Lesson 02: Reading Data

Learning the readr package

We will cover three main options how to importing data:

Below are examples on each of these methods to import data

Built-in Datasets

The iris dataset is commonly used and built into R; no additional downloads are necessary.

View(iris)
head(iris)


Reading Data from Local CSV File

Option 1: Import Dataset Button

Another option is to use the “Import Dataset” button in the environment pane of RStudio.

The data will now be available to you.

Option 2: Copy File Path Option

The readr package is the first package that we will learn about. This allows us to read in rectangular data like cvs files.

This is the typical way it goes when you have data located on your computer.

library(tidyverse)

dataset <- read_csv(file_path)

head(dataset)
Windows Users: This will not run

One backslash \

data1 <- read_csv("C:\Users\name\Downloads\dataset.csv")
Windows Users: Either of these options will run

Two backslashes \\

data1 <- read_csv("C:\\Users\\name\\Downloads\\dataset.csv")

One forwardslash /

data1 <- read_csv("C:/Users/name/Downloads/dataset.csv")


Option 3: URL Method

Often you get your data from online somewhere like GitHub or Kaggle. While there is the option to download the file, then read it from your computer locally, like we just did above, there’s also the option to read in data from a URL.

A benefit is that less memory is used on your computer. Another is that you can send your analysis with the data being read in from a URL, and others will be able to run the script without any data issues.

Below is an example.

library(tidyverse)

housing_data <- read_csv("https://raw.githubusercontent.com/saundersg/Statistics-Notebook/master/Data/Rent.csv")

How to Look at the Data?

view(): this opens a new tab to view the data in a spreadsheet style like Excel

head(): this returns the top six rows of a dataset, or the “head” of the data

head(housing_data)
View(housing_data)


Practices

First, let’s make a folder on your computer to use for bootcamp. I suggest putting it in your downloads folder or your documents folder and call it “R Bootcamp”.

Next, click the “Session” button at the top of RStudio, then “Set Working Directory”, then “Choose Directory” and use your computer’s file directory to navigate to the folder you made for bootcamp.

This set your “working directory” which basically makes it easier for us to deal with files.

Practice 1: 📃 Insurance 📑

Instructions:

  1. Click the link to download the insurance dataset.
  2. Use the two commands learned from above to look at the data.

Starter Code:

library(tidyverse)

insurance <- read_csv("path_to_dataset")
age sex bmi children smoker region charges
19 female 27.900 0 yes southwest 16884.924
18 male 33.770 1 no southeast 1725.552
28 male 33.000 3 no southeast 4449.462
33 male 22.705 0 no northwest 21984.471
32 male 28.880 0 no northwest 3866.855
31 female 25.740 0 no southeast 3756.622

Answer:

Code Solution 
library(tidyverse)

insurance <- read_csv("https://raw.githubusercontent.com/BrighamEaquinto/brighameaquinto.github.io/master/datasets/insurance.csv")

head(insurance)

View(insurance)

Practice 2: 🚗 Honda Cars 🚙

Instructions:

  1. Open this website in a new tab and copy the URL, or right-click and copy the URL.
  2. Use the two commands learned from above to look at the data.
Model Trim Year Mileage Price
Civic LX 2013 52911 9900
Civic LX 2017 27966 15495
Civic LX 2016 21639 14995
Civic LX 2017 21893 15595
Civic LX 2015 39868 11498
Civic LX 2015 26416 14000

Starter Code:

library(tidyverse)

bob <- read_csv("path_to_URL")

Answer:

Code Solution 
library(tidyverse)

bob <- read_csv("https://raw.githubusercontent.com/saundersg/Statistics-Notebook/master/Data/CivicCorolla.csv")

head(bob)

View(bob)

Practice 3: 🦷 ToothGrowth 😁

Instructions:

  1. Look at the ToothGrowth dataset (this dataset is built into R)
len supp dose
4.2 VC 0.5
11.5 VC 0.5
7.3 VC 0.5
5.8 VC 0.5
6.4 VC 0.5
10.0 VC 0.5

Answer:

Code Solution 
head(ToothGrowth)

View(ToothGrowth)

Practice 4: 📚 Student Performance Dataset 🧠

Instructions:

  1. Click the link to download the insurance dataset.
  2. Use the two commands learned from above to look at the data.

Starter Code:

library(tidyverse)

dataset <- read_csv("path_to_dataset")
gender race/ethnicity parental level of education lunch test preparation course math score reading score writing score
female group B bachelor’s degree standard none 72 72 74
female group C some college standard completed 69 90 88
female group B master’s degree standard none 90 95 93
male group A associate’s degree free/reduced none 47 57 44
male group C some college standard none 76 78 75
female group B associate’s degree standard none 71 83 78

Answer:

Code Solution 
head(student_performance)

View(student_performance)

Practice 5: 🏡 Rent Prices 🏘️

Instructions:

  1. Open this website and copy the URL.
  2. Use the two commands learned from above to look at the data.
Model Trim Year Mileage Price
Civic LX 2013 52911 9900
Civic LX 2017 27966 15495
Civic LX 2016 21639 14995
Civic LX 2017 21893 15595
Civic LX 2015 39868 11498
Civic LX 2015 26416 14000

Answer:

Code Solution 
head(bob)

View(bob)

Practice 6: 🌺 Iris 🌻

Instructions:

  1. Look at the iris dataset (this dataset is built into R)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa

Answer:

Code Solution 
head(iris)

View(iris)