Chapter 8 Reporting: hierarchical chart

Scenario. Upon reviewing the growth report you created, Lucy asked for a report that shows composition of Product Categories and Sub Categories based on certain filters, including Year, Country, Customer Gender, and Age Group.

Specifically, Lucy wants to see the report visualized using a hierarchical chart.

Load necessary libraries

library(tidyverse)
library(treemapify)
library(RColorBrewer)
library(knitr)
library(scales)

Load data for Lab7

my_data <- readRDS("./data/processing/data4week3.rds")

---

8.1 Explore the sales composition of bikes category for each age group. Which age group does the composition (rank of sales) differ than the rest?

ans_1 <- my_data %>%
    filter(`Product Category` == "Bikes") %>%
    group_by(`Age Group`, `Sub Category`) %>%
    summarise(Total = sum(Revenue)) %>%
    arrange(desc(Total)) %>% 
    ungroup() 

ans_1 %>%
  mutate_at(vars(Total), funs(scales::comma)) %>% 
  kable()

Age Group	Sub Category	Total
Adults	Road Bikes	15,723,543
Young Adults	Road Bikes	12,439,924
Adults	Mountain Bikes	10,534,548
Young Adults	Mountain Bikes	7,861,417
Youth	Road Bikes	5,140,111
Adults	Touring Bikes	4,078,076
Youth	Mountain Bikes	2,661,311
Young Adults	Touring Bikes	2,614,215
Youth	Touring Bikes	593,930
Seniors	Mountain Bikes	66,250
Seniors	Road Bikes	59,483
Seniors	Touring Bikes	9,326

Show answer on chart:

ggplot(ans_1, aes(area = Total, fill = `Sub Category`, group = `Age Group`, label = `Age Group` )) +
    geom_treemap(colour = "white", size = 2) +
    geom_treemap_text(colour = "black", place = "centre", grow = T, min.size = 3) +
    scale_fill_brewer(palette = "Set2", labels = paste(ans_1$`Sub Category`, '\t')) +
    theme(legend.position = "top",
          legend.title = element_blank(),
          legend.text = element_text(size = 14, face = "bold"))

Answer: Seniors (right upper corner)

---

Now explore the sales composition of Bikes category for each Age Group for the Male customers.

ans_2 <- my_data %>%
    filter(`Product Category` == "Bikes", `Customer Gender` == "M") %>%
    group_by(`Age Group`, `Sub Category`) %>%
    summarise(Total = sum(Revenue)) %>%
    arrange(desc(Total)) %>% 
    ungroup()

ans_2 %>% 
  mutate_at(vars(Total), funs(scales::comma)) %>% 
  kable()

Age Group	Sub Category	Total
Adults	Road Bikes	7,565,634
Young Adults	Road Bikes	6,393,719
Adults	Mountain Bikes	5,260,117
Young Adults	Mountain Bikes	3,660,311
Youth	Road Bikes	2,787,992
Adults	Touring Bikes	2,028,869
Youth	Mountain Bikes	1,406,305
Young Adults	Touring Bikes	1,276,009
Youth	Touring Bikes	330,496
Seniors	Road Bikes	44,154
Seniors	Mountain Bikes	41,447
Seniors	Touring Bikes	4,563

---

8.2 Now explore the sales composition of bikes category for each age group, for the Male customers. Which age group does the composition differ than the rest?

ggplot(ans_2, aes(area = Total, fill = `Sub Category`, group = `Sub Category`, label = `Age Group` )) +
    geom_treemap(colour = "white", size = 2) +
    geom_treemap_text(colour = "black", place = "centre", grow = T, min.size = 3) +
    scale_fill_brewer(palette = "Set2", labels = paste(ans_2$`Sub Category`, '\t')) +
    theme(legend.position = "top",
          legend.title = element_blank(),
          legend.text = element_text(size = 14, face = "bold"))

Answer 2: The composition are the same across Age Group for Male customers.

---

8.3 Clear all filters. Now, filter for the year 2016 and Germany. Rank the sales from the highest to lowest for the clothing category

ans_3 <- my_data %>%
    filter(Year == 2016, Country == "Germany", `Product Category` == "Clothing") %>%
    group_by(`Sub Category`) %>%
    summarise(Total = sum(Revenue)) %>%
    arrange(desc(Total)) %>% 
    ungroup()

ans_3 %>% 
  mutate_at(vars(Total), funs(scales::comma)) %>% 
  kable()

Sub Category	Total
Jerseys	121,186
Vests	25,190
Caps	20,441
Gloves	17,337
Shorts	6,066
Socks	3,646

Show answer on chart:

ggplot(ans_3, aes(x = reorder(`Sub Category`, -Total), y = Total, fill = `Sub Category`)) +
    geom_bar(stat = "identity", width = 0.5, show.legend = FALSE) +
    labs(x = "Sub Category") +
    scale_y_log10(breaks = c(1, 1e2, 1e3, 1e4, 2e4, 1e5),
                  expand = c(0.02, 0.0), labels = scales::dollar) +
    theme_classic() +
    theme(axis.title = element_text(size = 12, face = "bold"),
          axis.text = element_text(size = 11, colour = "black"))

---

8.4 Rank the sales from the highest to lowest for the clothing category. Keep the filter settings and add filter by male customers.

ans_4 <- my_data %>%
    filter(Year == 2016, Country == "Germany",
           `Product Category` == "Clothing", `Customer Gender` == "M") %>%
    group_by(`Sub Category`) %>%
    summarise(Total = sum(Revenue)) %>%
    arrange(desc(Total)) %>% 
    ungroup()

ans_4 %>% 
  mutate_at(vars(Total), funs(scales::comma)) %>% 
  kable()

Sub Category	Total
Jerseys	64,969
Vests	13,528
Gloves	11,511
Caps	9,776
Shorts	4,568
Socks	2,304

Show answer on chart:

ggplot(ans_4, aes(x = reorder(`Sub Category`, -Total), y = Total, fill = `Sub Category`)) +
    geom_bar(stat = "identity", width = 0.5, show.legend = FALSE) +
    labs(x = "Sub Category") +
    scale_y_log10(breaks = c(1, 10, 100, 1e3, 5e3, 15e3, 5e4),
                  expand = c(0.02, 0.0), labels = scales::dollar) +
    theme_classic() +
    theme(axis.title = element_text(size = 12, face = "bold"),
          axis.text = element_text(size = 11, colour = "black"))

---

8.5 Rank the sales from the highest to lowest for the clothing category. Keep the filter settings and add filter by youth age group

ans_5 <- my_data %>%
    filter(Year == 2016, Country == "Germany", `Product Category` == "Clothing",
           `Customer Gender` == "M", `Age Group` == "Youth") %>%
    group_by(`Sub Category`) %>%
    summarise(Total = sum(Revenue)) %>%
    arrange(desc(Total)) %>% 
    ungroup()

ans_5 %>% 
  mutate_at(vars(Total), funs(scales::comma)) %>% 
  kable()

Sub Category	Total
Jerseys	5,723
Gloves	3,208
Caps	1,119
Vests	536
Socks	219
Shorts	57

Show answer on chart:

ggplot(ans_5, aes(x = reorder(`Sub Category`, -Total), y = Total, fill = `Sub Category`)) +
    geom_bar(stat = "identity", width = 0.5, show.legend = FALSE) +
    labs(x = "Sub Category") +
    scale_y_log10(breaks = c(1, 10, 100, 1000, 5000),
                  expand = c(0.02, 0.0), labels = scales::dollar) +
    theme_classic() +
    theme(axis.title = element_text(size = 12, face = "bold"),
          axis.text = element_text(size = 11, colour = "black")
          )