• Data is not objective, and is not neutral. (Noble, O’Neill)
• Human motives influence the bias in the data. (Eviction Lab)
• Readers might not be aware of these biases. (Simpson’s Paradox, Asimov)
• Data needs to be looked at in context. (Klein, Klein and D’Ignazsio)
• Lack of depth/context can lead to unintentional bias. (Eviction Lab, O’Grada and Guinnane)
• In order for users to feel safe, they should understand how/where data is being collected. (Eviction Lab, Force 11)
• Not enough people know that data is not your own. (Gabourey)
• Verifying your data
• Make sure your survey design is good (Eviction Lab)
• Consent (What you Can’t and Shouldn’t do with social media)
• It is not always clear what your audience is when using social media to collect data  (Gabourey, Klein, Healy)
• Think about the financial incentives (Gabourey)
• Publishing accurate statistics makes it possible to infer personal information (Healy)

• Make sure that your data was ethically collected. (Pew, Healey)
• Use common language (Binder)
• Have variables that make sense
• Make sure that the layout of your interface is mobile accessible
• Use keys
• Make sure that your data that can be analyzed in many softwares (i.e. non-proprietary format)
• Pre-usage instructions (e.g. read me document)
• Being respectful – anticipating the expectations of the people whose data you are using (Noble)
• Sticking to larger datasets
• Only share the data you need/used for replication
• Account for the Hawthorn effect
• Do not share names and location.

• Make the presentation of the data understandable by anyone. Enough so that it is not necessary for you to be there to describe the data
• Use a shared vocabulary so people can understand your work without doing too much learning
• Make sources of information clear and available. Be clear about where the data is coming from and how you collected it (Klein and D’Ignazsio)
• Protect any personal information provided by people that you used for your data
• Be mindful of the purposes of your research
• Create visual / easy to understand methodology along with data (Drucker)
• Always cite our sources to ensure credibility & give credit to people/services that are typically overlooked
• Make sure any visualizations accurately represents the data
• Being transparent enough for replication purposes *(Force 11, Ramsey and Rockwell, Pew, Eviction Lab)
• Consider context when looking at data (Klein and D’Ignazsio)
• Being overall aware of potential subjectivity of data and how you yourself are able to skew the data.

Hello all,

We can certainly run each package at the start of each notebook, but if you are comfortable, you can also use the following code:

# List your packages here (this checks to see if tidyRSS and stringr are installed.
packages <- c("tidyRSS", "stringr")

# Run this without making changes
package.check <- lapply(packages, FUN = function(x) {
  if (!require(x, character.only = TRUE)) {
    install.packages(x, dependencies = TRUE)
    library(x, character.only = TRUE)
  }
})

Thing

work.csv

Here’s a helpful viz of how to imagine and visualize the different kinds of data structures:

Using your data models, I have created a form that captures a lot of the data you specified. Over the weekend, fill out the form for THREE people.