Simple tools for converting columns to new data types. Intuitive functions for columns with missing values.
hablar
give users an easy and effective way to work with data types. Additionally, it provides non-astonishing results when summarizing data.
The ambition is to lower the barrier to R but also provides simple tools that experienced R users could benefit from.
You can install hablar
from github with:
devtools::install_github("davidsjoberg/hablar")
The most useful function of hablar
is maybe convert. convert helps the user to quickly change data type of columns in a data frame. convert always converts factors to character before further conversion.
## convert column:# gear, vs to integers (int)# cyl to factor (fct)# am, gear and carb to character (chr)mtcars %>%convert(int(gear, vs),fct(cyl),chr(am, gear, carb))
#> # A tibble: 32 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr> <chr> <chr>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> # ... with 28 more rows
For more information type vignette("convert")
in the console.
A function for quick and dirty data type conversion. All columns are evaluated and converted to the simplest possible without loosing any information.
## convert all columns to characterdf <- mtcars %>% convert(chr(everything()))df
#> # A tibble: 32 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 21 6 160 110 3.9 2.62 16.46 0 1 4 4
#> 2 21 6 160 110 3.9 2.875 17.02 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> # ... with 28 more rows
## let retype guess the best data typedf %>% retype()
#> # A tibble: 32 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> # ... with 28 more rows
For more information type vignette("retype")
in the console.
Often summary function like min, max and mean return suprising results. Combining s
with your summary function ensures you that you will get a result, if there is one in your data.
## Base Rx <- c(NaN, 1, 2, NA)min(x)
#> [1] NA
## With smin(s(x))
#> [1] 1
For more information type vignette("s")
in the console.
Hablar means 'speak R' in spanish.