The following data objects are usually the inputs in R plots:

• vectors
• matrices
• data frames

Graph the number of males per lek

males <- c(5, 4, 6, 4, 9) # males per lek

plot(males)

cols <- topo.colors(5, alpha = 0.7)

Let's add a title, a line to connect the points, and some color:

plot(males, type = "o", col = "blue") #blue points overlayed by a line 
title(main = "Males per lek", col.main = "red", font.main = 4) 

Now let's add a red line for females and specify the y-axis range directly so it will be large enough to fit the female data:

# Define 2nd vector
females <- c(2, 5, 4, 5, 12) 

# Graph y axis that ranges from 0 to 12
plot(males, type = "o", col = "blue", ylim = c(0,12))

# females red dashed line and square points
lines(females, type = "o", pch = 22, lty = 2, col = "red") 

# title red, bold/italic font
title(main = "Males & females per lek", col.main = "red", font.main = 4) 

Now let's add a red line for females and specify the y-axis range directly so it will be large enough to fit the female data:

Next let's change the axes labels to match our data and add a legend

# Calculate range from 0 to max value of males and females
g_range <- range(0, females, males)

#Turn off axes and annotations (axis labels) so we can specify them ourself
plot(males, type = "o", col = "blue", ylim = g_range, 
   axes = FALSE, ann = FALSE)

Next let's change the axes labels to match our data and add a legend

Add the axis and box

plot(males, type = "o", col = "blue", ylim = g_range, 
   axes = FALSE, ann = FALSE)

# Make x axis using lek labels
axis(1, at = 1:5, lab = c("CCL","STR","SAT","SJA","SUR"))

# Make y axis with horizontal labels that display ticks at 
# every 4 marks. 4*0:g_range[2] is equivalent to c(0,4,8,12).
axis(2, las = 1, at = 4*0:g_range[2])

# Create box around plot
box()

Add the axis and box

Add the data, title and legend

plot(males, type = "o", col = "blue", ylim = g_range, 
   axes = FALSE, ann = FALSE)
axis(1, at = 1:5, lab = c("CCL","STR","SAT","SJA","SUR"))
axis(2, las = 1, at = 4*0:g_range[2])
box()

lines(females, type = "o", pch = 22, lty = 2, col = "red")
title(main = "Males and females in leks", col.main = "red", font.main = 4)

title(xlab = "Leks", col.lab = rgb(0,0.5,0))
title(ylab = "Total", col.lab = rgb(0,0.5,0))

legend(1, g_range[2], c("males","females"), cex = 0.8, 
   col = c("blue","red"), pch = 21:22, lty = 1:2);

Add the data, title and legend

Bar graph of male data

barplot(males)

Graph males with specified labels for axes.

barplot(males, main = "Males", xlab = "Leks", ylab = "Total", 
        names.arg=c("CCL", "STR", "SAT", "SJA", "SUR"), 
        border="blue", density = c(10, 20, 30, 40, 50))

Now let's graph the total number of birds per lek using some color and show a legend:

hummers <- cbind(males, females, juve = c(4, 4, 6, 6, 16))

barplot(hummers, main = "Hummingbirds", ylab = "Total",
   beside = TRUE, col = cols)

# Place the legend at the top-left corner with no frame  
legend("topleft", c("CCL", "STR", "SAT", "SJA", "SUR"), cex = 0.6, 
   bty = "n", fill = cols)

Now let's graph the total number of birds per day using some color and show a legend:

Check out the "AirPassengers" data set (deaths per year):

data(AirPassengers)

AirPassengers <- t(matrix(AirPassengers, ncol = 12))


colnames(AirPassengers) <- 1949:1960

1.1 Make a barplot for "casualty" counts for each month by year (similar to the one in the previous slides)

1.2 Use a single color for each bar (a total of 144 colors!)

For showing data distribution and normality

hist( rnorm(1000,5), col = topo.colors(1,  alpha = 0.3))

For showing proportions

pie(males, col = cols)

Add legend

pie(males, main="Males", col = cols,
   labels=c("CCL","STR","SAT","SJA","SUR"))

Create a colored dotchart for age classes by lek

dotchart(t(hummers), color=c("red","blue","darkgreen"),
   main="Dotchart for hummingbirds", cex=0.8)

plot(1, 1, xlim = c(1,5.5), ylim = c(0,7), type = "n", ann = FALSE) # Make an empty chart
text(1:5, rep(6, 5), labels = c(0:4), cex = 1:5, col= cols)# Plot digits 0-4 with increasing size/color

points(1:5, rep(5, 5), cex = 1:5, col = cols, pch = 0:4)# Plot symbols 0-4 with increasing size/color
text((1:5) + 0.4, rep(5, 5), cex=0.6, (0:4))

points(1:5, rep(4,5), cex = 2, pch = (5:9))# Plot symbols 5-9 with labels
text((1:5) + 0.4, rep(4,5), cex = 0.6, (5:9))

points(1:5, rep(3,5), cex=2, pch=(10:14))# Plot symbols 10-14 with labels
text((1:5)+0.4, rep(3,5), cex=0.6, (10:14))

points(1:5, rep(2,5), cex = 2, pch = 15:19)# Plot symbols 15-19 with labels
text(1:5 + 0.4, rep(2,5), cex=0.6, 15:19)

points(1:6 * 0.8 + 0.2, rep(1, 6), cex = 2, pch = 20:25) # Plot symbols 20-25 with labels
text(1:6 * 0.8 + 0.5, rep(1, 6), cex = 0.6, 20:25)

On hummingbird data

class<-c(rep("male",5),rep("female",5),rep("juvenile",5))
humm.df<-data.frame(class,total=c(males, females, c(4,4,6,6,16)),
                    Leks= rep(c("CCL","STR","SAT","SJA","SUR"),3))

boxplot(total~class,data = humm.df)

On hummingbird data

Changing color and orientation

humm.df2 <- data.frame(class, total = c(rnorm(5, 1, 1), rnorm(5, 3, 2), rnorm(5, 3, 1)),Leks = rep(c("CCL", "STR", "SAT", "SJA", "SUR"), 3))

boxplot(total ~ class, data = humm.df2, col = "yellow4", horizontal = TRUE)

Changing color and orientation

# height of president divided by height of most successful opponent: 
body.size <- c(3.745756, 1.725416, 3.905743, 6.216381, 4.516818, 5.049753, 
               4.922817, 4.961822, 6.387712, 6.360082, 3.673467, 6.403956, 
               3.360937, 4.721525, 5.963445, 5.648205, 4.250537, 4.581020, 
               6.081036, 5.994140, 3.883612, 4.457508, 1.892721, 3.409522,
               5.053790, 3.896708, 6.422167, 4.993130, 2.226273, 6.255924, 
               4.098446, 2.568278, 3.914492, 6.052945, 3.985166, 2.329352, 
               2.622594, 2.034283, 5.583006, 3.434656, 4.476129, 4.466925, 
               4.490384, 3.497488, 4.974221, 5.177449, 4.735471, 5.836074, 
               5.886631, 6.510843)

#Bill length in long-billed hermits
frequency <- c(6.455821, 5.139507, 6.108490, 8.623173, 6.710728, 8.029906, 
               7.469104, 6.906462, 8.603421, 7.284242, 5.979938, 7.143694, 
               6.096457, 5.999354, 7.850083, 7.785403, 5.907416, 7.901530, 
               7.937979, 7.114081, 6.179503, 7.143809, 6.380096, 6.769819, 
               7.385331, 6.539280, 8.525748, 7.068429, 6.181722, 7.508457, 
               7.860184, 5.886500, 5.968909, 8.239845, 6.530752, 5.949638, 
               4.630713, 5.908844, 7.291094, 6.046808, 6.816406, 6.908434, 
               6.724284, 4.804671, 8.942853, 6.950045, 7.461648, 7.487176,
               7.943633, 7.275438)

plot(body.size, frequency, col = "black", pch = 21, bg = "grey", cex = 2,
      ylab = "", xlab = "", axes = F)

axis(1)

axis(2) 

reg1 <- lm(frequency~body.size)
abline(reg1, lwd = 2) 

par(las = 0)

mtext("Body size (g)", side = 1, line = 2.5, cex = 1.5)

mtext("Frequency (kHz)", side = 2, line = 2.2, cex = 1.5)

text(3, 8, "r = .58", cex = 1.5)

2.1 Using the 'iris' data set example make a scatter plot showing the association between "sepal length" and "petal length"

2.2 Use different symbols for different species

2.3 Use a color gradient to show the size of "petal width" in the previous scatter plot

2.4 Add the "sepal width" as the size of the symbols

def.par <- par(no.readonly = TRUE)
xhist <- hist(body.size, plot = FALSE)
yhist <- hist(frequency, plot = FALSE)
top <- max(c(xhist$counts, yhist$counts))

nf <- layout(matrix(c(2,0,1,3),2,2,byrow = TRUE), c(3,1), c(1,3), TRUE)
layout.show(nf)

layout(matrix(c(2, 0, 1, 3),2, 2, byrow = TRUE), c(3,1), c(1,3), TRUE)

par(mar = c(3,3,1,1))

plot(body.size, frequency,  pch = 20, cex = 2)

par(mar = c(0,3,1,1))

barplot(xhist$counts, axes = FALSE, ylim = c(0, top), space = 0)

par(mar = c(3,0,1,1))

barplot(yhist$counts, axes = FALSE, xlim = c(0, top), space = 0,
        horiz = TRUE)

par(def.par)

Example with regression plot

#set working directory where you want to save plot
setwd("~/Desktop")
jpeg("reg plot.jpeg")
plot(body.size, frequency, col = "black", pch = 21, bg = "grey", cex = 2,
      ylab = "", xlab = "", axes = F)
axis(1)
axis(2) 
reg1 <- lm(frequency~body.size)
abline(reg1, lwd = 2) 
par(las = 0)
mtext("Body size (g)", side = 1, line = 2.5, cex = 1.5)
mtext("Frequency (kHz)", side = 2, line = 3.7, cex = 1.5)
text(3, 8, "r = .58", cex = 1.5)
dev.off()

Plots can be saved in other formats with these functions:

• tiff()

• png()

• pdf()

Using the 'iris' data set example:

3.1 Make a multipanel plot with 4 plots (2 rows and 2 columns) showing the association between "sepal length" and "petal length" for each species (first 3 panels) and for the whole data set (4th panel) (hint: you could use par(mfrow = c(2, 2)) instead of layout)

3.2 Add the species names to the titles for each plot

3.3 Use symbol size and color gradient to include information about "petal width" and "sepal width" (respectively) as in exercise 2.3 and 2.4

3.4 Save the plot as a tiff file. Make sure the font size is OK (not too big, not too small) in the saved image.