ggplot2: Curso en Harvard

#---Harvard Ejemplos de Estudio----------
#http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html
setwd("C:/Users/Jbustos/Documents/CURSO_R/Curso_R_INE_2018/datos/Rgraphics/Rgraphics")
housing <- read.csv("dataSets/landdata-states.csv")
head(housing[1:5])
hist(housing$Home.Value)

library(ggplot2)
ggplot(housing, aes(x = Home.Value)) +
  geom_histogram()

plot(Home.Value ~ Date,
     data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
       data=subset(housing, State == "TX"))
legend(1975, 400000,
       c("MA", "TX"), title="State",
       col=c("black", "red"),
       pch=c(1, 1))

ggplot(subset(housing, State %in% c("MA", "TX")),
       aes(x=Date,
           y=Home.Value,
           color=State))+
  geom_point()


help.search("geom_", package = "ggplot2")

hp2001Q1 <- subset(housing, Date == 2001.25) 
ggplot(hp2001Q1,
       aes(y = Structure.Cost, x = Land.Value)) +
  geom_point()


ggplot(hp2001Q1,
       aes(y = Structure.Cost, x = log(Land.Value))) +
  geom_point()


hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))

p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))

p1 + geom_point(aes(color = Home.Value)) +
  geom_line(aes(y = pred.SC))


p1 +
  geom_point(aes(color = Home.Value)) +
  geom_smooth()


p1 + 
  geom_text(aes(label=State), size = 3)


## install.packages("ggrepel") 
library("ggrepel")
p1 + 
  geom_point() + 
  geom_text_repel(aes(label=State), size = 3)



p1 +
  geom_point(aes(size = 2),# incorrect! 2 is not a variable
             color="red") # this is fine -- all points red


p1 +
  geom_point(aes(color=Home.Value, shape = region))


dat <- read.csv("dataSets/EconomistData.csv")
head(dat)

ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + geom_point()



args(geom_histogram)
args(stat_bin)


p2 <- ggplot(housing, aes(x = Home.Value))
p2 + geom_histogram()


p2 + geom_histogram(stat = "bin", binwidth=4000)


housing.sum <- aggregate(housing["Home.Value"], housing["State"], FUN=mean)
rbind(head(housing.sum), tail(housing.sum))


ggplot(housing.sum, aes(x=State, y=Home.Value)) + 
  geom_bar(stat="identity")

p3 <- ggplot(housing,
             aes(x = State,
                 y = Home.Price.Index)) + 
  theme(legend.position="top",
        axis.text=element_text(size = 6))
(p4 <- p3 + geom_point(aes(color = Date),
                       alpha = 0.5,
                       size = 1.5,
                       position = position_jitter(width = 0.25, height = 0)))


p4 + scale_x_discrete(name="State Abbreviation") +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"))


p4 +
  scale_x_discrete(name="State Abbreviation") +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"),
                         low = "blue", high = "red")

library(scales)
p4 +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"),
                         low = muted("blue"), high = muted("red"))

p4 +
  scale_color_gradient2(name="",
                        breaks = c(1976, 1994, 2013),
                        labels = c("'76", "'94", "'13"),
                        low = muted("blue"),
                        high = muted("red"),
                        mid = "gray60",
                        midpoint = 1994)


p5 <- ggplot(housing, aes(x = Date, y = Home.Value))
p5 + geom_line(aes(color = State))  


(p5 <- p5 + geom_line() +
    facet_wrap(~State, ncol = 10))


p5 + theme_linedraw()
p5 + theme_light()
p5 + theme_minimal() +
  theme(text = element_text(color = "turquoise"))



theme_new <- theme_bw() +
  theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
        text=element_text(size = 12, family = "Serif", color = "ivory"),
        axis.text.y = element_text(colour = "purple"),
        axis.text.x = element_text(colour = "red"),
        panel.background = element_rect(fill = "pink"),
        strip.background = element_rect(fill = muted("orange")))

p5 + theme_new


housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data = housing, mean)
ggplot(housing.byyear,
       aes(x=Date)) +
  geom_line(aes(y=Home.Value), color="red") +
  geom_line(aes(y=Land.Value), color="blue")


library(tidyr)
home.land.byyear <- gather(housing.byyear,
                           value = "value",
                           key = "type",
                           Home.Value, Land.Value)
ggplot(home.land.byyear,
       aes(x=Date,
           y=value,
           color=type)) +
  geom_line()


#----DESAFIO: PONGA TODO JUNTO Y CONSTRUYA ESTE GRAFICO DEL THE ECONOMIST
#Graph source: http://www.economist.com/node/21541178
#Building off of the graphics you created in the previous exercises,
#put the finishing touches to make it as close as possible to the original economist graph.


#Additional resources

#ggplot2 resources
#Mailing list: http://groups.google.com/group/ggplot2
#Wiki: https://github.com/hadley/ggplot2/wiki
#Website: http://had.co.nz/ggplot2/
#  StackOverflow: http://stackoverflow.com/questions/tagged/ggplot
#IQSS resources
#Research technology consulting: http://dss.iq.harvard.edu
#Workshops materials: http://dss.iq.harvard.edu/workshop-materials
#Workshop schedule and registration: http://dss.iq.harvard.edu/workshop-registration