File
menu, click on New project
, choose New directory
, then
Empty project
~/data-carpentry
)Files
tab on the right of the screen, click on New Folder
and
create a folder named data
within your newly created working directory.
(e.g., ~/data-carpentry/data
)data-carpentry-script.R
)Your working directory should now look like this:
Let's start by learning about our tool.
Point out the different windows in R.
R is a versatile, open source programming/scripting language that's useful both for statistics but also data science. Inspired by the programming language S.
Use #
signs to comment. Comment liberally in your R scripts. Anything to the
right of a #
is ignored by R.
<-
is the assignment operator. Assigns values on the right to objects on the
left. Mostly similar to =
but not always. Learn to use <-
as it is good
programming practice. Using =
in place of <-
can lead to issues down the
line.
=
should only be used to specify the values of arguments in functions for
instance read.csv(file="data/some_data.csv")
.
Let's look at a simple function call:
surveys <- read.csv(file="data/surveys.csv")
The file=
part inside the parentheses is called an argument, and most
functions use arguments. Arguments modify the behavior of the
function. Typically, they take some input (typically data) and other options to
change what the function will return, or how to treat the data provided.
Most functions can take several arguments, but most are specified by default so
you don't have to enter them. To see these default values, you can either type
args(read.csv)
or look at the help for this function (e.g., ?read.csv
).
args(surveys)
## Error: object 'surveys' not found
If you provide the arguments in the exact same order as they are defined you don't have to name them:
read.csv(file="data/surveys.csv", header=TRUE) # is identical to:
read.csv("data/surveys.csv", TRUE)
However, it's usually not recommended practice because it's a lot of remembering
to do, and if you share your code with others that includes less known functions
it makes your code difficult to read. (It's however OK to do so for basic
functions like mean
, min
, etc…)
Another advantage of naming arguments, is that the order doesn't matter:
read.csv(file="data/surveys.csv", header=TRUE) # is identical to:
read.csv(header=TRUE, file="data/surveys.csv")
There are two main ways of interacting with R: using the console or by using script files (plain text files that contain your code).
The recommended approach when working on a data analysis project is dubbed “the
source code is real”. The objects you are creating should be seen as disposable
as they are the direct realization of your code. Every object in your analysis
can be recreated from your code, and all steps are documented. Therefore, it is
best to enter as little commands as possible in the R console. Instead, all code
should be written in script files, and evaluated from there. The R console
should be used to inspect objects, test a function or get help. With this
approach, the .Rhistory
file automatically created during your session should
not be very useful.
Similarly, you should separate the original data (raw data) from intermediate
datasets that you may create for the need of a particular analysis. For
instance, you may want to create a data/
directory within your working
directory that stores the raw data, and have a data_output/
directory for
intermediate datasets and a figure_output/
directory for the plots you will
generate.
If you need help with a specific function, let's say barplot()
, you can type:
?barplot
If you just need to remind yourself of the names of the arguments, you can use:
args(lm)
If the function is part of a package that is installed on your computer but don't remember which one, you can type
??read.dna
If you are looking for a function to do a particular task, you can use
help.search()
(but only looks through the installed packages):
help.search("kruskal")
If you can't find what you are looking for, you can use the rdocumention.org website that search through the help files across all packages available.
Start by googling the error message. However, this doesn't always work very well because often, package developers rely too much on the error catching provided by R. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. “subscript out of bounds”).
However, you should check stackoverflow. Search using the [r]
tag. Most
questions have already been answered, but the challenge is to use the right
words in the search to find the answers:
http://stackoverflow.com/questions/tagged/r
The Introduction to R can also be dense for people with little programming experience but it is a good place to understand
The R FAQ is dense and technical but it is full of useful information.
The key to get help from someone is for them to grasp your problem rapidly. You should make it as easy as possible for them to pinpoint where the issue might be.
Try to use the correct words to describe your problem. For instance, a package is not the same thing as a library. Most people will understand what you meant, but others have really strong feelings about the difference in meaning. The key point is that it can make things confusing for people trying to help you. Be be as precise as possible when describing your problem
If possible, try to reduce what doesn't work to a simple reproducible
example. If you can reproduce the problem using a very small data.frame
instead of your 50,000 rows and 10,000 columns one, provide the small one with
the description of your problem. When appropriate, try to generalize what you
are doing so even people who are not in your field can understand the question.
To share an object with someone else, if it's relatively small, you can use the
function dput()
, it will output R code that can be used to recreate the exact same
object as the one in memory:
dput(head(iris)) # iris is an example data.frame that comes with R
## structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4),
## Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4,
## 1.4, 1.3, 1.5, 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2,
## 0.2, 0.2, 0.4), Species = structure(c(1L, 1L, 1L, 1L, 1L,
## 1L), .Label = c("setosa", "versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length",
## "Sepal.Width", "Petal.Length", "Petal.Width", "Species"), row.names = c(NA,
## 6L), class = "data.frame")
If the object is larger, provide either the raw file (i.e., your CSV file) with
your script up to the point of the error (and after removing everything that is
not relevant to your issue). Alternatively, in particular if your questions is
not related to a data.frame
, you can save any R object to a file:
saveRDS(iris, file="/tmp/iris.rds")
The content of this file is however not human readable and cannot be posted directly on stackoverflow. It can how be sent to someone by email who can read it with this command:
some_data <- readRDS(file="~/Downloads/iris.rds")
Last, but certainly not least, always include the output of sessionInfo()
as it provides critical information about your platform, the versions of R and
the packages that you are using, and other information that can be very helpful
to understand your problem.
sessionInfo()
## R version 3.1.1 (2014-07-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] knitr_1.6
##
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.5 formatR_1.0 stringr_0.6.2 tools_3.1.1
packageDescription("name-of-package")
. You may also want
to try to email the author of the package directly.