BTS 510 Lab 1

set.seed(12345)

Note

This first code chunk usually has more in it. See notes below. This code sets a random number seed so that if we do anything that requires a random number (e.g., create data with a random number generating function), it will produce the same values every time.

1 Learning objectives

Start using R and Rstudio
Start using Quarto documents as a reproducible method to present analyses along with narrative text

2 Where is the code???

At the top of the page, in the upper right, there is a button that reads “</> Code”
- Click on that and a window with the code that made this page will pop up
- Click on the little notepad icon in the upper right to copy the entire document

3 A few miscellaneous things about R and Quarto

3.1 About R

R is case sensitive
- mean is not the same as Mean
- The object b is not the same as the object B
Extra returns don’t matter
- Extra spaces generally don’t matter either
- I often add extra spaces, tabs, and returns to make the code more readable
The assignment operator is <-
- “Put whatever is on the right side in the object named on the left side”
Sometimes, equal isn’t equal
- If you’re talking about assigning something to a value, you can use =
- If you want to compare two values and make a decision based on it, you use ==
There isn’t a standard, widely-used naming convention for variables, so you should pick something and try to be consistent
- In general, variable names must start with a letter – no numbers or special characters
- There are some options for multi-word variable naming
  - variablename: Just smush it all together
  - variableName or VariableName: Camel case
  - variable_name or Variable_Name: Snake case
  - variable-name or Variable-Name: Kebab case
- In general, make the variable names as short as possible while keeping each name unique

3.2 About Quarto

Look at the top of the Quarto file. The section with --- at the top and bottom. This is the YAML (pronounced “yam-ull”). It provides options for the overall document.
- title: is pretty obvious
- format: tells R which output format to produce when you knit the document together
- This is an html document, but you can specify different or multiple output formats, like both HTML and PDF
  - There are additional options that are specific to this format
  - self-contained: true and self-contained-math: true make sure that any additional files that are created, like figures are part of the HTML file. If I didn’t include this and sent you to the HTML file, the figures would be missing.
  - html-math-method: katex is an option about the equations and math text. It’s probably not necessary, but I include it anyway.
  - number-sections: true and toc: true produce the table of contents on the right side of this page and number the sections and sub-sections (remember headings and organization?) to make it easier to navigate
  - code-tools: true adds the </> Code button at the top that shows the code that made this page. You can copy the code (using the little “Copy to clipboard” icon in the upper right) and paste it right into R
  - code-block-bg: true and code-block-border-left: "#31BAE9" make the code chunks a little more noticeable – they blended into the background too much otherwise.
  - The YAML is one of the few places that spacing matters. Note how each sub-option is indented more than it’s over-option. If you don’t get this right, the file won’t run
  - Read more about YAML options here
You can label each code chunk in the document
- This is a very smart thing to do – but I don’t do it consistently :(
- If you have an error, R will tell you which chunk has the problem
  - If the chunks are named, it will use the name so it’s easy to find
  - If they’re not named, it will tell you something like “Unnamed chunk 52”, which is less helpful
- Name your chunks with #| label: name-of-this-chunk
  - See the setup chunk above – that’s just its name, “setup”
There are other options for code chunks. Here are some useful ones
- echo: true: Repeats the code in the output
- warning: false: Suppress any warnings from R (not recommended)
- eval: false: Don’t run this code, just print it (useful for teaching)
- For any of these, you can swap from true to false or vice versa and get the opposite effect
- fig-cap: "My figure caption is cool": Adds a caption to the figure produced in that chunk – useful for accessibility
- More info here and here

4 Installing packages

You must install a new package the first time you use it

#install.packages(gapminder)

You only need to do this once. I have commented out this part of the code by putting # at the start of the line because I already have the package installed. If you try to install a package that is already installed, you will probably get an error.

Note

In my own work, I usually install packages via the Rstudio graphical user interface (GUI). I’m doing it this way for pedagogical reasons.

Alternatively, if you’re using Rstudio, you can quickly install packages in the “Packages” pane using the “Install” button, and exclude this code.

5 Loading packages

You must load or library() a package each time you use it. This means that you need to load it at the top of each markdown or quarto file you write.

library(gapminder)

This allows us to use the functions in these packages. If you’re loading multiple packages, just list each on it’s own line.

Note

In my own work, I usually load all packages in the “setup” chunk at the top of the document. I’m doing it this way for pedagogical reasons.

The gapminder package is a data package. It provides a subset of the data available at gapminder.org. You may have heard about this data from Hans Rosling’s very famous TED talk.

6 Reading in data

6.1 External data

You can read in data from almost any source.

Comma-separated values (CSV) file: Use read.csv() (built-in)
Excel: Use read_excel() function from readxl package
SAS, SPSS, Stata: Use the appropriate function from the haven package
SAS, SPSS, S, Stata, Systat, Epi Info, Minitab: Use the appropriate function from the foreign package

If you’re using Rstudio, you can read in most types of data using “Import Dataset” in the “Environment” tab in the upper right pane.

We’ll focus on this more next week. You’ll almost always be using external data (not internal R data) in your own research, so this is really important.

6.2 Data from a package

Some packages contain data. We will use the gapminder dataset from the gapminder package. Another great source of dataset is the datasets package, which includes commonly used datasets such as iris and mtcars. (Note that if you want to use iris or mtcars, you’ll need to load it – it is installed with R by default, so you don’t need to install it.)

To load a dataset, use the data() function

data(gapminder)

7 Looking at your data

There are a number of functions to help you examine your data. You should always look at your data to make sure it was read in correctly.

Note

What should your data look like? If you’re doing a typical experimental study, the organization of your dataset should follow a pretty standard format:

Each row is a unit of study (i.e., person, animal, petri dish)
Each column is a variable

There are some deviations from this

Longitudinal studies have multiple observations per unit and can be organized 2 different ways
- Wide or multivariate format: Each repeated measure is a new variable (e.g., X1, X2, X3 for the X variable at 3 different time points)
- Tall or stacked or univariate format: Each repeated measure is a new row, so each unit will have multiple rows of data (with an additional variable that indicates which time point is which)
Data from non-experiments is often structured differently or even unstructured
- Web scraping
- Data from devices or automated processes
- Data from someone who doesn’t know what they’re doing…

7.1 List the dataset

If you want to view the dataset, you can just name the dataset. (You can also print() the dataset for the same result.)

gapminder

# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

7.2 `head()` function

The head() function will show the head of the dataset. By default, this shows the first 6 rows of the dataset

head(gapminder)

# A tibble: 6 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

You can change the number of rows it shows using the n argument. Here, I’ve asked for the first 15 rows instead.

head(gapminder, n = 15)

# A tibble: 15 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
11 Afghanistan Asia       2002    42.1 25268405      727.
12 Afghanistan Asia       2007    43.8 31889923      975.
13 Albania     Europe     1952    55.2  1282697     1601.
14 Albania     Europe     1957    59.3  1476505     1942.
15 Albania     Europe     1962    64.8  1728137     2313.

Note

There is also a tail() function that – guess what – prints the last 6 rows of the dataset. If you wanted to check the end for some reason.

7.3 `str()` function

The str() (for “structure”) function tells you about the structure of the dataset. It shows the number of rows and columns, and summarizes some basic info about each variable (like whether it’s a number or character variable and the possible values).

str(gapminder)

tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
 $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
 $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
 $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
 $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

7.4 `glimpse()` function

The glimpse() function is in the dplyr package in the tidyverse. We’re going to talk about dplyr more next week, but I wanted to include this function here. This function shows you the number of rows and columns in the dataset. It also flips your dataset so that the variables are rows and prints the dataset out.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── 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

glimpse(gapminder)

Rows: 1,704
Columns: 6
$ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
$ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
$ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …

Note

Don’t worry about all those warnings that show up about tidyverse and such after library(tidyverse). That’s normal.

8 Activities

Complete these tasks by adding to this .qmd (quarto) file.

Render / knit the file together into an HTML file.
Render / knit the file together into a PDF file. You’ll need to install the tinytex package for this to work (or have LaTeX installed on your computer).
How many rows are in the gapminder dataset? Where did you find that out?
How many columns are in the gapminder dataset? Where did you find that out?
How many countries are represented in the gapminder dataset? Where did you find that out?
The gapminder dataset is longitudinal. Is it organized as tall or wide?

1 Learning objectives

2 Where is the code???

3 A few miscellaneous things about R and Quarto

3.1 About R

3.2 About Quarto

4 Installing packages

5 Loading packages

6 Reading in data

6.1 External data

6.2 Data from a package

7 Looking at your data

7.1 List the dataset

7.2 head() function

7.3 str() function

7.4 glimpse() function

8 Activities

7.2 `head()` function

7.3 `str()` function

7.4 `glimpse()` function