1. Bruno Rodrigues
  2. brotools

Overview

HTTPS SSH

Introduction

brotools is a package that includes some useful functions that makes my life easier. Maybe it'll make yours too. Install it with:

Installation

devtools::install_bitbucket("b-rodrigues/brotools")

Functions included

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()

Change the letter case of the column names of a list of datasets to uppercase with 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.

This function will probably get removed in the future, as janitor::clean_names() is much more interesting.

Is a value close to another? Find out with 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.

Filter a dataframe with various conditions with map_filter()

map_filter() returns a list of data frame objects where each data frame was filtered by one condition.

Get the mode of a distribution with modal_value()

Returns the mode of a distribution.

Join a list of datasets into one single dataset using 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).

Check if a value is not in a list with ni()

Returns TRUE if x is not in a list.

Only keep one row per individual with 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.

One helper function read_excel_clean() used by read_workbook()

read_excel_clean() is a wrapper around janitor::clean_names(readxl::read_excel()) and is used by read_workbook().

Read a lot of datasets at once easily with 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 an Excel workbook with 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:

Make a function work on a list using 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.