R03: R Basics, part 2

Meike Niederhausen and Nicky Wakim

2024-10-07

Last time

Downloaded R and Rstudio
If you are still having issues downloading, please come to my office hours!
Became familiar with the console and a script file
Did some math calculations in R!

Today, we’re going to work on…

Assigning things in R
Using functions to calculate
Familiarizing ourselves with common issues
Troubleshooting with different tools
Go over an example dataset

Where are we?

We will open RStudio on our computer (not R!)

Modern Dive

RStudio anatomy

Emma Rand

Variables (saved R objects)

Variables are used to store data, figures, model output, etc.

Can assign a variable using either = or <-
- Using <- is preferable for certain occasions
- I usually just use = because less typing hehe

Assign just one value:

x = 5
x

[1] 5

x <- 5
x

[1] 5

Assign a vector of values

Consecutive integers using :

a <- 3:10
a

[1]  3  4  5  6  7  8  9 10

Concatenate a string of numbers

b <- c(5, 12, 2, 100, 8)
b

[1]   5  12   2 100   8

Let’s try it out!

Create a new variable y that is assigned the value of 8
Create a new variable c that is assigned the vector of values 15 through 20
Create a new variable d that is assigned the vector of values 16 through 19 and 22.

Did you notice anything in the Environment section of Rstudio?

03:00

Doing math with variables

Math using variables with just one value

x <- 5
x

[1] 5

x + 3

[1] 8

y <- x^2
y

[1] 25

Math on vectors of values:
element-wise computation

a <- 3:6
a

[1] 3 4 5 6

a+2; a*3

[1] 5 6 7 8

[1]  9 12 15 18

a*a

[1]  9 16 25 36

Let’s try it out!

Use the variable name y to find the addition of y and 5
Add 5 to the vector c

02:00

Variables can include text (characters)

hi <- "hello"
hi

[1] "hello"

greetings <- c("Guten Tag", "Hola", hi)
greetings

[1] "Guten Tag" "Hola"      "hello"

Using functions

mean() is an example of a function
functions have “arguments” that can be specified within the ()
?mean in console will show help file for mean()

Function arguments specified by name:

mean(x = 1:4)

[1] 2.5

seq(from = 1, to = 12, by = 3)

[1]  1  4  7 10

seq(by = 3, to = 12, from = 1)

[1]  1  4  7 10

Function arguments not specified, but listed in order:

mean(1:4)

[1] 2.5

seq(1, 12, 3)

[1]  1  4  7 10

Now let’s use some functions for summary statistics

We will calculate the mean for c
Let’s also calculate the standard deviation for c
- Recall, our function is sd()
- Use ?sd in the console to identify the arguments for c

If you have more time, you can try to calculate the median and IQR for c

03:00

Common console errors (1/2)

Incomplete commands

When the console is waiting for a new command, the prompt line begins with >
- If the console prompt is +, then a previous command is incomplete
- You can finish typing the command in the console window

Example:

> 3 + (2*6
+ )

[1] 15

Common console errors (2/2)

Object is not found

This happens when text is entered for a non-existent variable (object)

Example:

hello

Error in eval(expr, envir, enclos): object 'hello' not found

Can be due to missing quotes

install.packages(dplyr)

Error in eval(expr, envir, enclos): object 'dplyr' not found

# correct code is: install.packages("dplyr")

Getting help with R

There are many ways to get help when you are stuck

Use the ? in front of the function name to get more information!
- Usually if I need help with the arguments for a function
Google or go to stackoverflow.com
- Often when I Google, I get redirected to something like stackoverflow
- For example, let’s say my mean function was outputting NA. I would Google something like “keep getting NA for mean in R” Then end up here
I can also go to my favorite AI tool to get help
- This is most useful for getting code started if it’s complicated (we’re not really at that level yet)
- I asked ChatGPT “can you give me the code for calculating the mean in R”
  - This is what I got
- For code generation, it gives you WAY too much
- I also asked ChatGPT “Why is the mean function in R giving me an NA?” (in above link)

Let’s try with an example dataset

Fisher’s (or Anderson’s) Iris data set

Data description:

n = 150
3 species of Iris flowers (Setosa, Virginica, and Versicolour)
- 50 measurements of each type of Iris
Variables:
- sepal length, sepal width, petal length, petal width, and species

Gareth Duffy

View the `iris` dataset

The iris dataset is already pre-loaded in base R and ready to use.
Type the following command in the console window
- Warning: this command cannot be rendered. It will give an error.

View(iris)

A new tab in the scripting window should appear with the iris dataset.

Data structure (1/2)

What are the different variable types in this data set?
We are going to use the str function
- Can you use the console to tell me what we can input into str?

Data structure (2/2)

What are the different variable types in this data set?
We are going to use the str function
- Can you use the console to tell me what we can input into str?

str(iris)   # structure of data

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Data set summary

Can we quickly summarize all the data?

summary(iris)

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50

Data set info

You can use different functions to find information on a data frame

dim(iris)

[1] 150   5

nrow(iris)

[1] 150

ncol(iris)

[1] 5

names(iris)

[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

We can also look at the Environment section

Take a moment to find the information on the iris data frame

Go to environment section to see the iris data frame

View the beginning or end of a dataset

These commands can be helpful if the data frame has a lot of rows

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

tail(iris)

    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
145          6.7         3.3          5.7         2.5 virginica
146          6.7         3.0          5.2         2.3 virginica
147          6.3         2.5          5.0         1.9 virginica
148          6.5         3.0          5.2         2.0 virginica
149          6.2         3.4          5.4         2.3 virginica
150          5.9         3.0          5.1         1.8 virginica

Specify how many rows to view at beginning or end of a dataset

head(iris, 3)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa

tail(iris, 2)

    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
149          6.2         3.4          5.4         2.3 virginica
150          5.9         3.0          5.1         1.8 virginica

The `$`

Suppose we want to single out the column of petal width values.
One way to do this is to use the $
- DatSetName$VariableName

iris$Petal.Width

  [1] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 0.2 0.2 0.1 0.1 0.2 0.4 0.4 0.3
 [19] 0.3 0.3 0.2 0.4 0.2 0.5 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.4 0.1 0.2 0.2 0.2
 [37] 0.2 0.1 0.2 0.2 0.3 0.3 0.2 0.6 0.4 0.3 0.2 0.2 0.2 0.2 1.4 1.5 1.5 1.3
 [55] 1.5 1.3 1.6 1.0 1.3 1.4 1.0 1.5 1.0 1.4 1.3 1.4 1.5 1.0 1.5 1.1 1.8 1.3
 [73] 1.5 1.2 1.3 1.4 1.4 1.7 1.5 1.0 1.1 1.0 1.2 1.6 1.5 1.6 1.5 1.3 1.3 1.3
 [91] 1.2 1.4 1.2 1.0 1.3 1.2 1.3 1.3 1.1 1.3 2.5 1.9 2.1 1.8 2.2 2.1 1.7 1.8
[109] 1.8 2.5 2.0 1.9 2.1 2.0 2.4 2.3 1.8 2.2 2.3 1.5 2.3 2.0 2.0 1.8 2.1 1.8
[127] 1.8 1.8 2.1 1.6 1.9 2.0 2.2 1.5 1.4 2.3 2.4 1.8 1.8 2.1 2.4 2.3 1.9 2.3
[145] 2.5 2.3 1.9 2.0 2.3 1.8

Example using the `$`

The $ is helpful if you want to create a new dataset for just that one variable, or, more commonly, if you want to calculate summary statistics for that one variable.

mean(iris$Petal.Width)

[1] 1.199333

sd(iris$Petal.Width)

[1] 0.7622377

median(iris$Petal.Width)

[1] 1.3

Inline code

With markdown you can also report R code output inline with the text instead of using a chunk.

Text in editor:

Output:

The mean petal width for all 3 species combined is 1.2 (SD = 0.8) cm.

Reporting summary statistics this way in a report, makes the numbers computationally reproducible.
For example, if this were for an abstract and a year later you are wondering where the numbers came from, your R code will tell you exactly which dataset was used to calculate the values.

R03: R Basics, part 2

Last time

Today, we’re going to work on…

Where are we?

We will open RStudio on our computer (not R!)

RStudio anatomy

Variables (saved R objects)

Let’s try it out!

Doing math with variables

Let’s try it out!

Variables can include text (characters)

Using functions

Now let’s use some functions for summary statistics

Common console errors (1/2)

Common console errors (2/2)

Getting help with R

There are many ways to get help when you are stuck

More on AI usage

Let’s try with an example dataset

Fisher’s (or Anderson’s) Iris data set

View the `iris` dataset

Data structure (1/2)

Data structure (2/2)

Data set summary

Data set info

Take a moment to find the information on the iris data frame

View the beginning or end of a dataset

Specify how many rows to view at beginning or end of a dataset

The `$`

Example using the `$`

Inline code

Some sources for useful base R commands

R03: R Basics, part 2

Last time

Today, we’re going to work on…

Where are we?

We will open RStudio on our computer (not R!)

RStudio anatomy

Variables (saved R objects)

Let’s try it out!

Doing math with variables

Let’s try it out!

Variables can include text (characters)

Using functions

Now let’s use some functions for summary statistics

Common console errors (1/2)

Common console errors (2/2)

Getting help with R

There are many ways to get help when you are stuck

More on AI usage

Let’s try with an example dataset

Fisher’s (or Anderson’s) Iris data set

View the iris dataset

Data structure (1/2)

Data structure (2/2)

Data set summary

Data set info

Take a moment to find the information on the iris data frame

View the beginning or end of a dataset

Specify how many rows to view at beginning or end of a dataset

The $

Example using the $

Inline code

Some sources for useful base R commands

View the `iris` dataset

The `$`

Example using the `$`