# Plotting
    data(mtcars)

    # basic scatterplot
    plot(x=mtcars$wt, y=mtcars$mpg)
    
    # same thing with formula syntax
    plot(mpg ~ wt, data=mtcars)
    
    # labeling axes and the title
    plot(x=mtcars$wt, y=mtcars$mpg,
         xlab = "Vehicle Weight",
         ylab = "Miles per Gallon",
         main = "My First Plot")
    
    # change the point character, color, and size
    plot(x=mtcars$wt, y=mtcars$mpg,
         col = "dodgerblue4", pch = 19, cex = 2.5)
    
    # change the axis limits
    plot(x=mtcars$wt, y=mtcars$mpg,
         xlim = c(-10, 10), 
         ylim = c(0, 100))
    
    # omit that "buffer space" next to axes
    plot(x=mtcars$wt, y=mtcars$mpg,
         xlim = c(-10, 10), 
         ylim = c(0, 100), 
         yaxs="i", xaxs="i")
    
    # add a line to a plot
    plot(x=mtcars$wt, y=mtcars$mpg)
    
        out <- lm(mpg ~ wt, data=mtcars)    
        coef(out)
        
    abline(coef(out), col="red")
    
    # use alpha function from scales package to make points transparent
    plot(x=mtcars$wt, y=mtcars$mpg, pch=19,
         col = scales::alpha("dodgerblue4", alpha=0.5))
    
    # put it together
    plot(x = log(mtcars$wt), 
         y = mtcars$mpg,
         xlab = "Log Vehicle Weight",
         ylab = "Miles per Gallon",
         main = "Plot with Reg Line",
         pch  = 19,
         col  = scales::alpha("dodgerblue4",0.8),
         xlim = c(0,2),
         ylim = c(0, 40),
         yaxs="i", xaxs="i")

    out <- lm(mpg ~ log(wt), data=mtcars)
    abline(coef(out), col="forestgreen", lwd=2)

    
    # color by number
    mtcars$cyl <- paste(mtcars$cyl, "cyl")
    table(mtcars$cyl)
    as.factor(mtcars$cyl)
    as.integer(as.factor(mtcars$cyl))
    
    colvec <- c("red", "blue", "green")
    colvec[as.factor(mtcars$cyl)]
    
    mtcars$cyl <- as.factor(mtcars$cyl)
    
    plot(x = log(mtcars$wt), 
         y = mtcars$mpg,
         pch = 19,
         col = colvec[mtcars$cyl])
    
    fancy <- c("dodgerblue4", "firebrick", "seagreen4")
    
    plot(x = log(mtcars$wt), 
         y = mtcars$mpg,
         pch=19,
         col = fancy[mtcars$cyl]  )
    
# histograms
    
    somedata <- rnorm(1000, mean=3, sd=5)
    
    hist(somedata)
    
    hist(somedata, col="dodgerblue3", border="grey", main="Title", xlab="Random Draws", ylab="", breaks=50)
    hist(somedata, col="dodgerblue3", border="grey", main="Title", xlab="Random Draws", ylab="", breaks=-25:25)
    
    abline(v=mean(somedata), col="red", lwd=3)
    abline(v=3, col="black", lty=4, lwd=3)
    
    text(3.1, 80, "True Mean = 3", col="red", pos=4)
    
    
## PRACTICE QUESTIONS
    
    #1. Use the 'diamonds' dataset in the 'ggplot2' package to make the following plot:
    #     x is 'log(carat)'
    #     y is 'log(price)'
    #     color the points with a different color for each level of 'clarity'
    #     (you may find that transparency is nice addition here)
    #     use pch to change the point character to a filled-in circle
    #     use cex to make the points small
    #     be sure to label the x-axis and y-axis, and add a title
    #
    #   Run a regression of log(price) on log(carat) using the diamonds dataset and plot
    #   the fitted regression line on the plot. Use the 'lwd' argument  to make the line 
    #   thicker and more noticeable.
    
    data(diamonds, package="ggplot2")
    str(diamonds)    
    
    
    
    


## CHALLENGE QUESTION

    data(mtcars)

    # A colleague sends you this figure: "heavier cars lose about 7 mpg per
    # 1000 lbs -- you can see it in the fitted line."

    plot(x = mtcars$wt, y = mtcars$mpg,
         pch = 19, col = "dodgerblue4",
         xlim = c(1, 4), ylim = c(10, 35),
         xlab = "Weight (1000 lbs)",
         ylab = "Miles per Gallon",
         main = "MPG vs Weight")

    out <- lm(mpg ~ wt, data = mtcars)
    abline(coef(out), col = "firebrick", lwd = 2)

    #   a) Count the points you can see. Now run nrow(mtcars). Reconcile the two
    #      numbers, and say what R printed to warn you that they differ.
    #   b) The red line is not fitted to the points on the plot. Report the slope
    #      of the line that IS drawn, and the slope of a line fitted only to the
    #      cars visible inside the axis limits. One of them is about -7.
    #   c) So the colleague's sentence describes the data you cannot see, using a
    #      line drawn from data that includes it. Which slope belongs on this
    #      figure as drawn? Defend your choice in one sentence.
    #   d) Redraw the figure so it is honest about all 32 cars. There is more than
    #      one defensible fix -- choose one and state what it costs the reader.
    #   e) Why is this class of bug worse in a plot than in a table?