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astudent’s guide to r 21
you will want to investigate the
knitr
/L
A
T
E
Xcapabili-
ties. For those who do not already know LAT
E
X, the sim-
pler RMarkdown system provides an easy entry into the
world of reproducible research methods. It also provides
agood facility for students to create homework and re-
ports that include text,
R
code,
R
output, and graphics.
To create a new RMarkdown file, select
File
,then
New
File
,then
RMarkdown
.The file will be opened with a short
template document that illustrates the mark up language.
The
mosaic
package includes two useful RMarkdown
templates for getting started:
fancy
includes bells and
whistles (and is intended to give an overview of features),
while
plain
is useful as a starting point for a new analy-
sis. These are accessed using the
Template
option when
creating a new RMarkdown file.
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22 horton, kaplan, pruim
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astudent’s guide to r 23
There is a button (marked with a question mark)
which provides a brief description of the supported markup
commands. The
RStudio
web site includes more extensive
tutorials on using RMarkdown.
Caution!
RMarkdown, and
knitr
/L
A
T
E
X
files do not have access to the
console environment, so the
code in them must be self-
contained.
It is important to remember that unlike
R
scripts,
which are executed in the console and have access to
the console environment, RMarkdown and
knitr
/L
A
T
E
X
files do not have access to the console environment This
is a good feature because it forces the files to be self-
contained, which makes them transferable and respects
good reproducible research practices. But beginners, es-
pecially if they adopt a strategy of trying things out in the
console and copying and pasting successful code from the
console to their file, will often create files that are incom-
plete and therefore do not compile correctly.
2.3 The Other Panels and Tabs
2.3.1 The History Tab
As commands are entered in the console, they appear in
the
History
tab. These histories can be saved and loaded,
there is a search feature to locate previous commands,
and individual lines or sections can be transferred back to
the console. Keeping the
History
tab open will allow you
to go back and see the previous several commands. This
can be especially useful when commands produce a fair
amount of output and so scroll off the screen rapidly.
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24 horton, kaplan, pruim
2.3.2 Communication between tabs
RStudio
provides several ways to move
R
code between
tabs. Pressing the
Run
button in the editing panel for an
R
script or RMarkdown or other file will copy lines of code
into the Console and run them.
2.3.3 The Files Tab
The
Files
tab provides a simple file manager. It can be
navigated in familiar ways and used to open, move, re-
name, and delete files. In the browser version of
RStudio
,
the
Files
tab also provides a file upload utility for moving
files from the local machine to the server. In RMarkdown
and knitr files one can also run the code in a particular
chunk or in all of the chunks in a file. Each of these fea-
tures makes it easy to try out code “live” while creating a
document that keeps a record of the code.
In the reverse direction, code from the history can be
copied either back into the console to run them again
(perhaps after editing) or into one of the file editing tabs
for inclusion in a file.
2.3.4 The Help Tab
The
Help
tab is where
RStudio
displays
R
help files. These
can be searched and navigated in the
Help
tab. You can
also open a help file using the
?
operator in the console.
For example the following command will provide the
help file for the logarithm function.
> ?log
2.3.5 The Environment Tab
The
Environment
tab shows the objects available to the
console. These are subdivided into data, values (non-
dataframe, non-function objects) and functions. The
broom icon can be used to remove all objects from the en-
vironment, and it is good to do this from time to time, es-
pecially when running in RStudio server or if you choose
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astudent’s guide to r 25
to save the environment when shutting down
RStudio
since in these cases objects can stay in the environment
essentially indefinitely.
2.3.6 The Plots Tab
Plots created in the console are displayed in the
Plots
tab.
For example, the following commands display the num-
ber of births in the United States for each day in 1978.
# this will make e lattice e graphics available to the e session
# as well as the e Births78 dataset
> require(mosaic)
> xyplot(births ~ dayofyear, data=Births78)
dayofyear
births
7000
8000
9000
10000
0
100
200
300
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From the
Plots
tab, you can navigate to previous plots and
also export plots in various formats after interactively
resizing them.
2.3.7 The Packages Tab
Much of the functionality of
R
is located in packages,
many of which can be obtained from a central clearing
house called CRAN (Comprehensive R Archive Network).
The
Packages
tab facilitates installing and loading pack-
ages. It will also allow you to search for packages that
have been updated since you installed them.
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3
One Quantitative Variable
3.1 Numerical summaries
R
includes a number of commands to numerically sum-
marize variables. These include the capability of calculat-
ing the mean, standard deviation, variance, median, five
number summary, interquartile range (IQR) as well as ar-
bitrary quantiles. We will illustrate these using the CESD
(Center for Epidemiologic Studies–Depression) measure
of depressive symptoms (which takes on values between
0and 60, with higher scores indicating more depressive
symptoms).
To improve the legibility of output, we will also set the
default number of digits to display to a more reasonable
level (see
?options()
for more configuration possibilities).
> require(mosaic)
> require(mosaicData)
> options(digits=3)
> mean(~ cesd, data=HELPrct)
[1] 32.8
Note that the
mean()
function in the
mosaic
package
supports a formula interface common to
lattice
graphics
and linear models (e.g.,
lm()
). The
mosaic
package pro-
vides many other functions that use the same notation,
which we will be using throughout this document.
Digging Deeper
If you have not seen the for-
mula notation before, the com-
panion book, Start Teaching with
Rprovides a detailed presen-
tation. Start Modeling with R,
another companion book, de-
tails the relationship between
the process of modeling and the
formula notation.
The same output could be created using the following
commands (though we will use the MOSAIC versions
when available).
28 horton, kaplan, pruim
> with(HELPrct, mean(cesd))
[1] 32.8
> mean(HELPrct$cesd)
[1] 32.8
Similar functionality exists for other summary statistics.
> sd(~ cesd, data=HELPrct)
[1] 12.5
> sd(~ cesd, data=HELPrct)^2
[1] 157
> var(~ cesd, data=HELPrct)
[1] 157
It is also straightforward to calculate quantiles of the
distribution.
> median(~ cesd, data=HELPrct)
[1] 34
By default, the
quantile()
function displays the quar-
tiles, but can be given a vector of quantiles to display.
Caution!
Not all commands have been
upgraded to support the for-
mula interface. For these
functions, variables within
dataframes must be accessed
using
with()
or the $ operator.
> with(HELPrct, quantile(cesd))
0%
25%
50%
75% 100%
1
25
34
41
60
> with(HELPrct, quantile(cesd, c(.025.975)))
2.5% 97.5%
6.3
55.0
astudent’s guide to r 29
Finally, the
favstats()
function in the
mosaic
package
provides a concise summary of many useful statistics.
> favstats(~ cesd, data=HELPrct)
min Q1 median Q3 max mean
sd
n missing
1 25
34 41
60 32.8 12.5 453
0
3.2 Graphical summaries
The
histogram()
function is used to create a histogram.
Here we use the formula interface (as discussed in the
Start Modeling with R book) to specify that we want a his-
togram of the CESD scores.
> histogram(~ cesd, data=HELPrct)
cesd
Density
0.00
0.01
0.02
0.03
0
20
40
60
We can use the
width()
and
center()
options to con-
trol the location of the bins.
> histogram(~ cesd, width=5center=2.5data=HELPrct)
30 horton, kaplan, pruim
cesd
Density
0.00
0.01
0.02
0.03
0
20
40
60
In the
HELPrct
dataset, approximately one quarter of
the subjects are female.
> tally(~ sex, data=HELPrct)
female
male
107
346
> tally(~ sex, format="percent"data=HELPrct)
female
male
23.6
76.4
It is straightforward to restrict our attention to just
the female subjects. If we are going to do many things
with a subset of our data, it may be easiest to make a new
dataframe containing only the cases we are interested
in. The
filter()
function in the
dplyr
package can be
used to generate a new dataframe containing just the
women or just the men (see also section 13.5). Once this
is created, the the
stem()
function is used to create a stem
and leaf plot.
Caution!
Note that the tests for equality
use two equal signs
> female <- filter(HELPrct, sex=='female')
> male <- filter(HELPrct, sex=='male')
> with(female, stem(cesd))
The decimal point is 1 digit(s) to the right of the |
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