Introduction to R and the tidyverse. Paolo Crosetto

Transcription

1 Introduction to R and the tidyverse Paolo Crosetto

2 Lecture 1: plotting

3 Before we start: Rstudio Interactive console Object explorer Script window Plot window

4 Before we start: R concatenate: c() assign: <- vector, matrices: rbind(), cbind() matrix extraction: [ ] variable extraction: $ data frames: mpg

5 Why do we plot Why do we want to plot data? we are human beings we are pattern recognizers we can see things we are not able to grasp from data good to explore a dataset and look for regularities good to convey a clear message to have fun (to show your colleagues how nice your plot is)

6 What do you see? Figure 1: Plots allow to convey a lot of information in a compact way

7 Good plots, bad plots It is important to make good plots i.e., plots that look good and are honest to the data it is very easy to hide the message rather than highlighting it it is very easy to mislead with a plot so let s start with a gallery of bad plots. Can you guess why they are bad?

8 Bad plotting 1 Figure 2:

9 Bad plotting 2 Figure 3:

10 Bad plotting 3 Figure 4:

11 Bad plotting 4 Figure 5:

12 Bad plotting 5 Figure 6:

13 Bad plotting 5 (really, you don t need 3D plots) Figure 7:

14 The road to good plotting know your data think before you hit the enter button sketch on paper first be honest draw your axis first choose your visualization wisely

15 Some data We will start by using the built-in dataset mpg mpg ## #... with 224 more rows, and 2 more variables: fl <chr> ## # A tibble: 234 x 11 ## manufacturer model displ year cyl trans ## <chr> <chr> <dbl> <int> <int> <chr> ## 1 audi a auto(l5) ## 2 audi a manual(m5) ## 3 audi a manual(m6) ## 4 audi a auto(av) ## 5 audi a auto(l5) ## 6 audi a manual(m5) ## 7 audi a auto(av) ## 8 audi a4 quattro manual(m5) ## 9 audi a4 quattro auto(l5) ## 10 audi a4 quattro manual(m6)

16 A look at the data model : model name displ : engine displacement, in litres year : year of manufacture cyl : number of cylinders trans : type of transmission drv : f = front-wheel drive, r = rear wheel drive, 4 = 4wd cty : city miles per gallon hwy : highway miles per gallon fl : fuel type class : type of car

17 We will be using ggplot2. Why? Advantages of ggplot2 consistent underlying grammar of graphics (Wilkinson, 2005) plot specification at a high level of abstraction very flexible theme system for polishing plot appearance mature and complete graphics system many users, active mailing list

18 What is a grammar of graphics? The basic idea: independently specify plot building blocks and combine them to create just about any kind of graphical display you want. Building blocks of a graph include: data aesthetic mapping geometric object statistical transformations scales coordinate system position adjustments faceting

19 Starting from the basics As in a grammar the minimal sentence is a subject in a plot the minimal object is data p <- ggplot(mpg) In a grammar, you need a verb. In plots, this is axis p <- ggplot(mpg, aes(x = displ, y = hwy)) Still no plot generated!

20 Generating a plot But you also need an object. In ggplot, this is geoms p + geom_point() 40 hwy

21 Generating a plot, 2 But you also need an object. In ggplot, this is geoms p + geom_smooth() ## `geom_smooth()` using method = 'loess' hwy 25 20

22 Generating a plot, 3 But you also need an object. In ggplot, this is geoms p + geom_smooth()+geom_point() ## `geom_smooth()` using method = 'loess' 40 hwy 30 20

23 The beauty of a grammar metaphor once you get the main idea, adding things is easy a plot is a sentence made with data you add layers with + as you would add words to a sentence as in grammar you use adjectives to give more nuanced meaning, in plots you could use + to add color, fill, size, shape, etc...

24 Adding meaning: color p + geom_point(aes(color=class)) hwy class 2seater compact midsize minivan pickup subcompact suv displ

25 Adding meaning: size p + geom_point(aes(size=cyl)) hwy cyl displ

26 Adding meaning: color AND size p + geom_point(aes(size = cyl, color=class)) compact hwy midsize minivan pickup subcompact suv displ cyl

27 Adding meaning: shape p + geom_point(aes(shape=fl)) hwy fl c d e p r displ

28 Adding meaning: all together p + geom_point(aes(color=manufacturer, shape =fl, size = cy mercury hwy nissan pontiac subaru toyota volkswagen displ fl c d e p

29 Facets sometimes sentences become a bit too long it is useful to split them up in shorter sentences for instance, you could first talk about a car, then another one in plots, you can split up the plot along a variable so that one plot is drawn for each level of a given variable, say type of fuel

30 Facets p + geom_point(aes(color=manufacturer, size = cyl))+facet_g hwy c d e p r hyundai jeep land rover lincoln mercury nissan pontiac subaru toyota volkswagen displ cyl

31 More details on the grammar A ggplot is made up of data (subject) axis (verb) geoms (object) aesthetic layers (size, fill color, shape, label,... ) facets (splitting sentences) And then you can change how things look and behave: - coordinate functions (changing the axis appearance and type) - scale functions (changing the appearance of the geoms) - theme functions (changing the appearance of the plot itself)

32 Exploring data with plots: one variable Plot types depend on the variable type one-variable plots, discrete variable: barplot one-variable plots, continuous variable: distribution, density

33 Barplots let s look at the drive type of the cars: front, rear, or 4wd p <- ggplot(mpg, aes(drv)) p + geom_bar() count

34 Barplots not so fancy. should we add color? p <- ggplot(mpg, aes(drv)) p + geom_bar(aes(color=drv)) 100 count drv 4 f r 0

35 Barplots ups. Maybe we meant fill? p <- ggplot(mpg, aes(drv)) p + geom_bar(aes(fill=drv)) 100 count drv 4 f r 0

36 Barplots nice. doesn t add much information, though. what if we cross it with car class? p <- ggplot(mpg, aes(drv)) p + geom_bar(aes(fill=class)) count class 2seater compact midsize minivan pickup subcompact suv

37 Barplots By default stacked. How to unstack? p <- ggplot(mpg, aes(drv)) p + geom_bar(aes(fill=class), position = position_dodge()) count class 2seater compact midsize minivan pickup subcompact suv 0

38 Barplots By default stacked. How to show relative weight? p <- ggplot(mpg, aes(drv)) p + geom_bar(aes(fill=class), position = position_fill()) 1.00 count class 2seater compact midsize minivan pickup subcompact suv 0.00

39 10 One variable, continuous: mpg on highway When the variable is continuous, it makes more sense to show distributions p <- ggplot(mpg, aes(hwy)) p + geom_histogram() ## `stat_bin()` using `bins = 30`. Pick better value with ` count 20

40 Histograms: binwidth p + geom_histogram(bins = 10) count hwy

41 Histograms: binwidth p + geom_histogram(bins = 100) count hwy

42 An alternative do histogram: dotplot p + geom_dotplot(binwidth = 0.5) count hwy

43 Continuous distribution: Kernel Density Estimation p + geom_density() 0.06 density hwy

44 Continuous distribution: Kernel Density Estimation p + geom_density(adjust = 3) density hwy

45 Continuous distribution: Kernel Density Estimation p + geom_density(adjust = 0.5) density hwy

46 Additional resources try to look for PlotCon2016 videos, and especially this one...