Chapter 2: Data Visualization

Question 1

What is the correct way to make the points blue in a scatterplot?

1. ggplot(persinj, aes(x = op_time, y = total, color = “blue”) + geom_point()
2. ggplot(persinj, aes(x = op_time, y = total)) + geom_point(colour = “blue)

Answer 1

2. is the correct way

remember that the aes() function is used to map variables in our dataset to visual properties of the graph. If we were to use the first choice, the function would look for a variable “blue” in our dataset, that doesn’t exist.
The mistake is setting an aesthetic to a constant value

Question 2

T/F: the aesthetics determine what relationships we want to see in the plot and the geoms determine how we want to see these relationships.

Answer 2

T

Question 3

How could we use the “color” aesthetic the right way in a ggplot?

Answer 3

we could use color = , alongside a comparison of two numeric variables
it will make the points on the graph different colours depending on the value of the factor variable

Question 4

can you set colour = factor variable without converting it to a factor variable first (in the aesthetics mapping)

Answer 4

no, you have to convert it as a factor using factor() function

Question 5

what are two common arguments of the geom_smooth() ?

Answer 5

method = “lm” and se = FALSE

Question 6

what are the 4 common arguments used in geom_point()?

Answer 6

color
alpha = how light/dark you want your points to be
shape
size

Question 7

what are the 2 common arguments of geom_bar()?

Answer 7

fill

alpha

Question 8

what are the 3 common arguments for geom_histogram()?

Answer 8

fill
alpha
bins = number of bins you want to use.

Question 9

what curve is usually used with geom_point?

Answer 9

geom_smooth(method = “lm”, se = FALSE)

Question 10

you are comparing 3 variables. two are numeric and one is a factor variable.
you want to make the colour of the points different based on the value of the factor variable.
you use geom_point() and geom_smooth() with se bounds.
how could you make sure that the se lines are filled in with the corresponding factor value (consistent with the points)?

Answer 10

ggplot(dataset, x = numeric, y = numeric, color = factor(categorical), fill = factor(categorical)) + geom_point() + geom_smooth(method = “lm”, se = TRUE)

consistent colouring between the SE lines and the smoothed curve. This is because we assigned the factor variable to both fill and colour.

Question 11

you are comparing 3 variables. two are numeric and one is a factor variable.
you want to make the colour of the points different based on the value of the factor variable.
you use geom_point() and geom_smooth() with an se bound.
how could you make sure that there is only one smoothed line and one se bound for all data points?

Answer 11

ggplot(dataset, aes(x = numeric var, y = numeric var)) + geom_point(aes(color = factor(categorical)) + geom_smooth(method = “lm”, se = TRUE)

• take it out of ggplot
• put it into geom_point as an aesthetic because we want the points to still be different colours
• se = TRUE in the geom_smooth

Question 12

T/F: aesthetic mappings in the original ggplot function will not be inherited to all geom functions.

Answer 12

False. they are

Question 13

What is faceting? What output does it provide when used? What function is used?

Answer 13

it’s a convenient way to categorize our data into distinct groups based on the value of our categorical predictors.
faceting displays the observations in separate plots produced for each value of the faceting variable placed side-by-side to ease comparison

the function used is facet_wrap()

Question 14

How do you use the facet_wrap() function?

Answer 14

ggplot(dataset, aes(x = numeric, y = numeric, color = factor(categorical)) + geom_point() + geom_smooth(method = “lm”, se = TRUE)
+ facet wrap(~ FACET VAR, ncol = n)

facet_wrap(~ FACET VAR, ncol = n) is added to the end of a ggplot

by default, the two graphs will be on the same scale so that they are easy to compare

Question 15

How do you add titles to a ggplot?

Answer 15

ggplot(dataset, aes(x = numeric, y = numeric, color = factor(categorical)) + geom_point() + geom_smooth(method = “lm”, se = TRUE)

+ labs( x = “x axis title”, y = “y axis title”, main = “main title”)

using the labs() function

Question 16

How do you display more than one graph on the page? What package?

Answer 16

library(gridExtra)

grid.arrage(graph1, graph2, …, graph n, ncol = n)

Question 17

How do you paste a graph into word from R?

Answer 17

right click on the graph > copy > go to word > right click > paste options: picture

Question 18

how do you alter the coordinates of a ggplot?

Answer 18

ggplot(dataset, aes(x = numeric, y = numeric, color = factor(categorical)) + geom_point() + geom_smooth(method = “lm”, se = TRUE)

+ coords_cartesian(ylim = c(number1, number2), xlim = c(400, 1000)

Question 19

What are the two components of exploratory data analysis?

Answer 19

1. descriptive statistics

2. visual aids (graphs)

Question 20

univariate data exploration: what 2 aspects of a numeric variable are you able to reveal using summary()

Answer 20

central tendency (mean and median - tell us the "size" of the variable)
dispersion (variance) 

can look for a skew in the variables data as well by comparing the mean and median.

Question 21

Univariate data exploration: what kind of graphs are used when exploring numeric variables?

Answer 21

Histograms: provide a visual summary of the count or relative frequency in each bin. we can learn about the shape of the distribution of a numeric variable

Boxplots: visualize the distribution of a numeric variable.
- boxplots don’t directly show the actual shape of the variable’s distribution, they offer a useful graphical summary of the key numeric statistics and allow for a visual comparison of the distributions of different numeric variables (relative magnitude of their mean and medians)

Question 22

TODO
if you want to compare the values of a numeric variable across different levels of a categorical predictor, what kind of graphical display could you use?

Answer 22

ggplot(dataset, x = factor(

Question 23

How could you calculate the summary statistics of a numeric variable for two groups of observations?

ex. separating the values of another numeric variable; you want the summary stats for y based on if x > 50 or x < 50?
ex. separating the values of a categorical predictor?

Answer 23

categorical:

dataset. value1 #, ]
dataset. under

Question 24

what should we do if we have a skewed target variable?

Answer 24

transformation. apply a monotonic concave function to shrink the outliers and symmetrize the overall distribution while preserving the ranks fo the observed values of the variable

Question 25

what kind of transformations can be done on a skewed target variable?

Answer 25

1. log-transformation (can only be done to strictly positive data)
2. sqrt (similar to the log transformation)

Question 26

how do we deal with natural outliers? 4

Answer 26

1. remove: if we know that it will not have a material effect
2. ignore: if the outliers make up an insignificant proportion of the data and are unlikely to create bias, then keep them
3. Modify: modify them to make them more reasonable
4. using robust model forms: instead of minimizing the squared error between predicted and observed, we could replace that with the absolute error. places a lot less weight on the observations that are outliers

Question 27

what could you use to look at the descriptive statistics for a categorical variable?

Answer 27

frequency tables.

table( dataset $ categorical ) / nrow( dataset)
this will produce a frequency table, it will tell you what the predominant level is of the categorical predictor.

Question 28

How could you graphically explore a categorical variable?

Answer 28

bar charts: extract the information in a frequency table and present the numeric counts visually

Question 29

what is bivariate data exploration used for?

Answer 29

relationships, patterns, outliers

Question 30

bivariate: numeric vs. numeric

what kind of descriptive statistics are to be used?

Answer 30

correlation coefficient!

use the cor() function

Question 31

bivariate: numeric vs. numeric

what kind of graph would you use to evaluate the relationship?

Answer 31

scatterplot 
ggplot(dataset, aes(x = numeric1, y = numeric2) + geom_point() + geom_smooth()

Question 32

bivariate: categorical vs. numeric

how could you use descriptive statistics to look at the relationship? How could you do this?

Answer 32

we can partition the data into different subsets, one for each level of the categorical variable, and compute the mean of the numeric variable within the subset

(using a tibble in the library(tidyverse)
* don’t need to know how to write that code

Question 33

bivariate: numeric vs. categorical
what if you wanted to look at a 3-way interaction between one numeric variable (target variable) and 2 categorical predictors?

Answer 33

use boxplots!

ggplot(dataset, aes(x = categorical1, y = numeric, fill = factor(categorical)) + geom_boxplot()

Question 34

bivariate: categorical vs. categorical

How could you use descriptive statistics to evaluate this relationship?

Answer 34

two-way frequency table using the table() function. the first argument is the rows and columns = 2nd argument

ex.
table(dataset $ categorical1, dataset $ categorical2)

Question 35

bivariate: categorical vs. categorical

how could you use graphical displays? what are the 3 options to display this graph?

Answer 35

split bar charts are a good way visualize the relationship

ggplot(dataset, aes(x = categorical1, y = categorical2)) + geom_bar(position = “ ….. “)

position can be three things:
position = “fill”
position = “dodge”
default value = stacked

fill is usually the most useful for depicting the interplay between two categorical variables