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VIS 1 - This graphic is a traditional stacked bar chart. This graphic works on the mpg dataset, which is built into the ggplot2 library. This means that you can access it simply by ggplot(mpg, ….). There is one modification above default in this graphic, I renamed the legend for more clarity.

#install.packages("ggplot2")
#install.packages("ggthemes")
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.2
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 3.5.2
library(datasets)
str(mpg)
## Classes 'tbl_df', 'tbl' and 'data.frame':    234 obs. of  11 variables:
##  $ manufacturer: chr  "audi" "audi" "audi" "audi" ...
##  $ model       : chr  "a4" "a4" "a4" "a4" ...
##  $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr  "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr  "f" "f" "f" "f" ...
##  $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr  "p" "p" "p" "p" ...
##  $ class       : chr  "compact" "compact" "compact" "compact" ...
g <- ggplot(mpg, aes(class, fill = trans)) + scale_fill_discrete(name = "Transmission")
g + geom_bar()

VIS 2 - This boxplot is also built using the mpg dataset. Notice the changes in axis labels, and an altered theme_XXXX

g1 <- ggplot(mpg, aes(manufacturer,hwy)) + labs(x= "Highway Fuel Efficiency(mile/gallon)", y= "Vehicle Manufacturer") + theme_classic() + coord_flip()
g1 + geom_boxplot()

VIS 3 - This graphic is built with another dataset diamonds a dataset also built into the ggplot2 package. For this one I used an additional package called library(ggthemes) check it out to reproduce this view.

str(diamonds)
## Classes 'tbl_df', 'tbl' and 'data.frame':    53940 obs. of  10 variables:
##  $ carat  : num  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  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
ggplot(diamonds, aes(price, fill = cut, colour = cut)) + 
  theme_economist() + 
  labs (title = "Diamond Price Density ", x = "Diamond Price (USD)", y = "Density") + geom_density(alpha = 0.3, size = 0.6)

Vis 4 For this plot we are changing vis idioms to a scatter plot framework. Additionally, I am using ggplot2 package to fit a linear model to the data all within the plot framework. Three are edited labels and theme modifications as well.

ggplot(iris, aes(Sepal.Length, Petal.Length)) + geom_point() +
  geom_smooth(method = lm) + theme_minimal() + labs(title = "Relationship between Petal and Sepal Length", x = "Iris Sepal Length", y = "Iris Petal Length")

Vis 5 - Finally, in this vis I extend on the last example, by plotting the same data but using an additional channel to communicate species level differences. Again I fit a linear model to the data but this time one for each species, and add additional theme and labeling modicitations.

ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + 
  geom_point() +
  geom_smooth(method = lm, se = FALSE) + 
  theme_minimal() + 
  theme(text = element_text(family = "serif"),
  axis.ticks = element_line(color = "black"),
  legend.position = "bottom" ) +
 labs(title = "Relationship between Petal and Sepal Length", subtitle = "Species level comparison", x = "Iris Sepal Length", y = "Iris Petal Length")