Introduction to R

Question 1

What is Haley’s model of the tools needed in a typical data science project?

Answer 1

Question 2

What does it mean to tidy data

Answer 2

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.

Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions.

Question 3

What does it mean to transform data

Answer 3

​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).

Question 4

What is wrangling data

Answer 4

Tidying and transforming, together are called wrangling because getting your data in a form that’s natural to work with often feels like a fight.

Question 5

What are the two engines of knowledge generation?

Answer 5

Visualization and Modeling

These have complementary strengths and weaknesses so any real analysis will iterate between them many times.

Question 6

What is a good visualisation

Answer 6

A good visualisation will show you things that you did not expect, or raise new questions about the data.

A good visualisation might also hint that you’re asking the wrong question, or you need to collect different data.

Visualisations can surprise you, but don’t scale particularly well because they require a human to interpret them.

Question 7

What are models? When should you use them? what can or can’t they do?

Answer 7

Models are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them.

Models are a fundamentally mathematical or computational tool, so they generally scale well. Even when they don’t, it’s usually cheaper to buy more computers than it is to buy more brains!

But every model makes assumptions, and by its very nature a model cannot question its own assumptions.

That means a model cannot fundamentally surprise you.

Question 8

What is the last step of data science?

Answer 8

Communication, an absolutely critical part of any data analysis project. It doesn’t matter how well your models and visualisation have led you to understand the data unless you can also communicate your results to others.

Question 9

How does programming tie in data science?

Answer 9

Programming is a cross-cutting tool that you use in every part of the project.

You don’t need to be an expert programmer to be a data scientist, but learning more about programming pays off because becoming a better programmer allows you to automate common tasks, and solve new problems with greater ease.

Question 10

What are the two camps analysis can be divided into?

Answer 10

Hypothesis generation and hypothesis confirmation (sometimes called confirmatory analysis).

Question 11

What are the two key regions in the R studio interface?

Answer 11

Question 12

What is tidyverse?

Answer 12

A collection of R packages

Question 13

What is a package in R

Answer 13

Packages are the fundamental units of reproducible R code.

They include reusable functions, the documentation that describes how to use them, and sample data.

Question 14

How do you install a package? Install the tidyverse package

Answer 14

install.packages(“tidyverse”)

Question 15

What’s the “prompt” in R?

Answer 15

Refers to the “>” in the local console

Question 16

When asking a question, what are the three things you need to include to make your example reproducible?

Answer 16

required packages, data, and code

Question 17

What is data exploration

Answer 17

Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again.

Question 18

What is the goal of data exploration?

Answer 18

The goal of data exploration is to generate many promising leads that you can later explore in more depth.

Question 19

What is the grammar of graphics?

Answer 19

A coherent system for describing and building graphs

Question 20

What is ggplot2

Answer 20

One of the more elegant and versatile systems for visualisation.

Located in the Tidyverse package

Question 21

How do you load a library? Use tidyverse as an example

Answer 21

library(tidyverse)

Question 22

What must you do with a package every time you start a new R session?

Answer 22

reload the package

Question 23

How do you call up a function from a package? Give an example.

Answer 23

> package:: function ( )

eg: > ggplot2:: ggplot ( )

Question 24

What is a data frame?

Answer 24

A data frame is a rectangular collection of variables (in the columns) and observations (in the rows).

Question 25

How do you learn more about a function or a package loaded into R?

Answer 25

”?”

eg. ?mpg

Question 26

Provide code for a reusable template for mapping graphs with ggplot2

Answer 26

ggplot (data = <data>) +</data>

<geom_function> (mapping = aes (<mappings>) )</mappings></geom_function>

Question 27

How do you begin a plot in ggplot?

Answer 27

ggplot ( ).

Question 28

What does the function “ggplot ( )” do?

Answer 28

“ggplot( )” creates a coordinate system that you can add layers to.

The first argument of ggplot ( ) is the dataset to use in the graph.

This creates an empty graph-eg ggplot (data = mpg)

Question 29

What does the function “geom_point ( )” do?

Answer 29

Adds a layer of points to your plot, which creates a scatterplot

Question 30

What is the “mapping” argument in a geom function?

Answer 30

This defines how variables in your dataset are mapped to visual properties.

Question 31

What is the “mapping” argument always paired with?

Answer 31

“aes ( )”

Question 32

What do the “aes ( )” arguments “x” and “y” specify?

Answer 32

Specify which variables to map to the x and y axes.

ggplot2 looks for the mapped variables in the “data” argument

Question 33

How can you add a third variable to a scatter plot?

Answer 33

You can add a third variable, like class, to a two dimensional scatterplot by mapping it to an aesthetic

Question 34

What is an aesthetic? Give an example:

Answer 34

An aesthetic is a visual property of the objects in your plot.

Aesthetics include things like the size, the shape, or the color of your points.

Question 35

What is scaling?

Answer 35

The process of assigning a unique level of the aesthetic ( such as a unique color) to each unique value of the variable

Question 36

What does the “alpha” aesthetic do?

Answer 36

controls the transparency of the points

Question 37

Give an example using ggplot and the mpg data set and anesthetic.

x = displ, y = hwy, alpha/shape = class

Answer 37

ggplot (data = mpg) +

geom_point ( mapping = aes ( x = displ, y = hwy, alpha = class) )

Question 38

What is a common problem regarding the “+” when creating ggplot2 graphics

Answer 38

Putting the + sign in the wrong place. It comes at the end, not the beginning.

Question 39

What are facets?

Answer 39

subplots that each display one subset of data.

Question 40

What are two types of facet functions?

Answer 40

facet_wrap ( )

facet_grid ( )

Question 41

What is a geom?

Answer 41

A geom is the geometrical object that a plot uses to represent data.

People often describe plots by the type of geom that the plot uses.

Question 42

How many geoms does ggplot2 have?

Answer 42

Over 30 geoms, and extension packages provide even more (see https://www.ggplot2-exts.org for a sampling)

Question 43

How can you display multiple geoms in the same plot?

Answer 43

To display multiple geoms in the same plot, add multiple geom functions to ggplot():

ggplot(data = mpg) +

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

Question 44

How can you avoid having to modify each function (and the inherent error potential this creates) when multiple geoms are being used in the same plot?

Answer 44

You can avoid this type of repetition by passing a set of mappings to ggplot().

ggplot2 will treat these mappings as global mappings that apply to each geom in the graph.

Question 45

How do you apply a set of mappings to ggplot ( ) function? Show by example.

Answer 45

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

geom_point() + geom_smooth()

Question 46

What does the “se” argument in geom_smooth( ) do?

Answer 46

Shading

Question 47

What is an additional feature seen with bar charts, histograms, and frequency polygons?

Answer 47

bar charts, histograms, and frequency polygons bin your data and then plot bin counts, the number of points that fall in each bin.

Question 48

What do “smoothers” do?

Answer 48

Smoothers fit a model to your data and then plot predictions from the model.

Question 49

What is stat?

Answer 49

The algorithm used to compute new values for a graph.

Question 50

You can generally use geoms and _____ interchangeably

Answer 50

stats

Question 51

Why can you use geoms and stats interchaneably

Answer 51

Because every geom has a default stat and every stat has a defaulg geom.

Question 52

What does stat_summary ( ) do?

Answer 52

Summarises the y values for each unique x value, to draw attention to the summary that you’re computing:

Question 53

How many stats does ggplot2 have? How can you see a complete list?

Answer 53

Over 20

ggplot2 cheat sheet

Question 54

How can you color a bar chart?

Answer 54

You can colour a bar chart using either the colour aesthetic, or, more usefully, fill.

Question 55

With regards to the “fill” aesthetic in bar graphs, what is necessary with regards to the height of the bars?

Answer 55

With the fill aesthetic, the heights of the bars need to be normalized.

Question 56

What does the position argument do?

Answer 56

Controls whether stacking is performed in a bar graph.

Question 57

How can you modify the position argument so it doesn’t stack the bars?

Answer 57

If you don’t want a stacked bar chart, you can use one of three other options: “identity”, “dodge”or “fill”.

Question 58

What does position = “identity” do?

Answer 58

position = “identity” will place each object exactly where it falls in the context of the graph. This is not very useful for bars, because it overlaps them.

Question 59

What does position = “fill” do? When should this be used?

Answer 59

position = “fill” works like stacking, but makes each set of stacked bars the same height. This makes it easier to compare proportions across groups.

Question 60

What does position = “dodge” do in a bar graph?

Answer 60

position = “dodge” places overlapping objects directly beside one another. This makes it easier to compare individual values.

Question 61

What is overplotting

Answer 61

When there exists observations with identical or similar values in a scatter plot. Because the points are rounded they may overlap and appear on top of each other.

Question 62

What is one approach to avoid overplotting?

Answer 62

You can avoid this gridding by setting the position adjustment to “jitter”. position = “jitter” adds a small amount of random noise to each point.

Question 63

What does coord_flip() do?

Answer 63

witches the x and y axes in a cartesian coordinate system

Question 64

What does coord_polar do?

Answer 64

coord_polar() uses polar coordinates. Polar coordinates reveal an interesting connection between a bar chart and a Coxcomb chart.

Question 65

What does geom_abline do?

Answer 65

Adds a reference line for the slope.

Question 66

What is the seven-parameter template applicable to most graphs which Hadley suggests?

Answer 66

ggplot ( data = <data> ) +</data>

<geom_function> (</geom_function>

mapping = aes (<mappings>),</mappings>

stat = <stat>,</stat>

position = <position></position>

) +

<coordinate_function> +</coordinate_function>

<facet_function></facet_function>

Question 67

What insight is the grammar of graphics based on?

Answer 67

Based on the insight that you can uniquely describe any plot as a combination of a dataset, a geom, a set of mappings, a stat, a position adjustment, a coordinate system, and a faceting scheme.

Question 68

What are five key key dplyr functions that allow you to solve the vast majority of your data manipulation challenges?

Answer 68

Pick observations by their values ( filter ( ) ).

Reorder the rows ( arrange ( ) ).

Pick variables by their names ( select ( ) ).

Create new variables with functions of existing variables ( mutate( ) ).

Collapse many values down to a single summary ( summarise( ) ).

along with group_by ( ) we have six functions which provide the verbs for a language of data manipulation

Question 69

What does group_by ( ) allow us to do in conjunction with the dplyr five key functions?

Answer 69

Changes the scope of each function from operating on the entire dataset to operating on it group-by-group.

Question 70

What function can we use to avoid the trouble created by floating point numbers?

Answer 70

near ( , )