Directions

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

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.
data<-Marriage
str(data)
## 'data.frame':    98 obs. of  15 variables:
##  $ bookpageID   : Factor w/ 49 levels "B230p1209","B230p1354",..: 5 6 7 8 9 1 2 3 4 16 ...
##  $ appdate      : Date, format: "1996-10-29" "1996-11-12" ...
##  $ ceremonydate : Date, format: "1996-11-09" "1996-11-12" ...
##  $ delay        : int  11 0 8 5 5 0 16 0 28 10 ...
##  $ officialTitle: Factor w/ 9 levels "BISHOP","CATHOLIC PRIEST",..: 4 6 6 7 7 6 6 6 6 7 ...
##  $ person       : Factor w/ 2 levels "Bride","Groom": 2 2 2 2 2 2 2 2 2 2 ...
##  $ dob          : Date, format: "1964-04-11" "1964-08-06" ...
##  $ age          : num  32.6 32.3 34.8 40.6 30 ...
##  $ race         : Factor w/ 4 levels "American Indian",..: 4 4 3 2 4 4 4 4 2 4 ...
##  $ prevcount    : int  0 1 1 1 0 1 1 1 0 3 ...
##  $ prevconc     : Factor w/ 2 levels "Death","Divorce": NA 2 2 2 NA NA 2 2 NA 2 ...
##  $ hs           : int  12 12 12 12 12 12 12 12 12 12 ...
##  $ college      : int  7 0 3 4 0 0 0 0 0 6 ...
##  $ dayOfBirth   : num  102 219 51 141 348 52 284 31 338 183 ...
##  $ sign         : Factor w/ 12 levels "Aquarius","Aries",..: 2 6 8 5 9 8 7 1 9 3 ...
summary(data$race)
## American Indian           Black        Hispanic           White 
##               1              22               1              74
summary(data$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
#Plotting age vs race where x axis is age and y axis iscount of people in that age group. Visual cues are used to distinguish people of different race.
ggplot(data, aes(x = age, fill = race)) + 
  geom_histogram(binwidth = 1, position = "dodge")

#For Atleast 5 variables:
ggplot(data = data, aes(x = age, y = college)) + 
  geom_point(aes(color = sign, shape = race), size = 2) +
  facet_wrap(~person)
## Warning: Removed 10 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.

  1. Boxplot Visualization

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

mpgdata<-mpg
mpgdata
## # 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
library(ggplot2)
plot <- ggplot(mpgdata,aes(manufacturer,hwy))
plot + geom_boxplot()+coord_flip()+labs(x="Vehicle Manufacturer", y="Highway Fuel Efficiency(miles/gallon) ")+theme_classic()

  1. Stacked Density Plot

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

library(ggthemes)
library(ggplot2)

diamonds_data<-diamonds
diamonds_data
## # 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
plot3 <- 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() 

plot3

  1. Sideways bar plot

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

penguins<-penguins
penguins
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
plot4<-penguins %>%
  count(sex, species) %>%
  ggplot() + 
  geom_col(aes(x = sex, y = n, fill = species)) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) + 
  facet_wrap(~species, ncol = 1) + 
  theme_minimal() + 
  theme(legend.position = 'none') + 
  labs(x = "Sex", y = "Count") + 
  coord_flip()

plot4

  1. Scatterplot

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

plot5<-ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point(aes(shape = species), size = 2) + 
  geom_smooth(method = 'lm', se = FALSE) +
  scale_color_manual(values = c("darkorange", "darkorchid", "cyan4")) + 
  labs(x = "Bill Length (mm)", y = "Bill Depth (mm)", color = "Species", shape = "Species")
plot5