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Lab 1C: Export, Upload, Import

Lab 1C - Export, Upload, Import

Directions: Follow along with the slides and answer the questions in bold font in your IDS 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 we 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 the IDS page.

  • Upload data to RStudio server

  • Import the data into R's working memory

Exporting

  • To begin, go to your class’ IDS 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.

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

  • Look for the option labeled Export Data. Click it.

  • 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.

  • Find the SurveyResponses.csv file you saved to your computer.

  • Hit the ok button a few times.

  • 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 data sets stored in its memory at a time.

    – By importing data, you are telling R that this is a data set that is important to store it in its memory so you can use it.

Importing

  • At the top of the Environment pane, click the Import Dataset button. Then, choose From CSV...

    – CSV is a standard data format used by many software programs.

  • Click the Browse... button in the upper right hand corner. Find your data file and click Open.

  • 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...

  • 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.

  • It is often 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 foodhabits.csv

Export, upload, import

  • After you Export, Upload, Import your data you're ready to analyze.

  • View your data, select a variable and try to make an appropriate plot for that variable.

    – If you're having issues, make sure you're spelling the name of your data correctly.