ggplot

[woneuy01]

One limitation is that ggplot is designed to work exclusively with data tables. In these data tables, rows have to be observations and columns have to be variables.

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ggplot2-cheatsheet.pdf

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You can associate a dataset x with a ggplot object with any of the 3 commands: ggplot(data = x) ggplot(x) x %>% ggplot()

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In ggplot2, graphs are created by adding layers to the ggplot object: DATA %>% ggplot() + LAYER_1 + LAYER_2 + ... + LAYER_N The geometry layer defines the plot type and takes the format geom_X where X is the plot type. Aesthetic mappings describe how properties of the data connect with features of the graph (axis position, color, size, etc.) Define aesthetic mappings with the aes function. aes uses variable names from the object component (for example, total rather than murders$total). geom_point creates a scatterplot and requires x and y aesthetic mappings. geom_text and geom_label add text to a scatterplot and require x, y, and label aesthetic mappings.

[woneuy01]

Code: Adding layers to a plot library(tidyverse) library(dslabs) data(murders) murders %>% ggplot() + geom_point(aes(x = population/10^6, y = total))

add points layer to predefined ggplot object

p <- ggplot(data = murders) p + geom_point(aes(population/10^6, total))

add text layer to scatterplot

p + geom_point(aes(population/10^6, total)) + geom_text(aes(population/10^6, total, label = abb))

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change the size of the points

p + geom_point(aes(population/10^6, total), size = 3) + geom_text(aes(population/10^6, total, label = abb))

move text labels slightly to the right

p + geom_point(aes(population/10^6, total), size = 3) + geom_text(aes(population/10^6, total, label = abb), nudge_x = 1)

[woneuy01]

simplify code by adding global aesthetic

p <- murders %>% ggplot(aes(population/10^6, total, label = abb)) p + geom_point(size = 3) + geom_text(nudge_x = 1.5) since the aes defined in p globally no need to add aes in geom_point and geom_text

local aesthetics override global aesthetics (redefining, even though there is global aesthetic)

p + geom_point(size = 3) + geom_text(aes(x = 10, y = 800, label = "Hello there!"))

[woneuy01]

Code: Log-scale the x- and y-axis

define p

library(tidyverse) library(dslabs) data(murders) p <- murders %>% ggplot(aes(population/10^6, total, label = abb))

log base 10 scale the x-axis and y-axis

p + geom_point(size = 3) + geom_text(nudge_x = 0.05) + scale_x_continuous(trans = "log10") + scale_y_continuous(trans = "log10")

efficient log scaling of the axes

p + geom_point(size = 3) + geom_text(nudge_x = 0.075) + scale_x_log10() + scale_y_log10()

[woneuy01]

Code: Add labels and title

p + geom_point(size = 3) + geom_text(nudge_x = 0.075) + scale_x_log10() + scale_y_log10() + xlab("Population in millions (log scale)") + ylab("Total number of murders (log scale)") + ggtitle("US Gun Murders in 2010")

5 redefine p to be everything except the points layer

p <- murders %>% ggplot(aes(population/10^6, total, label = abb)) + geom_text(nudge_x = 0.075) + scale_x_log10() + scale_y_log10() + xlab("Population in millions (log scale)") + ylab("Total number of murders (log scale)") + ggtitle("US Gun Murders in 2010")

[woneuy01]

make all points blue

p + geom_point(size = 3, color = "blue")

color points by region

p + geom_point(aes(col = region), size = 3) *col is color

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Code: Add a line with average murder rate

define average murder rate

r <- murders %>% summarize(rate = sum(total) / sum(population) * 10^6) %>% pull(rate)

basic line with average murder rate for the country

p + geom_point(aes(col = region), size = 3) + geom_abline(intercept = log10(r)) # slope is default of 1

change line to dashed and dark grey, line under points

p + geom_abline(intercept = log10(r), lty = 2, color = "darkgrey") + geom_point(aes(col = region), size = 3)

• lty = 2 (line type: dashed line)

Code: Change legend title p <- p + scale_color_discrete(name = "Region") # capitalize legend title

[woneuy01]

The ggthemes package adds additional themes.

theme used for graphs in the textbook and course

library(dslabs) ds_theme_set()

themes from ggthemes

library(ggthemes) p + theme_economist() # style of the Economist magazine p + theme_fivethirtyeight() # style of the FiveThirtyEight website

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Code: Putting it all together to assemble the plot

load libraries

library(tidyverse) library(ggrepel) library(ggthemes) library(dslabs) data(murders)

define the intercept

r <- murders %>% summarize(rate = sum(total) / sum(population) * 10^6) %>% .$rate

make the plot, combining all elements

murders %>% ggplot(aes(population/10^6, total, label = abb)) + geom_abline(intercept = log10(r), lty = 2, color = "darkgrey") + geom_point(aes(col = region), size = 3) + geom_text_repel() + scale_x_log10() + scale_y_log10() + xlab("Population in millions (log scale)") + ylab("Total number of murders (log scale)") + ggtitle("US Gun Murders in 2010") + scale_color_discrete(name = "Region") + theme_economist() *geom_text_repel() when text were too close or crowded

[woneuy01]

geom_histogram creates a histogram. Use the binwidth argument to change the width of bins, the fill argument to change the bar fill color, and the col argument to change bar outline color.

Code: Histograms in ggplot2

load heights data

library(tidyverse) library(dslabs) data(heights)

define p

p <- heights %>% filter(sex == "Male") %>% ggplot(aes(x = height))

basic histograms

p + geom_histogram() p + geom_histogram(binwidth = 1)

histogram with blue fill, black outline, labels and title

p + geom_histogram(binwidth = 1, fill = "blue", col = "black") + xlab("Male heights in inches") + ggtitle("Histogram") col = "black" means the line of bar is black

[woneuy01]

geom_density creates smooth density plots. Change the fill color of the plot with the fill argument.

p + geom_density() p + geom_density(fill = "blue")

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geom_qq creates a quantile-quantile plot. This geometry requires the sample argument. By default, the data are compared to a standard normal distribution with a mean of 0 and standard deviation of 1. This can be changed with the dparams argument, or the sample data can be scaled.

basic QQ-plot

p <- heights %>% filter(sex == "Male") %>% ggplot(aes(sample = height)) p + geom_qq()

QQ-plot against a normal distribution with same mean/sd as data

params <- heights %>% filter(sex == "Male") %>% summarize(mean = mean(height), sd = sd(height)) p + geom_qq(dparams = params) + geom_abline()

QQ-plot of scaled data against the standard normal distribution

heights %>% ggplot(aes(sample = scale(height)) + geom_qq() + geom_abline()

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histogram comparison Code: Grids of plots with the grid.extra package

define plots p1, p2, p3

p <- heights %>% filter(sex == "Male") %>% ggplot(aes(x = height)) p1 <- p + geom_histogram(binwidth = 1, fill = "blue", col = "black") p2 <- p + geom_histogram(binwidth = 2, fill = "blue", col = "black") p3 <- p + geom_histogram(binwidth = 3, fill = "blue", col = "black")

arrange plots next to each other in 1 row, 3 columns

library(gridExtra) grid.arrange(p1, p2, p3, ncol = 3)

[woneuy01]

To create a scatter plot, we add a layer with the function geom_point. The aesthetic mappings require us to define the x-axis and y-axis variables respectively. So the code looks like this: murders %>% ggplot(aes(x = , y = )) + geom_point() except we have to fill in the blanks to define the two variables x and y

[woneuy01]

library(dplyr) library(ggplot2) library(dslabs) data(murders)

edit the next line to add the label

murders %>% ggplot(aes(population, total, label = abb)) + geom_label()

[woneuy01]

Rewrite the code above to make the labels blue by adding an argument to geom_label

murders %>% ggplot(aes(population, total,label= abb)) + geom_label(color="blue")

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Rewrite the code above to make the label color correspond to the state's region. Because this is a mapping, you will have to do this through the aes function. Use the existing aes function inside of the ggplot function.

murders %>% ggplot(aes(population, total, label = abb, color=region)) + geom_label()

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p <- murders %>% ggplot(aes(population, total, label = abb, color = region)) + geom_label()

add a layer to add title to the next line

p + scale_x_log10() + scale_y_log10() + ggtitle("Gun murder data")

[woneuy01]

Create separate smooth density plots for males and females by defining group by sex. Use the existing aes function inside of the ggplot function.

add the group argument then a layer with +

heights %>% ggplot(aes(height,group=sex )) + geom_density()

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Change the density plots from the previous exercise to add color.

edit the next line to use color instead of group then add a density layer

heights %>% ggplot(aes(height, color = sex))+ geom_density()

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heights %>% ggplot(aes(height, fill = sex)) + geom_density()

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heights %>% ggplot(aes(height, fill = sex)) + geom_density(alpha = 0.2 )

• alpha is transparency