Whose data? Our data.

• Throughout the previous labs, we’ve been using data that was already loaded in RStudio.
  • But what if we want to analyze our own data?
• This lab is all about learning how to load our own participatory sensing data into RStudio.

Export, upload, import

• Before we can perform any analysis, we have to load data into R.
• When we want to get our participatory sensing data into RStudio, we:

  • Export the data from your class’ campaign page.

  • Upload data to RStudio server.

  • Import the data into R’s working memory.

    • NOTE: You can watch the following video for a step-by-step walk-through of the process:

Exporting

• To begin, go to the IDS Tools page.

  • Click on the Campaign Manager.

  • Fill in your username and password and click “Sign in.”

    

    Campaign manager

    If you forget your username or password, ask your teacher to remind you.

Campaign Manager

Campaign Manager

• After logging in, your screen should look similar to this.
• Click on the dropdown arrow for the campaign you are interested in downloading.
  • At this point in the course, it will most likely be the Food Habits campaign.

Dropdown Arrow

• The options for the dropdown menu will look like this:

  

  Campaign tab

• Click on the option labeled Export Data.

  • Remember where you save your file!

Exporting

• When you clicked the Export link, a .csv file was saved on your computer.
• Now that the file is on your computer, we need to upload it into RStudio.

Uploading

• Look at the four different panes in RStudio.
  • Find the pane with a Files tab.
  • Click it!

Upload Button

• Click the button on the Files pane that says “Upload”.
  • Click on “Choose File” and find the SurveyResponses.csv file you saved to your computer.
  • Hit the OK button.
• Voila!
  • If you look in the Files pane, you should be able to find your data!

Upload vs. Import

• By Uploading your data into RStudio you’ve really only given yourself access to it.
  • Don’t believe me? Look at the Environment pane … where’s your data?
• To actually use the data we need to Import it into your computer’s memory.
• To compute more quickly and efficiently, R will only keep a few datasets stored in its memory at a time.
  • By importing data, you are telling R that this is a dataset that is important to store it in its memory so you can use it.

Importing

import data

• On the Files pane, find the data you want to Import.
• Click on the name of the file and choose the option “Import Data set…”.

Data Preview

Data Preview

• You can give your data a name using the Name: field in the lower left corner.

What’s in a name?

• The name you give your data is what you will use when you write code to analyze your data.
  • Good names are short and descriptive.
  • For your food habits campaign, some good names to use would be “foodhabits” or even just “food”.
• When you’re ready, click the Import button.

read.csv()

• After you click Import you might notice something appeared in your console.

data.file <- read_csv("~/SurveyResponse.csv")
View(data.file)

• This is the actual code RStudio uses to read your data when you clicked the Import button.
  • So instead of using the RStudio buttons, we can actually Import by writing code similar to what was output into the console!
  • This will come in handy later in the course.

A word on staying organized…

Upload Button

• The Files tab has a few other features to help keep you organized.
  • SurveyResponse probably isn’t the best name for your data. Click Rename to give it a clearer name.
  • Often, it’s helpful to give your data file the same name as when you import your data.
  • So in this case, we could name our data file food.csv.

Analysis Time

• After you Export, Upload, and Import your data, you’re ready to analyze.
• View your data, then select a variable and try to make an appropriate plot for that variable.