anscombe


library(graphics) # not really needed
plot(y1 ~ x1, data = anscombe, pch = 16)

## this line is not really needed, as the graphics package is already
## attached when R starts


library(lattice)
xyplot(y1 ~ x1, data = anscombe, pch = 16)

library(ggplot2)
ggplot(data = anscombe) + geom_point(mapping = aes(x = x1, y = y1))


par(mfrow = c(2, 2))
plot(y1 ~ x1, data = anscombe, pch = 16)
plot(y2 ~ x2, data = anscombe, pch = 16)
plot(y3 ~ x3, data = anscombe, pch = 16)
plot(y4 ~ x4, data = anscombe, pch = 16)


anscombe.long <- 
    with(anscombe,
         data.frame(x = c(x1, x2, x3, x4), 
                    y = c(y1, y2, y3, y4),
                    which = rep(c("A", "B", "C", "D"), each = 11)))
anscombe.long


xyplot(y ~ x | which, data = anscombe.long, pch = 16, grid = TRUE)


ggplot(data = anscombe.long) + facet_wrap(~ which) +
    geom_point(aes(x = x, y = y))


for (w in c("A", "B", "C", "D")) {
    plot(y ~ x, data = anscombe.long, pch = 16, subset = (which == w))
    abline(coef(lm(y ~ x, data = anscombe.long, subset = (which == w))))
}


ggplot(data = anscombe.long) + facet_wrap(~ which) +
    geom_smooth(aes(x = x, y = y), method = "lm")


ggplot(data = anscombe.long) + facet_wrap(~ which) +
  geom_smooth(aes(x = x, y = y), method = "lm", se = FALSE) +
  geom_point(aes(x = x, y = y))
  

xyplot(y ~ x | which, data = anscombe.long, pch = 16,
       panel = panel.points)


xyplot(y ~ x | which, data = anscombe.long, pch = 16,
       panel = panel.lmline)


displayFunction <- function(x, y) {
    panel.grid(h = -1, v = -1)               ## add a reference grid
    panel.points(x, y, pch = 16)             ## draw the points
    panel.lmline(x, y, col = "grey50")       ## draw linear regression line
}


xyplot(y ~ x | which, data = anscombe.long,
       panel = displayFunction)


data(NHANES, package = "NHANES")
lattice::histogram( ~ BPSysAve | Gender, data = NHANES,
                   layout = c(1, 2), nint = 25,
                   type = "density", as.table = TRUE)


p <- ggplot(data = NHANES) + facet_wrap(~ Gender, ncol = 1)


p + stat_bin(aes(x = BPSysAve), bins = 25, geom = "point")


p + stat_bin(aes(x = BPSysAve), bins = 25, geom = "line")


p + stat_bin(aes(x = BPSysAve, y = after_stat(density)), 
             bins = 25, geom = "line")


p + stat_density(aes(x = BPSysAve))


p + stat_density(aes(x = BPSysAve), geom = "line")


p + stat_density(aes(x = BPSysAve, color = Race1), geom = "line")


p + stat_density(aes(x = 1/BPSysAve, fill = Race1), geom = "area")


p + stat_density(aes(x = 1/BPSysAve, color = Race1),
                 geom = "line", position = "identity")


gapminder <- read.delim("data/gapminder.tsv")
str(gapminder)


pgm <- ggplot(data = gapminder) + facet_wrap(~ year)


pgm + geom_point(aes(x = gdpPercap, y = lifeExp))


pgm + geom_point(aes(x = log(gdpPercap), y = lifeExp))


pgm + geom_point(aes(x = log(gdpPercap), y = lifeExp, color = continent))


pgm + geom_point())



## RGL

library(rgl)
with(subset(gapminder, year == 2002),
     plot3d(sqrt(pop), lifeExp, log(gdpPercap), type = "s", size = 1))


## plotly

q <- 
    ggplot(data = gapminder,
           mapping = aes(x = log(gdpPercap), y = lifeExp,
                         size = pop, color = continent,
                         label = country)) +
    facet_wrap(~ year) + geom_point()


library(plotly)

ggplotly(p)