This course will involve moving files around on your computer and downloading files.

If you are new to this - check out the videos on the resource page of the website.

R Projects are a super helpful feature of RStudio. They help you:

• Stay organized. R Projects help in organizing your work into self-contained directories (folders), where all related scripts, data, and outputs are stored together. This organization simplifies file management and makes it easier to locate and manage files associated with your analysis or project.

• Find the right files. When you open an R Project, RStudio automatically sets the working directory to the project’s directory. This is where RStudio “looks” for files. Because it’s always the Project folder, it can help avoid common issues with file paths.

• Be more reproducible. You can share the entire project directory with others, and they can replicate your environment and analysis without much hassle.

“The chance of the setwd() command having the desired effect – making the file paths work – for anyone besides its author is 0%. It’s also unlikely to work for the author one or two years or computers from now. The project is not self-contained and portable.”

• Jenny Bryan

Let’s go over how to create and use an R Project!

Let’s make an R Project so we can stay organized in the next steps. Click the new R Project button at the top left of RStudio:

In the New Project Wizard, click “New Directory”:

Click “New Project”:

Type in a name for your new folder.

Store it somewhere easy to find, such as your Desktop:

You now have a new R Project folder on your Desktop!

Make sure you add any scripts or data files to this folder as you go through your Intro to R lessons, or work on a new project. This will make sure R is able to “find” your files.

You can see what project you have open in the top right corner.

• ‘Reading in’ data is the first step of any real project/analysis
• R can read almost any file format, especially via add-on packages
• We are going to focus on simple delimited files first
  • comma separated (e.g. ‘.csv’)
  • tab delimited (e.g. ‘.txt’)

delimiters are symbols that separate cells in a simple-text file.

Youth Tobacco Survey (YTS) dataset:

“The YTS was developed to provide states with comprehensive data on both middle school and high school students regarding tobacco use, exposure to environmental tobacco smoke, smoking cessation, school curriculum, minors’ ability to purchase or otherwise obtain tobacco products, knowledge and attitudes about tobacco, and familiarity with pro-tobacco and anti-tobacco media messages.”

• Check out the data at: https://catalog.data.gov/dataset/youth-tobacco-survey-yts-data

• > File
• > Import Dataset
• > From Text (readr)
• > paste the url (http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv)
• > click “Update” and “Import”

Saves data in memory, not to hard drive

You see a preview of the data on the top left pane.

You see a new object called Youth_Tobacco_Survey_YTS_Data in your environment pane (top right). The table button opens the data for you to view.

R ran some code in the console (bottom left).

• > Download the data
• > Put data in the project folder
• > File, Import Dataset, From Text (readr)
• > browse for the file
• > click “Update” and “Import”

How can we get data into R?

A. From a URL

B. From a file we downloaded

C. Both of these!

🏠 Class Website

💻 Data Input Lab

Pros: easy!!

Cons: obscures some of what’s happening, others will have difficulty running your code

The tidyverse contains a package readr that is handy for importing data.

library(tidyverse)
dat <- read_csv(
  file = "http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv"
)

# `head` displays first few rows of a data frame. `tail()` works the same way.
head(dat, n = 5)

# A tibble: 5 × 31
   YEAR LocationAbbr LocationDesc TopicType     TopicDesc MeasureDesc DataSource
  <dbl> <chr>        <chr>        <chr>         <chr>     <chr>       <chr>     
1  2015 AZ           Arizona      Tobacco Use … Cessatio… Percent of… YTS       
2  2015 AZ           Arizona      Tobacco Use … Cessatio… Percent of… YTS       
3  2015 AZ           Arizona      Tobacco Use … Cessatio… Percent of… YTS       
4  2015 AZ           Arizona      Tobacco Use … Cessatio… Quit Attem… YTS       
5  2015 AZ           Arizona      Tobacco Use … Cessatio… Quit Attem… YTS       
# ℹ 24 more variables: Response <chr>, Data_Value_Unit <chr>,
#   Data_Value_Type <chr>, Data_Value <dbl>, Data_Value_Footnote_Symbol <chr>,
#   Data_Value_Footnote <chr>, Data_Value_Std_Err <dbl>,
#   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>, Sample_Size <dbl>,
#   Gender <chr>, Race <chr>, Age <chr>, Education <chr>, GeoLocation <chr>,
#   TopicTypeId <chr>, TopicId <chr>, MeasureId <chr>, StratificationID1 <chr>,
#   StratificationID2 <chr>, StratificationID3 <chr>, …

dat <- read_csv(
  file = "http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv"
)
# EQUIVALENT TO
dat <- read_csv(
  "http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv"
)

read_csv() needs an argument file = in quotation marks.

• can be path to a file on a website (URL)
• can be path in your local computer – absolute file path or relative file path

# URL
dat <- 
  read_csv("http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv")

# In project folder
dat <- 
  read_csv("Youth_Tobacco_Survey_YTS_Data.csv")

When we work in an R Project, our project folder is our working directory.

Working directory is a folder (directory) that RStudio will use to find files.

Run the getwd() function to determine your working directory.

# Get the working directory
getwd()

You can set the working directory manually with the setwd() function. But it’s easier to set up a project :)

# set the working directory
setwd("/Users/avahoffman/Desktop")

• the View() function shows your data in a new tab, in spreadsheet format
• be careful if your data is big!

View(dat)

read_tsv() can read tab delimited (separated) files.

read_delim() can be used to specify the delimiter.

• file is the path to your file, in quotes
• delim is what separates the fields within a record

## Examples
dat2 <- read_tsv(file = "table1.tsv", delim = "\t")

dat3 <- read_delim(file = "data.txt", delim = ":")

• readxl package can read excel files

• haven package has functions to read SAS, SPSS, Stata formats

• There are also resources for REDCap : REDCapR

There are also data importing functions provided in base R (rather than the readr package), like read.delim() and read.csv().

These functions have slightly different syntax for reading in data (e.g. header argument).

However, while many online resources use the base R tools, the latest version of RStudio switched to use these new readr data import tools, so we will use them in the class for slides. They are also up to two times faster for reading in large datasets, and have a progress bar which is nice.

• The str() function can tell you about data/objects.
• We will also discuss the glimpse() function later, which does something very similar.
• head() shows first few rows
• tail() shows the last few rows

R Projects can make it easier to find files.

Importing data manually:

• File > Import Dataset > From Text (readr)
• Paste the url / browse
• Click “Update” and “Import”
• Review the process: https://youtu.be/LEkNfJgpunQ

Importing data programmatically:

• read_csv() function from tidyverse (readr) package
• Use getwd() to check your working directory, where R looks for your data files

Look at your data!

• Check the environment for a data object
• View() gives you a preview of the data in a new tab

Other file types

• readr package: read_delim() for general delimited files
• other packages for more complicated files.

Don’t forget to use <- to assign your data to an object!

🏠 Class Website

💻 Data Input Lab

📃 Posit’s Data Import Cheatsheet

📃 Day 2 Cheatsheet

Image by Gerd Altmann from Pixabay