Topic: tidyr Goto Github
Some thing interesting about tidyr
Some thing interesting about tidyr
tidyr,Analyses and advanced visualisations
User: adam-dalbello
tidyr,GIS and R Project: Crime Analysis in Chapel Hill, North Carolina for 2018 to 2020
User: akunna1
tidyr,Carpentry-style lesson on how to use R, RStudio together with git & Github to promote Open Science practices.
Organization: carpentries-incubator
Home Page: https://carpentries-incubator.github.io/open-science-with-r/
tidyr,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
User: cosmoduende
tidyr,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.
User: dantesc03
tidyr,List of resources for learning R
User: data-datum
tidyr,Cookbook to provide solutions to common tasks and problems in using Polars with R
User: ddotta
Home Page: https://ddotta.github.io/cookbook-rpolars/
tidyr,Short programming tutorials pertaining to data analysis.
User: diyadas
tidyr,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
User: e-paj
tidyr,Magic potions to clean and transform your data 🧙
Organization: easystats
Home Page: https://easystats.github.io/datawizard/
tidyr,Collocates retriever and Collocation association measure
User: gederajeg
Home Page: https://gederajeg.github.io/collogetr/
tidyr,Predicting election results by county for the 2016 US general election with machine learning in R.
User: ijeffries
tidyr,Using R and text mining to mine sentiment from over 21,000 hotel reviews on resorts located in the Republic of Maldives.
User: ijeffries
tidyr,Analyzes the portrayal and themes of anime in NYT articles (1981-2023) using Semi-Supervised Topic Modeling to explore trends and diversification of media coverage over time.
User: jannik-hoffmann
tidyr,Course materials for CDS 101: Introduction to Computational and Data Sciences, offered at George Mason University
User: jkglasbrenner
Home Page: https://archive2019.cds101.com
tidyr,Exercise solutions to "R for Data Science"
User: jrnold
Home Page: https://jrnold.github.io/r4ds-exercise-solutions
tidyr,日本社会心理学会 第5回 春の方法論セミナー 「RとRStudio入門」用のリポジトリ。
User: kazutan
Home Page: https://kazutan.github.io/JSSP2018_spring/
tidyr,An exploratory data analysis and data visualization project using data from Spotify Web API
User: khanhnamle1994
tidyr,My data exploration and visualization projects for R Tidytuesday weekly community event.
User: leepingtay
tidyr,Extensions and extras for tidy processing.
User: m-clark
Home Page: https://m-clark.github.io/tidyext/
tidyr,R package with wrapper functions of the Tidyverse package suite.
User: meerapatelmd
Home Page: http://meerapatelmd.github.io/rubix
tidyr,A set of scripts to process stacked IO graphs for Wireshark data
User: mikael-ros
tidyr,#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
User: moriahtaylor1
tidyr,패스트캠퍼스 중급R 3기 수업자료
User: mrchypark
Home Page: https://mrchypark.github.io/dabrp_classnote3/#1
tidyr,Assignments of the "Data Analysis with R" course from Udacity.com. Learning gglot2, tidyr and dplyr
User: nicolasfguillaume
tidyr,A fluent code explorer for R. 🔍
User: nischalshrestha
tidyr,Data Wrangling with dplyr and tidyr - An introduction -- R Lunch 10.04.18 @ UniGe
User: pafonta
tidyr,Exploratory Data Analysis of Resume Names Dataset using R visualization packages
User: prathibha13
tidyr,I use various techniques for analyzing the Stanford Congressional Records. Specifically, we will be looking at
User: rcjansonvtfl
tidyr,In this project, an analysis of the investment process of the investor will be carried out. Data exploration, Data manipulation, Analysis of the investment process, Analyze the time until the first investment and Invest retention analysis
User: robyriyanto
tidyr,Slides and code presented in the second meetup of R-Ladies Frankfurt
User: sandrapintor
tidyr,Data Visualisation using R
User: sarathchandrikak
tidyr,This repo contains all the cheatsheets that I found Important.
User: satyam-bhalla
tidyr,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:
User: simonschoe
Home Page: https://simonschoe.github.io/introduction-to-the-tidyverse/
tidyr,Seurat meets tidyverse. The best of both worlds.
User: stemangiola
Home Page: https://stemangiola.github.io/tidyseurat/
tidyr,Brings SingleCellExperiment objects to the tidyverse
User: stemangiola
Home Page: https://stemangiola.github.io/tidySingleCellExperiment/index.html
tidyr,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.
User: tboudart
tidyr,Use machine learning libraries of R to build models that solve problems and predict business trends
Organization: trainingbypackt
tidyr,Fake News analysis and prediction in R Script. Naive Bayes, Random Forest, SVM, NNET, ROC, Confusion Matrix, Accuracy, F1 score.
User: trajceskijovan
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