CODEWITHSUNDEEP
rdplyr
Articolours
Articolours

Reputation: 61

Summarize one column, grouped by another in R

I believe this is a simple question, I think I just don't have the ability to logically think it out to be able to search it.

I have a table of data:

Column 1: Sex (M/F)
Column 2 Plays Sport (Y/N)

I need a summary table which shows:

Sex | Plays Sport Yes | Plays Sport No

I can't for the life of me figure out how to do it with dplyr.

Solution in base r would be preferred if not too complicated.

Upvotes: 1

Views: 107

Answers (3)

akrun
akrun

Reputation: 887108

We can use count with spread

library(tidyverse)
df1 %>%
   count(Sex, Sport) %>% 
   spread(Sport, n, fill = 0)
# A tibble: 2 x 3
#  Sex       N     Y
#   <chr> <dbl> <dbl>
#1 F         2     0
#2 M         3     1

data

df1 <- data.frame(Sex = c("M", "M", "F", "M", "M", "F"),
            Sport = c("N", "Y", "N", "N", "N", "N"), stringsAsFactors = FALSE)

Upvotes: 2

markus
markus

Reputation: 26343

You could use table

# dummy data
df1 <- data.frame(Sex = c("M", "M", "F", "M", "M", "F"),
                Sport = c("N", "Y", "N", "N", "N", "N"), stringsAsFactors = FALSE)
df1
#  Sex Sport
#1   M     N
#2   M     Y
#3   F     N
#4   M     N
#5   M     N
#6   F     N

Result

table(df1)
#   Sport
#Sex N Y
#  F 2 0
#  M 3 1

Here is another option with reshape2::dcast

reshape2::dcast(df1, Sex ~ paste0("Sport_", Sport), 
                fun.aggregate = length # default 
                )

Result

#  Sex Sport_N Sport_Y
#1   F       2       0
#2   M       3       1

Upvotes: 0

Jordo82
Jordo82

Reputation: 816

Using dplyr and making some assumptions about exactly what you're looking for:

library(tidyverse)

data <- data.frame(Sex = c("M", "F")[rbinom(10, 1, 0.5) + 1],
                   PlaysSport = c(TRUE, FALSE)[rbinom(10, 1, 0.5) + 1])

data %>% 
  group_by(Sex, PlaysSport) %>% 
  summarise(count = n())

# A tibble: 4 x 3
# Groups:   Sex [?]
      Sex   PlaysSport count
    <fctr>    <lgl>    <int>
1      F      FALSE     1
2      F       TRUE     3
3      M      FALSE     4
4      M       TRUE     2

Upvotes: 2

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