%d/%Y'))) align(twrs.re.x)='|rZZZZZ|' print(twrs.re.x,tabular.environment='tabularx',width='6.5in', scalebox=.8,caption.placement="top",hline.after=c(-1,0,nrow(twrs.re.x))) #put a blank row after each non-fund (a subtotal) blanksgo=vector() nb=which(!pme.x$isfund) if(length(nb)>=2) { nbbot=c(1,1+nb[-length(nb)]) nbtop=nb nbnbot=nbbot+(0:(-1+length(nb))) nbntop=nbtop+(0:(-1+length(nb))) } blanksgo=nbnbot[-1]-1 pme.xn=pme.x for (i in 1:length(nb)) { pme.xn[nbnbot[i]:nbntop[i],]=pme.x[nbbot[i]:nbtop[i],] if(i!=length(nb)) pme.xn[blanksgo[i],]=rep(NA,ncol(pme.xn)) } pme.xn$longname[blanksgo]='' rownames(pme.xn)=NULL blanksgo=c(blanksgo,nrow(pme.xn)) ## Adjust data frames for portfolio PME's if (port == "PE") pme.xn2=pme.xn[,c("longname", "vint", "commit", "drawn", "distributed", "unf", "appr", "Cash Adj NAV", "dpi", "Fund TVPI", "Fund IRR", "Russell 2K PME", "Fund IRR 2")] if (port == "RE") pme.xn2=pme.xn[,c("longname","vint","commit","drawn","distributed", "unf","appr","Cash Adj NAV","dpi","Fund TVPI","Fund IRR", "ODCE PME", "Fund IRR 2")] if (port == "POPP") pme.xn2=pme.xn[,c("longname", "vint", "commit", "drawn", "distributed", "unf", "appr", "Cash Adj NAV", "dpi", "Fund TVPI", "Fund IRR","Fixed 8 PME", "Fund IRR 2")] if (port == "PD") pme.xn2=pme.xn[,c("longname","vint","commit","drawn","distributed", "unf","appr","Cash Adj NAV","dpi","Fund TVPI", "Fund IRR", "Lev Loan+250 PME", "Fund IRR 2")] if (port == "OPP") pme.xn2=pme.xn[,c("longname","vint","commit","drawn","distributed", "unf","appr","Cash Adj NAV","dpi","Fund TVPI", "Fund IRR", "Fixed 8 PME", "Fund IRR 2")] if (port == "FARM") pme.xn2=pme.xn[,c("longname","vint","commit","drawn","distributed", "unf","appr","Cash Adj NAV","dpi","Fund TVPI", "Fund IRR", "CPIxFE+350 PME", "Fund IRR 2")] #format numbers pme.xn2$vint=gsub(paste(port,"V"),'',pme.xn2$vint) pme.xn2$commit=formatwblank(pme.xn2$commit) pme.xn2$unf=formatwblank(pme.xn2$unf) pme.xn2$drawn=formatwblank(pme.xn2$drawn/1000000) pme.xn2$distributed=formatwblank(pme.xn2$distributed/1000000) pme.xn2$appr=formatwblank(pme.xn2$appr/1000000) pme.xn2[ ,"Cash Adj NAV"]=formatwblank(pme.xn2[,'Cash Adj NAV']/1000000) pme.xn2$dpi=formatwblank(pme.xn2$dpi/100,2,' x') pme.xn2[ ,"Fund TVPI"]=formatwblank(pme.xn2[ , "Fund TVPI"],2,' x') pme.xn2[ ,"Fund IRR"]=formatwblank(pme.xn2[ , "Fund IRR"],2,'%') #test if (port == "PE") pme.xn2[ ,"Russell 2K PME"] = formatwblank(pme.xn2[ ,"Russell 2K PME"],2) if (port == "RE") pme.xn2[ ,"ODCE PME"] = formatwblank(pme.xn2[ ,"ODCE PME"],2) if (port == "POPP") pme.xn2[ ,"Fixed 8 PME"] = formatwblank(pme.xn2[ ,"Fixed 8 PME"],2) if (port == "PD") pme.xn2[ ,"Lev Loan+250 PME"] = formatwblank(pme.xn2[ ,"Lev Loan+250 PME"],2) if (port == "OPP") pme.xn2[ ,"Fixed 8 PME"] = formatwblank(pme.xn2[ ,"Fixed 8 PME"],2) if (port == "FARM") pme.xn2[ ,"CPIxFE+350 PME"] = formatwblank(pme.xn2[ ,"CPIxFE+350 PME"],2) # end test #pme.xn2[ ,"Russell 2K PME"] = formatwblank(pme.xn2[ ,"Russell 2K PME"],2) pme.xn2[,"Fund IRR 2"]=formatwblank(pme.xn2[,"Fund IRR 2"],2,'%') colnames(pme.xn2)=holdname=c("Fund Name","Vintage Year","Committed", paste("Called through",format(lastcfdate,"%m/%d/%Y")), paste("Distributed through",format(lastcfdate,"%m/%d/%Y")), paste("Unfunded as of",format(unfunded.date,"%m/%d/%Y")), "Est. Appreciation","Cash Adjusted Net Asset Value","DPI","TVPI Multiple", paste("IRR as of",format(valdate,"%m/%d/%Y")), paste("PME", format(valdate, "%m/%d/%Y")), "Most Recent IRR") #print the results nline=30 #lines per page nbreak=floor(nrow(pme.xn2)/nline) if(nbreak==0) breakvec=nrow(pme.xn2) if(nbreak>0) { rowgroups=blanksgo breakvec=vector() for (z in (1:nbreak)) { if (any(rowgroups<nline)) { breakvec[z]=max(rowgroups[rowgroups<nline])} else { breakvec[z]=nline } rowgroups=rowgroups-breakvec[z] rowgroups=rowgroups[rowgroups>0] } } breakvec=cumsum(breakvec) startrow=c(1,breakvec+1) breakvec=c(breakvec,nrow(pme.xn2)) for (z in 1:(nbreak+1)) { printrange=startrow[z]:breakvec[z] hlinevec=(blanksgo[which(blanksgo%in%printrange)])-(printrange[1]-1) captiontxt=port.longname if(nbreak>0) captiontxt=paste(captiontxt,z,"of",nbreak+1) pme.xt=xtable(pme.xn2[printrange,],caption=captiontxt) align(pme.xt)='l|lYZ|ZZZ|ZZ|ZZ|ZZZ|' print(pme.xt,tabular.environment='tabularx',floating.environment='sidewaystable', width='14in',scalebox=.65,include.rownames=FALSE,hline.after=c(-1,0,hlinevec)) } cat('\\clearpage') } ## @knitr print.reports port.names=as.character(unique(pme.df$Portfolio)) nullvec=sapply(port.names,print.portfolio) # ##print.re # print.portfolio("RE") # ## print.pe # print.portfolio("PE") # ## print.popp # print.portfolio("POPP") # ## print.opp # print.portfolio("OPP") # ## print.pd # print.portfolio("PD") #

e6a529edee5ad54e133f0b522650b43f7eddc826

44cf65e7ab4c487535d8ba91086b66b0b9523af6

/data/Newspapers/2001.11.22.editorial.68247.0774.r

58577a32b40697cacc8c4d959abfc6e8ae8d8d33

[]

no_license

narcis96/decrypting-alpha

f14a746ca47088ec3182d610bfb68d0d4d3b504e

5c665107017922d0f74106c13d097bfca0516e66

refs/heads/master

2021-08-22T07:27:31.764027

2017-11-29T12:00:20

111,142,761

0

1

null

MacCentralEurope

R

false

3,466

r

2001.11.22.editorial.68247.0774.r

uluitor progres ! americanii si tarile membre ale Uniunii Europene ne vor bombarda cu telegrame si scrisori de felicitare . Salutul lor cordial va deveni el insusi un eveniment politico - mediatic . celebrul Gioni Popescu , zis eminenta cenusie a Serviciului Roman de Informatii , zis Gioni Descurcaretuí , a fost propus de Radu Timofte pentru a ocupa postul de prim - adjunct al serviciului . si nu va trece multa vreme pina cind serviciile straine ne vor solicita sprijinul . " nu gasiti si voi niste marmura mai ieftina prin Romania ? " , " am putea procura si noi niste mobila sculptata in lemn de nuc sau trandafir ? " . in sfirsit , Romania va da raspunsul " profesional " adecvat : " Se rezolva , sefuí ! " . sa nu mai vorbim de " eminenta cenusie " . am putea presupune ca generalul Gioni Popescu a fost cel care a instrumentat toate scurgerile de informatii din structurile statului . el i - a depistat pe marii spioni prinsi in acesti ani . i - a vopsit , le - a pus catuse si i - a livrat pe tava lui Virgil Magureanu , lui Costin Georgescu si , de miine - poimiine , cu si mai multa autoritate , lui Radu Timofte . deci Gioni Popescu se afla , de fapt , la originea tuturor marilor victorii ale SRI , dar a fost baiat modest si n - a vrut sa iasa in fata ! sigur ca evolutia sa e impresionanta . De la un sef de complex " Universal Coop " , format din circiuma , depozit si dependinte , a evoluat . s - a calificat mai intii ca expert in parfumuri , spray - uri , televizoare color , Pepsi si , daca aveai nevoie , iti facea rost si de camasi chinezesti , de tenisi unguresti si de ciuperci vietnameze , inclusiv de muschiulet si fleica . inainte de a fi general , Gioni Popescu a fost un mare descurcaret in vremurile lui Nicolae Ceausescu . cum de s - a putut reprofila atit de spectaculos , devenind " as al informatiilor " , numai Dumnezeu stie ! de multa vreme el a fost banuit ca si sub " acoperirea " de general s - ar indeletnici cu aceleasi lucruri " Ce doriti sefuí " , terminate totdeauna cu acelasi " Se rezolva ! " . unii chiar l - au cercetat pentru ca a raspuns " mult prea corespunzator " in cazul vilei lui Virgil Magureanu , construita la Giurtelec . ba au existat sesizari ca generalul descurcaret ( cu facultatea facuta pe fuga dupa 1989 ) a sarit si ca problematica , si ca grad , si ca preocupari . ca problematica a avut de - a face cu exportul strategic de combustibili catre Iugoslavia ( zisa afacerea " Jimbolia " ) . Ca grad a ajuns general , iar ca preocupari a trecut de la frigidere , televizoare si videouri ( specializare antedecembrista ) la lucruri mai marunte ( cafea , tigari , vorbindu - se de diverse operatiuni , inclusiv " Tigareta " ) in perioada post Tovarasul si Tovarasa . pentru ca s - a dovedit expert in gospodarire , a avut in subordine inclusiv crescatoria de porci de la Craiova ( din gospodaria anexa a celebrei Regii Rasirom ) . cu siguranta , in comertul si relatiile cu arabii , Gioni Popescu i - a devansat si pe americani . prin 1992 - 1996 el ii vina deja pe talibani , dar nu avea cui sa - i predea . parola sa ar fi fost : " Tu cit dai ? " . daca ar fi pe televizoare , cafea , whisky , tigari , frigidere , ulei de masline si ficat de gisca , Gioni Popescu ne - ar baga sigur in NATO si in Uniunea Europeana . si toate serviciile secrete straine si - ar trimite oamenii la Bucuresti pentru a lua lectii de servire a sefilor si de sterpelit friptura din farfurie .

ab7f0153fca08c3381ee41336d37a5cd0220e8e6

ced1997e24a3100493ed8b4bdd116a19f101ffd7

/analyse-simulation-15-08.R

85bd762e63e6a28e3ff232fd08fccf1cdc8b6ee2

[]

no_license

tobiriebe/analyse

bb2db1c43d589d1da25fcb93032d0136454fcd6b

41ea6ab06f8f6f2fc3b258a6b772274ed5f0159f

refs/heads/master

2020-04-06T06:56:12.088598

2016-09-05T13:32:58

65,828,973

0

null

UTF-8

R

false

31,465

r

analyse-simulation-15-08.R

analyseSimulation <- function(dataFile) { library(pROC) library(Rmisc) data <- dataFile #save data file for later functions appearing Y <- dataFile$Y Ynoise <- dataFile$Ynoise yBin <- dataFile$yBin yBinNoise <- dataFile$yBinNoise originalX <- dataFile$originalX originalXnoise <- dataFile$originalXnoise coeffs <- dataFile$coeffs predictors <- dataFile$predictors kappa <- dataFile$kappa samples <- dataFile$samples simulations <- dataFile$simulations nOuterFolds <- dataFile$nOuterFolds redSteps <- dataFile$redSteps sampleRatio <- dataFile$sampleRatio fixedMstop <- dataFile$fixedMstop fixedNu <- dataFile$fixedNu offsetFinal <- dataFile$offsetFinal predModelList <- dataFile$predModelList offsetFinalClass <- dataFile$offsetFinalClass predModelListClass <- dataFile$predModelListClass predictionVector <- dataFile$predictionVector predictionVectorClass <- dataFile$predictionVectorClass offsetFinalNoise <- dataFile$offsetFinalNoise predModelListNoise <- dataFile$predModelListNoise predictionVectorNoise <- dataFile$predictionVectorNoise offsetFinalClassNoise <- dataFile$offsetFinalClassNoise predModelListClassNoise <- dataFile$predModelListClassNoise predictionVectorClassNoise <- dataFile$predictionVectorClassNoise #load(dataFile) # if (grepl(pattern = "_Plain", x = dataFile)){ # fileType <- fileTypePlain # } else { # fileType <- fileTypeVols # } if (!exists("redSteps")){ redSteps <- dim(predictionVector)[2] } #initalize variables for absolute errors AbsErrors <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save absolute errors after each simulation sumAbsErrors <- rep(0, redSteps) #vector to calculate sum of absolut errors AbsErrorsCI97.5 <- rep(0, redSteps)#vectors to save confidence intervals AbsErrorsCI2.5 <- rep(0, redSteps) AbsErrorsNoiseX <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save absolute errors after each simulation sumAbsErrorsNoiseX <- rep(0, redSteps)#vector to calculate sum of absolut errors AbsErrorsNoiseXCI97.5 <- rep(0, redSteps)#vectors to save confidence intervals AbsErrorsNoiseXCI2.5 <- rep(0, redSteps) AbsErrorsNoiseY <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save absolute errors after each simulation sumAbsErrorsNoiseY <- rep(0, redSteps)#vector to calculate sum of absolut errors AbsErrorsNoiseYCI97.5 <- rep(0, redSteps)#vectors to save confidence intervals AbsErrorsNoiseYCI2.5 <- rep(0, redSteps) AbsErrorsNoiseXNoiseY <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save absolute errors after each simulation sumAbsErrorsNoiseXNoiseY <- rep(0, redSteps)#vector to calculate sum of absolut errors AbsErrorsNoiseXNoiseYCI97.5 <- rep(0, redSteps) #vectors to save confidence intervals AbsErrorsNoiseXNoiseYCI2.5 <- rep(0, redSteps) #initialize variables for AUC AUCsb <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save AUCafter each simulation sumAUC <- rep(0, redSteps) #vector for sum of AUC and CIAUC2.5 <- rep(0, redSteps)#vectors to save confidence intervals CIAUC97.5 <- rep(0, redSteps) AUCNoiseX <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save AUCafter each simulation sumAUCNoiseX <- rep(0, redSteps) #vector for sum of AUC and CIAUCNoiseX2.5 <- rep(0, redSteps)#vectors to save confidence intervals CIAUCNoiseX97.5 <- rep(0, redSteps) AUCNoiseY <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save AUCafter each simulation sumAUCNoiseY <- rep(0, redSteps) #vector for sum of AUC and CIAUCNoiseY2.5 <- rep(0, redSteps)#vectors to save confidence intervals CIAUCNoiseY97.5 <- rep(0, redSteps) AUCNoiseXNoiseY <- matrix(0, nrow = redSteps, ncol = simulations)#matrix to save AUCafter each simulation sumAUCNoiseXNoiseY <- rep(0, redSteps) #vector for sum of AUC and CIAUCNoiseXNoiseY2.5<- rep(0, redSteps)#vectors to save confidence intervals CIAUCNoiseXNoiseY97.5<- rep(0, redSteps) #initialize variables for variance variance <- matrix(0, nrow = redSteps, ncol = simulations)#matrix save variance after each reduction step sumvariance <- rep(0, redSteps) #vector for sum of variance CIvariance2.5 <- rep(0, redSteps) #vector to save confidence intervals of variance CIvariance97.5 <- rep(0, redSteps) varianceNoiseX <- matrix(0, nrow = redSteps, ncol = simulations)#matrix save variance after each reduction step sumvarianceNoiseX <- rep(0, redSteps) #vector for sum of variance CIvariance2.5NoiseX <- rep(0, redSteps) #vector to save confidence intervals of variance CIvariance97.5NoiseX <- rep(0, redSteps) varianceNoiseY <- matrix(0, nrow = redSteps, ncol = simulations)#matrix save variance after each reduction step sumvarianceNoiseY <- rep(0, redSteps) #vector for sum of variance CIvariance2.5NoiseY <- rep(0, redSteps) #vector to save confidence intervals of variance CIvariance97.5NoiseY <- rep(0, redSteps) varianceNoiseXNoiseY <- matrix(0, nrow = redSteps, ncol = simulations)#matrix save variance after each reduction step sumvarianceNoiseXNoiseY <- rep(0, redSteps) #vector for sum of variance CIvariance2.5NoiseXNoiseY <- rep(0, redSteps) #vector to save confidence intervals of variance CIvariance97.5NoiseXNoiseY <- rep(0, redSteps) for (simulation in 1:simulations) { print(simulation) ########REGRESSION AbsErrors[,simulation] <- colSums(abs(t(predictionVector[simulation,,])-Y[,simulation]))/simulations AbsErrorsNoiseX[,simulation] <- colSums(abs(t(predictionVectorNoise[simulation,,])-Y[,simulation]))/simulations AbsErrorsNoiseY[,simulation] <- colSums(abs(t(predictionVector[simulation,,])-Ynoise[,simulation]))/simulations AbsErrorsNoiseXNoiseY[,simulation] <- colSums(abs(t(predictionVectorNoise[simulation,,])-Ynoise[,simulation]))/simulations #######CLASSIFICATION (VIA REGRESSION) for (reduction in 1:redSteps){ #print(reduction)#DEBUG #sumAUC[reduction] <- sumAUC[reduction] + auc(yBin[,simulation], predictionVectorClass[simulation, reduction, ]) #calculate AUC AUCsb[reduction, simulation] <- auc(yBin[,simulation], predictionVectorClass[simulation, reduction, ]) AUCNoiseX[reduction, simulation] <- auc(yBin[,simulation], predictionVectorClassNoise[simulation, reduction, ]) AUCNoiseY[reduction, simulation] <- auc(yBinNoise[,simulation], predictionVectorClass[simulation, reduction, ]) AUCNoiseXNoiseY[reduction, simulation] <- auc(yBinNoise[,simulation], predictionVectorClassNoise[simulation, reduction, ]) #calculate variance variance[reduction, simulation] <- var(predictionVector[simulation , reduction ,]) varianceNoiseX[reduction, simulation] <- var(predictionVectorNoise[simulation , reduction ,]) varianceNoiseY[reduction, simulation] <- var(predictionVector[simulation , reduction ,] - Ynoise[,simulation]) varianceNoiseXNoiseY[reduction, simulation] <- var(predictionVectorNoise[simulation , reduction ,] - Ynoise[,simulation] ) #print(variance2.5) #DEBUG } } #end simulation loop for (reduction in 1:redSteps){ #calculate confidence intervals for sum of absolut errors AbsErrorsCI <- CI(AbsErrors[reduction,]) #take all reductions steps over the simulations and calculate CI AbsErrorsCI97.5[reduction] <- AbsErrorsCI[1] #upper CI AbsErrorsCI2.5[reduction] <- AbsErrorsCI[3] #upper CI AbsErrorsCI <- CI(AbsErrorsNoiseX[reduction,]) #take all reductions steps over the simulations and calculate CI AbsErrorsNoiseXCI97.5[reduction] <- AbsErrorsCI[1] #upper CI AbsErrorsNoiseXCI2.5[reduction] <- AbsErrorsCI[3] #upper CI AbsErrorsCI <- CI(AbsErrorsNoiseY[reduction,]) #take all reductions steps over the simulations and calculate CI AbsErrorsNoiseYCI97.5[reduction] <- AbsErrorsCI[1] #upper CI AbsErrorsNoiseYCI2.5[reduction] <- AbsErrorsCI[3] #upper CI AbsErrorsCI <- CI(AbsErrorsNoiseXNoiseY[reduction,]) #take all reductions steps over the simulations and calculate CI AbsErrorsNoiseXNoiseYCI97.5[reduction] <- AbsErrorsCI[1] #upper CI AbsErrorsNoiseXNoiseYCI2.5[reduction] <- AbsErrorsCI[3] #upper CI #calculate confidence intervals for AUC CIAUC <- CI(AUCsb[reduction,]) #take all reductions steps over the simulations and calculate CI CIAUC97.5[reduction] <- CIAUC[1] #upper CI CIAUC2.5[reduction] <- CIAUC[3] #lower CI CIAUC <- CI(AUCNoiseX[reduction,]) #take all reductions steps over the simulations and calculate CI CIAUCNoiseX97.5[reduction] <- CIAUC[1] #upper CI CIAUCNoiseX2.5[reduction] <- CIAUC[3] #lower CI CIAUC <- CI(AUCNoiseY[reduction,]) #take all reductions steps over the simulations and calculate CI CIAUCNoiseY97.5[reduction] <- CIAUC[1] #upper CI CIAUCNoiseY2.5[reduction] <- CIAUC[3] #lower CI CIAUC <- CI(AUCNoiseXNoiseY[reduction,]) #take all reductions steps over the simulations and calculate CI CIAUCNoiseXNoiseY97.5[reduction] <- CIAUC[1] #upper CI CIAUCNoiseXNoiseY2.5[reduction] <- CIAUC[3] #lower CI #calculate confidence intervals for variance CIvariance <- CI(variance[reduction,]) #take all reductions steps over the simulations and calculate CI CIvariance2.5[reduction] <- CIvariance[3] #lower band CIvariance97.5[reduction] <- CIvariance[1] #upper band CIvariance <- CI(varianceNoiseX[reduction,]) #take all reductions steps over the simulations and calculate CI CIvariance2.5NoiseX[reduction] <- CIvariance[3] #lower band CIvariance97.5NoiseX[reduction] <- CIvariance[1] #upper band CIvariance <- CI(varianceNoiseY[reduction,]) #take all reductions steps over the simulations and calculate CI CIvariance2.5NoiseY[reduction] <- CIvariance[3] #lower band CIvariance97.5NoiseY[reduction] <- CIvariance[1] #upper band CIvariance <- CI(varianceNoiseXNoiseY[reduction,]) #take all reductions steps over the simulations and calculate CI CIvariance2.5NoiseXNoiseY[reduction] <- CIvariance[3] #lower band CIvariance97.5NoiseXNoiseY[reduction] <- CIvariance[1] #upper band } sumAUC <- rowSums(AUCsb)/simulations sumAUCNoiseX <- rowSums(AUCNoiseX)/simulations sumAUCNoiseY <- rowSums(AUCNoiseY)/simulations sumAUCNoiseXNoiseY <- rowSums(AUCNoiseXNoiseY)/simulations sumAbsErrors <- rowSums(AbsErrors)/simulations sumAbsErrorsNoiseX <- rowSums(AbsErrorsNoiseX)/simulations sumAbsErrorsNoiseY <- rowSums(AbsErrorsNoiseY)/simulations sumAbsErrorsNoiseXNoiseY <- rowSums(AbsErrorsNoiseXNoiseY)/simulations sumvariance <- rowSums(variance)/simulations sumvarianceNoiseX <- rowSums(varianceNoiseX)/simulations sumvarianceNoiseY <- rowSums(varianceNoiseY)/simulations sumvarianceNoiseXNoiseY <- rowSums(varianceNoiseXNoiseY)/simulations #save these values #####convergenceIteration(data)#after how many redSteps do analyse plots converge, for plots ####FOR LATER SAVE outcome of convergence#### redStepsconv <- convergenceIteration(data) #after how many reduction steps does boosting iteration converge save(redStepsconv, AbsErrors, AbsErrorsNoiseX, AbsErrorsNoiseY, AbsErrorsNoiseXNoiseY, sumAbsErrors, AbsErrorsCI97.5, AbsErrorsCI2.5, sumAbsErrorsNoiseX, AbsErrorsNoiseXCI97.5, AbsErrorsNoiseXCI2.5, sumAbsErrorsNoiseY, AbsErrorsNoiseYCI97.5, AbsErrorsNoiseYCI2.5, sumAbsErrorsNoiseXNoiseY, AbsErrorsNoiseXNoiseYCI97.5, AbsErrorsNoiseXNoiseYCI2.5, AUCsb, AUCNoiseX, AUCNoiseY, AUCNoiseXNoiseY, sumAUC, CIAUC97.5, CIAUC2.5, sumAUCNoiseX, CIAUCNoiseX97.5, CIAUCNoiseX2.5, sumAUCNoiseY, CIAUCNoiseY97.5, CIAUCNoiseY2.5, sumAUCNoiseXNoiseY, CIAUCNoiseXNoiseY97.5, CIAUCNoiseXNoiseY2.5, variance, varianceNoiseX, varianceNoiseY, varianceNoiseXNoiseY, sumvariance, CIvariance2.5, CIvariance97.5, sumvarianceNoiseX, CIvariance2.5NoiseX, CIvariance97.5NoiseX, sumvarianceNoiseY, CIvariance2.5NoiseY, CIvariance97.5NoiseY, sumvarianceNoiseXNoiseY, CIvariance2.5NoiseXNoiseY, CIvariance97.5NoiseXNoiseY, file = "EVAL.rda") nred <- redStepsconv[1,] #convergence of Iteration for AbsError nredNoiseX <- redStepsconv[2,] nredNoiseY <- redStepsconv[3,] nredNoiseXNoiseY <- redStepsconv[4,] meanred <- sum(nred)/simulations #mean of reduction Steps for Absolute Error over simulations meanredNoiseX <- sum(nredNoiseX)/simulations meanredNoiseY <- sum(nredNoiseY)/simulations meanredNoiseXNoiseY <- sum(nredNoiseXNoiseY)/simulations nredAUC <- redStepsconv[5,]#convergence of Iteration for AUC nredAUCNoiseX <- redStepsconv[6,] nredAUCNoiseY <- redStepsconv[7,] nredAUCNoiseXNoiseY <- redStepsconv[8,] meanredAUC <- sum(nredAUC)/simulations #mean of reduction Steps for AUC over simulations meanredAUCNoiseX <- sum(nredAUCNoiseX)/simulations meanredAUCNoiseY <- sum(nredAUCNoiseY)/simulations meanredAUCNoiseXNoiseY <- sum(nredAUCNoiseXNoiseY)/simulations #Plots pdf("plots.pdf") #save plot plot(sumAbsErrors, ylim=range(sumAbsErrors, AbsErrorsCI97.5, AbsErrorsCI2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of absolute Errors", main = "Sum of absolute Errors") lines(AbsErrorsCI97.5, col="red") #add CIs to plot lines(AbsErrorsCI2.5, col="red") #abline(h = sumAbsErrors[meanred], col = "green") #horizontal line that shows convergence plot(sumAbsErrorsNoiseX, ylim=range(sumAbsErrorsNoiseX, AbsErrorsNoiseXCI97.5, AbsErrorsNoiseXCI2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of absolute Errors", main = "Sum of absolute Errors for noisy X") lines(AbsErrorsNoiseXCI97.5, col="red") #add CIs to plot lines(AbsErrorsNoiseXCI2.5, col="red") #abline(h = sumAbsErrorsNoiseX[meanredNoiseX], col = "green") #horizontal line that shows convergence plot(sumAbsErrorsNoiseY, ylim=range(sumAbsErrorsNoiseY, AbsErrorsNoiseYCI97.5, AbsErrorsNoiseYCI2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of absolute Errors", main = "Sum of absolute Errors for noisy Y") lines(AbsErrorsNoiseYCI97.5, col="red") #add CIs to plot lines(AbsErrorsNoiseYCI2.5, col="red") #abline(h = sumAbsErrorsNoiseY[meanredNoiseY], col = "green") #horizontal line that shows convergence plot(sumAbsErrorsNoiseXNoiseY, ylim=range(sumAbsErrorsNoiseXNoiseY, AbsErrorsNoiseXNoiseYCI97.5, AbsErrorsNoiseXNoiseYCI2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of absolute Errors", main = "Sum of absolute Errors for noisy X and noisy Y") lines(AbsErrorsNoiseXNoiseYCI97.5, col="red") #add CIs to plot lines(AbsErrorsNoiseXNoiseYCI2.5, col="red") #abline(h = sumAbsErrorsNoiseXNoiseY[meanredNoiseXNoiseY], col = "green") #horizontal line that shows convergence # #relative values (to the basic boosting) for first and last simulation # plot(AbsErrors[,simulations]/AbsErrors[1, simulations], ylim=range(AbsErrors[,simulations]/AbsErrors[1, simulations], AbsErrors[,1]/AbsErrors[1, 1]), type="l", main = "Relative value of absolute error to basic boosting (first and last simulation)", xlab = "Iteration", ylab = "Relative value") # lines(AbsErrors[,1]/AbsErrors[1, 1], type="l", col = "blue") # abline(h = 1, col = "red") #if line lays above the line the Abs Error of basic boosting is higher # plot(AbsErrorsNoiseX[,simulations]/AbsErrorsNoiseX[1, simulations], ylim=range(AbsErrorsNoiseX[,simulations]/AbsErrorsNoiseX[1, simulations], AbsErrorsNoiseX[,1]/AbsErrorsNoiseX[1, 1]), type="l", main = "Relative value of absolute error to basic boosting for noisy X (first and last simulation)", xlab = "Iteration", ylab = "Relative value") # lines(AbsErrorsNoiseX[,1]/AbsErrorsNoiseX[1, 1], type="l", col = "blue") # abline(h = 1, col = "red") #if line lays above the line the Abs Error of basic boosting is higher # plot(AbsErrorsNoiseY[,simulations]/AbsErrorsNoiseY[1, simulations], ylim=range(AbsErrorsNoiseY[,simulations]/AbsErrorsNoiseY[1, simulations], AbsErrorsNoiseY[,1]/AbsErrorsNoiseY[1, 1]), type="l", main = "Relative value of absolute error to basic boosting for noisy Y (first and last simulation)", xlab = "Iteration", ylab = "Relative value") # lines(AbsErrorsNoiseY[,1]/AbsErrorsNoiseY[1, 1], type="l", col = "blue") # abline(h = 1, col = "red") #if line lays above the line the Abs Error of basic boosting is higher # plot(AbsErrorsNoiseXNoiseY[,simulations]/AbsErrorsNoiseXNoiseY[1, simulations], ylim=range(AbsErrorsNoiseXNoiseY[,simulations]/AbsErrorsNoiseXNoiseY[1, simulations], AbsErrorsNoiseXNoiseY[,1]/AbsErrorsNoiseXNoiseY[1, 1]), type="l", main = "Relative value of absolute error to basic boosting for noisy X and noisy Y (first and last simulation)", xlab = "Iteration", ylab = "Relative value") # lines(AbsErrorsNoiseXNoiseY[,1]/AbsErrorsNoiseXNoiseY[1, 1], type="l", col = "blue") # abline(h = 1, col = "red") #if line lays above the line the Abs Error of basic boosting is higher # #classification AUC #relative values (to the basic boosting) plot(sumAbsErrors/sumAbsErrors[1], main = "Relative value of absolute error to basic boosting ") plot(sumAbsErrorsNoiseX/sumAbsErrorsNoiseX[1], main = "Relative value of absolute error to basic boosting ") plot(sumAbsErrorsNoiseY/sumAbsErrorsNoiseY[1], main = "Relative value of absolute error to basic boosting ") plot(sumAbsErrorsNoiseXNoiseY/sumAbsErrorsNoiseXNoiseY[1], main = "Relative value of absolute error to basic boosting ") plot(sumAUC, ylim=range(sumAUC, CIAUC97.5, CIAUC2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of AUC", main = "Sum aof AUC") lines(CIAUC2.5, col="red") #add CIs to plot lines(CIAUC97.5, col="red") #abline(h = sumAUC[meanredAUC], col = "green") #horizontal line that shows convergence plot(sumAUCNoiseX, ylim=range(sumAUCNoiseX, CIAUCNoiseX97.5, CIAUCNoiseX2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of AUC", main = "Sum aof AUC for noisy X") lines(CIAUCNoiseX2.5, col="red") #add CIs to plot lines(CIAUCNoiseX97.5, col="red") #abline(h = sumAUCNoiseX[meanredAUCNoiseX], col = "green") #horizontal line that shows convergence plot(sumAUCNoiseY, ylim=range(sumAUCNoiseY, CIAUCNoiseY97.5, CIAUCNoiseY2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of AUC", main = "Sum aof AUC for noisy Y") lines(CIAUCNoiseY2.5, col="red") #add CIs to plot lines(CIAUCNoiseY97.5, col="red") #abline(h = sumAUCNoiseY[meanredAUCNoiseY], col = "green") #horizontal line that shows convergence plot(sumAUCNoiseXNoiseY, ylim=range(sumAUCNoiseXNoiseY, CIAUCNoiseXNoiseY97.5, CIAUCNoiseXNoiseY2.5), col='black', type="l", xlab = "Iteration", ylab = "Sum of AUC", main = "Sum aof AUC for noisy X and noisy Y") lines(CIAUCNoiseXNoiseY2.5, col="red") #add CIs to plot lines(CIAUCNoiseXNoiseY97.5, col="red") #abline(h = sumAUCNoiseXNoiseY[meanredAUCNoiseXNoiseY], col = "green") #horizontal line that shows convergence #variance plot(sumvariance, ylim=range(sumvariance, CIvariance2.5, CIvariance97.5), col='black', type = "l", xlab = "Iteration", ylab = "Sum of Variance", main = "Sum of Variance") lines(CIvariance97.5, col="red") #add CIs to plot lines(CIvariance2.5, col="red") plot(sumvarianceNoiseX, ylim=range(sumvarianceNoiseX, CIvariance2.5NoiseX, CIvariance97.5NoiseX), col='black', type = "l", xlab = "Iteration", ylab = "Sum of Variance", main = "Sum of Variance for noisy X") lines(CIvariance2.5NoiseX, col="red") #add CIs to plot lines(CIvariance97.5NoiseX, col="red") plot(sumvarianceNoiseY, ylim=range(sumvarianceNoiseY, CIvariance2.5NoiseY, CIvariance97.5NoiseY), col='black', type = "l", xlab = "Iteration", ylab = "Sum of Variance", main = "Sum of Variance for noisy Y") lines(CIvariance2.5NoiseY, col="red") #add CIs to plot lines(CIvariance97.5NoiseY, col="red") plot(sumvarianceNoiseXNoiseY, ylim=range(sumvarianceNoiseXNoiseY, CIvariance2.5NoiseXNoiseY, CIvariance97.5NoiseXNoiseY), col='black', type = "l", xlab = "Iteration", ylab = "Sum of Variance" , main = "Sum of Variance for noisy Y and noisy X") lines(CIvariance2.5NoiseXNoiseY, col="red") #add CIs to plot lines(CIvariance97.5NoiseXNoiseY, col="red") dev.off() #end of saving plot #par(mfrow=c(1,1)) } ######################################################################## ####################################################################### convergenceIteration <- function(dataFile){ ###FOR LATER -> give back maximum value for no convergence ###FOR LATER -> same for noisy X/Y library(pracma) library(pROC) #load(dataFile) Y <- dataFile$Y Ynoise <- dataFile$Ynoise yBin <- dataFile$yBin yBinNoise <- dataFile$yBinNoise originalX <- dataFile$originalX originalXnoise <- dataFile$originalXnoise coeffs <- dataFile$coeffs predictors <- dataFile$predictors kappa <- dataFile$kappa samples <- dataFile$samples simulations <- dataFile$simulations nOuterFolds <- dataFile$nOuterFolds redSteps <- dataFile$redSteps sampleRatio <- dataFile$sampleRatio fixedMstop <- dataFile$fixedMstop fixedNu <- dataFile$fixedNu offsetFinal <- dataFile$offsetFinal predModelList <- dataFile$predModelList offsetFinalClass <- dataFile$offsetFinalClass predModelListClass <- dataFile$predModelListClass predictionVector <- dataFile$predictionVector predictionVectorClass <- dataFile$predictionVectorClass offsetFinalNoise <- dataFile$offsetFinalNoise predModelListNoise <- dataFile$predModelListNoise predictionVectorNoise <- dataFile$predictionVectorNoise offsetFinalClassNoise <- dataFile$offsetFinalClassNoise predModelListClassNoise <- dataFile$predModelListClassNoise predictionVectorClassNoise <- dataFile$predictionVectorClassNoise grad <- 0 #variable for gradient of curve nred <- rep(0, simulations) #variable to count how many reduction iterations were done nredNoiseX <- rep(0, simulations) #variable to count how many reduction iterations were done nredNoiseY <- rep(0, simulations) #variable to count how many reduction iterations were done nredNoiseXNoiseY <- rep(0, simulations) #variable to count how many reduction iterations were done nredAUC <- rep(0, simulations) #variable to count how many reduction iterations were done nredAUCNoiseX <- rep(0, simulations) #variable to count how many reduction iterations were done nredAUCNoiseY <- rep(0, simulations) #variable to count how many reduction iterations were done nredAUCNoiseXNoiseY <- rep(0, simulations) #variable to count how many reduction iterations were done AbsError <- matrix(0, nrow = redSteps, ncol = simulations) #AbsError to stop reduction iteration AbsErrorNoiseX <- matrix(0, nrow = redSteps, ncol = simulations) #AbsError to stop reduction iteration AbsErrorNoiseY <- matrix(0, nrow = redSteps, ncol = simulations) #AbsError to stop reduction iteration AbsErrorNoiseXNoiseY <- matrix(0, nrow = redSteps, ncol = simulations) #AbsError to stop reduction iteration sumAUC <- matrix(0, nrow=redSteps, ncol = simulations) #sumAUC to stop reduction iteration sumAUCNoiseX <- matrix(0, nrow=redSteps, ncol = simulations) #sumAUC to stop reduction iteration sumAUCNoiseY <- matrix(0, nrow=redSteps, ncol = simulations) #sumAUC to stop reduction iteration sumAUCNoiseXNoiseY <- matrix(0, nrow=redSteps, ncol = simulations) #sumAUC to stop reduction iteration print(simulations) for (simulation in 1:simulations) { print(simulation) stopred <- 0 #calculate number of iteration when Absolute Error converges for (reduction in 1:redSteps){ #for sumabsErrors with no noisy X or Y if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } AbsError[reduction, simulation] <- sum(abs(t(predictionVector[simulation,reduction,])-Y[,simulation])) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(AbsError[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.07){ nred[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end iteration reduction stopred <- 0 for (reduction in 1:redSteps){ #for sumabsErrors with no noisy X or Y if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } AbsErrorNoiseX[reduction, simulation] <- sum(abs(t(predictionVectorNoise[simulation,reduction,])-Y[,simulation])) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(AbsErrorNoiseX[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.07){ nredNoiseX[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end iteration reduction stopred <- 0 for (reduction in 1:redSteps){ #for sumabsErrors with no noisy X or Y if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } AbsErrorNoiseY[reduction ,simulation] <- sum(abs(t(predictionVector[simulation, reduction,])-Ynoise[,simulation])) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(AbsErrorNoiseY[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.07){ nredNoiseY[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end iteration reduction stopred <- 0 for (reduction in 1:redSteps){ #for sumabsErrors with no noisy X or Y if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } AbsErrorNoiseXNoiseY[reduction ,simulation] <- sum(abs(t(predictionVectorNoise[simulation,,])-Ynoise[,simulation])) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(AbsErrorNoiseXNoiseY[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.01){ nredNoiseXNoiseY[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end iteration reduction #calculate number of iteration when AUC converges stopred <- 0 for (reduction in 1:redSteps){ if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } sumAUC[reduction, simulation] <- auc(yBin[,simulation], predictionVectorClass[simulation, reduction, ]) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(sumAUC[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.0001){ nredAUC[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end reduction stopred <- 0 for (reduction in 1:redSteps){ if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } sumAUCNoiseX[reduction, simulation] <- auc(yBin[,simulation], predictionVectorClassNoise[simulation, reduction, ]) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(sumAUCNoiseX[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.0001){ nredAUCNoiseX[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end reduction stopred <- 0 for (reduction in 1:redSteps){ if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } sumAUCNoiseY[reduction, simulation] <- auc(yBinNoise[,simulation], predictionVectorClass[simulation, reduction, ]) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(sumAUCNoiseY[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.0001){ nredAUCNoiseY[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end reduction stopred <- 0 for (reduction in 1:redSteps){ if(stopred == 1 ){ #if grad was small enough leave reduction iteration break } sumAUCNoiseXNoiseY[reduction, simulation] <- auc(yBinNoise[,simulation], predictionVectorClassNoise[simulation, reduction, ]) if(as.integer(reduction/10)==(reduction/10)){ #print(reduction) grad <- mean(gradient(sumAUCNoiseXNoiseY[(reduction-9):reduction])) #mean of gradient of error curve #print(paste("grad:", grad)) #DEBUG if(abs(grad) < 0.0001){ nredAUCNoiseXNoiseY[simulation] <- reduction #save for checking how many reduction iterations were made # print("stop") #DEBUG stopred <- 1 #if curve of errors is flat enough don't do the reduction iteration again } } }#end reduction }#end simulation rbind(nred, nredNoiseX, nredNoiseY, nredNoiseXNoiseY, nredAUC, nredAUCNoiseX, nredAUCNoiseY, nredAUCNoiseXNoiseY) #save values as matrix to use them analyse function }#end function setwd("/naslx/projects/ua341/di49suy/signal-noise-ratio/mytest-files/jobs/08") load("8-result.RData") analyseSimulation(result)

69e77210610c1a76aeef766f5d70cea258352054

2bec5a52ce1fb3266e72f8fbeb5226b025584a16

/pcIRT/R/LRT.MPRM.R

3bc3eafd93db1fbc77e384e3730ae63a0a42764e

[]

no_license

akhikolla/InformationHouse

4e45b11df18dee47519e917fcf0a869a77661fce

c0daab1e3f2827fd08aa5c31127fadae3f001948

refs/heads/master

2023-02-12T19:00:20.752555

2020-12-31T20:59:23

325,589,503

9

2

null

UTF-8

R

false

1,119

r

LRT.MPRM.R

#'@rdname lrt #'@method LRT MPRM #'@export LRT.MPRM <- function(object, splitcrit="score", ...){ if(is.character(splitcrit) && splitcrit == "score"){ sc <- rowSums(object$data) scm <- ifelse(sc > median(sc), 1,0) } else{ if(!is.vector(splitcrit)){stop("Error: split criterium has to be a vector!", call. = FALSE)} scm <- splitcrit } sp_dat <- split(as.data.frame(object$data), as.factor(scm), drop=FALSE) sp_res <- lapply(sp_dat, function(dat) MPRM(dat, start=as.vector(object$itempar[-nrow(object$itempar), -ncol(object$data)]))) sp_val <- sapply(sp_res, function(ex) ex$logLikelihood) sp_npar <- sapply(sp_res, function(ex) length(ex$estpar)) emp_Chi2 <- -2*(object$logLikelihood - sum(sp_val)) df <- sum(sp_npar) - length(object$estpar) pval <- 1-pchisq(emp_Chi2, df) itempar_split <- sapply(sp_res, function(re) list(re$itempar*(-1))) itemse_split <- sapply(sp_res, function(re) list(re$itempar_se)) res_lrt <- list(emp_Chi2 = emp_Chi2, df=df, pval=pval, itempar=itempar_split, item_se=itemse_split) class(res_lrt) <- "aLR" res_lrt }

a2f470ed1e23a9fe5801bf834bf350a537963957

a53b238211a2229b99941d5c9ced8b5dd42aa098

/man/getNOAAGuages.Rd

ef5127ca7476ea49609823f6b0eeca7e7d0bfa04

[ "CC0-1.0" ]

permissive

JerryHMartin/waterDataSupport

ca2695806cebc91f674ca37937d47ccc3bd3af76

5260b296294252f01c711a538eaff53eb869eb0e

refs/heads/master

2023-01-06T21:32:31.788682

2023-01-05T15:47:11

142,612,276

0

null

2020-07-14T18:27:11

2018-07-27T18:41:02

R

UTF-8

R

false

true

1,124

rd

getNOAAGuages.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/getNOAAGuages.R \name{getNOAAGuages} \alias{getNOAAGuages} \title{getNOAAGuages} \usage{ getNOAAGuages( siteID, plotmap = TRUE, zoomFactor = 10, limitToWatershed = TRUE, getElevations = TRUE, leafletmap = NULL ) } \arguments{ \item{siteID}{the USGS site identified for stream flow analysis} \item{plotmap}{TRUE if output plot is desired} \item{zoomFactor}{the zoom factor of the output plot} \item{limitToWatershed}{only pull datapoints on the watershed} \item{getElevations}{retrieve elevations of datapoints} \item{leafletmap}{pass a leaflet map arguements, note the output changes to a list if this parameter is invoked} } \description{ This function retrieves information on the hydraullic unit code given a USGS monitoring station ID. } \details{ Much of this code was drawn from this source. http://cyberhelp.sesync.org/leaflet-in-R-lesson/course/archive.html } \examples{ getNOAAGuages("02131000") } \keyword{NOAA} \keyword{USGS} \keyword{mapping} \keyword{precipitation}

dd13d140d5b064301632eebafa3c0c9a325476a0

9b9af459e4837a5d15c693c4ad0935be645a5667

/cachematrix.R

695acab0cca753d0014b3cf0fc6c3162d904804f

[]

no_license

billcary/ProgrammingAssignment2

0a92f8edd021eb07b51b0c0ea1e5654076d6a51d

5b732476eb3ccd374589a536a3fc6f6cf0efdc79

refs/heads/master

2020-12-25T12:17:25.306531

2014-07-23T02:48:29

null

0

null

UTF-8

R

false

1,966

r

cachematrix.R

## Put comments here that give an overall description of what your ## functions do ## This function creates a special "matrix" object ## that can cache its inverse. It provides methods ## to set and get the original matrix, as well as ## methods to set and get the inverse of the matrix. makeCacheMatrix <- function(mtrx = matrix()) { ## set initial values and initialize caching of both ## the original matrix and the inverse. ## Establish the cached value of the original matrix inv <- NULL set <- function(y) { mtrx <<- y inv <<- NULL } get <- function() mtrx ## provide a get method that returns orig. matrix setinv <- function(solve) inv <<- solve ## compute inverse using solve() getinv <- function() inv ## provide method to return the inverse ## expose methods created above for use by calling functions list(set = set, get = get, setinv = setinv, getinv = getinv) } ## This function computes the inverse of the special ## "matrix" returned by `makeCacheMatrix` above. If the inverse has ## already been calculated (and the matrix has not changed), then ## `cacheSolve` retrieves the inverse from the cache. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getinv() ## if cached value for inv exists, ## then return the value from the cache if(!is.null(inv)) { message("getting cached data") return(inv) } ## if the cached value of the inverse does not ## exist, then get the original matrix from the cache ## and then calculate its inverse using solve() data <- x$get() inv <- solve(data, ...) x$setinv(inv) ## set the value of the inverse in the cache inv ## return the inverse }

712e001b85c89e27462e388a88a901c3095efc8f

728315d8c5d09e13c67641030b92d59c5e7e2222

/easy/split_the_number.r

7e146b753d1a8e2d4fbb2064783c2042a0709049

[ "MIT" ]

permissive

shortthirdman/code-eval-challenges

88ea93c0e9385b2a0db95a05b1f3f753c900a62d

cf2197927830326539399fdd3e16c9b8a4468f7d

refs/heads/master

2023-03-06T19:34:44.607154

2023-02-26T13:30:56

92,970,178

4

0

MIT

2023-02-26T13:30:57

2017-05-31T17:14:59

Go

UTF-8

R

false

443

r

split_the_number.r

splitnum <- function(s) { r <- 0 v <- 0 o <- 1 d <- 1 for (i in 1:nchar(s[2])) { c <- substr(s[2], i, i) if (c == '+') { r <- r + v*o o <- 1 v <- 0 } else if (c == '-') { r <- r + v*o o <- -1 v <- 0 } else { v <- v*10 + as.integer(substr(s[1], d, d)) d <- d + 1 } } r + v*o } cat(sapply(strsplit(readLines(tail(commandArgs(), n=1)), " "), splitnum), sep="\n")

23f6b1accf2bd39f23e761114905feb69973c6da

2c6bdee82bc3df0a9ddf65e55e2dc4019bd22521

/SimpleRandom/server.R

ee1895c814979b1a4d5993c6d3528b242d3892ec

[]

no_license

homerhanumat/shinyGC

0eb1b55bcc8373385ea1091c36ccc1d577dc72fb

dc580961877af2459db8a69f09d539f37fd6e2ee

refs/heads/master

2021-07-11T18:20:53.862578

2021-06-21T19:18:32

37,830,825

4

2

null

UTF-8

R

false

4,490

r

server.R

library(shiny, quietly = TRUE) library(scales, quietly = TRUE) library(dplyr, quietly = TRUE) library(ggplot2, quietly = TRUE) library(tigerstats, quietly=TRUE) function(input, output) { rv <- reactiveValues( newVar = TRUE, variable = "income", factor = FALSE, psData = NULL, yMax = NULL ) observeEvent(input$variable, { rv$variable <- input$variable var <- imagpop[,rv$variable] if (is.factor(var)) { rv$factor <- TRUE } else { rv$factor <- FALSE den <- density(var) rv$yMax <- 1.5 * max(den$y) } }) observeEvent(input$sample, { rv$newVar <- FALSE pop <- imagpop[,rv$variable] samp <- sample(pop, size = input$n, replace = FALSE) popDF <- data.frame(values = pop, type = rep("Population",length(pop))) sampDF <- data.frame(values = samp, type = rep("Sample",input$n)) rv$psData <- rbind(popDF, sampDF) }) observeEvent(input$reset, { rv$newVar <- TRUE rv$variable <- "income" rv$factor <- FALSE rv$psData <- NULL }) output$newVar <- reactive({ rv$newVar }) outputOptions(output, "newVar", suspendWhenHidden = FALSE) output$initialPlot <- renderPlot({ validate( need(4 <= input$n && input$n <= 1000, message = "Sample size must be between 4 and 1000.") ) if ( ! rv$factor ) { ggplot(data = imagpop, aes_string(x = rv$variable), alpha = 0.5) + geom_density(fill = "red") + scale_y_continuous(limits = c(0,rv$yMax)) + labs(title = "Density Plot of the Population") } else { ggplot(data = imagpop, aes_string(x = rv$variable)) + geom_bar(aes(y = ..count../sum(..count..)),fill = "red") + scale_y_continuous(labels = percent) + labs(title = "Bar Graph of the Population", y = "Percent") } }) output$initialTable <- renderTable({ validate( need(4 <= input$n && input$n <= 1000, message = "Sample size must be between 4 and 1000.") ) form <- as.formula(paste0("~",rv$variable)) if ( ! rv$factor ) { favstats(form, data = imagpop)[1:8] } else { tab <- (rowPerc(xtabs(form, data = imagpop))) rownames(tab) <- "Population Percentages" tab } }) output$plot <- renderPlot({ validate( need(4 <= input$n && input$n <= 1000, message = "Sample size must be between 4 and 1000.") ) if ( ! rv$newVar ) { if ( ! rv$factor ) { dfSamp <- subset(rv$psData, type == "Sample") ggplot(data = rv$psData, aes(x = values, fill = type)) + geom_density(alpha = 0.5) + geom_rug(data = dfSamp, aes(x = values)) + labs(title = "Density Plot of the Population, with Sample", x = rv$variable) + scale_y_continuous(limits = c(0, rv$yMax)) + guides(fill = guide_legend(title = "Plot for:")) } else { df <- rv$psData %>% group_by(type, values) %>% summarize(n = n()) %>% mutate(relFreq = n/sum(n)) %>% mutate(pos = 0.5 * relFreq) %>% mutate(label = paste0(sprintf("%.1f", relFreq*100), "%")) ggplot(data = df, aes(x = values, fill = type)) + geom_bar(aes(y = relFreq), stat = "identity", position = "dodge") + scale_y_continuous(labels = percent) + geom_text(aes(x = values, y = pos, label = label, ymax = relFreq), position = position_dodge(width = 1),size = 5) + labs(title = "Density Plot of the Population, with Sample", x = rv$variable) + guides(fill = guide_legend(title = "Bars for:")) } } }) output$table <- renderTable({ validate( need(4 <= input$n && input$n <= 1000, message = "Sample size must be between 4 and 1000.") ) if (! is.null(rv$psData) ) { df2 <- rv$psData names(df2)[1] <- rv$variable if ( ! rv$factor ) { form <- as.formula(paste0(rv$variable," ~ type")) favstats(form, data = df2)[1:8] } else { form <- as.formula(paste0("~ type + ",rv$variable)) tab <- rowPerc(xtabs(form, data = df2)) tab } } }) }

d06411bcbdb200ceafe733963a6262a32b3f397e

b84d89b3f67fbd57e2d41f42c23c1f82fe7ba9fd

/R/fitted.TEfitAll.R

bc09bd16ba24957b992f9d769deea9fd31dbb1f2

[ "MIT" ]

permissive

akcochrane/TEfits

04305849bd8393c9e816312085a228ccdbd621e3

e11b07b2d9fed9eb6e8221c8cfdd86b9e287180e

refs/heads/master

2023-06-08T00:04:21.025346

2023-06-07T22:20:11

225,967,950

1

0

MIT

2023-06-07T22:20:13

2019-12-04T22:22:41

HTML

UTF-8

R

false

1,163

r

fitted.TEfitAll.R

#' Get fitted values and summary statistics from a set of TEfit models #' #' @param TEs3s A set of models fit by TEfitAll() #' #' @method fitted TEfitAll #' @export #' #' @examples #' \dontrun{ #' m <- TEfitAll(anstrain[,c('acc','trialNum')],groupingVar = anstrain$subID) #' fitted_data <- fitted(m) #' plot(fitted_data$meanPred) #' } #' fitted.TEfitAll <- function(TEs3s){ # loop through the fits, get the predVals, and calculate the mean/SE allPreds <- matrix(NA,length(TEs3s$allFitList),nrow(TEs3s$allFitList[[1]]$data)*10) ## make it plenty big for varieties of sizes maxTime <- 0 for (curGroup in 1:length(TEs3s$allFitList)){ curFit <- TEs3s$allFitList[[curGroup]] allPreds[curGroup,1:nrow(curFit$data)] <- curFit$model$fitVals if(maxTime<max(curFit$data[,2],na.rm=T)){maxTime <- max(curFit$data[,2],na.rm=T)} } allPreds <- allPreds[,apply(allPreds,2,function(x) !all(is.na(x)))]## trim the NAs meanPred <- apply(allPreds,2,mean,na.rm=T) ciPred <- qnorm(.975)*(apply(allPreds,2,sd,na.rm=T)/sqrt(apply(allPreds,2,function(x) sum(!is.na(x))))) return(list(allPreds=allPreds,meanPred=meanPred,ciPred=ciPred,maxTime=maxTime)) }

3b17042127610e4c1a4b8711b1428ff3edd46a49

63e5fc70d2e6233457fc9ad407d7e4984bfc8997

/man/plot_hisafe_voxels.Rd

9719889d54e242ab28a4c3070521efc8d412f520

[]

no_license

Boffiro/hisafer

32f648f6aca222d01006d25da1b237846b13113e

8773fe3d5d2aa6d307af0088a6f6e79cc9a087d0

refs/heads/master

2023-05-06T17:53:22.060974

2020-10-16T09:48:48

null

0

null

UTF-8

R

false

true

2,829

rd

plot_hisafe_voxels.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{plot_hisafe_voxels} \alias{plot_hisafe_voxels} \title{Tile plot of Hi-sAFe voxels output variable} \usage{ plot_hisafe_voxels( hop, variable, date.min = NA, date.max = NA, simu.names = "all", X = NA, Y = NA, Z = NA, summarize.by = "z", facet.simu = TRUE, facet.z = FALSE, vline.dates = NA, plot = TRUE ) } \arguments{ \item{hop}{An object of class hop.} \item{variable}{A character string of the name of the variable to plot.} \item{date.min}{A character vector containing the minimum date (yyyy-mm-dd) to include. If \code{NA}, the default, than the minimum value in the voxels profile is used} \item{date.max}{A character vector containing the minimum date (yyyy-mm-dd) to include. If \code{NA}, the default, than the maximum value in the voxels profile is used} \item{simu.names}{A character string containing the SimulationNames to include. Use "all" to include all available values.} \item{X}{A numeric vector of the x values of the voxels to include. If \code{NA}, the default, then all x values are used.} \item{Y}{A numeric vector of the y values of the voxels to include. If \code{NA}, the default, then all y values are used.} \item{Z}{A numeric vector of the z values of the voxels to include. If \code{NA}, the default, then all z values are used.} \item{summarize.by}{One of 'x', 'y', or 'z', indicating an axis over which to average voxel values. If \code{NA}, the default, then no averaging is done and each voxel is plotted as its own line.} \item{vline.dates}{A character vector of dates (yyyy-mm-dd) at which to plot dashed vertical reference lines. If \code{NA}, the default, then no reference lines are drawn.} \item{plot}{If \code{TRUE}, the default, a ggplot object is returned. If \code{FALSE}, the data that would create the plot is returned.} } \value{ Returns ggplot object. } \description{ Plots a tile plot of a single Hi-sAFe voxels output variable. If a single date is provided, SimulationName is used as the column facet. Otherwise, Date is used as the column facet. } \examples{ \dontrun{ # After reading in Hi-sAFe simulation data via: mydata <- read_hisafe(path = "./") # You can create a tile plot of waterAvailable: tile.plot <- plot_hisafe_voxels(mydata, "waterAvailable", paste(1998, 6:8, 1, sep = "-")) # Once you have the plot object, you can display it and save it: tile.plot ggsave_fitmax("tiled_waterAvailable.png", tile.plot) } } \seealso{ Other hisafe plot functions: \code{\link{plot_hisafe_annualcells}()}, \code{\link{plot_hisafe_bg}()}, \code{\link{plot_hisafe_cells}()}, \code{\link{plot_hisafe_monthcells}()}, \code{\link{plot_hisafe_scene}()}, \code{\link{plot_hisafe_tstress}()}, \code{\link{plot_hisafe_ts}()} } \concept{hisafe plot functions}

bb6107d4f95c04c9a2d6dc2b99b5c04e323f52c1

dd23f7848ee431d060d56af188a88d0dedaa0522

/3-exploratory-data-analysis/week4/plot1.R

bc82a7969d1c4a442ef4f0033e0cd8be775a1b76

[]

no_license

ryancey1/datascience-coursera

3d0b33d57dbb738399cfe09aba41230ea065318e

d71ae50686a3e00185bcf1debcb3e415692c58ff

refs/heads/main

2023-03-11T07:51:27.275516

2021-02-20T23:14:54

323,975,435

0

null

UTF-8

R

false

2,346

r

plot1.R

# plot1.R ----------------------------------------------------------------- # 1. Have total emissions from PM2.5 decreased in the United States from 1999 to # 2008? Using the `base` plotting system, make a plot showing the total PM2.5 # emission from all sources for each of the years 1999, 2002, 2005, and 2008. # HOUSEKEEPING ------------------------------------------------------------ ## load libraries library(dplyr) library(ggplot2) ## set working directory if (!grepl("3-exploratory-data-analysis/week4", getwd(), fixed = TRUE)) { setwd("3-exploratory-data-analysis/week4") } ## create directories if (!dir.exists("data")) { dir.create("data") } if (!dir.exists("plots")) { dir.create("plots") } ## download & extract RDS files if not already done if (!file.exists("exdata_data_NEI_data.zip")) { zip = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" file = "exdata_data_NEI_data.zip" download.file(url = zip, destfile = file) } if (!file.exists("data/Source_Classification_Code.rds") & !file.exists("data/summarySCC_PM25.rds")) { file = "exdata_data_NEI_data.zip" unzip(zipfile = file, exdir = "data/") } # read in RDS files if (!any(ls() == "NEI")) NEI <- readRDS(file = "data/summarySCC_PM25.rds") if (!any(ls() == "SCC")) SCC <- readRDS(file = "data/Source_Classification_Code.rds") # PLOT -------------------------------------------------------------------- # prepare data frame and variables for plot plot <- NEI %>% select(Emissions, year) %>% group_by(year) %>% summarize(across(.fns = sum), .groups = "keep") colors = c("red", "blue", "purple", "green") lm <- lm(Emissions ~ year, data = plot) # plot to PNG png("plots/plot1.png") bar <- barplot( Emissions ~ year, data = plot, col = colors, ylab = "PM2.5 Emissions (tons)", xlab = "", main = "Total PM2.5 Emissions in the United States\n(1999 - 2008)" ) lines( x = bar, y = lm$fitted.values, lty = 3, lwd = 2, col = "black" ) legend( "topright", legend = "Linear Regression", lty = 3, lwd = 2, col = "black", cex = 0.7, bty = "o" ) text( x = bar, y = plot$Emissions - 1.5e6, labels = as.integer(plot$Emissions), cex = 0.8 ) dev.off() ## clean up working environment rm(list = ls(pattern = "[^NEI|^SCC]"))

367d37293be18766da9d706edbfa86df875adcf6

5500882cda0a3b4af35d647acc4c36e3d8e5ba18

/hw3/hw3_code.R

7682c6ced0397cfc0ee3512c22b063331074fbec

[]

no_license

pinesol/texas

85b1fe1f5ffed386eb1fe3053d9649ca1b9ef442

cee029dd32bc21689f8d40063bffc7f1f3690efa

refs/heads/master

2021-01-21T13:33:39.735392

2016-05-11T02:07:06

52,619,333

1

0

null

UTF-8

R

false

13,004

r

hw3_code.R

#install.packages("lda") #install.packages("topicmodels") #install.packages("stm") #install.packages("LDAvis") # TODO save worksave so you can load it in the markdown file require(quanteda, warn.conflicts = FALSE, quietly = TRUE) library(topicmodels) data(immigNewsCorpus, package = "quantedaData") # Get 4 most common newspapers topPapers <- sort(table(immigNewsCorpus[["paperName"]]), decreasing = TRUE) reducedCorpus <- subset(immigNewsCorpus, paperName %in% names(topPapers)[1:4]) # Creates custom_stopwords vector load('~/texas/hw3/custom_stopwords.RData') news_dfm <- dfm(reducedCorpus, ignoredFeatures = custom_stopwords) news_dfm <- trim(news_dfm, minCount=30, minDoc=20) NUM_TOPICS <- 30 news_lda <- LDA(news_dfm, NUM_TOPICS, method='Gibbs', control=list(seed=2, burnin=100, thin=10, iter=1000)) get_terms(news_lda, 10) # Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 # "war" "ukip" "eu" "family" "english" # "world" "farage" "european" "life" "language" # "prince" "party" "europe" "back" "care" # "british" "racist" "britain" "father" "home" # "history" "leader" "cameron" "mother" "health" # "charles" "nigel" "referendum" "wife" "test" # "royal" "campaign" "union" "years" "speak" # "tour" "comments" "british" "day" "nhs" # "time" "european" "merkel" "home" "years" # "great" "candidate" "countries" "couple" "british" # Topic 1: British Royalty # Topic 2: UK Independence Party # Topic 3: Brexit # Topic 4: Family # Topic 5: Domestic Policy # Problem 1f ##Store the results of the distribution of topics over documents # subset the dfm to guardian and daily mail papers <- reducedCorpus$documents$paperName # function that finds the second max which.max2 <- function(x) { max(which(x == sort(x, partial=(NUM_TOPICS-1))[NUM_TOPICS-1])) } guardian_topic_dist <- news_lda@gamma[which(papers == 'guardian'),] top_guardian_topics <- apply(guardian_topic_dist, 1, which.max) second_top_guardian_topics <- apply(guardian_topic_dist, 1, which.max2) # TODO use paper names instead of dates guardian_topics <- data.frame(first=top_guardian_topics, second=second_top_guardian_topics) mail_topic_dist <- news_lda@gamma[which(papers == 'mail'),] top_mail_topics <- apply(mail_topic_dist, 1, which.max) second_top_mail_topics <- apply(mail_topic_dist, 1, which.max2) mail_topics <- data.frame(first=top_mail_topics, second=second_top_mail_topics) gmail_topics <- rbind(guardian_topics, mail_topics) gmail_topics$paper <- rep(c('guardian', "mail"), times=c(nrow(guardian_topics), nrow(mail_topics))) # plot z <- ggplot(gmail_topics, aes(x=1:nrow(gmail_topics), y=first, pch="First", color=paper)) + geom_point() z + geom_point(aes(x=1:nrow(gmail_topics), y=second, pch="Second", color=paper)) + theme_bw() + xlab(NULL)+ ylab("Topic Number") + theme(axis.ticks = element_blank(), axis.text.x = element_blank()) + labs(pch="Topic Order", color='Paper') + ggtitle("Paper Topics") # Problem 1g # Average contribution by a topic to a newspaper. # docs by topics matrix. each row is a histogram over the topics. first5_lda <- news_lda@gamma[,1:5] avgTopicProportions <- function(paper) { papers <- reducedCorpus$documents$paperName paper_first5_lda <- first5_lda[which(papers == paper),] ave_topic_proportions <- colSums(paper_first5_lda) / length(papers) names(ave_topic_proportions) <- c('British Royalty', 'UKIP', 'Brexit', 'Family', 'Domestic Policy') return(ave_topic_proportions) } avgTopicProportions('guardian') # British Royalty UKIP Brexit Family Domestic Policy # 0.006119630 0.010324730 0.008053529 0.008207289 0.006613840 # liberal paper, UKIP largest, Royalty lowest avgTopicProportions('mail') # British Royalty UKIP Brexit Family Domestic Policy # 0.007216055 0.011378225 0.007308273 0.012038282 0.008992935 # conservative paper, Family largest, Royalty lowest avgTopicProportions('telegraph') # British Royalty UKIP Brexit Family Domestic Policy # 0.008811142 0.012429343 0.011735386 0.008636098 0.007014645 # UKIP Largest, Domestic Policy Lowest avgTopicProportions('times') # British Royalty UKIP Brexit Family Domestic Policy # 0.005364085 0.009009731 0.008006753 0.007744099 0.005294511 # UKIP Largest, Domestic Policy Lowest # TODO problem 1h install.packages("LDAvis") library(LDAvis) # TODO the example given in topicmodelExamples2.R uses the lda library, not the topic models library # could you give us an example of how to use the LDAvis library with the topicsmodels library? #news_lda jsonLDA <- createJSON(phi=exp(news_lda@beta), theta=news_lda@gamma, doc.length=ntoken(news_dfm), vocab=news_lda@terms, term.frequency=colSums(news_dfm)) #install.packages('servr') #serVis(jsonLDA, out.dir = "visCollLDA", open.browser = TRUE) serVis(jsonLDA, open.browser = TRUE) # TODO require(png) # TODO grid.raster(readPNG()) # Using this library is not obvious at all # example: http://cpsievert.github.io/LDAvis/reviews/reviews.html # Problem 2 # Problem 2 Topic Stability news_lda_2 <- LDA(news_dfm, NUM_TOPICS, method='Gibbs', control=list(seed=3, burnin=100, thin=10, iter=1000)) # Problem 2b library(lsa) # For the cosine function closestTopicMap <- function(lda_1, lda_2) { # take the distribution of words for each topic from the first model, and exponentiate it to get probabilities. words_dist_1 <- exp(lda_1@beta) words_dist_2 <- exp(lda_2@beta) # given a single topics's word distribution, find all topic from the second lda that most closely matches it. closestTopic <- function(words_dist_row) { # bind the first arg of cosine similarity to the input word distribution. cosine_fn <- function(y) { cosine(words_dist_row, y) } # Get the cosine similarity to each, and return the index of the maximum element. which.max(apply(words_dist_2, 1, cosine_fn)) } apply(words_dist_1, 1, closestTopic) } lda_2_closest_topics <- closestTopicMap(news_lda_2, news_lda) for (i in 1:length(lda_2_closest_topics)) { print(paste('Topic', i, '->', lda_2_closest_topics[i])) } # Topic 1 -> 19 # Topic 2 -> 18 # Topic 3 -> 12 # Topic 4 -> 10 # Topic 5 -> 15 # Topic 6 -> 11 # Topic 7 -> 4 # Topic 8 -> 2 # Topic 9 -> 25 # Topic 10 -> 1 # Topic 11 -> 9 # Topic 12 -> 21 # Topic 13 -> 7 # Topic 14 -> 20 # Topic 15 -> 17 # Topic 16 -> 9 # Topic 17 -> 25 # Topic 18 -> 29 # Topic 19 -> 4 # Topic 20 -> 22 # Topic 21 -> 8 # Topic 22 -> 23 # Topic 23 -> 26 # Topic 24 -> 5 # Topic 25 -> 28 # Topic 26 -> 27 # Topic 27 -> 16 # Topic 28 -> 3 # Topic 29 -> 13 # Topic 30 -> 30 # Problem 2c # closest_topics maps topics from lda_1 to the most similar topics in lda_2 avg_terms_in_common <- function(lda_1, lda_2, closest_topics) { num_shared <- sapply(1:length(closest_topics), function(i) { length(intersect(terms(lda_1, 10)[,i], terms(lda_2, 10)[,closest_topics[i]])) }) mean(num_shared) } avg_terms_in_common(news_lda_2, news_lda, lda_2_closest_topics) # returns 6.666667 # Problem 2d small_lda_1 <- LDA(news_dfm, 10, method='Gibbs', control=list(seed=4, burnin=100, thin=10, iter=1000)) small_lda_2 <- LDA(news_dfm, 10, method='Gibbs', control=list(seed=5, burnin=100, thin=10, iter=1000)) small_closest_topics <- closestTopicMap(small_lda_1, small_lda_2) avg_terms_in_common(small_lda_1, small_lda_2, small_closest_topics) # Returns 5.6 # 10 topics are less stable than 30 # Problem 3 library(stm) install.packages("Rtsne") install.packages("geometry") # subset the dfm to guardian and daily mail mailGuardCorpus <- subset(immigNewsCorpus, paperName %in% c('mail', 'guardian')) paper <- mailGuardCorpus$documents$paperName text <- mailGuardCorpus$documents$texts # TODO chose 2015 arbitrarily. #date <- as.Date(as.numeric(mailGuardCorpus$documents$day) - 1, origin = "2014-01-01") # TODO numeric? days <- as.numeric(mailGuardCorpus$documents$day) mailGuard.df <- data.frame(paper, text, days)#TODO, date) processed_corpus <- textProcessor(mailGuard.df$text, metadata=mailGuard.df, customstopwords=custom_stopwords) ##remove some words for speed purposes # TODO wtf # TODO TODO ERROR # "Error in prepDocuments(processed$documents, processed_corpus$vocab, processed_corpus$meta, : # Your documents object has more unique features than your vocabulary file has entries." out_25 <- prepDocuments(processed_corpus$documents, processed_corpus$vocab, processed_corpus$meta, lower.thresh=25) # TODO wtf is the "spline of the date variable"? I'm ignoring it and just using the index. # NOTE reduce max EM iterations to 5 from 25 to make it faster. fitSpec25 <- stm(out_25$documents, out_25$vocab, K=0, init.type="Spectral", content=~paper, prevalence = ~paper + days, #smooth.spline(date), # TODO maybe just use numeric vector instead of date max.em.its=30, data=out_25$meta, seed=5926696) # Top 5 topics labelTopics(fitSpec25, 1:5) # Topic Words: # Topic 1: church, sex, christian, sexual, oppos, savil, spring # Topic 2: beauti, pop, cloth, cook, hate, knew, writer # Topic 3: birmingham, inspector, inquiri, gay, knew, friday, discuss # Topic 4: bnp, farag, ukip, confer, euro, extremist, nigel # Topic 5: bbcs, savil, leftw, broadcast, corpor, bbc, fee # Covariate Words: # Group guardian: grdn, newspaperid, caption, page, overwhelm, form, address # Group mail: newspapermailid, damonl, daim, newspap, mail, onlin, spark # Topic-Covariate Interactions: # Topic 1, Group guardian: anim, muslim, islam, religion, food, scare, recommend # Topic 1, Group mail: novemb, stress, briton, wintour, survey, betray, minimum # Topic 2, Group guardian: itali, sea, island, euro, rent, northern, coupl # Topic 2, Group mail: water, sea, murder, rape, road, wealthi, network # Topic 3, Group guardian: homosexu, asian, citizenship, teacher, guest, islam, facebook # Topic 3, Group mail: rowena, mason, brown, gordon, field, stanc, backbench # Topic 4, Group guardian: manifesto, surg, legitim, revolt, euroscept, poll, referendum # Topic 4, Group mail: ship, engin, human, eye, dream, smile, artist # Topic 5, Group guardian: terribl, corrupt, academ, ian, style, salari, bloodi # Topic 5, Group mail: offend, drug, releas, search, illeg, oper, approv # Names for the topics: # # 1: Christian sex # 2: Lifestyle # 3: Birmingham investigation? # 4: Right-wing politics # 5: Public broadcasting topic_names <- c('Christian sex', 'Lifestyle', 'Birmingham investigation', 'Right-wing politics', 'Public broadcasting') # TODO in the recitation code # TODO use labelTopics? # TODO estimateEffect method=difference # TODO use verbose_labels = F, label_type=custom, custom.albels = blah # visualization # TODO use estimateEffect method=continutuous ##change data types out_25$meta$paper<-as.factor(out_25$meta$paper) ##pick specifcation prep<-estimateEffect(topic_names ~ paper , fitSpec25, meta=out_25$meta) ##plot effects plot.estimateEffect(prep, covariate="paper", topics=1:5, model=out_25, method="difference", cov.value1 = "guardian", cov.value2 = "mail", xlab = "More Guardian......More Mail", xlim=c(-.1, .1), labeltype='custom', verboseLabels=F, custom.labels=topic_names) ##pick specifcation--over time prep <- estimateEffect(1:5 ~ s(days) , fitSpec25, meta=out_25$meta) ##plot effects plot.estimateEffect(prep, covariate="days", topics=1:5, model=out_25, method="continuous", labeltype='custom', verboseLabels=F, custom.labels=topic_names) ##### Problem 4 sotu_dfm <- dfm(subset(inaugCorpus, Year>1900)) nixon_index <- which(sotu_dfm@Dimnames$docs == "1969-Nixon") obama_index <- which(sotu_dfm@Dimnames$docs == "2009-Obama") # are these indices correct? df_fit <- textmodel_wordfish(sotu_dfm, c(obama_index, nixon_index)) # get index of left and rightmost things in df_fit@theta, then get their names # @theta: scores of each doc # Leftmost sotu_dfm@Dimnames$docs[which.min(df_fit@theta)] # "1993-Clinton" # Rightmost sotu_dfm@Dimnames$docs[which.max(df_fit@theta)] # "1909-Taft" # fascism df_fit@beta[which(df_fit@features == 'fascism')] # -4.291355 # Left???? #df_fit@features: words #df_fit@beta: scores for words. how much it discriminates on left v right. negative is left. # psi: fixed effects: how frequent or "stop wordy" a word is. ##most important features--word fixed effects ##guitar plot # plotting distriminating effect (beta) vs fixed effect (psi) words<-df_fit@psi names(words) <- df_fit@features sort(words)[1:50] sort(words, decreasing=T)[1:50] weights<-df_fit@beta plot(weights, words)

4ebcac1b25a286f51c94e1b1bf884617eedb3d4e

3682cbe55c4d8f6f8ec1cbef4f40d7f9d20734cb

/shinyAppProject1/server.R

0d1260a7b52716220ad44638b1fd8c21175691f9

[]

no_license

jeremiahkramer/WWTSLEDWestInternProject

f30c0d1d957733401a30c5856360e28e9d4fba16

0937f834196e81f17e9b671114ec5556e0dc70ec

refs/heads/master

2020-06-22T06:15:11.064774

2019-08-29T20:52:01

197,654,888

0

1

null

2019-08-05T19:21:28

2019-07-18T20:49:31

Python

UTF-8

R

false

2,076

r

server.R

library(shiny) library(ggplot2) library(DT) function(input, output) { #create pie chart output output$chart1 <- renderPlotly({ #handle if all P&L selected if(input$`P&L1` == "All" && input$AM == "none"){ #prepare dataframe for plot_ly chart_data <- data.frame( "Category" = unique(data$`P&L`), #add up GP by P&L GP = aggregate(as.numeric(data$GP), by = list(data$`P&L`), FUN = sum)[,2] ) } #handle if a P&L is selected with no AM if(input$`P&L1` != "All" && input$AM == "none"){ chart_data <- data[data$`P&L` == input$`P&L1`,] #get data for specific P&L #prepare dataframe for plot_ly chart_data <- data.frame( "Category" = unique(chart_data$`External Rep`), #add GP by AM GP = aggregate(as.numeric(chart_data$GP), by = list(chart_data$`External Rep`), FUN = sum)[,2] ) } #handle if an AM is selected if(input$AM != "none"){ #handle if specific AM selected if(input$AM != "All"){ chart_data <- data[data$`External Rep` == input$AM, ] #get data for specific AM } #prepare dataframe for plot_ly chart_data <- data.frame( "Category" = unique(chart_data$`Ship to Customer`), #add GP grouped by customer GP = aggregate(as.numeric(chart_data$GP), by = list(chart_data$`Ship to Customer`), FUN = sum)[,2] ) } plot_ly(chart_data, labels = ~Category, values = ~GP, type = 'pie') }) #possible second chart output$chart2 <- renderPlotly({ }) #create AM selection which depends on P&L selection output$secondSelection <- renderUI({ if(input$`P&L1` != "All"){ chart_data <- data[data$`P&L` == input$`P&L1`,] } selectizeInput("AM", "AM", c("none","All", unique(as.character((chart_data$`External Rep`))))) }) #create order option selection which will depend on AM selection output$thirdSelection <- renderUI({ selectizeInput("order_options", label = "Order Options", c("none", "% GP", "Status")) }) }

1295c5a4acbcef98e05464ebaf7f96ff35d597a1

e077f629946716c73ff9a53d82e3c8006c6dec2b

/man/neuromorpho_field_entries.Rd

f51879a9974f2bb0207e06b2867f2fb09e351931

[]

no_license

natverse/neuromorphr

8c8a006b51b560ed686ff6d4513dda267cefe55e

7f23e7861bcdea1206d3d43d43bd2f08667f69b9

refs/heads/master

2023-05-03T19:42:50.918806

2023-04-19T00:23:54

184,127,352

1

null

UTF-8

R

false

true

1,116

rd

neuromorpho_field_entries.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/search.R \name{neuromorpho_field_entries} \alias{neuromorpho_field_entries} \title{Return the available meta data entries for neuromorpho neuron fields} \usage{ neuromorpho_field_entries( field = "species", neuromorpho_url = "http://neuromorpho.org", ... ) } \arguments{ \item{field}{a valid neuron field, as returned by \code{neuromorpho_fields}} \item{neuromorpho_url}{the base URL for querying the neuromorpho database, defaults to \url{http://neuromorpho.org}} \item{...}{methods passed to \code{neuromorpho_async_req}, or in some cases, \code{neuromorphr:::neuromorpho_fetch}} } \description{ Returns a list of values present in the repository for the neuron field requested. These values can be used in the search criteria section of the custom queries. } \details{ All the data fields, and their entries, can be seen and explored on neuromorpho.org at \url{http://neuromorpho.org/MetaData.jsp}. } \seealso{ \code{\link{neuromorpho_neurons_info}}, \code{\link{neuromorpho_read_neurons}}, \code{\link{neuromorpho_fields}} }

3f6e5442fbbbd09caf0fe93e2c86b38b58fbaca3

0abc4ff64360255655b9f304716ad8122e18685a

/R/models/LinearRegression.R

4c6b727cfa9e30d6124467d9b66344bdf0aaa940

[]

no_license

rodrigoqaz/tcc-mba-fatec

01ceadded6c8e794fcc600779d3ecec087d1402e

df37f28526b8218e05b04d2ff55244a319059e92

refs/heads/master

2023-01-14T14:20:17.984056

2020-11-19T13:25:00

297,507,152

3

0

null

UTF-8

R

false

764

r

LinearRegression.R

# Linear Regression: library(tidyverse) library(tidymodels) LinearRegression <- function(train, test) { set.seed(4242) # Cria a receita: recipe <- train %>% recipe(Y~.) %>% update_role(day, new_role = "id variable") %>% step_corr(all_predictors()) %>% step_center(all_predictors(), -all_outcomes()) %>% step_scale(all_predictors(), -all_outcomes()) %>% prep() # Trata as bases de treino e teste: train <- recipe %>% bake(train) test <- recipe %>% bake(test) # Modelo de random forest lm <- linear_reg(penalty = 0.001, mixture = 0.5) %>% set_engine("glmnet") %>% fit(Y ~., data = train) # Gera as previsões predictions <- lm %>% predict(test) %>% pull() return(predictions) }

937e9b0f856bec1cfd644d6cdde19a737fd5c3da

577cecbf31ea2ea0e82851d6f1555853e3f4f9f1

/Fig4B_plot_insulation_Sox9.R

3c7c8081f6c358cf496a344d86058606128d1a97

[]

no_license

bianlab-hub/Chen_Sci_Adv_2022

a5972fc6e5b0e6711d739b74e51ded7b9c0125de

2072fc1e81a6d576de42de4e2d047d7b7e391b72

refs/heads/main

2023-04-13T00:27:23.818781

2023-02-28T10:18:36

547,825,253

0

null

UTF-8

R

false

1,074

r

Fig4B_plot_insulation_Sox9.R

library(dplyr) library(ggplot2) MC_insulation <- read.csv('MC_HiC_combined_50kb_insulation', sep='\t', header=T) MC_insulation_chr11<-dplyr::filter(MC_insulation,chrom == 'chr11') FL_insulation <- read.csv('FL_HiC_combined_50kb_insulation', sep='\t', header=T) FL_insulation_chr11<-dplyr::filter(FL_insulation,chrom == 'chr11') pdf(file = 'Sox9_Kcnj2_TAD_insulation_score_MC_vs_FL.pdf',width = 12,height = 6) ggplot()+geom_line(data = MC_insulation_chr11,aes(x = start, y = log2_insulation_score_500000,colour = "MC_insulation"),size=0.4)+ geom_line(data = FL_insulation_chr11,aes(x = start, y = log2_insulation_score_500000,colour = "FL_insulation"),size=0.4)+ xlab("chr11 Coordinates")+ylab("Insulation Score")+ylim(-1.5,1.5)+ theme_bw()+ theme( panel.grid = element_blank(), legend.position = c('none') )+ xlim(c(110000000,114000000))+ scale_colour_manual("",values = c("MC_insulation" = "red","FL_insulation"="blue"))+ ggtitle("Sox9-Kcnj2 locus")+geom_vline(xintercept = c(111555526,111833044),linetype = 'dashed') dev.off()

966bde1c73208ff6ad702d4f0255d048a7669c57

c445257a86a6ce0104edd866bd6a1cd1a776a695

/testing.R

ccd8cbd6016f39d218da8f5ac8f0789ab06a1221

[]

no_license

bweiher/recallR

969b67c5a060f5ea341723f2a759afaceba9b77b

d0f767a7ed3b15d3207c7f94f94f208249674266

refs/heads/master

2020-03-16T13:31:28.374522

2018-10-11T02:57:02

132,692,782

0

null

UTF-8

R

false

2,022

r

testing.R

library(glue) installed_packages <- as.character(as.data.frame(installed.packages())$Package) installed_packages_p <- paste(installed_packages, collapse = ",") file <- ".Rprofile" pat <- "#libs:" # TODO decide about methodology ,,, do we wanna # TODO do a check if its already been written to Rprofile. ~ DELTE In that case! # TODO or append missing items # TODO create central repoo # TODO creat permanunent backup solution # TODO a function that integrates with a github profile and pulls data from there.. eg .Rprofile register_libs <- function(file = ".Rprofile"){ setwd("~") if(file.exists(file)){ # .Rproilee exists lgl <- grepl(pat, readLines(file)) if(any(lgl)){ # libraries have been written already thing <- c(readLines(file), glue("{pat} {installed_packages_p")) writeLines(text = thing, con = ".Rprofile") shell(glue("echo {pat} {thing} > .Rprofile ")) } else { # this thingy already exists in .Rprofile shell(glue("echo {pat} {installed_packages} >> .Rprofile")) message(paste0("Libraries appended to .RProfile at ", getwd(), file)) } } # rprofile dont exist } register_libs() # find libs too lines <- readLines("~/.Rprofile") packages_registered <- trimws(strsplit(gsub(pattern = pat, x = lines[grepl("^#libs", lines)], replacement = ""),",")[[1]],"both") what_to_install <- setdiff(packages_registered, as.character(installed.packages()[,1])) # does the pat exist from above ? pat_exists_vector <- grepl(pat, lines) #paste(other_items[length(other_items)], "\n") # if it exists already, edit that line only... if(sum(pat_exists) > 0){ print("m") } # pull in other lines other_items <- lines[!pat_exists_vector] # item to adjust item_to_adjust <- lines[pat_exists_vector] lib_to_add <- "meowlibrary" # TODO add in collapse paste , item_to_adjust <- paste(trimws(item_to_adjust,"both"), trimws(lib_to_add, "both"), sep = ",") writeLines(text = c(other_items, item_to_adjust),con = "~/.Rprofile")

1ffa9821eadc9db422f2ddd5c2a95e7452c2cf64

c12ddf66b4ce31aec34d8fdd99c99fea12f36a81

/tests/testthat/helper-versioning.R

8223826c78adcb4b90a18c80a73261ddf68c04a2

[]

no_license

r-lib/oldie

ac49efbbde20e19d5a81961e74f27e1a4a304c81

36d18ec67997f748ee88391cf8b13da6a7a67a5e

refs/heads/master

2021-01-20T05:19:30.070679

2019-07-25T12:23:56

2019-07-25T12:24:12

101,428,392

18

4

null

2018-06-18T11:14:32

2017-08-25T17:48:09

R

UTF-8

R

false

385

r

helper-versioning.R

future_rlang_ver <- function() { version <- pkg_ver("rlang") version <- ver_trim(version, 3) version <- ver_bump(version, "minor") version[[3]] <- 0 version } past_rlang_ver <- function() { version <- pkg_ver("rlang") version <- ver_trim(version, 3) if (version[[3]] == 0) { version <- unbump(version, "minor") } else { version[[3]] <- 0 } version }

e0e9b22723fac1cb5ec9883d36dba4f85d8aeec0

ebbfc719a132c3716fedfa84942c600563f78392

/man/writeGhcn.Rd

4e819260178c2aedcb6f4bec5b4e487b414b8281

[]

no_license

cran/CHCN

48f859ae33d6d6edb8d72841762cf8451fcff21f

270b2dc2ebd78033ed0eccbc807d417e64671879

refs/heads/master

2021-01-23T02:30:16.880022

2012-06-07T00:00:00

null

0

null

UTF-8

R

false

689

rd

writeGhcn.Rd

\name{writeGhcn} \alias{writeGhcn} \title{A simple wrapper to \code{write.table} } \description{Simply writes a file to the data directory using \code{write.table} } \usage{ writeGhcn(data, directory = DATA.DIRECTORY, filename = "TaveCHCN.dat") } \arguments{ \item{data}{The data you want to write } \item{directory}{defaults to the processed data directory } \item{filename}{ The filename you want for the data } } \details{ Simply uses \code{write.table} to write the data } \value{side effect is a file is written } \author{Steven Mosher } \examples{\dontrun{ writeGhcn(data) } } \keyword{ files}

6f46476065fa4301455563118aff56af9dc40817

ddc2b096e681398f576a95e40c7fd366b65f50a2

/SDPSimulations/PrevalenceRegressions.R

588be5327cd89d7272b5882c34680ea4de401e25

[]

no_license

sbellan61/SDPSimulations

f334d96743c90d657045a673fbff309106e45fce

cfc80b116beafabe3e3aed99429fb03d58dc85db

refs/heads/master

2021-03-27T20:48:25.857117

2017-09-19T20:13:37

21,144,447

1

0

null

UTF-8

R

false

33,258

r

PrevalenceRegressions.R

#################################################################################################### ## Plot HIV prevalence & SDP vs fit transmission coefficients, and fit contact mixing coefficients. rm(list=ls()) # clear workspace library(metatest);library(coda);library(faraway); library(hier.part); library(AICcmodavg); library(abind) if(grepl('tacc', Sys.info()['nodename'])) setwd('/home1/02413/sbellan/DHSProject/SDPSimulations/') load("data files/allDHSAIS.Rdata") # DHS data load("data files/ds.nm.all.Rdata") # country names load('data files/dframe.Rdata') # country summary data (peak prevalence, country-prevalence, etc...) load("data files/draw.Rdata") # raw country summary data (peak prevalence, country-prevalence, etc...) load('data files/dframe.s.Rdata')# country summary data (peak prevalence, country-prevalence, etc...) by DHS survey load('data files/draw.s.Rdata')# country summary data (peak prevalence, country-prevalence, etc...) by DHS survey (unfiltered data) load('data files/pars.arr.ac.Rdata') # fitted transmission coefficients & other parameters for each assumed acute phase source('SimulationFunctions.R') ## add couple prevalence to dframe for(gg in 1:nrow(dframe)) { # for each country sel <- dat$group==dframe$country[gg] # select DHS data % of # coupled individuals that are HIV+ (1/discordant, 2/ ++ couple): dframe$cprev[gg] <- (sum(dat$ser[sel] %in% 2:3) + 2*sum(dat$ser[sel] == 1)) / (sum(sel)*2) } acutes <- as.numeric(in.arr[,1,2]) ## make a list of data frame with betas, and contact coefficients (log-ed) for each acute fit acs.to.do <- which(rowSums(!is.na(in.arr[,,2]))>0) rdatl <- list() for(aa in acs.to.do) { ## for each acute fit pars.arr <- out.arr[,,aa,] # select fitted parameters ## create data frame with couple prevalence, SDP, betas, & sexual mixing rates rdat <- data.frame(dframe$cprev, dframe$psdc, # DHS HIV prevalence in coupled indivs; proportion serodiscordant; ## b=before (pre-); e=extra; p=partner (within-) log(t(12*100*pars.arr['bp',c(2,1,3),])), log(t(12*100*pars.arr['be',c(2,1,3),])), # log(beta_within); log(beta_extra) log(t(12*100*pars.arr['bmb',c(2,1,3),])), log(t(12*100*pars.arr['bfb',c(2,1,3),])), # log(beta male pre-); log(beta female pre-) log(t(pars.arr['rr.ep',c(2,1,3),])), # log(extra- / within-) = log(extra-couple mixing coefficient) geometric mean across genders ## below: log(pre- / within-) = log(pre-couple mixing coefficient) (by gender) log(t(pars.arr['rr.bp.m',c(2,1,3),])), log(t(pars.arr['rr.bp.f',c(2,1,3),]))) names(rdat) <- c('cprev','psdc', # DHS HIV prevalence in coupled indivs; proportion serodiscordant; 'lbp','lbp.l','lbp.u', # log(beta_within) median & credible intervals (lower, upper) 'lbe','lbe.l','lbe.u', # ditto for log(beta_extra) 'lbmb','lbmb.l','lbmb.u', # log(male beta_pre) 'lbfb','lbfb.l','lbfb.u', # log(female beta_pre) 'lrr.ep','lrr.ep.l','lrr.ep.u', # log(extra-couple mixing coefficient) 'lrr.bp.m','lrr.bp.m.l','lrr.bp.m.u',# log(male pre-couple mixing coefficient) 'lrr.bp.f','lrr.bp.f.l','lrr.bp.f.u')# log(female pre-couple mixing coefficient) rdat$lgt.cprev <- logit(rdat$cprev) # logistic(DHS HIV prevalence in coupled individuals) rdat$lgt.pprev <- logit(dframe$peak.nart) # logistic(peak UNAIDS HIV prevalence in country) rdat$lgt.psdc <- logit(rdat$psdc) # logistic(DHS serodiscordant proportion) rdat$lgt.fD <- logit(rdat$psdc / (2*rdat$cprev * (1-rdat$cprev) / (2*rdat$cprev * (1-rdat$cprev) + rdat$cprev^2) )) # fancy D (normalized SDP) rdat$country <- dframe$country dat$fysa <- dat$tmar-dat$tfs dat$mysa <- dat$tmar-dat$tms rdat$fysa <- aggregate(dat$fysa, list(dat$group), mean)[,2]/12 rdat$mysa <- aggregate(dat$mysa, list(dat$group), mean)[,2]/12 rdat$mardur <- aggregate(dat$mardur.mon, list(dat$group), mean)[,2]/12 rdat$lmpret <- rdat$lrr.bp.m + log(rdat$mysa) # pre * time rdat$lfpret <- rdat$lrr.bp.f + log(rdat$fysa) # pre * time rdat$lextrat <- rdat$lrr.ep + log(rdat$mardur) # extra * time rdat$linft <- rdat$lbp + log(rdat$mardur) # infectivity * time rdat$lmysa <- log(rdat$mysa) rdat$lfysa <- log(rdat$fysa) rdat$lmardur <- log(rdat$mardur) rdat$lbp.w <- 1/((rdat$lbp.u - rdat$lbp.l)/(1.96*4))^2 # weights for each variable rdat$lbe.w <- 1/((rdat$lbe.u - rdat$lbe.l)/(1.96*4))^2 rdat$lbmb.w <- 1/((rdat$lbmb.u - rdat$lbmb.l)/(1.96*4))^2 rdat$lbfb.w <- 1/((rdat$lbfb.u - rdat$lbfb.l)/(1.96*4))^2 rdat$geom.w <- (rdat$lbp.w * rdat$lbe.w * rdat$lbmb.w * rdat$lbfb.w)^.25 # geometric average of weights rdatl[[aa]] <- rdat # add to array over acute fits } wts <- rdatl[[3]][,c('country','lbp.w','lbe.w','lbmb.w','lbfb.w','geom.w')] # weights wts outdir <- file.path('results','PrevFigs') sbpairs(wts[,-1], file.nm = file.path(outdir, 'wt correlations')) ## set plot parameters las <- 1 # axis text direction leg.ord <- order(dframe$cprev) # have legend order be by coupled prevalence so it matches top to bottom order of points cols <- rainbow(length(ds.nm))[leg.ord] # order colors similarly ylab <- '' # no ylabels r2 <- T # show p values on regression plots yaxt <- 'n' # no default axes xaxt <- 'n' leg.ord <- order(dframe$cprev) # countries ordered by HIV coupled prevalence in legend cols <- rainbow(length(ds.nm))[leg.ord] # same for colors ## plot axes, logistic y, log x, for different limits & labels (pre-determined) axis.fxn <- function(ylogit.axis, xlog.axis, ytype, xtype) { if(ylogit.axis) { if(ytype=='prev') { y.tcks <- c(seq(.01, .1, by = .01), seq(.1, .5, by = .1)) y.labels <- y.tcks y.labels[!y.labels %in% c(.01,.1,.5)] <- NA }else{ ## serodiscordant proportion (ranges .4-.8) y.tcks <- c(seq(.3, .9, by = .1), seq(.91,.95, by = .01)) y.labels <- y.tcks y.labels[!y.labels %in% c(.5,.9,.99)] <- NA } axis(2, at = logit(y.tcks), label = y.labels, las = 2) }else{ axis(2) ## not logistic } if(xlog.axis) { if(xtype=='within') { ## within-couple beta = HIV infectivity x.tcks <- c(1:9, seq(10,50, by = 10)) #.4,.9, by = .1), x.labels <- x.tcks x.labels[!x.labels %in% c(1,10,50)] <- NA axis(1, at = log(x.tcks), label = x.labels) }else{ if(xtype=='prev') { ## prevalence x.tcks <- c(seq(.01, .1, by = .01), seq(.1, .5, by = .1)) x.labels <- x.tcks x.labels[!x.labels %in% c(.01,.1,.5)] <- NA }else{ ## pre- & extra- contact mixing coefficients or betas (transmission coefficients) x.tcks <- c(seq(.1,.9, by = .1), 1:9, seq(10,100, by = 10)) x.labels <- x.tcks x.labels[!x.labels %in% c(.01,.1,1,10,100)] <- NA } } axis(1, at = log(x.tcks), label = x.labels) } } outdir <- file.path('results','PrevFigs') if(!file.exists(outdir)) dir.create(outdir) yvars <- c('lgt.cprev','lgt.pprev','lgt.psdc','lgt.fD') ytypes <- c('prev','prev','SDP','fancyD') ynames <- c('DHS prev','peak prev','SDP','fancyD') ytexts <- c('DHS HIV prevalence in couples', 'peak HIV prevalence (UNAIDS)','DHS serodiscordant proportion', 'fancy D') xclasses <- c('beta','contact') ylims <- list(c(.01, .55), c(.01, .55), c(.3, .99), c(.3, .99)) for(yv in 1:4) { ## for each Y variable for(xc in 1:2) { ## and each X class xclass <- xclasses[xc] if(xclass=='contact') { # name HIV infectivity & contact coefficients xvars <- c('lbp','lrr.ep','lrr.bp.m','lrr.bp.f') xlabs <- c('HIV infectivity (per 100 person-years)', expression(c['extra-couple']), expression(c['pre-couple,male']), expression(c['pre-couple,female'])) }else{ # name HIV infectivity & other betas xvars <- c('lbp','lbe','lbmb','lbfb') xlabs <- c('HIV infectivity (per 100 person-years)', expression(beta['extra-couple']), expression(beta['pre-couple,male']), expression(beta['pre-couple,female'])) } xtypes <- c('within', rep('contact',3)) # for axis function xlim <- list(c(.3,50),c(.08,100),c(.08,100),c(.08,100)) # xlimits for each x variable, same regardless of class ytype <- ytypes[yv] # yvar type yvar <- rdat[,yvars[yv]] # yvar pdf(file.path(outdir,paste0(ynames[yv],' vs ', xclass,'.pdf')), w = 6.5, h = 4.5) # initialize PDF for(aa in acs.to.do) { # for each assumed acute phase relative hazard rdat <- rdatl[[aa]] # pick fitted parameters from array for that acute phase relative hazard par(mar = c(4,3,2,.5), mfrow = c(2,2), oma = c(0,1,1,0), cex.lab = .8) for(xv in 1:4) { ## for each plot panel (each x variable) xvar <- rdat[,xvars[xv]] # get xvariable xvar.l <- rdat[,paste0(xvars[xv],'.l')] # get xvariable lower credible limit xvar.u <- rdat[,paste0(xvars[xv],'.u')] # get xvariable upper credible limit plot(0,0, type = 'n', xlab = xlabs[xv], ylab = ylab, las = las, yaxt = yaxt, xaxt = xaxt, # initialize plot xlim = log(xlim[[xv]]), ylim = logit(ylims[[yv]]), bty = 'n') axis.fxn(T,T, ytype=ytype, xtype=xtypes[xv]) # add axes using function above points(xvar, yvar, pch = 19, col = cols) # medians arrows(xvar.l, yvar, xvar.u, yvar, code = 3, col = cols, len = .05, angle = 90) # credible intervals ## weighted regression model (variance in xvars, from fitting transmission coefficients) mod <- lm(yvar ~ xvar, rdat, weights = 1/((xvar.u - xvar.l)/(1.96*2))^2) newx<- seq(min(xvar.l,na.rm=T), max(xvar.u,na.rm=T), l = 120) # sequence of x variables over which to predict prd<-predict(mod,newdata=data.frame(xvar = newx),interval = c("confidence"), level = 0.95,type="response") # predict ## plot thick solid regression line with thin dashed CI lines for(pl in 1:3) lines(newx,prd[,pl], lty=ifelse(pl==1,1,2), lwd = ifelse(pl==1,2,1)) if(r2) mtext(paste(expression(P), '=',signif(summary(mod)[[5]][2,4],2)), side = 3, line = 0, adj = .95, cex = .7) # add p value to plot } legend(.95*log(xlim[[xv]][1]), 1*logit(ylims[[yv]][2]), ncol = 2, # country legend rev(ds.nm[leg.ord]), col = rev(cols[leg.ord]), pch = 19, cex = .5, bg = 'white', title = 'from top to bottom') mtext(ytexts[yv], side = 2, outer = T, adj = .6, line = -.3) # add one y label mtext(paste('acute RR =', in.arr[aa,1,2],'during fit'), side = 3, line = -.5, outer=T) # show assumed acute relative hazard } dev.off() } } acs.to.do <- which(rowSums(!is.na(in.arr[,,2]))>0) fg.col <- 'black' wid <- 6.5 hei <- 4.5 cex <- 2 #################################################################################################### ## New version of figures with color scale for SDP & no arrows for CI's rmp <- colorRamp(c("red","yellow")) #create color ramp outdir <- file.path('results','PrevFigsNew') if(!file.exists(outdir)) dir.create(outdir) yvars <- c('lgt.cprev','lgt.pprev','lgt.psdc','lgt.fD') ytypes <- c('prev','prev')#,'SDP','fancyD') ynames <- c('DHS prev','peak prev')#,'SDP','fancyD') ytexts <- c('DHS HIV prevalence in couples', 'peak HIV prevalence (UNAIDS)')#,'DHS serodiscordant proportion', 'fancy D') xclasses <- c('beta','contact') ylims <- list(c(.01, .55), c(.01, .55), c(.3, .99), c(.3, .99)) for(yv in 1:2) { ## for each Y variable for(xc in 1:2) { ## and each X class xclass <- xclasses[xc] if(xclass=='contact') { # name HIV infectivity & contact coefficients xvars <- c('lbp','lrr.ep','lrr.bp.m','lrr.bp.f') xlabs <- c('HIV infectivity (per 100 person-years)', expression(c['extra-couple']), expression(c['pre-couple,male']), expression(c['pre-couple,female'])) xlabs <- c('HIV infectivity (per 100 person-years)', 'extra-couple mixing coefficient', 'male pre-couple mixing coefficient', 'female pre-couple mixing coefficient') xlim <- list(c(1,50),c(.08,50),c(.08,50),c(.08,50)) # xlimits for each x variable, same regardless of class }else{ # name HIV infectivity & other betas xvars <- c('lbp','lbe','lbmb','lbfb') xlabs <- c('HIV infectivity (per 100 person-years)', expression(beta['extra-couple']), expression(beta['pre-couple,male']), expression(beta['pre-couple,female'])) xlabs <- c('HIV infectivity (per 100 person-years)', 'extra-couple transmission coefficient', 'male pre-couple transmission coefficient', 'female pre-couple transmission coefficient') xlim <- list(c(.3,50),c(.08,100),c(.08,100),c(.08,100)) # xlimits for each x variable, same regardless of class } xtypes <- c('within', rep('contact',3)) # for axis function ytype <- ytypes[yv] # yvar type yvar <- rdat[,yvars[yv]] # yvar zvar <- rdat[,'psdc'] # zvar for point color zord <- order(order(zvar)) rzvar <- max(zvar)-min(zvar) zvar <- (zvar - min(zvar))/rzvar # scale so it's between 0 & 1 cols <- rgb(rmp(zvar), alpha = 200,max = 255) pdf(file.path(outdir,paste0(ynames[yv],' vs ', xclass,'-',fg.col,'.pdf')), w = wid, h = hei) # initialize PDF for(aa in acs.to.do) { # for each assumed acute phase relative hazard rdat <- rdatl[[aa]] # pick fitted parameters from array for that acute phase relative hazard layout(matrix(c(1,3,2,4,5,5),2,3), widths = c(1,1,.6), h = rep(1, 3)) par(mar = c(4,4,4,.5), cex.lab = .8, fg = fg.col, col.axis=fg.col, col.lab=fg.col) for(xv in 1:4) { ## for each plot panel (each x variable) xvar <- rdat[,xvars[xv]] # get xvariable xvar.l <- rdat[,paste0(xvars[xv],'.l')] # get xvariable lower credible limit xvar.u <- rdat[,paste0(xvars[xv],'.u')] # get xvariable upper credible limit plot(0,0, type = 'n', ylab = ylab, xlab='', las = las, yaxt = yaxt, xaxt = xaxt, # initialize plot xlim = log(xlim[[xv]]), ylim = logit(ylims[[yv]]), bty = 'n') mtext(xlabs[xv], side = 1, line = 2.3, adj = .5, cex = .75) axis.fxn(T,T, ytype=ytype, xtype=xtypes[xv]) # add axes using function above ## arrows(xvar.l, yvar, xvar.u, yvar, code = 3, col = cols, len = .05, angle = 90) # credible intervals ## weighted regression model (variance in xvars, from fitting transmission coefficients) mod <- lm(yvar ~ xvar, rdat, weights = 1/((xvar.u - xvar.l)/(1.96*2))^2) newx<- seq(min(xvar.l,na.rm=T), max(xvar.u,na.rm=T), l = 120) # sequence of x variables over which to predict prd<-predict(mod,newdata=data.frame(xvar = newx),interval = c("confidence"), level = 0.95,type="response") # predict ## plot thick solid regression line with thin dashed CI lines for(pl in 1:3) lines(newx,prd[,pl], lty=ifelse(pl==1,1,2), lwd = ifelse(pl==1,2,1)) points(xvar, yvar, cex = cex, pch = 19, col = cols) # medians text(xvar, yvar, c(16:1)[zord], cex = .5) if(r2) mtext(paste(expression(P), '=',signif(summary(mod)[[5]][2,4],2)), side = 3, line = 0, adj = .95, cex = .7) # add p value to plot } par(mar=rep(0,4)) plot(0,0, xlim = c(0, 10), ylim = c(0, 100), type = 'n', bty = 'n', axes = F, xlab = '', ylab ='') points(rep(.5,16), seq(40, 90, l = 16), pch = 19, cex = cex, col = cols[order(zvar)]) text(.5, seq(40, 90, l = 16), 16:1, cex = cex*.3) rdat$country[rdat$country=='WA'] <- 'West Africa' text(1, seq(40, 90, l = 16), paste0(rdat$country[order(zvar)], ' (',signif(rdat$psdc[order(zvar)],2)*100,'%)'), pos = 4) text(5, 100, 'country \n(serodiscordant \nproportion as %)') ## legend('topleft', ncol = 1, paste0(rdat$country, ' (',signif(zvar,2)*100,'%)'), ## pch = 19, col = cols, pt.cex = 1.5, cex = .8, bg = 'white') ## legend('topleft', ncol = 1, paste0(rdat$country, ' (',signif(zvar,2)*100,'%)'), ## pch = as.character(1:16), col = 'black', pt.cex = .5, cex = .8, bty = 'n') mtext(ytexts[yv], side = 2, outer = T, adj = .6, line = -1.5) # add one y label mtext(paste('acute RR =', in.arr[aa,1,2],'during fit'), side = 3, line = -2, outer=T) # show assumed acute relative hazard } dev.off() } } #################################################################################################### #################################################################################################### #################################################################################################### #################################################################################################### ## for PPT r2 <- T # show p values on regression plots acs.to.do <- which(in.arr[,2,2]==7) fg.col <- 'black' wid <- 5.5 hei <- 3.5 cex <- 2 #################################################################################################### ## New version of figures with color scale for SDP & no arrows for CI's rmp <- colorRamp(c("red","yellow")) #create color ramp outdir <- file.path('results','PrevFigsNew') if(!file.exists(outdir)) dir.create(outdir) yvars <- c('lgt.cprev','lgt.pprev','lgt.psdc','lgt.fD') ytypes <- c('prev','prev')#,'SDP','fancyD') ynames <- c('DHS prev','peak prev')#,'SDP','fancyD') ytexts <- c('HIV prevalence', 'peak HIV prevalence')#,'DHS serodiscordant proportion', 'fancy D') xclasses <- c('contact') ylims <- list(c(.01, .5), c(.01, .5), c(.3, .99), c(.3, .99)) for(yv in 2) { ## for each Y variable for(xc in 1) { ## and each X class xclass <- xclasses[xc] if(xclass=='contact') { # name HIV infectivity & contact coefficients xvars <- c('lbp','lrr.ep','lrr.bp.m','lrr.bp.f') xlabs <- c('intrinsic HIV transmission rate \n(per 100 person-years)', expression(c['extra-couple']), expression(c['pre-couple,male']), expression(c['pre-couple,female'])) xlabs <- c('intrinsic HIV transmission rate \n(per 100 person-years)', 'extra-couple mixing coefficient', 'male pre-couple mixing coefficient', 'female pre-couple mixing coefficient') xlim <- list(c(1,50),c(.08,50),c(.08,50),c(.08,50)) # xlimits for each x variable, same regardless of class }else{ # name HIV infectivity & other betas xvars <- c('lbp','lbe','lbmb','lbfb') xlabs <- c('HIV transmission rate (per 100 person-years)', expression(beta['extra-couple']), expression(beta['pre-couple,male']), expression(beta['pre-couple,female'])) xlabs <- c('HIV transmission rate (per 100 person-years)', 'extra-couple transmission coefficient', 'male pre-couple transmission coefficient', 'female pre-couple transmission coefficient') xlim <- list(c(.3,50),c(.08,100),c(.08,100),c(.08,100)) # xlimits for each x variable, same regardless of class } xtypes <- c('within', rep('contact',3)) # for axis function ytype <- ytypes[yv] # yvar type yvar <- rdat[,yvars[yv]] # yvar zvar <- rdat[,'psdc'] # zvar for point color zord <- order(order(zvar)) rzvar <- max(zvar)-min(zvar) zvar <- (zvar - min(zvar))/rzvar # scale so it's between 0 & 1 cols <- rgb(rmp(zvar), alpha = 200,max = 255) for(aa in acs.to.do) { # for each assumed acute phase relative hazard rdat <- rdatl[[aa]] # pick fitted parameters from array for that acute phase relative hazard for(xv in 1:4) { ## for each plot panel (each x variable) pdf(file.path(outdir,paste0(ynames[yv],' vs ', xclass,xv,'-',fg.col,'.pdf')), w = wid, h = hei) # initialize PDF layout(matrix(c(1,2),1,2), widths = c(1,.4)) par(mar = c(4,4,1,.5), oma = c(0,0,0,0), cex.lab = .8, fg = fg.col, col.axis=fg.col, col.lab=fg.col) xvar <- rdat[,xvars[xv]] # get xvariable xvar.l <- rdat[,paste0(xvars[xv],'.l')] # get xvariable lower credible limit xvar.u <- rdat[,paste0(xvars[xv],'.u')] # get xvariable upper credible limit plot(0,0, type = 'n', ylab = ytexts[yv], xlab='', las = las, yaxt = yaxt, xaxt = xaxt, # initialize plot xlim = log(xlim[[xv]]), ylim = logit(ylims[[yv]]), bty = 'n') mtext(xlabs[xv], side = 1, line = 2.3, adj = .5, cex = .75) axis.fxn(T,T, ytype=ytype, xtype=xtypes[xv]) # add axes using function above ## arrows(xvar.l, yvar, xvar.u, yvar, code = 3, col = cols, len = .05, angle = 90) # credible intervals ## weighted regression model (variance in xvars, from fitting transmission coefficients) mod <- lm(yvar ~ xvar, rdat, weights = geom.w) newx<- seq(min(xvar.l,na.rm=T), max(xvar.u,na.rm=T), l = 120) # sequence of x variables over which to predict prd<-predict(mod,newdata=data.frame(xvar = newx),interval = c("confidence"), level = 0.95,type="response") # predict ## plot thick solid regression line with thin dashed CI lines for(pl in 1:3) lines(newx,prd[,pl], lty=ifelse(pl==1,1,2), lwd = ifelse(pl==1,2,1)) points(xvar, yvar, cex = cex, pch = 19, col = cols) # medians text(xvar, yvar, c(16:1)[zord], cex = .5) if(r2) mtext(paste(expression(P), '=',signif(summary(mod)[[5]][2,4],2)), side = 3, line = -2, adj = .25, cex = .7) # add p value to plot par(mar=rep(0,4), cex = .8) plot(0,0, xlim = c(0, 10), ylim = c(0, 100), type = 'n', bty = 'n', axes = F, xlab = '', ylab ='') points(rep(.5,16), seq(0, 85, l = 16), pch = 19, cex = cex, col = cols[order(zvar)]) text(.5, seq(0, 85, l = 16), 16:1, cex = cex*.3) rdat$country[rdat$country=='WA'] <- 'West Africa' text(1, seq(0, 85, l = 16), paste0(rdat$country[order(zvar)], ' (',signif(rdat$psdc[order(zvar)],2)*100,'%)'), pos = 4, cex = cex*.5) text(5, 95, 'country \n(serodiscordant \nproportion as %)', cex = .9) par(xpd=NA) arrows(8,0,8,85, code = 2, len = .05, lwd = 3) mtext('serodiscordant proportion', side = 4, line = -1.3, cex = .8, adj = .4) dev.off() } } } } #################################################################################################### #################################################################################################### #################################################################################################### ## Figure 4 r2 <- T # show p values on regression plots acs.to.do <- which(in.arr[,2,2]==7) fg.col <- 'white' wid <- 5.5 hei <- 3.5 cex <- 2 #################################################################################################### ## New version of figures with color scale for SDP & no arrows for CI's rmp <- colorRamp(c("red","yellow")) #create color ramp outdir <- file.path('results','PrevFigsNew') if(!file.exists(outdir)) dir.create(outdir) yvars <- c('lgt.cprev','lgt.pprev','lgt.psdc','lgt.fD') ytypes <- c('prev','prev')#,'SDP','fancyD') ynames <- c('DHS prev','peak prev')#,'SDP','fancyD') ytexts <- c('HIV prevalence', 'peak HIV prevalence')#,'DHS serodiscordant proportion', 'fancy D') xclasses <- c('contact') ylims <- list(c(.01, .5), c(.01, .5), c(.3, .99), c(.3, .99)) for(yv in 2) { ## for each Y variable for(xc in 1) { ## and each X class xclass <- xclasses[xc] if(xclass=='contact') { # name HIV infectivity & contact coefficients xvars <- c('lbp','lrr.ep','lrr.bp.m','lrr.bp.f') xlabs <- c('intrinsic HIV transmission rate \n(per 100 person-years)', expression(c['extra-couple']), expression(c['pre-couple,male']), expression(c['pre-couple,female'])) xlabs <- c('intrinsic HIV transmission rate \n(per 100 person-years)', 'extra-couple mixing coefficient', 'male pre-couple mixing coefficient', 'female pre-couple mixing coefficient') xlabs <- c('intrinsic HIV transmission rate \n(per 100 person-years)', 'extra-couple mixing coefficient', 'male pre-couple mixing coefficient', 'female pre-couple mixing coefficient') xlim <- list(c(1,50),c(.08,50),c(.08,50),c(.08,50)) # xlimits for each x variable, same regardless of class }else{ # name HIV infectivity & other betas xvars <- c('lbp','lbe','lbmb','lbfb') xlabs <- c('HIV transmission rate (per 100 person-years)', expression(beta['extra-couple']), expression(beta['pre-couple,male']), expression(beta['pre-couple,female'])) xlabs <- c('HIV transmission rate (per 100 person-years)', 'extra-couple transmission coefficient', 'male pre-couple transmission coefficient', 'female pre-couple transmission coefficient') xlim <- list(c(.3,50),c(.08,100),c(.08,100),c(.08,100)) # xlimits for each x variable, same regardless of class } xtypes <- c('within', rep('contact',3)) # for axis function ytype <- ytypes[yv] # yvar type yvar <- rdat[,yvars[yv]] # yvar rdat$psdc.raw <- draw$psdc zvar <- rdat[,'psdc'] # zvar for point color zord <- order(order(zvar)) rzvar <- max(zvar)-min(zvar) zvar <- (zvar - min(zvar))/rzvar # scale so it's between 0 & 1 cols <- rgb(rmp(zvar), alpha = 200,max = 255) for(aa in acs.to.do) { # for each assumed acute phase relative hazard rdat <- rdatl[[aa]] # pick fitted parameters from array for that acute phase relative hazard pdf(file.path(outdir,paste0(ynames[yv],' vs ', xclass,'-',fg.col,'.pdf')), w = wid, h = hei) # initialize PDF layout(matrix(c(1,3,2,4,5,5),2,3), widths = c(1,1,.8)) cex.lab <- .7 par(mar = c(4.5,4,1.5,.5), oma = c(0,0,0,0), cex.lab = cex.lab, fg = fg.col, col.axis=fg.col, col.lab=fg.col) for(xv in 1:4) { ## for each plot panel (each x variable) xvar <- rdat[,xvars[xv]] # get xvariable xvar.l <- rdat[,paste0(xvars[xv],'.l')] # get xvariable lower credible limit xvar.u <- rdat[,paste0(xvars[xv],'.u')] # get xvariable upper credible limit plot(0,0, type = 'n', ylab = '', xlab='', las = las, yaxt = yaxt, xaxt = xaxt, # initialize plot xlim = log(xlim[[xv]]), ylim = logit(ylims[[yv]]), bty = 'n') mtext(xlabs[xv], side = 1, line = 2.8, adj = .5, cex = cex.lab) axis.fxn(T,T, ytype=ytype, xtype=xtypes[xv]) # add axes using function above ## arrows(xvar.l, yvar, xvar.u, yvar, code = 3, col = cols, len = .05, angle = 90) # credible intervals ## weighted regression model (variance in xvars, from fitting transmission coefficients) if(xv==1) { mod <- lm(yvar ~ xvar, rdat, weights = geom.w) newx<- seq(min(xvar.l,na.rm=T), max(xvar.u,na.rm=T), l = 120) # sequence of x variables over which to predict prd<-predict(mod,newdata=data.frame(xvar = newx),interval = c("confidence"), level = 0.95,type="response") # predict ## plot thick solid regression line with thin dashed CI lines for(pl in 1:3) lines(newx,prd[,pl], lty=ifelse(pl==1,1,2), lwd = ifelse(pl==1,2,1)) if(r2) mtext(paste(expression(P), '=',signif(summary(mod)[[5]][2,4],2)), side = 3, line = -2, adj = .25, cex = .7) # add p value to plot } points(xvar, yvar, cex = cex, pch = 19, col = cols) # medians text(xvar, yvar, c(16:1)[zord], cex = .5) } par(mar=rep(0,4), cex = .8) plot(0,0, xlim = c(0, 10), ylim = c(0, 100), type = 'n', bty = 'n', axes = F, xlab = '', ylab ='') fros <- 25 tos <- 85 points(rep(.5,16), seq(fros, tos, l = 16), pch = 19, cex = cex*.95, col = cols[order(zvar)]) text(.5, seq(fros, tos, l = 16), 16:1, cex = cex*.3) rdat$country[rdat$country=='WA'] <- 'West Africa' text(1, seq(fros, tos, l = 16), paste0(rdat$country[order(zvar)], ' (',signif(rdat$psdc[order(zvar)],2)*100,'%)'), pos = 4, cex = cex.lab) text(5, 95, 'country \n(serodiscordant \nproportion as %)', cex = .9) par(xpd=NA) arrows(8,fros,8,tos, code = 2, len = .05, lwd = 3) mtext('serodiscordant proportion', side = 4, line = -2, cex = .8, adj = .6) mtext('peak HIV prevalence', side = 2, line = -1, cex = .8, adj = .5, outer = T) dev.off() } } } #################################################################################################### ## Full model & univariate models acs.to.do <- 1:8 prev.type <- 'lgt.pprev' for(aa in acs.to.do) { modlist <- list(NA) rdat <- rdatl[[aa]] xvars <- c('lbp','lrr.ep','lrr.bp.m','lrr.bp.f') form4c <- as.formula(paste(prev.type, ' ~', paste(xvars, collapse = '+'))) modlist[[5]] <- lm(form4c, dat = rdat, weights = geom.w) modtab <- cbind(summary(modlist[[5]])[[5]], confint(modlist[[5]])) univtab <- modtab[-1,] # get rid of intercept row univtab[,] <- NA for(xx in 1:4) { xvar <- xvars[xx] temp.form <- as.formula(paste(prev.type, ' ~', xvar)) modlist[[xx]] <- lm(temp.form, dat = rdat, weights = geom.w) univtab[xx,] <- cbind(summary(modlist[[xx]])[[5]], confint(modlist[[xx]]))[-1,] } tab <- aictab(modlist, second.ord=T, modnames = c(xvars,'mult')) tab[,-1] <- signif(tab[,-1],3) write.csv(tab, file.path(outdir, paste0('aicc table Ac',acutes[aa],'.csv'))) un.est <- c(NA,paste0(signif(univtab[,'Estimate'],2), ' (', signif(univtab[,'2.5 %'],2), ', ', signif(univtab[,'97.5 %'],2), ')')) un.p <- c(NA,signif(univtab[,'Pr(>|t|)'],2)) mult.est <- paste0(signif(modtab[,'Estimate'],2), ' (', signif(modtab[,'2.5 %'],2), ', ', signif(modtab[,'97.5 %'],2), ')') mult.p <- signif(modtab[,'Pr(>|t|)'],2) outtab <- data.frame('univariate'=un.est, 'P'=un.p, 'multivariate'=mult.est, 'P'=mult.p)[-1,] rownames(outtab) <- c('transmission rate','extra-couple contact coefficient', 'male pre-couple contact coefficient', 'female pre-couple contact coefficient') outtab write.csv(outtab, file.path(outdir, paste0('final model table Ac',acutes[aa],'.csv'))) print(paste('R2 = ', summary(modlist[[1]])$r.squared, 'for acute = ', acutes[aa])) } #################################################################################################### ## Show evolution of {SDP,Prev} ove time ds <- function(x) (2*x*(1-x))/(2*x*(1-x) + x^2) # Null SDP model based on mixing SDP = (2p(1-p)) / (2p(1-p) + p^2) col <- 'black' cc <- F xlim <- c(0,.3) ylim <- c(0,1) pdf(file.path(outdir, paste('SDP vs prevalence by survey over time','curve'[cc],'lm.pdf')), w = 7, h=4) par(mar=c(4,5,1,1), col = col, fg = col, col.axis = col, col.main = col, col.lab=col, bty = 'l') plot(0,0, type = 'n', xlim=xlim, ylim = ylim, xlab = 'HIV prevalence', ylab = 'serodiscordance proportion', axes=F) if(cc) curve(ds(x), from = 0, to = .3, col = 'red', lty = 2, add=T) axis(2, seq(0,1,l=5), las = 2) axis(1, seq(0,.3,by=.05), las = 1) w.africa <- c("Burkina Faso", "Cameroon", 'Cote dIvoire', "Ghana", "Guinea", "Liberia", "Mali", "Niger", "Senegal", "Sierra Leone") temp <- draw.s[!draw.s$country %in% w.africa & !is.na(draw.s$country),] temp$country <- factor(temp$country) temp$col <- rainbow(nlevels(temp$country), alpha = .7)[as.numeric(temp$country)] points(temp$prev, temp$psdc, pch = 19, col = temp$col, cex = 2.5) leg <- as.character(unique(temp$country)) for(cc in 1:nlevels(temp$country)) { sel <- temp$country==levels(temp$country)[cc] leg[cc] <- paste(leg[cc], paste(round(temp$tint.yr[sel]), collapse = ', ')) lines(temp$prev[sel], temp$psdc[sel], col = temp$col[sel][1]) for(jj in which(sel)) text(temp$prev[sel], temp$psdc[sel], substr(round(temp$tint.yr[sel]),3,4), cex = .6) #, col = temp$col[sel]) } abline(lm(temp$psdc ~ temp$prev), col = 'black') legend('topright', leg = leg, col = unique(temp$col), pch = 19, bty = 'n', cex = .7, ncol=2) dev.off()

c7dbf01a5c3ed94ef37d14ecb2ac967420135c28

765e74a96d7a0a7d56c2cf056350ccf1265ee9f5

/R/cub_dist.R

4fa533b98f72163dc97396078e504b0866fa7fec

[]

no_license

fhernanb/cubm

a2a480af9a5516f6d6fc0dfe13fb553c705907bd

1c88e80f68e8ab21a681ecfd0d551e065de30f30

refs/heads/master

2021-07-02T06:53:45.658112

2020-12-07T21:58:35

74,044,405

4

7

null

2016-12-02T15:56:17

2016-11-17T16:14:06

R

UTF-8

R

false

4,253

r

cub_dist.R

#' cub distribution #' #' Density, distribution function, quantile function and random generation for the cub distribution given parameters pi and xi. #' #' @param x,q vector of quantiles. #' @param p vector of probabilities. #' @param pi uncertainty parameter belongs to \code{(0, 1]}. #' @param xi feeling parameter belongs to \code{[0, 1]}. #' @param n number of observations #' @param m the maximum value. #' @param log logical; if TRUE, densities are given as log. #' @param lower.tail logical; if TRUE (default), probabilities are \code{P[X <= x]} otherwise, \code{P[X > x]}. #' #' @examples #' # Examples with dcub #' #' dcub(x=4, pi=0.3, xi=0.7, m=5) #' dcub(x=1, pi=0.5, xi=0.4, m=8) #' dcub(x=c(4, 1), pi=c(0.3, 0.5), xi=c(0.7, 0.4), m=c(5, 8)) #' #' # Examples with pcub #' #' # low xi is associated with high ratings #' pcub(q=5, pi=0.5, xi=0.2, m=10, lower.tail=FALSE) #' #' # high pi is associated with indecision in choosing #' pcub(q=3, pi=0.9, xi=0.5, m=4) #' #' # probability for several quantiles #' pcub(q=c(1,3,5), pi=0.3, xi=0.6, m=5) #' #' # Examples with qcub #' #' # low xi is associated with high ratings #' qcub(p=0.1, pi=0.5, xi=0.1, m=7, lower.tail=TRUE) #' #' # high pi is associated with indecision in choosing #' qcub(p=0.86, pi=0.9, xi=0.5, m=4) #' #' #quantiles for several probabilities #' qcub(p=c(1,0.5,0.9), pi=0.3, xi=0.6, m=5) #' #' # Examples with rcub #' # Random sample, low xi is associated with high ratings #' x <- rcub(n=1000, pi=0.9, xi=0.1, m=5) #' barplot(prop.table(table(x))) #' #' # Random sample, low pi is associated with random choices #' y <- rcub(n=1000, pi=0.1, xi=0.1, m=5) #' barplot(prop.table(table(y))) #' @name cub_dist NULL #' @rdname cub_dist #' @importFrom stats dbinom #' @export dcub <-function(x, pi, xi, m, log=FALSE) { if(any(x %% 1 != 0)) stop(paste("x must be an integer number", "\n", "")) if (any(m <= 0)) stop(paste("m must be positive", "\n", "")) if (any(pi < 0 | pi > 1)) ## Era pi <= 0 stop(paste("pi must be in (0,1]", "\n", "")) if (any(xi < 0 | xi > 1)) stop(paste("xi must be in [0, 1]", "\n", "")) dens <- log(pi * dbinom(x=x-1, size=m-1, prob=1-xi) + (1 - pi) / m) dens[x <= 0 | x > m] <- -Inf if (log == FALSE) dens <- exp(dens) return(dens) } #' @rdname cub_dist #' @export pcub <- function(q, pi, xi, m, lower.tail=TRUE, log=FALSE) { if (any(m <= 1)) stop("m parameter must be greater than 1", "\n", "") if (any(pi <= 0 | pi > 1)) stop(paste("pi must be in (0,1]", "\n", "")) if (any(xi < 0 | xi > 1)) stop(paste("xi must be in [0, 1]", "\n", "")) # This is an auxiliar function ----------- aux <- function(q, pi, xi, m, lower.tail=TRUE, log=FALSE) { val <- seq(from=-100, to=q) prob <- dcub(x=val, pi=pi, xi=xi, m=m) sum(prob) } aux <- Vectorize(aux) # End of auxiliar function -------- p <- aux(q=q, pi=pi, xi=xi, m=m) if (lower.tail == FALSE) p <- 1-p if (log) p <- log(p) return(p) } #' @rdname cub_dist #' @export qcub <- function(p, pi, xi, m, lower.tail=TRUE, log=FALSE) { if (any(p < 0 | p > 1)) stop(paste("p must be in [0,1]", "\n", "")) if (any(m <= 1)) stop("m parameter must be greater than 1", "\n", "") if (any(pi <= 0 | pi > 1)) stop(paste("pi must be in (0,1]", "\n", "")) if (any(xi < 0 | xi > 1)) stop(paste("xi must be in [0, 1]", "\n", "")) if (lower.tail == FALSE) p <- 1 - p # This is an auxiliar function ----------- aux <- function(p, pi, xi, m) { cdf <- pcub(q=0:(m+1), pi=pi, xi=xi, m=m) findInterval(p, cdf) } aux <- Vectorize(aux) # End of auxiliar function -------- r <- aux(p=p, pi=pi, xi=xi, m=m) r } #' @rdname cub_dist #' @importFrom stats rbinom runif #' @export rcub <- function(n, pi, xi, m=5) { if (any(pi <= 0 | pi >1)) stop(paste("pi must be in (0,1]", "\n", "")) if (any(xi < 0 | xi > 1)) stop(paste("xi must be in [0, 1]", "\n", "")) if (any(m <= 1)) stop("m parameter must be greater than 1", "\n", "") # Define the component distributions rshifted.binom <- rbinom(n=n, size=m-1, prob=1-xi) + 1 rdiscrete.unif <- sample(1:m, n, replace=T) mixture <- runif(n) r <- ifelse(mixture < pi, rshifted.binom, rdiscrete.unif) return(r) }

40cb279c336514a19f1d9a393720178662cd724a

3d299999dd13ccfc9353379d5a34111364cf5a81

/R/main.R

b5337d861bf7e9931bf45a885d0e55d3e725ee8b

[]

no_license

shizelong1985/networkpanel

08777aa5b0549e873c1aa770bf0ea2fc30679ade

7aa7f68e0669369ee792f69392172abe459442b3

refs/heads/master

2023-03-17T06:54:14.324282

2020-06-19T08:09:45

null

0

null

UTF-8

R

false

4,597

r

main.R

#' Distance matrix normalization #' #' This function normalize the distance matrix #' @param dist The original distance matrix #' @keywords distance matrix #' @export #' @examples dist_normalize <- function(dist) { distNorm = matrix(0, nrow(dist), ncol(dist)) d = dist[upper.tri(dist)] d = rescale(d) distNorm[upper.tri(distNorm)] = d distNorm = distNorm + t(distNorm) distNorm } #' Pearson correlation between time series #' #' This function calcualte pearson correlation between time series #' @param ts1 The first time series #' @param ts2 The second time series #' @keywords pearson correlation #' @export #' @examples tsdiss.correlation <- function(ts1, ts2) { CorDistance(ts1, ts2) } #' Euclidean L2 distance between time series #' #' This function calcualte L2 distance between time series #' @param ts1 The first time series #' @param ts2 The second time series #' @keywords L2 distance #' @export #' @examples tsdiss.euclidean <- function(ts1, ts2) { diss.EUCL(ts1, ts2) } #' Manhattan L1 distance between time series #' #' This function calcualte L1 distance between time series #' @param ts1 The first time series #' @param ts2 The second time series #' @keywords L1 distance #' @export #' @examples tsdiss.manhattan <- function(ts1, ts2) { sum(abs(ts1-ts2)) } #' Infinite Norm between time series #' #' This function calcualte infinite norm between time series #' @param ts1 The first time series #' @param ts2 The second time series #' @keywords L infinity distance #' @export #' @examples tsdiss.infiniteNorm <- function(ts1, ts2) { max(abs(ts1-ts2)) } #' Time series clustering using community detection #' #' This function conduct time series clustering using community detection #' @param dist Distance matrix between every pair of time series. #' @param epsilon Parameter for the network construction method. #' @param communityDetectionFunc Community detection function. The igraph package has some community detection algorithms implemented. #' @keywords community detection #' @export #' @examples ts.community.detection <- function(dist,epsilon, communityDetectionFunc=cluster_louvain){ net = net.epsilon.create(dist, epsilon) communities = communityDetectionFunc(net) communities=membership(communities) #return(communities) } #' Network construction using epsilon threshold #' #' This function creats network using threshold epsilon #' @param dist Distance matrix between every pair of time series. #' @param epsilon Parameter for the network construction method. #' @keywords network construction #' @export #' @examples net.epsilon.create <- function(dist, epsilon) { n = matrix(0, ncol(dist), nrow(dist)) n[dist < epsilon] = 1; graph.adjacency(n, mode="undirected", diag=F) } #' Epanechnikov kernel function #' @param x Variable #' @keywords Epanechnikov kernel #' @export #' @examples epan <- function(x) { return(0.75*(1-x^2)*((sign(1-x^2)+1)/2))} #' NW estimator of nonparametric function #' @param X indepent variable with dimension T*1 #' @param Y dependent variable with dimension T*1 #' @param bw bandwidth #' @param N number of grid points #' @keywords NW estimator #' @export #' @examples m.i.hat <- function(X,Y,bw,N) { m.vec <- rep(0,N) for(j in 1:N) { rh <- sum(epan((X-j/N)/bw) * Y) fh <- sum(epan((X-j/N)/bw)) m.vec[j] <- rh/fh } return(m.vec) } #' Parallel distance matrix #' @param tsList indepent variable with dimension T*1 #' @param distFunc dependent variable with dimension T*1 #' @param cores bandwidt #' @keywords Parallel distance matrix #' @export #' @examples dist.parallel <- function(tsList, distFunc=tsdiss.euclidean, cores=2) { distFuncCompiled <- cmpfun(distFunc) tsListLength = length(tsList) combs = combn(tsListLength, 2) d = mcmapply(dist.parallel2.compute, x=combs[1,], y=combs[2,], MoreArgs=list(tsList=tsList, distFunc=distFuncCompiled), mc.cores=cores) dist = matrix(0, tsListLength, tsListLength) dist[lower.tri(dist)] = d dist = as.matrix(as.dist(dist)) return(dist) } #' calculate distance matrix #' @param x the first time series #' @param y the second time series #' @param tsList independent variable with dimension T*1 #' @param distFunc dependent variable with dimension T*1 #' @keywords Parallel distance matrix #' @export #' @examples dist.parallel2.compute <- function(x, y, tsList, distFunc) { distFunc(tsList[[x]], tsList[[y]]) }

036f2b5370a351fff28145bb2dc2ee390537f5cd

501e69463cc39cbb331a251b16c25ac4f4855caa

/man/vcov.Rd

62f1fec494d71b8d5130492699e379fe8c935817

[]

no_license

cran/mexhaz

22b3040da11a6cf5bcc16b957d3e3cd95e87d476

55e138836e58bbb04ec05eb5848afe60d43bab90

refs/heads/master

2022-11-10T11:59:07.330374

2022-10-31T13:47:48

58,455,607

0

null

UTF-8

R

false

694

rd

vcov.Rd

\name{vcov} \alias{vcov} %- Also NEED an '\alias' for EACH other topic documented here. \title{Method for extracting the covariance matrix} \description{ This is a generic function.} \usage{ vcov(object, ...)} %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{a fitted object from which the covariance matrix can be extracted.} \item{\dots}{may be required by some methods using this generic function.} } \value{ see the documentation for the specific class.} \examples{ ## See the specific class documentation. } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. %\keyword{models}

3bd1fac85bfd220876b43e558ce5797c8ad908d6

dc75a9160840901a4f6252c05e307bee4178c78b

/R/Chunk.R

a425179dff25393afa853093060c201ea80a4ee1

[]

no_license

lewinfox/balance-tracker

5c1b05324e87f8fd82503fb9fd7041e9ea76565d

6beec7a3476773a3ecf4563fae8d28e3b478d510

refs/heads/master

2020-07-02T22:14:26.306996

2019-08-10T21:46:49

201,684,036

0

null

UTF-8

R

false

1,416

r

Chunk.R

#' A single "lump" of cash #' #' A Chunk is the result of a single payment into an account. Money can be #' debited from a Chunk but not credited to it. The value of a Chunk cannot fall #' below zero - if this happens the Chunk is destroyed. Chunk <- R6::R6Class( classname = "Chunk", public = list( value = 0, birthdate = NULL, uuid = NULL, initialize = function(value = 0, birthdate = Sys.time()) { self$value <- value self$birthdate <- birthdate self$uuid <- uuid::UUIDgenerate() }, debit = function(amount) { self$value <- max(self$value - amount, 0) }, is_empty = function() { self$value == 0 }, print = function() { cat("Chunk", self$uuid, "\n") cat(" Value:", self$value, "\n") cat(" Birthdate:", self$birthdate, "\n") invisible(self) } ) ) #' A variant of the Chunk that allows overdrafts #' #' A BaseChunk is the first Chunk created in a Balance. Unline a regular Chunk, #' a BaseChunk has debit and credit methods. The value of a BaseChunk can #' decrease indefinitely (i.e. it can go overdrawn) but it can never increase #' above zero. BaseChunk <- R6::R6Class( classname = "BaseChunk", inherit = Chunk, public = list( credit = function(amount) { self$value <- min(self$value + amount, 0) }, debit = function(amount) { self$value <- self$value - amount } ) )

8793851aa9383e225af3b2fdb2de19416610d246

a1502451963856d226e8523097e89407b6ec85bc

/run_analysis.R

7875dae4082d8fe5aa43bbbe52f9a6d9db00154f

[]

no_license

Rowena752/Getting-and-Cleaning-Data-Course-Project

d0e224115923a346909a997b10c30aac9a51988f

b87a6649b97a9547e4f2d8df935aac083c4ca518

refs/heads/master

2020-05-25T02:00:03.160311

2017-03-14T22:30:52

84,900,001

0

null

UTF-8

R

false

3,761

r

run_analysis.R

packages <- c("data.table", "reshape2") sapply(packages, require, character.only=TRUE, quietly=TRUE) path <- getwd() path #Download the file and put in folder url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" f <- "Dataset.zip" if (!file.exists(path)) {dir.create(path)} download.file(url, file.path(path, f)) #Unzip the file unzip("./Dataset.zip") #Put unzipped files into UCI HAR Dataset folder pathIn <- file.path(path, "UCI HAR Dataset") #Read the subject files SubjectTrain <- read.table(file.path(pathIn, "train", "subject_train.txt")) SubjectTest <- read.table(file.path(pathIn, "test" , "subject_test.txt" )) #Read the activity files ActivityTrain <- read.table(file.path(pathIn, "train", "Y_train.txt")) ActivityTest <- read.table(file.path(pathIn, "test" , "Y_test.txt" )) #Read the features files fileToDataTable <- function (f) { df <- read.table(f) dtbl <- data.table(df) } Train <- fileToDataTable(file.path(pathIn, "train", "X_train.txt")) Test <- fileToDataTable(file.path(pathIn, "test" , "X_test.txt" )) # Merge the training and testing sets Subject <- rbind(SubjectTrain, SubjectTest) setnames(Subject, "V1", "subject") Activity <- rbind(ActivityTrain, ActivityTest) setnames(Activity, "V1", "activity_num") dtbl <- rbind(Train, Test) Subject <- cbind(Subject, Activity) dtbl <- cbind(Subject, dtbl) dtbl <- as.data.table(dtbl) setkey(dtbl, subject, activity_num) #Extract mean and std Features <- fread(file.path(pathIn, "features.txt")) setnames(Features, names(Features), c("feat_num", "feat_name")) Features <- Features[grepl("mean\$\$|std\$\$", feat_name)] Features$feat_code <- Features[, paste0("V", feat_num)] head(Features) Features$feat_code #Descriptive Activity Names select <- c(key(dtbl), Features$feat_code) dtbl <- dtbl[, select, with=FALSE] ActivityNames <- fread(file.path(pathIn, "activity_labels.txt")) setnames(ActivityNames, names(ActivityNames), c("activity_num", "activity_name")) dtbl <- merge(dtbl, ActivityNames, by="activity_num", all.x=TRUE) setkey(dtbl, subject, activity_num, activity_name) dtbl <- data.table(melt(dtbl, key(dtbl), variable.name="feat_code")) dtbl <- merge(dtbl, Features[, list(feat_num, feat_code, feat_name)], by="feat_code", all.x=TRUE) dtbl$activity <- factor(dtbl$activity_name) dtbl$feature <- factor(dtbl$feat_name) grepthis <- function (regex) { grepl(regex, dtbl$feature) } ## Features with 2 categories n <- 2 y <- matrix(seq(1, n), nrow=n) x <- matrix(c(grepthis("^t"), grepthis("^f")), ncol=nrow(y)) dtbl$featDomain <- factor(x %*% y, labels=c("Time", "Freq")) x <- matrix(c(grepthis("Acc"), grepthis("Gyro")), ncol=nrow(y)) dtbl$featInstrument <- factor(x %*% y, labels=c("Accelerometer", "Gyroscope")) x <- matrix(c(grepthis("BodyAcc"), grepthis("GravityAcc")), ncol=nrow(y)) dtbl$featAcceleration <- factor(x %*% y, labels=c(NA, "Body", "Gravity")) x <- matrix(c(grepthis("mean()"), grepthis("std()")), ncol=nrow(y)) dtbl$featVariable <- factor(x %*% y, labels=c("Mean", "SD")) ## Features with 1 category dtbl$featJerk <- factor(grepthis("Jerk"), labels=c(NA, "Jerk")) dtbl$featMagnitude <- factor(grepthis("Mag"), labels=c(NA, "Magnitude")) ## Features with 3 categories n <- 3 y <- matrix(seq(1, n), nrow=n) x <- matrix(c(grepthis("-X"), grepthis("-Y"), grepthis("-Z")), ncol=nrow(y)) dtbl$featAxis <- factor(x %*% y, labels=c(NA, "X", "Y", "Z")) #Create a tidy data set setkey(dtbl, subject, activity, featDomain, featAcceleration, featInstrument, featJerk, featMagnitude, featVariable, featAxis) tidy_data <- dtbl[, list(count = .N, average = mean(value)), by=key(dtbl)] # Creating a file for the tidy data set created write.table(tidy_data, file = "./tidy_data.txt", row.name = FALSE)

6df3956c417830d0034a6889cb495a47ab16eee9

5f5bdc7d277212c3f3b0d1d7721ae7813c7339f7

/shiny-retirement-planning-with-stocks/global.R

67804544d8438c9ace00178fb35968bc59951fd9

[]

no_license

michaelwfouts/shiny-retirement-planning-with-stocks

5cb4e44cb19c36b0b21fd62d95a6bdb61e8490b3

24e725a3a3279a3b859e7fb99d2ad4b3bd90d580

refs/heads/main

2023-08-11T06:06:31.622600

2021-09-17T12:29:26

407,531,472

0

null

UTF-8

R

false

192

r

global.R

# Load in modules library(shiny) library(plotly) library(dplyr) library(shinyWidgets) library(bslib) # Source module scripts source("R/infoModule.R") source("R/calculatorModule.R")

c0ba647a69741e11943c7330c2f826ad0d7b714f

f044402735a52fa040c5cbc76737c7950406f8b2

/BrCa_Age_Associated_TMA/Packages/biostatUtil/man/sem.Rd

9d214289bd6592396aece704c41e2251de268309

[]

no_license

BCCRCMO/BrCa_AgeAssociations

5cf34f3b2370c0d5381c34f8e0d2463354c4af5d

48a11c828a38a871f751c996b76b77bc33d5a3c3

refs/heads/master

2023-03-17T14:49:56.817589

2020-03-19T02:18:21

247,175,174

2

1

null

UTF-8

R

false

true

552

rd

sem.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{sem} \alias{sem} \title{Standard error of the mean} \usage{ sem(x, missing.value = NA, return.missing.value = NA) } \arguments{ \item{x}{input vector} \item{missing.value}{values that are missing} \item{return.missing.value}{the value to return where there are missing values} } \value{ The standard error of the mean of \code{x} } \description{ Standard error of the mean } \references{ http://en.wikipedia.org/wiki/Standard_error } \author{ Samuel Leung }

5f2548a2ad7abf957268affe68d354e7a29fa4fa

169a6494a475f42d0452d3ade4622bde1eb939cc

/R/scrapenames.r

48c17f68152a85535653388a6ff8cfff72f40310

[ "MIT" ]

permissive

ropensci/taxize

d205379bc0369d9dcdb48a8e42f3f34e7c546b9b

269095008f4d07bfdb76c51b0601be55d4941597

refs/heads/master

2023-05-25T04:00:46.760165

2023-05-02T20:02:50

1,771,790

224

75

NOASSERTION

2023-05-02T20:02:51

2011-05-19T15:05:33

R

UTF-8

R

false

5,141

r

scrapenames.r

#' @title Resolve names using Global Names Recognition and Discovery. #' #' @description Uses the Global Names Recognition and Discovery service, see #' http://gnrd.globalnames.org/ #' #' Note: this function sometimes gives data back and sometimes not. The API #' that this function is extremely buggy. #' #' @export #' @param url An encoded URL for a web page, PDF, Microsoft Office document, or #' image file, see examples #' @param file When using multipart/form-data as the content-type, a file may #' be sent. This should be a path to your file on your machine. #' @param text Type: string. Text content; best used with a POST request, see #' examples #' @param engine (optional) (integer) Default: 0. Either 1 for TaxonFinder, #' 2 for NetiNeti, or 0 for both. If absent, both engines are used. #' @param unique (optional) (logical) If `TRUE` (default), response has #' unique names without offsets. #' @param verbatim (optional) Type: boolean, If `TRUE` (default to #' `FALSE`), response excludes verbatim strings. #' @param detect_language (optional) Type: boolean, When `TRUE` (default), #' NetiNeti is not used if the language of incoming text is determined not to #' be English. When `FALSE`, NetiNeti will be used if requested. #' @param all_data_sources (optional) Type: boolean. Resolve found names #' against all available Data Sources. #' @param data_source_ids (optional) Type: string. Pipe separated list of #' data source ids to resolve found names against. See list of Data Sources #' http://resolver.globalnames.org/data_sources #' @param return_content (logical) return OCR'ed text. returns text #' string in `x$meta$content` slot. Default: `FALSE` #' @param ... Further args passed to [crul::verb-GET] #' @author Scott Chamberlain #' @return A list of length two, first is metadata, second is the data as a #' data.frame. #' @details One of url, file, or text must be specified - and only one of them. #' @examples \dontrun{ #' # Get data from a website using its URL #' scrapenames('https://en.wikipedia.org/wiki/Spider') #' scrapenames('https://en.wikipedia.org/wiki/Animal') #' scrapenames('https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0095068') #' scrapenames('https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0080498') #' scrapenames('http://ucjeps.berkeley.edu/cgi-bin/get_JM_treatment.pl?CARYOPHYLLACEAE') #' #' # Scrape names from a pdf at a URL #' url <- 'https://journals.plos.org/plosone/article/file?id= #' 10.1371/journal.pone.0058268&type=printable' #' scrapenames(url = sub('\n', '', url)) #' #' # With arguments #' scrapenames(url = 'https://www.mapress.com/zootaxa/2012/f/z03372p265f.pdf', #' unique=TRUE) #' scrapenames(url = 'https://en.wikipedia.org/wiki/Spider', #' data_source_ids=c(1, 169)) #' #' # Get data from a file #' speciesfile <- system.file("examples", "species.txt", package = "taxize") #' scrapenames(file = speciesfile) #' #' nms <- paste0(names_list("species"), collapse="\n") #' file <- tempfile(fileext = ".txt") #' writeLines(nms, file) #' scrapenames(file = file) #' #' # Get data from text string #' scrapenames(text='A spider named Pardosa moesta Banks, 1892') #' #' # return OCR content #' scrapenames(url='https://www.mapress.com/zootaxa/2012/f/z03372p265f.pdf', #' return_content = TRUE) #' } scrapenames <- function(url = NULL, file = NULL, text = NULL, engine = NULL, unique = NULL, verbatim = NULL, detect_language = NULL, all_data_sources = NULL, data_source_ids = NULL, return_content = FALSE, ...) { method <- tc(list(url = url, file = file, text = text)) if (length(method) > 1) { stop("Only one of url, file, or text can be used", call. = FALSE) } base <- "http://gnrd.globalnames.org/name_finder.json" if (!is.null(data_source_ids)) data_source_ids <- paste0(data_source_ids, collapse = "|") args <- tc(list(url = url, text = text, engine = engine, unique = unique, verbatim = verbatim, detect_language = detect_language, all_data_sources = all_data_sources, data_source_ids = data_source_ids, return_content = as_l(return_content))) cli <- crul::HttpClient$new(base, headers = tx_ual, opts = list(...)) if (names(method) == 'url') { tt <- cli$get(query = args) tt$raise_for_status() out <- jsonlite::fromJSON(tt$parse("UTF-8")) token_url <- out$token_url } else { if (names(method) == "text") { tt <- cli$post(body = list(text = text), encode = "form", followlocation = 0) } else { tt <- cli$post(query = argsnull(args), encode = "multipart", body = list(file = crul::upload(file)), followlocation = 0) } if (tt$status_code != 303) tt$raise_for_status() token_url <- tt$response_headers$location } st <- 303 while (st == 303) { dat <- crul::HttpClient$new(token_url, headers = tx_ual)$get() dat$raise_for_status() datout <- jsonlite::fromJSON(dat$parse("UTF-8")) st <- datout$status } meta <- datout[!names(datout) %in% c("names")] list(meta = meta, data = nmslwr(datout$names)) }

ddc63567bb8b0ce6718b9b4c1aabf46123e1056a

648ceb127101da98e0371f90e83c2613b20ee5d1

/R/get_all_funs.R

ba605d7babb31521fef48aaeac9b96c79d772188

[]

no_license

paulponcet/bazar

b561b9914300d4eb72d998028b4c2db061f9b07e

cacccfed36ed5650dbef2e78f584e0c07c321581

refs/heads/master

2021-01-11T21:59:55.950238

2019-07-13T23:51:42

78,890,817

0

null

2019-04-05T09:14:21

2017-01-13T22:12:23

R

UTF-8

R

false

871

r

get_all_funs.R

#' @title #' Functions exported by a package #' #' @description #' \code{get_all_funs} provides all the functions exported by a given #' installed package. #' #' @param pkg #' character. The package of interest. (Must be installed already.) #' #' @return #' A character vector, the functions exported. #' #' @export #' #' @examples #' get_all_funs("stats") #' get_all_funs <- function(pkg) { stopifnot(length(pkg) == 1L) if (pkg %in% c("Deducer", "DeducerExtra", "gstat", "rattle", "rggobi", "RGtk2", "spacetime", "translations")) { warning(paste0("get_all_funs() returns 'character(0)' for package ", pkg), call. = FALSE) return(character(0L)) } tryCatch(suppressWarnings(getNamespaceExports(pkg)), error = function(e) character(0L)) #unclass(lsf.str(envir = asNamespace(pkg), all = TRUE)) }

281dd1e188e0bdd1edff5ab887d91b94aa90fb68

fb0ac2162a0aece893c57fa06c2a4acced5f7cd4

/run_analysis.R

e982a3cf422a3bd531e96d224b739409d7344855

[]

no_license

JoeAllyn/Getting-and-Cleaning-Data-Project

b32a4b80d0af3f693132772aafc0f8fe5a866ffa

748513e7547d7da21fa9b2ff53192534ae9a3fbb

refs/heads/master

2020-06-22T16:30:31.173787

2016-11-23T15:23:10

74,586,370

0

null

UTF-8

R

false

2,558

r

run_analysis.R

# Getting and Cleaning Data - Project # Merges the training and the test sets to create one data set. # Download Data Files and Unzip filename <- "UCIHARDataset.zip" if(!file.exists(filename)) { fileurl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" download.file(fileurl, filename) } if(!file.exists("UCI HAR Dataset")) { unzip(filename) } #Load all datasets #Train datasets training <- read.table("./UCI HAR Dataset/train/X_train.txt") trainingAct <- read.table("./UCI HAR Dataset/train/y_train.txt") trainingSub <- read.table("./UCI HAR Dataset/train/subject_train.txt") #Test datasets testing <- read.table("./UCI HAR Dataset/test/X_test.txt") testingAct <- read.table("./UCI HAR Dataset/test/y_test.txt") testingSub <- read.table("./UCI HAR Dataset/test/subject_test.txt") #Features and Activity Labels features <- read.table("./UCI HAR Dataset/features.txt") activityLabels <- read.table("./UCI HAR Dataset/activity_labels.txt") #Combine Training and Activity Data training <- cbind(trainingSub, trainingAct, training) testing <- cbind(testingSub, testingAct, testing) combined <- rbind(training, testing) #Add Labels with Descriptive Variable Names featuresLabels <- as.character(features[,2]) featuresLabels <- gsub("[()-]","",featuresLabels) featuresLabels <- gsub("[-]","",featuresLabels) featuresLabels <- gsub("[,]","",featuresLabels) colnames(combined) <- c("subjectid", "activityid", featuresLabels) #Extracts only the measurements on the mean and standard deviation for each measurement. #Identify Columns with Mean and Std Dev Measures and IDs and Create New Dataset keepColumns <- colnames(combined) extractColumns <- (grepl("subjectid", keepColumns) | grepl("activityid", keepColumns) | grepl("*[Ss]td*", keepColumns) | grepl("*[Mm]ean*", keepColumns)) subCombined <- combined[ , extractColumns == TRUE] #Add descriptive activity names to name the activities in the data set subCombined$activityid <- factor(subCombined$activityid, levels = activityLabels[,1], labels = activityLabels[,2]) subCombined$subjectid <- as.factor(subCombined$subjectid) #Create a second, independent tidy data set with the average of each variable for each activity and each subject. tidyDataSet <- aggregate(. ~subjectid + activityid, data = subCombined , FUN = "mean") write.table(tidyDataSet, "tidyDataSet.txt", row.name=FALSE) #Code to read back in Tidy Data Set #data <- read.table("tidyDataSet.txt", header=TRUE) #View(data)

3e7fe210bb1ecfc04c9837c84d00b87320eef94d

f92a7585f054a393674716f4e178a78f099f4917

/man/common_lookup.Rd

32f94ddc7ea92f00f9b09b8d3a7cefd1aea512cb

[]

no_license

parvezrana/forvol

7a29c9c2c77777ddb0ed94c47f398b04056f2e84

767d1739061fce7fc207a500ebff0216173d6512

refs/heads/master

2020-03-14T13:42:17.524275

2018-01-03T18:09:12

null

0

null

UTF-8

R

false

true

435

rd

common_lookup.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/lookup.R \name{common_lookup} \alias{common_lookup} \title{Returns the common name of a species given some input FIA species code} \usage{ common_lookup(spcd, latin = FALSE) } \arguments{ \item{spcd}{The FIA species code} } \value{ A string of the species common name } \description{ Returns the common name of a species given some input FIA species code }