R and RStudio: the basics

installing R and RStudio

If you haven’t installed R and RStudio yet, please download and install them from here:

• https://www.r-project.org/
• https://posit.co/

installing R and RStudio

installing R and RStudio

installing R and RStudio

installing R and RStudio

let’s start with RStudio

RStudio

first let’s talk about what the different panes are.

What to notice: You should see four panes, and notice that there’s a file-menu-bar (like File, Edit, etc., and Help)

source pane

This is where you’ll write your scripts and code that you save.

This is where you’ll edit text based files in general.

You should try to use this pane a lot, because in general that means you’re leaning towards saving your work.

console pane

This is the console pane; This is where R commands are run and their text-based output is shown.

You’ll see that there are some friendly messages on startup about which version of R you’re running, your default settings, etc. Do read those and think about them since they’re put there to help you.

The bit about the ABSOLUTELY NO WARRANTY is really just saying you can’t sue the developers of R for giving out a free software program if it has a bug. The R community in general is very good about resolving bugs, so you shouldn’t let that alarm you.

Notice that this pane has a few more tabs to it, including the Terminal and Background Jobs. The Terminal tab is very close to the Terminal app on your computer if you’re running Mac OS or Linux. The Terminal is very handy to have closeby for programmers since it allows for very precise file manipulations and software operations.

environment pane

This pane is where you’ll be able to see what variables you have loaded in your current R session.

Notice that there’s also History, Connections, and a Tutorial pane.

The History pane will keep a history of your commands you’ve run, and the Connections pane is there in case you want to connect to online databases.

files pane

This is the files pane; here you can navigate your computers’ folders, open files, rename files, move files, delete files, etc.

Notice that this quadrant also has Plots, Packages, Help, Viewer, and Presentation tabs.

writing code

run button

the result of running that line

we can run more code

File → Save As

the print command

navigating using the file pane

navigating using the file pane

notice the path to the folder shown

pay attention to your working directory

what is a working directory?

the working directory is the default location where R will look for files you want to load and where it will save files.

example: let’s say your working directory is C:/Users/me/Documents/ and you instruct R to load file.txt — then R will look for file.txt in C:/Users/me/Documents/. you can also use full paths, so you can tell R that instead it should load C:/Users/me/Documents/specific_folder/file.txt.

the difference between file.txt vs. C:/Users/me/Documents/specific_folder/file.txt is that the first is a relative path (relative to the working directory) and the second is an absolute path.

you can think of the working directory as “what folder does R have open right now?”

you can set the working directory through commands

some helpful settings

highly suggested settings to disable

dark mode / other themes

dark mode / other themes

keyboard shortcuts

keyboard shortcuts

keyboard shortcuts

RStudio IDE cheatsheet

RStudio IDE cheatsheet

basics of R

Generally we would recommend that you run all your R code in RStudio

we’ll generally try to go in order of importance for this class

arithmetic

The usual arithmetic operators you’re familiar with are available to you

5 + 4  # evaluates to: 9
5 - 4  # evaluates to: 1
5 / 2  # => 2.5
5 * 2  # => 10
5^2    # => 25

arithmetic functions

• logarithms and exponentials: log2(x), log10(x), log(x), exp(x)
• trigonometric functions: cos(x), sin(x), tan(x), etc.
• other math functions: abs(x) (absolute value); sqrt(x) (square root)

log2(4) # => 2
abs(-5) # => 5
sqrt(9) # => 3
exp(1)  # => 2.718282

assigning variables

my_age <- 99  # numeric variable; a wizard never shares their age
my_name <- "Christian" # a character variable

# are you a data analyst? (yes/no) <==> (TRUE/FALSE)
is_a_data_analyst <- TRUE # boolean values

vectors

vectors create combinations of multiple values which are all of the same type (i.e., one of numeric, character, or logical).

in fancy computer science language, we would say this makes vectors a homogeneous data structure.

• create a vector using c() for concatenate
• for missing data, use NA (not available/not applicable) or NaN (not a number)
• extract individual items using my_vector[i] to get the ith element
• calculate statistics on vectors with: max(x), min(x), range(x), length(x), sum(x), mean(x), prod(x), sd(x), var(x), sort(x), summary(x)

an example working with vectors

classmate_ages <- c(24, 22, 24, 26, 23)
mean(classmate_ages) # => 23.8
max(classmate_ages) # => 26
summary(classmate_ages) 
#>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   22.0    23.0    24.0    23.8    24.0    26.0 

factors

one particularly important type of vector is a factor, which is used to store categorical variables where values are repeated frequently and there is a pre-specified set of distinct levels.

# create a factor: let's say we have a study with 5 participants
study_participant_genders <- factor(
  c('non-binary', 
    'transgender female', 
    'non-binary', 
    'cis female', 
    'transgender female'))

# create a frequency table
table(study_participant_genders)
#> study_participant_genders
#>         cis female         non-binary transgender female 
#>                  1                  2                  2 

# get a list of the distinct levels
levels(study_participant_genders)
#> [1] "cis female"         "non-binary"         "transgender female"

lists

lists are an important heterogeneous data structure

create a list using list(), extract elements from it using my_list[[i]] to get the ith element from it, or use the my_list$element_name syntax if the elements in the list are named.

harrys_family <- list(
  father = "James Potter",
  mother = "Lily Evans",
  aunt = "Petunia Dursley",
  uncle = "Vernon Dursley",
  cousin = "Dudley Dursley",
  parents_age = 21,
  adoptive_family_ages = c(44, 50, 18)
)

$father
[1] "James Potter"

$mother
[1] "Lily Evans"

$aunt
[1] "Petunia Dursley"

$uncle
[1] "Vernon Dursley"

$cousin
[1] "Dudley Dursley"

$parents_age
[1] 21

$adoptive_family_ages
[1] 44 50 18

data frames

data frames are a data structure composed of multiple columns of data, all of which have the same length, but which may differ in type.

here’s an example:

   name  age height_cm is_from_middle_earth
    Tom    4        90                FALSE
  Jerry    1        10                FALSE
Legolas 2000       180                 TRUE
  Gimli  140       137                 TRUE

data frames will be the most important data structure in this class!

as you might have guessed, the corresponding fancy computer science lingo would be to say that data frames are a heterogeneous data structure.

matrices

matrices are data structures made up of columns of data, all of the same type, all with the same length.

some functions to use with matrices

• to create matrices, use: matrix(), cbind(), rbind()
• to name their rows or columns: rownames() and colnames()
• to check and convert: is.matrix() and as.matrix()
• transpose a matrix: t()
• get dimensions: ncol() nrow(), dim()
• subset a matrix: my_matrix[row, col]
• calculations with numeric matrices: rowSums(), colSums(), rowMeans(), colMeans()

matrices

pup_stats <- matrix(
  data = c(
  3.8, 3.6, 3.7, 3.8, 3.5,     # 1st column
  8.5,   6, 7.7,   8, 8.2,     # 2nd column
  1,    .9, 1.2, 1.1,  .9),    # 3rd column
  ncol = 3, nrow = 5)
  
colnames(pup_stats) <- 
  c('body_length_ft', 
  'ear_width_in', 
  'nose_width_in')
  
rownames(pup_stats) <- 
  c('hodu', 
    'coco-chanel', 
    'sabre',
    'kissy',
    'mochi')

          body_length_ft ear_width_in nose_width_in
hodu                 3.8          8.5           1.0
coco-chanel          3.6          6.0           0.9
sabre                3.7          7.7           1.2
kissy                3.8          8.0           1.1
mochi                3.5          8.2           0.9

installing packages

R packages are extensions to the R programming language that contain code, data, and documentation.

R packages provide reusable R functions, documentation on how to use them, tests, and sample data.

# to install a package from CRAN use install.packages("packageName") with quotes
install.packages("tidyverse")
#> trying URL 'https://cloud.r-project.org/bin/macosx/big-sur-arm64/contrib/4.2/tidyverse_1.3.2.tgz'
#> Content type 'application/x-gzip' length 425892 bytes (415 KB)
#> ==================================================
#> downloaded 415 KB
#> 
#> 
#> The downloaded binary packages are in
#>  /var/folders/m8/2_hpqf1n5g3__1ps7nn8t31r0000gn/T//RtmpxHXjRQ/downloaded_packages

CRAN is the Comprehensive R Archive Network which acts to guarantee that packages downloaded from CRAN will compile, have documentation, and follow conventional norms about how packages should work.

installing packages

packages can also be installed from GitHub repositories using the {devtools} package.

install.packages("devtools")
library(devtools)

# call devtools::install_github("username/repositoryName")
devtools::install_github("wilkelab/ggtext")

loading packages

# to load a package use library(packageName) without quotes

library(tidyverse)
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.3.2 ──
#> ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
#> ✔ tibble  3.1.7     ✔ dplyr   1.0.9
#> ✔ tidyr   1.2.0     ✔ stringr 1.4.0
#> ✔ readr   2.1.2     ✔ forcats 0.5.1
#> ── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()

getting help

Use help() or ? to get help on a specific function.

help(mean) # or run ?mean
#> mean                   package:base                    R Documentation
#> 
#> Arithmetic Mean
#> 
#> Description:
#> 
#>      Generic function for the (trimmed) arithmetic mean.
#> 
#> Usage:
#> 
#>      mean(x, ...)
#> 
#>      ## Default S3 method:
#>      mean(x, trim = 0, na.rm = FALSE, ...)
#> Arguments:
#> 
#>        x: An R object.  Currently there are methods for numeric/logical
#>           vectors and date, date-time and time interval objects.
#>           Complex vectors are allowed for ‘trim = 0’, only.
#> 
#>     trim: the fraction (0 to 0.5) of observations to be trimmed from
#>           each end of ‘x’ before the mean is computed.  Values of trim
#>           outside that range are taken as the nearest endpoint.
#> 
#>    na.rm: a logical evaluating to ‘TRUE’ or ‘FALSE’ indicating whether
#>           ‘NA’ values should be stripped before the computation
#>           proceeds.
#> 
#>      ...: further arguments passed to or from other methods.
#> 
#> Value:
#> 
#>      If ‘trim’ is zero (the default), the arithmetic mean of the values
#>      in ‘x’ is computed, as a numeric or complex vector of length one.
#>      If ‘x’ is not logical (coerced to numeric), numeric (including
#>      integer) or complex, ‘NA_real_’ is returned, with a warning.
#> 
#>      If ‘trim’ is non-zero, a symmetrically trimmed mean is computed
#>      with a fraction of ‘trim’ observations deleted from each end
#>      before the mean is computed.
#> 
#> References:
#> 
#>      Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S
#>      Language_.  Wadsworth & Brooks/Cole.
#> 
#> See Also:
#> 
#>      ‘weighted.mean’, ‘mean.POSIXct’, ‘colMeans’ for row and column
#>      means.
#> Examples:
#> 
#>      x <- c(0:10, 50)
#>      xm <- mean(x)
#>      c(xm, mean(x, trim = 0.10))

getting help

Using the help() command for most functions is straightforward, but there are situations where you need to be a little bit careful.

• if you are looking for help for a function from a specific package, especially where that function appears in multiple packages, you should run help(filter, package='dplyr').
• if you’re trying to get help on a function in R that is a symbol, like the arithmetic operators (/, +, -, *, ^), or the assignment operator <-, then you should put it in quotes when you call help: help("*")
• there’s more advice on getting help from inside R here:
  https://www.r-project.org/help.html

builtin example datasets

there are a handful of builtin example datasets you can use to test out ideas on.

mtcars
iris

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
         5.1         3.5          1.4         0.2     setosa
         4.9         3.0          1.4         0.2     setosa
         4.7         3.2          1.3         0.2     setosa
         4.6         3.1          1.5         0.2     setosa
         5.0         3.6          1.4         0.2     setosa
         5.4         3.9          1.7         0.4     setosa
         4.6         3.4          1.4         0.3     setosa
         5.0         3.4          1.5         0.2     setosa
         4.4         2.9          1.4         0.2     setosa
         4.9         3.1          1.5         0.1     setosa
         5.4         3.7          1.5         0.2     setosa
         4.8         3.4          1.6         0.2     setosa
         4.8         3.0          1.4         0.1     setosa
         4.3         3.0          1.1         0.1     setosa
         5.8         4.0          1.2         0.2     setosa
         5.7         4.4          1.5         0.4     setosa
         5.4         3.9          1.3         0.4     setosa
         5.1         3.5          1.4         0.3     setosa
         5.7         3.8          1.7         0.3     setosa
         5.1         3.8          1.5         0.3     setosa
         5.4         3.4          1.7         0.2     setosa
         5.1         3.7          1.5         0.4     setosa
         4.6         3.6          1.0         0.2     setosa
         5.1         3.3          1.7         0.5     setosa
         4.8         3.4          1.9         0.2     setosa
         5.0         3.0          1.6         0.2     setosa
         5.0         3.4          1.6         0.4     setosa
         5.2         3.5          1.5         0.2     setosa
         5.2         3.4          1.4         0.2     setosa
         4.7         3.2          1.6         0.2     setosa
         4.8         3.1          1.6         0.2     setosa
         5.4         3.4          1.5         0.4     setosa
         5.2         4.1          1.5         0.1     setosa
         5.5         4.2          1.4         0.2     setosa
         4.9         3.1          1.5         0.2     setosa
         5.0         3.2          1.2         0.2     setosa
         5.5         3.5          1.3         0.2     setosa
         4.9         3.6          1.4         0.1     setosa
         4.4         3.0          1.3         0.2     setosa
         5.1         3.4          1.5         0.2     setosa
         5.0         3.5          1.3         0.3     setosa
         4.5         2.3          1.3         0.3     setosa
         4.4         3.2          1.3         0.2     setosa
         5.0         3.5          1.6         0.6     setosa
         5.1         3.8          1.9         0.4     setosa
         4.8         3.0          1.4         0.3     setosa
         5.1         3.8          1.6         0.2     setosa
         4.6         3.2          1.4         0.2     setosa
         5.3         3.7          1.5         0.2     setosa
         5.0         3.3          1.4         0.2     setosa
         7.0         3.2          4.7         1.4 versicolor
         6.4         3.2          4.5         1.5 versicolor
         6.9         3.1          4.9         1.5 versicolor
         5.5         2.3          4.0         1.3 versicolor
         6.5         2.8          4.6         1.5 versicolor
         5.7         2.8          4.5         1.3 versicolor
         6.3         3.3          4.7         1.6 versicolor
         4.9         2.4          3.3         1.0 versicolor
         6.6         2.9          4.6         1.3 versicolor
         5.2         2.7          3.9         1.4 versicolor
         5.0         2.0          3.5         1.0 versicolor
         5.9         3.0          4.2         1.5 versicolor
         6.0         2.2          4.0         1.0 versicolor
         6.1         2.9          4.7         1.4 versicolor
         5.6         2.9          3.6         1.3 versicolor
         6.7         3.1          4.4         1.4 versicolor
         5.6         3.0          4.5         1.5 versicolor
         5.8         2.7          4.1         1.0 versicolor
         6.2         2.2          4.5         1.5 versicolor
         5.6         2.5          3.9         1.1 versicolor
         5.9         3.2          4.8         1.8 versicolor
         6.1         2.8          4.0         1.3 versicolor
         6.3         2.5          4.9         1.5 versicolor
         6.1         2.8          4.7         1.2 versicolor
         6.4         2.9          4.3         1.3 versicolor
         6.6         3.0          4.4         1.4 versicolor
         6.8         2.8          4.8         1.4 versicolor
         6.7         3.0          5.0         1.7 versicolor
         6.0         2.9          4.5         1.5 versicolor
         5.7         2.6          3.5         1.0 versicolor
         5.5         2.4          3.8         1.1 versicolor
         5.5         2.4          3.7         1.0 versicolor
         5.8         2.7          3.9         1.2 versicolor
         6.0         2.7          5.1         1.6 versicolor
         5.4         3.0          4.5         1.5 versicolor
         6.0         3.4          4.5         1.6 versicolor
         6.7         3.1          4.7         1.5 versicolor
         6.3         2.3          4.4         1.3 versicolor
         5.6         3.0          4.1         1.3 versicolor
         5.5         2.5          4.0         1.3 versicolor
         5.5         2.6          4.4         1.2 versicolor
         6.1         3.0          4.6         1.4 versicolor
         5.8         2.6          4.0         1.2 versicolor
         5.0         2.3          3.3         1.0 versicolor
         5.6         2.7          4.2         1.3 versicolor
         5.7         3.0          4.2         1.2 versicolor
         5.7         2.9          4.2         1.3 versicolor
         6.2         2.9          4.3         1.3 versicolor
         5.1         2.5          3.0         1.1 versicolor
         5.7         2.8          4.1         1.3 versicolor
         6.3         3.3          6.0         2.5  virginica
         5.8         2.7          5.1         1.9  virginica
         7.1         3.0          5.9         2.1  virginica
         6.3         2.9          5.6         1.8  virginica
         6.5         3.0          5.8         2.2  virginica
         7.6         3.0          6.6         2.1  virginica
         4.9         2.5          4.5         1.7  virginica
         7.3         2.9          6.3         1.8  virginica
         6.7         2.5          5.8         1.8  virginica
         7.2         3.6          6.1         2.5  virginica
         6.5         3.2          5.1         2.0  virginica
         6.4         2.7          5.3         1.9  virginica
         6.8         3.0          5.5         2.1  virginica
         5.7         2.5          5.0         2.0  virginica
         5.8         2.8          5.1         2.4  virginica
         6.4         3.2          5.3         2.3  virginica
         6.5         3.0          5.5         1.8  virginica
         7.7         3.8          6.7         2.2  virginica
         7.7         2.6          6.9         2.3  virginica
         6.0         2.2          5.0         1.5  virginica
         6.9         3.2          5.7         2.3  virginica
         5.6         2.8          4.9         2.0  virginica
         7.7         2.8          6.7         2.0  virginica
         6.3         2.7          4.9         1.8  virginica
         6.7         3.3          5.7         2.1  virginica
         7.2         3.2          6.0         1.8  virginica
         6.2         2.8          4.8         1.8  virginica
         6.1         3.0          4.9         1.8  virginica
         6.4         2.8          5.6         2.1  virginica
         7.2         3.0          5.8         1.6  virginica
         7.4         2.8          6.1         1.9  virginica
         7.9         3.8          6.4         2.0  virginica
         6.4         2.8          5.6         2.2  virginica
         6.3         2.8          5.1         1.5  virginica
         6.1         2.6          5.6         1.4  virginica
         7.7         3.0          6.1         2.3  virginica
         6.3         3.4          5.6         2.4  virginica
         6.4         3.1          5.5         1.8  virginica
         6.0         3.0          4.8         1.8  virginica
         6.9         3.1          5.4         2.1  virginica
         6.7         3.1          5.6         2.4  virginica
         6.9         3.1          5.1         2.3  virginica
         5.8         2.7          5.1         1.9  virginica
         6.8         3.2          5.9         2.3  virginica
         6.7         3.3          5.7         2.5  virginica
         6.7         3.0          5.2         2.3  virginica
         6.3         2.5          5.0         1.9  virginica
         6.5         3.0          5.2         2.0  virginica
         6.2         3.4          5.4         2.3  virginica
         5.9         3.0          5.1         1.8  virginica

data packages

there are also packages that contain data; some that are particularly often referenced the {nycflights13} and {palmerpenguins} packages.

install.packages("nycflights13")
install.packages("palmerpenguins")

library(nycflights13)
library(palmerpenguins)

# you can use the help() or ? commands to 
# see documentaiton for data too
?flights
?penguins

getting help

in this class, we are encouraging you to reference online materials including:

• R for Data Science Online Community https://www.rfordatasci.com/
• StackOverflow: https://stackoverflow.com/questions/tagged/r?tab=Votes
• the RStudio Community: https://community.rstudio.com/
• the /r/rstats reddit: https://www.reddit.com/r/rstats/
• twitter / mastodon: the #rstats hashtag, https://www.t4rstats.com/
• R Bloggers https://www.r-bloggers.com/
• Cross Validated: https://stats.stackexchange.com/questions?tab=Votes

learning how to help yourself learn R is one of the key skills we want you to take away from this class

key takeaways

• R and RStudio are available for you to download, for free
• the basic classes of data you should become familiar with are:
  • numeric, character, logical
• the basic data structures you should become familiar with are:
  • vectors, lists, data frames, matrices
• get help in R using help() or ?, or search online