Instructions
Use this template for the in-class activity. Create a graph to addresses each of the following insights from the global superstore data set. Where applicable, your graph and/or description should clearly explain any relevant definitions. For example, if you are discussing the “most important” market, then you should explain how you are measuring “most important”. For each graph, include a 1-2 sentence description with a deeper analysis of the main insight of the graph. * Insight #1:** The most profitable sub-categories are in Technology.
Create a graph to describe the most profitable subcategories and fill the color by category. The graph should look like this:

Your description here

# INSERT CODE HERE

Insight #2: There is seasonality in our global sales.

Your description here

View(dat)
help(lubridate)
## starting httpd help server ... done
library(zoo)
## Warning: package 'zoo' was built under R version 3.4.2
## 
## Attaching package: 'zoo'
## 
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
dat1 <- dat %>%
  mutate(Date2 = as.yearqtr(as.Date(dat$`Order Date`, format = "%m/%d/%Y"))) %>%
  group_by(Date2, Market) %>%
  summarise(Sales2 = sum(Sales))

dat1 %>%
  ggplot(aes(x = Date2, y = Sales2, color = Market)) +
  geom_line() +
  theme_minimal() ##Need scales::comma to change y-axis from scientific notation
## Don't know how to automatically pick scale for object of type yearqtr. Defaulting to continuous.

* Insight #3:** The most important market is…

Your description here. Remember to explain the measure of importance and narrate your graph.

# INSERT CODE HERE

Insight #4: (Find your own)

Your description here

# INSERT CODE HERE