CODEWITHSUNDEEP
rdataframelapply
JabrahM
JabrahM

Reputation: 27

Computing average of columns of a list of dataframes in R?

I have a list of data frames that looks like the following:

$central_nervous_system
     DepMap_ID                Lineage       ABCA2
133 ACH-000025 central_nervous_system  0.06953841
134 ACH-000036 central_nervous_system -0.20757324
135 ACH-000040 central_nervous_system -0.07189173
          ABCA3       ABCA5        ABCB9      ABCC10
133 -0.20215981  0.02591981 -0.124328522 -0.19439091
134 -0.16144270  0.08592305 -0.101500474 -0.01984359
135 -0.06166222 -0.26031989  0.009193998 -0.33360141

with 26 dataframes in total. I want to generate another list of dataframes, or a table or a list that has averages of all the columns except the first two (because they are not numerical). My approach so far has been:

lineage_avged <- lapply(x,colMeans(x[3:ncol(lineage_data)], na.rm = TRUE))

but it isn't working, i am assuming because lapply shouldn't be used here.

Upvotes: 0

Views: 374

Answers (3)

Duck
Duck

Reputation: 39613

You can try this solution. I also include an example with other data.

lineage_avged <- lapply(YourList,function(x) colMeans(x[,-c(1,2)], na.rm = TRUE))

Example

#Create list
List <- split(iris,iris$Species)
#Function
lineage_avged <- lapply(List,function(x) colMeans(x[,-5], na.rm = TRUE))

$setosa
Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
       5.006        3.428        1.462        0.246 

$versicolor
Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
       5.936        2.770        4.260        1.326 

$virginica
Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
       6.588        2.974        5.552        2.026

Upvotes: 2

SebSta
SebSta

Reputation: 496

Why not use apply?

library(tidyverse)

a <-tribble(~DepMap_ID,                ~Lineage,       ~BCA2,
 "ACH-000025", "central_nervous_system",  0.06953841,
 "ACH-000036", "central_nervous_system", -0.20757324,
 "ACH-000040", "central_nervous_system", -0.07189173)
b <-tribble( ~ABCA3,      ~ABCA5,       ~ABCB9 ,     ~ABCC10,
 -0.20215981,  0.02591981, -0.124328522, -0.19439091,
 -0.16144270,  0.08592305, -0.101500474, -0.01984359,
 -0.06166222, -0.26031989,  0.009193998, -0.33360141)

df <- cbind(a,b)

get_mean <- function(x){
   apply(x[,-c(1,2)], 2, mean, na.rm=T)
}

To then apply it to all of your lists:

lapply(data, get_mean)

Upvotes: 0

Daniel O
Daniel O

Reputation: 4358

Example data:

x <- list(mtcars,mtcars)

Code:

sapply(x, function(df) apply(df[,-(1:2)], 2, mean))

#or use: sapply(x, function(df) colMeans(df[,-(1:2)]))

output:

           [,1]       [,2]
disp 230.721875 230.721875
hp   146.687500 146.687500
drat   3.596563   3.596563
wt     3.217250   3.217250
qsec  17.848750  17.848750
vs     0.437500   0.437500
am     0.406250   0.406250
gear   3.687500   3.687500
carb   2.812500   2.812500

Upvotes: 3

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