Problem Set 4 - More ggplot2

More Visualization Processes

Directions

The objective of this assignment is to complete and explain basic plots before moving on to more complicated ways to graph data.

• To ensure accuracy pay close attention to axes and labels, you will be evaluated based on the accuracy and expository nature of your graphics. Make sure your axis labels are easy to understand and are comprised of full words with units if necessary.

Each question is worth 5 points.

To submit this homework you will create the document in Rstudio, using the knitr package (button included in Rstudio) and then submit the document to your Rpubs account. Once uploaded you will submit the link to that document on Canvas. Please make sure that this link is hyper linked and that I can see the visualization and the code required to create it (echo=TRUE).

Questions

1. For The following questions use the Marriage data set from the mosaicData package.

• Create an informative and meaningful data graphic.

• Identify each of the visual cues that you are using, and describe how they are related to each variable.

• Create a data graphic with at least five variables (either quantitative or categorical). For the purposes of this exercise, do not worry about making your visualization meaningful—just try to encode five variables into one plot.

• We are mapping the x-axis to the ‘hs’variable , the y-acis to the ’race’varible, and the fill aesthetic to the ’race’ varible to colorful.We are using the visual curs of position x and y coordinates to encode the varabile of age, and the visual cur of color(fill aesthetic) to encode the varible of race.

data("Marriage")

ggplot(data = Marriage, aes(x = hs, fill = race))+
  geom_histogram(binwidth = 1 , position = "dodge")

summary(Marriage$race)

## American Indian           Black        Hispanic           White 
##               1              22               1              74

summary(Marriage$sign)

##    Aquarius       Aries      Cancer   Capricorn      Gemini         Leo 
##           7          10           8           2           9           7 
##       Libra      Pisces Saggitarius     Scorpio      Taurus       Virgo 
##           7          16           9           7           6          10

ggplot(Marriage, aes(x=delay, y=age, color=race, shape=sign, size=hs)) +
  geom_point() +
  facet_wrap(~sign)

## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 12. Consider
## specifying shapes manually if you must have them.

## Warning: Removed 55 rows containing missing values (geom_point).

Your objective for the next four questions will be write the code necessary to exactly recreate the provided graphics.

2. Boxplot Visualization

This boxplot was built using the mpg dataset. Notice the changes in axis labels.

data("mpg")
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…
## # … with 224 more rows

q2<- ggplot(mpg, aes(manufacturer, hwy)) 
q2 + geom_boxplot()  + 
  labs( y = "Vehicle Manufacturer", x = "HFE(miles/gallon)") + 
  theme_classic()

3. Stacked Density Plot

This graphic is built with the diamonds dataset in the ggplot2 package.

data("diamonds")
diamonds

## # A tibble: 53,940 × 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # … with 53,930 more rows

q3 <- ggplot(diamonds, aes(price, colour = cut, fill = cut)) +
  geom_density(alpha = 0.2) + 
   scale_fill_discrete() +
  labs(x = "Diamond Price ", y = "Density", title = "Diamond Price Density")+
  theme_bw() 
q3

4. Sideways bar plot

This graphic uses the penguins dataset and shows the counts between males and females by species.

data("penguins")
penguins

## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_…¹ body_…² sex    year
##    <fct>   <fct>              <dbl>         <dbl>      <int>   <int> <fct> <int>
##  1 Adelie  Torgersen           39.1          18.7        181    3750 male   2007
##  2 Adelie  Torgersen           39.5          17.4        186    3800 fema…  2007
##  3 Adelie  Torgersen           40.3          18          195    3250 fema…  2007
##  4 Adelie  Torgersen           NA            NA           NA      NA <NA>   2007
##  5 Adelie  Torgersen           36.7          19.3        193    3450 fema…  2007
##  6 Adelie  Torgersen           39.3          20.6        190    3650 male   2007
##  7 Adelie  Torgersen           38.9          17.8        181    3625 fema…  2007
##  8 Adelie  Torgersen           39.2          19.6        195    4675 male   2007
##  9 Adelie  Torgersen           34.1          18.1        193    3475 <NA>   2007
## 10 Adelie  Torgersen           42            20.2        190    4250 <NA>   2007
## # … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
## #   ²​body_mass_g

ggplot(penguins, aes(x = species, fill = sex)) +
  geom_bar(position = "dodge") +
  labs(title = "Counts between males and females by species",
       x = "Species",
       y = "Count")

5. Scatterplot

This figure examines the relationship between bill length and depth in the penguins dataset.

data("penguins")
penguins

## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_…¹ body_…² sex    year
##    <fct>   <fct>              <dbl>         <dbl>      <int>   <int> <fct> <int>
##  1 Adelie  Torgersen           39.1          18.7        181    3750 male   2007
##  2 Adelie  Torgersen           39.5          17.4        186    3800 fema…  2007
##  3 Adelie  Torgersen           40.3          18          195    3250 fema…  2007
##  4 Adelie  Torgersen           NA            NA           NA      NA <NA>   2007
##  5 Adelie  Torgersen           36.7          19.3        193    3450 fema…  2007
##  6 Adelie  Torgersen           39.3          20.6        190    3650 male   2007
##  7 Adelie  Torgersen           38.9          17.8        181    3625 fema…  2007
##  8 Adelie  Torgersen           39.2          19.6        195    4675 male   2007
##  9 Adelie  Torgersen           34.1          18.1        193    3475 <NA>   2007
## 10 Adelie  Torgersen           42            20.2        190    4250 <NA>   2007
## # … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
## #   ²​body_mass_g

q5 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm), color = species)+ 
  geom_point(aes(shape = species,color = species), size = 1) +
  labs(title = "Bill Length vs. Depth",
       x = "Bill Length (mm)",
       y = "Bill Depth (mm)", 
       shape = "Species")+
  geom_smooth(method = "lm", aes(color = species))

q5