hablar

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.

Installation

You can install hablar from github with:

# install.packages("devtools")
devtools::install_github("davidsjoberg/hablar")

convert

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.

retype

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 character
df <- 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 type
df %>% 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.

s

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 R
x <- c(NaN, 1, 2, NA)
min(x)

#> [1] NA

## With s
min(s(x))

#> [1] 1

For more information type vignette("s") in the console.

Note

Hablar means 'speak R' in spanish.