How to add an empty column to a list of dataframes in r

Question

Say I have the list of dataframes:

#Example data frame columns
Image <- c("001", "001", "001", "001", "001", "001", "001", "001", "001", "001", "001", "001")
Size <- c("Big", "Small", "Medium", "Tiny", "Big", "Small", "Medium", "Tiny", "Big", "Small", "Medium", "Tiny")
n <- c(111778, 56, 7099, 3, 3682081, 88, 9078, 7, 198346, 422, 30077, 8)

#make example data frame
data <- data.frame(Image, Size, n)

#Split dataframe into a list of dataframes
df <- split(data, f = data$Image) 
df

How could I add an empty column (named new), to each of the dataframes contained in this list.

I have tried

df$new <- NA

But nothing happens with no error

There are much more complicated answers to this question, based on specific conditions etc but I get lost trying to simplify it!

Answer 1

Two problems:

1. Your n has an extra comma ,, remove it.

2. Your df is a list, not a frame, so you cannot just reference a column in it with the $ special operator. You can either add the new column before splitting it, or add it specially to each split-frame.

First option:

data$new <- NA
df <- split(data, f = data$Image) 
df
# $`001`
#    Image   Size       n new
# 1    001    Big  111778  NA
# 2    001  Small      56  NA
# 3    001 Medium    7099  NA
# 4    001   Tiny       3  NA
# 5    001    Big 3682081  NA
# 6    001  Small      88  NA
# 7    001 Medium    9078  NA
# 8    001   Tiny       7  NA
# 9    001    Big  198346  NA
# 10   001  Small     422  NA
# 11   001 Medium   30077  NA
# 12   001   Tiny       8  NA

Second option, add to each frame in the list:

### original data, without `new`
df <- split(data, f = data$Image) 
df <- lapply(df, `[<-`, "new", value = NA)
df
# $`001`
#    Image   Size       n new
# 1    001    Big  111778  NA
# 2    001  Small      56  NA
# 3    001 Medium    7099  NA
# 4    001   Tiny       3  NA
# 5    001    Big 3682081  NA
# 6    001  Small      88  NA
# 7    001 Medium    9078  NA
# 8    001   Tiny       7  NA
# 9    001    Big  198346  NA
# 10   001  Small     422  NA
# 11   001 Medium   30077  NA
# 12   001   Tiny       8  NA

---

Data

data <- structure(list(Image = c("001", "001", "001", "001", "001", "001", "001", "001", "001", "001", "001", "001"), Size = c("Big", "Small", "Medium", "Tiny", "Big", "Small", "Medium", "Tiny", "Big", "Small", "Medium", "Tiny"), n = c(111778, 56, 7099, 3, 3682081, 88, 9078, 7, 198346, 422, 30077, 8)), row.names = c(NA, -12L), class = "data.frame")

Answer 2

Data

Image <- c("001", "001", "001", "001", "001", "001", "001", "001", "002", "002", "002", "002")
Size <- c("Big", "Small", "Medium", "Tiny", "Big", "Small", "Medium", "Tiny", "Big", "Small", "Medium", "Tiny")
n <- c(111778, 56, 7099, 3, 3682081, 88, 9078, 7, 198346, 422, 30077, 8)

data <- data.frame(Image, Size, n)
df <- split(data, f = data$Image) 

Code

library(purrr)
library(dplyr)
map(df,function(x) x %>% mutate(New = NA))

Output

$`001`
  Image   Size       n New
1   001    Big  111778  NA
2   001  Small      56  NA
3   001 Medium    7099  NA
4   001   Tiny       3  NA
5   001    Big 3682081  NA
6   001  Small      88  NA
7   001 Medium    9078  NA
8   001   Tiny       7  NA

$`002`
   Image   Size      n New
9    002    Big 198346  NA
10   002  Small    422  NA
11   002 Medium  30077  NA
12   002   Tiny      8  NA