1.1 Source

The source is where your R ‘scripts’ go. Your script is simply a text document in which you write most of your code. Open a new script under the File menu (File -> New File -> R Script). Now, into your script, type the following piece of code.

print('pumped for R!')

Great! Now you need to run your code piece of code. To do this, make sure your cursor is somewhere on the line of code you just typed, then hit ‘Command + Enter’ on MacOS, or ‘Control + Enter’ on WindowsOS. There is also a ‘Run’ button at the top right of the source panel, but you should not need this too often.

To run a chunk of code, you can highlight the relevant code, then use the ‘Command + Enter’ keys.

You can open your script in a new window. To do this, click on the third icon from the left in the toolbar directly above the script.

You can change the theme of the editor. To do this, select the ‘Tools’ menu. Then select ‘Global Options’. Choose the ‘Appearance’ tab. Pick a sexy theme.

Data tables (spreadsheets) are also viewed in the source pane. To see this, type and run the code below. (cars is the name of a dataset that comes loaded with R.)

View(cars)

Question: How many rows and columns are there in the cars dataset?

1.2 Console

The console, the bottom left pane, shows the lines of code that you have run as well as the output generated by those lines of code.

You can also type code directly into the console. Try doing that now. Type a random piece of code and press ‘Enter’.

Question: How is code written in the console different from that in the script?

1.3 Environment, History, Connections etc.

The Environment tab shows datasets that are loaded. To explore this tab, let’s load one of R’s built-in datasets. Run the following code, either from your script or your console:

data(USArrests)
USArrests

Now that the dataset is loaded, you can click on the blue dropdown icon to the left of the dataset to reveal a summary. Clicking directly on the dataset opens it in a ‘View’ tab.

The History tab gives you a history of past code that has been run.

The Connections tab allows you to connect to external databases. You will not need this.

Question: Are all US states included in this dataset?

You can remove an object from the workspace with the rm() function:

# remove the 'cars' data frame
rm(cars)

# remove everything in the workspace
rm( list=ls() )

That last command is pretty difficult to remember. Just Google “how to clear workspace in R” when you forget it.

1.4 Files, Plots, Packages, Help, Viewer etc.

Let’s look now at the bottom right pane. We will consider each tab there in order.

First the Files tab. You can load data into your R workspace from the ‘Files’ panel but this is not recommended. We will look at better ways to load in data soon.

Next, the Plots tab. Here is where your plots show up. Try generating a simple plot with the following code:

plot(cars)

You can zoom into the plot, and save it as well, by clicking on the respective icons above the plot. Try this.

Question: How many different image formats are available for exported plots?

Next, the Packages tab is where you can install and load R packages. Most of the functionality of R comes from its huge library of packages. Let’s install the ‘ggplot2’ package, which is a popular graphics package that we will need later. Click on the ‘Install’ icon and type in ggplot2.

Now, we need to load the package into our library in order to use it. Do this by ticking the checkbox to the left of the package name.

We can also install and load a package in the R script or console, completely avoiding the dialogue boxes. This is shown below:

install.packages('ggplot2')
library(ggplot2)

Next up, The Help tab shows the documentation for different functions and data frames. Try running each line below to see what documentation looks like:

?plot
?print
?cars

Study the format of these help files. You will need to use them often.

2.1 Comments

By the way, have you noticed the lines preceded by a # symbol above? Anything written after that symbol is treated as a comment. Comments are a great way of keeping track of what each step of your code is doing. In general, you should write a comment before each ‘chunk’ of code. You will thank yourself for this in the future when you are trying to decipher what your code does. Others who read your code will also greatly appreciate all comments.

3.1 Arithmetic functions

Let’s look first at some arithmetic functions. Run these and observe their output.

# square root
sqrt(9)

# log base 8
log(32, 2)

# truncate 
trunc(4.3)

Your turn: 1. Write code to round the number 3.56 to its nearest whole number. 2. Write code to find the log, base 7, of the number 117649. Use Google to find out what the needed functions are.

3.2 Statistical functions

R has several built-in statistical functions. Run the code lines below.

# arithmetic mean
mean(4,5)

# median 
median(5,6,7)

Hmmm the answers to those probably do not look right. Welcome to your first syntax problem! Most of your time learning R (or any other programming language) will be spent trying to figure what went wrong with your syntax.

Here, the problem is this: when you want to refer to a bunch objects as a group in R, you need to ‘concatenate’ them into a ‘vector’. A vector is a fancy name for a list.

Let’s try that again.

mean(  c(3,4,5)  )

median(  c(5,6,7)  )

That’s more like it!

Note that R is ‘space-insensitive’. So you can use as many or as few spaces as you like. In the above code, we used more white space than necessary.

Your turn: Write code to calculate the variance and standard deviation of the following set of numbers: 3,4,7,18,22,78. Use Google to find out what the functions for variance and standard deviation are.

3.3 Text functions

Now, some text functions. When you want something to be treated as text, you enclose it in single or double quotation marks. A piece of text enclosed in quotation marks is called a ‘string’. Let’s look at some simple text/string functions. Run the following pieces of code.

# change case
toupper('He unfollowed me!')

tolower('WOW')

# paste a bunch of text together
paste("cat", "and", "dog")

# Find and substitute
gsub('Celeste', 'Ceci', 'My name is Celeste. Celeste is my name') 

This last command does the following:

• Takes the sentence ‘My Name is Celeste’
• Then substitutes all instances of ‘Celeste’ with ‘Ceci’

Your turn: 1. Use the gsub function to replace the numerals in the paragraph below with the spelled-out numbers:

“So far we have seen 3 cases in Orange County. In San Francisco, that number is 5. Although 3 and 5 are small numbers, health officials are worried”

3.4 Writing your own functions

As you advance in programming expertise, you will eventually need to write your own functions. Let’s look at this very cursorily. Below, we write a function to calculate the roots of a quadratic equation.

# Define the function 'getroots' that takes in values a, b and c
getroots <- function(a,b,c) {
  root1 <- (-b + sqrt(b^2 - 4*a*c)) / 2*a # then applies the quadratic formula 
  root2 <- (-b - sqrt(b^2 - 4*a*c)) / 2*a
    return(c(root1, root2))  # then returns both roots
}

Now, you should test that function on the following quadratic to see if it works. \[x^2 + 3x - 4 \]

Your turn : Write a function that takes in a set of numbers, then outputs the mean, median and mode, in that order, of the set of numbers.