Extract specific attribute of an object stored in a list

Question

I am trying to determine effect size for each column in a dataframe with ~700 columns. A representative dataset looks like this:

subjects <- c('1', '2', '3', '4', '5', '6')
obs1 <- c(828.4, 632.7, 616.0, 732.0, 886.7, 901.9)
obs2 <- c(9084.5, 10388.8, 11941.5, 8903.4, 9066.6, 10648.5)
obs3 <- c(1293.5, 1648.6, 873.0, 1406.7, 2050.1, 1069.7)
df <- data.frame(subjects, obs1, obs2, obs3)
df

I accomplished calculating cohen's d for each column this like so:

z <- mapply(effsize::cohen.d, df[1:3,], df[4:6,])

This results in a list with my results. I want to extract and return a list or vector of the 'estimate' attribute for each object within this list. I can access them individually like so:

z[[3]]

After looking at other stack overflow questions, I found some useful advice (e.g., access attributes of objects stored in lists). After trying the following method:

lapply(z, function(x) x[[3]])

I get a subscript out of bounds error. What am I missing here? There must be something I am not understanding about accessing attributes. If I remove a pair of brackets I can see some of the attribute names, but none of the values.

EDIT: As per @Roland's suggestion, I used Map rather than mapply and now the lapply function works.

Answer 1

Here's a solution with purrr:

library(purrr)

z <- map2(dat[, 1:2], dat[, 3:4], effsize::cohen.d)

map(z, 3)

$dv1
[1] -1.62089

$dv2
[1] -1.45319

Example data

set.seed(123)
n <- 10
dat <- data.frame(dv1 = rnorm(n), 
                  dv2 = rnorm(n), 
                  iv1 = sample(c(1:2), size = n, replace = TRUE),
                  iv2 = sample(c(1:2), size = n, replace = TRUE))