validate
janitor
| validate | janitor | |
|---|---|---|
| 1 | 2 | |
| 401 | 1,341 | |
| 0.5% | - | |
| 3.7 | 6.2 | |
| 12 days ago | 2 months ago | |
| R | R | |
| - | GNU General Public License v3.0 or later |
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validate
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How to verify your data?
To me it sounds as if you want to test your data in between steps or at the end. Two tools come to mind: https://docs.ropensci.org/assertr/ and https://github.com/data-cleaning/validate
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.
What are some alternatives?
tidyverse - Easily install and load packages from the tidyverse
IntRo - Introduction to R for health data
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.
datapasta - On top of spaghetti, all covered in cheese....
desctable - An R package to produce descriptive and comparative tables
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