Mutate one df based on the sum of a column in another df matching criteria

I have df1 that looks like this:

state	zip code	type
NC	00001	person
NC	00001	property
NC	00000	person
NC	00001	property

And I want to create df2 where each zip code is a single observation, with columns counting the number of times each 'type' appears across each zip code from df1:

zip code	person	property
00000	250	45
00001	115	150

I created df2 so far which is a list of all unique zip codes, and tried the following method, which resulted in the column 'person' being added but every value is the same number, being the sum of each row where "person" appears across the entire dataset of df1.

df2 <- mutate(df2, person = sum(df1$type == "person"))

The end goal is to use a for loop to create multiple additional columns for each "type". I tried the previous method as well as the following method:

for(i in nrow(df2)){
df2 <- mutate(df2, person = sum(df1[i,"type"] == "person"))
}

But now, I get the column added with zeroes for every row.

Am I going about this the right way? How can I fix this?

Solution

You can use dplyr::count followed by tidyr::pivot_wider:

library(dplyr)
library(tidyr)

df %>% 
  group_by(zip_code, type) %>%
  count() %>%
  pivot_wider(names_from = type, values_from = n)

Output:

  zip_code person property
     <int>  <int>    <int>
1    12345      1        2
2    54321      1       NA

Data (note I changed your zip codes just for ease of identification)

df <- read.table(text = "state  zip_code    type
NC  12345   person
NC  12345   property
NC  54321   person
NC  12345   property", h = TRUE)