How to lag and calculate difference for every data frame in a list?

I have a list containing 981 data frames. Each data.frame has the same structure.

I want to lag one column (called growth) to calculate the growth over time (from one observation to another) for each data frame.

I tried lapply by somehow could not get it done.

my_list <- 
  list(
    data.frame(time = 1:10, growth = rnorm(10, mean = 1.3, sd = 2)),
    data.frame(time = 1:10, growth = rnorm(10, mean = 1.3, sd = 2)),
    data.frame(time = 1:10, growth = rnorm(10, mean = 1.3, sd = 2))
  )

Solution

If you are not able to share real data you can create a fake dataset to make the post reproducible.

If I have understood you correctly here is what you can do with lapply

lapply(list_df, function(x) {x$difference <- c(NA, diff(x$growth)); x})

#[[1]]
#   growth b difference
#1       3 a         NA
#2       8 b          5
#3       4 c         -4
#4       7 d          3
#5       6 e         -1
#6       1 f         -5
#7      10 g          9
#8       9 h         -1
#9       2 i         -7
#10      5 j          3

#[[2]]
#   growth b difference
#1      10 a         NA
#2       5 b         -5
#3       6 c          1
#4       9 d          3
#5       1 e         -8
#6       7 f          6
#7       8 g          1
#8       4 h         -4
#9       3 i         -1
#10      2 j         -1

The tidyverse way to do the same would be

library(dplyr)
library(purrr)

map(list_df,. %>% mutate(difference = c(NA, diff(growth))))

map(list_df,. %>% mutate(difference = growth - lag(growth)))

data

set.seed(123)
list_df <- list(data.frame(growth = sample(10), b = letters[1:10]), 
               data.frame(growth = sample(10), b = letters[1:10]))