CODEWITHSUNDEEP
rdplyrsummarize
jay.sf
jay.sf

Reputation: 73342

Summarize means of data frames in a list

I have some data frames in a list with all having the same structure – in this example the variables a, b and c. Now I want to summarize the means of the values across the list.

# list of 10 random data frames
n <- 1e1
initSeed <- 1234
set.seed(initSeed)
(seedVec <- sample.int(n = 1e3, size = n, replace = FALSE))
lst <- lapply(1:n, function(i){
  set.seed(seedVec[i])
a <- rnorm(24,1,.1)
b <- rnorm(24,2,.2)
c <- rnorm(24,3,.3)
df <- data.frame(a,b,c)
})

I attempted to feed dplyr with lst %>% summarize_all(mean) but he won't like lists. The formula below gives me the means of each data frame in the list, but not yet the means of these variables a, b and c across all data frames. 

lapply(1:10, function(n){
  lst[n] %>%
    data.frame() %>%
    summarize_all(mean)
})

So i wanted to make a new data frame with the summarized outputs in order to summarize them again, but this fails and both my extended formula and a related answer are throwing the Error in lst[[idx]] : subscript out of bounds Here is my attempt:

df1 <- as.data.frame(setNames(replicate(3,numeric(0), simplify = FALSE), 
                                 letters[1:3]))
lapply(1:10, function(n){
  lst[n] %>%
    data.frame() %>%
    summarize_all(mean) %>%
    rbind(df1, lst[n])
})

df1 %>% summarize_all(mean)

How could I get what I want?

Upvotes: 0

Views: 3073

Answers (1)

Andrew Gustar
Andrew Gustar

Reputation: 18425

You can do this with purrr

purrr::map_df(lst, function(df){summarize_all(df,mean)})

           a        b        c
1  0.9917488 1.995821 3.121970
2  1.0007174 2.029938 2.962271
3  0.9582000 2.007167 3.046708
4  0.9745993 1.938877 3.015066
5  1.0050672 1.932359 3.052645
6  1.0196390 2.034723 2.998995
7  0.9717243 1.914532 3.024200
8  0.9954225 1.991664 2.981958
9  1.0148424 1.975775 2.949854
10 1.0014377 2.023839 2.976223

Or in base R...

t(sapply(lst,colMeans))
              a        b        c
 [1,] 0.9917488 1.995821 3.121970
 [2,] 1.0007174 2.029938 2.962271
 [3,] 0.9582000 2.007167 3.046708
 [4,] 0.9745993 1.938877 3.015066
 [5,] 1.0050672 1.932359 3.052645
 [6,] 1.0196390 2.034723 2.998995
 [7,] 0.9717243 1.914532 3.024200
 [8,] 0.9954225 1.991664 2.981958
 [9,] 1.0148424 1.975775 2.949854
[10,] 1.0014377 2.023839 2.976223

Upvotes: 2

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