Chapter 2 Code

Question 1

How can we load a dataframe if it is from a package in R?

Answer 1

data <- nhanes

Question 2

How we can we load a data frame from a file?

Answer 2

data <- read.csv(“nhanes.csv”)

Question 3

How do we write data to a file?

Answer 3

write.csv(data, “file_name”)

write.csv(data, “file_name”, row.names=TRUE)

Question 4

How do we look at the structure of the data?

Answer 4

str(data)

Question 5

How do you ensure that there is only complete data in the dataset?

Answer 5

use na.omit()

data <- na.omit(data)

Question 6

How do you print a statement in R?

Answer 6

paste0()

Question 7

What function was used to get the mode of a vector?

Answer 7

getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}

Question 8

What does tabulate() do?

Answer 8

Takes an integer-valued vector bin and counts the number of times each integer occurs in it

Question 9

What does match(x, y) do?

Answer 9

Matches each element of x to its corresponding position in y.

Question 10

What function is used to determines the location (index) of the (first) minimum or maximum of a numeric (or logical) vector?

Answer 10

which.max()

Question 11

What function can you use to return the statistical mode?

Answer 11

R does not have a built-in function to compute the statistical mode. This is why you need to use a custom function like getmode to:

Identify the unique values.
Count how often each unique value occurs.
Return the value with the highest frequency.

Question 12

What are the measures of location?

Answer 12

mean(data)
median(data)

and mode - need to create a function

Question 13

How is R indexed?

Answer 13

Indexed from 1

Question 14

How do you convert a numeric variable to a categorical variable?

Answer 14

data$var <- as.factor(data$var)

Question 15

How can you quickly see the quartiles of data?

Answer 15

summary(data)

Question 16

What are the measures of variation?

Answer 16

Range
- Can do max(data) - min(data)
- Can do range(data)[2] - range(data)[1]

IQR
- IQR(data)
- summary(data)[5] - summary(data)[2]

Variance
- var(data)

Use summary(data) to get the quartiles

Question 17

How do we calculate frequencies to probabilities?

Answer 17

prob <- freq / sum(freq)

Question 18

What are the measures of heterogeneity?

Answer 18

Gini index of heterogeneity
Entropy

Question 19

How do we calculate the Gini Index of Heterogeneity?

Answer 19

G <- 1 - sum(prob^2)

Question 20

Why would you multiply the Gini index by k / k-1 ?
What code is used to do this?

Answer 20

K/k-1 is a correction factor that adjusts the Gini index to account for the number of categories in the dataset.

This normalises the index to range from 0 to 1.

G prime -
k <- length(example)
G_norm <- G * (k/(k-1))

Question 21

How do you calculate entropy?

Answer 21

E <- -sum(prob * log10(prob))

Question 22

How do you normalise the entropy value?

Answer 22

Divide by log(k) where k = length(data)

E_norm <- E / log10(k)

Question 23

What is the Gini() function from DescTools used for?

Answer 23

The Gini concentration index - used for measuring concentration

Question 24

How do you calculate the Gini concentration index?

Answer 24

Gini(data)

Manual process:
n <- length(data)
n_list <- c(1:n)

example_1 <- sort(example_1)

Fi <- n_list / n

xj <- cumsum(example_1)
N <- sum(example_1)
Qi <- xj / N

FiQi <- Fi-Qi
FiQi <- (head(sumFiQi, -1))
sumFiQi <- sum(FiQi)

R <- sumFiQi / sumFi

Question 25

If we want to work on models and then check the results, what should we do?

Answer 25

Split the data into a training and test set

Question 26

How do we split the data into a training and test set?

Answer 26

createDataPartition()

in_train <- createDataPartition(data$column, p = 0.8, list = FALSE)

Question 27

How do you add labels to a graph?

Answer 27

+ labs(title=”yx”)