library(tidyverse)
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library(ggplot2)
library(gghighlight)
library(psych)
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library(stringr)
library(lubridate)
library(knitr)
library(kableExtra)
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## group_rows
sessionInfo()
## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22631)
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sales <- read.csv("orders (6).csv")
tickets <- read.csv("tickets.csv")
Top 10 cities by number of attendees
sales$Shipping.city <- tolower(sales$Shipping.city)
cities <- sales %>%
select(Number,
City = Shipping.city) %>%
mutate_if(is.character,str_trim) %>%
arrange(City) %>%
group_by(City) %>%
count()
colnames(cities)[2] <- "Total"
top10_cities <- cities[order(desc(cities$Total)),]
top10_cities$City_Se <- str_to_sentence(top10_cities$City)
top10_cities <- as.data.frame(head(top10_cities, 10)) %>%
select(City = City_Se,
Total)
ggplot() +
geom_col(top10_cities,
mapping = aes(x = reorder(City, -Total),
y = Total),
fill = "black") +
labs(x = "City",
y = "Tickets Sold",
title = "Top 10 attendee origen") +
scale_y_continuous(expand = c(0,0),
limits = c(0, 45)) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
Timeline of ticket sales
Timeline <- sales %>%
select(Date) %>%
group_by(Date) %>%
count()
colnames(Timeline)[2] <- "Total"
Timeline$date <- dmy(Timeline$Date)
highlight_max <- Timeline[which.max(Timeline$Total),]
highlight_max$xend <- as.Date("2023-11-20")
highlight_max$xend1 <- as.Date("2023-11-15")
highlight_max$yend <- 44
highlight_max$label <- "49 tickets sold on 17/11/23"
second_max <- arrange(Timeline, desc(Total))
second_max <- second_max[2,]
second_max$xend <- as.Date("2023-11-19")
second_max$xend1 <- as.Date("2023-11-26")
second_max$yend <- 15
second_max$label <- "23 tickets sold on 19/11/23"
ordered <- arrange(Timeline, desc(Total))
top10_days <- head(ordered, 10)
ggplot() +
geom_line(Timeline,
mapping = aes(x = date,
y = Total)) +
geom_point(top10_days,
mapping = (aes(x = date,
y = Total,
color = "Top 10 Days")),
color = "black",
size = 3) +
geom_segment(highlight_max,
mapping = aes(xend = xend,
yend = yend,
x = highlight_max$date,
y = highlight_max$Total),
colour = "green") +
geom_label(aes(label = highlight_max$label,
x = highlight_max$xend1,
y = highlight_max$yend),
hjust = 0,
vjust = 1,
fill = "green",
colour = "black",
alpha= 1) +
geom_segment(second_max,
mapping = aes(xend = xend1,
yend = yend,
x = date,
y = Total),
colour = "red") +
geom_label(aes(label = second_max$label,
x = second_max$xend1,
y = second_max$yend),
hjust = 0,
vjust = 1,
fill = "red",
colour = "black",
alpha= 1) +
labs(title = "Timeline of Sales",
x = "Date",
y = "Total Sales") +
scale_x_date(date_breaks = "1 month",
minor_breaks = "1 day",
date_labels = "%b %Y",
limits = c(as.Date("2023-11-15"), NA)) +
scale_y_continuous(expand = c(0, 0)) +
coord_cartesian(expand = FALSE,
ylim = c(0, 50)) +
theme_bw()
top_10_days <- top10_days %>%
select(Total) %>%
kbl() %>%
kable_paper(full_width = F,
position = "center")
## Adding missing grouping variables: `Date`
top_10_days
|
Date
|
Total
|
|
17/11/2023
|
49
|
|
19/11/2023
|
23
|
|
18/11/2023
|
18
|
|
16/01/2024
|
12
|
|
21/01/2024
|
10
|
|
26/01/2024
|
10
|
|
05/02/2024
|
8
|
|
17/01/2024
|
8
|
|
25/01/2024
|
8
|
|
18/01/2024
|
7
|
On the day Attendance and Drop
tickets1 <- tickets %>%
select(Bought = Item.count,
Attended = Attended) %>%
summarise(sum(Bought),
sum(Attended))
colnames(tickets1)[1] <- "Bought"
colnames(tickets1)[2] <- "Attended"
tickets1$AvgAttend <- round((tickets1$Attended/tickets1$Bought *100),2)
tickets1$Drop <- (100 - tickets1$AvgAttend)
Intake <- tickets1 %>%
select(Bought,
Attended,
Percentage_Atteded = AvgAttend,
Drop_Percentage = Drop) %>%
kbl() %>%
kable_paper(full_width = F,
position = "center")
Intake
|
Bought
|
Attended
|
Percentage_Atteded
|
Drop_Percentage
|
|
414
|
369
|
89.13
|
10.87
|