Customize R Studio

Let’s add a little style.

Change fonts and colors

1. Go to Tools on the top navigation bar
   
2. Choose Global Options...
3. Choose Appearance with the paint bucket
4. Increase the overall Zoom to 125%
   
5. Increase the Editor Font size
6. Pick an Editor theme you like
   • The default is Textmate

Let’s make a droid!

Try adding the code below to your R script to create an object called droid.

Assignment operator

# Create a new object
droid <- "bb7"

droid 

# Give it a new value
droid <- "bb8"

droid 

To run a line of code in your script, click the cursor anywhere on that line and press CTRL+ENTER.

The droid wants a friend. Let’s create a Wookie named Chewbacca.

wookie <- "Chewbacca"

wookie

Break some things

# Try this:
wookie <- Chewbacca

Drop and remove data

You can drop objects with the remove function rm(). Try it out on some of your wookies.

# Delete objects to clean-up your Environment
rm(wookie)

rm(wookie2)

What’s a good name?

Everything has a name in R and you can name things almost anything you like, even TOP_SECRET_shhhhhh... or data_McData_face.

Sadly, there are a few restrictions. R doesn’t like names to include spaces or special characters found in math equations, like +, -, *, \, /, =, !, or ).

# What happens when you add these to your R script?
n wookies <- 5

n*wookies <- 5

n_wookies <- 5

n.wookies <- 5

wookies! <- "Everyone"

# Names cannot begin with a number
1st_wookie <- "Chewbacca"

88b <- "droid"

# But they can contain numbers
wookie1 <- "Chewbacca"

bb8 <- "droid"

# What if you have 10,000 wookies?
n_wookies <- 10,000  # Error

n_wookies <- 10000

Collect multiple items

We can put multiple values inside c() to make a vector of items. It’s like a chain of items, where each additional item is connected by a comma. The c stands for concatenate or to collect.

Let’s use c() to create a few vectors of names.

# Create a character vector and name it starwars_characters
starwars_characters <- c("Luke", "Leia", "Han Solo")

# Print starwars_characters
starwars_characters

## [1] "Luke"     "Leia"     "Han Solo"

# Create a numeric vector and name it starwars_ages
starwars_ages  <- c(19,19,25)

# Print the ages to the console
starwars_ages

## [1] 19 19 25

Make a table

A table in R is known as a data frame. We can think of it as a group of columns, where each column is made from a vector. Data frames in R have columns of data that are all the same length.

Let’s make a data frame with two columns to hold the character names and their ages.

# Create table with 2 columns: characters & ages
starwars_df <- data.frame(characters = starwars_characters,
                          ages       = starwars_ages)

# Print the starwars_df data frame to the console
starwars_df

##   characters ages
## 1       Luke   19
## 2       Leia   19
## 3   Han Solo   25

starwars_df <- data.frame(characters  = starwars_characters, 
                          ages        = starwars_ages, 
                          fathers     = __________________)

starwars_df <- data.frame(characters  = starwars_characters, 
                          ages        = starwars_ages, 
                          fathers     = c("Darth", "Darth", "Unknown"))

# View the "ages" column in starwars_df
starwars_df$ages

## [1] 19 19 25

New Message

item,origin,destination,amount,units,price_per_pound  
Flight recorder,Outskirts,Niima Outpost,887,Tons,590.93  
Proximity sensor,Outskirts,Raiders,7081,Tons,1229.03  
Aural sensor,Tuanul,Raiders,707,Tons,145.27  
Electromagnetic filter,Tuanul,Niima Outpost,107,Tons,188.2  
...  

CSV to the rescue

The main data format used in R is the CSV (comma-separated values). A CSV is a simple text file that can be opened in R and most other data tools, including Excel. It looks squished together as plain text, but that’s okay! When opened in R, the text becomes a familiar looking table with columns and rows.

Before we launch ahead, let’s add a package to R that will help us read CSV files.

Read the data

Let’s load the readr package to use the read_csv() function.

#install.packages("readr")
library(readr)

read_csv("https://mn-r.netlify.com/data/starwars_scrap_jakku.csv")

## # A tibble: 1,132 × 7
##    receipt_date item                         origin desti…¹ amount units price…²
##    <chr>        <chr>                        <chr>  <chr>    <dbl> <chr>   <dbl>
##  1 4/1/2013     Flight recorder              Outsk… Niima …    887 Tons     591.
##  2 4/2/2013     Proximity sensor             Outsk… Raiders   7081 Tons    1229.
##  3 4/3/2013     Vitus-Series Attitude Thrus… Reest… Raiders   4901 Tons     226.
##  4 4/4/2013     Aural sensor                 Tuanul Raiders    707 Tons     145.
##  5 4/5/2013     Electromagnetic discharge f… Tuanul Niima …    107 Tons     188.
##  6 4/6/2013     Proximity sensor             Tuanul Trade …  32109 Tons    1229.
##  7 4/7/2013     Hyperdrive motivator         Tuanul Trade …    862 Tons    1485.
##  8 4/8/2013     Landing jet                  Reest… Niima …  13944 Tons    1497.
##  9 4/9/2013     Electromagnetic discharge f… Crate… Raiders   7788 Tons     188.
## 10 4/10/2013    Sublight engine              Outsk… Niima …  10642 Tons    7211.
## # … with 1,122 more rows, and abbreviated variable names ¹​destination,
## #   ²​price_per_pound
## # ℹ Use `print(n = ...)` to see more rows

Name the data

Where did the data go after you read it into R? When we want to work with the data in R, we need to give it a name with the assignment operator: <-.

# Read in scrap data and set name to "scrap"
scrap <- read_csv("https://mn-r.netlify.com/data/starwars_scrap_jakku.csv")

# Type the name of the table to view it in the console
scrap

## # A tibble: 1,132 × 7
##    receipt_date item                         origin desti…¹ amount units price…²
##    <chr>        <chr>                        <chr>  <chr>    <dbl> <chr>   <dbl>
##  1 4/1/2013     Flight recorder              Outsk… Niima …    887 Tons     591.
##  2 4/2/2013     Proximity sensor             Outsk… Raiders   7081 Tons    1229.
##  3 4/3/2013     Vitus-Series Attitude Thrus… Reest… Raiders   4901 Tons     226.
##  4 4/4/2013     Aural sensor                 Tuanul Raiders    707 Tons     145.
##  5 4/5/2013     Electromagnetic discharge f… Tuanul Niima …    107 Tons     188.
##  6 4/6/2013     Proximity sensor             Tuanul Trade …  32109 Tons    1229.
##  7 4/7/2013     Hyperdrive motivator         Tuanul Trade …    862 Tons    1485.
##  8 4/8/2013     Landing jet                  Reest… Niima …  13944 Tons    1497.
##  9 4/9/2013     Electromagnetic discharge f… Crate… Raiders   7788 Tons     188.
## 10 4/10/2013    Sublight engine              Outsk… Niima …  10642 Tons    7211.
## # … with 1,122 more rows, and abbreviated variable names ¹​destination,
## #   ²​price_per_pound
## # ℹ Use `print(n = ...)` to see more rows

?read_csv

read_csv("https://itep-r.netlify.com/data/starwars_scrap_jakku.csv", 
         skip = 1)

# Read in 100 rows
small_data <- read_csv("https://mn-r.netlify.com/data/starwars_scrap_jakku.csv", 
                       skip = 1, 
                       n_max = 100)

# Remove the data
rm(small_data)

Plot the data, Plot the data, Plot the data

In data analysis you’ll want to look at your data early and often. For that we use a new package called ggplot2!

Install the package by running

install.packages("ggplot2")

A column plot

Here’s a simple chart showing the total amount of scrap sold from each origin location.

library(ggplot2)

ggplot(scrap, aes(y = amount, x = origin)) + 
  geom_col() +
  labs(title = "Which origin sold the most scrap?") + 
  theme_gray()

## Warning: Removed 910 rows containing missing values (position_stack).

Well, well, well, it looks like there is an All category we should look into more. Either there is a town hogging all the scrap or the data needs some cleaning.

Got questions

Lost in an ERROR message? Is something behaving strangely and want to know why? Want ideas for a new chart?

Go to Help! for troubleshooting, cheatsheets, and other learning resources.

Key terms

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