NNNWeek 2 - Basic R, Data Structure/Manipulation

Question 1

What are logical operators used in?

Answer 1

Data management

Question 2

What do logical operators return in R?

Answer 2

True or false (a Boolean value)

Question 3

To find outliers, values greater than or less than a specific score, or check values fall within a certain range, or recode continous variables in categorial variables (low/medium/high) what would you use?

Answer 3

A logical operator

Question 4

What might a logical operator look like?

Question 5

How do operators deal with data - where do they pull from?

Answer 5

Operators take data on the LEFT hand side an a few options arguments on the right hand side.

Question 6

In an analysis, we often subset data. Why would we do this?

Answer 6

Because subsetting means we can select a portion of data that is relevant in the criteria you are wanting to use.

Question 7

If you wanted to exclude outliers or select participants who meet a certain criteria in your data, what task would you use ?

Answer 7

Subset the data

Question 8

True or false: logical operators are commonly used in subsetting to pick specific rows of a dataset or specific values from a single variable?

Answer 8

TRUE

Question 9

If we wanted to run analyses in data excluding people outside 25 years of age and 20 years of age, which logical operator would we use in subsetting?

Answer 9

%gele% because it captures people greater than or equal to.

You can see we are working with just a single variable as Age is only word to the left of the logical operator. If more than one, would be & or vertical line if more than one. e.g [Age == 18 & Female == 1]

Question 10

What operators do we use to chain conitions together?

Answer 10

& or the single vertical line

e.g

d[Age == 18 & Female == 1, .(UserID, Age)]

Question 11

What if you had several values that you wanted to test within a data set, what operator could do this?

Answer 11

the %in% one.

select anyone whose age is in 18, 19, or 20

d[Age %in% c(18, 19, 20), .(UserID, Age)]

So this is saying, in this variable of AGE on the left, find anyone whose age is in 18,19 or 20.

Question 12

If you had two variables to consider - not just Age, but also Gender as an example, how would you chain these together?

Answer 12

Using brackets! (parentheses)

So say you wanted to see a 19 year old female participant or 18 year old male participants. You’ve used vertical line below to put two together but could do & potentially..

d[(Age == 19 & Female == 1) | (Age == 18 & Female == 0),
.(UserID, Age)]

Question 13

What is this operator asking the data to pull out?

d[Age < 20, .(UserID, Age)]

Answer 13

Show anyone under the age of 20.

Question 14

What is this operator asking the data to pull out?

## anyone age 20 or under
d[Age <= 20, .(UserID, Age)]

Answer 14

Show anyone age 20 or under

Question 15

What does the operator ! say

Answer 15

who is NOT

Question 16

What does this operator say?

d[Age %!in% c(18, 19, 20), .(UserID, Age)]

Answer 16

Return data for the variable age for anyone who is NOT age 18, 19 or 20.

Question 17

What does the function is.na() mean?

Answer 17

It returns true if a value is NA. So if we don’t want missing values, we might include is.na in an operation order like this:

d2

Question 18

How many main parts do data tables have?

Answer 18

DT , i , J and by

Question 19

What does DT represent in a data table?

Answer 19

The name of the data table

Question 20

What does i represent in datatable?

Answer 20

Which row(s) we want to select

Question 21

if We leave the i portion blank, what does this mean?

Answer 21

Select ALL rows

Question 22

What does the J represent?

Answer 22

Columns/variables.

That could be selecting only certain columns/variables to display or creating / modifying a column or variable.

Question 23

DT i and J mean

Answer 23

Name of data table, which rows we want to select, and columns and variables

Question 24

What does by mean - last part of DT argument

Answer 24

By represents a grouping variable or some way of organising out operations

Question 25

If you wanted to perform the SAME operation for each ID in the data set, which part of the data.tables would be important - the i J or by?

Answer 25

The by as it is the grouping variable and organises out operations

Question 26

If there are NO commas in data.tables, what is this telling R?

Answer 26

Give me the rows/cases that match my criteria, and also give me all the columns.variables.

Question 27

What is the below asking for?

d2 3][, Count := .N,

by = UserID]

[Count >= 30]

Answer 27

Assign to d2, in this order:

Here, the order is:

first, remove missing stress observations,

second take only surveys/rows where stress scores > 3,

third, count how many observations are not missing by ID.

Finally:

we are only taking people with 30 non missing stress values > 3

Question 28

We have decided that negative affect scores above 4 are outliers and that participant 56 was an outlier overall. We can exclude ID 56 and select only observations with negative affect at or below 4 as below.

What would this equation look like?

Answer 28

d[UserID != 56 & NegAff <= 4,

.(UserID, NegAff)]

Question 29

How can you ask R to show you the type of data a particular variable is?

Answer 29

Code:

class(dataset$variable)

EXAMPLE:
your dataset is metacars. you want to know what type of data the cartype is.

equation would be:
class (metacars$cartype)

Question 30

What is the logical data type?

Answer 30

Used for logical data, which are either TRUE or FALSE. If data are logical, it is a very efficient format and useful for many cases. Logical variables can be compared. Arithmetic can be used for logical variables, in which case TRUE is treated as 1 and FALSE as 0.

Question 31

What is the Integer data type?

Answer 31

Used for integer type data, that is whole numbers like 0, 1, 2. For variables that are only whole numbers, integer format is more efficient than real numbers or numeric data (e.g., 1.4).

Question 32

What is the Numbers/real/numeric data type?

Answer 32

Used for real numbers, such as 1.1, 4.8. It also can be used for integer data (i.e., whole numbers only) but is a less efficient format. In R these are represented by the class numeric abbreviated num.

Question 33

What is the Text/character/string data type?

Answer 33

Used for text type data, such as names, qualitative data, etc. Also, any numbers can be stored as strings. In R these are represented by the class character abbreviated chr. Character data do not work with arithmetic operators, but can be sorted (e.g., alphabetically).

Question 34

Why might we treat variables that are only whole numbers with INTEGER format instead of real numbers/numeric data?

Answer 34

Because integer format is more efficient than real numbers are real number will show you the decimals.