ggplot stacked and side by side bar charts.R

##################################################################################
# the object of this series on ggplot2 is not to learn ggplot2 from the bottom   #
# up, there are books and books on it. it's supposed to give you a really handy  #
# framework to quickly produce when you need to utilizing excel instincts.       #
#                                                                                #
# As such, despite not always necessary, i reshape the data first as you would   #
# with Pivot Tables in Excel and then graph them. this simplifies things even if #
# code is slightly longer.                                                       #  
##################################################################################

rm(list = ls())
getwd()

library(ggplot2)
library(dplyr)
library(tidyr)

df <- read.csv("https://www.dropbox.com/s/kj9yioc24iq4pdb/superstore.csv?dl=1")
write.csv("superstore.csv")

############## BAR AND COLUMN PLOTS ############################################## 

#footnote (not so important, just explaining why i choose to do it in a certain way)

ggplot(data=df, aes(x=Order.Priority))+
  geom_bar()  # this will work for counting items per category

ggplot(data=df, aes(x=Order.Priority, y = Sales))+
  geom_bar()  # but this will not. it will throw exception

ggplot(data=df, aes(x=Order.Priority, y = Sales))+
  geom_bar(stat = "identity") # this solves it, but can get confusin. i'd rather reshape my data!

######################################
# so the longer way - the excel way... #
######################################

# 1) MAKE SURE YOUR CATEGORICAL FIELDS ARE FACTORS FIRST!
  
str(df)

# 2) reshape your data and pivot to the data you need. in this case - count of items
#     by order priority
 
 df1 <- df %>% 
   select(Province,Order.Priority) %>% 
   group_by (Province,Order.Priority) %>% 
   summarise(TotalOrders = n()) # if you don't know this, go to my videos about pivoting data


# 3) create the base of your chart: data, x/y variables, sorting, and color


 p<-ggplot(data=df1, aes(x=Province, 
                         y=TotalOrders,
                         fill = Order.Priority
 ))
 p + geom_bar(stat = "identity")
 

  # 4) add layers. there are countless options but i think these will cover 90% of your
  #     needs. use this as a template.
 
 p +
   geom_bar(stat = "identity", #confusing subject, when plotting pivoted data, always leave "identity"
            width = 0.6,      # how wide (0-1) the bars are
            color = "black",  # the outline color of the bars
            size = 0.5,         # the thickness of the outline
            alpha = 0.7) +    # the opaqueness of the fill colors
   theme_minimal()+           # the background
   theme(legend.position = "right", axis.text.x = element_text(angle = 45, hjust = 0.8)) +                         # legend position
   labs(x= "Order Priority by Province", y = "Total Orders", title = "Total Orders by Priority",
        caption = "2016 data")+  # axis labels and footnote
   scale_x_discrete(limits = c("Ontario", "Alberta","British Columbia", "Quebec")) + # filter only certain factors
   geom_text(aes(label = TotalOrders), position = position_stack(vjust = 0.5), size = 3) + # the labels
   #coord_flip()+              # flip from column to bar horizontal

   
   # 5) same, but side by side with position_dodge()
   
   p +
   geom_bar(position = position_dodge(),
            stat = "identity", #confusing subject, when plotting pivoted data, always leave "identity"
            width = 0.6,      # how wide (0-1) the bars are
            color = "black",  # the outline color of the bars
            size = 0.5,         # the thickness of the outline
            alpha = 0.7) +    # the opaqueness of the fill colors
   theme_minimal()+           # the background
   theme(legend.position = "right", axis.text.x = element_text(angle = 45, hjust = 0.8)) +                         # legend position
   labs(x= "Order Priority by Province", y = "Total Orders", title = "Total Orders by Priority",
        caption = "2016 data")+  # axis labels and footnote
   scale_x_discrete(limits = c("Ontario", "Alberta","British Columbia", "Quebec")) + # filter only certain factors
   geom_text(aes(label = TotalOrders), position = position_dodge(width = 0.7), vjust = -0.5, size = 3,check_overlap = TRUE) + # the labels
   #coord_flip()+              # flip from column to bar horizontal