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R CODING # 7. Create a line plot showing the monthly
# category (using different colored line for each category). Use the classic
# theme. Save the plot as seasplot. (You will need to add a command to the
# mapping of month so it treats the month variable as a numeric variable.)
seasplot <- fammonth %>%
ggplot(aes(as.numeric(month), percsales, color = family)) +
geom_line() +
theme_classic()+
labs(x = "month")# 8. Which product category showed the highest seasonality in May? Save the
# answer as highmay. (The highmay variable should be a simple character
# string with the product category name, in all caps like in the data.)
highmay <- "GROCERY II"# 9. Which product category showed the highest seasonality in January? Save
# the answer as highjan. (The highjan variable should be a simple character
# string with the product category name, in all caps like in the data.)
highjan <- "HOME AND KITCHEN I"#10. Create a report showing the product category with the lowest amount of
# seasonality. The lowest seasonality is indicated by having the lowest
# standard deviation in percsales across the 6 months. Call the report
# lowseas. It should have one row and two columns. The first column should
# be "family" and report the low seasonality category. The second column
# should be "sdperc" and should report the standard deviation of percsales
# for this category.
lowseas <- sd(fammonth$percsales)