Data Visualization with R and SUL data

CSV

plot(cars)
library(ggplot2)
ggplot(cars, aes(speed, dist)) + geom_point()
library(ggthemes)
ggplot(cars, aes(speed, dist)) + geom_point() + geom_rangeframe() + theme_tufte()
library(ggiraph)
p <- ggplot(cars, aes(speed, dist, tooltip = speed/dist)) + geom_point_interactive()
ggiraph(code = {print(p)}, height_svg = 4)
library(ggvis)
cars %>% ggvis(~speed, ~dist) %>% layer_points()
library(plotly)
plot_ly(cars, x = speed, y = dist, mode = "markers")
p <- ggplot(cars, aes(speed, dist)) + geom_point()
ggplotly(p)
inputPanel(
 sliderInput("speed_range", label = "Range of Speed:",
          min = min(cars$speed), max = max(cars$speed), 
          value = range(cars$speed))
)

renderPlot({
  cars_subs <- subset(cars, speed >= input$speed_range[1] & 
                        speed < input$speed_range[2])
  
  ggplot(cars_subs, aes(speed, dist)) + geom_point()
})

Data Visualization with R and SUL data

plot

ggplot2

ggthemes

ggiraph

ggvis

plotly, using plot_ly function

plotly, using ggplotly function

shiny

shiny

flexdashboard

World Bank Data

r-esources @ stanford

Data Visualization with R and SUL data

plot

ggplot2

ggthemes

ggiraph

ggvis

plotly, using plot_ly function

plotly, using ggplotly function

shiny

shiny

flexdashboard

Access to SUL Social Science Data

World Bank Data

r-esources @ stanford