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install.packages("plotly")
## Installing package into 'C:/Users/tresa/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)
## package 'plotly' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\tresa\AppData\Local\Temp\RtmpmwO2ux\downloaded_packages
library(plotly) #To load the plotly library for interactive visualizations
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
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##     layout
library(ggplot2) #To load the ggplot library

ply <- ggplot(mpg, aes(x = displ, y = cty, color = class, size = cyl)) + #Assigning the ggplot output
  geom_point(alpha=0.5) +
  facet_wrap(~year) +
  labs(title = "City Fuel Economy by Car Type & Engine Size",
       x = "Engine Displacement in Litres",
       y = "City Fuel Economy (mpg)",
       color = "Car Class",
       size = "Number of Cylinders") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(text = element_text(size = 12, family = "Comic Sans MS", color = "navy"), 
                                            #Defining the theme parameters to be used across the plots
        panel.background = element_rect(fill = "white"),  #The background is set to White
        panel.grid.major.y = element_line(color = "grey", linetype = "dashed"),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        strip.background = element_rect(fill = "white", color="grey"))

ply   #Calling the assigned ggplot output
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plotly1 <- ggplotly(ply)  #Transforming the ggplot output 'ply' with ggplotly to make the visualization interactive and assigning to another object 'plotly1'

plotly1  #Calling the assigned ggplotly for the final output

#Comment: The above ggplotly is an interactive visualization that shows more details for each point on the plot when hovered on top of the point, and with the ability to show specific groups of points by double-clicking on a particular car class from the list of legends.

# Creating the box and whisker plot for the two variables - class & cty and assigning to object 'ply2'
ply2 <- ggplot(mpg, aes(x = class, y = cty)) + #Defining data & mapping: x-axis as 'class' & y-axis, 'cty'
  geom_point(size=0.5) +              #To create the scatter plot
  geom_boxplot() +                    #To create the box plot 
    labs(title = "City Fuel Economy by Car Class", #For labeling of the chart: x, y-axes and title.
       x = "Car Class",
       y = "City Fuel Economy (mpg)") +
theme(axis.text.x = element_text(colour="grey40", size=12, angle=45, hjust=1, vjust=1))+
# Setting the fonts color & size, Rotating and Adjusting the x-axis labels for better readability
theme(text = element_text(size = 12, family = "Comic Sans MS", color = "navy"), 
                                            #Defining the theme parameters to be used across the plots
        panel.background = element_rect(fill = "white"),  #The background is set to white and no grids
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        strip.background = element_rect(fill = "white", color="grey"))

ply2    #Calling the assigned ggplot output
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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plotly2 <- ggplotly(ply2) #Transforming the ggplot output 'ply' with ggplotly to make the visualization interactive and assigning to another object 'plotly1'

plotly2    #Calling the assigned ggplotly for the final output

#Comment: The above ggplotly is an interactive visualization that shows more details for each box plot when hovered on top of it. We can reveal details such as Min, Max, Median, etc.

library(gapminder)

# Filtering the data for five specific countries of choice using the pipe operator
ply3 <-  gapminder %>%             #Selection of the data of reference and then piped through a filter
    filter (country %in% c("United States", "China", "India", "Brazil", "Germany")) %>% #Filtering                                                          for selected countries of choice in data frame
  ggplot(aes(x=gdpPercap, y=lifeExp, color=country)) + #Defining the mapping values
  geom_point() +                                      #Plotting the scatter plot for the countries
  geom_line() +   #Adding a line to join the plotted points
  scale_x_log10() +                                 # Log-transforming the scale of GDP per capita
  labs(title="GDP per Capita vs Life Expectancy", #Adding the x-,y- and title labels
       x="GDP per Capita (log scale)",
       y="Life Expectancy",
       color="Country") +
  theme(text = element_text(size = 12, family = "Comic Sans MS", color = "navy"), 
                                            #Defining the theme parameters to be used across the plots
        panel.background = element_rect(fill = "white"),  #The background is set to white and no grids
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        strip.background = element_rect(fill = "white", color="grey"))

ply3   #Calling the assigned ggplot output
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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plotly3 <- ggplotly(ply3) #Transforming the ggplot output 'ply' with ggplotly to make the visualization interactive and assigning to another object 'plotly1'

plotly3 #Calling the assigned ggplotly for the final output

#Comment: The above ggplotly is an interactive visualization that shows more details for each point on the plot when hovered on top of the point, and with the ability to show data for specific country by double-clicking on a particular country from the list of legends.

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.