Binning the Numeric Data

The binning() converts a numeric variable to a categorization variable.

binning(
  x,
  nbins,
  type = c("quantile", "equal", "pretty", "kmeans", "bclust"),
  ordered = TRUE,
  labels = NULL,
  approxy.lab = TRUE
)

Arguments

x	numeric. numeric vector for binning.
nbins	integer. number of intervals(bins). required. if missing, nclass.Sturges is used.
type	character. binning method. Choose from "quantile", "equal", "pretty", "kmeans" and "bclust". The "quantile" sets breaks with quantiles of the same interval. The "equal" sets breaks at the same interval. The "pretty" chooses a number of breaks not necessarily equal to nbins using base::pretty function. The "kmeans" uses stats::kmeans function to generate the breaks. The "bclust" uses e1071::bclust function to generate the breaks using bagged clustering. "kmeans" and "bclust" was implemented by classInt::classIntervals() function.
ordered	logical. whether to build an ordered factor or not.
labels	character. the label names to use for each of the bins.
approxy.lab	logical. If TRUE, large number breaks are approximated to pretty numbers. If FALSE, the original breaks obtained by type are used.

Value

An object of bins class. Attributes of bins class is as follows.

class : "bins"
type : binning type, "quantile", "equal", "pretty", "kmeans", "bclust".
breaks : breaks for binning. the number of intervals into which x is to be cut.
levels : levels of binned value.
raw : raw data, numeric vector corresponding to x argument.

Details

This function is useful when used with the mutate/transmute function of the dplyr package.

See vignette("transformation") for an introduction to these concepts.

Examples

# \donttest{
# Generate data for the example
heartfailure2 <- heartfailure
heartfailure2[sample(seq(NROW(heartfailure2)), 20), "platelets"] <- NA

# Binning the platelets variable. default type argument is "quantile"
bin <- binning(heartfailure2$platelets)
# Print bins class object
bin
#> binned type: quantile
#> number of bins: 10
#> x
#>  [25100,151533.3] (151533.3,196000]   (196000,221000] (221000,236066.7] 
#>                28                29                29                26 
#> (236066.7,260000]   (260000,265000]   (265000,283000]   (283000,319000] 
#>                28                29                27                28 
#>   (319000,376200]   (376200,850000]              <NA> 
#>                27                28                20 

# Summarise bins class object
summary(bin)
#>               levels freq       rate
#> 1   [25100,151533.3]   28 0.09364548
#> 2  (151533.3,196000]   29 0.09698997
#> 3    (196000,221000]   29 0.09698997
#> 4  (221000,236066.7]   26 0.08695652
#> 5  (236066.7,260000]   28 0.09364548
#> 6    (260000,265000]   29 0.09698997
#> 7    (265000,283000]   27 0.09030100
#> 8    (283000,319000]   28 0.09364548
#> 9    (319000,376200]   27 0.09030100
#> 10   (376200,850000]   28 0.09364548
#> 11              <NA>   20 0.06688963

# Plot bins class object
plot(bin)

# Using labels argument
bin <- binning(heartfailure2$platelets, nbins = 4,
              labels = c("LQ1", "UQ1", "LQ3", "UQ3"))
bin
#> binned type: quantile
#> number of bins: 4
#> x
#>  LQ1  UQ1  LQ3  UQ3 <NA> 
#>   70   70   70   69   20 

# Using another type argument
bin <- binning(heartfailure2$platelets, nbins = 5, type = "equal")
bin
#> binned type: equal
#> number of bins: 5
#> x
#>  [25100,190080] (190080,355060] (355060,520040] (520040,685020] (685020,850000] 
#>              51             191              32               3               2 
#>            <NA> 
#>              20 
bin <- binning(heartfailure2$platelets, nbins = 5, type = "pretty")
bin
#> binned type: pretty
#> number of bins: 5
#> x
#>     [0,2e+05] (2e+05,4e+05] (4e+05,6e+05] (6e+05,8e+05] (8e+05,1e+06] 
#>            59           202            15             2             1 
#>          <NA> 
#>            20 
bin <- binning(heartfailure2$platelets, nbins = 5, type = "kmeans")
bin
#> binned type: kmeans
#> number of bins: 5
#> x
#>  [25100,187000] (187000,269000] (269000,355500] (355500,582000] (582000,850000] 
#>              47             127              68              34               3 
#>            <NA> 
#>              20 
bin <- binning(heartfailure2$platelets, nbins = 5, type = "bclust")
bin
#> binned type: bclust
#> number of bins: 5
#> x
#>  [25100,182500] (182500,457500] (457500,681500] (681500,796000] (796000,850000] 
#>              44             226               7               1               1 
#>            <NA> 
#>              20 

x <- sample(1:1000, size = 50) * 12345679
bin <- binning(x)
bin
#> binned type: quantile
#> number of bins: 7
#> x
#>  [3.45679e+08,2.432687e+09] (2.432687e+09,3.783069e+09] 
#>                           7                           7 
#> (3.783069e+09,4.983539e+09] (4.983539e+09,7.582598e+09] 
#>                           7                           8 
#> (7.582598e+09,8.779541e+09] (8.779541e+09,1.062963e+10] 
#>                           7                           7 
#> (1.062963e+10,1.212346e+10] 
#>                           7 
bin <- binning(x, approxy.lab = FALSE)
bin
#> binned type: quantile
#> number of bins: 7
#> x
#>    [345679012,2432686652]   (2432686652,3783068779]   (3783068779,4983539090] 
#>                         7                         7                         7 
#>   (4983539090,7582598464]   (7582598464,8779541437]  (8779541437,10629629619] 
#>                         8                         7                         7 
#> (10629629619,12123456778] 
#>                         7 

# extract binned results
extract(bin)
#>  [1] (2432686652,3783068779]   (8779541437,10629629619] 
#>  [3] (4983539090,7582598464]   (2432686652,3783068779]  
#>  [5] (2432686652,3783068779]   (4983539090,7582598464]  
#>  [7] [345679012,2432686652]    (3783068779,4983539090]  
#>  [9] (4983539090,7582598464]   (3783068779,4983539090]  
#> [11] (10629629619,12123456778] (2432686652,3783068779]  
#> [13] (10629629619,12123456778] (10629629619,12123456778]
#> [15] (4983539090,7582598464]   (3783068779,4983539090]  
#> [17] (8779541437,10629629619]  [345679012,2432686652]   
#> [19] (7582598464,8779541437]   (4983539090,7582598464]  
#> [21] (4983539090,7582598464]   (3783068779,4983539090]  
#> [23] (7582598464,8779541437]   (4983539090,7582598464]  
#> [25] (2432686652,3783068779]   (8779541437,10629629619] 
#> [27] (10629629619,12123456778] [345679012,2432686652]   
#> [29] (7582598464,8779541437]   [345679012,2432686652]   
#> [31] (10629629619,12123456778] (7582598464,8779541437]  
#> [33] (4983539090,7582598464]   (2432686652,3783068779]  
#> [35] (7582598464,8779541437]   (7582598464,8779541437]  
#> [37] (7582598464,8779541437]   (10629629619,12123456778]
#> [39] [345679012,2432686652]    (8779541437,10629629619] 
#> [41] (8779541437,10629629619]  [345679012,2432686652]   
#> [43] (8779541437,10629629619]  (2432686652,3783068779]  
#> [45] (10629629619,12123456778] (3783068779,4983539090]  
#> [47] (8779541437,10629629619]  (3783068779,4983539090]  
#> [49] [345679012,2432686652]    (3783068779,4983539090]  
#> 7 Levels: [345679012,2432686652] < ... < (10629629619,12123456778]

# -------------------------
# Using pipes & dplyr
# -------------------------
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# Compare binned frequency by death_event
heartfailure2 %>%
  mutate(platelets_bin = binning(heartfailure2$platelets) %>%
           extract()) %>%
  group_by(death_event, platelets_bin) %>%
  summarise(freq = n()) %>%
  arrange(desc(freq)) %>%
  head(10)
#> `summarise()` has grouped output by 'death_event'. You can override using the `.groups` argument.
#> # A tibble: 10 x 3
#> # Groups:   death_event [1]
#>    death_event platelets_bin      freq
#>    <fct>       <ord>             <int>
#>  1 No          (265000,283000]      23
#>  2 No          (236066.7,260000]    22
#>  3 No          (221000,236066.7]    20
#>  4 No          (196000,221000]      19
#>  5 No          (283000,319000]      19
#>  6 No          (151533.3,196000]    18
#>  7 No          (319000,376200]      18
#>  8 No          (376200,850000]      18
#>  9 No          [25100,151533.3]     16
#> 10 No          (260000,265000]      16

 # Compare binned frequency by death_event using Viz
 heartfailure2 %>%
   mutate(platelets_bin = binning(heartfailure2$platelets) %>%
            extract()) %>%
   target_by(death_event) %>%
   relate(platelets_bin) %>%
   plot()
# }

Arguments

Value

Details

See also

Examples