Make Friends with R and RStudio

Author

Jihong Zhang

Make Friends with R and RStudio

Welcome to your first steps in applied multivariate statistics! In this tutorial, you will learn how to use R and RStudio to run code, work with data, create plots, and support reproducible analyses.

1. What Are R and RStudio?

  • R (required): A powerful programming language for statistical computing and graphics.
  • RStudio: An integrated development environment (IDE) that provides an editor, Console, object viewer, plot viewer, package tools, and help system for R. Download RStudio Desktop.

Install R before installing RStudio. R performs the computations; RStudio provides the interface for working with R.

2. Getting Started with RStudio

RStudio is designed specifically for R programming and organizes the tools needed for an analysis in one interface.

Key Features of RStudio

  • R-focused design: Built specifically for R programming and statistical analysis
  • Four-panel layout: Organized interface with Source, Console, Environment, and Files/Plots panels
  • Package management: Easy package installation and loading through GUI
  • Project management: RStudio Projects for organized, reproducible workflows
  • Git integration: Built-in version control support
  • Script editor: Write, save, and rerun analysis code in .R files

RStudio Interface Layout

  • Source Editor (top-left): Write and edit .R scripts with syntax highlighting
  • Console (bottom-left): Interactive R console for running commands
  • Environment/History (top-right): View objects, variables, and command history
  • Files/Plots/Packages/Help (bottom-right): Navigate files, view plots, manage packages, access help

RStudio interface with red labels identifying the Source pane at top left, Console at bottom left, Environment at top right, and Output at bottom right.

The RStudio interface labeled with its Source, Console, Environment, and Output panes.

Screenshot source: Posit RStudio User Guide: Pane Layout.

Getting Started with RStudio

  1. Download: Get RStudio Desktop from posit.co/download/rstudio-desktop/
  2. Create projects: Use File → New Project for organized workflows
  3. Customize layout: Tools → Global Options → Pane Layout to adjust panels
  4. Install packages: Use Tools → Install Packages or the Packages panel

Essential RStudio Features

  • Code completion: Tab completion for functions and variables
  • Help integration: F1 on functions for instant help
  • Object inspector: Click objects in Environment to view details
  • Plot history: Navigate through previous plots in Plots panel
  • Addins: Extend functionality with community-developed tools

RStudio Keyboard Shortcuts

  • Run code: Ctrl+Enter (Cmd+Enter on Mac)
  • New R script: Ctrl+Shift+N (Cmd+Shift+N)
  • Save the current script: Ctrl+S (Cmd+S)
  • Source the current script: Ctrl+Shift+Enter (Cmd+Shift+Enter)
  • Go to line: Ctrl+G (Cmd+G)

A Basic RStudio Workflow

  • Create or open an RStudio Project for the analysis.
  • Write commands in an .R script rather than relying only on the Console.
  • Run one line or a selected block with Ctrl+Enter (Cmd+Enter on Mac).
  • Inspect data and model objects in the Environment pane.
  • Review figures in the Plots pane and documentation in the Help pane.
  • Save the script so the analysis can be rerun and checked.

3. Running R Code in RStudio

You can run R commands directly in the Console, but an .R script creates a record that you can revise and rerun. Enter the following commands in a script, then run each line with Ctrl+Enter (Cmd+Enter on Mac):

RStudio Source pane showing an R script containing View(mtcars), with the Run and Source buttons visible in the toolbar.

Use the Run button or Ctrl+Enter to execute the current line from an R script.

Screenshot source: Posit RStudio User Guide: Executing Code.

1 + 1
[1] 2
mean(c(1, 2, 3))
[1] 2
print("Hello, world!")

4. Install and Load Packages

  • Use install.packages() once per machine; load each time with library().
install.packages(c("tidyverse", "readr", "ggplot2", "here"))
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(here)
here() starts at /Users/jihong/Documents/Projects/website-jihong

You can also select Install in the Packages pane, enter the package names, and select Install in the dialog.

RStudio Install Packages dialog with tidyverse entered as the package name and the Install dependencies option selected.

Installing the tidyverse package through RStudio’s Install Packages dialog.

Screenshot source: Posit RStudio User Guide: Packages Pane.

5. Importing Data

  • Prefer readr::read_csv() for CSV; read.csv() is the base R alternative.

Task: Import a CSV with RStudio

  1. Select File → Import Dataset → From Text (readr) or use Import Dataset in the Environment pane.
  2. Select heights.csv and inspect the data preview, variable names, and inferred data types.
  3. Review the Code Preview, then select Import.
  4. Copy the generated import code into your .R script so the step is reproducible.

RStudio Import Text Data wizard showing a CSV URL, a preview of penguin data, import options, and generated readr code in the Code Preview area.

RStudio’s Import Text Data wizard previews the data, import settings, and generated R code.

Screenshot source: Posit RStudio User Guide: Local Data.

Task: Import the Same File with Code

Create a folder called data in your project folder, download heights.csv into it, and run one of the following commands:

# Read CSV with readr
height_data <- readr::read_csv(here::here("data", "heights.csv"))

# Base R alternative
height_data_base <- read.csv(here::here("data", "heights.csv"))

6. Inspecting Data in the Data Viewer

Use View() or select a data-frame object in the Environment pane to open RStudio’s spreadsheet-like Data Viewer.

mpg_data <- ggplot2::mpg |>
  dplyr::select(manufacturer, model, displ, cty, hwy)

View(mpg_data)

In the Data Viewer, try these tasks:

  1. Select a column name to sort the rows.
  2. Select Filter to define column-specific filters.
  3. Use the search box to find matching values across columns.
  4. Return to the script before changing the data.

RStudio Data Viewer showing manufacturer, model, engine displacement, city mileage, and highway mileage columns with Filter and Search controls.

The RStudio Data Viewer displaying selected variables from the mpg data.

Sorting and filtering in the Data Viewer help you inspect the data, but they do not document a reproducible transformation. Record substantive data changes in the R script.

Screenshot source: Posit RStudio User Guide: Data Viewer.

7. Exporting Data

  • Save data to disk using write_csv() or write.csv().
readr::write_csv(
  height_data,
  here::here("outputs", "clean-height-data.csv")
)

write.csv(
  height_data_base,
  here::here("outputs", "clean-height-data-base.csv"),
  row.names = FALSE
)

8. Basic Data Wrangling with dplyr

  • Core verbs: select(), filter(), mutate(), summarize(), group_by().
library(dplyr)

mtcars_summary <- mtcars |>
  group_by(cyl) |>
  summarize(mean_mpg = mean(mpg), .groups = "drop")

head(mtcars_summary)
# A tibble: 3 × 2
    cyl mean_mpg
  <dbl>    <dbl>
1     4     26.7
2     6     19.7
3     8     15.1

9. Basic Plot with ggplot2

  • Create a scatterplot and map aesthetics.
library(ggplot2)

mpg_plot <- ggplot(
  mtcars,
  aes(x = wt, y = mpg, color = factor(cyl))
) +
  geom_point(size = 2) +
  labs(color = "Cylinders", x = "Weight", y = "MPG")

mpg_plot

Task: Save the Plot Reproducibly

The Export button in the Plots pane can save a figure interactively. Recording ggsave() in the script also preserves the file name and dimensions.

dir.create(here::here("outputs"), showWarnings = FALSE)

ggplot2::ggsave(
  filename = here::here("outputs", "mtcars-scatterplot.png"),
  plot = mpg_plot,
  width = 6,
  height = 4
)

Workflow source: Posit RStudio User Guide: Get Started.

10. Working Directories and Projects

  • Use RStudio Projects and here::here() for reliable paths.
getwd()
[1] "/Users/jihong/Documents/Projects/website-jihong/teaching/2024-07-21-applied-multivariate-statistics-esrm64503/Lecture01"
here::here()
[1] "/Users/jihong/Documents/Projects/website-jihong"

Use File → New Project to start in a new directory, organize an existing directory, or obtain a project from version control.

RStudio New Project Wizard offering New Directory, Existing Directory, and Version Control as three project-creation choices.

The New Project Wizard in RStudio.

Screenshot source: Posit RStudio User Guide: RStudio Projects.

11. Reproducibility

  • Record your session details for reproducibility.
sessionInfo()
R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.5.2

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0  
 [5] dplyr_1.2.0     purrr_1.2.1     readr_2.2.0     tidyr_1.3.2    
 [9] tibble_3.3.1    ggplot2_4.0.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] Matrix_1.7-4       gtable_0.3.6       jsonlite_2.0.0     compiler_4.5.2    
 [5] tidyselect_1.2.1   Rcpp_1.1.1         unigd_0.2.0        systemfonts_1.3.1 
 [9] scales_1.4.0       png_0.1-8          yaml_2.3.12        fastmap_1.2.0     
[13] reticulate_1.45.0  lattice_0.22-9     R6_2.6.1           labeling_0.4.3    
[17] generics_0.1.4     knitr_1.51         htmlwidgets_1.6.4  rprojroot_2.1.1   
[21] tzdb_0.5.0         pillar_1.11.1      RColorBrewer_1.1-3 rlang_1.1.7       
[25] stringi_1.8.7      xfun_0.56          S7_0.2.1           otel_0.2.0        
[29] timechange_0.4.0   cli_3.6.5          withr_3.0.2        magrittr_2.0.4    
[33] digest_0.6.39      grid_4.5.2         hms_1.1.4          lifecycle_1.0.5   
[37] vctrs_0.7.1        evaluate_1.0.5     glue_1.8.0         farver_2.1.2      
[41] httpgd_2.0.4       rmarkdown_2.30     tools_4.5.2        pkgconfig_2.0.3   
[45] htmltools_0.5.9

12. Getting Help

Use ?function_name or help("function_name") to open documentation in the Help pane. Use example() to run a function’s documented examples.

?mean
help("mean")
example(mean)

RStudio Console on the left showing paste0 output and Help pane on the right displaying examples for the base paste function.

RStudio’s Console and Help pane displaying an executed example and function documentation.

Screenshot source: Posit RStudio User Guide: Pane Layout and Help.

13. Next Steps

  • Explore the tidyverse (readr, dplyr, tidyr, ggplot2)
  • Organize each analysis in an RStudio Project
  • Save your commands in .R scripts so you can rerun and inspect them
  • Practice by importing a dataset, cleaning it, summarizing, and plotting
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