Table of Contents:

01-Introduction

  • Import: First you must import your data into R.

  • Tidy: Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation.

  • Transform: Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing speed from distance and time), and calculating a set of summary statistics (like counts or means).

  • Wrangling: Together, tidying and transforming are called wrangling, because getting your data in a form that’s natural to work with often feels like a fight!

  • Two main engines of knowledge generation:

    • Visualisation: is a fundamentally human activity. Does not scale well as it requires a human to interpret.
    • Modelling: are complementary tools to visualisation. Works well when the questions are precisely defined. Scales well, as not human involvement.

  • Communication: Last and the most critical step.

  • Programming: Cuts across all aspects of the project.

Importing Packages

# install.packages(c("tidyverse","nycflights13", "gapminder", "Lahman"))
lapply(c("tidyverse","nycflights13", "gapminder", "Lahman"), library, character.only = TRUE)

02-Explore

Introduction

Lets explore some useful tools that have an immediate payoff:

  • Visualisation
  • Transformation
  • Data Exploration - curiosity and scepticism to ask and answer interesting questions about data

03-Data Visualization

First Steps

Using example dataset from ggplot2 on Cars, lets try to answer the following questions

  • Do cars with big engines use more fuel than cars with small engines?

  • What does the relationship between engine size and fuel efficiency look like?

    • Is it positive? Negative? Linear? Nonlinear?
mpg
## # A tibble: 234 x 11
##    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    class
##    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> <chr>
##  1 audi         a4         1.8  1999     4 auto(l~ f        18    29 p     comp~
##  2 audi         a4         1.8  1999     4 manual~ f        21    29 p     comp~
##  3 audi         a4         2    2008     4 manual~ f        20    31 p     comp~
##  4 audi         a4         2    2008     4 auto(a~ f        21    30 p     comp~
##  5 audi         a4         2.8  1999     6 auto(l~ f        16    26 p     comp~
##  6 audi         a4         2.8  1999     6 manual~ f        18    26 p     comp~
##  7 audi         a4         3.1  2008     6 auto(a~ f        18    27 p     comp~
##  8 audi         a4 quat~   1.8  1999     4 manual~ 4        18    26 p     comp~
##  9 audi         a4 quat~   1.8  1999     4 auto(l~ 4        16    25 p     comp~
## 10 audi         a4 quat~   2    2008     4 manual~ 4        20    28 p     comp~
## # ... with 224 more rows

Graphing

Plot # 1: displ vs hwy
  • displ - engine displacement, in litres (engine size)
  • hwy - highway miles per gallon (car’s efficiency)
ggplot(data=mpg, aes(x=displ,y=hwy)) +
  geom_point(mapping = aes(x=displ,y=hwy)) +
  geom_smooth(method = "lm", se = TRUE, level=0.95)

plot shows negative relationship between engine size (displ) and efficiency (hwy)

Plot # 2: cyl vs hwy
  • displ - engine displacement, in litres (engine size)
  • hwy - highway miles per gallon (car’s efficiency)
ggplot(data=mpg, aes(x=hwy,y=cyl)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE, level=0.95)

plot shows negative relationship between # of cylinders (cyl) and efficiency (hwy)

Plot # 3: OMG!! 2 seaters have a differentiated mileage
  • Mix and match color with size, shape and alpha
ggplot(data=mpg, aes(x=displ,y=hwy)) +
  geom_point(mapping = aes(x=displ,y=hwy,color=class)) +
  geom_smooth(method = "lm", se = TRUE, level=0.95)
## `geom_smooth()` using formula 'y ~ x'

Plot # 4: Conditional coloring
ggplot(data=mpg, aes(x=displ,y=hwy)) +
  geom_point(mapping = aes(x=displ,y=hwy,color=displ<5)) +
  geom_smooth(method = "lm", se = TRUE, level=0.95)
## `geom_smooth()` using formula 'y ~ x'

Facets

Plot # 5: by Class

Clear view of the class variable. But the linear regression gave more insight than the facets.

ggplot(data=mpg, aes(x=displ,y=hwy)) +
  geom_point(mapping = aes(x=displ,y=hwy,color=displ<5)) +
  facet_wrap(~class, nrow = 2)

Plot # 5: drv ~ cyl
ggplot(data=mpg, aes(x=displ,y=hwy)) +
  geom_point(mapping = aes(x=displ,y=hwy,color=hwy<25)) +
  facet_grid(cyl~drv)

Geom objects

Plot # 6 - GEOM_SMOOTH
ggplot(data=mpg, aes(x=displ,y=hwy)) +
  geom_smooth(mapping = aes(x=displ,y=hwy))

Plot # 7 - GEOM_POINT
ggplot(data=mpg, aes(x=displ,y=hwy)) +
  geom_point(mapping = aes(x=displ,y=hwy))

Plot # 8 - GEOM_SMOOTH with Linetype
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

Plot # 9 - Multiple geoms in the same plot
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

Plot # 10 - display different aesthetics in different layers
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()

Statistical transformations

The figure below describes how this process works with geom_bar()
Plot 11 - default stat is count
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y= stat(prop) , group=1))

Plot 12 - default stat changed from count to other
demo <- tribble(
  ~cut,         ~freq,
  "Fair",       1610,
  "Good",       4906,
  "Very Good",  12082,
  "Premium",    13791,
  "Ideal",      21551
)

ggplot(data = demo) +
  geom_bar(mapping = aes(x = cut, y = freq), stat = "identity")

Plot 13 - stat_summary
ggplot(data = diamonds) + 
  stat_summary(
    mapping = aes(x = cut, y = depth),
    fun.min = min,
    fun.max = max,
    fun = median
  )

Position adjustments

Plot 14 - color
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, colour = cut))

Plot 15 - fill
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = cut))

Plot 16 - clarity
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity))

Plot 17 - 100 perc stacked - position fill
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")

Plot 18 - position dodge
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

Plot 19 - position jitter
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")

Coordinate systems

Plot 20 - coord_flip()
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot()

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot() +
  coord_flip()

Plot 21 - coord_quickmap()
nz <- map_data("nz")

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

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

Plot 22 - coord_polar()
bar <- ggplot(data = diamonds) + 
  geom_bar(
    mapping = aes(x = cut, fill = cut), 
    show.legend = FALSE,
    width = 1
  ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL)

bar + coord_flip()

bar + coord_polar()

The layered grammar of graphics