Installing R Packages from the CRAN Repository: Alternative Method For the full information about the usage of the install.packages() function, including all its possible parameters and their detailed descriptions, check the R Documentation page. However, this function has many optional parameters that can be useful in some cases (and we'll see some examples soon).
This is usually enough for the majority of cases. In both examples above, we passed in only one argument to the install.packages() function, which is actually the value of the pkgs parameter. Selecting the necessary topic, we'll open the page with all the R packages relevant to that category, as well as an exhaustive description of usage for each of them. Here, we find a convenient list of task categories (topics) and a brief description of each. If we're unsure which R module to use for a certain data science task, we can visit the CRAN Task Views page. In this case, we need to apply first the c() function to create a character vector containing all the desired packages as its items: install.packages(c('readr', 'ggplot2', 'tidyr'))Ībove, we've installed three R packages: the already-familiar readr, ggplot2 (for data visualization), and tidyr (for data cleaning). We can use the same function to install several R packages at once. Make sure that the name of the package is in quotation marks. Here, we've installed the readr R package used for reading data from the files of different types: comma-separated values (CSV), tab-separated values (TSV), fixed-width files, etc. To install an R package from CRAN, we can use the install.packages() function: install.packages('readr') Most often, you'll use this repository to install various R packages. The Comprehensive R Archive Network ( CRAN) repository stores thousands of stable R packages designed for a variety of data-related tasks. Installing R Packages from the CRAN Repository To start working with the packages in R, we first need to install them.
This popularity is because R offers an impressive choice of data science-oriented packages, which are collections of methods for implementing specific functionality not included in basic R. R is a programming language for statistical computing, especially efficient for performing data science tasks. But before we can put its packages to work, we need to install them. R is a powerhouse programming language with many data science applications.
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