Scatter plots to show relationship between two variables

To show a scatter plot, use the plot function.

?plot.default
plot(x, y = NULL, type = "p", xlim = NULL, ylim = NULL, main = NULL, sub = NULL, 
     xlab = NULL, ylab = NULL, axes = TRUE, ...)

pch

plot(cars$dist, cars$speed)

plot(cars$dist, cars$speed, pch = 10, cex = 1.2, col = "blue")

Plot is a generic function (you can “plot” many different types of objects); plot can draw many types of objects, including vectors, tables, and time series. If you need to plot something generated by another package, chances are that package will work with plot to do that. For example, if we have a phylogeny, and plot can draw it.

if(!require("ape")) install.packages("ape")

## Loading required package: ape

tree = ape::rtree(n = 10)
plot(tree)

Histogram to show distribution of a continous varaible

When performing data analysis, it’s often very important to understand the shape of a data distribution. Looking at a distribution can tell you whether there are outliers in the data, or whether a certain modeling technique will work on your data, or simply how many observations are within a certain range of values.

The best known technique for visualizing a distribution is the histogram.

hist(cars$speed)

hist(rnorm(n = 1000, mean = 2, sd = 2))

hist(rpois(n = 100, lambda = 1))

Boxplot

Another very useful way to visualize a distribution is a box plot. A box plot is a compact way to show the distribution of a variable. The box shows the interquartile range. The interquartile range contains values between the 25th and 75th percentile; the line inside the box shows the median. The two “whiskers” on either side of the box show the adjacent values. The adjacent values are intended to show extreme values, but they don’t always extend to the absolute maximum or minimum value. The upper adjacent value is the value of the largest observation that is less than or equal to the upper quartile plus 1.5 times the length of the interquartile range; the lower adjacent value is the value of the smallest observation that is greater than or equal to the lower quartile less 1.5 times the length of the interquartile range. When there are values far outside the range we would expect for normally distributed data, those outlying values are plotted separately.

boxplot

boxplot(mtcars$mpg)

boxplot(mtcars$mpg ~ mtcars$cyl)

Save figures as external files

Graphics in R are plotted on a graphics device. You can generate graphics in common formats using the bmp, jpeg, png, and tiff devices. Other devices include postscript, pdf, pictex (to generate LaTeX/PicTeX), xfig, and bitmap.

pdf(file = "file_name.pdf", width = 7, height = 5)
boxplot(mtcars$mpg ~ mtcars$cyl)
dev.off()

We can save multiple figures in one file, which can be useful when we explore the data.

pdf(file = "file_name.pdf", width = 7, height = 5, onefile = TRUE)
for(i in 1:8){
  set.seed(i)
  plot(1:10, rnorm(10))
}
dev.off()

A data visualization, is composed of geometric shapes, that take on aesthetics which represent variables from a data set. ggplot() + aes() + geom_*() or ggplot(data, aes()) + geom_*()

ggplot2 is a package developed by Hadley Wickham (yes, he also developed the whole tidyverse with others) based on the idea of grammar of graphics – a concept created by Leland Wilkinson (Wilkinson 2005, a book). ggplot2 graphics are built up from modular logical pieces, we can add layers with +.

ggplot2 supports a continuum of expertise. One can get started right away and can also build complex, publication quality figures with some extra effort and prctice.

From the reading chapter: The components of ggplot2’s grammar of graphics are

• Basic:

1. one or more datasets, ggplot2 works with data frames only, no vectors.
2. one or more geometric objects that serve as the visual representations of the data, – for instance, points, lines, rectangles, contours,
3. descriptions of how the variables in the data are mapped to visual properties (aesthetics) of the geometric objects, and an associated scale (e. g., linear, logarithmic, rank),

• Advanced:

4. one or more coordinate systems,
5. statistical summarization rules,
6. a facet specification, i.e. the use of multiple similar subplots to look at subsets of the same data,
7. optional parameters that affect the layout and rendering, such text size, font and alignment, legend positions.

Some terminology

• ggplot - The main function where you specify the dataset and variables to plot
• geoms - geometric objects
  • geom_point(), geom_bar(), geom_density(), geom_line(), geom_area(), etc.
• aes - aesthetics
  • shape, transparency (alpha), color, fill, linetype.
• scales Define how your data will be plotted
  • continuous, discrete, log

Examples

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

library(ggplot2)
ggplot(data = iris) # nothing yet

ggplot(data = iris) +
  geom_point(aes(x = Sepal.Length, y = Sepal.Width))

# can also set aes() within ggplot()
# this is ** Global setting **
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) + 
  geom_point()

# increase point size
myplot <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width))
myplot + geom_point(size = 3)

# add color
myplot + geom_point(aes(color = Species), size = 3)

# different shape?
myplot + geom_point(aes(color = Species, shape = Species), size = 3)

set.seed(123)
d <- diamonds[sample(1:nrow(diamonds), 1000), ]

More examples

boxplot(mtcars$mpg ~ mtcars$cyl)

ggplot(mtcars, aes(x = as.factor(cyl), y = mpg)) +
  geom_boxplot()

hist(d2 <- rnorm(n = 1000, mean = 2, sd = 2))

# ggplot2 must work with a data frame, not a vector!
ggplot(as.data.frame(d2), aes(x = d2)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(as.data.frame(d2), aes(x = d2)) +
  geom_histogram(binwidth = 1, color = "lightblue")

ggplot(as.data.frame(d2), aes(x = d2)) +
  geom_histogram(binwidth = 1, color = "lightblue", fill = "blue")

library(dplyr, warn.conflicts = FALSE)
group_by(iris, Species) %>% summarise(l = sum(Sepal.Length))

## # A tibble: 3 × 2
##   Species        l
##   <fct>      <dbl>
## 1 setosa      250.
## 2 versicolor  297.
## 3 virginica   329.

# bar plot
ggplot(iris, aes(Species, Sepal.Length)) +
  geom_col()

(d3 <- tidyr::pivot_longer(iris, -Species))

## # A tibble: 600 × 3
##    Species name         value
##    <fct>   <chr>        <dbl>
##  1 setosa  Sepal.Length   5.1
##  2 setosa  Sepal.Width    3.5
##  3 setosa  Petal.Length   1.4
##  4 setosa  Petal.Width    0.2
##  5 setosa  Sepal.Length   4.9
##  6 setosa  Sepal.Width    3  
##  7 setosa  Petal.Length   1.4
##  8 setosa  Petal.Width    0.2
##  9 setosa  Sepal.Length   4.7
## 10 setosa  Sepal.Width    3.2
## # ℹ 590 more rows

ggplot(d3, aes(x = Species, y = value, fill = name)) +
  geom_col()

ggplot(d3, aes(x = Species, y = value, fill = name)) +
  geom_col(position = "dodge")

More examples

# density plot
head(faithful)

##   eruptions waiting
## 1     3.600      79
## 2     1.800      54
## 3     3.333      74
## 4     2.283      62
## 5     4.533      85
## 6     2.883      55

ggplot(faithful, aes(x = waiting)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(faithful, aes(x = waiting)) + geom_density()

ggplot(faithful, aes(x = waiting)) +
  geom_density(fill = "blue", alpha = 0.3)

Colors!

Use color-blind friendly, print friendly colors!

Some useful links:

• Colors in R
• R color cheatsheet
• colorspace R package

library(RColorBrewer)
display.brewer.all()

ggplot(d3, aes(x = Species, y = value, fill = name)) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Set1")

ggplot(d3, aes(x = Species, y = value, fill = name)) +
  geom_col(position = "dodge") +
  scale_fill_viridis_d()

ggplot(d3, aes(x = Species, y = value, fill = name)) +
  geom_col(position = "dodge") +
  scale_fill_viridis_d(option = "magma")

Facet

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  facet_grid(Species ~ .)

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  facet_grid(Species ~ .) +
  theme(legend.position = "none") # remove legend

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  facet_grid(. ~ Species) +
  theme(legend.position = "none") # remove legend

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  facet_wrap(~ Species) +
  theme(legend.position = "none") # remove legend

Add smoothers

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  geom_smooth(method = "lm")

## `geom_smooth()` using formula = 'y ~ x'

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~ Species) +
  theme(legend.position = "none") # remove legend

## `geom_smooth()` using formula = 'y ~ x'

More themes

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~ Species) +
  labs(x = "Species length",
       y = "Sepal width",
       color = "Species names") +
  theme(legend.position = "bottom", 
        legend.key = element_rect(fill = NA),
        strip.background = element_rect(fill = NA)
        ) 

## `geom_smooth()` using formula = 'y ~ x'

p = ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point()
p + theme_bw() # comes with ggplot2

p + theme_dark()

p + theme_void()

p + theme_minimal()

There is a ggthemes package for you to use.

library(ggthemes)
p + ggthemes::theme_excel()

p + ggthemes::theme_wsj()

p + ggthemes::theme_economist()

p + ggthemes::theme_solarized()

Save ggplot figures

ggsave(filename = "path/to/figure.pdf", plot = p, 
       width = 7, height = 5)

Putting multiple ggplot2 figures together

Sometimes we need to put multiple figures together as a larger figure. We won’t cover this here, but you can check out these R packages:

• cowplot
• patchwork