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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
NAand irrational valuesInf,NaNis dominant. For example, in Rsum(c(1, 2, NA))isNAand not 3. Inhablarthe addition of an underscoresum_(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
hablartheconvert()function always changes the type of the values. -
Finding duplicates, and rows with
NAcan be cumbersome. The functionsfind_duplicates()andfind_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
#> <dbl> <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 13
#> name height mass hair_color skin_color eye_color birth_year gender homeworld
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Arve… NA NA brown fair brown NA male <NA>
#> 2 Finn NA NA black dark dark NA male <NA>
#> 3 Rey NA NA brown light hazel NA female <NA>
#> 4 Poe … NA NA brown light brown NA male <NA>
#> # … with 2 more rows, and 4 more variables: 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 13
#> name height mass hair_color skin_color eye_color birth_year gender homeworld
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Arve… 100 NA brown fair brown NA male <NA>
#> 2 Finn 100 NA black dark dark NA male <NA>
#> 3 Rey 100 NA brown light hazel NA female <NA>
#> 4 Poe … 100 NA brown light brown NA male <NA>
#> # … with 2 more rows, and 4 more variables: 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()andzero_if()if_inf()andinf_if()if_nan()andnan_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.