best package r

R interface to the metricsgraphics JavaScript library for bare-bones line, scatterplot and bar charts. lme4/nlme - Linear and Non-linear mixed effects models, randomForest - Random forest methods from machine learning, multcomp - Tools for multiple comparison testing, vcd - Visualization tools and tests for categorical data, glmnet - Lasso and elastic-net regression methods with cross validation, caret - Tools for training regression and classification models. Base R handles most of these functions; but if you have huge files, these packages offer faster and standardized way to read CSVs and similar files into R. readr has been around for awhile; vroom is a speedier alternative, useful for larger data sets. Do you want to install a package from GitHub but can't remember the creator's name -- or just don't feel like typing it out? R wrapper for the powerful and flexible ECharts JavaScript library. CRAN. When you run render, R Markdown will replace the code with its results and then export your report as an HTML, pdf, or MS Word document, or a HTML or pdf slideshow. CRAN. The R programming language provides a huge list of different R packages, containing many tools and functions for statistics and data science.. Her book Practical R for Mass Communication and Journalism was published in December 2018. Just use the functions read.csv, read.table, and read.fwf. tidyverse - An opinionated collection of R packages designed for data science that share an underlying design philosophy, grammar, and data structures. read_excel("my-spreadsheet.xls", sheet = 1). CRAN.

CRAN. With githubinstall, simply run githubinstall("packagename") and the function will suggest an account; you just respond Y to install or n if it's the wrong one. d <- diamonds[sample(nrow(diamonds), 1000), ], R wrapper for the robust and well documented Highcharts JavaScript library, one of my favorite choices for presentation-quality interactive graphics. Many useful R function come in packages, free libraries of code written by R's active user community. This package offer an easy way to read in shape files and join data files with geographic info, as well as do some exploratory mapping. CRAN. This makes it easy to download numerical and geospatial info in R-ready format. The package lets you create your own geofacet visualizations using ggplot2 and built-in grids such as US states and EU countries.

An alternative to t-tests, producing posterior estimates for group means and standard deviations and their differences and effect sizes. Unsubscribe at any time. data.table - An alternative way to organize data sets for very, very fast operations.

R Markdown - The perfect workflow for reproducible reporting. Basic data cleaning made easy, such as finding duplicates by multiple columns, making R-friendly column names and removing empty columns. R can handle plain text files – no package required. tidymodels - A collection of packages for modeling and machine learning using tidyverse principles. If Shiny is too complex and involved for your needs, this package offers a simpler (if somewhat less robust) solution based on R Markdown. To install a package from CRAN, use the command install.packages("packagename") -- of course substituting the actual package name for packagename and putting it in quotation marks. Thanks for subscribing! For example, if you are usually working with data frames, probably you will have heard about dplyr or data.table, two of the most popular R packages. CRAN. GitHub. If you want to use data from a Google API in an R project and there's not yet a specific package for that API, this is the place to turn for authenticating CRAN. Part of JournalDev IT Services Private Limited. CRAN. A GIS game-changer for R. CRAN. dplyr - Essential shortcuts for subsetting, summarizing, rearranging, and joining together data sets. GitHub.

This RStudio add-in offers a drag-and-drop interface for ggplot2. CRAN. plot_layout() adds ability to set columns, rows, and relative sizes of each component graphic. See 4 data wrangling tasks in R for advanced beginners. Import and edit Microsoft Word and PowerPoint documents, making it easy to add R-generated analysis and visualizations to existing as well as new reports and presentations. See the, data import, data visualization, data analysis. Your email address will not be published. RMySQL, RPostgresSQL, RSQLite - If you'd like to read in data from a database, these packages are a good place to start. rgl - Interactive 3D visualizations with R. htmlwidgets - A fast way to build interactive (javascript based) visualizations with R. Packages that implement htmlwidgets include: googleVis - Let's you use Google Chart tools to visualize data in R. Google Chart tools used to be called Gapminder, the graphing software Hans Rosling made famous in hie TED talk.

This interface to the Google Maps Direction and Distance Matrix APIs let you analyze and map distances and driving routes. Yihui Xie & others (knitr), RStudio (rmarkdown).

R is preferred by several data scientists and statisticians for its beautifully formatted … Create a sortable, searchable table in one line of code with this R interface to the jQuery DataTables plug-in. CRAN. It also offers compression that doesn't slow data access too much, as well as the ability to import a specific range of rows (by row number). Super easy way to convert data between different R time-series data formats: xts, data frame, zoo, tsibble, and more. Plus some basic analysis functions. A must-have if you're interested in reproducible research and automating the journey from data analysis to report creation. If you've ever wanted to tear your hair out over an Excel file with merged cells, data in column headers, headers mixed in data, and key information in color coding, this is the package for you. CRAN. While I rarely need to create "geofacets" -- maps with same-sized blocks in geospatially appropriate locations -- this package is so cool that I had to include it. Seeks to find packages that had previously been installed on your system and need to be re-installed after upgrading R. CRAN. CRAN. R interface to the dimple JavaScript library with numerous customization options. qplot(factor(myfactor), data=mydf, geom="bar", fill=factor(myfactor)). We promise not to spam you.

How to reshape data with tidyr’s new pivot functions, How to add color to your ggplot2 text in R, Mozilla sees 'collateral damage' in DOJ antitrust fight with Google, Warning: Multiple Windows 10 retirements ahead, 8 fast fixes for common Chrome OS problems. While I often prefer dplyr, data.table has many fans for its speed with large data sets and concise syntax. This package is another that aims to solve one problem, and solve it well: package installation. Recent functionality adds support for simple features, interactive maps and creating leaflet objects. ggplot2. read_csv(myfile.csv) or vroom(myfile.csv), Hadley Wickham (readr), Jim Hester (vroom). CRAN. cSplit(mydata, "multi_val_column", sep = ",", direction = "long"). I don't use this package often for its main purpose of pulling down background map tiles, it's also useful for geocoding addresses with the Google Maps API with its geocode and mutate_geocode functions.

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