• What is a (data) plot?
• What are the three most important data plots?

How would you describe this plot?

Elements of a plot

• data
• aesthetics: mapping of variables to graphical elements
• geom: type of plot structure to use
• transformations: log scale, …

Additional components

• layers: multiple geoms, multiple data sets, annotation
• facets: show subsets in different plots
• themes: modifying style

Extracted from http://openexchangerates.org, extracted using the json api, with the R package, jsonlite.

library(readr)
rates <- read_csv("http://dicook.github.io/Monash-R/data/rates.csv")
rates[1:5,1:8]
#> Source: local data frame [5 x 8]
#> 
#>         date      AED      AFN      ALL     AMD     ANG      AOA      ARS
#>       (date)    (dbl)    (dbl)    (dbl)   (dbl)   (dbl)    (dbl)    (dbl)
#> 1 2015-02-23 3.672900 57.33792 123.8969 478.692 1.78968 105.9075 8.702166
#> 2 2015-02-24 3.672069 57.35200 123.7132 478.608 1.78958 106.1014 8.696728
#> 3 2015-02-25 3.673324 57.32655 123.5259 478.616 1.78954 106.1913 8.715239
#> 4 2015-02-26 3.673028 57.52745 124.5801 480.294 1.78956 106.3042 8.720107
#> 5 2015-02-27 3.672648 57.33172 124.8491 478.812 1.78958 106.3389 8.721236

If you'd like to collect exchange rates yourself, see here.

qplot(date, AUD, data=rates)

qplot(date, AUD, data=rates, geom="line")

qplot(date, AUD, data=rates, geom=c("line", "point"))

• data: rates
• aesthetics: x=date, y=AUD
• geom: point, line

ggplot(data=rates, aes(x=date, y=AUD)) + geom_point() + geom_line()

ggplot(data=rates, aes(x=date, y=AUD)) + geom_line() +
  geom_line(aes(y=NZD), colour="blue") + 
  geom_line(aes(y=GBP), colour="red")

• That code is clunky!
• Better to rearrange data, and then let ggplot2 handle the colors, legends, …

rates.sub <- select(rates, date, AUD, NZD, GBP)
rates.sub.m <- gather(rates.sub, currency, rate, -date)
qplot(date, rate, data=rates.sub.m, geom="line", colour=currency)

## Emphasis on magnitude of cross rate

• On this scale we see that the GBP has a much lower ratio with the USD than AUD and NZD. This is information that is easy to obtain by eyeballing the numbers.
• We are probably more interested in comparing the trend of rates, which would suggest to standardise the rates. We will use the traditional definition of standardising.

rates.sub <- mutate(rates.sub, AUD=scale(AUD), NZD=scale(NZD), GBP=scale(GBP))
rates.sub$date <- as.Date(rates.sub$date)
rates.sub.m <- gather(rates.sub, currency, rate, -date)
qplot(date, rate, data=rates.sub.m, geom="line", colour=currency)

Set the linetype/shape to be different for the different currencies.

Set the linetype/shape to be different for the different currencies.

qplot(date, rate, data=rates.sub.m, geom="line", colour=currency, linetype=currency, lwd=I(2))

qplot(date, rate, data=rates.sub.m, geom=c("line", "point"), colour=currency, linetype=currency, shape=currency)

qplot(AUD, NZD, data=rates.sub) + theme(aspect.ratio=1)

qplot(AUD, NZD, data=rates.sub, geom="line") + theme(aspect.ratio=1)

Problem: line only connects points from left to right along the x axis

qplot(AUD, NZD, data=rates.sub, geom="path", colour=order(date)) + theme(aspect.ratio=1)

qplot(AUD, NZD, data=rates.sub, geom=c("density2d", "point")) + theme(aspect.ratio=1)

AUD <- rates[,c("date", "AUD")]
AUD.1 <- lag(AUD$AUD, 1)
AUD.2 <- lag(AUD$AUD, 2)
AUD.7 <- lag(AUD$AUD, 7)
qplot(AUD, AUD.1, data=rates.sub) + theme(aspect.ratio=1)
qplot(AUD, AUD.2, data=rates.sub) + theme(aspect.ratio=1)
qplot(AUD, AUD.7, data=rates.sub) + theme(aspect.ratio=1)

Look up geom_rug in the ggplot2 cheat sheet and add the marginal distributions to a scatterplot of AUD and NZD.

qplot(AUD, data=rates.sub, geom="histogram") 

Only one variable was passed to the plot command, but two axes are shown in the histogram. What happened? What is plotted on the vertical axis?

qplot(AUD, data=rates.sub, geom="density", fill=I("black")) 

• What ways have we seen data variables mapped to graphical elements so far?
• What are the natural ways to map a categorical variable to a graphical element?

qplot(date, rate, data=rates.sub.m, geom="line", colour=currency) +
  xlab("Date") + ylab("Standardized rates") + 
  ggtitle("Cross rates 23/2/2015-11/11/2015")

qplot(date, rate, data=rates.sub.m, geom="line", colour=currency) +
  xlab(expression(Date[i]^2~ mu ~ pi * sigma)) + ylab("Standardized rates") + 
  ggtitle("Cross rates 23/2/2015-11/11/2015")

rates.sub.m$date <- as.POSIXct(rates.sub.m$date)
p <- qplot(date, rate, data = rates.sub.m, geom = "line", colour = currency) +
  scale_x_datetime(breaks = date_breaks("1 month"), labels = date_format("%b")) +
  scale_y_continuous("Standardized rates") 
p

p + theme(legend.position = "bottom")

library(ggthemes)
p + theme_tufte()

p + theme_economist()

p + scale_color_brewer("", palette = "Dark2")

library(dichromat)
clrs <- hue_pal()(3)
p + scale_color_manual("", values=clrs) + theme(legend.position = "none")
clrs <- dichromat(hue_pal()(3))
p + scale_color_manual("", values=clrs) + theme(legend.position = "none")

library(RColorBrewer)
clrs <- brewer.pal(3, "Dark2")
p + scale_color_manual("", values=clrs) + theme(legend.position = "none")
clrs <- dichromat(brewer.pal(3, "Dark2"))
p + scale_color_manual("", values=clrs) + theme(legend.position = "none")

• Qualitative: categorical variables
• Sequential: low to high numeric values
• Diverging: negative to positive values

p <- qplot(date, rate, data = rates.sub.m, geom = "line", colour = currency, linetype=I(2)) +
  scale_x_datetime(breaks = date_breaks("1 month"), labels = date_format("%b")) +
  scale_y_continuous("Standardized rates") 
p + geom_smooth() + scale_color_brewer(palette="Dark2")

How has the EUR changed relative to the USD over the course of this year? What about the JPY?

qplot(date, rate, data=rates.sub2.m, group=currency, geom="line", alpha=I(0.5)) + 
  facet_wrap(~cl, ncol=3)

library(maps)
world <- map_data("world")
ggplot(data=world) + geom_path(aes(x=long, y=lat,  group=group, order=order)) + theme_solid()
ggplot(data=world) + geom_polygon(aes(x=long, y=lat, group=group, order=order), 
      fill="grey70") + theme_solid() 

rates.map <- merge(rates.countries, world, by.x="name", by.y="region")
rates.map <- rates.map[order(rates.map$order),]
rates.map$cl <- factor(rates.map$cl)
ggplot(data=rates.map) + geom_polygon(aes(x=long, y=lat, group=group, order=order, fill=cl)) + scale_fill_brewer(palette="Dark2") + 
  theme_solid() + theme(legend.position="None")

Map and line plots

p1 <- ggplot(data=subset(rates.map, cl==3)) + 
  geom_polygon(aes(x=long, y=lat, group=group, order=order), fill="#1B9E77") + 
  geom_path(aes(x=long, y=lat, group=group, order=order), 
            data=world, colour="grey90") + 
  theme_solid() + theme(legend.position="None")
p2 <- ggplot(data=subset(rates.sub2.m, cl==3)) +
  geom_line(aes(x=date, y=rate, group=currency), 
            alpha=0.2, colour="#1B9E77") + theme(legend.position="None")
p3 <- ggplot(aes(x=long, y=lat, group=group, order=order), 
             data=subset(rates.map, cl==2)) + 
  geom_polygon(fill="#D95F02") + 
  geom_path(data=world, colour="grey90") +
  theme_solid() + theme(legend.position="None")
p4 <- ggplot(data=subset(rates.sub2.m, cl==2)) +
  geom_line(aes(x=date, y=rate, group=currency), 
            alpha=0.2, colour="#D95F02") + theme(legend.position="None")

library(gridExtra)
grid.arrange(p1, p2, p3, p4, ncol=2)

!! Requires internet connection

library(ggmap)
melb <- get_map(location=c(144.9631, -37.8136))
ggmap(melb) + theme_solid()

poll_loc <- read_csv("http://dicook.github.io/Monash-R/data/polling-places.csv")
ggmap(melb) + geom_point(data=poll_loc, aes(x=Long, y=Lat)) + theme_solid()

Work out how to get a watercolor map as the background.

melb <- get_map(location=c(144.9631, -37.8136), maptype="watercolor")
ggmap(melb) + theme_solid()

Plots

• Barcharts
• Boxplots

Data

• subset of OECD PISA 2012 survey
• subset of 2006 NRC ratings of US graduate programs

internet <- read_csv("http://dicook.github.io/Monash-R/data/internet.csv")
qplot(`Social networks`, data=internet, geom="bar", binwidth=0.5) + 
  facet_grid(Gender~name)

ggplot( data=internet) + geom_bar(aes(x=`Social networks`, fill=Gender)) +
  facet_wrap(~name, ncol=5) + theme(legend.position="bottom")

ggplot( data=internet) + geom_bar(aes(x=`Social networks`, fill=Gender),
                                  position="dodge") +
  facet_wrap(~name, ncol=5) + theme(legend.position="bottom")

grad <- read_csv("http://dicook.github.io/Monash-R/data/graduate-programs.csv")
qplot(subject, AvGREs, data=grad, geom="boxplot") 

How do the four programs compare in terms of average number of publications? Number of students?

• Hierarchy of mappings: (first) position along an axis - (last) color (Cleveland, 1984; Heer and Bostock, 2009)
• Pre-attentive: Some elements are noticed before you even realise it.
• Color: (pre-attentive) palettes - qualitative, sequential, diverging.
• Proximity: Place elements for primary comparison close together.
• Change blindness: When focus is interrupted differences may not be noticed.

Can you find the odd one out?

Is it easier now?

What's wrong with this plot? Can you answer: Is the proportion of girls who use social networks every day (4) higher than boys, in Australia? And is this different in Germany?

Is this easier? But what about answering this: Are German girls more likely to report using social networks once or twice per month (1) than Japanese girls?

Are German girls more likely to report using social networks once or twice per month (1) than Japanese girls? Easier?

Using rmarkdown generate a document with several plots to answer these questions:

• How has the JPY changed relative to the USD over the course of this year?
• How do the four programs compare in terms of average number of publications? Number of students?
• Do boys tend to download music more than girls?

• Cheat sheet
• ggplot2: Elegant Graphics for Data Analysis, Hadley Wickham, web site
• R Graphics Cookbook, Winston Chang
• Q/A site: http://stackoverflow.com
• Dynamic Documents with R and knitr, Yihui Xie,
• Naomi Robbins, Creating More Effective Graphs
• Antony Unwin, Graphical Data Analysis with R

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