Nguyen Viet Anh 110035101

library(esquisse)
library(dplyr)
library(ggplot2)
library(scales)
library(readxl)

bike_orderlines <- read_excel("bike_orderlines.xlsx")
bike_orderlines$year <- format(as.Date(bike_orderlines$order_date, format="%Y/%m/%d"),"%Y")

bikes <- bike_orderlines %>% 
         select(total_price,category_2, year) %>%
         group_by(year,category_2)%>%
         summarise(total_price) %>%
         group_by(year, category_2) %>%               summarise_at(vars(total_price),funs(sum(.,na.rm=TRUE)))

ggplot(bikes) +
  aes(x = year, fill = category_2, y = total_price) +
  scale_fill_brewer(palette = "Set7")+
  theme_bw() +
  theme(legend.position = "bottom") +
  labs(
    y = "Revenue",
    title = "Revenue by year",
    subtitle = "Upward trend"
  ) +
  facet_wrap(vars(category_2), scales = "free_y")+
  geom_col() +
  geom_smooth(aes(group=1),method="lm", se =FALSE)+
  scale_y_continuous(labels=scales::dollar_format())

bikes$numyear <- as.numeric(as.character(bikes$year))
ggplot(bikes) +
  aes(x = numyear, y = total_price, fill = category_2) +
  geom_area(color = "black") +
  scale_fill_brewer(palette = "white", direction = 1) +
  labs(
    y = "Revenue",
    x = "Year",
    title = "Sale Over Year by Category 2",
    subtitle = "Sale Trending Up",
    fill = "2nd category"
  ) +
  theme_bw() +
  theme(
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "bold"),
    axis.title.y = element_text(face = "bold")
  ) + scale_y_continuous(labels=scales::dollar_format())