Tufte style visualizations in R using Plotly

April 19, 2016
By

(This article was first published on R – Modern Data, and kindly contributed to R-bloggers)

This post is inspired by Lukasz Piwek’s awesome Tufte in R post. We’ll try to replicate Tufte’s visualization practices in R using Plotly. You can read more about Edward Tufte here.

One easy way to replicate the graphs showcased on Lukasz’s blog would be to simply use ggplotly() on his ggplot2() code.

We’ll use plot_ly() instead.

Minimal Line Plot

library(plotly)

x <- 1967:1977
y <- c(0.5,1.8,4.6,5.3,5.3,5.7,5.4,5,5.5,6,5)
hovertxt <- paste("Year: ", x, "
", "Exp: ", y) plot_ly(x = x, y = y, mode = "line + markers", name = "", marker = list(color = "#737373", size = 8), line = list(width = 1), showlegend = F, hoverinfo = "text", text = hovertxt) %>% add_trace(x = c(1966.75, 1977.25), y = c(5, 5), mode = "lines", line = list(dash = "5px", color = "#737373"), showlegend = F, hoverinfo = "none") %>% add_trace(x = c(1966.75, 1977.25), y = c(6, 6), mode = "lines", line = list(dash = "5px", color = "#737373"), showlegend = F, hoverinfo = "none") %>% layout(xaxis = list(title = "", showgrid = F, tickmode = "array", type = "linear", autorange = F, range = c(1966.75, 1977.25), tickvals = x, tickfont = list(family = "serif", size = 10), ticks = "outside"), yaxis = list(title = "", showgrid = F, tickmode = "array", type = "linear", tickvals = 1:6, ticktext = paste0("$", c(300, 320, 340, 360, 380, 400)), tickfont = list(family = "serif", size = 10), ticks = "outside"), margin = list(r = 20), annotations = list( list(xref = "x", yref = "y", x = 1977.25, y = 5.5, text = "5%", showarrow = F, ax = 0, ay = 0), list(xref = "x", yref = "y", x = 1976, y = 1.5, align = "right", text = "Per capita
budget expenditures
in constant dollars", showarrow = F, ax = 0, ay = 0) ))

Range-frame (or quartile-frame) scatterplot

library(plotly)

x <- mtcars$wt
y <- mtcars$mpg
hovertxt <- paste("Weight:", x, "
", "Miles per gallon: ", y) plot_ly(x = x, y = y, mode = "markers", marker = list(color = "#737373"), hoverinfo = "text", text = hovertxt) %>% layout(xaxis = list(title = "Car weight (lb/1000)", titlefont = list(family = "serif"), showgrid = F, tickmode = "array", tickvals = summary(x), ticktext = round(summary(x), 1), tickfont = list(family = "serif", size = 10), ticks = "outside"), yaxis = list(title = "Miles per gallon of fuel", titlefont = list(family = "serif"), showgrid = F, tickmode = "array", tickvals = summary(y), ticktext = round(summary(y), 1), tickfont = list(family = "serif", size = 10), ticks = "outside"))

Dot-dash (Rug) plot

library(plotly)
library(dplyr)

x <- mtcars$wt
y <- mtcars$mpg
hovertxt <- paste("Weight:", x, "
", "Miles per gallon: ", y) ds <- data.frame(x, y, labelsx = round(x, 0), labelsy = round(y,0)) ds <- ds %>% arrange(x) ds$labelsx <- c(rep("", 7), 2, rep("", 12), 3, rep("", 7), 4, rep("", 2), 5) ds <- ds %>% arrange(y) ds$labelsy <- c(rep("", 1), 10, rep("", 5), 15, rep("", 8), 20, rep("", 8), 26, rep("", 5), 34) plot_ly(ds, x = x, y = y, mode = "markers", marker = list(color = "#737373"), hoverinfo = "text", text = hovertxt) %>% layout(xaxis = list(title = "Car weight (lb/1000)", titlefont = list(family = "serif"), showgrid = F,tickmode = "array", tickvals = x, ticktext = labelsx, ticklen = 10, tickfont = list(family = "serif", size = 10), ticks = "outside"), yaxis = list(title = "Miles Per Gallon of Fuel", titlefont = list(family = "serif"), showgrid = F,tickmode = "array", tickvals = y, ticktext = labelsy, ticklen = 10, tickfont = list(family = "serif", size = 10), ticks = "outside"))

Minimal Boxplot

This one needs a little bit of work. Since geom_tufteboxplot() is not yet supported, using ggplotly() won’t work either.

library(plotly)

# Empty plot
p <- plot_ly()

vec <- sort(unique(quakes$mag))

# Each mean (dot) and quartile (line - segment) will have to be added as a separate trace

for(i in vec){
  summ <- boxplot.stats(subset(quakes, mag == i)$stations)$stats
  hovertxt <- paste("Mean:", summ[3], "
", "IQR:", IQR(subset(quakes, mag == i)$stations)) p <- add_trace(p, x = i, y = summ[3], mode = "markers", hoverinfo = "text", text = hovertxt, marker = list(color = "#737373", size = 6), evaluate = T, showlegend = F) p <- add_trace(p, x = c(i, i), y = c(summ[1], summ[2]), mode = "lines", hoverinfo = "none", marker = list(color = "#737373"), line = list(width = 1), evaluate = T, showlegend = F) p <- add_trace(p, x = c(i, i), y = c(summ[4], summ[5]), mode = "lines", hoverinfo = "none", marker = list(color = "#737373"), line = list(width = 1), evaluate = T, showlegend = F) } # Layout options p <- layout(p, xaxis = list(showgrid = F, nticks = length(vec)), yaxis = list(showgrid = F), annotations = list( list(xanchor = "left", x = 4, y = 120, text = "Number of stations
reporting Richter Magnitude
of Fiji earthquakes (n=1000)", align = "left", showarrow = F, ax = 0, ay = 0))) p

Minimal Barchart

library(psych)
library(reshape2)

ds <- melt(colMeans(msq[,c(2,7,34,36,42,43,46,55,68)],na.rm = T)*10)
ds$trait <- rownames(ds)
hovertxt <- paste(ds$trait, ":", round(ds$value,3))

plot_ly(ds, x = 1:nrow(ds), y = value, type = "bar", marker = list(color = "#737373"), 
        hoverinfo = "text", showlegend = F, text = hovertxt) %>% 
  
  add_trace(x = c(0.4, 9.6, NA, 0.4, 9.6, NA, 0.4, 9.6, NA, 0.4, 9.6, NA, 0.4, 9.6), 
            y = c(1, 1, NA, 2, 2, NA, 3, 3, NA, 4, 4, 5, 5), mode = "lines", 
            marker = list(color = "white"), showlegend = F) %>% 
  
  layout(xaxis = list(title = "", tickmode = "array", tickvals = 1:nrow(ds), ticktext = trait,
                      tickfont = list(family = "serif", size = 10)),
         yaxis = list(title = "", showgrid = F), 
         annotations = list(
           list(x = 1, xanchor = "left", y = 6, showarrow = F, ax = 0, ay = 0, align = "left",
                text = "Average scores
on negative emotion traits
from 3896 participants
(Watson et al., 1988)")))

To leave a comment for the author, please follow the link and comment on their blog: R – Modern Data.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)