library(tidyverse)
library(ggplot2)

help.search("geom_", package = "ggplot2")
# geom TAB - review menu

?diamonds
ggplot(data=diamonds)  + geom_point(mapping = aes(x=carat, y=price))
# how else can I express this
??geom

ggplot(data=diamonds)  + geom_point(mapping = aes(x=carat, y=price), color = "blue")
# why is the color outside of aes?
# want a different shade consult
# http://research.stowers.org/mcm/efg/R/Color/Chart/

ggplot(data=diamonds)  + geom_point(mapping = aes(x=carat, y=price), color = "lightskyblue")

# built-in help in R
colors()

# exmample of setting the colot aesthetic outside of the aes function
ggplot(data=diamonds)  + geom_point(mapping = aes(x=carat, y=price), color = "lightskyblue")

# transform the data in the first level of the graphic
ggplot(data=diamonds, aes(x=log10(carat), y=log10(price))) + geom_point(color = "lightskyblue")
# transform the scale of the data to log 10
ggplot(diamonds, aes(x=carat, y=price)) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10()

# specifying aes arguments by position
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10()

# transforming the coordinate system:
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  coord_trans(x = "log10", y = "log10")

# coordinate transformation are done after any statistical transformations
# scale transforations are done BEFORE statistical functions

ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10() +
  scale_y_log10()

# look at the relationship between city and highway miles per gallon and the class of car
ggplot(mpg,
       aes(x=cty,
           y=hwy,
           color=class))+
  geom_point()

# now lets add another geometry that smooths the data
# it adds a line and a ribbon that should represent spread
ggplot(mpg,
       aes(x=cty,
           y=hwy))+
  geom_point() + geom_smooth()

# labels for points
ggplot(filter(mpg, model== "a4"),
       aes(x=cty,
           y=hwy))+
   geom_text(aes(label=cyl), size = 5)

#ggplot(data = <DATA>) +
#  <GEOM_FUNCTION>(
#    mapping = aes(<MAPPINGS>),
#    stat = <STAT>,
#    position = <POSITION>
#  ) +
#  <COORDINATE_FUNCTION> +
#  <FACET_FUNCTION>
p <- ggplot(mpg, aes(displ, cty)) + geom_point()

p + facet_grid(. ~ cyl)
p + facet_grid(drv ~ .)
p + facet_grid(drv ~ cyl)
ggplot(data=df, aes(x=fan))+stat_count()

# the histogram geometry that sets the binwidth
ggplot(mpg, aes(x = cyl)) +
  geom_histogram(stat = "bin", binwidth=1)

# geom_bar does a statistica; transformation counts records

ggplot(mpg, aes(x = cyl)) +
  geom_bar()

ggplot(diamonds, aes(x = cut, y = carat)) +
  geom_bar(stat="identity")

statsum <- ggplot(data = diamonds) +
  stat_summary(mapping = aes(x = cut, y = depth),
               fun.ymin = min,
               fun.ymax = max,
               fun.y = median)

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = color, y = ..prop.., group = color))

ggplot(data = diamonds) +
  stat_summary(
    mapping = aes(x = cut, y = depth),
    fun.ymin = min,
    fun.ymax = max,
    fun.y = median
  )

# no points basic color
ggplot(diamonds) +
  geom_pointrange(
    aes(x = cut, y = depth), stat = "summary",
    fun.ymin = min,
    fun.ymax = max,
    fun.y = median
  )

#with detailed points
ggplot(mpg, aes(trans, cty)) +
  geom_point() +
  stat_summary(geom = 'pointrange', fun.ymin = min, fun.ymax = max, fun.y = mean, colour = "red", size = 1)

# without points
ggplot(mpg, aes(trans, cty)) +
  stat_summary(geom = 'pointrange', fun.ymin = min, fun.ymax = max, fun.y = mean, colour = "red", size = 1)

#Scales
#Aesthetic mapping (i.e., with aes()) only says that a variable should be mapped #to an aesthetic. It doesn't say how that should happen. For example, when #mapping a variable to shape with aes(shape = x) you don't say what shapes #should be used. Similarly, aes(color = z) doesn't say what colors should be # used. Describing what colors/shapes/sizes etc. to use is done by modifying the # corresponding scale. In ggplot2 scales include

#position
#color and fill
#size
#shape
#line type
#Scales are modified with a series of functions using a scale_<aesthetic>_<type> #naming scheme.