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I would like to a categorize numeric variable in my data.frame object with the use of dplyr (and have no idea how to do it).

Without dplyr, I would probably do something like:

df <- data.frame(a = rnorm(1e3), b = rnorm(1e3))
df$a <- cut(df$a , breaks=quantile(df$a, probs = seq(0, 1, 0.2)))

and it would be done. However, I strongly prefer to do it with the use of some dplyr function (mutate, I suppose) in the chain sequence of other actions I do perform over my data.frame.

zx8754
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Marta Karas
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    At a guess (from google and reading the online manual, I've never used `dplyr`) I'd say `mutate( df , a = cut( a , breaks = quantile( a , probs = seq( 0 , 1 , 0.2 ) ) ) )`... – Simon O'Hanlon Apr 18 '14 at 22:55

2 Answers2

40

The ggplot2 package has 3 functions that work well for these tasks:

  • cut_number(): Makes n groups with (approximately) equal numbers of observation
  • cut_interval(): Makes n groups with equal range
  • cut_width: Makes groups of width width

My go-to is cut_number() because this uses evenly spaced quantiles for binning observations. Here's an example with skewed data.

library(tidyverse)

skewed_tbl <- tibble(
    counts = c(1:100, 1:50, 1:20, rep(1:10, 3), 
               rep(1:5, 5), rep(1:2, 10), rep(1, 20))
    ) %>%
    mutate(
        counts_cut_number   = cut_number(counts, n = 4),
        counts_cut_interval = cut_interval(counts, n = 4),
        counts_cut_width    = cut_width(counts, width = 25)
        ) 

# Data
skewed_tbl
#> # A tibble: 265 x 4
#>    counts counts_cut_number counts_cut_interval counts_cut_width
#>     <dbl> <fct>             <fct>               <fct>           
#>  1      1 [1,3]             [1,25.8]            [-12.5,12.5]    
#>  2      2 [1,3]             [1,25.8]            [-12.5,12.5]    
#>  3      3 [1,3]             [1,25.8]            [-12.5,12.5]    
#>  4      4 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  5      5 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  6      6 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  7      7 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  8      8 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  9      9 (3,13]            [1,25.8]            [-12.5,12.5]    
#> 10     10 (3,13]            [1,25.8]            [-12.5,12.5]    
#> # ... with 255 more rows

summary(skewed_tbl$counts)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    1.00    3.00   13.00   25.75   42.00  100.00

# Histogram showing skew
skewed_tbl %>%
    ggplot(aes(counts)) +
    geom_histogram(bins = 30)


# cut_number() evenly distributes observations into bins by quantile
skewed_tbl %>%
    ggplot(aes(counts_cut_number)) +
    geom_bar()


# cut_interval() evenly splits the interval across the range
skewed_tbl %>%
    ggplot(aes(counts_cut_interval)) +
    geom_bar()


# cut_width() uses the width = 25 to create bins that are 25 in width
skewed_tbl %>%
    ggplot(aes(counts_cut_width)) +
    geom_bar()

Created on 2018-11-01 by the reprex package (v0.2.1)

Matt Dancho
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34
set.seed(123)
df <- data.frame(a = rnorm(10), b = rnorm(10))

df %>% mutate(a = cut(a, breaks = quantile(a, probs = seq(0, 1, 0.2))))

giving:

                 a          b
1  (-0.586,-0.316]  1.2240818
2   (-0.316,0.094]  0.3598138
3      (0.68,1.72]  0.4007715
4   (-0.316,0.094]  0.1106827
5     (0.094,0.68] -0.5558411
6      (0.68,1.72]  1.7869131
7     (0.094,0.68]  0.4978505
8             <NA> -1.9666172
9   (-1.27,-0.586]  0.7013559
10 (-0.586,-0.316] -0.4727914
G. Grothendieck
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  • What is the `dplyr` benefit of doing `df %.% mutate( a = ... )` over `df <- mutate( df , a = ... )`. Does it change by reference the first way? – Simon O'Hanlon Apr 18 '14 at 23:07
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    It improves readability while you are executing series of actions over one `data.farme` - instead of using nested functions, you can write them sequentially with the use of `%.%` and - therefore - read code in a *from-left-to-right* manner (not: *from-inside-to-outside*). More here: http://blog.rstudio.org/2014/01/17/introducing-dplyr/ – Marta Karas Apr 18 '14 at 23:11