The idea of {ggexplorer} is to create plots with minimal effort using {ggplot2} for the purpose of exploring and analyzing data.
Install the development version from GitHub:
remotes::install_github("zpio/ggexplorer")Explore
mtcars <- mtcars %>% mutate(across(c(am, carb, cyl, gear, vs), as.factor))exploreData(mtcars)# A tibble: 11 × 7
variable type n NAs NAsPct unique uniquePct
<chr> <chr> <int> <int> <dbl> <int> <dbl>
1 am numeric 32 0 0 2 0.062
2 carb numeric 32 0 0 6 0.188
3 cyl numeric 32 0 0 3 0.094
4 disp numeric 32 0 0 27 0.844
5 drat numeric 32 0 0 22 0.688
6 gear numeric 32 0 0 3 0.094
7 hp numeric 32 0 0 22 0.688
8 mpg numeric 32 0 0 25 0.781
9 qsec numeric 32 0 0 30 0.938
10 vs numeric 32 0 0 2 0.062
11 wt numeric 32 0 0 29 0.90
exploreNumeric(mtcars)# A tibble: 11 × 14
variable n na naPct min max Q1 mean median Q3 sd zeros negatives outliers
<chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int>
1 am 32 0 0 0 1 0 0.41 0 1 0.5 19 0 0
2 carb 32 0 0 1 8 2 2.81 2 4 1.62 0 0 1
3 cyl 32 0 0 4 8 4 6.19 6 8 1.79 0 0 0
4 disp 32 0 0 71.1 472 121. 231. 196. 326 124. 0 0 0
5 drat 32 0 0 2.76 4.93 3.08 3.6 3.7 3.92 0.53 0 0 0
6 gear 32 0 0 3 5 3 3.69 4 4 0.74 0 0 0
7 hp 32 0 0 52 335 96.5 147. 123 180 68.6 0 0 1
8 mpg 32 0 0 10.4 33.9 15.4 20.1 19.2 22.8 6.03 0 0 0
9 qsec 32 0 0 14.5 22.9 16.9 17.8 17.7 18.9 1.79 0 0 1
10 vs 32 0 0 0 1 0 0.44 0 1 0.5 18 0 0
11 wt 32 0 0 1.51 5.42 2.58 3.22 3.33 3.61 0.98 0 0 2
exploreCategory(mtcars)# A tibble: 16 × 6
variable levels freq N ratio rank
<chr> <chr> <int> <int> <dbl> <int>
1 am 0 19 32 0.594 1
2 am 1 13 32 0.406 2
3 carb 2 10 32 0.312 1
4 carb 4 10 32 0.312 1
5 carb 1 7 32 0.219 3
6 carb 3 3 32 0.094 4
7 carb 6 1 32 0.031 5
8 carb 8 1 32 0.031 5
9 cyl 8 14 32 0.438 1
10 cyl 4 11 32 0.344 2
11 cyl 6 7 32 0.219 3
12 gear 3 15 32 0.469 1
13 gear 4 12 32 0.375 2
14 gear 5 5 32 0.156 3
15 vs 0 18 32 0.562 1
16 vs 1 14 32 0.438 2
descriptiveStats(mtcars)# A tibble: 6 × 18
variable n na mean median sd se skewness kurtosis min max IQR Q1 Q3 p10 p25 p50 p75
<chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 disp 32 0 231. 196. 124. 21.9 0.400 1.91 71.1 472 205. 121. 326 80.6 121. 196. 326
2 drat 32 0 3.6 3.7 0.53 0.095 0.279 2.44 2.76 4.93 0.84 3.08 3.92 3.01 3.08 3.70 3.92
3 hp 32 0 147. 123 68.6 12.1 0.761 3.05 52 335 83.5 96.5 180 66 96.5 123 180
4 mpg 32 0 20.1 19.2 6.03 1.06 0.640 2.80 10.4 33.9 7.38 15.4 22.8 14.3 15.4 19.2 22.8
5 qsec 32 0 17.8 17.7 1.79 0.316 0.387 3.55 14.5 22.9 2.01 16.9 18.9 15.5 16.9 17.7 18.9
6 wt 32 0 3.22 3.33 0.98 0.173 0.444 3.17 1.51 5.42 1.03 2.58 3.61 1.96 2.58 3.32 3.61
correlate(mtcars)# A tibble: 15 × 4
var1 var2 variables cor
<chr> <chr> <fct> <dbl>
1 disp wt disp - wt 0.888
2 disp hp disp - hp 0.791
3 mpg drat mpg - drat 0.681
4 hp wt hp - wt 0.659
5 mpg qsec mpg - qsec 0.419
6 drat qsec drat - qsec 0.091
7 wt qsec wt - qsec -0.175
8 disp qsec disp - qsec -0.434
9 hp drat hp - drat -0.449
10 hp qsec hp - qsec -0.708
11 disp drat disp - drat -0.71
12 drat wt drat - wt -0.712
13 mpg hp mpg - hp -0.776
14 mpg disp mpg - disp -0.848
15 mpg wt mpg - wt -0.868
Charts
