My Introduction

Whats Covered

I had to split this up becasue of the size of the document.

  • Doc A
    • Introduction
    • Data
    • Aesthetics
  • Doc B
    • Geometries
    • qplot and wrap-up

Aditional Resources

   

Geometries

Scatter Plots

  • 37 geometries. whaa!
    • abline, area, bar, bin2d, blank, boxplot
    • contour, crossbar, density, density2d, dotplot
    • errorbar, errorbarh, freqpoly, hex, histogram, hline
    • jitter, line, linerange, map, path, point, pointrange
    • polygon, quantile, raster, rect, ribbon, rug
    • segment, smooth, step, text, tile, violin, vline
  • 3 common plots
    • scatter plots – points, jitter, abline
    • bar plots – histogram, bar, errorbar
    • line plots – line
  • You can also add a nother variable, such as summary statistics in the geom_point
  • Jitter and alpha are common to deal with overplotting

– Scatter plots and jittering (1)

# Shown in the viewer:
ggplot(mtcars, aes(x = cyl, y = wt)) +
  geom_point()

# Solutions:
# 1 - With geom_jitter()
ggplot(mtcars, aes(x = cyl, y = wt)) +
  geom_jitter()

  • Thats a little too much jitter
  • We lose the sense of separate variables
  • We can adjust the jitter width to fix this
# 2 - Set width in geom_jitter()
ggplot(mtcars, aes(x = cyl, y = wt)) +
  geom_jitter(width = 0.1)

# 3 - Set position = position_jitter() in geom_point() ()
ggplot(mtcars, aes(x = cyl, y = wt)) +
  geom_point(position = position_jitter(0.1))

– Scatter plots and jittering (2)

# Examine the structure of Vocab
library(car)
str(Vocab)
## 'data.frame':    21638 obs. of  4 variables:
##  $ year      : int  2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 ...
##  $ sex       : Factor w/ 2 levels "Female","Male": 1 1 2 1 2 2 1 2 2 1 ...
##  $ education : int  9 14 14 17 14 14 12 10 11 9 ...
##  $ vocabulary: int  3 6 9 8 1 7 6 6 5 1 ...
# Basic scatter plot of vocabulary (y) against education (x). Use geom_point()
ggplot(Vocab, aes(education, vocabulary)) + 
  geom_point()

# Use geom_jitter() instead of geom_point()
ggplot(Vocab, aes(education, vocabulary)) + 
  geom_jitter()

# Using the above plotting command, set alpha to a very low 0.2
ggplot(Vocab, aes(education, vocabulary)) + 
  geom_jitter(alpha = 0.2)

# Using the above plotting command, set the shape to 1
ggplot(Vocab, aes(education, vocabulary)) + 
  geom_jitter(alpha = 0.2, shape = 1)

Bar Plots

– Histograms

# 1 - Make a univariate histogram
ggplot(mtcars, aes(x = mpg)) +
  geom_histogram()

  • By default the bin width is the range/30
# 2 - Plot 1, plus set binwidth to 1 in the geom layer
ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(binwidth = 1)

  • We also have access to internal data frame for the plot
  • We can change the y aes to ..density..
  • This is calculated internally in the summary stats just like count
# 3 - Plot 2, plus MAP ..density.. to the y aesthetic (i.e. in a second aes() function)
ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(aes(y = ..density..), binwidth = 1)

# 4 - plot 3, plus SET the fill attribute to "#377EB8"
ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(aes(y = ..density..), binwidth = 1, fill = "#377EB8")

– Position

# Draw a bar plot of cyl, filled according to am
ggplot(mtcars, aes(x = cyl, fill = am)) +
  geom_bar()

  • stack is the default
# Change the position argument to stack
ggplot(mtcars, aes(x = cyl, fill = am)) +
  geom_bar(position = "stack")

# Change the position argument to fill
ggplot(mtcars, aes(x = cyl, fill = am)) +
  geom_bar(position = "fill")

# Change the position argument to dodge
ggplot(mtcars, aes(x = cyl, fill = am)) +
  geom_bar(position = "dodge")

– Overlapping bar plots

# 1 - The last plot form the previous exercise
# ggplot(mtcars, aes(x = cyl, fill = am)) + 
#   geom_bar(position = "dodge")

# 2 - Define posn_d with position_dodge()
posn_d <- position_dodge(width = 0.2)

# 3 - Change the position argument to posn_d
ggplot(mtcars, aes(x = cyl, fill = am)) + 
  geom_bar(position = posn_d)

# 4 - Use posn_d as position and adjust alpha to 0.6
ggplot(mtcars, aes(x = cyl, fill = am)) + 
  geom_bar(position = posn_d, alpha = 0.6)

– Overlapping histograms

# A basic histogram, add coloring defined by cyl 
ggplot(mtcars, aes(mpg, fill = cyl)) +
  geom_histogram(binwidth = 1)

# Change position to identity 
ggplot(mtcars, aes(mpg, fill = cyl)) +
  geom_histogram(binwidth = 1, position = 'identity')

  • Now the bars are not stacked, but some are hidden behind others
# Change geom to freqpoly (position is identity by default) 
ggplot(mtcars, aes(mpg, col = cyl)) +
  geom_freqpoly(binwidth = 1)

  • This would look much better with a more full histogram

– Bar plots with color ramp, part 1

  • Color brewer is a great package for working with colors
  • Its worth its own chapter probably.
# Example of how to use a brewed color palette
ggplot(mtcars, aes(x = cyl, fill = am)) +
  geom_bar() +
  scale_fill_brewer(palette = "Set1")

# Use str() on Vocab to check out the structure
Vocab$education <- as.factor(Vocab$education)
Vocab$vocabulary <- as.factor(Vocab$vocabulary)
str(Vocab)
## 'data.frame':    21638 obs. of  4 variables:
##  $ year      : int  2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 ...
##  $ sex       : Factor w/ 2 levels "Female","Male": 1 1 2 1 2 2 1 2 2 1 ...
##  $ education : Factor w/ 21 levels "0","1","2","3",..: 10 15 15 18 15 15 13 11 12 10 ...
##  $ vocabulary: Factor w/ 11 levels "0","1","2","3",..: 4 7 10 9 2 8 7 7 6 2 ...
# Plot education on x and vocabulary on fill
# Use the default brewed color palette
ggplot(Vocab, aes(x = education, fill = vocabulary)) +
  geom_bar(position = 'fill') + 
  scale_fill_brewer()

  • There are only 9 colors in the “Blues” palette, but we have 11 categories under vocabulary
    • So we get an error. And a weird looking chart. groups 9 and 10 are blank
    • We need to create our own palette with the same number of colors as groups

– Bar plots with color ramp, part 2

  • A quick example of how the colorRampPalette works
    • It returns a function that can be used to make a new palette
    • You can just use two colors and it will scale between them
    • Or you can use many colors or an existing palette
new_col <- colorRampPalette(c("#FFFFFF", "#0000FF"))
new_col(4) # the newly extrapolated colours
## [1] "#FFFFFF" "#AAAAFF" "#5555FF" "#0000FF"
munsell::plot_hex(new_col(4)) # Quick and dirty plot

library(RColorBrewer)

# Final plot of last exercise
ggplot(Vocab, aes(x = education, fill = vocabulary)) +
  geom_bar(position = "fill") +
  scale_fill_brewer()

# Definition of a set of blue colors
blues <- brewer.pal(9, "Blues") # from the RColorBrewer package
blues
## [1] "#F7FBFF" "#DEEBF7" "#C6DBEF" "#9ECAE1" "#6BAED6" "#4292C6" "#2171B5"
## [8] "#08519C" "#08306B"
# 1 - Make a color range using colorRampPalette() and the set of blues
blue_range <- colorRampPalette(blues)

# This is our new pallete. We can create it with as many colors as we want. 
munsell::plot_hex(blue_range(11)) 

# 2 - Use blue_range to adjust the color of the bars, use scale_fill_manual()
ggplot(Vocab, aes(x = education, fill = vocabulary)) +
  geom_bar(position = "fill") +
  scale_fill_manual(values = blue_range(11))

- Nice. Thats much better.

– Overlapping histograms (2)

# 1 - Basic histogram plot command
ggplot(mtcars, aes(mpg)) + 
  geom_histogram(binwidth = 1)

# 2 - Plot 1, Expand aesthetics: am onto fill
ggplot(mtcars, aes(mpg, fill = am)) + 
  geom_histogram(binwidth = 1)

# 3 - Plot 2, change position = "dodge"
ggplot(mtcars, aes(mpg, fill = am)) + 
  geom_histogram(binwidth = 1, position = "dodge")

# 4 - Plot 3, change position = "fill"
## In this case, none of these positions really work well, because it's difficult to compare the distributions directly.
ggplot(mtcars, aes(mpg, fill = am)) + 
  geom_histogram(binwidth = 1, position = "fill")

# 5 - Plot 4, plus change position = "identity" and alpha = 0.4
ggplot(mtcars, aes(mpg, fill = am)) + 
  geom_histogram(binwidth = 1, 
    position = "identity",
    alpha = 0.4)

# 6 - Plot 5, plus change mapping: cyl onto fill
ggplot(mtcars, aes(mpg, fill = cyl)) + 
  geom_histogram(binwidth = 1, 
    position = "identity",
    alpha = 0.4)

Line Plots - Time Series

– Line plots

# Print out head of economics
head(economics)
## # A tibble: 6 x 6
##         date   pce    pop psavert uempmed unemploy
##       <date> <dbl>  <int>   <dbl>   <dbl>    <int>
## 1 1967-07-01 507.4 198712    12.5     4.5     2944
## 2 1967-08-01 510.5 198911    12.5     4.7     2945
## 3 1967-09-01 516.3 199113    11.7     4.6     2958
## 4 1967-10-01 512.9 199311    12.5     4.9     3143
## 5 1967-11-01 518.1 199498    12.5     4.7     3066
## 6 1967-12-01 525.8 199657    12.1     4.8     3018
# Plot unemploy as a function of date using a line plot
ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line()

# Adjust plot to represent the fraction of total population that is unemployed
ggplot(economics, aes(x = date, y = unemploy/pop)) +
  geom_line()

– Periods of recession

# Basic line plot
# ggplot(economics, aes(x = date, y = unemploy/pop)) +
#   geom_line()

# Expand the following command with geom_rect() to draw the recess periods
ggplot(economics, aes(x = date, y = unemploy/pop)) +
  geom_rect(data = recess,
         aes(xmin = begin, 
             xmax = end, 
             ymin = -Inf, 
             ymax = Inf),
            inherit.aes = FALSE,
            fill = "red", 
            alpha = 0.2
            ) + 
  geom_line()

– Multiple time series, part 1

# Check the structure as a starting point
str(fish.species)
## 'data.frame':    61 obs. of  8 variables:
##  $ Year    : int  1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 ...
##  $ Pink    : int  100600 259000 132600 235900 123400 244400 203400 270119 200798 200085 ...
##  $ Chum    : int  139300 155900 113800 99800 148700 143700 158480 125377 132407 113114 ...
##  $ Sockeye : int  64100 51200 58200 66100 83800 72000 84800 69676 100520 62472 ...
##  $ Coho    : int  30500 40900 33600 32400 38300 45100 40000 39900 39200 32865 ...
##  $ Rainbow : int  0 100 100 100 100 100 100 100 100 100 ...
##  $ Chinook : int  23200 25500 24900 25300 24500 27700 25300 21200 20900 20335 ...
##  $ Atlantic: int  10800 9701 9800 8800 9600 7800 8100 9000 8801 8700 ...
# Use gather to go from fish.species to fish.tidy
fish.tidy <- gather(fish.species, Species, Capture, -Year)

str(fish.tidy)
## 'data.frame':    427 obs. of  3 variables:
##  $ Year   : int  1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 ...
##  $ Species: chr  "Pink" "Pink" "Pink" "Pink" ...
##  $ Capture: int  100600 259000 132600 235900 123400 244400 203400 270119 200798 200085 ...

– Multiple time series, part 2

# Recreate the plot shown on the right
ggplot(fish.tidy, aes(x = Year, y = Capture, col = Species)) +
  geom_line()

   

qplot and wrap-up

qplot

  • qplot is sort of the base R eqivalent in terms of syntax
    • you can make charts quick and easy. It will guess the geom for you
  • But its better just to know the ggplot layers and be explicit and build your plots.
    • This is intuative and not that much more typing.
    • Its still good to know about qplot because you will see it

– Using qplot

# The old way (shown)
plot(mpg ~ wt, data = mtcars) # formula notation

with(mtcars, plot(wt, mpg)) # x, y notation

# Using ggplot:
ggplot(mtcars, aes(wt, mpg)) +
  geom_point()

# Using qplot:
qplot(wt, mpg, data = mtcars)

– Using aesthetics

# Categorical variable mapped onto size:
# cyl
qplot(wt, mpg, data = mtcars, size = factor(cyl))

# gear
qplot(wt, mpg, data = mtcars, size = factor(gear))

# Continuous variable mapped onto col:
# hp
qplot(wt, mpg, data = mtcars, col = hp)

# qsec
qplot(wt, mpg, data = mtcars, col = qsec)

– Choosing geoms, part 1

# qplot() with x only
qplot(x = factor(cyl), data = mtcars)

# qplot() with x and y
qplot(x = factor(cyl), y = factor(vs), data = mtcars)

# qplot() with geom set to jitter manually
qplot(x = factor(cyl), y = factor(vs), data = mtcars, geom = 'jitter')

– Choosing geoms, part 2 - dotplot

# cyl and am are factors, wt is numeric
class(mtcars$cyl)
## [1] "numeric"
class(mtcars$am)
## [1] "numeric"
class(mtcars$wt)
## [1] "numeric"
# "Basic" dot plot, with geom_point():
ggplot(mtcars, aes(cyl, wt, col = am)) +
  geom_point(position = position_jitter(0.2, 0))

# 1 - "True" dot plot, with geom_dotplot():
ggplot(mtcars, aes(cyl, wt, fill = am)) +
  geom_dotplot(binaxis = "y", stackdir = "center")

# 2 - qplot with geom "dotplot", binaxis = "y" and stackdir = "center"
qplot(
  cyl, wt, 
  data = mtcars, 
  fill = am, 
  geom = "dotplot", 
  binaxis = "y", 
  stackdir = "center"
)

Wrap-up

– Chicken weight

# ChickWeight is available in your workspace
# 1 - Check out the head of ChickWeight
head(ChickWeight)
## Grouped Data: weight ~ Time | Chick
##   weight Time Chick Diet
## 1     42    0     1    1
## 2     51    2     1    1
## 3     59    4     1    1
## 4     64    6     1    1
## 5     76    8     1    1
## 6     93   10     1    1
# 2 - Basic line plot
ggplot(ChickWeight, aes(x = Time, y = weight)) + 
  geom_line(aes(group = Chick))

# 3 - Take plot 2, map Diet onto col.
ggplot(ChickWeight, 
    aes(x = Time, y = weight, col = Diet)) + 
  geom_line(
    aes(group = Chick))

# 4 - Take plot 3, add geom_smooth()
ggplot(ChickWeight, 
    aes(x = Time, y = weight, col = Diet)) + 
  geom_line(
    aes(group = Chick), alpha = 0.3) + 
  geom_smooth(lwd = 2, se = F)

– Titanic

# titanic is avaliable in your workspace
# 1 - Check the structure of titanic
str(titanic)
## 'data.frame':    714 obs. of  4 variables:
##  $ Survived: int  0 1 1 1 0 0 0 1 1 1 ...
##  $ Pclass  : int  3 1 3 1 3 1 3 3 2 3 ...
##  $ Sex     : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 1 1 1 ...
##  $ Age     : num  22 38 26 35 35 54 2 27 14 4 ...
# 2 - Use ggplot() for the first instruction
ggplot(titanic, 
    aes(x = Pclass, fill = Sex)) + 
  geom_bar(
    position = "dodge")

# 3 - Plot 2, add facet_grid() layer
ggplot(titanic, 
    aes(x = Pclass, fill = Sex)) + 
  geom_bar(
    position = "dodge") +
  facet_grid(. ~ Survived)

# 4 - Define an object for position jitterdodge, to use below
posn.jd <- position_jitterdodge(0.5, 0, 0.6)

# 5 - Plot 3, but use the position object from instruction 4
ggplot(titanic, 
    aes(x = Pclass, y = Age, col = Sex)) + 
  geom_point(
    size = 3, alpha = 0.5, position = posn.jd) +
  facet_grid(. ~ Survived)