tidyr
There are 3 repositories under tidyr topic.
elbersb / 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.
Language:R 572jrnold / r4ds-exercise-solutions
Exercise solutions to "R for Data Science"
Language:R 315- Language:Python 255
- datawizard
easystats / datawizard
Magic potions to clean and transform your data 🧙
Language:R 184 stemangiola / tidyseurat
Seurat meets tidyverse. The best of both worlds.
Language:R 142nischalshrestha / Unravel
A fluent code explorer for R. 🔍
Language:R 100- spotify-artists-analysis
khanhnamle1994 / spotify-artists-analysis
An exploratory data analysis and data visualization project using data from Spotify Web API
Language:R 69 ddotta / cookbook-rpolars
Cookbook to provide solutions to common tasks and problems in using Polars with R
Language:CSS 46data-datum / learning_R
List of resources for learning R
Brings SingleCellExperiment objects to the tidyverse
Language:R 34- Language:Jupyter Notebook 15
moriahtaylor1 / tidy-tuesday
#TidyTuesday is a weekly social data project in R which encourages participants to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem
Language:HTML 13cosmoduende / r-netflix-data-analysis
Análisis y visualización de datos con R de historial de actividad en Netflix de una cuenta personal. Visualización de maratones de series más vistas y frecuencia de actividad por días, meses y años
Language:R 9ivelasq / 2023-03-22_whats-new-in-the-tidyverse
2023-03-22 R-Ladies Rome Presentation
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Satyam-Bhalla / Cheatsheets
This repo contains all the cheatsheets that I found Important.
TrainingByPackt / Applied-Supervised-Learning-with-R
Use machine learning libraries of R to build models that solve problems and predict business trends
Language:Jupyter Notebook 9carpentries-incubator / open-science-with-r
Carpentry-style lesson on how to use R, RStudio together with git & Github to promote Open Science practices.
Language:Python 8- Language:R 6
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- Intro2Bioinfo_course
pesteller / Intro2Bioinfo_course
Crash course for 1st Year PhD students on the basics of bioinformatics (July 2022).
Language:HTML 5 - Language:R 4
nicolasfguillaume / Exploratory-Data-Analysis-with-R
Assignments of the "Data Analysis with R" course from Udacity.com. Learning gglot2, tidyr and dplyr
- Language:R 3
- election-predictions
ianjeffries / election-predictions
Predicting election results by county for the 2016 US general election with machine learning in R.
DanteSc03 / Time-Spent
Statistics project in R about time spent, relating data to current and past issues. Our data source is the OWID website where we collected data from the data tables.
Language:R 2Drev917 / DEN-MLR-Model
Multiple linear regression models to predict non-airline revenues at DEN airport in R.
e-paj / Machine-Learning-Projects-in-R
This repository contains all the projects/case studies done using Machine Learning methods. This is in conjunction with another repository. Difference being that R would be the main software used here
Language:R 2- tidytuesday_projects
leepingtay / tidytuesday_projects
My data exploration and visualization projects for R Tidytuesday weekly community event.
Language:R 2 meerapatelmd / rubix
R package with wrapper functions of the Tidyverse package suite.
Language:R 2RCJansonVTFL / Text-Analytics-with-Congressional-Speeches
I use various techniques for analyzing the Stanford Congressional Records. Specifically, we will be looking at
Language:R 2simonschoe / introduction-to-the-tidyverse
This lecture is part of the "Machine Learning in R" graduate course held at University of Münster, School of Business and Economics (winter term 2021/22). :mortar_board:
Language:HTML 2tboudart / Financial-Markets-Regression-Analysis
My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.
- Data-Visualisation-Designs
adam-dalbello / Data-Visualisation-Designs
Analyses and advanced visualisations
Language:R 1 AmirMotefaker / Sentiment-Analysis-Model-using-R
build a sentiment analysis model which will allow us to categorize words based on their sentiments, that is whether they are positive, negative and also the magnitude of it.
Language:R 1mikael-ros / wiresharkR
A set of scripts to process stacked IO graphs for Wireshark data
Language:R 1
