distinct cases for two variables by grouping and counting - r

Question

We can use the following data frame as an example:
Case <- c("Siddhartha", "Siddhartha", "Siddhartha", "Paul", "Paul", "Paul", "Hannah", "Herbert")
Procedure <- c("1", "1", "2", "3", "3", "4", "1", "1")
Location <- c("a", "a", "b", "a", "a", "b", "c", "a")
(df <- data.frame(Case, Procedure, Location))
Case Procedure Location
1 Siddhartha 1 a
2 Siddhartha 1 a
3 Siddhartha 2 b
4 Paul 3 a
5 Paul 3 a
6 Paul 4 b
7 Hannah 1 c
8 Herbert 1 a
Now i do the following:
df %>%
count(Location, Procedure) %>%
pivot_wider(names_from = Location, values_from = n, values_fill = list(n = 0))
which gives me:
# A tibble: 4 x 4
Procedure a b c
<fct> <int> <int> <int>
1 1 3 0 1
2 3 2 0 0
3 2 0 1 0
4 4 0 1 0
This is not exactly, what i want though. What i want is the following data frame:
# A tibble: 4 x 4
Procedure a b c
<fct> <int> <int> <int>
1 1 2 0 1
2 3 1 0 0
3 2 0 1 0
4 4 0 1 0
Notice the difference in Procedure 1 and 3.
So what i would like is a function, that counts the number of DISTINCT cases for each Procedures AND each location. Also that function should be working on varying data frames, where there are different (unknown) cases and procedures.
For the original data frame
df %>%
distinct() %>%
count(Location, Procedure) %>%
pivot_wider(names_from = Location, values_from = n, values_fill = list(n = 0))
does not work, since it is ignoring the "distinct". What works (also for the original data frame!) is the following:
df %>%
group_by(Procedure, Location) %>%
summarise(Anzahl = n_distinct(Case))
That gives me the following though:
# A tibble: 5 x 3
# Groups: Procedure [4]
Procedure Location Anzahl
<fct> <fct> <int>
1 1 a 2
2 1 c 1
3 2 a 1
4 3 b 1
5 4 b 1
But how to implement the "pivot_wider" function, so it is also sorted by location? If i try to add it, i get the following error:
"Error: This tidyselect interface doesn't support predicates yet.
i Contact the package author and suggest using eval_select()."
Also it is very confusing to me, why the solution of Ronak works for the example data frame but not for the original. I can't spot important differences in these two data frames.
Regards

Answer 1

You can do it with a single call to pivot_wider and take advantage of the argument values_fn, which applies a function to the values
df %>%
pivot_wider(names_from = Location,
values_from = Case,
values_fn = list(Case = n_distinct),
values_fill = list(Case = 0))
which gives,
# A tibble: 4 x 4
Procedure a b c
<fct> <int> <int> <int>
1 1 2 0 1
2 2 0 1 0
3 3 1 0 0
4 4 0 1 0

Answer 2

A simple fix is to add distinct or unique before counting
library(dplyr)
library(tidyr)
df %>%
distinct() %>%
count(Location, Procedure) %>%
pivot_wider(names_from = Location, values_from = n, values_fill = list(n = 0))
# A tibble: 4 x 4
# Procedure a b c
# <chr> <int> <int> <int>
#1 1 2 0 1
#2 3 1 0 0
#3 2 0 1 0
#4 4 0 1 0
For OP's data they need :
df %>%
group_by(Procedure, Location) %>%
summarise(Anzahl = n_distinct(Case)) %>%
pivot_wider(names_from = Location, values_from = Anzahl,
values_fill = list(Anzahl = 0))