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Finalize materials #2

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c200b10
intro
NateRByers Apr 27, 2024
44dd264
lessons 3 through 5
NateRByers Apr 28, 2024
e2897b0
yaml 3
NateRByers Apr 30, 2024
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2 changes: 1 addition & 1 deletion R/build_all_lessons.R
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
#' @export
build_lessons <- function(source_dir = file.path(getwd(), "source"), learnr = TRUE, github = TRUE) {
# Validate base directory
if (!dir.exists(base_dir)) {
if (!dir.exists(source_dir)) {
stop("The specified base directory does not exist.")
}

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106 changes: 72 additions & 34 deletions docs/1-Introduction/readme.md
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Expand Up @@ -12,7 +12,7 @@ This lesson is a part of the Introduction to R for Air Quality Data Science. The
- [Basic Math](#basic-math)
- [Order of Operations](#order-of-operations)

- [Note on Comments and Code Blocks](#note-on-comments-and-code-blocks)
- [Comments and Code Blocks](#comments-and-code-blocks)

- [Variables](#variables)

Expand All @@ -29,7 +29,16 @@ This lesson is a part of the Introduction to R for Air Quality Data Science. The

## What is R?

R is a free, open-source computing language. It was originally written by statisticians for doing statistical analysis in academia. In recent years it has become more widely used in many industries for performing a variety of data science tasks such as:
R is a free, open-source computing language. It was originally written
by statisticians for doing statistical analysis in academia. In recent
years it has become more widely used in many industries for performing
a variety of data science tasks such as:

- reading and writing files,
- data transformation,
- graphic visualization,
- geographic mapping,
- and predictive modeling.


## Why Use a Programming Language?
Expand All @@ -44,6 +53,18 @@ Many data analysis tasks can be accomplished with spreadsheets and other busines
BI. When should you move beyond BI tools and use a high-level programming language like R? Below are a few scenarios where a language
like R is more advantageous than a BI tool.

- If you cannot access data easily in your BI tool, R can read just about any data source.
- If you need to download, save, or otherwise process a large number of files, R can automate those tasks in a way that BI tools cannot.
- Custom data transformations that are not possible in BI tools can be done with R.
- Custom data visualizations that are not available in BI tools can be done with R.
- Predictive modeling that is not available in BI tools, or only in a rudimentary way, can be done in R.

BI tools are more advantageous if you need enterprise wide dashboards,
or tools that are more easily accessible to a wider audience. If there
are few occasions where you need custom visualizations or transformations,
or if you do not need automation in your work, you may not need to learn
a programming language.


## Install R and RStudio

Expand All @@ -55,7 +76,7 @@ To download R, [see this page](https://cran.r-project.org/). You will need to se
system (PC or Mac). Accept the default options during the installation.


Once you have installed R, you can open the program itself. On PC, if you have selected the desktop shortcut during installation, the R icon
Once you have installed R, you can open the program itself. On a PC, if you have selected the desktop shortcut during installation, the R icon
will look like this:


Expand Down Expand Up @@ -108,7 +129,7 @@ Open up a script if you haven't already (“File” -> “New File” -> “R Sc
the lines into your script.


```{r ex-eiHaz-1, exercise = FALSE, eval = TRUE, exercise.cap = 'Practice Basic Math Operations'}
```{r ex-HdgAY-1, exercise = FALSE, eval = TRUE, exercise.cap = 'Practice Basic Math Operations'}
10 + 5
10 - 5
10 * 5
Expand All @@ -135,17 +156,17 @@ R follows the usual order of arithmetical operations and uses parentheses for gr
see the different values that are returned.


```{r ex-C58km-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Understanding Order of Operations'}
```{r ex-xEdMY-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Understanding Order of Operations'}
10 - 3 / 5

```

```{r ex-2P9sT-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Practicing Grouping with Parentheses'}
```{r ex-VGcxo-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Practicing Grouping with Parentheses'}
(10 - 3) / 5

```

## Note on Comments and Code Blocks
## Comments and Code Blocks

To write a comment in your script that will not be evaluated, type `#` in front of your comment. The text after `#` will not be evaluated. There is
no multi-line commenting in R, so every comment line must begin with the `#` character.
Expand All @@ -154,36 +175,43 @@ no multi-line commenting in R, so every comment line must begin with the `#` cha
Run all of the code below and see what gets returned in the R console (bottom left panel in RStudio).


```{r ex-7hEfM-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Experiment with Comments'}
```{r ex-RC8UP-1, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Experiment with Comments'}
# Full line comment
5^2 # partial line comment

```

In the example above and the previous section, you have seen the R code and its output. The code blocks with output look like this, with `1+1` being
In the example above and the previous section, you can see the R code and its output. The code blocks with output look like this, with `1+1` being
the R code and `## [1] 2` being the output:


```{r ex-l629B-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Code and Output Example'}
```{r ex-rNPTd-2, exercise = FALSE, eval = TRUE, exercise.cap = 'Code and Output Example'}
1+1

```

However, in the R console the code and output would look like this:


```{r ex-hkQIj-3, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Console Code Execution'}
1 + 1
```{r ex-FA0xU-3, exercise = FALSE, eval = FALSE, echo = TRUE, exercise.cap = 'Console Code Execution'}
> 1 + 1
[1] 2

```

The R code in code blocks do not show the `>` part of the console, called
the prompt, and the output block places two comment marks `##` before the
output. This is to make it possible to copy and paste the text into your
R console and run it without causing an error.


## Variables

A variable is a letter or combination of alphanumeric characters that is used to store data. To create a variable in R, use the less-than character
with the dash to create an arrow symbol pointing left `<-`. Below, the variables `x` and `y` are created by assigning some numbers to them.


```{r ex-JADmU-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Creating Variables'}
```{r ex-1jvTE-1, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Creating Variables'}
x <- 10
y <- 5
x + y
Expand All @@ -198,12 +226,12 @@ In RStudio, you will see the variables we created in the top right panel.
If you've already created a variable, you can replace the value with another value.


```{r ex-Y1soB-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Exploring Variable Assignment'}
```{r ex-urj0S-2, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Exploring Variable Assignment'}
x

```

```{r ex-O0ucr-3, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Updating Variable Values'}
```{r ex-VcnkF-3, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Updating Variable Values'}
x <- 20
x

Expand All @@ -216,13 +244,17 @@ In the top right panel you can see that the number stored in the variable `x` ha

There are 3 important rules to remember when creating variable names:

1. You can't start your variable name with a number.
2. You can't use spaces or special characters ($,%,#,-). Periods `.` and underscores `_` are ok.
3. Capitalization __DOES__ matter in R. That is, R will consider `y` and `Y` to be different variables.


Try running the following code and you will see that in your global environment there are two different objects listed.


```{r ex-5emEn-4, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Variable Naming Rules'}
x <- 5
X <- 5
```{r ex-DdKbR-4, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Variable Naming Rules'}
y <- 5
Y <- 10

```

Expand All @@ -237,27 +269,33 @@ R has three main data types:
| numeric | numbers | `1`, `3.14`, `log(10)` |
| logical | binary | `TRUE`, `FALSE` |

The `character` type requires single or double quotes. The logical values
`TRUE` and `FALSE` should not be quoted and require full caps.
The `character` type requires single or double quotes. The numeric type
must be unquoted numbers, and the full-caps logical values `TRUE` and
`FALSE` must also be unquoted.


## Grouping Data

There are several ways to group data to make them easier to work with:
There are several ways to store groups of data to make them easier to work with:

- A __vector__ stores multiple values of the same type (e.g. all numeric values).
- A __list__ stores multiple values of different types (e.g. some numbers and character values).
- A __matrix__ is a table of values with only one data type.
- A __data frame__ is a table of values that can have columns with different data types (e.g. a numeric column and a logical column).


## Vectors

A vector variable can contain only one type of data (numeric, character, or logical). We use `c()` to create vectors.


```{r ex-7qwhu-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Creating Numeric Vectors'}
```{r ex-0xxM6-1, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Creating Numeric Vectors'}
x <- c(1, 2, 3, 4, 5)
x

```

```{r ex-f4koA-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Creating Character Vectors'}
```{r ex-1IrUs-2, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Creating Character Vectors'}
fruit <- c("apples", "bananas", "oranges")
fruit

Expand All @@ -267,7 +305,7 @@ If you try to type in text without using quotations marks for character values (
running the code below.


```{r ex-kAAyQ-3, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Error Without Quotes'}
```{r ex-JT90l-3, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Error Without Quotes'}
fruit <- c(apples, bananas, oranges)

```
Expand All @@ -277,7 +315,7 @@ find them and it returns an error. The members of a vector can be accessed by us
`fruit` vector, you can use the single bracket with the number 3:


```{r ex-cYSCq-4, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Accessing Vector Elements'}
```{r ex-P8WcO-4, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Accessing Vector Elements'}
fruit[3]

```
Expand All @@ -287,7 +325,7 @@ fruit[3]
Lists are like vectors but can contain any mix of data types. We use `list()` to create a list variable.


```{r ex-3RJPl-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Creating Lists'}
```{r ex-7M0x4-1, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Creating Lists'}
x <- list("Benzene", 1.3, TRUE)
x

Expand All @@ -298,15 +336,15 @@ is the second value in the list, so it is shown below the double bracket `[[2]]`
list.


```{r ex-NeQlK-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Accessing List Elements'}
```{r ex-xYfvK-2, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Accessing List Elements'}
x[[2]]

```

Lists can also contain vectors and other lists.


```{r ex-ZvztJ-3, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Lists Containing Vectors and Lists'}
```{r ex-Hwf7U-3, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Lists Containing Vectors and Lists'}
my_vector <- c(1, 2, 3)
my_list <- list("Benzene", 1.3, TRUE)
y <- list(TRUE, my_vector, my_list)
Expand All @@ -318,7 +356,7 @@ In this example, you can use two double brackets to access the value `1.3` by se
`my_list`:


```{r ex-w7rur-4, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Nested List Access'}
```{r ex-bf5fg-4, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Nested List Access'}
y[[3]][[2]]

```
Expand All @@ -329,7 +367,7 @@ Data frames are data tables in R. We use `data.frame()` to create a data frame o
vectors of the same length and use them to create a data frame.


```{r ex-w74nJ-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Creating Data Frames'}
```{r ex-TuNdl-1, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Creating Data Frames'}
pollutant <- c("Benzene", "Toluene", "Xylenes")
concentration <- c(1.3, 5.5, 6.0)
carcinogen <- c(TRUE, FALSE, FALSE)
Expand All @@ -339,10 +377,10 @@ my_data_frame
```

The output above shows a table with the vector variable names as column names, and the vector values below the respective column name. If you try to
create a data frame where the vectors are not all the same length, you will see the error shown below.
create a data frame where the vectors are not all the same length, you will get an error.


```{r ex-ExWhT-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Data Frame Dimension Mismatch Error'}
```{r ex-R6bar-2, exercise = TRUE, exercise.eval = FALSE, exercise.cap = 'Data Frame Dimension Mismatch Error'}
pollutant <- c("Benzene", "Toluene")
concentration <- c(1.3, 5.5, 6.0)
carcinogen <- c(TRUE, FALSE, FALSE)
Expand Down Expand Up @@ -463,7 +501,7 @@ v <- c(1, 2, 3, 4, 5)

### Exercise 4

Create a list `l` that contains a number (e.g., 5), a string (e.g., 'apple'), and a logical value (e.g., TRUE).
Create a list `l` that contains the number 5, the string 'apple', and the logical value `TRUE`.

<details><summary>Click for Hint</summary>

Expand Down Expand Up @@ -496,7 +534,7 @@ l <- list(5, 'apple', TRUE)

### Exercise 5

Create a data frame `df` with two columns, `name` and `age`, each containing three entries of your choosing.
Create a data frame `df` with two columns, `name` and `age`. Use the names 'Alice', 'Bob', and 'Charlie' and give them each an age.

<details><summary>Click for Hint</summary>

Expand Down
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