Leslie's DA Journey: Information Dashboard Design

Load Libraries

gt creates static tables.
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 react-able tables with easy-to-use and highly-customizable functions and themes.

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packages = c('tidyverse', 'lubridate', 'ggthemes', 'reactable', 
             'reactablefmtr', 'gt', 'gtExtras')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

Import Data

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coffeechain <- readRDS('data/CoffeeChain.rds')

Plot Bullet Chart using ggplot

The code chunk below is used to aggregate Sales and Budgeted Sales at the Product level.

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

The code chunk below is used to plot the bullet charts using ggplot2 functions.

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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()

Plot Sparklines using ggplot

The following code chunk prepares the data for sparklines plot.

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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)

The code chunk below is used to compute the minimum, maximum and end othe the month sales.

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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))

The code chunk below is used to compute the 25 and 75 quantiles.

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

The following code chunk is used to plot the sparklines.

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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())

Putting Together a Dashboard

The following code chunk plots a simple bullet chart.

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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

Before we can prepare the sales report by product by using gtExtras functions, code chunk below will be used to prepare the data.

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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()

It is important to note that one of the requirement 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.

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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]>

Plotting sales report in sparklines plot.

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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

Calculating the summary stats using the code chunk below:

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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

Below code chunk adds statistics into the table:

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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)

Update table:

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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

The following codes combines the bullet chart and the sparklines:

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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)

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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