histogramR is a tool based on dplyr and ggplot2 that creates classical frequency distribution tables, histograms and frequency polygons. Also, a comparison between number of classes compute methods (Sturges, Friedman-Diaconis and Scott) are performed. This package is part of a final work in Computational Statistics course at the Master of Applied Statistics in Universidad del Norte, Colombia.
histogramR is stored in this github repository, thus package devtools is needed to install. If you are on a fresh install of R, then following code will install a lot of packages.
install.packages("devtools")
devtools::install_github("rodianf/histogramR")
library(histogramR)This function creates a classical frequency distribution table, of class tibble, with five columns.
-
variable name: Class intervals computed by selected method, default is "Sturges".
-
f: Counts or frequency of the variable in a class interval.
-
rf: Relative frequency or density.
-
cf: Cummulative frequency.
-
crf: Cummulative relative frequency.
As the return object is a tibble, functions from dplyr can be applied. To include in Rmarkdown use knitr::kable for better results.
Classes with zero frequency are dropped from table. This is caused by function group_by from dplyr package, however a correction for this behavior will be implemented soon. See tidyverse/dplyr#3492.
library(MASS)
#>
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#>
#> select
data("Melanoma")
attach(Melanoma)
tab_freq(thickness)
#> # A tibble: 8 x 5
#> thickness f rf cf crf
#> <chr> <int> <dbl> <int> <dbl>
#> 1 [0, 2) 109 0.532 109 0.532
#> 2 [2, 4) 51 0.249 160 0.780
#> 3 [4, 6) 21 0.102 181 0.883
#> 4 [6, 8) 12 0.0585 193 0.941
#> 5 [8, 10) 4 0.0195 197 0.961
#> 6 [12, 14) 6 0.0293 203 0.990
#> 7 [14, 16) 1 0.00488 204 0.995
#> 8 [16, 18] 1 0.00488 205 1.00
tab_freq(thickness, nclass = "FD")
#> # A tibble: 14 x 5
#> thickness f rf cf crf
#> <chr> <int> <dbl> <int> <dbl>
#> 1 [0, 1) 56 0.273 56 0.273
#> 2 [1, 2) 53 0.259 109 0.532
#> 3 [2, 3) 24 0.117 133 0.649
#> 4 [3, 4) 27 0.132 160 0.780
#> 5 [4, 5) 13 0.0634 173 0.844
#> 6 [5, 6) 8 0.0390 181 0.883
#> 7 [6, 7) 4 0.0195 185 0.902
#> 8 [7, 8) 8 0.0390 193 0.941
#> 9 [8, 9) 3 0.0146 196 0.956
#> 10 [9, 10) 1 0.00488 197 0.961
#> 11 [12, 13) 5 0.0244 202 0.985
#> 12 [13, 14) 1 0.00488 203 0.990
#> 13 [14, 15) 1 0.00488 204 0.995
#> 14 [17, 18] 1 0.00488 205 1.00
tab_freq(thickness) %>%
rename("Frequency" = f,
"Relative frequency" = rf)
#> # A tibble: 8 x 5
#> thickness Frequency `Relative frequency` cf crf
#> <chr> <int> <dbl> <int> <dbl>
#> 1 [0, 2) 109 0.532 109 0.532
#> 2 [2, 4) 51 0.249 160 0.780
#> 3 [4, 6) 21 0.102 181 0.883
#> 4 [6, 8) 12 0.0585 193 0.941
#> 5 [8, 10) 4 0.0195 197 0.961
#> 6 [12, 14) 6 0.0293 203 0.990
#> 7 [14, 16) 1 0.00488 204 0.995
#> 8 [16, 18] 1 0.00488 205 1.00
tab_freq(thickness, nclass = "scott") %>%
rename("Frequency" = f,
"Relative frequency" = rf) %>%
knitr::kable()| thickness | Frequency | Relative frequency | cf | crf |
|---|---|---|---|---|
| [0, 2) | 109 | 0.5317073 | 109 | 0.5317073 |
| [2, 4) | 51 | 0.2487805 | 160 | 0.7804878 |
| [4, 6) | 21 | 0.1024390 | 181 | 0.8829268 |
| [6, 8) | 12 | 0.0585366 | 193 | 0.9414634 |
| [8, 10) | 4 | 0.0195122 | 197 | 0.9609756 |
| [12, 14) | 6 | 0.0292683 | 203 | 0.9902439 |
| [14, 16) | 1 | 0.0048780 | 204 | 0.9951220 |
| [16, 18] | 1 | 0.0048780 | 205 | 1.0000000 |
This function creates an histogram and frequency polygon or a cummulative frequency polygon. The return object is a ggplot2 plot, thus layers can be applied.
plot_freq(thickness)
plot_freq(thickness, nclass = "FD", density = TRUE)
plot_freq(thickness, nclass = "scott", density = TRUE, cfp = TRUE) +
theme_classic()
This function compare the methods for calculation of the number of classes from a numerical random variable. Uses plot_freq function to generate plots. Generics as print, summary and ggplot can be used.
nc_comp(thickness)
#> Class number methods comparison.
#>
#> method nclasses
#> 1 Sturges 9
#> 2 FD 20
#> 3 scott 10
comparison <- nc_comp(thickness)
print(comparison)
#> Class number methods comparison.
#>
#> method nclasses
#> 1 Sturges 9
#> 2 FD 20
#> 3 scott 10
summary(comparison)
#> Class number methods comparison.
#>
#> method nclasses
#> 1 Sturges 9
#> 2 FD 20
#> 3 scott 10
#>
#> Summary of input vector:
#>
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.10 0.97 1.94 2.92 3.56 17.42
ggplot(comparison)