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77272144d21534a6e4839e53d7d6bf88550840bd | 05b5b6430374ef292e9ae18c4b4ed455af85248c | /R/womenWork.R | 0a7bada18028b774aba355714e3da960165852de | [] | no_license | tanetpongc/BayesLearnSU | 1a7aa580709694776db05afd290f5fe12fc27468 | ea8546c14a4cd951a3e43522a1813067b9935cc8 | refs/heads/master | 2023-03-27T03:53:46.270172 | 2021-03-26T18:30:31 | 2021-03-26T18:30:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 698 | r | womenWork.R | #' Data on working status
#'
#' A dataset on the working status (works or does not work) for 200 women.
#'
#' @format A data frame with 200 rows and 9 variables:
#' \describe{
#' \item{work}{Whether or not the woman works.}
#' \item{constant}{Constant/intercept.}
#' \item{husbandInc}{Husband's income.}
#' \item{educYears}{Years of education.}
#' \item{expYears}{Years of experience.}
#' \item{expYears2}{(Years of experience/10)^2.}
#' \item{age}{Age.}
#' \item{nSmallChild}{Number of children at or under the age of six
#' in the household.}
#' \item{nBigChild}{Number of children older than six in the household.
#' Not counting the husband.}
#' ...
#' }
#'
"womenWork"
|
2be3185a8ea3dc51507d803024f3dbfbf6ab872a | 0701bed484ef0ba4a10914161fc33e85c480052c | /code/08-suit-expanded-results-tableS2.R | 38d680584712235c1a86f088d687e6bc23e2f243 | [] | no_license | mmw590/rubberxbiodiversityCB | c762f6b50bd592aef2cfdc91cb47318658161539 | 6e4ae3c11e8281a813667fd835ecf941cbac7f2f | refs/heads/master | 2022-11-11T19:08:39.075313 | 2020-06-30T09:50:37 | 2020-06-30T09:50:37 | 267,854,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,587 | r | 08-suit-expanded-results-tableS2.R | #### rubberxbiodiversityCB
#### 08-suit-expanded-results-tableS2.R ####
rm(list=ls())
library(raster)
library(dplyr)
library(tidyr)
library(data.table)
library(scales) #for hue_pal
library(ggplot2)
library(cowplot)
###### DF of cells with suit, rich/vuln suit_vuln_vals3 #######
### Africa
list.files('output/afr/biodiversity_rasters/', pattern="std_mask_ext.tif")
mat_rich <- raster('output/afr/biodiversity_rasters/rescaled_twice_afr_combspp_richness_std_mask_ext.tif')
mat_vulnA <- raster('output/afr/biodiversity_rasters/rescaled_twice_afr_combspp_vuln_all_std_mask_ext.tif')
mat_vulnT <- raster('output/afr/biodiversity_rasters/rescaled_twice_afr_combspp_vuln_threat_std_mask_ext.tif')
mat_suit <- raster('output/afr/various_rasters/extendedsuit_afr_0-6.tif')
mat_pa <- raster('output/afr/various_rasters/protected_areas_afr.tif')
mat_acc <- raster('output/afr/various_rasters/accessibility_afr.tif')
mat_carb <- raster('output/afr/various_rasters/carbon_afr.tif')
#### Get mat_forest from mat_land_use
mat_land_use_cci <- raster('output/afr/various_rasters/land_use_africa_CCI.tif')
reclassify.df <- read.csv('data/mat_land_use_reclassifyMW.csv')
mat_forest <- subs(mat_land_use_cci, reclassify.df, by='NB_LAB', which='forest', subsWithNA=FALSE)
### Mask unsuitable LU from mat_suit
mat_land_use <- subs(mat_land_use_cci, reclassify.df, by='NB_LAB', which='hassparse', subsWithNA=FALSE)
mat_suit <- raster::mask(mat_suit, mat_land_use, maskvalue=NA, updatevalue=NA)
mat_suit <- raster::mask(mat_suit, mat_land_use, maskvalue=0, updatevalue=0)
### Stack
suit_vuln_stack_afr <- stack(mat_suit, mat_pa, mat_rich, mat_vulnA, mat_vulnT, mat_forest, mat_acc, mat_carb)
suit_vuln_spdf_afr <- rasterToPoints(suit_vuln_stack_afr, spatial=TRUE, progress='text')
suit_vuln_vals_afr <- as.data.frame(suit_vuln_spdf_afr)
suit_vuln_vals_afr <- suit_vuln_vals_afr %>%
rename(suit = names(suit_vuln_vals_afr)[1],
pa = names(suit_vuln_vals_afr)[2],
rich = names(suit_vuln_vals_afr)[3],
vulnA = names(suit_vuln_vals_afr)[4],
vulnT = names(suit_vuln_vals_afr)[5],
forest = names(suit_vuln_vals_afr)[6],
acc = names(suit_vuln_vals_afr)[7],
carb = names(suit_vuln_vals_afr)[8]) %>%
mutate(conc = 0, region = 'afr')
head(suit_vuln_vals_afr)
# SSEA
list.files('output/ssea/biodiversity_rasters/', pattern="std_mask_ext.tif")
mat_rich <- raster('output/ssea/biodiversity_rasters/rescaled_twice_ssea_combspp_richness_std_mask_ext.tif')
mat_vulnA <- raster('output/ssea/biodiversity_rasters/rescaled_twice_ssea_combspp_vuln_all_std_mask_ext.tif')
mat_vulnT <- raster('output/ssea/biodiversity_rasters/rescaled_twice_ssea_combspp_vuln_threat_std_mask_ext.tif')
mat_suit <- raster('output/ssea/various_rasters/extendedsuit_ssea_0-6.tif')
mat_pa <- raster('output/ssea/various_rasters/protected_areas_ssea.tif')
mat_conc <- raster('output/ssea/various_rasters/concessions_ssea.tif')
mat_acc <- raster('output/ssea/various_rasters/accessibility_ssea.tif')
mat_carb <- raster('output/ssea/various_rasters/carbon_ssea.tif')
#### Get mat_forest from mat_land_use
mat_land_use_cci <- raster('output/ssea/various_rasters/land_use_ssea.tif')
reclassify.df <- read.csv('data/mat_land_use_reclassifyMW.csv')
mat_forest <- subs(mat_land_use_cci, reclassify.df, by='NB_LAB', which='forest', subsWithNA=FALSE)
### Mask unsuitable LU from mat_suit
mat_land_use <- subs(mat_land_use_cci, reclassify.df, by='NB_LAB', which='hassparse', subsWithNA=FALSE)
mat_suit <- raster::mask(mat_suit, mat_land_use, maskvalue=NA, updatevalue=NA)
mat_suit <- raster::mask(mat_suit, mat_land_use, maskvalue=0, updatevalue=0)
### Stack raster
suit_vuln_stack_ssea <- stack(mat_suit, mat_pa, mat_rich, mat_vulnA, mat_vulnT, mat_forest, mat_acc, mat_carb, mat_conc )
suit_vuln_spdf_ssea <- rasterToPoints(suit_vuln_stack_ssea, spatial=TRUE, progress='text') #%>% st_as_sf() #takes a minute using sf
suit_vuln_vals_ssea <- as.data.frame(suit_vuln_spdf_ssea)
suit_vuln_vals_ssea <- suit_vuln_vals_ssea %>%
rename(suit = names(suit_vuln_vals_ssea)[1],
pa = names(suit_vuln_vals_ssea)[2],
rich = names(suit_vuln_vals_ssea)[3],
vulnA = names(suit_vuln_vals_ssea)[4],
vulnT = names(suit_vuln_vals_ssea)[5],
forest = names(suit_vuln_vals_ssea)[6],
acc = names(suit_vuln_vals_ssea)[7],
carb = names(suit_vuln_vals_ssea)[8],
conc = names(suit_vuln_vals_ssea)[9]) %>%
mutate(region = 'ssea')
head(suit_vuln_vals_ssea)
rm(list=setdiff(ls(), c("GISfolder", "suit_vuln_vals_afr", "suit_vuln_vals_ssea", "suit_vuln_stack_afr", "suit_vuln_stack_ssea")))
### Combine
suit_vuln_vals <- rbind(suit_vuln_vals_afr, suit_vuln_vals_ssea)
suit_vuln_vals <- suit_vuln_vals %>%
#mutate(suitclass = cut(suit, breaks=c(-0.1, 0.2, 0.4, 0.6, 0.8, 1), right=TRUE)) %>%
mutate(vulnAclass = cut(vulnA, breaks=c(-0.1, 0.2, 0.4, 0.6, 0.8, 1), right=TRUE)) %>%
mutate(vulnTclass = cut(vulnT, breaks=c(-0.1, 0.2, 0.4, 0.6, 0.8, 1), right=TRUE)) %>%
mutate(richclass = cut(rich, breaks=c(-0.1, 0.2, 0.4, 0.6, 0.8, 1), right=TRUE))
# Exclude when both suit and rich/vuln are NAs
# Only incld min suitability, i.e. suit >0
suit_vuln_vals2 <- suit_vuln_vals %>%
dplyr::filter(!(is.na(.$suit)==TRUE & is.na(.$rich)==TRUE & is.na(.$vulnA)==TRUE & is.na(.$vulnT)==TRUE)) %>%
dplyr::filter(suit > 0) #125527
# Exclude PAs, exclude suit <0. Include only non-NA suit AND non-NA (rich OR vuln) #98999obs
suit_vuln_vals3 <- suit_vuln_vals %>%
dplyr::filter(is.na(.$suit)==FALSE) %>% filter(is.na(.$rich)==FALSE | is.na(.$vulnA)==FALSE | is.na(.$vulnT)==FALSE ) %>%
dplyr::filter(suit > 0) %>%
dplyr::filter(pa == 0) %>%
dplyr::filter(conc == 0) #exclude PAs and conc
head(suit_vuln_vals3) #102311
#### Write suit_vuln_vals3.csv ####
fwrite(suit_vuln_vals3, 'output/suit_vuln_vals3_ext.csv')
suit_vuln_vals3ext <- fread('output/suit_vuln_vals3_ext.csv')
suit_vuln_vals3ext %>% dplyr::filter(suit >=2 ) %>% group_by(region) %>% summarize(n=n())
##################### Table S2 (area of all classes) ###################################
head(suit_vuln_vals3)
unique(suit_vuln_vals3$vulnTclass)
suit_vuln_vals3[suit_vuln_vals3$vulnT == 0, ] #checking for no 0s
# Master table
aoc_tbl <- expand.grid(vulnTclass=unique(suit_vuln_vals3$vulnTclass), suit=c(6:2), region=c('afr', 'ssea'))
#Table S1
aoc_tbl_vulnT <- suit_vuln_vals3 %>%
group_by(region, suit, vulnTclass) %>%
summarize(Mha = n()/100) %>%
arrange(region)
aoc_tbl <- left_join(aoc_tbl, aoc_tbl_vulnT)
aoc_tbl_sum <- aoc_tbl %>% dplyr::select(-region) %>% group_by(suit, vulnTclass) %>% summarize_all(sum, na.rm=TRUE) %>% mutate(region='z.both')
aoc_tbl_sum <- bind_rows(aoc_tbl, aoc_tbl_sum) %>% arrange(region, suit, vulnTclass)
aoc_tbl_wide <- aoc_tbl_sum %>% spread(vulnTclass, Mha, fill='-') %>% arrange(region, desc(suit))
write.csv(aoc_tbl_wide, 'output/results/TableS2.csv', row.names=FALSE)
#### Numbers for the Results ####
# Of land available for rubber expansion, just 0.1 Mha in Africa and none in Asia/New Guinea had high bioclimatic suitability for rubber (suitability >0.8) (Figure 1).
suit_vuln_vals3 %>% filter(suit == 6) %>% group_by(region) %>% summarize(n = n(), Mha = n()*0.01) #43.3, 147 Mha in afr and ssea highly suitable
suit_vuln_vals3 %>% filter(suit == 5) %>% group_by(region) %>% summarize(n = n(), Mha = n()*0.01) #277 and 135 Mha high suitable
# Among these highly suitable areas, none were minimal in extinction vulnerability (vulnerability ≤0.2) (Figure 1 and Table S1; see also Text S1).
suit_vuln_vals3ext %>% filter(suit==6 & vulnT <= 0.2) %>% group_by(region, suit, vulnTclass) %>% summarize(n = n(), Mha = n()*0.01) %>% arrange(region, suit)
# >> win-win: 0.29, 22.6 in afr and ssea
# We identified 19.5 Mha of land meeting our criteria for ‘areas of compromise’ (Table S1, see also Text S1).
suit_vuln_vals3 %>% filter(suit >= 2 & vulnT <= 0.4) %>% summarize(n = n(), Mha = n()*0.01)
# 733.79 Mha
# Africa had fewer areas of compromise (8.7 Mha) compared to Asia and New Guinea (11.9 Mha),
suit_vuln_vals3ext %>% filter(suit >= 2 & vulnT <= 0.4) %>% group_by(region) %>% summarize(n = n(), Mha = n()*0.01) #601 Mha afr and 133 Mha in ssea. Afr has lot more.
# For Text 2 Result (vulnA)
# Of land available for rubber expansion, just 0.1 Mha in Africa and none in Asia/New Guinea had high bioclimatic suitability for rubber (suitability >0.8) (Figure 1).
suit_vuln_vals3 %>% filter(suit == 6) %>% group_by(region) %>% summarize(n = n(), Mha = n()*0.01) #43.3 and 147
suit_vuln_vals3 %>% filter(suit == 5) %>% group_by(region) %>% summarize(n = n(), Mha = n()*0.01) #277 and 135 Mha high suitable
# Among these highly suitable areas, none were minimal in extinction vulnerability (vulnerability ≤0.2) (Figure 1 and Table S1; see also Text S1).
suit_vuln_vals3 %>% filter(suit==6 & vulnA <= 0.2) %>% group_by(region, suit, vulnAclass) %>% summarize(n = n(), Mha = n()*0.01) %>% arrange(region, suit)
# >> win-win: 0.3, 3.27 in afr and ssea
# We identified 19.5 Mha of land meeting our criteria for ‘areas of compromise’ (Table S1, see also Text S1).
suit_vuln_vals3 %>% filter(suit >= 2 & vulnA <= 0.4) %>% summarize(n = n(), Mha = n()*0.01)
# 295.89 Mha
# Africa had fewer areas of compromise (8.7 Mha) compared to Asia and New Guinea (11.9 Mha),
suit_vuln_vals3 %>% filter(suit >= 2 & vulnA <= 0.4) %>% group_by(region) %>% summarize(n = n(), Mha = n()*0.01) #185 Mha afr and 111 Mha in ssea. Afr has lot more.
|
121372e48006c58a2ac4b91e4d78dbbd12969e0e | 4ececcb5166ef1c848a22a54961aa7341f8bc8fa | /analyze_data.R | a4f376fd2332ceb5cebff740f7c7807ab6d5e97a | [] | no_license | pgrimm52/Math-640-Final-Project | f595ed07e60843960c32856023c0101d0bf6f589 | 478397466141d26c058858bbed24cfa1c0a0dbc2 | refs/heads/master | 2020-03-14T09:50:37.906525 | 2018-05-10T06:05:59 | 2018-05-10T06:05:59 | 131,553,523 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,697 | r | analyze_data.R | ########################
# File: analyze_data.R
# Purpose: to conduct core sampling and analysis
# Output: console, graphs
########################
###############
# A. Setup
###############
rm(list=ls())
# Load packages and helper functions
source("helper_functions.R")
# Load data
tweet_storms <- readRDS("tweet_storms.rds")
all_storms <- tweet_storms %>% pull(days_elapsed)
pre_election <- tweet_storms %>% filter(!post_election) %>% pull(days_elapsed)
post_election <- tweet_storms %>% filter(post_election) %>% pull(days_elapsed)
###############
# B. Examine different posteriors
# (always source sampler file before runs, since posteriors overwrite each other)
###############
# LOG-NORMAL #############################
source("lognormal_sampler.R")
# NON-INFORMATIVE
# 1. No tuning necessary
# 2. Sample
lognorm_samples_noninf <- lognormSamp(data=all_storms, B=10000,
a=1, b=10,
c=1, d=1000)
# 3. Check for convergence
mcmcplot2(lognorm_samples_noninf$mu)
mcmcplot2(lognorm_samples_noninf$sig2)
# 4. Grab Log-normal parameters
posterior_summary(lognorm_samples_noninf$mu)
posterior_summary(lognorm_samples_noninf$sig2)
final_lognorm <- lognorm_samples_noninf
# INFORMATIVE
# 1. No tuning necessary
# 2. Sample
lognorm_samples_inf <- lognormSamp(data=all_storms, B=10000,
a=1, b=10,
c=0.752763, d=1)
# 3. Check for convergence
mcmcplot2(lognorm_samples_inf$mu)
mcmcplot2(lognorm_samples_inf$sig2)
# 4. Grab Log-normal parameters
posterior_summary(lognorm_samples_inf$mu)
posterior_summary(lognorm_samples_inf$sig2)
# GAMMA #############################
source("gamma_sampler.R")
# NON-INFORMATIVE
# 1. Tune with Gamma proposal
tune_acceptance_rate(
a_vals = seq(60, 70, by=1),
b_vals = seq(60, 70, by=1),
sampler = gammaSamp,
data = all_storms,
B = 10000,
p=1, q=0, r=0, s=0,
alpha_start = 1, beta_start = 1) # settle on Gamma(60, 63) with ~43% acceptance
# 2. Sample
gamma_samples_noninf.chain1 <- gammaSamp(
seed = 1, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=1, q=0, r=0, s=0,
alpha_start = 0.7, beta_start = 0.2)
gamma_samples_noninf.chain2 <- gammaSamp(
seed = 2, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=1, q=0, r=0, s=0,
alpha_start = 1.1, beta_start = 0.3)
gamma_samples_noninf.chain3 <- gammaSamp(
seed = 3, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=1, q=0, r=0, s=0,
alpha_start = 0.9, beta_start = 0.4)
gamma_samples_noninf.chain4 <- gammaSamp(
seed = 4, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=1, q=0, r=0, s=0,
alpha_start = 0.8, beta_start = 0.5)
# 3. Check for convergence
mcmcplot2(gamma_samples_noninf.chain1$alpha)
mcmcplot2(gamma_samples_noninf.chain2$alpha)
mcmcplot2(gamma_samples_noninf.chain3$alpha)
mcmcplot2(gamma_samples_noninf.chain4$alpha)
gelman_rubin(
gamma_samples_noninf.chain1$alpha,
gamma_samples_noninf.chain2$alpha,
gamma_samples_noninf.chain3$alpha,
gamma_samples_noninf.chain4$alpha)
mcmcplot2(gamma_samples_noninf.chain1$beta)
mcmcplot2(gamma_samples_noninf.chain2$beta)
mcmcplot2(gamma_samples_noninf.chain3$beta)
mcmcplot2(gamma_samples_noninf.chain4$beta)
gelman_rubin(
gamma_samples_noninf.chain1$beta,
gamma_samples_noninf.chain2$beta,
gamma_samples_noninf.chain3$beta,
gamma_samples_noninf.chain4$beta)
show_thinning_options(gamma_samples_noninf.chain1$alpha) # 10 is best
show_thinning_options(gamma_samples_noninf.chain1$beta) # 10 is best
# 4. Grab Gamma parameters
final_gamma <- NULL
final_gamma$alpha <- c(
thin(gamma_samples_noninf.chain1$alpha, 10),
thin(gamma_samples_noninf.chain1$alpha, 10),
thin(gamma_samples_noninf.chain1$alpha, 10),
thin(gamma_samples_noninf.chain1$alpha, 10))
final_gamma$beta <- c(
thin(gamma_samples_noninf.chain1$beta, 10),
thin(gamma_samples_noninf.chain1$beta, 10),
thin(gamma_samples_noninf.chain1$beta, 10),
thin(gamma_samples_noninf.chain1$beta, 10))
posterior_summary(final_gamma$alpha)
posterior_summary(final_gamma$beta)
# INFORMATIVE
# 1. Tune with Gamma proposal
# Not necessary
# 2. Sample
gamma_samples_inf.chain1 <- gammaSamp(
seed = 1, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=10, q=10, r=10, s=10,
alpha_start = 0.7, beta_start = 0.2)
gamma_samples_inf.chain2 <- gammaSamp(
seed = 2, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=10, q=10, r=10, s=10,
alpha_start = 1.1, beta_start = 0.3)
gamma_samples_inf.chain3 <- gammaSamp(
seed = 3, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=10, q=10, r=10, s=10,
alpha_start = 0.9, beta_start = 0.4)
gamma_samples_inf.chain4 <- gammaSamp(
seed = 4, data = all_storms, B = 20000,
a1 = 60, b1 = 63,
p=10, q=10, r=10, s=10,
alpha_start = 0.8, beta_start = 0.5)
# 3. Check for convergence
mcmcplot2(gamma_samples_inf.chain1$alpha)
mcmcplot2(gamma_samples_inf.chain2$alpha)
mcmcplot2(gamma_samples_inf.chain3$alpha)
mcmcplot2(gamma_samples_inf.chain4$alpha)
gelman_rubin(
gamma_samples_inf.chain1$alpha,
gamma_samples_inf.chain2$alpha,
gamma_samples_inf.chain3$alpha,
gamma_samples_inf.chain4$alpha)
mcmcplot2(gamma_samples_inf.chain1$beta)
mcmcplot2(gamma_samples_inf.chain2$beta)
mcmcplot2(gamma_samples_inf.chain3$beta)
mcmcplot2(gamma_samples_inf.chain4$beta)
gelman_rubin(
gamma_samples_inf.chain1$beta,
gamma_samples_inf.chain2$beta,
gamma_samples_inf.chain3$beta,
gamma_samples_inf.chain4$beta)
show_thinning_options(gamma_samples_inf.chain1$alpha) # 10 is best
show_thinning_options(gamma_samples_inf.chain1$beta) # 10 is best
# 4. Grab Gamma parameters
posterior_summary(c(
thin(gamma_samples_inf.chain1$alpha, 10),
thin(gamma_samples_inf.chain1$alpha, 10),
thin(gamma_samples_inf.chain1$alpha, 10),
thin(gamma_samples_inf.chain1$alpha, 10)))
posterior_summary(c(
thin(gamma_samples_inf.chain1$beta, 10),
thin(gamma_samples_inf.chain1$beta, 10),
thin(gamma_samples_inf.chain1$beta, 10),
thin(gamma_samples_inf.chain1$beta, 10)))
# WEIBULL #############################
source("weibull_sampler.R")
# 1. Tune with Gamma proposal
tune_acceptance_rate(
a_vals = seq(60, 70, by=1),
b_vals = seq(60, 70, by=1),
sampler = weibullSamp,
data = all_storms,
B = 20000,
theta_start = 1, lambda_start = 1) # settle on Gamma(60, 67) with ~44% acceptance
# 2. Sample
weibull_samples.chain1 <- weibullSamp(
seed = 1, data = all_storms, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.8, lambda_start = 2)
weibull_samples.chain2 <- weibullSamp(
seed = 2, data = all_storms, B = 20000,
a1 = 60, b1 = 67,
theta_start = 1.1, lambda_start = 4)
weibull_samples.chain3 <- weibullSamp(
seed = 3, data = all_storms, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.9, lambda_start = 3.5)
weibull_samples.chain4 <- weibullSamp(
seed = 4, data = all_storms, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.9, lambda_start = 2.5)
# 3. Check for convergence
mcmcplot2(weibull_samples.chain1$theta)
mcmcplot2(weibull_samples.chain2$theta)
mcmcplot2(weibull_samples.chain3$theta)
mcmcplot2(weibull_samples.chain4$theta)
gelman_rubin(
weibull_samples.chain1$theta,
weibull_samples.chain2$theta,
weibull_samples.chain3$theta,
weibull_samples.chain4$theta)
mcmcplot2(weibull_samples.chain1$lambda)
mcmcplot2(weibull_samples.chain2$lambda)
mcmcplot2(weibull_samples.chain3$lambda)
mcmcplot2(weibull_samples.chain4$lambda)
gelman_rubin(
weibull_samples.chain1$lambda,
weibull_samples.chain2$lambda,
weibull_samples.chain3$lambda,
weibull_samples.chain4$lambda)
show_thinning_options(weibull_samples.chain1$theta) # 5 is best
show_thinning_options(weibull_samples.chain1$lambda) # 5 is best
# 4. Grab Weibull parameters
final_weibull <- NULL
final_weibull$theta <- c(
thin(weibull_samples.chain1$theta, 5),
thin(weibull_samples.chain1$theta, 5),
thin(weibull_samples.chain1$theta, 5),
thin(weibull_samples.chain1$theta, 5))
final_weibull$lambda <- c(
thin(weibull_samples.chain1$lambda, 5),
thin(weibull_samples.chain1$lambda, 5),
thin(weibull_samples.chain1$lambda, 5),
thin(weibull_samples.chain1$lambda, 5))
posterior_summary(final_weibull$theta)
posterior_summary(final_weibull$lambda)
###############
# C. Decide on best likelihood (probably tie between weibull, gamma)
###############
# Graphical comparison to empirical density
plot(density(all_storms, from=0), lwd=2, ylim=c(0, 0.4), xlim=c(0,20),
main="Model Selection", xlab="Days Elapsed")
legend(10, 0.4,
legend=c("Empirical", "Log-normal", "Gamma", "Weibull"),
col=c("black", "green", "blue", "red"), lty=c(2,2,2,2), lwd=c(1,2,2,2), cex=1)
curve(dlnorm(x, 0.434, sqrt(1.878)), col="green", lwd=2, lty=2, add=TRUE)
curve(dgamma(x, 0.901, 0.301), col="blue", lwd=2, lty=2, add=TRUE)
curve(dweibull(x, 0.908, 2.877), col="red", lwd=2, lty=2, add=TRUE)
# Show replicated moment distribution with original data
show_replicate_analysis(all_storms, rlnorm, final_lognorm$mu, sqrt(final_lognorm$sig2)) # remember sqrt!
show_replicate_analysis(all_storms, rgamma, final_gamma$alpha, final_gamma$beta)
show_replicate_analysis(all_storms, rweibull, final_weibull$theta, final_weibull$lambda)
dev.off()
# Decide based on DIC
calc_DIC(
data = all_storms,
ddistribution = dlnorm,
params = cbind(final_lognorm$mu, sqrt(final_lognorm$sig2)))
calc_DIC(
data = all_storms,
ddistribution = dgamma,
params = cbind(final_gamma$alpha, final_gamma$beta))
calc_DIC(
data = all_storms,
ddistribution = dweibull,
params = cbind(final_weibull$theta, final_weibull$lambda))
###############
# D. Compare credible intervals of pre- and post-election
# (there is a difference, but not stat significant because intervals overlap)
###############
# Use Weibull because paramters have interesting interpretation
source("weibull_sampler.R")
# Train using pre-election data
pre_weibull_samples.chain1 <- weibullSamp(
seed = 1, data = pre_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.8, lambda_start = 2)
pre_weibull_samples.chain2 <- weibullSamp(
seed = 2, data = pre_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 1.1, lambda_start = 4)
pre_weibull_samples.chain3 <- weibullSamp(
seed = 3, data = pre_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.9, lambda_start = 3.5)
pre_weibull_samples.chain4 <- weibullSamp(
seed = 4, data = pre_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.9, lambda_start = 2.5)
mcmcplot2(pre_weibull_samples.chain1$theta)
mcmcplot2(pre_weibull_samples.chain2$theta)
mcmcplot2(pre_weibull_samples.chain3$theta)
mcmcplot2(pre_weibull_samples.chain4$theta)
gelman_rubin(
pre_weibull_samples.chain1$theta,
pre_weibull_samples.chain2$theta,
pre_weibull_samples.chain3$theta,
pre_weibull_samples.chain4$theta)
mcmcplot2(pre_weibull_samples.chain1$lambda)
mcmcplot2(pre_weibull_samples.chain2$lambda)
mcmcplot2(pre_weibull_samples.chain3$lambda)
mcmcplot2(pre_weibull_samples.chain4$lambda)
gelman_rubin(
pre_weibull_samples.chain1$lambda,
pre_weibull_samples.chain2$lambda,
pre_weibull_samples.chain3$lambda,
pre_weibull_samples.chain4$lambda)
show_thinning_options(pre_weibull_samples.chain1$theta) # 5 is best
show_thinning_options(pre_weibull_samples.chain1$lambda) # 5 is best
pre_weibull <- NULL
pre_weibull$theta <- c(
thin(pre_weibull_samples.chain1$theta, 5),
thin(pre_weibull_samples.chain1$theta, 5),
thin(pre_weibull_samples.chain1$theta, 5),
thin(pre_weibull_samples.chain1$theta, 5))
pre_weibull$lambda <- c(
thin(pre_weibull_samples.chain1$lambda, 5),
thin(pre_weibull_samples.chain1$lambda, 5),
thin(pre_weibull_samples.chain1$lambda, 5),
thin(pre_weibull_samples.chain1$lambda, 5))
posterior_summary(pre_weibull$theta)
posterior_summary(pre_weibull$lambda)
# Train using post-election data
post_weibull_samples.chain1 <- weibullSamp(
seed = 1, data = post_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.8, lambda_start = 2)
post_weibull_samples.chain2 <- weibullSamp(
seed = 2, data = post_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 1.1, lambda_start = 4)
post_weibull_samples.chain3 <- weibullSamp(
seed = 3, data = post_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.9, lambda_start = 3.5)
post_weibull_samples.chain4 <- weibullSamp(
seed = 4, data = post_election, B = 20000,
a1 = 60, b1 = 67,
theta_start = 0.9, lambda_start = 2.5)
mcmcplot2(post_weibull_samples.chain1$theta)
mcmcplot2(post_weibull_samples.chain2$theta)
mcmcplot2(post_weibull_samples.chain3$theta)
mcmcplot2(post_weibull_samples.chain4$theta)
gelman_rubin(
post_weibull_samples.chain1$theta,
post_weibull_samples.chain2$theta,
post_weibull_samples.chain3$theta,
post_weibull_samples.chain4$theta)
mcmcplot2(post_weibull_samples.chain1$lambda)
mcmcplot2(post_weibull_samples.chain2$lambda)
mcmcplot2(post_weibull_samples.chain3$lambda)
mcmcplot2(post_weibull_samples.chain4$lambda)
gelman_rubin(
post_weibull_samples.chain1$lambda,
post_weibull_samples.chain2$lambda,
post_weibull_samples.chain3$lambda,
post_weibull_samples.chain4$lambda)
show_thinning_options(post_weibull_samples.chain1$theta) # 5 is best
show_thinning_options(post_weibull_samples.chain1$lambda) # 5 is best
post_weibull <- NULL
post_weibull$theta <- c(
thin(post_weibull_samples.chain1$theta, 5),
thin(post_weibull_samples.chain1$theta, 5),
thin(post_weibull_samples.chain1$theta, 5),
thin(post_weibull_samples.chain1$theta, 5))
post_weibull$lambda <- c(
thin(post_weibull_samples.chain1$lambda, 5),
thin(post_weibull_samples.chain1$lambda, 5),
thin(post_weibull_samples.chain1$lambda, 5),
thin(post_weibull_samples.chain1$lambda, 5))
posterior_summary(post_weibull$theta)
posterior_summary(post_weibull$lambda)
# Look at median of distribution via parameters
pre_median <- pre_weibull$lambda * log(2)^(1/pre_weibull$theta)
post_median <- post_weibull$lambda * log(2)^(1/post_weibull$theta)
posterior_summary(pre_median)
posterior_summary(post_median)
caterplot2(pre_median, "Pre-\nElection", post_median, "Post-\nElection")
title(main="Median Days Elapsed\nSince Last Tweetstorm",
xlab="Time (days)")
# Look at Prob(> 1 week silence)
n_pre <- post_weibull$theta %>% length
n_post <- post_weibull$theta %>% length
mean(rweibull(n_pre, pre_weibull$theta, pre_weibull$lambda) > 7)
mean(rweibull(n_post, post_weibull$theta, post_weibull$lambda) > 7)
###############
# E. How well does pre-election model/data do for post-election realized data?
###############
plot(density(post_election, from=0), lwd=2, col="black", xlim=c(0, 20),
main="Pre-election model vs.\npost-election data")
curve(dweibull(x, 0.8359, 3.4118), add=TRUE, col="purple", lty=2, lwd=2)
legend(6, 0.2,
legend=c("Empirical (post-election)", "Model (pre-election)"),
col=c("black", "purple", "blue", "red"), lty=c(1,2), lwd=c(2,2), cex=1)
|
4df015addbd7c6ea29b25bb512de75773297c600 | d17f6fda2c41536d3eb7e60c3429f9630cb7697f | /man/dim.DGEobj.Rd | 4384be1d626b2746bad216231c0c89b6934cdd8e | [] | no_license | klmedeiros-ag/DGEobj | 049ba274ab76b31eaf2c178467e18b3af39871fc | fd68063215058b50ab0c12e7dd018abf15b5aa73 | refs/heads/develop | 2023-01-10T07:09:38.089778 | 2020-11-03T03:25:42 | 2020-11-03T03:25:42 | 304,167,313 | 0 | 0 | null | 2020-11-03T03:25:43 | 2020-10-15T00:25:22 | R | UTF-8 | R | false | true | 469 | rd | dim.DGEobj.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{dim.DGEobj}
\alias{dim.DGEobj}
\title{DGEobj dimensions}
\usage{
\method{dim}{DGEobj}(x)
}
\arguments{
\item{x}{A class DGEobj created by function initDGEobj()}
}
\value{
An integer vector [r,c] with a length of 2.
}
\description{
Reports the dimensions of the assay slot (row = genes; col = samples) in a DGEobj.
}
\author{
John Thompson
}
\keyword{DGEobj}
\keyword{RNA-Seq,}
|
006dd1a30ad6a2c9a5e88f4f17f2cbcc836b815e | 729078c88d61973a94248bed6896648f95bc2fdb | /run_analysis.R | c57872af0c3400ffc73a42918d1c860a594b43cc | [] | no_license | jmscarrillo/Getting-and-Cleaning-Data-Course-Project | 3180f22131112344e4897c2b78e14f1586e4e75a | 743ed4bdcb55dbea0aed90b76137edce218dcc3a | refs/heads/master | 2021-09-03T09:54:15.347846 | 2018-01-08T06:26:52 | 2018-01-08T06:26:52 | 115,909,744 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,874 | r | run_analysis.R | ## Coursera - Data Science - Universidad Johns Hopkins
## Getting and Cleaning Data - Week 4
## Course Project
## José Mª Sebastián Carrillo
##########
## Step 0A.- Check libraries in the current system.
##########
if (!require('dplyr')) {
stop('The package dplyr was not installed!')
}
library(dplyr)
##########
## Step 0B.- Check if the downloaded data already exists.
##########
currentFolder <- getwd()
dataFileZip <- "UCI_HAR_Dataset.zip"
# Verify the file downloaded
if (!file.exists(dataFileZip)){
dataFileZipUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(dataFileZipUrl, dataFileZip, method="curl")
}
# Verify the folder with the data uncompressed
dataFolderName <- "UCI HAR Dataset"
dataFolder <- file.path(currentFolder, dataFolderName)
if (!dir.exists(dataFolder)) {
unzip(dataFileZip)
}
##########
## Step 01.- Merges the training and the test sets to create one data set.
##########
# Author <- "José Mª Sebastián Carrillo"
# Read the training data files
trainingSubjects <- read.table(file.path(dataFolderName, "train", "subject_train.txt"), header = FALSE)
trainingValues <- read.table(file.path(dataFolderName, "train", "X_train.txt"), header = FALSE)
trainingActivity <- read.table(file.path(dataFolderName, "train", "y_train.txt"), header = FALSE)
# Read the test data files
testSubjects <- read.table(file.path(dataFolderName, "test", "subject_test.txt"), header = FALSE)
testValues <- read.table(file.path(dataFolderName, "test", "X_test.txt"), header = FALSE)
testActivity <- read.table(file.path(dataFolderName, "test", "y_test.txt"), header = FALSE)
# Read the features data file
dataFeatures = read.table(file.path(dataFolderName, "features.txt"), header = FALSE, as.is = TRUE)
colnames(dataFeatures) <- c("featureId", "featureLabel")
# Read the activity labels data file
activityLabels = read.table(file.path(dataFolderName, "activity_labels.txt"), header = FALSE, as.is = TRUE)
colnames(activityLabels) <- c("activityId", "activityLabel")
# Combine all data in one
allData <- rbind(
cbind(trainingSubjects, trainingActivity, trainingValues),
cbind(testSubjects, testActivity, testValues)
)
colnames(allData) <- c("subjectId", "activityId", dataFeatures$featureLabel)
# Delete the variables which we don't need to use more (memory efficiency purposes)
rm(trainingSubjects, trainingActivity, trainingValues,
testSubjects, testActivity, testValues)
##########
## Step 02.- Extracts only the measurements on the mean and standard deviation
## for each measurement.
##########
# Obtains the target columns (key columns [subjectId,activityId] and data columns [mean,std])
targetColumns <- grepl("subjectId|activityId|mean|std", colnames(allData))
# Constructs the dataset with the data required
targetData <- allData[, targetColumns]
# Delete the variables which we don't need to use more (memory efficiency purposes)
rm(allData)
##########
## Step 03.- Uses descriptive activity names to name the activities in the data set.
## Step 04.- Appropriately labels the data set with descriptive variable names.
##########
# Obtains the columns for apply the descriptive names
targetDataColumns <- colnames(targetData)
# Remove all the special characters
targetDataColumns <- gsub("[\\(\\)-]", "", targetDataColumns)
# Apply the correct names
targetDataColumns <- gsub("mean$", "Mean", targetDataColumns)
targetDataColumns <- gsub("std$", "StandardDeviation", targetDataColumns)
targetDataColumns <- gsub("mean", "Mean_", targetDataColumns)
targetDataColumns <- gsub("std", "StandardDeviation_", targetDataColumns)
targetDataColumns <- gsub("Acc", "Accelerometer_", targetDataColumns)
targetDataColumns <- gsub("Gyro", "Gyroscope_", targetDataColumns)
targetDataColumns <- gsub("Mag", "Magnitude_", targetDataColumns)
targetDataColumns <- gsub("Freq", "Frequency_", targetDataColumns)
targetDataColumns <- gsub("Body", "Body_", targetDataColumns)
targetDataColumns <- gsub("Gravity", "Gravity_", targetDataColumns)
targetDataColumns <- gsub("^t", "Time_", targetDataColumns)
targetDataColumns <- gsub("^f", "Frequency_", targetDataColumns)
targetDataColumns <- gsub("_$", "", targetDataColumns)
# Rename the targetData column names with the correct ones
colnames(targetData) <- targetDataColumns
##########
## Step 05.- From the data set in step 4, creates a second, independent tidy data set
## with the average of each variable for each activity and each subject.
##########
# Using the pipe operator, obtains the mean of all values, grouping by "subject" and "activity".
tidyDataSet <- targetData %>%
group_by(subjectId, activityId) %>%
summarise_all(funs(mean))
# output to file "tidy_data_set.txt"
write.table(tidyDataSet, "tidy_data_set.txt", row.names = FALSE,
quote = FALSE)
|
b25b575afad6eb86f097e36f670d0192d65cedc6 | 5ffe62a29eec525f568d2e71b854d3ce442e98ef | /Genotyping/Analysis/APCL/090313/Wright1969 neighborhood 090402.R | 9910571e54523c1104c2e8a3dd3e812245abeadc | [
"MIT"
] | permissive | pinskylab/PhilippinesAnemonefish2008 | df3ac238f4db30d90cd564f68a23a2bf8eff86f6 | 4b21bc01c6be794181feff95b44f7e0295ecf061 | refs/heads/main | 2023-08-11T06:47:06.704422 | 2021-09-30T17:27:36 | 2021-09-30T17:27:36 | 307,220,641 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 633 | r | Wright1969 neighborhood 090402.R | # recreating figures on p. 304 in Wright 1969 Evolution and the Genetics of Populations Vol 2 The Theory of Gene Frequencies
x= seq(0, 10, by=0.1)
sigma=1
ynorm=(1/(sigma*sqrt(2*pi)))*exp(-x^2/(2*sigma^2))
a = 1/1
b= .5
b^(-2*a)*gamma(3*a)/gamma(a) # sigma2
y0 = (b^a)/(2*gamma(a+1))
ylepto = y0*exp(-b*x^(1/a))
plot(x, ynorm, type="l", ylim=c(0,.45))
lines(x,ylepto, col="red")
a = seq(1e-40,2, by=0.01)
kurt = gamma(a)*gamma(5*a)/(gamma(3*a)^2) -3 # kurtosis for the lepto curve
mult = 2^(a+1)*gamma(a+1)*(gamma(a)/gamma(3*a))^0.5 # multiplier for sigma*d to get N
plot(kurt, mult, type="l")
abline(v=0, lty=3)
hist(mult) |
c28866a1136dc016926424454349f8b1322f5a6b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tm/examples/removePunctuation.Rd.R | fa1e621b5885a40e083d59b8358cb2984873ff7b | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 464 | r | removePunctuation.Rd.R | library(tm)
### Name: removePunctuation
### Title: Remove Punctuation Marks from a Text Document
### Aliases: removePunctuation removePunctuation.character
### removePunctuation.PlainTextDocument
### ** Examples
data("crude")
inspect(crude[[14]])
inspect(removePunctuation(crude[[14]]))
inspect(removePunctuation(crude[[14]],
preserve_intra_word_contractions = TRUE,
preserve_intra_word_dashes = TRUE))
|
4b0f3f76f97e83fe34601a7e9fc9371447e6ff1a | 332698c619dee288b4aa016119adea1910c4b3b5 | /man/Schumaker.Rd | bd1023045cdc554e8235043cedf566dfe7e9a89d | [] | no_license | cran/schumaker | 7226c538c6b233bec3be1699d0c1e0e33eb840a7 | 95741199393695b602b1f38891dbc282df9fe40c | refs/heads/master | 2021-09-25T18:08:32.832453 | 2021-09-09T21:00:03 | 2021-09-09T21:00:03 | 40,399,779 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,532 | rd | Schumaker.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Schumaker.R
\name{Schumaker}
\alias{Schumaker}
\title{Create a Schumaker spline}
\usage{
Schumaker(
x,
y,
gradients = NA,
Vectorised = TRUE,
Extrapolation = c("Curve", "Constant", "Linear"),
edgeGradients = c(NA, NA)
)
}
\arguments{
\item{x}{A vector of x coordinates}
\item{y}{A corresponding vector of y coordinates}
\item{gradients}{(Optional) A corresponding vector of gradiants at the data points. If this is NA then it will be estimated.}
\item{Vectorised}{This is a boolean parameter. Set to TRUE if you want to be able to input vectors to the created spline. If you will only input single values set this to FALSE as it is a bit faster.}
\item{Extrapolation}{This determines how the spline function responds when an input is recieved outside the domain of x. The options are "Curve" which outputs the result of the point on the quadratic curve at the nearest interval, "Constant" which outputs the y value at the end of the x domain and "Linear" which extends the spline using the gradiant at the edge of x.}
\item{edgeGradients}{This gives the options of specifing the gradients at either edge of the domain. By default this is c(NA,NA) meaning that the defaults from the original paper are used. If this is set to c(0,NA) for instance this will mean that the left edge gradient is zero and the right edge gradient is as recommended in the original paper. This setting has no impact if a full set of gradients is input.}
}
\value{
A list with 3 spline functions and a table with spline intervals and coefficients. The first spline is the schumaker spline, the second spline is the first derivative of the schumaker spline, the third spline is the second derivative of the schumaker spline. Each function takes an x value (or vector if Vectorised = TRUE) and outputs the interpolated y value (or relevant derivative).
}
\description{
Create a Schumaker spline
}
\examples{
x = seq(1,6)
y = log(x)
SSS = schumaker::Schumaker(x,y, Vectorised = TRUE)
xarray = seq(1,6,0.01)
Result = SSS$Spline(xarray)
Result2 = SSS$DerivativeSpline(xarray)
Result3 = SSS$SecondDerivativeSpline(xarray)
plot(xarray, Result, ylim=c(-0.5,2))
lines(xarray, Result2, col = 2)
lines(xarray, Result3, col = 3)
}
\references{
Schumaker, L.L. 1983. On shape-preserving quadratic spline interpolation. SIAM Journal of Numerical Analysis 20: 854-64.
Judd (1998). Numerical Methods in Economics. MIT Press
}
|
7acc94712b10defbee90211fde67c3053b711791 | e3dc7bc2d94d4c37044298658b6f023b27a73986 | /04_Codes/VBP/01_Readin_Data.R | 073d1bceff6dc31eadd231632aa97721bf6e949f | [] | no_license | Zaphiroth/Bluebook_2021 | 83083aeddb9d77997ff62453e86dd62de28da28d | 3478927c75b19a25f76265d9322d5b6ecf5a7da5 | refs/heads/main | 2023-03-25T06:51:30.107261 | 2021-03-22T09:53:02 | 2021-03-22T09:53:02 | 346,550,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,033 | r | 01_Readin_Data.R | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# ProjectName: Bluebook 2021-VBP
# Purpose: Readin
# programmer: Zhe Liu
# Date: 2021-03-11
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
##---- Readin raw data ----
## PCHC code
pchc.mapping <- read.xlsx("02_Inputs/Universe_PCHCCode_20210303.xlsx", sheet = "PCHC")
pchc.mapping1 <- pchc.mapping %>%
filter(!is.na(`单位名称`), !is.na(PCHC_Code)) %>%
group_by(province = `省`, city = `地级市`, district = `区[县/县级市】`, hospital = `单位名称`) %>%
summarise(pchc = first(PCHC_Code)) %>%
ungroup()
pchc.mapping2 <- pchc.mapping %>%
filter(!is.na(ZS_Servier.name), !is.na(PCHC_Code)) %>%
group_by(province = `省`, city = `地级市`, district = `区[县/县级市】`, hospital = `ZS_Servier.name`) %>%
summarise(pchc = first(PCHC_Code)) %>%
ungroup()
pchc.mapping3 <- bind_rows(pchc.mapping1, pchc.mapping2) %>%
distinct(province, city, district, hospital, pchc)
pchc.mapping4 <- pchc.mapping3 %>%
group_by(pchc) %>%
summarise(province = first(na.omit(province)),
city = first(na.omit(city)),
district = first(na.omit(district))) %>%
ungroup()
## product info
nfc.info <- read.xlsx("02_Inputs/Product standardization master data-A-S-0106_updated.xlsx") %>%
distinct(packid = stri_pad_left(PACK_ID, 7, 0),
nfc1 = NFC1_NAME_CH)
##---- Raw data ----
## Community 100
raw.100.19 <- read_csv('02_Inputs/data/shequ_100_bjjszj_19_packid_moleinfo.csv',
locale = locale(encoding = 'GB18030'))
raw.100.20 <- read_csv('02_Inputs/data/shequ_100_bjjszj_20_packid_moleinfo.csv',
locale = locale(encoding = 'GB18030'))
raw.total <- bind_rows(raw.100.19, raw.100.20) %>%
distinct(year = as.character(Year),
quarter = Quarter,
date = gsub('/', '', Month),
province = gsub('省|市', '', Province),
city = if_else(City == '市辖区', '北京', gsub('市', '', City)),
district = County,
hospital = Hospital_Name,
atc2 = stri_sub(ATC4_Code, 1, 3),
atc3 = stri_sub(ATC4_Code, 1, 4),
atc4 = ATC4_Code,
molecule = Molecule_Desc,
packid = stri_pad_left(packcode, 7, 0),
units = Volume,
sales = Value) %>%
mutate(
market = case_when(
atc2 %in% c("C10", "C11") ~ 'LIP',
atc2 %in% c("C02", "C03", "C07", "C08", "C09") ~ "HTN",
atc4 == "L01H2" & molecule %in% c("ERLOTINIB", "GEFITINIB", "ICOTINIB", "AFATINIB") ~
"ONG-TKI",
atc4 == "N03A0" & molecule %in% c("CARBAMAZEPINE", "LAMOTRIGINE", "LEVETIRACETAM",
"OXCARBAZEPINE", "VALPROIC ACID") ~
"EPI",
atc4 == "J01D1" & molecule %in% c("CEFDINIR", "CEFIXIME", "CEFACLOR",
"CEFPROZIL", "CEFUROXIME", "CEFADROXIL",
"CEFUROXIME AXETIL") ~
"ORAL CEF",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(market)) %>%
left_join(pchc.mapping3, by = c('province', 'city', 'district', 'hospital')) %>%
filter(!is.na(pchc)) %>%
group_by(year, quarter, date, province, city, district, pchc, market, atc4, molecule, packid) %>%
summarise(units = sum(units, na.rm = TRUE),
sales = sum(sales, na.rm = TRUE)) %>%
ungroup() %>%
filter(units > 0, sales > 0)
# chk <- raw.total %>%
# filter(is.na(pchc),
# grepl('中心', hospital),
# grepl('社区', hospital),
# !grepl('卫生院|卫生室|卫生站|服务站|社区站|医院', hospital)) %>%
# distinct(province, city, district, hospital) %>%
# arrange(province, city, district, hospital)
write_feather(raw.total, "03_Outputs/VBP/01_Bluebook_2020_VBP_Raw.feather")
|
8530deb9cc421e5f87eb8664414e3feaf59590f4 | f81ac43a1d02013a9cb9eebc2a7d92da4cae9169 | /R/xl_write.R | 4dd8b447099bfbf44b06e9d883a64b51e42fd4b4 | [] | no_license | gdemin/expss | 67d7df59bd4dad2287f49403741840598e01f4a6 | 668d7bace676b555cb34d5e0d633fad516c0f19b | refs/heads/master | 2023-08-31T03:27:40.220828 | 2023-07-16T21:41:53 | 2023-07-16T21:41:53 | 31,271,628 | 83 | 15 | null | 2022-11-02T18:53:17 | 2015-02-24T17:16:42 | R | UTF-8 | R | false | false | 34,574 | r | xl_write.R | #' Write tables and other objects to an xlsx file with formatting
#'
#' Note that \code{openxlsx} package is required for these functions. It can be
#' install by printing \code{install.packages('openxlsx')} in the console. On
#' Windows system you also may need to
#' install \href{https://cran.r-project.org/bin/windows/Rtools/}{rtools}. You
#' can export several tables at once by combining them in a list. See examples.
#' If you need to write all tables to the single sheet you can use
#' \code{xl_write_file}. It automatically creates workbook, worksheet and save
#' *.xlsx file for you.
#' @param obj \code{table} - result of \link{cro}, \link{fre} and etc.
#' \code{obj} also can be data.frame, list or other objects.
#' @param wb xlsx workbook object, result of \link[openxlsx]{createWorkbook} function.
#' @param sheet character or numeric - worksheet name/number in the workbook \code{wb}
#' @param filename A character string naming an xlsx file. For \code{xl_write_file}.
#' @param sheetname A character name for the worksheet. For \code{xl_write_file}.
#' @param row numeric - starting row for writing data
#' @param col numeric - starting column for writing data
#' @param rownames logical should we write data.frame row names?
#' @param colnames logical should we write data.frame column names?
#' @param remove_repeated Should we remove duplicated row or column labels in
#' the rows/columns of the etable? Possible values: "all", "rows", "columns", "none".
#' @param format_table logical should we format table? If FALSE all format arguments will be ignored.
#' @param borders list Style of the table borders. List with two named elements:
#' \code{borderColour} and \code{borderStyle}. For details see
#' \link[openxlsx]{createStyle} function. If it is NULL then no table borders will
#' be produced.
#' @param header_format table header format - result of the \link[openxlsx]{createStyle} function.
#' @param main_format result of the \link[openxlsx]{createStyle} function.
#' Format of the table main area except total rows. Total rows is rows which
#' row labels contain '#'.
#' @param row_labels_format result of the \link[openxlsx]{createStyle} function.
#' Format of the row labels area except total rows. Total rows is rows which
#' row labels contain '#'.
#' @param total_format result of the \link[openxlsx]{createStyle} function.
#' Format of the total rows in the table main area. Total rows is rows which
#' row labels contain '#'.
#' @param total_row_labels_format result of the \link[openxlsx]{createStyle} function.
#' Format of the total rows in the row labels area. Total rows is rows which
#' row labels contain '#'.
#' @param top_left_corner_format result of the \link[openxlsx]{createStyle} function.
#' @param row_symbols_to_remove character vector. Perl-style regular expressions
#' for substrings which will be removed from row labels.
#' @param col_symbols_to_remove character vector. Perl-style regular expressions
#' for substrings which will be removed from column names.
#' @param other_rows_formats named list. Names of the list are perl-style
#' regular expression patterns, items of the list are results of the
#' \link[openxlsx]{createStyle} function. Rows in the main area which row
#' labels contain pattern will be formatted according to the appropriate style.
#' @param other_row_labels_formats named list. Names of the list are perl-style
#' regular expression patterns, items of the list are results of the
#' \link[openxlsx]{createStyle} function. Rows in the row labels area which row
#' labels contain pattern will be formatted according to the appropriate style.
#' @param other_cols_formats named list. Names of the list are perl-style
#' regular expression patterns, items of the list are results of the
#' \link[openxlsx]{createStyle} function. Columns in the main area which column
#' labels contain pattern will be formatted according to the appropriate style.
#' @param other_col_labels_formats named list. Names of the list are perl-style
#' regular expression patterns, items of the list are results of the
#' \link[openxlsx]{createStyle} function. Columns in the header area which column
#' labels contain pattern will be formatted according to the appropriate style.
#' @param additional_cells_formats list Each item of the list is list which
#' consists of two elements. First element is two columns matrix or data.frame
#' with row number and column numbers in the main area of the table. Such
#' matrix can be produced with code \code{which(logical_condition, arr.ind =
#' TRUE)}. Instead of matrix one can use function which accepts original table
#' (\code{obj}) and return such matrix. Second element is result of the
#' \link[openxlsx]{createStyle} function. Cells in the main area will be
#' formatted according to this style.
#' @param caption_format result of the \link[openxlsx]{createStyle} function.
#' @param gap integer. Number of rows between list elements.
#' @param ... further arguments for \code{xl_write}
#' @return invisibly return vector with rows and columns (\code{c(rows,
#' columns)}) occupied by outputted object.
#'
#' @examples
#' \dontrun{
#' library(openxlsx)
#' data(mtcars)
#' # add labels to dataset
#' mtcars = apply_labels(mtcars,
#' mpg = "Miles/(US) gallon",
#' cyl = "Number of cylinders",
#' disp = "Displacement (cu.in.)",
#' hp = "Gross horsepower",
#' drat = "Rear axle ratio",
#' wt = "Weight (lb/1000)",
#' qsec = "1/4 mile time",
#' vs = "Engine",
#' vs = c("V-engine" = 0,
#' "Straight engine" = 1),
#' am = "Transmission",
#' am = c("Automatic" = 0,
#' "Manual"=1),
#' gear = "Number of forward gears",
#' carb = "Number of carburetors"
#' )
#'
#' # create table with caption
#' mtcars_table = cross_cpct(mtcars,
#' cell_vars = list(cyl, gear),
#' col_vars = list(total(), am, vs)
#' ) %>%
#' set_caption("Table 1")
#'
#'
#' wb = createWorkbook()
#' sh = addWorksheet(wb, "Tables")
#' # export table
#' xl_write(mtcars_table, wb, sh)
#' saveWorkbook(wb, "table1.xlsx", overwrite = TRUE)
#'
#' ## quick export
#' xl_write_file(mtcars_table, "table1.xlsx")
#'
#' ## custom cells formatting
#' wb = createWorkbook()
#' sh = addWorksheet(wb, "Tables")
#'
#' # we want to mark cells which are greater than total column
#' my_formatter = function(tbl){
#' greater_than_total = tbl[,-1]>tbl[[2]]
#' which(greater_than_total, arr.ind = TRUE)
#' }
#' # export table
#' xl_write(mtcars_table, wb, sh,
#' additional_cells_formats = list(
#' list(my_formatter, createStyle(textDecoration = "bold", fontColour = "blue"))
#' )
#' )
#' saveWorkbook(wb, "table_with_additional_format.xlsx", overwrite = TRUE)
#'
#' ## automated report generation on multiple variables with the same banner
#'
#' banner = with(mtcars, list(total(), am, vs))
#'
#' # create list of tables
#' list_of_tables = lapply(mtcars, function(variable) {
#' if(length(unique(variable))<7){
#' cro_cpct(variable, banner) %>% significance_cpct()
#' } else {
#' # if number of unique values greater than seven we calculate mean
#' cro_mean_sd_n(variable, banner) %>% significance_means()
#'
#' }
#'
#' })
#'
#'
#' wb = createWorkbook()
#' sh = addWorksheet(wb, "Tables")
#' # export list of tables with additional formatting
#' xl_write(list_of_tables, wb, sh,
#' # remove '#' sign from totals
#' col_symbols_to_remove = "#",
#' row_symbols_to_remove = "#",
#' # format total column as bold
#' other_col_labels_formats = list("#" = createStyle(textDecoration = "bold")),
#' other_cols_formats = list("#" = createStyle(textDecoration = "bold")),
#' )
#' saveWorkbook(wb, "report.xlsx", overwrite = TRUE)
#' }
#' @export
xl_write = function(obj, wb, sheet, row = 1, col = 1, ...){
if(!requireNamespace("openxlsx", quietly = TRUE)){
stop("xl_write: 'openxlsx' is required for this function. Please, install it with 'install.packages('openxlsx')'.")
}
UseMethod("xl_write")
}
#' @export
#' @rdname xl_write
xl_write_file = function(obj, filename, sheetname = "Tables", ...){
wb = openxlsx::createWorkbook()
sh = openxlsx::addWorksheet(wb, sheetName = sheetname)
xl_write(obj, wb = wb, sheet = sh, ...)
openxlsx::saveWorkbook(wb, file = filename, overwrite = TRUE)
}
#' @export
#' @rdname xl_write
xl_write.default = function(obj, wb, sheet, row = 1, col = 1, rownames = FALSE, colnames = !is.atomic(obj), ...){
force(colnames)
if(!is.data.frame(obj)) {
obj = as.sheet(obj)
}
openxlsx::writeData(wb = wb,
sheet = sheet,
x = obj,
startCol = col,
startRow = row,
colNames = colnames,
rowNames = rownames
)
invisible(c(NROW(obj) + colnames, NCOL(obj) + rownames))
}
#' @export
#' @rdname xl_write
xl_write.list = function(obj, wb, sheet, row = 1, col = 1, gap = 1, ...){
stopifnot(
is.numeric(gap),
length(gap)==1,
!is.na(gap),
gap>0
)
col_shift = 0
row_shift = 0
for(each in obj){
res = xl_write(each, wb = wb,
sheet = sheet,
row = row,
col = col,
...)
col_shift = max(col_shift, res[2])
row_shift = row_shift + res[1] + gap
row = row + res[1] + gap
}
invisible(c(row_shift - gap, col_shift))
}
#' @export
#' @rdname xl_write
xl_write.etable = function(obj,
wb,
sheet,
row = 1,
col = 1,
remove_repeated = c("all", "rows", "columns", "none"),
format_table = TRUE,
borders = list(borderColour = "black", borderStyle = "thin"),
header_format = openxlsx::createStyle(
fgFill = "#EBEBEB",
halign = "left",
wrapText = FALSE
),
main_format = openxlsx::createStyle(
halign = "right",
numFmt = format(0, nsmall = get_expss_digits())
),
row_labels_format = openxlsx::createStyle(
halign = "left"
),
total_format = openxlsx::createStyle(
fgFill = "#EBEBEB",
border = "TopBottom",
borderStyle = "thin",
halign = "right",
numFmt = "0"
),
total_row_labels_format = openxlsx::createStyle(
fgFill = "#EBEBEB",
border = "TopBottom",
borderStyle = "thin",
halign = "left"
),
top_left_corner_format = header_format,
row_symbols_to_remove = NULL,
col_symbols_to_remove = NULL,
other_rows_formats = NULL,
other_row_labels_formats = NULL,
other_cols_formats = NULL,
other_col_labels_formats = NULL,
additional_cells_formats = NULL,
...){
if(NCOL(obj)==0) return(invisible(c(NROW(obj), 0)))
recode(obj) = is.nan ~ NA
if(getRversion()>="3.5.0"){
obj = type.convert(obj, as.is = TRUE)
} else {
for(i in seq_along(obj)){
if(is.character(obj[[i]])) obj[[i]] = type.convert(obj[[i]], as.is = TRUE)
}
}
remove_repeated = match.arg(remove_repeated)
header = t(split_labels(colnames(obj), remove_repeated = remove_repeated %in% c("all", "columns")))[,-1, drop = FALSE]
row_labels = split_labels(obj[[1]], remove_repeated = remove_repeated %in% c("all", "rows"))
if(NCOL(header)>0) {
header = header[rowSums(!is.na(header) & (header != "")) >
0, , drop = FALSE]
for(pattern in col_symbols_to_remove){
header[] = gsub(pattern, "", header, perl = TRUE)
}
}
recode(header) = "" ~ NA
for(pattern in row_symbols_to_remove){
row_labels[] = gsub(pattern, "", row_labels, perl = TRUE)
}
# max(1, NCOL(row_labels)) for zero-rows table
top_left_corner = matrix(NA, ncol = max(1, NCOL(row_labels)), nrow = NROW(header))
if (!is.null(colnames(obj)) && !(colnames(obj)[1] %in%
c(NA, "row_labels", ""))) {
top_left_corner[nrow(top_left_corner), 1] = colnames(obj)[1]
}
xy = c(col, row)
openxlsx::writeData(wb,
sheet = sheet,
x = as.sheet(top_left_corner),
xy = xy,
keepNA = FALSE,
rowNames = FALSE,
colNames = FALSE)
rng = c(xy[1] + NCOL(top_left_corner), xy[2] )
openxlsx::writeData(wb,
sheet = sheet,
x = as.sheet(header),
xy = rng,
keepNA = FALSE,
rowNames = FALSE,
colNames = FALSE)
rng = c(xy[1], xy[2] + NROW(top_left_corner))
openxlsx::writeData(wb,
sheet = sheet,
x = as.sheet(row_labels),
xy = rng,
keepNA = FALSE,
rowNames = FALSE,
colNames = FALSE)
rng = c(xy[1] + NCOL(top_left_corner), xy[2] + NROW(top_left_corner))
openxlsx::writeData(wb,
sheet = sheet,
x = obj[, -1, drop = FALSE],
xy = rng,
keepNA = FALSE,
rowNames = FALSE,
colNames = FALSE)
if(format_table){
table_structure = get_table_structure(obj)
if(!is.null(header_format)){
xl_format_header(wb, sheet, row, col,
table_structure,
header_format,
borders = borders
)
}
if(!is.null(top_left_corner_format)){
xl_format_top_left_corner(wb, sheet, row, col,
table_structure,
top_left_corner_format,
borders = borders
)
}
### rows
main_format = c("^[^#]*$" = main_format, "#" = total_format, other_rows_formats)
for(pattern in names(main_format)){
xl_format_main_rows(wb, sheet, row, col,
table_structure,
grep(pattern, obj[[1]], perl = TRUE),
main_format[[pattern]]
)
}
row_labels_format = c("^[^#]*$" = row_labels_format, "#" = total_row_labels_format, other_row_labels_formats)
for(pattern in names(row_labels_format)){
xl_format_row_labels(wb, sheet, row, col,
table_structure,
grep(pattern, obj[[1]], perl = TRUE),
row_labels_format[[pattern]]
)
}
### columns
for(pattern in names(other_cols_formats)){
xl_format_main_cols(wb, sheet, row, col,
table_structure,
col_numbers = grep(pattern, colnames(obj)[-1], perl = TRUE),
other_cols_formats[[pattern]]
)
}
for(pattern in names(other_col_labels_formats)){
xl_format_col_labels(wb, sheet, row, col,
table_structure,
col_numbers = grep(pattern, colnames(obj)[-1], perl = TRUE),
other_col_labels_formats[[pattern]]
)
}
### cells
for(each in additional_cells_formats){
coord_matrix = each[[1]]
if(is.function(coord_matrix)){
coord_matrix = coord_matrix(obj)
}
if(is.data.frame(coord_matrix)){
coord_matrix = as.matrix(coord_matrix)
}
xl_format_cells(wb, sheet, row, col,
table_structure,
row_numbers = coord_matrix[,1],
col_numbers = coord_matrix[,2],
format = each[[2]]
)
}
xl_format_entire_table(wb,
sheet,
row,
col,
table_structure,
borders = borders
)
}
invisible(c(NROW(obj) + NROW(top_left_corner),
NCOL(obj) + NCOL(top_left_corner) - 1)) # -1 because we don't need to count row_labels column
}
#' @export
#' @rdname xl_write
xl_write.with_caption = function(obj,
wb,
sheet,
row = 1,
col = 1,
remove_repeated = c("all", "rows", "columns", "none"),
format_table = TRUE,
borders = list(borderColour = "black", borderStyle = "thin"),
header_format = openxlsx::createStyle(
fgFill = "#EBEBEB",
halign = "left",
wrapText = FALSE
),
main_format = openxlsx::createStyle(
halign = "right",
numFmt = format(0, nsmall = get_expss_digits())
),
row_labels_format = openxlsx::createStyle(
halign = "left"
),
total_format = openxlsx::createStyle(
fgFill = "#EBEBEB",
border = "TopBottom",
borderStyle = "thin",
halign = "right",
numFmt = "0"
),
total_row_labels_format = openxlsx::createStyle(
fgFill = "#EBEBEB",
border = "TopBottom",
borderStyle = "thin",
halign = "left"
),
top_left_corner_format = header_format,
row_symbols_to_remove = NULL,
col_symbols_to_remove = NULL,
other_rows_formats = NULL,
other_row_labels_formats = NULL,
other_cols_formats = NULL,
other_col_labels_formats = NULL,
additional_cells_formats = NULL,
caption_format = openxlsx::createStyle(
textDecoration = "bold",
halign = "left"
),
...){
caption_res = xl_write(get_caption(obj),
wb = wb,
sheet = sheet,
row = row,
col = col
)
if(!is.null(caption_format)){
openxlsx::addStyle(
wb = wb,
sheet = sheet,
style = caption_format,
rows = row:(row + caption_res[1] - 1),
cols = col:(col + caption_res[2] - 1),
gridExpand = TRUE,
stack = TRUE
)
}
table_row = row + caption_res[1]
obj = set_caption(obj, NULL)
table_res = xl_write(obj,
wb = wb,
sheet = sheet,
row = table_row,
col = col,
remove_repeated = remove_repeated,
format_table = format_table,
borders = borders,
header_format = header_format,
main_format = main_format,
row_labels_format = row_labels_format,
total_format = total_format,
total_row_labels_format = total_row_labels_format,
top_left_corner_format = top_left_corner_format,
row_symbols_to_remove = row_symbols_to_remove,
col_symbols_to_remove = col_symbols_to_remove,
other_rows_formats = other_rows_formats,
other_row_labels_formats = other_row_labels_formats,
other_cols_formats = other_cols_formats,
other_col_labels_formats = other_col_labels_formats,
additional_cells_formats = additional_cells_formats
)
invisible(c(
caption_res[1] + table_res[1],
max(caption_res[2], table_res[2], na.rm = TRUE)
))
}
###########################
get_table_structure = function(tbl){
# to pass CRAN check
row_labels_width = NULL
header_height = NULL
header_width = NULL
#####
header = t(split_labels(colnames(tbl), remove_repeated = TRUE))[,-1, drop = FALSE]
table_structure = list(
row_labels_width = max(1, NCOL(split_labels(tbl[[1]]))), # for zero-rows table
header_height = NROW(header),
header_width = NCOL(header)
)
table_structure = within(table_structure, {
total_row_numbers = grep("#", tbl[[1]], fixed = TRUE)
table_width = row_labels_width - 1 + NCOL(tbl)
not_total_row_numbers = (1:NROW(tbl)) %d% total_row_numbers
table_height = header_height + NROW(tbl)
tbl_colnames = colnames(tbl)
vertical_divisors = get_vertical_divisors(colnames(tbl))
})
table_structure
}
get_vertical_divisors = function (tbl_colnames){
if(length(tbl_colnames)<2) return(list())
vapply(
header_groups(tbl_colnames),
"[[",
FUN.VALUE = numeric(1),
USE.NAMES = FALSE,
1
)
}
xl_format_entire_table = function(wb, sheet, row, col, table_structure, borders){
height = table_structure$table_height
width = table_structure$table_width
header_height = table_structure$header_height
vertical_divisors = table_structure$vertical_divisors
table_end_col = col + width - 1
table_end_row = row + height - 1
if(is.null(borders)){
borders = list(borderStyle = "none", borderColour = "black")
}
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row,
cols = col:table_end_col,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "top"
),
gridExpand = TRUE,
stack = TRUE)
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = table_end_row,
cols = col:table_end_col,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "bottom"
),
gridExpand = TRUE,
stack = TRUE)
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row:table_end_row,
cols = col,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "left"
),
gridExpand = TRUE,
stack = TRUE)
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row:table_end_row,
cols = table_end_col,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "right"
),
gridExpand = TRUE,
stack = TRUE)
if((row + header_height)>=table_end_row) return(NULL)
for(each in vertical_divisors){
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = (row + header_height):table_end_row,
cols = col + each + table_structure$row_labels_width - 1 - 1,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "left"
),
gridExpand = TRUE,
stack = TRUE)
}
}
xl_format_header = function(wb, sheet, row, col, table_structure, format, borders){
if(table_structure$table_width<=table_structure$row_labels_width) return(NULL)
row_labels_width = table_structure$row_labels_width
height = table_structure$header_height
width = table_structure$header_width
tbl_colnames = table_structure$tbl_colnames
table_end_col = col + row_labels_width + width - 1
header_end_row = row + height - 1
header_start_col = col + row_labels_width
if(is.null(borders)){
borders = list(borderStyle = "none", borderColour = "black")
}
# horizontal divizors
hlines = lapply(strsplit(tbl_colnames[-1], split = "|", fixed = TRUE), function(each){
each = trimws(each)
# each!="" - if we have empty element then we don't draw line but move down
res = seq_along(each)[c(each[-1]!="", FALSE)]
if(is.na(each[1]) || each[1]==""){
# if first element is empty then we don't draw line after it
res = res[-1]
}
res
})
for(i in seq_along(hlines)){
if(length(hlines[i])>0){
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row + hlines[[i]] - 1,
cols = header_start_col + i - 1,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "bottom"
),
gridExpand = TRUE,
stack = TRUE)
}
}
if(width>1){
if(header_end_row>row){
for( i in header_end_row:(row + 1)){
vertical_divisors = get_vertical_divisors(tbl_colnames)
for(each in vertical_divisors){
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = i,
cols = col + row_labels_width + each - 1 - 1,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "left"
),
gridExpand = FALSE,
stack = TRUE)
}
tbl_colnames = gsub("\\|[^\\|]*$", "", tbl_colnames, perl = TRUE)
}
}
for(each in seq_along(tbl_colnames)[-1]){
if(tbl_colnames[each]!=tbl_colnames[each-1]){
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row,
cols = col + row_labels_width + each - 1 - 1,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "left"
),
gridExpand = FALSE,
stack = TRUE)
}
}
}
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = header_end_row,
cols = header_start_col:table_end_col,
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "bottom"
),
gridExpand = TRUE,
stack = TRUE)
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row:header_end_row,
cols = header_start_col:table_end_col,
format,
gridExpand = TRUE,
stack = TRUE)
}
xl_format_top_left_corner = function(wb, sheet, row, col, table_structure, format, borders){
row_labels_width = table_structure$row_labels_width
height = table_structure$header_height
width = table_structure$header_width
header_end_row = row + height - 1
header_start_col = col + row_labels_width
if(is.null(borders)){
borders = list(borderStyle = "none", borderColour = "black")
}
if(col<header_start_col){
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = header_end_row,
cols = col:(header_start_col-1),
openxlsx::createStyle(
borderStyle = borders$borderStyle,
borderColour = borders$borderColour,
border = "bottom"
),
gridExpand = TRUE,
stack = TRUE)
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row:header_end_row,
cols = col:(header_start_col-1),
format,
gridExpand = TRUE,
stack = TRUE)
}
}
###########################
xl_format_row_labels = function(wb,
sheet,
row,
col,
table_structure,
row_numbers,
format){
end_col = col + table_structure$row_labels_width - 1
row_numbers = row_numbers + row + table_structure$header_height - 1
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row_numbers,
cols = col:end_col,
format,
gridExpand = TRUE,
stack = TRUE)
}
##########################
xl_format_main_rows = function(wb,
sheet,
row,
col,
table_structure,
row_numbers,
format){
if(table_structure$table_width<=table_structure$row_labels_width) return(NULL)
start_col = col + table_structure$row_labels_width
end_col = col + table_structure$table_width - 1
row_numbers = row_numbers + row + table_structure$header_height - 1
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row_numbers,
cols = start_col:end_col,
format,
gridExpand = TRUE,
stack = TRUE)
}
###########################
xl_format_col_labels = function(wb,
sheet,
row,
col,
table_structure,
col_numbers,
format){
end_row = col + table_structure$header_height - 1
col_numbers = col + col_numbers + table_structure$row_labels_width - 1
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row:end_row,
cols = col_numbers,
format,
gridExpand = TRUE,
stack = TRUE)
}
##########################
xl_format_main_cols = function(wb,
sheet,
row,
col,
table_structure,
col_numbers,
format){
if(table_structure$table_height<=table_structure$header_height) return(NULL)
start_row = row + table_structure$header_height
end_row = row + table_structure$table_height - 1
col_numbers = col + col_numbers + table_structure$row_labels_width - 1
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = start_row:end_row,
cols = col_numbers,
format,
gridExpand = TRUE,
stack = TRUE)
}
########
xl_format_cells = function(wb,
sheet,
row,
col,
table_structure,
row_numbers,
col_numbers,
format
){
col_numbers = col_numbers + col + table_structure$row_labels_width - 1
row_numbers = row_numbers + row + table_structure$header_height - 1
openxlsx::addStyle(wb = wb,
sheet = sheet,
rows = row_numbers,
cols = col_numbers,
format,
gridExpand = FALSE,
stack = TRUE)
}
|
6fd8b4f0860695f9508222cc2705748d427c53a8 | 0574e86c20d46f2cdc14481da00ebb75d6a23f6d | /plot_location/rbind data.R | aef11721b61eaf0323de8092a18c48a3ba8a2cd5 | [] | no_license | andy400400/FB_insights_crawler_2 | b2361dfae4f479209308f8237bb878d1d4ab8dde | 7feefc60a12079837747057595e68f9308344069 | refs/heads/master | 2021-01-01T05:11:05.689134 | 2016-04-26T01:43:03 | 2016-04-26T01:43:03 | 56,816,259 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | r | rbind data.R | #chiao,ilan,lotung,suao,toucheng
#dongshan,jhuangwei,sanxing,wujie,yuanshan
city_all<-c("chiao","ilan","lotung","suao","toucheng","dongshan","jhuangwei","sanxing","wujie","yuanshan")
all_data_total<-NULL
for (z in 1:10) {
city<-city_all[z]
al<-dir('C:/pro/plot/8_season_data')
al_length<-length(al)
al_choose<-NULL
for (x in 1:al_length) {
if (grepl(city,al[x])) {
al_choose<-c(al_choose,al[x])
}
}
data_total<-NULL
bl_length<-length(al_choose)
options(digits = 20)
for (y in 1:bl_length) {
data<-read.csv(paste('C:/pro/plot/8_season_data/',al_choose[y],sep = ""))
data_nrow<-nrow(data)
zero<-cbind(c(1:data_nrow),c(y))
season<-zero[,2]
data<-cbind(data,season)
data<-data[,-1]
data<-data[,-1]
data_total<-rbind(data_total,data)
}
all_data_total<-rbind(all_data_total,data_total)
}
claen_data<-NULL
all_data_total_nrow<-nrow(all_data_total)
for (a in 1:all_data_total_nrow) {
if (all_data_total[a,5]>0) {
claen_data<-rbind(claen_data,all_data_total[a,])
}
}
nrow(claen_data)
write.csv(claen_data,'C:/pro/plot/season_data/location.csv')
|
7ba4be6728d5eb52cada6ec5855ae8fbbba9e013 | edd6a9dd0f4ddb95b5c7f43226b775aa83b12dca | /LinReg/man/LinReg-package.Rd | 861901615942ca53f502c8e534d78a2000d61b90 | [
"MIT"
] | permissive | aleka769/A94Lab4 | 0d63a254c9af704f19863165db67ccce0053f98d | b77bb436222adf12bb762deaaaa5a7da0020b4e4 | refs/heads/master | 2020-03-28T22:45:48.557157 | 2018-09-24T09:23:21 | 2018-09-24T09:23:21 | 149,256,127 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 572 | rd | LinReg-package.Rd | \name{LinReg-package}
\alias{LinReg-package}
\alias{LinReg}
\docType{package}
\title{
\packageTitle{LinReg}
}
\description{
\packageDescription{LinReg}
}
\details{
The DESCRIPTION file:
\packageDESCRIPTION{LinReg}
\packageIndices{LinReg}
~~ An overview of how to use the package, including ~~
~~ the most important functions ~~
}
\author{
\packageAuthor{LinReg}
Maintainer: \packageMaintainer{LinReg}
}
\references{
~~ Literature or other references for background information ~~
}
\keyword{ package }
\seealso{
Optional links to other man pages, e.g.
}
\examples{
}
|
b1d0e25caa3a938a911aeb0c05fd85f6f0179bdd | fdab02d7da7d230cf6a47d02e382af3d9143eb51 | /Plot1.R | 32582bacfb9d67679e933acf1add7e90acd65c6b | [] | no_license | Koketa13/ExData_Plotting1 | 0c921c697892d9addd97801ecd735517022e4ff2 | 6e8449733683bc52383163dbdd1f3ccc9fcfe723 | refs/heads/master | 2022-11-22T23:21:21.817555 | 2020-07-28T17:59:39 | 2020-07-28T17:59:39 | 283,276,316 | 0 | 0 | null | 2020-07-28T17:03:29 | 2020-07-28T17:03:28 | null | UTF-8 | R | false | false | 626 | r | Plot1.R | #Load data.table package
library("data.table")
#Read Data and Store as Variable PowerCon
PowerCon <- data.table::fread(input = "household_power_consumption.txt", na.strings="?")
#Change Date Column to Date Type
PowerCon[, Date := lapply(.SD, as.Date, "%d/%m/%Y"), .SDcols = c("Date")]
#Filter to Desired Dates 02/01/2007 - 02/02/2007
PowerCon <- PowerCon[(Date >= "2007-02-01") & (Date <= "2007-02-02")]
#Create PNG File
png("plot1.png", width=480, height=480)
#Create Plot 1
hist(PowerCon[, Global_active_power], main="Global Active Power",
xlab="Global Active Power (kilowatts)", ylab="Frequency", col="Red")
dev.off() |
aafdf0ae43b13ea30aa566cdc28680f2bb0bd3c4 | cc5db289ea31cd38d8b59dd8f76b18bff6af32d1 | /starList.R | 0ae491a933d9c76b05a7f3c1ccf598b8d5f0d6b7 | [] | no_license | hoarika727/CSP571_Movie_Profits_Project | e6b55262a98948a091a70d56e01d09493faca5f8 | e864dabcddfe1c9cc9e767451f7eebe8d1fde4c7 | refs/heads/master | 2022-05-27T14:42:20.167776 | 2020-05-03T13:45:44 | 2020-05-03T13:45:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 492 | r | starList.R | library(stringr)
dfA <- read.csv("A-Listers of Hollywood.csv"
, header = TRUE)
dfB <- read.csv("B-Listers Celebrities.csv"
, header = TRUE)
dfA <- as.data.frame(dfA)
dfB <- as.data.frame(dfB)
dfA['Rank'] <- 1
dfB['Rank'] <- 2
df <- rbind(dfA[c('Name', 'Rank')], dfB[c('Name', 'Rank')])
temp <- str_split_fixed(df$Name, " ")
df['FirstName'] <- temp[,1]
df['LastName'] <- temp[,2]
df$Name <- tolower(as.character(df$Name))
write.csv(df, 'celebrity_ranking.csv')
|
bef169cf2d17d2b2d6d8f90585414484ce71092e | d1da1616ec40038d486348b8a1aea22cc171118d | /man/get_oldest_article.Rd | c793e17ff962c7a86200281765ee6c5bb9e35d55 | [] | no_license | RILAB/scholar | f0b6283d299a01c6af18a7cf6c55103931af2718 | 2f288a58ed3bc4798d1341db044a390464e64e20 | refs/heads/master | 2021-01-17T22:57:52.186525 | 2015-04-24T22:50:50 | 2015-04-24T22:50:50 | 34,545,748 | 1 | 1 | null | 2015-04-24T22:49:57 | 2015-04-24T22:49:57 | null | UTF-8 | R | false | false | 394 | rd | get_oldest_article.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{get_oldest_article}
\alias{get_oldest_article}
\title{Gets the year of the oldest article for a scholar}
\usage{
get_oldest_article(id)
}
\arguments{
\item{id}{a character string giving the Google Scholar ID}
}
\value{
the year of the oldest article
}
\description{
Gets the year of the oldest article published by a given scholar.
}
|
cfdd7bab92e2bf36f150f908a432e0b198f6a53d | 29585dff702209dd446c0ab52ceea046c58e384e | /varhandle/R/check.numeric.R | 147a7a763d6893545a3b0841fee063178d99de9d | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,447 | r | check.numeric.R | ## Function Description:
## A function to assess if a vector can be interpreted as numbers
# * it can get a parameter to trim white space
# * it can get a parameter to return the numeric vector or just one simple TRUE or FALSE
check.numeric <- function(v=NULL, rm.na=TRUE){
#----[ checking the input ]----#
if(is.null(v)){
stop("The parameter \"v\" is not defined. It can be character vector or factor vector.")
}else if(!class(v) %in% c("character", "factor")){
if(class(v) %in% c("numeric", "integer")){
warning(paste("The input is already of class", class(v)))
}else{
stop("The parameter \"v\" can only be a character vector or factor vector.")
}
}
if(rm.na!=TRUE & rm.na!=FALSE){
stop("The parameter \"rm.na\" should be either TRUE or FALSE.")
}
#----[ pre-processing ]----#
# convert to character if it is vector
if(class(v)=="factor"){
v <- as.character(v)
}
#----[ processing ]----#
if(rm.na){
# if it has some NAs
if(any(is.na(v))){
# remove NAs
v <- v[-pin.na(v)]
}
}
if(length(grep(pattern="^(-|\\+)?\\d+(\\.?\\d+)?$", x=v, invert=TRUE))==0){
# everything is numbers, so change it to numeric
output <- TRUE
}else{
output <- FALSE
}
# return the result
return(output)
} |
7f9d6fd25146918a34e58dac6eea0d322b0cd9ed | 39e15d93f848ab782e6b7e5703633d83ad201594 | /UI.R | b92ca9dff27f9588896ec51e5785f883e9a573d4 | [] | no_license | SaiSurve/ShinyApp | 45d0da3c3e169a30cd47920fd46f3dcec31649c6 | 48c2495e7739ba1cef7cbb3a8c0738e5e5f55a51 | refs/heads/master | 2020-05-05T03:15:48.565510 | 2014-09-04T11:53:23 | 2014-09-04T11:53:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,573 | r | UI.R | #HML Query -- Shiny implementation
#User Interface
#Load relevant libraries
library(shiny)
library(stringr)
shinyUI(
navbarPage("Retail Analytics Interface",
tabPanel("HML Segments"
,titlePanel(HTML('<p><img src="logo.png"/></p>'))
,sidebarLayout(
sidebarPanel(
# Pure Variables of Select Input
h4("Select the Required Columns:")
,checkboxInput("pur_var_01", "Loyalty Card Number", TRUE)
# ,checkboxInput("pur_var_02", "Customer Number", FALSE)
# Derived/Aggregate Variables of Select Input
,checkboxInput("agg_var_01", "Rank Result", FALSE)
,checkboxInput("agg_var_02", "Decile of Customer", FALSE)
,checkboxInput("agg_var_03", "Segment Name", FALSE)
,br()
# Case Variables of Select Input
,h4("Options Available Based on Selected Columns:")
,conditionalPanel(condition = "input.agg_var_01 |input.agg_var_02|input.agg_var_03|input.agg_var_04"
,selectInput("cas_var_01", "Ranking of shoppers based on:"
,list("Sales" = "SALES","Units" = "UNITS","Visits" = "VISITS")
)
)
,conditionalPanel(condition = "input.agg_var_03"
,h5("Choose Decile Threshod for Segments"),numericInput("cas_var_01_02", label = "Heavy Shopper if Decile <",value = "2")
,numericInput("cas_var_02_02", label = "Light Shopper if Decile >=",value = "5")
)
,br()
# Filters Required
,h4("Put Filters on:")
,checkboxInput("fil_bool_01", "Division Number", FALSE)
,conditionalPanel(condition = "input.fil_bool_01"
,textInput("fil_input_cd_01", label = "Division Number in (comma delimited):",value = "1005")
)
,checkboxInput("fil_bool_02", "Product Number", FALSE)
,conditionalPanel(condition = "input.fil_bool_02"
,textInput("fil_input_cd_02", label = "Product Number in (comma delimited):",value = "380000-EA")
)
,checkboxInput("fil_bool_03", "Start Transaction Date", FALSE)
,conditionalPanel(condition = "input.fil_bool_03","Range:"
,div(class='row',div(class="span2 offset1"
,dateInput("fil_input_dt_01_03", label = "End Date",value = Sys.Date()-1)
)
,div(class="span2 offset1"
,numericInput("fil_input_in_02_03", label = "# Weeks",value = 54)
)
)
)
,checkboxInput("fil_bool_04", "Product Segment Code", FALSE)
,conditionalPanel(condition = "input.fil_bool_04"
,textInput("fil_input_cd_04", label = "Segment Code in (comma delimited):",value = "1005")
)
,tags$hr()
)#end of sidebarPanel
,mainPanel(
tabsetPanel(
tabPanel("Code",verbatimTextOutput("TestQuery"))
,tabPanel("Results",tableOutput("table"))
,tabPanel("Summary",plotOutput("plot"))
)
)#end of mainPanel
)#end of sidebarLayout
)#end of tabPanel
)#end of navbarPage
)#end of shinyUI
|
e4434f3579d5fab586b88b4cfe93301aa319c367 | 576d4312d705f8c410c02925c9e54d1bd09a3a4c | /2_add_layouts/support_scripts/layout_handling.R | de26e7055db7e602b7f74be155e171dfb0a59064 | [] | no_license | mronzetti/dsfworld | d512383472a7f6b89ba7b48e64152b108faab0d6 | 7a5d5714d3a1696af39494bdf58ba38527f244dc | refs/heads/master | 2022-06-22T05:03:46.552146 | 2020-05-09T00:44:22 | 2020-05-09T00:44:22 | 274,525,085 | 0 | 0 | null | 2020-06-23T22:55:07 | 2020-06-23T22:55:07 | null | UTF-8 | R | false | false | 4,717 | r | layout_handling.R | #
# new daughter layout function
df_to_layout <- function(df, layout_type) {
df_m <- set_names( df , c("type","row",as.numeric( df [1,-c(1,2)]))) %>%
. [ -1 , -1] %>%
reshape2::melt( . ,id.vars = "row") %>%
mutate( . , well = as_vector(map2( . $row, . $variable, paste0)) ) %>%
set_names( . , c("row", "column", layout_type, "well"))
df_m
}
make_layout <- function( filename ) { # from path to raw layout to a final fomatted layout file
# read the layout file, and split each layout into an individual
layout_list <- data.table::fread( filename, header = TRUE) %>%
as_tibble() %>%
split( . , . $Type)
# put into a merge-able form
layout <- df_to_layout(layout_list[[1]], names(layout_list)[[1]])[c(1,2,4)] # initialize the list
for (i in c(1:length(layout_list))) {
layout <- layout %>%
mutate("var" = as_vector(df_to_layout(layout_list[[i]], layout_type = names(layout_list)[[i]])[3] )) %>% # append the column of interest
set_names(c(names(layout), names(layout_list)[[i]])) # rename based on the column of interest
}
layout <- layout %>%
unite("condition", c(4:ncol(.)), remove = FALSE) %>% # create a unique column, used to define groups after averaging
mutate_if(is.factor, as.character)
layout
}
nest_raw <- function( data_raw ) {
df <- data_raw %>% # this is the active dataframe, used for plotting and calculations
gather(well, value, -Temperature) %>% # call whatever column names "well"
group_by(well) %>%
mutate(value_norm = BBmisc::normalize(value, method = "range", range = c(0,1)), ###### if we do this as a mutate, it ignores the groups!!!!!!!
Temperature_norm = BBmisc::normalize(Temperature, method = "range", range = c(0,1))) %>%
nest_legacy() %>%
#plyr::mutate(new_names = well)
plyr::mutate(condition = well)
}
parse_well_vec <- function( well_vec ){
print("parsing well vec")
l <- list(
col = parse_number(well_vec),
row = str_extract_all(well_vec, "[A-Z; a-z]", simplify = TRUE)
%>% str_to_upper(locale = "en")
%>% as_vector()
)
l
}
add_standardized_wells <- function( df, make_factor ) {
df %>%
mutate(row_ = parse_well_vec(.$well)$row,
col_ = parse_well_vec(.$well)$col) %>%
mutate(well_ = map2(.$row_, .$col_, paste0) %>% as_vector()) %>%
mutate(well_f_ = factor(.$well_, levels = make_well_names("ROWS", "1"))) # as a factor, so things will order correctly
}
ensure_standardized_wells <- function( df ) {
if ( all(c("well_", "well_f_", "row_", "col_") %in% names(df)) == FALSE ) { # if standardized well columns are missing
df_out <- df %>%
add_standardized_wells()
} else {
df_out <- df
}
df_out
}
join_layout_nest <- function(by_well, layout) {
by_well_ <- ensure_standardized_wells(by_well) # this will always be fresh, un-layout-joined dataframe
layout_ <- ensure_standardized_wells(layout)
l_names <- names(layout_)
dup_cols <- l_names[!l_names %in% c("well_", "well_f_", "row_", "col_")]
#dup_cols <- names(layout_)[!c(names(layout_) %in% c("well_", "well_f_", "row_", "col_"))]
common_cols <- c("well","well_", "well_f_", "row_", "col_", "row", "column") %>%
.[. %in% names(layout_) ]
if (!"condition" %in% names(layout_)) { # this should never happen....
print("no_cond see join_layout_nest in layout_handling.R")
layout_ <- layout_ %>%
unite("condition", -one_of(c("well","well_", "well_f_", "row_", "col_", "row", "column")), remove = FALSE)
} else { # if "condition" is present in the layout
if ( all(l_names[!l_names == "condition"] %in% common_cols) == TRUE ) { # if "condition" is the only column unique to the layout
common_cols <- c("well_", "well_f_", "row_", "col_", "row", "column") # retain the well column
print("all names in common cols ZZ")
} ### IX THIS--laout needs to have something left to combine
}
layout_ <- layout_ %>%
select(-condition) %>%
unite("condition", -one_of(common_cols), remove = FALSE) %>%
mutate_if(is.factor, as.character)
by_well_ %>%
unnest_legacy() %>%
dplyr::select(-one_of(dup_cols )) %>% # if it's already in layout, drop it
dplyr::left_join(., layout_, by = c("well_", "well_f_", "row_", "col_")) %>%
group_by(condition, Temperature) %>%
dplyr::mutate(mean = mean(value),
sd = sd(value),
mean_norm = mean(value_norm),
sd_norm = sd(value_norm)) %>%
ungroup() %>%
nest(data = c(Temperature, value, mean, sd, Temperature_norm, value_norm, mean_norm, sd_norm)) # THIS IS NOT nest_legacy
}
|
3d4385092da88d9e4a85c2349dc628eeac3e3a32 | 154f590295a74e1ca8cdde49ecbb9cbb0992147e | /R/dh8.R | f8490813aba76bd62e20847b99f36b3b478f6e25 | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer",
"CC0-1.0"
] | permissive | klingerf2/EflowStats | 2e57df72e154581de2df3d5de3ebd94c3da0dedf | 73891ea7da73a274227212a2ca829084149a2906 | refs/heads/master | 2017-12-07T10:47:25.943426 | 2016-12-28T20:52:42 | 2016-12-28T20:52:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,253 | r | dh8.R | #' Function to return the DH8 hydrologic indicator statistic for a given data frame
#'
#' This function accepts a data frame that contains a column named "discharge" and calculates
#' DH8; Variability of annual maximum of 7-day moving average flows. Compute the standard deviation for the
#' maximum 7-day moving averages. DH8 is 100 times the standard deviation divided by the mean (percent-spatial).
#'
#' @param qfiletempf data frame containing a "discharge" column containing daily flow values
#' @return dh8 numeric containing DH8 for the given data frame
#' @export
#' @examples
#' qfiletempf<-sampleData
#' dh8(qfiletempf)
dh8 <- function(qfiletempf) {
qfiletempf <- qfiletempf[order(qfiletempf$date),]
meandh8 <- dh3(qfiletempf, pref = "mean")
day7mean <- rollmean(qfiletempf$discharge, 7, align = "right",
na.pad = TRUE)
day7rollingavg <- data.frame(qfiletempf, day7mean)
rollingavgs7day <- subset(day7rollingavg, day7rollingavg$day7mean !=
"NA")
max7daybyyear <- aggregate(rollingavgs7day$day7mean,
list(rollingavgs7day$wy_val), max, na.rm=TRUE)
sddh8 <- sd(max7daybyyear$x)
dh8 <- round((sddh8 * 100)/meandh8,digits=2)
return(dh8)
} |
ac57f42818cebf868f39bda80afabfe834b21848 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/8820_2/rinput.R | 35facd5035e72b7c38addf53030d13bc2332bc0a | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("8820_2.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="8820_2_unrooted.txt") |
de9e4847e882d1e20c91c14f1b9dc1f98bea04d2 | 558e72eb5e4e6f0014e5ae5fcd17583df5c18aa7 | /binary_data_to_pairwise_distance.R | b696bb9f210386da11955736bd3773fccbb3f560 | [] | no_license | woodlaur189/HGT_identification_in_evol_exps | 2f9acd3f01e1b335693df1d3aa105034ea6c3477 | 758b4262fa52219e78d2816e66313383fc36fa35 | refs/heads/main | 2023-06-12T10:50:39.482245 | 2021-07-08T03:52:41 | 2021-07-08T03:52:41 | 348,955,586 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 648 | r | binary_data_to_pairwise_distance.R | #install.packages("proxy")
#install.packages("spaa")
library(proxy)
bin_data_file='/Users/lwoo0005/Documents/Laura_stuff/H_py_An/Concatanated_maxHGT_p12_genomes/numberup_pangenomes/22-8-20_roary_90_evolved_50up_w_parents_split/gene_presence_absence.Rtab'
a<-read.table(bin_data_file, header=TRUE)
b<-as.data.frame.matrix(a)
d <- b[,-1]
rownames(d) <- b[,1]
pairwise_list <- dist2list(proxy::dist(d, by_rows = FALSE, method = "Euclidean"))
write.csv(pairwise_list, "/Users/lwoo0005/Documents/Laura_stuff/H_py_An/Concatanated_maxHGT_p12_genomes/numberup_pangenomes/22-8-20_roary_90_evolved_50up_w_parents_split/gene_presence_absence_euclidean.csv")
|
2645d5acd59e678d6897e04c3d4f94b5720784ba | fac5f5c0c0a66f44d4da97cad8914715104f0b8e | /man/minosse.data.Rd | 861f5459675aaf96fd2391a8f31f8f491a339e0e | [] | no_license | carotenuto79/PaleoCore | 33831ae48a8bfd5620e155a8c0d3ceae17c434a8 | 6809e03a0e279fa8964297c07a0f21cde263f539 | refs/heads/master | 2020-06-24T05:40:42.052684 | 2019-08-28T12:12:32 | 2019-08-28T12:12:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 7,695 | rd | minosse.data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/minosse.data.R
\name{minosse.data}
\alias{minosse.data}
\title{Creates model's predictors}
\usage{
minosse.data(obj,species_name,domain,coc.by="locality",min.occs=3,
abiotic.covs=NULL,combine.covs=TRUE,reduce_covs_by="pca",covs_th=0.95,
c.size="mean",bkg.predictors="presence",min.bkg=NULL,
sampling.by.distance=TRUE,prediction.ground=NULL,crop.by.mcp=FALSE,
projection=NULL,lon_0=NULL,lat_0=NULL,n.clusters="automatic",seed=NULL)
}
\arguments{
\item{obj}{A n x m dataframe where n are the single occurrences and m are the following columns: spec (the species name), x and y (longitude and latitude in decimal degrees, respectively) and loc_id (an id identifying the fossil locality).}
\item{species_name}{Character. The name of the species whose geographic range is to be estimated.}
\item{domain}{Character. Eithter "land", for terrestrial species, or "sea", for marine species.}
\item{coc.by}{Character. Either "locality" or "cell" to enable cooccurence analysis. See details below.}
\item{min.occs}{Either numeric or numeric vector of length 2. The number occurrences below which to discard a species from being either valid predictors either a target. If ony one value is provided, the threshold is the same for both target and predicors.}
\item{abiotic.covs}{the raster or rasters' stack of additional environmental predictors.}
\item{combine.covs}{Logical. Should minosse.data collate species and abiotic predictors when performing variables' number reduction? Default TRUE See details.}
\item{reduce_covs_by}{Character. The method used for predictors' number reduction. Available strategies are "pca", "variance" or "corr". See details.}
\item{covs_th}{Numeric. The threshold value used for predictors' number reduction strategy. See details.}
\item{c.size}{Numeric. When prediction.ground is null this is the (square) cell resolution in meters for spatial interpolations. Ignored if prediction.ground is a raster.}
\item{bkg.predictors}{The number of pseudo absences to be simulated for each predictor species. If "presence", the pseudo absences number equals the presences in each species.}
\item{min.bkg}{Numeric. If bkg.predictors is set to "presence", this is the minimum number of pseudo absences to simulate if a species occurrence number is below this value.}
\item{sampling.by.distance}{Logical. TRUE for a distace-based density pseudo absences simulation. FALSE for a pure spatial random distribution of pseudo absences.}
\item{prediction.ground}{Either a raster or a SpatialPolygons class object where to perform all the spatial interpolations and target species prediction.}
\item{crop.by.mcp}{Logical. If TRUE, interpoalations and prediction are restricted to the prediction.grund area delimited by the MCP enclosing the fossil occurrences of the whole dataset. Default FALSE}
\item{projection}{Character. This argument works only if prediction.ground is NULL. This is the euqual-area projection for spatial interpolations. A character string in the proj4 format or either "moll" (Mollweide) or "laea" (Lambert Azimuthal equal area) projections (see details).}
\item{lon_0}{Numeric. Only if prediction.ground is NULL. The longitude of the projection centre used when setting either "moll" or "laea" projections. If NULL the mean longitude of the whole fossil record is used. Default NULL.}
\item{lat_0}{Numeric. Only if prediction.ground is NULL. The latitude of the projection centre used when setting "laea" projection. If NULL the mean latitude of whole fossil record is used. Default NULL.}
\item{n.clusters}{Numeric. The number of cores to use during spatial interpolations. If "automatic", the number of used cores is equal to the number of predictors. If preductors' number > the avaialble cores, all cores - 1 is then used. Default "automatic".}
\item{seed}{Numeric. The seed number for experiment replication.}
}
\value{
a list of three objects to be used with minosse.target function. The first element of the list is the dataset of target species occurrences. The second object is the raster stack of predictor species. The third object, if present, is the result of the cooccurrence analysis.
}
\description{
This function creates both the response variable and the predictor variables to be used with minosse.target function
}
\details{
In minosse.data there are different strategies for predictor species (covariates) dimension reduction. The first one considers only the species that are significantly related (positively or negatively) to the target species, then discarding all the others.
This first stratery uses the cooccurrence analysis that can be performed either at the locality level, i.e. by seeking pattern of cooccurrence whithin the species list of any single fossil locality, or at the cell level, i.e. by considering lists of
unique species occurring inside the squared cell of the prediction ground. A cell based analysis is useful when having many low-richness fossil localities. If the significantly relationships is less than 4, then all the species are considered. Other
strategies can be used for predictors' dimensionality reduction. These additional strategies are performed over the predictors'maps and can employ one of the following methods: Principal Component Analysis ("pca"), Variance Inflation Factor ("vif") and correlation ("corr").
These strategies need a threshold value ("covs_th") to be set in order to select the predictors to retain. If the strategy is "pca", then the covs_th is the percentage (from 1 to 100) of variance to be explained by PCA axes. If the strategy is "corr", then covs_th is any
number between 0 and 1 indicating the correlation between predictors below which predictor species can be retained. If the strategy is "variance", then covs_th is any mumber higher than zero indicating the higher varaince inflation that can be achieved by the predictor.
See details of vif function in the usdm package for further explanations. If abiotic.covs is not null, the combine.covs argument indicates if performing predictors maps number reduction by including (TRUE) or excluding (FALSE) abiotic covariates. If FALSE, abiotic covariates
are always included in the final dataset of predictors. Spatial interpolations always need equal area coordinates reference system to be used. The user can specify its own projected CRS (in the proj4 format, see https://proj4.org/operations/projections/index.html) or can use predefined choices like "laea" (for Lambert Azimuthal equal area) or "moll" (for Mollweide) projections. When setting predefined projections,
the user can specify the projection centre's coordinates in decimal degrees by lon_0 and lat_0 arguments. If both lon_0 and lat_0 are NULL, the mean longitide and latitude of the whole fossil record are used. Warning: If not NULL, the prediction.ground's coordinates reference system has the priority over all the other projection settings.
}
\examples{
\donttest{
library(raster)
data(lgm)
raster(system.file("exdata/prediction_ground.gri", package="PaleoCore"))->prediction_ground
minosse_dat<-minosse.data(obj=lgm,species_name="Mammuthus_primigenius",
domain="land",coc.by="locality",min.occs=3,abiotic.covs=NULL,
combine.covs=TRUE,reduce_covs_by="pca",covs_th=0.95,c.size="mean",
bkg.predictors="presence",min.bkg=100,sampling.by.distance=TRUE,
prediction.ground=prediction_ground,crop.by.mcp=FALSE,projection=NULL,lon_0=NULL,lat_0=NULL,
n.clusters="automatic",seed=625)
}
}
\author{
Francesco Carotenuto, francesco.carotenuto@unina.it
}
|
17655c838f4be6ca67a1715cf424d1ed59a0fcbb | 8a27a13d8e2839958d408671414dea775f3700e9 | /man/get_official_observed_seasonal_quantities.Rd | 09da0692656ccadc2ecd6b16203c80b54c915554 | [] | no_license | reichlab/2017-2018-cdc-flu-contest | a0a59cabb65ce374d3e2313248ca98fbf247b33c | dcb99465bccbe1167e196878182b2e84749b6d87 | refs/heads/master | 2021-03-22T03:32:05.682003 | 2018-09-19T19:28:48 | 2018-09-19T19:28:48 | 90,150,606 | 2 | 3 | null | 2018-03-06T19:57:54 | 2017-05-03T13:13:00 | R | UTF-8 | R | false | true | 1,214 | rd | get_official_observed_seasonal_quantities.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{get_official_observed_seasonal_quantities}
\alias{get_official_observed_seasonal_quantities}
\title{Compute season onset, peak week, and peak incidence}
\usage{
get_official_observed_seasonal_quantities(data, season, incidence_var)
}
\arguments{
\item{data}{a data frame containing at minimum columns named season,
season_week and a column with some sort of incidence measure}
\item{season}{the season to look at}
\item{incidence_var}{a character string naming the variable in the data
argument containing a measure of incidence, or an integer index}
}
\value{
a list with four entries:
1) observed_onset_week, either an integer between first_CDC_season_week
and last_CDC_season_week (inclusive), or "none"
2) observed_peak_week, an integer between first_CDC_season_week and
last_CDC_season_week (inclusive)
3) observed_peak_inc, a numeric with the maximum value of the specified
incidence measure between first_CDC_season_week and last_CDC_season_week
4) observed_peak_inc_bin, character name of incidence bin for peak incidence
}
\description{
Compute season onset, peak week, and peak incidence
}
|
4244a79eddb6415abe974d1cd85e02ca523c1998 | aef1475c158fb5b1f8689a8c975453e16e012d92 | /make_data.R | f241b28e7f679df38b6913d3b2958cdf190a30ca | [] | no_license | haututu/fear_of_covid_validation | 726703e2ff8866ee91a7ec3e61d55b1a538a53e8 | f710217c51f80ff4cb3d4f6e6b11bc4bf15a151e | refs/heads/master | 2022-07-19T06:13:44.052073 | 2020-05-24T07:04:29 | 2020-05-24T07:04:29 | 264,552,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,643 | r | make_data.R | library(tidyverse)
library(lavaan)
library(haven)
library(brms)
library(eRm)
# Load data
covid_dat <- bind_rows(
read_sav("data/L3Clean.sav") %>% select(Gender, Age, Ethnicity, starts_with("FofCOVID_"), starts_with("PVD_"), starts_with("Lockdown"), starts_with("Behaviour_C"), Political_Beliefs, FearCovid) %>% mutate(lockdown = "lvl3"),
read_sav("data/L4Clean.sav") %>% select(Gender, Age, Ethnicity, starts_with("FofCOVID_"), starts_with("PVD_"), starts_with("Lockdown"), starts_with("Behaviour_C"), Political_Beliefs, FearCovid = FearofCovid) %>% mutate(lockdown = "lvl4")
) %>%
mutate(Gender = case_when(
Gender == 1 ~ "male",
Gender == 2 ~ "female",
TRUE ~ "other"
),
Ethnicity = case_when(
Ethnicity == 1 ~ "european",
Ethnicity %in% 2:3 ~ "maori_poly",
Ethnicity == 4 ~ "asian",
TRUE ~ "other"
)
) %>%
filter(!is.na(Gender) & !is.na(Age) & !is.na(FearCovid)) %>%
na.omit() %>%
mutate(id = row_number())
covid_lvl3 <- read_sav("data/L3Clean.sav") %>%
select(Gender, Age, Ethnicity, starts_with("FofCOVID_"), starts_with("PVD_"), starts_with("WEMWBS"), FearCovid) %>%
mutate(lockdown = "lvl3") %>%
mutate(Gender = case_when(
Gender == 1 ~ "male",
Gender == 2 ~ "female",
TRUE ~ "other"
),
Ethnicity = case_when(
Ethnicity == 1 ~ "european",
Ethnicity %in% 2:3 ~ "maori_poly",
Ethnicity == 4 ~ "asian",
TRUE ~ "other"
)
) %>%
filter(!is.na(Gender) & !is.na(Age) & !is.na(FearCovid)) %>%
na.omit() %>%
mutate(id = row_number())
# Demographic information
# Total number of participants for each level
read_sav("data/L3Clean.sav") %>% summarise(n())
read_sav("data/L4Clean.sav") %>% select(FearCovid = FearofCovid) %>% mutate(lockdown = "lvl4") %>% na.omit() %>% summarise(n())
covid_dat %>%
group_by(lockdown) %>%
summarise(female_percent = mean(Gender == "female"),
age_mean = mean(Age),
age_sd = sd(Age),
age_max = max(Age),
european_percent = mean(Ethnicity == "european"),
maori_poly_percent = mean(Ethnicity == "maori_poly"),
asian_percent = mean(Ethnicity == "asian"),
other_percent = mean(Ethnicity == "other"),
size = n()
) %>%
gather(key, value, -1) %>%
spread(lockdown, value) %>%
knitr::kable() %>%
kableExtra::kable_styling()
# Fear of covid ############################################
covid_cols <- colnames(covid_dat)[grepl("FofCOVID", colnames(covid_dat))]
covid_fear_lvl3 <- cfa(
paste("fear =~", paste0(covid_cols, collapse = " + ")),
data = covid_dat %>% filter(lockdown == "lvl3")
)
covid_fear_lvl4 <- cfa(
paste("fear =~", paste0(covid_cols, collapse = " + ")),
data = covid_dat %>% filter(lockdown == "lvl4")
)
summary(covid_fear_lvl3, fit=TRUE, standardized=TRUE)
summary(covid_fear_lvl4, fit=TRUE, standardized=TRUE)
# PVD ##################################################
infect_cols <- colnames(covid_dat)[grepl("PVD_", colnames(covid_dat)) & grepl("_[2568]|1[024]", colnames(covid_dat))]
germ_cols <- colnames(covid_dat)[grepl("PVD_", colnames(covid_dat)) & !grepl("_[2568]|1[024]", colnames(covid_dat))]
covid_pvd_lvl3 <- cfa(
paste(
paste("infect =~", paste0(infect_cols, collapse = " + ")),
paste("germ =~", paste0(germ_cols, collapse = " + ")),
sep="\n"
),
data = covid_dat %>% filter(lockdown == "lvl3")
)
covid_pvd_lvl4 <- cfa(
paste(
paste("infect =~", paste0(infect_cols, collapse = " + ")),
paste("germ =~", paste0(germ_cols, collapse = " + ")),
sep="\n"
),
data = covid_dat %>% filter(lockdown == "lvl4")
)
# Mental wellbeing ############################################
mwb_cols <- colnames(covid_lvl3)[grepl("WEMWBS", colnames(covid_lvl3))]
covid_mwb <- cfa(
paste("MWB =~", paste0(mwb_cols, collapse = " + ")),
data = covid_lvl3
)
covid_fear_for_mwb <- cfa(
paste("fear =~", paste0(covid_cols, collapse = " + ")),
data = covid_lvl3
)
summary(covid_fear, fit=TRUE, standardized=TRUE)
summary(covid_mwb, fit=TRUE, standardized=TRUE)
######################################### Comparisons ################################################
# Infect to fear
cor.test(predict(covid_pvd_lvl3)[,1], predict(covid_fear_lvl3))
cor.test(predict(covid_pvd_lvl4)[,1], predict(covid_fear_lvl4))
# Germ to fear
cor.test(predict(covid_pvd_lvl3)[,2], predict(covid_fear_lvl3))
cor.test(predict(covid_pvd_lvl4)[,2], predict(covid_fear_lvl4))
# MWB to fear
cor.test(predict(covid_mwb), predict(covid_fear_for_mwb))
cor.test(filter(covid_dat, lockdown == "lvl4")$Political_Beliefs, predict(covid_fear_lvl4), method = "spearman")
cor.test(filter(covid_dat, lockdown == "lvl3")$Political_Beliefs, predict(covid_fear_lvl3), method = "spearman")
# Cronback alpha
covid_dat %>% filter(lockdown == "lvl3") %>% select(covid_cols) %>% na.omit() %>% ltm::cronbach.alpha(standardized = TRUE)
covid_dat %>% filter(lockdown == "lvl4") %>% select(covid_cols) %>% na.omit() %>% ltm::cronbach.alpha(standardized = TRUE)
covid_dat %>% filter(lockdown == "lvl3") %>% select(infect_cols) %>% na.omit() %>% ltm::cronbach.alpha(standardized = TRUE)
covid_dat %>% filter(lockdown == "lvl4") %>% select(infect_cols) %>% na.omit() %>% ltm::cronbach.alpha(standardized = TRUE)
covid_dat %>% filter(lockdown == "lvl3") %>% select(germ_cols) %>% na.omit() %>% ltm::cronbach.alpha(standardized = TRUE)
covid_dat %>% filter(lockdown == "lvl4") %>% select(germ_cols) %>% na.omit() %>% ltm::cronbach.alpha(standardized = TRUE)
covid_lvl3 %>% filter(lockdown == "lvl3") %>% select(mwb_cols) %>% na.omit() %>% ltm::cronbach.alpha(standardized = TRUE)
|
d0d762dcdfd6882e285ad612ef854d534fc324d9 | c13c41582b93e1ec4df4d3fd15c9b461ba926d72 | /R/makeqtl.R | f793397f92610908871774c1c47ffa6ca002abe3 | [] | no_license | pjotrp/rqtl | 834979ea3e6453637dee4b7a53432b3c61b26f44 | d7f377b50771d9f0862d1590bf05add06982cb35 | refs/heads/master | 2020-06-02T06:57:46.392621 | 2009-06-26T21:27:15 | 2009-06-26T21:27:15 | 127,591 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,356 | r | makeqtl.R | ######################################################################
#
# makeqtl.R
#
# copyright (c) 2002-9, Hao Wu and Karl W. Broman
# last modified Feb, 2009
# first written Apr, 2002
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License,
# version 3, as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but without any warranty; without even the implied warranty of
# merchantability or fitness for a particular purpose. See the GNU
# General Public License, version 3, for more details.
#
# A copy of the GNU General Public License, version 3, is available
# at http://www.r-project.org/Licenses/GPL-3
#
# Part of the R/qtl package
# Contains: makeqtl, replaceqtl, addtoqtl, dropfromqtl, locatemarker
# print.qtl, summary.qtl, print.summary.qtl, reorderqtl
# plot.qtl
# print.compactqtl, summary.compactqtl, print.summary.compactqtl
#
######################################################################
######################################################################
#
# This is the function to construct an object of class "qtl"
# The phenotype data and genotype data for a given list of
# chromosome and locations will be extracted from the input
# "cross" object
#
######################################################################
makeqtl <-
function(cross, chr, pos, qtl.name, what=c("draws", "prob"))
{
if( !sum(class(cross) == "cross") )
stop("The first input variable must be an object of class cross")
# cross type
type <- class(cross)[1]
chrtype <- sapply(cross$geno, class)
sexpgm <- getsex(cross)
what <- match.arg(what)
themap <- pull.map(cross)
# try to interpret chr argument
if(!is.character(chr))
chr <- as.character(chr)
# chr, pos and qtl.name must have the same length
if(length(chr) != length(pos))
stop("Input chr and pos must have the same length.")
else if( !missing(qtl.name) )
if( length(chr) != length(qtl.name) )
stop("Input chr and qtl.name must have the same length.")
# local variables
n.ind <- nrow(cross$pheno) # number of individuals
n.pos <- length(chr) # number of selected markers
n.gen <- NULL
# initialize output object
qtl <- NULL
# take out the imputed genotypes and/or genoprobs for the
# selected markers (if there are there)
if(what == "draws") { # pull out draws
if(!("draws" %in% names(cross$geno[[1]])))
stop("You must first run sim.geno.")
# take out imputed genotype data
n.draws <- dim(cross$geno[[1]]$draws)[3] # number of draws
# initialize geno matrix for selected markers
geno <- array(rep(0, n.ind*n.pos*n.draws),
dim=c(n.ind, n.pos, n.draws))
for(i in 1:n.pos) {
# get the index for this chromosome
i.chr <- which(chr[i]==names(cross$geno))
if(length(i.chr) == 0) # no this chromosome in cross
stop("There's no chromosome number ", chr[i], " in input cross object")
i.pos <- pos[i] # marker position
# make the genetic map for this chromosome
if("map" %in% names(attributes(cross$geno[[i.chr]]$draws)))
map <- attr(cross$geno[[i.chr]]$draws,"map")
else {
stp <- attr(cross$geno[[i.chr]]$draws, "step")
oe <- attr(cross$geno[[i.chr]]$draws, "off.end")
if("stepwidth" %in% names(attributes(cross$geno[[i.chr]]$draws)))
stpw <- attr(cross$geno[[i.chr]]$draws, "stepwidth")
else stpw <- "fixed"
map <- create.map(cross$geno[[i.chr]]$map,stp,oe,stpw)
}
# pull out the female map if there are sex-specific maps
if(is.matrix(map)) map <- map[1,]
# locate this marker (given chromosome and position)
marker.idx <- locatemarker(map, i.pos, i.chr, flag="draws")
if(length(marker.idx) > 1)
stop("Multiple markers at the same position; run jittermap.")
# if everything is all right, take the genotype
geno[,i,] <- cross$geno[[i.chr]]$draws[,marker.idx,]
pos[i] <- map[marker.idx]
# no. genotypes
n.gen[i] <- length(getgenonames(type,chrtype[i.chr],"full",sexpgm, attributes(cross)))
# Fix up X chromsome here
if(chrtype[i.chr]=="X")
geno[,i,] <- reviseXdata(type,"full",sexpgm,draws=geno[,i,,drop=FALSE],
cross.attr=attributes(cross))
}
# give geno dimension names
# the 2nd dimension called "Q1", "Q2", etc.
dimnames(geno) <- list(NULL, paste("Q", 1:n.pos, sep=""), NULL)
# output
qtl$geno <- geno
}
else { # pull out probs
if(!("prob" %in% names(cross$geno[[1]])))
stop("You must first run calc.genoprob.")
# initialize prob matrix
prob <- vector("list",n.pos)
# locate the marker
for(i in 1:n.pos) {
# get the index for this chromosome
i.chr <- which(chr[i]==names(cross$geno))
if(length(i.chr) == 0) # no this chromosome in cross
stop("There's no chromosome number ", chr[i], " in input cross object")
i.pos <- pos[i] # marker position
if("map" %in% names(attributes(cross$geno[[i.chr]]$prob)))
map <- attr(cross$geno[[i.chr]]$prob,"map")
else {
stp <- attr(cross$geno[[i.chr]]$prob, "step")
oe <- attr(cross$geno[[i.chr]]$prob, "off.end")
if("stepwidth" %in% names(attributes(cross$geno[[i.chr]]$prob)))
stpw <- attr(cross$geno[[i.chr]]$prob, "stepwidth")
else stpw <- "fixed"
map <- create.map(cross$geno[[i.chr]]$map,stp,oe,stpw)
}
# pull out the female map if there are sex-specific maps
if(is.matrix(map)) map <- map[1,]
# locate this marker (given chromosome and position)
marker.idx <- locatemarker(map, i.pos, i.chr, flag="prob")
if(length(marker.idx) > 1)
stop("Multiple markers at the same position; run jittermap.")
# take genoprob
if(chrtype[i.chr] == "X") { # fix X chromosome probs
prob[[i]] <- reviseXdata(type, "full", sexpgm,
prob=cross$geno[[i.chr]]$prob[,marker.idx,,drop=FALSE],
cross.attr=attributes(cross))[,1,]
}
else
prob[[i]] <- cross$geno[[i.chr]]$prob[,marker.idx,]
pos[i] <- map[marker.idx]
# no. genotypes
n.gen[i] <- ncol(prob[[i]])
}
qtl$prob <- prob
}
if(missing(qtl.name)) { # no given qtl names
dig <- 1
if(what=="draws")
step <- attr(cross$geno[[i.chr]]$draws, "step")
else
step <- attr(cross$geno[[i.chr]]$prob, "step")
if(!is.null(step)) {
if(step > 0) dig <- max(dig, -floor(log10(step)))
}
else {
if(what=="draws")
stepw <- attr(cross$geno[[i.chr]]$draws, "stepwidth")
else
stepw <- attr(cross$geno[[i.chr]]$prob, "stepwidth")
if(!is.null(stepw) && stepw > 0) dig <- max(dig, -floor(log10(stepw)))
}
# make qtl names
qtl.name <- paste( paste(chr,sep=""), charround(pos,dig), sep="@")
}
# output object
qtl$name <- qtl.name
qtl$altname <- paste("Q", 1:n.pos, sep="")
qtl$chr <- chr
qtl$pos <- pos
qtl$n.qtl <- n.pos
qtl$n.ind <- nind(cross)
qtl$n.gen <- n.gen
class(qtl) <- "qtl"
attr(qtl, "map") <- themap
qtl
}
######################################################################
#
# This is the function to replace one QTL by another.
#
######################################################################
replaceqtl <-
function(cross, qtl, index, chr, pos, qtl.name, drop.lod.profile=TRUE)
{
if(class(qtl) != "qtl")
stop("qtl should have class \"qtl\".")
if(any(index < 1 | index > qtl$n.qtl))
stop("index should be between 1 and ", qtl$n.qtl)
if(length(index) != length(chr) || length(index) != length(pos))
stop("index, chr, and pos should all have the same length.")
if(!missing(qtl.name) && length(index) != length(qtl.name))
stop("index and qtl.name should have the same length.")
if("geno" %in% names(qtl)) what <- "draws"
else what <- "prob"
if(missing(qtl.name))
newqtl <- makeqtl(cross, chr, pos, what=what)
else
newqtl <- makeqtl(cross, chr, pos, qtl.name=qtl.name, what=what)
if(what=="draws") {
qtl$geno[,index,] <- newqtl$geno
}
else {
qtl$prob[index] <- newqtl$prob
}
qtl$name[index] <- newqtl$name
qtl$chr[index] <- newqtl$chr
qtl$pos[index] <- newqtl$pos
if(qtl$n.ind != newqtl$n.ind) stop("Mismatch in no. individuals")
qtl$n.gen[index] <- newqtl$n.gen
if(drop.lod.profile)
attr(qtl, "lodprofile") <- NULL
qtl
}
######################################################################
#
# This is the function to add a QTL to given qtl object
#
######################################################################
addtoqtl <-
function(cross, qtl, chr, pos, qtl.name, drop.lod.profile=TRUE)
{
if(class(qtl) != "qtl")
stop("qtl should have class \"qtl\".")
if("geno" %in% names(qtl)) what <- "draws"
else what <- "prob"
if(missing(qtl.name))
newqtl <- makeqtl(cross, chr, pos, what=what)
else
newqtl <- makeqtl(cross, chr, pos, qtl.name=qtl.name, what=what)
if(what=="draws") {
do <- dim(qtl$geno)
dn <- dim(newqtl$geno)
if(do[1] != dn[1] || do[3] != dn[3])
stop("Mismatch in number of individuals or number of imputations.")
temp <- array(dim=c(do[1], do[2]+dn[2], do[3]))
temp[,1:ncol(qtl$geno),] <- qtl$geno
temp[,-(1:ncol(qtl$geno)),] <- newqtl$geno
colnames(temp) <- paste("Q", 1:ncol(temp), sep="")
qtl$geno <- temp
}
else {
qtl$prob <- c(qtl$prob, newqtl$prob)
}
qtl$name <- c(qtl$name, newqtl$name)
qtl$chr <- c(qtl$chr, newqtl$chr)
qtl$pos <- c(qtl$pos, newqtl$pos)
qtl$n.qtl <- qtl$n.qtl + newqtl$n.qtl
qtl$altname <- paste("Q", 1:qtl$n.qtl, sep="")
if(qtl$n.ind != newqtl$n.ind)
stop("Mismatch in no. individuals")
qtl$n.gen <- c(qtl$n.gen, newqtl$n.gen)
attr(qtl, "formula") <- NULL
attr(qtl, "pLOD") <- NULL
if(drop.lod.profile)
attr(qtl, "lodprofile") <- NULL
qtl
}
######################################################################
#
# This is the function to drop a QTL from a given qtl object
#
######################################################################
dropfromqtl <-
function(qtl, index, chr, pos, qtl.name, drop.lod.profile=TRUE)
{
if(class(qtl) != "qtl")
stop("qtl should have class \"qtl\".")
if(!missing(chr) || !missing(pos)) {
if(missing(chr) || missing(pos))
stop("Give both chr and pos, or give name, or give a numeric index")
if(!missing(qtl.name) || !missing(index))
stop("Give chr and pos or qtl.name or numeric index, but not multiple of these.")
if(length(chr) != length(pos))
stop("chr and pos must have the same lengths.")
todrop <- NULL
for(i in seq(along=chr)) {
m <- which(qtl$chr == chr[i])
if(length(m) < 1)
stop("No QTL on chr ", chr[i], " in input qtl object.")
for(j in seq(along=m)) {
d <- abs(qtl$pos[m[j]] - pos[i])
if(min(d) > 10) stop("No qtl near position ", pos[i], " on chr ", chr[i])
wh <- m[d==min(d)]
if(length(wh) > 1)
stop("Multiple QTL matching chr ", chr[i], " at pos ", pos[i])
if(min(d) > 1)
warning("No QTL on chr ", chr[i], " exactly at ", pos[i],
"; dropping that at ", qtl$pos[wh])
todrop <- c(todrop, wh)
}
}
todrop <- unique(todrop)
}
else if(!missing(qtl.name)) {
if(!missing(index))
stop("Give chr and pos or qtl.name or numeric index, but not multiple of these.")
m <- match(qtl.name, qtl$name)
if(all(is.na(m))) # if no matches, try "altname"
m <- match(qtl.name, qtl$altname)
if(any(is.na(m)))
warning("Didn't match QTL ", qtl.name[is.na(m)])
todrop <- m[!is.na(m)]
}
else {
if(missing(index))
stop("Give chr and pos or qtl.name or numeric index, but not multiple of these.")
if(any(index < 1 | index > qtl$n.qtl))
stop("index should be between 1 and ", qtl$n.qtl)
todrop <- index
}
# input drop is an integer index
# get the index for exclusing drop QTL
idx <- setdiff(1:qtl$n.qtl, todrop)
# result object
qtl$name <- qtl$name[idx]
qtl$chr <- qtl$chr[idx]
qtl$pos <- qtl$pos[idx]
qtl$n.qtl <- qtl$n.qtl - length(todrop)
qtl$altname <- paste("Q", 1:qtl$n.qtl, sep="")
qtl$n.ind <- qtl$n.ind
qtl$n.gen <- qtl$n.gen[idx]
if("geno" %in% names(qtl)) {
qtl$geno <- qtl$geno[,idx,,drop=FALSE]
colnames(qtl$geno) <- paste("Q", 1:ncol(qtl$geno), sep="")
}
if("prob" %in% names(qtl))
qtl$prob <- qtl$prob[idx]
attr(qtl, "formula") <- NULL
attr(qtl, "pLOD") <- NULL
if(drop.lod.profile)
attr(qtl, "lodprofile") <- NULL
qtl
}
##################################################################
#
# locate the marker on a genetic map. Choose the nearest
# one if there's no marker or pseudomarker one the given
# location
#
# This is the internal function and not supposed to be used by user
#
###################################################################
locatemarker <-
function(map, pos, chr, flag)
{
marker.idx <- which(map == pos)
if( length(marker.idx)==0 ) {
# there's no this marker, take the nearest marker instead
# if there's a tie, take the first nearst one
m.tmp <- abs(pos-map)
marker.idx <- which(m.tmp==min(m.tmp))[[1]]
}
if(length(marker.idx) > 1)
marker.idx <- marker.idx[sample(length(marker.idx))]
marker.idx
}
# print QTL object
print.qtl <-
function(x, ...)
{
print(summary(x))
}
# summary of QTL object
summary.qtl <-
function(object, ...)
{
if(is.null(object) || length(object) == 0) {
class(object) <- "summary.qtl"
return(object)
}
if("geno" %in% names(object)) {
type <- "draws"
n.draws <- dim(object$geno)[3]
}
else type <- "prob"
output <- data.frame(name=object$name, chr=object$chr, pos=object$pos, n.gen=object$n.gen)
rownames(output) <- object$altname
attr(output, "type") <- type
if(type=="draws") attr(output, "n.draws") <- n.draws
class(output) <- c("summary.qtl", "data.frame")
if("formula" %in% names(attributes(object)))
attr(output, "formula") <- attr(object, "formula")
if("pLOD" %in% names(attributes(object)))
attr(output, "pLOD") <- attr(object, "pLOD")
output
}
# print summary of QTL object
print.summary.qtl <-
function(x, ...)
{
if(is.null(x) || length(x) == 0) {
cat(" Null QTL model\n")
}
else {
type <- attr(x, "type")
if(type=="draws")
thetext <- paste("imputed genotypes, with", attr(x, "n.draws"), "imputations.")
else thetext <- "genotype probabilities."
cat(" QTL object containing", thetext, "\n\n")
print.data.frame(x, digits=5)
}
if("formula" %in% names(attributes(x))) {
form <- attr(x, "formula")
if(!is.character(form)) form <- deparseQTLformula(form)
cat("\n Formula:")
w <- options("width")[[1]]
printQTLformulanicely(form, " ", w+5, w)
}
if("pLOD" %in% names(attributes(x)))
cat("\n pLOD: ", round(attr(x, "pLOD"),3), "\n")
}
######################################################################
# plot locations of QTLs on the genetic map
######################################################################
plot.qtl <-
function(x, chr, horizontal=FALSE, shift=TRUE,
show.marker.names=FALSE, alternate.chrid=FALSE, ...)
{
if(!("qtl" %in% class(x)))
stop("input should be a qtl object")
if(length(x) == 0)
stop(" There are no QTL to plot.")
map <- attr(x, "map")
if(is.null(map))
stop("qtl object doesn't contain a genetic map.")
if(missing(chr))
chr <- names(map)
else {
chr <- matchchr(chr, names(map))
map <- map[chr]
class(map) <- "map"
}
if(horizontal)
plot.map(map, horizontal=horizontal, shift=shift,
show.marker.names=show.marker.names, alternate.chrid=alternate.chrid,
ylim=c(length(map)+0.5, 0), ...)
else
plot.map(map, horizontal=horizontal, shift=shift,
show.marker.names=show.marker.names, alternate.chrid=alternate.chrid,
xlim=c(0.5,length(map)+1), ...)
whchr <- match(x$chr, names(map))
thepos <- x$pos
thepos[is.na(whchr)] <- NA
if(any(!is.na(thepos))) {
whchr <- whchr[!is.na(whchr)]
if(shift) thepos <- thepos - sapply(map[whchr], min)
if(is.matrix(map[[1]])) whchr <- whchr - 0.3
if(length(grep("^.+@[0-9\\.]+$", x$name)) == length(x$name))
x$name <- x$altname
if(horizontal) {
arrows(thepos, whchr - 0.35, thepos, whchr, lwd=2, col="red", len=0.05)
text(thepos, whchr-0.4, x$name, col="red", adj=c(0.5,0))
}
else {
arrows(whchr + 0.35, thepos, whchr, thepos, lwd=2, col="red", len=0.05)
text(whchr+0.4, thepos, x$name, col="red", adj=c(0,0.5))
}
}
attr(qtl, "formula") <- NULL
attr(qtl, "pLOD") <- NULL
invisible()
}
######################################################################
#
# This is the function to reorder the QTL within a QTL object
#
######################################################################
reorderqtl <-
function(qtl, neworder)
{
if(class(qtl) != "qtl")
stop("qtl should have class \"qtl\".")
if(missing(neworder)) {
if(!("map" %in% names(attributes(qtl))))
stop("No map in the qtl object; you must provide argument 'neworder'.")
chr <- names(attr(qtl, "map"))
thechr <- match(qtl$chr, chr)
if(any(is.na(thechr)))
stop("Chr ", paste(qtl$chr[is.na(thechr)], " "), " not found.")
neworder <- order(thechr, qtl$pos)
}
curorder <- seq(qtl$n.qtl)
if(length(neworder) != qtl$n.qtl ||
!all(curorder == sort(neworder)))
stop("neworder should be an ordering of the integers from 1 to ", qtl$n.qtl)
if(qtl$n.qtl == 1)
stop("Nothing to do; just one qtl.")
if("geno" %in% names(qtl))
qtl$geno <- qtl$geno[,neworder,]
else
qtl$prob <- qtl$prob[neworder]
qtl$name <- qtl$name[neworder]
qtl$chr <- qtl$chr[neworder]
qtl$pos <- qtl$pos[neworder]
attr(qtl, "formula") <- NULL
attr(qtl, "pLOD") <- NULL
if("lodprofile" %in% names(attributes(qtl))) {
lodprof <- attr(qtl, "lodprofile")
if(length(lodprof) == length(neworder))
attr(qtl, "lodprofile") <- lodprof[neworder]
}
qtl
}
# print compact version of QTL object
print.compactqtl <-
function(x, ...)
{
print(summary(x))
}
summary.compactqtl <-
function(object, ...)
{
class(object) <- c("summary.compactqtl", "list")
object
}
print.summary.compactqtl <-
function(x, ...)
{
if(is.null(x) || length(x) == 0)
cat("Null QTL model\n")
else {
temp <- as.data.frame(x)
rownames(temp) <- paste("Q", 1:nrow(temp), sep="")
print.data.frame(temp)
}
if("formula" %in% names(attributes(x))) {
form <- attr(x, "formula")
if(!is.character(form)) form <- deparseQTLformula(form)
cat(" Formula:")
w <- options("width")[[1]]
printQTLformulanicely(form, " ", w+5, w)
}
if("pLOD" %in% names(attributes(x)))
cat(" pLOD: ", round(attr(x, "pLOD"),3), "\n")
}
# end of makeqtl.R
|
be9b2cf0cbedf898fdaaba0e01c5675e35fb506c | 88ceb298e0147b3f2ff4e9c13e4d65ad4723fb2e | /R/handleContribution.R | 23543a0d42a2fd26077feea9e75f840f3f3da9c8 | [] | no_license | Sage-Bionetworks/rSCR | 43edda65c1a4a265aebcef7846d2474618a2067f | 17a4297acc96bc3c32a96475f0d03b209c53024e | refs/heads/master | 2020-05-17T09:14:56.287852 | 2013-01-22T21:56:02 | 2013-01-22T21:56:02 | 5,533,164 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,353 | r | handleContribution.R | #
# These functions handle the work associated with
# gathering the information about the contributed
# dataset(s).
#
setMethod(
f = "handleContribution",
signature = signature("Project", "list","character"),
definition = function(project, contribution, logFile) {
# First remove any annotations set to NA
if(sum(is.na(contribution))){
contribution <- contribution[-which(is.na(contribution))]
}
# Next, make sure the user has provided the required information.
if(! all(c("data.url","study.name","data.name", "data.type","data.status") %in% names(contribution))){
cat(setdiff(c("data.url","study.name","data.name", "data.type","data.status"), names(contribution)),"\n")
stop("Mising one of study.name, data.url, data.name, data.type, or data.status. Please try again.\n")
}
# Set the data.lastUpdate to NA if it wasn't provided
if(! "data.lastUpdate" %in% names(contribution)){
# Last update not included. Set to NA
contribution$data.lastUpdate = format(Sys.time(), "%d-%m-%Y")
}
contribution$parentId <- propertyValue(project,"id")
if(any(names(contribution) == "tissue")){
names(contribution)[which(names(contribution) == "tissue")] <- 'tissueType'
}
if(any(names(contribution) == "platformName")){
names(contribution)[which(names(contribution) == "platformName")] <- 'platform'
}
return(contribution)
}
)
setMethod(
f = "handleContribution",
signature = signature("Folder", "list","character"),
definition = function(project, contribution, logFile) {
# First remove any annotations set to NA
if(sum(is.na(contribution))){
contribution <- contribution[-which(is.na(contribution))]
}
# Next, make sure the user has provided the required information.
if(! all(c("data.url","study.name","data.name", "data.type","data.status") %in% names(contribution))){
cat(setdiff(c("data.url","study.name","data.name", "data.type","data.status"), names(contribution)),"\n")
stop("Mising one of study.name, data.url, data.name, data.type, or data.status. Please try again.\n")
}
# Set the data.lastUpdate to NA if it wasn't provided
if(! "data.lastUpdate" %in% names(contribution)){
# Last update not included. Set to NA
contribution$data.lastUpdate = format(Sys.time(), "%d-%m-%Y")
}
contribution$parentId <- propertyValue(project,"id")
if(any(names(contribution) == "tissue")){
names(contribution)[which(names(contribution) == "tissue")] <- 'tissueType'
}
if(any(names(contribution) == "platformName")){
names(contribution)[which(names(contribution) == "platformName")] <- 'platform'
}
return(contribution)
}
)
setMethod(
f = "handleContribution",
signature = signature("NULL", "data.frame", "character"),
definition = function(project, contribution, logFile){
# here we assume user has supplied a dataframe, with
# each row containing a single contribution
if(! all(c("data.url","study.name","data.name", "data.type","data.status", "parentId") %in% names(contribution))){
stop("Mising one of study.name, data.url, data.name, data.type, or data.status. Please try again.\n")
}
proj <- contribution$parentId
projects <- sapply(unique(proj), handleProject)
names(projects) <- sapply(projects, function(project){
propertyValue(project,"id")
})
contribution <- lapply(1:nrow(contribution),function(x){ as.list(contribution[x,])})
sapply(contribution, function(x){
project <- projects[[as.character(x$parentId)]]
handleContribution(project, x, logFile)
}) -> contribution
tmp <- list()
if(class(contribution) == "matrix"){
tmp <- apply(contribution,2,as.list)
contribution <- tmp
}
contribution
}
)
setMethod(
f = "handleContribution",
signature = signature("NULL", "character", "character"),
definition = function(project, contribution, logFile){
# here we assume user has supplied a file. we assume
# each row contains a single contribution
if(!file.exists(contribution)) {
stop("Cannot find file", contribution,"\n. Please try again.")
}
contribution <- read.delim(contribution,
sep="\t",
quote="",
strip.white=TRUE,
stringsAsFactors=FALSE)
contribution <- handleContribution(project, contribution, logFile)
return(contribution)
}
)
|
4b60eccda6ce8ba7a25b98622aef24ec16305796 | b0dc3cd68fbfdc23a614c05841656c624a7a1c4d | /R_en_VisualStudio.r | 3fb5f855c6999901574d95a725640885bba42ad5 | [] | no_license | iJosepe/R-q-R | 8c944daee531f5faa222fa8b2d08a83d65f6936c | 841b5c1005a1a7219a4878bec901340c0e6d6c2e | refs/heads/main | 2023-03-23T01:35:29.882453 | 2021-03-20T09:54:38 | 2021-03-20T09:54:38 | 345,773,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,582 | r | R_en_VisualStudio.r | v <- c(4,2,-8)
print(v)
frutas <- c(15, 100, 2, 30)
names(frutas) <- c("naranja", "pera", "manzana", "durazno")
print(frutas)
frutas["manzana"] <- 8
print(frutas)
u <- 2:33
v <- c(4, 5, 6)
w <- c(u, v)
print(u,v,w)
# CONSTRUCCION DE MATRICES
(m <- 11:30) # Un vector con 20 números
# Para convertirla en matriz simplemente se especifica el atributo dim
dim(m) <- c(4,5)
m
rownames(m) <- c("uno", "dos", "tres", "cuatro" )
colnames(m) <- c('UNO','DOS','TRES','CUATRO','CINCO')
m
m1 <- rbind(c(1.5, 3.2, -5.5), c(0, -1.1, 60))
m1
# install.packages('tidyverse')
library(tidyverse)
radio <- 0:10 # Vector de radios
area <- pi*radio^2 # Vector de áreas
tit <- "Áreas de círculos" # Título del gráfico
plot(radio, area, # x=radio y=area
type="b", # "both", puntos y líneas
main=tit,
xlab="Radio (r)",
ylab=expression(Area == pi*r^2), # Una expresión
col="orange", # color (naranja)
pch=20) # tipo de símbolo para punto
# Operaciones con matrices
(A <- matrix(1:6, 3, 2))
(B <- rbind(7:9, 10:12))
A %*% B
# Matriz traspuesta
t(A)
# FACTORES Y CADENAS DE CARACTERES
persona <- c("Hugo", "Paco", "Luis", "Petra", "Maria", "Fulano",
"Sutano", "Perengano", "Metano", "Etano", "Propano")
mes.nacimiento <- c("Dic", "Feb", "Oct", "Mar", "Feb", "Nov",
"Abr", "Dic", "Feb", "Oct", "Dic")
print(persona[7]); print(mes.nacimiento[7])
# Uso de paste()
paste(persona[7], "nacio en el mes de", mes.nacimiento[7])
# Funcion de conversion as.factor()
Fmes.nacimiento <- as.factor(mes.nacimiento)
Fmes.nacimiento
# y generamos ahora la impresión con el factor
paste(persona[7], "nació en el mes de", Fmes.nacimiento[7])
# La función table() toma típicamente como argumento un factor y regresa
# como resultado justamente la frecuencia de aparición de los
# niveles en el vector de índices.
table(Fmes.nacimiento)
# Incluimos todos los meses en la tabla de frecuencias
meses <- c("Ene","Feb","Mar","Abr","May","Jun","Jul","Ago",
"Sep","Oct","Nov","Dic")
# Se incluyen meses que no están el el vector original
FFmes.nacimiento <- factor(mes.nacimiento, levels=meses)
FFmes.nacimiento
# Ahora la tabla de frecuencias es:
table(FFmes.nacimiento)
# Acceso a los elementos de un factor
# Un elemento individual del factor:
Fmes.nacimiento[10]
# Un elemento individual de los niveles:
levels(Fmes.nacimiento)[3]
# LISTAS Una lista, de la clase list, es una clase de datos que puede
# contener cero o más elementos, cada uno de los cuales puede ser
# de una clase distinta.
familia <- list("Maria", "Juan", 10, c("Hugo", "Petra"), c(8,6))
familia
familia <- list(madre="Maria", padre="Juan", casados=10,
hijos=c("Hugo", "Petra"), edades=c(8, 6))
familia
# Acceso a los elementos individuales de una lista
familia$madre
familia[['madre']]
# Acceso de escritura
familia[['padre']] <- 'Juan Pedro'
familia$padre
familia$"madre" <- "Maria Candelaria"
mm <- "madre"
familia[[mm]]
familia[[ paste("ma", "dre", sep="") ]]
## DATA FRAMES
# Un data frame es una lista, cuyos componentes pueden ser vectores,
# matrices o factores.
(m <- cbind(ord=1:3, edad=c(30L, 26L, 9L)) )
(v <- c(1.80, 1.72, 1.05) )
df <- data.frame(familia=c("Padre", "Madre", "Hijo"),m, estatura=v)
df
# Usamos read.table()
mi.tabla <- read.table('Rtext.txt')
colnames(mi.tabla)
rownames(mi.tabla)
mi.tabla$Piso
mi.tabla[[2]]
mi.tabla[2]
mi.tabla[3, 2]
mi.tabla[3, 2] <- 106
mi.tabla
mi.tabla$Calentador
class(mi.tabla$Calentador)
mi.tabla$Total <- mi.tabla$Precio * mi.tabla$Area
mi.tabla
|
596279a76d224f7c47a73e637c8f74ef9887df1d | 7622e144ac5196eab93a451b7178de4097b21c3d | /tests/testthat/test_sentiment_computation.R | f09261ab167f84a00546f0a4d2e2dd06ee9b638f | [] | no_license | kbenoit/sentometrics | 6ed0d4bbeddc1732528e3c6e6d9157e252de0f3f | 41a4806911da38a799b23218918935dc09ef39a6 | refs/heads/master | 2021-06-13T22:17:04.756279 | 2021-03-03T16:34:54 | 2021-03-03T16:34:54 | 171,403,511 | 1 | 0 | null | 2019-02-19T04:12:49 | 2019-02-19T04:12:49 | null | UTF-8 | R | false | false | 10,127 | r | test_sentiment_computation.R |
# test_file("tests/testthat/test_sentiment_computation.R")
context("Sentiment computation")
library("data.table")
library("quanteda")
library("tm")
library("stringi")
set.seed(123)
# sento_corpus creation
data("usnews")
corpus <- sento_corpus(corpusdf = usnews[1:250, ])
# SimpleCorpus creation
txt <- system.file("texts", "txt", package = "tm")
scorp <- tm::SimpleCorpus(tm::DirSource(txt))
# scorp$content[1] <- "A text for which we want to calculate above average sentiment."
# scorp$content[2] <- "A text for which we want to calculate below average sentiment."
scorp$content[3] <- quanteda::texts(corpus)[3]
# VCorpus creation
reuters <- system.file("texts", "crude", package = "tm")
vcorp <- tm::VCorpus(tm::DirSource(reuters))
# corpus with multiple languages
usnews[["language"]] <- "en"
usnews[["language"]][1:100] <- "fr"
corpusLang <- sento_corpus(corpusdf = usnews[1:250, ])
# lexicons creation
data("list_lexicons")
lex <- sento_lexicons(list_lexicons[c("GI_en", "LM_en", "HENRY_en")],
list_valence_shifters[["en"]])
# lexSimple <- sento_lexicons(list_lexicons[c("GI_en", "LM_en", "HENRY_en")]) # same as lex[1:3]
lexSplit <- sento_lexicons(list_lexicons[c("GI_en", "LM_en", "HENRY_en")], do.split = TRUE)
lexClust <- sento_lexicons(list_lexicons[c("GI_en", "LM_en", "HENRY_en")],
list_valence_shifters[["en"]][, c("x", "t")])
lEn <- sento_lexicons(list("HENRY_en" = list_lexicons$HENRY_en), list_valence_shifters$en)
lFr <- sento_lexicons(list("HENRY_fr" = list_lexicons$HENRY_en, "FEEL" = list_lexicons$FEEL_fr))
lexLang <- lexWrong <- list(en = lEn, fr = lFr)
names(lexWrong)[2] <- "frr"
### tests from here ###
sanity_sentiment <- function(texts, lexicon, valence = NULL) {
setkey(lexicon, "x")
if (!is.null(valence)) setkey(valence, "x")
out <- rep(NA, length(texts))
for (i in seq_along(texts)) {
x <- texts[i]
tok <- stringi::stri_split_boundaries(
stringi::stri_trans_tolower(x), type = "word", skip_word_none = TRUE, skip_word_number = TRUE
)[[1]]
lo <- which(tok %in% lexicon[["x"]])
m <- tok[lo]
sc <- lexicon[m, y]
before <- sapply(lo - 1, max, 1)
vals <- rep(1, length(sc))
if (!is.null(valence)) {
val <- which(tok[before] %in% valence$x)
v <- tok[before][val]
vals[val] <- valence[v, y]
}
ss <- sum(sc * vals)
out[i] <- ss
}
out
}
sentimentList <- list(
s1 = compute_sentiment(quanteda::texts(corpus), lex, how = "counts"),
s2 = compute_sentiment(quanteda::texts(corpus), lex[1:3], how = "counts"),
s3 = compute_sentiment(quanteda::texts(corpus), lex, how = "proportional"),
s4 = compute_sentiment(quanteda::texts(corpus), lex, how = "proportionalPol"),
s5 = compute_sentiment(quanteda::corpus(usnews[1:250, "texts"]), lex, how = "counts"),
s6 = compute_sentiment(quanteda::corpus(usnews[1:250, c("texts", "wsj", "economy")], text_field = "texts"),
lex, how = "counts"),
s7 = compute_sentiment(corpus, lex, how = "counts"),
s8 = compute_sentiment(quanteda::texts(corpus), lexSplit, how = "counts"),
# s9 = compute_sentiment(quanteda::texts(corpus), lex, how = "TF", nCore = 2), # no multicore computation in CRAN checks
s10 = compute_sentiment(quanteda::texts(corpus), lexClust, how = "counts"),
s11 = compute_sentiment(corpus, lexClust, how = "proportional"),
s12 = compute_sentiment(quanteda::texts(corpus), lexClust, how = "proportionalPol"),
s13 = compute_sentiment(corpus, lex, how = "exponential"),
s14 = compute_sentiment(corpus, lex, how = "inverseExponential"),
s15 = compute_sentiment(corpus, lex, how = "UShaped"),
s16 = compute_sentiment(corpus, lex, how = "inverseUShaped"),
# s17 = compute_sentiment(corpus, lex, how = "TF"),
# s18 = compute_sentiment(corpus, lex, how = "logarithmicTF"),
# s19 = compute_sentiment(corpus, lex, how = "augmentedTF"),
# s20 = compute_sentiment(corpus, lex, how = "IDF"),
s21 = compute_sentiment(corpus, lex, how = "TFIDF"),
# s22 = compute_sentiment(corpus, lex, how = "logarithmicTFIDF"),
# s23 = compute_sentiment(corpus, lex, how = "augmentedTFIDF"),
s24 = compute_sentiment(corpusLang, lexLang, how = "proportionalSquareRoot")
)
# compute_sentiment
# load(system.file("extdata", "test_data.rda", package = "sentometrics")) # benchmark sentiment scores
# test_that("Agreement between legacy benchmark and current produced sentiment scores", {
# expect_equal(test_data, sentimentList[1:11])
# })
test_that("Agreement between sentiment scores on document-level across input objects", {
expect_true(all(unlist(lapply(sentimentList, function(s) nrow(s) == 250))))
expect_true(all(unlist(lapply(sentimentList[-1], function(s) all(s$word_count == sentimentList$s1$word_count)))))
expect_true(all(sentimentList$s8[, c("GI_en_POS", "LM_en_POS", "HENRY_en_POS")] >= 0))
expect_true(all(sentimentList$s8[, c("GI_en_NEG", "LM_en_NEG", "HENRY_en_NEG")] <= 0))
expect_equivalent(sentimentList$s1[, c("GI_en", "LM_en", "HENRY_en")],
sentimentList$s5[, c("GI_en", "LM_en", "HENRY_en")])
expect_equivalent(sentimentList$s6[, -c(1:2)],
sentimentList$s7[, colnames(sentimentList$s6)[-c(1:2)], with = FALSE])
expect_error(compute_sentiment(quanteda::texts(corpus), lex, how = "notAnOption"))
expect_warning(compute_sentiment(quanteda::texts(corpus), lex, how = "counts", nCore = -1))
expect_error(compute_sentiment(quanteda::texts(corpus), list_lexicons))
expect_true(all.equal(sentimentList$s3[3, -1],
compute_sentiment(scorp[3], lex, how = "proportional")[, -1]))
# expect_warning(compute_sentiment(vcorp, lex, how = "proportional"))
expect_error(compute_sentiment(corpusLang, lex, how = "proportional"))
expect_true("language" %in% colnames(quanteda::docvars(corpusLang)))
expect_error(compute_sentiment(corpusLang, lexWrong, how = "proportional"))
expect_true(all.equal(sentimentList$s1$GI_en, sanity_sentiment(quanteda::texts(corpus), lex$GI_en, lex$valence)))
expect_true(all.equal(sentimentList$s2$GI_en, sanity_sentiment(quanteda::texts(corpus), lex$GI_en)))
})
sentimentSentenceList <- list(
s1 = compute_sentiment(quanteda::texts(corpus), lexClust, how = "counts", do.sentence = TRUE),
s2 = compute_sentiment(quanteda::corpus(usnews[1:250, "texts"]),
lexClust, how = "counts", do.sentence = TRUE),
s3 = compute_sentiment(quanteda::corpus(usnews[1:250, c("texts", "wsj", "economy")], text_field = "texts"),
lexClust, how = "counts", do.sentence = TRUE),
s4 = compute_sentiment(corpus, lexClust, how = "proportionalSquareRoot", do.sentence = TRUE),
s5 = compute_sentiment(corpusLang, lexLang, how = "proportional", do.sentence = TRUE),
s6 = compute_sentiment(corpus, lex[1:3], how = "TFIDF", do.sentence = TRUE),
s7 = compute_sentiment(corpus, lex, how = "inverseUShaped", do.sentence = TRUE)
)
# test_that("Agreement between sentiment scores on sentence-level across input objects", {
# expect_true(all(unlist(lapply(sentimentSentenceList, function(s) nrow(s) == 2658))))
# expect_true(all(unlist(lapply(sentimentSentenceList[1:4], function(s)
# all(s$word_count == sentimentSentenceList$s1$word_count)))))
# expect_true(all(unlist(lapply(sentimentSentenceList, function(s)
# sum(s$word_count) == sum(sentimentSentenceList$s1$word_count)))))
# expect_true(all(c("GI_en", "LM_en", "HENRY_en") %in%
# colnames(compute_sentiment(scorp[3], lexClust, how = "counts", do.sentence = TRUE))))
# })
# sento_lexicons
test_that("Proper fails when issues with lexicons and valence shifters input", {
expect_error(sento_lexicons(list("heart--break--hotel" = list_lexicons[["LM_en"]], "good" = list_lexicons[["GI_en"]])))
expect_error(sento_lexicons(list_lexicons["GI_en"], valenceIn = data.table(x = rep("w", 10))))
expect_error(sento_lexicons(list_lexicons["GI_en"], valenceIn = data.table(x = "w", wrong = 1:3)))
expect_error(sento_lexicons(list_lexicons["GI_en"], valenceIn = data.table(x = "w", t = 2:5)))
expect_error(sento_lexicons(list_lexicons$FEEL_nl_tr))
expect_error(sento_lexicons(list(list_lexicons$LM_en, list_lexicons$GI_en)))
expect_error(sento_lexicons(list(a = list_lexicons[[1]], b = list_lexicons[[2]], a = list_lexicons[[3]])))
expect_error(sento_lexicons(list_lexicons[1:3], valenceIn = letters))
})
test_that("Proper fails when trying to modify a sento_lexicons object", {
expect_error(lex["valence"])
expect_error(lex[0])
expect_error(lex[length(lex) + 1])
expect_error(lex[1] <- lexSplit[3])
expect_error(lex[[1]] <- lexSplit[[1]])
expect_error(lex$HENRY_en <- lexSplit$HENRY_en_POS)
expect_error(names(lex)[1] <- names(lex)[2])
})
# as.sentiment
sA <- sAw1 <- sAw2 <- sAw3 <- sentimentList[["s7"]]
colnames(sAw1)[1:3] <- letters[1:3]
colnames(sAw2)[5:6] <- letters[1]
sAw3[[7]] <- "notNumeric"
test_that("Correct or failed conversion to a sentiment object", {
expect_true(inherits(as.sentiment(sA), "sentiment"))
expect_error(as.sentiment(sAw1))
expect_error(as.sentiment(sAw2))
expect_error(as.sentiment(sAw3))
})
# merge.sentiment
sB <- sA
sB$id <- paste0("idNew", 1:nrow(sB))
test_that("Correct binding of several sentiment objects", {
expect_true(inherits(merge(sentimentList$s1, sentimentList$s2), "data.table"))
expect_true(nrow(merge(sA, sB, sA)) == (2 * nrow(sA)))
expect_true(ncol(merge(sentimentList$s7, sentimentList$s11)) == ncol(sentimentList$s7))
})
# tf-idf comparison sentometrics vs. quanteda
toks <- stri_split_boundaries(stri_trans_tolower(quanteda::texts(corpus)), type = "word", skip_word_none = TRUE)
dfmQ <- quanteda::dfm(as.tokens(toks)) %>% dfm_tfidf(k = 1)
posScores <- rowSums(as.matrix(quanteda::dfm_select(dfmQ, lex$GI_en[y == 1, x])))
negScores <- rowSums(as.matrix(quanteda::dfm_select(dfmQ, lex$GI_en[y == -1, x])))
test_that("Same tf-idf scoring for sentometrics and quanteda", {
expect_equal(compute_sentiment(quanteda::texts(corpus), lex[-length(lex)], tokens = toks, "TFIDF")[["GI_en"]],
unname(posScores - negScores))
})
|
4bd7693634a136b690b1742364d8ca0ee85a3f76 | 2429f45453e4a19b2a32c03916dff8da073bedec | /PartyVoteLoyalty.R | 13925806e3b4cdf15cc28bf92f0a61a89c511a2f | [] | no_license | scottcame/wa-leg-harvester | f595d42929e246f1b75efc7bfb5f1f67435f33f6 | 120a706a98e83b7cf2d0873eafe65538272f5e2a | refs/heads/master | 2020-04-21T11:13:50.691596 | 2020-01-19T20:14:30 | 2020-01-19T20:14:30 | 169,516,373 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,528 | r | PartyVoteLoyalty.R | # Party loyalty in legislative votes
library(ggrepel)
library(ggthemes)
library(scales)
library(sf)
downloadData()
LoyaltyDf <- RollCalls %>%
group_by(Chamber, MemberName, District, Position, Party) %>%
summarize(Loyalty=mean(WithPartyMajority)) %>%
group_by(Chamber) %>%
arrange(Chamber, Loyalty) %>% ungroup()
ChamberLoyaltyDf <- RollCalls %>%
group_by(Chamber, Party) %>%
summarize(MeanChamberPartyLoyalty=mean(WithPartyMajority), MedianChamberPartyLoyalty=median(WithPartyMajority))
LoyaltyDf <- inner_join(LoyaltyDf, ChamberLoyaltyDf, by=c('Chamber', 'Party')) %>%
mutate(MeanRelativeLoyalty=100*(Loyalty-MeanChamberPartyLoyalty), MedianRelativeLoyalty=100*(Loyalty-MedianChamberPartyLoyalty))
LoyaltyDf %>%
top_n(5, -Loyalty) %>%
ungroup() %>%
mutate(Loyalty=scales::percent(Loyalty)) %>%
filter(Chamber=='Senate') %>% select(-Chamber) %>%
rename(`Votes with Party`=Loyalty, Member=MemberName) %>%
kableExtra::kable(format='rst')
RollCalls %>% group_by(Chamber, Party) %>%
summarize(Loyalty=mean(WithPartyMajority)) %>%
mutate(Loyalty=scales::percent(Loyalty)) %>%
rename(`Votes with Party`=Loyalty) %>%
kableExtra::kable(format='rst')
LoyaltyDf %>%
filter(Chamber=='House') %>%
select(District, Position, Loyalty) %>%
spread(Position, Loyalty, sep='Loyalty_') %>%
inner_join(LoyaltyDf %>%
filter(Chamber=='House') %>%
select(District, Position, Party) %>%
spread(Position, Party, sep='Party_') %>%
mutate(Party=case_when(
PositionParty_1==PositionParty_2 ~ PositionParty_1,
TRUE ~ 'Split'
)) %>% select(District, Party)) %>%
filter(PositionLoyalty_1 < .98 | PositionLoyalty_2 < .98) %>%
ggplot() + geom_label_repel(aes(x=PositionLoyalty_1, y=PositionLoyalty_2, label=District, fill=Party), min.segment.length = 10) +
scale_x_continuous(breaks=c(.7, .75, .8, .85, .9, .95, 1), limits=c(.7, 1.05), labels=percent) +
scale_y_continuous(breaks=c(.7, .75, .8, .85, .9, .95, 1), limits=c(.7, 1.05), labels=percent) +
scale_fill_manual(values=c('D'='CornflowerBlue', 'R'='Tomato', 'Split'='MediumPurple')) +
coord_equal() +
theme_minimal() +
labs(x='Position 1: % of votes with party', y='Position 2: % of votes with party',
title='Party Loyalty of Representatives from the Same District', subtitle='Washington House of Representatives, 2019 Session',
caption='Note: Excludes Districts where both reps voted with party > 98 % of time')
waLegShp <- read_sf('/opt/data/Shapefiles/cb_2017_53_sldl_500k/cb_2017_53_sldl_500k.shp')
loyaltyChoropleth <- function(chamber, position, personLabel) {
relativeLoyaltyLimit <- LoyaltyDf %>% filter(Chamber==chamber) %>% filter(is.na(position) | Position==position) %>% .$MeanRelativeLoyalty %>% range() %>% abs() %>% max()
caption <- NULL
if (position) {
caption <- paste0('Map depicts party voting loyalty of representatives from Position ', position, '\n')
}
ggplot(waLegShp %>%
inner_join(LoyaltyDf %>% filter(Chamber==chamber) %>% filter(is.na(position) | Position==position) %>% mutate(District=as.character(District)), by=c('NAME'='District'))) +
geom_sf(aes(fill=Loyalty), color='grey70') +
scale_fill_gradient(low='#f7fbff', high='#08519c', labels=percent, breaks=c(.7, .8, .9, 1), limits=c(.7, 1)) +
theme_void() +
theme(panel.grid.major=element_line(color="transparent"), legend.position = 'bottom', legend.title = element_text(size=10),
legend.key.width = unit(50, 'points')) +
labs(
fill='% of votes with party majority: ',
title=paste0('Party Voting Loyalty in Washington State ', chamber, ' (2019 Session)'),
subtitle=paste0('Representatives in Position #', position),
caption=paste0(#caption,
'Legislative vote data from Washington State Legislature, available at https://data.world/scottcame/washington-legislature-2019'
)
)
}
loyaltyChoropleth('House', 1, 'Representative')
loyaltyChoropleth('House', 2, 'Representative')
#loyaltyChoropleth('Senate', NA, 'Senator')
# look at loyalty compared to margin of victory in 2018 election
results2018 <- read_csv('~/git-repos/openelections/openelections-data-wa/2018/20181106__wa__general__precinct.csv', col_types=cols(.default=col_character())) %>%
filter(!is.na(district)) %>%
filter(grepl(x=office, pattern='^State')) %>%
filter(candidate != 'Registered Voters') %>%
filter(candidate != 'Write-in') %>%
select(office, district, candidate, party, votes) %>%
mutate(votes=as.integer(votes)) %>%
group_by(office, district, candidate, party) %>% summarize(votes=sum(votes)) %>%
filter(!grepl(x=office, pattern='Senat')) %>%
mutate(position=gsub(x=office, pattern='.+Pos\\. ([12]).+', replacement='\\1')) %>%
group_by(district, position) %>% mutate(totalVotes=sum(votes)) %>% filter(votes==max(votes)) %>%
ungroup() %>%
mutate(WinMargin=votes/totalVotes) %>% mutate(district=as.integer(district), position=as.integer(position)) %>%
select(District=district, Position=position, WinMargin2018=WinMargin)
LoyaltyDf <- LoyaltyDf %>% inner_join(results2018, by=c('District', 'Position')) %>%
mutate(PartyFull=case_when(Party=='R' ~ 'Republican', TRUE ~ 'Democrat'))
LoyaltyDf %>%
ggplot() + geom_point(aes(x=Loyalty, y=WinMargin2018, color=PartyFull), size=2.5) +
geom_label_repel(data=LoyaltyDf %>%
filter(Loyalty < .88 | (Loyalty < .9 & WinMargin2018 > .7) | grepl(x=MemberName, pattern='Apple')) %>%
mutate(label=paste0(MemberName, ' (', Party, '-', District, ')')),
mapping=aes(x=Loyalty, y=WinMargin2018, label=label), min.segment.length = .05, box.padding = .8, size=3) +
scale_color_manual(values=c('Republican'='#ae0305', 'Democrat'='#003464')) +
scale_y_continuous(labels=percent) +
scale_x_continuous(labels=percent) +
theme_hc() +
labs(
title='Do close elections compel legislators to cross the aisle more often?',
subtitle='Washington State House Members: Margin of victory in 2018 and party voting loyalty in the House during the 2019 session',
x='% of votes with party majority', y='% of vote won in 2018 general election', color=NULL,
caption=paste0('Election results data from Open Elections (@openelex)\n',
'Legislative vote data from Washington State Legislature, available at https://data.world/scottcame/washington-legislature-2019')
)
|
ca8bfd55bd6bc6798ad4031b5aee9021a7c0eaee | faa067ff528a08fc6622988f9619f3389636107f | /EvaluationStats/main.R | 6039d2e7ccc9363a0769724f11e2852d85745f6d | [] | no_license | donairo/90percent | d33d895e465d655ec8d2649291e35e2ef6eb13f0 | 2e677cd04d21ec93bb975bcc73c06f424315b5aa | refs/heads/master | 2020-04-28T13:25:34.565949 | 2019-04-09T01:46:31 | 2019-04-09T01:46:31 | 175,306,499 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,481 | r | main.R | mydata <- read.csv("../data/loginData.csv")
attach(mydata)
print(mydata)
print("for TEXT21")
print("Mean for total login")
print(mean(Logins.Total[scheme=="testtextrandom"]))
print("Mean for successful login")
print(mean(Logins.success[scheme=="testtextrandom"]))
print("Mean for Failed login")
print(mean(Logins.Failure[scheme=="testtextrandom"]))
print("Median for total login")
print(median(Logins.Total[scheme=="testtextrandom"]))
print("Median for successful login")
print(median(Logins.success[scheme=="testtextrandom"]))
print("Median for Failed login")
print(median(Logins.Failure[scheme=="testtextrandom"]))
print("sd for Total login")
print(sd(Logins.Total[scheme=="testtextrandom"]))
print("sd for Successful login")
print(sd(Logins.success[scheme=="testtextrandom"]))
print("sd for Failed login")
print(sd(Logins.Failure[scheme=="testtextrandom"]))
print("Mean for successful login time")
print(mean(Logins.avgSuccess[scheme=="testtextrandom"]))
print("Mean for failed login time")
print(mean(Logins.avgFail[scheme=="testtextrandom"]))
print("Median for successful login time")
print(median(Logins.avgSuccess[scheme=="testtextrandom"]))
print("Median for failed login time")
print(median(Logins.avgFail[scheme=="testtextrandom"]))
print("sd for successful login time")
print(sd(Logins.avgSuccess[scheme=="testtextrandom"]))
print("sd for failed login time")
print(sd(Logins.avgFail[scheme=="testtextrandom"]))
hist(Logins.Total[scheme=="testtextrandom"], ylim=c(0,7),xlim=c(0,35), breaks=7, main="Histogram of text21 Total Logins", xlab = "Total logins")
hist(Logins.success[scheme=="testtextrandom"], ylim=c(0,18), xlim=c(0,25), main="Histogram of Succesful text21 Logins", xlab = "Succesful logins")
hist(Logins.Failure[scheme=="testtextrandom"], ylim=c(0,18), xlim=c(0,12), main="Histogram of Failed text21 Logins", xlab = "Failed logins")
hist(Logins.avgFail[scheme=="testtextrandom"], ylim=c(0,10), xlim=c(0,30000), main="Histogram of time for Failed text21 Logins", xlab = "time of Failed logins (ms)")
hist(Logins.avgSuccess[scheme=="testtextrandom"], ylim=c(0,8), xlim=c(0,30000), main="Histogram of time for Successful text21 Logins", xlab = "time of Successful logins (ms)")
boxplot(Logins.avgFail[scheme=="testtextrandom"], ylim=c(0,30000),main ="BoxPlot of time for text21 Failed Logins", ylab = "time of Failed logins (ms)")
boxplot(Logins.avgSuccess[scheme=="testtextrandom"],main ="BoxPlot of text21 time for Successful Logins", ylab = "time of Successful logins (ms)")
print("for IMAGE21")
print("Mean for total login")
print(mean(Logins.Total[scheme=="testpasstiles"]))
print("Mean for successful login")
print(mean(Logins.success[scheme=="testpasstiles"]))
print("Mean for Failed login")
print(mean(Logins.Failure[scheme=="testpasstiles"]))
print("Median for total login")
print(median(Logins.Total[scheme=="testpasstiles"]))
print("Median for successful login")
print(median(Logins.success[scheme=="testpasstiles"]))
print("Median for Failed login")
print(median(Logins.Failure[scheme=="testpasstiles"]))
print("sd for Total login")
print(sd(Logins.Total[scheme=="testpasstiles"]))
print("sd for Successful login")
print(sd(Logins.success[scheme=="testpasstiles"]))
print("sd for Failed login")
print(sd(Logins.Failure[scheme=="testpasstiles"]))
print("Mean for successful login time")
print(mean(Logins.avgSuccess[scheme=="testpasstiles"]))
print("Mean for failed login time")
print(mean(Logins.avgFail[scheme=="testpasstiles"]))
print("Median for successful login time")
print(median(Logins.avgSuccess[scheme=="testpasstiles"]))
print("Median for failed login time")
print(median(Logins.avgFail[scheme=="testpasstiles"]))
print("sd for successful login time")
print(sd(Logins.avgSuccess[scheme=="testpasstiles"]))
print("sd for failed login time")
print(sd(Logins.avgFail[scheme=="testpasstiles"]))
hist(Logins.Total[scheme=="testpasstiles"], ylim=c(0,10),xlim=c(0,40), main="Histogram of image21 Total Logins", xlab = "Total logins")
hist(Logins.success[scheme=="testpasstiles"], ylim=c(0,8), xlim=c(0,20), main="Histogram of Succesful image21 Logins", xlab = "Succesful logins")
hist(Logins.Failure[scheme=="testpasstiles"], ylim=c(0,12), xlim=c(0,25), main="Histogram of Failed image21 Logins", xlab = "Failed logins")
hist(Logins.avgFail[scheme=="testpasstiles"], ylim=c(0,10), xlim=c(0,60000), main="Histogram of time for Failed image21 Logins", xlab = "time of Failed logins (ms)")
hist(Logins.avgSuccess[scheme=="testpasstiles"], ylim=c(0,10), xlim=c(0,50000), main="Histogram of time for Successful image21 Logins", xlab = "time of Successful logins (ms)")
boxplot(Logins.avgFail[scheme=="testpasstiles"], ylim= c(0,50000),main ="BoxPlot of time for image21 Failed Logins", ylab = "time of Failed logins (ms)")
boxplot(Logins.avgSuccess[scheme=="testpasstiles"],ylim=c(0,30000),main ="BoxPlot of image21 time for Successful Logins", ylab = "time of Successful logins (ms)")
print("for ColorAnimalPair")
print("Mean for total login")
print(mean(Logins.Total[scheme=="ColorAnimalPair"]))
print("Mean for successful login")
print(mean(Logins.success[scheme=="ColorAnimalPair"]))
print("Mean for Failed login")
print(mean(Logins.Failure[scheme=="ColorAnimalPair"]))
print("Median for total login")
print(median(Logins.Total[scheme=="ColorAnimalPair"]))
print("Median for successful login")
print(median(Logins.success[scheme=="ColorAnimalPair"]))
print("Median for Failed login")
print(median(Logins.Failure[scheme=="ColorAnimalPair"]))
print("sd for Total login")
print(sd(Logins.Total[scheme=="ColorAnimalPair"]))
print("sd for Successful login")
print(sd(Logins.success[scheme=="ColorAnimalPair"]))
print("sd for Failed login")
print(sd(Logins.Failure[scheme=="ColorAnimalPair"]))
print("Mean for successful login time")
print(mean(Logins.avgSuccess[scheme=="ColorAnimalPair"]))
print("Mean for failed login time")
print(mean(Logins.avgFail[scheme=="ColorAnimalPair"]))
print("Median for successful login time")
print(median(Logins.avgSuccess[scheme=="ColorAnimalPair"]))
print("Median for failed login time")
print(median(Logins.avgFail[scheme=="ColorAnimalPair"]))
print("sd for successful login time")
print(sd(Logins.avgSuccess[scheme=="ColorAnimalPair"]))
print("sd for failed login time")
print(sd(Logins.avgFail[scheme=="ColorAnimalPair"]))
hist(Logins.Total[scheme=="ColorAnimalPair"], ylim=c(0,8),xlim=c(0,8), main="Histogram of ColorAnimalPair Total Logins", xlab = "Total logins")
hist(Logins.success[scheme=="ColorAnimalPair"], ylim=c(0,6), xlim=c(0,5), main="Histogram of Succesful ColorAnimalPair Logins", xlab = "Succesful logins")
hist(Logins.Failure[scheme=="ColorAnimalPair"], ylim=c(0,6), xlim=c(0,6), main="Histogram of Failed ColorAnimalPair Logins", xlab = "Failed logins")
hist(Logins.avgFail[scheme=="ColorAnimalPair"], ylim=c(0,4), xlim=c(0,60000), main="Histogram of time for Failed ColorAnimalPair Logins", xlab = "time of Failed logins (ms)")
hist(Logins.avgSuccess[scheme=="ColorAnimalPair"], ylim=c(0,6), xlim=c(0,80000), main="Histogram of time for Successful ColorAnimalPair Logins", xlab = "time of Successful logins (ms)")
boxplot(Logins.avgFail[scheme=="ColorAnimalPair"], ylim= c(0,50000),main ="BoxPlot of time for ColorAnimalPair Failed Logins", ylab = "time of Failed logins (ms)")
boxplot(Logins.avgSuccess[scheme=="ColorAnimalPair"],ylim=c(0,70000),main ="BoxPlot of ColorAnimalPair time for Successful Logins", ylab = "time of Successful logins (ms)")
|
cb08d2260ab684fdc4d48a4d8fec3ffd086b86f9 | fd595e4213f4857e26ec3bf937caa2ef95b20dd2 | /server.R | 7fdc5bf0477973e3d994ae24850b52b721f4265d | [] | no_license | EarnestlyFrank/SCCounties | 7daa46d947f208d708f27eff4e10fc101d0fd05d | 9e883092a51f2c7a5ebab4ea22f2363527c353e2 | refs/heads/master | 2020-03-12T16:53:14.101305 | 2019-02-11T23:17:55 | 2019-02-11T23:17:55 | 130,725,442 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,898 | r | server.R | require(leaflet)
require(RColorBrewer)
require(scales)
require(lattice)
require(dplyr)
require(rgdal)
require(rgeos)
function(input, output, session) {
##Data import
inputs <- read.delim("Extracted DataNoError.txt")
names <- c(
"Number of self-employed adults",
"Percent of working adults who are self-employed",
"Number of children without health insurance",
"Percent of children without health insurance",
"Median family income",
"Percent of adults with a high school education or higher",
"Percent of adults with a bachelor degree or higher",
"Number of households living in the same house as last year",
"Percent of households living in the same house as last year",
"Median value of owner occupied housing units",
"Number of vacant housing units",
"Percent of housing units vacant",
"Number of housing units without a full kitchen",
"Percent of housing units without a full kitchen",
"Percent turnout in 2016 general election",
"Median age",
"Male life expectancy",
"Female life expectancy"
)
colnames(inputs) <- names
County <- readOGR("SC/SC_County.shp",
layer = "SC_County",
GDAL1_integer64_policy = TRUE)
County <- spTransform(County, CRS("+proj=longlat +ellps=GRS80"))
centers <- data.frame(gCentroid(County, byid = T))
tagText <- function(cName, depVar, indepVar, cID)
as.character(paste(
tags$h4(cName),
tags$br(),
paste0("Independent: ", names[which(names(inputs) == indepVar)], ": ", inputs[cID, which(names(inputs) == indepVar)]),
tags$br(),
paste0("Dependent: ", names[which(names(inputs) == depVar)], ": ", inputs[cID, which(names(inputs) == depVar)])
))
## Interactive Map ###########################################
# Create the map
output$map <- renderLeaflet({
leaflet(
data = County,
options = leafletOptions(
crs = leafletCRS(proj4def = County@proj4string),
zoomControl = FALSE,
minZoom = 8,
maxZoom = 8,
dragging = FALSE
)
) %>%
addTiles(urlTemplate = "//{s}.tiles.mapbox.com/v3/jcheng.map-5ebohr46/{z}/{x}/{y}.png",
attribution = 'Maps by <a href="http://www.mapbox.com/">Mapbox</a>') %>%
setView(lng = -80.9,
lat = 33.9,
zoom = 8) %>%
addPolygons(
color = "#444444",
weight = 1,
smoothFactor = 0.75,
opacity = 1.0,
fillOpacity = 0.5,
fillColor = "blue",
highlightOptions = highlightOptions(
color = "white",
weight = 2,
bringToFront = T
),
layerId = County@data$ID
)
})
output$qqPlot <- renderPlot({
qqnorm(resid(lm(inputs[, which(names(inputs) == input$dep)] ~ inputs[, which(names(inputs) == input$indep)],
data = inputs)))
})
output$histIndep <- renderPlot({
hist(inputs[, which(names(inputs) == input$indep)],
xlab = names[which(names(inputs) == input$indep)],
main = "Independent Variable")
})
output$histDep <- renderPlot({
hist(inputs[, which(names(inputs) == input$dep)],
xlab = names[which(names(inputs) == input$dep)],
main = "Dependent Variable"
)
})
output$scatter <- renderPlot({
plot(
inputs[, which(names(inputs) == input$dep)] ~ inputs[, which(names(inputs) == input$indep)],
data = inputs,
xlim = range(inputs[, which(names(inputs) == input$indep)]),
ylim = range(inputs[, which(names(inputs) == input$dep)]),
xlab = names[which(names(inputs) == input$indep)],
ylab = names[which(names(inputs) == input$dep)]
)
abline(lm(inputs[, which(names(inputs) == input$dep)] ~ inputs[, which(names(inputs) == input$indep)]))
})
output$analysis <- renderText({
deps <- inputs[, which(names(inputs) == input$dep)]
indeps <- inputs[, which(names(inputs) == input$indep)]
model <- lm(deps ~ indeps, data = inputs)
results <- anova(model)
sumText <- paste(
"<br>The independent variable has a median of ", median(indeps),
", a mean of ", signif(mean(indeps),4), ", and a standard deviation of ", signif(sd(indeps),4), "<br/>",
"<br>The dependent variable has a median of ", median(deps), ", a mean of ",
signif(mean(deps),4), ", and a standard deviation of ", signif(sd(deps),4), "<br/>", sep = "")
if (results$`Pr(>F)`[1] < 0.05) {
conclude <- paste(
"The linear regression has a p-value less than 0.05, meaning there is sufficient evidence to conclude that ",
tolower(input$indep),
" is connected to ",
tolower(input$dep),
". The equation is as follows: <br/>y = ", signif(as.numeric(model$coefficients[1]), 4),
" + ", signif(as.numeric(model$coefficients[2]), 4), "x.<br/>",
"This means that for every increase of 1 unit in ", tolower(input$indep),
" there is a change of ", signif(as.numeric(model$coefficients[2]), 4),
" +/- ", signif(as.numeric(summary(model)$coefficients[4]), 4), " units in ", tolower(input$dep), ". ",
sep = ""
)
}
else{
conclude <- paste(
"There is not sufficient evidence to conclude that ",
tolower(input$indep),
" is connected to ",
tolower(input$dep),
".",
sep = ""
)
}
if (input$numIn < max(inputs[, which(names(inputs) == input$indep)]) &&
input$numIn > min(inputs[, which(names(inputs) == input$indep)])) {
x.value <- data.frame(indeps = input$numIn)
# x.indep[1:length(inputs[, 1])] <- input$numIn
# x.indep <- as.data.frame(x.indep)
confintValues <-
predict(
model,
x.value,
type = "response",
interval = "prediction",
level = 0.95
)
confintText <- paste(
"With 95% confidence, the ",
tolower(input$dep),
" in a county with a ",
tolower(input$indep),
" equal to ",
input$numIn,
" is (",
signif(confintValues[2], 4),
", ",
signif(confintValues[3], 4),
").",
sep = ""
)
}
else{
confintText <- "The input value is outside the scope of the model. It would be improper to extrapolate. "
}
corText <- paste(
"The correlation (R<sup>2</sup>) between ",
tolower(input$indep),
" and ",
tolower(input$dep),
" is ",
signif(cor(inputs[, which(names(inputs) == input$dep)], inputs[, which(names(inputs) == input$indep)], use = "c")^2, 4),
". This means that ", 100*signif(cor(inputs[, which(names(inputs) == input$dep)], inputs[, which(names(inputs) == input$indep)], use = "c")^2, 4),
"% of the variation between the two variables can be explained by the regression model. ",
"The R<sup>2</sup> value has a P-value of ",
signif(cor.test(inputs[, which(names(inputs) == input$dep)], inputs[, which(names(inputs) == input$indep)], use = "c")$p.value, 4),
sep = ""
)
HTML(paste0(sumText,
'<br/>',
conclude,
'<br/><br/>',
confintText,
'<br/><br/>',
corText))
})
showCountyPopup <- function(cID, lat, lng) {
cName = County@data$GEO_id2[cID]
content <- tagText(cName, input$dep, input$indep, cID)
leafletProxy("map") %>% addPopups(lng, lat, content, layerId = cID)
}
# When map is clicked, show a popup with city info
observe({
leafletProxy("map") %>% clearPopups()
event <- input$map_shape_click
if (is.null(event))
return()
isolate({
showCountyPopup(event$id, event$lat, event$lng)
})
})
} |
048b7c3c46de0c6a5562b9b8ae678036ac53d9f2 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.database/R/dynamodb_interfaces.R | 02dca9a603c590df49fc9e8842b0bd2d690fe02b | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | false | 227,856 | r | dynamodb_interfaces.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common populate
#' @include dynamodb_service.R
NULL
.dynamodb$batch_execute_statement_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Statements = structure(list(structure(list(Statement = structure(logical(0), tags = list(type = "string")), Parameters = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), ConsistentRead = structure(logical(0), tags = list(type = "boolean")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$batch_execute_statement_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Responses = structure(list(structure(list(Error = structure(list(Code = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), TableName = structure(logical(0), tags = list(type = "string")), Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list")), ConsumedCapacity = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$batch_get_item_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RequestItems = structure(list(structure(list(Keys = structure(list(structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "list")), AttributesToGet = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ConsistentRead = structure(logical(0), tags = list(type = "boolean")), ProjectionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$batch_get_item_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Responses = structure(list(structure(list(structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "list"))), tags = list(type = "map")), UnprocessedKeys = structure(list(structure(list(Keys = structure(list(structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "list")), AttributesToGet = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ConsistentRead = structure(logical(0), tags = list(type = "boolean")), ProjectionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsumedCapacity = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$batch_write_item_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RequestItems = structure(list(structure(list(structure(list(PutRequest = structure(list(Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), DeleteRequest = structure(list(Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "map")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), ReturnItemCollectionMetrics = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$batch_write_item_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(UnprocessedItems = structure(list(structure(list(structure(list(PutRequest = structure(list(Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), DeleteRequest = structure(list(Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "map")), ItemCollectionMetrics = structure(list(structure(list(structure(list(ItemCollectionKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), SizeEstimateRangeGB = structure(list(structure(logical(0), tags = list(type = "double"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "map")), ConsumedCapacity = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$create_backup_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), BackupName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$create_backup_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupDetails = structure(list(BackupArn = structure(logical(0), tags = list(type = "string")), BackupName = structure(logical(0), tags = list(type = "string")), BackupSizeBytes = structure(logical(0), tags = list(type = "long")), BackupStatus = structure(logical(0), tags = list(type = "string")), BackupType = structure(logical(0), tags = list(type = "string")), BackupCreationDateTime = structure(logical(0), tags = list(type = "timestamp")), BackupExpiryDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$create_global_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string")), ReplicationGroup = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$create_global_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableDescription = structure(list(ReplicationGroup = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalTableArn = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), GlobalTableStatus = structure(logical(0), tags = list(type = "string")), GlobalTableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$create_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), BillingMode = structure(logical(0), tags = list(type = "string")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SSESpecification = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableClass = structure(logical(0), tags = list(type = "string")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$create_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableDescription = structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableStatus = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), BillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexStatus = structure(logical(0), tags = list(type = "string")), Backfilling = structure(logical(0), tags = list(type = "boolean")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), LatestStreamLabel = structure(logical(0), tags = list(type = "string")), LatestStreamArn = structure(logical(0), tags = list(type = "string")), GlobalTableVersion = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), RestoreSummary = structure(list(SourceBackupArn = structure(logical(0), tags = list(type = "string")), SourceTableArn = structure(logical(0), tags = list(type = "string")), RestoreDateTime = structure(logical(0), tags = list(type = "timestamp")), RestoreInProgress = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ArchivalSummary = structure(list(ArchivalDateTime = structure(logical(0), tags = list(type = "timestamp")), ArchivalReason = structure(logical(0), tags = list(type = "string")), ArchivalBackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$delete_backup_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$delete_backup_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupDescription = structure(list(BackupDetails = structure(list(BackupArn = structure(logical(0), tags = list(type = "string")), BackupName = structure(logical(0), tags = list(type = "string")), BackupSizeBytes = structure(logical(0), tags = list(type = "long")), BackupStatus = structure(logical(0), tags = list(type = "string")), BackupType = structure(logical(0), tags = list(type = "string")), BackupCreationDateTime = structure(logical(0), tags = list(type = "timestamp")), BackupExpiryDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), SourceTableDetails = structure(list(TableName = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableCreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), ItemCount = structure(logical(0), tags = list(type = "long")), BillingMode = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SourceTableFeatureDetails = structure(list(LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamDescription = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TimeToLiveDescription = structure(list(TimeToLiveStatus = structure(logical(0), tags = list(type = "string")), AttributeName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$delete_item_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), Expected = structure(list(structure(list(Value = structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), Exists = structure(logical(0), tags = list(type = "boolean")), ComparisonOperator = structure(logical(0), tags = list(type = "string")), AttributeValueList = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map")), ConditionalOperator = structure(logical(0), tags = list(type = "string")), ReturnValues = structure(logical(0), tags = list(type = "string")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), ReturnItemCollectionMetrics = structure(logical(0), tags = list(type = "string")), ConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$delete_item_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Attributes = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsumedCapacity = structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), ItemCollectionMetrics = structure(list(ItemCollectionKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), SizeEstimateRangeGB = structure(list(structure(logical(0), tags = list(type = "double"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$delete_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$delete_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableDescription = structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableStatus = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), BillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexStatus = structure(logical(0), tags = list(type = "string")), Backfilling = structure(logical(0), tags = list(type = "boolean")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), LatestStreamLabel = structure(logical(0), tags = list(type = "string")), LatestStreamArn = structure(logical(0), tags = list(type = "string")), GlobalTableVersion = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), RestoreSummary = structure(list(SourceBackupArn = structure(logical(0), tags = list(type = "string")), SourceTableArn = structure(logical(0), tags = list(type = "string")), RestoreDateTime = structure(logical(0), tags = list(type = "timestamp")), RestoreInProgress = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ArchivalSummary = structure(list(ArchivalDateTime = structure(logical(0), tags = list(type = "timestamp")), ArchivalReason = structure(logical(0), tags = list(type = "string")), ArchivalBackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_backup_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_backup_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupDescription = structure(list(BackupDetails = structure(list(BackupArn = structure(logical(0), tags = list(type = "string")), BackupName = structure(logical(0), tags = list(type = "string")), BackupSizeBytes = structure(logical(0), tags = list(type = "long")), BackupStatus = structure(logical(0), tags = list(type = "string")), BackupType = structure(logical(0), tags = list(type = "string")), BackupCreationDateTime = structure(logical(0), tags = list(type = "timestamp")), BackupExpiryDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), SourceTableDetails = structure(list(TableName = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableCreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), ItemCount = structure(logical(0), tags = list(type = "long")), BillingMode = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SourceTableFeatureDetails = structure(list(LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamDescription = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TimeToLiveDescription = structure(list(TimeToLiveStatus = structure(logical(0), tags = list(type = "string")), AttributeName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_continuous_backups_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_continuous_backups_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ContinuousBackupsDescription = structure(list(ContinuousBackupsStatus = structure(logical(0), tags = list(type = "string")), PointInTimeRecoveryDescription = structure(list(PointInTimeRecoveryStatus = structure(logical(0), tags = list(type = "string")), EarliestRestorableDateTime = structure(logical(0), tags = list(type = "timestamp")), LatestRestorableDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_contributor_insights_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), IndexName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_contributor_insights_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), IndexName = structure(logical(0), tags = list(type = "string")), ContributorInsightsRuleList = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ContributorInsightsStatus = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp")), FailureException = structure(list(ExceptionName = structure(logical(0), tags = list(type = "string")), ExceptionDescription = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_endpoints_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_endpoints_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Endpoints = structure(list(structure(list(Address = structure(logical(0), tags = list(type = "string")), CachePeriodInMinutes = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_export_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ExportArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_export_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ExportDescription = structure(list(ExportArn = structure(logical(0), tags = list(type = "string")), ExportStatus = structure(logical(0), tags = list(type = "string")), StartTime = structure(logical(0), tags = list(type = "timestamp")), EndTime = structure(logical(0), tags = list(type = "timestamp")), ExportManifest = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), ExportTime = structure(logical(0), tags = list(type = "timestamp")), ClientToken = structure(logical(0), tags = list(type = "string")), S3Bucket = structure(logical(0), tags = list(type = "string")), S3BucketOwner = structure(logical(0), tags = list(type = "string")), S3Prefix = structure(logical(0), tags = list(type = "string")), S3SseAlgorithm = structure(logical(0), tags = list(type = "string")), S3SseKmsKeyId = structure(logical(0), tags = list(type = "string")), FailureCode = structure(logical(0), tags = list(type = "string")), FailureMessage = structure(logical(0), tags = list(type = "string")), ExportFormat = structure(logical(0), tags = list(type = "string")), BilledSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_global_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_global_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableDescription = structure(list(ReplicationGroup = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalTableArn = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), GlobalTableStatus = structure(logical(0), tags = list(type = "string")), GlobalTableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_global_table_settings_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_global_table_settings_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string")), ReplicaSettings = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaBillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ReplicaProvisionedReadCapacityUnits = structure(logical(0), tags = list(type = "long")), ReplicaProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaProvisionedWriteCapacityUnits = structure(logical(0), tags = list(type = "long")), ReplicaProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaGlobalSecondaryIndexSettings = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), IndexStatus = structure(logical(0), tags = list(type = "string")), ProvisionedReadCapacityUnits = structure(logical(0), tags = list(type = "long")), ProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedWriteCapacityUnits = structure(logical(0), tags = list(type = "long")), ProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_import_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ImportArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_import_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ImportTableDescription = structure(list(ImportArn = structure(logical(0), tags = list(type = "string")), ImportStatus = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), ClientToken = structure(logical(0), tags = list(type = "string")), S3BucketSource = structure(list(S3BucketOwner = structure(logical(0), tags = list(type = "string")), S3Bucket = structure(logical(0), tags = list(type = "string")), S3KeyPrefix = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), ErrorCount = structure(logical(0), tags = list(type = "long")), CloudWatchLogGroupArn = structure(logical(0), tags = list(type = "string")), InputFormat = structure(logical(0), tags = list(type = "string")), InputFormatOptions = structure(list(Csv = structure(list(Delimiter = structure(logical(0), tags = list(type = "string")), HeaderList = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure")), InputCompressionType = structure(logical(0), tags = list(type = "string")), TableCreationParameters = structure(list(TableName = structure(logical(0), tags = list(type = "string")), AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), BillingMode = structure(logical(0), tags = list(type = "string")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), SSESpecification = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), StartTime = structure(logical(0), tags = list(type = "timestamp")), EndTime = structure(logical(0), tags = list(type = "timestamp")), ProcessedSizeBytes = structure(logical(0), tags = list(type = "long")), ProcessedItemCount = structure(logical(0), tags = list(type = "long")), ImportedItemCount = structure(logical(0), tags = list(type = "long")), FailureCode = structure(logical(0), tags = list(type = "string")), FailureMessage = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_kinesis_streaming_destination_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_kinesis_streaming_destination_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), KinesisDataStreamDestinations = structure(list(structure(list(StreamArn = structure(logical(0), tags = list(type = "string")), DestinationStatus = structure(logical(0), tags = list(type = "string")), DestinationStatusDescription = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_limits_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_limits_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AccountMaxReadCapacityUnits = structure(logical(0), tags = list(type = "long")), AccountMaxWriteCapacityUnits = structure(logical(0), tags = list(type = "long")), TableMaxReadCapacityUnits = structure(logical(0), tags = list(type = "long")), TableMaxWriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Table = structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableStatus = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), BillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexStatus = structure(logical(0), tags = list(type = "string")), Backfilling = structure(logical(0), tags = list(type = "boolean")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), LatestStreamLabel = structure(logical(0), tags = list(type = "string")), LatestStreamArn = structure(logical(0), tags = list(type = "string")), GlobalTableVersion = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), RestoreSummary = structure(list(SourceBackupArn = structure(logical(0), tags = list(type = "string")), SourceTableArn = structure(logical(0), tags = list(type = "string")), RestoreDateTime = structure(logical(0), tags = list(type = "timestamp")), RestoreInProgress = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ArchivalSummary = structure(list(ArchivalDateTime = structure(logical(0), tags = list(type = "timestamp")), ArchivalReason = structure(logical(0), tags = list(type = "string")), ArchivalBackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_table_replica_auto_scaling_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_table_replica_auto_scaling_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableAutoScalingDescription = structure(list(TableName = structure(logical(0), tags = list(type = "string")), TableStatus = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), IndexStatus = structure(logical(0), tags = list(type = "string")), ProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_time_to_live_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$describe_time_to_live_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TimeToLiveDescription = structure(list(TimeToLiveStatus = structure(logical(0), tags = list(type = "string")), AttributeName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$disable_kinesis_streaming_destination_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), StreamArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$disable_kinesis_streaming_destination_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), StreamArn = structure(logical(0), tags = list(type = "string")), DestinationStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$enable_kinesis_streaming_destination_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), StreamArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$enable_kinesis_streaming_destination_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), StreamArn = structure(logical(0), tags = list(type = "string")), DestinationStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$execute_statement_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Statement = structure(logical(0), tags = list(type = "string")), Parameters = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), ConsistentRead = structure(logical(0), tags = list(type = "boolean")), NextToken = structure(logical(0), tags = list(type = "string")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$execute_statement_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Items = structure(list(structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string")), ConsumedCapacity = structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), LastEvaluatedKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$execute_transaction_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TransactStatements = structure(list(structure(list(Statement = structure(logical(0), tags = list(type = "string")), Parameters = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), ClientRequestToken = structure(logical(0), tags = list(idempotencyToken = TRUE, type = "string")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$execute_transaction_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Responses = structure(list(structure(list(Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list")), ConsumedCapacity = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$export_table_to_point_in_time_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableArn = structure(logical(0), tags = list(type = "string")), ExportTime = structure(logical(0), tags = list(type = "timestamp")), ClientToken = structure(logical(0), tags = list(idempotencyToken = TRUE, type = "string")), S3Bucket = structure(logical(0), tags = list(type = "string")), S3BucketOwner = structure(logical(0), tags = list(type = "string")), S3Prefix = structure(logical(0), tags = list(type = "string")), S3SseAlgorithm = structure(logical(0), tags = list(type = "string")), S3SseKmsKeyId = structure(logical(0), tags = list(type = "string")), ExportFormat = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$export_table_to_point_in_time_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ExportDescription = structure(list(ExportArn = structure(logical(0), tags = list(type = "string")), ExportStatus = structure(logical(0), tags = list(type = "string")), StartTime = structure(logical(0), tags = list(type = "timestamp")), EndTime = structure(logical(0), tags = list(type = "timestamp")), ExportManifest = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), ExportTime = structure(logical(0), tags = list(type = "timestamp")), ClientToken = structure(logical(0), tags = list(type = "string")), S3Bucket = structure(logical(0), tags = list(type = "string")), S3BucketOwner = structure(logical(0), tags = list(type = "string")), S3Prefix = structure(logical(0), tags = list(type = "string")), S3SseAlgorithm = structure(logical(0), tags = list(type = "string")), S3SseKmsKeyId = structure(logical(0), tags = list(type = "string")), FailureCode = structure(logical(0), tags = list(type = "string")), FailureMessage = structure(logical(0), tags = list(type = "string")), ExportFormat = structure(logical(0), tags = list(type = "string")), BilledSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$get_item_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), AttributesToGet = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ConsistentRead = structure(logical(0), tags = list(type = "boolean")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), ProjectionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$get_item_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsumedCapacity = structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$import_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ClientToken = structure(logical(0), tags = list(idempotencyToken = TRUE, type = "string")), S3BucketSource = structure(list(S3BucketOwner = structure(logical(0), tags = list(type = "string")), S3Bucket = structure(logical(0), tags = list(type = "string")), S3KeyPrefix = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), InputFormat = structure(logical(0), tags = list(type = "string")), InputFormatOptions = structure(list(Csv = structure(list(Delimiter = structure(logical(0), tags = list(type = "string")), HeaderList = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure")), InputCompressionType = structure(logical(0), tags = list(type = "string")), TableCreationParameters = structure(list(TableName = structure(logical(0), tags = list(type = "string")), AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), BillingMode = structure(logical(0), tags = list(type = "string")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), SSESpecification = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$import_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ImportTableDescription = structure(list(ImportArn = structure(logical(0), tags = list(type = "string")), ImportStatus = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), ClientToken = structure(logical(0), tags = list(type = "string")), S3BucketSource = structure(list(S3BucketOwner = structure(logical(0), tags = list(type = "string")), S3Bucket = structure(logical(0), tags = list(type = "string")), S3KeyPrefix = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), ErrorCount = structure(logical(0), tags = list(type = "long")), CloudWatchLogGroupArn = structure(logical(0), tags = list(type = "string")), InputFormat = structure(logical(0), tags = list(type = "string")), InputFormatOptions = structure(list(Csv = structure(list(Delimiter = structure(logical(0), tags = list(type = "string")), HeaderList = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure")), InputCompressionType = structure(logical(0), tags = list(type = "string")), TableCreationParameters = structure(list(TableName = structure(logical(0), tags = list(type = "string")), AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), BillingMode = structure(logical(0), tags = list(type = "string")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), SSESpecification = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), StartTime = structure(logical(0), tags = list(type = "timestamp")), EndTime = structure(logical(0), tags = list(type = "timestamp")), ProcessedSizeBytes = structure(logical(0), tags = list(type = "long")), ProcessedItemCount = structure(logical(0), tags = list(type = "long")), ImportedItemCount = structure(logical(0), tags = list(type = "long")), FailureCode = structure(logical(0), tags = list(type = "string")), FailureMessage = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_backups_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer")), TimeRangeLowerBound = structure(logical(0), tags = list(type = "timestamp")), TimeRangeUpperBound = structure(logical(0), tags = list(type = "timestamp")), ExclusiveStartBackupArn = structure(logical(0), tags = list(type = "string")), BackupType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_backups_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupSummaries = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), BackupArn = structure(logical(0), tags = list(type = "string")), BackupName = structure(logical(0), tags = list(type = "string")), BackupCreationDateTime = structure(logical(0), tags = list(type = "timestamp")), BackupExpiryDateTime = structure(logical(0), tags = list(type = "timestamp")), BackupStatus = structure(logical(0), tags = list(type = "string")), BackupType = structure(logical(0), tags = list(type = "string")), BackupSizeBytes = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "list")), LastEvaluatedBackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_contributor_insights_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_contributor_insights_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ContributorInsightsSummaries = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), IndexName = structure(logical(0), tags = list(type = "string")), ContributorInsightsStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_exports_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableArn = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_exports_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ExportSummaries = structure(list(structure(list(ExportArn = structure(logical(0), tags = list(type = "string")), ExportStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_global_tables_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ExclusiveStartGlobalTableName = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer")), RegionName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_global_tables_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTables = structure(list(structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string")), ReplicationGroup = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list")), LastEvaluatedGlobalTableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_imports_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableArn = structure(logical(0), tags = list(type = "string")), PageSize = structure(logical(0), tags = list(type = "integer")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_imports_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ImportSummaryList = structure(list(structure(list(ImportArn = structure(logical(0), tags = list(type = "string")), ImportStatus = structure(logical(0), tags = list(type = "string")), TableArn = structure(logical(0), tags = list(type = "string")), S3BucketSource = structure(list(S3BucketOwner = structure(logical(0), tags = list(type = "string")), S3Bucket = structure(logical(0), tags = list(type = "string")), S3KeyPrefix = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), CloudWatchLogGroupArn = structure(logical(0), tags = list(type = "string")), InputFormat = structure(logical(0), tags = list(type = "string")), StartTime = structure(logical(0), tags = list(type = "timestamp")), EndTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_tables_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ExclusiveStartTableName = structure(logical(0), tags = list(type = "string")), Limit = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_tables_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), LastEvaluatedTableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_tags_of_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$list_tags_of_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$put_item_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), Expected = structure(list(structure(list(Value = structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), Exists = structure(logical(0), tags = list(type = "boolean")), ComparisonOperator = structure(logical(0), tags = list(type = "string")), AttributeValueList = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValues = structure(logical(0), tags = list(type = "string")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), ReturnItemCollectionMetrics = structure(logical(0), tags = list(type = "string")), ConditionalOperator = structure(logical(0), tags = list(type = "string")), ConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$put_item_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Attributes = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsumedCapacity = structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), ItemCollectionMetrics = structure(list(ItemCollectionKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), SizeEstimateRangeGB = structure(list(structure(logical(0), tags = list(type = "double"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$query_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), IndexName = structure(logical(0), tags = list(type = "string")), Select = structure(logical(0), tags = list(type = "string")), AttributesToGet = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), Limit = structure(logical(0), tags = list(type = "integer")), ConsistentRead = structure(logical(0), tags = list(type = "boolean")), KeyConditions = structure(list(structure(list(AttributeValueList = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), ComparisonOperator = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "map")), QueryFilter = structure(list(structure(list(AttributeValueList = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), ComparisonOperator = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "map")), ConditionalOperator = structure(logical(0), tags = list(type = "string")), ScanIndexForward = structure(logical(0), tags = list(type = "boolean")), ExclusiveStartKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), ProjectionExpression = structure(logical(0), tags = list(type = "string")), FilterExpression = structure(logical(0), tags = list(type = "string")), KeyConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$query_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Items = structure(list(structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "list")), Count = structure(logical(0), tags = list(type = "integer")), ScannedCount = structure(logical(0), tags = list(type = "integer")), LastEvaluatedKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsumedCapacity = structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$restore_table_from_backup_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TargetTableName = structure(logical(0), tags = list(type = "string")), BackupArn = structure(logical(0), tags = list(type = "string")), BillingModeOverride = structure(logical(0), tags = list(type = "string")), GlobalSecondaryIndexOverride = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), LocalSecondaryIndexOverride = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), SSESpecificationOverride = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$restore_table_from_backup_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableDescription = structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableStatus = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), BillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexStatus = structure(logical(0), tags = list(type = "string")), Backfilling = structure(logical(0), tags = list(type = "boolean")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), LatestStreamLabel = structure(logical(0), tags = list(type = "string")), LatestStreamArn = structure(logical(0), tags = list(type = "string")), GlobalTableVersion = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), RestoreSummary = structure(list(SourceBackupArn = structure(logical(0), tags = list(type = "string")), SourceTableArn = structure(logical(0), tags = list(type = "string")), RestoreDateTime = structure(logical(0), tags = list(type = "timestamp")), RestoreInProgress = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ArchivalSummary = structure(list(ArchivalDateTime = structure(logical(0), tags = list(type = "timestamp")), ArchivalReason = structure(logical(0), tags = list(type = "string")), ArchivalBackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$restore_table_to_point_in_time_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(SourceTableArn = structure(logical(0), tags = list(type = "string")), SourceTableName = structure(logical(0), tags = list(type = "string")), TargetTableName = structure(logical(0), tags = list(type = "string")), UseLatestRestorableTime = structure(logical(0), tags = list(type = "boolean")), RestoreDateTime = structure(logical(0), tags = list(type = "timestamp")), BillingModeOverride = structure(logical(0), tags = list(type = "string")), GlobalSecondaryIndexOverride = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), LocalSecondaryIndexOverride = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), SSESpecificationOverride = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$restore_table_to_point_in_time_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableDescription = structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableStatus = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), BillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexStatus = structure(logical(0), tags = list(type = "string")), Backfilling = structure(logical(0), tags = list(type = "boolean")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), LatestStreamLabel = structure(logical(0), tags = list(type = "string")), LatestStreamArn = structure(logical(0), tags = list(type = "string")), GlobalTableVersion = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), RestoreSummary = structure(list(SourceBackupArn = structure(logical(0), tags = list(type = "string")), SourceTableArn = structure(logical(0), tags = list(type = "string")), RestoreDateTime = structure(logical(0), tags = list(type = "timestamp")), RestoreInProgress = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ArchivalSummary = structure(list(ArchivalDateTime = structure(logical(0), tags = list(type = "timestamp")), ArchivalReason = structure(logical(0), tags = list(type = "string")), ArchivalBackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$scan_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), IndexName = structure(logical(0), tags = list(type = "string")), AttributesToGet = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), Limit = structure(logical(0), tags = list(type = "integer")), Select = structure(logical(0), tags = list(type = "string")), ScanFilter = structure(list(structure(list(AttributeValueList = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), ComparisonOperator = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "map")), ConditionalOperator = structure(logical(0), tags = list(type = "string")), ExclusiveStartKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), TotalSegments = structure(logical(0), tags = list(type = "integer")), Segment = structure(logical(0), tags = list(type = "integer")), ProjectionExpression = structure(logical(0), tags = list(type = "string")), FilterExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsistentRead = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$scan_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Items = structure(list(structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "list")), Count = structure(logical(0), tags = list(type = "integer")), ScannedCount = structure(logical(0), tags = list(type = "integer")), LastEvaluatedKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsumedCapacity = structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$tag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$tag_resource_output <- function(...) {
list()
}
.dynamodb$transact_get_items_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TransactItems = structure(list(structure(list(Get = structure(list(Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), TableName = structure(logical(0), tags = list(type = "string")), ProjectionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$transact_get_items_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ConsumedCapacity = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list")), Responses = structure(list(structure(list(Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$transact_write_items_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TransactItems = structure(list(structure(list(ConditionCheck = structure(list(Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), TableName = structure(logical(0), tags = list(type = "string")), ConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Put = structure(list(Item = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), TableName = structure(logical(0), tags = list(type = "string")), ConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Delete = structure(list(Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), TableName = structure(logical(0), tags = list(type = "string")), ConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Update = structure(list(Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), UpdateExpression = structure(logical(0), tags = list(type = "string")), TableName = structure(logical(0), tags = list(type = "string")), ConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), ReturnItemCollectionMetrics = structure(logical(0), tags = list(type = "string")), ClientRequestToken = structure(logical(0), tags = list(idempotencyToken = TRUE, type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$transact_write_items_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ConsumedCapacity = structure(list(structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure"))), tags = list(type = "list")), ItemCollectionMetrics = structure(list(structure(list(structure(list(ItemCollectionKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), SizeEstimateRangeGB = structure(list(structure(logical(0), tags = list(type = "double"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "map"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$untag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), TagKeys = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$untag_resource_output <- function(...) {
list()
}
.dynamodb$update_continuous_backups_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), PointInTimeRecoverySpecification = structure(list(PointInTimeRecoveryEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_continuous_backups_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ContinuousBackupsDescription = structure(list(ContinuousBackupsStatus = structure(logical(0), tags = list(type = "string")), PointInTimeRecoveryDescription = structure(list(PointInTimeRecoveryStatus = structure(logical(0), tags = list(type = "string")), EarliestRestorableDateTime = structure(logical(0), tags = list(type = "timestamp")), LatestRestorableDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_contributor_insights_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), IndexName = structure(logical(0), tags = list(type = "string")), ContributorInsightsAction = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_contributor_insights_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), IndexName = structure(logical(0), tags = list(type = "string")), ContributorInsightsStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_global_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string")), ReplicaUpdates = structure(list(structure(list(Create = structure(list(RegionName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Delete = structure(list(RegionName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_global_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableDescription = structure(list(ReplicationGroup = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalTableArn = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), GlobalTableStatus = structure(logical(0), tags = list(type = "string")), GlobalTableName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_global_table_settings_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string")), GlobalTableBillingMode = structure(logical(0), tags = list(type = "string")), GlobalTableProvisionedWriteCapacityUnits = structure(logical(0), tags = list(type = "long")), GlobalTableProvisionedWriteCapacityAutoScalingSettingsUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure")), GlobalTableGlobalSecondaryIndexSettingsUpdate = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedWriteCapacityUnits = structure(logical(0), tags = list(type = "long")), ProvisionedWriteCapacityAutoScalingSettingsUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaSettingsUpdate = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaProvisionedReadCapacityUnits = structure(logical(0), tags = list(type = "long")), ReplicaProvisionedReadCapacityAutoScalingSettingsUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure")), ReplicaGlobalSecondaryIndexSettingsUpdate = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedReadCapacityUnits = structure(logical(0), tags = list(type = "long")), ProvisionedReadCapacityAutoScalingSettingsUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaTableClass = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_global_table_settings_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalTableName = structure(logical(0), tags = list(type = "string")), ReplicaSettings = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaBillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ReplicaProvisionedReadCapacityUnits = structure(logical(0), tags = list(type = "long")), ReplicaProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaProvisionedWriteCapacityUnits = structure(logical(0), tags = list(type = "long")), ReplicaProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaGlobalSecondaryIndexSettings = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), IndexStatus = structure(logical(0), tags = list(type = "string")), ProvisionedReadCapacityUnits = structure(logical(0), tags = list(type = "long")), ProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedWriteCapacityUnits = structure(logical(0), tags = list(type = "long")), ProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_item_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), Key = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), AttributeUpdates = structure(list(structure(list(Value = structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), Action = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "map")), Expected = structure(list(structure(list(Value = structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), Exists = structure(logical(0), tags = list(type = "boolean")), ComparisonOperator = structure(logical(0), tags = list(type = "string")), AttributeValueList = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "map")), ConditionalOperator = structure(logical(0), tags = list(type = "string")), ReturnValues = structure(logical(0), tags = list(type = "string")), ReturnConsumedCapacity = structure(logical(0), tags = list(type = "string")), ReturnItemCollectionMetrics = structure(logical(0), tags = list(type = "string")), UpdateExpression = structure(logical(0), tags = list(type = "string")), ConditionExpression = structure(logical(0), tags = list(type = "string")), ExpressionAttributeNames = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "map")), ExpressionAttributeValues = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ReturnValuesOnConditionCheckFailure = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_item_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Attributes = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), ConsumedCapacity = structure(list(TableName = structure(logical(0), tags = list(type = "string")), CapacityUnits = structure(logical(0), tags = list(type = "double")), ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), Table = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map")), GlobalSecondaryIndexes = structure(list(structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "double")), WriteCapacityUnits = structure(logical(0), tags = list(type = "double")), CapacityUnits = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "map"))), tags = list(type = "structure")), ItemCollectionMetrics = structure(list(ItemCollectionKey = structure(list(structure(list(S = structure(logical(0), tags = list(type = "string")), N = structure(logical(0), tags = list(type = "string")), B = structure(logical(0), tags = list(type = "blob")), SS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NS = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), BS = structure(list(structure(logical(0), tags = list(type = "blob"))), tags = list(type = "list")), M = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "map")), L = structure(list(structure(logical(0), tags = list(type = "structure"))), tags = list(type = "list")), "NULL" = structure(logical(0), tags = list(type = "boolean")), BOOL = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "map")), SizeEstimateRangeGB = structure(list(structure(logical(0), tags = list(type = "double"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_table_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), BillingMode = structure(logical(0), tags = list(type = "string")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexUpdates = structure(list(structure(list(Update = structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure")), Create = structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedThroughput = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure")), Delete = structure(list(IndexName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), SSESpecification = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), ReplicaUpdates = structure(list(structure(list(Create = structure(list(RegionName = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), TableClassOverride = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Update = structure(list(RegionName = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), TableClassOverride = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Delete = structure(list(RegionName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), TableClass = structure(logical(0), tags = list(type = "string")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_table_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableDescription = structure(list(AttributeDefinitions = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), AttributeType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), TableStatus = structure(logical(0), tags = list(type = "string")), CreationDateTime = structure(logical(0), tags = list(type = "timestamp")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), TableSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), TableArn = structure(logical(0), tags = list(type = "string")), TableId = structure(logical(0), tags = list(type = "string")), BillingModeSummary = structure(list(BillingMode = structure(logical(0), tags = list(type = "string")), LastUpdateToPayPerRequestDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), LocalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), KeySchema = structure(list(structure(list(AttributeName = structure(logical(0), tags = list(type = "string")), KeyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), Projection = structure(list(ProjectionType = structure(logical(0), tags = list(type = "string")), NonKeyAttributes = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure")), IndexStatus = structure(logical(0), tags = list(type = "string")), Backfilling = structure(logical(0), tags = list(type = "boolean")), ProvisionedThroughput = structure(list(LastIncreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), LastDecreaseDateTime = structure(logical(0), tags = list(type = "timestamp")), NumberOfDecreasesToday = structure(logical(0), tags = list(type = "long")), ReadCapacityUnits = structure(logical(0), tags = list(type = "long")), WriteCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), IndexSizeBytes = structure(logical(0), tags = list(type = "long")), ItemCount = structure(logical(0), tags = list(type = "long")), IndexArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), StreamSpecification = structure(list(StreamEnabled = structure(logical(0), tags = list(type = "boolean")), StreamViewType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), LatestStreamLabel = structure(logical(0), tags = list(type = "string")), LatestStreamArn = structure(logical(0), tags = list(type = "string")), GlobalTableVersion = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaStatus = structure(logical(0), tags = list(type = "string")), ReplicaStatusDescription = structure(logical(0), tags = list(type = "string")), ReplicaStatusPercentProgress = structure(logical(0), tags = list(type = "string")), KMSMasterKeyId = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedThroughputOverride = structure(list(ReadCapacityUnits = structure(logical(0), tags = list(type = "long"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaInaccessibleDateTime = structure(logical(0), tags = list(type = "timestamp")), ReplicaTableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), RestoreSummary = structure(list(SourceBackupArn = structure(logical(0), tags = list(type = "string")), SourceTableArn = structure(logical(0), tags = list(type = "string")), RestoreDateTime = structure(logical(0), tags = list(type = "timestamp")), RestoreInProgress = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), SSEDescription = structure(list(Status = structure(logical(0), tags = list(type = "string")), SSEType = structure(logical(0), tags = list(type = "string")), KMSMasterKeyArn = structure(logical(0), tags = list(type = "string")), InaccessibleEncryptionDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), ArchivalSummary = structure(list(ArchivalDateTime = structure(logical(0), tags = list(type = "timestamp")), ArchivalReason = structure(logical(0), tags = list(type = "string")), ArchivalBackupArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), TableClassSummary = structure(list(TableClass = structure(logical(0), tags = list(type = "string")), LastUpdateDateTime = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure")), DeletionProtectionEnabled = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_table_replica_auto_scaling_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(GlobalSecondaryIndexUpdates = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedWriteCapacityAutoScalingUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), TableName = structure(logical(0), tags = list(type = "string")), ProvisionedWriteCapacityAutoScalingUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure")), ReplicaUpdates = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), ReplicaGlobalSecondaryIndexUpdates = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), ProvisionedReadCapacityAutoScalingUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaProvisionedReadCapacityAutoScalingUpdate = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicyUpdate = structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_table_replica_auto_scaling_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableAutoScalingDescription = structure(list(TableName = structure(logical(0), tags = list(type = "string")), TableStatus = structure(logical(0), tags = list(type = "string")), Replicas = structure(list(structure(list(RegionName = structure(logical(0), tags = list(type = "string")), GlobalSecondaryIndexes = structure(list(structure(list(IndexName = structure(logical(0), tags = list(type = "string")), IndexStatus = structure(logical(0), tags = list(type = "string")), ProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list")), ReplicaProvisionedReadCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaProvisionedWriteCapacityAutoScalingSettings = structure(list(MinimumUnits = structure(logical(0), tags = list(type = "long")), MaximumUnits = structure(logical(0), tags = list(type = "long")), AutoScalingDisabled = structure(logical(0), tags = list(type = "boolean")), AutoScalingRoleArn = structure(logical(0), tags = list(type = "string")), ScalingPolicies = structure(list(structure(list(PolicyName = structure(logical(0), tags = list(type = "string")), TargetTrackingScalingPolicyConfiguration = structure(list(DisableScaleIn = structure(logical(0), tags = list(type = "boolean")), ScaleInCooldown = structure(logical(0), tags = list(type = "integer")), ScaleOutCooldown = structure(logical(0), tags = list(type = "integer")), TargetValue = structure(logical(0), tags = list(type = "double"))), tags = list(type = "structure"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure")), ReplicaStatus = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_time_to_live_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TableName = structure(logical(0), tags = list(type = "string")), TimeToLiveSpecification = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), AttributeName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.dynamodb$update_time_to_live_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TimeToLiveSpecification = structure(list(Enabled = structure(logical(0), tags = list(type = "boolean")), AttributeName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
|
13f786418a273afeeee093fa55e9d0e13bebc5ba | 7e02e90d63b317ce890145788f512a5f0aa022d9 | /inst/examples/examples_pars.R | 453e18f0c228157e7c8c6d44a5d8962bc76b2e7f | [] | no_license | mrjoh3/d3wordcloud | 86cfaccd5314c6c132f3732fd9c27762c18dbcad | 81c52a2e5249dc8f96a63e59d88bd7085e0ed7f0 | refs/heads/master | 2021-01-18T07:15:07.803195 | 2016-04-28T09:44:25 | 2016-04-28T09:44:25 | 56,191,178 | 1 | 0 | null | 2016-04-13T22:49:47 | 2016-04-13T22:49:47 | null | UTF-8 | R | false | false | 2,805 | r | examples_pars.R | #' ---
#' title: "R package: d3wordcloud"
#' author: "Joshua Kunst"
#' output:
#' html_document:
#' theme: journal
#' toc: yes
#' ---
#' # Installation
#+ echo=FALSE
#' devtools::install_github("jbkunst/d3wordcloud")
#' # First Use
library("d3wordcloud")
words <- c("I", "love", "this", "package", "but", "I", "don't", "like", "use", "wordclouds!", "voila")
freqs <- rev(seq(length(words))) + 10
d3wordcloud(words, freqs)
#' # Requeriments for the Examples
#+ warnings=FALSE, message=FALSE
library("d3wordcloud")
library("tm")
library("dplyr")
library("viridis")
data(crude)
crude <- tm_map(crude, removePunctuation)
crude <- tm_map(crude, function(x){ removeWords(x, stopwords()) })
tdm <- TermDocumentMatrix(crude)
m <- as.matrix(tdm)
v <- sort(rowSums(m), decreasing = TRUE)
d <- data.frame(word = names(v), freq = v)
d <- d %>% tbl_df()
d <- d %>% arrange(desc(freq))
d <- d %>% head(100)
words <- d$word
freqs <- d$freq
#' # Examples
#' ## Simple
d3wordcloud(words, freqs)
#' ## Colors
d3wordcloud(words, freqs, colors = "#FFAA00")
#' Each word has its own color, *only* hedecimal format!
#'
set.seed(123)
colors <- sample(substr(rainbow(length(words)), 0 , 7))
d3wordcloud(words, freqs, colors = colors)
#' We can add a gradient between colors *acording the freq (size)*
d3wordcloud(words, freqs, colors = c("#FF0000", "#00FF00", "#0000FF"))
d3wordcloud(words, freqs, colors = substr(viridis(10, 1), 0 , 7))
#' ## Fonts
#' Only works when you have a web connection and it works only with fonts on https://www.google.com/fonts
#' https://www.google.com/fonts/specimen/Erica+One
d3wordcloud(words, freqs, font = "Erica One", padding = 5)
#' https://www.google.com/fonts/specimen/Anton
d3wordcloud(words, freqs, font = "Anton", padding = 7)
#' ## Spiral
d3wordcloud(words,freqs, spiral = "archimedean") # default
d3wordcloud(words,freqs, spiral = "rectangular")
#' ## Scale Size
d3wordcloud(words,freqs, size.scale = "linear") # default
d3wordcloud(words,freqs, size.scale = "log")
d3wordcloud(words,freqs, size.scale = "sqrt")
#' ## Scale Color
#' Just work only when you put some colors with length(colors) != length(words)
#'
#' The differences between colors are minimal but exists!
colors <- substr(viridis::viridis(3), 0 , 7)
colors
d3wordcloud(words,freqs, colors = colors, color.scale = "linear") # default
d3wordcloud(words,freqs, colors = colors, color.scale = "log")
d3wordcloud(words,freqs, colors = colors, color.scale = "sqrt")
#' ## Rotation
d3wordcloud(words, freqs, rotate.min = 0, rotate.max = 0)
d3wordcloud(words, freqs, rotate.min = 45, rotate.max = 45)
d3wordcloud(words, freqs, rotate.min = -180, rotate.max = 180)
#' ## Tooltips
d3wordcloud(words, freqs, tooltip = TRUE)
#' ## Change size
d3wordcloud(words, freqs, rangesizefont = c(10, 20))
|
c9c9511e1f00c3978176e6a50d57c171115cfc0f | b8fc18056d960ddeb3861d961f5534f05400261d | /functions/GMfreq_updated.R | 3bda0b6e3dd7c9058a4c7ae9ed943b0c8a0dafed | [] | no_license | jpmtavares/vaRiants-annotation | 8532599a9a8390e3e9e7fa0e80697aa1e9eb39cb | fc58d61ed31c4a4a6511fb75107a51da8d6bc374 | refs/heads/master | 2021-01-20T09:19:57.972829 | 2018-12-13T13:14:19 | 2018-12-13T13:14:19 | 90,236,524 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,323 | r | GMfreq_updated.R | ##########################################################
# #
# GenoMed Frequency #
# #
##########################################################
GMfrequency<-function(GM_freq,path){
#get date of the last todas_ file
updated_todas<-max(str_extract(list.files(paste(path,"/Archive/VCF/",sep=""), pattern = "todas_"),
"[0-9]{8}"))
#get date of the last GMfreq file
updated_GMfreq<-max(str_extract(list.files("./sources/", pattern = "GM_freq"),
"[0-9]{8}"))
if(updated_todas>updated_GMfreq){
#___________________________________________________________
# import todas.vcf.gz
#___________________________________________________________
todas<-read.vcf(paste(path,"/Archive/VCF/todas_",updated_todas,".vcf.gz",sep=""))
todas<-todas$vcf
#___________________________________________________________
# no. of samples and alleles
#___________________________________________________________
nsamples<-todas %>%
ncol()-9
nalleles<-nsamples*2
#___________________________________________________________
# get number of mutated alleles per sample
#___________________________________________________________
alleles<-todas %>%
mutate_at(vars(-CHROM, -POS, -ID, -REF, -ALT, -QUAL, -FILTER, -INFO, -FORMAT),
funs(replace(., grepl("0/", .), 1))) %>%
mutate_at(vars(-CHROM, -POS, -ID, -REF, -ALT, -QUAL, -FILTER, -INFO, -FORMAT),
funs(replace(., grepl("1/", .), 2))) %>%
select(-CHROM, -POS, -ID, -REF, -ALT, -QUAL, -FILTER, -INFO, -FORMAT) %>%
replace(., is.na(.), 0) %>%
mutate_if(sapply(., is.character), as.numeric)
#___________________________________________________________
# get number of mutated samples, homozygous and MAF
#___________________________________________________________
inHouse<-alleles %>%
transmute(inHouse_samples=apply(alleles, 1, function(x) length(which(x>0)))) %>%
mutate(inHouse_homozygous=apply(alleles, 1, function(x) length(which(x==2)))) %>%
mutate(inHouse_maf=rowSums(alleles)/nalleles)
#___________________________________________________________
# get output table
#___________________________________________________________
freq<-data.frame(todas[,c("CHROM", "POS", "REF", "ALT")],
inHouse) %>%
mutate(CHROM=paste("chr",CHROM, sep="")) %>%
set_names(c("Chr","Position","Ref","Alt","inHouse_samples", "inHouse_homozygous", "inHouse_MAF"))
#___________________________________________________________
# write.table
#___________________________________________________________
output<-paste("GM_freq",format(Sys.time(), "%Y%m%d"),".txt",sep="")
write.table(freq, paste("./sources/",output,sep=""), row.names=F, col.names=T,
quote=F,sep="\t")
#___________________________________________________________
# save to bcbio_pipeline.Rdata
#___________________________________________________________
save(refSeqGenes,freq,file="./sources/bcbio_pipeline.Rdata")
}else{
freq<-GM_freq
}
return(freq)
} |
c0db9ce595f948659c315d4f8d8b1c3a652ce8f0 | 07448c8fc0a594be3b1ed086a87e63c8499b5353 | /plot3.R | 0f47c890d7661ee9a35f8b1563daf4ec5b44c1a9 | [] | no_license | davidliziyi/ExData_Plotting1 | 2752a3743a91cb1bf98e0d48fd95238680ef1081 | 8e27faf429d15ea484001172c0c5efbbb7b90071 | refs/heads/master | 2020-03-07T03:02:22.001258 | 2018-03-30T08:52:12 | 2018-03-30T08:52:12 | 127,224,250 | 0 | 0 | null | 2018-03-29T02:15:02 | 2018-03-29T02:15:01 | null | UTF-8 | R | false | false | 983 | r | plot3.R | library(data.table)
data <- fread("household_power_consumption.txt", sep = ";", na.strings = "?")[
, Date := as.Date(Date, "%d/%m/%Y")][
Date >= as.Date("2007-02-01") & Date <= as.Date("2007-02-02")][
, "DateTime" := as.POSIXct(paste(Date, Time),
format = "%Y-%m-%d %H:%M:%S",
tz = "UTC")][
, c("Date", "Time") := NULL]
with(data, plot(DateTime, Sub_metering_1, type = "n",
ylab = "Energy sub metering"))
with(data, lines(DateTime, Sub_metering_1, type = "l", col = "black"))
with(data, lines(DateTime, Sub_metering_2, type = "l", col = "red"))
with(data, lines(DateTime, Sub_metering_3, type = "l", col = "blue"))
with(data, legend("topright", legend = c("Sub_metering_1",
"Sub_metering_2",
"Sub_metering_3"),
col = c("black", "red", "blue"), lty = 1, cex = 0.5))
dev.copy(png, file = "plot3.png")
dev.off()
|
63cafd410107dd8a4d780a92d09d8be03006fc44 | 962e4bb67effdb987626e91017c8aa8d45b9128a | /cachematrix.R | 8aede38024eb79d7f36bb9e6ee67fd6ebcd6cdd9 | [] | no_license | ABHAY2000/ProgrammingAssignment2 | ba9396edd868623006e7064a840de1c04cd10496 | abf1e016c62b3dee0266a5042d3a3651d5f27aa0 | refs/heads/master | 2021-05-23T08:51:23.201445 | 2020-04-05T10:46:14 | 2020-04-05T10:46:14 | 253,207,404 | 0 | 0 | null | 2020-04-05T10:22:16 | 2020-04-05T10:22:15 | null | UTF-8 | R | false | false | 510 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
matrix(x)
}
## Write a short comment describing this function
## This function computes the inverse of the special "matrix" returned by makeCacheMatrix above.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
1/x
}
|
e3c7a0f11eaceed5dfaa46dae335bb56c7662848 | 9e01415c74ccf8ff485b20dc44bb85ad98ab3cd8 | /man/exp.loss.k.Rd | 8382c456a4c86b6cfb2bbf86774c1df1310640ba | [] | no_license | mst1g15/biasedcoin | 4846ca74b277f442026dd9a74008eae6cd4fb18c | 0728c0c9ec43075c0e965a73128c0066bd677c4b | refs/heads/master | 2020-04-22T03:54:41.755315 | 2019-11-25T21:58:08 | 2019-11-25T21:58:08 | 170,105,256 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,351 | rd | exp.loss.k.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/linear_nonmyop.R
\name{exp.loss.k}
\alias{exp.loss.k}
\title{Break down the expected future objective function by cases for every combination of:
1) future possible covariate
2) future possible treatment
Find a weighted average across all these cases}
\usage{
\method{exp}{loss.k}(z.now, t.now, zp, N, design, int, lossfunc, dyn = NULL,
...)
}
\arguments{
\item{z.now}{vector of covariate values for current unit}
\item{t.now}{treatment of current unit}
\item{zp}{vector of probabilities for each level of covariate z (needs to in the same order as all.z)}
\item{N}{natural number greater than 0 for horizon}
\item{design}{design matrix constructed for all units up until the current unit}
\item{int}{set to NULL if there are no interactions, set to T of there are interactions}
\item{lossfunc}{the objective function to minimize}
\item{dyn}{set to NULL of there are no dynamic covariates, set to T if there are dynamic covariates}
\item{...}{further arguments to be passed to <lossfunc>}
}
\value{
design matrix D
}
\description{
Break down the expected future objective function by cases for every combination of:
1) future possible covariate
2) future possible treatment
Find a weighted average across all these cases
}
|
bdabd2175e4f07f0eb6043db001eac9631b9f8a3 | 258917ad60335fcf744a7ece7b1f220e420c7050 | /scripts/R/algo_tests/sam_quantitative.R | 8bfd6077045699bb2b9f869d74416aed6ac780d9 | [] | no_license | whtbowers/TCGA-SARC_graphs | 67d5b3f99a581d41061de1f34fac698a626a0b34 | 0def2a5b80f65e3cc9172a3d7507023fd402f2eb | refs/heads/master | 2020-04-21T01:11:23.761625 | 2019-03-08T09:39:00 | 2019-03-08T09:39:00 | 169,216,606 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 195 | r | sam_quantitative.R | setwd("C:/Users/wbowers/Documents/tcga_replication_2/data")
library(samr)
#Quantitative example
quant.data <- read.csv(paste(path.package("samr"), "/excel/Quantitative.csv", sep = ""))
|
b69ec717360115a142e1aac24ca99ea523894998 | a68a3cf38cc6f08462bfa30f99f7c838af432743 | /plot-clmx01-firm-count.R | 5561957a2f08e48b07f023276ff360603b871e10 | [] | no_license | alexchinco/clmx01 | 32f540b57666c1e981f183f5f9398d23d081be1c | 33c76e76ad2f4d78082caba568681280b7a924c9 | refs/heads/master | 2021-01-13T01:45:36.106313 | 2013-05-04T16:09:57 | 2013-05-04T16:09:57 | 9,856,188 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,149 | r | plot-clmx01-firm-count.R |
## Prep workspace
rm(list=ls())
library(foreign)
library(grid)
library(plyr)
library(ggplot2)
library(tikzDevice)
library(reshape)
library(vars)
scl.str.DAT_DIR <- "~/Dropbox/research/trading_on_coincidences/data/"
scl.str.DAT_NAME <- "clmx01-observations-per-month.csv"
scl.str.FIG_DIR <- "~/Dropbox/research/trading_on_coincidences/figures/"
## Load CLMX01 firm count data
mat.dfm.CLMX01 <- read.csv(paste(scl.str.DAT_DIR, scl.str.DAT_NAME, sep = ""), stringsAsFactors = FALSE)
mat.dfm.CLMX01$t <- mat.dfm.CLMX01$year + (mat.dfm.CLMX01$month - 1)/12
mat.dfm.CLMX01 <- mat.dfm.CLMX01[, names(mat.dfm.CLMX01) %in% c("indAbrv", "obsPerMonth", "t")]
names(mat.dfm.CLMX01) <- c("ind", "N", "t")
## Plot firm count time series
mat.dfm.PLOT <- mat.dfm.CLMX01
theme_set(theme_bw())
scl.str.RAW_FILE <- 'clmx02-firm-count-per-industry'
scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='')
scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='')
scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='')
scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='')
scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='')
tikz(file = scl.str.TEX_FILE, height = 11, width = 24, standAlone=TRUE)
obj.gg2.PLOT <- ggplot()
obj.gg2.PLOT <- obj.gg2.PLOT + geom_path(data = mat.dfm.PLOT,
aes(x = t,
y = N,
group = ind
),
size = 1.25
)
obj.gg2.PLOT <- obj.gg2.PLOT + facet_wrap(~ind, ncol = 7)
obj.gg2.PLOT <- obj.gg2.PLOT + ylab('Number of Firms')
obj.gg2.PLOT <- obj.gg2.PLOT + xlab('')
obj.gg2.PLOT <- obj.gg2.PLOT + opts(legend.position = "none")
print(obj.gg2.PLOT)
dev.off()
system(paste('pdflatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE)
system(paste('convert -density 450', file.path(scl.str.PDF_FILE), ' ', file.path(scl.str.PNG_FILE)))
system(paste('mv ', scl.str.PNG_FILE, ' ', scl.str.FIG_DIR, sep = ''))
system(paste('rm ', scl.str.TEX_FILE, sep = ''))
system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = ''))
system(paste('rm ', scl.str.AUX_FILE, sep = ''))
system(paste('rm ', scl.str.LOG_FILE, sep = ''))
mat.dfm.PLOT <- ddply(mat.dfm.CLMX01,
c("t"),
function(X)sum(X$N)
)
names(mat.dfm.PLOT) <- c("t", "N")
asdasd;
theme_set(theme_bw())
scl.str.RAW_FILE <- 'clmx02-firm-count-total'
scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='')
scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='')
scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='')
scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='')
scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='')
tikz(file = scl.str.TEX_FILE, height = 11, width = 24, standAlone=TRUE)
obj.gg2.PLOT <- ggplot()
obj.gg2.PLOT <- obj.gg2.PLOT + geom_path(data = mat.dfm.PLOT,
aes(x = t,
y = N,
group = ind
),
size = 1.25
)
obj.gg2.PLOT <- obj.gg2.PLOT + facet_wrap(~variable, ncol = 7)
obj.gg2.PLOT <- obj.gg2.PLOT + ylab('Number of Firms')
obj.gg2.PLOT <- obj.gg2.PLOT + xlab('')
obj.gg2.PLOT <- obj.gg2.PLOT + opts(legend.position = "none")
print(obj.gg2.PLOT)
dev.off()
system(paste('pdflatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE)
system(paste('convert -density 450', file.path(scl.str.PDF_FILE), ' ', file.path(scl.str.PNG_FILE)))
system(paste('mv ', scl.str.PNG_FILE, ' ', scl.str.FIG_DIR, sep = ''))
system(paste('rm ', scl.str.TEX_FILE, sep = ''))
system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = ''))
system(paste('rm ', scl.str.AUX_FILE, sep = ''))
system(paste('rm ', scl.str.LOG_FILE, sep = ''))
|
7a27446f7784c9727fa25831153b632a9d60189f | f7546999748d00b74db8551ed65e02cc564f7f4f | /R/plotIdentifiableZone.R | 22ae93f3c4d161bc489bfb8bfa78c494113351ea | [] | no_license | cran/CHAT | b5887ac9eb97d1deace91cd638477e5d7bf56319 | 1819354a80335e6d92384002b90899b70c60719f | refs/heads/master | 2021-01-19T08:15:18.756721 | 2014-02-10T00:00:00 | 2014-02-10T00:00:00 | 19,303,604 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,318 | r | plotIdentifiableZone.R | plotIdentifiableZone <- function(nt,nb,add=FALSE,legend=TRUE,title=TRUE){
ps<-seq(0,1,by=0.002)
n<-length(ps)
na<-nt-nb
Nt<-nt*ps+2*(1-ps)
fla<-na*ps/Nt ## low limit of major allele, early
fha<-(na*ps+1-ps)/Nt ## high limit major allele, early
flb<-nb*ps/Nt ## low limit of minor allele, early
fhb<-(nb*ps+1-ps)/Nt ## high limit of minor allele, early
flp<-rep(0,n) ## low limit in aneuploid, late
fhp<-ps/Nt ## high limit in aneuploid, late
flpp<-rep(0,n) ## low limit in euploid
fhpp<-(1-ps)/Nt ## high limit in euploid
col.list=rep(rgb(0,100,0,alpha=30,maxColorValue=255),4)
if(!add){
par(mar=c(4,4,2,0.1),xpd=TRUE)
plot(0,0,cex=0,xlim=c(0,1),ylim=c(0,1),xlab='sAGP',ylab='SAF')
}
polygon(c(ps,ps[n:1]),c(fla,fha[n:1]),col=col.list[1],border=NA)
polygon(c(ps,ps[n:1]),c(flb,fhb[n:1]),col=col.list[2],border=NA)
polygon(c(ps,ps[n:1]),c(flp,fhp[n:1]),col=col.list[3],border=NA)
polygon(c(ps,ps[n:1]),c(flpp,fhpp[n:1]),col=col.list[4],border=NA)
col.borders=c('red2','orange3','dark green','deepskyblue')
if(legend){
legend(0.05,0.99,legend=c(expression(A[1]),expression(A[2]),'B','C'),lty=1,lwd=2,col=col.borders)
}
col.ccf=rgb(50,0,0,alpha=100,maxColorValue=255)
if(nt==2&nb==0){
polygon(c(0,0,1/3),c(0,0.5,1/3),col=col.ccf,border=NA)
}
delta=0.005
if(nt==3&nb==1){
xx=c(0,0,seq(0,0.5,by=0.002),seq(0.5,0,by=-0.002))
yy=c(0,0.5,1/(2+seq(0,0.5,by=0.002)),2*seq(0.5,0,by=-0.002)/(2+seq(0.5,0,by=-0.002)))
polygon(xx,yy,col=col.ccf,border=NA)
}
lines(ps+2*delta,fla+delta,col=col.borders[1],lwd=2)
lines(ps+2*delta,fha+delta,col=col.borders[1],lwd=2)
segments(2*delta,delta,2*delta,0.5+delta,col=col.borders[1],lwd=2)
lines(ps+delta,flb+delta/2,col=col.borders[2],lwd=2)
lines(ps+delta,fhb+delta/2,col=col.borders[2],lwd=2)
segments(delta,delta/2,delta,0.5+delta/2,col=col.borders[2],lwd=2)
lines(ps,flp-delta/2,col=col.borders[3],lwd=2)
lines(ps,fhp-delta/2,col=col.borders[3],lwd=2)
segments(1,0-delta/2,1,1/nt-delta/2,col=col.borders[3],lwd=2)
lines(ps,flpp,col=col.borders[4],lwd=2)
lines(ps,fhpp,col=col.borders[4],lwd=2)
segments(0,0,0,0.5,col=col.borders[4],lwd=2)
if(title)title(bquote(n[t]~'='~.(nt)~','~n[b]~'='~.(nb)),cex.main=2)
} |
043dcefd674b2b6db79fba259da19a61b16bce0e | efb670f72cb42c1e3a62c0e9c22db5a037e4ba48 | /plot1.R | baa12752e6467c4a0b94b31d6dfbf077729f1b33 | [] | no_license | nargis-parween/ExData_Plotting1 | 4de3241a1f44135bd79c97e127e375bde6d767a2 | 7a60df64d8bcfb5776dcdf424494f1ea36c41800 | refs/heads/master | 2020-04-18T12:07:41.491768 | 2019-01-25T10:10:55 | 2019-01-25T10:10:55 | 167,510,515 | 0 | 0 | null | 2019-01-25T08:12:54 | 2019-01-25T08:12:54 | null | UTF-8 | R | false | false | 383 | r | plot1.R | data<-read.table("./household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors = F, dec=".")
subsetData<-data[data$Date %in% c("1/2/2007","2/2/2007"),]
globalActivePower<-as.numeric(subsetData$Global_active_power)
png("plot1.png", width = 480, height = 480)
hist(globalActivePower,col="red",main="Global Active Power",xlab="Global Active Power (kilowatts)")
dev.off()
|
a4ac9798596b5875024671bbf120cbb3fa6c63d3 | 6b3ca6134352e6692069d0a7e57c3a7ba14e21ae | /man/IsFixedRandomization.Rd | 949914d06907917b9e8adf583d0845df5c7b6afe | [
"Apache-2.0"
] | permissive | ovative-group/GeoexperimentsResearch | fe5baeec316ecc19c99fc621cd3f56e960ad2849 | c50a1d6f4b21ea7624c27cec5374f4a3a3d76c0e | refs/heads/master | 2020-07-05T08:07:57.434144 | 2019-08-15T17:29:42 | 2019-08-15T17:29:42 | 202,583,766 | 0 | 0 | null | 2019-08-15T17:27:12 | 2019-08-15T17:27:11 | null | UTF-8 | R | false | false | 562 | rd | IsFixedRandomization.Rd | % Copyright (C) 2017 Google, Inc.
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geostrata.R
\name{IsFixedRandomization}
\alias{IsFixedRandomization}
\title{Test if randomizing the geostrata can lead only to a single outcome.}
\usage{
IsFixedRandomization(geostrata)
}
\arguments{
\item{geostrata}{a GeoStrata object.}
}
\value{
\code{TRUE} if randomizing the geostrata can only lead to a single
\code{GeoAssignment}, \code{FALSE} otherwise.
}
\description{
Test if randomizing the geostrata can lead only to a single outcome.
}
|
1304a5b0384bb4da482cbb0ea39151c9bd63be18 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RMark/examples/get.real.Rd.R | d3ff3db1bd2c8444383b34819636e2bd9ccc0f4e | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,156 | r | get.real.Rd.R | library(RMark)
### Name: get.real
### Title: Extract or compute sets of real parameters
### Aliases: get.real
### Keywords: utility
### ** Examples
data(example.data)
pregion=list(formula=~region)
PhiAge=list(formula=~Age)
mod=mark(example.data,model.parameters=list(p=pregion,Phi=PhiAge),
groups=c("sex","age","region"),age.var=2,initial.ages=c(0,1,2),threads=1)
# extract list of Phi parameter estimates for all groups in PIM format
Phi.estimates=get.real(mod,"Phi")
# print out parameter estimates in triangular PIM format
for(i in 1:length(Phi.estimates))
{
cat(names(Phi.estimates)[i],"\n")
print(Phi.estimates[[i]]$pim,na.print="")
}
require(plotrix)
#extract parameter estimates of capture probability p with se and conf intervals
p.table=get.real(mod,"p",se=TRUE)
print(p.table[p.table$region==1,]) # print values from region 1
estimates=by(p.table$estimate,p.table$region,mean)
lcl=by(p.table$lcl,p.table$region,mean)
ucl=by(p.table$ucl,p.table$region,mean)
plotCI(c(1:4),estimates,ucl-estimates,estimates-lcl,xlab="Region",
ylab="Capture probability",
ylim=c(.5,1),main="Capture probability estimates by region")
|
d064696770740697cf85b19ff55de06e1ec8667c | 563f6e7cdf6916d8cd9a86c7affa4295dcac0695 | /some_setup_for_benchtestr.R | 8e578f1ca12e5d31c737bcb5a1a6156f7b4762fb | [] | no_license | igorgeyn/benchtestr | 1a80b489e738cdfd0ec3ef0cdb7d477aa960f43c | ed9604b859a9568d788068c07cb7ab78ec34fc37 | refs/heads/main | 2023-05-30T15:00:41.948710 | 2021-06-11T22:10:33 | 2021-06-11T22:10:33 | 366,471,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,268 | r | some_setup_for_benchtestr.R | #####
# This file is to set up some of the things that are going to end up in the `benchtestr` packcage
# but which need a bit of TLC.
#####
library(haven)
library(foreign)
library(readstata13)
library(dplyr)
### dehejia and wahba data from here:
### https://users.nber.org/~rdehejia/data/.nswdata3.html
## make sure the files are in the wd, and then:
# this is Dehejia and Wahba's sample of the NSW data
setwd('C:/Users/Igor/Desktop/grad school/coursework/SP21/200C/package/benchmarkr/package_prep')
nsw_dehejia_wahba <- readstata13::read.dta13('nsw_dw.dta')
save(nsw_dehejia_wahba, file = "nsw_dehejia_wahba.rda")
# this is the data for the PSID (1-3)
# and the CPS controls.
# NOTE THAT AS OF THIS WIRTING THE LINK
# ABOVE HAD INCOMPLETE DATA
# (delete comment once resolved)
psid_controls_1 <- readstata13::read.dta13('psid_controls.dta')
psid_controls_2 <- readstata13::read.dta13('psid_controls2.dta') # missing
psid_controls_3 <- readstata13::read.dta13('psid_controls3.dta')
cps_controls_1 <- readstata13::read.dta13('cps_controls.dta')
cps_controls_2 <- readstata13::read.dta13('cps_controls2.dta')
cps_controls_3 <- readstata13::read.dta13('cps_controls3.dta') # missing
# make changes, append, and save as .RDA
# PSID files
psid_controls_1 <- psid_controls_1 %>% mutate(source = 'psid1')
# psid_controls_2 <- psid_controls_2 %>% mutate(source = 'psid2') # missing
psid_controls_3 <- psid_controls_3 %>% mutate(source = 'psid3')
psid_controls_dw <- rbind(psid_controls_1, psid_controls_3)
save(psid_controls_dw, file = "psid_controls_dw.rda")
# make changes, append, and save as .RDA
# CPS files
cps_controls_1 <- cps_controls_1 %>% mutate(source = 'cps1')
# cps_controls_2 <- cps_controls_2 %>% mutate(source = 'cps2') # missing
cps_controls_3 <- cps_controls_3 %>% mutate(source = 'cps3')
cps_controls_dw <- rbind(cps_controls_1, cps_controls_2)
save(cps_controls_dw, file = "cps_controls_dw.rda")
### doing similar to the above but for the Tennessee data:
# need this for some basic data prep:
require('some_basic_data_exploration.R')
setwd('C:/Users/Igor/Desktop/grad school/coursework/SP21/200C/package/benchmarkr/benchtestr/data')
save(tenn_star_df, file = "star_tenn_experiment.rda")
save(tenn_compar_df, file = "star_tenn_comparison.rda")
|
932047ea01bd1ca70d3e829936b46b49f0c66bdc | a6876c8263c51762ff796eeb0a2fee454d81be5c | /playground/sim1_legacy.R | 8657b1035c6ac0554d4830225e31f7b68a4d6f56 | [] | no_license | jinhao-luo/stat440-project | 212df513f2f7cc14f7ae54ca2038ae75bcc869dc | c8c5eb725db4497b2b69f69d2be02ae965ea8ed1 | refs/heads/master | 2022-12-21T04:19:38.901484 | 2020-09-26T01:34:09 | 2020-09-26T01:34:09 | 249,228,125 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,873 | r | sim1_legacy.R | require("TMB")
source("smfret-functions.R")
gr_mod <- "builtin"
# gr_mod <- "main"
dyn.load(dynlib(gr_mod))
#' simulation 1
#'
#' @param beta0 the value of beta0
#' @param beta1 the value of beta1
#' @param gamma Scalar mean reversion parameter
#' @param mu Scalar mean parameter
#' @param sigma Scalar diffusion parameter
#' @param dt Interobservation time.
#' @param n_obs Number of observations to generate.
#' @param n_dataset Number of dataset to simulate for each `\beta`
#' @return a vector of rmse: rmse for inference on each simulated dataset
sim_1 <- function(beta0=10, beta1=0.5, gamma = 1, mu = 10, sigma = sqrt(2*gamma), dt = 1,
n_obs = 99, n_dataset = 100, method="BFGS") {
theta <- list(mu=mu, sigma=sigma, gamma=gamma, t = 1 / gamma, tau = sigma / sqrt(2 * gamma))
test_output <- replicate(n_dataset, expr = {
test_detail <- list()
X <- ou_sim(gamma, mu, sigma, dt, n_obs)
Y <- y_sim(X, beta0, beta1)
test_detail$Y <- Y
if (anyNA(Y)) {
param_names <- c("sigma", "gamma", "mu", "t", "tau")
param <- rep(NA, length(param_names))
names(param) <- param_names
test_detail$param <- param
} else {
param <- list(gamma = 1, mu = 0, sigma = 1, X=rep(0, n_obs))
data <- list(model_type = "ou", dt = dt, y = Y, beta0 = beta0, beta1 = beta1)
f <- MakeADFun(data = data, parameters = param, random = c("X"), silent = TRUE)
# param <- list(gamma = 1, mu = 0, sigma = 1)
# data <- list(model_type = "ou", niter = 1000, dt = dt, beta0 = beta0, beta1 = beta1, y = Y)
# f <- MakeADFun(data = data, parameters = param)
result <- optim(par = f$par, fn = f$fn, gr = f$gr, control=list(maxit=1000,reltol=1e-8), method=method)
param <- result$par
param["t"] <- 1/param["gamma"]
param["tau"] <- param["sigma"] / sqrt(2*param["gamma"])
test_detail$theta_hat <- param
}
test_detail
})
theta_hat <- apply(test_output, 2, function(tc) {tc$theta_hat})
# get number of NAs in simulation
num_na <- sum(is.na(theta_hat["t",]))
# calulate rmse
rmse <- sapply(rownames(theta_hat), function (j) {
sqrt(mean((theta_hat[j,]-theta[[j]])^2, na.rm=TRUE))/theta[[j]]
})
sim_output <- list(rmse=signif(rmse,2), num_na=num_na, details=test_output, true_param=theta)
sim_output
}
sim_1()
test_cases <- expand.grid(beta0=10, beta1=0.5, gamma=c(0.1,1,10), mu=c(1, 10), n_obs=c(99,199,299))
result <- apply(test_cases, 1, function(tc) {
sim_1(beta0=tc[["beta0"]], beta1=tc[["beta1"]], mu=tc[["mu"]], gamma=tc[["gamma"]], n_dataset = 100, n_obs=tc[["n_obs"]])
})
cbind(test_cases, t(sapply(1:nrow(test_cases), function(i) {c(theta=result[[i]]$true_param, rmse=result[[i]]$rmse)})))
|
633491a5652e3373c0a9891237c427deb5198fef | 44de14d4766f804955ff82adcb7b05c6eb99befd | /inst/SHINYstan/server.R | 93febed7171fc57f89403a63cbe802802f75d2bc | [
"MIT"
] | permissive | bibliophiledd/SHINYstan | e9d761750d9e9312b58d89e183a6d2519653f1df | 615bff0088de21962011e0c6aa2a92c47b1a11b5 | refs/heads/master | 2020-12-26T00:46:17.614197 | 2014-10-19T02:41:18 | 2014-10-19T02:41:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,927 | r | server.R | library(shiny)
# load the helper functions
source("functions/SHINYstan_helpers.R", local=TRUE)
# Extract the content of the shiny_stan_object slots
object <- shiny_stan_object
samps_all <- object@samps_all
samps_post_warmup <- object@samps_post_warmup
sampler_params <- object@sampler_params
nIter <- object@nIter
warmup_val <- object@nWarmup
fit_summary <- object@summary
param_names <- object@param_names
# Begin shinyServer -------------------------------------------------------
# _________________________________________________________________________
shinyServer(function(input, output) {
# Preliminaries -----------------------------------------------------------
# _________________________________________________________________________
# reactive function to get samples for a single parameter
par_samps_all <- reactive({
param <- input$param
p <- which(param_names == param)
samps_all[,,p]
})
par_samps_post_warmup <- reactive({
param <- input$param
p <- which(param_names == param)
samps_post_warmup[,,p]
})
# Output ------------------------------------------------------------------
# _________________________________________________________________________
#### TEXT: parameter name ####
output$param_name <- renderText({
input$param
})
#### TABLE: summary stats (single parameter) ####
output$parameter_summary <- renderTable({
do.call(".param_summary", args = list(
param = input$param,
r_e = fit_summary[input$param,c("Rhat","n_eff")],
dat = par_samps_post_warmup(),
warmup_val = warmup_val
))
}, include.rownames = FALSE, display = c("s","f","d",rep("f",5)))
#### PLOT: trace (single parameter) ####
output$trace_plot <- renderPlot({
zoom <- input$tracezoom
do.call(".param_trace", args = list(
param = input$param,
dat = par_samps_all(),
chain = input$trace_chain,
warmup_val = warmup_val,
x1 = ifelse(zoom, input$xzoom[1], NA),
x2 = ifelse(zoom, input$xzoom[2], NA),
y1 = ifelse(zoom, input$yzoom[1], NA),
y2 = ifelse(zoom, input$yzoom[2], NA)
))
})
#### PLOT: density (single parameter) ####
output$density_plot <- renderPlot({
customize <- input$dens_customize
do.call(".param_dens", args = list(
param = input$param,
dat = par_samps_post_warmup(),
chain = input$dens_chain,
warmup_val = warmup_val,
fill_color = ifelse(customize, input$dens_fill_color, "black"),
line_color = ifelse(customize, input$dens_line_color, "lightgray"),
point_est = ifelse(customize, input$dens_point_est, "None"),
CI = ifelse(customize, input$dens_ci, "None")
))
})
#### PLOT: contour (two parameters) ####
output$contour_plot <- renderPlot({
customize <- input$contour_customize
do.call(".param_contour", args = list(
samps = samps_post_warmup,
param = input$param,
param2 = input$param2_contour,
type = ifelse(customize, input$contour_type, "Point"),
nBins = ifelse(customize, input$contour_bins, 10),
high_color = ifelse(customize, input$contour_high_color, "skyblue"),
low_color = ifelse(customize, input$contour_low_color, "navyblue")
))
})
#### DATATABLE: summary stats (all parameters) ####
output$all_summary <- renderDataTable({
.all_summary(fit_summary)
}, options = list(scrollY = 500, scrollX = 500))
#### PLOT: median, CI, and density (multiple parameters) ####
calc_height_plot_param_vertical <- reactive({
params <- input$params_to_plot
LL <- length(params)
N <- ifelse(LL < 10, 10, LL)
round(400*N/10)
})
output$plot_param_vertical <- renderPlot({
customize <- input$param_plot_customize
do.call(".plot_param_vertical", args = list(
samps = samps_post_warmup,
params = input$params_to_plot,
CI.level = input$CI_level/100,
show.options = input$show_options,
point_est = ifelse(customize, input$param_plot_point_est, "Median"),
fill_color = ifelse(customize, input$param_plot_fill_color, "gray"),
outline_color = ifelse(customize, input$param_plot_outline_color, "black"),
est_color = ifelse(customize, input$param_plot_est_color, "turquoise4")
))
}, height = calc_height_plot_param_vertical)
#### PLOT: Rhat (all parameters) ####
output$rhatplot <- renderPlot({
.rhat_plot(fit_summary)
}, height = .calc_height_fixed(param_names))
#### TABLE: summary stats (sampler) ####
output$sampler_summary <- renderTable({
do.call(".sampler_summary", args = list(
sampler_params = sampler_params,
inc_warmup = input$sampler_warmup,
warmup_val = warmup_val
))
})
}) # End shinyServer |
2331cd624b06c7e74b1a7ae08bcecf3ac2f3d6a1 | cf75f57c49e44070bfb93e04b28e9386a2d2783a | /man/summary.isolationForest.Rd | ea228ae6aca433d157cdded6fb66511c410bd3f0 | [] | no_license | ficol/ZUM | f7975b259f2991a56fe4fd14cff5278051ed9953 | 7e9d3ce484ff47e6246f2c5e6719668c66385114 | refs/heads/main | 2023-08-17T08:12:40.055435 | 2021-08-08T03:48:17 | 2021-08-08T03:48:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 374 | rd | summary.isolationForest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iforest.R
\name{summary.isolationForest}
\alias{summary.isolationForest}
\title{Prints summary information of isolation forest}
\usage{
\method{summary}{isolationForest}(X, ...)
}
\arguments{
\item{X}{Isolation forest object}
}
\description{
This prints parameters of created isolation forest.
}
|
507b1a3b8f0b9da0d806443ca795ebd0909c0d90 | 6bd40decfeb72585c4497f0acb8c1de4c01d4bfe | /get_vst_crs.R | 12d3c5653c182b6f7611c21366b19a95f60de6c1 | [] | no_license | mbjoseph/neon-veg | 5051deed3f90607448d239886417411f033ff191 | 68a68f6d1b9cb7aed9b2cf31f7ef80cd580bbfe6 | refs/heads/master | 2020-03-13T07:14:21.108134 | 2018-04-25T19:56:06 | 2018-04-25T19:56:06 | 131,021,537 | 0 | 0 | null | 2018-04-25T19:56:23 | 2018-04-25T14:46:52 | R | UTF-8 | R | false | false | 617 | r | get_vst_crs.R | get_vst_crs <- function(woody_path){
print("Retrieving CRS...")
# define path to vst_plotperyear table,
# which contains geodetic datum and UTM zone
vst_path <- paste(woody_path,
list.files(path = woody_path,
pattern = "plotperyear"),
sep="/")
vst_data <- read.csv(vst_path)
datum <- as.character(vst_data$geodeticDatum[1])
zone <- gsub("[^0-9\\.]", "", vst_data$utmZone[1])
coord_ref <- CRS(paste("+proj=utm +zone=",zone,
" +datum=",datum," +units=m",sep=""))
return(coord_ref)
} |
23299b164a895fb86a89738ae07decc7ac299470 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MARX/examples/selection.lag.lead.Rd.R | 5d72aa91f3ea8f372ca1ca4ef95b914ad5fc0b72 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 290 | r | selection.lag.lead.Rd.R | library(MARX)
### Name: selection.lag.lead
### Title: The lag-lead model selection for MARX function
### Aliases: selection.lag.lead
### Keywords: causal-noncausal selection
### ** Examples
data <- sim.marx(c('t',3,1), c('t',3,1),100,0.5,0.4,0.3)
selection.lag.lead(data$y,data$x,2)
|
1138902931d634f397a7ffe8a2a7babfe57433ae | 1fe3f749bc6daa62a16f3a263ba4f03845340cc9 | /Whale foraging energetics.R | 753cd406fda66a05265f761273dbb6dcf0fd8325 | [] | no_license | mssavoca/Whale-futures-project | 73cbca73ff38c9a54174777b9a68383def9e98e1 | 2827cafed878bfd5af893b7cbd5556af7862c610 | refs/heads/master | 2020-04-18T04:28:48.204550 | 2019-05-09T15:56:54 | 2019-05-09T15:56:54 | 167,240,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,980 | r | Whale foraging energetics.R | #############################################
# Preliminary figures for whale futures paper
#############################################
# load packages
library(ggplot2)
library(dplyr)
library(readxl)
library(ggsci)
library(tidyverse)
library(mgcv)
library(lme4)
# formula for standard error
SE = function(x){sd(x)/sqrt(sum(!is.na(x)))}
# load data
d_full_NULL <- read.csv("Cetacea model output NULL_EXTANT.csv")
d_full_NULL$Species <- as.character(d_full_NULL$Species)
# the equivalent to an "if" statement
d_full_NULL$Species[d_full_NULL$Species == "bonarensis"] <- "bonaerensis"
d_full_BOUT <- read.csv("Cetacea model output BOUT_EXTANT.csv")
d_full_BOUT$Species <- as.character(d_full_BOUT$Species)
# the equivalent to an "if" statement
d_full_BOUT$Species[d_full_BOUT$Species == "bonarensis"] <- "bonaerensis"
# sweet tidy code from Max
d_sum_NULL = d_full_NULL %>% group_by(Genus, Species) %>% summarize(wgtMeanNULL = weighted.mean(Prey.W..g., Percent), medNULL = median(Prey.W..g., Percent))
d_sum_BOUT = d_full_BOUT %>% group_by(Genus, Species) %>% summarize(wgtMeanBOUT = weighted.mean(Prey.W..g., Percent), medBOUT = median(Prey.W..g., Percent))
RorqualData <- read_csv("lunge_rates_from_Paolo.csv")
RorqualData$`deployment-time_h` <- (RorqualData$`deployment-time_secs`)/60/60
OdontoceteData <- read_csv("foragestats_combined_ko2.csv")
OdontoceteData <- separate(OdontoceteData, Species, into = c("Genus", "Species"), sep = "_")
OdontoceteData <- full_join(OdontoceteData, d_sum_NULL, by = "Species", all = TRUE)
OdontoceteData <- full_join(OdontoceteData, d_sum_BOUT, by = "Species", all = TRUE)
OdontoceteData <- select(OdontoceteData, -Genus.y, -Genus.x)
en_df <- merge(RorqualData, OdontoceteData, by = "ID", all.x = TRUE, all.y = TRUE)
# coalescing to merge two columns into one
en_df$TotalFeedingEvents = coalesce(en_df$total_lunges, en_df$total_buzz_count)
head(en_df$TotalFeedingEvents)
en_df$TotalTagTime_h = coalesce(en_df$`deployment-time_h`, en_df$total_duration_h)
head(en_df$TotalTagTime_h)
# could also try transmute(iris, sepal = Sepal.Length + Sepal. Width) to drop original columns
en_df <- filter(en_df, !Species %in% NA) #removes rows with NA in Species column
en_df$Species <- gsub("_", " ", en_df$Species) #replaces underscore with space in the Species column
en_df$taxa <- gsub("M", "Mysticete", en_df$taxa)
en_df$taxa <- gsub("O", "Odontocete", en_df$taxa)
#creating new columns
en_df$feeding_rate = en_df$TotalFeedingEvents/en_df$TotalTagTime_h
en_df$en_h1 = en_df$Prey_E_kJ*en_df$feeding_rate # NEED TO CHANGE TO MEDIAN AND/OR WEIGHTED MEAN INSTEAD OF "Prey_E_kJ"
en_df$en_h2 = en_df$en_h1*2
en_df$en_h3 = en_df$en_h1*3
en_df$en_h4 = en_df$en_h1*4
en_df$en_h5 = en_df$en_h1*5
en_df$en_h6 = en_df$en_h1*6
# en_df$en_h2 = en_df$en_h1*7
# en_df$en_h3 = en_df$en_h1*8
# en_df$en_h4 = en_df$en_h1*9
# en_df$en_h5 = en_df$en_h1*10
# en_df$en_h6 = en_df$en_h1*11
# en_df$en_h6 = en_df$en_h1*12
# energy acquired in a day
en_df$en_day = en_df$en_h1*24
en_df$corr_en_day = en_df$en_day/en_df$Body_mass_kg
# mass of prey consumed in an hour / day, using the point estimates Danuta gave me, the median and weighted means of the prey
en_df$prey_wt_g_h1 = en_df$Prey_wt_g*en_df$feeding_rate # values from Danuta
en_df$prey_wt_g_day = en_df$prey_wt_g_h1*24 # values from Danuta
en_df$med_prey_wt_g_NULL_h1 = en_df$medNULL*en_df$feeding_rate
en_df$wgtMean_prey_wt_g_NULL_h1 = en_df$wgtMeanNULL*en_df$feeding_rate
en_df$med_prey_wt_g_BOUT_h1 = en_df$medBOUT*en_df$feeding_rate
en_df$wgtMean_prey_wt_g_BOUT_h1 = en_df$wgtMeanBOUT*en_df$feeding_rate
# make the wide dataset long (i.e., tidy)
en_df_tidy = gather(en_df, hour, en_per_hour, 58:63)
#turn certain values into numbers
en_df_tidy$hour = ifelse(en_df_tidy$hour == "en_h1", 1,
ifelse(en_df_tidy$hour == "en_h2", 2,
ifelse(en_df_tidy$hour == "en_h3", 3,
ifelse(en_df_tidy$hour == "en_h4", 4,
ifelse(en_df_tidy$hour == "en_h5", 5, 6)))))
en_df_tidy$corr_en_per_hour = en_df_tidy$en_per_hour/en_df_tidy$Body_mass_kg
#en_df_tidy = spread(en_df_tidy, hour, en_h1_corr_mass:en_h6_corr_mass)
#to check if working
#View(en_df_tidy[en_df_tidy$ID == "bb12_214a",])
# remove blank rows
en_df_tidy <- en_df_tidy[-which(is.na(en_df_tidy$en_per_hour)), ]
# data summary filtering by lunge quality, removing any NA Species or NAs in feeding rate, filtering to tak
Sp_sum = en_df_tidy %>%
drop_na(feeding_rate) %>%
filter(lunge_quality %in% c("ok", "good", NA, "good dives", "good_dives")) %>%
filter(sonar_exp %in% c("none", NA) & TotalTagTime_h > 23) %>% #Filtering to only dives of more than 24 h
group_by(taxa, Species) %>%
dplyr::summarize(mean_en_per_hour = mean(en_per_hour),
mean_corr_en_per_hour = mean(corr_en_per_hour),
mean_prey_wt_g_per_hour = mean(prey_wt_g_h1),
mean_prey_wt_g_per_day = mean(prey_wt_g_day),
SD_mean_prey_wt_g_per_day = sd(prey_wt_g_day),
SE_mean_prey_wt_g_per_day = SE(prey_wt_g_day),
mean_prey_wt_kg_3mo = mean((prey_wt_g_day/1000)*90),
SE_prey_wt_kg_3mo = SE((prey_wt_g_day/1000)*90),
mean_prey_wt_kg_6mo = mean((prey_wt_g_day/1000)*180),
SE_prey_wt_kg_6mo = SE((prey_wt_g_day/1000)*180),
mean_prey_wt_kg_9mo = mean((prey_wt_g_day/1000)*270),
SE_prey_wt_kg_9mo = SE((prey_wt_g_day/1000)*270),
med_prey_wt_g_NULL_per_hour = mean(med_prey_wt_g_NULL_h1),
wgtMean_prey_wt_g_NULL_per_hour = mean(wgtMean_prey_wt_g_NULL_h1),
med_prey_wt_g_BOUT_per_hour = mean(med_prey_wt_g_BOUT_h1),
wgtMean_prey_wt_g_BOUT_per_hour = mean(wgtMean_prey_wt_g_BOUT_h1),
Prey_wt_kg_3mo_NULLmed = mean((med_prey_wt_g_BOUT_h1/1000)*24*90),
Prey_wt_kg_6mo_NULLmed = mean((med_prey_wt_g_BOUT_h1/1000)*24*180),
Prey_wt_kg_9mo_NULLmed = mean((med_prey_wt_g_BOUT_h1/1000)*24*270),
Prey_wt_kg_3mo_NULLwgtMean = mean((wgtMean_prey_wt_g_NULL_h1/1000)*24*90),
Prey_wt_kg_6mo_NULLwgtMean = mean((wgtMean_prey_wt_g_NULL_h1/1000)*24*180),
Prey_wt_kg_9mo_NULLwgtMean = mean((wgtMean_prey_wt_g_NULL_h1/1000)*24*270))
# make the wide dataset long (i.e., tidy); CHANGE AS N ECESSARY FOR DIFFERENT PLOTS
Sp_sum_tidy = Sp_sum %>%
gather(months_feeding, kg_consumed, c(9,11,13)) %>%
mutate(errBar = case_when(
months_feeding == "mean_prey_wt_kg_3mo" ~ SE_prey_wt_kg_3mo,
months_feeding == "mean_prey_wt_kg_6mo" ~ SE_prey_wt_kg_6mo,
months_feeding == "mean_prey_wt_kg_9mo" ~ SE_prey_wt_kg_9mo
))
# Max's cool tidy code to look at feeding rates by rorqual species
en_df_tidy %>% filter(TotalTagTime_h > 24) %>% group_by(species) %>% summarize(meanFeedRate = 24*mean(feeding_rate))
en_df_tidy %>% filter(TotalTagTime_h > 24) %>% ggplot(aes(x = 24*feeding_rate, color = species)) + geom_density()
# # looking at weighted means for NULL an BOUT fin and blue whale
# fin_NULL <- weighted.mean(c(2740, 6000, 12900, 27840, 60000, 129240, 278520, 600000), c(1.9, 7.5, 12.1, 17.8, 22.7, 22.7, 12.8, 2.5))
# fin_BOUT <- weighted.mean(c(6000, 12900, 27840, 60000, 129240, 278520, 600000), c(0.5, 3.7, 8.3, 13.9, 24.7, 30.9, 18))
#
# blue_NULL <- weighted.mean(c(6160, 13400, 28810, 62176, 134000, 288636, 622028, 1340000), c(1.9, 7.4, 12.2, 17.3, 23, 22.8, 12.9, 2.5))
# blue_BOUT <- weighted.mean(c(13400, 28810, 62176, 134000, 288636, 622028, 1340000), c(1, 3.3, 8.2, 17, 28.8, 29.9, 11.8))
##########################
# Plot of energy in by time
##########################
plot_en_per_h_w_avg <- ggplot() +
geom_point(data=en_df_tidy, aes(hour, log(corr_en_per_hour), color = Species, shape = Species), alpha = 0.2) +
scale_shape_manual(name = "Species",
labels = c("Balaenoptera bonaerensis","Balaenoptera musculus","Balaenoptera physalus","Berardius bairdii",
"Globicephala macrorhynchus", "Globicephala melas","Grampus griseus", "Megaptera novaeangliae",
"Mesoplodon densirostris","Orcinus orca","Phocoena phocoena", "Physeter macrocephalus", "Ziphius cavirostris"),
values = c(0,1,2,3,4,5,6,7,8,9,10,12,13,14)) +
#geom_path(data=en_df_tidy, aes(hour, log(corr_en_per_hour), color = Species), group=en_df_tidy$ID, alpha = 0.1) +
geom_point(data = Sp_sum, aes(hour, log(mean_corr_en_per_hour), color = Species, shape = Species), size = 4) +
geom_path(data = Sp_sum, aes(hour, log(mean_corr_en_per_hour), color = Species, group = Sp_sum$Species)) +
geom_smooth(data=en_df_tidy, aes(hour, log(corr_en_per_hour)), color = "black", linetype="dashed") +
scale_fill_manual(values = c("Berardius_bairdii","Globicephala_macrorhynchus", "Globicephala_melas","Grampus_griseus", "Mesoplodon_densirostris",
"Orcinus_orca","Phocoena_phocoena", "Physeter_macrocephalus", "Ziphius_cavirostris"),
labels = c("Berardius bairdii","Globicephala macrorhynchus", "Globicephala melas","Grampus griseus", "Mesoplodon densirostris",
"Orcinus orca","Phocoena phocoena", "Physeter macrocephalus", "Ziphius cavirostris")) +
facet_grid(.~taxa) +
theme_bw() +
labs(x = "Time (hours)", y = "log[Energy gain corrected for body mass (kJ/kg)]")
plot_en_per_h_w_avg
####################################################
# Plot of prey wt consumed by season
####################################################
prey_wt_consumed_season <- ggplot(filter(Sp_sum_tidy, Species %in% c("musculus", "physalus", "novaeangliae")),
aes(x = months_feeding, y=kg_consumed, fill = Species)) +
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = kg_consumed - errBar, ymax = kg_consumed + errBar),
stat = "identity", position="dodge", color = "black")
prey_wt_consumed_season
################################################
# Scaling plot of energy in per day by body size
################################################
S1 <- ggplot(data=en_df_tidy, aes(x = log10(Body_mass_kg), y=log10(en_day), color = Species)) +
geom_point(alpha = 0.5) +
geom_smooth(aes(group = taxa), method = lm) +
geom_abline(intercept = 0, slope = 1, linetype ="dashed") +
S1
theme_bw() + guides(size=FALSE, color=FALSE) +
ylim(1,7.25) + xlim(1,6.25) +
theme(axis.text=element_text(size=14), axis.title=element_text(size=16,face="bold")) +
labs(x = "log[Mass (kg)]", y = "log[Prey Energy (kJ)]")
fig_3a + scale_color_manual(values = cols)
# graph, energy in corrected for mass
plot_corr_en_per_h <- ggplot(en_df_tidy, aes(hour, log(corr_en_per_hour), color = Species)) +
geom_point(alpha = 0.2) +
geom_path(group=en_df_tidy$ID, alpha = 0.2) +
geom_smooth(aes(group=en_df_tidy$Species)) + geom_smooth(color = "black", linetype="dashed") +
theme_bw()
plot_corr_en_per_h
#get data ready to plot
Sp_sum = as.data.frame(Sp_sum)
Sp_sum$Species = as.factor(Sp_sum$Species)
scale_fill_manual(name="My new legend", values=c("brown1","darkolivegreen4","burlywood3", labels=c("condition1", "condition2", "condition3")) +
# graph
plot_en_per_h <- ggplot(en_df_tidy, aes(hour, log(en_per_hour/Body_mass_kg), color = Species)) +
geom_point() +
geom_path(group=en_df_tidy$ID)
#geom_smooth(aes(group=en_df_tidy$Species), color = "black")
plot_en_per_h
a=en_df_tidy %>% as.data.frame()
plot_en_per_h <- ggplot() +
geom_point(a, aes(hour, log(en_per_hour), color = Species)) +
geom_line(group=en_df_tidy$ID)+
geom_point(a,aes(hour,corr_en_per_hour),color="black")
plot_en_per_h
|
6bc2fe7650d58d845f2b04135ca0a89a0927627c | ccb34867e8a558f8c32dbd49d4a5a41fbd837c32 | /man/add_forecast_dates.Rd | 7eb8e016a86d8969a8639cafc98cdc2cece1f866 | [] | no_license | cran/dateutils | 5e828b7daed410a5149b270f3e204a42f7214620 | 85960aa5ef9e3b21f1e5ac7184b7b863774da510 | refs/heads/master | 2023-09-06T03:22:32.461198 | 2021-11-10T14:50:10 | 2021-11-10T14:50:10 | 426,859,304 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 830 | rd | add_forecast_dates.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{add_forecast_dates}
\alias{add_forecast_dates}
\title{Add NA values to the tail of a wide data.table}
\usage{
add_forecast_dates(
dt,
horizon = 1,
frq = c("month", "week", "quarter", "year"),
date_name = "ref_date"
)
}
\arguments{
\item{dt}{data.table in wide format}
\item{horizon}{number of periods to add at specified `frq`}
\item{frq}{frequency for aggregation, one of `"month"`, `"week"`, `"quarter"`, or `"year"`}
\item{date_name}{name of date column}
}
\value{
NA-filled data.table in wide format
}
\description{
Add NA values to the tail of a wide data.table to be filled by forecasting routines
}
\examples{
add_forecast_dates(fred[series_name == "gdp constant prices"],frq="quarter")
}
|
b45949bf93729b9debc37918eb84ce858b6b75c4 | 26b7a8eee606fb480b62c4c958249e40ee52e5a9 | /Data_Fusion_Functions.R | 3d1e0584eef4fc96bce21bf9575d59df7681a69e | [
"Apache-2.0"
] | permissive | eleafeit/data_fusion | c9269a643b4ce039066268bad0ce322a4d74bc5a | 604e03e09ee0b66988adec505e9216e9fbbfcc66 | refs/heads/master | 2020-03-25T12:07:00.546162 | 2019-03-01T18:02:00 | 2019-03-01T18:02:00 | 143,760,838 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,432 | r | Data_Fusion_Functions.R | # Functions for data fusion
# Elea McDonnell Feit, eleafeit@gmail.com
# 11 March 2016
data.mvn.split <- function(K1=2, K2=2, Kb=3, N1=100, N2=100,
mu=rep(0, K1+K2+Kb), Sigma=diag(1, K1+K2+Kb))
{
y <- mvrnorm(n=N1+N2, mu=mu, Sigma=Sigma)
list(data=list(K1=K1, K2=K2, Kb=Kb, N1=N1, N2=N2,
y1=as.matrix(y[1:N1, 1:K1], col=K1),
y2=as.matrix(y[N1+1:N2, K1+1:K2], col=K2),
yb=as.matrix(y[,K1+K2+1:Kb], col=Kb)),
true=list(mu=mu, Sigma=Sigma,
y1mis=y[1:N1, K1+1:K2],
y2mis=y[N1+1:N2, 1:K1]))
}
data.mvp.split <- function(K1=2, K2=2, Kb=3, N1=100, N2=100,
mu=rep(0, K1+K2+Kb), Sigma=diag(1, K1+K2+Kb))
{
z <- mvrnorm(n=N1+N2, mu=mu, Sigma=Sigma)
y <- z
y[y>0] <- 1
y[y<0] <- 0
y1mis <- y[1:N1, K1+1:K2]
y2mis <- y[N1+1:N2, 1:K1]
y[1:N1, K1+1:K2] <- NA
y[N1+1:N2, 1:K1] <- NA
true=list(mu=mu, Sigma=Sigma, z=z, y=y, y1mis=y1mis, y2mis=y2mis)
y[is.na(y)] <- 0
data=list(K1=K1, K2=K2, Kb=Kb, N1=N1, N2=N2, y=y)
list(data=data, true=true)
}
plot.post.density <- function(m.stan, pars, true, prefix=NULL){
for (i in 1:length(pars)) {
draws <- As.mcmc.list(m.stan, pars=pars[i])
if (!is.null(prefix)) {
filename <- paste(prefix, "Post", pars[i], ".png", sep="")
png(filename=filename, width=600, height=400)
}
beanplot(data.frame(draws[[1]]),
horizontal=TRUE, las=1, what=c(0, 1, 1, 0), side="second",
main=paste("Posterior Density of", pars[[i]]))
if (!is.null(prefix)) dev.off()
}
}
plot.true.v.est <- function(m.stan, pars, true, prefix=NULL){
for (i in 1:length(pars)) {
draws <- As.mcmc.list(m.stan, pars=pars[i])
est <- summary(draws)
if (!is.null(prefix)) {
filename <- paste(prefix, "TrueVEst", pars[i], ".png", sep="")
png(filename=filename, width=600, height=400)
}
plot(true[[i]], est$quantiles[,3], col="blue",
xlab=paste("True", pars[i]),
ylab=paste("Estiamted", pars[i], "(posterior median)"))
abline(a=0, b=1)
arrows(true[[i]], est$quantiles[,3], true[[i]], est$quantiles[,1],
col="gray90", length=0)
arrows(true[[i]], est$quantiles[,3], true[[i]], est$quantiles[,5],
col="gray90", length=0)
points(true[[i]], est$quantiles[,3], col="blue")
if (!is.null(prefix)) dev.off()
}
} |
7cbf352294dfafee3bf3ae45a1d98834ea3f75bf | fca98fdbd1e4d7d5c9db53e4f275b61454d4a9df | /Homework1/Homework1-Exercise4.R | e3f1a3d656c1e83ac9f86bc2e94494429828a963 | [] | no_license | buzduganalex1/Special-Chapters-on-Artificial-Intelligence | bad231a3773557ad9c31c38169f3c45b87816e3b | 74802f6470f2f8e58e59570ff2e0f1164f807d26 | refs/heads/master | 2020-04-01T21:58:26.875613 | 2019-01-18T20:26:04 | 2019-01-18T20:26:04 | 153,684,480 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 402 | r | Homework1-Exercise4.R | source("Functions.R")
x <- readinteger()
length(x)
print(x)
maxValue <- max(x)
minValue <- min(x)
meanValue <- mean(x)
medianValue <- median(x)
standardDeviation <- sd(x)
standardization <- scale(x)
sort(x, FALSE)
print(maxValue)
print(minValue)
print(meanValue)
print(medianValue)1
print(standardDeviation)
print(standardization)
##(x-mean(x))/sd(x) -- standardization
|
24edd807236546817d417f9e0acc3609e791440a | 710db2115cc82992be5419d9f1379faecd32f6cb | /getdata/run_analysis.R | bd9829b376fdce5281bd502a8ab7ccf042b5d715 | [] | no_license | asmolich/datasciencecoursera | 836c1508f249c886a388ee67c9e9211466a7b2df | d95478923557ceae1001a77e2b4da61464f339ea | refs/heads/master | 2020-04-06T06:41:26.959012 | 2014-08-25T08:22:21 | 2014-08-25T08:22:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,012 | r | run_analysis.R | data.file <- 'https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip'
local.data.file <- './data/UCI_HAR_Dataset.zip'
local.data.dir <- './data/UCI HAR Dataset'
tidy.data.file <- './tidy-UCI-HAR-dataset.txt'
tidy.avgs.data.file <- './tidy-UCI-HAR-avgs-dataset.txt'
# Make sure the original data file is in the data directry, downloading
# it if needed (and allowed)
if (!file.exists("data")) {
message("Creating data directory")
dir.create("data")
}
if (! file.exists(local.data.file)) {
message("Downloading data")
download.file(data.file, destfile = local.data.file, method = 'curl')
unzip(local.data.file, exdir="data")
}
# Fail if unzipped directory does not exist
if (! file.exists(local.data.dir)) {
stop('Directory does not exist.')
}
# Read activity labels
acts <- read.table(paste(local.data.dir, 'activity_labels.txt', sep = '/'),
col.names = c('id', 'name'))
# Read feature labels
features <- read.table(paste(local.data.dir, 'features.txt', sep = '/'),
col.names = c('id', 'name'))
# Read the plain data files, assigning sensible column names
train.X <- read.table(paste(local.data.dir, 'train', 'X_train.txt', sep = '/'),
col.names = features$name)
train.y <- read.table(paste(local.data.dir, 'train', 'y_train.txt', sep = '/'),
col.names = c('activity'))
train.subject <- read.table(paste(local.data.dir, 'train', 'subject_train.txt',
sep = '/'),
col.names = c('subject'))
test.X <- read.table(paste(local.data.dir, 'test', 'X_test.txt', sep = '/'),
col.names = features$name)
test.y <- read.table(paste(local.data.dir, 'test', 'y_test.txt', sep = '/'),
col.names = c('activity'))
test.subject <- read.table(paste(local.data.dir, 'test', 'subject_test.txt',
sep = '/'),
col.names = c('subject'))
# Merge the training and test sets
X <- rbind(train.X, test.X)
y <- rbind(train.y, test.y)
subject <- rbind(train.subject, test.subject)
# Extract just the mean and SD features
# Note that this includes meanFreq()s - it's not clear whether we need those,
# but they're easy to exlude if not needed.
X <- X[, grep('mean|std', features$name)]
# Convert activity labels to meaningful names
y$activity <- acts[y$activity,]$name
# Merge partial data sets together
tidy.data.set <- cbind(subject, y, X)
# Dump the data set
write.csv(tidy.data.set, tidy.data.file)
# Compute the averages grouped by subject and activity
tidy.avgs.data.set <- aggregate(tidy.data.set[, 3:dim(tidy.data.set)[2]],
list(tidy.data.set$subject,
tidy.data.set$activity),
mean)
names(tidy.avgs.data.set)[1:2] <- c('subject', 'activity')
# Dump the second data set
write.csv(tidy.avgs.data.set, tidy.avgs.data.file) |
868a97b937d45936bb5d599a5d42fcc3fbc7d08f | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_PenmanMonteith/AFL_ET0_PenmanMonteith/ET0_PenmanMonteith_valgrind_files/1615842159-test.R | 0a1ec9f972d6367029e22d6f5b7cff943a92ad60 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 988 | r | 1615842159-test.R | testlist <- list(G = numeric(0), Rn = numeric(0), atmp = numeric(0), ra = numeric(0), relh = c(-6.87353716589742e-83, 179.214603488924, -6.67707850404722e+133, 5.32948612168953e-320, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rs = numeric(0), temp = c(8.5728629954997e-312, 1.56898424065867e+82, 8.96970809549085e-158, -1.3258495253834e-113, 2.79620616433656e-119, -6.80033518839086e+41, 2.68298522855314e-211, 1444042902784.06, 6.68889884134308e+51, -4.05003163986346e-308, -3.52601820453991e+43, -1.49815227045093e+197, -2.61605817623304e+76, -1.18078903777423e-90, 1.86807199752012e+112, -5.58551357556946e+160, 2.00994342527714e-162, -1.0474771182888e-279, -3.91881664584645e-291, 1.05691417767735e+254, -1.44288984971022e+71))
result <- do.call(meteor:::ET0_PenmanMonteith,testlist)
str(result) |
859ec6f518f617440297dc01d28c5b7d95d94355 | e54953d5d7a5ffc1ac2e30b83644e77800a37c68 | /cachematrix.R | 2ff46fe236957e82fe361b71171c6f2300c18ea5 | [] | no_license | gnupate/ProgrammingAssignment2 | ec77077fe691f2dbea77cd04c08a323bf3688e2d | c62ddb66a0515686de0290e2252b1ca1968c8262 | refs/heads/master | 2020-12-26T04:38:50.385893 | 2015-05-22T17:36:36 | 2015-05-22T17:36:36 | 36,083,291 | 0 | 0 | null | 2015-05-22T16:38:14 | 2015-05-22T16:38:14 | null | UTF-8 | R | false | false | 1,715 | r | cachematrix.R | ## makeCacheMatrix - creates a new matrix that can keep cached copies of it's inverse
## cacheSolve - finds the inverse of a CacheMatrix, checking for a cached inverse first
## solution by Pat Eyler, based on makeVector and cachemean from assignment
## makeCacheMatrix creates a new version of a matrix with set, get, setinverse,
## and getinverse functions
## if given a matrix as it's argument, it copies the values in it to the new matrix
##
## the set function will overwrite the values in the CacheMatrix with the matrix provided
##
## the get function will return the matrix
##
## the setinverse function will set the cached inverse as whatever argument is provided
##
## the get inverse function will return whatever value is currently cached
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
## build the set, get, setinverse, and getinverse functions
set <- function(y) {
x <<- y
i <<- NULL
}
get <- function() x
setinverse <- function(solution) i <<- solution
getinverse <- function() i
# build a list of the 4 functions and return it
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## cacheSolve will check to see if there is a cached copy of
## the inverse of the matrix and return it if available.
## Otherwise it will calculate the inverse, cache it, and return it
cacheSolve <- function(x, ...) {
## first use getinverse to see if there's a cached version
i <- x$getinverse()
if(!is.null(i)) { ## if there's a cached version, return it
return(i)
} #otherwise find the inverse and cache it
data <- x$get()
i <- solve(data, ...)
x$setinverse(i)
i
}
|
95ba656acd9fef1a47216d61e729672ef2913c55 | cbd70b829a3dffc23bffe01929d7732f2df815f5 | /exploratory/detrended_visualization.R | fe691168ae99968cf49e47a72ab1628dbdaf431c | [] | no_license | kimberlyroche/ROL | 603ac7a71a0487b1ff999a14cc34681090e299fc | d33c8f63c692a55cdca9c551b718f1c04929d6f8 | refs/heads/master | 2021-02-18T23:58:43.631388 | 2020-12-08T23:47:55 | 2020-12-08T23:47:55 | 245,254,434 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,739 | r | detrended_visualization.R | library(phyloseq)
library(ROL)
library(ggplot2)
# to do: (1) run this for many different individuals
# (2) incorporate their lagged AC
# (3) ribbon plot for Lambda, Lambda_detrended
tax_level <- "ASV"
data <- load_data(tax_level = tax_level, host_sample_min = 75, count_threshold = 5, sample_threshold = 0.2)
metadata <- sample_data(data)
cat(paste0("There are ",length(unique(metadata$sname))," unique hosts in this data set and a total of ",phyloseq::nsamples(data)," samples!\n"))
params <- formalize_parameters(data)
for(host in unique(metadata$sname)) {
cat("Fitting",host,"\n")
fit_GP(data, host = host, tax_level = tax_level, SE_days_to_baseline = 90, alr_ref = params$alr_ref, MAP = TRUE)
}
use_Eta <- FALSE
use_detrended <- FALSE
df <- data.frame(x = c(), y = c(), host = c())
for(host in unique(metadata$sname)) {
fit <- readRDS(paste0("output/model_fits/ASV_MAP/",host,"_bassetfit.rds"))
# calculate autocorrelation for Lambda
observations <- fit$X[1,]
Lambda <- fit$fit$Lambda[,,1] # this is D-1 taxa x N samples
Eta <- fit$fit$Eta[,,1]
Theta <- fit$fit$Theta(fit$fit$X)
Gamma <- fit$fit$Gamma(fit$fit$X)
Gamma_sqrt <- chol(Gamma)
Lambda_detrended <- Theta + (Lambda - Theta)%*%solve(Gamma_sqrt)
lags <- list()
for(i in 1:(length(observations)-1)) {
for(j in (i+1):length(observations)) {
diff_week <- round(abs(observations[i] - observations[j])/7)
if(is.na(diff_week)) {
cat(i,",",j,"\n")
}
lag_str <- as.character(diff_week)
if(use_Eta) {
if(use_detrended) {
Eta_detrended <- Lambda_detrended + (Eta - Lambda)
ij_correlation <- cor(Eta_detrended[,i], Eta_detrended[,j])
} else {
ij_correlation <- cor(fit$fit$Eta[,i,1], fit$fit$Eta[,j,1])
}
} else {
if(use_detrended) {
ij_correlation <- cor(Lambda_detrended[,i], Lambda_detrended[,j])
} else {
ij_correlation <- cor(Lambda[,i], Lambda[,j])
}
}
if(lag_str %in% names(lags)) {
lags[[lag_str]] <- c(lags[[lag_str]], ij_correlation)
} else {
lags[[lag_str]] <- c(ij_correlation)
}
}
}
df <- rbind(df, data.frame(x = as.numeric(names(lags)), y = sapply(lags, function(lag) mean(lag)), host = host))
}
p <- ggplot(df[df$x < 104,]) +
geom_smooth(aes(x = x, y = y)) +
geom_point(aes(x = x, y = y)) +
xlab("lag (weeks)") +
ylab("ACF")
show(p)
if(use_Eta) {
save_file <- "Eta"
} else {
save_file <- "Lambda"
}
if(use_detrended) {
save_file <- paste0(save_file, "_detrended")
}
save_file <- paste0(save_file, ".png")
ggsave(paste0("C:/Users/kim/Desktop/",save_file), p, dpi = 100, units = "in", height = 6, width = 10)
|
e88a4b532cae62224dca90e72c9d9c3a821ca642 | 5f73594e48bc6913ece984b713f227762b00aa94 | /tfa-package/man/world_pop.Rd | 08446f5d835bd296dea1d011d30b5db1c4bb2061 | [] | no_license | raquelsimoes/tfa | 962eb8bc29f94448d68348da0a94ad39546869f3 | 2debe0c04ceee03f383331a1001ba94f6438fcd1 | refs/heads/master | 2023-09-02T22:51:50.623832 | 2021-11-23T08:57:26 | 2021-11-23T08:57:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 488 | rd | world_pop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{world_pop}
\alias{world_pop}
\title{World population}
\format{
A data frame with 376 rows and 2 variables:
\itemize{
\item \strong{Zip} Zip code.
\item \strong{Area} Name of area.
}
}
\source{
Gapminder project (http://gapm.io/dl_pop).
}
\usage{
world_pop
}
\description{
A dataset containing with populations for each country 1800-2100.
}
\examples{
world_pop
}
\keyword{datasets}
|
56c1be9c75d5ef963fcc141956c4994f1825e0f6 | 619c4321ffc122fa0333a7e35e6fba16d1f9368b | /man/FarmerB_N_sec35middle_2016.Rd | 63d44b3c4c8a317b305c1b59cf096bf120ae2b2c | [
"MIT"
] | permissive | paulhegedus/OFPEDATA | 4a6c3add78208f0250139c55d061f31775d56789 | ae70c5d8ad4fe154fd4b746a7ec43f3031a6e0a9 | refs/heads/master | 2022-12-22T21:51:29.566593 | 2020-09-14T00:14:19 | 2020-09-14T00:14:19 | 278,233,610 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,290 | rd | FarmerB_N_sec35middle_2016.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FarmerB_N_sec35middle_2016.R
\docType{data}
\name{FarmerB_N_sec35middle_2016}
\alias{FarmerB_N_sec35middle_2016}
\title{FarmerB_N_sec35middle_2016}
\format{
A data frame with 5802 rows and 10 variables:
\describe{
\item{\code{id}}{integer ID integer for each data point.}
\item{\code{client_nam}}{character Name of client (farmer/company.}
\item{\code{farm_name}}{character Name of farm.}
\item{\code{field_name}}{character Field name.}
\item{\code{vrappratev}}{double As-applied variable rate (gal/ac).}
\item{\code{time}}{character Time of application.}
\item{\code{latitude}}{character Latitude in decimal degrees.}
\item{\code{longitude}}{character Longitude in decimal degrees.}
\item{\code{orig_file}}{character Original file name of shapefile.}
\item{\code{geometry}}{list Coordinates of application polygons.}
}
}
\usage{
FarmerB_N_sec35middle_2016
}
\description{
Variable rate nitrogen fertilizer polygon data from Farmer B's
John Deere sprayer in 2016. Product is 32\% UAN, applied as a liquid in
gallons per acre. Use a conversion factor of 3.5 gal/acre to get to
lbs N/acre. Data is raw experimental data with attributes recorded by
Farmer B in sec35middle.
}
\details{
DETAILS
}
\keyword{datasets}
|
864be1b8cc589fcdeff104e9abf7641793deeed8 | e3d8bbcc3424296f5d3e0cc3b516b31173776c2f | /R/Day 1 - Data generation solution.R | 5e30dc136a36b1a0ce3dff9355391e364d3c65ec | [] | no_license | gbisschoff/grad-training | 5ed911f5d8e14ff02caf34e57f688aa4b132ef07 | 586db2156ee787841c109b1668d750c981f11535 | refs/heads/master | 2021-05-14T11:43:35.395413 | 2018-01-24T13:10:56 | 2018-01-24T13:10:56 | 116,390,664 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,429 | r | Day 1 - Data generation solution.R | # Day 1 - Data generation solution
PD<-dlnorm(seq(0.05,3,by = 0.05), meanlog = 0, sdlog = 1, log = FALSE)/6
generate_account<-function(id,PD){
id<-paste("account",id,sep = "-")
segment<-sample(LETTERS[1:3],1)
start_balance<-runif(1,5000,6000)
interest_rate<-runif(1,0.05,0.20)/12
n<-60
pmt<-start_balance*(interest_rate*(1+interest_rate)^n)/((1+interest_rate)^n-1)
for(i in 1:60){
if(i==1){
balance_contractual<-start_balance*(1+interest_rate)-pmt
made_payment<-rbinom(1,1,1-PD[i])
balance_actual<-start_balance*(1+interest_rate)-pmt*made_payment[i]
}else{
balance_contractual<-c(balance_contractual,balance_contractual[i-1]*(1+interest_rate)-pmt)
made_payment<-c(made_payment,rbinom(1,1,1-PD[i]))
balance_actual<-c(balance_actual,balance_actual[i-1]*(1+interest_rate)-pmt*made_payment[i])
}
}
data<-data.frame(
id=id,
segment=segment,
start_balance=start_balance,
interest_rate=interest_rate,
n=n,
age=1:n,
pmt=pmt,
balance_contractual=balance_contractual,
made_payment=made_payment,
balance_actual=balance_actual,
arrears=balance_contractual-balance_actual,
cd_bucket=-ceiling((balance_contractual-balance_actual)/pmt)
)
}
account<-lapply(
1:100,
generate_account,
PD=PD
)
data<-dplyr::bind_rows(account)
# Export
data%>%rio::export("Data/tranition_data.csv")
|
8457a611f119666463295d72012c01365618907f | 83900fae385d232cc5d84b18d3fa9bdaf5a05432 | /KnnProject.R | cd44f1dc1aa24ac79c209e44e3efe47963ce08a7 | [] | no_license | vinaysheelwagh/machine-Learning-model-evaluation | 5eca7f93acae0ea6500b74fd0b1463160a16fca1 | 23f6b6e967b3273538ce1e673441606e81f4c33b | refs/heads/master | 2020-12-15T17:15:21.069015 | 2020-01-20T20:33:20 | 2020-01-20T20:33:20 | 235,191,396 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,558 | r | KnnProject.R | # Load packages
packages <- c("dummies","descr","ROSE","ggplot2", "dplyr", "MASS" ,"pROC","caret","e1071","corrplot","data.table","Amelia","arm","ModelMetrics","class")
lapply(packages, library, character.only = TRUE)
knnShoppersData <- read.csv("C:\\Users\\Vinaysheel Wagh\\Downloads\\online_shoppers_intention.csv",header=TRUE,sep=",")
str(knnShoppersData)
knnShoppersData[ knnShoppersData == "?"] <- NA
colSums(is.na(knnShoppersData))
knnShoppersData$VisitorType <- as.numeric(knnShoppersData$VisitorType)
knnShoppersData$Month <- as.numeric(knnShoppersData$Month)
knnShoppersData$Weekend <- as.numeric(knnShoppersData$Weekend)
knnShoppersData$Revenue <- as.numeric(knnShoppersData$Revenue)
str(knnShoppersData)
M <- cor(knnShoppersData)
corrplot(M, method="circle")
knnShoppersData$VisitorType<- as.factor(knnShoppersData$VisitorType)
knnShoppersData$Month<- as.factor(knnShoppersData$Month)
knnShoppersData$Weekend<- as.factor(knnShoppersData$Weekend)
knnShoppersData$Revenue<- as.factor(knnShoppersData$Revenue)
n <- sapply(knnShoppersData, function(x){is.numeric(x)})
numerics <- knnShoppersData[,n]
summary(numerics)
normalize <- function(x){return((x-min(x))/(max(x)-min(x)))}
numericsNormal <- normalize(numerics)
summary(numericsNormal)
shoppersDataKnn <- knnShoppersData[,!n]
shoppersDataKnn <- cbind(shoppersDataKnn,numericsNormal)
str(shoppersDataKnn)
#############Feature selection
shoppersDataKnn<- subset(shoppersDataKnn,select = c(1,4,5,6,7,8,9,10,13))
str(shoppersDataKnn)
#########################
set.seed(1400)
id <- sample(2,nrow(shoppersDataKnn),prob= c(0.70,0.30),replace=T)
knnTrain <-shoppersDataKnn[id==1,]
knnTest <-shoppersDataKnn[id==2,]
library(class)
knnmodel <- knn(train = knnTrain[,-2],test = knnTest[,-2],cl=knnTrain[,2],k=4)
### Model Evaluation
caret::confusionMatrix(knnmodel, knnTest[,2]) #Accuracy is 84.14% k=4
#######Performance tunning#############
#knnModel value contains value of resampling results across tunning parameters that can be used to explain performance tunning
i=1 # declaration to initiate for loop
k.optm=1 # declaration to initiate for loop
for (i in 1:30){
knn.mod <- knn(train=knnTrain[,-2], test=knnTest[,-2], cl=knnTrain[,2], k=i)
k.optm[i] <- 100 * sum(knnTestLabel == knn.mod)/NROW(knnTest[,2])
k=i
cat(k,'=',k.optm[i],'\n') # to print % accuracy k=10 gives accuracy of 85.01% after that success rate becomes constant for increase in k value
}
|
fc9a94aaeaa02ff2c226c84f5ecc7f5fa1f74757 | 0575a2c951639cfe77812dd33f8f024898b4a932 | /man/make_labels_quarters.Rd | aaf048dcc2817f3f559e7b4fbd3c4933a4837b4f | [
"MIT"
] | permissive | bayesiandemography/demprep | 8f400672fbbee9d92f852056d4ff7cb31a7fc87a | 3aa270ff261ab13570f3ba261629031d38773713 | refs/heads/master | 2021-12-29T04:00:24.929536 | 2021-12-16T22:15:01 | 2021-12-16T22:15:01 | 204,109,024 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,752 | rd | make_labels_quarters.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make_labels.R
\name{make_labels_quarters}
\alias{make_labels_quarters}
\title{Make Quarters labels}
\usage{
make_labels_quarters(x)
}
\arguments{
\item{x}{A list of Date vectors.}
}
\value{
A character vector
}
\description{
Make labels with the format expected for an
object of class \code{"Quarters"}
(as defined in the demarray package).
This function would not normally be called directly
by end users.
}
\details{
\code{x} is a list of Date vectors of length 2.
If elements of a vector are non-NA, then the second
element must one quarter after the first.
Any non-NA elements must be the first day of a quarter.
The vectors cannot overlap.
The elements of \code{x} are converted into
labels as follows:
\tabular{ll}{
Element \tab Label \cr
\code{as.Date(c("2020-01-01", "2020-04-01"))} \tab \code{"2020 Q1"} \cr
\code{as.Date(c(NA, "2020-01-01"))} \tab \code{"<2020 Q1"} \cr
\code{as.Date(c("2020-01-01", NA))} \tab \code{"2020 Q1+"} \cr
\code{as.Date(c(NA, NA))} \tab \code{NA} \cr
}
}
\examples{
x <- list(as.Date(c("2020-10-01", "2021-01-01")),
as.Date(c(NA, "2020-01-01")),
as.Date(c(NA_character_, NA_character_)),
as.Date(c("2025-01-01", NA)),
as.Date(c("2020-01-01", "2020-04-01")))
make_labels_quarters(x)
}
\seealso{
\code{\link{make_labels_categories}},
\code{\link{make_labels_triangles}},
\code{\link{make_labels_directions}},
\code{\link{make_labels_quantiles}},
\code{\link{make_labels_integers}},
\code{\link{make_labels_intervals}},
\code{\link{make_labels_quantities}},
\code{\link{make_labels_months}},
\code{\link{make_labels_dateranges}},
\code{\link{make_labels_datepoints}}
}
\keyword{internal}
|
5fa1de807098c63f56f2b86b8f60079bc9394f23 | b9c02efd9d459a64beb87a7d459f128e9365d18b | /R/filter_trace_length_percentile.R | a19aa2d164691e9cd9fa457612c13693e686e8bb | [] | no_license | cran/edeaR | 0cc89ba53b436438f2ef21904605ae68ee91f039 | 4a77e919794ac7e6c65fdac4bb015a7e0c5562ea | refs/heads/master | 2023-05-14T01:22:27.650585 | 2023-04-27T07:33:06 | 2023-04-27T07:33:06 | 54,409,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 266 | r | filter_trace_length_percentile.R |
filter_trace_length_percentile <- function(eventlog,
percentage,
reverse)
{
eventlog %>%
trace_length("case") %>%
slice(1:ceiling(n()*percentage)) %>%
pull(1) -> case_selection
filter_case(eventlog, case_selection, reverse)
}
|
c9c6864605f4e8564ffb31781d829b205fe3bf84 | 3c78721fd0fe8fcf8b058a734f84157b5d2a0b55 | /man/cigarFromMSA.Rd | c6a93392aa64ae56ae29d8f9d8d1fd085787f8cd | [] | no_license | HLindsay/AlignmentTools | 424acc8b2695a8ce77dcca41d05805e72719f41c | 4eea239de9593829560b02a41eef359eac6afac6 | refs/heads/master | 2021-08-19T04:06:39.511149 | 2017-11-24T17:55:15 | 2017-11-24T17:55:15 | 111,944,363 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,024 | rd | cigarFromMSA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cigarFromMSA.R
\docType{methods}
\name{cigarFromMSA}
\alias{cigarFromMSA}
\alias{cigarFromMSA,MsaDNAMultipleAlignment-method}
\alias{cigarFromMSA,DNAStringSet-method}
\title{CIGAR from Multiple Sequence Alignment}
\usage{
cigarFromMSA(aln, ...)
\S4method{cigarFromMSA}{MsaDNAMultipleAlignment}(aln, ..., ref = NULL)
\S4method{cigarFromMSA}{DNAStringSet}(aln, ..., ref = NULL)
}
\arguments{
\item{aln}{A multiple sequence alignment}
\item{...}{Extra arguments}
\item{ref}{character(1) Name of sequence in aln to use as a
reference (Default: NULL)}
\item{ref}{character(1) name of the alignment in alns to use as a reference
(Default: NULL)}
}
\description{
Construct a CIGAR string from a multiple sequence
alignment
}
\examples{
aln <- DNAStringSet(c("AA--CC","AATTCC","AATTC-"))
names(aln) <- c("A","B","C")
cigarFromMSA(aln)
cigarFromMSA(aln, ref = "A")
cigarFromMSA(aln, ref = "B")
cigarFromMSA(aln, ref = "C")
}
\author{
Helen Lindsay
}
|
bc240847c4da9196cb4122958dec6ed446683e6a | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Miller-Marin/trafficlight-controller/tlc02-nonuniform-depth-11/tlc02-nonuniform-depth-11.R | c4d4bac12f781f85c09d488b1b1234203d4fc389 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 76 | r | tlc02-nonuniform-depth-11.R | 97c52f06a567f0fcdfb0944fe72bc6cd tlc02-nonuniform-depth-11.qdimacs 2749 7240 |
3e2b8ccc136a1f90ae3fa6229ee8678c2dd2eaae | a5e0580feef4a95e17d90f032ed610d1056eb5a2 | /plot3.R | 699110202a4e8bf42a0a091dd467bf026a65ca34 | [] | no_license | aloha6131/exploratory-data-analysis | 8d8d9c7be3d48310e1e4883eb897e1e137ebc146 | a18c9600ca5e5752552ebee70cdb97f5467e83b6 | refs/heads/master | 2021-01-01T04:03:54.672870 | 2017-07-24T16:35:13 | 2017-07-24T16:35:13 | 97,117,013 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,183 | r | plot3.R | #### plot3.R ####
Sys.setlocale(category = "LC_ALL", "en_US.UTF-8")
if(!file.exists("PowerData.zip")) {
fileURL<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileURL,"PowerData.zip")
unzip("PowerData.zip")
}
data <- read.csv("household_power_consumption.txt", sep = ";", na.strings="?")
# data <- read.csv("exploratory-data-analysis/household_power_consumption.txt", sep = ";")
data <- data[as.character(data$Date) %in% c('2/2/2007','1/2/2007'),]
data$DateTime <- strptime(paste(data$Date,data$Time), format="%d/%m/%Y %H:%M:%S")
## Plot Submetering_1
plot(x=data$DateTime,
y=data$Sub_metering_1,
type="l",
col="black",
xlab="",
ylab="Energy sub metering"
)
## Plot sub_metering_2
lines(x=data$DateTime,
y=data$Sub_metering_2,
type="l",
col="red")
## Plot sub_metering_3
lines(x=data$DateTime,
y=data$Sub_metering_3,
type="l",
col="blue")
legend("topright",
legend = c('Sub_metering_1', 'Sub_metering_2', 'Sub_metering_3'),
lwd=1,
col=c("black", "red", "blue"))
dev.copy(png, filename="plot3.png", width=480, height=480)
dev.off()
|
4652b1037659c94abec363cf928d7c73c89ffe99 | 795007f0a113ff47068bfa5207cb627f4a354b77 | /R/cleanQ.R | a7e895b850c6fc4b9dd37dac6aa6bc1f072afd26 | [] | no_license | soohyuna/tidyQ | d6cacbf5ff28931d1aac58268c20503db28a9a14 | f84c8359b1279d8e0da1ff6b7cbe9542e8cb5a7f | refs/heads/master | 2021-05-26T04:51:26.794845 | 2020-09-03T15:42:15 | 2020-09-03T15:42:15 | 127,472,337 | 1 | 2 | null | 2019-07-31T20:31:22 | 2018-03-30T20:56:26 | R | UTF-8 | R | false | false | 889 | r | cleanQ.R | #' A function to clean a xls imported data frame
#'
#' @param df A data frame.
#' @param sep_samp Separate sample column into individual columns.
#' @param sep_var Variables of variables to be separated.
#' @return Data frame suitable for downstream tidyQ analysis.
#' @keywords clean, tidy, import
#' @export
cleanQ <- function(df, sep_samp = FALSE, sep_var) {
#rawCT <- paste0("C","\u0442")
clean_df <- df %>%
dplyr::rename("Sample" = `Sample Name`,
"Gene" = `Target Name`) %>%
dplyr::select(-(Task:`RQ Max`)) %>%
dplyr::filter(!CT %in% c("NA","Undetermined")) %>%
dplyr::mutate_at(vars(1:Gene), as.factor) %>%
dplyr::mutate_at(vars(CT), as.numeric)
if(sep_samp == FALSE){
return(clean_df)
} else if(sep_samp == TRUE){
clean_df %>%
tidyr::separate(Sample,
into = sep_var,
sep = "_")
}
}
|
2e4e8cb2ae58d2aea97db001ec7e7d14ddb2573a | 99278dd1a2fb43892203f02ec4ae0f1f373d557b | /man/l_ccmaps.Rd | 844baec9dde7f6dc4cca171749b85f02069b697d | [] | no_license | z267xu/loon.micromaps | d7b09e328d76f064f1a1bdd2e5cee6a728cb6ce3 | e61d45e14fafae1cbd9ea52f3cf00b552e29652d | refs/heads/master | 2020-03-28T21:09:46.880675 | 2018-09-23T15:14:18 | 2018-09-23T15:14:18 | 149,134,122 | 0 | 0 | null | 2018-09-17T14:01:46 | 2018-09-17T14:01:46 | null | UTF-8 | R | false | true | 4,609 | rd | l_ccmaps.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/l_ccmaps.R
\name{l_ccmaps}
\alias{l_ccmaps}
\title{Conditioned Choropleth Maps (Conditioned Micromaps) in loon}
\usage{
l_ccmaps(tt = tktoplevel(), cc_inspector = TRUE, title = "CCmaps",
spdf, respvar, respvar.lab = NULL, cond1var, cond1var.lab = NULL,
cond2var, cond2var.lab = NULL, respbreaks = 2, cond1breaks = 2,
cond2breaks = 2, respscale = c("actual", "percent", "log"),
cond1scale = c("actual", "percent", "log"), cond2scale = c("actual",
"percent", "log"), size = 10, seg1col = "blue",
seg2col = "darkgrey", seg3col = "red", optimize = FALSE,
otry = 20)
}
\arguments{
\item{tt}{Tk top level window. Defaults to a new window}
\item{cc_inspector}{Whether to draw the custom inspector for CCmaps, which
allows for variable selection, variable label update, font size adjustment
and option to optimize \eqn{R^2}. Defaults to TRUE. Once created, the inspector
can only be closed when the main display window is closed}
\item{title}{Title of the map. Appears in the title bar of the toplevel window.
Defaults to "CCmaps"}
\item{spdf}{\code{SpatialPolygonsDataFrame} object to hold polygon coordinates
and attributes. It should contain all variables used in analysis}
\item{respvar}{Name of the response value variable}
\item{respvar.lab}{Label for response value variable for slider. Defaults to NULL,
in which case \code{respvar} is used}
\item{cond1var}{Name of the first conditioning variable (controls
panel assignment in the vertical direction)}
\item{cond1var.lab}{Label for first conditional variable for slider. Defaults to NULL,
in which case \code{cond1} is used}
\item{cond2var}{Name of the second conditioning variable (controls
panel assignment in the horizontal direction)}
\item{cond2var.lab}{Label for second conditional variable for slider. Defaults to NULL,
in which case \code{cond2} is used}
\item{respbreaks}{Determines how the response data is divided into three groups
for coloring scheme. It can either be the integer 2 or a numeric vector of
two middle break points. Defaults to 2, in which case the response values are divided
into tertiles}
\item{cond1breaks}{Similar to \code{respbreaks}; determines how the first conditioning
variable values are divided into three groups for panel membership}
\item{cond2breaks}{Similar to \code{respbreaks}; determines how the second
conditioning variable values are divided into three groups for panel membership}
\item{respscale}{What scale to use for drawing the response variable slider. Must be
one of three values - actual (unchanged), percent (quantiles) and log}
\item{cond1scale}{What scale to use for drawing the first conditioning variable slider.
Must be one of three values - actual (unchanged), percent (quantiles) and log}
\item{cond2scale}{What scale to use for drawing the second conditioning variable slider.
Must be one of three values - actual (unchanged), percent (quantiles) and log}
\item{size}{Font size for model value labels and\eqn{R^2}. Defaults to size 10}
\item{seg1col}{Color of first interval of points by \code{respvar} value.
Cannot be 'cornsilk'. Defaults to 'blue'}
\item{seg2col}{Color of second interval of points by \code{respvar} value.
Cannot be 'cornsilk'. Defaults to 'darkgrey'}
\item{seg3col}{Color of third interval of points by \code{respvar} value.
Cannot be 'cornsilk'. Defaults to 'red'}
\item{optimize}{Logical value indicating whether panel assignment should be
optimized for \eqn{R^2}. Requires a long time to compute. Defaults to FALSE}
\item{otry}{Integer (greater than 0) indicating number of values to try
for optimization (see above). Required if \code{optimize = TRUE}.
Defaults to 20. A higher \code{otry} value leads to more precise estimates
at the cost of longer computation time}
}
\value{
An object of classes \code{l_ccmaps} and \code{loon}, containing the
Tk toplevel window, \code{respvar} value, \code{cond1var} value,
\code{cond2var} value, and the handles for the \code{loon} map plot objects
in list form
}
\description{
2-way panel of maps for visualizing multivariate data
}
\examples{
\dontrun{
## Get data
library(rgdal)
library(maptools)
columbus <- readOGR(system.file("shapes/columbus.shp", package = "maptools")[1], verbose = F)
## Plot
cc <- l_ccmaps(title = "Columbus Residential Burglaries and Vehicle Thefts",
spdf = columbus,
respvar = "CRIME", cond1var = "PLUMB", cond2var = "HOVAL",
respscale = "actual", cond1scale = "actual", cond2scale = "actual",
optimize = FALSE)
}
}
|
1b7a902e81ef0d2f09f2129a736cc53fa888810f | 007c8de3044306dcdab75eec267541d986d83203 | /R/get_data.R | ced982acf5fcd8065230595a7f7a14a83c90ddf2 | [] | no_license | patperu/refpal | 69e92999b4a7d47d8b2edb765517d4863a3054c2 | 78dfcd71a3689f4674235fa28808c441e0367894 | refs/heads/master | 2021-01-01T03:36:33.447705 | 2016-05-01T16:28:25 | 2016-05-01T16:28:25 | 56,458,166 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 439 | r | get_data.R | #' get results
#'
#' @export
#' @param url An URL
get_refdata <- function(url) {
# url <- "http://referendum2016.comune.palermo.it/AFFLSEZ_1_82053_R1.xml"
x <- xml2::read_xml(url) %>%
rvest::xml_nodes('SV') %>%
rvest::html_attrs()
x <- data.frame(do.call("rbind", x), stringsAsFactors = FALSE)
x$TOTVOT <- as.numeric(x$TOTVOT)
x$ELETTORI <- as.numeric(x$ELETTORI)
x$pct_vot <- x$TOTVOT / x$ELETTORI * 100
x
}
|
6d90ffff9b170d197655099980f0f652e14d3049 | eae65f35c5096b07b944b15a28d598b3e4879cb7 | /gwmodel_final.R | 8802c267703c3123c13d7fd5dc2eb684c23d5f87 | [
"MIT"
] | permissive | ptjacobsen/Spatial-MN-Lakes | d8973eda2292ebd39e3e951b591afc4c8feb6912 | 99bfed23091e4525b86ef0259401b717d705b8d4 | refs/heads/master | 2020-03-22T14:19:28.863897 | 2018-08-27T16:24:43 | 2018-08-27T16:24:43 | 140,170,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,703 | r | gwmodel_final.R | setwd('/home/ptjacobsen/Geocomputation/Dissertation/MN Lakes/')
library(GWmodel)
library(sp)
library(rgdal)
source('P/gwmodel_plotting.R')
read_lc_file <- function(ring_size,year) {
lc <- read.csv(paste0('D/Land Cover/Land Cover Surrounding Lakes ',ring_size,'ring ',year,'.csv'))
lc$wet <- (lc$OW.share + lc$WW.share + lc$EHW.share)
lc$developed <- (lc$D.OS.share + lc$D.LI.share + lc$D.MI.share + lc$D.HI.share)
lc$natural <- (lc$DF.share + lc$EF.share + lc$MF.share + lc$S.share + lc$GL.share)
lc$ag <- (lc$PH.share + lc$CC.share)
return(lc)
}
read_lc_data <- function(ring_size) {
lc2001 <- read_lc_file(ring_size,2001)
lc2006 <- read_lc_file(ring_size,2006)
lc2011 <- read_lc_file(ring_size,2011)
lc <- data.frame(dowlknum = lc2001$dowlknum)
for (v in names(lc2001)) {
if (v=='dowlknum') {
next
}
lc[,v] <- (lc2001[,v] + lc2006[,v] + lc2011[,v]) / 3
}
return(lc)
}
data <- read.csv('D/Water Samples/by lake.csv')
data<- merge(data,read_lc_data('1k'),by='dowlknum')
lakes <- readOGR(dsn="D/DNR HYDRO/lakes clean",layer="lakes clean", stringsAsFactors=F)
lakes@data$lakeidx <- 1:nrow(lakes)
lakes_subset <- cbind(coordinates(lakes),lakes@data[,c('dowlknum','shape_Area','lakeidx')])
names(lakes_subset)[1:2] <- c('X','Y')
data <- merge(data,lakes_subset,by='dowlknum')
data <- merge(data,read.csv('D/Land Cover/surrounding building count.csv'),by='dowlknum')
#significant drop in observations here. probably selection bias
data <- merge(data,read.csv('D/Bathymetry/Lake Depths.csv'),by='dowlknum')
adj_dmat_all <- readRDS('D/adjusted_dmat.rds')
lakes_used <- as.character(unique(data$dowlknum))
adj_dmat <- adj_dmat_all[lakes_used,lakes_used]
data_spdf0 <- SpatialPointsDataFrame(data[,c('X','Y')],data,proj4string = lakes@proj4string)
#convert dist to km. There somes to be some sort of interger overflow in gwmodel when working with UTM coordinates. No errors when using Km
adj_dmat <- adj_dmat / 1000
data_spdf <- SpatialPointsDataFrame(data[,c('X','Y')]/1000,data,proj4string = lakes@proj4string)
vrn_mn_carto <- gen_mn_cartogram(data_spdf0)
fm <- tsi ~ wet + developed + natural + ag + building.per.km.shore + log(shape_Area) + shallow.share + abs_depth
lm1 <- lm(fm,data_spdf)
myplot(lm1$residuals,vrn_mn_carto)
bw <- bw.gwr(fm,data_spdf,kernel='bisquare',dMat = adj_dmat,adaptive = T) #got the same doing AIC approach
bw_nodist <- bw.gwr(fm,data_spdf,kernel='bisquare',adaptive = T)
gwm1 <- gwr.basic(fm,data_spdf,bw=bw,kernel='bisquare',dMat=adj_dmat,adaptive = T)
gwm1_nodist <- gwr.basic(fm,data_spdf,bw=bw_nodist,kernel='bisquare',adaptive=T)
####wow look at how much better my distances are
myplot(gwm1$SDF@data$developed,vrn_mn_carto)
#show extreme cn numbers
coldiag <- gwr.collin.diagno(fm,data_spdf,bw=bw,kernel='bisquare',dMat=adj_dmat,adaptive=T)
myplot(coldiag$local_CN,vrn_mn_carto)
#show VIF summaries
colnames(coldiag$VIF) <- attr(terms(fm),'term.labels')
summary(coldiag$VIF)
#make PCA indexes
pca <- princomp(data_spdf@data[,14:28],cor=T)
pca$sdev^2
vari_share <- pca$sdev^2/sum(pca$sdev^2)
sum(vari_share[1:4])
pca$loadings
data_spdf@data$pca_forest <- pca$scores[,1]
data_spdf@data$pca_ag <- pca$scores[,2]
data_spdf@data$pca_wood_crop <- pca$scores[,3]
data_spdf@data$pca_water_ef <- pca$scores[,4]
#data_spdf@data$pca_barren <- pca$scores[,5]
#new gwm.
#reasonable parameters, only slight decrease in r2
fm2 <- tsi ~ pca_forest + pca_ag + pca_wood_crop + pca_water_ef + building.per.km.shore + log(shape_Area) + shallow.share + abs_depth
bw2 <- bw.gwr(fm2,data_spdf,kernel='bisquare',dMat = adj_dmat,adaptive = T)
gwm2 <- gwr.basic(fm2,data_spdf,bw=bw2,kernel='bisquare',dMat=adj_dmat,adaptive = T)
coldiag2 <- gwr.collin.diagno(fm2,data_spdf,bw=bw,kernel='bisquare',dMat=adj_dmat,adaptive=T)
#CN still kinda high
myplot(coldiag2$local_CN,vrn_mn_carto) #reigned in but generally high for us still
#check VIF. whats the problem now?
colnames(coldiag2$VIF) <- attr(terms(fm2),'term.labels')
summary(coldiag2$VIF)
#top two pca are high
#not enough local variation in land cover to effectively model
myplot(coldiag2$VIF[,'pca_forest'],vrn_mn_carto)
#such limited local variability
myplot(gwm2$SDF@data$Stud_residual,vrn_mn_carto)
myplot(gwm2$SDF@data$pca_ag,vrn_mn_carto)
myplot(gwm2$SDF@data$pca_ag,vrn_mn_carto)
#apply ridge?
bw3 <- bw.gwr.lcr(fm2,data_spdf,kernel='bisquare', adaptive=T, lambda.adjust=T,dMat=adj_dmat,cn.thresh=30)
gwm3 <- gwr.lcr(fm2,data_spdf, bw=bw3, kernel="bisquare",adaptive=T, lambda.adjust = T, dMat=adj_dmat,cn.thresh = 30)
#thats the best we can do
#now interpret.
#houses don't make sense but track something else
|
aae00a2dea01a05919522f47bf4a69d9ca7d5a8e | a3768945aa97c3e226fceaa1cdd5ec4ae79ecd3e | /tm_overall_scraper.R | 5e1c7a33ffe2656993abb021a054a5508ac90aa7 | [] | no_license | Rahminatti/useful_nba_datasets | 89c2d1799130dbdfd4e8ce527e840b0e6061f8dc | ce78ef0ff888a9d99daf6cbe2acf4c92ffca0570 | refs/heads/master | 2022-12-06T02:15:19.202488 | 2020-08-19T13:14:38 | 2020-08-19T13:14:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,018 | r | tm_overall_scraper.R | ####################################################
# This script simply scraped the team stats for each
# team from 2005 to 2018 form bball reference.
####################################################
setwd("~/Desktop/Threes and Layups Articles/Useful Things")
source('tm_table_scrape.R')
years <- seq(2005, 2020,1)
tab <- list()
for(i in 1:length(years)){
yr <- years[i]
misc <- tm_table_scrape(yr = yr, id = 'misc_stats_link')
opp_per_poss <- tm_table_scrape(yr = yr, id ='opponent-stats-per_poss_link')
tm_per_poss <- tm_table_scrape(yr = yr, id = 'team-stats-per_poss_link')
opp_shoot <- tm_table_scrape(yr = yr, id ='opponent_shooting_link')
tm_shoot <- tm_table_scrape(yr = yr, id ='team_shooting_link')
misc <- merge(misc, tm_per_poss, by = 'tm')
misc <- merge(misc, opp_per_poss, by = 'tm')
misc <- merge(misc, tm_shoot, by = 'tm')
misc <- merge(misc, opp_shoot, by = 'tm')
tab[[i]] <- misc
print(yr)
}
tm_stats <- do.call('rbind', tab)
write.csv(tm_stats, 'tm.data.05.20.csv', row.names = F)
|
1127b2e43b89db5987f40d5bccc3b2442a7ef156 | a3b4006dd6b77ff0818c751d9c6ae6d32c8a0377 | /tests/testthat/test-compatibility.R | cc41a49103124c628112c6964e287909eb7ac1c5 | [
"BSD-2-Clause",
"MIT"
] | permissive | tidyverse/readxl | 98a61dbb111848100783d5c977becee6cf3cd749 | 3aa8c2ddf9f1d8921f2a8b42ae0bdfa69a22ed9b | refs/heads/main | 2023-07-20T16:12:41.510109 | 2023-07-07T02:58:27 | 2023-07-07T02:58:27 | 32,161,666 | 432 | 130 | NOASSERTION | 2023-02-08T23:07:26 | 2015-03-13T14:50:20 | C++ | UTF-8 | R | false | false | 3,196 | r | test-compatibility.R | test_that("can read document from google doc", {
iris_1 <- read_excel(test_sheet("iris-excel-xlsx.xlsx"))
iris_2 <- read_excel(test_sheet("iris-google-doc.xlsx"))
expect_equal(iris_1, iris_2)
})
## #180, #152, #99
## Some 3rd-party s/w writes xls where lastcol is 0-indexed, like lastrow
## Changed an inequality in xls_cell() in xls.c to accomodate this
## WriteXLS is (or, rather, wraps) such s/w, so it's good source of such xls.
## WriteXLS::WriteXLS(head(mtcars), file.path("tests", "testthat", "sheets", "mtcars.xls"))
test_that("can tolerate xls that underreports number of columns", {
df <- read_excel(test_sheet("mtcars.xls"))
expect_identical(ncol(df), ncol(mtcars))
})
## #80
## The infamous Ekaterinburg sheet is written by an unspecified BI tool.
## Tricky for several reasons:
## * Worksheet target paths demand full lookup (#233)
## * Worksheet target paths are prefixed with `/xl/` (#294)
## * Nonstandard XML namespace prefixes (#295)
test_that("we can finally read Ekaterinburg", {
ek <- read_excel(test_sheet("Ekaterinburg_IP_9.xlsx"), skip = 2)
expect_identical(ek[[1, 2]], "27.05.2004")
})
## #309
## A BIFF5 xls from an unspecified 3rd party tool, that uses a very antiquated
## practice of storing text strings in LABEL records.
## Exposed the fact that the libxls patch in #293 is beneficial for indexing
## the shared string table, but causes difficulty when parsing LABEL records.
## We have a small patch now in libxls for that.
test_that("we can read the BIFF5, LABEL record sheet", {
df <- read_excel(
test_sheet("biff5-label-records.xls"),
skip = 2,
na = c("", "--")
)
expect_identical(dim(df), c(14L, 4L))
expect_identical(df$Date[c(1, 14)], c("21/01/2017", "21/01/2017"))
expect_identical(df$Time[c(1, 14)], c("01:00", "14:00"))
})
## https://github.com/tidyverse/readxl/pull/429
## <c r="C2" s="1" t="str"><f>A2 + B2</f></c>
test_that("formula cell with no v node does not cause crash", {
df <- read_excel(test_sheet("missing-v-node-xlsx.xlsx"))
expect_identical(df$`A + B`, NA)
})
## https://github.com/tidyverse/readxl/issues/435
## https://source.opennews.org/articles/how-we-found-new-patterns-la-homeless-arrest/
## LAPD uses a tool to produce xlsx that implements the minimal SpreadsheetML
## package structure described on pp65-66 of ECMA 5th edition
test_that("we can read LAPD arrest sheets", {
expect_no_error(
lapd <- read_excel(test_sheet("los-angeles-arrests-xlsx.xlsx"), skip = 2)
)
expect_identical(dim(lapd), c(193L, 36L))
expect_match(lapd$ARR_LOC[9], "HOLLYWOOD")
expect_identical(lapd$CHG_DESC[27], "EX CON W/ A GUN")
})
# https://github.com/tidyverse/readxl/issues/611
# xls file produced by ABBYY FineReader (OCR of PDFs)
# inspired libxls to add support for rich-text strings in BIFF5
# https://github.com/libxls/libxls/commit/b6d9d872756f69780b743dbaec9cd2ec30c37740
test_that("we can read xls from ABBYY FineReader", {
expect_no_error(
abbyy <- read_excel(
test_sheet("biff5-rich-text-string.xls"),
col_names = FALSE,
n_max = 1
)
)
expect_equal(nrow(abbyy), 1)
expect_equal(ncol(abbyy), 1)
expect_match(abbyy[[1,1]], "^ELECTORAL")
})
|
eac2d2d677d605426296600ae6602bf7046fdf4b | c998dd2344823de1b36045f5a940e116401414c0 | /vector.R | e68d7338339ebd15f596efae7262370588d32291 | [] | no_license | shivangg99/analytics | f77b040ef2c9ff39682649056fa13fb86f9a8e80 | 183e5e43e275c4dd609003707d64fd9db7a27127 | refs/heads/master | 2020-03-28T15:22:30.651594 | 2018-09-16T11:54:05 | 2018-09-16T11:54:05 | 148,586,231 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,417 | r | vector.R | #vector----
a<-1:10
a
x=c(1,2,3)
x1=1:1000000
length(x1)
x2=seq(10,100,2)
x2
?seq
x3=seq(from=10,to=100,by=4)
x3
#numeric vector----
(marks=rnorm(30,mean=60,sd=10))
mean(marks)
median(marks)
mode(marks) #no mode
sd(marks)
var(marks)
summary(marks)
boxplot(marks)
length(marks)
range(marks)
str(marks)
class(marks)
hist(marks)
plot(density(marks))
#character vector----
(names=c('Ram','Shyam','Robin'))
length(names)
mean(names) #not possible
class(names)
summary(names)
gender=c('M','F','M')
summary(gender)
genderF=factor(gender) #categorical data conversioon
summary(genderF)
(grades=c('A','B','C','D','A','D','A'))
gradesF=factor(grades,ordered=T)
summary(gradesF)
gradesF
gradesF1=factor(grades,ordered=T,levels=c('D','B','A','C'))
summary(gradesF1)
gradesF1
table(gradesF1)
table(gender)
barplot(table(gradesF1))
pie(table(gradesF1))
median(gradesF1)
#logical vector----
(married = c(T,F,T,F,T,F))
#married
table(married)
summary(married)
class(married)
sum(married) #True means 1 False means 0
#subset of marks
marks
trunc(marks);round(marks);floor(marks);ceiling(marks)
(marks1=trunc(marks))
marks1[1]
marks1[18]
marks1[1:5]
(marks1[-2])
marks1[c(1,5,10,30)]
mean(marks1[c(1,5,10,30)])
(marks1[marks1>60])
marks1>60
length(marks1[marks1>60 & marks<75])
set.seed(1234)
gender2 = sample(c('M','F'),size=100000,replace=T,prob=c(0.3,0.7))
#gender2
table(gender2)
prop.table(table(gender2))
|
55787a8e4b99ce80dc0e33316a13d212a25b83fb | c7e56ac69989a3668fa2d97cac45a15792ebe982 | /NSFgrants/newgraphs.R | a12447eed6087186781e66ce173a0d94fe9d58d6 | [] | no_license | kaleidopop/NSFgrants | 641662970b194008d24084f280fe0431dcff2758 | 2ad1892785997ceb4830bad6cc59c5d3acf24d52 | refs/heads/master | 2021-01-10T09:37:25.890298 | 2015-12-21T23:46:34 | 2015-12-21T23:46:34 | 48,297,812 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,977 | r | newgraphs.R |
############################################################################################### INTRO
### Idenitfy the gap
ggplot(schoolsall,aes(x=obligation_action_date, y = logcost)) + geom_point(aes(color= action_type ))
ggplot(schoolsall,aes(x=obligation_action_date, y = fed_funding_amount/100000)) + geom_point(aes(color= action_type ))
## Winter holidays and increase towards the end of the year
ggplot(newdata1, aes(y=logcost, x=obligation_action_date)) + geom_point(aes(color = action_type))
ggplot(newdata1, aes(y=logcost, x=obligation_action_date)) + geom_point(aes(color = action_type)) + facet_wrap(~action_type)
#identify the gap
ggplot(newdata5, aes(y=logcost, x=obligation_action_date)) + geom_point(aes(color = action_type))
## Negative values (log) or no entries
ggplot(newdata5, aes(y=logcost, x=obligation_action_date)) + geom_point(aes(color = action_type))+ facet_wrap(~action_type)
## Negative values
ggplot(newdata5, aes(y=fed_funding_amount/100000, x=obligation_action_date)) + geom_point(aes(color = action_type))+ facet_wrap(~action_type)
## Cont/NEw/ Revision - difference in frequency and central cost
ggplot(schoolsall, aes(x=log(costmon), fill=action_type)) + geom_density(alpha = 0.6)
ggplot(schoolsall, aes(x=monstar, y=log(costmon), fill=action_type)) + geom_boxplot()+ facet_wrap(~action_type)
#### NEW/CONT... - length and cost
ggplot(schoolsall, aes(y=res_duration, x=log(costmon))) + geom_point(aes(color = action_type))+ facet_wrap(~action_type)
############################################################################################### FIELDS
### Highest cumulative fields
ggplot(sumbyprog, aes(x=fed_funding_amount, y=reorder(cfda_program_title, fed_funding_amount), fill = fiscal_year)) +
geom_point(size=3) +
theme(axis.text.x = element_text(angle=60, hjust = 1),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(color="grey60", linetype='dashed'))
## state summaries by type of research
bigstates <- schoolsall[which(schoolsall$principal_place_state_code == "CA" | schoolsall$principal_place_state_code == "DC" | schoolsall$principal_place_state_code == "MA"| schoolsall$principal_place_state_code == "NY"| schoolsall$principal_place_state_code == "PA"| schoolsall$principal_place_state_code == "TX"), ]
ggplot(bigstates, aes(x=principal_place_state_code, y=log(fed_funding_amount), fill = principal_place_state_code)) + geom_boxplot() + facet_wrap(~cfda_program_title) +guides(colour = guide_legend(override.aes = list(size=12)))
############################################################################################### STATES
### HIghest cumulative states
ggplot(sumbystate, aes(x=fed_funding_amount, y=reorder(principal_place_state_code, fed_funding_amount), fill = fiscal_year)) +
geom_point(size=3) +
theme_bw() +
theme(axis.text.x = element_text(angle=60, hjust = 1),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(color="grey60", linetype='dashed'))
### types of research - by new/cont/revise
ggplot(bigstates, aes(x=principal_place_state_code, y=log(costmon), fill = action_type)) + geom_boxplot() + facet_wrap(~action_type) + guides(colour = guide_legend(override.aes = list(size=12)))
### types of research by recipient types
ggplot(bigstates, aes(x=principal_place_state_code, y=log(costmon), fill = principal_place_state_code)) + geom_boxplot() + facet_wrap(~recipient_type) +guides(colour = guide_legend(override.aes = list(size=12)))
############################################################################################### MONTHS
ggplot(sumbymon, aes(x=fed_funding_amount, y=mondec, fill = fiscal_year)) +
geom_point(size=3) +
theme_bw() +
theme(axis.text.x = element_text(angle=60, hjust = 1),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(color="grey60", linetype='dashed'))
## Months do change
ggplot(schoolsall, aes(x=log(costmon), fill=mondec)) + geom_density(alpha = 0.1) + facet_wrap(~action_type) +geom_vline(aes(xintercept=mean(logcost), na.rm=T), color="red", linetype="dashed", size=1)
### Density grid - new thingy
ggplot(schoolsall, aes(x=mondec, y=log(costmon), fill=mondec)) + geom_boxplot() + facet_wrap(~action_type)
############################################################################################### RECIPIENT TYPE
## Highest cumulative type of recipient
ggplot(sumbyrect, aes(x=fed_funding_amount, y=recipient_type, fill = fiscal_year)) +
geom_point(size=3) +
theme_bw() +
theme(axis.text.x = element_text(angle=60, hjust = 1),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(color="grey60", linetype='dashed'))
|
1ccab7209a6de6115b38c5892b441bdb11537ac5 | d2bccceacfb75e84c076a53725ce0f2cc42c89a5 | /R/Essay 1/tmp2.R | f87e8d79000219fe5754f92154e74e40cf26be3e | [] | no_license | DawnGnius/Nonparameter | 596f9caf7ba3eede694ca709489c049871a9d1b8 | b323eeffa5791b3fa9f4a94507f0422a2f763612 | refs/heads/master | 2020-07-24T15:11:44.302449 | 2019-12-16T05:21:22 | 2019-12-16T05:21:22 | 207,965,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,008 | r | tmp2.R | # check if Z is decreasing with h
set.seed(111)
n <- 200
Num.Cmp <- 8
pro <- rep(1/8, Num.Cmp)
multi <- sample(1:Num.Cmp, n, replace = T, prob=pro)
mu <- 3*((2/3)^(1:Num.Cmp)-1)
sigma <- (2/3)^(1:Num.Cmp)
x <- NULL
for (ii in 1:Num.Cmp) {
com_txt <- paste("com", ii, " <- rnorm(length(which(multi==", ii, ")), mean=", mu[ii], ", sd=", sigma[ii], ")",sep="")
eval(parse(text=com_txt))
com_txt <- paste("x <- c(x, com", ii, ")", sep="")
eval(parse(text=com_txt))
}
rodeo.local.bw <- function(xx, x, h.init=5/log(log(n)), beta=0.9, cn=log(n)/n){
# bandwidth selection
# para: xx target point at which we want to estimate f(x)
# para: x samples
# para: beta learning rate
# value: h.init bandwidth
h1 <- h.init
while(TRUE){
Z.i <- ((xx - x)^2 - h1^2) * exp(- (xx - x)^2 / h1^2 / 2) / h1^4 / sqrt(2*pi)
Z <- mean(Z.i)
s <- var(Z.i)
lam <- sqrt(2*s*log(n*cn)) / 10
if (abs(Z) > lam) {
h1 <- h1 * beta
} else {
return(h1)
}
}
} |
0a3f6052fa9a9f0f2b3078ef5480b68942db1029 | e39d5cdbf492b38b7e0fe310aff51c78fa09a557 | /R/getSampleSimilarity.R | 07da76f8631df7386793134bcacf355c875998e1 | [
"MIT"
] | permissive | quevedor2/WadingPool | 4589c991e2421af5e860f55a37f3069995a6465c | 29790a79d7826b64fadc627cba115d7880e295e0 | refs/heads/master | 2023-05-04T15:28:09.913925 | 2021-05-21T12:50:04 | 2021-05-21T12:50:04 | 339,831,237 | 1 | 1 | MIT | 2021-03-16T23:45:42 | 2021-02-17T19:19:40 | R | UTF-8 | R | false | false | 7,681 | r | getSampleSimilarity.R | #' Generates a similarity matrix
#' @description Takes a path to a directory that contains
#' the [CHR]_filt.snps files and processes each to generate
#' a similarity matrix using a given similarityFun. If no
#' similarityFun is given, it generates one using the default
#' Jaccard metric.
#'
#' @param similarityFun Similarity function with two parameters, 'i' and 'j'.
#' @param rm_nocov If no filtering process took place prior, setting this to TRUE
#' will remove all SNPs where no sample have any coverage (Default=FALSE)
#' @param sample_matrix character: path to csv or vcf file to run similarity match on
#' @param matchmode character: Either 'autosome' or 'chrM' (Default=autosome)
#' @param rem_ref boolean: Removes reference matches in chrM sample matching (0/0 and
#' 0/0) (Default=TRUE)
#' @param samples character: Comma-separated list of ordered samples for the matrix
#'
#' @importFrom utils read.csv
#' @importFrom stats na.omit
#' @importFrom assertthat assert_that
#' @return
#' Returns a list of similarity matrices, matrix of number of SNPs found between
#' two samples, and a matrix of number of heterozygous SNPs between two samples
#' @export
getSampleSimilarity <- function(sample_matrix, samples=NULL, matchmode='autosome',
similarityFun=NULL, rm_nocov=FALSE, rem_ref=TRUE){
assert_that(file.exists(sample_matrix), msg="sample_matrix must be a file path containing the samples to match")
## Read in the VCF or CSV file
mat <- switch(matchmode,
"autosome"={
mat <- read.csv(sample_matrix, header = TRUE)
if(!is.null(samples)) colnames(mat) <- strsplit(samples, ",")[[1]]
mat
},
"chrM"={
mega_vcf = read.csv(sample_matrix, sep = "\t", comment.char = "#")
mat = mega_vcf[,10:ncol(mega_vcf)]
if(!is.null(samples)) colnames(mat) <- strsplit(samples, ",")[[1]]
mat
},
stop("matchmode must be either 'autosome' or 'chrM'"))
## Run the similarity checks
mats <- switch(matchmode,
"autosome"=.autosomeMatch(mat, rm_nocov, similarityFun),
"chrM"=.chrmMatch(mat, rem_ref, similarityFun),
stop("matchmode must be either 'autosome' or 'chrM'"))
return(mats)
}
#' Sim-N plotter
#' @description Plot the similarity and n-matrix
#'
#' @param sim_mat Similarity matrix
#' @param n_mat Matrix of N's, same size and format as sim_mat
#' @param mid_diag boolean: Set diagonal of similarity matrix to midpoint (Default=FALSE)
#' @param midpoint Midpoint value of colour range (Default=0.5)
#'
#' @import ggplot2
#' @importFrom reshape2 melt
#' @export
plotSampleSimilarity <- function(sim_mat, n_mat, midpoint=0.5, mid_diag=FALSE){
# Order the matrix based on hclusts
cl <- hclust(dist(sim_mat))
# Remove the sim=1 of intersects by setting to midpoint
if(mid_diag){
diag(sim_mat) <- midpoint
}
# Melt and combine similarity and n-matrices
m_sim <- melt(sim_mat[cl$order,cl$order])
m_n <- melt(n_mat[cl$order,cl$order])
m_sim_n <- merge(m_sim, m_n, by=c('Var1', 'Var2'))
colnames(m_sim_n) <- c('Var1', 'Var2', 'similarity', 'n')
m_sim_n$Var1 <- factor(m_sim_n$Var1)
m_sim_n$Var2 <- factor(m_sim_n$Var2)
# Heatmap visualization of two-factor clustered matrix
ggplot(m_sim_n, aes_string(y='Var1', x='Var2')) +
geom_point(aes_string(colour='similarity', size='n')) +
scale_color_gradient2(low='blue', mid='gray', high='red',
midpoint=midpoint) +
theme_bw()
}
.autosomeMatch <- function(mat, rm_nocov, similarityFun){
mat <- as.matrix(mat)
storage.mode(mat) <- 'integer'
mat <- as.data.frame(mat)
if(rm_nocov){
# Removes SNPs where no samples have coverage for it
mat[mat==0] <- NA # Remove no coverage SNPs
nonhet_idx <- apply(mat,1,function(i) {
i <- na.omit(as.integer(i))
any(i==2) | (any(i==1) & any(i==3))
})
mat[!nonhet_idx,] <- NA # Removes SNPs that have no het cov
}
if(is.null(similarityFun)){
similarityFun <- function(i,j){
# Heterozygous SNPs in i and j
# Divided by
# SNPs heterozygous in i or j +
# SNPs that are ref in i and alt in j (vice versa)
ihet <- i==2
jhet <- j==2
#ihom <- (i==1 | i==3)
# i[which(ihom)] != j[which(ihom)]
sum(ihet & jhet, na.rm=T) /
(sum(ihet | jhet, na.rm=T)) #+ sum(i[which(ihom)] != j[which(ihom)], na.rm=T))
}
}
getN <- function(i,j){ sum(!is.na(i) & !is.na(j)) }
getHet <- function(i,j){ sum((i==2) & (j==2),na.rm=T) }
simmat <- allbyall(mat, margin=2, fun=similarityFun)
nmat <- allbyall(mat, margin=2, fun=getN)
hetmat <- allbyall(mat, margin=2, fun=getHet)
list("sim"=simmat,
"n"=nmat,
"het"=hetmat)
}
.chrmMatch <- function(mat, rem_ref, similarityFun){
# Parse haplotype caller into the first column (i.e. 0/0, or 1/1, or 0/1).
sample_geno <- apply(mat, 2, .getGT)
sample_count <- .cleanGenotype(as.matrix(sample_geno))
colnames(sample_count) <- colnames(mat)
## Read all the samples and count the genotypes####
if(is.null(similarityFun)){
similarityFun <- function(i,j){
# Checks that each samples SNP matches the other samples SNP
comp_idx <- c(1:length(i))
if(rem_ref){
#print("Removing SNPs which are ref in both samples")
refs <- i == '0/0' & j=='0/0'
comp_idx <- comp_idx[-which(refs)]
}
m <- sum(i[comp_idx] == j[comp_idx])
n <- length(comp_idx)
jacc <- sum(m)/n
jacc
# return(c(jacc,n))
}
}
getN <- function(i,j){
comp_idx <- c(1:length(i))
if(rem_ref){
#print("Removing SNPs which are ref in both samples")
refs <- i == '0/0' & j=='0/0'
comp_idx <- comp_idx[-which(refs)]
}
return(length(comp_idx))
}
jacc_mat <- allbyall(sample_count, margin=2, fun=similarityFun)
n_mat <- allbyall(sample_count, margin=2, fun=getN)
list("sim"=jacc_mat,
"n"=n_mat)
}
.getGT <- function(x, gt_idx=1){
split_gt <- strsplit(as.vector(x), split = ":")
sapply(split_gt, function(i) i[[gt_idx]])
}
.cleanGenotype <- function(b) {
# GT genotype, encoded as alleles values separated by either of ”/” or “|”,
# e.g. The allele values are 0 for the reference allele (what is in the
# reference sequence), 1 for the first allele listed in ALT, 2 for the
# second allele list in ALT and so on. For diploid calls examples could be
# 0/1 or 1|0 etc. For haploid calls, e.g. on Y, male X, mitochondrion,
# only one allele value should be given. All samples must have GT call
# information; if a call cannot be made for a sample at a given locus, ”.”
# must be specified for each missing allele in the GT field (for example
# ./. for a diploid). The meanings of the separators are:
stopifnot(any(class(b)=='matrix'))
b <- gsub("\\|", "/", b) # convert phased to unphased
b[b == '.'] <- "0/0" # convert No-call to HOMREF
# Deeper sanity checks
splgt <- sapply(strsplit(as.vector(b), "/"), function(i) i[1:2])
class(splgt) <- 'integer'
# CHECK_1: Flips 1/0 to 0/1
hetidx <- which(splgt[2,] != splgt[1,])
rev_hetidx <- splgt[1,hetidx] > splgt[2,hetidx]
if(any(rev_hetidx)){
for(i in which(rev_hetidx)){
splgt[,hetidx[i]] <- rev(splgt[,hetidx[i]])
}
}
# CHECK_2: ...
if(FALSE){
}
genotype <- matrix(apply(splgt, 2, paste, collapse="/"), ncol=ncol(b))
return(genotype)
} |
69eeb07364acfb2125f5a39f35d943aee0054c0f | 7b08112912ba3ee4dc20488b687c817d2134798c | /geo_data_preprocessing.R | ab5883dcf8246f97574e66c2a87b3ff9594503c6 | [] | no_license | heonedream/geo_learn | ef1efbd0db3c91aa44c986d9459756d2db90306b | 0489923b548cceb0fdec4b58d1aa48b85a879fbc | refs/heads/master | 2020-05-22T18:12:10.329945 | 2017-03-18T06:05:09 | 2017-03-18T06:05:09 | 84,713,068 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,149 | r | geo_data_preprocessing.R | source("http://bioconductor.org/biocLite.R")
biocLite("affy")
biocLite("affyPLM")
biocLite("RColorBrewer")
biocLite("limma")
biocLite("pheatmap")
###########################
#set the working directiory
setwd("E:\\geo_learn\\T")
setwd("E:/geo_learn/T")
library(affy)
Data<-ReadAffy()
sampleNames(Data)
image(Data[,1])
library(affyPLM)
library(RColorBrewer)
#convert the AffyBatch into PLMset
Pset<-fitPLM(Data)
#creat the corlor scheme
colors<-brewer.pal(5,"Set3")
#boxplot RLE(Relative log Expression)
Mbox(Pset,col=colors,main="RLE",las=1)
#assess RNA degradation in Affymetrix GeneChip data
data.deg<-AffyRNAdeg(Data)
#plot the RNA degradation
plotAffyRNAdeg(data.deg,col=colors)
#add legend
legend("topleft",sampleNames(Data),col = colors,lwd=1,inset = 0.05,cex = 0.5)
#Data cleaning
#converts the affybatch into expressionset rma(robust multi-array average)
eset.rma<-rma(Data)
#get the express data matrix from the expressionset
tumor_exprs<-exprs(eset.rma)
#preserved the dataset
write.csv(tumor_exprs,file = "tumor_exprs.csv")
###############################
#deal with the normal data
setwd("E:\\geo_learn\\N")
Data.normal<-ReadAffy()
sampleNames(Data.normal)
image(Data.normal[,1])
Pset<-fitPLM(Data.normal)
Mbox(Pset,col=colors,main="RLE",las=1)
data.deg<-AffyRNAdeg(Data.normal)
plotAffyRNAdeg(data.deg,col=colors)
legend("topleft",sampleNames(Data.normal),col = colors,lwd=1,inset = 0.05,cex = 0.5)
eset.rma<-rma(Data.normal)
normal_exprs<-exprs(eset.rma)
write.csv(normal_exprs,file = "normal_exprs.csv")
###############################
#combined the normal and cancer expression dataset
setwd("E:\\geo_learn\\combined")
#input the expression and information frome the GSE
expression.normal=read.csv(file = "normal_exprs.csv")
names(expression.normal)[1]="probe"
expression.tumor=read.csv(file = "tumor_exprs.csv")
names(expression.tumor)[1]="probe"
gene_names=read.table(file = "GPL97-17394_cleaned.txt",header = T,sep = '\t')
names(gene_names)[1]="probe"
#merge by the probe id
expression.whole=merge(expression.normal,expression.tumor,by="probe")
expression.whole=merge(expression.whole,gene_names,by="probe")
head(expression.whole)
|
e98f6188a2cb17773fd5adf6c15ff0df52d44600 | 16787859609e486c6e32ceaadbe683c235f27c28 | /R/initExtension.R | 70c56722144cdf0c4631f54413957ad317ae0c9a | [] | no_license | duncantl/RSQLiteUDF | f5be24d291794c05e8a493717154f5ab475bb1e9 | facde807e1403157d08df578a81d2f4828e09bc0 | refs/heads/master | 2021-01-10T12:21:15.928711 | 2021-01-02T03:25:48 | 2021-01-02T03:25:48 | 46,583,707 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 431 | r | initExtension.R | initExtension = sqliteExtension =
function(db, dll = getLoadedDLLs()[[pkg]][["path"]],
pkg = "RSQLite")
{
if (!db@loadable.extensions)
stop("Loadable extensions are not enabled for this db connection", call. = FALSE)
dll = path.expand(dll)
if(!file.exists(dll))
stop("Cannot find the extension file")
dbGetQuery(db, sprintf("SELECT load_extension('%s')", dll))
TRUE
}
|
d60741a430e0e60d579f71daf3dd6da636faf21c | 22d901378a2b4a0c40f1e245ac8409b0ffdddfd0 | /actresses/sridevi.R | 583aff623a5d2f31b94e74737fe3b770aa4f7ad0 | [] | no_license | puneeth019/Bollywoodata | af494b0f235e27c8b7428049d2cb2d90082df876 | ab42780776920a8e9bd412a41acdc1ad899759f7 | refs/heads/master | 2020-05-21T23:55:29.807646 | 2017-03-27T09:01:21 | 2017-03-27T09:01:21 | 65,877,598 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,902 | r | sridevi.R | # Sample script to scrape table from webpage
library(rvest) # Load `rvest` package
library(dplyr) # Load `dplyr` pacakge
library(stringr) # Load `stringr` package
setwd("~/Documents/DA/Projects/Project1/actresses/")
# Set Working directory
file_url <- "https://en.wikipedia.org/wiki/Sridevi_filmography"
# Assign the wiki url to `file_url`
# Scrape `Tamil` movies
table_sridevi_tamil <- file_url %>%
read_html() %>%
html_nodes(xpath='//*[@id="mw-content-text"]/table[2]') %>%
html_table(fill = TRUE, trim = TRUE, header = TRUE)
table_sridevi_tamil <- table_sridevi_tamil[[1]]
# convert `table_sridevi` from `list` into `data.frame`
# clean text in column-1
table_sridevi_tamil$Year <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_tamil$Year)
table_sridevi_tamil$Year <- str_trim(string = table_sridevi_tamil$Year)
# clean text in column-2
table_sridevi_tamil$Film <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_tamil$Film)
table_sridevi_tamil$Film <- str_trim(string = table_sridevi_tamil$Film)
# clean text in column-3
table_sridevi_tamil$Role <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_tamil$Role)
table_sridevi_tamil$Role <- str_trim(string = table_sridevi_tamil$Role)
# clean text in column-4
table_sridevi_tamil$Source <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_tamil$Source)
table_sridevi_tamil$Source <- str_trim(string = table_sridevi_tamil$Source)
# Assign language in column-5
table_sridevi_tamil$Language <- "Tamil"
# Scrape `Malayalam` movies
table_sridevi_malayalam <- file_url %>%
read_html() %>%
html_nodes(xpath= '//*[@id="mw-content-text"]/table[3]') %>%
html_table(fill = TRUE, trim = TRUE, header = TRUE)
table_sridevi_malayalam <- table_sridevi_malayalam[[1]]
# convert `table_sridevi` from `list` into `data.frame`
# clean text in column-1
table_sridevi_malayalam$Year <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_malayalam$Year)
table_sridevi_malayalam$Year <- str_trim(string = table_sridevi_malayalam$Year)
# clean text in column-2
table_sridevi_malayalam$Film <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_malayalam$Film)
table_sridevi_malayalam$Film <- str_trim(string = table_sridevi_malayalam$Film)
# clean text in column-3
table_sridevi_malayalam$Role <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_malayalam$Role)
table_sridevi_malayalam$Role <- str_trim(string = table_sridevi_malayalam$Role)
# clean text in column-4
table_sridevi_malayalam$Source <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_malayalam$Source)
table_sridevi_malayalam$Source <- str_trim(string = table_sridevi_malayalam$Source)
# Assign language in column-5
table_sridevi_malayalam$Language <- "Malayalam"
# Scrape `Telugu` movies
table_sridevi_telugu <- file_url %>%
read_html() %>%
html_nodes(xpath= '//*[@id="mw-content-text"]/table[4]') %>%
html_table(fill = TRUE, trim = TRUE, header = TRUE)
table_sridevi_telugu <- table_sridevi_telugu[[1]]
# convert `table_sridevi` from `list` into `data.frame`
# clean text in column-1
table_sridevi_telugu$Year <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_telugu$Year)
table_sridevi_telugu$Year <- str_trim(string = table_sridevi_telugu$Year)
# clean text in column-2
table_sridevi_telugu$Film <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_telugu$Film)
table_sridevi_telugu$Film <- str_trim(string = table_sridevi_telugu$Film)
# clean text in column-3
table_sridevi_telugu$Role <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_telugu$Role)
table_sridevi_telugu$Role <- str_trim(string = table_sridevi_telugu$Role)
# clean text in column-4
table_sridevi_telugu$Source <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_telugu$Source)
table_sridevi_telugu$Source <- str_trim(string = table_sridevi_telugu$Source)
# Assign language in column-5
table_sridevi_telugu$Language <- "Telugu"
# Scrape `Kannada` movies
table_sridevi_kannada <- file_url %>%
read_html() %>%
html_nodes(xpath= '//*[@id="mw-content-text"]/table[5]') %>%
html_table(fill = TRUE, trim = TRUE, header = TRUE)
table_sridevi_kannada <- table_sridevi_kannada[[1]]
# convert `table_sridevi` from `list` into `data.frame`
# clean text in column-1
table_sridevi_kannada$Year <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_kannada$Year)
table_sridevi_kannada$Year <- str_trim(string = table_sridevi_kannada$Year)
# clean text in column-2
table_sridevi_kannada$Film <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_kannada$Film)
table_sridevi_kannada$Film <- str_trim(string = table_sridevi_kannada$Film)
# clean text in column-3
table_sridevi_kannada$Role <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_kannada$Role)
table_sridevi_kannada$Role <- str_trim(string = table_sridevi_kannada$Role)
# clean text in column-4
table_sridevi_kannada$Source <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_kannada$Source)
table_sridevi_kannada$Source <- str_trim(string = table_sridevi_kannada$Source)
# Assign language in column-5
table_sridevi_kannada$Language <- "Kannada"
# Scrape `Hindi` movies
table_sridevi_hindi <- file_url %>%
read_html() %>%
html_nodes(xpath= '//*[@id="mw-content-text"]/table[6]') %>%
html_table(fill = TRUE, trim = TRUE, header = TRUE)
table_sridevi_hindi <- table_sridevi_hindi[[1]]
# convert `table_sridevi` from `list` into `data.frame`
table_sridevi_hindi <- select(table_sridevi_hindi, Year:Source)
# clean text in column-1
table_sridevi_hindi$Year <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_hindi$Year)
table_sridevi_hindi$Year <- str_trim(string = table_sridevi_hindi$Year)
# clean text in column-2
table_sridevi_hindi$Film <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_hindi$Film)
table_sridevi_hindi$Film <- str_trim(string = table_sridevi_hindi$Film)
# clean text in column-3
table_sridevi_hindi$Role <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_hindi$Role)
table_sridevi_hindi$Role <- str_trim(string = table_sridevi_hindi$Role)
# clean text in column-4
table_sridevi_hindi$Source <- gsub(pattern = "^$", replacement = NA_character_, x = table_sridevi_hindi$Source)
table_sridevi_hindi$Source <- str_trim(string = table_sridevi_hindi$Source)
# Assign language in column-5
table_sridevi_hindi$Language <- "Hindi"
# Combine data
table_sridevi <- rbind(table_sridevi_tamil, table_sridevi_malayalam, table_sridevi_telugu,
table_sridevi_kannada, table_sridevi_hindi)
# Remove `Source` column
table_sridevi <- select(table_sridevi, -Source)
write.csv(x = table_sridevi, file = "sridevi.csv")
|
a095268d29e84195332411653b1ad12d1fe08339 | 91f820de113ba22fabcf3ba10ed650229381d24a | /untap.1.0.R | bc5af1915a4b81088f623f9a4a3b208a7d0e02ed | [
"MIT"
] | permissive | wsupko/ontap | 421d1156de855d5c1316ca08bbff28efcafa86a8 | b5517c9f7f1ab901a9e27b0a297ad529427f934b | refs/heads/master | 2021-05-04T14:45:38.725112 | 2018-02-15T15:49:16 | 2018-02-15T15:49:16 | 120,211,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,882 | r | untap.1.0.R | #'---
#'title: "Ontap"
#'author: "Wojciech Supko"
#'date: "4 lutego 2018"
#'output: github_document
#'---
#+ r setup, include=FALSE
knitr::opts_chunk$set(echo = TRUE)
### Install (if not available already) required libraries ###
if (!'data.table' %in% installed.packages()) {install.packages('data.table')}
if (!'RPostgreSQL' %in% installed.packages()) {install.packages('RPostgreSQL')}
if (!'ggplot2' %in% installed.packages()) {install.packages('ggplot2')}
if (!'scales' %in% installed.packages()) {install.packages('scales')}
### Load required libraries ###
library(data.table)
library(RPostgreSQL)
library(ggplot2)
library(scales)
library(lubridate)
#+ r DataInput, include = FALSE
Imp.All <- fread('./data/in/Data.Sample.txt')
Imp.All[, wd := lubridate::wday(Podlaczenie, label = T, abbr = T)]
Imp.All[, id := 1:.N]
Imp.All[, Cena := max(Cena1, Cena2, Cena2, na.rm = T), by = id]
#+ DataManipulation
#' Spojrzmy na zbior danych
#' Inicjujemy funkcje str()
str(Imp.All)
#' Zauwazylem piwa, ktore nie posiadaja ceny - multitapy nie lubia sie dzielic czy blad?
#' Takich rekordów jest:
Imp.All[Cena1 == 0 & Cena2== 0 & Cena3 == 0, .N]
Proc.Count <- merge(Imp.All[Cena == 0, .(noprice = .N), Multitap],
Imp.All[, .(total = .N), Multitap],
by = 'Multitap', all.y = T); Proc.Count[is.na(noprice), noprice := 0]
Proc.Count[, perc := noprice/total]
ggplot(Proc.Count) + geom_histogram(aes(perc), binwidth = .05)
#' ## Data Visualization
#' #### Liczba podłączeń w maju 2018
#+ r, echo = FALSE
ggplot(Imp.All[, .(ct = .N), .(Date = as.Date(Podlaczenie), wd)]) +
geom_bar(aes(x = Date, y = ct, fill = wd), stat = 'identity') +
theme_minimal() +
scale_fill_brewer(palette = 'Dark2') +
theme(legend.position = 'bottom', legend.title = element_blank(),
axis.title = element_blank())
|
54c96e0763a435dd85843492f551aa37dc8a95ce | c3352c6e4471c5e7c8682825b84dd8270b31507d | /man/triangulate_quads.Rd | f21cd4c7dff19c06534e0078f51b3920fdc4083c | [] | no_license | MilesMcBain/quadmesh | 2f6f7a17276a55ffb74fc2fdfa39d12a41dd54b3 | 712dfa1d9c05d936222c0e5768866ed118690623 | refs/heads/master | 2020-04-07T04:44:14.081500 | 2018-11-18T09:57:59 | 2018-11-18T09:57:59 | 158,069,412 | 1 | 0 | null | 2018-11-18T09:49:24 | 2018-11-18T09:49:24 | null | UTF-8 | R | false | true | 1,374 | rd | triangulate_quads.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/primitives.R
\name{triangulate_quads}
\alias{triangulate_quads}
\title{Triangles from quads}
\usage{
triangulate_quads(quad_index, clockwise = FALSE)
}
\arguments{
\item{quad_index}{the 'ib' index of quads from 'quadmesh'}
\item{clockwise}{if true triangles are wound clockwise, if false
anticlockwise. This affects which faces rendering engines consider to be
the 'front' and 'back' of the triangle. If your mesh appears 'inside out',
try the alternative setting.}
}
\value{
matrix of triangle indices
}
\description{
Convert quad index to triangles, this converts the 'rgl mesh3d (ib)' quad
index to the complementary triangle index '(it)'.
}
\details{
Triangle pairs from each quad are interleaved in the result, so that neighbour
triangles from a single quad are together.
}
\examples{
triangulate_quads(cbind(c(1, 2, 4, 3), c(3, 4, 6, 5)))
qm <- quadmesh(raster::crop(etopo, raster::extent(140, 160, -50, -30)))
tri <- triangulate_quads(qm$ib)
plot(t(qm$vb))
tri_avg <- colMeans(matrix(qm$vb[3, tri], nrow = 3), na.rm = TRUE)
scl <- function(x) (x - min(x))/diff(range(x))
tri_col <- grey(seq(0, 1, length = 100))[scl(tri_avg) * 99 + 1]
## tri is qm$ib converted to triangles for the same vertex set
polygon(t(qm$vb)[rbind(tri, NA), ])
polygon(t(qm$vb)[rbind(tri, NA), ], col = tri_col)
}
|
47868ece1b257e6e16d2c0057da95d57a8ab8b3d | 0a906cf8b1b7da2aea87de958e3662870df49727 | /ggforce/inst/testfiles/enclose_points/libFuzzer_enclose_points/enclose_points_valgrind_files/1609956110-test.R | 56b322269a52575726fe4f30481a3eea0fb9df85 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 116 | r | 1609956110-test.R | testlist <- list(id = -1L, x = numeric(0), y = NaN)
result <- do.call(ggforce:::enclose_points,testlist)
str(result) |
c3702fef92a0de52afb6ef6e7e5f35db4ef99c2d | f55448b4b0e3c848c4f6904a27df040b6023d3e5 | /R/ttt_fs.R | 7f3e0cfd6d9595b4ca0a2c4e79ce466312222949 | [
"MIT"
] | permissive | rich-iannone/ttt | dcb2308871b3b636de2fb3d6e24ecfdec15c6232 | 507b1fcb1bbe95bed55dd2aeeafbd4232a0ebe1f | refs/heads/main | 2023-06-22T21:14:43.835098 | 2018-05-28T16:14:17 | 2018-05-28T16:14:17 | 126,098,267 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 929 | r | ttt_fs.R |
#' Get location of the ttt_dir
#'
#' Get the directory path for the hidden ttt directory.
#' @export
where_ttt_dir <- function() {
path.expand("~/Documents/.ttt/")
}
#' Get a vector of files in ttt_dir
#'
#' Get a vector of all files in the ttt directory.
#' @param full_names an option for showing the
#' directory paths prepended to the file names.
#' full path
#' @export
show_ttt_files <- function(full_names = FALSE) {
where_ttt_dir() %>% list.files(full.names = full_names)
}
#' Delete all files in ttt_dir
#'
#' Delete all of the files in the ttt directory.
#' @export
delete_all_ttt_files <- function() {
file_count <-
list.files(path = where_ttt_dir(), pattern = "*.csv") %>%
length()
invisible(
file.remove(
where_ttt_dir() %>%
list.files(full.names = TRUE)))
message(
paste0(
"All ", file_count, " CSV file(s) in `",
where_ttt_dir(), "` have been removed"))
}
|
4135d6768f76dd9b4db9488821e186dd2f3544d4 | 82ab2c8f0a83950560bc22803cc38cd816c0527c | /man/get_routes_by_search_text.Rd | f6800fe67dc769b9408aa5a1524bb51e915ad287 | [
"MIT"
] | permissive | Tina-ye112/kiaora | ae65a496617f40f26543db0b2936dde9a2d3ac4c | dad037e2e26d0ad937634fb925a5bafb15ec5de5 | refs/heads/master | 2023-03-07T22:18:22.779104 | 2021-02-22T20:27:30 | 2021-02-22T20:27:30 | 318,342,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 473 | rd | get_routes_by_search_text.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/akl_transport.R
\name{get_routes_by_search_text}
\alias{get_routes_by_search_text}
\title{Get routes by search text}
\usage{
get_routes_by_search_text(search_text = NULL)
}
\arguments{
\item{search_text}{string text to search}
}
\value{
A tibble
}
\description{
Query routes by search text
}
\examples{
\dontrun{
get_routes_by_search_text(search_text = "Albany")
}
# get routes by search_text
}
|
68f353af534cb7451da19323ab4bdfbf6c204cc2 | 93286036db17d0a5d6f49031ff109e48ee4688d0 | /scrabble-score/scrabble-score.R | bc3782476c66a2f0820fcd9a6b652d046e35c21f | [
"MIT"
] | permissive | stephenfeagin/exercism-r | 378dbf2f1c89e1d0703a48cbb2ab52df37f40056 | 0ac8a5b4a0e726fa97dce2b7e9ae9ffaa77e08f0 | refs/heads/master | 2020-04-11T19:02:55.171832 | 2018-12-17T01:23:32 | 2018-12-17T01:23:32 | 162,020,200 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 584 | r | scrabble-score.R | scrabble_score <- function(input){
# Split the input into lowercase characters
chars <- strsplit(tolower(input), "")[[1]]
# Make a lookup list for the scores
lookup <- list(
"aeioulnrst" = 1,
"dg" = 2,
"bcmp" = 3,
"fhvwy" = 4,
"k" = 5,
"jx" = 8,
"qz" = 10
)
# Lookup the score based on the name of element in the lookup list
scores <- vapply(chars, function(x) {
item <- grep(x, names(lookup))
lookup[[item]]
}, numeric(1))
# Return the sum of the scores
sum(scores)
}
|
356fb8ac30422f501067114dd28c5de60bb9759e | 801b67f78c8f78cb40ce641e4282c73a9b34d634 | /XOR Neural Net Expanded Example.R | 71b45dfe7c405cec4c2f53a45cb6c08cfbcda754 | [] | no_license | NerdParker/SVM-Xor-Neural_Network_Machine_Learning | c0ee43e0a13e42b31244332880c2f676ca4a0568 | b2c5291952f689bcb27ce510a00ff4a69ab583de | refs/heads/master | 2020-06-01T15:14:53.375197 | 2020-02-28T01:08:47 | 2020-02-28T01:08:47 | 190,829,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,942 | r | XOR Neural Net Expanded Example.R | install.packages("neuralnet")
install.packages("ggplot2")
# Sample multivariate Gaussian distributions
library(MASS)
# R neural networks package
library(neuralnet)
covariance_matrix <- matrix(c(0.05, 0, 0, 0.05), 2, 2)
covariance_matrix
# Number of points in each row
points_row <- 4000
# Rows 1-4
one <- mvrnorm(points_row, c(0, 0), covariance_matrix)
two <- mvrnorm(points_row, c(0, 1), covariance_matrix)
three <- mvrnorm(points_row, c(1, 0), covariance_matrix)
four <- mvrnorm(points_row, c(1, 1), covariance_matrix)
# Stacks points
all_rows <- rbind(one, two, three, four)
plot(all_rows)
values <- rep(c(0, 1, 1, 0), each = points_row)
plot(values)
# Combine points and values
xor_data <- as.data.frame(cbind(values, all_rows))
colnames(xor_data) <- c("values", "a", "b")
plot(xor_data)
# Number of rows we want to look at
number_rows <- 20
# Views rows
visualize_rows <- sample(1:nrow(xor_data), number_rows)
xor_data[visualize_rows, ]
library(ggplot2)
# Creates a nice visual of our data
ggplot(xor_data, aes(x = a, y = b, color = factor(values))) + geom_point() +
scale_color_manual(name = "Values", values = c("gold", "maroon"),
labels = c("False", "True")) +
ggtitle("XOR Data") + xlab("A") + ylab("B")
# Creates a neural net
xor_neurel_net <- neuralnet("values ~ a + b", data = xor_data, threshold = 1,
# Number of units containing one hidden layer
hidden = c(20),
# Classification
linear.output = F,
# Error Function
err.fct = "ce",
# Activation Function
act.fct = "logistic")
# Error reached so far
cat(sprintf("Error minimized to: %f", xor_neurel_net$result.matrix[c('error'), ]))
# Run a test on data, displays it and our prediction
initial_test <- data.frame(x = c(0, 0, 1, 1),
y = c(0, 1, 0, 1),
true_value = c(0, 1, 1, 0))
prediction <- compute(xor_neurel_net, initial_test[, c("x", "y")])$net.result
cbind(initial_test, prediction)
# Running a larger set of test data
larger_test <- data.frame(x = runif(10), y = runif(10))
larger_prediction <- compute(xor_neurel_net, larger_test)$net.result
cbind(larger_test, larger_prediction )
# Number of interpolating points
interpolating_points <- 100
a_values <- seq(0, 1, len = interpolating_points)
b_values <- seq(0, 1, len = interpolating_points)
test_points <- as.data.frame(expand.grid(a_values, b_values))
colnames(test_points) <- c("a", "b")
predictions <- compute(xor_neurel_net, test_points)$net.result
ggplot() + geom_point(aes(x = test_points$a, y = test_points$b, color = predictions)) +
scale_color_gradient("Prediction", low = "purple", high = "gold") +
ggtitle("Neural Network Decision Pattern") + xlab("A") + ylab("B") |
a8c689fca5438ba81db81e0e468b68e0a20fd7d6 | 195c2364d6cbf6031e13e4aa5afa37a14b541e4b | /assessent3.r | f8e4a3e67454f8cbcc9740b4180a20746ed50999 | [] | no_license | Dulan-A/Learning-0 | 8170fbac50eaf946b321ad0b049c4223c74e18ed | 5f822c720a243138d35730dd164c719d7a3bbe64 | refs/heads/master | 2023-01-14T09:30:53.729378 | 2020-11-07T12:06:46 | 2020-11-07T12:06:46 | 289,175,709 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 696 | r | assessent3.r | library(Lahman)
top <- Batting %>%
filter(yearID == 2016) %>%
arrange(desc(HR)) %>% # arrange by descending HR count
slice(1:10) # take entries 1-10
top %>% as_tibble()
Master %>% as_tibble()
top_names <- top %>% left_join(Master) %>% select(playerID, nameFirst, nameLast, HR)
top_names
top_salary <- Salaries %>% filter(yearID == 2016) %>% right_join(top_names) %>% select(nameFirst, nameLast, teamID, HR, salary)
top_salary
awards_2016 <- AwardsPlayers %>% filter(yearID == 2016) %>% select(playerID, awardID)
top10_awards <- inner_join(top_names, awards_2016)
top10_awards
col_awards <- awards_2016$playerID
col_top10 <- top_names$playerID
setdiff(col_awards, col_top10)
|
be37bcd14301c5edbb290031490c32620bc8ddc2 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gtkWidgetGetAllocation.Rd | f714c62811d8b3c61dbad79ce640168cafa8649f | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 492 | rd | gtkWidgetGetAllocation.Rd | \alias{gtkWidgetGetAllocation}
\name{gtkWidgetGetAllocation}
\title{gtkWidgetGetAllocation}
\description{Retrieves the widget's allocation.}
\usage{gtkWidgetGetAllocation(object)}
\arguments{\item{\verb{object}}{a \code{\link{GtkWidget}}}}
\details{Since 2.18}
\value{
A list containing the following elements:
\item{\verb{allocation}}{a pointer to a \code{\link{GtkAllocation}} to copy to. \emph{[ \acronym{out} ]}}
}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
|
7bbdb99fcc9e9e87c47dd05241bd1ea18718b11a | 6f9c463239c2150428f2cc3079d1768dbb885634 | /calibration/explore_hydro_params_using_response_curve.r | da8e756bf5acbf2abb4d750564fbb02f3af91321 | [] | no_license | Dave-Evans/wisconsinRiverTMDL | ebd1bb43a6c37a79bbed848dcd7da41fb263d634 | 3f6b8f91c59db4056944454ed466a2ac8a1e44bb | refs/heads/master | 2021-01-14T11:25:48.196968 | 2014-12-10T19:41:58 | 2014-12-10T19:41:58 | 27,941,509 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,674 | r | explore_hydro_params_using_response_curve.r | # run SWAT for different crops using a range of bio E
# This should be run with MONTHLY output (code 0)
wd = "H:\\WRB\\Scenarios\\Default\\TxtInOut"
setwd(wd)
dir_out = "H:/sim_flow/hyd_params"
source("C:/Users/evansdm/Documents/Code/validation/functions_query_output.r")
file_obs_flow_lu = "T:/Projects/Wisconsin_River/GIS_Datasets/observed/gauge_basin_lookup.csv"
obs_flow_lu = read.csv(file_obs_flow_lu)
obs_flow_lu = subset(obs_flow_lu,
Keep == 1,
select = c("Flow_Name", "WRB_SubbasinID"))
# move swat executable
file.copy("C:/SWAT/ArcSWAT/SWAT_64rel.exe", paste(wd, "SWAT_64rel.exe", sep="\\"))
# surlag = 0.05 to 24;4.000
# esco = 0 to 1; default: 0.950
# epco = 0 to 1; default: 1.00
# sftmp = -5 to 5; default: 1.000
# smtmp = 05 to 5; defualt: 0.500
valTbl = list(seq(0, 1, length.out=10),
seq(0, 1, length.out=10),
seq(0.05, 24, length.out=10),
seq(-5, 5, length.out=10),
seq(-5, 5, length.out=10))
dflts = list(0.950,
1.00,
4.000,
1.000,
0.500)
#shortening and/or lengthening to make 5 chars
names(valTbl) = c("ESCO:", "EPCO:", "SURLA", "SFTMP", "SMTMP")
names(dflts) = c("ESCO:", "EPCO:", "SURLA", "SFTMP", "SMTMP")
# default alphaBF: 0.0480
for (abf in 1:2){
# second time around input default alpha bf
if (abf == 2){
alphaBF = "Default"
files.gw = list.files(wd, pattern = "*.gw")
for (fl in files.gw){
gw.txt = readLines(paste(wd, fl, sep = '/'))
bf.ind = which(substr(gw.txt, 23, 31) == "ALPHA_BF ")
substr(gw.txt[bf.ind], 11, 15) = format(0.500, digits = 4, nsmall = 3)
writeLines(gw.txt, paste(wd, fl, sep = '/'))
}
} else {
alphaBF = "Region"
}
for (param in names(valTbl)) {
vals = valTbl[[param]]
for (val in vals){
print(paste("Running SWAT, changing:",param, "to", val))
basins.bsn = readLines(paste(wd, "basins.bsn", sep="\\"))
par.ind = which(substr(basins.bsn, 23, 27) == param)
substr(basins.bsn[par.ind], 11, 16) =
format(val, digits=3, nsmall=3, width = 6)
writeLines(basins.bsn, paste(wd, "basins.bsn", sep="\\"))
bat = tempfile(pattern="runswat_", fileext=".bat")
writeLines(paste("cd ", wd, "\nSWAT_64rel.exe", sep=""), bat)
system(bat)
dat = readLines(paste(wd, "output.rch", sep="\\"))
dat = dat[10:length(dat)]
select_cols = list(
cols = c("RCH", "MON", "FLOW_OUT"),
dtypes = c(as.integer, as.integer, as.numeric)
)
modData = matrix(NA, nrow=length(dat), ncol=length(select_cols$cols))
modData = as.data.frame(modData)
names(modData) = select_cols$cols
for (row in 1:length(select_cols$cols)) {
col_name = select_cols$cols[row]
dtype = select_cols$dtypes[row][[1]]
vals = query_output.rch(dat, col_name)
vals = sapply(vals, dtype)
modData[col_name] = data.frame(vals, stringsAsFactors=F)
}
modData = subset(modData, RCH %in% obs_flow_lu$WRB_SubbasinID & MON < 13)
fl_name = paste(substr(param,1,4), val, alphaBF, "flowout.csv", sep = '_')
write.csv(modData, paste(dir_out, fl_name, sep = '/'), row.names=F)
}
substr(basins.bsn[par.ind], 11, 16) =
format(dflts[[param]], digits=2, nsmall=3, width = 6)
writeLines(basins.bsn, paste(wd, "basins.bsn", sep="\\"))
}
}
|
6e61129fafb2eb724729a5c1c3c0c34c13aa512b | 81ee39303513c84cc83ca7751bcc35bbc90087d8 | /man/E.MTAB.386.Rd | 4619aee9dcdf53b856736ca2352928c4992460c5 | [] | no_license | bhklab/MetaGxData | 547531d9fa5fd80d04e75a094332143786b0ab4c | 0d253edc56cf74b299bed618cd0d5cbc94dd08fe | refs/heads/master | 2021-01-15T12:10:38.309031 | 2020-12-03T14:38:38 | 2020-12-03T14:38:38 | 43,975,358 | 2 | 3 | null | null | null | null | UTF-8 | R | false | false | 204,744 | rd | E.MTAB.386.Rd | \name{ E.MTAB.386_eset }
\alias{ E.MTAB.386_eset }
\docType{data}
\title{ Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer. }
\description{ Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials. }
\usage{data( E.MTAB.386_eset )}
\format{
\preformatted{
experimentData(eset):
Experiment data
Experimenter name: Bentink S, Haibe-Kains B, Risch T, Fan J-B, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane A, Drapkin R, Quackenbush J, Matulonis UA: Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer. PLoS ONE 2012, 7:e30269.
Laboratory: Bentink, Matulonis 2012
Contact information:
Title: Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer.
URL:
PMIDs: 22348002
Abstract: A 212 word abstract is available. Use 'abstract' method.
Information is available on: preprocessing
notes:
platform_title:
Illumina humanRef-8 v2.0 expression beadchip
platform_shorttitle:
Illumina humanRef-8 v2.0
platform_summary:
illuminaHumanv2
platform_manufacturer:
Illumina
platform_distribution:
commercial
platform_accession:
GPL6104
version:
2015-09-22 19:06:44
featureData(eset):
An object of class 'AnnotatedDataFrame'
featureNames: ILMN_1343291 ILMN_1651228 ... ILMN_1815951 (12449
total)
varLabels: probeset gene EntrezGene.ID best_probe
varMetadata: labelDescription
}}
\details{
\preformatted{
assayData: 12449 features, 129 samples
Platform type:
Overall survival time-to-event summary (in years):
Call: survfit(formula = Surv(time, cens) ~ -1)
n events median 0.95LCL 0.95UCL
129.00 73.00 3.51 2.68 4.13
---------------------------
Available sample meta-data:
---------------------------
unique_patient_ID:
DFCI.1 DFCI.10 DFCI.100 DFCI.101 DFCI.102 DFCI.103 DFCI.104 DFCI.105
1 1 1 1 1 1 1 1
DFCI.106 DFCI.107 DFCI.108 DFCI.109 DFCI.11 DFCI.110 DFCI.111 DFCI.112
1 1 1 1 1 1 1 1
DFCI.113 DFCI.114 DFCI.115 DFCI.116 DFCI.117 DFCI.118 DFCI.119 DFCI.12
1 1 1 1 1 1 1 1
DFCI.120 DFCI.121 DFCI.122 DFCI.123 DFCI.124 DFCI.125 DFCI.126 DFCI.127
1 1 1 1 1 1 1 1
DFCI.128 DFCI.129 DFCI.13 DFCI.130 DFCI.131 DFCI.132 DFCI.14 DFCI.15
1 1 1 1 1 1 1 1
DFCI.16 DFCI.17 DFCI.18 DFCI.19 DFCI.2 DFCI.20 DFCI.21 DFCI.22
1 1 1 1 1 1 1 1
DFCI.23 DFCI.24 DFCI.25 DFCI.26 DFCI.27 DFCI.28 DFCI.29 DFCI.3
1 1 1 1 1 1 1 1
DFCI.30 DFCI.31 DFCI.32 DFCI.33 DFCI.34 DFCI.35 DFCI.36 DFCI.37
1 1 1 1 1 1 1 1
DFCI.38 DFCI.39 DFCI.4 DFCI.40 DFCI.41 DFCI.42 DFCI.44 DFCI.45
1 1 1 1 1 1 1 1
DFCI.46 DFCI.47 DFCI.48 DFCI.49 DFCI.50 DFCI.51 DFCI.52 DFCI.53
1 1 1 1 1 1 1 1
DFCI.54 DFCI.55 DFCI.56 DFCI.57 DFCI.58 DFCI.59 DFCI.6 DFCI.60
1 1 1 1 1 1 1 1
DFCI.61 DFCI.62 DFCI.63 DFCI.64 DFCI.65 DFCI.66 DFCI.67 DFCI.68
1 1 1 1 1 1 1 1
DFCI.69 DFCI.7 DFCI.70 (Other)
1 1 1 30
sample_type:
tumor
129
histological_type:
ser
129
primarysite:
ov
129
summarygrade:
high
129
summarystage:
early late
1 128
tumorstage:
2 3 4
1 109 19
substage:
a b c NA's
5 12 93 19
age_at_initial_pathologic_diagnosis:
Min. 1st Qu. Median Mean 3rd Qu. Max.
21.00 50.00 66.00 60.71 72.00 95.00
days_to_death:
Min. 1st Qu. Median Mean 3rd Qu. Max.
3.9 516.9 917.1 1007.0 1401.0 2724.0
vital_status:
deceased living
73 56
debulking:
optimal suboptimal NA's
98 28 3
uncurated_author_metadata:
Source.Name: DFCI-100///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=44.42>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 33.77 months///Characteristics.TimeToProgression.: 15.87 months///Hybridization.Name: RNA-hyb-DFCI-100///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-100///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-101///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=40.9>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 14.2 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-101///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-101///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-102///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=39.93>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 21.87 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-102///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-102///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-103///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=45.24>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 45.2 months///Characteristics.TimeToProgression.: 6.87 months///Hybridization.Name: RNA-hyb-DFCI-103///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-103///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-104///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=54.92>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 53.67 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-104///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-104///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-105///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=39.47>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIa///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 35.63 months///Characteristics.TimeToProgression.: 30.43 months///Hybridization.Name: RNA-hyb-DFCI-105///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-105///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-106///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=50.35>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 40.73 months///Characteristics.TimeToProgression.: 2.77 months///Hybridization.Name: RNA-hyb-DFCI-106///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-106///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-107///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=43.63>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 79.6 months///Characteristics.TimeToProgression.: 60.47 months///Hybridization.Name: RNA-hyb-DFCI-107///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-107///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-108///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=69.91>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 14.93 months///Characteristics.TimeToProgression.: 4.7 months///Hybridization.Name: RNA-hyb-DFCI-108///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-108///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-109///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=79.12>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 20.07 months///Characteristics.TimeToProgression.: 15.87 months///Hybridization.Name: RNA-hyb-DFCI-109///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-109///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-10///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=64.94>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 1.53 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-10///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-10///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-110///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=77.76>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 62.5 months///Characteristics.TimeToProgression.: 58.9 months///Hybridization.Name: RNA-hyb-DFCI-110///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-110///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-111///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=71.29>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 30.67 months///Characteristics.TimeToProgression.: 24.17 months///Hybridization.Name: RNA-hyb-DFCI-111///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-111///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-112///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=73.54>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 31.97 months///Characteristics.TimeToProgression.: 4.1 months///Hybridization.Name: RNA-hyb-DFCI-112///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-112///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-113///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=79.47>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 23.6 months///Characteristics.TimeToProgression.: 5.97 months///Hybridization.Name: RNA-hyb-DFCI-113///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-113///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-114///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=80.91>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 18.2 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-114///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-114///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-115///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.6>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 41.83 months///Characteristics.TimeToProgression.: 32.67 months///Hybridization.Name: RNA-hyb-DFCI-115///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-115///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-116///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=72.33>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 20.37 months///Characteristics.TimeToProgression.: 14.67 months///Hybridization.Name: RNA-hyb-DFCI-116///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-116///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-117///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=65.58>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 43.93 months///Characteristics.TimeToProgression.: 9.9 months///Hybridization.Name: RNA-hyb-DFCI-117///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-117///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-118///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=69.39>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 15.03 months///Characteristics.TimeToProgression.: 3.97 months///Hybridization.Name: RNA-hyb-DFCI-118 rep1 / RNA-hyb-DFCI-118 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-118 rep1 / RNA-hyb-DFCI-118 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-119///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=66.53>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 63.3 months///Characteristics.TimeToProgression.: 39.6 months///Hybridization.Name: RNA-hyb-DFCI-119///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-119///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-11///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=68.58>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 16.13 months///Characteristics.TimeToProgression.: 10.73 months///Hybridization.Name: RNA-hyb-DFCI-11///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-11///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-120///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=77.11>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 46.63 months///Characteristics.TimeToProgression.: 11 months///Hybridization.Name: RNA-hyb-DFCI-120 rep1 / RNA-hyb-DFCI-120 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-120 rep1 / RNA-hyb-DFCI-120 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-121///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=71.53>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIa///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 62.87 months///Characteristics.TimeToProgression.: 21 months///Hybridization.Name: RNA-hyb-DFCI-121///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-121///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-122///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=77.1>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 21.4 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-122///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-122///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-123///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.59>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 58.77 months///Characteristics.TimeToProgression.: 9.63 months///Hybridization.Name: RNA-hyb-DFCI-123///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-123///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-124///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=95.13>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 9.2 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-124///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-124///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-125///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=69.1>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 61.9 months///Characteristics.TimeToProgression.: 18.5 months///Hybridization.Name: RNA-hyb-DFCI-125///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-125///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-126///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=77.07>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 18 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-126///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-126///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-127///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.86>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 49.37 months///Characteristics.TimeToProgression.: 13 months///Hybridization.Name: RNA-hyb-DFCI-127 rep1 / RNA-hyb-DFCI-127 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-127 rep1 / RNA-hyb-DFCI-127 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-128///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=73.28>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 28.43 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-128///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-128///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-129///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=68.33>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 90.8 months///Characteristics.TimeToProgression.: 9.87 months///Hybridization.Name: RNA-hyb-DFCI-129 rep1 / RNA-hyb-DFCI-129 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-129 rep1 / RNA-hyb-DFCI-129 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-12///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=68.23>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 30.57 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-12///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-12///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-130///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.97>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 68.73 months///Characteristics.TimeToProgression.: 6.27 months///Hybridization.Name: RNA-hyb-DFCI-130 rep1 / RNA-hyb-DFCI-130 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-130 rep1 / RNA-hyb-DFCI-130 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-131///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=68.28>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 21.73 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-131 rep1 / RNA-hyb-DFCI-131 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-131 rep1 / RNA-hyb-DFCI-131 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-132///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=75.63>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 78.3 months///Characteristics.TimeToProgression.: 21.03 months///Hybridization.Name: RNA-hyb-DFCI-132 rep1 / RNA-hyb-DFCI-132 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-132 rep1 / RNA-hyb-DFCI-132 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-13///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=65.52>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 33.77 months///Characteristics.TimeToProgression.: 9.43 months///Hybridization.Name: RNA-hyb-DFCI-13///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-13///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-14///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=81.56>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 2.33 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-14///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-14///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-15///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=71.64>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 16.2 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-15///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-15///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-16///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=69.08>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 12.33 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-16///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-16///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-17///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.21>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 86.17 months///Characteristics.TimeToProgression.: 81.4 months///Hybridization.Name: RNA-hyb-DFCI-17///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-17///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-18///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=70.94>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 24.63 months///Characteristics.TimeToProgression.: 2.57 months///Hybridization.Name: RNA-hyb-DFCI-18///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-18///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-19///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=76.01>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 12.07 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-19///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-19///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-1///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=77.74>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 28.03 months///Characteristics.TimeToProgression.: 8 months///Hybridization.Name: RNA-hyb-DFCI-1///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-1///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-20///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=66.14>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 67.73 months///Characteristics.TimeToProgression.: 60.3 months///Hybridization.Name: RNA-hyb-DFCI-20///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-20///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-21///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=71>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 15.93 months///Characteristics.TimeToProgression.: 4.6 months///Hybridization.Name: RNA-hyb-DFCI-21///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-21///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-22///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=78.13>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 16.6 months///Characteristics.TimeToProgression.: 10.37 months///Hybridization.Name: RNA-hyb-DFCI-22 rep1 / RNA-hyb-DFCI-22 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-22 rep1 / RNA-hyb-DFCI-22 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-23///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=76.71>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 36.37 months///Characteristics.TimeToProgression.: 32.17 months///Hybridization.Name: RNA-hyb-DFCI-23///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-23///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-24///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=68.72>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Unknown///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 35.4 months///Characteristics.TimeToProgression.: 8.57 months///Hybridization.Name: RNA-hyb-DFCI-24///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-24///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-25///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=78.03>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Unknown///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 24.03 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-25///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-25///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-26///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=72.88>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 40.03 months///Characteristics.TimeToProgression.: 6.1 months///Hybridization.Name: RNA-hyb-DFCI-26///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-26///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-27///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=72.39>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIa///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 32.57 months///Characteristics.TimeToProgression.: 7.53 months///Hybridization.Name: RNA-hyb-DFCI-27///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-27///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-28///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=76.02>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 17.93 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-28///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-28///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-29///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=68.68>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 21.27 months///Characteristics.TimeToProgression.: 3.43 months///Hybridization.Name: RNA-hyb-DFCI-29///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-29///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-2///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=75.53>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 13.33 months///Characteristics.TimeToProgression.: 8.27 months///Hybridization.Name: RNA-hyb-DFCI-2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-30///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=74.96>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 19.57 months///Characteristics.TimeToProgression.: 7.2 months///Hybridization.Name: RNA-hyb-DFCI-30///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-30///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-31///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=66.42>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 50.3 months///Characteristics.TimeToProgression.: 33.77 months///Hybridization.Name: RNA-hyb-DFCI-31///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-31///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-32///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.68>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 53.97 months///Characteristics.TimeToProgression.: 16.87 months///Hybridization.Name: RNA-hyb-DFCI-32///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-32///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-33///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=74.2>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 34.77 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-33///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-33///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-34///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=89.36>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 6.63 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-34///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-34///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-35///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=71.92>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 50.67 months///Characteristics.TimeToProgression.: 6.93 months///Hybridization.Name: RNA-hyb-DFCI-35///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-35///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-36///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=65.01>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 23.23 months///Characteristics.TimeToProgression.: 6 months///Hybridization.Name: RNA-hyb-DFCI-36///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-36///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-37///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=68.53>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 38.03 months///Characteristics.TimeToProgression.: 4.07 months///Hybridization.Name: RNA-hyb-DFCI-37///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-37///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-38///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=65.95>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 62.07 months///Characteristics.TimeToProgression.: 58.33 months///Hybridization.Name: RNA-hyb-DFCI-38///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-38///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-39///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=69.32>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Unknown///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 58.37 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-39///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-39///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-3///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=66.92>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 17.47 months///Characteristics.TimeToProgression.: 7.5 months///Hybridization.Name: RNA-hyb-DFCI-3///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-3///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-40///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=74.03>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 61.5 months///Characteristics.TimeToProgression.: 56.73 months///Hybridization.Name: RNA-hyb-DFCI-40///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-40///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-41///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.96>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 39.9 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-41///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-41///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-42///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=72.09>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 46.7 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-42///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-42///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-44///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=75.66>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 13.3 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-44///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-44///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-45///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=75.15>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 33.07 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-45///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-45///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-46///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=71.48>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 30.93 months///Characteristics.TimeToProgression.: 12.6 months///Hybridization.Name: RNA-hyb-DFCI-46///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-46///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
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Source.Name: DFCI-47///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=21.95>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 50.3 months///Characteristics.TimeToProgression.: 4.17 months///Hybridization.Name: RNA-hyb-DFCI-47///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-47///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-48///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=51.5>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 63.8 months///Characteristics.TimeToProgression.: 60 months///Hybridization.Name: RNA-hyb-DFCI-48///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-48///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-49///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=46.2>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 19.73 months///Characteristics.TimeToProgression.: 2.73 months///Hybridization.Name: RNA-hyb-DFCI-49///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-49///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-4///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=79.29>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 49.2 months///Characteristics.TimeToProgression.: 11.1 months///Hybridization.Name: RNA-hyb-DFCI-4///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-4///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-50///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=49.15>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 14.2 months///Characteristics.TimeToProgression.: 5.53 months///Hybridization.Name: RNA-hyb-DFCI-50///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-50///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-51///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=50.55>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 45.83 months///Characteristics.TimeToProgression.: 10.87 months///Hybridization.Name: RNA-hyb-DFCI-51///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-51///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-52///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=50.97>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 18.23 months///Characteristics.TimeToProgression.: 2.53 months///Hybridization.Name: RNA-hyb-DFCI-52///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-52///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-53///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=52.41>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 26.97 months///Characteristics.TimeToProgression.: 22.53 months///Hybridization.Name: RNA-hyb-DFCI-53///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-53///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-54///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=50.96>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 16.03 months///Characteristics.TimeToProgression.: 11.03 months///Hybridization.Name: RNA-hyb-DFCI-54///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-54///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-55///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=52.79>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIb///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 16.2 months///Characteristics.TimeToProgression.: 3.87 months///Hybridization.Name: RNA-hyb-DFCI-55///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-55///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-56///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=47.06>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 21.43 months///Characteristics.TimeToProgression.: 10.77 months///Hybridization.Name: RNA-hyb-DFCI-56///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-56///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-57///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=53.22>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 18.03 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-57///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-57///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-58///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=52.88>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 32.07 months///Characteristics.TimeToProgression.: 2.5 months///Hybridization.Name: RNA-hyb-DFCI-58///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-58///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-59///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=42.55>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 67.5 months///Characteristics.TimeToProgression.: 8.3 months///Hybridization.Name: RNA-hyb-DFCI-59///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-59///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-60///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=51.73>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 15.77 months///Characteristics.TimeToProgression.: 0.97 months///Hybridization.Name: RNA-hyb-DFCI-60///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-60///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-61///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=45.9>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIa///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 38 months///Characteristics.TimeToProgression.: 13.33 months///Hybridization.Name: RNA-hyb-DFCI-61///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-61///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-62///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=51.13>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 0.13 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-62 rep1 / RNA-hyb-DFCI-62 rep2///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-62 rep1 / RNA-hyb-DFCI-62 rep2///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-63///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=54.07>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 17.07 months///Characteristics.TimeToProgression.: 0 months///Hybridization.Name: RNA-hyb-DFCI-63///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-63///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-64///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=45.35>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IV///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 33.43 months///Characteristics.TimeToProgression.: 3.53 months///Hybridization.Name: RNA-hyb-DFCI-64///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-64///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-65///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=48.09>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIa///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 57.73 months///Characteristics.TimeToProgression.: 18.87 months///Hybridization.Name: RNA-hyb-DFCI-65///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-65///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-66///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=49.65>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 23 months///Characteristics.TimeToProgression.: 9.63 months///Hybridization.Name: RNA-hyb-DFCI-66///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-66///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-67///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=38.15>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 31 months///Characteristics.TimeToProgression.: 1.13 months///Hybridization.Name: RNA-hyb-DFCI-67///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-67///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-68///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=51.23>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 34.23 months///Characteristics.TimeToProgression.: 5.73 months///Hybridization.Name: RNA-hyb-DFCI-68///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-68///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-69///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=43.62>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 0///Characteristics.EventProgression.: 0///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 39.77 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-69///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-69///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-6///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=67.63>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 4.8 months///Characteristics.TimeToProgression.: null months///Hybridization.Name: RNA-hyb-DFCI-6///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-6///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-70///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=53.29>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: No///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 24.03 months///Characteristics.TimeToProgression.: 9.17 months///Hybridization.Name: RNA-hyb-DFCI-70///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-70///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
Source.Name: DFCI-71///Characteristics.Age.: Age <has_measurement <Measurement <has_units <Unit <time unit <time unit=years>>>> <has_value <has_value=45.5>>>>///Characteristics.Channel.: Cy3///Characteristics.DiseaseStage.: IIIc///Characteristics.DiseaseState.: serous ovarian cancer///Term.Source.REF: SNOMED Clinical Terms///Characteristics.EventDeath.: 1///Characteristics.EventProgression.: 1///Characteristics.OptimalDebulking.: Yes///Characteristics.Organism.: Homo sapiens///Characteristics.Sex.: female///Term.Source.REF.1: EFO///Characteristics.SurvivalTime.: 12.3 months///Characteristics.TimeToProgression.: 2.03 months///Hybridization.Name: RNA-hyb-DFCI-71///Array.Design.REF: A-MEXP-931///Scan.Name: RNA-hyb-DFCI-71///Derived.Array.Data.Matrix.File: normalized.txt.proc///Comment..Derived.ArrayExpress.FTP.file.: ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/MTAB/E-MTAB-386/E-MTAB-386.processed.1.zip
1
(Other)
30
}}
\keyword{datasets}
|
f61136a654921cf7abb2dcc6f2af73e4bea173df | e089b43cad0c3b820ca64fc3fc9a52c690cd428d | /man/theme_soe.Rd | a97b4c6369ab5623360192f44ee8be97850a7677 | [
"LicenseRef-scancode-proprietary-license",
"OGL-Canada-2.0",
"Apache-2.0"
] | permissive | bcgov/envreportutils | d4f2149a57a5c05c3d1313ba81b8cd8dde25247d | 65371ee1cd6a9f35dd761ac9be582eb01a77cca7 | refs/heads/master | 2022-11-04T16:34:25.314578 | 2022-08-23T21:31:10 | 2022-08-23T21:31:10 | 34,545,077 | 13 | 4 | Apache-2.0 | 2022-11-03T22:41:29 | 2015-04-24T22:29:40 | R | UTF-8 | R | false | true | 490 | rd | theme_soe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme_soe.r
\name{theme_soe}
\alias{theme_soe}
\title{Default theme for EnvReportBC graphs and plots}
\usage{
theme_soe(base_size = 12, base_family = "Verdana")
}
\arguments{
\item{base_size}{base font size (default = 12)}
\item{base_family}{base font family (default = Verdana)}
}
\value{
returns a plot theme
}
\description{
Default theme for EnvReportBC graphs and plots
}
\keyword{plotting}
\keyword{theme}
|
a9be94cb6d8c2b32f1a7e46a6759f85bfdb4bdd7 | 2d02b08eb66d83596e238bc1eeb3a7f24d23cfff | /cooccurence.R | 0d1464de3395c0b0a1dba2626443678531345cdc | [
"MIT"
] | permissive | coreyabshire/ivmooc-gtap | a8b9339055578dc61bc2db6a05f2b655650ae6f4 | 886f954b56054b5fa6a79c3ed1038effacd898f0 | refs/heads/master | 2021-01-01T18:42:53.711835 | 2015-05-02T17:51:32 | 2015-05-02T17:51:32 | 32,621,491 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,738 | r | cooccurence.R | ## Co-occurence network
# Inspired by: http://stackoverflow.com/questions/13281303/creating-co-occurrence-matrix
#
# co-occurrence of exported commodities
#
load("trade.clean")
ctrade = subset(trade, value > 10000)
ctr1995 = subset(ctrade, year == 2009)
dim(ctr1995)
colnames(ctr1995)
head(ctr1995)
ctr1995$imp = NULL
ctr1995$year = NULL
ctr1995$value = 1
ctr1995 = unique(ctr1995)
# reorder the columns
ctr1995 = ctr1995[c("exp", "comm", "value")]
#making the boolean matrix
library(reshape2)
ctr1995 <- melt(ctr1995)
w <- dcast(ctr1995, comm~exp)
x <- as.matrix(w[,-1])
x[is.na(x)] <- 0
x <- apply(x, 2, function(x) as.numeric(x > 0)) #recode as 0/1
v <- x %*% t(x) #the magic matrix
diag(v) <- 0 #repalce diagonal
dimnames(v) <- list(w[, 1], w[,1]) #name the dimensions
v
# graphing
library(igraph)
g <- graph.adjacency(v, weighted=TRUE, mode ='undirected')
g <- simplify(g)
# set labels and degrees of vertices
V(g)$label <- V(g)$name
V(g)$degree <- degree(g)
plot(g, main="year 2009 - value > 10000")
#
# co-occurrence of exported regions
#
topRegions1995 = Sum_x_ExpYear %>%
filter(year == 1995) %>%
ungroup %>% arrange(desc(commval)) %>%
select(exp)
top10 = topRegions1995[1:25,][[1]]
load("trade.plus")
rtrade = subset(tradeplus, exp %in% top10 & imp %in% top10)
# rtrade = subset(rtrade, value > 10000)
rtr1995 = subset(rtrade, year == 2009)
rtr1995 <- rtr1995 %>%
group_by(exp, imp) %>%
summarise(weight = sum(weight)) %>%
arrange() %>% ungroup()
dim(rtr1995)
colnames(rtr1995)
head(rtr1995)
# rtr1995$value = 1
rtr1995$commval = rtr1995$commval/10000
rtr1995 = unique(rtr1995)
tv = rtr1995 %>% select(exp, imp, comm, value) %>% group_by(exp, imp) %>% summarise(val = sum(value))
tw = rtr1995 %>% select(exp, imp, comm, weight) %>% group_by(exp, imp) %>% summarise(weight = sum(weight))
#making the boolean matrix
library(reshape2)
m <- melt(tw)
w <- dcast(tw, exp~imp)
x <- as.matrix(w[,-1])
x[is.na(x)] <- 0
x <- apply(x, 2, function(x) as.numeric(x > 0.1)) #recode as 0/1
v <- x %*% t(x) #the magic matrix
vdiag(v) <- 0 #repalce diagonal
dimnames(v) <- list(w[, 1], w[,1]) #name the dimensions
# v = x
# graphing
library(igraph)
g <- graph.adjacency(v, weighted=TRUE, mode ='directed')
g <- simplify(g)
# set labels and degrees of vertices
V(g)$label <- V(g)$name
V(g)$degree <- degree(g)
egam <- (log(E(g)$weight)+.4) / max(log(E(g)$weight)+.4)
E(g)$color <- rgb(.5, .5, 0, egam)
E(g)$width <- egam * 5
layout1 <- layout.fruchterman.reingold(g)
plot(g, layout=layout1, main="year 2009 - value > 10000")
|
7f9fdd7f227f246290656189d5ef986d8442dcca | 0c925dc2d7aa2461f20b42d86fd66619a3bd31e0 | /man/filter_for_loinc.Rd | 0da6f7605a0f10a72ca22e0002d5d689fd785d59 | [] | no_license | meerapatelmd/metaoriteSQL | cfc69887ed06c552ac67256fc95b530263b29c65 | 932cc16b3dace2319fc8373cab82e5d8a8facc52 | refs/heads/master | 2023-01-29T07:09:09.547666 | 2020-11-29T21:31:03 | 2020-11-29T21:31:03 | 299,144,522 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 273 | rd | filter_for_loinc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filter_for_loinc.R
\name{filter_for_loinc}
\alias{filter_for_loinc}
\title{Filter MRCONSO output for LOINC}
\usage{
filter_for_loinc(mrconso_df)
}
\description{
Filter MRCONSO output for LOINC
}
|
6baa9d73795effd30e39725caef2d1c87cf35f80 | 55f7894436bc7b3b6e51631daa9a0bf11a91debb | /R/onlineCPD-internal.R | 09d2f8889bce09e841273df0e3e7c59c0f7cfdc6 | [] | no_license | cran/onlineCPD | fe2c0a36a7618c3cfb69eebd372a8e48c6738533 | 721bda0483a9f8e5737ca8b24ea1ddad1eeb0d8e | refs/heads/master | 2020-12-08T06:37:31.528416 | 2016-08-23T20:00:17 | 2016-08-23T20:00:17 | 66,390,808 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,246 | r | onlineCPD-internal.R | .Random.seed <-
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|
66f5b6fd575396fbed055d4bbc403069c1d56db0 | 1a604107dfff182200e5438a4fd5d90a8dc1f377 | /pred_find_exact_string_match.R | d1a4207b9f1d88f7da4c087444b1b39cba7647e4 | [] | no_license | Bixi81/R-ml | 5ef025de1612b5589641d315857d79e6ea29aca4 | 1a4a478d3f005a29f75d5d8280ddc287c7a5be1e | refs/heads/master | 2021-07-03T08:17:14.218404 | 2020-11-16T08:56:10 | 2020-11-16T08:56:10 | 194,518,570 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 206 | r | pred_find_exact_string_match.R | mylist = list("rm", "ff", "ffu", "fkw")
mylist[1]
# Find the position of an exact match
# Use word boundaries
grep("\\bff\\b", mylist)
mylist[grep("\\bff\\b", mylist)+1]
# Or which
which(mylist == "ff")
|
711f882acee54d08681f75527a74bb2f7dcc6609 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /ggforce/inst/testfiles/enclose_points/libFuzzer_enclose_points/enclose_points_valgrind_files/1609955648-test.R | 6561814f2aa6e71cd1334c0873c760107f194e38 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,257 | r | 1609955648-test.R | testlist <- list(id = integer(0), x = c(0, 0, NaN, NaN, 1.08646184497373e-311, NaN, 3.08608224361818e-319, 9.61235670663551e+281, 3.96573944649364e-317, 0, 4.53801546776667e+279, 9.80104716176339e+281, 2.88109526018606e+284, 7.06327445644536e-304, 7.1071553048134e-15, 6.8181059126092e-322, 0, -3.27585619210153e+221, 2.12186639171417e-314, 2.12186652758222e-314, -1.07556276931065e-204, 2.25317852920819e-310, 3.18618424339872e-58, 2.18356286902537e-310, 1.39067111850155e-309, 1.25986739689518e-321, 9.61276248427429e+281, 6.02760087926321e-322, 0, 9.61208401266328e+281, 4.7783079911955e-299, 2.52435529615491e-29, 2.47032822920623e-322, 1.08646182031046e-311, 3.96567917048485e-317, 0, 0, 2.18060768306417e-106, 5.4273824231201e-315, 2.68221493545489e+154, 7.18426768903719e-109, 1.92740794802915e-310, 0, 4.24395910650158e-314, -5.4866012032577e+303, 2.781342323134e-309, NaN, -5.82900159119719e+303, NaN, NaN), y = c(2.11373912349891e-314, -1.16211202458355e+306, 2.35683994576271e-306, 2.52467545024877e-321, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
result <- do.call(ggforce:::enclose_points,testlist)
str(result) |
c1f3fad5f8484b7d7b18cf72fdf3c6159cbbe511 | 6cb3879ab6685ca0812185d61dc7c88f2cb29e7a | /Problem 30/Problem 30.R | d02c8498fc8a1182ed46b3fa2193817884b9e3b5 | [] | no_license | samuelweller21/Project-Euler | d471d7002ec2af45bcdd5299441f97eabfe5232f | 0b301ab818449265212a353b3c2b2d21bf039c31 | refs/heads/main | 2023-03-29T00:07:04.335150 | 2021-04-05T19:22:22 | 2021-04-05T19:22:22 | 354,944,630 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 365 | r | Problem 30.R | max = 200000
return.sum = function(x, p) {
sum = 0
for (j in 1:nchar(toString(i))) {
l = as.integer(substr(toString(i), j,j))^p
sum = sum + l
}
return (sum)
}
big.sum = 0
for (i in 1:max) {
sum = return.sum(i,5)
if (sum == i) {
cat(i, "\n")
big.sum = big.sum + sum
}
}
#Minus 1
cat("Total: ", big.sum, "\n") |
3049068a12953364c58bdaddad39a13fc31af346 | aebca85114388224fc24481fdfce04be048110db | /man/renamecols.Rd | 6dd72c8988756426b69864675f9bd5c91aa55ea1 | [] | no_license | mssm-msf-2019/BiostatsALL | 4f79f2fbb823db8a0cbe60172b3dcd54eac58539 | 0623dd13db576b2501783b31d08ae43340f2080b | refs/heads/master | 2020-05-25T15:16:01.949307 | 2019-05-21T18:11:12 | 2019-05-21T18:11:12 | 187,864,190 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 473 | rd | renamecols.Rd | % Generated by roxygen2 (4.1.0.9001): do not edit by hand
% Please edit documentation in R/renamecols.R
\name{renamecols}
\alias{renamecols}
\title{A function that changes the names of a dataframe columns}
\usage{
renamecols(db, suffix)
}
\arguments{
\item{db}{dataframe}
\item{suffixb}{dataframe}
}
\description{
This function changes the names of a dataframe columns adding a suffix to the current names (uses "_" to separate coulmn name and suffix)
}
|
011574b0564c97c62add2901f54ce38a592d1607 | e8b72b3d1ddc401b3afcfdb539698a61ca03511a | /random forest of compay sales.r | 6e4bdb4be24eb3abbe965c790a413db063f82c54 | [] | no_license | lalitha2997/Random-Forest | 4df705ae2ecab86198de38705175523b6d287377 | 5b6ec7cf916e7150500834e3b2f69567f6155d18 | refs/heads/main | 2023-04-23T15:30:59.232971 | 2021-05-08T13:18:43 | 2021-05-08T13:18:43 | 365,516,572 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,188 | r | random forest of compay sales.r | ##importing company data set
company=read.csv(file.choose())
View(company)
company$Sales=cut(company$Sales,c(0,5,10,15),labels = c('low','avg','high'))
View(company)
##performing some EDA tequnics
table(company$Sales)
summary(company)
plot(company)
boxplot(company)
sum(is.na(company))
##visuvalization using density plot
library(ggplot2)
ggplot(data=company,aes(x =company$Sales, fill = company$Sales)) +
geom_density(alpha = 0.9, color = 'black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'sales variable in company datas set')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
###
ggplot(data=company,aes(x=company$CompPrice,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for comprice varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$Income,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for income varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$Advertising,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for Advertising varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$Population,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for population varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$Price,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for price varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$ShelveLoc,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for Shelveloc varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$Age,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for age varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$Education,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for Education varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$Urban,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for urban varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##
ggplot(data=company,aes(x=company$US,fill=company$Sales))+
geom_density(alpha=0.9,color='black')+
theme(panel.background = element_rect(fill = 'peachpuff'))+
labs(title = 'company data for US varable')+
theme(plot.title = element_text(hjust = 0.5),plot.background =
element_rect('aquamarine4'))
##handling missing values or data
summary(company)
p=function(x){sum(is.na(x))/length(x)*100}
p
apply(company, 2,p)
library(mice)
library(VIM)
md.pattern(company)
md.pairs(company)
marginplot(company[,c("Sales","Price")])
##impute
impute=mice(company,m=3,seed = 123)
print(impute)
impute$imp$Sales
company[1]
summary(company$Sales)
#complete data
company1=complete(impute,1)
company1
company1[1]
#distribution of observed /imputed values
stripplot(impute,pch=20,cex=1.2)
xyplot(impute,Sales~Price,pch=20,cex=1)
View(company)
View(company1)
#spiltting data
set.seed(1234)
id=sample(2,nrow(company1),prob = c(0.8,0.2),replace = T)
training=company1[id==1,]
testing=company1[id==2,]
library(randomForest)
str(company1)
com=randomForest(Sales~.,data =company1)
com
pred=predict(com,newdata = training,type = 'class')
pred
pred1=predict(com,newdata = testing,type = 'class')
pred1
library(caret)
con1=confusionMatrix(table(pred,training$Sales))
con1
con=confusionMatrix(table(pred1,testing$Sales))
con
varImpPlot(com)
plot(com, lwd=1)
err=legend("topright", colnames(com$err.rate), col=1:4, cex=0.8,fill=1:4)
err
|
ffeb08c4f2459cae3d0733127f820c95589b1ceb | b6a204dd6f659d91a371ae79bd86fe646ee86169 | /unique_rank.R | 55b312581e6e62c977e1363e774cd6496f4087cf | [] | no_license | LIUXXiaotong/simulation_ranks-of-the-subjective-likelihoods | dbb3a45d1a95605b240f26de5c2f8f7fd4328014 | 5b65f731b4692407a4e5b8ef21925bd5c0be2b35 | refs/heads/master | 2022-11-30T09:25:48.734364 | 2020-08-17T12:57:21 | 2020-08-17T12:57:21 | 253,451,047 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,471 | r | unique_rank.R | library(tidyverse)
options(digits = 4) ### reason: the after the mathmatical operations, the maximum digit we have is four
### rank function
myRank <- function(x) {
rank(-x, ties.method = "average")
}
ranking <- function (b, a_b, a_B) { ##input: P(B) & P(A|B) & P(A|¬B)
a = a_b * b + a_B * (1-b) ## P(A) = P(A|B)*P(B) + P(A|¬B)*P(¬B)
#### ---------------------
### calculate simple likelihoods
marginal_value = numeric(4)
names(marginal_value) <- c("P(A)", "P(¬A)", "P(B)", "P(¬B)")
marginal_value[1] = a
marginal_value[2] = 1-a
marginal_value[3] = b
marginal_value[4] = 1-b
rank_marginal <- myRank(marginal_value)
### conditional likelihoods
###P(¬A|B)
A_b = 1 - a_b
###P(¬A|¬B)
A_B = 1 - a_B
### --------------------------------
### calculate consjunctions
conjunction_value = numeric(4)
names(conjunction_value) <- c("P(A^B)", "P(A^¬B)", "P(¬A^B)", "P(¬B^¬A)")
### conjuctions
conjunction_value[1] = a_b*b
conjunction_value[2] = a_B*(1-b)
conjunction_value[3] = A_b*b
conjunction_value[4] = A_B*(1-b)
### disjunctios
disjunction_value = numeric(4)
names(disjunction_value) <- c("P(A∨B)", "P(A∨¬B)", "P(¬A∨B)", "P(¬B∨¬A)")
disjunction_value[1] = a + b - conjunction_value["P(A^B)"] ## math: P(A∨B) = P(A) + P(B) - P(A^B)
disjunction_value[2] = a + (1-b) - conjunction_value["P(A^¬B)"]
disjunction_value[3] = (1-a) + b - conjunction_value["P(¬A^B)"]
disjunction_value[4] = (1-a) + (1-b) - conjunction_value["P(¬B^¬A)"]
### ---------------------- ranking the prob
rank_conjunction = myRank(conjunction_value)
rank_disjunction = myRank(disjunction_value)
return( c(rank_marginal, rank_conjunction, rank_disjunction) )
}
b <- seq(0, 1, by = 0.01)
length(b)
a_b <- seq(0, 1, by = 0.01)
length(a_b)
a_B <- seq(0, 1, by = 0.01)
length(a_B)
rank_value = matrix (0, length(b)*length(a_b)*length(a_B), 12)
colnames(rank_value) <- c("P(A)", "P(¬A)", "P(B)", "P(¬B)",
"P(A^B)", "P(A^¬B)", "P(¬A^B)", "P(¬B^¬A)" ,
"P(A∨B)", "P(A∨¬B)", "P(¬A∨B)", "P(¬B∨¬A)")
m <- 1
for ( i in seq_along(b) ) {
for ( k in seq_along(a_b) ) {
for ( j in seq_along(a_B) ) {
rank_value[m, ] <- ranking(b[i], a_b[k], a_B[j])
m = m + 1
}
}
}
( unique_rank <- unique(rank_value) %>% as.data.frame() )
write.csv(unique_rank, file = 'unique_rank.csv')
|
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