I want create (dummy) variables that show whether an observation is in a group of observations (Identifiable by a common Group_ID) with a certain combination of characteristics across that group. The code example makes it clearer what I exactly mean.
I tried combinations of group_by and caret::dummyVars, but had no success. I am running out of ideas - any help would be appreciated very much.
library(tidyverse)
# Input data
# please note: in my case each value of the column Role will appear only once per Group_ID.
input_data <- tribble( ~Group_ID, ~Role, ~Income,
#--|--|----
1, "a", 3.6,
1, "b", 8.5,
2, "a", 7.6,
2, "c", 9.5,
2, "d", 9.7,
3, "a", 1.6,
3, "b", 4.5,
3, "c", 2.7,
3, "e", 7.7,
4, "b", 3.3,
4, "c", 6.2,
)
# desired output
output_data <- tribble( ~Group_ID, ~Role, ~Income, ~Role_A, ~Role_B, ~Role_C, ~Role_D, ~Role_E, ~All_roles,
#--|--|----
1, "a", 3.6, 1, 1, 0, 0, 0, "ab",
1, "b", 8.5, 1, 1, 0, 0, 0, "ab",
2, "a", 7.6, 1, 0, 1, 1, 0, "acd",
2, "c", 9.5, 1, 0, 1, 1, 0, "acd",
2, "d", 9.7, 1, 0, 1, 1, 0, "acd",
3, "a", 1.6, 1, 1, 1, 0, 1, "abce",
3, "b", 4.5, 1, 1, 1, 0, 1, "abce",
3, "c", 2.7, 1, 1, 1, 0, 1, "abce",
3, "e", 7.7, 1, 1, 1, 0, 1, "abce",
4, "b", 3.3, 0, 1, 1, 0, 0, "bc",
4, "c", 6.2, 0, 1, 1, 0, 0, "bc"
)
The following takes advantage of base R modeling functions to create the dummies.
First, create a model matrix with no intercept.
fit <- lm(Group_ID ~ 0 + Role, input_data)
m <- model.matrix(fit)
Now, process that matrix by noting that the dummies the question asks for are the sums by groups of Group_ID.
input_data %>%
bind_cols(m %>% as.data.frame()) %>%
group_by(Group_ID) %>%
mutate_at(vars(matches("Role[[:alpha:]]")), sum) %>%
mutate(all_roles = paste(Role, collapse = ""))
## A tibble: 11 x 9
## Groups: Group_ID [4]
# Group_ID Role Income Rolea Roleb Rolec Roled Rolee all_roles
# <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
# 1 1 a 3.6 1 1 0 0 0 ab
# 2 1 b 8.5 1 1 0 0 0 ab
# 3 2 a 7.6 1 0 1 1 0 acd
# 4 2 c 9.5 1 0 1 1 0 acd
# 5 2 d 9.7 1 0 1 1 0 acd
# 6 3 a 1.6 1 1 1 0 1 abce
# 7 3 b 4.5 1 1 1 0 1 abce
# 8 3 c 2.7 1 1 1 0 1 abce
# 9 3 e 7.7 1 1 1 0 1 abce
#10 4 b 3.3 0 1 1 0 0 bc
#11 4 c 6.2 0 1 1 0 0 bc