Hands-on 10

pacman::p_load(lubridate, ggthemes, reactable, reactablefmtr, gt, gtExtras, tidyverse, svglite)

library(RODBC)
con <- odbcConnectAccess2007('../data/Coffee Chain.mdb')
coffeechain <- sqlFetch(con, 'CoffeeChain Query')
write_rds(coffeechain, "../data/CoffeeChain.rds")
odbcClose(con)

coffeechain <- read_rds("../data/rds/CoffeeChain.rds")

product <- coffeechain %>%
  group_by(`Product`) %>%
  summarise(`target` = sum(`Budget Sales`),
            `current` = sum(`Sales`)) %>%
  ungroup()

ggplot(product, aes(Product, current)) + 
  geom_col(aes(Product, max(target) * 1.01),
           fill="grey85", width=0.85) +
  geom_col(aes(Product, target * 0.75),
           fill="grey60", width=0.85) +
  geom_col(aes(Product, target * 0.5),
           fill="grey50", width=0.85) +
  geom_col(aes(Product, current), 
           width=0.35,
           fill = "black") + 
  geom_errorbar(aes(y = target,
                    x = Product, 
                    ymin = target,
                    ymax= target), 
                width = .4,
                colour = "red",
                size = 1) +
  coord_flip()

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

sales_report <- coffeechain %>%
  filter(Date >= "2013-01-01") %>%
  mutate(Month = month(Date)) %>%
  group_by(Month, Product) %>%
  summarise(Sales = sum(Sales)) %>%
  ungroup() %>%
  select(Month, Product, Sales)

`summarise()` has grouped output by 'Month'. You can override using the
`.groups` argument.

mins <- group_by(sales_report, Product) %>% 
  slice(which.min(Sales))
maxs <- group_by(sales_report, Product) %>% 
  slice(which.max(Sales))
ends <- group_by(sales_report, Product) %>% 
  filter(Month == max(Month))

quarts <- sales_report %>%
  group_by(Product) %>%
  summarise(quart1 = quantile(Sales, 
                              0.25),
            quart2 = quantile(Sales, 
                              0.75)) %>%
  right_join(sales_report)

Joining with `by = join_by(Product)`

ggplot(sales_report, aes(x=Month, y=Sales)) + 
  facet_grid(Product ~ ., scales = "free_y") + 
  geom_ribbon(data = quarts, aes(ymin = quart1, max = quart2), 
              fill = 'grey90') +
  geom_line(size=0.3) +
  geom_point(data = mins, col = 'red') +
  geom_point(data = maxs, col = 'blue') +
  geom_text(data = mins, aes(label = Sales), vjust = -1) +
  geom_text(data = maxs, aes(label = Sales), vjust = 2.5) +
  geom_text(data = ends, aes(label = Sales), hjust = 0, nudge_x = 0.5) +
  geom_text(data = ends, aes(label = Product), hjust = 0, nudge_x = 1.0) +
  expand_limits(x = max(sales_report$Month) + 
                  (0.25 * (max(sales_report$Month) - min(sales_report$Month)))) +
  scale_x_continuous(breaks = seq(1, 12, 1)) +
  scale_y_continuous(expand = c(0.1, 0)) +
  theme_tufte(base_size = 3, base_family = "Helvetica") +
  theme(axis.title=element_blank(), axis.text.y = element_blank(), 
        axis.ticks = element_blank(), strip.text = element_blank())

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

product %>%
  gt::gt() %>%
  gt_plt_bullet(column = current, 
              target = target, 
              width = 60,
              palette = c("lightblue", 
                          "black")) %>%
  gt_theme_538()

Product	current
Amaretto
Caffe Latte
Caffe Mocha
Chamomile
Colombian
Darjeeling
Decaf Espresso
Decaf Irish Cream
Earl Grey
Green Tea
Lemon
Mint
Regular Espresso

report <- coffeechain %>%
  mutate(Year = year(Date)) %>%
  filter(Year == "2013") %>%
  mutate (Month = month(Date, 
                        label = TRUE, 
                        abbr = TRUE)) %>%
  group_by(Product, Month) %>%
  summarise(Sales = sum(Sales)) %>%
  ungroup()

`summarise()` has grouped output by 'Product'. You can override using the
`.groups` argument.

report %>%
  group_by(Product) %>%
  summarize('Monthly Sales' = list(Sales), 
            .groups = "drop")

# A tibble: 13 × 2
   Product           `Monthly Sales`
   <chr>             <list>         
 1 Amaretto          <dbl [12]>     
 2 Caffe Latte       <dbl [12]>     
 3 Caffe Mocha       <dbl [12]>     
 4 Chamomile         <dbl [12]>     
 5 Colombian         <dbl [12]>     
 6 Darjeeling        <dbl [12]>     
 7 Decaf Espresso    <dbl [12]>     
 8 Decaf Irish Cream <dbl [12]>     
 9 Earl Grey         <dbl [12]>     
10 Green Tea         <dbl [12]>     
11 Lemon             <dbl [12]>     
12 Mint              <dbl [12]>     
13 Regular Espresso  <dbl [12]>     

report %>%
  group_by(Product) %>%
  summarize('Monthly Sales' = list(Sales), 
            .groups = "drop") %>%
   gt() %>%
   gt_plt_sparkline('Monthly Sales',
                    same_limit = FALSE)

Product	Monthly Sales
Amaretto
Caffe Latte
Caffe Mocha
Chamomile
Colombian
Darjeeling
Decaf Espresso
Decaf Irish Cream
Earl Grey
Green Tea
Lemon
Mint
Regular Espresso

report %>% 
  group_by(Product) %>% 
  summarise("Min" = min(Sales, na.rm = T),
            "Max" = max(Sales, na.rm = T),
            "Average" = mean(Sales, na.rm = T)
            ) %>%
  gt() %>%
  fmt_number(columns = 4,
    decimals = 2)

Product	Min	Max	Average
Amaretto	1016	1210	1,119.00
Caffe Latte	1398	1653	1,528.33
Caffe Mocha	3322	3828	3,613.92
Chamomile	2967	3395	3,217.42
Colombian	5132	5961	5,457.25
Darjeeling	2926	3281	3,112.67
Decaf Espresso	3181	3493	3,326.83
Decaf Irish Cream	2463	2901	2,648.25
Earl Grey	2730	3005	2,841.83
Green Tea	1339	1476	1,398.75
Lemon	3851	4418	4,080.83
Mint	1388	1669	1,519.17
Regular Espresso	890	1218	1,023.42

spark <- report %>%
  group_by(Product) %>%
  summarize('Monthly Sales' = list(Sales), 
            .groups = "drop")

sales <- report %>% 
  group_by(Product) %>% 
  summarise("Min" = min(Sales, na.rm = T),
            "Max" = max(Sales, na.rm = T),
            "Average" = mean(Sales, na.rm = T)
            )

sales_data = left_join(sales, spark)

Joining with `by = join_by(Product)`

sales_data %>%
  gt() %>%
  gt_plt_sparkline('Monthly Sales',
                   same_limit = FALSE)

Product	Min	Max	Average	Monthly Sales
Amaretto	1016	1210	1119.000
Caffe Latte	1398	1653	1528.333
Caffe Mocha	3322	3828	3613.917
Chamomile	2967	3395	3217.417
Colombian	5132	5961	5457.250
Darjeeling	2926	3281	3112.667
Decaf Espresso	3181	3493	3326.833
Decaf Irish Cream	2463	2901	2648.250
Earl Grey	2730	3005	2841.833
Green Tea	1339	1476	1398.750
Lemon	3851	4418	4080.833
Mint	1388	1669	1519.167
Regular Espresso	890	1218	1023.417

Reflection:

Connect to database and display data in report format