Once opened, the R console looks very plain.
-
+
RStudio makes R much more user friendly. It's free and can be downloaded from the [Posit website](https://posit.co/download/rstudio-desktop/).
Accept the defaults during installation. It's not necessary to open RStudio to use R, but in these sessions we will assume that RStudio is your
@@ -95,29 +95,29 @@ interface to R.
On a PC, the RStudio desktop icon looks like this:
-
+
When you first open RStudio, this is what you see:
-
+
The left panel is the console for R. Type `1 + 1` in the console then hit "Enter" and R will return the answer.
-
+
It's a good idea to use a script so you can save your code. Open a new script by selecting "File" -> "New File" -> "R Script" and it will appear
in the top left panel of RStudio.
-
+
This is a text document that can be saved. Go to "File" -> "Save As" and you can save the file with a `.R` extension. You can type and run more
than one line at a time by highlighting and clicking the "Run" button on the script tool bar.
-
+
The bottom right panel can be used to find and open files, view plots, load packages, and look at help pages. The top right panel gives you information
about what variables you're working with during your R session.
@@ -143,7 +143,7 @@ in the R language.
| Operator | Meaning | Example |
-| --- | --- | --- |
+| :--- | :--- | :--- |
| + | addition | 2 + 2 |
| - | subtraction | 2 - 2 |
| * | multiplication | 2 * 2 |
@@ -221,7 +221,7 @@ x + y
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.
@@ -240,7 +240,7 @@ x
In the top right panel you can see that the number stored in the variable `x` has changed.
-
+
There are 3 important rules to remember when creating variable names:
@@ -264,7 +264,7 @@ R has three main data types:
| Type | Description | Examples |
-| --- | --- | --- |
+| :--- | :--- | :--- |
| character | letters and words | `z`, `red`, `H2O` |
| numeric | numbers | `1`, `3.14`, `log(10)` |
| logical | binary | `TRUE`, `FALSE` |
diff --git a/docs/2-Functions-and-Importing-Data/readme.md b/docs/2-Functions-and-Importing-Data/readme.md
index d4192f7..4880f4e 100644
--- a/docs/2-Functions-and-Importing-Data/readme.md
+++ b/docs/2-Functions-and-Importing-Data/readme.md
@@ -89,7 +89,7 @@ Many functions also have additional options you can choose, which are called the
In RStudio, you will see the help page for `mean()` in the bottom right corner panel.
-
+
On the help page, under `Usage`, you see `mean(x, ...)`. This means that the only thing that necessarily has to go into `( )` is `x`. On the help page under `Arguments` you will find a description of what `x` needs to be: a numeric or logical vector.
@@ -301,12 +301,12 @@ It's not available because we need to install the package first (again, like ini
In the bottom right panel of RStudio, click on the "Packages" tab then click "Install Packages" in the tool bar.
-
+
A window will pop up. Start typing "EnvStats" into the "Packages" box, select that package, and click "Install".
-
+
Now that we've installed the package, we still can't use the function we want. We need to load the package first (opening the app). We use the `library()` function to do this.
@@ -377,7 +377,14 @@ library(readxl)
Use the `read_excel()` function from the `readxl` package to read emissions data from [this Excel workbook](https://github.com/LADCO/training-r-intro/blob/main/data/emissions_IL_2022.xlsx). Download the file to your working directory and read the first worksheet (named "UNIT_DATA"), skipping the first 6 rows.
-```{r ex-42c0a66e0643, exercise = TRUE, exercise.eval = FALSE, exercise.lines = 5, exercise.cap = 'Read and Inspect Excel Data'}
+```{r ex-4cbff1905b04, exercise = FALSE, eval = FALSE, exercise.cap = 'Read and Inspect Excel Data'}
+library(readxl)
+emissions <- read_excel("emissions_IL_2022.xlsx", sheet = "UNIT_DATA", skip = 6)
+head(emissions)
+
+```
+
+```{r ex-42c0a66e0643, echo = FALSE, eval = TRUE, exercise.cap = 'Read and Inspect Excel Data'}
library(readxl)
emissions <- read_excel("./data/emissions_IL_2022.xlsx", sheet = "UNIT_DATA", skip = 6)
head(emissions)
diff --git a/docs/3-Subsetting-Sorting-and-Combining/readme.md b/docs/3-Subsetting-Sorting-and-Combining/readme.md
index 0e5dafe..5471a26 100644
--- a/docs/3-Subsetting-Sorting-and-Combining/readme.md
+++ b/docs/3-Subsetting-Sorting-and-Combining/readme.md
@@ -99,7 +99,7 @@ View(chicago_air)
```
-
+
By inspecting the data frame this way, you can see that the records are daily values of ozone, temperature, and air pressure. For more information about the data set you can type a question mark in from the name of the data frame variable in the console.
diff --git a/docs/4-Writing-Functions-Conditionals-and-Loops/readme.md b/docs/4-Writing-Functions-Conditionals-and-Loops/readme.md
index b65c65a..3cdae61 100644
--- a/docs/4-Writing-Functions-Conditionals-and-Loops/readme.md
+++ b/docs/4-Writing-Functions-Conditionals-and-Loops/readme.md
@@ -28,10 +28,10 @@ This lesson assumes you are familiar with the material in the previous lessons:
- Functions and Importing Data
- Subsetting, Sorting, and Combining Data Frames
-The data for these lessons is available from this package. It is assumed that this package is already installed and loaded into the R session. If you need to refer to the package, simply refer to it as "this package".
+The data for these lessons is available from this package or the `region5air` package.
-```{r ex-b0dd7d25d817, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Load Data from This Package'}
+```{r ex-868d2c8c5b75, exercise = FALSE, exercise.eval = TRUE, exercise.cap = 'Load Data from the Package'}
# Assuming the package is already loaded
data(chicago_air)
@@ -61,7 +61,7 @@ We can write a simple function that prints something to the console. Here is a
function named `print_hello`.
-```{r ex-a3bc32a64175, exercise = TRUE, exercise.eval = TRUE, exercise.cap = 'Simple Function to Print Hello'}
+```{r ex-a3bc32a64175, exercise = TRUE, exercise.eval = TRUE, eval = TRUE, exercise.cap = 'Simple Function to Print Hello'}
print_hello <- function() {
print("Hello")
diff --git a/docs/5-Plotting/readme.md b/docs/5-Plotting/readme.md
index a447fd1..1a2441f 100644
--- a/docs/5-Plotting/readme.md
+++ b/docs/5-Plotting/readme.md
@@ -337,7 +337,7 @@ Plots can be saved in RStudio using the "Export" button at the top of the "Plots
pane.
-
+
You can also save a plot made by `ggplot2` using the `ggsave()` function.
diff --git a/docs/6-Basic-Statistics/readme.md b/docs/6-Basic-Statistics/readme.md
index e8757ca..c315c9a 100644
--- a/docs/6-Basic-Statistics/readme.md
+++ b/docs/6-Basic-Statistics/readme.md
@@ -219,7 +219,7 @@ Below is a reference table of a few popular tests for categorical data analysis
| test | function |
-| --- | --- |
+| :--- | :--- |
| Chi Square Test | `chisq.test()` |
| Fisher's Test | `fisher.test()` |
| Analysis of Variance | `aov()` |
diff --git a/inst/tutorials/1-Introduction/lesson.Rmd b/inst/tutorials/1-Introduction/lesson.Rmd
index dd3130e..7aed79d 100644
--- a/inst/tutorials/1-Introduction/lesson.Rmd
+++ b/inst/tutorials/1-Introduction/lesson.Rmd
@@ -14,11 +14,13 @@ output:
runtime: shiny_prerendered
---
+
```{r setup, include=FALSE}
library(learnr)
library(gradethis)
library(trainingRIntro)
library(shiny)
+
```
This lesson is a part of the Introduction to R for Air Quality Data Science. The sections below provide a basic introduction to R, including how to install and set up R and RStudio, an overview of R syntax, and how to perform simple operations.
@@ -82,7 +84,7 @@ in the R language.
| Operator | Meaning | Example |
-| --- | --- | --- |
+| :--- | :--- | :--- |
| + | addition | 2 + 2 |
| - | subtraction | 2 - 2 |
| * | multiplication | 2 * 2 |
@@ -160,7 +162,7 @@ x + y
In RStudio, you will see the variables we created in the top right panel.
-
+
-
-This lesson covers how to write your own R functions. It also
+explains how to automate your code using if() and
+ifelse() functions, for loops, and the apply
+function.
print(options("tutorial.storage"))## $tutorial.storage
+## NULLPrerequisites
+This lesson assumes you are familiar with the material in the +previous lessons:
+-
+
- Functions and Importing Data +
- Subsetting, Sorting, and Combining Data Frames +
The data for these lessons is available from this package or the
+region5air package.
# Assuming the package is already loaded
+
+data(chicago_air)Writing Functions
+As discussed in the second lesson on
+functions, R can execute a saved chunk of code by running the name
+of a function, like mean(). The name mean is
+saved like a variable name, but since the name refers to a function, the
+thing that’s saved is not a data object but lines of R code.
To save your own function, use this construction:
+my_function_name <- function() {
+
+ # lines of R code
+
+}We can write a simple function that prints something to the console.
+Here is a function named print_hello.
print_hello <- function() {
+
+ print("Hello")
+
+}print_hello()Arguments
+Most of the functions we have used in these lessons have had at least
+one argument inside of the parentheses. The print_hello()
+function did not have any arguments, so we could run it without putting
+anything inside (). We could modify the function to take an
+argument that pastes some text to the printed message.
Here we recreate the same function, but this time we add an argument
+text inside of the parentheses.
print_hello <- function(text) {
+
+ message <- paste("Hello", text)
+
+ print(message)
+
+}Now there are two lines of code inside of the curly braces. The first
+line uses the paste() function to concatenate two character
+values. The first value will always be “Hello”. The second value will be
+supplied by the user in the text argument.
print_hello(text = "everybody!")Default Values
+We can create a function with more than one argument, and set default +values when needed. Suppose we would like to make a function that checks +if a measurement is greater than a criteria pollutant standard. We could +make a simple function that takes two arguments: one for the measurement +value, and one for the standard value.
+standard_violated <- function(measurement, standard) {
+
+ measurement > standard
+
+}This function will simply return a TRUE or
+FALSE value, depending on whether the measurement is
+greater than the standard or not.
standard_violated(measurement = 84, standard = 70)We could write a more specific function for checking a value against
+the ozone standard. For this function, we want to keep the
+standard parameter but make sure the default is
+70. It may be that we typically want to use this function
+to check against the current 8-hour ozone standard in parts per billion,
+but have the flexibility to use a different value.
To set a default value, we use = 70 when we create the
+function.
standard_violated <- function(measurement, standard = 70) {
+
+ measurement > standard
+
+}Now, when we use the function, it is not necessary to set the
+standard argument.
standard_violated(measurement = 50)Positional Arguments
+One final note on functions in R. When a function is created, the
+order of the arguments are important. The user can supply values for the
+arguments in the order they appeared in the parentheses of the
+function( ){} call, without writing out the argument
+names.
For example, we can supply two numbers to the
+standard_violated() function that we created above, without
+writing out the measurement and standard
+arguments. When R executes the function, it will assign the numbers to
+the arguments based on the position in the parentheses.
Here we show that using two numbers in a different order will return +different outputs.
+standard_violated(60, 70)standard_violated(70, 60)if Functions
+Writing custom functions is a good way to standardize some R code
+that can be reused on different data sets. It’s also helpful to write
+code that will execute different lines of code depending on different
+scenarios. The functions if() and ifelse()
+allow you to control what code is executed based on a logical
+condition.
The if() function takes the logical condition in the
+parentheses. The code that will run if the logical expression is
+TRUE is placed inside curly braces. Below is the outline
+(not actual R code).
if(<logical expression>) {
+
+ <code that will run if the logical expression is TRUE>
+
+}The key word else can also be used with another set of
+curly braces to hold code that will only run if the logical expression
+returns FALSE.
if(<logical expression>) {
+
+ <code that will run if the logical expression is true>
+
+} else {
+
+ <code that will run if the logical expression is false>
+
+}For example, if we wanted to print a message based on the value of +ozone, we could use this construction:
+ozone <- 0.075
+
+if(ozone > 0.065) {
+
+ print("Potential Health Effects")
+
+} else {
+
+ print("All Good")
+
+}Since the expression ozone > 0.065 resolves to
+TRUE, the code in the first set of curly braces is run. If
+we set the ozone variable to 0.06, then the
+logical expression will resolve to FALSE and the code in
+the second curly braces will run.
ozone <- 0.06
+
+if(ozone > 0.065) {
+
+ print("Potential Health Effects")
+
+} else {
+
+ print("All Good")
+
+}The ifelse() function is another way to use a logical
+condition that determines which output is returned. Here is the
+outline
ifelse(<test>, <yes>, <no>)The test argument is the logical expression,
+yes is the value returned if the expression resolves to
+TRUE, and no is the value returned if the
+expression resolves to FALSE.
Here we accomplish the same task as the previous example. This time
+we use the ifelse() function to create a variable named
+message.
ozone_value <- 0.06
+
+message <- ifelse(ozone_value > 0.065, "Potential Health Effects", "All Good")
+
+print(message)For loops
+Like most programming languages, R has for and while loops. This
+tutorial will cover just for loops and move on to apply()
+functions, which are more commonly used in R.
For loops are used to repeat an operation a set number of times. Here +is the basic outline:
+for(i in sequence){
+
+ <code that will run a set number of times>
+
+}The sequence parameter is typically a vector. The
+i parameter is a variable that will take on the values in
+the sequence vector. For instance, if sequence
+was the vector c(1, 2, 3) then the i variable
+will take on each of those values in turn.
This example simply prints the values of the vector as the for loop +progresses.
+for(i in c(1, 2, 3)) {
+
+ print(i)
+
+}Typically when we use loops, we want to perform the same operation on
+different sets of data and save the results. To do this using the
+for() function in R, we need to create an empty vector (or
+list or data frame) and save the results during each iteration.
Here is an example data frame we will use. It represents a few values +from three monitors.
+monitors <- data.frame(monitor1 = c(50, 60, 58, 52),
+ monitor2 = c(55, 59, 65, 61),
+ monitor3 = c(70, 62, 68, 71))
+
+monitorsIn the code below, we create an empty vector called max_values. As +the for() function loops through the vector c(1, 2, 3), the data frame +columns are accessed using square brackets [ , i]. Each max value is +saved to the max_values vector using square brackets [i].
+max_values <- c()
+
+for(i in c(1, 2, 3)) {
+
+ max_values[i] <- max(monitors[, i])
+
+}
+
+max_valuesapply function
+Although the for loop is common in programming languages, it is not
+the most efficient way to apply a function to different sets of data in
+R. The most efficient way to do loops in R is with the
+apply() family of functions, including
+lapply(), tapply(), and mapply().
+This section will demonstrate how to use the apply()
+function.
The apply() function takes a data frame (or matrix) as
+the first argument. The second argument determines if each loop will
+apply to the rows (1) or columns (2). And the
+third argument is the function you want to apply to each row or column.
+Additional arguments can be used to pass on to the function being
+applied to each row or column.
The example below applies the max() function to the
+monitors data frame from the previous section.
monitors_max <- apply(monitors, MARGIN = 2, FUN = max)
+
+monitors_maxThe MARGIN argument is set to 2 because we are applying the max() +function to the columns of the data frame. Also notice that we do not +need to create an initial empty vector, as we did with the for() +function. The returned value is a named vector that is as long as the +number of columns in the data frame.
+We could also find the mean of each row in the monitors
+data frame. To do this, we would set the MARGIN argument to
+1.
apply(monitors, MARGIN = 1, FUN = mean)Exercises
+Exercise 1
+Write a function named ppm_to_ppb that converts a value
+from parts per million to parts per billion.
# Your code here# Define the function `ppm_to_ppb` with one argument for the value you want to convert.# Inside the function, multiply the input value by 1000 to convert it to parts per billion.# Return the converted value.ppm_to_ppb <- function(value) {
+ value * 1000
+}Exercise 2
+Write a function named check_value that prints “warning”
+if a value is above a threshold, and “OK” if it’s below. Make the
+threshold an argument in the function.
# Your code here# Define the function `check_value` with two arguments: `value` and `threshold`.# Use an `if` statement to check if `value` is greater than `threshold`.# Print "warning" if the value is above the threshold, otherwise print "OK".check_value <- function(value, threshold) {
+ if(value > threshold) {
+ print("warning")
+ } else {
+ print("OK")
+ }
+}Exercise 3
+Use the for() function to loop through the
+temp column of the data from this package and print any
+value that is above 90 degrees.
# Your code here# Access the `temp` column of the data frame within the loop.# Use a `for` loop to iterate over each value in the `temp` column.# Inside the loop, use an `if` statement to check if the current temperature value is greater than 90.# If a value is above 90, use `print()` to display it.for (i in chicago_air$temp) {
+ if(i > 90) {
+ print(i)
+ }
+}Exercise 4
+Use the apply() function to create a vector of the mean
+values from the numeric columns in the data from this package.
# Your code here# First, subset the data frame to include only the numeric columns you wish to analyze.# Use the `apply()` function with the subsetted data frame, specifying `MARGIN = 2` to apply the function across columns.# Use the `mean` function as the `FUN` argument, including `na.rm = TRUE` to handle missing values.chicago_numeric <- chicago_air[, c("ozone", "temp", "pressure")]
+mean_values <- apply(chicago_numeric, MARGIN = 2, FUN = mean, na.rm = TRUE)
+mean_valuesNext Lesson
+You have completed Lesson . Click the button below to mark it as +complete and move on to the next lesson.
+ ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +