Practical-Applications-in-R-for-Psychologists
janitor
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| Practical-Applications-in-R-for-Psychologists | janitor | |
|---|---|---|
| 1 | 2 | |
| 125 | 1,341 | |
| - | - | |
| 5.5 | 6.2 | |
| 7 months ago | about 2 months ago | |
| R | R | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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Practical-Applications-in-R-for-Psychologists
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Book/resources recommendations for doctoral-level stats/data-analysis in Psychology
https://quantpsych.net/web-applications/ https://jkkweb.sitehost.iu.edu/KruschkeFreqAndBayesAppTutorial.html https://rpsychologist.com/viz https://www.sas.upenn.edu/~baron/from_cattell/rpsych/rpsych.html https://ladal.edu.au/tutorials.html https://bookdown.org/paul/computational_social_science/ https://github.com/mattansb/Practical-Applications-in-R-for-Psychologists https://github.com/seanchrismurphy/A-Psychologists-Guide-to-R https://cu-psych-computing.github.io/cu-psych-comp-tutorial/tutorials/r-extra/accelerated-ggplot2/ggplot_summer2018_part2/#1-overview https://www.andrewheiss.com/blog/2022/05/20/marginalia/ https://www.statisticshowto.com/ https://benwhalley.github.io/just-enough-r/ https://vasishth.github.io/Freq_CogSci/ https://uoepsy.github.io/ https://pittmethods.github.io/r4ss/ https://www.sas.upenn.edu/~baron/from_cattell/rpsych/rpsych.html
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?
easystats - :milky_way: The R easystats-project
tidyverse - Easily install and load packages from the tidyverse
desctable - An R package to produce descriptive and comparative tables
IntRo - Introduction to R for health data
report - :scroll: :tada: Automated reporting of objects in R
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.
TidyTuesday - My contributions to the #TidyTuesday challenge, a weekly data visualization challenge. All plots are 💯 created in R with ggplot2.
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