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7 Exploratory Data Analysis
7.1 Introduction
This will also use data from the nycflights13 package. The ggbeeswarm, lvplot, and ggstance packages provide some additional functions used in some solutions.
7.2 Questions
7.3 Variation
Exercise 7.3.1
Explore the distribution of each of the x
, y
, and z
variables in diamonds
. What do you learn?
Think about a diamond and how you might decide which dimension is the length, width, and depth.
First, I’ll calculate summary statistics for these variables and plot their distributions.
summary(select(diamonds, x, y, z))
#> x y z
#> Min. : 0.00 Min. : 0.0 Min. : 0.0
#> 1st Qu.: 4.71 1st Qu.: 4.7 1st Qu.: 2.9
#> Median : 5.70 Median : 5.7 Median : 3.5
#> Mean : 5.73 Mean : 5.7 Mean : 3.5
#> 3rd Qu.: 6.54 3rd Qu.: 6.5 3rd Qu.: 4.0
#> Max. :10.74 Max. :58.9 Max. :31.8
There several noticeable features of the distributions:

x
andy
are larger thanz
,  there are outliers,
 they are all right skewed, and
 they are multimodal or “spiky”.
The typical values of x
and y
are larger than z
, with x
and y
having interquartile
ranges of 4.7–6.5, while z
has an interquartile range of 2.9–4.0.
There are two types of outliers in this data.
Some diamonds have values of zero and some have abnormally large values of x
, y
, or z
.
summary(select(diamonds, x, y, z))
#> x y z
#> Min. : 0.00 Min. : 0.0 Min. : 0.0
#> 1st Qu.: 4.71 1st Qu.: 4.7 1st Qu.: 2.9
#> Median : 5.70 Median : 5.7 Median : 3.5
#> Mean : 5.73 Mean : 5.7 Mean : 3.5
#> 3rd Qu.: 6.54 3rd Qu.: 6.5 3rd Qu.: 4.0
#> Max. :10.74 Max. :58.9 Max. :31.8
These appear to be either data entry errors, or an undocumented convention in the dataset for indicating missing values. An alternative hypothesis would be that values of zero are the
result of rounding values like 0.002
down, but since there are no diamonds with values of 0.01, that does not seem to be the case.
filter(diamonds, x == 0  y == 0  z == 0)
#> # A tibble: 20 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 1 Premium G SI2 59.1 59 3142 6.55 6.48 0
#> 2 1.01 Premium H I1 58.1 59 3167 6.66 6.6 0
#> 3 1.1 Premium G SI2 63 59 3696 6.5 6.47 0
#> 4 1.01 Premium F SI2 59.2 58 3837 6.5 6.47 0
#> 5 1.5 Good G I1 64 61 4731 7.15 7.04 0
#> 6 1.07 Ideal F SI2 61.6 56 4954 0 6.62 0
#> # … with 14 more rows
There are also some diamonds with values of y
and z
that are abnormally large.
There are diamonds with y == 58.9
and y == 31.8
, and one with z == 31.8
.
These are probably data errors since the values do not seem in line with the values of
the other variables.
diamonds %>%
arrange(desc(y)) %>%
head()
#> # A tibble: 6 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 2 Premium H SI2 58.9 57 12210 8.09 58.9 8.06
#> 2 0.51 Ideal E VS1 61.8 55 2075 5.15 31.8 5.12
#> 3 5.01 Fair J I1 65.5 59 18018 10.7 10.5 6.98
#> 4 4.5 Fair J I1 65.8 58 18531 10.2 10.2 6.72
#> 5 4.01 Premium I I1 61 61 15223 10.1 10.1 6.17
#> 6 4.01 Premium J I1 62.5 62 15223 10.0 9.94 6.24
diamonds %>%
arrange(desc(z)) %>%
head()
#> # A tibble: 6 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.51 Very Good E VS1 61.8 54.7 1970 5.12 5.15 31.8
#> 2 2 Premium H SI2 58.9 57 12210 8.09 58.9 8.06
#> 3 5.01 Fair J I1 65.5 59 18018 10.7 10.5 6.98
#> 4 4.5 Fair J I1 65.8 58 18531 10.2 10.2 6.72
#> 5 4.13 Fair H I1 64.8 61 17329 10 9.85 6.43
#> 6 3.65 Fair H I1 67.1 53 11668 9.53 9.48 6.38
Initially, I only considered univariate outliers. However, to check the plausibility
of those outliers I would informally consider how consistent their values are with
the values of the other variables. In this case, scatter plots of each combination
of x
, y
, and z
shows these outliers much more clearly.
Removing the outliers from x
, y
, and z
makes the distribution easier to see.
The right skewness of these distributions is unsurprising; there should be more smaller diamonds than larger ones and these values can never be negative.
More interestingly, there are spikes in the distribution at certain values.
These spikes often, but not exclusively, occur near integer values.
Without knowing more about diamond cutting, I can’t say more about what these spikes represent. If you know, add a comment.
I would guess that some diamond sizes are used more often than others, and these spikes correspond to those sizes.
Also, I would guess that a diamond cut and carat value of a diamond imply values of x
, y
, and z
.
Since there are spikes in the distribution of carat sizes, and only a few different cuts, that could result in these spikes.
I’ll leave it to readers to figure out if that’s the case.
filter(diamonds, x > 0, x < 10) %>%
ggplot() +
geom_histogram(mapping = aes(x = x), binwidth = 0.01) +
scale_x_continuous(breaks = 1:10)
filter(diamonds, y > 0, y < 10) %>%
ggplot() +
geom_histogram(mapping = aes(x = y), binwidth = 0.01) +
scale_x_continuous(breaks = 1:10)
filter(diamonds, z > 0, z < 10) %>%
ggplot() +
geom_histogram(mapping = aes(x = z), binwidth = 0.01) +
scale_x_continuous(breaks = 1:10)
According to the documentation for diamonds
, x
is length, y
is width, and z
is depth.
If documentation were unavailable, I would compare the values of the variables to match them to the length, width, and depth.
I would expect length to always be less than width, otherwise the length would be called the width.
I would also search for the definitions of length, width, and depth with respect to diamond cuts.
Depth can be expressed as a percentage of the length/width of the diamond, which means it should be less than both the length and the width.
summarise(diamonds, mean(x > y), mean(x > z), mean(y > z))
#> # A tibble: 1 x 3
#> `mean(x > y)` `mean(x > z)` `mean(y > z)`
#> <dbl> <dbl> <dbl>
#> 1 0.434 1.00 1.00
It appears that depth (z
) is always smaller than length (x
) or width (y
), perhaps because a shallower depth helps when setting diamonds in jewelry and due to how it affect the reflection of light.
Length is more than width in less than half the observations, the opposite of my expectations.
Exercise 7.3.2
Explore the distribution of price. Do you discover anything unusual or surprising? (Hint: Carefully think about the binwidth
and make sure you try a wide range of values.)
 The price data has many spikes, but I can’t tell what each spike corresponds to. The following plots don’t show much difference in the distributions in the last one or two digits.
 There are no diamonds with a price of $1,500 (between $1,455 and $1,545, including).
 There’s a bulge in the distribution around $750.
The last digits of prices are often not uniformly distributed. They are often round, ending in 0 or 5 (for onehalf). Another common pattern is ending in 99, as in $1999. If we plot the distribution of the last one and two digits of prices do we observe patterns like that?
diamonds %>%
mutate(ending = price %% 10) %>%
ggplot(aes(x = ending)) +
geom_histogram(binwidth = 1, center = 0)
Exercise 7.3.3
How many diamonds are 0.99 carat? How many are 1 carat? What do you think is the cause of the difference?
There are more than 70 times as many 1 carat diamonds as 0.99 carat diamond.
diamonds %>%
filter(carat >= 0.99, carat <= 1) %>%
count(carat)
#> # A tibble: 2 x 2
#> carat n
#> <dbl> <int>
#> 1 0.99 23
#> 2 1 1558
I don’t know exactly the process behind how carats are measured, but some way or another some diamonds carat values are being “rounded up” Presumably there is a premium for a 1 carat diamond vs. a 0.99 carat diamond beyond the expected increase in price due to a 0.01 carat increase.^{7}
To check this intuition, we would want to look at the number of diamonds in each carat range to see if there is an unusually low number of 0.99 carat diamonds, and an abnormally large number of 1 carat diamonds.
diamonds %>%
filter(carat >= 0.9, carat <= 1.1) %>%
count(carat) %>%
print(n = Inf)
#> # A tibble: 21 x 2
#> carat n
#> <dbl> <int>
#> 1 0.9 1485
#> 2 0.91 570
#> 3 0.92 226
#> 4 0.93 142
#> 5 0.94 59
#> 6 0.95 65
#> 7 0.96 103
#> 8 0.97 59
#> 9 0.98 31
#> 10 0.99 23
#> 11 1 1558
#> 12 1.01 2242
#> 13 1.02 883
#> 14 1.03 523
#> 15 1.04 475
#> 16 1.05 361
#> 17 1.06 373
#> 18 1.07 342
#> 19 1.08 246
#> 20 1.09 287
#> 21 1.1 278
Exercise 7.3.4
Compare and contrast coord_cartesian()
vs xlim()
or ylim()
when zooming in on a histogram. What happens if you leave binwidth
unset? What happens if you try and zoom so only half a bar shows?
The coord_cartesian()
function zooms in on the area specified by the limits,
after having calculated and drawn the geoms.
Since the histogram bins have already been calculated, it is unaffected.
ggplot(diamonds) +
geom_histogram(mapping = aes(x = price)) +
coord_cartesian(xlim = c(100, 5000), ylim = c(0, 3000))
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
However, the xlim()
and ylim()
functions influence actions before the calculation
of the stats related to the histogram. Thus, any values outside the x and ylimits
are dropped before calculating bin widths and counts. This can influence how
the histogram looks.
ggplot(diamonds) +
geom_histogram(mapping = aes(x = price)) +
xlim(100, 5000) +
ylim(0, 3000)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: Removed 14714 rows containing nonfinite values (stat_bin).
#> Warning: Removed 6 rows containing missing values (geom_bar).
7.4 Missing values
Exercise 7.4.1
What happens to missing values in a histogram? What happens to missing values in a bar chart? Why is there a difference?
Missing values are removed when the number of observations in each bin are calculated. See the warning message: Removed 9 rows containing nonfinite values (stat_bin)
diamonds2 < diamonds %>%
mutate(y = ifelse(y < 3  y > 20, NA, y))
ggplot(diamonds2, aes(x = y)) +
geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: Removed 9 rows containing nonfinite values (stat_bin).
In the geom_bar()
function, NA
is treated as another category. The x
aesthetic in geom_bar()
requires a discrete (categorical) variable, and missing values act like another category.
diamonds %>%
mutate(cut = if_else(runif(n()) < 0.1, NA_character_, as.character(cut))) %>%
ggplot() +
geom_bar(mapping = aes(x = cut))
In a histogram, the x
aesthetic variable needs to be numeric, and stat_bin()
groups the observations by ranges into bins.
Since the numeric value of the NA
observations is unknown, they cannot be placed in a particular bin, and are dropped.
Exercise 7.4.2
What does na.rm = TRUE
do in mean()
and sum()
?
This option removes NA
values from the vector prior to calculating the mean and sum.
7.5 Covariation
7.5.1 A categorical and continuous variable
Exercise 7.5.1.1
Use what you’ve learned to improve the visualization of the departure times of cancelled vs. noncancelled flights.
Exercise 7.5.1.2
What variable in the diamonds dataset is most important for predicting the price of a diamond? How is that variable correlated with cut? Why does the combination of those two relationships lead to lower quality diamonds being more expensive?
What are the general relationships of each variable with the price of the diamonds?
I will consider the variables: carat
, clarity
, color
, and cut
.
I ignore the dimensions of the diamond since carat
measures size, and thus incorporates most of the information contained in these variables.
Since both price
and carat
are continuous variables, I use a scatter plot to visualize their relationship.
However, since there is a large number of points in the data, I will use a boxplot by binning carat
, as suggested in the chapter:
ggplot(data = diamonds, mapping = aes(x = carat, y = price)) +
geom_boxplot(mapping = aes(group = cut_width(carat, 0.1)), orientation = "x")
Note that the choice of the binning width is important, as if it were too large it would obscure any relationship, and if it were too small, the values in the bins could be too variable to reveal underlying trends.
Version 3.3.0 of ggplot2 introduced changes to boxplots that may affect the orientation.
This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail
If you are getting something different with your code check the version of ggplot2.
Use orientation = "x"
(vertical boxplots) or orientation = "y"
(horizontal boxplots) to explicitly specify how the geom should treat these axes.
The variables color
and clarity
are ordered categorical variables.
The chapter suggests visualizing a categorical and continuous variable using frequency polygons or boxplots.
In this case, I will use a box plot since it will better show a relationship between the variables.
There is a weak negative relationship between color
and price
.
The scale of diamond color goes from D (best) to J (worst).
Currently, the levels of diamonds$color
are in the wrong order.
Before plotting, I will reverse the order of the color
levels so they will be in increasing order of quality on the xaxis.
The color
column is an example of a factor variable, which is covered in the
“Factors” chapter of R4DS.
There is also weak negative relationship between clarity
and price
.
The scale of clarity goes from I1 (worst) to IF (best).
For both clarity
and color
, there is a much larger amount of variation within each category than between categories.
Carat is clearly the single best predictor of diamond prices.
Now that we have established that carat appears to be the best predictor of price, what is the relationship between it and cut? Since this is an example of a continuous (carat) and categorical (cut) variable, it can be visualized with a box plot.
There is a lot of variability in the distribution of carat sizes within each cut category. There is a slight negative relationship between carat and cut. Noticeably, the largest carat diamonds have a cut of “Fair” (the lowest).
This negative relationship can be due to the way in which diamonds are selected for sale. A larger diamond can be profitably sold with a lower quality cut, while a smaller diamond requires a better cut.
Exercise 7.5.1.3
Install the ggstance package, and create a horizontal box plot.
How does this compare to using coord_flip()
?
Earlier, we created this horizontal box plot of the distribution hwy
by class
, using geom_boxplot()
and coord_flip()
:
ggplot(data = mpg) +
geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy)) +
coord_flip()
In this case the output looks the same, but x
and y
aesthetics are flipped.
library("ggstance")
ggplot(data = mpg) +
geom_boxploth(mapping = aes(y = reorder(class, hwy, FUN = median), x = hwy))
Current versions of ggplot2 (since version 3.3.0) do not require coord_flip()
.
All geoms can choose the direction.
The direction is be inferred from the aesthetic mapping.
In this case, switching x
and y
produces a horizontal boxplot.
The orientation
argument is used to explicitly specify the axis orientation of the plot.
Exercise 7.5.1.4
One problem with box plots is that they were developed in an era of much smaller datasets and tend to display a prohibitively large number of “outlying values”.
One approach to remedy this problem is the letter value plot.
Install the lvplot package, and try using geom_lv()
to display the distribution of price vs cut.
What do you learn?
How do you interpret the plots?
Like boxplots, the boxes of the lettervalue plot correspond to quantiles. However, they incorporate far more quantiles than boxplots. They are useful for larger datasets because,
 larger datasets can give precise estimates of quantiles beyond the quartiles, and
 in expectation, larger datasets should have more outliers (in absolute numbers).
The lettervalue plot is described in Hofmann, Wickham, and Kafadar (2017).
Exercise 7.5.1.5
Compare and contrast geom_violin()
with a faceted geom_histogram()
, or a colored geom_freqpoly()
.
What are the pros and cons of each method?
I produce plots for these three methods below. The geom_freqpoly()
is better
for lookup: meaning that given a price, it is easy to tell which cut
has the
highest density. However, the overlapping lines makes it difficult to distinguish how the overall distributions relate to each other.
The geom_violin()
and faceted geom_histogram()
have similar strengths and
weaknesses.
It is easy to visually distinguish differences in the overall shape of the
distributions (skewness, central values, variance, etc).
However, since we can’t easily compare the vertical values of the distribution,
it is difficult to look up which category has the highest density for a given price.
All of these methods depend on tuning parameters to determine the level of
smoothness of the distribution.
ggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) +
geom_freqpoly(mapping = aes(color = cut), binwidth = 500)
ggplot(data = diamonds, mapping = aes(x = price)) +
geom_histogram() +
facet_wrap(~cut, ncol = 1, scales = "free_y")
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The violin plot was first described in Hintze and Nelson (1998).
Exercise 7.5.1.6
If you have a small dataset, it’s sometimes useful to use geom_jitter()
to see the relationship between a continuous and categorical variable.
The ggbeeswarm package provides a number of methods similar to geom_jitter()
.
List them and briefly describe what each one does.
There are two methods:

geom_quasirandom()
produces plots that are a mix of jitter and violin plots. There are several different methods that determine exactly how the random location of the points is generated. 
geom_beeswarm()
produces a plot similar to a violin plot, but by offsetting the points.
I’ll use the mpg
box plot example since these methods display individual points, they are better suited for smaller datasets.
ggplot(data = mpg) +
geom_quasirandom(mapping = aes(
x = reorder(class, hwy, FUN = median),
y = hwy
))
ggplot(data = mpg) +
geom_quasirandom(
mapping = aes(
x = reorder(class, hwy, FUN = median),
y = hwy
),
method = "tukey"
)
ggplot(data = mpg) +
geom_quasirandom(
mapping = aes(
x = reorder(class, hwy, FUN = median),
y = hwy
),
method = "tukeyDense"
)
ggplot(data = mpg) +
geom_quasirandom(
mapping = aes(
x = reorder(class, hwy, FUN = median),
y = hwy
),
method = "frowney"
)
7.5.2 Two categorical variables
Exercise 7.5.2.1
How could you rescale the count dataset above to more clearly show the distribution of cut within color, or color within cut?
To clearly show the distribution of cut
within color
, calculate a new variable prop
which is the proportion of each cut within a color
.
This is done using a grouped mutate.
diamonds %>%
count(color, cut) %>%
group_by(color) %>%
mutate(prop = n / sum(n)) %>%
ggplot(mapping = aes(x = color, y = cut)) +
geom_tile(mapping = aes(fill = prop))
Similarly, to scale by the distribution of color
within cut
,
diamonds %>%
count(color, cut) %>%
group_by(cut) %>%
mutate(prop = n / sum(n)) %>%
ggplot(mapping = aes(x = color, y = cut)) +
geom_tile(mapping = aes(fill = prop))
I add limit = c(0, 1)
to put the color scale between (0, 1).
These are the logical boundaries of proportions.
This makes it possible to compare each cell to its actual value, and would improve comparisons across multiple plots.
However, it ends up limiting the colors and makes it harder to compare within the dataset.
However, using the default limits of the minimum and maximum values makes it easier to compare within the dataset the emphasizing relative differences, but harder to compare across datasets.
Exercise 7.5.2.2
Use geom_tile()
together with dplyr to explore how average flight delays vary by destination and month of year.
What makes the plot difficult to read?
How could you improve it?
flights %>%
group_by(month, dest) %>%
summarise(dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
ggplot(aes(x = factor(month), y = dest, fill = dep_delay)) +
geom_tile() +
labs(x = "Month", y = "Destination", fill = "Departure Delay")
#> `summarise()` regrouping output by 'month' (override with `.groups` argument)
There are several things that could be done to improve it,
 sort destinations by a meaningful quantity (distance, number of flights, average delay)
 remove missing values
How to treat missing values is difficult.
In this case, missing values correspond to airports which don’t have regular flights (at least one flight each month) from NYC.
These are likely smaller airports (with higher variance in their average due to fewer observations).
When we group all pairs of (month
, dest
) again by dest, we should have a total count of 12 (one for each month) per group (dest
).
This makes it easy to filter.
flights %>%
group_by(month, dest) %>% # This gives us (month, dest) pairs
summarise(dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
group_by(dest) %>% # group all (month, dest) pairs by dest ..
filter(n() == 12) %>% # and only select those that have one entry per month
ungroup() %>%
mutate(dest = reorder(dest, dep_delay)) %>%
ggplot(aes(x = factor(month), y = dest, fill = dep_delay)) +
geom_tile() +
labs(x = "Month", y = "Destination", fill = "Departure Delay")
#> `summarise()` regrouping output by 'month' (override with `.groups` argument)
Exercise 7.5.2.3
Why is it slightly better to use aes(x = color, y = cut)
rather than aes(x = cut, y = color)
in the example above?
It’s usually better to use the categorical variable with a larger number of categories or the longer labels on the y axis. If at all possible, labels should be horizontal because that is easier to read.
However, switching the order doesn’t result in overlapping labels.
diamonds %>%
count(color, cut) %>%
ggplot(mapping = aes(y = color, x = cut)) +
geom_tile(mapping = aes(fill = n))
Another justification, for switching the order is that the larger numbers are at the top when x = color
and y = cut
, and that lowers the cognitive burden of interpreting the plot.
7.5.3 Two continuous variables
Exercise 7.5.3.1
Instead of summarizing the conditional distribution with a box plot, you could use a frequency polygon.
What do you need to consider when using cut_width()
vs cut_number()
?
How does that impact a visualization of the 2d distribution of carat
and price
?
Both cut_width()
and cut_number()
split a variable into groups.
When using cut_width()
, we need to choose the width, and the number of
bins will be calculated automatically.
When using cut_number()
, we need to specify the number of bins, and
the widths will be calculated automatically.
In either case, we want to choose the bin widths and number to be large enough to aggregate observations to remove noise, but not so large as to remove all the signal.
If categorical colors are used, no more than eight colors should be used
in order to keep them distinct. Using cut_number
, I will split carats into
quantiles (five groups).
ggplot(
data = diamonds,
mapping = aes(color = cut_number(carat, 5), x = price)
) +
geom_freqpoly() +
labs(x = "Price", y = "Count", color = "Carat")
Alternatively, I could use cut_width
to specify widths at which to cut.
I will choose 1carat widths. Since there are very few diamonds larger than
2carats, this is not as informative. However, using a width of 0.5 carats
creates too many groups, and splitting at nonwhole numbers is unappealing.
Exercise 7.5.3.2
Visualize the distribution of carat
, partitioned by price
.
Plotted with a box plot with 10 bins with an equal number of observations, and the width determined by the number of observations.
ggplot(diamonds, aes(x = cut_number(price, 10), y = carat)) +
geom_boxplot() +
coord_flip() +
xlab("Price")
Plotted with a box plot with 10 equalwidth bins of $2,000. The argument boundary = 0
ensures that first bin is $0–$2,000.
Exercise 7.5.3.3
How does the price distribution of very large diamonds compare to small diamonds. Is it as you expect, or does it surprise you?
The distribution of very large diamonds is more variable. I am not surprised, since I knew little about diamond prices. After the fact, it does not seem surprising (as many thing do). I would guess that this is due to the way in which diamonds are selected for retail sales. Suppose that someone selling a diamond only finds it profitable to sell it if some combination size, cut, clarity, and color are above a certain threshold. The smallest diamonds are only profitable to sell if they are exceptional in all the other factors (cut, clarity, and color), so the small diamonds sold have similar characteristics. However, larger diamonds may be profitable regardless of the values of the other factors. Thus we will observe large diamonds with a wider variety of cut, clarity, and color and thus more variability in prices.
Exercise 7.5.3.4
Combine two of the techniques you’ve learned to visualize the combined distribution of cut, carat, and price.
There are many options to try, so your solutions may vary from mine. Here are a few options that I tried.
Exercise 7.5.3.5
Two dimensional plots reveal outliers that are not visible in one dimensional plots.
For example, some points in the plot below have an unusual combination of x
and y
values, which makes the points outliers even though their x
and y
values appear normal when examined separately.
ggplot(data = diamonds) +
geom_point(mapping = aes(x = x, y = y)) +
coord_cartesian(xlim = c(4, 11), ylim = c(4, 11))
Why is a scatterplot a better display than a binned plot for this case?
In this case, there is a strong relationship between \(x\) and \(y\). The outliers in this case are not extreme in either \(x\) or \(y\). A binned plot would not reveal these outliers, and may lead us to conclude that the largest value of \(x\) was an outlier even though it appears to fit the bivariate pattern well.
The later chapter Model Basics discusses fitting models to bivariate data and plotting residuals, which would reveal this outliers.
7.6 Patterns and models
7.7 ggplot2 calls
7.8 Learning more
References
Hintze, Jerry L., and Ray D. Nelson. 1998. “Violin Plots: A Box PlotDensity Trace Synergism.” The American Statistician 52 (2). Taylor & Francis: 181–84. https://doi.org/10.1080/00031305.1998.10480559.
Hofmann, Heike, Hadley Wickham, and Karen Kafadar. 2017. “LetterValue Plots: Boxplots for Large Data.” Journal of Computational and Graphical Statistics 26 (3). Taylor & Francis: 469–77. https://doi.org/10.1080/10618600.2017.1305277.
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