Within dplyr::group_by, obtain the number of observations for ONE of multiple grouping variables

It's very possible this has been asked before, however I am having a very difficult time articulating my problem.

Within my data, I have 3 variables, LOCATION, TOPIC, and RESPONSE. I would like to calculate the distribution for each combination of TOPIC and RESPONSE by LOCATION.

Create toy data and perform initial data prep

responses <- data.frame(LOCATION = c("LOC_A", "LOC_A", "LOC_A", "LOC_A", "LOC_A", 
"LOC_A", "LOC_A", "LOC_A", 
"LOC_B", "LOC_B", "LOC_B", "LOC_B", "LOC_B", 
"LOC_C", "LOC_C", "LOC_C", "LOC_C", "LOC_C", "LOC_C", 
"LOC_C", "LOC_C", "LOC_C", "LOC_C", "LOC_C", "LOC_C", 
"LOC_C", "LOC_C", "LOC_C", "LOC_C", "LOC_C"),
TOPIC = c("Dogs", "Dogs", "Dogs", "Dogs", "Dogs", "Dogs", 
"Lizards", "Lizards", "Lizards",
"Lizards", "Lizards", "Lizards", "Lizards", "Lizards", 
"Lizards", "Lizards", "Snakes", "Snakes", "Snakes", "Snakes", "Snakes", 
"Snakes", "Dogs", "Snakes", "Dogs", "Snakes", "Dogs", 
"Snakes", "Dogs", "Snakes"),
RESP = c("Agree", "Disagree", "Agree", "Disagree", "Agree", 
"Disagree", "Agree", "Disagree", 
"Agree", "Disagree", "Agree", "Disagree", "Neither", "Agree",
"Neither", "Agree", "Neither", "Agree", "Neither", 
"Agree", "Neither", "Agree", "Agree", "Neither", 
"Agree", "Neither", "Agree", "Disagree", "Disagree",
"Neither"))
# Obtain counts for each combination of levels
distribution <- responses %>% 
table() %>% 
as.data.frame() %>% 
# Make it more readable
dplyr::arrange(LOCATION, TOPIC, RESP) 

Here is an example solution which uses a loop to create my desired output:

# ugly loop solution :(
# Initialize output container
out <- list()
# Iterate over each location
for(loc in unique(distribution$LOCATION)){
# Subset distribution for this location
thisDist <- dplyr::filter(distribution, LOCATION == loc)
# Calculate percent of each response for this location
thisDist$percent <- thisDist$Freq/sum(thisDist$Freq)
# Store distribution df with percent column
out[[loc]] <- thisDist
}
# combine output into single df
out <- do.call("rbind", out)

What I would like to have is a concise tidyverse solution. Here is some pseudo-code which describes my imaginary solution.

# Imaginary tidyverse solution :)
out <- distribution %>% 
group_by(LOCATION, TOPIC, RESP) %>% 
summarise(#percent = Freq/(sum(<all-Freq-values-for-this-group's-LOCATION-value>))
)

What I'm looking to do here is obtain the sum of all Freq values for the LOCATION value of the current group. Is there a nice way to do this within a group_by/summarise?

Thanks for reading, I hope this isn't completely inscrutable.

Is this what you're looking for?

distribution %>%
  mutate(percent = Freq/sum(Freq), .by = LOCATION)
#    LOCATION   TOPIC     RESP Freq    percent
# 1     LOC_A    Dogs    Agree    3 0.37500000
# 2     LOC_A    Dogs Disagree    3 0.37500000
# 3     LOC_A    Dogs  Neither    0 0.00000000
# 4     LOC_A Lizards    Agree    1 0.12500000
# 5     LOC_A Lizards Disagree    1 0.12500000
# 6     LOC_A Lizards  Neither    0 0.00000000
# 7     LOC_A  Snakes    Agree    0 0.00000000
# 8     LOC_A  Snakes Disagree    0 0.00000000
# 9     LOC_A  Snakes  Neither    0 0.00000000
# 10    LOC_B    Dogs    Agree    0 0.00000000
# 11    LOC_B    Dogs Disagree    0 0.00000000
# 12    LOC_B    Dogs  Neither    0 0.00000000
# 13    LOC_B Lizards    Agree    2 0.40000000
# 14    LOC_B Lizards Disagree    2 0.40000000
# 15    LOC_B Lizards  Neither    1 0.20000000
# 16    LOC_B  Snakes    Agree    0 0.00000000
# 17    LOC_B  Snakes Disagree    0 0.00000000
# 18    LOC_B  Snakes  Neither    0 0.00000000
# 19    LOC_C    Dogs    Agree    3 0.17647059
# 20    LOC_C    Dogs Disagree    1 0.05882353
# 21    LOC_C    Dogs  Neither    0 0.00000000
# 22    LOC_C Lizards    Agree    2 0.11764706
# 23    LOC_C Lizards Disagree    0 0.00000000
# 24    LOC_C Lizards  Neither    1 0.05882353
# 25    LOC_C  Snakes    Agree    3 0.17647059
# 26    LOC_C  Snakes Disagree    1 0.05882353
# 27    LOC_C  Snakes  Neither    6 0.35294118

If you have dplyr older than 1.1, then use

distribution %>%
  group_by(LOCATION) %>%
  mutate(percent = Freq/sum(Freq))

The key is not to use summarise but mutate.

out <- distribution %>% 
ungroup() %>% 
group_by(LOCATION) %>% 
mutate(percent = Freq/ sum(Freq))