- #R studio library install#
- #R studio library drivers#
- #R studio library driver#
- #R studio library software#
A perfect way to explore data and share findings with non-programmers. Shiny - Easily make interactive, web apps with R. Glmnet - Lasso and elastic-net regression methods with cross validationĬaret - Tools for training regression and classification models To report results Vcd - Visualization tools and tests for categorical data Multcomp - Tools for multiple comparison testing RandomForest - Random forest methods from machine learning Lme4/ nlme - Linear and Non-linear mixed effects models This collection includes rsample, parsnip, recipes, broom, and many other general and specialized packages listed here.Ĭar - car's Anova function is popular for making type II and type III Anova tables. Tidymodels - A collection of packages for modeling and machine learning using tidyverse principles.
#R studio library software#
Google Chart tools used to be called Gapminder, the graphing software Hans Rosling made famous in hie TED talk. GoogleVis - Let's you use Google Chart tools to visualize data in R. Packages that implement htmlwidgets include: Htmlwidgets - A fast way to build interactive (javascript based) visualizations with R. Rgl - Interactive 3D visualizations with R Ggvis - Interactive, web based graphics built with the grammar of graphics. ggplot2 lets you use the grammar of graphics to build layered, customizable plots. Ggplot2 - R's famous package for making beautiful graphics. Lubridate - Tools that make working with dates and times easier. Stringr - Easy to learn tools for regular expressions and character strings. Use the gather and spread functions to convert your data into the tidy format, the layout R likes best. Tidyr - Tools for changing the layout of your data sets. dplyr is our go to package for fast data manipulation. This collection includes all the packages in this section, plus many more for data import, tidying, and visualization listed here.ĭplyr - Essential shortcuts for subsetting, summarizing, rearranging, and joining together data sets. Tidyverse - An opinionated collection of R packages designed for data science that share an underlying design philosophy, grammar, and data structures. If you have even more exotic data, consult the CRAN guide to data import and export.įor more information about using R with databases see db.
Just use the functions read.csv, read.table, and read.fwf. R can handle plain text files – no package required. Haven - Enables R to read and write data from SAS, SPSS, and Stata. csv's.įoreign - Want to read a SAS data set into R? Or an SPSS data set? Foreign provides functions that help you load data files from other programs into R. You can also just export your spreadsheets from Excel as. XLConnect, xlsx - These packages help you read and write Micorsoft Excel files from R. Choose the package that fits your type of database. RMySQL, RPostgresSQL, RSQLite - If you'd like to read in data from a database, these packages are a good place to start.
#R studio library drivers#
Note: RStudio professional products come with professional drivers for some of the most popular databases.
#R studio library driver#
Odbc - Use any ODBC driver with the odbc package to connect R to your database. Packages that connect R to databases depend on the DBI package. To load dataĭBI - The standard for for communication between R and relational database management systems. But you don't have to take our word for it, these packages are also some of the top most downloaded R packages. We've used each of these, and found them to be outstanding – we've even written some of them. To help you out, we've compiled this guide to some of the best. There are thousands of helpful R packages for you to use, but navigating them all can be a challenge. Once you have a package installed, you can make its contents available to use in your current R session by running library("") R will download the package from CRAN, so you'll need to be connected to the internet.
#R studio library install#
To install an R package, open an R session and type at the command line install.packages("") Many useful R function come in packages, free libraries of code written by R's active user community.
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