Introduction to R and RStudio

R
RStudio
Posit
Metaphors with Data and Geometry
Author

Arvind V.

Published

November 14, 2022

Modified

September 25, 2024

Abstract
How to use R and RStudio

Goals

At the end of this Lab, we will:

  • have installed R and RStudio on our machines
  • understood how to add additional R-packages for specific features and graphic capability
  • run code within RStudio and interpret the results
  • have learnt to look for help within R and RStudio
  • learnt to use Quarto in R, which a document format for reproducible report generation

Introduction to R and RStudio

This guide will lead you through the steps to install and use R, a free and open-source software environment for statistical computing and graphics.

What is R?

  • R is the name of the programming language itself, based off S from Bell Labs, which users access through a command-line interpreter (>)

What is RStudio?

  • RStudio is a powerful and convenient user interface that allows you to access the R programming language along with a lot of other bells and whistles that enhance functionality (and sanity).

Install R

Install R from CRAN, the Comprehensive R Archive Network. Please choose a precompiled binary distribution for your operating system.

Check in

Launch R by clicking this logo in your Applications. You should see one console with a command line interpreter. Try typing 2 + 2 and check !

Close R.

Install RStudio

Install the free, open-source edition of RStudio: https://posit.co/download/rstudio-desktop/

RStudio provides a powerful user interface for R, called an integrated development environment. RStudio includes:

  • a console (the standard command line interface: >),
  • a syntax-highlighting editor that supports direct code execution, and
  • tools for plotting, history, debugging and work space management.

Check in

Launch RStudio.You should get a window similar to the screenshot you see here:

RStudio Default Window

RStudio Default Window

but yours will be empty. Look at the bottom left pane: this is the same console window you saw when you opened R in step Section 3.1.

  • Place your cursor where you see > and type x <- 2 + 2 again hit enter or return, then type x, and hit enter/return again.
  • If [1] 4 prints to the screen, you have successfully installed R and RStudio, and you can move onto installing packages.

Installation Slides

View slides in full screen

Install packages

The version of R that you just downloaded is considered base R, which provides you with good but basic statistical computing and graphics powers. For analytical and graphical super-powers, you’ll need to install add-on packages, which are user-written, to extend/expand your R capabilities. Packages can live in one of two places:

  • They may be carefully curated by CRAN (which involves a thorough submission and review process), and thus are easy install using install.packages("name_of_package", dependencies = TRUE) in your CONSOLE.

  • Personal repositories of packages created by practitioners, which are usually in Github.

Place your cursor in the CONSOLE again (where you last typed x and [4] printed on the screen). You can use the first method to install the following packages directly from CRAN, all of which we will use:

Type these commands in your CONSOLE:

install.packages("knitr")
install.packages("tidyverse")
install.packages("ggformula")
install.packages("babynames")
Installation and Usage of R Packages!

  • To install a package, you put the name of the package in quotes as in install.packages("name_of_package"). Mind your use of quotes carefully with packages.

  • To use an already installed package, you must load it first, as in library(name_of_package), leaving the name of the package bare. You only need to do this once per RStudio session.

  • If you want help, no quotes are needed: help(name_of_package) or ?name_of_package.
  • If you want the citation for a package (and you should give credit where credit is due), ask R as in citation("name_of_package").

Using Quarto

We will get acquainted with the Quarto Document format, which allows us to mix text narrative, code, code-developed figures and items from the web in a seamless document. Quarto can be used to generate multiple formats such as HTML, Word, PDF from the same text/code file.

Something that can:

  • provide a visualization
  • provide insight
  • tell a story
  • is reproducible
  • be a call to action or a recommendation
  • impress colleagues, bosses, and faculty

Setting up Quarto

Quarto is already installed along with RStudio!! We can check if all is in order by running a check in the Terminal in RStudio.

The commands are:

  1. quarto check install
  2. quarto check knitr

If these come out with no errors then we are ready to fire up our first Quarto document.

Practice

Let us now create a brand new Quarto document, create some graphs in R and add some narrative text and see how we can generate our first report!

  1. Fire up a new Quarto document by going to: File -> New File -> Quarto Document.
  2. Give a title to your document ( “My First Quarto Document”, for example.
  3. Change the author name to your own! Keep HTML as your output format
  4. Switch to Visual mode, if it is not already there. Use the visual mode tool bar.

  1. Click on the various buttons to see what happens. Try to create Sections, code chunks, embedding images and tables.
Add Anything Shortcut

Try the “add anything” shortcut! Type “/” anywhere in your Quarto Doc, while in Visual Mode, and choose what you want to add from the drop-down menu!

  1. Create a code chunk as shown below. You can either use the visual tool bar to create it, or simply hit the copy button in the code chunk display on this website and paste the results into your Quarto document. Check every step!
```{r}
#| label: setup

# library(knitr) # to use this….document! More later!!
library(tidyverse) # Data Management and Plotting!!
library(babynames) # A package containing, yes, Baby Names
library(ggformula)
```

  1. Hit the green “play” button to run this “setup” chunk to include in your R session all the installed packages you need.

  2. Let us greet our data first !!

```{r}
#| label: babynames-data
glimpse(babynames)
head(babynames)
tail(babynames)
names(babynames)
```
Rows: 1,924,665
Columns: 5
$ year <dbl> 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880,…
$ sex  <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", …
$ name <chr> "Mary", "Anna", "Emma", "Elizabeth", "Minnie", "Margaret", "Ida",…
$ n    <int> 7065, 2604, 2003, 1939, 1746, 1578, 1472, 1414, 1320, 1288, 1258,…
$ prop <dbl> 0.07238359, 0.02667896, 0.02052149, 0.01986579, 0.01788843, 0.016…
[1] "year" "sex"  "name" "n"    "prop"
  1. If you have done the above and produced sane-looking output, you are ready for the next bit. Use the code below to create a new data frame called my_name_data.
```{r}
#| label: manipulate-name-data
my_name_data <- babynames %>%
  filter(name == "Arvind" | name == "Aravind") %>%
  filter(sex == "M")
```
  • The first bit makes a new dataset called my_name_data that is a copy of the babynames dataset
  • the %>% (pipe) tells you we are doing some other stuff to it later.1
  • The second bit filters our babynames to only keep rows where the name is either Arvind or Aravind (read | as “or”.)
  • The third bit applies another filter to keep only those where sex is male.

Let’s check out the data.

```{r}
my_name_data
glimpse(my_name_data)
```
Rows: 61
Columns: 5
$ year <dbl> 1970, 1972, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983,…
$ sex  <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", …
$ name <chr> "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvi…
$ n    <int> 5, 8, 7, 5, 9, 6, 7, 6, 8, 6, 7, 7, 7, 13, 8, 11, 6, 8, 12, 10, 1…
$ prop <dbl> 2.620e-06, 4.780e-06, 4.310e-06, 3.060e-06, 5.260e-06, 3.510e-06,…
  1. Again, if you have sane-looking output here, move along to plotting the data!
```{r}
#| label:  plot-name-data

plot <- gf_line(prop ~ year,
  color = ~name,
  data = my_name_data
)
```

Now if you did this right, you will not see your plot!

  1. Because we saved the ggplot with a name (plot), R just saved the object for you. But check out the top right pane in RStudio again: under the Environment pane you should see plot, so it is there, you just have to ask for it. Here’s how:
```{r}
plot
```

  1. Now hit the Render button on your Visual toolbar and see what happens!! Try to use the drop down menu next to it and see if there are more output file options!!

Make a new name plot!

  1. Edit my code above to create a new dataset. Pick 2 names to compare how popular they each are (these could be different spellings of your own name, like I did, but you can choose any 2 names that are present in the dataset), and create a new data object with a new name.

  2. Write narratives comments wherever suitable in your Quarto document. Make sure you don’t type inside your code chunks. See if you can write your comments in sections which you can create with your visual tool bar, or by using the “add anything” shortcut.

  3. Save your work ( your Quarto document itself) so you can share your favorite plot.

  4. Share your Plot: You will not like the looks of your plot if you mouse over to Export and save it. Instead, use ggplot2’s command for saving a plot with sensible defaults.

Type help(ggsave) in your Console.

```{r}
#| label: Saving

ggsave("file_name_here.pdf", plot) # please make the filename unique!
```

Conclusions

We have installed R, RStudio and created our Quarto document, complete with graphs and narrative text. We also rendered our Quarto doc into HTML and other formats!

References

  1. https://www.markdowntutorial.com

  2. https://ysc-rmarkdown.netlify.app/slides/01-basics.html Nice RMarkdown presentation and “code movies” !

  3. https://rmarkdown.rstudio.com/index.html

  4. Samantha Csik. Customizing Quarto websites. https://ucsb-meds.github.io/customizing-quarto-websites/#/title-slide

  5. Reproducible Reporting with Quarto. https://book.rwithoutstatistics.com/quarto-chapter

  6. Thomas Mock.(). Quarto in Two Hours https://jthomasmock.github.io/quarto-in-two-hours/

  7. https://quarto.org/docs/get-started/hello/rstudio.html

  8. https://quarto.org/docs/authoring/markdown-basics.html How to do more with Quarto HTML format

  9. https://apps.machlis.com/shiny/quartotips/

Assignment(s)

  1. Complete the markdown tutorial in [reference 1]
  2. Look through the Slides in [reference 2]
  3. Create a fresh Quarto document and use as many as possible of the RMarkdown constructs from the Cheatsheet [reference 1]

Readings

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Footnotes

  1. Insert the pipe character using the keyboard shortcutCTRL + SHIFT + M.↩︎