Bar chart creation

Give shape to the data with dplyr

Load and prepare the data:

## Observations: 80
## Variables: 18
## $ ProductType           <chr> "PC", "PC", "PC", "Laptop", "Laptop", "A...
## $ ProductNum            <dbl> 101, 102, 103, 104, 105, 106, 107, 108, ...
## $ Price                 <dbl> 949.00, 2249.99, 399.00, 409.99, 1079.99...
## $ x5StarReviews         <dbl> 3, 2, 3, 49, 58, 83, 11, 33, 16, 10, 21,...
## $ x4StarReviews         <dbl> 3, 1, 0, 19, 31, 30, 3, 19, 9, 1, 2, 25,...
## $ x3StarReviews         <dbl> 2, 0, 0, 8, 11, 10, 0, 12, 2, 1, 2, 6, 5...
## $ x2StarReviews         <dbl> 0, 0, 0, 3, 7, 9, 0, 5, 0, 0, 4, 3, 0, 8...
## $ x1StarReviews         <dbl> 0, 0, 0, 9, 36, 40, 1, 9, 2, 0, 15, 3, 1...
## $ PositiveServiceReview <dbl> 2, 1, 1, 7, 7, 12, 3, 5, 2, 2, 2, 9, 2, ...
## $ NegativeServiceReview <dbl> 0, 0, 0, 8, 20, 5, 0, 3, 1, 0, 1, 2, 0, ...
## $ Recommendproduct      <dbl> 0.9, 0.9, 0.9, 0.8, 0.7, 0.3, 0.9, 0.7, ...
## $ BestSellersRank       <dbl> 1967, 4806, 12076, 109, 268, 64, NA, 2, ...
## $ ShippingWeight        <dbl> 25.80, 50.00, 17.40, 5.70, 7.00, 1.60, 7...
## $ ProductDepth          <dbl> 23.94, 35.00, 10.50, 15.00, 12.90, 5.80,...
## $ ProductWidth          <dbl> 6.62, 31.75, 8.30, 9.90, 0.30, 4.00, 10....
## $ ProductHeight         <dbl> 16.89, 19.00, 10.20, 1.30, 8.90, 1.00, 1...
## $ ProfitMargin          <dbl> 0.15, 0.25, 0.08, 0.08, 0.09, 0.05, 0.05...
## $ Volume                <dbl> 12, 8, 12, 196, 232, 332, 44, 132, 64, 4...

Using dplyr to prepare the data:

• Create the total volume, total number of products, mean profit percentage and total profits for each category:

ex_prod %>%
  mutate(Total_profit = Price*Volume*ProfitMargin) %>% 
  group_by(ProductType) %>%
  summarise(Total_Volume = sum(Volume), 
            Total_Profits = sum(Total_profit)) %>% 
  mutate(Total_Profits_Dol = paste(round((Total_Profits), 0),"$")) %>% 
  arrange(desc(Total_Profits)) -> categories_analysis 

kable(categories_analysis %>% select(-Total_Profits)) %>% 
  kable_styling(bootstrap_options = "striped", full_width = F, position = "center")

ProductType	Total_Volume	Total_Profits_Dol
ExtendedWarranty	9876	706083 $
Display	2428	268089 $
GameConsole	8720	223998 $
Smartphone	1808	81927 $
Tablet	948	73639 $
Software	4268	71774 $
Accessories	25548	54640 $
Printer	2036	51800 $
Laptop	516	39151 $
PC	116	11203 $
Netbook	92	3848 $
PrinterSupplies	44	452 $

Building the plot

1. Create the geometries

p1 <- categories_analysis %>%
  arrange(Total_Profits) %>%
   ggplot(aes(x = reorder(ProductType, Total_Profits))) +
    geom_col(aes(y = Total_Profits), fill = "gold") +
    geom_col(aes(y = -Total_Volume), fill = "dodgerblue3") +
    geom_label(aes(x = ProductType, y = Total_Profits, 
                   label = Total_Profits_Dol),
               hjust = 0, 
               vjust = 0.4, 
               colour = "goldenrod3", 
               fill = NA, 
               label.size = NA, 
               size = 3.5) +
    geom_label(aes(x = ProductType, y = -Total_Volume, 
                   label = round(Total_Volume, 0)),
               hjust = 1, 
               vjust = 0.4, 
               colour = "dodgerblue4", 
               fill = NA, 
               label.size = NA, 
               size = 3.5)
p1

2. Turn the plot

p2 <- p1 +
  coord_flip()
p2

3. Define the dimensions

p3 <- p2 +
  scale_y_continuous(limits = c(-80000, 780000), labels = NULL)
p3

4. Add titles and personalize the theme

# Create a graph to understand Blackwell's business
p4 <- p3 +
  labs(title = "Blackwell overview",
         subtitle = "Total Volume (items) vs Total Profits ($)") +
  theme_void()
p4

5. Place the titles and plot information

p5 <- p4 +
theme(axis.title.x = element_blank(), 
           axis.title.y = element_blank(), 
           axis.text.y = element_text())
p5