Visualisasi Data

Most of the content refer to https://ggplot2-book.org/, so, for more detail, please read the website.

1. Introduction

The goal of this chapter is to teach you how to produce useful graphics with ggplot2 as quickly as possible. You’ll learn the basics of ggplot() along with some useful “recipes” to make the most important plots.

ggplot() allows you to make complex plots with just a few lines of code because it’s based on a rich underlying theory, the grammar of graphics.

library(ggplot2)

2. Fuel Economy Data

We’ll mostly use one data set that’s bundled with ggplot2: mpg. It includes information about the fuel economy of popular car models in 1999 and 2008, collected by the US Environmental Protection Agency, http://fueleconomy.gov. You can access the data by loading ggplot2:

str(mpg)

## tibble [234 x 11] (S3: tbl_df/tbl/data.frame)
##  $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
##  $ model       : chr [1:234] "a4" "a4" "a4" "a4" ...
##  $ displ       : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr [1:234] "f" "f" "f" "f" ...
##  $ cty         : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr [1:234] "p" "p" "p" "p" ...
##  $ class       : chr [1:234] "compact" "compact" "compact" "compact" ...

The variables are mostly self-explanatory:

manufacturer : manufacturer name
model : model name
displ : engine displacement, in litres
year : year of manufacture
cyl : number of cylinders
trans : type of transmission
drv : the type of drive train, where f = front-wheel drive r = rear wheel drive, 4 = 4wd
cty : city miles per gallon
hwy : highway miles per gallon
fl : fuel type (diesel, petrol, electric, etc.)
class : “type” of car (compact, SUV, minivan etc.)

This dataset suggests many interesting questions. - How are engine size and fuel economy related? - Do certain manufacturers care more about fuel economy than others? - Has fuel economy improved in the last ten years?

We will try to answer some of these questions, and in the process learn how to create some basic plots with ggplot2.

3. Key Components

Every ggplot2 plot has three key components:

data,
A set of aesthetic mappings between variables in the data and visual properties, and
At least one layer which describes how to render each observation. Layers are usually created with a geom function.

Here’s a simple example:

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point()

This produces a scatterplot defined by:

Data: mpg.
Aesthetic mapping: engine size mapped to x position, fuel economy to y position.
Layer: points.

Pay attention to the structure of this function call: data and aesthetic mappings are supplied in ggplot(), then layers are added on with “+”. This is an important pattern, and as you learn more about ggplot2 you’ll construct increasingly sophisticated plots by adding on more types of components.

Almost every plot maps a variable to x and y, so naming these aesthetics is tedious, so the first two unnamed arguments to aes() will be mapped to x and y. This means that the following code is identical to the example above:

ggplot(mpg, aes(displ, hwy)) +
  geom_point()

Exercise

How would you describe the relationship between cty and hwy? Do you have any concerns about drawing conclusions from that plot?
What does ggplot(mpg, aes(model, manufacturer)) + geom_point() show? Is it useful? How could you modify the data to make it more informative?
Describe the data, aesthetic mappings and layers used for each of the following plots. You’ll need to guess a little because you haven’t seen all the datasets and functions yet, but use your common sense! See if you can predict what the plot will look like before running the code.

ggplot(mpg, aes(cty, hwy)) + geom_point()
ggplot(diamonds, aes(carat, price)) + geom_point()
ggplot(economics, aes(date, unemploy)) + geom_line()
ggplot(mpg, aes(cty)) + geom_histogram()

ggplot(mpg, aes(cty, hwy)) + geom_point()

ggplot(diamonds, aes(carat, price)) + geom_point()

ggplot(economics, aes(date, unemploy)) + geom_line()

ggplot(mpg, aes(cty)) + geom_histogram()

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

4. Colour, size, shape and other aesthetic attributes

To add additional variables to a plot, we can use other aesthetics like colour, shape, and size. These work in the same way as the x and y aesthetics, and are added into the call to aes():

aes(displ, hwy, colour = class)
aes(displ, hwy, shape = drv)
aes(displ, hwy, size = cyl)

ggplot2 takes care of the details of converting data (e.g., ‘f’, ‘r’, ‘4’) into aesthetics (e.g., ‘red’, ‘yellow’, ‘green’) with a scale.

ggplot(mpg, aes(displ, cty, shape = drv)) +
  geom_point()

ggplot(mpg, aes(displ, cty, size = cyl)) +
  geom_point()

There is one scale for each aesthetic mapping in a plot. The scale is also responsible for creating a guide, an axis or legend, that allows you to read the plot, converting aesthetic values back into data values. For now, we’ll stick with the default scales provided by ggplot2.

To learn more about those outlying variables in the previous scatterplot, we could map the class variable to colour:

ggplot(mpg, aes(displ, cty, colour = class)) +
  geom_point()

This gives each point a unique colour corresponding to its class. The legend allows us to read data values from the colour, showing us that the group of cars with unusually high fuel economy for their engine size are two seaters: cars with big engines, but lightweight bodies. If you want to set an aesthetic to a fixed value, without scaling it, do so in the individual layer outside of aes(). Compare the following two plots:

str(mpg)

## tibble [234 x 11] (S3: tbl_df/tbl/data.frame)
##  $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
##  $ model       : chr [1:234] "a4" "a4" "a4" "a4" ...
##  $ displ       : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr [1:234] "f" "f" "f" "f" ...
##  $ cty         : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr [1:234] "p" "p" "p" "p" ...
##  $ class       : chr [1:234] "compact" "compact" "compact" "compact" ...

ggplot(mpg, aes(displ, hwy)) + geom_point(aes(colour = "blue"))

ggplot(mpg, aes(displ, hwy)) + geom_point(colour = "blue")

In the first plot, the value “blue” is scaled to a pinkish colour, and a legend is added. In the second plot, the points are given the R colour blue.

Different types of aesthetic attributes work better with different types of variables. For example, colour and shape work well with categorical variables, while size works well for continuous variables. The amount of data also makes a difference: if there is a lot of data it can be hard to distinguish different groups. An alternative solution is to use facetting, as described next.

When using aesthetics in a plot, less is usually more. It’s difficult to see the simultaneous relationships among colour and shape and size, so exercise restraint when using aesthetics. Instead of trying to make one very complex plot that shows everything at once, see if you can create a series of simple plots that tell a story, leading the reader from ignorance to knowledge.

Exercise

Experiment with the colour, shape and size aesthetics. What happens when you map them to continuous values? What about categorical values? What happens when you use more than one aesthetic in a plot? //fm aslinya kosong , tambah sendiri bwh ini

ggplot(mpg,aes(manufacturer, hwy))+
geom_point()

ggplot(mpg,aes(drv, hwy,size=cyl))+
geom_point()

ggplot(mpg,aes(drv, cyl,size=hwy))+
geom_point()

table(mpg$year)

## 
## 1999 2008 
##  117  117

ggplot(mpg,aes(year,hwy, colour=class))+
geom_point()

What happens if you map a continuous variable to shape? Why? What happens if you map trans to shape? Why? gk cocok, shape sm color
How is drive train related to fuel economy? How is drive train related to engine size and class? //fm kosong,tambah baru

ggplot(mpg,aes(drv,hwy))+
geom_point()

5. Facetting

Another technique for displaying additional categorical variables on a plot is facetting. Facetting creates tables of graphics by splitting the data into subsets and displaying the same graph for each subset.

There are two types of facetting: grid and wrapped. Wrapped is the most useful, so we’ll discuss it here, and you can learn about grid facetting later. To facet a plot you simply add a facetting specification with facet_wrap(), which takes the name of a variable preceded by ~.

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  facet_wrap(~class)

nambah sendiri latihan

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  facet_wrap(~fl)

str(mpg)

## tibble [234 x 11] (S3: tbl_df/tbl/data.frame)
##  $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
##  $ model       : chr [1:234] "a4" "a4" "a4" "a4" ...
##  $ displ       : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr [1:234] "f" "f" "f" "f" ...
##  $ cty         : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr [1:234] "p" "p" "p" "p" ...
##  $ class       : chr [1:234] "compact" "compact" "compact" "compact" ...

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  facet_wrap(~year)

ggplot(mpg, aes(displ, hwy,color=class)) + 
  geom_point() + 
  facet_wrap(~year)

ggplot(mpg, aes(displ, hwy,color=drv)) + 
  geom_point() + 
  facet_wrap(~year)

Exercise

What happens if you try to facet by a continuous variable like hwy? What about cyl? What’s the key difference? klau memisah variabel kontinue bisa/tdk, trgantung variabel nya m58
Use facetting to explore the 3-way relationship between fuel economy, engine size, and number of cylinders. How does facetting by number of cylinders change your assessement of the relationship between engine size and fuel economy? //fm cek recorder j 1.08 //tambah baru

# ggplot(mpg,aes(manufacturer, model))+
#   geom_point()+
# 
# ggplot(mpg,aes(manufacturer, model,size=hwy))+
#   geom_point()+
# 
# ggplot(mpg,aes(class, drv,size=hwy))+
#   geom_point()+

Read the documentation for facet_wrap(). What arguments can you use to control how many rows and columns appear in the output?

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth()

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

ggplot(mpg, aes(displ, hwy,color=drv)) + 
  geom_point() + 
  geom_smooth()

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

What does the scales argument to facet_wrap() do? When might you use it? //fm lat cb

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth()+
  facet_wrap(~drv)

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

6. Plot geom()

You might guess that by substituting geom_point() for a different geom function, you’d get a different type of plot. That’s a great guess!

In the following sections, you’ll learn about some of the other important geoms provided in ggplot2.

6.1. Adding a smoother to a plot

If you have a scatterplot with a lot of noise, it can be hard to see the dominant pattern. In this case it’s useful to add a smoothed line to the plot with geom_smooth()

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth()

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

An important argument to geom_smooth() is the method, which allows you to choose which type of model is used to fit the smooth curve:

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth(span = 0.2)

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

#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth(span = 1)

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

#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Loess does not work well for large datasets, so an alternative smoothing algorithm is used when n is greater than 1,000

Method = “gam” fits a generalised additive model provided by the mgcv package. You need to first load mgcv, then use a formula like formula = y ~ s(x) or y ~ s(x, bs = “cs”) (for large data). This is what ggplot2 uses when there are more than 1,000 points. //fm j.18.30

library(mgcv)

## Loading required package: nlme

## This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth(method = "gam", formula = y ~ s(x))

//fm add

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth(method = "gam", formula = y ~ s(x))

  facet_wrap(~year)

## <ggproto object: Class FacetWrap, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetWrap, Facet, gg>

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth(method = "gam", formula = y ~ s(x))

  facet_wrap(~drv)

## <ggproto object: Class FacetWrap, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetWrap, Facet, gg>

6.2. Boxplots and jittered points

When a set of data includes a categorical variable and one or more continuous variables, you will probably be interested to know how the values of the continuous variables vary with the levels of the categorical variable. Say we’re interested in seeing how fuel economy varies within cars that have the same kind of drivetrain //j1.23

ggplot(mpg, aes(drv, hwy)) + 
  geom_point()

Because there are few unique values of both drv and hwy, there is a lot of overplotting. Many points are plotted in the same location, and it’s difficult to see the distribution. There are three useful techniques that help alleviate the problem:

Jittering, geom_jitter(), adds a little random noise to the data which can help avoid overplotting.
Boxplots, geom_boxplot(), summarise the shape of the distribution with a handful of summary statistics.
Violin plots, geom_violin(), show a compact representation of the “density” of the distribution, highlighting the areas where more points are found.

These are illustrated below: //j1.24.30 fm

ggplot(mpg, aes(drv, hwy)) + geom_jitter()

ggplot(mpg, aes(drv, hwy)) + geom_boxplot()

ggplot(mpg, aes(drv, hwy)) + geom_violin()

Each method has its strengths and weaknesses. Boxplots summarise the bulk of the distribution with only five numbers, while jittered plots show every point but only work with relatively small datasets. Violin plots give the richest display, but rely on the calculation of a density estimate, which can be hard to interpret.

//fm add

ggplot(mpg, aes(drv, cty)) + geom_jitter()

ggplot(mpg, aes(drv, cty)) + geom_boxplot()

ggplot(mpg, aes(drv, cty)) + geom_violin()

ggplot(mpg, aes(year, cty)) + geom_jitter()

ggplot(mpg, aes(year, cty)) + geom_boxplot()

## Warning: Continuous x aesthetic -- did you forget aes(group=...)?

ggplot(mpg, aes(year, cty)) + geom_violin()

ggplot(mpg, aes(class, cty)) + geom_jitter()

ggplot(mpg, aes(class, cty)) + geom_boxplot()

ggplot(mpg, aes(class, cty)) + geom_violin()

6.3. Histograms and frequency polygons

Histograms and frequency polygons show the distribution of a single numeric variable. They provide more information about the distribution of a single group than boxplots do, at the expense of needing more space.//j1.30

ggplot(mpg, aes(hwy)) + geom_histogram()

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

#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(mpg, aes(hwy)) + geom_freqpoly()

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

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

You can control the width of the bins with the binwidth argument (if you don’t want evenly spaced bins you can use the breaks argument). It is very important to experiment with the bin width. The default just splits your data into 30 bins, which is unlikely to be the best choice. You should always try many bin widths, and you may find you need multiple bin widths to tell the full story of your data.//j1.31 bindwidth

ggplot(mpg, aes(hwy)) + 
  geom_freqpoly(binwidth = 2.5)

ggplot(mpg, aes(hwy)) + 
  geom_freqpoly(binwidth = 1)

ggplot(mpg, aes(hwy)) + 
  geom_histogram(binwidth = 2.5)

ggplot(mpg, aes(hwy)) + 
  geom_histogram(binwidth = 1)

To compare the distributions of different subgroups, you can map a categorical variable to either fill (for geom_histogram()) or colour (for geom_freqpoly()). It’s easier to compare distributions using the frequency polygon because the underlying perceptual task is easier. You can also use facetting: this makes comparisons a little harder, but it’s easier to see the distribution of each group

ggplot(mpg, aes(displ, colour = drv)) + 
  geom_freqpoly(binwidth = 0.5)

ggplot(mpg, aes(displ, fill = drv)) + 
  geom_histogram(binwidth = 0.5) + 
  facet_wrap(~drv, ncol = 1)

ggplot(mpg, aes(displ, colour = drv)) + 
  geom_freqpoly(binwidth = 0.5)

ggplot(mpg, aes(displ, fill = drv)) + 
  geom_histogram(binwidth = 0.5) + 
  facet_wrap(~drv, ncol = 1)

6.4. Bar charts

The discrete analogue of the histogram is the bar chart, geom_bar(). It’s easy to use: //m1 recorder new add class

ggplot(mpg, aes(manufacturer)) + 
  geom_bar()

ggplot(mpg, aes(class)) + 
  geom_bar()

6.5. Time series with line and path plots

Line and path plots are typically used for time series data. Line plots join the points from left to right, while path plots join them in the order that they appear in the dataset (in other words, a line plot is a path plot of the data sorted by x value). Line plots usually have time on the x-axis, showing how a single variable has changed over time. Path plots show how two variables have simultaneously changed over time, with time encoded in the way that observations are connected.

Because the year variable in the mpg dataset only has two values, we’ll show some time series plots using the economics dataset, which contains economic data on the US measured over the last 40 years. The figure below shows two plots of unemployment over time, both produced using geom_line(). The first shows the unemployment rate while the second shows the median number of weeks unemployed. We can already see some differences in these two variables, particularly in the last peak, where the unemployment percentage is lower than it was in the preceding peaks, but the length of unemployment is high. //m9.20

ggplot(economics, aes(date, unemploy / pop)) +
  geom_line()

ggplot(economics, aes(date, uempmed)) +
  geom_line()

To examine this relationship in greater detail, we would like to draw both time series on the same plot. We could draw a scatterplot of unemployment rate vs. length of unemployment, but then we could no longer see the evolution over time. The solution is to join points adjacent in time with line segments, forming a path plot.

Below we plot unemployment rate vs. length of unemployment and join the individual observations with a path. Because of the many line crossings, the direction in which time flows isn’t easy to see in the first plot. In the second plot, we colour the points to make it easier to see the direction of time.

ggplot(economics, aes(unemploy / pop, uempmed)) + 
  geom_path() +
  geom_point()

year <- function(x) as.POSIXlt(x)$year + 1900
ggplot(economics, aes(unemploy / pop, uempmed)) + 
  geom_path(colour = "grey50") +
  geom_point(aes(colour = year(date)))

Exercise

6 Exercises 1. What’s the problem with the plot created by ggplot(mpg, aes(cty, hwy)) + geom_point()? Which of the geoms described above is most effective at remedying the problem?

One challenge with ggplot(mpg, aes(class, hwy)) + geom_boxplot() is that the ordering of class is alphabetical, which is not terribly useful. How could you change the factor levels to be more informative?
Rather than reordering the factor by hand, you can do it automatically based on the data: ggplot(mpg, aes(reorder(class, hwy), hwy)) + geom_boxplot(). What does reorder() do? Read the documentation.
Explore the distribution of the carat variable in the diamonds dataset. What binwidth reveals the most interesting patterns?
Explore the distribution of the price variable in the diamonds data. How does the distribution vary by cut?
You now know (at least) three ways to compare the distributions of subgroups: geom_violin(), geom_freqpoly() and the colour aesthetic, or geom_histogram() and facetting. What are the strengths and weaknesses of each approach? What other approaches could you try?
Read the documentation for geom_bar(). What does the weight aesthetic do?
Using the techniques already discussed in this chapter, come up with three ways to visualise a 2d categorical distribution. Try them out by visualising the distribution of model and manufacturer, trans and class, and cyl and trans.

7. Modifying the axes

xlab() and ylab() modify the x- and y-axis labels: //m12.20

ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1 / 3)

ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1 / 3) + 
  xlab("city driving (mpg)") + 
  ylab("highway driving (mpg)")

# Remove the axis labels with NULL
ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1 / 3) + 
  xlab(NULL) + 
  ylab(NULL)

xlim() and ylim() modify the limits of axes:

ggplot(mpg, aes(drv, hwy)) +
  geom_jitter(width = 0.25)

ggplot(mpg, aes(drv, hwy)) +
  geom_jitter(width = 0.25) + 
  xlim("f", "r") + 
  ylim(20, 30)

## Warning: Removed 137 rows containing missing values (geom_point).

#> Warning: Removed 137 rows containing missing values (geom_point).
  
# For continuous scales, use NA to set only one limit
ggplot(mpg, aes(drv, hwy)) +
  geom_jitter(width = 0.25, na.rm = TRUE) + 
  ylim(NA, 30)

Visualisasi Data

Fahmi Agil Winata

10/17/2020

1. Introduction

2. Fuel Economy Data

3. Key Components

Exercise

4. Colour, size, shape and other aesthetic attributes

Exercise

5. Facetting

Exercise

6. Plot geom()

6.1. Adding a smoother to a plot

6.2. Boxplots and jittered points

6.3. Histograms and frequency polygons

6.4. Bar charts

6.5. Time series with line and path plots

Exercise

7. Modifying the axes