Create a pivot table with multiple hierarchical column groups - r

Question

I'm trying to create a pivot table (to later be rendered in markdown). However, I can't find a way to produce multiple pivot columns.
my data:
| ID | group | var1 | var2 |
| -: |:-----:|:------:|:------:|
| 1 | A | 1 | 2 |
| 2 | B | 3 | 4 |
| 3 | C | 5 | 6 |
| 4 | A | 7 | 8 |
| 5 | B | 9 | 10 |
| 6 | C | 11 | 12 |
required table:
| | groupA | groupB | groupC |
| ID | var1 | var2 | var1 | var2 | var1 | var2 |
| -: |:------:|:------:|:------:|:------:|:------:|:------:|
| 1 | 1 | 2 | | | | |
| 2 | | | 3 | 4 | | |
| 3 | | | | | 5 | 6 |
| 4 | 7 | 8 | | | | |
| 5 | | | 9 | 10 | | |
| 6 | | | | | 11 | 12 |
Obviously the result is not a dataframe or a tibble.
How can such a table be created?

Answer 1

if this is your example data df:
df <- structure(list(ID = 1:6, group = c("A", "B", "C", "A", "B", "C"
), var1 = c(1, 3, 5, 7, 9, 11), var2 = c(2, 4, 6, 8, 10, 12)), class = "data.frame", row.names = c(NA,
-6L))
... you can generate the table structure and column headers like this:
library(tidyr)
df %>%
pivot_longer(cols = starts_with('var'),
names_to = 'var_name',
values_to = 'value'
) %>%
pivot_wider(id_cols = ID,
names_from = c('group', 'var_name'),
names_sep = '\n', ## wrap line after group name
values_from = 'value'
)
Note that AFAIK having the group names span the variable columns would require some separate fiddling between the steps of reshaping your data (see above) and producing the markdown.

Answer 2

Adding on #I_O data transformation, the header for the groups you could achieve with the kableExtra package, i.e.
library(dplyr)
library(tidyr)
library(kableExtra)
options(knitr.kable.NA = '')
df %>%
pivot_longer(cols = starts_with('var'),
names_to = 'var_name',
values_to = 'value'
) %>% pivot_wider(id_cols = ID,
names_from = c('group', 'var_name'),
names_sep = '\n', ## wrap line after group name
values_from = 'value'
) %>%
kbl(col.names = c("ID", "var1", "var2","var1", "var2","var1", "var2")) %>%
add_header_above(c(" ", "groupA" = 2,"groupB" = 2,"groupC" = 2 )) %>%
kable_styling(bootstrap_options = "striped", full_width = F)

Answer 3

Using reshape2
library(reshape2)
dcast(
melt(
df,
id.vars=c("grp1","grp2"),
measure.vars=c("var1","var2")
),
grp1~grp2+variable,
value.var="value"
)
grp1 A_var1 A_var2 B_var1 B_var2 C_var1 C_var2
1 1 1 2 NA NA NA NA
2 2 NA NA 3 4 NA NA
3 3 NA NA NA NA 5 6
4 4 7 8 NA NA NA NA
5 5 NA NA 9 10 NA NA
6 6 NA NA NA NA 11 12

Answer 4

There are two separate issues here. One is how to print a hierarchical table in R. There are a few ways to do this, mostly producing latex or html tables. For a hierarchical table printing in the R console, one option is to use tabular from the tables package:
library(tables)
library(dplyr)
fm <- function(x) if(length(x) == 0) "" else x
tabular( (ID) ~ group*(var1 + var2)*(`---`=fm),
data=mutate(df, ID = factor(ID), group = factor(group)))
#>
#> group
#> A B C
#> var1 var2 var1 var2 var1 var2
#> ID --- --- --- --- --- ---
#> 1 1 2
#> 2 3 4
#> 3 5 6
#> 4 7 8
#> 5 9 10
#> 6 11 12
The second, perhaps more important issue is how to store and work with hierarchical tabular structures. This is possible with nested tibbles. In your case, we can do something like:
library(tidyr)
nested_df <- complete(df, ID, group) %>%
nest_by(ID, group) %>%
pivot_wider(names_from = group, values_from = data)
nested_df
#> # A tibble: 6 x 4
#> ID A B C
#> <int> <list<tibble[,2]>> <list<tibble[,2]>> <list<tibble[,2]>>
#> 1 1 [1 x 2] [1 x 2] [1 x 2]
#> 2 2 [1 x 2] [1 x 2] [1 x 2]
#> 3 3 [1 x 2] [1 x 2] [1 x 2]
#> 4 4 [1 x 2] [1 x 2] [1 x 2]
#> 5 5 [1 x 2] [1 x 2] [1 x 2]
#> 6 6 [1 x 2] [1 x 2] [1 x 2]
To access, say, the var1 and var2 columns for group A we would do:
nested_df %>% select(A) %>% unnest(A)
# A tibble: 6 x 2
var1 var2
<dbl> <dbl>
1 1 2
2 NA NA
3 NA NA
4 7 8
5 NA NA
6 NA NA
Created on 2022-05-25 by the reprex package (v2.0.1)