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")

2. Change the month to a numerical value by using as.numeric:

df_fb$Month <- as.numeric(df_fb$Month)

3. Now, use ggplot to make a plot of closing prices versus months.
4. Plot the data using geom_point (color=red).
5. Change the x scale to show each month, and label the x-axis, such that each month is shown.
6. Title your plot Monthly closing stock prices: Facebook.
7. 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))+
    ggtitle("Monthly Closing Stock Prices: Facebook")+theme_classic()

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.

3. Use the ggplot options geom_polygon and coord_map to create the map.
4. 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")
head(us_data)

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")]

2. 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

3. Make a scatterplot for the preceding pairs for grade A, then fit a linear regression model.
4. Determine what are the correlations of the preceding pairs.

Outcome:

Answer to step 4: The correlations are as follows:

1. 93%
2. 85%