a3-JoshLovering

README.md

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-Assignment 3 - Replicating a Classic Experiment  
-===
-
-For the scope of this project, assume the role of a scientist who runs experiments for a living.
-
-Q: How do we know that bar charts are "better" than pie charts?  
-A: Controlled experiments!
-
-In this assignment you'll implement a simple controlled experiment using some of the visualizations you’ve been building in this class. 
-You'll need to develop support code for the experiment sequence, results file output, and other experiment components. 
-(These are all simple with Javascript buttons and forms.)
-The main goals for you are to a) test three competing visualizations, b) implement data generation and error calculation functions inspired by Cleveland and McGill's 1984 paper, c) run the experiment with 10 participants (or a trial equivalent), and d) do some basic analysis and reporting of the results.
-
-For this assignment you should aim to write everything from scratch. For experimentation it is often necessary to control all elements of the chart.
-You should definitely *reference* demo programs from books or the web, and if you do please provide a References section with links at the end of your Readme.
-
-Requirements
----
-
-- Look it over Cleveland and McGill's original experiment (see the section below) and [watch this video](experiment-example.mp4) to get a sense of the experiment structure and where your visualizations will go.
-- When viewing the example experiment video, note the following:
-    - Trials are in random order.  
-    - Each trial has a randomly generated set of 5-10 data points.  
-    - Two of these data points are marked.  
-    - (Note: the experiment UI and User Experience could be better. Plenty of design achievements here).
-- Implement the data generation code **as described in the Cleveland & McGill paper**. 
-    - The goal is to generate a set of random datapoints (usually 5 or 10, with values be between 0 and 100) and to mark two of them for comparison in the trial. 
-- Add 3 visualizations (i.e. conditions) to your experiment. When you are adding these visualizations, think about *why* these visualizations are interesting to test. In other words, keep in mind a *testable hypothesis* for each of the added visualization. Some good options include bar charts, pie charts, stacked-bar charts, and treemaps. You can also rotate your bar chart to be horizontal or upside-down as one of your conditions. You are encouraged to test unorthodox charts -- radar charts come to mind, but there are MANY possibilities here-- feel free to be creative!
-    - Follow the style from Cleveland and McGill closely (e.g. no color, simple lines) unless you are specifically testing a hypothesis (e.g. color versus no color). Pay attention to spacing between elements like bars. Do not mark bars for comparison using color-- this makes the perceptual task too easy.
-- After each trial, implement code that grades and stores participant’s responses.
-- At the end of the experiment, to get the data, one easy option use Javascript to show the data from the current experiment\* (i.e. a comma separated list in a text box) and copy it into your master datafile. See the Background section below for an example of what this file should look like. (\*Alternately implement a server, if you're experienced with that sort of thing.)
-
-- Figure out how to calculate "Error", the difference between the true percentage and the reported percentage.
-- Scale this error using Cleveland and McGill’s log-base-2 error equation. For details, see the background section (there’s a figure with the equation). This becomes your “Error” column in the output. Make sure you use whole percentages (not decimal) in the log-base-2 equation. Make sure you handle the case of when a person gets the exact percentage correct (log-base-2 of 1/8 is -3, it is better to set this to 0). 
-- Run your experiment with 10 or more participants, or-- make sure you get at least 200 trials **per visualization type** in total.  
-    - Grab friends or people in the class.   
-    - Run at least 20 trials per visualization type, per participant. This is to ensure that you cover the range of possible answers (e.g. 5%, 10%, ..., 95%)
-- Make sure to save the resulting CSV after each participant. Compile the results into a master csv file (all participants, all trials).
-- Produce a README with figures that shows the visualizations you tested and results, ordered by best performance to worst performance. Follow the modern Cleveland-McGill figure below -- though note that using names instead of icons is fine.
-- To obtain the ranking, calculate and report the average log2Error for each visualization across all trials and participants. This should be straightforward to do in a spreadsheet.
-- Use Bootstrapped 95\% confidence intervals for your error upper and lower bounds. Include these in your figures. Bootstrapped confidence intervals are easily implemented in R + ggplot2 using the `stat_summary` geom. You can also use Excel, Python, or many many other tools. Bootstrapped 95% CIs are **very** useful in modern experiment practice.
-- Include example images of each visualization as they appeared in your experiment (i.e. if you used a pie chart show the actual pie chart you used in the experiment along with the markings, not an example from Google Images).
-
-## General Requirements
-
-0. Your code should be forked from the GitHub repo and linked using GitHub pages.
-2. Your project should use d3 to build visualizations. 
-3. Your writeup (readme.md in the repo) should contain the following:
-
-- Working link to the experiment hosted on gh-pages or some other site.
-- Concise description and screenshot of your experiment.
-- Description of the technical achievements you attempted with this project.
-- Description of the design achievements you attempted with this project.
-
-Background
----
-
-In 1984, William Cleveland and Robert McGill published the results of several controlled experiments that pitted bar charts against pies and stacked-bar variants. 
-Their paper (http://www.cs.ubc.ca/~tmm/courses/cpsc533c-04-spr/readings/cleveland.pdf) (http://info.slis.indiana.edu/~katy/S637-S11/cleveland84.pdf) is considered a seminal paper in data visualization.
-In particular, they ran a psychology-style experiment where users were shown a series of randomly-generated charts with two graphical elements marked like this:
-
-![cleveland bar chart](img/cleveland-bar.png)
-
-Participants were then asked, "What percentage is the smaller of the larger?". 
-This was repeated hundreds of time with varying data and charts. 
-By the end of the study, Cleveland and McGill had amassed a large dataset that looked like this:
-
-![cleveland table](img/cleveland-table.png)
-
-__Log-base-2 or "cm-error"__: The true percent is the actual percentage of the smaller to the larger, while the reported percent is what participants reported. 
-Cleveland and McGill recognized that their analyses would be biased if they took `abs(ReportedPercent – TruePercent)` as their score for error. 
-To compensate, they came up with a logarithmic scale for error with this equation:
-
-![cleveland equation](img/cleveland-equation.png)
-
-You’ll be implementing this error score as part of the lab. 
-(Hint: it’s not a trick question, this is just to familiarize you with the experiment protocol). 
-With this Cleveland-McGill error score you can better compare the performance of the charts you test to figure out which one performs the best.
-
-As a baseline, compare your average Error scores to the following chart, which include both Cleveland and McGill’s results as well as more recent extensions of this experiment (lower error indicates better performance, and error bars are bootstrapped 95% confidence intervals (`http://en.wikipedia.org/wiki/Confidence_interval#Meaning_and_interpretation`)):
-
-![cleveland results](img/cleveland-results.png)
-
-GitHub Details
----
-
-- Fork the GitHub Repository. You now have a copy associated with your username.
-- Make changes to index.html to fulfill the project requirements. 
-- Make sure your "master" branch matches your "gh-pages" branch. See the GitHub Guides referenced above if you need help.
-- Edit this README.md with a link to your gh-pages site: e.g. http://YourUsernameGoesHere.github.io/Experiment/index.html
-- Replace this file (README.md) with your writeup and Design/Technical achievements.
-- To submit, make a [Pull Request](https://help.github.com/articles/using-pull-requests/) on the original repository.
-- Name your submission using the following scheme: 
-```
-a3-FirstLastnameMember1-FirstLastnameMember2-FirstLastnameMember3-...
-```
+# Assignment 3 - Replicating a Classic Experiment  
+
+[Experiment Hosted on GitHub Pages](https://jalovering.github.io/datavis_course_a3_experiment/)
+
+# Experiment Description
+The hypothesis being tested in this experiment was that adding categorical coloring to a bar chart would improve the accuracy of predicting bar height. Participants were asked to predict the percentage of a smaller bar to that of a larger bar in various charts. The three color palettes chosen to conduct this experiment were:
+    1. Plain (no categorical varibality)
+    2. Grayscale (colorless categorical variability)
+    3. Colored (colored categorical variability)
+Thirteen participants were presented with 20 of each type of chart, in random order. There were 260 data points collected for each chart, totalling 780 predictions. The three chart types are presented below.
+
+![Three Chart Types](chart_types.png)
+
+The first page of the experiment explained the instructions and provided relevant information to the participants.
+
+![Page 1](screen1.PNG)
+
+The second page of the experiment was where the participants would spend most of the time. This page generated random charts and collected participant predictions.
+
+![Page 2](screen2.PNG)
+
+The third and final page presented the participant with their average log2error for each type of chart as well as instructions for submitting their data.
+
+![Page 3](screen3.PNG)
+
+
+# Experiment Results
+In conclusion, there was no evidence to support that adding color to a bar plot would improve the accuracy of predicting bar height. While individual participants had varying averages across the different charts, the compiled averages were extremely close. The Grayscale had a mean error of 1.525, followed by 1.591 and 1.592 from the Plain and Colored charts, respectively. The variability between these results is too minor to justify concluding any chart type performed better than another. The 95% confidence intervals are displayed below.
+
+![Confidence Intervals](confidence_interval.png)
+
+# Referenced Code
+- https://www.d3-graph-gallery.com/graph/barplot_basic.html
+
+This example was referenced for the structure of creating a barplot in d3 v4.
+
+# Achievements
+## Design Achievements
+My design achievement was making this experiment as user friendly as possible. To start, I used JavaScript Bootstrap to make the interface. Boostrap results in a clean, that most users would already be familiar with the style. Furthermore, for the colored plot, I used a colorblind friendly palette. I found the palette on a [popular website made by David Nichols](https://davidmathlogic.com/colorblind/#%23332288-%23117733-%2344AA99-%2388CCEE-%23DDCC77-%23CC6677-%23AA4499-%23882255). 
+
+## Technical Achievements
+My technical achievement was collecting user inputs as variables in order to use the data throughout the remainder of the experiment. By doing this, I was able to compile the predictions in a dictionary alongside the type of chart and true value. On the results page, I created a link for the user to press which would open their JSON results in a new tab to copy and send to me. Each dictionary could be appended to a main dictionary within my analysis notebook to allow for easy compilation of results. The final implication of this method was that I was able to provide the user with their average log2error at the end of the experiment.