I get n/a in columns when using the pivot_wider function

Question

when I execute the following code:

data_ikea_wider <- data_ikea_longer %>%
  pivot_wider(id_cols = c(Record_no
                          , Geography
                          , City
                          , Country
                          , City.Country
                          , Year)
                          , names_from = Category, values_from = Value)

The columns just have n/a's as shown in the attached print screen.

What am I doing wrong? Thanks!

Answer 1

Getting NA s from a pivot is not unexpected, it means that not all of your id columns have all "columns".

For example,

dat <- data.frame(col1 = c(1,1,2), col2 = c('a', 'b', 'a'), val = 1:3)
dat
#   col1 col2 val
# 1    1    a   1
# 2    1    b   2
# 3    2    a   3

If we want to pivot keeping col1 as an id, and turning col2 values into new columns, then it should be apparent that we'll end up with two rows (ida 1 and 2 ), and two new columns ( a and b ) to replace col2 and val . Unfortunately, since we only have three rows, the 2 rows 2 columns = 4 cells will not be completely filled with 3 values, so one will be NA :

pivot_wider(dat, col1, names_from = col2, values_from = val)
# # A tibble: 2 x 3
#    col1     a     b
#   <dbl> <int> <int>
# 1     1     1     2
# 2     2     3    NA

If you see this and are surprised, thinking that you actually have the data ... then you should check your data importing and filtering to make sure you did not inadvertently remove it (or it was not provided initially).

Answer 2

We could use dcast from data.table

library(data.table)
setDT(dat)[, col1 ~ col2, value.var = 'val')