Package 'mosaicData' reference manual

Title:	Project MOSAIC Data Sets
Description:	Data sets from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.
Authors:	Randall Pruim <[email protected]>, Daniel Kaplan <[email protected]>, Nicholas Horton <[email protected]>
Maintainer:	Randall Pruim <[email protected]>
License:	GPL (>=2)
Version:	0.20.4
Built:	2024-09-12 08:29:41 UTC
Source:	https://github.com/projectmosaic/mosaicdata

Alcohol Consumption per Capita

Description

These data provide per capita alcohol consumption values for many countries in 2005 and 2008. There are also a few countries for which there are data in other years.

Usage

data(Alcohol)
data(Alcohol)

Format

A data frame with 411 observations on the following variables.

country: country name
year: year
alcohol: estimated per capita alcohol consumption for adults (15+) in litres pure alcohol

Source

Gapminder (https://www.gapminder.org/data/)

Examples

data(Alcohol)
# There are only a few observations in years other than 2005 and 2008
subset(Alcohol, ! year %in% c(2005,2008))
data(Alcohol)
# There are only a few observations in years other than 2005 and 2008
subset(Alcohol, ! year %in% c(2005,2008))

US Births in 1969 - 1988

Description

A day by day record of the number of births in each US State.

Usage

data(Birthdays)
data(Birthdays)

Format

A data frame with 374221 observations on the following variables.

state: state where child was born
year: year (1969-1988)
month: month (1-12)
day: day of month
date: date as a date object
wday: Day of week (ordered factor)
births: number of births

Examples

data(Birthdays)
if (require(mosaic)) {
  MI <- Birthdays |> filter(state == "MI")
  gf_point(births ~ date, Birthdays, data = MI)
  gf_line(births ~ date, Birthdays, data = MI, color = ~ wday)
  gf_line(births ~ date,
    data = Birthdays |> group_by(date) |> summarise(births = sum(births)))
  }
data(Birthdays)
if (require(mosaic)) {
  MI <- Birthdays |> filter(state == "MI")
  gf_point(births ~ date, Birthdays, data = MI)
  gf_line(births ~ date, Birthdays, data = MI, color = ~ wday)
  gf_line(births ~ date,
    data = Birthdays |> group_by(date) |> summarise(births = sum(births)))
  }

US Births

Description

Number of births in the United States. There are several data sets covering different date ranges and obtaining data from different sources.

Usage

data(Births)

data(Births78)

data(Births2015)

data(BirthsSSA)

data(BirthsCDC)
data(Births)

data(Births78)

data(Births2015)

data(BirthsSSA)

data(BirthsCDC)

Format

A data.frame with the following 8 variables.

date: Date
births: Number of births on date (integer)
wday: Day of week (ordered factor)
year: Year (integer)
month: Month (integer)
day_of_year: Day of year (integer)
day_of_month: Day of month (integer)
day_of_week: Day of week (integer)

Details

There are some overlapping dates in the various data sets, but the number of births does not always agree due to the different sources of the data. See the examples.

Source

Data source for Births: National Vital Statistics System natality data, as provided by Google BigQuery and exported to csv by Robert Kern https://www.mechanicalkern.com:443/static/birthdates-1968-1988.csv.
Data source for BirthsSSA US Social Security Administration, as curated at https://github.com/fivethirtyeight/data/tree/master/births
Data source for BirthsCDC US Centers for Disease Control, as curated at https://github.com/fivethirtyeight/data/tree/master/births
Data source for Births2015: Obtained from the National Center for Health Statistics, National Vital Statistics System, Natality, 2015 data.

Examples

data(Births78)
data(Births2015)
data(Births)
data(BirthsSSA)
data(BirthsCDC)
# date ranges for the different data sets
lapply(
  list(Births = Births, Births78 = Births78, Biths2015 = Births2015, BirthsSSA = BirthsSSA,
       BirthsCDC = BirthsCDC),
       function(x) range(x$date))
range(Births78$date)
range(Births2015$date)
range(Births$date)
range(BirthsSSA$date)
range(BirthsCDC$date)

# Births and Births78 have slightly different numbers of births

if(require(ggplot2)) {
  ggplot(data = Births, aes(x = date, y = births, colour = ~ wday)) +
    stat_smooth(se = FALSE, alpha = 0.8, geom = "line")
  ggplot(data = Births, aes(x = day_of_year, y = births, colour = ~ wday)) +
    geom_point(size = 0.4, alpha = 0.5) +
    stat_smooth(se = FALSE, geom = "line", alpha = 0.6, size = 1.5)
  if (require(dplyr)) {
    ggplot(
     data =  bind_cols(Births |> filter(year == 1978),
                       Births78 |> rename(births78 = births)),
     aes(x = births - births78)
     ) +
     geom_histogram(binwidth = 1)
  }
}

if(require(ggplot2)) {
  ggplot(data = Births, aes(x = date, y = births, colour = ~ wday)) +
    stat_smooth(se = FALSE, alpha = 0.8, geom = "line")
  ggplot(data = Births, aes(x = day_of_year, y = births, colour = ~ wday)) +
    geom_point(size = 0.4, alpha = 0.5) +
    stat_smooth(se = FALSE, geom = "line", alpha = 0.6, size = 1.5)
  if (require(dplyr)) {
    ggplot(
     data =  bind_cols(Births |> filter(year == 1978),
                       Births78 |> rename(births78 = births)),
     aes(x = births - births78)
     ) +
     geom_histogram(binwidth = 1)

    # SSA records more births than CDC
    ggplot(
     data =  bind_cols(BirthsSSA |> filter(year <= 2003) |> rename(SSA = births),
                       BirthsCDC |> filter(year >= 2000) |> rename(CDC = births)),
     aes(x = SSA - CDC)
     ) +
     geom_histogram(binwidth = 10)
  }
}



data(Births78)
data(Births2015)
data(Births)
data(BirthsSSA)
data(BirthsCDC)
# date ranges for the different data sets
lapply(
  list(Births = Births, Births78 = Births78, Biths2015 = Births2015, BirthsSSA = BirthsSSA,
       BirthsCDC = BirthsCDC),
       function(x) range(x$date))
range(Births78$date)
range(Births2015$date)
range(Births$date)
range(BirthsSSA$date)
range(BirthsCDC$date)

# Births and Births78 have slightly different numbers of births

if(require(ggplot2)) {
  ggplot(data = Births, aes(x = date, y = births, colour = ~ wday)) +
    stat_smooth(se = FALSE, alpha = 0.8, geom = "line")
  ggplot(data = Births, aes(x = day_of_year, y = births, colour = ~ wday)) +
    geom_point(size = 0.4, alpha = 0.5) +
    stat_smooth(se = FALSE, geom = "line", alpha = 0.6, size = 1.5)
  if (require(dplyr)) {
    ggplot(
     data =  bind_cols(Births |> filter(year == 1978),
                       Births78 |> rename(births78 = births)),
     aes(x = births - births78)
     ) +
     geom_histogram(binwidth = 1)
  }
}

if(require(ggplot2)) {
  ggplot(data = Births, aes(x = date, y = births, colour = ~ wday)) +
    stat_smooth(se = FALSE, alpha = 0.8, geom = "line")
  ggplot(data = Births, aes(x = day_of_year, y = births, colour = ~ wday)) +
    geom_point(size = 0.4, alpha = 0.5) +
    stat_smooth(se = FALSE, geom = "line", alpha = 0.6, size = 1.5)
  if (require(dplyr)) {
    ggplot(
     data =  bind_cols(Births |> filter(year == 1978),
                       Births78 |> rename(births78 = births)),
     aes(x = births - births78)
     ) +
     geom_histogram(binwidth = 1)

    # SSA records more births than CDC
    ggplot(
     data =  bind_cols(BirthsSSA |> filter(year <= 2003) |> rename(SSA = births),
                       BirthsCDC |> filter(year >= 2000) |> rename(CDC = births)),
     aes(x = SSA - CDC)
     ) +
     geom_histogram(binwidth = 10)
  }
}

Standard Deck of Cards

Description

A character vector with two or three character representations of each card in a standard 52-card deck.

Usage

Cards
Cards

Details

The 2 of clubs is represented as "2C", while the 10 of diamonds is "10D".

Examples

if (require(mosaic)) {
  deal(Cards, 13)        # bridge hand
  deal(Cards, 5)         # poker hand
  shuffle(Cards)         # shuffled deck
}

if (require(mosaic)) {
  deal(Cards, 13)        # bridge hand
  deal(Cards, 5)         # poker hand
  shuffle(Cards)         # shuffled deck
}

CoolingWater

Description

Temperature of a mug of water as it cools

Usage

data(CoolingWater)
data(CoolingWater)

Format

A data frame with 222 observations of the following variables.

time: time in minutes
temp: temperature in Celsius

Details

The water was poured into a mug and a temperature probe inserted into the water with a few seconds of the pour.

Source

These data were collected Stan Wagon to help his mathematical modeling students explore Newton's Law of Cooling and the ways that the law is really only an approximation. More about Stan: http://stanwagon.com.

Examples

data(CoolingWater)
if (require(ggformula)) {
  gf_point(temp ~ time, data = CoolingWater, alpha = 0.5)
}

data(CoolingWater)
if (require(ggformula)) {
  gf_point(temp ~ time, data = CoolingWater, alpha = 0.5)
}

Countries

Description

A data frame containing country names as used by Gapminder and the maps package to facilitate conversation between the two.

Usage

data(Countries)
data(Countries)

Format

A data frame with 258 observations on the following variables.

maptools: region name http://mappinghacks.com/ data sets
gapminder: country name in Gapminder data sets
maps: region name in maps data sets

Details

The "countries" in the maps data include several other geographic regions (bodies of water, islands belonging to other countries, Hawaii, etc.) that are not countries. Furthermore, the maps countries do not include many of the countries that have been created since ca. 2000. The mapping is therefore many-to-many, and also includes some NAs when there is no appropriate mapping. Bodies of water in the maps data, for example, are not assigned a country in the Gapminder.

Examples

data(Countries)
subset(Countries, maps=="Yugoslavia")  # Where has Yugoslavia gone?
subset(Countries, is.na(gapminder))    # Things from maps with no Gapminder equivalent
subset(Countries, is.na(maps))         # Things from Gapminder with no maps equivalent
data(Countries)
subset(Countries, maps=="Yugoslavia")  # Where has Yugoslavia gone?
subset(Countries, is.na(gapminder))    # Things from maps with no Gapminder equivalent
subset(Countries, is.na(maps))         # Things from Gapminder with no maps equivalent

Data from the 1985 Current Population Survey (CPS85)

Description

The Current Population Survey (CPS) is used to supplement census information between census years. These data consist of a random sample of persons from the CPS85, with information on wages and other characteristics of the workers, including sex, number of years of education, years of work experience, occupational status, region of residence and union membership.

Usage

data(CPS85)
data(CPS85)

Format

A data frame with 534 observations on the following variables.

wage: wage (US dollars per hour)
educ: number of years of education
race: a factor with levels NW (nonwhite) or W (white)
sex: a factor with levels F M
hispanic: a factor with levels Hisp NH
south: a factor with levels NS S
married: a factor with levels Married Single
exper: number of years of work experience (inferred from age and educ)
union: a factor with levels Not Union
age: age in years
sector: a factor with levels clerical const manag manuf other prof sales service

Details

Data are from 1985. The data file is recoded from the original, which had entirely numerical codes.

Source

Data are from https://dasl.datadescription.com

References

Berndt, ER. The Practice of Econometrics 1991. Addison-Wesley.

Examples

data(CPS85)

data(CPS85)

Weight of dimes

Description

Weights of a sample of dimes.

Usage

data(Dimes)
data(Dimes)

Format

A data frame with 30 observations on the following 2 variables.

mass: mass of dime in grams
year: year the dime was minted

Details

These data were collected on a sample taken from a large sack of dimes for the purpose of estimating the total number of dimes in the sack based on the weights of the individual dimes.

Source

Data were collected by Michael Stob.

Galton's dataset of parent and child heights

Description

In the 1880's, Francis Galton was developing ways to quantify the heritability of traits. As part of this work, he collected data on the heights of adult children and their parents.

Usage

data(Galton)
data(Galton)

Format

A data frame with 898 observations on the following variables.

family: a factor with levels for each family
father: the father's height (in inches)
mother: the mother's height (in inches)
sex: the child's sex: F or M
height: the child's height as an adult (in inches)
nkids: the number of adult children in the family, or, at least, the number whose heights Galton recorded.

Details

Entries were deleted for those children whose heights were not recorded numerically by Galton, who sometimes used entries such as "tall", "short", "idiotic", "deformed" and so on.

Source

The data were transcribed by J.A. Hanley who has published them at http://www.medicine.mcgill.ca/epidemiology/hanley/galton/

References

"Transmuting" women into men: Galton's family data on human stature. (2004) The American Statistician, 58(3):237-243.

Examples

data(Galton)

data(Galton)

Data from the Child Health and Development Studies

Description

Birth weight, date, and gestational period collected as part of the Child Health and Development Studies in 1961 and 1962. Information about the baby's parents — age, education, height, weight, and whether the mother smoked is also recorded.

Usage

data(Gestation)
data(Gestation)

Format

A data frame with 1236 observations on the following variables.

id: identification number
plurality: all "single fetus" in this data set
outcome: all "live birth" (survived at least 28 days) in this data set
date: birth date where 1096=January 1, 1961
gestation: length of gestation (in days)
wt: birth weight (in ounces)
parity: total number of previous pregnancies (including fetal deaths and still births)
sex: "male"
race: mother's race: "asian", "black", "mex", "mixed", or "white"
age: mother's age in years at termination of pregnancy
ed: mother's education
ht: mother's height in inches to the last completed inch
wt.1: mother's prepregnancy weight (in pounds)
drace: father's race
dage: father's age (in years)
ded: father's education
dht: father's height in inches to the last completed inch
dwt: father's weight (in pounds)
marital: marital status

inc: family yearly income in $2500 increments
smoke: does mother smoke? (never, smokes now, until current pregnancy, once did, not now)
time: time since quitting smoking (never smoked, still smokes, during current preg, within 1 year, 1 to 2 years ago, 2 to 3 years ago, 3 to 4 years ago, 5 to 9 years ago, 10+ years ago, quit and don't know
number: number of cigarettes smoked per day for past and current smokers (never, 1-4, 5-9, 10-14, 15-19, 20-29, 30-39, 40-60, 60+, smoke but don't know)

Details

The data were presented by Nolan and Speed to address the question of whether there is a link between maternal smoking and the baby's health for male births.

Source

The book by Nolan and Speed describes the data in more detail and provides an Internet site for accessing them: https://www.stat.berkeley.edu/users/statlabs/

References

D Nolan and T Speed. Stat Labs: Mathematical Statistics Through Applications (2000), Springer-Verlag.

Examples

data(Gestation)

data(Gestation)

Goose Permit Study

Description

237 hunters were each offered one of 11 cash amounts (bids) ranging from $1 to $200 in return for their goose permits. Hunters returned either their permit or the cash.

Usage

data(GoosePermits)
data(GoosePermits)

Format

A data.frame with 11 observations on the following 3 variables.

bid: amount offered for permit (US $) (numeric)
keep: number of hunters who kept the permit and returned the cash (numeric)
sell: number of hunters who kept the cash and returned the permit (numeric)

Source

Bishop and Heberlein. "Measuring values of extramarket goods: are indirect measures biased?". Amer. J. Agr. Econ. 61, 1979. Available at https://onlinelibrary.wiley.com/doi/abs/10.2307/3180348

Examples

data(GoosePermits)

goose.model <-
  glm( cbind(keep, sell) ~ log(bid), data = GoosePermits, family = binomial())
if (require(ggformula)) {
  y.hat <- makeFun(goose.model)
  gf_point( (keep/(keep+sell)) ~ bid, data = GoosePermits, ylim = c(0,1.05)) |>
  gf_fun(y.hat(b) ~ b, add = TRUE, color = "red", alpha = 0.5)
}

data(GoosePermits)

goose.model <-
  glm( cbind(keep, sell) ~ log(bid), data = GoosePermits, family = binomial())
if (require(ggformula)) {
  y.hat <- makeFun(goose.model)
  gf_point( (keep/(keep+sell)) ~ bid, data = GoosePermits, ylim = c(0,1.05)) |>
  gf_fun(y.hat(b) ~ b, add = TRUE, color = "red", alpha = 0.5)
}

Data from a heat exchanger laboratory

Description

These data were collected by engineering students at Calvin College. The apparatus consists of concentric pipes insulated from the environment so that as nearly as can be managed the only heat exchange is between the hot and cold water.

Usage

data(HeatX)
data(HeatX)

Format

A data frame with 6 observations on the following variables.

trial: trial number
T.cold.in: temperature (C) of the cold water as it enters the apparatus
T.cold.out: temperature (C) of the cold water as it leaves the apparatus
m.cold: flow rate (L/min) of the cold water
T.hot.in: temperature (C) of the hot water as it enters the apparatus
T.hot.out: temperature (C) of the hot water as it leaves the apparatus
m.hot: flow rate (L/min) of the hot water

Examples

# We can test for heat exchange with the environment by checking to see if the
# heat gained by the cold water matches the heat lost by the hot water.
C_p <- 4.182 / 60  # / 60 because measuring m in L/min
HeatX2 <-
  dplyr::mutate(HeatX,
    Q.cold = m.cold * C_p * (T.cold.out - T.cold.in),
    Q.hot  = m.hot * C_p * (T.hot.out- T.hot.in),
    Q.env  = Q.cold + Q.hot
  )
if (require(ggformula)) {
  gf_jitter( "" ~ Q.env, data = HeatX2, alpha = 0.6, size = 4,
    width = 0, height = 0.1, seed = 123) |>
  gf_labs(y = "")
}
if (require(mosaic)) {
  t.test( ~Q.env, data = HeatX2 )
}
# We can test for heat exchange with the environment by checking to see if the
# heat gained by the cold water matches the heat lost by the hot water.
C_p <- 4.182 / 60  # / 60 because measuring m in L/min
HeatX2 <-
  dplyr::mutate(HeatX,
    Q.cold = m.cold * C_p * (T.cold.out - T.cold.in),
    Q.hot  = m.hot * C_p * (T.hot.out- T.hot.in),
    Q.env  = Q.cold + Q.hot
  )
if (require(ggformula)) {
  gf_jitter( "" ~ Q.env, data = HeatX2, alpha = 0.6, size = 4,
    width = 0, height = 0.1, seed = 123) |>
  gf_labs(y = "")
}
if (require(mosaic)) {
  t.test( ~Q.env, data = HeatX2 )
}

Health Evaluation and Linkage to Primary Care

Description

The HELP study was a clinical trial for adult inpatients recruited from a detoxification unit. Patients with no primary care physician were randomized to receive a multidisciplinary assessment and a brief motivational intervention or usual care, with the goal of linking them to primary medical care.

Usage

data(HELPfull)
data(HELPfull)

Format

A data frame with 1472 observations on the following variables.

ID: Subject ID
A10: Marital Status (1=Married, 2=Remarried, 3=Widowed, 4= Separated, 5=Divorced, 6=Never Married
A11A: Do you currently have a living mother? (0=No, 1= Yes
A11B: Do you currently have a living father? (0=No, 1=Yes
A11C: Do you currently have siblings? (0=No, 1=Yes
A11D: Do you currently have a partner (0=No, 1=Yes)
A11E: Do you currently have children? (0=No, 1=Yes)
A12B_REC: Hollingshead category (recode) (0=Cat 1,2,3, 1=Cat 4,5,6, 2=Cat 7,8,9)
A12B: Hollingshead categories (1=Major profess, 2=Lesser professional, 3=Minor professional, 4=Clerical/sales, 5=Skilled manual, 6=Semi-skilled, 7=Unskilled, 8= Homemaker, 9=No occupation)
A13: Usual employment pattern in last 6 months (1=Full time, 2=Part time, 3=Student, 4=Unemployed, 5=Control envir)
A14A: Lived alone-last 6 months (0=No, 1=Yes)
A14B: Lived with a partner-last 6 months (0=No, 1=Yes
A14C: Lived with parent(s)-last 6 months (0=No, 1=Yes)
A14D: Lived with children-last 6 months (0=No, 1=Yes)
A14E: Lived with other family-last 6 months (0=No, 1=Yes
A14F: Lived with friend(s)-last 6 months (0=No, 1=Yes)
A14G_T: a factor with levels 1/2 WAY HOUSE 3/4 HOUSE ANCHOR INN ARMY ASSOCIATES BOARDERS BOYFRIENDS MOM CORRECTIONAL FACILIT CRACK HOUSE DEALER ENTRE FAMILIA FENWOOD GAVIN HSE GIRLFRIENDS DAUGHTE GIRLFRIENDS SON GIRLFRIENDS CHILDREN GIRLFRIENDS DAUGHTER GROUP HOME HALF-WAY HOUSE HALFWAY HOUSE HALFWAY HOUSES HALFWAY HSE HOLDING UNIT HOME BORDER HOMELESS HOMELESS SHELTER IN JAIL IN PROGRAMS INCARCERATED JAIL JAIL HALFWAY HOUSE JAIL, SHELTER JAIL, STREET JAIL/PROGRAM JAIL/SHELTER JAILS LANDLADY LANDLORD LODGING HOUSE MERIDIAN HOUSE NURSING HOME ON THE STREET PARTNERS MOTHER PARTNERS CHILD PARTNERS CHILDREN PRDGRAMS PRISON PROGRAM PROGRAM MTHP PROGRAM ROOMMATES PROGRAM SOBER HOUSE PROGRAM-RESIDENTIAL PROGRAM/HALFWAY HOUS PROGRAM/JAIL PROGRAM/SHELTER PROGRAM/SHELTERS PROGRAMS PROGRAMS SUBSTANCE PROGRAMS/SHELTER PROGRAMS/SHELTERS PROGRAMS/SHELTERS/DE PROJECT SOAR RESIDENTIAL FACILITY RESIDENTIAL PROGRAM ROOMING HOUSE ROOMING HOUSE (RELIG ROOMMATE ROOMMATES ROOMMATES AT TRANSIT RYAN HOUSE SALVATION ARMY SHELTER SHELTER/HALFWAY HSE SHELTER/HOTEL SHELTER/PROGRAM SHELTERS SHELTERS/HOSPITALS SHELTERS/JAIL SHELTERS/PROGRAMS SHELTERS/STREETS SOBER HOUSE SOBER HOUSING SOUTH BAY JAIL STEPSON STREET STREETS SUBSTANCE ABUSE TREA TRANSITIONAL HOUSE VA SHELTER
A14G: Lived w/ other-last 6 months (0=No, 1=Yes)
A15A: #nights in overnight shelter-last 6 months
A15B: # nights on street-last 6 months
A15C: # months in jail-last 6 months
A16A: # months in overnight shelter-last 5 years
A16B: # moths on street-last 5 years
A16C: # months in jail-last 5 years
A17A: Received SSI – past 6 months (0=No, 1=Yes)
A17B: Received SSDI – past 6 months (0=No, 1=Yes)
A17C: Received AFDC – past 6 months (0=No, 1=Yes)
A17D: Received EAEDC – past 6 months (0=No, 1=Yes)
A17E: Received WIC – past 6 months (0=No, 1=Yes)
A17F: Received unemployment benefits – past 6 months (0=No, 1=Yes)
A17G: Received Workman's Compensation – past 6 months (0=No, 1=Yes)
A17H: Received Child Support – past 6 months (0=No, 1=Yes)
A17I_T: a factor with levels DISABLED VETERAN EBT (FOOD STAMPS) EMERGENCY FOOD STAMP FOOD STAMP FOOD STAMPS FOOD STAMPS/VETERAN FOOD STAMPS/VETERANS INSURANCE SETTLEMENT PENSION CHECK SECTION 8 SERVICE CONNECTED DI SOCIAL SECURITY SSDI FOR SON SURVIVORS BENEFITS TEMPORARY DISABILITY VA BENEFITS-DISABILI VA COMPENSATION VA DISABILITY PENSIO VETERAN BENEFITS VETERANS SERVICES VETERANS AFFAIRS
A17I: Received other income – past 6 months (0=No, 1=Yes)
A18_REC1: Most money made in 1 year (recode) (0=$19,000 or less, 1=$20,000-$49,000, 2=$50,000 or more)
A18_REC2: Most money made-continuous recode
A18: Most money made in any 1 year-last 5 years (1=<5000, 2=5000-10000, 3=11000-19000, 4=20000-29000, 5=30000-39000, 6=40000-49000, 7=50000+
A1: Gender (1=Male, 2=Female)
A9: Years of education completed
ABUSE2: Type of abuse (0=No abuse, 1=Physical only, 2=Sexual only, 3=Physical and sexual)
ABUSE3: Type of abuse (0=No abuse, 1=Physical only, 2=Sexual +/- physical (0=No, 1=Yes)
ABUSE: Abuse-physical or sexual (0=No abuse, 1=Family abuse, 2=Stranger only abuse)
AGE: Age in years
ALCOHOL: 1st/2nd drug of coice=Alcohol (0=No, 1=Yes)
ALCQ_30: Total number drinks past 30 days
ALONE6M: Usually lived alone past 6 months (0=No, 1=Yes)
ALT_TRT: Alternative tratments (0=No, 1=Yes)
ANYSUBSTATUS: Used alcohol, heroin, or cocaine since leaving detox-6 months
ANY_INS: Did you have health insurance in past 6 months (0=No, 1=Yes)
ANY_UTIL: Any recent health utilization (0=No, 1=Yes)
ANY_VIS_CUMUL: Cumulative # visits to regular doctor's office
ANY_VIS: # visits to regular doctor's office–This time point
B10: Any physcal/emotional problem interfere with social activities-last 4 weeks (1=All of the time, 2=Most of the time, 3=Some of the time, 4= A lttle of time, 5= None of the time)
B11A: I seem to get sick easier than other people (1=Definitely true, 2=Mostly True, 3=Don't know, 4=Mostly false, 5=Definitely false)
B11B: I am as healthy as anybody I know (1=Definitely true, 2=Mostly true, 3=Don't know, 4=Mostly false, 5=Definitely False)
B11C: I expect my health to get worse (1=Definitely true, 2=Mostly true, 3=Don't know, 3=Mostly false, 5=Definitely false)
B11D: My health is excellent (1=Definitely true, 2=Mostly true, 3=Don't know, 4=Mostly false, 5=Definitely false)
B1: In general, how is your health (1=Excellent, 2=Very Good, 3=Good, 4=Fair, 5=Poor)
B2: Compared to 1 year ago, how is your health now (1=Much better, 2=Somewhat better, 3=About the same, 4=Somewhat worse, 5=Much worse)
B3A: Does health limit you in vigorous activity (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3B: Does your health limit you in moderate activity (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3C: Does health limit you in lift/carry groceries (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3D: Does health limit you in climb several stair flights (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3E: Does health limit you in climb 1 stair flight (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3F: Does health limit you in bend/kneel/stoop (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3G: Does health limit you in walking >1 mile (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3H: Does health limit you in walking sevral blocks (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3I: Does health limit you in walking 1 block (1=Limited a lot, 2=Limited a little, 3=Not limited)
B3J: Does health limit you in bathing/dressing self (1=Limited a lot, 2=Limited a little, 3=Not limited)
B4A: Cut down work/activity due to physical health-last 4 weeks (0=No, 1=Yes)
B4B: Accomplish less due to phys health-last 4 weeks (0=No, 1=Yes)
B4C: Lim wrk/act type due to phys health-last 4 weeks (0=No, 1=Yes)
B4D: Diff perf work due to phys health-last 4 weeks (0=No, 1=Yes)
B5A: Cut wrk/act time due to emot prbs-last 4 weeks (0=No, 1=Yes)
B5B: Accomplish ess due to emot probs-last 4 weeks (0=No, 1=Yes)
B5C: <carefl w/wrk/act due to em prb-last 4 weeks (0=No, 1=Yes)
B6: Ext phys/em intf w/norm soc act-last 4 weeks (1=Not al all, 2=Slightly, 3=Moderately, 4=Quite a bit, 5=Extremely)
B7: Amount of bodily pain – past 4 weeks (1=None, 2=Very mild, 3= Mild, 4=Moderate, 5= Severe, 6= Very severe)
B8: Amount of pain interfering with normal work-last 4 weeks (1=Not at all, 2=A little bit, 3=Moderately, 4=Quite a bit, 5=Extremely
B9A: Did you feel full of pep – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9B: Have you been nervous – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9C: Felt nothing could cheer you-last 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9D: Have you felt calm/peaceful – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9E: Did you have a lot of energy – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9F: Did you feel downhearted – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9G: Did you feel worn out – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9H: Have you been a happy pers – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
B9I: Did you feel tired – past 4 weeks (1=All of the time, 2=Most of the time, 3 = Good bit of the time, 4=Some of the time, 5=A little of time, 6=None of the time)
BIRTHPLC: Where born (recode) (0=USA, 1=Foreign)
BP: SF-36 pain index (0-100)
C1A: Tolf by MD had seix, epil, convuls (0=No, 1=Yes)
C1B: Told by MD had asthma, emphysema, chr lung dis (0=No, 1=Yes)
C1C: Told by MD had MI (0=No, 1=Yes)
C1D: Told by MD had CHF (0=No, 1=Yes)
C1E: Told by MD had other heart dis (req med) (0=No, 1=Yes)
C1F: Told by MD had HBP (0=No, 1=Yes)
C1G: Told by MD had chronic liver disease (0=No, 1=Yes)
C1H: Told by MD had kidney failure (0=No, 1=Yes)
C1I: Told by MD had chronic art, osteoarth (0=No, 1=Yes)
C1J: Told by MD had peripheral neuropathy (0=No, 1=Yes)
C1K: Ever told by MD had cancer (0=No, 1=Yes)
C1L: Ever told by MD had diabetes (0=No, 1=Yes)
C1M: Ever told by MD had stroke (0=No, 1=Yes)
C2A1: Have you ever had skin infections (0=No, 1=Yes)
C2A2: Have you had skin infections – past 6 months (0=No, 1=Yes)
C2B1: Have you ever had pneumonia (0=No, 1=Yes)
C2B2: Have you had pneumonia – past 6 months (0=No, 1=Yes)
C2C1: Have you ever had septic arthritis (0=No, 1=Yes)
C2C2: Have you had septic arthritis – past 6 months (0=No, 1=Yes)
C2D1: Have you ever had TB (0=No, 1=Yes)
C2D2: Have you had TB-last 6 months (0=No, 1=Yes)
C2E1: Have you ever had endocarditis (0=No, 1=Yes)
C2E2: Have you had endocarditis – past 6 months (0=No, 1=Yes)
C2F1: Have you ever had an ulcer (0=No, 1=Yes)
C2F2: Have you had an ulcer – past 6 months (0=No, 1=Yes)
C2G1: Have you ever had pancreatitis (0=No, 1=Yes)
C2G2: Have you had pancreatitis – past 6 months (0=No, 1=Yes)
C2H1: Ever had abdom pain req overnt hosp stay (0=No, 1=Yes)
C2H2: Abdom pain req ovrnt hosp stay-last 6 months (0=No, 1=Yes)
C2I1: Have you ever vomited blood (0=No, 1=Yes)
C2I2: Have you vomited blood – past 6 months (0=No, 1=Yes)
C2J1: Have you ever had hepatitis (0=No, 1=Yes)
C2J2: Have you had hepatitis – past 6 months (0=No, 1=Yes)
C2K1: Ever had blood clots in legs/lungs (0=No, 1=Yes)
C2K2: Blood clots in legs/lungs – past 6 months (0=No, 1=Yes)
C2L1: Have you ever had osteomyelitis (0=No, 1=Yes)
C2L2: Have you had osteomyelitis – past 6 months (0=No, 1=Yes)
C2M1: Chest pain using cocaine req ER/hosp (0=No, 1=Yes)
C2M2: Chest pain using coc req ER/hosp-last 6 months (0=No, 1=Yes)
C2N1: Have you ever had jaundice (0=No, 1=Yes)
C2N2: Have you had jaundice – past 6 months (0=No, 1=Yes)
C2O1: Lower back pain > 3 months req med attn (0=No, 1=Yes)
C2O2: Lwr back pain >3 months req med attention-last 6 months (0=No, 1=Yes)
C2P1: Ever had seizures or convulsions (0=No, 1=Yes)
C2P2: Had seizures or convulsions – past 6 months (0=No, 1=Yes)
C2Q1: Ever had drug/alcohol overdose requiring ER attention (0=No, 1=Yes)
C2Q2: Drug/alcohol overdose req ER attn (0=No, 1=Yes)
C2R1: Have you ever had a gunshot wound (0=No, 1=Yes)
C2R2: Had a gunshot wound – past 6 months (0=No, 1=Yes)
C2S1: Have you ever had a stab wound (0=No, 1=Yes)
C2S2: Have you had a stab wound – past 6 months (0=No, 1=Yes)
C2T1: Ever had accident/falls req med attn (0=No, 1=Yes)
C2T2: Had accident/falls req med attn – past 6 months (0=No, 1=Yes)
C2U1: Ever had fract/disloc to bones/joints (0=No, 1=Yes)
C2U2: Fract/disloc to bones/joints – past 6 months (0=No, 1=Yes)
C2V1: Ever had injury from traffic accident (0=No, 1=Yes)
C2V2: Had injury from traffic accident – past 6 months (0=No, 1=Yes)
C2W1: Have you ever had a head injury (0=No, 1=Yes)
C2W2: Have you had a head injury – past 6 months (0=No, 1=Yes)
C3A1: Have you ever had syphilis (0=No, 1=Yes)
C3A2: # times had syphilis
C3A3: Have you had syphilis in last 6 months (0=No, 1=Yes)
C3B1: Have you ever had gonorrhea (0=No, 1=Yes)
C3B2: # times had gonorrhea
C3B3: Have you had gonorrhea in last 6 months (0=No, 1=Yes)
C3C1: Have you ever had chlamydia (0=No, 1=Yes)
C3C2: # of times had Chlamydia
C3C3: Have you had chlamydia in last 6 months (0=No, 1=Yes)
C3D: Have you ever had genital warts (0=No, 1=Yes)
C3E: Have you ever had genital herpes (0=No, 1=Yes)
C3F1: Have you ever had other STD's (not HIV) (0=No, 1=Yes)
C3F2: # of times had other STD's (not HIV)
C3F3: Had other STD's (not HIV)-last 6 months (0=No, 1=Yes)
C3F_T: a factor with levels 7 CRABS CRABS - TRICHONOMIS CRABS, HEP B DOESNT KNOW NAME HAS HAD ALL 3 ABC HEP B HEP B, TRICAMONAS HEP. B HEPATITIS B HEPATITS B TRICHAMONAS VAGINALA TRICHAMONIS TRICHOMONAS TRICHOMONIASIS TRICHOMONIS TRICHOMONIS VAGINITI TRICHOMORAS TRICHONOMIS
C3G1: Have you ever been tested for HIV/AIDS (0=No, 1=Yes)
C3G2: # times tested for HIV/AIDS
C3G3: Have you been tested for HIV/AIDS-last 6 months (0=No, 1=Yes)
C3G4: What was the result of last test (1=Positive, 2=Negative, 3=Refused, 4=Never got result, 5=Inconclusive
C3H1: Have you ever had PID (0=No, 1=Yes)
C3H2: # of times had PID
C3H3: Have you had PID in last 6 months (0=No, 1=Yes)
C3I: Have you ever had a Pap smear (0=No, 1=Yes)
C3J: Have you had a Pap smear in last 3 years (0=No, 1=Yes)
C3K_M: How many months pregnant
C3K: Are you pregnant (0=No, 1=Yes)
CESD_CUT: CES-D score > 21 y/n (0=No, 1=Yes)
CES_D: CES-D score, measure of depressive symptoms, high scores are worse
CHR_6M: Chronic medical conds/HIV – past 6m y/n (0=No, 1=Yes)
CHR_EVER: Chronic medical conds/HIV-ever y/n (0=No, 1=Yes)
CHR_SUM: Sum chronic medical conds/HIV ever
CNTRL: InDUC-2L-Control score
COC_HER: 1st/2nd drug of choice=cocaine or heroine (0=No, 1=Yes)
CUAD_C: CUAD-Cocaine
CUAD_H: CUAD-Heroin
CURPHYAB: Current abuse-physical (0=No, 1=Yes)
CURPHYSEXAB: Curent abuse-physical or sexual (0=No abuse, 1=Physical only, 2=Sexual +/- physical)
CURSEXAB: Current abuse-sexual (0=No, 1=Yes)
C_AU: ASI-Composite score for alcohol use
C_DU: ASI-Composite score for drug use
C_MS: ASI-Composite medical status
D1: $ of times hospitalized for med probs
D2: Take prescription medicdation regularly for physical problem (0=No, 1=Yes)
D3_REC: Any medical problems past 30d y/n (0=No, 1=Yes)
D3: # days had med probs-30 days bef detox
D4_REC: Bothered by medical problems y/n (0=No, 1=Yes)
D4: How bother by med prob-30days bef detox (0=Not at all, 1=Slightly, 2=Moderately, 3=Considerably, 4=Extremely)
D5_REC: Medical trtmt is important y/n (0=No, 1=Yes)
D5: How import is trtmnt for these med probs (0=Not at all, 1=Slightly, 2= Moderately, 3= Considerably, 4= Extremely
DAYSANYSUB: time (days) from baseline to first alcohol, heroin, or cocaine since leaving detox-6m
DAYSDRINK: Time (days) from baseline to first drink since leaving detox-6m
DAYSLINK: Time (days) to linkage to primary care within 12 months (by administrative record)
DAYS_SINCE_BL: # of days from baseline to current interview
DAYS_SINCE_PREV: # of days from previous to current interview
DEAD: a numeric vector
DEC_AM: SOCRATES-Ambivalence-Decile
DEC_RE: SOCRATES-Recognition-Decile
DEC_TS: SOCRATES-Taking steps-Decile
DRINKSTATUS: Drank alcohol since leaving detox-6m
DRUGRISK: RAB-Drug risk total
E10A: have you been to med clinic-last 6 months (0=No, 1=Yes)
E10B1_R: Mental health treatment past 6m y/n (0=No, 1=Yes)
E10B1: # x visit ment health clin/prof-last 6 months
E10B2_R: Med clinic/private MD past 6m y/n (0=No, 1=Yes)
E10B2: # x visited med clin/priv MD-last 6 months
E10C19: Visited private MD-last 6 months (0=No, 1=Yes)
E11A: Did you stay ovrnite/+ in hosp-last 6 months (0=No, 1=Yes)
E11B: # times ovrnight/+ in hosp-last 6 months
E11C: Total # nights in hosp-last 6 months
E12A: Visited Hosp ER for med care – past 6 months (0=No, 1=Yes)
E12B: # times visited hosp ER-last 6 months
E13: Tlt # visits to MDs-last 2 weeks bef detox
E14A: Recd trtmt from acupuncturist-last 6 months (0=No, 1=Yes)
E14B: Recd trtmt from chiropractor-last 6 months (0=No, 1=Yes)
E14C: Trtd by hol/herb/hom med prac-last 6 months (0=No, 1=Yes)
E14D: Recd trtmt from spirit healer-last 6 months (0=No, 1=Yes)
E14E: Have you had biofeedback-last 6 months (0=No, 1=Yes)
E14F: Have you underwent hypnosis-last 6 months (0=No, 1=Yes)
E14G: Received other treatment-last 6 months (0=No, 1=Yes)
E15A: Tried to get subst ab services-last 6 months (0=No, 1=Yes)
E15B: Always able to get subst ab servies (0=No, 1=Yes)
E15C10: My insurance didn't cover services (0=No, 1=Yes)
E15C11: There were no beds available at the prog (0=No, 1=Yes)
E15C12: Other reason not get sub ab services (0=No, 1=Yes)
E15C1: I could not pay for services (0=No, 1=Yes)
E15C2: I did not know where to go for help (0=No, 1=Yes)
E15C3: Couldn't get to services due to transp prob (0=No, 1=Yes)
E15C4: The offie/clinic hrs were inconvenient (0=No, 1=Yes)
E15C5: Didn't speak/understnd Englsh well enough (0=No, 1=Yes)
E15C6: Afraid other might find out about prob (0=No, 1=Yes)
E15C7: My substance abuse interfered (0=No, 1=Yes)
E15C8: Didn't have someone to watch my children (0=No, 1=Yes)
E15C9: I did not want to lose my job (0=No, 1=Yes)
E16A10: I do not want to lose my job (0=No, 1=Yes)
E16A11: My insurance doesn't cover charges (0=No, 1=Yes)
E16A12: I do not feel I need a regular MD (0=No, 1=Yes)
E16A13: Other reasons don't have regular MD (0=No, 1=Yes)
E16A1: I cannot pay for services (0=No, 1=Yes)
E16A2: I am not eligible for free care (0=No, 1=Yes)
E16A3: I do not know where to go (0=No, 1=Yes)
E16A4: Can't get to services due to trans prob (0=No, 1=Yes)
E16A5: a numeric vectorOffice/clinic hours are inconvenient (0=No, 1=Yes)
E16A6: I don't speak/understnd enough English (0=No, 1=Yes)
E16A7: Afraid othrs find out about my health prob (0=No, 1=Yes)
E16A8: My substance abuse interferes (0=No, 1=Yes)
E16A9: I don't have someone to watch my children (0=No, 1=Yes)
E16A_DD: Barrier to regular MD: dislike docs/system (0=No, 1=Yes)
E16A_IB: Barrier to regular MD: internal barriers (0=No, 1=Yes)
E16A_RT: Barrier to regular MD: red tape (0=No, 1=Yes)
E16A_TM: Barrier to regular MD: time restrictions (0=No, 1=Yes)
E18A: I could not pay for services (0=No, 1=Yes)
E18B: I did not know where to go for help (0=No, 1=Yes)
E18C: Couldn't get to services due to transp prob (0=No, 1=Yes)
E18D: The office/clinic hrs were inconvenient (0=No, 1=Yes)
E18F: Afraid others might find out about prob (0=No, 1=Yes)
E18G: My substance abuse interfered (0=No, 1=Yes)
E18H: Didn't have someone to watch my children (0=No, 1=Yes)
E18I: I did not want to lose my job (0=No, 1=Yes)
E18J: My insurance didn't cover services (0=No, 1=Yes)
E18K: There were no beds available at the prog (0=No, 1=Yes)
E18L: I do not need substance abuse services (0=No, 1=Yes)
E18M: Other reason not get sub ab services (0=No, 1=Yes)
E2A: Detox prog for alcohol or drug prob-last 6 months (0=No, 1=Yes)
E2B: # times entered a detox prog-last 6 months
E2C: # nights ovrnight in detox prg-last 6 months
E3A: Holding unit for drug/alcohol prob-last 6 months (0=No, 1=Yes)
E3B: # times in holding unity=last 6 months
E3C: # total nights in holding unit-last 6 months
E4A: In halfway hse/resid facil-last 6 months (0=No, 1=Yes)
E4B: # times in hlfwy hse/res facil-last 6 months
E4C: Ttl nites in hlfwy hse/res fac-last 6 months
E5A: In day trtmt prg for alcohol/drug-last 6 months (0=No, 1=Yes)
E5B: Total # days in day trtmt prg-last 6 months
E6: In methadone maintenance prg-last 6 months (0=No, 1=Yes)
E7A: Visit outpt prg subst ab couns-last 6 months (0=No, 1=Yes)
E7B: # visits outpt prg subst ab couns-last 6 months
E8A1: Saw MD/H care worker regarding alcohol/drugs-last 6 months (0=No, 1=Yes)
E8A2: Saw Prst/Min/Rabbi re alcohol/drugs-last 6 months (0=No, 1=Yes)
E8A3: Employ Asst Prg for alcohol/drug prb-last 6 months (0=No, 1=Yes)
E8A4: Oth source cnsl for alcohol/drug prb-last 6 months (0=No, 1=Yes)
E9A: AA/NA/slf-hlp for drug/alcohol/emot-last 6 months (0=No, 1=Yes)
E9B: How often attend AA/NA/slf-hlp-last 6 months (1=Daily, 2=2-3 Times/week, 3=Weekly, 4=Every 2 weeks, 5=Once/month
EPI_6M2B: Episodic(C2A-C2O)-6m y/n (0=No, 1=Yes)
EPI_6M: Episodic (C2A-C2O,C2R-C2U, STD)-6m y/n (0=No, 1=Yes)
EPI_SUM: Sum episodic (C2A-C2O, C2R-C2U, STD)-6m
F1A: Bothered by thngs not generally bothered by (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1B: My appetite was poor (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1C: Couldn't shake blues evn w/fam+frnds hlp (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1D: Felt I was just as good as other people (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1E: Had trouble keeping mind on what doing (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1F: I felt depressed (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1G: I felt everything I did was an effort (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1H: I felt hopeful about the future (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1I: I thought my life had been a failure (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1J: I felt fearful (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1K: My sleep was restless (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1L: I was happy (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1M: I talked less than usual (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1N: I felt lonely (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1O: People were unfriendly (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1P: I enjoyed life (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1Q: I had crying spells (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1R: I felt sad (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1S: I felt that people dislike me (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
F1T: I could not get going (0=Rarely/never, 1=Some of the time, 2=Occas/moderately, 3=Most of the time)
FAMABUSE: Family abuse-physical or sexual (0=No, 1=Yes)
FRML_SAT: Formal substance abuse treatment y/n (0=No, 1=Yes)
G1A_30: Diff contr viol beh-sig per last 30 days (0=No, 1=Yes)
G1A: Diff contr viol beh for sig time per evr (0=No, 1=Yes)
G1B_30: Had thoughts of suicide-last 30 days (0=No, 1=Yes)
G1B_REC: Suicidal thoughts past 30 days y/n (0=No, 1=Yes)
G1B: Ever had thoughts of suicide (0=No, 1=Yes)
G1C_30: Attempted suicide-last 30 days (0=No, 1=Yes)
G1C: Attempted suicide ever (0=No, 1=Yes)
G1D_30: Prescr med for psy/emot prob-last 30 days (0=No, 1=Yes)
G1D_REC: Prescribed psych meds past 30 days y/n (0=No, 1=Yes)
G1D: Prescr med for pst/emot prob ever (0=No, 1=Yes)
GH: SF-36 general health perceptions (0-100)
GOV_SUPP: Received government support past 6 m (0=No, 1=Yes)
GROUP: Randomization Group (0=Control, 1=Clinic)
H10_30: # days in last 30 bef detox used cannabis
H10_LT: # years regularly used cannabis
H10_PRB: Problem sub: marijuana, cannabis (0=No, 1=Yes)
H10_RT: Route of admin of cannabis (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H11_30: # days in last 30 bef detox used halluc
H11_LT: # years regularly used hallucinogens
H11_PRB: Problem sub: hallucinogens (0=No, 1=Yes)
H11_RT: Route of admin of hallucinogens (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H12_30: # days in last 30 bef detox used inhalant
H12_LT: # years regularly used inhalants
H12_PRB: Problem sub: inhalants (0=No, 1=Yes)
H12_RT: Route of admin of inhalants (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H13_30: # days used >1 sub/day-last 30 bef detox
H13_LT: # years regularly used >1 subst/day
H13_RT: Route of admin of >1 subst/day (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H14: According to interviewer, which substance is main problem (0=No problem, 1=Alcohol, 2=Alcohol to intox, 3=Heroin 4=Methadone, 5=Other opiate/analg, 6=Barbituates, 7=Sed/hyp/tranq, 8=Cocaine, 9=Amphetamines, 10=Marij/cannabis, 15=Alcohol and one or more drug, 16=More than one drug
H15A: # times had alcohol DTs
H15B: # times overdosed on drugs
H16A: $ spent on alcohol-last 30 days bef detox
H16B: $ spent on drugs-last 30 days bef detox
H17A: # days had alcohol prob-last 30 days bef det
H17B: # days had drug prob-last 30 days bef det
H18A: How troubled by alcohol probs-last 30 days (0=Not at all, 1=Slightly, 2=Moderately, 3=Considerably, 4=Extremely)
H18B: How troubled by drug probs-last 30 days (0=Not at all, 1=Slightly, 2=Moderately, 3=Considerably, 4=Extremely)
H19A: How import is treatment for alcohol problems now (0=Not at all, 1=Slightly, 2=Moderately, 3=Considerably, 4=Extremely)
H19B: How important is trtmnt for drug probs now (0=Not at all, 1=Slightly, 2=Moderately, 3=Considerably, 4=Extremely)
H1_30: # days in past 30 bef detox used alcohol
H1_LT: # years regularly used alcohol
H1_RT: Route of administration use alcohol (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H2_30: #days in 3- bef detox use alcohol to intox
H2_LT: # years regularly used alcohol to intox
H2_PRB: Problem sub: alcohol to intox (0=No, 1=Yes)
H2_RT: Route of admin use alcohol to intox (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H3_30: # days in past 30 bef detox used heroin
H3_LT: # years regularly used heroin
H3_PRB: Problem sub: heroin (0=No, 1=Yes)
H3_RT: Route of administration of heroin (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H4_30: # days used methadone-last 30 bef detox
H4_LT: # years regularly used methadone
H4_PRB: Problem sub: methadone (0=No, 1=Yes)
H4_RT: Route of administration of methadone (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H5_30: # days used opiates/analg-last 30 bef detox
H5_LT: # years regularly used oth opiates/analg
H5_PRB: Problem sub: other opiates/analg (0=No, 1=Yes)
H5_RT: Route of admin of other opiates/analg (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H6_30: # days in past 30 before detox used barbiturates
H6_LT: # years regularly used barbiturates
H6_PRB: Problem sub: barbiturates (0=No, 1=Yes)
H6_RT: Route of admin of barbiturates (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H7_30: # days used sed/hyp/trnq-last 30 bef det
H7_LT: # years regularly used sed/hyp/trnq
H7_PRB: Problem sub: sedat/hyp/tranq (0=No, 1=Yes)
H7_RT: Route of admin of sed/hyp/trnq (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H8_30: # days in last 30 bef detox used cocaine
H8_LT: # years regularly used cocaine
H8_PRB: Problem sub: cocaine (0=No, 1=Yes)
H8_RT: Route of admin of cocaine (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
H9_30: # days in last 30 bef detox used amphet
H9_LT: # years regularly used amphetamines
H9_PRB: Problem sub: amphetamines (0=No, 1=Yes)
H9_RT: Route of admin of amphetamines (0=N/A. 1=Oral, 2=Nasal, 3=Smoking, 4=Non-IV injection, 5=IV)
HOMELESS: Homeless-shelter/street past 6 m (0=No, 1=Yes)
HS_GRAD: High school graduate (0=No, 1=Yes)
HT: Raw SF-36 health transition item
I1: Avg # drinks in last 30 days bef detox
I2: Most drank any 1 day in last 30 bef detox
I3: On days used heroin, avg # bags used
I4: Most bags heroin used any 1 day – 30 before det
I5: Avg $ amt of heroin used per day
I6A: On days used cocaine, avg # bags used
I6B: On days used cocaine, avg # rocks used
I7A: Mst bgs cocaine use any 1 day-30 bef det
I7B: Mst rcks cocaine use any 1 day-30 bef det
I8: Avg $ amt of cocaine used per day
IMPUL2: Inventory of Drug Use Consequences InDUC-2L-Impulse control-Raw (w/0 M23)
IMPUL: Inventory of Drug Use Consequences InDUL-2L-Impulse control-Raw
INDTOT2: InDUC-2L-Total drlnC-Raw- w/o M23 and M48
INDTOT: InDUC-2LTotal drlnC sore-Raw
INTER: InDUC-2L-Interpersonal-Raw
INTRA: InDUC-2L-Intrapersonal-Raw
INT_TIME1: # of months from baseline to current interview
INT_TIME2: # of months from previous to current interview
J10A: Get physically sick when stop using heroin (0=No, 1=Yes)
J10B: Ever use heroin to prevent getting sick (0=No, 1=Yes)
J1: Evr don't stop using cocaine when should (0=No, 1=Yes)
J2: Ever tried to cut down on cocaine (0=No, 1=Yes)
J3: Does cocaine take up a lot of your time (0=No, 1=Yes)
J4: Need use > cocaine to get some feeling (0=No, 1=Yes)
J5A: Get physically sick when stop using cocaine (0=No, 1=Yes)
J5B: Ever use cocaine to prevent getting sick (0=No, 1=Yes)
J6: Ever don't stop using heroin when should (0=No, 1=Yes)
J7: Ever tried to cut down on heroin (0=No, 1=Yes)
J8: Does heroin take up a lot of your time (0=No, 1=Yes)
J9: Need use > heroin to get some feeling (0=No, 1=Yes)
JAIL_5YR: Any jail time past 5 years y/n (0=No, 1=Yes)
JAIL_MOS: Total months in jail past 5 years
K1: Do you currently smoke cigarettes (1=Yes-every day, 2=Yes-some days, 3=No-former smoker, 4=No-never>100 cigarettes
K2: Avg # cigarettes smoked per day
K3: Considering quitting cigarettes within next 6 months (0=No, 1=Yes)
L10: Have had blkouts as result of drinkng (0=No, never, 1=Sometimes, 2=Often, 3=Alm evry time drink)
L11: Do you carry bottle or keep close by (0=No, 1=Some of the time, 2=Most of the time)
L12: After abstin end up drink heavily again (0=No, 1=Sometimes, 2=Almost evry time)
L13: Passed out due to drinking-last 12 months (0=No, 1=Once, 2=More than once)
L14: Had convuls following period of drinkng (0=No, 1=Once, 2=Several times)
L15: Do you drink throughout the day (0=No, 1=Yes)
L16: After drinkng heavily was thinkng unclear (0=No, 1=Yes, few hrs, 2=Yes,1-2 days, 3=Yes, many days)
L17: D/t drinkng felt heart beat rapidly (0=No, 1=Once, 2=Several times)
L18: Do you constntly think about drinkng/alcohol (0=No, 1=Yes)
L19: D/t drinkng heard things not there (0=No, 1=Once, 2= Several times)
L1: How often drink last time drank (1=To get high/less, 2=To get drunk, 3=To pass out)
L20: Had weird/fright sensations when drinkng (0=No, 1=Once or twice, 2=Often)
L21: When drinkng felt things rawl not there (0=No, 1=Once, 2=Several times)
L22: With respect to blackouts (0=Never had one, 1=Had for <1hr, 2=Had several hrs, 3=Had for day/+)
L23: Ever tried to cut down on drinking & failed (0=No, 1=Once, 2=Several times)
L24: Do you gulp drinks (0=No, 1=Yes)
L25: After taking 1 or 2 drinks can you stop (0=No, 1=Yes)
L2: Often have hangovers Sun or Mon mornings (0=No, 1=Yes)
L3: Have you had the shakes when sobering (0=No, 1=Sometimes, 2=Alm evry time drink)
L4: Do you get physically sick as reslt of drinking (0=No, 1=Sometimes, 2=Alm evry time drink)
L5: have you had the DTs (0=No, 1=Once, 2=Several times
L6: When drink do you stumble/stagger/weave (0=No, 1=Sometimes, 2=Often)
L7: D/t drinkng felt overly hot/sweaty (0=No, 1=Once, 2=Several times)
L8: As result of drinkng saw thngs not there (0=No, 1=Once, 2=Several times)
L9: Panic because fear not have drink if need it (0=No, 1=Yes)
LINKSTATUS: Linked to primary care within 12 months (by administrative record)
M10: Using alcohol/1 drug caused > use othr drugs (0=No, 1=Yes)
M11: I have been sick/vomited aft alcohol/drug use (0=No, 1=Yes)
M12: I have been unhappy because of alcohol/drug use (0=No, 1=Yes)
M13: Lost weight/eaten poorly due to alcohol/drug use (0=No, 1=Yes)
M14: Fail to do what expected due to alcohol/drug use (0=No, 1=Yes)
M15: Using alcohol/drugs has helped me to relax (0=No, 1=Yes)
M16: Felt guilt/ashamed because of my alcohol drug use (0=No, 1=Yes)
M17: Said/done emarras thngs when on alcohol/drug (0=No, 1=Yes)
M18: Personality changed for worse on alcohol/drug (0=No, 1=Yes)
M19: Taken foolish risk when using alcohol/drugs (0=No, 1=Yes)
M1: Had hangover/felt bad aftr using alcohol/drugs (0=No, 1=Yes)
M20: Gotten into trouble because of alcohol/drug use (0=No, 1=Yes)
M21: Said cruel things while using alcohol/drugs (0=No, 1=Yes)
M22: Done impuls thngs regret due to alcohol/drug use (0=No, 1=Yes)
M23: Gotten in physical fights when use alcohol/drugs (0=No, 1=Yes)
M24: My physical health was harmed by alcohol/drug use (0=No, 1=Yes)
M25: Using alcohol/drug helped me have more + outlook (0=No, 1=Yes)
M26: I have had money probs because of my alcohol/drug use (0=No, 1=Yes)
M27: My love relat harmed due to my alcohol/drug use (0=No, 1=Yes)
M28: Smoked tobacco more when using alcohol/drugs (0=No, 1=Yes)
M29: My physical appearance harmed by alcohol/drug use (0=No, 1=Yes)
M2: Felt bad about self because of alcohol/drug use (0=No, 1=Yes)
M30: My family hurt because of my alcohol drug use (0=No, 1=Yes)
M31: Close relationsp damaged due to alcohol/drug use (0=No, 1=Yes)
M32: Spent time in jail because of my alcohol/drug use (0=No, 1=Yes)
M33: My sex life suffered due to my alcohol/drug use (0=No, 1=Yes)
M34: Lost interst in activity due to my alcohol/drug use (0=No, 1=Yes)
M35: Soc life> enjoyable when using alcohol/drug (0=No, 1=Yes)
M36: Spirit/moral life harmed by alcohol/drug use (0=No, 1=Yes)
M37: Not had kind life want due to alcohol/drug use (0=No, 1=Yes)
M38: My alcohol/drug use in way of personal growth (0=No, 1=Yes)
M39: My alcohol/drug use damaged soc life/reputat (0=No, 1=Yes)
M3: Missed days wrk/sch because of alcohol/drug use (0=No, 1=Yes)
M40: Spent/lost too much $ because alcohol/drug use (0=No, 1=Yes)
M41: Arrested for DUI of alcohol or oth drugs (0=No, 1=Yes)
M42: Arrested for offenses rel to alcohol/drug use (0=No, 1=Yes)
M43: Lost marriage/love relat due to alcohol/drug use (0=No, 1=Yes)
M44: Susp/fired/left job/sch due to alcohol/drug use (0=No, 1=Yes)
M45: I used drugs moderately w/o having probs (0=No, 1=Yes)
M46: I have lost a friend due to my alcohol/drug use (0=No, 1=Yes)
M47: Had an accident while using alcohol/drugs (0=No, 1=Yes)
M48: Physically hurt/injured/burned when using alcohol/drugs (0=No, 1=Yes)
M49: I injured someone while using alcohol/drugs (0=No, 1=Yes)
M4: Fam/frinds worry/compl about alcohol/drug use (0=No, 1=Yes)
M50: Damaged things/prop when using alcohol/drugs (0=No, 1=Yes)
M5: I have enjoyed drinking/using drugs (0=No, 1=Yes)
M6: Qual of work suffered because of alcohol/drug use (0=No, 1=Yes)
M7: Parenting ability harmed by alcohol/drug use (0=No, 1=Yes)
M8: Trouble sleeping/nightmares aftr alcohol/drugs (0=No, 1=Yes)
M9: Driven motor veh while undr inf alcohol/drugs (0=No, 1=Yes)
MAR_STAT: Marital status (recode) (0=Married, 1=Not married)
MCS: Standardized mental component scale-00
MD_LANG: Lang prefer to speak to MD (recode) (0=English, 1=Other lang)
MH: SF-36 mental health index (0-100)
MMSEC: MMSEC
N1A: My friends give me the moral support I need (0=No, 1=Yes)
N1B: Most people closer to friends than I am (0=No, 1=Yes)
N1C: My friends enjoy hearing what I think (0=No, 1=Yes)
N1D: I rely on my friends for emot support (0=No, 1=Yes)
N1E: Friend go to when down w/o feel funny later (0=No, 1=Yes)
N1F: Frnds and I open re what thnk about things (0=No, 1=Yes)
N1G: My friends sensitive to my pers needs (0=No, 1=Yes)
N1H: My friends good at helping me solve probs (0=No, 1=Yes)
N1I: have deep sharing relat w/ a # of frnds (0=No, 1=Yes)
N1J: When confide in frnds makes me uncomfort (0=No, 1=Yes)
N1K: My friends seek me out for companionship (0=No, 1=Yes)
N1L: Not have as int relat w/frnds as others (0=No, 1=Yes)
N1M: Recent good idea how to do somethng frm frnd (0=No, 1=Yes)
N1N: I wish my friends were much different (0=No, 1=Yes)
N2A: My family gives me the moral support I need (0=No, 1=Yes)
N2B: Good ideas of how do/make thngs from fam (0=No, 1=Yes)
N2C: Most peop closer to their fam than I am (0=No, 1=Yes)
N2D: When confide make close fam membs uncomf (0=No, 1=Yes)
N2E: My fam enjoys hearing about what I think (0=No, 1=Yes)
N2F: Membs of my fam share many of my intrsts (0=No, 1=Yes)
N2G: I rely on my fam for emot support (0=No, 1=Yes)
N2H: Fam memb go to when down w/o feel funny (0=No, 1=Yes)
N2I: Fam and I open about what thnk about thngs (0=No, 1=Yes)
N2J: My fam is sensitive to my personal needs (0=No, 1=Yes)
N2K: Fam memb good at helping me solve probs (0=No, 1=Yes)
N2L: Have deep sharing relat w/# of fam membs (0=No, 1=Yes)
N2M: Makes me uncomf to confide in fam membs (0=No, 1=Yes)
N2N: I wish my family were much different (0=No, 1=Yes)
NUM_BARR: # of perceived barriers to linkage
NUM_INTERVALS: Number of 6-month intervals from previous to current interview
O1A: # people spend tx w/who drink alcohol (1=None, 2= A few, 3=About half, 4= Most, 5=All)
O1B_REC: Family/friends heavy drinkers y/n (0=No, 1=Yes)
O1B: # people spend tx w/who are heavy drinkrs (1=None, 2= A few, 3=About half, 4= Most, 5=All)
O1C_REC: Family/friends use drugs y/n (0=No, 1=Yes)
O1C: # people spend tx w/who use drugs (1=None, 2= A few, 3=About half, 4= Most, 5=All)
O1D_REC: Family/fiends support abst. y/n (0=No, 1=Yes)
O1D: # peop spend tx w/who supprt your abstin (1=None, 2= A few, 3=About half, 4= Most, 5=All)
O2_REC: Live-in partner drinks/drugs y/n (0=No, 1=Yes)
O2: Does live-in part/spouse drink/use drugs (0=No, 1=Yes, 2=N/A)
P1A: Physical abuse/assault by family members/person I know (0=No, 1=Yes, 7=Not sure)
P1B: Age first physically assaulted by person I know
P1C: Physically assaulted by person I know-last 6 months (0=No, 1=Yes)
P2A: Physical abuse/assault by stranger (0=No, 1=Yes, 7=Not sure)
P2B: Age first physically assaulted by stranger
P2C: Physically assaulted by stranger-last 6 months (0=No, 1=Yes)
P3: Using drugs/alcohol when physically assaulted (1=Don't know, 2=Never, 3=Some cases, 4=Most cases, 5=All cases, 9=Never assaulted)
P4: Person who physically assaulted you was using alcohol/drugs (1=Don't know, 2=Never, 3=Some cases, 4=Most cases, 5=All cases, 9=Never assaulted)
P5A: Sexual abuse/assault by family member/person you know (0=No, 1= Yes, 7=Not sure)
P5B: Age first sexually assaulted by person you know
P5C: Sexually assaulted by person you know-last 6 months (0=No, 1=Yes)
P6A: Sexual abuse/assault by stranger (0=No, 1=Yes, 7=Not sure)
P6B: Age first sexually assaulted by stranger
P6C: Sexually assaulted by stranger-last 6 months (0=No, 1=Yes)
P7: Using drugs/alcohol when sexually assaulted (1=Don't know, 2=Never, 3=Some cases, 4=Most cases, 5=All cases, 9=Never assaulted)
P8: Person who sexually assaulted you using alcohol/drugs (1=Don't know, 2=Never, 3=Some cases, 4=Most cases, 5=All cases, 9=Never assaulted)
PCP_ID: a numeric vector
PCS: Standardized physical component scale-00
PC_REC7: Primary cared received: Linked & # visits (0=Not linked, 1=Linked, 1 visit, 2=Linked, 2 visits, 3=Linked, 3 visits, 4=Linked, 4 visits, 5= Linked, 5 visits, 6=Linked, 6+visits)
PC_REC: Primary care received: Linked & # visits (0=Not linked, 1=Linked, 1 visit, 2=Linked, 2+ visits)
PF: SF-36 physical functioning (0-100)
PHSXABUS: Any abuse (0=No, 1=Yes)
PHYABUSE: Physical abuse-stranger or family (0=No, 1=Yes)
PHYS2: InDUC-2L-Physical 9Raw (w/o M48)
PHYS: InDUC-2L-Physical-Raw
POLYSUB: Polysubstance abuser y/n (0=No, 1=Yes)
PREV_TIME: Previous interview time
PRIMLANG: First language (recode) (0=English, 1=Other lang)
PRIMSUB2: First drug of choice (no marijuana) (0=None, 1=Alcohol, 2=Cocaine, 3=Heroin, 4=Barbituates, 5=Benzos, 6=Marijuana, 7=Methadone, 8=Opiates)
PRIM_SUB: First drug of choice (0=None, 1=Alcohol, 2=Cocaine, 3=Heroin, 4=Barbituates, 5=Benzos, 6=Marijuana, 7=Methadone, 8=Opiates)
PSS_FA: Perceived social support-family
PSS_FR: Perceived social support-friends
Q10: How would you describe yourself (0=Straight, 1=Gay/bisexual)
Q11: # men had sex w/in past 6 months (0=0 men, 1=1 man, 2=2-3 men, 3=4+ men
Q12: # women had sex w/in past 6 months (0=0 women, 1=1woman, 2=2-3 women, 3=4+ women
Q13: # times had sex In past 6 months (0=Never, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q14: How often had sex to get drugs-last 6 months (0=Never, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q15: How often given drugs to have sex-last 6 months (0=Never, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q16: How often were you paid for sex-last 6 months (0=Never, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q17: How often you pay pers for sex-last 6 months (0=Never, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q18: How often use condoms during sex=last 6 months (0=No sex/always, 1=Most of the time, 2=Some of the time, 3=None of the time)
Q19: Condoms are too much of a hassle to use (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
Q1A: Have you ever injected drugs (0=No, 1=Yes)
Q1B: Have you injected drugs-last 6 months (0=No, 1=Yes)
Q20: Safer sex is always your responsibility (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
Q2: Have you shared needles/works-last 6 months (0=No/Not shot up, 3=Yes)
Q3: # people shared needles w/past 6 months (0=No/Not shot up, 1=1 other person, 2=2-3 diff people, 3=4/+ diff people)
Q4: How often been to shoot gall/hse-last 6 months (0=Never, 1=Few times or less, 2= Few times/month, 3= Once or more/week)
Q5: How often been to crack house-last 6 months (0=Never, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q6: How often shared rinse-water-last 6 months (0=Nevr/Not shot up, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q7: How often shared a cooker-last 6 months (0=Nevr/Not shot up, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q8: How often shared a cotton-last 6 months (0=Nevr/Not shot up, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
Q9: How often use syringe to div drugs-last 6 months (0=Nevr/Not shot up, 1=Few times or less, 2=Few times/month, 3=Once or more/week)
R1A: I really want to change my alcohol/drug use (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1B: Sometimes I wonder if I'm an alcohol/addict (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1C: Id I don't change alcohol/drug probs will worsen (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1D: I started making changes in alcohol/drug use (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1E: Was using too much but managed to change (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1F: I wonder if my alcohol/drug use hurting othrs (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1G: I am a prob drinker or have drug prob (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1H: Already doing thngs to change alcohol/drug use (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1I: have changed use-trying to not slip back (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1J: I have a serious problem w/ alcohol/drugs (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1K: I wonder if I'm in control of alcohol/drug use (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1L: My alcohol/drug use is causing a lot of harm (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1M: Actively cutting down/stopping alcohol/drug use (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1N: Want help to not go back to alcohol/drugs (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1O: I know that I have an alcohol/drug problem (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1P: I wonder if I use alcohol/drugs too much (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1Q: I am an alcoholic or drug addict (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1R: I am working hard to change alcohol/drug use (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
R1S: Some changes-want help from going back (1=Strongly disagree, 2=Disagree, 3= Agree, 4=Strongly agree)
RABSCALE: RAB scale sore
RACE2: Race (recode) (1=White, 2=Minority)
RACE: Race (recode) (1=Afr Amer/Black, 2=White, 3=Hispanic, 4=Other)
RAWBP: Raw SF-36 pain index
RAWGH: Raw SF-36 general health perceptions
RAWMH: Raw SF-36 mental health index
RAWPF: Raw SF-36 physical functioning
RAWRE: Raw SF-36 role-emotional
RAWRP: Raw SF-36 role-physical
RAWSF: Raw SF-36 social functioning
RAWVT: Raw SF-36 vitality
RAW_ADS: ADS score
RAW_AM: SOCRATES-Ambivalence-Raw
RAW_RE: SOCRATES-Recognition-Raw
RAW_TS: SOCRATES-Taking steps-Raw
RCT_LINK: Did subject link to primary care (RCT)–This time point (0=No, 1=Yes)
REALM2: REALM score (dichotomous) (1=0-60, 2=61-66)
REALM3: REALM score (categorical) (1=0-44), 2=45-60), 3=61-66)
REALM: REALM score
REG_MD: Did subject report having regular doctor–This time point (0=No, 1=Yes)
RE: SF-36 role-emotional (0-100)
RP: SF-36 role physical (0-100)
S1A: At interview pt obviously depressed/withdrawn (0=No, 1=Yes)
S1B: at interview pt obviously hostile (0=No, 1=Yes)
S1C: At interview patientt obviously anxious/nervous (0=No, 1=Yes)
S1D: Trouble with real tst/thght dis/par at interview (0=No, 1=Yes)
S1E: At interview pt trbl w/ compr/concen/rememb (0=No, 1=Yes)
S1F: At interview pt had suicidal thoughts (0=No, 1=Yes)
SATREAT: Any BSAS substance abuse this time point (0=No, 1=Yes)
SECD_SUB: Second drug of choice (0=None, 1=Alcohol, 3=Cocaine, 3=Heroine, 4=Barbituates, 5=Benzos, 6=Marijuana, 7=Methadone, 8=Opiates)
SER_INJ: Recent (6m) serious injury y/n (0=No, 1=Yes)
SEXABUSE: Sexual abuse-stranger or family (0=No, 1=Yes)
SEXRISK: RAB-Sex risk total
SF: SF-36 social functioning (0-100)
SMOKER: Current smoker (every/some days) y/n (0=No, 1=Yes)
SR: InDUC-2L-Social responsibility-Raw
STD_6M: Had an STD past 6m y/n (0=No, 1=Yes)
STD_EVER: Ever had an STD y/n (0=No, 1=Yes)
STRABUSE: Stranger abuse-physical or sexual (0=No, 1=Yes)
T1B: # days in row continued to drink
T1C: Longest period abstain-last 6 months (alcohol)
T1: Have used alcohol since leaving River St. (0=No, 1=Yes)
T2B: # days in row continued to use heroin
T2C: Longest period abstain-last 6 months (heroin)
T2: Have used heroin since leaving River St (0=No, 1=Yes)
T3B: # days in row continued to use cocaine
T3C: Longest period abstain-last 6 months (cocaine)
T3: Have used cocaine since leaving River St (0=No, 1=Yes)
TIME: Interview time point
TOTALRAB: RAB-Total RAB sore
U10A: # times been to regular MDs office-pst 6 months
U10B: # times saw regular MD in office-pst 6 months
U10C: # times saw oth prof in office-pst 6 months
U11: Rate convenience of MD office location (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U12: Rate hours MD office open for medical appointments (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U13: Usual wait for appointment when sick (unscheduled) (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U14: Time wait for appointment to start at MD office (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U15A: DO you pay for any/all of MD visits (0=No, 1=Yes)
U15B: How rate amt of $ you pay for MD visits (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U16A: Do you pay for any/all of prescript meds (0=No, 1=Yes)
U16B: Rate amt $ pay for meds/prescript trtmnts (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U17: Ever skip meds/trtmnts because too expensive (1=Yes, often, 2=Yes, occasionally, 3=No, never)
U18A: Ability to reach MC office by phone (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U18B: Ability to speak to MD by phone if need (1=Very poor, 2=Poor, 3=Fair, 4=Good, 5=Very good, 6=Excellent)
U19: How often see regular MD when have regular check-up (1=Always, 2=Almost always, 3=A lot of the time, 4=Some of the time, 5=Almost never, 6=Never)
U1: It is important to have a regular MD (1=Strongly agree, 2=Agree, 3=Uncertain, 4=Disagree, 5=Strongly Disagree)
U20: When sick + go to MD how often see regular MD (1=Always, 2=Almost always, 3=A lot of the time, 4=Some of the time, 5=Almost never, 6=Never)
U21A: How thorough MD exam to check health prb (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U21B: How often question if MD diagnosis right (1=Always, 2=Almost always, 3=A lot of the time, 4=Some of the time, 5=Almost never, 6=Never)
U22A: Thoroughness of MD questions re symptoms (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U22B: Attn MD gives to what you have to say (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U22C: MD explanations of health problems/treatments need (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U22D: MD instructions re symptom report/further care (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U22E: MD advice in decisions about your care (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U23: How often leave MD office with unanswd quests (1=Always, 2=Almost always, 3=A lot of the time, 4=Some of the time, 5=Almost never, 6=Never)
U24A: Amount of time your MD spends with you (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U24B: MDs patience w/ your questions/worries (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U24C: MDs friendliness and warmth toward you (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U24D: MDs caring and concern for you (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U24E: MDs respect for you (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U25A: Reg MD ever talked to you about smoking (0=No, 1=Yes)
U25B: Reg MD ever talked to you about alcohol use (0=No, 1=Yes)
U25C: Reg MD ever talk to you about seat belt use (0=No, 1=Yes)
U25D: Reg MD ever talked to you about diet (0=No, 1=Yes)
U25E: Reg Mdever talked to you about exercise (0=No, 1=Yes)
U25F: Reg MD ever talked to you about stress (0=No, 1=Yes)
U25G: Reg MD ever talked to you about safe sex (0=No, 1=Yes)
U25H: Reg MD ever talked to you about drug use (0=No, 1=Yes)
U25I: Reg MD ever talked to you about HIV testing (0=No, 1=Yes)
U26A: Cut/quit smoking because of MDs advice (0=No, 1=Yes)
U26B: Tried to drink less alcohol because of MD advice (0=No, 1=Yes)
U26C: Wore my seat belt more because of MDs advice (0=No, 1=Yes)
U26D: Changed diet because of MDs advice (0=No, 1=Yes)
U26E: Done more exercise because MDs advice (0=No, 1=Yes)
U26F: Relax/reduce stress because of MDs advice (0=No, 1=Yes)
U26G: Practiced safer sex because of MDs advice (0=No, 1=Yes)
U26H: Tried to cut down/quit drugs because MD advice (0=No, 1=Yes)

U26I: Got HIV tested because of MDs advice (0=No, 1=Yes)

U27A: I can tell my MD anything (1=Strongly agree, 2= Agree, 3= Not sure, 4=Disagree, 5=Strongly disagree)

U27B: My MD pretends to know thngs if not sure (1=Strongly agree, 2= Agree, 3= Not sure, 4=Disagree, 5=Strongly disagree)

U27C: I trust my MDs judgment re my med care (1=Strongly agree, 2= Agree, 3= Not sure, 4=Disagree, 5=Strongly disagree)

U27D: My MD cares > about < costs than my health (1=Strongly agree, 2= Agree, 3= Not sure, 4=Disagree, 5=Strongly disagree)

U27E: My MD always tell truth about my health (1=Strongly agree, 2= Agree, 3= Not sure, 4=Disagree, 5=Strongly disagree)

U27F: My MD cares as much as I about my health (1=Strongly agree, 2= Agree, 3= Not sure, 4=Disagree, 5=Strongly disagree)

U27G: My MD would try to hide a mistake in trtmt (1=Strongly agree, 2= Agree, 3= Not sure, 4=Disagree, 5=Strongly disagree)

U28: How much to you trust this MD (0=Not at all, 1=1, 2=2, 3=3, 4=4, 5=5, 6=6, 7=7, 8=8, 9=9, 10=Completely)

U29A: MDs knowledge of your entire med history (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)

U29B: MD knowledge of your response-home/work/sch (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)

U29C: MD knowledge of what worries you most-health (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)

U29D: MDs knowledge of you as a person (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)

U2A: I cannot pay for services (0=No, 1=Yes)
U2B: I am not eligible for free care (0=No, 1=Yes)
U2C: I do not know where to go (0=No, 1=Yes)
U2D: Can't get services due to transport probs (0=No, 1=Yes)
U2E: Office/clinic hours are inconvenient (0=No, 1=Yes)
U2F: I do not speak/understand English well (0=No, 1=Yes)
U2G: Afraid others discover health prb I have (0=No, 1=Yes)
U2H: My substance abuse interferes (0=No, 1=Yes)
U2I: I do not have a babysitter (0=No, 1=Yes)
U2J: I do not want to lose my job (0=No, 1=Yes)
U2K: My insurance does not cover services (0=No, 1=Yes)
U2L: Medical care is not important to me (0=No, 1=Yes)
U2M: I do not have time (0=No, 1=Yes)
U2N: Med staff do not treat me with respect (0=No, 1=Yes)
U2O: I do not trust my doctors or nurses (0=No, 1=Yes)
U2P: Often been unsatisfied w/my med care (0=No, 1=Yes)
U2Q_T: a factor with many levels
U2Q: Other reason hard to get regular med care (0=No, 1=Yes)
U2R: a factor with levels 7 A B C D E F G H I J K L M N O P Q
U30: MD would know what want done if unconscious (1=Strongly agree, 2=Agree, 3=Not sure, 4= Disagree, 5=Strongly disagree)

U31: Oth MDs/RNs who play role in your care (0=No, 1=Yes)

" *

U32A: Their knowledge of you as a person (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U32B: The quality of care they provide (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U32C: Coordination between them and your regular MD (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U32D_T: N/A, only my regular MD does this
U32D: Their explanation of your health prbs/trtmts need (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U33: Amt regular MD knows about care from others (1=Knows everything, 2=Knows almost everything, 3=Knows some things, 4=Knows very little, 5=Knows nothing)
U34: Has MD ever recommended you see MD specialists (0=No, 1=Yes)
U35A: How helpful MD in deciding on specialist (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U35B: How helpful MD getting appointment with specialist (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U35C: MDs involvement when you trtd by specialist (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U35D: MDs communication w/your specialists/oth MDs (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U35E: MD help in explain what specialists said (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U35F: Quality of specialists MD sent you to (1=Very poor, 2= Poor, 3=Fair, 4=Good, 5= Very good, 6= Excellent)
U36: How many minutes to get to MDs office (1=<15, 2=16-30. 3=31-60, 4=More than 60)
U37: When sick+call how long take to see you (1=Same day, 2=Next day, 3=In 2-3 days, 4=In 4-5 days, 5=in >5 days)
U38: How many minutes late appointment usually begin (1=None, 2=<5 minutes, 3=6-10 minutes, 4=11-20 minutes, 5=21-30 minutes, 6=31-45 minutes, 7=>45 minutes)
U39: How satisfied are you w/your regular MD (1=Completely satisfied, 2=Very satisfied, 3=Somewhat satisfied, 4=Neither, 5=Somewhat dissatisfied, 6=Very dissatisfied, 7=Completely dissatisfied)
U3A: Has MD evr talked to you about drug use (0=No, 1=Yes)
U3B: Has MD evr talked to you about alcohol use (0=No, 1=Yes)
U4: Is there an MD you consider your regular MD (0=No, 1=Yes)
U5: Have you seen any MDs in last 6 months (0=No, 1=Yes)
U6A: Would you go to this MD if med prb not emergency (0=No, 1=Yes)
U6B: Think one of these could be your regular MD (0=No, 1=Yes)
U7A_T: a factor with levels ARTHRITIS DOCTOR CHIROPRACTOR COCAINE STUDY DETOX DOCTOR DO EAR DOCTOR EAR SPECIALIST EAR, NOSE, & THROAT. EAR/NOSE/THROAT ENT FAMILY PHYSICIAN GENERAL MEDICINE GENERAL PRACTICE GENERAL PRACTITIONER GENERAL PRACTITIONER HEAD & NECK SPECIALIST HERBAL/HOMEOPATHIC/ACUPUNCTURE ID DOCTOR MAYBE GENERAL PRACTITIONER MEDICAL STUDENT NEUROLOGIST NURSE NURSE PRACTITIONER NURSE PRACTITIONER ONCOLOGIST PRENATAL PRIMARY PRIMARY CARE PRIMARY CARE PRIMARY CARE DOCTOR PRIMARY CARE THERAPIST UROLOGIST WOMENS CLINIC BMC
U7A: What type of MD is your regular MD/this MD (1=OB/GYN, 2=Family medicine, 3=Pediatrician, 4=Adolescent medicine, 5=Internal medicine, 6=AIDS doctor, 7=Asthma doctor, 8=Pulmonary doctor, 9=Cardiologist, 10=Gastroen)
U8A: Only saw this person once (=Only saw once)
U8B: Saw this person for < 6 months (1 = <6 months)
U8C: Saw this person for 6 months - 1 year (2=Between 6 months & 1 year)
U8D: Saw this person for 1-2 years (3 = 1-2 years)
U8E: Saw this person for 3-5 years (4 = 3-5 years)
U8F: Saw this person for more than 5 years (5 = >5 years)
UNEMPLOY: Usually unemployed last 6 months (0=No, 1=Yes)
V1: Ever needed to drink much more to get effect (0=No, 1=Yes)
V2: Evr find alcohol had < effect than once did (0=No, 1=Yes)
VT: SF-36 vitality 0-100)
Z1: Breath Alcohol Concentration:1st test
Z2: Breath Alcohol Concentration:2nd test

Details

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 primary care clinic to which they would be referred or were homeless. Patients with established primary care relationships they planned to continue, significant dementia, specific plans to leave the Boston area that would prevent research participation, failure to provide contact information for tracking purposes, or pregnancy were excluded.

Subjects were interviewed at baseline during their detoxification stay and follow-up interviews were undertaken every 6 months for 2 years. A variety of continuous, count, discrete, and survival time predictors and outcomes were collected at each of these five occasions.

This dataset is a superset of the HELPmiss and HELPrct datasets which include far fewer variables. Full details of the survey instruments are available at the following link.

Source

https://nhorton.people.amherst.edu/help/

References

Samet JH, Larson MJ, Horton NJ, Doyle K, Winter M, and Saitz R. Linking alcohol and drug-dependent adults to primary medical care: A randomized controlled trial of a multi-disciplinary health intervention in a detoxification unit. Addiction, 2003; 98(4):509-516.

Examples

data(HELPfull)

data(HELPfull)

Health Evaluation and Linkage to Primary Care

Description

Usage

data(HELPmiss)
data(HELPmiss)

Format

Data frame with 470 observations on the following variables.

age: subject age at baseline (in years)
anysub: use of any substance post-detox: a factor with levels no yes
cesd: Center for Epidemiologic Studies Depression measure of depressive symptoms at baseline (higher scores indicate more symptoms)
d1: lifetime number of hospitalizations for medical problems (measured at baseline)
daysanysub: time (in days) to first use of any substance post-detox
dayslink: time (in days) to linkage to primary care
drugrisk: Risk Assessment Battery drug risk scale at baseline
e2b: number of times in past 6 months entered a detox program (measured at baseline)
female: 0 for male, 1 for female
sex: a factor with levels male female
g1b: experienced serious thoughts of suicide in last 30 days (measured at baseline): a factor with levels no yes
homeless: housing status: a factor with levels housed homeless
i1: average number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline)
i2: maximum number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline)
avg_drinks: average number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline). Same as i1.
max_drinks: maximum number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline). Same as i2.
id: subject identifier
indtot: Inventory of Drug Use Consequences (InDUC) total score (measured at baseline)
linkstatus: post-detox linkage to primary care (0 = no, 1 = yes)
link: post-detox linkage to primary care: no yes
mcs: SF-36 Mental Component Score (measured at baseline, higher scores are better)
pcs: SF-36 Physical Component Score (measured at baseline, higher scores are better)
pss_fr: perceived social support by friends (measured at baseline)
racegrp: race/ethnicity: levels black hispanic other white
satreat: any BSAS substance abuse treatment at baseline: no yes
sexrisk: Risk Assessment Battery sex risk score (measured at baseline)
substance: primary substance of abuse: alcohol cocaine heroin
treat: randomized to HELP clinic: no yes

Details

This dataset is a superset of the HELPrct data with 17 subjects with partially observed data on some of the baseline variables. This is a subset of the HELPfull data which includes 5 timepoints and many additional variables.

Source

https://nhorton.people.amherst.edu/help/

References

Examples

data(HELPmiss)

data(HELPmiss)

Health Evaluation and Linkage to Primary Care

Description

Usage

data(HELPrct)
data(HELPrct)

Format

Data frame with 453 observations on the following variables.

age: subject age at baseline (in years)
anysub: use of any substance post-detox: a factor with levels no yes
cesd: Center for Epidemiologic Studies Depression measure at baseline (high scores indicate more depressive symptoms)
d1: lifetime number of hospitalizations for medical problems (measured at baseline)
hospitalizations: lifetime number of hospitalizations for medical problems (measured at baseline)
daysanysub: time (in days) to first use of any substance post-detox
dayslink: time (in days) to linkage to primary care
drugrisk: Risk Assessment Battery drug risk scale at baseline
e2b: number of times in past 6 months entered a detox program (measured at baseline)
female: 0 for male, 1 for female
sex: a factor with levels male female
g1b: experienced serious thoughts of suicide in last 30 days (measured at baseline): a factor with levels no yes
homeless: housing status: a factor with levels housed homeless
i1: average number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline)
i2: maximum number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline)
id: subject identifier
indtot: Inventory of Drug Use Consequences (InDUC) total score (measured at baseline)
linkstatus: post-detox linkage to primary care (0 = no, 1 = yes)
link: post-detox linkage to primary care: no yes
mcs: SF-36 Mental Component Score (measured at baseline, lower scores indicate worse status)
pcs: SF-36 Physical Component Score (measured at baseline, lower scores indicate worse status)
pss_fr: perceived social support by friends (measured at baseline, higher scores indicate more support)
racegrp: race/ethnicity: levels black hispanic other white
satreat: any BSAS substance abuse treatment at baseline: no yes
sexrisk: Risk Assessment Battery sex risk score (measured at baseline)
substance: primary substance of abuse: alcohol cocaine heroin
treat: randomized to HELP clinic: no yes

Details

This data set is a subset of the HELPmiss data set restricted to the 453 subjects who were fully observed on the age, cesd, d1, female, sex, g1b, homeless, i1, i2, indtot, mcs, pcs, pss_fr, racegrp, satreat, substance, treat, and sexrisk variables. (There is some missingness in the other variables.) HELPmiss contains 17 additional subjects with partially observed data on some of these baseline variables. This is also a subset of the HELPfull data which includes 5 timepoints and many additional variables.

Note

The \code{HELPrct} data set was originally named \code{HELP} but has
been renamed to avoid confusion with the \code{help} function.

Source

https://nhorton.people.amherst.edu/help/

References

Examples

data(HELPrct)

data(HELPrct)

Foot measurements in children

Description

These data were collected by a statistician, Mary C. Meyer, in a fourth grade classroom in Ann Arbor, MI, in October 1997. They are a convenience sample — the kids who were in the fourth grade.

Usage

data(KidsFeet)
data(KidsFeet)

Format

A data frame with 39 observations on the following variables.

name: a factor with levels corresponding to the name of each child
birthmonth: the month of birth
birthyear: the year of birth
length: length of longer foot (in cm)
width: width of longer foot (in cm)
sex: a factor with levels B G
biggerfoot: a factor with levels L R
domhand: a factor with levels L R

Details

Quoted from the source: "From a very young age, shoes for boys tend to be wider than shoes for girls. Is this because boys have wider feet, or because it is assumed that girls, even in elementary school, are willing to sacrifice comfort for fashion? To assess the former, a statistician measures kids' feet."

References

Mary C. Meyer (2006) "Wider Shoes for Wider Feet?" Journal of Statistics Education 14(1), http://jse.amstat.org/v14n1/datasets.meyer.html.

Examples

data(KidsFeet)

data(KidsFeet)

Marriage records

Description

Marriage records from the Mobile County, Alabama, probate court.

Usage

data(Marriage)
data(Marriage)

Format

A data frame with 98 observations on the following variables.

bookpageID: a factor with levels for each book and page (unique identifier)
appdate: date on which the application was filed
ceremonydate: date of the ceremony
delay: number of days between the application and the ceremony
officialTitle: a factor with levels BISHOP CATHOLIC PRIEST CHIEF CLERK CIRCUIT JUDGE ELDER MARRIAGE OFFICIAL MINISTER PASTOR REVEREND
person: a factor with levels Bride Groom
dob: a factor with levels corresponding to the date of birth of the person
age: age of the person (in years)
race: a factor with levels American Indian Black Hispanic White
prevcount: the number of previous marriages of the person, as listed on the application
prevconc: the way the last marriage ended, as listed on the application
hs: the number of years of high school education, as listed on the application
college: the number of years College education, as listed on the application. Where no number was listed, this field was left blank, unless less than 12 years High School was reported, in which case it was entered as 0.
dayOfBirth: the day of birth, as a number from 1 to 365 counting from January 1
sign: the astrological sign, with levels Aquarius Aries Cancer Capricorn Gemini Leo Libra Pisces Sagittarius Scorpio Taurus Virgo

Details

The calculation of the astrological sign may not correctly sort people directly on the borders between signs. This variable is not part of the original record.

Source

The records were collected through http://www.mobilecounty.org/probatecourt/recordssearch.htm

Examples

data(Marriage)

data(Marriage)

Mites and Wilt Disease

Description

Data from an experiment to test whether exposure to mites protects against Wilt Disease in cotton plants.

Usage

data(Mites)
data(Mites)

Format

A data frame with 47 observations on the following variables.

treatment: a factor with levels mites and no mites
outcome: a factor with levels wilt and no wilt

Details

Researchers suspected that attack of a plant by one organism induced resistance to subsequent attack by a different organism. Individually potted cotton plants were randomly allocated to two groups: infestation by spider mites or no infestation. After two weeks the mites were dutifully removed by a conscientious research assistant, and both groups were inoculated with Verticillium, a fungus that causes Wilt disease. More information can be found at https://www.causeweb.org/cause/webinar/activity/2010-01/.

Source

Statistics for the Life Sciences, Third Edition; Myra Samuels & Jeffrey Witmer (2003), page 409.

Examples

data(Mites)
if (require(mosaic)) {
  tally(~ treatment + outcome, data=Mites)
  tally(~ outcome | treatment, format="percent", data=Mites)
}

data(Mites)
if (require(mosaic)) {
  tally(~ treatment + outcome, data=Mites)
  tally(~ outcome | treatment, format="percent", data=Mites)
}

Volume of Users of a Rail Trail

Description

The Pioneer Valley Planning Commission (PVPC) collected data north of Chestnut Street in Florence, MA for ninety days from April 5, 2005 to November 15, 2005. Data collectors set up a laser sensor, with breaks in the laser beam recording when a rail-trail user passed the data collection station.

Usage

data(RailTrail)
data(RailTrail)

Format

A data frame with 90 observations on the following variables.

hightemp: daily high temperature (in degrees Fahrenheit)
lowtemp: daily low temperature (in degrees Fahrenheit)
avgtemp: average of daily low and daily high temperature (in degrees Fahrenheit)
spring: indicator of whether the season was Spring
summer: indicator of whether the season was Summer
fall: indicator of whether the season was Fall
cloudcover: measure of cloud cover (in oktas)
precip: measure of precipitation (in inches)
volume: estimated number of trail users that day (number of breaks recorded)
weekday: logical indicator of whether the day was a non-holiday weekday
dayType: one of "weekday" or "weekend"

Details

There is a potential for error when two users trigger the infrared beam at exactly the same time since the counter would only logs one of the crossings. The collectors left the motion detector out during the winter, but because the counter drops data when the temperature falls below 14 degrees Fahrenheit, there is no data for the cold winter months.

Source

Pioneer Valley Planning Commission

References

http://www.fvgreenway.org/pdfs/Northampton-Bikepath-Volume-Counts%20_05_LTA.pdf

Examples

data(RailTrail)

data(RailTrail)

Volume of Users of a Massachusetts Rail Trail

Description

Usage

data(Riders)
data(Riders)

Format

A data frame with 90 observations on the following 12 variables.

date: date of data collection (POSIXct)
day: a factor with levels Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday.
highT: high temperature for the day (in degrees Fahrenheit)
lowT: low temperature for the day (in degrees Fahrenheit)
hi: shorter name for highT
lo: shorter name for lowT
precip: inches of precipitation
clouds: measure of cloud cover (in oktas)
riders: estimated number of trail crossings that day (number of breaks recorded)
ct: shorter name for riders
weekday: type of day: a factor with levels N (weekend or holiday) Y (non-holiday weekday)
wday: shorter name for weekday

Details

Source

Pioneer Valley Planning Commission, http://www.fvgreenway.org/pdfs/Northampton-Bikepath-Volume-Counts%20_05_LTA.pdf

References

"Rail trails and property values: Is there an association?", Nicholas J. Horton and Ella Hartenian (Journal of Statistics Education, 2015), http://www.amstat.org/publications/jse/v23n2/horton.pdf

Examples

data(Riders)
str(Riders)

data(Riders)
str(Riders)

Houses in Saratoga County (2006)

Description

Data on houses in Saratoga County, New York, USA in 2006

Usage

data(SaratogaHouses)
data(SaratogaHouses)

Format

A data frame with 1728 observations on the following 16 variables.

price: price (US dollars)
lotSize: size of lot (acres)
age: age of house (years)
landValue: value of land (US dollars)
livingArea: living are (square feet)
pctCollege: percent of neighborhood that graduated college
bedrooms: number of bedrooms
fireplaces: number of fireplaces
bathrooms: number of bathrooms (half bathrooms have no shower or tub)
rooms: number of rooms
heating: type of heating system
fuel: fuel used for heating
sewer: type of sewer system
waterfront: whether property includes waterfront
newConstruction: whether the property is a new construction
centralAir: whether the house has central air

Source

Data collected by Candice Corvetti and used in the "Stat 101" case study "How much is a Fireplace Worth". See also https://www.saratogacountyny.gov/departments/real-property-tax-service-agency/

State by State SAT data

Description

SAT data assembled for a statistics education journal article on the link between SAT scores and measures of educational expenditures

Usage

data(SAT)
data(SAT)

Format

A data frame with 50 observations on the following variables.

state: a factor with names of each state
expend: expenditure per pupil in average daily attendance in public elementary and secondary schools, 1994-95 (in thousands of US dollars)
ratio: average pupil/teacher ratio in public elementary and secondary schools, Fall 1994
salary: estimated average annual salary of teachers in public elementary and secondary schools, 1994-95 (in thousands of US dollars)
frac: percentage of all eligible students taking the SAT, 1994-95
verbal: average verbal SAT score, 1994-95
math: average math SAT score, 1994-95
sat: average total SAT score, 1994-95

Source

http://www.amstat.org/publications/jse/secure/v7n2/datasets.guber.cfm

References

Deborah Lynn Guber, "Getting what you pay for: the debate over equity in public school expenditures" (1999), Journal of Statistics Education 7(2).

Examples

data(SAT)
if (require(ggformula)) {
  gf_point(sat ~ expend, data = SAT, color = "blue", alpha = 0.5) |>
    gf_lm()
  gf_text(sat ~ expend, data = SAT, label = ~ abbreviate(SAT$state, 3),
    inherit = FALSE)
}
data(SAT)
if (require(ggformula)) {
  gf_point(sat ~ expend, data = SAT, color = "blue", alpha = 0.5) |>
    gf_lm()
  gf_text(sat ~ expend, data = SAT, label = ~ abbreviate(SAT$state, 3),
    inherit = FALSE)
}

Snowfall data for Grand Rapids, MI

Description

Official snowfall data by month and season for Grand Rapids, MI, going back to 1893.

Usage

data(SnowGR)
data(SnowGR)

Format

A data frame with 119 observations of the following variables.

SeasonStart: Year in which season started (July is start of season)
SeasonEnd: Year in which season ended (June is end of season)
Jul: Inches of snow in July
Aug: Inches of snow in August
Sep: Inches of snow in September
Oct: Inches of snow in October
Nov: Inches of snow in November
Dec: Inches of snow in December
Jan: Inches of snow in January
Feb: Inches of snow in February
Mar: Inches of snow in March
Apr: Inches of snow in April
May: Inches of snow in May
Jun: Inches of snow in June
Total: Inches of snow for entire season (July-June)

Source

These data were compiled by Laura Kapitula from data available from NOAA. The original URL used (http://www.crh.noaa.gov/grr/climate/data/grr/snowfall/) is no longer in service.

Examples

data(SnowGR)
if (require(ggformula)) {
  df_stats(~ Total, data = SnowGR)
  gf_histogram( ~ Total, data = SnowGR)
  gf_point(Total ~ SeasonStart, data = SnowGR) |>
    gf_smooth()

  if (require(tidyr) && require(dplyr)) {
    Snow2 <-
      SnowGR |>
      pivot_longer(Jul:Total, names_to = "month", values_to = "snowfall") |>
      filter(month != "Total") |>
      mutate(month = factor(month, levels = unique(month)))
    gf_violin(snowfall ~ month, data = Snow2, scale = "width")
  }
}

data(SnowGR)
if (require(ggformula)) {
  df_stats(~ Total, data = SnowGR)
  gf_histogram( ~ Total, data = SnowGR)
  gf_point(Total ~ SeasonStart, data = SnowGR) |>
    gf_smooth()

  if (require(tidyr) && require(dplyr)) {
    Snow2 <-
      SnowGR |>
      pivot_longer(Jul:Total, names_to = "month", values_to = "snowfall") |>
      filter(month != "Total") |>
      mutate(month = factor(month, levels = unique(month)))
    gf_violin(snowfall ~ month, data = Snow2, scale = "width")
  }
}

100 m Swimming World Records

Description

World records for men and women over time from 1905 through 2004.

Usage

data(SwimRecords)
data(SwimRecords)

Format

A data frame with 62 observations of the following variables.

time: time (in seconds) of the world record
year: Year in which the record was set
sex: a factor with levels M and F

Examples

data(SwimRecords)
if (require(ggformula)) {
  gf_point(time ~ year, data = SwimRecords, color = ~ sex)
}

data(SwimRecords)
if (require(ggformula)) {
  gf_point(time ~ year, data = SwimRecords, color = ~ sex)
}

Cherry Blossom Race

Description

The Cherry Blossom 10 Mile Run is a road race held in Washington, D.C. in April each year. (The name comes from the famous cherry trees that are in bloom in April in Washington.) The results of this race are published. This data frame contains the results from the 2005 race.

Usage

data(TenMileRace)
data(TenMileRace)

Format

A data frame with 8636 observations on the following variables.

state: State of residence of runner.
time: Official time from starting gun to finish line.
net: The recorded time (in seconds) from when the runner crossed the starting line to when the runner crossed the finish line. This is generally less than the official time because of the large number of runners in the race: it takes time to reach the starting line after the gun has gone off.
age: Age of runner in years.
sex: A factor with levels F M.

Examples

data(TenMileRace)
if (require(ggformula)) {
  gf_point(net ~ age | sex, data = TenMileRace, color = ~sex, alpha = 0.1) |>
  gf_density2d(color = "gray40")
  lm(net ~ age + sex, data = TenMileRace)
}

data(TenMileRace)
if (require(ggformula)) {
  gf_point(net ~ age | sex, data = TenMileRace, color = ~sex, alpha = 0.1) |>
  gf_density2d(color = "gray40")
  lm(net ~ age + sex, data = TenMileRace)
}

Utility bills

Description

Data from utility bills at a residence. Utilities2 is a similar data set with some additional variables.

Usage

data(Utilities)
data(Utilities)

Format

A data frame containing 117 observations for the following variables.

month: month (coded as a number)
day: day of month on which bill was calculated
year: year of bill
temp: average temperature (F) for billing period
kwh: electricity usage (kwh)
ccf: gas usage (ccf)
thermsPerDay: a numeric vector
billingDays: number of billing days in billing period
totalbill: total bill (in dollars)
gasbill: gas bill (in dollars)
elecbill: electric bill (in dollars)
notes: notes about the billing period

Source

Daniel T. Kaplan, Statistical modeling: A fresh approach, 2009.

Examples

data(Utilities)
if (require(ggformula)) {
  gf_point(gasbill ~ temp, data = Utilities)
}

data(Utilities)
if (require(ggformula)) {
  gf_point(gasbill ~ temp, data = Utilities)
}

Utility bills

Description

Data from utility bills at a private residence. This is an augmented version of Utilities.

Usage

data(Utilities2)
data(Utilities2)

Format

A data frame containing 117 observations for the following variables.

month: month (coded as a number)
day: day of month on which bill was calculated
year: year of bill
temp: average temperature (F) for billing period
kwh: electricity usage (kwh)
ccf: gas usage (ccf)
thermsPerDay: a numeric vector
billingDays: number of billing days in billing period
totalbill: total bill (in dollars)
gasbill: gas bill (in dollars)
elecbill: electric bill (in dollars)
notes: notes about the billing period
ccfpday: average gas usage per day (Utilities2 only)
kwhpday: average electric usage per day (Utilities2 only)
gasbillpday: gas bill divided by billing days (Utilities2 only)
elecbillpday: electric bill divided by billing days a numeric vector (Utilities2 only)
totalbillpday: total bill divided by billing days a numeric vector (Utilities2 only)
therms: thermsPerDay * billingDays (Utilities2 only)
monthsSinceY2K: months since 2000 (Utilities2 only)

Source

Daniel T. Kaplan, Statistical modeling: A fresh approach, 2009.

Examples

data(Utilities2)
if (require(ggformula)) {
  gf_point(gasbillpday ~ temp, data = Utilities2)
}

data(Utilities2)
if (require(ggformula)) {
  gf_point(gasbillpday ~ temp, data = Utilities2)
}

Weather

Description

2016-17 weather in several cities

Usage

data(Weather)
data(Weather)

Format

A data frame with weather-related variables for several world cities.

city: City name.
date: Date.
year: Numeric year.
month: Numeric month.
day: Numeric day.
high_temp, avg_temp, low_temp: High, average, and low temperature for the day in degrees F.
high_dewpt, avg_dewpt, low_dewpt: High, average, and low dew point for the day in degrees F.
high_humidity, avg_humidity, low_humidity: High, average, and low relative humidity.
high_hg, avg_hg, low_hg: High, average, and low sea level pressure in inches of mercury.
high_vis, avg_vis, low_vis: High, average, and low visability for the day in miles.
high_wind, avg_wind, low_wind: High, average, and low wind speed for the day in mph.
precip: Precipitation for the day – a character vale; T means "trace amount".
events: Character string naming weather events on the day (Rain, Fog, Snow, etc.)

Source

These data were downloaded from WeatherUnderground in January 2018.

Examples

if (require(dplyr)) {
  Weather |>
    group_by(city, year) |>
    summarise(
      min_temp = min(low_temp),
      max_temp = max(high_temp)
      )
}

if (require(ggformula)) {
  Weather |>
    gf_linerange(low_temp + high_temp ~ date | city ~ ., 
    color = ~ (high_temp + low_temp) / 2, show.legend = FALSE) |>
    gf_refine(scale_color_gradientn(colors = rev(rainbow(5))))
}

if (require(dplyr)) {
  Weather |>
    group_by(city, year) |>
    summarise(
      min_temp = min(low_temp),
      max_temp = max(high_temp)
      )
}

if (require(ggformula)) {
  Weather |>
    gf_linerange(low_temp + high_temp ~ date | city ~ ., 
    color = ~ (high_temp + low_temp) / 2, show.legend = FALSE) |>
    gf_refine(scale_color_gradientn(colors = rev(rainbow(5))))
}

Data from the Whickham survey

Description

Data on age, smoking, and mortality from a one-in-six survey of the electoral roll in Whickham, a mixed urban and rural district near Newcastle upon Tyne, in the UK. The survey was conducted in 1972-1974 to study heart disease and thyroid disease. A follow-up on those in the survey was conducted twenty years later.

Usage

data(Whickham)
data(Whickham)

Format

A data frame with 1314 observations on women for the following variables.

outcome: survival status after 20 years: a factor with levels Alive Dead
smoker: smoking status at baseline: a factor with levels No Yes
age: age (in years) at the time of the first survey

Details

This dataset contains a subset of the survey sample: women who were classified as current smokers or as never having smoked. The data were synthesized from the summary description tables given in the Appleton et al al paper.

References

DR Appleton, JM French, MPJ Vanderpump. "Ignoring a covariate: an example of Simpson's paradox". (1996) American Statistician, 50(4):340-341.

Examples

data(Whickham)

data(Whickham)

Package 'mosaicData'

Help Index

Alcohol Consumption per Capita

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US Births in 1969 - 1988

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Data from the 1985 Current Population Survey (CPS85)

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Galton's dataset of parent and child heights

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Data from the Child Health and Development Studies

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Goose Permit Study

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Data from a heat exchanger laboratory

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