STATS 60 Rlab Session 2

Lists

In all the data structures so far, the elements have to be of the same type. To have elements on different types in one data structure, we can use a list, which we create with list(). We can think of a list as a collection of key-value pairs. Keys should be strings.

person <- list(name = "John Doe", age = 26)
person

## $name
## [1] "John Doe"
## 
## $age
## [1] 26

Lists

The str function can be used to inspect what is inside person:

str(person)

## List of 2
##  $ name: chr "John Doe"
##  $ age : num 26

To access the name element person, we have 2 options:

person[["name"]]

## [1] "John Doe"

person$name

## [1] "John Doe"

Lists

The elements of a list can be anything, even another data structure! Let’s add the names of John’s children to the person object:

person$children <- c("Ross", "Robert")
str(person)

## List of 3
##  $ name    : chr "John Doe"
##  $ age     : num 26
##  $ children: chr [1:2] "Ross" "Robert"

To see the keys associated with a list, use the names() function:

names(person)

## [1] "name"     "age"      "children"

What is a data frame?

A data frame is R’s data structure for storing datasets
- First row: variable/covariate/feature names
- Each subsequent row represents one observation
- Each column contains the values of that variable across observations

A special type of list:

list keys are variable names of the dataset
list values are all vectors of the same length (no. of observations)

Data frame

data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

Data Frame

We can use “help” menu on the bottom right corner of the Rstudio to check for the meaning of the variable names:

Data Frame

View(mtcars)

Data Frame

head(mtcars)     ## return the first 6 rows of the data set, also works with "tail"

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

names(mtcars)

##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
## [11] "carb"

dim(mtcars)

## [1] 32 11

Data Frame

Instead of using built-in data sets, we can also let R read from local files

# First, set your working directory
setwd("~/Desktop/")
carSpeeds <- read.csv(file = 'data/car-speeds.csv')

Can also read data from website!

df <- read.csv("https://stats60.github.io/Rlab/worldbank_data_tidy.csv",
               stringsAsFactors = FALSE)

Words vs. pictures

“The simple graph has brought more information to the data analyst’s mind than any other device.” - John Tukey

library(ggplot2)
p_base <- ggplot(data = df, mapping = aes(y = mpg, x = weight))
p_scatter <- p_base + geom_point(aes(col = cylinders), size = 2)
p_scatter

Two classes of variables in statistics

Continuous variable: Variable takes on values which fall on the real number line (or part of it)
- E.g. height, exam score, attendance count
Categorical variable: Variable takes on values which fall into discrete categories
- E.g. ice-cream flavor, country of origin

Barplots: counts for a categorical variable

What is the distribution of cylinders in my dataset?

ggplot(data = mtcars) +
    geom_bar(aes(x = factor(cyl))) +
    ggtitle("Count by cylinders") +
    xlab("No. of cylinders")

Histograms: counts for a continuous variable

What is the distribution of miles per gallon in my dataset?

p_hist <- ggplot(data = mtcars) + 
    geom_histogram(aes(x = mpg), breaks = seq(10, 35, 5)) +
    ggtitle("Histogram of miles per gallon")
p_hist

ggplot(data = mtcars) + 
    geom_histogram(aes(x = mpg)) +
    ggtitle("Histogram of miles per gallon")

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

Scatterplots: continuous variable vs. continuous variable

What is the relationship between mpg and weight?

ggplot(data = df) + 
    geom_point(mapping = aes(y = mpg, x = weight), size = 2) + 
    ggtitle("Miles per gallon vs. weight")

Lineplots: continuous variable vs. time variable

What is the relationship between mpg and time?

Boxplots

For each value of cylinder, what is the distribution of mpg like?

ggplot(data = df) + 
    geom_boxplot(aes(cylinders,mpg)) +
    ggtitle("Distribution of mpg by cylinders")

Violinplots

For each value of cylinder, what is the distribution of mpg like?

ggplot(data = df) + 
    geom_violin(aes(cylinders,mpg)) +
    ggtitle("Distribution of mpg by cylinders")

Heatmaps: categorical variable vs. categorical variable

How often does each pair of cylinder and gear occur in the dataset?

Summary

1 categorical variable: barplot
1 continuous variable: histogram
Continuous vs. continuous: scatterplot
Continuous vs. time: lineplot
Continuous vs. categorical: boxplots & violin plots
Categorical vs. categorical: heatmap

Data visualization in R: 2 broad approaches

base R

Data visualization in R: 2 broad approaches

`ggplot2`

3 essential elements of graphics: data, geometries, aesthetics

Data: Dataset we are using for the plot

##     mpg weight cylinders
## 1  21.0  2.620         6
## 2  21.0  2.875         6
## 3  22.8  2.320         4
## 4  21.4  3.215         6
## 5  18.7  3.440         8
## 6  18.1  3.460         6
## 7  14.3  3.570         8
## 8  24.4  3.190         4
## 9  22.8  3.150         4
## 10 19.2  3.440         6

3 essential elements of graphics: data, geometries, aesthetics

Geometries: Visual elements used for our data

E.g. point, line, histogram, bar, boxplot

Geom: point

3 essential elements of graphics: data, geometries, aesthetics

Aesthetics: Defines the data columns which affect various aspects of the geom

E.g. x, y, color, fill, size, alpha, line type, line width
Which aesthetics you use depend on the geometries you choose

3 different aesthetics:

x-axis: weight
y-axis: mpg
color: cylinders
shape, size, etc. take on default values, not determined by data

Examples of other aesthetics

x-axis: weight
y-axis: mpg
size: cylinders
alpha: weight

p_base + geom_point(aes(size = cylinders, alpha = weight))

Examples of other aesthetics

x-axis: weight
y-axis: mpg
color: cylinders
shape: cylinders

p_base + geom_point(aes(col = cylinders, shape=cylinders), size = 3)

`ggplot2` code

ggplot()

`ggplot2` code

ggplot() +
    geom_histogram(data = df, mapping = aes(x = mpg))

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

`ggplot2` code

ggplot() +
    geom_boxplot(data = df, mapping = aes(x = cylinders, y = mpg))

`ggplot2` code

ggplot() +
    geom_point(data = df, 
               mapping = aes(x = weight, y = mpg, col = cylinders),
               shape = 15)

Layers: Combining multiple plots into one graphic

We can have more than one layer in a graphic.

= +

Each layer contains (essentially):

1 dataset, 1 geometric object, aesthetic mappings

`ggplot2` code

ggplot() +
    geom_boxplot(data = df, mapping = aes(x = cylinders, y = mpg)) +
    geom_point(data = df, mapping = aes(x = cylinders, y = mpg), 
               position = "jitter")

`ggplot2` code

When layers share attributes, we only have to type them once:

ggplot(data = df, mapping = aes(x = cylinders, y = mpg)) +
    geom_boxplot() +
    geom_point(position = "jitter")

`ggplot2` code

We can drop data = if it is the first argument of ggplot()
We can drop mapping = if:
- it is the second argument of ggplot()
- it is the first argument of a geom_xx() function

ggplot(df, aes(x = cylinders, y = mpg)) +
    geom_boxplot() +
    geom_point(position = "jitter")

Scales

Aesthetics only tell you which column corresponds with which aesthetic (e.g. cylinder -> color, mpg -> x)
Does not tell you how to do the mapping
- E.g. Which color should represent which cylinder value?
- E.g. What should the range of the x-axis be?
Scales define that for you
(On the bright side, defaults usually ok)

Examples of scales (Source: A Layered Grammar of Graphics)

Scales example: colors

Manually chosen colors

p_scatter + scale_color_manual(values=c("gold2", "darkorange","firebrick"))

Facets

Plotting different parts of our data on different canvases
Can give a clearer, less cluttered picture
We can facet by rows and/or columns

p_scatter + facet_wrap(~cylinders)

Themes

Refers to all non-data ink

Titles, axis ticks & labels, background color, legend, etc.
Can manually set each item, or use preset themes

ggplot2’s default theme

p_scatter

Minimal theme

p_scatter + theme_minimal()

STATS 60 Rlab Session 2

Project proposal

Agenda for today

Lists

Lists

Lists

What is a data frame?

Data frame

Data Frame

Data Frame

Data Frame

Data Frame

Words vs. pictures

Two classes of variables in statistics

Barplots: counts for a categorical variable

Histograms: counts for a continuous variable

Scatterplots: continuous variable vs. continuous variable

Lineplots: continuous variable vs. time variable

Boxplots

Violinplots

Heatmaps: categorical variable vs. categorical variable

Summary

Data visualization in R: 2 broad approaches

base R

Data visualization in R: 2 broad approaches

ggplot2

3 essential elements of graphics: data, geometries, aesthetics

3 essential elements of graphics: data, geometries, aesthetics

3 essential elements of graphics: data, geometries, aesthetics

Examples of other aesthetics

Examples of other aesthetics

ggplot2 code

ggplot2 code

ggplot2 code

ggplot2 code

Layers: Combining multiple plots into one graphic

ggplot2 code

ggplot2 code

ggplot2 code

Scales

Scales example: colors

Facets

Themes

More pre-set themes

More pre-set themes

We’ve only scratched the surface!

`ggplot2`

`ggplot2` code

`ggplot2` code

`ggplot2` code

`ggplot2` code

`ggplot2` code

`ggplot2` code

`ggplot2` code