https://github.com/b-rodrigues/brotools
https://github.com/b-rodrigues/brotools
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/b-rodrigues/brotools
- Owner: b-rodrigues
- Created: 2018-03-13T15:18:20.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-04-12T20:34:59.000Z (almost 5 years ago)
- Last Synced: 2024-08-13T07:11:17.890Z (6 months ago)
- Language: R
- Size: 1.96 MB
- Stars: 21
- Watchers: 5
- Forks: 8
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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- jimsghstars - b-rodrigues/brotools - (R)
README
### 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_github("b-rodrigues/brotools")
```
### Functions included
Here is the list of the included functions:
* `around()`
* `map_filter()`
* `ni()`
* `one_row()`
* `read_list()`
* `to_map()`
### 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.
### 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.
### 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.
### 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.