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astudent’s guide to r 101
> stateTraffic <- longTraffic %>%
select(year, deathRate, state) %>%
mutate(year=paste("deathRate.", year, sep="")) %>%
spread(year, deathRate)
> stateTraffic
state deathRate.1951 deathRate.1952 deathRate.1953 deathRate.1954 deathRate.1955
1
ny
13.9
13.8
14.4
13.0
13.5
2
cn
13.0
10.8
12.8
10.8
14.0
3
ma
10.2
10.0
11.0
10.5
11.8
4
ri
8.0
8.5
8.5
7.5
10.0
deathRate.1956 deathRate.1957 deathRate.1958 deathRate.1959
1
13.4
13.3
13.0
12.9
2
12.1
11.9
10.1
10.0
3
11.0
10.2
11.8
11.0
4
8.2
9.4
8.6
9.0
13.9 Derived variable creation
Anumber of functions help facilitate the creation or re-
coding of variables.
13.9.1 Creating categorical variable from a quantita-
tive variable
Next we demonstrate how to create a three-level categor-
ical variable with cuts at 20 and 40 for the CESD scale
(which ranges from 0 to 60 points).
> favstats(~ cesd, data=HELPrct)
min Q1 median Q3 max mean
sd
n missing
1 25
34 41
60 32.8 12.5 453
0
> HELPrct <- mutate(HELPrct, cesdcut = cut(cesd,
breaks=c(0, 20, 40, 60), include.lowest=TRUE))
> bwplot(cesd ~ cesdcut, data=HELPrct)
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102 horton, kaplan, pruim
cesd
0
10
20
30
40
50
60
[0,20]
(20,40]
(40,60]
l
l
l
The
ntiles()
function can be
used to automate creation of
groups in this manner.
It might be preferable to give better labels.
> HELPrct <- mutate(HELPrct, cesdcut = cut(cesd,
labels=c("low", "medium", "high"),
breaks=c(0, 20, 40, 60), include.lowest=TRUE))
> bwplot(cesd ~ cesdcut, pch="|"data=HELPrct)
cesd
0
10
20
30
40
50
60
low
medium
high
The
derivedFactor()
function is even more general
and can also be used for this purpose.
> HELPrct <- mutate(HELPrct,
anothercut = derivedFactor(
low = cesd >= 0 & cesd d <= 20,
medium = cesd d > 20 & cesd <= 40,
high = cesd > 40))
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astudent’s guide to r 103
13.9.2 Reordering factors
By default R uses the first level in lexicographic order as
the reference group for modeling. This can be overriden
using the
relevel()
function (see also
reorder()
).
> tally(~ substance, data=HELPrct)
alcohol cocaine
heroin
177
152
124
> coef(lm(cesd ~ substance, data=HELPrct))
(Intercept) substancecocaine
substanceheroin
34.373
-4.952
0.498
> HELPrct <- mutate(HELPrct, subnew = relevel(substance,
ref="heroin"))
> coef(lm(cesd ~ subnew, data=HELPrct))
(Intercept) subnewalcohol subnewcocaine
34.871
-0.498
-5.450
13.10 Group-wise statistics
It can often be useful to calculate summary statistics by
group, and add these into a dataset. The
group
_
by()
function in the
dplyr
package facilitates this process.
Here we demonstrate how to add a variable containing
the median age of subjects by substance group.
> favstats(age ~ substance, data=HELPrct)
substance min Q1 median
Q3 max mean
sd
n missing
1
alcohol
20 33
38.0 43.0
58 38.2 7.65 177
0
2
cocaine
23 30
33.5 37.2
60 34.5 6.69 152
0
3
heroin
19 27
33.0 39.0
55 33.4 7.99 124
0
> ageGroup <- HELPrct %>%
group
_
by(substance) %>%
summarise(agebygroup = mean(age))
> ageGroup
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104 horton, kaplan, pruim
Source: local data frame [3 x 2]
substance agebygroup
(fctr)
(dbl)
1
alcohol
38.2
2
cocaine
34.5
3
heroin
33.4
> nrow(ageGroup)
[1] 3
> nrow(HELPrct)
[1] 453
> HELPmerged <- left
_
join(ageGroup, HELPrct, by="substance")
> favstats(agebygroup ~ substance, data=HELPmerged)
substance
min
Q1 median
Q3
max mean sd
n missing
1
alcohol 38.2 38.2
38.2 38.2 38.2 38.2
0 177
0
2
cocaine 34.5 34.5
34.5 34.5 34.5 34.5
0 152
0
3
heroin 33.4 33.4
33.4 33.4 33.4 33.4
0 124
0
> nrow(HELPmerged)
[1] 453
13.11 Accounting for missing data
Missing values arise in almost all real world investiga-
tions. R uses the
NA
character as an indicator for missing
data. The
HELPmiss
dataframe within the
mosaicData
package includes all n= 470 subjects enrolled at baseline
(including the n = 17 subjects with some missing data
who were not included in
HELPrct
).
> smaller <- select(HELPmiss, cesd, drugrisk, indtot, mcs, pcs,
substance)
> dim(smaller)
[1] 470
6
> summary(smaller)
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astudent’s guide to r 105
cesd
drugrisk
indtot
mcs
pcs
Min.
: 1.0
Min.
: 0.00
Min.
: 4.0
Min.
: 6.8
Min.
:14.1
1st Qu.:25.0
1st Qu.: 0.00
1st Qu.:32.0
1st Qu.:21.7
1st Qu.:40.3
Median :34.0
Median : 0.00
Median :37.5
Median :28.6
Median :48.9
Mean
:32.9
Mean
: 1.87
Mean
:35.7
Mean
:31.5
Mean
:48.1
3rd Qu.:41.0
3rd Qu.: 1.00
3rd Qu.:41.0
3rd Qu.:40.6
3rd Qu.:57.0
Max.
:60.0
Max.
:21.00
Max.
:45.0
Max.
:62.2
Max.
:74.8
NA's
:2
NA's
:14
NA's
:2
NA's
:2
substance
alcohol:185
cocaine:156
heroin :128
missing:
1
Of the 470 subjects in the 6 variable dataframe, only
the
drugrisk
,
indtot
,
mcs
,and
pcs
variables have missing
values.
> favstats(~ mcs, data=smaller)
min
Q1 median
Q3
max mean
sd
n missing
6.76 21.7
28.6 40.6 62.2 31.5 12.8 468
2
> with(smaller, sum(is.na(mcs)))
[1] 2
> nomiss <- na.omit(smaller)
> dim(nomiss)
[1] 453
6
> nrow(nomiss)
[1] 453
> ncol(nomiss)
[1] 6
> favstats(~ mcs, data=nomiss)
min
Q1 median
Q3
max mean
sd
n missing
6.76 21.8
28.6 40.9 62.2 31.7 12.8 453
0
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106 horton, kaplan, pruim
Alternatively, we could generate the same dataset us-
ing logical conditions.
> nomiss <- filter(smaller,
(!is.na(mcs) & !is.na(indtot) & !is.na(drugrisk)))
> dim(nomiss)
[1] 453
6
14
Health Evaluation (HELP) Study
Many of the examples in this guide utilize data from the
HELP study, a randomized clinical trial for adult inpa-
tients recruited from a detoxification unit. Patients with
no primary care physician were randomized to receive
amultidisciplinary assessment and a brief motivational
intervention or usual care, with the goal of linking them
to primary medical care. Funding for the HELP study
was provided by the National Institute on Alcohol Abuse
and Alcoholism (R01-AA10870, Samet PI) and National
Institute on Drug Abuse (R01-DA10019, Samet PI). The
details of the randomized trial along with the results from
aseries of additional analyses have been published
1
.
1
J. H. Samet, M. J. Larson, N. J.
Horton, K. Doyle, M. Winter,
and R. Saitz. Linking alcohol
and drug dependent adults to
primary medical care: A ran-
domized controlled trial of a
multidisciplinary health inter-
vention in a detoxification unit.
Addiction, 98(4):509–516, 2003;
J. Liebschutz, J. B. Savetsky,
R. Saitz, N. J. Horton, C. Lloyd-
Travaglini, and J. H. Samet. The
relationship between sexual and
physical abuse and substance
abuse consequences. Journal
of Substance Abuse Treatment,
22(3):121–128, 2002; and S. G.
Kertesz, N. J. Horton, P. D.
Friedmann, R. Saitz, and J. H.
Samet. Slowing the revolving
door: stabilization programs
reduce homeless persons’ sub-
stance use after detoxification.
Journal of Substance Abuse Treat-
ment, 24(3):197–207, 2003
Eligible subjects were adults, who spoke Spanish or
English, reported alcohol, heroin or cocaine as their first
or second drug of choice, resided in proximity to the pri-
mary care clinic to which they would be referred or were
homeless. Patients with established primary care rela-
tionships they planned to continue, significant dementia,
specific plans to leave the Boston area that would prevent
research participation, failure to provide contact informa-
tion for tracking purposes, or pregnancy were excluded.
Subjects were interviewed at baseline during their
detoxification stay and follow-up interviews were under-
taken every 6 months for 2 years. A variety of continuous,
count, discrete, and survival time predictors and out-
comes were collected at each of these five occasions. The
Institutional Review Board of Boston University Medical
Center approved all aspects of the study, including the
creation of the de-identified dataset. Additional privacy
protection was secured by the issuance of a Certificate of
Confidentiality by the Department of Health and Human
108 horton, kaplan, pruim
Services.
The
mosaicData
package contains several forms of the
de-identified HELP dataset. We will focus on
HELPrct
,
which contains 27 variables for the 453 subjects with min-
imal missing data, primarily at baseline. Variables in-
cluded in the HELP dataset are described in Table 14.1.
More information can be found at:
http://www.amherst.
edu/~nhorton/r2
.A copy of the study instruments can be
found at:
http://www.amherst.edu/~nhorton/help
.
Table 14.1: Annotated description of variables in the
HELPrct
dataset
VARIABLE
DESCRIPTION (VALUES)
NOTE
age
age at baseline (in years) (range
19–60)
anysub
use of any substance post-detox
see also
daysanysub
cesd
Center for Epidemiologic Stud-
ies Depression scale (range 0–60,
higher scores indicate more depres-
sive symptoms)
d1
how many times hospitalized for
medical problems (lifetime) (range
0–100)
daysanysub
time (in days) to first use of any
substance post-detox (range 0–268)
see also
anysubstatus
dayslink
time (in days) to linkage to primary
care (range 0–456)
see also
linkstatus
drugrisk
Risk-Assessment Battery (RAB)
drug risk score (range 0–21)
see also
sexrisk
e2b
number of times in past 6 months
entered a detox program (range
1–21)
female
gender of respondent (0=male,
1=female)
g1b
experienced serious thoughts of
suicide (last 30 days, values 0=no,
1=yes)
homeless
1or more nights on the street or
shelter in past 6 months (0=no,
1=yes)
astudent’s guide to r 109
i1
average number of drinks (standard
units) consumed per day (in the
past 30 days, range 0–142)
see also
i2
i2
maximum number of drinks (stan-
dard units) consumed per day (in
the past 30 days range 0–184)
see also
i1
id
random subject identifier (range
1–470)
indtot
Inventory of Drug Use Conse-
quences (InDUC) total score (range
4–45)
linkstatus
post-detox linkage to primary care
(0=no, 1=yes)
see also
dayslink
mcs
SF-36 Mental Component Score
(range 7-62, higher scores are bet-
ter)
see also
pcs
pcs
SF-36 Physical Component Score
(range 14-75, higher scores are
better)
see also
mcs
pss
_
fr
perceived social supports (friends,
range 0–14)
racegrp
race/ethnicity (black, white, his-
panic or other)
satreat
any BSAS substance abuse treat-
ment at baseline (0=no, 1=yes)
sex
sex of respondent (male or female)
sexrisk
Risk-Assessment Battery (RAB) sex
risk score (range 0–21)
see also
drugrisk
substance
primary substance of abuse (alco-
hol, cocaine or heroin)
treat
randomization group (randomize to
HELP clinic, no or yes)
Notes: Observed range is provided (at baseline) for con-
tinuous variables.
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