Coding Basics

R as a calculator

1000 / 10
## [1] 100
x <- 1000 / 10
# x

Assignment symbol

What’s in a name?

Calling Functions

Use of ‘TAB’

seq(from = 1, to = 10, by = 1)
##  [1]  1  2  3  4  5  6  7  8  9 10

continuation character ‘+’

seq(from = 1, to = 10)
##  [1]  1  2  3  4  5  6  7  8  9 10

Printing to Screen

y <- seq(from = 1, to = 10)
y
##  [1]  1  2  3  4  5  6  7  8  9 10

Running R code

1+2
## [1] 3

Getting help

  • Google
  • Stackoverflow

Ch2 Introduction to Data Exploration

Ch3 Data Visualization

Set up

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

data

mpg
## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # ℹ 224 more rows

aesthetics

  • x
  • y
  • color
  • size
  • alpha
  • shape
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# Add a third variable: color
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

# Add a third variable: size
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))
## Warning: Using size for a discrete variable is not advised.

# Add a third variable: alpha
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))
## Warning: Using alpha for a discrete variable is not advised.

# Add a third variable: shape
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))
## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 7 values. Consider specifying shapes manually if you need
##   that many have them.
## Warning: Removed 62 rows containing missing values or values outside the scale range
## (`geom_point()`).

# Setting aesthetics manually
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

common problems

  • Sometimes you’ll run the code and nothing happens.
  • Putting the + in the wrong place.

How to get help

  • ? function name
  • Select the function name and press F1
  • Read the error message
  • Google the error message

facets

# facet your plot by a single variable
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

# facet your plot on the combination of two variables
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

geometric objects

different visual object to represent data

# scatterplot
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# smooth line
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

not every aesthetic works with every geom

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

two geoms in the same graph!

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv)) +
    
  # Add another geom
  geom_point(mapping = aes(x = displ, y = hwy, color = drv))
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

local vs. global mappings This makes it possible to display different aesthetics in different layers.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

specify different data for each layer

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

statistical transformation

position adjustments

adjustments for bar charts

# to place each object exactly where it falls
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar(position = "identity")

# to compare proportions across groups
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar(position = "fill")

# to compare individual values
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar(position = "dodge")

adjustments for scatterplots

# overlapping
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point()

# jitter
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_jitter()

coordinate systems

switch x and y

# original
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot()

# switch x and y
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot() +
  coord_flip()

set the aspect ratio correctly for maps

nz <- map_data("nz")

ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black") +
  coord_quickmap()

Polar coordinates reveal an interesting connection between a bar chart and a Coxcomb chart.

bar <- ggplot(data = diamonds) + 
  geom_bar(
    mapping = aes(x = cut, fill = cut), 
    show.legend = FALSE,
    width = 1
  ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL)

bar + coord_flip()

bar + coord_polar()

the layered grammar of graphics

The grammar of graphics is based on the insight that you can uniquely describe any plot as a combination of:

  • a dataset,
  • a geom,
  • a set of mappings,
  • a stat,
  • a position adjustment,
  • a coordinate system, and
  • a faceting scheme.