library(tidyverse)
library(ggplot2)

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

# 2. Investigate the diamonds dataset and create a scatter plot of carat versus price
?diamonds

# 3. how else can I express this chart ??
??geom
# 4. Change the color of the points to a different color 
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/

# 5. Change to one from the colors listed in the chart above 
ggplot(data=diamonds)  + geom_point(mapping = aes(x=carat, y=price), color = "lightskyblue")

# 6. Investigate the colors() function using the built-in help in R
colors()


# 7. create another scatter plot where carat and price are transformed with the log10 function in the first level of the graphic

# 8. transform the scale of the data to log 10 using one of the scale_* functions 


# 9. specifying aes arguments by position as opposed to x=, y=


# 10. transforming the coordinate system for the plot from #7
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

# 11. add a smoothing line using geom_smooth to plot 10 and plot 11


# 12. look at the relationship between city and highway miles per gallon and the 
#      class of car so 3 variables represented in your graph


# 13. 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()

# 14. plot the data for car model "a4" and add labels for points


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

# 15 add a facet grid for cyl variable
p + facet_grid(. ~ cyl)
# 16. now add a facet grid for drv.
p + facet_grid(drv ~ .)
# 16. now add a facet grid for cyl and drv.
p + facet_grid(drv ~ cyl)

ggplot(data=df, aes(x=fan))+stat_count()

# 17 create for the cyl variavle - using the histogram geometry that sets the binwidth


# 18 now use geom_bar to generate the data/statistics; transformation counts records

# 19 using the diamonds dataset and geom_bar plot cut versus carat

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

# 20 use stat_summary to sisplay the min, max, median for depth for each diamond cut

# 21 use geom_bar to create proportion plot 


#22 use geom_bar to create a stacked bar 

#23 


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

# 24 use stat_summary and the geom = 'pointrange' to create a summary of trans to cty for mpg
# where you set the color of the summary points

# 25 remove the detailed points from the chart above  

#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.