Create mean dataframe from a list of dataframes

Question

I've got a list of more than 6000 dataframes with about 6000 observations of 2000 variables. I would like to calculate a mean dataframe of 6000x2000 in which each value is the mean of that same position across all dataframes (as example: mean.df[1,1] = mean(df1[1,1],df2[1,1],df3[1,1],.....dfN[1,1] )

I've prepared a short example to illustrate what I need

# Build list of dataframes
df.X <- data.frame("A1" = seq(1,5,1), "A2" = seq(2,10,2) , "A3" = seq(10,18,2))
df.Y <- data.frame("B1" = seq(0,8,2), "B2" = seq(4,8,1) , "B3" = seq(10,18,2))
df.Z <- data.frame("C1" = seq(2,10,2), "C2" = 1:5 , "C3" = seq(10,18,2))
df.list <- list(df.X,df.Y,df.Z)

#Desired output
m1  M2  M3 
1.0 2.3 10.0
2.7 3.7 12.0
4.3 5.0 14.0
6.0 6.3 16.0
7.7 7.7 18.0

I have made some attempts with purrr with reduce and map but had no success. Mabye apply?

Thanks in advance

Answer 1

Base R
Sum all elements of the list together, and divide by number of elements. Does not work with NA-elements!

Reduce(`+`, df.list) / length(df.list)

        A1       A2 A3
1 1.000000 2.333333 10
2 2.666667 3.666667 12
3 4.333333 5.000000 14
4 6.000000 6.333333 16
5 7.666667 7.666667 18

All there is left to do is setting the colnames.

Answer 2

An option is also to convert to array and use colMeans

apply(array(unlist(df.list), c(dim(df.list[[1]]), length(df.list))), 2, rowMeans)

-output

          [,1]     [,2] [,3]
[1,] 1.000000 2.333333   10
[2,] 2.666667 3.666667   12
[3,] 4.333333 5.000000   14
[4,] 6.000000 6.333333   16
[5,] 7.666667 7.666667   18

Answer 3

A tidyverse solution. We can combine all data frames, create an ID column by each Group , and calculate the mean. This solution is not as succinct as Wimpel's solution, but it can handle missing values by setting na.rm = TRUE in the summarize and across call.

library(tidyverse)

df_mean <- df.list %>%
  map_dfr(~setNames(.x, paste0("A", 1:ncol(.x))), .id = "Group") %>%
  group_by(Group) %>%
  mutate(ID = 1:n()) %>%
  group_by(ID) %>%
  summarize(across(-Group, .fns = mean, na.rm = TRUE)) %>%
  select(-ID)
# # A tibble: 5 x 3
#    A1    A2    A3
#    <dbl> <dbl> <dbl>
# 1  1     2.33    10
# 2  2.67  3.67    12
# 3  4.33  5       14
# 4  6     6.33    16
# 5  7.67  7.67    18