Creating Dataframes through R For Loop - r

Question

Fairly new to R, so any guidance is appreciated.
GOAL: I'm trying to create hundreds of dataframes in a short script. They follow a pattern, so I thought a For Loop would suffice, but the data.frame function seems to ignore the variable nature of the variable, reading it as it appears. Here's an example:
# Defining some dummy variables for the sake of this example
dfTitles <- c("C2000.AMY", "C2000.ACC", "C2001.AMY", "C2001.ACC")
Copes <- c("Cope1", "Cope2", "Cope3", "Cope4")
Voxels <- c(1:338)
# (Theoretically) creating a separate dataframe for each of the terms in 'dfTitles'
for (i in dfTitles){
i <- data.frame(matrix(0, nrow = 4, ncol = 338, dimnames = list(Copes, Voxels)))
}
# Trying an alternative method
for (i in 1:length(dfTitles))
{dfTitles[i] <- data.frame(matrix(0, nrow = 4, ncol = 338, dimnames = list(Copes, Voxels)))}
This results in the creation of one dataframe named 'i', in the former, or a list of 4, in the case of the latter. Any ideas? Thank you!
PROBABLY UNNECESSARY BACKGROUND INFORMATION: We're using fMRI data to run an analysis which will run correlations across stimuli, brain voxels, brain regions, and participants. We're correlating whole matrices, so separating the values (aka COPEs) into separate dataframes by both Participant ID and Brain Region is going to make the next step much much easier. I already had tried the next step after having loaded and sorted the data into one large dataframe and it was a big pain in the butt.

Answer 1

rm(list=ls)
dfTitles <- c("C2000.AMY", "C2000.ACC", "C2001.AMY", "C2001.ACC")
Copes <- c("Cope1", "Cope2", "Cope3", "Cope4")
Voxels <- c(1:3)
# (Theoretically) creating a separate dataframe for each of the terms in 'dfTitles'
nr <- length(Voxels)
nc <- length(Copes)
N <- length(dfTitles) # Number of data frames, same as length of dfTitles
DF <- vector(N, mode="list")
for (i in 1:N){
DF[[i]] <- data.frame(matrix(rnorm(nr*nc), nrow = nr))
dimnames(DF[[i]]) <- list(Voxels, Copes)
}
names(DF) <- dfTitles
DF[1:2]
$C2000.AMY
Cope1 Cope2 Cope3 Cope4
1 -0.8293164 -1.813807 -0.3290645 -0.7730110
2 -1.1965588 1.022871 -0.7764960 -0.3056280
3 0.2536782 -0.365232 2.3949076 0.5672671
$C2000.ACC
Cope1 Cope2 Cope3 Cope4
1 -0.7505513 1.023325 -0.3110537 -1.4298174
2 1.2807725 1.216997 1.0644983 1.6374749
3 1.0047408 1.385460 0.1527678 0.1576037

Answer 2

When creating objects in a for loop, they need to be saved somewhere before the next iteration of the loop, or it gets overwritten.
One way to handle that is to create an empty list or vector with c()before the beginning of your loop, and append the output of each run of the loop.
Another way to handle it is to assign the object to your environment before moving on to the next iteration of the loop.
# Defining some dummy variables for the sake of this example
dfTitles <- c("C2000.AMY", "C2000.ACC", "C2001.AMY", "C2001.ACC")
Copes <- c("Cope1", "Cope2", "Cope3", "Cope4")
Voxels <- c(1:338)
# initialize a list to store the data.frame output
df_list <- list()
for (d in dfTitles) {
# create data.frame with the dfTitle, and 1 row per Copes observation
df <- data.frame(dfTitle = d,
Copes = Copes)
# append columns for Voxels
# setting to NA, can be reassigned later as needed
for (v in Voxels) {
df[[paste0("Voxel", v)]] <- NA
}
# store df in the list as the 'd'th element
df_list[[d]] <- df
# or, assign the object to your environment
# assign(d, df)
}
# data.frames can be referenced by name
names(df_list)
head(df_list$C2000.AMY)