Description: Scores by reproducibility criterion
Coder name(s): Althea ArchMiller
Load libraries
library(ezknitr)
library(knitr)
library(devtools)
library(ggplot2)
Clear environment and set seed
remove(list=ls())
set.seed(8675)
load(file = "data/processed_data/averages_of_reviewed_studies.Rdata")
plottingdata <- as.data.frame(matrix(NA, nrow = 30, ncol = 8))
colnames(plottingdata) <- c("Score", "SD", "upper", "lower",
"Category", "Question", "Response", "n")
Yes, graphs
plottingdata$Score[1] <-
mean(na.exclude(averages$graphsReproduced[averages$dataAvailable=="True"]))
plottingdata$SD[1] <-
sd(na.exclude(averages$graphsReproduced[averages$dataAvailable=="True"]))
plottingdata$Category[1] <- "Figures reproduced"
plottingdata$Question[1] <- "Q5: Data available?"
plottingdata$Response[1] <- "Yes"
plottingdata$n[1] <- length(na.exclude(averages$graphsReproduced[averages$dataAvailable=="True"]))
No, graphs
plottingdata$Score[2] <-
mean(na.exclude(averages$graphsReproduced[averages$dataAvailable=="False"]))
plottingdata$SD[2] <-
sd(na.exclude(averages$graphsReproduced[averages$dataAvailable=="False"]))
plottingdata$Category[2] <- "Figures reproduced"
plottingdata$Question[2] <- "Q5: Data available?"
plottingdata$Response[2] <- "No"
plottingdata$n[2] <-
length(na.exclude(averages$graphsReproduced[averages$dataAvailable=="False"]))
Yes, numbers
plottingdata$Score[3] <-
mean(na.exclude(averages$numbersReproduced[averages$dataAvailable=="True"]))
plottingdata$SD[3] <-
sd(na.exclude(averages$numbersReproduced[averages$dataAvailable=="True"]))
plottingdata$Category[3] <- "Numbers reproduced"
plottingdata$Question[3] <- "Q5: Data available?"
plottingdata$Response[3] <- "Yes"
plottingdata$n[3] <-
length(na.exclude(averages$numbersReproduced[averages$dataAvailable=="True"]))
No, numbers
plottingdata$Score[4] <-
mean(na.exclude(averages$numbersReproduced[averages$dataAvailable=="False"]))
plottingdata$SD[4] <-
sd(na.exclude(averages$numbersReproduced[averages$dataAvailable=="False"]))
plottingdata$Category[4] <- "Numbers reproduced"
plottingdata$Question[4] <- "Q5: Data available?"
plottingdata$Response[4] <- "No"
plottingdata$n[4] <-
length(na.exclude(averages$numbersReproduced[averages$dataAvailable=="False"]))
Yes, conclusions
plottingdata$Score[5] <-
mean(na.exclude(averages$conclusionsReproduced[averages$dataAvailable=="True"]))
plottingdata$SD[5] <-
sd(na.exclude(averages$conclusionsReproduced[averages$dataAvailable=="True"]))
plottingdata$Category[5] <- "Conclusions reproduced"
plottingdata$Question[5] <- "Q5: Data available?"
plottingdata$Response[5] <- "Yes"
plottingdata$n[5] <-
length(na.exclude(averages$conclusionsReproduced[averages$dataAvailable=="True"]))
No, conclusions
plottingdata$Score[6] <-
mean(na.exclude(averages$conclusionsReproduced[averages$dataAvailable=="False"]))
plottingdata$SD[6] <-
sd(na.exclude(averages$conclusionsReproduced[averages$dataAvailable=="False"]))
plottingdata$Category[6] <- "Conclusions reproduced"
plottingdata$Question[6] <- "Q5: Data available?"
plottingdata$Response[6] <- "No"
plottingdata$n[6] <-
length(na.exclude(averages$conclusionsReproduced[averages$dataAvailable=="False"]))
Yes, graphs
plottingdata$Score[7] <-
mean(na.exclude(averages$graphsReproduced[averages$preProcessed=="Raw format"]))
plottingdata$SD[7] <-
sd(na.exclude(averages$graphsReproduced[averages$preProcessed=="Raw format"]))
plottingdata$Category[7] <- "Figures reproduced"
plottingdata$Question[7] <- "Q6: Raw data?"
plottingdata$Response[7] <- "Yes"
plottingdata$n[7] <-
length(na.exclude(averages$graphsReproduced[averages$preProcessed=="Raw format"]))
No, graphs
plottingdata$Score[8] <-
mean(na.exclude(averages$graphsReproduced[averages$preProcessed=="Pre-processed"]))
plottingdata$SD[8] <-
sd(na.exclude(averages$graphsReproduced[averages$preProcessed=="Pre-processed"]))
plottingdata$Category[8] <- "Figures reproduced"
plottingdata$Question[8] <- "Q6: Raw data?"
plottingdata$Response[8] <- "No"
plottingdata$n[8] <-
length(na.exclude(averages$graphsReproduced[averages$preProcessed=="Pre-processed"]))
Yes, numbers
plottingdata$Score[9] <-
mean(na.exclude(averages$numbersReproduced[averages$preProcessed=="Raw format"]))
plottingdata$SD[9] <-
sd(na.exclude(averages$numbersReproduced[averages$preProcessed=="Raw format"]))
plottingdata$Category[9] <- "Numbers reproduced"
plottingdata$Question[9] <- "Q6: Raw data?"
plottingdata$Response[9] <- "Yes"
plottingdata$n[9] <-
length(na.exclude(averages$numbersReproduced[averages$preProcessed=="Raw format"]))
No, numbers
plottingdata$Score[10] <-
mean(na.exclude(averages$numbersReproduced[averages$preProcessed=="Pre-processed"]))
plottingdata$SD[10] <-
sd(na.exclude(averages$numbersReproduced[averages$preProcessed=="Pre-processed"]))
plottingdata$Category[10] <- "Numbers reproduced"
plottingdata$Question[10] <- "Q6: Raw data?"
plottingdata$Response[10] <- "No"
plottingdata$n[10] <-
length(na.exclude(averages$numbersReproduced[averages$preProcessed=="Pre-processed"]))
Yes, conclusions
plottingdata$Score[11] <-
mean(na.exclude(averages$conclusionsReproduced[averages$preProcessed=="Raw format"]))
plottingdata$SD[11] <-
sd(na.exclude(averages$conclusionsReproduced[averages$preProcessed=="Raw format"]))
plottingdata$Category[11] <- "Conclusions reproduced"
plottingdata$Question[11] <- "Q6: Raw data?"
plottingdata$Response[11] <- "Yes"
plottingdata$n[11] <-
length(na.exclude(averages$conclusionsReproduced[averages$preProcessed=="Raw format"]))
No, conclusions
plottingdata$Score[12] <-
mean(na.exclude(averages$conclusionsReproduced[averages$preProcessed=="Pre-processed"]))
plottingdata$SD[12] <-
sd(na.exclude(averages$conclusionsReproduced[averages$preProcessed=="Pre-processed"]))
plottingdata$Category[12] <- "Conclusions reproduced"
plottingdata$Question[12] <- "Q6: Raw data?"
plottingdata$Response[12] <- "No"
plottingdata$n[12] <-
length(na.exclude(averages$conclusionsReproduced[averages$preProcessed=="Pre-processed"]))
Yes, graphs
plottingdata$Score[13] <-
mean(na.exclude(averages$graphsReproduced[averages$codeBased=="True"]))
plottingdata$SD[13] <-
sd(na.exclude(averages$graphsReproduced[averages$codeBased=="True"]))
plottingdata$Category[13] <- "Figures reproduced"
plottingdata$Question[13] <- "Q8: Code based?"
plottingdata$Response[13] <- "Yes"
plottingdata$n[13] <-
length(na.exclude(averages$graphsReproduced[averages$codeBased=="True"]))
No, graphs
plottingdata$Score[14] <-
mean(na.exclude(averages$graphsReproduced[averages$codeBased=="False"]))
plottingdata$SD[14] <-
sd(na.exclude(averages$graphsReproduced[averages$codeBased=="False"]))
plottingdata$Category[14] <- "Figures reproduced"
plottingdata$Question[14] <- "Q8: Code based?"
plottingdata$Response[14] <- "No"
plottingdata$n[14] <-
length(na.exclude(averages$graphsReproduced[averages$codeBased=="False"]))
Yes, numbers
plottingdata$Score[15] <-
mean(na.exclude(averages$numbersReproduced[averages$codeBased=="True"]))
plottingdata$SD[15] <-
sd(na.exclude(averages$numbersReproduced[averages$codeBased=="True"]))
plottingdata$Category[15] <- "Numbers reproduced"
plottingdata$Question[15] <- "Q8: Code based?"
plottingdata$Response[15] <- "Yes"
plottingdata$n[15] <-
length(na.exclude(averages$numbersReproduced[averages$codeBased=="True"]))
No, numbers
plottingdata$Score[16] <-
mean(na.exclude(averages$numbersReproduced[averages$codeBased=="False"]))
plottingdata$SD[16] <-
sd(na.exclude(averages$numbersReproduced[averages$codeBased=="False"]))
plottingdata$Category[16] <- "Numbers reproduced"
plottingdata$Question[16] <- "Q8: Code based?"
plottingdata$Response[16] <- "No"
plottingdata$n[16] <-
length(na.exclude(averages$numbersReproduced[averages$codeBased=="False"]))
Yes, conclusions
plottingdata$Score[17] <-
mean(na.exclude(averages$conclusionsReproduced[averages$codeBased=="True"]))
plottingdata$SD[17] <-
sd(na.exclude(averages$conclusionsReproduced[averages$codeBased=="True"]))
plottingdata$Category[17] <- "Conclusions reproduced"
plottingdata$Question[17] <- "Q8: Code based?"
plottingdata$Response[17] <- "Yes"
plottingdata$n[17] <-
length(na.exclude(averages$conclusionsReproduced[averages$codeBased=="True"]))
No, conclusions
plottingdata$Score[18] <-
mean(na.exclude(averages$conclusionsReproduced[averages$codeBased=="False"]))
plottingdata$SD[18] <-
sd(na.exclude(averages$conclusionsReproduced[averages$codeBased=="False"]))
plottingdata$Category[18] <- "Conclusions reproduced"
plottingdata$Question[18] <- "Q8: Code based?"
plottingdata$Response[18] <- "No"
plottingdata$n[18] <-
length(na.exclude(averages$conclusionsReproduced[averages$codeBased=="False"]))
Yes, graphs
plottingdata$Score[19] <-
mean(na.exclude(averages$graphsReproduced[averages$codeAvailable=="True"]))
plottingdata$SD[19] <-
sd(na.exclude(averages$graphsReproduced[averages$codeAvailable=="True"]))
plottingdata$Category[19] <- "Figures reproduced"
plottingdata$Question[19] <- "Q7: Code available?"
plottingdata$Response[19] <- "Yes"
plottingdata$n[19] <-
length(na.exclude(averages$graphsReproduced[averages$codeAvailable=="True"]))
No, graphs
plottingdata$Score[20] <-
mean(na.exclude(averages$graphsReproduced[averages$codeAvailable=="False"]))
plottingdata$SD[20] <-
sd(na.exclude(averages$graphsReproduced[averages$codeAvailable=="False"]))
plottingdata$Category[20] <- "Figures reproduced"
plottingdata$Question[20] <- "Q7: Code available?"
plottingdata$Response[20] <- "No"
plottingdata$n[20] <-
length(na.exclude(averages$graphsReproduced[averages$codeAvailable=="False"]))
Yes, numbers
plottingdata$Score[21] <-
mean(na.exclude(averages$numbersReproduced[averages$codeAvailable=="True"]))
plottingdata$SD[21] <-
sd(na.exclude(averages$numbersReproduced[averages$codeAvailable=="True"]))
plottingdata$Category[21] <- "Numbers reproduced"
plottingdata$Question[21] <- "Q7: Code available?"
plottingdata$Response[21] <- "Yes"
plottingdata$n[21] <-
length(na.exclude(averages$numbersReproduced[averages$codeAvailable=="True"]))
No, numbers
plottingdata$Score[22] <-
mean(na.exclude(averages$numbersReproduced[averages$codeAvailable=="False"]))
plottingdata$SD[22] <-
sd(na.exclude(averages$numbersReproduced[averages$codeAvailable=="False"]))
plottingdata$Category[22] <- "Numbers reproduced"
plottingdata$Question[22] <- "Q7: Code available?"
plottingdata$Response[22] <- "No"
plottingdata$n[22] <-
length(na.exclude(averages$numbersReproduced[averages$codeAvailable=="False"]))
Yes, conclusions
plottingdata$Score[23] <-
mean(na.exclude(averages$conclusionsReproduced[averages$codeAvailable=="True"]))
plottingdata$SD[23] <-
sd(na.exclude(averages$conclusionsReproduced[averages$codeAvailable=="True"]))
plottingdata$Category[23] <- "Conclusions reproduced"
plottingdata$Question[23] <- "Q7: Code available?"
plottingdata$Response[23] <- "Yes"
plottingdata$n[23] <-
length(na.exclude(averages$conclusionsReproduced[averages$codeAvailable=="True"]))
No, conclusions
plottingdata$Score[24] <-
mean(na.exclude(averages$conclusionsReproduced[averages$codeAvailable=="False"]))
plottingdata$SD[24] <-
sd(na.exclude(averages$conclusionsReproduced[averages$codeAvailable=="False"]))
plottingdata$Category[24] <- "Conclusions reproduced"
plottingdata$Question[24] <- "Q7: Code available?"
plottingdata$Response[24] <- "No"
plottingdata$n[24] <-
length(na.exclude(averages$conclusionsReproduced[averages$codeAvailable=="False"]))
Yes, graphs
plottingdata$Score[25] <-
mean(na.exclude(averages$graphsReproduced[averages$openSource=="True"]))
plottingdata$SD[25] <-
sd(na.exclude(averages$graphsReproduced[averages$openSource=="True"]))
plottingdata$Category[25] <- "Figures reproduced"
plottingdata$Question[25] <- "Q9: Open source?"
plottingdata$Response[25] <- "Yes"
plottingdata$n[25] <-
length(na.exclude(averages$graphsReproduced[averages$openSource=="True"]))
No, graphs
plottingdata$Score[26] <-
mean(na.exclude(averages$graphsReproduced[averages$openSource=="False"]))
plottingdata$SD[26] <-
sd(na.exclude(averages$graphsReproduced[averages$openSource=="False"]))
plottingdata$Category[26] <- "Figures reproduced"
plottingdata$Question[26] <- "Q9: Open source?"
plottingdata$Response[26] <- "No"
plottingdata$n[26] <-
length(na.exclude(averages$graphsReproduced[averages$openSource=="False"]))
Yes, numbers
plottingdata$Score[27] <-
mean(na.exclude(averages$numbersReproduced[averages$openSource=="True"]))
plottingdata$SD[27] <-
sd(na.exclude(averages$numbersReproduced[averages$openSource=="True"]))
plottingdata$Category[27] <- "Numbers reproduced"
plottingdata$Question[27] <- "Q9: Open source?"
plottingdata$Response[27] <- "Yes"
plottingdata$n[27] <-
length(na.exclude(averages$numbersReproduced[averages$openSource=="True"]))
No, numbers
plottingdata$Score[28] <-
mean(na.exclude(averages$numbersReproduced[averages$openSource=="False"]))
plottingdata$SD[28] <-
sd(na.exclude(averages$numbersReproduced[averages$openSource=="False"]))
plottingdata$Category[28] <- "Numbers reproduced"
plottingdata$Question[28] <- "Q9: Open source?"
plottingdata$Response[28] <- "No"
plottingdata$n[28] <-
length(na.exclude(averages$numbersReproduced[averages$openSource=="False"]))
Yes, conclusions
plottingdata$Score[29] <-
mean(na.exclude(averages$conclusionsReproduced[averages$openSource=="True"]))
plottingdata$SD[29] <-
sd(na.exclude(averages$conclusionsReproduced[averages$openSource=="True"]))
plottingdata$Category[29] <- "Conclusions reproduced"
plottingdata$Question[29] <- "Q9: Open source?"
plottingdata$Response[29] <- "Yes"
plottingdata$n[29] <-
length(na.exclude(averages$conclusionsReproduced[averages$openSource=="True"]))
No, conclusions
plottingdata$Score[30] <-
mean(na.exclude(averages$conclusionsReproduced[averages$openSource=="False"]))
plottingdata$SD[30] <-
sd(na.exclude(averages$conclusionsReproduced[averages$openSource=="False"]))
plottingdata$Category[30] <- "Conclusions reproduced"
plottingdata$Question[30] <- "Q9: Open source?"
plottingdata$Response[30] <- "No"
plottingdata$n[30] <-
length(na.exclude(averages$conclusionsReproduced[averages$openSource=="False"]))
plottingdata$upper <- plottingdata$Score + plottingdata$SD
plottingdata$lower <- plottingdata$Score - plottingdata$SD
print(plottingdata)
## Score SD upper lower Category
## 1 3.200000 1.8234583 5.023458 1.3765417 Figures reproduced
## 2 3.166667 1.5275252 4.694192 1.6391414 Figures reproduced
## 3 3.100000 1.2449900 4.344990 1.8550100 Numbers reproduced
## 4 3.153846 1.5053324 4.659179 1.6485138 Numbers reproduced
## 5 3.500000 1.6583124 5.158312 1.8416876 Conclusions reproduced
## 6 3.576923 1.6689087 5.245832 1.9080143 Conclusions reproduced
## 7 3.800000 1.3509256 5.150926 2.4490744 Figures reproduced
## 8 2.916667 1.6213537 4.538020 1.2953129 Figures reproduced
## 9 3.750000 0.8803408 4.630341 2.8696592 Numbers reproduced
## 10 2.833333 1.5423320 4.375665 1.2910014 Numbers reproduced
## 11 4.333333 1.4023789 5.735712 2.9309544 Conclusions reproduced
## 12 3.166667 1.6283474 4.795014 1.5383193 Conclusions reproduced
## 13 3.227273 1.7372915 4.964564 1.4899812 Figures reproduced
## 14 3.083333 1.3197222 4.403056 1.7636111 Figures reproduced
## 15 3.136364 1.6138604 4.750224 1.5225032 Numbers reproduced
## 16 3.142857 1.1073349 4.250192 2.0355223 Numbers reproduced
## 17 3.272727 1.8078113 5.080539 1.4649159 Conclusions reproduced
## 18 4.000000 1.2583057 5.258306 2.7416943 Conclusions reproduced
## 19 3.666667 1.6007811 5.267448 2.0658856 Figures reproduced
## 20 2.500000 2.1794495 4.679449 0.3205505 Figures reproduced
## 21 3.500000 1.5411035 5.041104 1.9588965 Numbers reproduced
## 22 2.666667 2.0816660 4.748333 0.5850007 Numbers reproduced
## 23 3.722222 1.6791201 5.401342 2.0431021 Conclusions reproduced
## 24 2.500000 2.1794495 4.679449 0.3205505 Conclusions reproduced
## 25 3.444444 1.5500896 4.994534 1.8943548 Figures reproduced
## 26 2.875000 1.6201852 4.495185 1.2548148 Figures reproduced
## 27 3.333333 1.4142136 4.747547 1.9191198 Numbers reproduced
## 28 2.944444 1.4457793 4.390224 1.4986651 Numbers reproduced
## 29 3.444444 1.5092309 4.953675 1.9352136 Conclusions reproduced
## 30 3.666667 1.8027756 5.469442 1.8638910 Conclusions reproduced
## Question Response n
## 1 Q5: Data available? Yes 5
## 2 Q5: Data available? No 12
## 3 Q5: Data available? Yes 5
## 4 Q5: Data available? No 13
## 5 Q5: Data available? Yes 5
## 6 Q5: Data available? No 13
## 7 Q6: Raw data? Yes 5
## 8 Q6: Raw data? No 12
## 9 Q6: Raw data? Yes 6
## 10 Q6: Raw data? No 12
## 11 Q6: Raw data? Yes 6
## 12 Q6: Raw data? No 12
## 13 Q8: Code based? Yes 11
## 14 Q8: Code based? No 6
## 15 Q8: Code based? Yes 11
## 16 Q8: Code based? No 7
## 17 Q8: Code based? Yes 11
## 18 Q8: Code based? No 7
## 19 Q7: Code available? Yes 9
## 20 Q7: Code available? No 3
## 21 Q7: Code available? Yes 9
## 22 Q7: Code available? No 3
## 23 Q7: Code available? Yes 9
## 24 Q7: Code available? No 3
## 25 Q9: Open source? Yes 9
## 26 Q9: Open source? No 8
## 27 Q9: Open source? Yes 9
## 28 Q9: Open source? No 9
## 29 Q9: Open source? Yes 9
## 30 Q9: Open source? No 9
Compile raw data in long format
tempdata1 <- averages[,c("studyID", "graphsReproduced",
"codeRunsAsIs", "dataAvailable", "preProcessed",
"codeBased", "codeAvailable","openSource" )]
tempdata2 <- averages[,c("studyID", "numbersReproduced",
"codeRunsAsIs", "dataAvailable", "preProcessed",
"codeBased", "codeAvailable","openSource" )]
tempdata3 <- averages[,c("studyID", "conclusionsReproduced",
"codeRunsAsIs", "dataAvailable", "preProcessed",
"codeBased", "codeAvailable","openSource" )]
colnames(tempdata1) <-
colnames(tempdata2) <-
colnames(tempdata3) <- c("studyID", "Score",
"codeRunsAsIs", "dataAvailable", "preProcessed",
"codeBased", "codeAvailable","openSource" )
tempdata1$Category <- "Figures reproduced"
tempdata2$Category <- "Numbers reproduced"
tempdata3$Category <- "Conclusions reproduced"
plottingdata.long <- rbind(tempdata1, tempdata2, tempdata3)
tempdata.a <- plottingdata.long[,c("studyID", "Score", "Category", "codeRunsAsIs")]
tempdata.b <- plottingdata.long[,c("studyID", "Score", "Category", "dataAvailable")]
tempdata.c <- plottingdata.long[,c("studyID", "Score", "Category", "preProcessed")]
tempdata.d <- plottingdata.long[,c("studyID", "Score", "Category", "codeBased")]
tempdata.e <- plottingdata.long[,c("studyID", "Score", "Category", "codeAvailable")]
tempdata.f <- plottingdata.long[,c("studyID", "Score", "Category", "openSource")]
tempdata.b$question <- "Q5: Data available?"
tempdata.c$question <- "Q6: Raw data?"
tempdata.d$question <- "Q8: Code based?"
tempdata.e$question <- "Q7: Code available?"
tempdata.f$question <- "Q9: Open source?"
tempdata.b$response <- ifelse(tempdata.b$dataAvailable=="True", yes = "Yes", no = "No")
tempdata.c$response <- ifelse(tempdata.c$preProcessed=="Raw format", yes = "Yes", no = "No")
tempdata.d$response <- ifelse(tempdata.d$codeBased=="True", yes = "Yes", no = "No")
tempdata.e$response <- ifelse(tempdata.e$codeAvailable=="True", yes = "Yes", no = "No")
tempdata.f$response <- ifelse(tempdata.f$openSource=="True", yes = "Yes", no = "No")
colnames(tempdata.f) <-
colnames(tempdata.e) <-
colnames(tempdata.d) <-
colnames(tempdata.c) <-
colnames(tempdata.b) <- c("studyID", "Score", "Category", "originalResponse",
"Question", "Response")
plottingdata.longComplete <- rbind(tempdata.b, tempdata.c, tempdata.d,
tempdata.e, tempdata.f)
# Remove NAs from response column
plottingdata.longComplete <- plottingdata.longComplete[!is.na(plottingdata.longComplete$Response),]
plottingdata$cutoff <- ifelse(test = plottingdata$upper >= 5, yes = 5, no = NA)
plottingdata$upper <- ifelse(test = plottingdata$upper > 5, yes = 5, no = plottingdata$upper)
ggplot(data = plottingdata.longComplete,
aes(x = Question, y = Score, shape = Response, colour=Response))+
geom_pointrange(data = plottingdata,
aes(ymin = lower, ymax = upper, shape = Response),
position = position_dodge(width = 0.6))+
geom_jitter(position = position_jitterdodge(jitter.width = 0.15,
jitter.height = 0.15,
dodge.width = 0.7), color="black")+
facet_wrap(~Category, nrow=3)+
#ylim(1,5.9)+
theme_classic()+
theme(legend.position = "top")+
ylab("Reproducibility score")
## Warning: Removed 4 rows containing missing values (geom_point).
devtools::session_info()
## - Session info ----------------------------------------------------------
## setting value
## version R version 3.4.3 (2017-11-30)
## os Windows >= 8 x64
## system x86_64, mingw32
## ui RStudio
## language (EN)
## collate English_United States.1252
## ctype English_United States.1252
## tz America/Chicago
## date 2020-03-09
##
## - Packages --------------------------------------------------------------
## package * version date lib source
## assertthat 0.2.0 2017-04-11 [1] CRAN (R 3.4.4)
## backports 1.1.2 2017-12-13 [1] CRAN (R 3.4.4)
## bindr 0.1.1 2018-03-13 [1] CRAN (R 3.4.4)
## bindrcpp 0.2.2 2018-03-29 [1] CRAN (R 3.4.4)
## callr 3.1.0 2018-12-10 [1] CRAN (R 3.4.4)
## cli 1.1.0 2019-03-19 [1] CRAN (R 3.4.4)
## colorspace 1.3-2 2016-12-14 [1] CRAN (R 3.4.4)
## crayon 1.3.4 2017-09-16 [1] CRAN (R 3.4.4)
## desc 1.2.0 2018-05-01 [1] CRAN (R 3.4.4)
## devtools * 2.0.1 2018-10-26 [1] CRAN (R 3.4.4)
## digest 0.6.18 2018-10-10 [1] CRAN (R 3.4.4)
## dplyr 0.7.8 2018-11-10 [1] CRAN (R 3.4.4)
## evaluate 0.12 2018-10-09 [1] CRAN (R 3.4.4)
## ezknitr * 0.6 2016-09-16 [1] CRAN (R 3.4.4)
## fs 1.2.6 2018-08-23 [1] CRAN (R 3.4.4)
## ggplot2 * 3.1.0 2018-10-25 [1] CRAN (R 3.4.4)
## glue 1.3.0 2018-07-17 [1] CRAN (R 3.4.4)
## gtable 0.2.0 2016-02-26 [1] CRAN (R 3.4.4)
## highr 0.7 2018-06-09 [1] CRAN (R 3.4.4)
## knitr * 1.21 2018-12-10 [1] CRAN (R 3.4.4)
## labeling 0.3 2014-08-23 [1] CRAN (R 3.4.1)
## lazyeval 0.2.1 2017-10-29 [1] CRAN (R 3.4.4)
## magrittr 1.5 2014-11-22 [1] CRAN (R 3.4.3)
## memoise 1.1.0 2017-04-21 [1] CRAN (R 3.4.4)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 3.4.4)
## pillar 1.3.0 2018-07-14 [1] CRAN (R 3.4.4)
## pkgbuild 1.0.2 2018-10-16 [1] CRAN (R 3.4.4)
## pkgconfig 2.0.2 2018-08-16 [1] CRAN (R 3.4.4)
## pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.4.4)
## plyr 1.8.4 2016-06-08 [1] CRAN (R 3.4.4)
## prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.4.4)
## processx 3.3.0 2019-03-10 [1] CRAN (R 3.4.4)
## ps 1.2.1 2018-11-06 [1] CRAN (R 3.4.4)
## purrr 0.2.5 2018-05-29 [1] CRAN (R 3.4.4)
## R.methodsS3 1.7.1 2016-02-16 [1] CRAN (R 3.4.1)
## R.oo 1.22.0 2018-04-22 [1] CRAN (R 3.4.4)
## R.utils 2.7.0 2018-08-27 [1] CRAN (R 3.4.4)
## R6 2.3.0 2018-10-04 [1] CRAN (R 3.4.4)
## Rcpp 1.0.0 2018-11-07 [1] CRAN (R 3.4.4)
## remotes 2.1.0 2019-06-24 [1] CRAN (R 3.4.3)
## rlang 0.3.4 2019-04-07 [1] CRAN (R 3.4.4)
## rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.4.4)
## rstudioapi 0.8 2018-10-02 [1] CRAN (R 3.4.4)
## scales 1.0.0 2018-08-09 [1] CRAN (R 3.4.4)
## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.4.4)
## stringi 1.2.4 2018-07-20 [1] CRAN (R 3.4.4)
## stringr 1.3.1 2018-05-10 [1] CRAN (R 3.4.4)
## testthat 2.1.1 2019-04-23 [1] CRAN (R 3.4.4)
## tibble 1.4.2 2018-01-22 [1] CRAN (R 3.4.4)
## tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.4.4)
## usethis * 1.4.0 2018-08-14 [1] CRAN (R 3.4.4)
## withr 2.1.2 2018-03-15 [1] CRAN (R 3.4.4)
## xfun 0.4 2018-10-23 [1] CRAN (R 3.4.4)
## yaml 2.2.0 2018-07-25 [1] CRAN (R 3.4.4)
##
## [1] C:/Users/aarchmil/Documents/R/win-library/3.4
## [2] C:/Program Files/R/R-3.4.3/library
spun with: ezknitr::ezspin(file = “programs/04_reproducibility_criteria_figure.R”, keep_md = FALSE, out_dir = “html_reports”, fig_dir = “figures”)