1.1.1

Question 1

What does the function library() do?

Answer 1

Loads packages

Question 2

What function reads data into R?

Answer 2

read_csv(“”)

Question 3

What does head() do?

Answer 3

Shows the first six rows of data by default

Question 4

What does the function head(data, n = x) do?

Answer 4

Changes the number of rows displayed in head()

Question 5

What is the function dim() used for?

Answer 5

Looking at how big the data is

Question 6

What do the left and right number represent in the output of dim()?

Answer 6

Left = rows
Right = columns

Question 7

What are observational units?

Answer 7

Individual entities on which data are collected (normally rows)

Question 8

What are variables?

Answer 8

Any characteristic that varies between observational units (usually columns)

Question 9

What does the dollar sign $ allow us to do?

Answer 9

Extract a specific variable from a dataframe

Question 10

What is a vector?

Answer 10

Each variable in a dataframe

Question 11

What are the square brackets [] used for?

Answer 11

Indexing (finding specific entries in the data)

Question 12

What functions are used to find specific values in vectors and dataframes?

Answer 12

Use vector[entry] for vectors/single sequence of values
Use dataframe[rows, columns] for dataframes

Question 13

What happens if we leave either rows or columns blank in the dataframe[rows, columns] function?

Answer 13

We will get out all of them

Question 14

How can we identify a column using quotation marks?

Answer 14

dataframe[“column name”]

Question 15

How can we combine multiple rows/columns using the combine function?

Answer 15

Dataframe[c(n1,n2), c(n3,n4)]

Question 16

What function allows us to specify a sequence?

Answer 16

Colon from:to:

Question 17

How can we combine the $ and [] function?

Answer 17

Dataframe$variable[row/column/vector]

Question 18

What are conditions?

Answer 18

Conditions e.g. > or == allow us to access entries of data for which the condition is true
e.g.
somevalues[somevalues > 20]
starwars[starwars2$species == “Droid”, ]

Question 19

What is the with() function and an example of using it?

Answer 19

A function that creates a container where variables of a data frame are available without the need to use $
e.g.
with(starwars2, starwars2[species == “Droid”, ]

Question 20

What is data cleaning?

Answer 20

Identifying incorrect/incomplete/irrelevant data
Replacing/modifying/deleting them

Question 21

How can we replace/overwrite/reassign parts of the data? Give an example

Answer 21

dataframe[row, column] <- “entry”
e.g.
starwars2[2,6] <- “Droid”

Question 22

How can we assign selected entries to a new value? Give an example

Answer 22

Dataframe[dataframe$column==”entry”, “column name/number”] <- “entry”
e.g.
starwars2[starwars2$homeworld==”Naboo”, “species”] <- “Nabooian”
OR
starwars2$species[starwars2$homeworld==”Naboo”] <- “Nabooian”

Question 23

How can we rewrite existing columns or add new columns in a dataframe, and what are 2 examples?

Answer 23

Dataframe$entry <- dataframe$entry / or * or + or – n
Dataframe$entry2 <- dataframe$entry / or * or + or – n
e.g. starwars2$height <- starwars2$height / 100 or starwars2$height2 <- starwars2$height / 100

Question 24

How can we remove rows/columns from dataframes and what is an example?

Answer 24

dataframe <- dataframe[-row(s)/space, column(s)/space]
e.g.
starwars2[-c(35, 75),]

Question 25

What are categorical variables and levels?

Answer 25

Categorical variables = tell us what group/character each individual belongs to Level = each distinct group/category

Question 26

What are nominal variables?

Answer 26

Unordered categorical variables with no intrinsic ordering among levels

Question 27

What are ordinal variables?

Answer 27

Categorical variables which levels possess some kind of order

Question 28

What are binary categorical variables?

Answer 28

Categorical variables with only 2 possible levels e.g. yes or no

Question 29

What are numerical/quantitative variables

Answer 29

Variables consisting of numbers that represent a measurable quantity

Question 30

What are continuous numerical variables?

Answer 30

Variables which can take any real number within the specified range of measurement e.g. height

Question 31

What are discrete numerical variables?

Answer 31

Variables which can only take integer number values e.g. number of siblings

Question 32

Why do we need to tell R explicitly what type of data each variable is?

Answer 32

Different types of data get treated differently by functions

Question 33

If data is neither numerical or categorical, what does it get treated as?

Answer 33

Characters (symbols)

Question 34

How are categorical types of data set and checked in R?

Answer 34

Set as: as.factor() or factor() Check is: is.factor(dataframe$entry)

Question 35

How are ordinal types of data set and checked in R?

Answer 35

Set as: as.ordered() or factor(... , ordered = TRUE) Check is: is.ordered(dataframe$entry)

Question 36

How are continuous types of data set and checked in R?

Answer 36

Set as: as. numeric() Check is: is.numeric(dataframe$entry)

Question 37

How are character types of data set and checked in R?

Answer 37

Set as: as.character() Check is: is.character(dataframe$entry)

Question 38

What does the function 'class' do and how can we use it?

Answer 38

Check what type of data each entry is e.g. class(dataframe$entry)

Question 39

How can we modify the class of a variable and what is an example?

Answer 39

dataframe$entry <- as/class(dataframe$entry) e.g. starwars2$species <- as.factor(starwars2$species)

Question 40

What does != and ! mean in R?

Answer 40

!= means “is not equal to” ! means “not”

Question 41

What do <, <=, >, and >= mean in R?

Answer 41

< means less than <= means less than or equal to > means greater than >= means greater than or equal to

Question 42

What does the function levels() do?

Answer 42

See the possible response options of a factor (categorical) variable