ggplot2: Barplots

One R Tip A Day uses R basic graphics to visualise migration to the United States during 1820-2006.

Again, as usual, let’s reproduce this in ggplot2.

First, load ggplot2 and dataset

> library(ggplot2) > df <- structure(c(106487, 495681, 1597442, 2452577, 2065141, 2271925, 4735484, 3555352, 8056040, 4321887, 2463194, 347566, 621147, 1325727, 1123492, 800368, 761550, 1359737, 1073726, 36, 53, 141, 41538, 64759, 124160, 69942, 74862, 323543, 247236, 112059, 16595, 37028, 153249, 427642, 1588178, 2738157, 2795672, 2265696, 11951, 33424, 62469, 74720, 166607, 404044, 426967, 38972, 361888, 1143671, 1516716, 160037, 354804, 996944, 1716374, 1982735, 3615225, 4486806, 3037122, 17, 54, 55, 210, 312, 358, 857, 350, 7368, 8443, 6286, 1750, 7367, 14092, 28954, 80779, 176893, 354939, 446792, 33333, 69911, 53144, 29169, 18005, 11704, 13363, 18028, 46547, 14574, 8954, 2483, 14693, 25467, 25215, 41254, 46237, 98263, 185986), .Dim = c(19, 5), .Dimnames = list(c("1820-30", "1831-40", "1841-50", "1851-60", "1861-70", "1871-80", "1881-90", "1891-00", "1901-10", "1911-20", "1921-30", "1931-40", "1941-50", "1951-60", "1961-70", "1971-80", "1981-90", "1991-00", "2001-06"), c("Europe", "Asia", "Americas", "Africa", "Oceania")))

The regional data is presented in columns. We could use it in this format and manually add all the regional series, however with little data manipulation it is possible to considerably speed up the following steps.

> df.m <- melt(df) > df.m <- rename(df.m, c(X1 = "Period", X2 = "Region"))

Now everything is set to start designing the plot.

> a <- ggplot(df.m, aes(x = Period, y = value/1e+06, fill = Region)) + opts(title = "Migration to the United States by Source Region (1820-2006)") + labs(x = NULL, y = "Number of People (in millions)n", fill = NULL) > b <- a + geom_bar(stat = "identity", position = "stack")

There are a few problems with this plot: the title is not completely visible, also x-axes labels are unreadable. We will fix them after first changing the colour palette.

The default colours are nice, still I prefer ColorBrewer palettes.

> b <- b + scale_fill_brewer(palette = "Set1")

Next we will make changes to the default plotting theme used, this will also take care of the few problems highlighted above.

> immigration_theme <- theme_update(axis.text.x = theme_text(angle = 90, hjust = 1), panel.grid.major = theme_line(colour = "grey90"), panel.grid.minor = theme_blank(), panel.background = theme_blank(), axis.ticks = theme_blank(), legend.position = "none") > b

Alternative visualisations

Next I would also like to present two alternative views of the same data using faceting capabilities of ggplot2.

> c <- b + facet_grid(Region ~ .) + opts(legend.position = "none")

Additionally, it would be interesting to see the change in total immigration on the same graph. In order to do this, a new dataframe with the annual totals will be created and later merged with the existing dataset (variable names in both dataframes should be identical for this to work). Then we just change the dataframe the plot is based on.

> total <- cast(df.m, Period ~ ., sum) > total <- rename(total, c(`(all)` = "value")) > total$Region <- "Total" > df.m.t <- rbind(total, df.m) > c1 <- c %+% df.m.t

Update: Hadley suggested in the comments to “try scale=”free_y” and possibly space=”free” so it’s possible to see the counts for the smaller regions.”

Here goes:

> c2 <- c1 + facet_grid(Region ~ ., scale = "free_y")

Use of opts()

Next, note how the formatting is changed with the opts() function instead of a theme update.

> d <- a + geom_bar(aes(x = Region, y = value/1e+06)) + theme_grey() + facet_wrap(~Period) + scale_fill_brewer(palette = "Set1") + opts(axis.text.x = theme_text(angle = 90, hjust = 1)) + opts(axis.ticks = theme_blank()) + opts(legend.position = "none") + opts(panel.grid.minor = theme_blank())