Data Viz Resources

• Tufte: Visual Display of Quantitative Information
• Cleveland: The Elements of Graphing Data
• Yau: Visualize This
• Cairo: Truthful Art, The: Data, Charts, and Maps for Communication

Compelling - Hans Rosling

http://www.youtube.com/embed/jbkSRLYSojo?rel=0

Exercise: Hans Rosling Discussion

• What did you learn from this movie?
• How did Hans Rosling use data visualization to tell a story?
• What principles from the visualization would you like to be able to do?

Why use Graphics

• Why do you, or have you, in the past used data graphics?
  • Exploratory Graphics
  • Publication Graphics
  • Presentation Graphics

Exercise: Visualizing Patterns Over Time

• What are we looking for with data over time?

Solution: Visualizing Patterns Over Time

• What are we looking for with data over time?
  • Trends (increasing/decreasing)
  • Are season cycles present?
• Identifying these patterns requires looking beyond single points
• We are also interested in looking at more the data in more detail
  • Are there outliers?
  • Do any time periods look out of place?
  • Are there spikes or dips?
  • What causes any of these irregularities?

Capital Bikeshare Data

library(readr)
bike.data <- read_csv('http://www.math.montana.edu/ahoegh/teaching/stat408/datasets/Bike.csv')

## Rows: 10886 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl  (11): season, holiday, workingday, weather, temp, atemp, humidity, wind...
## dttm  (1): datetime
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Capital Bikeshare Data

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(dplyr)
bike.data <- bike.data %>% mutate(year = as.factor(year(datetime)), month = as.factor(month(datetime)))
monthly.counts <- bike.data %>% group_by(month) %>% summarize(num_bikes = sum(count)) %>% arrange(month)
monthly.counts

## # A tibble: 12 × 2
##    month num_bikes
##    <fct>     <dbl>
##  1 1         79884
##  2 2         99113
##  3 3        133501
##  4 4        167402
##  5 5        200147
##  6 6        220733
##  7 7        214617
##  8 8        213516
##  9 9        212529
## 10 10       207434
## 11 11       176440
## 12 12       160160

Discrete Points: Bar Charts

Exercise: Visualizing Proportions

• What to look for in proportions?

Visualizing Proportions

• What to look for in proportions?
  • Generally looking for maximum, minimum, and overall distribution.
• Many of the figures we have discussed are useful here as well: for example, stacked bar charts or points to look at changes in proportions over time.
• Another possibility is the waffle plot we’ve previously seen.

Exercise: Visualizing Relationships

• When considering relationships between variables, what are we looking for?

Visualizing Relationships

• When considering relationships between variables, what are we looking for?
  • If something goes up, do other variables have a positive relationship, negative relationship, or no relationship.
  • What is the distribution of your data? (both univariate and multivariate)