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.
summary(Marriage)
##      bookpageID    appdate            ceremonydate            delay       
##  B230p1209: 2   Min.   :1996-10-29   Min.   :1996-11-09   Min.   : 0.000  
##  B230p1354: 2   1st Qu.:1997-04-22   1st Qu.:1997-04-26   1st Qu.: 0.000  
##  B230p1665: 2   Median :1997-11-26   Median :1997-11-26   Median : 3.000  
##  B230p1948: 2   Mean   :1997-12-04   Mean   :1997-12-10   Mean   : 5.673  
##  B230p539 : 2   3rd Qu.:1998-07-13   3rd Qu.:1998-07-31   3rd Qu.: 9.000  
##  B230p677 : 2   Max.   :1999-02-05   Max.   :1999-02-06   Max.   :28.000  
##  (Other)  :86                                                             
##            officialTitle   person        dob                  age       
##  MARRIAGE OFFICIAL:44    Bride:49   Min.   :1924-05-21   Min.   :16.27  
##  PASTOR           :22    Groom:49   1st Qu.:1955-03-02   1st Qu.:21.66  
##  MINISTER         :20               Median :1965-05-04   Median :31.90  
##  BISHOP           : 2               Mean   :1963-06-15   Mean   :34.51  
##  CATHOLIC PRIEST  : 2               3rd Qu.:1976-02-07   3rd Qu.:42.82  
##  CHIEF CLERK      : 2               Max.   :1982-07-20   Max.   :74.25  
##  (Other)          : 6                                                   
##               race      prevcount         prevconc        hs       
##  American Indian: 1   Min.   :0.0000   Death  : 7   Min.   : 8.00  
##  Black          :22   1st Qu.:0.0000   Divorce:43   1st Qu.:12.00  
##  Hispanic       : 1   Median :1.0000   NA's   :48   Median :12.00  
##  White          :74   Mean   :0.7755                Mean   :11.68  
##                       3rd Qu.:1.0000                3rd Qu.:12.00  
##                       Max.   :5.0000                Max.   :12.00  
##                                                                    
##     college        dayOfBirth              sign   
##  Min.   :0.000   Min.   :  6.00   Pisces     :16  
##  1st Qu.:0.000   1st Qu.: 81.25   Aries      :10  
##  Median :1.000   Median :166.50   Virgo      :10  
##  Mean   :1.625   Mean   :177.76   Gemini     : 9  
##  3rd Qu.:2.000   3rd Qu.:263.75   Saggitarius: 9  
##  Max.   :7.000   Max.   :358.00   Cancer     : 8  
##  NA's   :10                       (Other)    :36
head(Marriage)
##   bookpageID    appdate ceremonydate delay     officialTitle person        dob
## 1   B230p539 1996-10-29   1996-11-09    11    CIRCUIT JUDGE   Groom 1964-04-11
## 2   B230p677 1996-11-12   1996-11-12     0 MARRIAGE OFFICIAL  Groom 1964-08-06
## 3   B230p766 1996-11-19   1996-11-27     8 MARRIAGE OFFICIAL  Groom 1962-02-20
## 4   B230p892 1996-12-02   1996-12-07     5          MINISTER  Groom 1956-05-20
## 5   B230p994 1996-12-09   1996-12-14     5          MINISTER  Groom 1966-12-14
## 6  B230p1209 1996-12-26   1996-12-26     0 MARRIAGE OFFICIAL  Groom 1970-02-21
##        age     race prevcount prevconc hs college dayOfBirth        sign
## 1 32.60274    White         0     <NA> 12       7        102       Aries
## 2 32.29041    White         1  Divorce 12       0        219         Leo
## 3 34.79178 Hispanic         1  Divorce 12       3         51      Pisces
## 4 40.57808    Black         1  Divorce 12       4        141      Gemini
## 5 30.02192    White         0     <NA> 12       0        348 Saggitarius
## 6 26.86301    White         1     <NA> 12       0         52      Pisces
ggplot(Marriage,aes(x=college,y=age))+
  geom_point(aes(color=race))+
               scale_colour_manual(values=c("blue","pink","orange","green"))+
  labs(color="race", title = "Years in College and Age", x="Years in College", y="Age in Years")
## Warning: Removed 10 rows containing missing values (geom_point).

ggplot(Marriage,aes(x=delay,y=officialTitle))+
  geom_point(aes(color=sign,shape=race))+scale_color_discrete()+
  labs(color="sign",x="Delay", y= "Official Title", title="Official Title and Marriage Delays")

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.

head(mpg)
## # A tibble: 6 × 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(l5)   f        18    29 p     compa…
## 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
## 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
## 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
## 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
## 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…
ggplot(mpg,aes(x=hwy,y=manufacturer))+
  geom_boxplot()+
  theme_classic()+
  labs (x="highway efficiency", y= "vehicle manufacturer")

  1. Stacked Density Plot

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

head(diamonds)
## # A tibble: 6 × 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
library(viridisLite)
library(viridis)
ggplot(diamonds,aes(x=price,color=cut,fill=cut))+
  geom_density(alpha=.2)+
  scale_fill_discrete()+
  scale_color_viridis(discrete=TRUE)+
  labs(fill="Cut",color="Cut", x="Diamond Price", y="Density")

  1. Sideways bar plot

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

head(penguins)
## # A tibble: 6 × 8
##   species island    bill_length_mm bill_depth_mm flipper_l…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
## # … with abbreviated variable names ¹​flipper_length_mm, ²​body_mass_g
ggplot(penguins,aes(x=sex,fill=species))+
  geom_bar(alpha=0.8)+
  scale_fill_manual(values=c("pink","purple","cyan4"),guide=FALSE)+
  theme_minimal()+
  facet_wrap(~species,ncol=1)+
  coord_flip()+
  labs(y="Count", x="Sex")
## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.

  1. Scatterplot

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

ggplot(data=penguins,aes(x=bill_length_mm,y=bill_depth_mm))+
  geom_point(aes(color=species,shape=species),size=2)+
  guides(shape="none")+
  geom_smooth(method=lm,se=FALSE,aes(color=species))+
  scale_color_manual(values=c("pink","orange","purple"))+
  labs(color="Species",x="Bill Length",y="Bill Depth")