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Data visuals 2017
Supplementary notes for CJ Brown’s talks on dataviz in 2017 for Griffith
University’s honours students and the UQ Winterschool in
Bioinformatics.
Skip to the quiz
Structure of this talk
 Tools for dataviz
 Eleven principles for effective dataviz
 Breaking the rules
 Change the world
The ideal dataviz tool
 Sensible and clean defaults
 Fast and convenient production of common graphics
 Convenient to plot statistical models
 Flexible enough to realize our creative thoughts
R integrates different tools
 Data merging
 Maps
 Graphs
 Analysis
 Word processing
 Presentations (including this one)
R is a flexible tool
 Flexibility is also it’s weakness
 Hard to start
 So many options to do the same thing
 Steep learning curve
 Often combine with other tools
Graphics packages in R
The dominant options are the base graphics R comes shipped with and the
ggplot2
package.
Plot your data
Make your own Datasaurus dozen
The datasaurus is a great example of why you should view your data,
invented by Alberto Cairo. See Steph Locke’s code and package on
github for making this in R.
library(datasauRus)
datnames < rev(unique(datasaurus_dozen$dataset))
nlevels < length(datnames)
for (i in 1:nlevels){
i < which(datasaurus_dozen$dataset == datnames[i])
plot(datasaurus_dozen$x[i], datasaurus_dozen$y[i],
xlab = "x", ylab = "y", las = 1)
Sys.sleep(1)
}
Convince yourself that the mean, sd and correlation is the same in all
of these plots:
library(dplyr)
datasaurus_dozen %>% group_by(dataset) %>%
summarize(meanx = mean(x), meany = mean(y),
sdx = sd(x), sdy = sd(y),
corr = cor(x,y))
## # A tibble: 13 × 6
## dataset meanx meany sdx sdy corr
##
## 1 away 54.26610 47.83472 16.76982 26.93974 0.06412835
## 2 bullseye 54.26873 47.83082 16.76924 26.93573 0.06858639
## 3 circle 54.26732 47.83772 16.76001 26.93004 0.06834336
## 4 dino 54.26327 47.83225 16.76514 26.93540 0.06447185
## 5 dots 54.26030 47.83983 16.76774 26.93019 0.06034144
## 6 h_lines 54.26144 47.83025 16.76590 26.93988 0.06171484
## 7 high_lines 54.26881 47.83545 16.76670 26.94000 0.06850422
## 8 slant_down 54.26785 47.83590 16.76676 26.93610 0.06897974
## 9 slant_up 54.26588 47.83150 16.76885 26.93861 0.06860921
## 10 star 54.26734 47.83955 16.76896 26.93027 0.06296110
## 11 v_lines 54.26993 47.83699 16.76996 26.93768 0.06944557
## 12 wide_lines 54.26692 47.83160 16.77000 26.93790 0.06657523
## 13 x_shape 54.26015 47.83972 16.76996 26.93000 0.06558334
We can also save these as .png images to make a .gif image (see also
here)
for (ilvs in 1:nlevels){
i < which(datasaurus_dozen$dataset == datnames[ilvs])
thiscol < ifelse(datnames[ilvs] == "dino", "darkseagreen", "grey20")
png(filename = paste0("datasaurus/",datnames[ilvs],".png"))
plot(datasaurus_dozen$x[i], datasaurus_dozen$y[i],
xlab = "x", ylab = "y", las = 1,
xlim = c(10, 105), ylim = c(5, 105), col = thiscol, pch = 16)
dev.off()
}
Clarity not simplicity
I give the example of the famout ‘hockey stick’ graph of Northern
Hemisphere
temperatures.
Dataviz are models
Any visualization is a model
Alberto Cairo 2016
Different viz models for the same data
Three ways of visualising the same xy data
Each of these graphs of the same data has a slightly different
interpretation.
x < 1:100
y < x + rnorm(100, sd=30)
plot(x,y, pch = 16, col = grey(0.5, 0.5))
mod1 < lm(y ~ x)
plot(x,y, col = 'white')
abline(mod1, lwd = 3, col = 'red')
library(MASS)
filled.contour(kde2d(x,y), scale = F)
plot(x,y, pch = 16, col = grey(0.5, 0.5))
abline(mod1, lwd = 3, col = 'red')
Models help clarify complex datasets
Effect size often has to been seen to be understood
When doing confirmatory analysis, we might want to know how strong an
effect is. Data viz is very useful for this task. Lets compare two
datasets that have similar pvalues, but very different effect sizes
set.seed(42)
x < rnorm(1000)
set.seed(420)
y < 5*x + 3 + rnorm(1000, sd = 15)
set.seed(420)
y2 < 5*x + 3 + rnorm(1000, sd = 1)
mod1 < lm(y ~ x)
mod2 < lm(y2 ~ x)
#Compare the pvalues on the slopes
summary(mod1)
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## 43.201 10.330 0.395 9.634 46.694
##
## Coefficients:
## Estimate Std. Error t value Pr(>t)
## (Intercept) 2.8054 0.4614 6.080 1.71e09 ***
## x 4.2096 0.4603 9.145 < 2e16 ***
## 
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.59 on 998 degrees of freedom
## Multiple Rsquared: 0.07732, Adjusted Rsquared: 0.07639
## Fstatistic: 83.63 on 1 and 998 DF, pvalue: < 2.2e16
summary(mod2)
##
## Call:
## lm(formula = y2 ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## 2.88004 0.68868 0.02634 0.64229 3.11291
##
## Coefficients:
## Estimate Std. Error t value Pr(>t)
## (Intercept) 2.98703 0.03076 97.11 <2e16 ***
## x 4.94731 0.03069 161.21 <2e16 ***
## 
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9724 on 998 degrees of freedom
## Multiple Rsquared: 0.963, Adjusted Rsquared: 0.963
## Fstatistic: 2.599e+04 on 1 and 998 DF, pvalue: < 2.2e16
par(mfrow = c(1,2))
plot(x,y, pch = 16, col = grey(0.5,0.5), las = 1)
abline(mod1, lwd = 2, col = 'red')
plot(x,y2, pch = 16, col = grey(0.5,0.5), las = 1)
abline(mod2, lwd = 2, col = 'red')
Superplots
Andrew Gelman coined the term
superplots for
plotting different models on multiple panels of a graph so you can
visually compare them.
For instance, say we have several timeseries and we want to know if
they deviate from each other signficantly. An easy way to compare them
is to fit splines to each timeseries and then just plot them next to
each other, with SEs. Then we can compare visually for ‘signficant’
differences.
Here’s some code to simulate three madeup series. The first two have
the same trend, but different observation errors, the third has a
different trend:
tmax < 50
drift < c(10, 5)
sd < 40
sdobs < 200
set.seed(5)
yzero < cumsum(rnorm(n=tmax, mean=drift[1], sd=sd))
y1 < yzero + rnorm(n = tmax, mean = 0, sd = sdobs)
y2 < yzero + rnorm(n = tmax, mean = 0, sd = sdobs)
y3 < cumsum(rnorm(n=tmax, mean=drift[2], sd=sd)) +
rnorm(n = tmax, mean = 0, sd = sdobs)
dat < data.frame(ts = rep(letters[1:3], each = tmax), x = rep(1:tmax, 3), y = c(y1, y2, y3))
We can easily plot these three series using ggplot2
and automatically
add a spline.
library(ggplot2)
ggplot(dat, aes(x = x, y = y)) +
geom_point() +
facet_grid(.~ts) +
stat_smooth(method = "loess", se = TRUE) +
theme(axis.text = element_text(size=14),
axis.title = element_text(size=16,face="bold"),
strip.text.x = element_text(size = 16),
panel.background = element_rect(fill = 'white', colour = 'white'))
Length is most accurate
Ways of comparing data in order from most accurate (top) to more generic
(bottom).
Comparing volume and area
Compare these. Note that if we compare circles we should use area, not
the radius or diameter to scale their size.
n < c(10, 5)
barplot(n, col = 'skyblue', xaxt = 'n', yaxt = 'n')
rad1 < 1
area1 < pi*(rad1^2)
area2 < area1/2
rad2 < sqrt(area2/pi)
par(mfrow = c(1,2), mar = c(0,0,0,0))
pie(1, col = 'skyblue', labels = NA, border = NA, radius = rad1)
pie(1, col = 'skyblue', labels = NA, border = NA, radius = rad2)
Exploration of data
Let’s create a point cloud to demonstrate some data exploration
techniques
set.seed(42)
x < rnorm(1000)
y < 5*x + 3 + rnorm(1000, sd = 15)
plot(x,y, pch = 16, col = grey(0.5,0.5), las = 1)
Can’t see alot here. A linear model might help us explore if there is
any trend going on:
mod1 < lm(y ~ x)
plot(x,y, pch = 16, col = grey(0.5,0.5), las = 1)
abline(mod1, lwd = 2, col = 'red')
xnew < seq(min(x), max(x), length.out = 100)
pmod < predict(mod1, newdata =data.frame(x=xnew), se = T)
lines(xnew, pmod$fit + pmod$se.fit, lwd = 2, col = 'red', lty = 2)
lines(xnew, pmod$fit  pmod$se.fit, lwd = 2, col = 'red', lty = 2)
What about identifying extreme points, that may be worth investigating
further? We can pick out points that are greater than 2SDs from the
trend:
modresid < resid(mod1)
sd2 < sd(modresid)*2
ipt < which(abs(modresid) > sd2)
plot(x,y, pch = 16, col = grey(0.5,0.5), las = 1)
abline(mod1, lwd = 2, col = 'red')
points(x[ipt], y[ipt], pch = 16, col = rgb(1,0,0, 0.6))
Effect size
Don’t waste digital ink
Plots with less ‘addons’ tend to communicate the key message more
clearly. For instance, just like excel plots dont:
x < rnorm(100)
dat < data.frame(x = x, y = 0.25*x + rnorm(100, sd = 0.2))
library(ggplot2)
library(ggthemes)
ggplot(dat, aes(x = x, y = y)) + geom_point() +
theme_excel() + theme(axis.text=element_text(size=20),
axis.title=element_text(size=20))
You can get additional themes for ggplot2 using this excellent
package.
A cleaner view:
ggplot(dat, aes(x = x, y = y)) + geom_point() +
theme_base() + theme(axis.text=element_text(size=20),
axis.title=element_text(size=20))
Or simply:
plot(dat$x, dat$y, xlab = "x", ylab = "y", las = 1)
A good principle is to not use ‘ink’ on figures that isn’t needed to
communicate your message. Tufte
takes the ‘less ink’ philosophy to the extreme:
ggplot(dat, aes(x = x, y = y)) + geom_point() +
theme_tufte() + theme(axis.text=element_text(size=20),
axis.title=element_text(size=20))
When is ggplot2 appropriate, or when should I use base R?
In general I think ggplot2 is appropriate for
problems of intermediate complexity. Like this:
Base R is great if you just want to plot a barplot quickly, or do an xy
plot. ggplot2 comes into its own for slight more complex plots, like
having multiple panels for different groups or colouring lines by a 3rd
factor. But once you move to really complex plots, like overlaying a
subplot on a map, ggplot2 becomes very difficult, if not impossible. At
that point it is better to move back to Base R. ggplot2 can also get
very fiddly if you are very specific about your plots, like having
certain colours, or the labels in a certain way.
As an example, ggplot2 is great for data like this:
x1 < rnorm(30)
grps < letters[c(rep(1, 10), rep(2, 10), rep(3, 10))]
y1 < x1 + c(rep(1, 10), rep(1, 10), rep(2, 10)) + rnorm(30)
dat < data.frame(x = x1, grps = grps, y = y1)
head(dat)
## x grps y
## 1 0.19033984 a 6.159889e01
## 2 0.07173877 a 7.577584e05
## 3 0.00285171 a 1.003277e+00
## 4 1.10821896 a 7.302877e01
## 5 0.93519177 a 2.662337e+00
## 6 1.48583945 a 1.588023e+00
ggplot(dat, aes(x = x1, y = y1, color = grps)) +
geom_point() + theme_bw()
It is also pretty handy for faceting:
ggplot(dat, aes(x = x1, y = y1)) +
geom_point() + facet_wrap(~grps)+
theme_bw()
The key with ggplot2 is to have your data in a dataframe.
In reality both ggplot2 and base R graphics are worth learning, but I
would start with learning the basics of base R graphics and then move
onto ggplot2 if you want to quickly plot lots of structured datasets.
Pie graphs vs bar graphs
In Mariani et al. they plot rates of seafood fraud by several European
countries. While its a foundational study that establishes improvement
in the accuracy of food labelling, their graphics could be improved in
several ways.
First they use perspective pies. This makes it incredibly hard to
compare the two groups (fish that are labelled/mislabelled). Humans are
very bad at comparing angles and pretty bad at comparing areas. With the
perspective you can’t even compare the areas properly. They do provide
the raw numbers, but then, why bother with the pies?
Note that the % pies misrepresent the data slightly because the %
figures are actually odds ratios (mislabels / correct labels), rather
than percent (mislabeels / total samples).
Second the pies are coloured red/green, which will be hard for redgreen
colourblind people to see.
Third, they have coloured land blue on their map, so it appears to be
ocean at first look.
Fourth, the map is not really neccessary. There are no spatial patterns
going on that the authors want to draw attention to. I guess having a
map does emphasize that the study is in Europe. Finally, the size of
each pie is scaled to the sample size, but the scale bar for the sample
size shows a sample of only 30, whereas most of their data are for much
larger samples sizes (>200). Do you get the impression from the pies
that the UK has 668 samples, whereas Ireland only has 187? Therefore,
from this graphic we have no idea what sample size was used in each
country.
In fact, all the numbers that are difficult to interpret in the figure
are very nicely presented in Table 1.
Below is a start at improving the presentation. For instance, you could
do a simple bar chart, ordering by rate of mislabelling.
cnames < c('Ireland' ,'UK','Germany','France','Spain','Portugal')
corrlab < c(180, 647, 145, 146, 267, 178)
mislab < c(7, 21, 9, 4, 24, 12)
misrate < 100*signif(mislab / (corrlab + mislab),2)
corrrate < 100  misrate
ord < order(misrate, decreasing = T)
y < rbind(corrrate, misrate)
par(mar = c(5,4,4,7))
barplot(y[,ord], names.arg = cnames[ord], col = c('skyblue','tomato'), ylab = 'Labelling rate (%)', las = 2)
legend(x=7.5, y = 90, legend = c("Mislabelled", "Correctly labelled"), pch = 16, col = c('tomato','skyblue'), xpd = NA, cex = 0.7)
You could add another subfigure to this, showing the rate by different
species too.
The barplot doesn’t communicate the sample size, but then that is
probably not the main point. The sample sizes are probably best reported
in the table
If we felt the map was essential, then putting barcharts on it would be
more informative. It is not that easy to add barcharts ontop of an
existing map in R, so I would recommend creating the barcharts
seperately, then adding them on in Illustrator or Powerpoint.
We can make a basic map like this:
library(maps)
library(maptools)
map('world', xlim = c(20, 20), ylim = c(35, 60), col = 'grey', fill = T)
Then create some nice barcharts. We write a loop so we get one barchart
for each country.
nc < length(cnames)
par(mfrow = c(2,3), oma = c(1,1,1,3))
for (i in 1:nc){
y < c(mislab[i], corrlab[i])
barplot(y, names.arg = '', las = 2, col = c('tomato','skyblue'), ylim = c(0, corrlab[i]), main = cnames[i], cex.main = 2.4, yaxt = 'n')
byy < signif(max(y),2)/3
yat < c(0, min(y), max(y))
axis(2, at = yat, las = 2, cex.axis = 2, labels = F)
axis(2, at = yat[2:3], las = 2, cex.axis = 2, labels = T)
}
legend(x = 2.8, y = 500, legend = c('Fraud', 'Correct'), pch = 15, col = c('tomato','skyblue'), xpd = NA, cex = 2, bty = 'n')
Scaling matters
It can be misleading to present % and proportion data on axes that are
not scaled 0 – 100%. For instance, compare these three graphs:
y < c(70, 72, 68, 73)
x < 1:4
xnams < LETTERS[1:4]
par(mfrow = c(1,3), oma = c(1,1,1,3), mar = c(5,6,2,2))
plot(x,y, pch = 3, cex = 2, las = 1, xaxt = 'n', xlab = '', ylab = 'Percent', cex.axis = 2, cex.lab = 2, tcl = 0.5, xlim = c(0, 5), col = 'red', lwd = 3)
axis(1, at = x, labels = xnams, cex.axis = 2, tcl = 0.5)
barplot(y, names.arg = xnams, las = 1, cex.axis = 2, cex.lab = 2, cex.names = 2, ylab = 'Percent')
barplot(y, names.arg = xnams, las = 1, cex.axis = 2, cex.lab = 2, cex.names = 2, ylab = 'Percent', ylim = c(0, 100))
Interpreting rates
The units you use affect how people interpret your graph.
People are bad at interpreting rates, we just can’t get our heads around
accumulation very well. Here is a numerical example. Check out the below
figure and ask yourself:
At what time is the number of people in the shopping centre declining?
Would you say it is at point A, B, C or D?
Before you proceed with code below, take the poll:
Here is how we made the figure and generated the data:
par(mar = c(4,4.5,2,2), mgp = c(3,1,0))
plot(times, inrate_err, type = 'l', xlab = 'Hour of day', ylab = 'People per 10 minutes', las = 1, cex.axis = 2, lwd = 3, col = 'darkblue', cex.lab = 2, ylim = c(0, 12))
lines(times, outrate_err, lwd = 3, col = 'tomato')
abline(v = 12, lwd = 2, col = grey(0.5,0.5))
text(12, 13, 'A', xpd = NA, cex = 2)
abline(v = 13.5, lwd = 2, col = grey(0.5,0.5))
text(13.5, 13, 'B', xpd = NA, cex = 2)
abline(v = 14.2, lwd = 2, col = grey(0.5,0.5))
text(14.2, 13, 'C', xpd = NA, cex = 2)
abline(v = 15.8, lwd = 2, col = grey(0.5,0.5))
text(15.8, 13, 'D', xpd = NA, cex = 2)
legend('bottomleft', legend = c('Entering', 'Leaving'), lwd = 2, col = c('darkblue','tomato'), cex = 1.5, xpd = NA)
Let’s plot the total number of people:
par(mar = c(5,5.5,2,2), mgp = c(4,1,0))
plot(times, cumsum(inrate_err)  cumsum(outrate_err), type = 'l', xlab = 'Hour of day', ylab = 'People in shopping centre', las = 1, cex.axis = 2, lwd = 3, col = 'darkblue', cex.lab = 2, ylim = c(0, 120))
abline(v = 12, lwd = 2, col = grey(0.5,0.5))
text(12, 130, 'A', xpd = NA, cex = 2)
abline(v = 13.5, lwd = 2, col = grey(0.5,0.5))
text(13.5, 130, 'B', xpd = NA, cex = 2)
abline(v = 14.1, lwd = 2, col = 'tomato')
text(14.2, 130, 'C', xpd = NA, cex = 2)
abline(v = 15.8, lwd = 2, col = grey(0.5,0.5))
text(15.8, 130, 'D', xpd = NA, cex = 2)
Hopefully the answer is obvious now. So the right scales can help make
interpretation much easier.
Choosing colour scales
Alot of thought should go into choosing colour scales for graphs for
instance will it print ok? will colour blind people be able to see
this? does the scale create artificial visual breaks in the data?
Luckily there is a package to help you make the right decision for a
colour scale, it is called RColorBrewer
. Check out colorbrewer.org for
a helpful interactive web interface for choosing colours.
First let’s load some sea surface temperature data
as a raster:
library(raster)
r < raster("MeanAVHRRSST")
library(RColorBrewer)
par(mfrow = c(1,2))
plot(r, col = rev(brewer.pal(11, "Spectral")), asp = NA)
plot(r, col = brewer.pal(11, "Purples"), asp = NA)
## Warning in brewer.pal(11, "Purples"): n too large, allowed maximum for palette Purples is 9
## Returning the palette you asked for with that many colors
Using redgreen palettes makes it hard for colour blind people. Also,
using a diverging palette makes it look like there is something
important about the middle point (yellow). A better palette to use would
be one of the sequential ones, “Purples” shown here.
To make it easier to understand, let’s look at these again as contour
plots. I will use a more appropriate diverging palette to the redgreen
one though.
z < matrix(rep(1:10, 10), nrow = 10)
filled.contour(z, col = brewer.pal(9, 'Reds'), nlevels = 10)
filled.contour(z, col = brewer.pal(9, 'RdBu'), nlevels = 10)
Notice the diverging pallette creates an artificial split at yellow
One of the only legitimate uses for pie graphs (I think) is visualising
the colour scales. Here is how:
reds < brewer.pal(9, 'Reds')
greens < brewer.pal(9, 'Greens')
blues < brewer.pal(9, 'Blues')
rdylgn < brewer.pal(9, 'RdYlGn')
rdbu < brewer.pal(9, 'RdBu')
dark2 < brewer.pal(8, 'Dark2')
par(mfrow = c(2,3), mar = c(0,0,0,0), oma = c(0,0,0,0))
pie(rep(1, 9), col = reds)
pie(rep(1, 9), col = greens)
pie(rep(1, 9), col = blues)
pie(rep(1, 9), col = rdylgn)
pie(rep(1, 9), col = rdbu)
pie(rep(1, 9), col = dark2)
Breaking the rules – stick with convention
Sometimes you might want to bend or break the principles above in order
to stick with convention. This is useful if people are used to viewing
data in a particular way. For instance, geneticists are fond of pie
graphs in haplotype networks. Here is some code I modified from
here
to make one (note that in this dataset there is no haplotype diversity
so we artificially mix them up so you can see the pies here):
library(pegas)
library(RColorBrewer)
data(woodmouse)
x < woodmouse[sample(15, size = 110, replace = TRUE), ]
h < haplotype(x)
net < haploNet(h)
mixed_up < rep(letters[1:5], each=22)
ind.hap2 < with(
utils::stack(setNames(attr(h, "index"), rownames(h))),
table(hap=ind, pop = mixed_up[values])
)
plot(net, size = attr(net, "freq"), scale.ratio = 2, cex = 0.8, pie = ind.hap2, bg = brewer.pal(8, "Dark2"))
legend("bottomright", colnames(ind.hap2), col = brewer.pal(8, "Dark2"), pch=20)
Interactive dataviz
Interactive visuals can overcome the dilemma of
having too much complexity to show, but also wanting the viewer to
explore the details. For instance, you could use the dygraphs
package to zoom in on a date range.
We will get some recent data on wave heights from QLD government. First
we load the data directly from the
web
and process it to correctly label dates and times, then just select the
Signficant wave height variable at Tweed Heads.
waves <read.csv(url("http://www.ehp.qld.gov.au/datasets/waves/wave7dayopdata.csv?timestamp=20170705EST210650"), skip = 1)
waves$time < as.POSIXct(strptime(as.character(waves$DateTime), format = "%Y%m%dT%H:%M:%S"))
waves2 < subset(waves, (Hsig > 0) & (Site == "Tweed Heads"))[,c("time", "Hsig")]
waves_xts < xts::xts(waves2$Hsig, order.by = waves2$time)
Now have our data, we can look at it using the dygraphs
package. We will do two things: add
a date range selector and also add a ‘roll’ which will smooth over a
certain period of our choosing:
library(dygraphs)
dygraph(waves_xts, main = "Significant wave height  Tweed Heads") %>%
dyRoller(rollPeriod = 50) %>%
dyRangeSelector()
I haven’t put it up on the webpage here, but have a go yourself.
Anatomy of a simple chart
The construction of a simple chart in R can be a surprisingly long piece
of code. Here is an example to get you started. Don’t be afraid to
experiment!
#  #
# Make some data
#  #
set.seed(42)
n < 11
x < rnorm(n, mean = seq(18, 25, length.out = n))
y < rnorm(n, mean =seq(26, 18, length.out = n))
z < rnorm(n, mean = 22)
t < 2005:(2005+n1)
datnames < c('Almonds', 'Peanuts', 'Hazelnuts')
plot(t, x)
lines(t, y)
lines(t, z)
Which look terrible. Let’s build a better chart.
# Package for colours
library(RColorBrewer)
#Set axis limits
ymax < 30
ylim < c(15, ymax)
xlim < c(min(t), max(t))
#Define colours
cols < brewer.pal(3, 'Dark2')
#Parameters for plotting
lwd < 2
xlabels < seq(min(t), max(t), by = 5)
ylabels < seq(0, ymax, by = 5)
#Set the window params
par(mar = c(5,5,4,4))
#Build the plot
plot(t, x, type = 'l', bty = 'n', xaxt = 'n', yaxt = 'n',
ylim = ylim, xlim = xlim, lwd = lwd, col = cols[1],
xaxs = 'i', yaxs = 'i',
xlab = 'Time (yrs)',
ylab = '',
main = 'Changing price of nuts ($/kg)')
#Add more lines
lines(t, y, lwd = lwd, col = cols[2])
lines(t, z, lwd = lwd, col = cols[3])
#Add labels to lines
text(t[n], x[n], datnames[1], adj = c(0, 0), xpd = NA, col = cols[1])
text(t[n], y[n], datnames[2], xpd = NA, adj = c(0, 0), col = cols[2])
text(t[n], z[n], datnames[3], xpd = NA, adj = c(0, 0), col = cols[3])
# Add custom axes
axis(1, col = 'white', col.ticks = 'black', labels = xlabels, at = xlabels)
axis(1, col = 'white', col.ticks = 'black', labels = NA, at = t)
axis(2, col = 'white', col.ticks = 'black', las =1, labels = ylabels, at = ylabels)
Resources and further reading

An infographic of chart types: Visual
vocabulary
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