library(tidyverse)
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## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
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library(lubridate)
library(scales)
##
## Attaching package: 'scales'
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## discard
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## col_factor
library(tidyquant)
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## method from
## as.zoo.data.frame zoo
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# Load libraries
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(RColorBrewer)
library(zoo)
# Libraries
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(RColorBrewer)
library(zoo)
bike_orderlines_wrangled <- read_csv("/Users/macstore/Downloads/bike_orderlines.csv")
## Rows: 15644 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): model, category_1, category_2, frame_material, bikeshop_name, city...
## dbl (5): order_id, order_line, quantity, price, total_price
## dttm (1): order_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(zoo) # for as.yearqtr
quarterly_sales <- bike_orderlines_wrangled %>%
mutate(year_quarter = as.yearqtr(order_date)) %>%
group_by(year_quarter) %>%
summarise(total_sales = sum(total_price), .groups = "drop")
ggplot(quarterly_sales, aes(year_quarter, total_sales)) +
geom_line(color = "#2C6BED", linewidth = 1.2) +
geom_point(color = "#2C6BED", size = 2) +
scale_y_continuous(labels = dollar_format()) +
labs(
title = "Total Sales",
subtitle = "Quarterly Sales Trends",
x = NULL,
y = "Revenue (USD)"
) +
theme_tq() +
theme(
plot.title = element_text(face = "bold"),
axis.title.y = element_text(size = 10)
)
monthly_sales <- bike_orderlines_wrangled %>%
mutate(year_month = floor_date(order_date, "month")) %>%
group_by(year_month) %>%
summarise(total_sales = sum(total_price), .groups = "drop")
ggplot(monthly_sales, aes(year_month, total_sales)) +
geom_point(color = "black", size = 1.8, alpha = 0.8) +
geom_smooth(
method = "loess",
color = "#2C6BED",
fill = "grey70",
linewidth = 1.2,
se = TRUE
) +
scale_y_continuous(labels = dollar_format()) +
labs(
title = "Total Sales",
subtitle = "Monthly Sales Trends",
x = NULL,
y = "Revenue (USD)"
) +
theme_tq() +
theme(
plot.title = element_text(face = "bold"),
axis.title.y = element_text(size = 10)
)
## `geom_smooth()` using formula = 'y ~ x'
quarterly_category_sales <- bike_orderlines_wrangled %>%
filter(category_1 == "Road") %>%
mutate(year_quarter = as.yearqtr(order_date)) %>%
group_by(year_quarter, category_2) %>%
summarise(total_sales = sum(total_price), .groups = "drop")
quarterly_category_sales %>%
filter(category_2 == "Elite Road") %>%
ggplot(aes(year_quarter, total_sales)) +
geom_line(color = "#2c3e50", linewidth = 1.2) +
geom_point(color = "#2c3e50", size = 2) +
scale_y_continuous(labels = dollar_format()) +
labs(x = NULL, y = NULL) +
theme_tq()
quarterly_category_sales %>%
filter(category_2 == "Endurance Road") %>%
ggplot(aes(year_quarter, total_sales)) +
geom_line(color = "red", linewidth = 1.2) +
geom_point(color = "red", size = 2) +
scale_y_continuous(labels = dollar_format()) +
labs(x = NULL, y = NULL) +
theme_tq()
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(zoo)
library(RColorBrewer)
# Filter for Road bikes and aggregate by quarter and category_2
road_quarterly <- bike_orderlines_wrangled %>%
filter(category_1 == "Road") %>%
mutate(year_quarter = as.yearqtr(order_date)) %>%
group_by(year_quarter, category_2) %>%
summarise(revenue = sum(total_price), .groups = "drop") %>%
arrange(year_quarter)
# Define categories order (FIXED NAMES)
road_quarterly <- road_quarterly %>%
mutate(
category_2 = factor(
category_2,
levels = c(
"Elite Road",
"Endurance Road",
"Road Triathlon",
"Cyclocross"
)
)
)
# Create color palette
road_colors <- brewer.pal(4, "Blues")
# Plot with facets (matches PDF layout)
ggplot(road_quarterly, aes(x = year_quarter, y = revenue)) +
geom_line(color = "#2c3e50", linewidth = 1.2) +
geom_point(color = "#2c3e50", size = 2) +
facet_wrap(~ category_2, ncol = 1, scales = "free_y") +
scale_y_continuous(labels = dollar_format(), limits = c(0, NA)) +
labs(title = "Sales By Category 2", x = "", y = "") +
theme_tq() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
strip.text = element_text(face = "bold"),
legend.position = "none"
)
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(RColorBrewer)
# Filter for Road bikes and aggregate by month
road_monthly <- bike_orderlines_wrangled %>%
filter(category_1 == "Road") %>%
mutate(year_month = floor_date(order_date, "month")) %>%
group_by(year_month, category_2) %>%
summarise(revenue = sum(total_price), .groups = "drop") %>%
arrange(year_month)
# Factor categories (FIXED spelling)
road_monthly <- road_monthly %>%
mutate(
category_2 = factor(
category_2,
levels = c(
"Elite Road",
"Endurance Road",
"Road Triathlon",
"Cyclocross"
)
)
)
# Create color palette
road_colors <- brewer.pal(4, "Blues")
ggplot(road_monthly, aes(x = year_month, y = revenue)) +
geom_point(size = 1, alpha = 0.5, color = "#2c3e50") +
geom_smooth(
method = "loess",
se = TRUE,
fill = "#A8DADC",
alpha = 0.3,
linewidth = 1.2,
color = "#2c3e50"
) +
facet_wrap(~ category_2, ncol = 1, scales = "free_y") +
scale_y_continuous(labels = dollar_format(), limits = c(0, NA)) +
scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
labs(title = "Sales By Category 2", x = "", y = "") +
theme_tq() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
strip.text = element_text(face = "bold"),
legend.position = "none"
)
## `geom_smooth()` using formula = 'y ~ x'
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(RColorBrewer)
# Filter for Road bikes and aggregate by week
road_weekly <- bike_orderlines_wrangled %>%
filter(category_1 == "Road") %>%
mutate(year_week = floor_date(order_date, "week")) %>%
group_by(year_week, category_2) %>%
summarise(revenue = sum(total_price), .groups = "drop") %>%
arrange(year_week)
# Factor categories (FIXED spelling)
road_weekly <- road_weekly %>%
mutate(
category_2 = factor(
category_2,
levels = c(
"Elite Road",
"Endurance Road",
"Road Triathlon",
"Cyclocross"
)
)
)
# Create color palette (reuse if already created)
road_colors <- brewer.pal(4, "Blues")
# Plot with facets (weekly view)
ggplot(road_weekly, aes(x = year_week, y = revenue)) +
geom_point(size = 0.8, alpha = 0.4, color = "#2c3e50") +
geom_smooth(
method = "loess",
se = FALSE,
span = 0.15,
linewidth = 1.2,
color = "#2c3e50"
) +
facet_wrap(~ category_2, ncol = 1, scales = "free_y") +
scale_y_continuous(labels = dollar_format(), limits = c(0, NA)) +
scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
labs(title = "Sales By Category 2", x = "", y = "") +
theme_tq() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
strip.text = element_text(face = "bold"),
legend.position = "none"
)
## `geom_smooth()` using formula = 'y ~ x'
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(zoo)
library(RColorBrewer)
# Filter for Mountain bikes and aggregate by quarter
mountain_quarterly <- bike_orderlines_wrangled %>%
filter(category_1 == "Mountain") %>%
mutate(year_quarter = as.yearqtr(order_date)) %>%
group_by(year_quarter, category_2) %>%
summarise(revenue = sum(total_price), .groups = "drop") %>%
arrange(year_quarter)
# Define mountain categories (ordered)
mountain_quarterly <- mountain_quarterly %>%
mutate(
category_2 = factor(
category_2,
levels = c(
"Cross Country Race",
"Trail",
"Over Mountain",
"Sport",
"Fat Bike"
)
)
)
# Create color palette
mountain_colors <- brewer.pal(5, "Blues")
# Plot with facets
ggplot(mountain_quarterly, aes(x = year_quarter, y = revenue)) +
geom_line(color = "#2c3e50", linewidth = 1.2) +
geom_point(color = "#2c3e50", size = 2) +
facet_wrap(~ category_2, ncol = 1, scales = "free_y") +
scale_y_continuous(labels = dollar_format(), limits = c(0, NA)) +
labs(title = "Sales By Category 2", x = "", y = "") +
theme_tq() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
strip.text = element_text(face = "bold"),
legend.position = "none"
)
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(RColorBrewer)
# Filter for Mountain bikes and aggregate by month
mountain_monthly <- bike_orderlines_wrangled %>%
filter(category_1 == "Mountain") %>%
mutate(year_month = floor_date(order_date, "month")) %>%
group_by(year_month, category_2) %>%
summarise(revenue = sum(total_price), .groups = "drop") %>%
arrange(year_month)
# Factor categories (ordered)
mountain_monthly <- mountain_monthly %>%
mutate(
category_2 = factor(
category_2,
levels = c(
"Cross Country Race",
"Trail",
"Over Mountain",
"Sport",
"Fat Bike"
)
)
)
# Create color palette (reuse if already created)
mountain_colors <- brewer.pal(5, "Blues")
# Plot with facets (monthly view)
ggplot(mountain_monthly, aes(x = year_month, y = revenue)) +
geom_point(size = 1, alpha = 0.5, color = "#2c3e50") +
geom_smooth(
method = "loess",
se = TRUE,
fill = "#A8DADC",
alpha = 0.3,
linewidth = 1.2,
color = "#2c3e50"
) +
facet_wrap(~ category_2, ncol = 1, scales = "free_y") +
scale_y_continuous(labels = dollar_format(), limits = c(0, NA)) +
scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
labs(title = "Sales By Category 2", x = "", y = "") +
theme_tq() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
strip.text = element_text(face = "bold"),
legend.position = "none"
)
## `geom_smooth()` using formula = 'y ~ x'
library(tidyverse)
library(lubridate)
library(scales)
library(tidyquant)
library(RColorBrewer)
# Filter for Mountain bikes and aggregate by month
mountain_monthly <- bike_orderlines_wrangled %>%
filter(category_1 == "Mountain") %>%
mutate(year_month = floor_date(order_date, "month")) %>%
group_by(year_month, category_2) %>%
summarise(revenue = sum(total_price), .groups = "drop") %>%
arrange(year_month)
# Factor categories (ordered)
mountain_monthly <- mountain_monthly %>%
mutate(
category_2 = factor(
category_2,
levels = c(
"Cross Country Race",
"Trail",
"Over Mountain",
"Sport",
"Fat Bike"
)
)
)
# Create color palette (reuse if already created)
mountain_colors <- brewer.pal(5, "Blues")
# Plot with facets (monthly view)
ggplot(mountain_monthly, aes(x = year_month, y = revenue)) +
geom_point(size = 1, alpha = 0.5, color = "#2c3e50") +
geom_smooth(
method = "loess",
se = TRUE,
fill = "#A8DADC",
alpha = 0.3,
linewidth = 1.2,
color = "#2c3e50"
) +
facet_wrap(~ category_2, ncol = 1, scales = "free_y") +
scale_y_continuous(labels = dollar_format(), limits = c(0, NA)) +
scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
labs(title = "Sales By Category 2", x = "", y = "") +
theme_tq() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
strip.text = element_text(face = "bold"),
legend.position = "none"
)
## `geom_smooth()` using formula = 'y ~ x'