midtermR

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summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

library(scales)
library(tidyverse)
library(dbplyr)
library(esquisse)
library(readxl)
bikex <- read_excel("bikex.xlsx")
bikex$year <- format(as.Date(bikex$order_date, format="%Y/%m/%d"),"%Y")
bikes<-bikex%>%select(total_price,category_2,year)
bikey<-bikes%>%group_by(year,category_2)
biebie<-bikey%>%summarise(total_price)
byebye<-biebie%>%group_by(year,category_2)%>%summarise_at(vars(total_price),funs(sum(.,na.rm=TRUE)))


ggplot(byebye) +
  aes(x = year, fill = category_2, y = total_price) +
  scale_fill_hue(direction = 1) +
  labs(
    x = "Year",
    y = "Revenue",
    title = "Revenue by Year",
    subtitle = "Upward Trend"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  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())

byebye$numyear<-as.numeric(as.character(byebye$year))
byebye$category_2<-fct_reorder(byebye$category_2,byebye$total_price)
ggplot(byebye) +
  aes(x = numyear, y = total_price, fill = category_2 ) +
  geom_area(size = 1.5,position = position_stack(reverse = T)) +
  scale_fill_hue(direction = 1) +
  labs(
    x = "Year",
    y = "Revenue",
    title = "Sales over year by Category_2",
    subtitle = "Sales Trending up"
  ) +
  theme_minimal()+scale_y_continuous(labels = scales::dollar_format())+
  guides(fill=guide_legend(reverse = T))

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.