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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 rowsFor 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 66The 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 rowsThere 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 rowsAll variables with only integer were converted to type integer. For
more information type vignette("retype") in the
console.
Hablar means ‘speak R’ in Spanish.