• What is Statistics?
  • hypothesis tests
  • pattern finding
  • predictive modeling
  • storytelling with data can help you solve real-world problems (predicting unrest, decreasing crime) or it can help you stay more informed

Hans Rosling’s 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four

Why do you, or have you, in the past used data graphics?

• Exploratory Graphics
• Publication Graphics
• Presentation Graphics

Source: https://mtstateintrostats.github.io/IntroStatTextbook/index.html

R Shiny provides a way to create interactive visualizations and web applets.

There are two key components of an R Shiny Script:

1. The ui (user interface) provides a way for the user to interact with the visualization and for the program to capture input values.
2. The server piece takes those outputs and applies them to R code.

# Define server logic required to draw a histogram
server <- function(input, output) {
  
  output$distPlot <- renderPlot({
    # generate bins based on input$bins from ui.R
    x    <- faithful[, 2] 
    bins <- seq(min(x), max(x), length.out = input$bins + 1)
    
    # draw the histogram with the specified number of bins
    hist(x, breaks = bins, col = 'darkgray', border = 'white')
  })
}

    input.bins <- 30 # number of bins
    x   <- faithful[, 2] 
    bins <- seq(min(x), max(x), length.out = input.bins + 1)
      # draw the histogram with the specified number of bins
    hist(x, breaks = bins, col = 'darkgray', border = 'white')

# Define UI for application that draws a histogram
ui <- fluidPage(
   # Application title
   titlePanel("Old Faithful Geyser Data"),
   # Sidebar with a slider input for number of bins 
   sidebarLayout(
      sidebarPanel(
         sliderInput("bins", 
                     "Number of bins:",
                     min = 1,
                     max = 50,
                     value = 30)
      ),
      # Show a plot of the generated distribution
      mainPanel(
         plotOutput("distPlot")
      )
   )
)

# Define server logic required to draw a histogram
server <- function(input, output) {
   
   output$distPlot <- renderPlot({
      # generate bins based on input$bins from ui.R
      x    <- faithful[, 2] 
      bins <- seq(min(x), max(x), length.out = input$bins + 1)
      
      # draw the histogram with the specified number of bins
      hist(x, breaks = bins, col = 'darkgray', border = 'white')
   })
}

# Run the application 
shinyApp(ui = ui, server = server)

• Shiny Web Applet
• Shiny in RMarkdown HTML file
• shinyapps.io

• Open the default Shiny Web App in R that contains the interactive histogram of Old faithful eruptions. Change the color of the histogram and reload the app.

• Open the default Shiny document R Markdown file and change the system file to use a different embedded Shiny application example than “06_tabsets”.

• Google Trend Index
• Shiny app with SAT data by Nick Horton
• RStudio built-in Shiny examples

The babynames library contains a (giant) dataset called babynames, which contains the full baby name data provided by the Social Security Administration (SSA). Type the following to explore the data set and its variables:

library(babynames)  # You will most likely have to install this
data(babynames)  # Load data into environment
?babynames
head(babynames)

Using the babynames data set, implement your own version of this app from the SSA.

• 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?

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

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), .groups = 'drop') %>% 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

monthly.counts %>% 
  ggplot(aes(y = num_bikes, x = month)) + 
  geom_bar(stat = 'identity') + xlab('Month') + 
  ylab('Bike Rentals') + 
  labs(title = 'Bike Rentals per Month in 2011-2012 \n Capital Bikeshare in Washington, DC', 
       caption = 'Source: www.capitalbikeshare.com')

bike.counts <- aggregate(cbind(bike.data$casual,bike.data$registered),
                         by=list(bike.data$month), sum)
barplot(t(as.matrix(bike.counts[,-1])), 
        names.arg =collect(select(monthly.counts, month))[[1]], 
        xlab='Month', sub ='Source: www.capitalbikeshare.com', 
        ylab='Bike Rentals', 
        main='Bike Rentals per Month in 2011 - 2012 \n Capital Bikeshare in Washington, DC',
        col=c("darkblue","red"),legend.text = c("Casual", "Registered"),
        args.legend = list(x = "topleft"))

plot(rowSums(bike.counts[,-1])~bike.counts[,1],xlab='Month',
     sub ='Source: www.capitalbikeshare.com', ylab='Bike Rentals', 
     main='Bike Rentals per Month \n Capital Bikeshare in Washington, DC',
     col=c("darkblue"),pch=16,axes=F,
     ylim=c(0,max(rowSums(bike.counts[,-1]))))
axis(2)
axis(1,at=1:12)
box()

mean_temp <- bike.data %>% group_by(month) %>%
  summarize(mean_temp = mean(temp),.groups = 'drop') %>% 
  mutate(month = as.numeric(month))

ggplot(aes(y=temp, x= month), data = bike.data) +
  geom_jitter(alpha = .1) + 
  geom_line(inherit.aes = F, aes(y = mean_temp, x = month),
            data = mean_temp, color = 'red', lwd = 2) +
  ylab('Average Temp (C)') + xlab('Month') + 
  labs(title = 'Average Temperature in Washington, DC', 
                       caption = 'Source: www.capitalbikeshare.com')

• 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, which we will not cover, are plotting with rectangles known as a tree map.

• 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)

bike.data$tempF <- bike.data$temp * 1.8 + 32
plot(bike.data$count~bike.data$tempF,pch=16,
     col=rgb(100,0,0,10,max=255),ylab='Hourly Bike Rentals',
     xlab='Temp (F)',sub ='Source: www.capitalbikeshare.com',
     main='Hourly Bike Rentals by Temperature')
bike.fit <- loess(count~tempF,bike.data)
temp.seq <- seq(min(bike.data$tempF),max(bike.data$tempF))
lines(predict(bike.fit,temp.seq)~temp.seq,lwd=2)

pairs(bike.data[,c(12,15,8)])

par(mfcol=c(2,2),oma = c(1,0,0,0))
bike.data$tempF <- bike.data$temp * 1.8 + 32
plot(bike.data$count~bike.data$tempF,pch=16,col=rgb(100,0,0,10,max=255),
     ylab='Hourly Bike Rentals',xlab='Temp (F)',
     main='Hourly Bike Rentals by Temperature')
bike.fit <- loess(count~tempF,bike.data)
temp.seq <- seq(min(bike.data$tempF),max(bike.data$tempF))
lines(predict(bike.fit,temp.seq)~temp.seq,lwd=2)

plot(bike.data$count~bike.data$humidity,pch=16,
     col=rgb(100,0,100,10,max=255),
     ylab='Hourly Bike Rentals',xlab='Humidity (%)',
     main='Hourly Bike Rentals by Humidity')
bike.fit <- loess(count~humidity,bike.data)
humidity.seq <- seq(min(bike.data$humidity),max(bike.data$humidity))
lines(predict(bike.fit,humidity.seq)~humidity.seq,lwd=2)

plot(bike.data$count~bike.data$windspeed,pch=16,col=rgb(0,0,100,10,max=255),
     ylab='Hourly Bike Rentals',xlab='Windspeed (MPH)',main='Hourly Bike Rentals by Windspeed')
bike.fit <- loess(count~windspeed,bike.data)
windspeed.seq <- seq(min(bike.data$windspeed),max(bike.data$windspeed))
lines(predict(bike.fit,windspeed.seq)~windspeed.seq,lwd=2)

plot(bike.data$count~as.factor(bike.data$weather),col=rgb(0,100,0,255,max=255),
     ylab='Hourly Bike Rentals',xlab='Weather Conditions',main='Hourly Bike Rentals by Weather')

mtext('Source: www.capitalbikeshare.com', outer = TRUE, cex = .9, side=1)
par(mfcol=c(1,1),oma = c(0,0,0,0))

hist(bike.data$tempF,prob=T, main='Temperature (F)',col='red',xlab='')

par(mfrow=c(2,1))
bike.data$reltempF <- bike.data$atemp * 1.8 + 32

hist(bike.data$tempF,prob=T,breaks='FD',
     main='Temperature (F)',col='red',xlab='',
     xlim=c(0,max(c(bike.data$reltempF,bike.data$tempF))))
hist(bike.data$reltempF,prob=T,breaks='FD',
     main='Relative Temperature (F)',col='orange',xlab='',
     xlim=c(0,max(c(bike.data$reltempF,bike.data$tempF))))