R; separate columns with comma into rows [closed] - r

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I'm having trouble to split column into multiple rows even after using separate_rows function.
It gives me the following error..
Error: Can't subset columns that don't exist.
INPUT:
ID Colours Shapes
1 Red Triangle
1 Red Square
2 Green, Black Circle
2 Green, Black Triangle
3 Blue Square
3 Blue Oval
OUTPUT:
ID Colours Shapes
1 Red Triangle
1 Red Square
2 Green Circle
2 Green Triangle
2 Black Circle
2 Black Triangle
3 Blue Square
3 Blue Oval

Answer 1

I tried to use separate_rows with your data and I had no problems:
df <- data.frame(ID = c(1,1,2,2,3,3),
Colours = c("Red", "Red", "Green, Black", "Green, Black", "Blue", "Blue"),
Shapes = c("Triangle", "Square", "Circle", "Triangle", "Square", "Oval"))
library(tidyr)
df %>% separate_rows(Colours, sep = ", ")
#> # A tibble: 8 x 3
#> ID Colours Shapes
#> <dbl> <chr> <chr>
#> 1 1 Red Triangle
#> 2 1 Red Square
#> 3 2 Green Circle
#> 4 2 Black Circle
#> 5 2 Green Triangle
#> 6 2 Black Triangle
#> 7 3 Blue Square
#> 8 3 Blue Oval

Answer 2

Try this. You can use tidyverse functions to separate rows by comma. The solution will work for n elements separated by comma. Initially, reshape data to long with pivot_longer(), then separate rows with separate_rows(). As ids for rows were necessary you can reshape to wide to obtain the expected output. Finally, use fill() to complete the missing values and arrange() to give the desired order. Here the code:
library(tidyverse)
#Code
newdf <- df %>% mutate(id=row_number()) %>%
pivot_longer(-c(ID,id)) %>%
separate_rows(value,sep=',') %>%
mutate(value=trimws(value)) %>%
group_by(id,name) %>% mutate(id2=row_number()) %>%
pivot_wider(names_from = name,values_from=value) %>%
fill(Shapes) %>% ungroup() %>% select(-c(id,id2)) %>%
arrange(ID,Colours)
Output:
# A tibble: 8 x 3
ID Colours Shapes
<int> <chr> <chr>
1 1 Red Triangle
2 1 Red Square
3 2 Black Circle
4 2 Black Triangle
5 2 Green Circle
6 2 Green Triangle
7 3 Blue Square
8 3 Blue Oval