Getting started with R

February 19, 2018
By

[This article was first published on R – William E. J. Doane PhD, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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Download and install R. Download and install RStudio. Read R for Data Science.

R provides the backend: the programming language specification and the interpreter.

RStudio provides the frontend: the user interface that allows you to interact with R, visualize data, and manage the files associated with your analyses.

R for Data Science introduces you to the tidyverse way of programming. There are basically methods of programming in R: “base R”, which has been around since the R language was first conceived (and before, since R is itself based on the S language), and the tidyverse, a newer approach that focuses on leveraging a consistent structure to your data and developing a grammar for data ingest, data wrangling, data visualization, and data storage.

Base R tends to be dense in meaning where the Tidyverse tends to be consistent and to breakdown complex processes into a set of discrete steps:

base R Tidyverse
mtcars[2, "cyl"] library(tidyverse)
mtcars %>%
select(cyl) %>%
slice(2)
mtcars[mtcars$cyl == 4, c("hp", "mpg")] library(tidyverse)
mtcars %>%
filter(cyl == 4) %>%
select(hp, mpg)

 

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