janitor
IntRo
Our great sponsors
| janitor | IntRo | |
|---|---|---|
| 2 | 1 | |
| 1,341 | 9 | |
| - | - | |
| 6.2 | 6.9 | |
| about 2 months ago | 3 months ago | |
| R | ||
| GNU General Public License v3.0 or later | - |
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janitor
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Working with columns names that are numbers (in this case, years)
I would just clean the names and work with those. Then there is no need to use backticks. Read about the function clean_names in the janitor vignette: https://github.com/sfirke/janitor
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R Libraries Every Data Scientist Should Know - Pyoflife
I just stumbled across Janitor which can help you clean colum names easily.
IntRo
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Courses on R for Medical Research
I’ve prepared a 5-session introduction to R with the tidyverse, mainly focussing on the basics and data management (which is more important than stats when you start, IMO). Have a look https://github.com/andreamazzella/IntRo
What are some alternatives?
tidyverse - Easily install and load packages from the tidyverse
Data-science-best-resources - Carefully curated resource links for data science in one place
Practical-Applications-in-R-for-Psychologists - Lesson files for Practical Applications in R for Psychologists.
tidylog - Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.
desctable - An R package to produce descriptive and comparative tables
datapasta - On top of spaghetti, all covered in cheese....
tidytext - Text mining using tidy tools :sparkles::page_facing_up::sparkles:
parquetize - R package that allows to convert databases of different formats to parquet format
tidyquery - Query R data frames with SQL