Built-in help pages

There is an online help facility that can help you to see what a particular
function is supposed to do.

If you know the name of the function that you need help with, the
help() function is likely sufficient.

It may be called with a string or function name as an argument, or you

can simply put a question mark (?) in front of your query.

2
Built-in help pages

For example, for help on the q() function, type

?q

or
help(q)

or just hit the F1 key while pointing at q in RStudio. Any of these will
open a help page containing a description of the function for quitting R.

3
Quitting R

To quit your R session, type q()

q()

If you then hit the Enter key, you will be asked whether to save an image
of the current workspace, or not, or to cancel.

The workspace image contains a record of the computations you’ve

done, and may contain some saved results.

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Functions

Most of the work in R is done using functions.

For example, we saw that to quit R we type q(). This tells R to call the
function named q.

The brackets surround the argument list, which in this case contains
nothing: we just want R to quit, and do not need to tell it how.

5
q is a function

Attempting to quit without the brackets gives:

q

## function (save = "default", status = 0, runLast = TRUE)

## .Internal(quit(save, status, runLast))
## <bytecode: 0x55b98f4fb3b8>
## <environment: namespace:base>

This has happened because q is a function that is used to

tell R to quit.

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q is a function

Typing q by itself tells R to show us the contents of the function q. By

typing q(), we are telling R to call the function q.

The action of this function is to quit R.

q has three arguments: save, status, and runLast.

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Default Values of Parameters

Each argument has a default value: "default", 0, and TRUE

respectively.

What happens when we execute q() is that R calls the q function with
the arguments set to their default values.

8
Changing from the Defaults

To change from the default values, specify them in the function call.

Arguments are identified by their position or by their name.

For example,

q("no") # these calls tell R to quit without

q(save = "no") # saving the workspace image

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Changing from the Defaults

If we had given two arguments without names, they would apply to save
and status.

If we want to accept the defaults of the early parameters but change later
ones, we give the name when calling the function, e.g.
q(runLast = FALSE)

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Changing from the Defaults

Alternatively, commas can be used to mark the missing arguments, e.g.

q( , , FALSE)

It is a good idea to use named arguments when calling a function which

has many arguments or when using uncommon arguments, because it
reduces the risk of specifying the wrong argument, and makes your
code easier to read.

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Named storage: Graphics Example

The amounts of time spent by a person watching 4 different types of TV

shows were measured.

15% of the time was spent on sports, 10% on game shows, 30% on
movies, and 45% on comedies. Set up a pie chart and a bar chart.

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Named storage: Graphics Example

First, you need to set up an object that contains the information to be

plotted. Here, an object called tv is assigned the required information.
tv <- c("sports" = 15, "game shows"= 10,
"movies" = 30, "comedies" = 45)

The pie() function can then be used to create the pie chart.

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Named storage: Graphics Example
pie(tv)

game shows

movies
sports

comedies

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Named storage: Bar Chart Example
barplot(tv)
40
30
20
10
0

sports game shows movies comedies

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Polling question - Yes or No?

My friend has baked 3 apple pies, 4 blueberry pies and 7 cherry pies.
Create a pie chart for these data. The first step is:

pies <- c(apple = 3, blueberry = 4, cherry = 7)

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Polling question - Answer

No! Don’t forget the quotation marks.

pies <- c("apple" = 3, "blueberry" = 4, "cherry" = 7)

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Polling question - Yes or No?

The second step to create the pie chart is for my friend’s pie data is

pie(pies)

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Polling question - Answer

Yes!
pie(pies)

blueberry
apple

cherry

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R is case-sensitive

Let’s try to find the length of the longest river:

MAX(rivers)

## Error in MAX(rivers): could not find function "MAX"

Now try

max(rivers)

## [1] 3710

The function max() is built in to R. R would consider MAX to be a

different function, because it is case-sensitive: m is different from M.

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R is case-sensitive

If you really want a function called MAX to do the work of max, you would
type
MAX <- max

Now, MAX will do what you want:

MAX(rivers)

## [1] 3710

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Listing the objects in the workspace

Our workspace now contains some objects that we have created.

A list of all objects in the current workspace can be printed to the screen
using the objects() function:
objects()

## [1] "MAX" "pies" "tv"

The same result is obtained with the alias function

ls()

## [1] "MAX" "pies" "tv"

Remember that if we quit our R session without saving the workspace

image, then these objects will disappear.
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The simplest model for random noise - the runif function

Suppose you are measuring a length with a ruler that gives you
accuracy to the nearest millimeter.

When you measure the length of your pencil as 273 millimeters, the truth
could be anywhere between 272.5 and 273.5 millimeters.

The uniform distribution on the interval [−.5, .5] provides a model for the
error in your measurement, and we can simulate values from this
distribution using the runif() function.

Following is a sample of 4 such simulated values:

runif(4, min = -.5, max = .5)

## [1] -0.05448498 0.32835214 0.46347435 0.47992511

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Packages

One of the major strengths of R is the availability of add-on packages

that have been created by statisticians and computer scientists from
around the world.

There are thousands of packages, e.g. graphics, ggplot2, and MPV.

A package contains functions and data which extend the abilities of R.

Every installation of R contains a number of packages by default (e.g.

base, stats, and graphics) which are automatically loaded when you
start R.

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Packages

To load an additional package, for example, called DAAG, type

library(DAAG)

If you get a warning that the package is can’t be found, then the package
doesn’t exist on your computer, but it can likely be installed. Try

install.packages("DAAG")

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Packages

In RStudio, it may be simpler to use the Tools menu.

26
Packages

Choose “Install Packages”:

27
Packages

Type in the name of the package you are requesting, and click “Install”:

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Packages

Before DAAG is installed, if we type;

> seedrates
Error: object 'seedrates' not found

Once DAAG is installed, it can be loaded using the library() function,

and you can access data frames and functions that were not avaiable
previously. For example, the seedrates data frame is now available:
seedrates

## rate grain
## 1 50 21.2
## 2 75 19.9
## 3 100 19.2
## 4 125 18.4
## 5 150 17.9
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Polling question - Yes or No?

To install the MPV package, type

install.packages(MPV)

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Polling question - Answer

No! We forgot the quotation marks again.

install.packages("MPV")

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Using one object from a package at a time

MPV::p2.12
## temp usage
## 1 21 185.79
The MPV package is installed on my
## 2 24 214.47
system, but I have not loaded it. I
## 3 32 288.03
only want to access the p2.12 data
## 4 47 424.84
frame and nothing else.
## 5 50 454.68
## 6 59 539.03
To do this, just type the package ## 7 68 621.55
name (MPV), followed by two colons ## 8 74 675.06
(::) and the object name you seek. ## 9 62 562.03
## 10 50 452.93
## 11 41 369.95
## 12 30 273.98

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Polling question - Yes or No?

If I don’t load the DAAG package, but I want to assign seedrates to an

object called plantingData, the following should work:

plantingData <- DAAG::seedrates

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Polling question - Answer

Yes!

plantingData <- DAAG::seedrates

Look at the result:

plantingData
The contents of
## rate grain
plantingData
## 1 50 21.2
are the same as
## 2 75 19.9
the contents of
## 3 100 19.2
seedrates.
## 4 125 18.4
## 5 150 17.9

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Packages

You might want to know which packages are loaded into your system
already.

To see which packages are loaded, run

search()

## [1] ".GlobalEnv" "package:DAAG" "package:stat

## [4] "package:graphics" "package:grDevices" "package:util
## [7] "package:datasets" "package:methods" "Autoloads"
## [10] "package:base"

(Your list will likely be different from mine.)

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Packages

This list also indicates the search order: a package can only contain one
function of any given name, but the same name may be used in another
package.

When you use that function, R will choose it from the first package in the
search list.

If you want to force a function to be chosen from a particular package,

prefix the name of the function with the name of the package and ::,
e.g.

stats::median(x)

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Packages

Thousands of contributed packages are available, though you likely

have only a few dozen installed on your computer.

If you try to use one that isn’t already there, you will receive an error
message:

library(notInstalled)

## Error in library(notInstalled): there is no package

called ’notInstalled’

This means that the package doesn’t exist on your computer, but it
might be available in a repository online.

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Packages

The biggest repository of R packages is known as CRAN. To install a

package from CRAN, you can run a command like

install.packages("knitr")

or, within RStudio, click on the Packages tab in the Output Pane,
choose Install, and enter the name in the resulting dialog box.

38
Packages

Because there are so many contributed packages, it is hard to know

which one to use to solve your own problems.

If you can’t get help from someone with more experience, you can get
information from the CRAN task views at
https://cloud.r-project.org/web/views.

These are reviews of available packages written by experts in dozens of

different subject areas.

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