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Lab 1C: Export, Upload, Import

Lab 1C - Export, Upload, Import

Directions: Follow along with the slides, completing the questions in blue on your computer, and answering the questions in red in your journal.

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’s campaign page.

    – Upload data to RStudio server

    – Import the data into R's working memory

Exporting

  • To begin, go to the IDS Tools page

    – Click on the Campaign Manager.

    – Fill in your username and password and click "Sign in".

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

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.

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

  • 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!

  • 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 in its memory so you can use it.

Importing

  • On the Files pane, find the data you want to import.

  • Click on the name of the file and choose the option "Import Dataset..."

  • If you do not see the data in your Environment pane, see the instructions on the next slide.

Importing with Google Drive

  • If you see the data in your Environment pane, skip this slide.

  • If you do not see the data in your Environment pane and you got the following error message, follow the steps below for an alternate method of importing your data.

  • Your teacher will provide you with an ID to store in an RScript and run.
    Example:
    id <- "1hMVVxWhSv31jCNuKILRhNRTdUknNIOyb"

  • Use the read.csv command below to read in the file.
    Example:
    new_file_name <- read.csv(sprintf("https://docs.google.com/uc?id=%s&export=download", id), stringsAsFactors=T)

  • You should now see your file in your Environment!

  • If you'd like to save the data file to your "Files" in your project, run the following code:
    write_csv(new_file_name, "new_file_name.csv")

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.

  • Make sure the name under Import Options matches the name in the Code Preview before you click Import.

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...

  • The Files tab has a few other features to help keep you organized.

    – CampaignName - Ids Teacher Year Semester 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, Import your data, you're ready to analyze.

  • (1) View your data and select a variable. Write and run code to create an appropriate plot for that variable.

  • (2) What variable did you choose? What does the plot tell us about that variable?

  • Summarize the process:

    – (3) What might your future self forget about this process? Summarize the steps you took in this lab to use new data in RStudio.