Customize R Studio

Data awaits us…

Start an R project

Let’s make a new project for our Jakku mischief.

Step 1: Start a new project

• In Rstudio select File from the top menu bar
• Choose New Project…
• Choose New Directory
• Choose New Project
• Enter a project name such as "starwars"
• Select Browse… and choose a folder where you normally perform your work.
• Click Create Project

Step 2: Open a new script

• File > New File > R Script
• Click the floppy disk save icon
• Give it a name: jakku.R or lesson1.R will work well

Assignment operator

# Create a new object
droid <- "bb8"

droid 

wookie <- "Chewbacca"

wookie

Break some things

# To save text to a character object you need quotation marks: "text"

# Try this:
wookie <- Chewbacca

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

Copy objects

# To copy an object, assign it to a new name
wookie2 <- wookie


# Or overwrite an object with new "text"
wookie <- "Tarfful"
  
wookie


# Did this change the value stored in wookie2?
wookie2  

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. You can even name your data TOP_SECRET_shhhhhh... or Luke_I_am_your_father or data_McData_face.

Sadly, there are a few minor restrictions. Names cannot include spaces or special characters that might be found in math equations, like +, -, *, \, /, =, !, or ).

n wookies <- 5

n*wookies <- 5

n_wookies <- 5

n.wookies <- 5


all_the_wookies! <- "Everyone on Kashyyyk"

# You can add one wookie
n_wookies <- n_wookies + 1


# But what if you have 10,000 wookies?
n_wookies <- 10,000


# They also cannot begin with a number.
1st_wookie <- "Chewbacca"

88b <- "droid"

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

bb8 <- "droid"

Multiple items

We can add 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 to concatenate or to combine values into a vector.

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 to the console
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 table with 2 columns to hold the character names and their ages.

# Create table with columns "character" and "ages" with values from the starwars_names and starwars_ages vectors
starwars_df <- data.frame(character = starwars_characters,
                          ages  = starwars_ages)

# Print the starwars_df data frame to the console
starwars_df

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

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

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

Show all values in a $column_name

Use the $ sign after the name of your table to see the values in one of your columns.

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

## [1] 19 19 25

Leave a #comment

The lines of code in the scripts that start with a # in front are called comments. Every line that starts with a # is ignored and won’t be run as R code. You can use the # to add notes in your script to make it easier for others and yourself to understand what is happening and why. You can also use comments to add warnings or instructions for others using your code.

New Message (1)

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 stats software, 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.

📦 Add new packages

What is an R package?

A package is a small add-on for R, it’s like a phone App for your phone. They add capabilities like statistical functions, mapping powers, and special charts to R. In order to use a new package we first need to install it. Let’s try it!

Add a package to your library

1. Open RStudio
2. Type install.packages("readr") in the lower left console
3. Press Enter
4. Wait two seconds
5. Open the Packages tab in the lower right window of RStudio to see the packages in your library
   • Use the search bar to find the readr package

Your installed packages are stored in your R library. The Packages tab on the right shows all of the available packages installed in your library. When you want to use one of them, you load it in R. Loading a package is like opening an App on your phone. To load a package we use the library() function. Once you load it, the package will stay loaded until you close RStudio.

Let’s load the readr package so we can use the read_csv() function to read the Jakku scrap data.

Read the data

library(readr)

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

## Parsed with column specification:
## cols(
##   receipt_date = col_character(),
##   item = col_character(),
##   origin = col_character(),
##   destination = col_character(),
##   amount = col_double(),
##   units = col_character(),
##   price_per_pound = col_double()
## )

## # A tibble: 1,132 x 7
##    receipt_date item       origin  destination amount units price_per_pound
##    <chr>        <chr>      <chr>   <chr>        <dbl> <chr>           <dbl>
##  1 4/1/2013     Flight re~ Outski~ Niima Outp~    887 Tons             591.
##  2 4/2/2013     Proximity~ Outski~ Raiders       7081 Tons            1229.
##  3 4/3/2013     Vitus-Ser~ Reestki Raiders       4901 Tons             226.
##  4 4/4/2013     Aural sen~ Tuanul  Raiders        707 Tons             145.
##  5 4/5/2013     Electroma~ Tuanul  Niima Outp~    107 Tons             188.
##  6 4/6/2013     Proximity~ Tuanul  Trade cara~  32109 Tons            1229.
##  7 4/7/2013     Hyperdriv~ Tuanul  Trade cara~    862 Tons            1485.
##  8 4/8/2013     Landing j~ Reestki Niima Outp~  13944 Tons            1497.
##  9 4/9/2013     Electroma~ Crater~ Raiders       7788 Tons             188.
## 10 4/10/2013    Sublight ~ Outski~ Niima Outp~  10642 Tons            7211.
## # ... with 1,122 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://itep-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 x 7
##    receipt_date item       origin  destination amount units price_per_pound
##    <chr>        <chr>      <chr>   <chr>        <dbl> <chr>           <dbl>
##  1 4/1/2013     Flight re~ Outski~ Niima Outp~    887 Tons             591.
##  2 4/2/2013     Proximity~ Outski~ Raiders       7081 Tons            1229.
##  3 4/3/2013     Vitus-Ser~ Reestki Raiders       4901 Tons             226.
##  4 4/4/2013     Aural sen~ Tuanul  Raiders        707 Tons             145.
##  5 4/5/2013     Electroma~ Tuanul  Niima Outp~    107 Tons             188.
##  6 4/6/2013     Proximity~ Tuanul  Trade cara~  32109 Tons            1229.
##  7 4/7/2013     Hyperdriv~ Tuanul  Trade cara~    862 Tons            1485.
##  8 4/8/2013     Landing j~ Reestki Niima Outp~  13944 Tons            1497.
##  9 4/9/2013     Electroma~ Crater~ Raiders       7788 Tons             188.
## 10 4/10/2013    Sublight ~ Outski~ Niima Outp~  10642 Tons            7211.
## # ... with 1,122 more rows

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

# Read in 100 rows
small_data <- read_csv("https://itep-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 it is really important to look at your data early and often. For that, let’s add a new package called ggplot2!

Install it by running the following in your Console:

• install.packages("ggplot2")

NOTE

You can also install packages from the Packages tab in the lower right window of RStudio.

A column plot

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

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.5.3

ggplot(scrap, aes(y = amount, x = origin)) + 
  geom_col() +
  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.

RECAP

• What packages have we added to our library?

• What new functions have we learned?

REVIEW: Functions & arguments

Functions perform steps based on inputs called arguments and usually return an output object. There are functions in R that are really complex but most boil down to the same general setup:

new_output <- function(argument_input1, argument_input2)

You can make your own functions in R and name them almost anything you like, even my_amazing_starwars_function().

You can think of a function like a plan for making Clone Troopers.

create_clones(host       = "Jango Fett", 
              n_troopers = 2000)

The function above creates Clone Troopers based on two arguments: the host and n_troopers. When we have more than one argument, we use a comma to separate them. With some luck, the function will successfully return a new object - a group of 2,000 Clone Troopers.

The sum() function

We can use the sum() function to find the sum age of our Star Wars characters.

# Call the sum function with starwars_ages as input
ages_sum <- sum(starwars_ages) # Assigns the output to starwars_ages_sum

# Print the starwars_ages_sum value to the console
ages_sum

## [1] 63

The sum() function takes the starwars_ages vector as input, performs a calculation, and returns a number. Note that we assigned the output to the name ages_sum.

If we don’t assign the output it will be printed to the console and not saved.

# Alternative without assigning output
sum(starwars_ages) 

## [1] 63

NOTE

The original starwars_ages vector has not changed. Each function has its own “environment” and its calculations happen inside a bubble. In general, what happens inside a function won’t change your objects outside of the function.

starwars_ages

## [1] 19 19 25