Getting Help

Help | Using RStudio

We can use RStudio help:

RStudio help

Help | inside R

We can also use R help system:

If we don’t know exactly the name of the function you are looking for, you can type on the console:

help.search("normal distribution")

or alternatively:

??"normal distribution"

If we know the name of the function:

help(rnorm)

or alternatively:

?rnorm

Help | inside R

We can also search for operators, for instance:

?"+"

Help | inside R

Most pages will have an example section. We can run all the examples from a given function using:

example(rnorm)


rnorm> require(graphics)

rnorm> dnorm(0) == 1/sqrt(2*pi)
[1] TRUE

rnorm> dnorm(1) == exp(-1/2)/sqrt(2*pi)
[1] TRUE

rnorm> dnorm(1) == 1/sqrt(2*pi*exp(1))
[1] TRUE

rnorm> ## Using "log = TRUE" for an extended range :
rnorm> par(mfrow = c(2,1))

rnorm> plot(function(x) dnorm(x, log = TRUE), -60, 50,
rnorm+      main = "log { Normal density }")


rnorm> curve(log(dnorm(x)), add = TRUE, col = "red", lwd = 2)

rnorm> mtext("dnorm(x, log=TRUE)", adj = 0)

rnorm> mtext("log(dnorm(x))", col = "red", adj = 1)

rnorm> plot(function(x) pnorm(x, log.p = TRUE), -50, 10,
rnorm+      main = "log { Normal Cumulative }")


rnorm> curve(log(pnorm(x)), add = TRUE, col = "red", lwd = 2)

rnorm> mtext("pnorm(x, log=TRUE)", adj = 0)

rnorm> mtext("log(pnorm(x))", col = "red", adj = 1)

rnorm> ## if you want the so-called 'error function'
rnorm> erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1

rnorm> ## (see Abramowitz and Stegun 29.2.29)
rnorm> ## and the so-called 'complementary error function'
rnorm> erfc <- function(x) 2 * pnorm(x * sqrt(2), lower = FALSE)

rnorm> ## and the inverses
rnorm> erfinv <- function (x) qnorm((1 + x)/2)/sqrt(2)

rnorm> erfcinv <- function (x) qnorm(x/2, lower = FALSE)/sqrt(2)

Help | inside R

If we are not sure about the name of the function, we can search using the apropos function:

apropos("rno")

[1] "psmirnov" "qsmirnov" "rnorm"    "rsmirnov"

apropos("norm")

 [1] "dlnorm"        "dnorm"         "norm"          "normalizePath"
 [5] "plnorm"        "pnorm"         "qlnorm"        "qnorm"        
 [9] "qqnorm"        "rlnorm"        "rnorm"

Note: You can even use regular expressions in the search string.

Help | inside R

The find() function tells what package a function is in, for instance:

find("rnorm")

[1] "package:stats"

Help | inside R

Many R packages also have vignettes (more detailed descriptions on how the packages work).

To List vignettes from loaded (attached packages):

vignette(all = FALSE)

To list vignettes from all installed packages:

vignette(all = TRUE)

A friendlier option is:

browseVignettes()

Help | inside R

If you know the name of the vignette use:

vignette("reshape")

Help | outside R

The R manuals

Go to https://cran.r-project.org/ and select Manuals on the left bar.

Help | outside R

The R forums and lists

RStudio Community is a friendly forum to ask questions about R and RStudio.

R also has a user list, to search it go to https://cran.r-project.org/ and select Search

Notes: You can also use RSiteSearch("rnorm") from the R console

Help | outside R

Google

The trick to search google is to add “in R”, or just “R” after the search term:

Go to https://google.com

and try searching for random normal numbers in r

Help | outside R

Other good sources for help are:

Stack Overflow for R problems (make sure to use the [r] tag)
Cross Validated for statistical problems (you can also use the [r] tag)

Help | Before asking a question

Before asking a question make sure that you have:

Searched before asking the question
Narrowed down the problem
Been explicit about what you are trying to do and provide an example of what you want to accomplish.

Help | Reproducible example

Be sure to provided a reproducible example.

Include the list of packages that you have loaded
Provide a minimal dataset
The absolute minimum code that you have that narrows down the problem
The expected output or end result

Help | Reproducible example

For more on this take a look at:

Cheatsheets

Rstudio provides a wide selection of cheatsheets which contain excellent information, particularly when starting out:

Cheatsheets – RStudio

Let’s practice

Try to do the exercises below.