Dynamically Name R Data Frame in a For Loop

Question

I'm using a For loop to create 100 datasets according to some specifications. My end goal is to have 1 dataset containing each iterated dataset (i.e., dataset 1 through 100).

My current solution is inelegant. I export each individual data frame (called Dataset) to a csv then merge them outside R. With each iteration i of the For loop, my data frame is overwritten.

Trackfile=1:100
for (i in Trackfile){
  d.cor <- .10 # Desired correlation
  Dataset <- as.data.frame(mvrnorm(20, mu = c(0,0), 
                                   Sigma = matrix(c(1,d.cor,d.cor,1), ncol = 2), 
                                   empirical = TRUE))
  write.csv(Dataset, paste0("C:/",d.cor," ",i,".csv"))
}

I believe the solution is to dynamically name the data frame according to the iteration (i) such that the data frames are named dataset1, dataset2...dataset100, then merge them. But I've struggled to find a solution for dynamically naming data frames embedded in a For loop. I'm a novice in R, please help!

"I believe the solution is to dynamically name the data frame": No! Please don't! Initialize an empty list of length 100 and place each data set in the list in turn. They can then be combined into one object right in R. How you do that depends on what you mean by "merge" though. — joran
– joran, Commented Mar 9, 2018 at 17:26
Why don't you create a dataframe and keep adding rows to it from the for loop. Alternatively, you can create a list/array/vector of dataframes. — rnso
– rnso, Commented Mar 9, 2018 at 17:27

dcarlson · Accepted Answer · 2018-03-09 17:59:50Z

1

R handles this easily. Here is an approach, but it may need modification depending on what you want to do with all these random data sets. This builds a list with 100 matrices labeled "data001" to "data100":

library(MASS)
d.cor <- .10
DATA <- replicate(100,  mvrnorm(n=20, mu=c(0, 0), Sigma=matrix(c(1,
     d.cor, d.cor, 1), ncol=2), empirical=TRUE), simplify=FALSE)
names <- paste0("data", sprintf("%0003d", 1:100))
names(DATA) <- names
head(DATA[["data099"]])
#              [,1]         [,2]
#  [1,]  1.94086111  1.570299681
#  [2,] -0.74071651 -0.664948968
#  [3,] -1.02952487 -0.704650191
#  [4,]  0.85203916  0.698703243
#  [5,] -0.08673212  1.668412324
#  [6,]  0.88828524  0.001039757
save(DATA, file="AllData.RData")

This code creates a list containing 100 matrices and names each matrix. You can access a particular matrix with the name or number, DATA[["data099")]] or DATA[[99]]. It is saved as "AllData.RData" so that you can retrieve it with load("AllData.RData"). Depending on what you plan to do with this data, a list is probably more flexible than 100 separate files.

edited Mar 9, 2018 at 17:59

answered Mar 9, 2018 at 17:49

dcarlson

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Pusheenicorn Over a year ago

That worked dcarlson, I really appreciate it thank you! :) And thank you joran and rnso for the comments. rnso, you pointed me in the right direction and I believe I have an alternative solution, posted below. Cheers everyone!

Pusheenicorn · Accepted Answer · 2018-03-09 19:37:46Z

0

Thanks rnso for pointing me towards an alternative solution:

trial=NULL
Trackfile=1:10
for (i in Trackfile){
  d.cor <- .10 # Desired correlation
  Dataset <- as.data.frame(mvrnorm(20, mu = c(0,0), 
                                   Sigma = matrix(c(1,d.cor,d.cor,1), ncol = 2), 
                                   empirical = TRUE))
  trial = rbind(trial, data.frame(Dataset$V1, Dataset$V2))
}
print(trial)
print(Dataset)

Thanks stackoverflow community. I really appreciate it.

answered Mar 9, 2018 at 19:37

Pusheenicorn

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