Helpful Code

When using R, several tasks are used quite often without a detailed explanation given for each use. We therefore have created a reference page for the most common uses.

Extracting Columns from Data Frames as Vectors

Generally, a column in a data frame contains the values for a specific variable. Thus, we often wish to extract a column from a data frame as a vector of values so that we can work with it.

pers <- read.csv('https://faculty.ung.edu/rsinn/data/personality.csv')

Option 1: $

head

The R function head() displays the column titles and first few rows of a data frame. We often find it useful to inspect a compact version of a data frame instead of producing hundreds of rows in the display.

To extract the perfectionism scores column of data using the dollar sign method, we proceed as follows:

perfect <- pers$Perf
head(perfect, 5)

1. 105
2. 105
3. 73
4. 90
5. 95

Option 2: [Row, Column] Format

To extract the perfectionism scores column of data using the Rows and columns of the data frame, we leave the row indicator empty and specific a column as shown:

perfect2 <- pers[ , 'Perf']
head(perfect2, 5)

1. 105
2. 105
3. 73
4. 90
5. 95

The column may described either by number (shown below) or by name (as shown above). The perfectionism scores are stored in column #27.

perfect3 <- pers[ , 27]
head(perfect3, 5)

1. 105
2. 105
3. 73
4. 90
5. 95

All methods shown work properly and, as one can see, display identical results.

Subsetting a Data Frame

What if we wish to compare the biological sexes on the narcissism variable? Then, we need to create a subset of narcissism for both sexes, male and female. Working with the females first, we see the following:

females <- subset(pers, Sex == 'F')
head(females, 5)

	Age	Yr	Sex	G21	Corps	Res	Greek	VarsAth	Honor	GPA	...	Perf	OCD	Play	Extro	Narc	HSAF	HSSE	HSAG	HSSD	PHS
2	20	3	F	N	N	2	Y	N	Y	3.95	...	105	3	172	16	11	46	52	26	33	SE
4	27	3	F	Y	N	3	N	N	N	2.84	...	90	9	160	16	10	51	51	23	19	SE
6	22	3	F	Y	N	2	Y	N	N	2.63	...	114	20	133	10	9	40	27	31	28	AG
8	20	3	F	N	N	1	Y	N	N	3.30	...	142	17	168	16	9	55	45	24	29	AF
9	22	2	F	Y	N	1	N	N	N	3.02	...	119	16	141	10	9	52	47	32	26	SE

Grid of Graphics

We often wish to show 2 or more graphical displays for a specific data set while minimizing the space required to do so. We will use two functions to assist us:

1. layout()

2. matrix()

Warning

We use the option lcm() to specify the height of the graphical display in centimeters. Values between 5 and 12 generally work well, and some guesswork is typically required.

We create a plot called plt because it’s made up of 2 different graphical pieces: the qqnorm plot and qqline superimposed on top. Since we wish to display these 2 elements together, we surround them with { } and store them as the single graphical item plt.

For an example, let’s display a histogram and a QQ plot for the naricissism variable of the personality data frame:

data <- pers[ , 'Narc']

layout(matrix(c(1,2), ncol = 2), lcm(8))
hist(data)
plt <- { qqnorm(data, main = 'QQ Plot: Narcissism') ; qqline(data) }