Outline

We will spend next couple lectures studying some R packages for typical data science projects.

A typical data science project:

Tidyverse

Data visualization

“The simple graph has brought more information to the data analyst’s mind than any other device.”

John Tukey

mpg data

Aesthetic mappings | r4ds chapter 3.3

Scatter plot

  • hwy vs displ

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

  • An aesthetic maps data to a specifc feature of plot.

  • Check available aesthetics for a geometric object by ?geom_point.

Color of points

  • Color points according to class:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Size of points

  • Assign different sizes to points according to class:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))

Transparency of points

  • Assign different transparency levels to points according to class:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))
    ## Warning: Using alpha for a discrete variable is not advised.

Shape of points

  • Assign different shapes to points according to class:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

  • Maximum of 6 shapes at a time. By default, additional groups will go unplotted.

Manual setting of an aesthetic

  • Set the color of all points to be blue:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Facets | r4ds chapter 3.5

Facets

  • Facets divide a plot into subplots based on the values of one or more discrete variables.

  • A subplot for each car type:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

  • A subplot for each car type and drive:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ class)

Geometric objects | r4ds chapter 3.6

geom_smooth(): smooth line

  • hwy vs displ line:

    ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

Different line types

  • Different line types according to drv:

    ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

Different line colors

  • Different line colors according to drv:

    ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, color = drv))

Points and lines

  • Lines overlaid over scatter plot:

    ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

  • Same as

    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth()

Aesthetics for each geometric object

  • Different aesthetics in different layers:

    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)

Bar charts | r4ds chapter 3.7

diamonds data

  • diamonds data:

    diamonds
    ## # A tibble: 53,940 x 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7 0.24  Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8 0.26  Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9 0.22  Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10 0.23  Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # … with 53,930 more rows

Bar chart

  • geom_bar() creates bar chart:

    ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))

  • Bar charts, like histograms, frequency polygons, smoothers, and boxplots, plot some computed variables instead of raw data.

  • Check available computed variables for a geometric object via help:

    ?geom_bar

  • Use stat_count() directly:

    ggplot(data = diamonds) + 
  stat_count(mapping = aes(x = cut))

  • stat_count() has a default geom geom_bar().

  • Display frequency instead of counts:

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

    Note the aesthetics mapping group=1 overwrites the default grouping (by cut) by considering all observations as a group. Without this we get

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

  • Color bar:

    ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, colour = cut))

  • Fill color:

    ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = cut))

  • Fill color according to another variable:

    ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity))

Positional arguments | r4ds chapter 3.8

Coordinate systems | r4ds chapter 3.9

ggplot(nz, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

Graphics for communications | r4ds chapter 28

Title

  • Figure title should be descriptive:

    ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(title = "Fuel efficiency generally decreases with engine size")

Subtitle and caption

  • ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) + 
  labs(
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )

Axis labels

  • ggplot(mpg, aes(displ, hwy)) +
geom_point(aes(colour = class)) +
geom_smooth(se = FALSE) +
labs(
  x = "Engine displacement (L)",
  y = "Highway fuel economy (mpg)"
)

Math equations

  • df <- tibble(x = runif(10), y = runif(10))
ggplot(df, aes(x, y)) + geom_point() +
  labs(
    x = quote(sum(x[i] ^ 2, i == 1, n)),
    y = quote(alpha + beta + frac(delta, theta))
  )

  • ?plotmath

Annotations

  • Create labels

    best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)
best_in_class
    ## # A tibble: 7 x 11
## # Groups:   class [7]
##   manufacturer model  displ  year   cyl trans drv     cty   hwy fl    class
##   <chr>        <chr>  <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 chevrolet    corve…   5.7  1999     8 manu… r        16    26 p     2sea…
## 2 dodge        carav…   2.4  1999     4 auto… f        18    24 r     mini…
## 3 nissan       altima   2.5  2008     4 manu… f        23    32 r     mids…
## 4 subaru       fores…   2.5  2008     4 manu… 4        20    27 r     suv  
## 5 toyota       toyot…   2.7  2008     4 manu… 4        17    22 r     pick…
## 6 volkswagen   jetta    1.9  1999     4 manu… f        33    44 d     comp…
## 7 volkswagen   new b…   1.9  1999     4 manu… f        35    44 d     subc…

  • Annotate points

    ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = class)) +
  geom_text(aes(label = model), data = best_in_class)

  • ggrepel package automatically adjust labels so that they don’t overlap:

    library("ggrepel")
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(size = 3, shape = 1, data = best_in_class) +
  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)

Scales

  • ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))

    automatically adds scales

    ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_colour_discrete()

  • breaks

    ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_y_continuous(breaks = seq(15, 40, by = 5))

  • labels

    ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL)

  • Plot y-axis at log scale:

    ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  scale_y_log10()

  • Plot x-axis in reverse order:

    ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  scale_x_reverse()

Legends

  • Set legend position: "left", "right", "top", "bottom", none:

    ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) + 
  theme(legend.position = "left")

Zooming

  • Without clipping (removes unseen data points)

    ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30))

  • With clipping (removes unseen data points)

    ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  xlim(5, 7) + ylim(10, 30)

  • ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  scale_x_continuous(limits = c(5, 7)) +
  scale_y_continuous(limits = c(10, 30))
  • mpg %>%
  filter(displ >= 5, displ <= 7, hwy >= 10, hwy <= 30) %>%
  ggplot(aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth()

Themes

  • ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  theme_bw()

Saving plots

ggplot(mpg, aes(displ, hwy)) + geom_point()

ggsave("my-plot.pdf")
## Saving 7 x 5 in image

Cheat sheet

RStudio cheat sheet is extremely helpful.