datapasta
On top of spaghetti, all covered in cheese.... (by MilesMcBain)
janitor
simple tools for data cleaning in R (by sfirke)
| datapasta | janitor | |
|---|---|---|
| 3 | 2 | |
| 884 | 1,341 | |
| - | - | |
| 0.0 | 6.2 | |
| about 2 years ago | 2 months ago | |
| R | R | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
datapasta
Posts with mentions or reviews of datapasta.
We have used some of these posts to build our list of alternatives
and similar projects.
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How many ways to munge data into R? Which is the quickest, or most effective, what is reproducable?
I think you are looking for datapasta package. https://github.com/MilesMcBain/datapasta
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List of Free Agents
If you’re planning on importing the data into R, you can skip Excel altogether. Paste it as a tribble with the datapasta package/add-on.
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Monthly to quarterly
Next time, please post a small, sample dataset. If you use datapasta, you can copy and paste a tribble verison of your data into your RStudio session.
janitor
Posts with mentions or reviews of janitor.
We have used some of these posts to build our list of alternatives
and similar projects.
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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?
When comparing datapasta and janitor you can also consider the following projects:
dplyr - dplyr: A grammar of data manipulation
tidyverse - Easily install and load packages from the tidyverse
esquisse - RStudio add-in to make plots interactively with ggplot2
IntRo - Introduction to R for health data
excel.link - Convenient Data Exchange between R and Microsoft Excel
Practical-Applications-in-R-for-Psychologists - Lesson files for Practical Applications in R for Psychologists.
xlsx - An R package to interact with Excel files using the Apache POI java library
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.