## ----Q2, echo=SOLUTIONS, eval=SOLUTIONS--------------------------------------- library(ggplot2) scotpho <- read.table('data/scotpho_alcohol_admissions.txt', header = TRUE, sep = "\t", stringsAsFactors = TRUE) ## ----Q4, echo=SOLUTIONS, eval=SOLUTIONS, results='hide'----------------------- str(scotpho) summary(scotpho) nrow(scotpho) # 330 table(scotpho$area_type) # Council area Scotland # 320 10 # 33 areas, 10 years each. Every area is complete: table(scotpho$area_name) unique(table(scotpho$area_name)) # 10 - so every area has all 10 years range(scotpho$year) # 2010 2019 # Why it's worth checking: if an area were missing a year, the line would be # drawn straight through the gap, joining the points either side of it. # Nothing would warn you, and the plot would imply data you don't have. ## ----Q5, echo=SOLUTIONS, eval=SOLUTIONS, fig.width=8, fig.height=4.5---------- scot <- scotpho[scotpho$area_name == "Scotland", ] ggplot(data = scot, aes(x = year, y = measure)) + geom_line() + geom_point() # What is wrong with this plot for anyone other than you? # - the axes are labelled year and measure, which are column names rather # than English # - there are no units anywhere, so 673 could be anything # - there is no title, so the reader does not know what is being counted # - the x axis is broken at 2010, 2012.5, 2015 and 2017.5, and there is no # such year as 2012.5 # - the y axis runs from about 665 to 764 rather than from zero, which # exaggerates the decline # - there is nothing to say where the data came from # # The plot is not wrong. It is just not very useful to anybody who doesn't already # have the dataset. ## ----Q6, echo=SOLUTIONS, eval=SOLUTIONS, results='hide', fig.width=8, fig.height=4.5---- # a) the three areas in one dataframe areas <- c("Scotland", "Glasgow City", "Aberdeenshire") three_areas <- scotpho[scotpho$area_name %in% areas, ] nrow(three_areas) # 30 - three areas, ten years each # b) one geom_line(), three lines, and a legend you didn't have to write ggplot(data = three_areas, aes(x = year, y = measure, colour = area_name)) + geom_line(linewidth = 1) # Glasgow City is roughly twice the Scottish average and Aberdeenshire is # roughly half of it. In 2019 the rates were 1169, 673 and 314 admissions per # 100,000. The national line, on its own, describes almost nobody. ## ----Q7a, echo=SOLUTIONS, eval=SOLUTIONS, results='hide', fig.width=8, fig.height=4.5---- cols <- palette.colors(3, palette = "Okabe-Ito") cols # "#000000" "#E69F00" "#56B4E9" black, orange, sky blue ggplot(data = three_areas, aes(x = year, y = measure, colour = area_name)) + geom_line(linewidth = 1) + scale_colour_manual(values = cols) ## ----Q7b, echo=SOLUTIONS, eval=SOLUTIONS, fig.width=8, fig.height=4.5--------- greys <- grey(c(0, 0.64, 0.62)) ggplot(data = three_areas, aes(x = year, y = measure, colour = area_name)) + geom_line(linewidth = 1) + scale_colour_manual(values = greys) # The orange and the sky blue are different enough on screen, but in grey they # come out at 0.64 and 0.62, which is practically the same. Anyone printing # your poster in black and white can't tell Glasgow from Scotland. ## ----Q7c, echo=SOLUTIONS, eval=SOLUTIONS, fig.width=8, fig.height=4.5--------- ggplot(data = three_areas, aes(x = year, y = measure, colour = area_name, linetype = area_name)) + geom_line(linewidth = 1) + scale_colour_manual(values = cols) # mapping both colour and linetype to the same variable gives you a single # legend showing both, which is what you want. Try it with greys instead of # cols and you'll find the plot still reads perfectly well. ## ----Q8, echo=SOLUTIONS, eval=SOLUTIONS, fig.width=9, fig.height=5------------ ggplot(data = three_areas, aes(x = year, y = measure, colour = area_name, linetype = area_name)) + geom_line(linewidth = 1) + scale_colour_manual(values = cols) + scale_x_continuous(breaks = seq(2010, 2019, by = 2)) + scale_y_continuous(limits = c(0, 1600)) + labs(x = "Year", y = "Hospital admissions per 100,000 people", title = "Alcohol-related hospital admissions, 2010 to 2019", caption = "Source: Scottish Public Health Observatory. Rates are age-sex standardised.", colour = NULL, linetype = NULL) + theme_minimal() + theme(legend.position = "bottom", plot.caption = element_text(hjust = 0)) # plot.caption = element_text(hjust = 0) pushes the source note over to the # left. ggplot right aligns it by default, which doesn't look quite right. # Should the y axis start at zero? # Starting at zero, as here, shows the true relative size of the difference # between the areas, and Glasgow really is about four times Aberdeenshire. # Starting at the minimum instead would fill the panel with the year to year # wiggles and make a fairly modest national decline look dramatic. For a rate # like this, where zero is meaningful and the comparison between areas is the # point, starting at zero is the honest choice. For something like average age, # where zero is nowhere near the data, it wouldn't make much sense. Whichever # you go for, just be able to say why. ## ----Q9, echo=SOLUTIONS, eval=SOLUTIONS, fig.width=9, fig.height=5------------ # give the plot a name. Nothing is drawn until you ask for it by name admissions_plot <- ggplot(data = three_areas, aes(x = year, y = measure, colour = area_name, linetype = area_name)) + geom_line(linewidth = 1) + scale_colour_manual(values = cols) + scale_x_continuous(breaks = seq(2010, 2019, by = 2)) + scale_y_continuous(limits = c(0, 1600)) + labs(x = "Year", y = "Hospital admissions per 100,000 people", title = "Alcohol-related hospital admissions, 2010 to 2019", caption = "Source: Scottish Public Health Observatory. Rates are age-sex standardised.", colour = NULL, linetype = NULL) + theme_minimal() + theme(legend.position = "bottom", plot.caption = element_text(hjust = 0)) admissions_plot # draw it in the plot pane # pdf: a vector format, so it stays sharp at any size. Sizes are in inches. ggsave('output/ex6_admissions.pdf', plot = admissions_plot, width = 10, height = 5.625, units = "in") # png: pixels, so the resolution matters. 10 x 5.625 inches at 300 dpi gives # you a 3000 x 1687 pixel image that will print cleanly. ggsave('output/ex6_admissions.png', plot = admissions_plot, width = 10, height = 5.625, units = "in", dpi = 300) # Use the pdf for anything going to a printer and the png for anything going # into a Word document, PowerPoint or a web page. If your poster template asks # for a particular figure size, set width and height to that size here rather # than resizing the image afterwards, which is what makes text look squashed # or fuzzy. # If you leave out plot = admissions_plot, ggsave() saves the last plot you # drew. That's usually the one you wanted, but not always, so naming it is # safer.