Scroll down for the fun bits!

Data Cleaning

Putting all the boring deleting of personally identifiable data and cleaning in here.

raw <- read_csv("Lizard - A Slate Pro-202404140002.csv", show_col_types = FALSE)


#Fixing the date_time
raw <- raw %>% 
  mutate(`Booking Date`= parse_date_time(`Booking Date`, c("%d/%m/%Y %I:%M:%S %p"))) %>%
  mutate(`Modified Date` = parse_date_time(`Modified Date`, c("%d/%m/%Y %I:%M:%S %p")))

#Fixing attendee type
raw <- raw %>%
  mutate(`Attendee Type` = factor(`Attendee Type`)) %>%
  mutate(`Attendee Type` = fct_recode(`Attendee Type`, 
             Student = "NUS Student", 
             Public = "I am a Member of the Public",
             Staff = "NUS Staff"))

#Adding a "Pax" row
raw <- raw %>%
  mutate(Pax = `Line Number`*Quantity)

#Adding a "Total Cost" rowx
raw <- raw %>%
  mutate(`Total Cost` = `Unit Amount`*Quantity)

#Removing tickets of quantity 0
raw <- raw %>% filter(Quantity>0)

#Removing Personal Identifiers
receipt <- raw %>% select(`Booking Date`, `Modified Date`, `Attendee Type`, `Line Number`, `Unit Amount`, `Unit Tax`, Quantity, Pax, `Total Cost`)

str(receipt)

Important Numbers

Total Seats Bought

total_seats_bought <- sum(receipt$Pax)

There have been 136 out 240 seats bought. That’s 56.67%

Total Money Made

Will we be able to break even? Probably not. But is it fun to see? Kinda.

total_money <- sum(receipt$`Unit Amount`*receipt$Quantity)
after_gst <- sum(receipt$`Unit Amount`*receipt$Quantity - receipt$`Unit Tax`*receipt$Quantity)

We’ve received $1722, and made $1579.81 after GST. Wait how much money do we have to make back again?

Types of Seats

type_of_seats <- receipt %>% group_by(`Line Number`) %>% summarise(`Total Tix Bought` = sum(Quantity))
type_of_seats <- type_of_seats %>% rename(`Seat Type` = `Line Number`)
type_of_seats
## # A tibble: 2 × 2
##   `Seat Type` `Total Tix Bought`
##         <dbl>              <dbl>
## 1           1                 30
## 2           2                 53

30 out of 70 single seat tix have been bought. That’s 42.86%. 53 out of 110 double seat tix have been bought. That’s 48.18%.

Demographics

Who’s buying the tickets? Whether it be members of the public, or NUS Students, or Staff.

people <- receipt %>% group_by(`Attendee Type`) %>% summarise(`Total No. of People` = sum(Pax)) %>% arrange(desc(`Total No. of People`))
people
## # A tibble: 3 × 2
##   `Attendee Type` `Total No. of People`
##   <fct>                           <dbl>
## 1 Student                            91
## 2 Public                             35
## 3 Staff                              10

GRAPH!

day_tix <- receipt %>% mutate (day = date(`Booking Date`)) %>% 
  group_by(day, `Attendee Type`) %>% 
  summarise(total = sum(Pax)) %>% 
  complete(`Attendee Type`, fill = list(total = 0)) %>% 
  group_by(`Attendee Type`) %>% 
  mutate(cum = cumsum(total)) %>%
  pivot_wider(names_from = `Attendee Type`, values_from = c(total, cum)) %>%
  mutate (total_Total = total_Public+total_Staff+total_Student) %>%
  mutate (cum_Total = cum_Public+cum_Staff+cum_Student) %>%
  pivot_longer(cols = c(grep("cum_|total_", names(.), value = TRUE)),
               names_to = c(".value", "Attendee Type"),
               names_sep = "_") 
## `summarise()` has grouped output by 'day'. You can override using the `.groups`
## argument.
day_tix |>
  ggplot(mapping = aes(x = day, y = cum, color = `Attendee Type`)) +
  geom_point() +
  geom_line() +
  theme_minimal() + 
  geom_text_repel(aes(label = cum), size = 4)+
  labs(x = "Date", y = "Cumulative Tickets Bought")

Cheeky Stats

Things I will add: