Homework_11
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()###############################
#FUNCTION: t_test
# purpose: to take the values of two different numbers, in our case it will be eye sizes, and perform a t-test
# input: a data file which will consist of 2 columns (left and right eyesize) and compare a t.test across them
# output: the p-value of this test
#------------------------------
t_test <- function(d=NULL) {
if(is.null(d)) {
x_var<-rnorm(10)
y_var<-rnorm(10)
d<-data.frame(x_var,y_var)}
.<- t.test(data=d , d[,1] , d[,2], paired=TRUE)
.<-print(.$p.value)
return(.)
}
t_test()## [1] 0.3885487## [1] 0.3885487#--------------------------------------------
# Global variables
file_folder <- "RandomFiles/"
file_out <- "StatsSummary.csv"
file_names <- list.files(path=file_folder)
#--------------------------------------------
ID<-seq_along(file_names)#column with the file number
pval<-rep(NA)#column to be filled with NA
stats_out <- data.frame(ID, file_names, pval)# make data frame
print(stats_out)## ID file_names pval
## 1 1 48 MO A.csv NA
## 2 2 48 MO B.csv NA
## 3 3 48 UIC A.csv NA
## 4 4 48 UIC B.csv NA# batch process by looping through individual files
for (i in seq_along(file_names)) {
data <- read.csv(file=paste(file_folder,file_names[i],sep=""),
sep=",",
header=TRUE) # read in next data file
glimpse(data)
summary(data)
print(data)
d_clean <- data[complete.cases(data),] # get clean cases
.<-t_test(d_clean)
print(d_clean)
stats_out[i,3]<-unlist(.) #this adds the p value and run to the dataframe under the section I created which was filled with NA
}## Rows: 6
## Columns: 2
## $ Left.eye <int> 312, 298, 251, 319, 318, 335
## $ Right.eye <int> 310, 303, 377, 330, 314, 328
## Left.eye Right.eye
## 1 312 310
## 2 298 303
## 3 251 377
## 4 319 330
## 5 318 314
## 6 335 328
## [1] 0.3543283
## Left.eye Right.eye
## 1 312 310
## 2 298 303
## 3 251 377
## 4 319 330
## 5 318 314
## 6 335 328
## Rows: 7
## Columns: 2
## $ Left.eye <int> 411, 397, 454, 478, 419, 546, 455
## $ Right.eye <int> 303, 563, 464, 460, 482, 550, 517
## Left.eye Right.eye
## 1 411 303
## 2 397 563
## 3 454 464
## 4 478 460
## 5 419 482
## 6 546 550
## 7 455 517
## [1] 0.4541286
## Left.eye Right.eye
## 1 411 303
## 2 397 563
## 3 454 464
## 4 478 460
## 5 419 482
## 6 546 550
## 7 455 517
## Rows: 9
## Columns: 2
## $ Left.eye <int> 510, 452, 578, 648, 438, 507, 557, NA, NA
## $ Right.eye <int> 512, 685, 455, 508, 650, 450, 650, 450, 648
## Left.eye Right.eye
## 1 510 512
## 2 452 685
## 3 578 455
## 4 648 508
## 5 438 650
## 6 507 450
## 7 557 650
## 8 NA 450
## 9 NA 648
## [1] 0.6044909
## Left.eye Right.eye
## 1 510 512
## 2 452 685
## 3 578 455
## 4 648 508
## 5 438 650
## 6 507 450
## 7 557 650
## Rows: 8
## Columns: 2
## $ Left.eye <int> 569, 424, 402, 550, 553, 589, 432, 403
## $ Right.eye <int> 636, 506, 606, 573, 421, NA, NA, NA
## Left.eye Right.eye
## 1 569 636
## 2 424 506
## 3 402 606
## 4 550 573
## 5 553 421
## 6 589 NA
## 7 432 NA
## 8 403 NA
## [1] 0.4192725
## Left.eye Right.eye
## 1 569 636
## 2 424 506
## 3 402 606
## 4 550 573
## 5 553 421#set up output file and incorporate time stamp and minimal metadata
write.table(cat("# Summary stats for ",
"batch processing of regression models","\n",
"# timestamp: ",as.character(Sys.time()),"\n",
"# JAG","\n",
"# ------------------------", "\n",
"\n",
file=file_out,
row.names="",
col.names="",
sep=""))## ""# now add the data frame
write.table(x=stats_out,
file=file_out,
row.names=FALSE,
col.names=TRUE,
sep=",",
append=FALSE)
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