How to aggregate R dataframe of two columns based on values of another

Question

My dataframe is as follows in which gender=="1" refers to men and gender=="2" refers to women, Occupations go from A to U and year goes from 2010 to 2018 (I give you a small example)

Gender   Occupation    Year
1            A         2010
1            A         2010
2            A         2010
1            B         2010
2            B         2010
1            A         2011
2            A         2011
1            C         2011
2            C         2011

I want an output that sums the number of rows in which gender and year and occupation is distinct like you can see next:

Year | Occupation | Men | Woman
2010 |      A     |  2  |   1
2010 |      B     |  1  |   1
2011 |      A     |  1  |   1
2011 |      C     |  1  |   1

I have tried the following:

Nr_gender_occupation <- data %>%
   group_by(year, occupation) %>%
   summarise(
      Men = aggregate(gender=="1" ~ occupation, FUN= count),
      Women = aggregate(gender=="2" ~ occupation, FUN=count)
)

Just curious (for the sake of assumptions and possible data loss), are you concerned with non-binary gender values? — r2evans
– r2evans, Commented Apr 30, 2021 at 18:03

LMc · Accepted Answer · 2021-04-30 17:50:40Z

3

You can also do a count within your groups:

library(dplyr)

df %>% 
  group_by(Occupation, Year) %>% 
  summarize(Men = sum(Gender == 1),
            Woman = sum(Gender == 2), .groups = "drop")

Output

  Occupation  Year   Men Woman
  <chr>      <dbl> <int> <int>
1 A           2010     2     1
2 A           2011     1     1
3 B           2010     1     1
4 C           2011     1     1

answered Apr 30, 2021 at 17:50

LMc

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Comments

akrun · Accepted Answer · 2021-04-30 17:53:42Z

We could use the index in 'Gender' to change the values, then with pivot_wider from tidyr reshape the data into 'wide' format

library(dplyr)
library(tidyr)
data %>%
 mutate(Gender = c("Male", "Female")[Gender]) %>%
 pivot_wider(names_from = Gender, values_from = Gender, values_fn = length)

-output

# A tibble: 4 x 4
#  Occupation  Year  Male Female
#  <chr>      <int> <int>  <int>
#1 A           2010     2      1
#2 B           2010     1      1
#3 A           2011     1      1
#4 C           2011     1      1

Or use table with unnest

library(tidyr)
data %>%
   group_by(Year, Occupation) %>%
   summarise(out = list(table(Gender)), .groups = 'drop') %>%     
   unnest_wider(out)

Or we can use count with pivot_wider

data %>%
  count(Gender, Occupation, Year) %>%
  pivot_wider(names_from = Gender, values_from = n)

data

data <- structure(list(Gender = c(1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), 
    Occupation = c("A", "A", "A", "B", "B", "A", "A", "C", "C"
    ), Year = c(2010L, 2010L, 2010L, 2010L, 2010L, 2011L, 2011L, 
    2011L, 2011L)), class = "data.frame", row.names = c(NA, -9L
))

ThomasIsCoding · Accepted Answer · 2021-04-30 18:15:28Z

1

A data.table option using dcast

dcast(setDT(df), Year + Occupation ~ c("Men", "Woman")[Gender])

gives

   Year Occupation Men Woman
1: 2010          A   2     1
2: 2010          B   1     1
3: 2011          A   1     1
4: 2011          C   1     1

answered Apr 30, 2021 at 18:15

ThomasIsCoding

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