Using group_by() to collapse a dataset in R based on conditions

Question

I am trying to collapse a dataset based on conditions and groupings from another dataset. My current dataframe looks like this

For every 'RollNo' in every 'congress' I want a new variable indicating if the two senators in the same state voted together (1,0) and are in the same party (1,0)

congress	sen	RollNo	state	Vote	Party
106	Jay	1	Ark	1	Rep
106	Mary	1	Ark	1	Dem
106	Bill	2	Ten	2	Dem
106	Kevin	2	Ten	1	Dem
108	Sue	1	Ore	2	Rep
108	Sally	1	Ore	2	Rep
108	Lisa	3	SDak	1	Rep
108	Penny	3	SDak	2	Rep
109	Jay	1	Mich	1	Dem
109	Mary	1	Mich	9	Rep
109	Rudy	5	Cal	1	Dem
109	Niles	5	Cal	1	Dem

The new dataframe should look like this:

congress	RollNo	state	Pair_Vote	Pair_Party
106	1	Ark	1	0
106	2	Ten	0	1
108	1	Ore	1	1
108	3	SDak	0	1
109	1	Mich	0	0
109	5	Cal	1	1

I have tried the code below, tweaked it several times. My my dataset returns with the same observation and two new columns empty vectors for my new variables.

library(dplyr)
dataframe['Pair_Vote'] <- NA
dataframe['Pair_Party'] <- NA
newdata <- dataframe %>% group_by(congress, RollNo, state) %>% 
  mutate(Pair_Vote - case_when(any(Vote == Vote) ~ 1, FALSE ~ 0))

I'm at a loss.

Answer 1

Use mutate when you want to add columns to a data frame as-is, use summarize when you want the result to have one row per group. Your output has one row per group, so we will use summarize.

And vote == vote won't do much useful, let's use n_distinct to count distinct values.

dataframe %>% 
  group_by(congress, RollNo, state) %>% 
  summarize(
    Pair_Vote = ifelse(n_distinct(Vote) == 1, 1, 0),
    Pair_Party = ifelse(n_distinct(Party) == 1, 1, 0)
  )