# install.packages(c("tidyverse", "GGally"))

# to.install <- c("magittr", "purrr", 
#  "ggplot2", "dplyr", "broom", "GGally")
# install.packages(to.install)

require(ggplot2)

head(mtcars)

require(ggplot2)

gp <- ggplot(aes(x = wt, y = mpg), 
             data = mtcars)

gp + geom_point()

gp + geom_point(size = 4)

gp + geom_point(aes(size = wt))

gp <- ggplot(aes(x = wt, y = mpg), 
             data = mtcars) + 
    geom_point()

gp + scale_y_continuous(limits=c(1, 40)) +
   scale_x_continuous(limits=c(0, 6))

gp + scale_y_continuous(limits=c(1, 35),
                                breaks=seq(1, 35, 5)) +
   scale_x_continuous(limits=c(1.5, 5.5),
                     breaks=seq(1.5, 5.5, 1))

gp <- ggplot(aes(x=wt, y=mpg, color=factor(vs)), 
             data=mtcars)
gp + geom_point()

mtcars$vs <- as.factor(mtcars$vs)
gp <-ggplot(aes(x=wt, y=mpg, color=vs), 
             data=mtcars)
gp + geom_point()

data(Burt, package = 'carData')
Burt$class <- as.factor(Burt$class)

gp <- ggplot(aes(x = IQbio, y = IQfoster), data = Burt)
gp + geom_point(aes(color = class, 
             shape = class))

gp <- ggplot(aes(x = IQbio, y = IQfoster), data = Burt)
gp + geom_point(aes(color = class, 
             shape = class)) +
      geom_smooth(method = "lm", se = FALSE)

gp + geom_point(aes(color = class, 
             shape = class)) +
      geom_smooth(aes(color = class), method = "lm", se = FALSE)

gp <- ggplot(aes(x=wt, y=mpg), 
             data=mtcars)
gp + geom_point() + geom_density2d()

require(dplyr)
require(broom)

corr <- mtcars  %$%
             cor.test(mpg, wt) %>%
             tidy %>%
             mutate_if(is.numeric, round, 4)

corr

text = paste0('r = ', corr$estimate, ', ',
             ifelse(corr$p.value <= 0,
                           'p < 0.05',
                   paste('p = ', corr$p.value))
             )

text

gp <- ggplot(aes(x = wt, y = mpg), 
             data = mtcars)
gp + geom_point() + geom_smooth(method = "lm", se = FALSE) +
    annotate('text',  x = 4.5, y = 35, label=text)

require(tidyr)
require(purrr)

data(Burt, package = 'carData')


corr <- Burt %>% group_by(class) %>%
  nest() %>% 
  mutate(Cor = map(data, ~ cor.test(.$IQbio, .$IQfoster)),
         p   = map_dbl(Cor, 'p.value'),
         est = map_dbl(Cor, 'estimate')
             ) %>%
  mutate_if(is.numeric, round, 4) %>%
  select(class, p, est, Cor)

text <- corr %>%
  mutate(
        text = paste0('r = ', est, ', ',
             ifelse(p <= 0.01,
                           'p < 0.05',
                   paste('p = ', p))))


Burt$class <- as.factor(Burt$class)

gp <- ggplot(aes(x = IQbio, y = IQfoster), 
             data = Burt) 


corrp <- gp + geom_point(aes(color = class, 
             shape=class)) +
      geom_smooth(aes(color = class), method = "lm", se = FALSE) +
      geom_text(aes(x = 120, y = 137, color="high", 
                    label=subset(text, class == "high")$text)) +
      geom_text(aes(x = 118, y = 109, color="medium", 
                    label=subset(text, class == "medium")$text)) +
      geom_text(aes(x = 124, y = 103, color="low", 
                    label=subset(text, class == "low")$text))

corrp

data(Salaries, package = "carData")
Salaries$rank <- as.factor(Salaries$rank)

gp <- ggplot(aes(x = salary, y = yrs.since.phd), 
             data = Salaries) + 
    geom_point(aes(color = rank, 
             shape = rank)) +
    geom_smooth(method = "lm") +
   scale_y_continuous(limits = c(0, 60)) +
   scale_x_continuous(limits = c(50000, 240000), 
                      breaks = seq(50000, 240000, by = 10000))

gp + theme(axis.text.x = 
           element_text(angle = 90, hjust = 1))

corrp + theme_bw() +  scale_colour_grey() +
  theme(axis.line = element_line(colour = "black")
        ,plot.background = element_blank()
        ,panel.grid.major = element_blank()
        ,panel.grid.minor = element_blank()
        ,strip.background = element_blank()
        ,panel.border = element_blank() 
        ,legend.title=element_blank()
        ,legend.key = element_blank())

require(GGally)

cols = c('mpg', 'wt', 'hp', 'qsec')
ggpairs(mtcars, columns = cols)

data(Salaries, package = "carData")


gp <- ggplot(aes(x=yrs.since.phd, y=salary), 
             data=Salaries) + 
    geom_point() +
    geom_smooth(method = "lm", se = FALSE, colour="gray") +
    theme_bw() +
    theme(axis.line = element_line(colour = "black")
        ,plot.background = element_blank()
        ,panel.grid.major = element_blank()
        ,panel.grid.minor = element_blank()
        ,strip.background = element_blank()
        ,panel.border = element_blank() 
        ,legend.title=element_blank()
        ,legend.key = element_blank())  +
    xlab('Years since Ph.D.') +
    ylab('Salary')

ggsave("salaries_by_year_scatterplot.pdf", device = "pdf",
       width = 12, height = 8,
       units = "cm", dpi = 300)

ggsave("salaries_by_year_scatterplot.tiff", device = "tiff",
       width = 12, height = 8,
       units = "cm", dpi = 300)