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The mission of hablar is for you to get non-astonishing results! That means that functions return what you expected. R has some intuitive quirks that beginners and experienced programmers fail to identify. Some of the first weird features of R that hablar solves:

• Missing values NA and irrational values Inf, NaN is dominant. For example, in R sum(c(1, 2, NA)) is NA and not 3. In hablar the addition of an underscore sum_(c(1, 2, NA)) returns 3, as is often expected.

• Factors (categorical variables) that are converted to numeric returns the number of the category rather than the value. In hablar the convert() function always changes the type of the values.

• Finding duplicates, and rows with NA can be cumbersome. The functions find_duplicates() and find_na() make it easy to find where the data frame needs to be fixed. When the issues are found the utility replacement functions, e.g. if_else_(), if_na(), zero_if() easily fixes many of the most common problems you face.

hablar follows the syntax API of tidyverse and works seamlessly with dplyr and tidyselect.

You can install hablar from CRAN:

install.packages("hablar")

Or preferably:

if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, hablar)

The most useful function of hablar is maybe convert. convert helps the user to quickly and dynamically change data type of columns in a data frame. convert always converts factors to character before further conversion. Works with tidyselect.

mtcars %>% 
  convert(int(cyl, am),
          fct(disp:drat),
          chr(contains("w")))

#> # A tibble: 32 x 11
#>     mpg   cyl disp  hp    drat  wt     qsec    vs    am  gear  carb
#>   <dbl> <int> <fct> <fct> <fct> <chr> <dbl> <dbl> <int> <dbl> <dbl>
#> 1  21       6 160   110   3.9   2.62   16.5     0     1     4     4
#> 2  21       6 160   110   3.9   2.875  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  3.215  19.4     1     0     3     1
#> # ... with 28 more rows

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

Often summary function like min, max and mean return surprising results. Combining _ with your summary function ensures you that you will get a result, if there is one in your data. It ignores irrational numbers like Inf and NaN as well as NA. If all elements are NA, Inf, NaN it returns NA.

starwars %>% 
  summarise(min_height_baseR = min(height),
            min_height_hablar = min_(height))

#> # A tibble: 1 x 2
#>   min_height_baseR min_height_hablar
#>              <int>             <int>
#> 1               NA                66

The function min_ omitted that the variable height contained NA. For more information type vignette("s") in the console.

When cleaning data you spend a lot of time understanding your data. Sometimes you get more row than you expected when doing a left_join(). Or you did not know that certain column contained missing values NA or irrational values like Inf or NaN.

In hablar the find_* functions speeds up your search for the problem. To find duplicated rows you simply df %>% find_duplicates(). You can also find duplicates in in specific columns, which can be useful before joins.

# Create df with duplicates
df <- mtcars %>% 
  bind_rows(mtcars %>% slice(1, 5, 9))

# Return rows with duplicates in cyl and am
df %>% 
  find_duplicates(cyl, am)

#> # A tibble: 35 x 11
#>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 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 31 more rows

There are also find functions for other cases. For example find_na() returns rows with missing values.

starwars %>% 
  find_na(height)

#> # A tibble: 6 x 14
#>   name     height  mass hair_color skin_color eye_color birth_year sex   gender 
#>   <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  
#> 1 Arvel C~     NA    NA brown      fair       brown             NA male  mascul~
#> 2 Finn         NA    NA black      dark       dark              NA male  mascul~
#> 3 Rey          NA    NA brown      light      hazel             NA fema~ femini~
#> 4 Poe Dam~     NA    NA brown      light      brown             NA male  mascul~
#> # ... with 2 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

If you rather want a Boolean value instead then e.g. check_duplicates() returns TRUE if the data frame contains duplicates, otherwise it returns FALSE.

Let’s say that we have found a problem is caused by missing values in the column height and you want to replace all missing values with the integer 100. hablar comes with an additional ways of doing if-or-else.

starwars %>% 
  find_na(height) %>% 
  mutate(height = if_na(height, 100L))

#> # A tibble: 6 x 14
#>   name     height  mass hair_color skin_color eye_color birth_year sex   gender 
#>   <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  
#> 1 Arvel C~    100    NA brown      fair       brown             NA male  mascul~
#> 2 Finn        100    NA black      dark       dark              NA male  mascul~
#> 3 Rey         100    NA brown      light      hazel             NA fema~ femini~
#> 4 Poe Dam~    100    NA brown      light      brown             NA male  mascul~
#> # ... with 2 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

In the chunk above we successfully replaced all missing heights with the integer 100. hablar also contain the self explained:

• if_zero() and zero_if()
• if_inf() and inf_if()
• if_nan() and nan_if()

which works in the same way as the examples above.

A function for quick and dirty data type conversion. All columns are evaluated and converted to the simplest possible without loosing any information.

mtcars %>% 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

All variables with only integer were converted to type integer. For more information type vignette("retype") in the console.

Hablar means ‘speak R’ in Spanish.