brotools
is a package that includes some useful functions to make my life easier.
Here is the list of the included functions:
all_data_to_upper()
around()
map_filter()
modal_value()
multi_join()
ni()
one_row()
read_excel_clean()
read_list()
read_workbook()
to_map()
all_data_to_upper()
This function changes the letter case of the column names of the datasets stored in a list to upper case. This is useful for merging datasets with the same column names, but with different letter cases.
around()
This function is useful if you want to test the equality of two values when these values are different
by a very little epsilon
.
If x > y - eps and x < y + eps, around()
returns TRUE
, if not, FALSE
.
map_filter()
map_filter()
returns a list of data frame objects where each data frame was filtered by one condition.
modal_value()
This is basically a wrapper around quantile(x, 0.5)
. Might get removed in future versions.
multi_join()
multi_join()
solves the problem of merging a lot of datasets together. It takes a list of datasets
as an input, and outputs a tibble
(an enhanced version of base R's data.frames
).
ni()
Returns TRUE
if x is not in a list.
one_row()
This function is useful to remove duplicate lines in a dataframe. The user can specify the variables that will be used to check for duplicates in the data frames.
read_excel_clean()
sude by read_workbook()
read_excel_clean()
is a wrapper around janitor::clean_names(readxl::read_excel())
and is used
by read_workbook()
.
read_list()
read_list()
works by giving it a list of datasets in your current working directory and a read
function, such as readr::read_csv()
in case you want to read .csv
files and puts them in a
list. You can then use the above functions on this list of datasets.
read_workbook()
read_workbook()
reads an .xlsx
file into R. It is a wrapper around various pre-existing
functions. The only argument of read_workbook()
is a path to an .xlsx
file. The output is a
list where each element is a data.frame
object representing each one of the sheets in the .xlsx
file. So for instance, a .xlsx
file with four sheets, named sheet1
, sheet2
, sheet3
and
sheet4
, gets imported into R in a named list where the first element is a data.frame
named also
sheet1
and containing the data from sheet1
, the second element, ... etc. If one loads the data
into a variable called workbook
, here is what it looks like:
to_map()
After having read a lot of datasets into a list, to_map()
allows you to make any function work on
this list of datasets. So for example, there is no need to use an anonymous function in map()
to get the
summary statistics of each dataframe of the list.
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