Lecture 1

Question 1

What are descriptive statistics?

Answer 1

Summary statistics - sum up features of a data sample.

Descriptive statistics (PLURAL) is the process of analyzing those statistics.

Question 2

What is exploratory data analysis?

Answer 2

The critical process of performing investigations on data to:
discover patterns
spot anomalies
test hypothesis AND
check assumptions
with the help of summary statistics and graphical representations.

Question 3

Discrete VS Continuous Probability Distributions

Answer 3

DISCRETE: Data can only take on certain values, for example, integers.
CONTINUOUS: Data can take on any value within a specified range (which may be infinite).

For a discrete distribution, probabilities can be assigned to the values in the distribution - for example, the probability that a web page will have 12 clicks in an hour is 0.15.

In contrast, in a continuous distribution, the probability associated with any particular value is null. Therefore, continuous distributions are normally described in terms of probability density, which can be converted into the probability that a value will fall within a certain range.

Question 4

Console window

Answer 4

Where you type R commands

Question 5

Source file

Answer 5

Instead of entering commands one at a time in the Console window, you can run a set of commands from a source file.

Question 6

q()

Answer 6

To quit R

Question 7

You can perform mathematical calculations in R.

Answer 7

Answers will appear in Console window.

Question 8

Generating and Displaying an object

Answer 8

Generate: x=5
Display: x

Question 9

ls()

Answer 9

To list objects available in the current R session
e.g.
x=5
x2=9
COMMAND: ls(pattern=”x”)
CONSOLE: “x” “x2”

NOTE: Pattern - Optional. Only names matching pattern are returned.

Question 10

exists(“name of object”)

Answer 10

Checks whether or not specified object exists. Returns TRUE or FALSE.
e.g.
COMMAND: exists(“x”)
CONSOLE: TRUE

Question 11

rm(“name of object”)

Answer 11

Removes previously defined object from the workspace
e.g.
rm(“x”)

Question 12

Workspace

Answer 12

Objects that you create during an R session are held in memory.

The collection of objects that you currently have is called the workspace. The workspace can be found in the Environment tab.

REMEMBER: The workspace is not saved unless you tell R to save it. This means that your objects are lost when you close R without saving the objects OR when R/your system crashes during a session.

When you close the R-GUI or the R console window, the system will ask you if you want to save the workspace image. If you select the ‘save the workspace image’ option, all the objects in your current R session will be saved in a file (.RData).

Question 13

getwd()

Answer 13

To find out what the current working directory is

Question 14

save.image()

Answer 14

To save to the current working directory

Question 15

save.image(“Working Directory”)

Answer 15

To save to a specific file/location

Question 16

If you have saved a workspace image and you start R the next time…

Answer 16

It will restore the workspace. All previously saved objects will be available again.

Question 17

R gets confused if you use a path like: c:\mydocuments\myfile.txt
BECAUSE R sees \ as an escape character. Instead, use…

Answer 17

c:\my documents\myfile.txt
OR
c:/mydocuments/myfile.txt

Question 18

help(options)

Answer 18

To learn about available options.

Question 19

options(…)

Answer 19

Allow the user to set and examine a variety of global options which affect the way in which R computes and displays its results.
e.g.
options(digits=3)

Question 20

history()

Answer 20

Displays last 25 commands

Question 21

history(max.show=Inf)

Answer 21

Displays all previous commands.

Question 22

savehistory(file = “name of file”)

Answer 22

Saves command history.

Default is “.Rhistory”

Question 23

loadhistory(file = “name of file”)

Answer 23

Recalls command history.

Default is “.Rhistory”

Question 24

help.start()

Answer 24

General help.

Question 25

help(foo) OR ?foo

Answer 25

Help about function foo.

The name ‘foo’ is not a real name: it is a placeholder used to represent the name of any desired thing.

Question 26

apropos(“foo”)

Answer 26

Lists all functions containing string foo.

The apropos() function in R is used to return a vector with the names of objects matching or containing the input character partially.

Question 27

example(foo)

Answer 27

Shows an example of function foo.

Question 28

data()

Answer 28

To see available datasets. Depends on what packages you have loaded.

Question 29

help(datasetname)

Answer 29

Provides details on sample dataset.

Question 30

R package OR R library

Answer 30

The system allows you to write new functions and package those functions - an extension of the system.

When you download R, a number of packages (around 30) are downloaded as well.

To use a function in an R package, that package must be attached to the system.

When you start R, not all the downloaded packages are attached. Only seven packages are attached to the system by default.

You can use function search to see the list of packages that are currently attached to the system. This list is also called the search path. (COMMAND: search() OR searchpaths()). You can also use the Packages window.

In order to be able to use a package, make sure it is activated by clicking on the check box. This can be done with the command library(package name) as well.

If a package is not available in the list, click on “Install Package”, enter the name, and click on “Install”. You may use the code: install.packages(“package name”) as well.

Question 31

It is not recommended to do so but…

Answer 31

R allows the user to give an object a name that already exists. If you are not sure if a name already exists, enter the name in the console and see if R can find it. R will look for the object in all the libraries (packages) that are currently attached to the system. R will not warn you when you use an existing name.
e.g.
mean = 10
mean
The object mean already exists in the base package, but is now masked by your object mean. To get a list of all masked objects, use the function conflicts. (COMMAND: conflicts())

You can safely remove the object mean with the function rm() without risking deletion of the mean function.

Question 32

Condition for existence of inverse of a matrix

Answer 32

A matrix’s rank should be full for the existence of its inverse.

Question 33

Basic Calculations

Answer 33

+ Addition
- Subtraction
* Multiplication
/ Division
^ OR ** Exponent
x%%y Modulus (x mod y)
x%/%y Integer division