## Exercise: Telling Stories with Data

• What does statistics mean to you?
• data visualization?

## 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?

## Exercise: Visualizing Patterns Over Time

• What are we looking for with data over time?

## Capital Bikeshare Data

url <- 'http://www.math.montana.edu/ahoegh/teaching/stat408/datasets/Bike.csv'
head(bike.data)
##              datetime season holiday workingday weather temp  atemp
## 1 2011-01-01 00:00:00      1       0          0       1 9.84 14.395
## 2 2011-01-01 01:00:00      1       0          0       1 9.02 13.635
## 3 2011-01-01 02:00:00      1       0          0       1 9.02 13.635
## 4 2011-01-01 03:00:00      1       0          0       1 9.84 14.395
## 5 2011-01-01 04:00:00      1       0          0       1 9.84 14.395
## 6 2011-01-01 05:00:00      1       0          0       2 9.84 12.880
##   humidity windspeed casual registered count
## 1       81    0.0000      3         13    16
## 2       80    0.0000      8         32    40
## 3       80    0.0000      5         27    32
## 4       75    0.0000      3         10    13
## 5       75    0.0000      0          1     1
## 6       75    6.0032      0          1     1
bike.data$year <- substr(bike.data$datetime,1,4)
bike.data$month <- substr(bike.data$datetime,6,7)
monthly.counts <- summarize(group_by(bike.data,month), sum(count))
colnames(monthly.counts)[2] <- 'Num.Bikes'
head(monthly.counts)
## # A tibble: 6 x 2
##   month Num.Bikes
##   <chr>     <int>
## 1    01     79884
## 2    02     99113
## 3    03    133501
## 4    04    167402
## 5    05    200147
## 6    06    220733

## Exercise: Patterns over Time

Consider the number of bike rentals per hour per season - Is this an example of continuous or discrete time? - Make a figure to display your findings

## Exercise: Visualizing Proportions

• What to look for in proportions?

## Exercise: Visualizing Relationships

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