jutilities
This a collection of convenience functions for data handling, plotting,
and analysis within the tidyverse syntax. Most of these functions are
wrappers for tasks I perform regularly (e. g., describe variables, add
mean indices) and wanted to integrate them into %>% pipes with just
one line.
This is all very much work in progress.
Installation
Install from GitHub:
install.packages("devtools")
devtools::install_github("joon-e/jutilities")Instructions
jutilities are to be used with the tidyverse. Most functions will
return a tibble and can thus be easily integrated into %>% pipes.
library(tidyverse)
library(jutilities)Describe continous variables
describe() computes several measures of central tendency and
variability for all specified variables:
diamonds %>%
describe(x, y, z)
#> # A tibble: 3 x 11
#> variable N Missing M SD Min Max Range Mdn Q25 Q75
#> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 x 53940 0 5.73 1.12 0 10.7 10.7 5.7 4.71 6.54
#> 2 y 53940 0 5.73 1.14 0 58.9 58.9 5.71 4.72 6.54
#> 3 z 53940 0 3.54 0.706 0 31.8 31.8 3.53 2.91 4.04If no variables are specified, all numeric variables are described:
diamonds %>%
describe()
#> # A tibble: 7 x 11
#> variable N Missing M SD Min Max Range Mdn Q25
#> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 carat 53940 0 7.98e-1 4.74e-1 0.2 5.01e0 4.81e0 0.7 0.4
#> 2 depth 53940 0 6.17e+1 1.43e+0 43 7.90e1 3.60e1 61.8 61
#> 3 price 53940 0 3.93e+3 3.99e+3 326 1.88e4 1.85e4 2401 950
#> 4 table 53940 0 5.75e+1 2.23e+0 43 9.50e1 5.20e1 57 56
#> 5 x 53940 0 5.73e+0 1.12e+0 0 1.07e1 1.07e1 5.7 4.71
#> 6 y 53940 0 5.73e+0 1.14e+0 0 5.89e1 5.89e1 5.71 4.72
#> 7 z 53940 0 3.54e+0 7.06e-1 0 3.18e1 3.18e1 3.53 2.91
#> # ... with 1 more variable: Q75 <dbl>describe_groups() outputs the same statistics for one variable,
grouped by one or more grouping variables.
diamonds %>%
describe_groups(price, cut, color)
#> # A tibble: 35 x 12
#> # Groups: cut [5]
#> cut color N Missing M SD Min Max Range Mdn Q25
#> <ord> <ord> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Fair D 163 0 4291. 3286. 536 16386 15850 3730 2204.
#> 2 Fair E 224 0 3682. 2977. 337 15584 15247 2956 1590.
#> 3 Fair F 312 0 3827. 3223. 496 17995 17499 3035 1642.
#> 4 Fair G 314 0 4239. 3610. 369 18574 18205 3057 1985
#> 5 Fair H 303 0 5136. 3886. 659 18565 17906 3816 2458.
#> 6 Fair I 175 0 4685. 3730. 735 18242 17507 3246 2324
#> 7 Fair J 119 0 4976. 4050. 416 18531 18115 3302 2313
#> 8 Good D 662 0 3405. 3175. 361 18468 18107 2728. 957.
#> 9 Good E 933 0 3424. 3331. 327 18236 17909 2420 969
#> 10 Good F 909 0 3496. 3202. 357 18686 18329 2647 1214
#> # ... with 25 more rows, and 1 more variable: Q75 <dbl>Describe categorical variables
cat_ftable() outputs a frequency table including relative, valid, and
cumulative frequencies for one categorical variable.
diamonds %>%
cat_ftable(cut)
#> # A tibble: 5 x 7
#> cut n cum.n percent cum.percent valid.percent cum.valid.percent
#> <ord> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 Fair 1610 1610 0.0298 0.0298 0.0298 0.0298
#> 2 Good 4906 6516 0.0910 0.121 0.0910 0.121
#> 3 Very Good 12082 18598 0.224 0.345 0.224 0.345
#> 4 Premium 13791 32389 0.256 0.600 0.256 0.600
#> 5 Ideal 21551 53940 0.400 1 0.400 1cat_xtable outputs contigency tables for one column variable and one
or more row variables:
diamonds %>%
cat_xtable(cut, color, clarity)
#> # A tibble: 56 x 8
#> color clarity Fair Good `Very Good` Premium Ideal total
#> <ord> <ord> <int> <int> <int> <int> <int> <dbl>
#> 1 D I1 4 8 5 12 13 42
#> 2 D SI2 56 223 314 421 356 1370
#> 3 D SI1 58 237 494 556 738 2083
#> 4 D VS2 25 104 309 339 920 1697
#> 5 D VS1 5 43 175 131 351 705
#> 6 D VVS2 9 25 141 94 284 553
#> 7 D VVS1 3 13 52 40 144 252
#> 8 D IF 3 9 23 10 28 73
#> 9 E I1 9 23 22 30 18 102
#> 10 E SI2 78 202 445 519 469 1713
#> # ... with 46 more rowsSetting the argument percentages = TRUE changes the output to relative
frequencies:
diamonds %>%
cat_xtable(cut, color, percentages = TRUE)
#> # A tibble: 7 x 7
#> color Fair Good `Very Good` Premium Ideal total
#> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 D 0.101 0.135 0.125 0.116 0.132 0.126
#> 2 E 0.139 0.190 0.199 0.169 0.181 0.182
#> 3 F 0.194 0.185 0.179 0.169 0.178 0.177
#> 4 G 0.195 0.178 0.190 0.212 0.227 0.209
#> 5 H 0.188 0.143 0.151 0.171 0.145 0.154
#> 6 I 0.109 0.106 0.0997 0.104 0.0971 0.101
#> 7 J 0.0739 0.0626 0.0561 0.0586 0.0416 0.0521A Chi² test can be optionally computed by setting the argument chisq = TRUE. Test results will be displayed in a console message:
diamonds %>%
cat_xtable(cut, color, chisq = TRUE)
#> Chi² = 310.317901, df = 24.000000, p = 0.000000
#> # A tibble: 7 x 7
#> color Fair Good `Very Good` Premium Ideal total
#> <ord> <int> <int> <int> <int> <int> <dbl>
#> 1 D 163 662 1513 1603 2834 6775
#> 2 E 224 933 2400 2337 3903 9797
#> 3 F 312 909 2164 2331 3826 9542
#> 4 G 314 871 2299 2924 4884 11292
#> 5 H 303 702 1824 2360 3115 8304
#> 6 I 175 522 1204 1428 2093 5422
#> 7 J 119 307 678 808 896 2808Modify data
add_index() adds a rowwise mean index columns of the specified
variables to the dataset. The second argument (first argument if used in
a pipe) should be the name of the index column:
diamonds %>%
add_index(meanxyz, x, y, z)
#> # A tibble: 53,940 x 11
#> carat cut color clarity depth table price x y z meanxyz
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 3.45
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 3.35
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 3.48
#> 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 3.69
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 3.81
#> 6 0.24 Very Go~ J VVS2 62.8 57 336 3.94 3.96 2.48 3.46
#> 7 0.24 Very Go~ I VVS1 62.3 57 336 3.95 3.98 2.47 3.47
#> 8 0.26 Very Go~ H SI1 61.9 55 337 4.07 4.11 2.53 3.57
#> 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 3.38
#> 10 0.23 Very Go~ H VS1 59.4 61 338 4 4.05 2.39 3.48
#> # ... with 53,930 more rowsSet the argument type = "sum" to create a sum index instead:
diamonds %>%
add_index(sumxyz, x, y, z, type = "sum")
#> # A tibble: 53,940 x 11
#> carat cut color clarity depth table price x y z sumxyz
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 10.4
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 10.0
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 10.4
#> 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 11.1
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 11.4
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 10.4
#> 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 10.4
#> 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 10.7
#> 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 10.1
#> 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 10.4
#> # ... with 53,930 more rowsadd_label adds a text label column of a numeric variable to the
dataset, for example to be used as labels in plots.
diamonds %>%
add_label(labelz, z)
#> # A tibble: 53,940 x 11
#> carat cut color clarity depth table price x y z labelz
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <chr>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 2.31
#> 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 2.48
#> 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 2.47
#> 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 2.53
#> 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 2.49
#> 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 2.39
#> # ... with 53,930 more rowsBy default, add_label rounds to two decimal places. You can change
this by setting the decimal.places argument:
diamonds %>%
add_label(labelz, z, decimal.places = 0)
#> # A tibble: 53,940 x 11
#> carat cut color clarity depth table price x y z labelz
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <chr>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 2
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 2
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 2
#> 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 3
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 3
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 2
#> 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 2
#> 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 3
#> 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 2
#> 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 2
#> # ... with 53,930 more rowsAnalyze data
Correlations
correlate() computes correlations coefficients and p-values for all
combinations of the specified variables. By default, Pearson correlation
coefficients are computed. Set the argument type = "spearman" to
compute Spearman ranked correlations coefficicents instead.
diamonds %>%
correlate(x, y, z)
#> # A tibble: 3 x 5
#> var1 var2 r_pearson n p.value
#> <chr> <chr> <dbl> <int> <dbl>
#> 1 x y 0.975 53940 0
#> 2 x z 0.971 53940 0
#> 3 y z 0.952 53940 0T-tests
ttest computes t-Tests for one grouping variable and one or more test
variables. Output statistics include descriptives, t-values, degrees of
freedom, p-values, and Cohen’s d:
diamonds %>%
filter(cut == "Ideal" | cut == "Fair") %>%
droplevels() %>%
ttest(cut, price, x, y, z)
#> Group 1: Fair, Group 2: Ideal
#> # A tibble: 4 x 10
#> variable M1 SD1 M2 SD2 deltaM t df p
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 price 4.36e3 3.56e+3 3.46e3 3.81e+3 901. 9.75 1895. 6.08e- 22
#> 2 x 6.25e0 9.64e-1 5.51e0 1.06e+0 0.739 29.5 1914. 1.34e-157
#> 3 y 6.18e0 9.56e-1 5.52e0 1.07e+0 0.663 26.6 1925. 8.22e-133
#> 4 z 3.98e0 6.52e-1 3.40e0 6.58e-1 0.581 34.5 1862. 3.75e-202
#> # ... with 1 more variable: d <dbl>