Create new columns based on 2 columns - r

Question

So I have this kind of table df
Id
Type
QTY
unit
1
A
5
1
2
B
10
2
3
C
5
3
2
A
10
4
3
B
5
5
1
C
10
6
I want to create this data frame df2
Id
A_QTY
A_unit
B_QTY
B_unit
C_QTY
C_unit
1
5
1
0
0
10
6
2
10
4
10
2
0
0
3
0
0
5
5
5
3
This means that I want to create a new column for every "Type's" "QTY" and "unit" for each "Id". I was thinking to use a loop to first create a new column for each Type, to get something like this :
Id
Type
QTY
unit
A_QTY
A_unit
B_QTY
B_unit
C_QTY
C_unit
1
A
5
1
5
1
0
0
0
0
2
B
10
2
0
0
10
2
0
0
3
C
5
3
0
0
0
0
5
3
2
A
10
4
10
4
0
0
0
0
3
B
5
5
0
0
5
5
0
0
1
C
10
6
0
0
0
0
10
6
, and then group_by() to agregate them resulting in df2. But I get stuck when it comes to creating the new columns. I have tried the for loop but my level on R is still not that great yet. I can't manage to create new columns from those existing columns...
I'll appreciate any suggestions you have for me!

Answer 1

You can use pivot_wider from the tidyr package:
library(dplyr)
library(tidyr)
df %>%
pivot_wider(names_from = "Type", # Columns to get the names from
values_from = c("QTY", "unit"), # Columns to get the values from
names_glue = "{Type}_{.value}", # Column naming
values_fill = 0, # Fill NAs with 0
names_vary = "slowest") # To get the right column ordering
output
# A tibble: 3 × 7
Id A_QTY A_unit B_QTY B_unit C_QTY C_unit
<int> <int> <int> <int> <int> <int> <int>
1 1 5 1 0 0 10 6
2 2 10 4 10 2 0 0
3 3 0 0 5 5 5 3

Answer 2

library(tidyverse)
df %>%
pivot_longer(-c(Id, Type)) %>%
mutate(name = str_c(Type, name, sep = "_")) %>%
select(-Type) %>%
pivot_wider(names_from = "name", values_from = "value", values_fill = 0)
# A tibble: 3 × 7
Id A_QTY A_unit B_QTY B_unit C_QTY C_unit
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 5 1 0 0 10 6
2 2 10 4 10 2 0 0
3 3 0 0 5 5 5 3