Batch processing: repeat lecture materials but with a different simulated data set (donโt have a real data set to use yet)
####################################
# FUNCTION: file_builder
# purpose: create a set of random files for regression
# input: file_n = number of files to create
# : file_folder = name of folder for random files
# : file_size = c(min,max) number of rows in file
# : file_na = number on average of NA values per column
# output: set of random files
#----------------------------------
file_builder <- function(file_n=10, # create 10 files
file_folder="RandomFilesHW11/",
file_size=c(10,20), # between 10 and 20 rows
file_na=5) { # 5 NAs present in the data
for (i in seq_len(file_n)) {
file_length <- sample(file_size[1]:file_size[2],size=1) # get the number of rows
var_x <- runif(file_length) # create random x
var_y <- runif(file_length) # create random y
df <- data.frame(var_x,var_y) # bind into a data frame
bad_vals <- rpois(n=1,lambda=file_na) # determine NA number; poisson is integer distribution and lambda will take value from NA (5, defined above)
df[sample(nrow(df),size=bad_vals),1] <- NA # random NA in var_x
df[sample(nrow(df),size=bad_vals),2] <- NA # random NA in var_y
# create path: RandomFile/ranFile001.csv (for e.g.)
file_label <- paste(file_folder,
"ranFile",
formatC(i,
width=3,
format="d",
flag="0"),
".csv",sep="") # padded 0s to get everything in the right order, e.g. make sure file 001 comes before file 010, etc.
# set up data file and incorporate time stamp and minimal metadata
write.table(cat("# Simulated random data file for batch processing","\n",
"# timestamp: ",as.character(Sys.time()),"\n",
"# HRS","\n",
"# ------------------------", "\n",
"\n",
file=file_label,
row.names="",
col.names="",
sep=""))
# now add the data frame
write.table(x=df,
file=file_label,
sep=",",
row.names=FALSE,
append=TRUE) # need append=TRUE - means that it'll open the same file but will put everything below what we did; the code will work without the line but it'll overwrite everything we just coded
}
}
####################################
# FUNCTION: reg_stats
# purpose: fits linear model, extracts statistics
# input: 2-column data frame (x and y)
# output: slope, p-value, and r2
#----------------------------------
reg_stats <- function(d=NULL) {
if(is.null(d)) {
x_var <- runif(10)
y_var <- runif(10)
d <- data.frame(x_var,y_var)
}
. <- lm(data=d,d[,2]~d[,1])
. <- summary(.)
stats_list <- list(slope=.$coefficients[2,1],
p_val=.$coefficients[2,4],
r2=.$r.squared)
return(stats_list)
}
### Body of script for batch processing of regression models
# Global variables
file_folder <- "RandomFilesHW11/"
n_files <- 10
file_out <- "StatsSummaryHW11.csv"
# Create 10 random data sets
dir.create(file_folder)## Warning in dir.create(file_folder): 'RandomFilesHW11' already existsfile_builder(file_n=n_files)## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to file## ""## Warning in write.table(x = df, file = file_label, sep = ",", row.names =
## FALSE, : appending column names to filefile_names <- list.files(path=file_folder)
head(file_names)## [1] "ranFile001.csv" "ranFile002.csv" "ranFile003.csv" "ranFile004.csv"
## [5] "ranFile005.csv" "ranFile006.csv"# Create data frame to hold file summary statistics
ID <- seq_along(file_names)
file_name <- file_names
slope <- rep(NA,n_files)
p_val <- rep(NA,n_files)
r2 <- rep(NA,n_files)
stats_out <- data.frame(ID, file_name, slope, p_val, r2)
# batch process by looping through individual files and reading them in
for (i in seq_along(file_names)) {
data <- read.table(file=paste(file_folder,file_names[i],sep=""),
sep=",",
header=TRUE) # read in next data file
d_clean <- data[complete.cases(data),] # get clean cases
. <- reg_stats(d_clean) # pull out regression stats from clean file
stats_out[i,3:5] <- unlist(.) # un-list, copy into last 3 columns
}
# 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",
"# NJG","\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=TRUE)## Warning in write.table(x = stats_out, file = file_out, row.names = FALSE, :
## appending column names to file## Organizing Source Files
# Logging
library(logger)
log_layout(layout_glue_colors)
log_threshold(TRACE)
mylog <- tempfile() # set up a temporary file to record the log
log_appender(appender_tee(mylog)) # append log statements to temp file
# using log statements
log_info()
log_trace()
log_debug()
for (i in 1:5) {
log_debug('running file #',i)
}
# consider using log statements as annotation to code
z <- function(x=NULL){log_info(x)}
# now create a snippet
#---------------------------------------
z('read input')
#
#---------------------------------------
z('source functions')
#
# close the log file
cat(readLines(mylog),file="logfile.txt",sep="\n")
# write the entire logfile once to the screen
cat("#---------------",
"logfile.txt: ",
readLines(mylog),sep="\n",
"#---------------")## #---------------
## logfile.txt:
## INFO [2022-04-27 10:18:35]
## TRACE [2022-04-27 10:18:35]
## DEBUG [2022-04-27 10:18:35]
## DEBUG [2022-04-27 10:18:35] running file #1
## DEBUG [2022-04-27 10:18:35] running file #2
## DEBUG [2022-04-27 10:18:35] running file #3
## DEBUG [2022-04-27 10:18:35] running file #4
## DEBUG [2022-04-27 10:18:35] running file #5
## INFO [2022-04-27 10:18:35] read input
## INFO [2022-04-27 10:18:35] source functions
## #---------------# clean up and remove temporary file from memory
unlink(mylog)
rm(mylog)
## Using a progress bar (helpful so we can see how far we get in case code fails)
# "Old school"
for (i in 1:10) {
Sys.sleep(0.1) # at each loop, stop for 0.1 sec
if(i%%10==0) cat(i) else if(i%%5==0) cat('.')
} # first part is the same, if it's divisible by 10 print the number; else if divisible by 5, print period. and nothing happens if not divisible by 5 or 10.## .10