# GGPlot2 Extras

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library(swirl)

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swirl()

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| you did then. If you are new, call yourself something unique.

What shall I call you? Krishnakanth Allika

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1: Exploratory Data Analysis

2: Take me to the swirl course repository!

Selection: 1

| Please choose a lesson, or type 0 to return to course menu.

```
1: Principles of Analytic Graphs 2: Exploratory Graphs
3: Graphics Devices in R 4: Plotting Systems
5: Base Plotting System 6: Lattice Plotting System
7: Working with Colors 8: GGPlot2 Part1
9: GGPlot2 Part2 10: GGPlot2 Extras
11: Hierarchical Clustering 12: K Means Clustering
13: Dimension Reduction 14: Clustering Example
15: CaseStudy
```

Selection: 10

| Attempting to load lesson dependencies...

| Package ‘ggplot2’ loaded correctly!

| | 0%

| GGPlot2_Extras. (Slides for this and other Data Science courses may be found at

| github https://github.com/DataScienceSpecialization/courses/. If you care to use

| them, they must be downloaded as a zip file and viewed locally. This lesson

| corresponds to 04_ExploratoryAnalysis/ggplot2.)

...

|= | 2%

| In this lesson we'll go through a few more qplot examples using diamond data which

| comes with the ggplot2 package. This data is a little more complicated than the mpg

| data and it contains information on various characteristics of diamonds.

...

|=== | 4%

| Run the R command str with the argument diamonds to see what the data looks like.

str(diamonds)

`tibble [53,940 x 10] (S3: tbl_df/tbl/data.frame) $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ... $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ... $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ... $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ... $ depth : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ... $ table : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ... $ price : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ... $ x : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ... $ y : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ... $ z : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...`

| Keep working like that and you'll get there!

|==== | 6%

| From the output, how many characteristics of diamonds do you think this data

| contains?

1: 53950

2: 53940

3: 10

4: 5394

Selection: 3

| You are doing so well!

|====== | 7%

| From the output of str, how many diamonds are characterized in this dataset?

1: 53950

2: 10

3: 5394

4: 53940

Selection: 1

| You're close...I can feel it! Try it again.

| The output says there are 53940 observations of 10 variables. This is followed by a

| 10-long list of characteristics (carat, cut, color, etc.) that can apply to

| diamonds.

1: 5394

2: 53940

3: 10

4: 53950

Selection: 2

| You got it!

|======= | 9%

| Now let's plot a histogram of the price of the 53940 diamonds in this dataset.

| Recall that a histogram requires only one variable of the data, so run the R command

| qplot with the first argument price and the argument data set equal to diamonds.

| This will show the frequency of different diamond prices.

qplot(price,data=diamonds)

`stat_bin()`

using`bins = 30`

. Pick better value with`binwidth`

.

| Excellent work!

|========= | 11%

| Not only do you get a histogram, but you also get a message about the binwidth

| defaulting to range/30. Recall that range refers to the spread or dispersion of the

| data, in this case price of diamonds. Run the R command range now with

| diamonds$price as its argument.

range(diamonds$price)

[1] 326 18823

| You nailed it! Good job!

|========== | 13%

| We see that range returned the minimum and maximum prices, so the diamonds vary in

| price from $326 to $18823. We've done the arithmetic for you, the range (difference

| between these two numbers) is $18497.

...

|=========== | 15%

| Rerun qplot now with 3 arguments. The first is price, the second is data set equal

| to diamonds, and the third is binwidth set equal to 18497/30). (Use the up arrow to

| save yourself some typing.) See if the plot looks familiar.

qplot(price,data=diamonds,binwidth=18497/30)

| All that practice is paying off!

|============= | 17%

| No more messages in red, but a histogram almost identical to the previous one! If

| you typed 18497/30 at the command line you would get the result 616.5667. This means

| that the height of each bin tells you how many diamonds have a price between x and

| x+617 where x is the left edge of the bin.

...

|============== | 19%

| We've created a vector containing integers that are multiples of 617 for you. It's

| called brk. Look at it now.

brk

`[1] 0 617 1234 1851 2468 3085 3702 4319 4936 5553 6170 6787 7404 [14] 8021 8638 9255 9872 10489 11106 11723 12340 12957 13574 14191 14808 15425 [27] 16042 16659 17276 17893 18510 19127`

| You are amazing!

|================ | 20%

| We've also created a vector containing the number of diamonds with prices between

| each pair of adjacent entries of brk. For instance, the first count is the number of

| diamonds with prices between 0 and $617, and the second is the number of diamonds
| with prices between $617 and $1234. Look at the vector named counts now.

counts

`[1] 4611 13255 5230 4262 3362 2567 2831 2841 2203 1666 1445 1112 987 [14] 766 796 655 606 553 540 427 429 376 348 338 298 305 [27] 269 287 227 251 97`

| You nailed it! Good job!

|================= | 22%

| See how it matches the histogram you just plotted? So, qplot really works!

...

|=================== | 24%

| You're probably sick of it but rerun qplot again, this time with 4 arguments. The

| first 3 are the same as the last qplot command you just ran (price, data set equal

| to diamonds, and binwidth set equal to 18497/30). (Use the up arrow to save yourself

| some typing.) The fourth argument is fill set equal to cut. The shape of the

| histogram will be familiar, but it will be more colorful.

qplot(price,data=diamonds,binwidth=18497/30,fill=cut)

| You're the best!

|==================== | 26%

| This shows how the counts within each price grouping (bin) are distributed among the

| different cuts of diamonds. Notice how qplot displays these distributions relative

| to the cut legend on the right. The fair cut diamonds are at the bottom of each bin,

| the good cuts are above them, then the very good above them, until the ideal cuts

| are at the top of each bin. You can quickly see from this display that there are

| very few fair cut diamonds priced above $5000.

...

|===================== | 28%

| Now we'll replot the histogram as a density function which will show the proportion

| of diamonds in each bin. This means that the shape will be similar but the scale on

| the y-axis will be different since, by definition, the density function is

| nonnegative everywhere, and the area under the curve is one. To do this, simply call

| qplot with 3 arguments. The first 2 are price and data (set equal to diamonds). The

| third is geom which should be set equal to the string "density". Try this now.

qplot(price,data=diamonds,geom="density")

| Your dedication is inspiring!

|======================= | 30%

| Notice that the shape is similar to that of the histogram we saw previously. The

| highest peak is close to 0 on the x-axis meaning that most of the diamonds in the

| dataset were inexpensive. In general, as prices increase (move right along the

| x-axis) the number of diamonds (at those prices) decrease. The exception to this is

| when the price is around $4000; there's a slight increase in frequency. Let's see if

| cut is responsible for this increase.

...

|======================== | 31%

| Rerun qplot, this time with 4 arguments. The first 2 are the usual, and the third is

| geom set equal to "density". The fourth is color set equal to cut. Try this now.

qplot(price,data=diamonds,geom="density",color=cut)

| Keep working like that and you'll get there!

|========================== | 33%

| See how easily qplot did this? Four of the five cuts have 2 peaks, one at price

| $1000 and the other between $4000 and $5000. The exception is the Fair cut which has
| a single peak at $2500. This gives us a little more understanding of the histogram

| we saw before.

...

|=========================== | 35%

| Let's move on to scatterplots. For these we'll need to specify two variables from

| the diamond dataset.

...

|============================= | 37%

| Let's start with carat and price. Use these as the first 2 arguments of qplot. The

| third should be data set equal to the dataset. Try this now.

qplot(carat,price,data=diamonds)

| You got it right!

|============================== | 39%

| We see the positive trend here, as the number of carats increases the price also

| goes up.

...

|=============================== | 41%

| Now rerun the same command, except add a fourth parameter, shape, set equal to cut.

qplot(carat,price,data=diamonds,shape=cut)

Warning message:

Using shapes for an ordinal variable is not advised

| You are doing so well!

|================================= | 43%

| The same scatterplot appears, except the cuts of the diamonds are distinguished by

| different symbols. The legend at the right tells you which symbol is associated with

| each cut. These are small and hard to read, so rerun the same command, except this

| time instead of setting the argument shape equal to cut, set the argument color

| equal to cut.

qplot(carat,price,data=diamonds,color=cut)

| Excellent job!

|================================== | 44%

| That's easier to see! Now we'll close with two, more complicated scatterplot

| examples.

...

|==================================== | 46%

| We'll rerun the plot you just did (carat,price,data=diamonds and color=cut) but add

| an additional parameter. Use geom_smooth with the method set equal to the string

| "lm".

qplot(carat,price,data=diamonds,color=cut)+geom_smooth(method="lm")

`geom_smooth()`

using formula 'y ~ x'

| That's a job well done!

|===================================== | 48%

| Again, we see the same scatterplot, but slightly more compressed and showing 5

| regression lines, one for each cut of diamonds. It might be hard to see, but around

| each line is a shadow showing the 95% confidence interval. We see, unsurprisingly,

| that the better the cut, the steeper (more positive) the slope of the lines.

...

|====================================== | 50%

| Finally, let's rerun that plot you just did qplot(carat,price,data=diamonds,

| color=cut) + geom_smooth(method="lm") but add one (just one) more argument to qplot.

| The new argument is facets and it should be set equal to the formula .~cut. Recall

| that the facets argument indicates we want a multi-panel plot. The symbol to the

| left of the tilde indicates rows (in this case just one) and the symbol to the right

| of the tilde indicates columns (in this five, the number of cuts). Try this now.

qplot(carat,price,data=diamonds,color=cut,facets=.~cut)+geom_smooth(method="lm")

`geom_smooth()`

using formula 'y ~ x'

| You are quite good my friend!

|======================================== | 52%

| Pretty good, right? Not too difficult either. Let's review what we learned!

...

|========================================= | 54%

| Which types of plot does qplot plot?

1: box and whisker plots

2: histograms

3: all of the others

4: scatterplots

Selection: 3

| You are doing so well!

|=========================================== | 56%

| Any and all of the above choices work; qplot is just that good. What does the gg in

| ggplot2 stand for?

1: good grief

2: goto graphics

3: grammar of graphics

4: good graphics

Selection: 3

| You are amazing!

|============================================ | 57%

| True or False? The geom argument takes a string for a value.

1: False

2: True

Selection: 2

| You are really on a roll!

|============================================== | 59%

| True or False? The method argument takes a string for a value.

1: True

2: False

Selection: 1

| You got it!

|=============================================== | 61%

| True or False? The binwidth argument takes a string for a value.

1: True

2: False

Selection: 2

| You nailed it! Good job!

|================================================ | 63%

| True or False? The user must specify x- and y-axis labels when using qplot.

1: False

2: True

Selection: 1

| That's correct!

|================================================== | 65%

| Now for some ggplots.

...

|=================================================== | 67%

| First create a graphical object g by assigning to it the output of a call to the

| function ggplot with 2 arguments. The first is the dataset diamonds and the second

| is a call to the function aes with 2 arguments, depth and price. Remember you won't

| see any result.

g<-ggplot(data=diamonds,aes(depth,price))

| You are quite good my friend!

|===================================================== | 69%

| Does g exist? Yes! Type summary with g as an argument to see what it holds.

summary(g)

`data: carat, cut, color, clarity, depth, table, price, x, y, z [53940x10] mapping: x = ~depth, y = ~price faceting: <ggproto object: Class FacetNull, 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 FacetNull, Facet, gg>`

| That's a job well done!

|====================================================== | 70%

| We see that g holds the entire dataset. Now suppose we want to see a scatterplot of

| the relationship. Add to g a call to the function geom_point with 1 argument, alpha

| set equal to 1/3.

g+geom_point(alpha=1/3)

| You're the best!

|======================================================== | 72%

| That's somewhat interesting. We see that depth ranges from 43 to 79, but the densest

| distribution is around 60 to 65. Suppose we want to see if this relationship

| (between depth and price) is affected by cut or carat. We know cut is a factor with

| 5 levels (Fair, Good, Very Good, Premium, and Ideal). But carat is numeric and not a

| discrete factor. Can we do this?

...

|========================================================= | 74%

| Of course! That's why we asked. R has a handy command, cut, which allows you to

| divide your data into sets and label each entry as belonging to one of the sets, in

| effect creating a new factor. First, we'll have to decide where to cut the data.

...

|========================================================== | 76%

| Let's divide the data into 3 pockets, so 1/3 of the data falls into each. We'll use

| the R command quantile to do this. Create the variable cutpoints and assign to it

| the output of a call to the function quantile with 3 arguments. The first is the

| data to cut, namely diamonds$carat; the second is a call to the R function seq. This

| is also called with 3 arguments, (0, 1, and length set equal to 4). The third

| argument to the call to quantile is the boolean na.rm set equal to TRUE.

cutpoints<-quantile(diamonds$carat,seq(0,1,length=4),na.rm=TRUE)

| Keep working like that and you'll get there!

|============================================================ | 78%

| Look at cutpoints now to understand what it is.

cutpoints

```
0% 33.33333% 66.66667% 100%
0.20 0.50 1.00 5.01
```

| You got it right!

|============================================================= | 80%

| We see a 4-long vector (explaining why length was set equal to 4). We also see that

| .2 is the smallest carat size in the dataset and 5.01 is the largest. One third of

| the diamonds are between .2 and .5 carats and another third are between .5 and 1

| carat in size. The remaining third are between 1 and 5.01 carats. Now we can use the

| R command cut to label each of the 53940 diamonds in the dataset as belonging to one

| of these 3 factors. Create a new name in diamonds, diamonds$car2 by assigning it the
| output of the call to cut. This command takes 2 arguments, diamonds$carat, which is

| what we want to cut, and cutpoints, the places where we'll cut.

diamonds$car2<-cut(diamonds$carat,cutpoints)

| You are quite good my friend!

|=============================================================== | 81%

| Now we can continue with our multi-facet plot. First we have to reset g since we

| changed the dataset (diamonds) it contained (by adding a new column). Assign to g

| the output of a call to ggplot with 2 arguments. The dataset diamonds is the first,

| and a call to the function aes with 2 arguments (depth,price) is the second.

g<-ggplot(data=diamonds,aes(depth,price))

| You're the best!

|================================================================ | 83%

| Now add to g calls to 2 functions. This first is a call to geom_point with the

| argument alpha set equal to 1/3. The second is a call to the function facet_grid

| using the formula cut ~ car2 as its argument.

g+geom_point(alpha=1/3)+facet_grid(cut~car2)

| That's correct!

|================================================================== | 85%

| We see a multi-facet plot with 5 rows, each corresponding to a cut factor. Not

| surprising. What is surprising is the number of columns. We were expecting 3 and got

| 4. Why?

...

|=================================================================== | 87%

| The first 3 columns are labeled with the cutpoint boundaries. The fourth is labeled

| NA and shows us where the data points with missing data (NA or Not Available)

| occurred. We see that there were only a handful (12 in fact) and they occurred in

| Very Good, Premium, and Ideal cuts. We created a vector, myd, containing the indices

| of these datapoints. Look at these entries in diamonds by typing the expression

| diamonds[myd,]. The myd tells R what rows to show and the empty column entry says to

| print all the columns.

diamonds[myd,]

`# A tibble: 12 x 11 carat cut color clarity depth table price x y z car2 <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <fct> 1 0.2 Premium E SI2 60.2 62 345 3.79 3.75 2.27 NA 2 0.2 Premium E VS2 59.8 62 367 3.79 3.77 2.26 NA 3 0.2 Premium E VS2 59 60 367 3.81 3.78 2.24 NA 4 0.2 Premium E VS2 61.1 59 367 3.81 3.78 2.32 NA 5 0.2 Premium E VS2 59.7 62 367 3.84 3.8 2.28 NA 6 0.2 Ideal E VS2 59.7 55 367 3.86 3.84 2.3 NA 7 0.2 Premium F VS2 62.6 59 367 3.73 3.71 2.33 NA 8 0.2 Ideal D VS2 61.5 57 367 3.81 3.77 2.33 NA 9 0.2 Very Good E VS2 63.4 59 367 3.74 3.71 2.36 NA 10 0.2 Ideal E VS2 62.2 57 367 3.76 3.73 2.33 NA 11 0.2 Premium D VS2 62.3 60 367 3.73 3.68 2.31 NA 12 0.2 Premium D VS2 61.7 60 367 3.77 3.72 2.31 NA`

| You're the best!

|==================================================================== | 89%

| We see these entries match the plots. Whew - that's a relief. The car2 field is, in

| fact, NA for these entries, but the carat field shows they each had a carat size of

| .2. What's going on here?

...

|====================================================================== | 91%

| Actually our plot answers this question. The boundaries for each column appear in

| the gray labels at the top of each column, and we see that the first column is

| labeled (0.2,0.5]. This indicates that this column contains data greater than .2 and

| less than or equal to .5. So diamonds with carat size .2 were excluded from the car2

| field.

...

|======================================================================= | 93%

| Finally, recall the last plotting command

| (g+geom_point(alpha=1/3)+facet_grid(cut~car2)) or retype it if you like and add

| another call. This one to the function geom_smooth. Pass it 3 arguments, method set

| equal to the string "lm", size set equal to 3, and color equal to the string "pink".

play()

| Entering play mode. Experiment as you please, then type nxt() when you are ready to

| resume the lesson.

diamonds[is.na(diamonds$car2),]

`# A tibble: 12 x 11 carat cut color clarity depth table price x y z car2 <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <fct> 1 0.2 Premium E SI2 60.2 62 345 3.79 3.75 2.27 NA 2 0.2 Premium E VS2 59.8 62 367 3.79 3.77 2.26 NA 3 0.2 Premium E VS2 59 60 367 3.81 3.78 2.24 NA 4 0.2 Premium E VS2 61.1 59 367 3.81 3.78 2.32 NA 5 0.2 Premium E VS2 59.7 62 367 3.84 3.8 2.28 NA 6 0.2 Ideal E VS2 59.7 55 367 3.86 3.84 2.3 NA 7 0.2 Premium F VS2 62.6 59 367 3.73 3.71 2.33 NA 8 0.2 Ideal D VS2 61.5 57 367 3.81 3.77 2.33 NA 9 0.2 Very Good E VS2 63.4 59 367 3.74 3.71 2.36 NA 10 0.2 Ideal E VS2 62.2 57 367 3.76 3.73 2.33 NA 11 0.2 Premium D VS2 62.3 60 367 3.73 3.68 2.31 NA 12 0.2 Premium D VS2 61.7 60 367 3.77 3.72 2.31 NA`

nxt()

| Resuming lesson...

| Finally, recall the last plotting command

| (g+geom_point(alpha=1/3)+facet_grid(cut~car2)) or retype it if you like and add

| another call. This one to the function geom_smooth. Pass it 3 arguments, method set

| equal to the string "lm", size set equal to 3, and color equal to the string "pink".

g+geom_point(alpha=1/3)+facet_grid(cut~car2)+geom_smooth(method="lm",size=3,color="pink")

`geom_smooth()`

using formula 'y ~ x'

| Keep up the great work!

|========================================================================= | 94%

| Nice thick regression lines which are somewhat interesting. You can add labels to

| the plot if you want but we'll let you experiment on your own.

...

|========================================================================== | 96%

| Lastly, ggplot2 can, of course, produce boxplots. This final exercise is the sum of

| 3 function calls. The first call is to ggplot with 2 arguments, diamonds and a call

| to aes with carat and price as arguments. The second call is to geom_boxplot with no

| arguments. The third is to facet_grid with one argument, the formula . ~ cut. Try

| this now.

ggplot(diamonds,aes(carat,price))+geom_boxplot()+facet_grid(.~cut)

Warning message:

Continuous y aesthetic -- did you forget aes(group=...)?

| Perseverance, that's the answer.

|============================================================================ | 98%

| Yes! A boxplot looking like marshmallows about to be roasted. Well done and

| congratulations! You've finished this jewel of a lesson. Hope it paid off!

...

|=============================================================================| 100%

| Would you like to receive credit for completing this course on Coursera.org?

1: Yes

2: No

Selection: 1

What is your email address? [email protected]

What is your assignment token? xXxXxxXXxXxxXXXx

Grade submission succeeded!

| Excellent job!

| You've reached the end of this lesson! Returning to the main menu...

| Please choose a course, or type 0 to exit swirl.

1: Exploratory Data Analysis

2: Take me to the swirl course repository!

Selection: 0

| Leaving swirl now. Type swirl() to resume.

rm(list=ls())

*Last updated 2020-05-09 22:04:44.535875 IST*

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