Categorize numeric variable with mutate

Question

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.

Answer 1

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)}

Answer 2

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