Math 430: Lecture 1b

Introduction to toolkit

Professor Catalina Medina

Coding foundations with R

R as a calculator

2 + 2
[1] 4
sqrt(6 *(18 / 5)^2 * exp(13))
[1] 5865.327

What you see is

  • a line of code (starting with the code line number “1”)
  • a line of output (starting with “[1]”)

Creating an object

We can save things into an object using the assignment operator <-

my_calculation <- 2 * 4

I can name it almost whatever I wish

magical_purple_dragon <- 2 * 4

Names of objects:

  • must begin with a letter
  • contain letters &/or numbers
  • separate with “_” (underscore) or a “.” (period)

Naming an object

You can view what is in an object

favorite_color <- "olive green"

favorite_color
[1] "olive green"

R is case sensitive

Favorite_color
Error: object 'Favorite_color' not found

Tip

Let R autocomplete for you to avoid spelling mistakes

Vectors

Objects can store vectors also

apple_colors <- c("green", "yellow", "red")
apple_colors
[1] "green"  "yellow" "red"

We can also do elementwise math with vectors

x <- c(3, 6, 9, 12)
y <- x / 3
y
[1] 1 2 3 4
x - y
[1] 2 4 6 8

Data frames

The most common object type we will use are data frames

apple_data <- data.frame(
  apple_colors = c("green", "yellow", "red"), 
  apple_count = c(3, 17, 8)
)

apple_data
  apple_colors apple_count
1        green           3
2       yellow          17
3          red           8

Vocabulary

do(something)

do() is a function;
something is the argument of the function.

do(something, colorful)

do() is a function;
something is the first argument of the function;
colorful is the second argument of the function.

Example function calls

Sometimes you may only want to specify one argument

pnorm(1.65)
[1] 0.9505285

Sometimes you want to provide multiple arguments

my_data <- c(2, 4, 7, 3, 6, 2, 5, 6)

t.test(my_data, alternative = "two.sided", conf.level = 0.95)

    One Sample t-test

data:  my_data
t = 6.4362, df = 7, p-value = 0.0003549
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 2.767658 5.982342
sample estimates:
mean of x 
    4.375

Getting Help

In order to get any help we can use ? followed by function (or object) name.

?t.test

Using comments with # in your code for less obvious tasks can be helpful

# This is just a comment and is not processed as code
# Converted from Celsius to Fahrenheit. F = (C * 9 / 5) + 32
my_temperature <- 36.7
my_temperature <- (my_temperature * 9 / 5) + 32
my_temperature
[1] 98.06

Tip

AI tools can be helpful for finding functions and providing examples.

Warning

You should not copy paste code from my slides or from the internet. Part of learning to code is building up your muscle memory.

Do not assume that AI tools will be correct. Even if the code runs it may not be the proper way of doing something.

If the code example is very long or does not use functions we discussed in class, refine your question.

Literate programming with Quarto documents in RStudio

Quarto

Quarto parts

Quarto parts

Quarto parts

Quarto parts

Quarto parts

Quarto parts

Quarto parts

Slides for this course

Slides that you are currently looking at are also written in Quarto. You can take a look at them on in the GitHub repository I use to make the slides.

R packages

Phone apps vs. R packages

When you buy a new phone it comes with some apps pre-installed.

  • Calendar
  • Email
  • Messages

If you want to use a different app you can install it.

  • Instagram
  • GMail
  • BlueSky

When you download R for the first time to your computer. It comes with some packages already installed. You can also install many other R packages.

R packages

What do R packages have? All sorts of things but mainly

  • functions

  • datasets

In order to use a package you have to:

  1. install it (once per device)
install.packages("tidyverse")
  1. load it (in each Quarto document)
library(tidyverse)

R packages

Try running the following code and look at the error:

beep()
Error in beep(): could not find function "beep"

The function beep() is from the beepr package, so we have to (1) make sure it is installed and then (2) load it

library(beepr)
beep()

Getting to know data

Dear Mona, Which State Has the Worst Drivers?

library(fivethirtyeight)
library(tidyverse)
data("bad_drivers")

When you are given some code like this one in your lecture notes or assignments, you should run it first before beginning to code. As we progress in the course, you’ll have a deeper understanding of what the starter codes do.

Data frames

Data frame

ncol(bad_drivers)
[1] 8
nrow(bad_drivers)
[1] 51

  • The data frame has 8 variables (state, num_drivers, perc_speeding, perc_not_distracted, perc_no_previous, insurance_premiums, losses).

  • The data frame has 51 cases or observations. Each case represents a US state (or District of Columbia).

Data documentation

If a dataset is part of an R package you can look at its help documentation.

?bad_drivers

In general we use a data dictionary for information on a data set. The data dictionary at minimum should contain information describing each column in the data set.

Functions for viewing data frames

View top of data frame

head(bad_drivers)
# A tibble: 6 × 8
  state      num_drivers perc_speeding perc_alcohol perc_not_distracted
  <chr>            <dbl>         <int>        <int>               <int>
1 Alabama           18.8            39           30                  96
2 Alaska            18.1            41           25                  90
3 Arizona           18.6            35           28                  84
4 Arkansas          22.4            18           26                  94
5 California        12              35           28                  91
6 Colorado          13.6            37           28                  79
# ℹ 3 more variables: perc_no_previous <int>, insurance_premiums <dbl>,
#   losses <dbl>

View bottom of data frame

tail(bad_drivers)
# A tibble: 6 × 8
  state         num_drivers perc_speeding perc_alcohol perc_not_distracted
  <chr>               <dbl>         <int>        <int>               <int>
1 Vermont              13.6            30           30                  96
2 Virginia             12.7            19           27                  87
3 Washington           10.6            42           33                  82
4 West Virginia        23.8            34           28                  97
5 Wisconsin            13.8            36           33                  39
6 Wyoming              17.4            42           32                  81
# ℹ 3 more variables: perc_no_previous <int>, insurance_premiums <dbl>,
#   losses <dbl>

View structure of data frame

glimpse(bad_drivers)
Rows: 51
Columns: 8
$ state               <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "Calif…
$ num_drivers         <dbl> 18.8, 18.1, 18.6, 22.4, 12.0, 13.6, 10.8, 16.2, 5.…
$ perc_speeding       <int> 39, 41, 35, 18, 35, 37, 46, 38, 34, 21, 19, 54, 36…
$ perc_alcohol        <int> 30, 25, 28, 26, 28, 28, 36, 30, 27, 29, 25, 41, 29…
$ perc_not_distracted <int> 96, 90, 84, 94, 91, 79, 87, 87, 100, 92, 95, 82, 8…
$ perc_no_previous    <int> 80, 94, 96, 95, 89, 95, 82, 99, 100, 94, 93, 87, 9…
$ insurance_premiums  <dbl> 784.55, 1053.48, 899.47, 827.34, 878.41, 835.50, 1…
$ losses              <dbl> 145.08, 133.93, 110.35, 142.39, 165.63, 139.91, 16…

Functions to import and export data

Import

unemployment <- read_csv("http://csuci-math430.github.io/lectures/week-01/data/unemployment_rate_by_age_groups.csv")

Export

write_csv(unemployment, file = "unemployment-data.csv")

Tip

A lot of headaches can be prevented from using read_csv() instead of read.csv()!