#-------------------------------------
#9           Graphing by Group. 
#            Updated Feb 6 2019
#-------------------------------------
#
#Exercise #2: What was the percent of cancelled flights each day 
#among flights that were supposed to leave during the Hurricane Sandy time period? 
#Create a data frame called departing_cancellations from the ny data. 
#Hint: You first want to filter for flights leaving JFK, LGA, and EWR; 
#group by day; summarize the number of cancellations and total flights; 
#and then calculate percents. 


install.packages('geofacet')
install.packages('RColorBrewer')
library(geofacet)
library(RColorBrewer)
library(dplyr)
library(ggplot2)

#Load this file
ny <- rio::import("https://raw.githubusercontent.com/profrobwells/Data-Analysis-Class-Jour-405v-5003/master/Data/ny.csv")

#Check it out
View(ny)
ncol(ny)
nrow(ny)
colnames(ny)

#Filter file on airport departures
departing_cancellations <- ny %>%
  filter(ORIGIN %in% c("JFK", "LGA", "EWR")) %>%
  filter(FL_DATE >= "2012-10-27", FL_DATE <= "2012-11-03",
  ) %>%
  group_by (FL_DATE, ORIGIN) %>%
  summarize(
    TotalCancelled = sum(CANCELLED),
    TotalFlights = n(),
    PctCancelled = (TotalCancelled / TotalFlights) * 100
  )

ggplot(departing_cancellations, aes(x=FL_DATE, y=PctCancelled, fill=ORIGIN)) + 
  geom_col(position="dodge") +
  theme(axis.text.x = element_text(angle = 45, vjust = 1.2, hjust = 1.1))

#Add your own colors
ggplot(departing_cancellations, aes(x=FL_DATE, y=PctCancelled, fill=ORIGIN)) + 
  geom_col(position = "dodge") +
  scale_fill_manual(values = c("black", "darkgrey", "white"))

#Get ugly
ggplot(departing_cancellations, aes(x=FL_DATE, y=PctCancelled, fill=ORIGIN)) + 
  geom_col(position = "dodge") +
  scale_fill_manual(values = c("pink", "green", "orange"))

#Multiple Lines on a Graph
#Geom_Line, Geom_point, Geom_bar
#How to alter the colors in a chart.


mpg <- as_data_frame(mpg)
View(mpg)

#test with colors
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = manufacturer, size=hwy))

#Geom_bar vs geom_col
#There are two types of bar charts: geom_bar() and geom_col(). 
#geom_bar() uses stat_count() by default: it counts the number of cases at each x position. geom_col() uses stat_identity(): it leaves the data as is.
#geom_bar() makes the height of the bar proportional to the number of cases in each group 
#(or if the weight aesthetic is supplied, the sum of the weights).

#If you want the heights of the bars to represent values in the data, use geom_col() instead. 

ggplot(data = mpg) + 
  geom_col(aes(x = displ, y = hwy))

#Geom_bar example
g <- ggplot(mpg, aes(class))
# Number of cars in each class:
g + geom_bar()

#See how it automatically counts the cars
ggplot(mpg, aes(manufacturer)) +
  geom_bar()

#fix the axis
ggplot(mpg, aes(manufacturer)) +
  geom_bar() +
theme(axis.text.x = element_text(angle = 45, vjust = 1.2, hjust = 1.1))


#geom_bar() understands the following aesthetics (required aesthetics are in bold):
#x
#y
#alpha
#colour
#fill
#group
#linetype
#size

# Bar charts are automatically stacked when multiple bars are placed
# at the same location. The order of the fill is designed to match
# the legend
g + geom_bar(aes(fill = drv))

#flip chart
g +
  geom_bar(aes(fill = drv), position = position_stack(reverse = TRUE)) +
  coord_flip() +
  theme(legend.position = "top")

#References
https://ggplot2.tidyverse.org/reference/geom_bar.html
https://plot.ly/ggplot2/geom_line/
  
  #Exercise stopped 2-5-19 #

#geom_line
#Aesthetics
#x
#y
#alpha
#colour
#linetype
#size

library(plotly)

ggplot(mpg, aes(x = year, y = hwy)) + 
geom_line()











#facet_grid

ggplot(departing_cancellations, aes(x=FL_DATE, y=PctCancelled, fill=ORIGIN)) + 
  geom_bar(stat="identity") +
  theme(axis.text.x= element_text(angle=45, hjust = 1.3, vjust = 1.2)) +
  facet_grid(. ~ ORIGIN)

#You can also add fill=ORIGIN to aes(x=FL_DATE, y=PctCancelled) 
#if you’d like the graphs to have different colors.

departing_cancelled_raw <- filter(sandyCancelled, ORIGIN %in% c("JFK", "LGA", "EWR"))

ggplot(departing_cancelled_raw, aes(x=FL_DATE)) +
  geom_bar

#by airport
ggplot(departing_cancelled_raw, aes(x=FL_DATE)) +
  geom_bar() +
  theme(axis.text.x= element_text(angle=45, hjust = 1.3, vjust = 1.2)) +
  facet_grid(. ~ ORIGIN)

ggplot(departing_cancelled_raw, aes(x=FL_DATE)) +
  geom_bar() +
  theme(axis.text.x= element_text(angle=45, hjust = 1.3, vjust = 1.2)) +
  facet_grid(CARRIER ~ ORIGIN)


#housing prices
homeprices <- rio::import("data/state_statistics_for_download.xls", skip = 4)
str(homeprices)
head(homeprices)
summary(homeprices)
Hmisc::describe(homeprices$year_quarter)
unique(homeprices$year_quarter)
homeprices <- janitor::clean_names(homeprices)
head(homeprices)

homeprices <- filter(homeprices, !(is.na(state)))

#graph home prices by state
ggplot(homeprices, aes(x=year_quarter, y=median_price, group = state)) +
  geom_line() +
  facet_wrap(~ state, ncol = 8)

#clean up
options(scipen = 999)
ggplot(homeprices, aes(x=year_quarter, y=median_price, group = state)) +
  geom_line() +
  facet_wrap(~state, ncol = 12)  + 
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank()
  )

#Map
pacman::p_load(geofacet)
get_grid_names()
grid_preview(us_state_grid2)
facet_geo(~ state, grid = "us_state_grid2")

ggplot(homeprices, aes(x=year_quarter, y=median_price, group = state)) +
  geom_line() +
  facet_geo(~ state, grid = "us_state_grid2") +
  theme(axis.text.x= element_blank(),
        axis.ticks.x=element_blank() ,
        axis.ticks.y=element_blank() ,
        axis.text.y=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank()
  ) +
  ggtitle("Quarterly Median Home Prices 2000-2010") 


delayed_ny <- sandyFlights%>%
  filter(CANCELLED != 1, ORIGIN %in% c("JFK", "LGA", "EWR"))

ggplot(delayed_ny, aes(x = AIR_TIME, y = DEP_DELAY, color = ORIGIN)) +
  geom_point(position = "jitter")

filter(delayed_ny, is.na(AIR_TIME))

ggplot(departing_cancellations, aes(x=FL_DATE, y=PctCancelled, fill=ORIGIN)) + 
  geom_col(position = "dodge") +
  scale_fill_manual(values = c("black", "darkgrey", "white"))

install.packages("ggiraph")
library(ggiraph)

lga$tooltip <- paste0(lga$CARRIER, " Flight ", lga$FL_NUM, " from ", lga$ORIGIN, " to ", 
                      lga$DEST, " on ", lga$FL_DATE)
lga$clickjs = paste0("alert(\"",lga$tooltip, "\")" )

myscatterplot <- ggplot(lga, aes(x = AIR_TIME, y = DEP_DELAY, 
                                 color = ORIGIN, tooltip = tooltip, onclick = clickjs)) +
  geom_point_interactive(position = "jitter") +
  scale_color_brewer(palette = "Dark2")

ggiraph(code = {print(myscatterplot)})


pacman::p_load(dplyr, lubridate, testthat)