Hands-on_Ex09: Information Dashboard Design using R

Author

Li Ziyi

Published

March 3, 2023

Modified

March 18, 2023

1 Packages installation

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

Take note that - gtExtras provides some additional helper functions to assist in creating beautiful tables with gt, an R package specially designed for anyone to make wonderful-looking tables using the R programming language.

reactablefmtr provides various features to streamline and enhance the styling of interactive reactable tables with easy-to-use and highly-customizable functions and themes.

2 Data

2.1 Dataset

For this exercise, a personal database in Microsoft Access mdb format called Coffee Chain will be used.

To import that, odbcConnectAccess() from RODBC package will be used.

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

2.2 Data preparation

This step below is used if coffeechain is already available in R.

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

Sales and Budgeted Sales data at Product level is aggregated below.

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

3 Bullet chart in ggplot2

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",
                linewidth = 1) +
  coord_flip()

4 Sparklines in ggplot2

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)

Minimum, maximum, 25th & 75th percentiles are prepared below. End of the month sales are also prepared.

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)

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) +
  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())

5 Static information dashboard design using gt and gtExtras

5.1 Simeple bullet chart

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

5.2 Sparklines using gtExtras

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()

Take note that one of the requirements of gtExtras functions is that almost exclusively they require you to pass data.frame with list columns. In view of this, code chunk below will be used to convert the report data.frame into list columns.

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')

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

5.3 To add statistics elements

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

5.4 To combine the data frame

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)

5.5 Plot the updated data table

sales_data %>%
  gt() %>%
  gt_plt_sparkline('Monthly Sales')

Product	Min	Max	Average
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

5.6 To combine with bullet chart and sparklines

bullet <- coffeechain %>%
  filter(Date >= "2013-01-01") %>%
  group_by(`Product`) %>%
  summarise(`Target` = sum(`Budget Sales`),
            `Actual` = sum(`Sales`)) %>%
  ungroup() 

sales_data = sales_data %>%
  left_join(bullet)

sales_data %>%
  gt() %>%
  gt_plt_sparkline('Monthly Sales') %>%
  gt_plt_bullet(column = Actual, 
                target = Target, 
                width = 28,
                palette = c("lightblue", 
                          "black")) %>%
  gt_theme_538()

Product	Min	Max	Average
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