#### Overview of this book

Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques. You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries. By the end of this book, you’ll have created data visualizations that will impress your clients.
Title Page
Packt Upsell
Preface
Free Chapter
Basic Plotting in ggplot2
Grammar of Graphics and Visual Components
Other Books You May Enjoy
Index

## Chapter 3:  Advanced Geoms and Statistics

The following are the activity solutions for this chapter.

### Activity: Using Density Plots to Compare Distributions

Steps for Completion:

1. Use the `RestaurantTips` dataset in `Lock5data`.
2. Compare the TIP amount for various days. Use `aes=color` for `geom_density` command.
3. Superimpose all of the plots.
4. Use the `scale_x_continuous` command for the x-axis tick marks.

### Activity: Plot the Monthly Closing Stock Prices and the Mean Values

Steps for Completion:

1. Use the `strftime` command to get the month from each date and make another variable (`Month`), as follows:
`df_fb\$Month <- strftime(df_fb\$Date,"%m")`
1. Change the month to a numerical value by using `as.numeric`:
`df_fb\$Month <- as.numeric(df_fb\$Month)`
1. Now, use ggplot to make a plot of closing prices versus months.
2. Plot the data using `geom_point (color=red)`.
3. Change the x scale to show each month, and label the x-axis, such that each month is shown.
5. Use `geom_line(stat='summary',fun.y=mean)` to plot the mean.

Outcome:

The complete code is shown as follows:

```ggplot(df_fb, aes(Month,Close)) + geom_point(color="red",alpha=1/2,position = position_jitter(h=0.0,w=0.0
))+
geom_line(stat='summary',fun.y=mean, color="blue",size=1)+
scale_x_continuous(breaks=seq(0,13,1))+
```

### Activity: Creating a Variable-Encoded Regional Map

Steps for Completion:

1. Merge the `USStates` data with `states_map`.
2. Before merging, change the `states` variable in `USStates` to the same format used in `states_map`.
1. Use the ggplot options `geom_polygon` and `coord_map` to create the map.
2. For aesthetics, run the following code and specify `x=long`, `y=lat``group=group`, and `fill=ObamaVote`.

Outcome:

The complete code is shown as follows:

```USStates\$Statelower <- as.character(tolower(USStates\$State))
glimpse(USStates)
us_data <- merge(USStates,states_map,by.x="Statelower",by.y="region")

### Activity: Studying Correlated Variables

Steps for Completion:

1. Make a subset of the `loan` dataset by using some of the following variables:
```df3_1 <- df3[,c("funded_amnt","annual_inc","dti","inq_last_6mths",
"total_acc","total_pymnt_inv")]```
1. Use `cor` for the preceding `loan` data subset, and then choose two highly correlated variables in the `loan` dataset. Use pairs, as follows:
```total_rec_prncp and total_pymnt_int
funded_amnt,total_pymnt_inv```
1. Make a scatterplot for the preceding pairs for grade A, then fit a linear regression model.
2. Determine what are the correlations of the preceding pairs.

Outcome:

Answer to step 4: The correlations are as follows:

1. 93%
2. 85%