How to run the same code on multiple different datasets in R

Question

I am trying to run a series of fixed effects linear regressions on six different sets of data. For each dataset, I would like to run the regression multiple times on subsets of the data.

I have developed code to do this once, for one dataset. But I would like to write generic code, so that I can run this for each of the six separate sets of data.

This is what I have so far using an example dataset:

month <- (rep(0:35, 36))
monthfact <- as.factor(month)
prodid2<- as.character(rep(112:147, 36))
log_value <- rnorm(1296)
exp_share <- abs(rnorm(1296))
regdat <- data.frame(month, monthfact, prodid2, log_value, exp_share)
#Subset the data into 24 datasets, each of which includes a 13 month window
subfun<-function(x,y,z) {  subset(x,y>=z & y<=z+12)}
dsets <- lapply(1:24, function(x) subfun(regdat, regdat$month, x-1)) 
#Writing a function for running linear regressions

lmfun<-function(data){  lm(log_value~monthfact+prodid2, data = data, 
weights = data$exp_share)}
#Apply the function to all the datasets in the list
linreg<-lapply(dsets,lmfun)
coefs<-lapply(linreg,coef)
#Choose only the coefficients for month 
coefs <- as.data.frame(lapply(coefs, function(x) {x[2:13]}))
#Add in a row of 0 values for the baseline month
baseline<-rep(0,each=24)
coefs<-rbind(baseline,coefs)

#Compute the index using the dataframe created
FEindexes<-data.frame(lapply(coefs, function(x) (exp(x))/(exp(x[1]))))
splices<-FEindexes[2,]
splices <- apply(splices, 1, cumprod)
splices <- c(1,splices[1:23])
FEindex13<-t(FEindexes[13,])
FEWS<-splices*FEindex13
FEWS<-as.data.frame(FEWS[2:24])
firstFEWS<-as.data.frame(FEindexes[,1])
colnames(firstFEWS) <- "FEWS_index"
colnames(FEWS) <- "FEWS_index"
FEWS<-rbind(firstFEWS,FEWS)
View(FEWS)

I would like to run all of this code on 6 different datasets, and wondered if there's a way to do this in R without having to re-run all the code 6 times?

Thanks very much for your help.

Answer 1

You example code is a bit complex so I will explain it with a simpler example:

If you are ok to split your R-script you could use one script with all the functions you want to execute and a second script in which you call the first script via source(...) with different data sets. Very simple example: save this script as "my_functions.R" in your working directory (or specify the files location when you call source() ):

plot(my.data)

Assuming you have list with all your data sets (but also works with data frame columns or what ever structure), call the first script via "source()":

list.of.my.data <- list(a=1:10, b=11:20, c=21:30)
for (i in 1:length(list.of.my.data)){
  my.data <- list.of.my.data[[i]]
  source("my_functions.R")
  }

Instead, if you prefer to keep everything in one R-script you could write one huuuge function and call this function with every data set as input:

# Example: set of data frames in a list
list.of.data.sets <- list(a=data.frame(x=1:10, y=1:10),
  b=data.frame(x=1:10, y=11:20),
  c=data.frame(x=1:10, y=21:30)
  )
# The meta function where you define all the things you want to do to your data sets:
my.meta.function <- function(my.data, color.parameter, size.parameter){
  plot(y~x, data=my.data, cex=size.parameter, col=color.parameter) 
  my.mean <- mean(my.data$y)
  return(my.mean)
  }
# Call the function for each data set with a for-loop:
for(i in 1:length(list.of.data.sets)){
    my.meta.function(my.data=list.of.data.sets[[i]], size.parameter=4, color.parameter=20)
    }
# Call the function for each data set with lapply (faster!):
results.of.all.data.sets <- lapply(list.of.data.sets, FUN=my.meta.function, size.parameter=4, color.parameter=20)