Day 1 Review

  • the RStudio Editor (top) is for static code like scripts or R Markdown documents
  • The console is for testing code (bottom) - best to save your code though!
  • R code goes within what is called a chunk (the gray box with a green play button)
  • Objects (like nouns) are data or variables.

Day 1 Review

  • R functions as a calculator
  • Use <- to save (assign) values to objects
  • Functions (like verbs) perform specific tasks in R and are found within packages
  • Use c() to combine vectors
  • length(), class(), and str() tell you information about an object
  • Install packages with install.packages()
  • Load packages with library()
  • Get help with ? or help pane

Day 1 Review

  • Make sure we have installed and loaded the tidyverse package!

Outline

  • Part 0: A little bit of set up!
  • Part 1: reading in manually (point and click) (.csv)
  • Part 2: checking data & multiple file formats (.xlsx)

Part 0: Setup - R Project

New 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:

The New R Project button is highlighted.

New R Project

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

In the New Project Wizard, the 'New Directory' option is highlighted.

New R Project

Click “New Project”:

In the New Project Wizard, the 'New Project' option is highlighted.

New R Project

Type in a name for your new folder.

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

In the New Project Wizard, the new project has been given a name and is going to be stored in the Desktop directory. The 'Create Project' button is highlighted.

New R Project

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

Make sure you add any scripts or data files to this folder as we go through today’s lesson. This will make sure R is able to “find” your files.

The image shows an image of an arrow pointing to the newly created R project repository.

New R Project

Pause here! Confirm the Project is set up.

Part 1: Getting data into R (manual/point and click, .csv)

Data Input

  • ‘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’)

Data Input

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.”

Data Input: Dataset Location

Import Dataset

Import Dataset

Gif showing the process of importing a dataset via readr.

What Just Happened?

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

The image shows an image of an arrow pointing to the newly created R project repository.

What Just Happened?

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.

The image shows an image of an arrow pointing to the newly created R project repository.

What Just Happened?

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

The image shows an image of an arrow pointing to the newly created R project repository.

Browsing for Data on Your Machine

The image shows an image of an arrow pointing to the newly created R project repository.

Summary

Let’s practice!

Importing “states” data

Looking at the code

library(readr)
states <- read_csv("~/Downloads/states.csv")
View(states)

Notice that the part in quotes is a location on my computer. If I move the file, I won’t be able to use the same code again. Better to move that file to my project folder (instead of my downloads for example) for longer-term storage.

Part 2: Checking data & Other formats

Data Input: Checking the data

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

Screenshot of the RStudio console. 'View(dat)' has been typed and the data appears in table format.

Data Input: Checking the data

The str() function can tell you about data/objects(different variables and their classes - more on this later).

str(states)
spec_tbl_df [52 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ entity                    : chr [1:52] "Alabama" "Alaska" "Arizona" "Arkansas" ...
 $ state_abb                 : chr [1:52] "AL" "AK" "AZ" "AR" ...
 $ state_area_sq_miles       : num [1:52] 51609 589757 113909 53104 158693 ...
 $ state_division            : chr [1:52] "East South Central" "Pacific" "Mountain" "West South Central" ...
 $ state_region              : chr [1:52] "South" "West" "West" "South" ...
 $ population                : num [1:52] 4903185 731545 7278717 3017804 39512223 ...
 $ births_in_2021            : num [1:52] 58054 9367 77916 35965 420608 ...
 $ fertility_rate_per_1000   : num [1:52] 59.5 64.9 55.5 61.7 52.8 52.5 52.1 56.5 54.9 55.9 ...
 $ cesarean_percent          : num [1:52] 35.1 24.2 28.7 34.3 30.8 27.3 35.4 31.9 35.8 35.1 ...
 $ life_expect               : num [1:52] 73.2 76.6 76.3 73.8 79 78.3 78.4 76.7 77.5 75.6 ...
 $ cancer_rate_per_100000    : num [1:52] 160 156 135 168 132 ...
 $ cancer_mortality          : num [1:52] 10429 1093 12813 6516 59503 ...
 $ Administered_Dose1_Pop_Pct: num [1:52] 64.8 72.8 77.1 69.6 84.3 83.3 95 87.7 82.1 68.1 ...
 $ Series_Complete_Pop_Pct   : num [1:52] 53 64.9 65.8 56.7 74.4 73.2 82.8 72.9 69.2 57.1 ...
 - attr(*, "spec")=
  .. cols(
  ..   entity = col_character(),
  ..   state_abb = col_character(),
  ..   state_area_sq_miles = col_double(),
  ..   state_division = col_character(),
  ..   state_region = col_character(),
  ..   population = col_double(),
  ..   births_in_2021 = col_double(),
  ..   fertility_rate_per_1000 = col_double(),
  ..   cesarean_percent = col_double(),
  ..   life_expect = col_double(),
  ..   cancer_rate_per_100000 = col_double(),
  ..   cancer_mortality = col_double(),
  ..   Administered_Dose1_Pop_Pct = col_double(),
  ..   Series_Complete_Pop_Pct = col_double()
  .. )
 - attr(*, "problems")=<externalptr>

Data Input: Excel files

  • Getting data from Excel is a bit more complicated. You have to download the file, either through R or manually.
  • R does not know how to read excel files by default. We will use a package called readxl to do that.

Let’s bring the following into R:

Looking at the code:

library(readxl)
url <- "https://hutchdatascience.org/SeattleStatSummer_R/data/asthma.xlsx"
destfile <- "asthma.xlsx"
curl::curl_download(url, destfile)
asthma <- read_excel(destfile)
View(asthma)

Let’s practice!

Importing tuberculosis data

Looking at the code

library(readxl)
tb_incidence <- read_excel("tb_incidence.xlsx")
View(tb_incidence)

Modifying the code

You can name the dataset whatever you want, it’s an object in your Environment now.

library(readxl)
my_data <- read_excel("tb_incidence.xlsx")
View(my_data)

Summary

  • > File
  • > Import Dataset
  • > From Text (readr) OR From Excel
  • > paste the url or Browse for the file
  • > click “Update” and “Import”
  • > save the code for later!

🏠 Workshop Website