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Summer R Bootcamp

PS 3 Solutions

 

 

Problem 1 (Use Base R)

  1. Compare the average age of local population for the US and non-US stores by displaying their boxplots. Customize your boxplots as follows: add labels, change the colors, and add notches.
data1 <- read.csv(file = "carseats.csv", header = T, stringsAsFactors = T)

boxplot(data1$Age ~ data1$US,
        
        xlab = "Store Loc.",
        
        ylab = "Age",
        
        names = c("Non-US", "US"),
        
        main = "Age of Population for the US and non-US stores",
        
        col = c("lightblue", "lightcoral"),
        
        border = c("dodgerblue", "red"),
        
        notch = T)

 

  1. Summarize the quality of shelving locations by displaying its barplot. Customize it as discussed in the previous question ( no notches here).
barplot(table(data1$ShelveLoc),
        
        xlab = "Quality of Shelving Loc.",
        
        ylab = "Num. of Stores",
        
        main = "Quality of Shelving Loc. Count",
        
        col = "lightblue",
        
        border = "blue")

 

  1. Plot a side-by-side barplots displaying the frequency of ShelveLoc variable in urban and rural areas. Customize your plot as discussed above.
barplot(table(data1[, c("ShelveLoc", "Urban")]),
        
        beside = TRUE,
        
        legend.text = c("Bad", "Good", "Medium"),
        
        ylab = "Num. of Stores",
        
        xlab = "Store Location: Urban",
        
        main = "Qual. of Shelving Loc. vs Urban/Rural",
        
        col = c("lightblue", "lightcoral", "orange"))

 

  1. Plot the Sales variable against the Price variable. Label the plot and axes accordingly, change the shape of points to triangular, change the color of points based on whether the store is located in urban (orange color) or rural (green color) area and add the corresponding legend to the plot, change the size of points to 0.9.
colors <- c("green", "orange")

plot(x = data1$Price,
     
     y = data1$Sales,
     
     xlab = "Price",
     
     ylab = "Sales",
     
     main = "Sales vs Price",
     
     col = colors[data1$Urban],
     
     pch = 2,
     
     cex  = 0.9)


legend("topleft", legend = c("Rural", "Urban"), pch = 2, col = colors)

 

  1. Combine a histogram of the Price variable and a boxplot of the Population variable in one image. Customize both plots as discussed in part (1) (no notches for the histogram).
par(mfrow = c(1, 2))

hist(data1$Price,
     
     xlab = "Price",
     
     ylab = "Num. of Stores",
     
     main = "Histogram of Price",
     
     col = "pink",
     
     border = "violetred")


boxplot(data1$Population,
        
        xlab = "Population",
        
        ylab = "Values",
        
        main = "Boxplot of Population",
        
        col = "lightblue",
        
        border = "dodgerblue")

 

Problem 2 (Use ggplot2 Package)

  1. Plot a histogram of the Sale_Price variable: add labels (title, subtitle, x and y labels); adjust the limits of the x axis by adding new ticks to it (step size = 100,000) and changing the angle of ticks to 60; change the theme to minimal; change the colors of bins and bin outlines (it is up to you to pick colors); add a blue vertical line that represents the mean value of the variable.

data <- read.csv("ames_housing.csv", header = T, stringsAsFactors = TRUE)

library(tidyverse)
#> ── Attaching core tidyverse packages ──── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.0     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

ggplot(data = data) +
    
geom_histogram(aes(x = Sale_Price),
               
               fill="steelblue1",
               
               color="steelblue") +
  
  geom_vline(aes(xintercept = mean(Sale_Price)),
               
           color = "blue",
               
           size = 1) +

  labs(title = "Sale Price Histogram",
       
       subtitle = "From ames_housing dataset",
       
       x = "Sale Price",
       
       y = "Frequency") +
  
  scale_x_continuous(breaks = seq(0, 750000, 100000), limits = c(0, 750000)) +
  
  theme_minimal() +
  
  theme(axis.text.x=element_text(angle = 60))
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2
#> 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where
#> this warning was generated.
#> `stat_bin()` using `bins = 30`. Pick better value with
#> `binwidth`.
#> Warning: Removed 1 row containing non-finite outside the scale range
#> (`stat_bin()`).
#> Warning: Removed 2 rows containing missing values or values outside
#> the scale range (`geom_bar()`).

 

  1. Use a stacked barplot to visualize the relationship between these two categorical variables (make sure the bars have the same height); add labels to the plot (title, subtitle, x and y labels).
ggplot(data = data) +
  
  geom_bar(mapping = aes(x = Roof_Style, fill = Fence),
           
           position = "fill") +
  
  labs(title="Distribution of Fence for Different Roof Style",
      
      subtitle="From ames_housing dataset",
      
      x="Roof Style",
      
      y="Frequentist Probability")

 

  1. Plot a boxplot of the Sales_Price variable reflecting the North_Ames and College_Creek neighborhoods; add black dots to these boxplots that represent the average prices in these groups; make boxplots transparent (use transparency rate of 0.45).

data1 <- data %>% filter(Neighborhood %in% c("College_Creek", "North_Ames"))

ggplot(data = data1) +
  
geom_boxplot(aes(x = Neighborhood, y = Sale_Price), alpha = 0.45) +
  
stat_summary(aes(x = Neighborhood, y = Sale_Price),
               
             fun = "mean",
               
             geom = "point",
               
             color = "black")

 

  1. Add a new variable to the dataset (name it Rooms) that will split all homes in the dataset into the following three groups based on the total number of rooms above the ground: "2-4", "5-8", and "9 or more". In other words, you need to create a factor variable with 3 levels by converting the TotRms_AbvGrd variable. (Hint: you might want to check out the case_when() function from dpyr package).

    Plot a barplot of the Rooms variable: add labels to this barplot that display the exact frequency of each category. Plot another barplot of the Rooms variable with labels that display the frequency of each category as percentages. Now create a composition (assemble) of these plots (it is up to you to pick a layout). In other words, combine these barplots in one plot.

categorize <- function(x) {
  
  case_when(x<=4 & x>=2 ~ "2-4",
            
            x<=8 & x>=5 ~ "5-8",
            
            x>=9 ~ "9 or more") %>%
    
  as.factor()
  
}

data <- data %>%
  
  mutate(Rooms = categorize(TotRms_AbvGrd))
gg1 <- ggplot(data = data) +
  
  geom_bar(mapping = aes(x = Rooms), fill = "gray70") +
  
  geom_text(aes(x = Rooms, label = ..count..),
            
            stat = "count",
            
            colour = "black")

data2 <- data %>%
  
  count(Rooms) %>%
  
  mutate(perc = n / sum(n))

gg2 <- ggplot(data2, aes(x = Rooms, y = perc)) +
  
  geom_bar(stat = "identity", fill = "gray70") +
  ## add percentage labels
  
  geom_text(aes(label = scales::percent(perc))) +
  
  scale_y_continuous(labels = scales::percent) +
  
  labs(y = "Percentage")

library(patchwork)

gg1/gg2
#> Warning: The dot-dot notation (`..count..`) was deprecated in
#> ggplot2 3.4.0.
#> ℹ Please use `after_stat(count)` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where
#> this warning was generated.

 

  1. Plot the Sale_Price variable (Y) against the Gr_Liv_Area variable (X): add labels to the scatterplot (title, subtitle, caption, x and y labels) and change their colors; change the size and shape of datapoints; change the color of datapoints based on the Rooms variable; move the legends to the left.
  ggplot(data = data) + 
  
  geom_point(mapping = aes(x = Gr_Liv_Area, y = Sale_Price, color = Rooms),
               
             size = 0.8,
             
             shape = 22) +
  
  labs(x = "Above Ground Living Area",
       
       y = "Sale Price",
       
       title = "Sale Price - Above Ground Living Area Scatter Plot",
       
       subtitle = "From ames_housing data",
       
       caption = "The plot resembles a correlation between sale price and above ground living area.") +
  
  theme(plot.title=element_text(size=15, face="bold", color = "red"),
             
        axis.title.x=element_text(color = "blue"),
             
        axis.title.y=element_text(color = "blue"),
        
        legend.position = "left")

 

  1. Plot the Sale_Price variable (Y) against the Gr_Liv_Area variable (X) for each level of the Rooms variable separately, using facets.

ggplot(data = data) + 
    
  geom_point(mapping = aes(x = Gr_Liv_Area, y = Sale_Price),
             
             size = 0.8,
             
             shape = 22,
             
             color = "steelblue") +
    
  facet_wrap(~ Rooms)