knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(hablar) library(dplyr) library(knitr) options(tibble.print_min = 4L, tibble.print_max = 4L)
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
.
A common issue in R is how R treats missing values (i.e. NA
). Sometimes NA
in your data frame means that there is missing values in the sense that you need to estimate or replace them with values. But often it is not a problem! Often NA
means that there is no value, and should not be. hablar
provide useful functions that handle NA
intuitively. Let's take a simple example:
df <- tibble(name = c("Fredrik", "Maria", "Astrid"), graduation_date = as.Date(c("2016-06-15", NA, "2014-06-15")), age = c(21L, 16L, 23L)) df
min()
to min_()
The graduation_date
is missing for Maria. In this case it is not because we do not know. It is because she has not graduated yet, she is younger than Fredrik and Astrid. If we would like to know the first graduation date of the three observation in R with a naive min()
we get NA
. But with min_()
from hablar
we get the minimum value that is not missing. See:
df %>% mutate(min_baseR = min(graduation_date), min_hablar = min_(graduation_date))
The hablar
package provides the same functionality for
max_()
mean_()
median_()
sd_()
first_()
... and more. For more documentation type help(min_())
or vignette("s")
for an in-depth description.
In hablar
the function convert
provides a robust, readable and dynamic way to change type of a column.
mtcars %>% convert(int(cyl, am), num(disp:drat))
The above chunk converts the columns cyl
and am
to integers, and the columns disp
through drat
to numeric. If a column is of type factor
it always converts it to character before further conversion.
With convert
and tidyselect
you can easily change type of a wide range of columns.
mtcars %>% convert( chr(last_col()), # Last colum to character int(1:2), # First two columns to integer fct(hp, wt), # hp and wt to factors dte(vs), # vs to date (if you really want) num(contains("car")) # car as in carb to numeric )
For more information, see help(hablar)
or vignette("convert")
.
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)
There are also find functions for other cases. For example find_na()
returns rows with missing values.
starwars %>% find_na(height)
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))
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.
The generic function if_else_()
provides the same rigidity as if_else()
in dplyr
but ads some flexibility. In dplyr
you need to specify which type NA
should have. In if_else_()
you can write:
starwars %>% mutate(skin_color = if_else_(hair_color == "brown", NA, hair_color))
In if_else()
from dplyr
you would have had to specified NA_character_
.
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