CHFLS {HSAUR2} | R Documentation |
The Chinese Health and Family Life Survey sampled $60$ villages and urban neighborhoods chosen in such a way as to represent the full geographical and socioeconomic range of contemporary China.
data("CHFLS")
A data frame with 1534 observations on the following 10 variables.
R_region
Coastal South
, Coastal Easth
,
Inlands
, North
, Northeast
, Central West
.R_age
R_edu
Never attended school
< Elementary school
< Junior high school
< Senior high school
< Junior college
< University
.R_income
R_health
Poor
< Not good
< Fair
< Good
< Excellent
.R_height
R_happy
Very unhappy
< Not too happy
<
Somewhat happy
< Very happy
.A_height
A_edu
Never attended school
< Elementary school
<
Junior high school
< Senior high school
< Junior college
< University
.A_income
Contemporary China is on the leading edge of a sexual revolution, with tremendous regional and generational differences that provide unparalleled natural experiments for analysis of the antecedents and outcomes of sexual behavior. The Chinese Health and Family Life Study, conducted 1999–2000 as a collaborative research project of the Universities of Chicago, Beijing, and North Carolina, provides a baseline from which to anticipate and track future changes. Specifically, this study produces a baseline set of results on sexual behavior and disease patterns, using a nationally representative probability sample. The Chinese Health and Family Life Survey sampled 60 villages and urban neighborhoods chosen in such a way as to represent the full geographical and socioeconomic range of contemporary China excluding Hong Kong and Tibet. Eighty-three individuals were chosen at random for each location from official registers of adults aged between 20 and 64 years to target a sample of 5000 individuals in total. Here, we restrict our attention to women with current male partners for whom no information was missing, leading to a sample of 1534 women. The data have been extracted as given in the example section.
http://www.spc.uchicago.edu/prc/chfls.php
William L. Parish, Edward O. Laumann, Myron S. Cohen, Suiming Pan, Heyi Zheng, Irving Hoffman, Tianfu Wang, and Kwai Hang Ng. (2003), Population-Based Study of Chlamydial Infection in China: A Hidden Epidemic. Journal of the American Medican Association, 289(10), 1265–1273.
## Not run: library("foreign") ### data see http://www.spc.uchicago.edu/prc/chfls.php chfls1 <- url("http://www.src.uchigaco.edu/datalib/chfls/data/chfls1.dta") chfls1 <- read.stata("chfls1.dta", to.data.frame = TRUE) tmp <- chfls1[, c("REGION6", "ZJ05", "ZJ06", "A35", "ZJ07", "ZJ16M", "INCRM", "JK01", "JK02", "JK20", "HY04", "HY07", "A02", "AGEGAPM", "A07M", "A14", "A21", "A22M", "A23", "AX16", "INCAM", "SEXNOW", "ZW04")] names(tmp) <- c("Region", "Rgender", ### gender of respondent "Rage", ### age of respondent "RagestartA", ### age of respondent at beginning of relationship with partner A "Redu", ### education of respondent "RincomeM", ### rounded monthly income of respondent "RincomeComp", ### inputed monthly income of respondent "Rhealth", ### health condition respondent "Rheight", ### respondent's height "Rhappy", ### respondent's happiness "Rmartial", ### respondent's marital status "RhasA", ### R has current A partner "Agender", ### gender of partner A "RAagegap", ### age gap "RAstartage", ### age at marriage "Aheight", ### height of partner A "Aedu", ### education of partner A "AincomeM", ### rounded partner A income "AincomeEst", ### estimated partner A income "orgasm", ### orgasm frequency "AincomeComp", ### imputed partner A income "Rsexnow", ### has sex last year "Rhomosexual") ### R is homosexual ### code missing values tmp$AincomeM[tmp$AincomeM < 0] <- NA tmp$RincomeM[tmp$RincomeM < 0] <- NA tmp$Aheight[tmp$Aheight < 0] <- NA olevels <- c("never", "rarely", "sometimes", "often", "always") tmpA <- subset(tmp, Rgender == "female" & Rhomosexual != "yes" & orgasm ### 1534 subjects dim(tmpA) CHFLS <- tmpA[, c("Region", "Rage", "Redu", "RincomeComp", "Rhealth", "Rheight", "Rhappy", "Aheight", "Aedu", "AincomeComp")] names(CHFLS) <- c("R_region", "R_age", "R_edu", "R_income", "R_health", "R_height", "R_happy", "A_height", "A_edu", "A_income") levels(CHFLS$R_region) <- c("Coastal South", "Coastal Easth", "Inlands", "North", "Northeast", "Central West") CHFLS$R_edu <- ordered(as.character(CHFLS$R_edu), levels = c("no school", "primary", "low mid", "up mid", "j col", "univ/grad")) levels(CHFLS$R_edu) <- c("Never attended school", "Elementary school", "Junior high school", "Senior high school", "Junior college", "University") CHFLS$A_edu <- ordered(as.character(CHFLS$A_edu), levels = c("no school", "primary", "low mid", "up mid", "j col", "univ/grad")) levels(CHFLS$A_edu) <- c("Never attended school", "Elementary school", "Junior high school", "Senior high school", "Junior college", "University") CHFLS$R_health <- ordered(as.character(CHFLS$R_health), levels = c("poor", "not good", "fair", "good", "excellent")) levels(CHFLS$R_health) <- c("Poor", "Not good", "Fair", "Good", "Excellent") CHFLS$R_happy <- ordered(as.character(CHFLS$R_happy), levels = c("v unhappy", "not too", "relatively", "very")) levels(CHFLS$R_happy) <- c("Very unhappy", "Not too happy", "Relatively happy", "Very happy") ## End(Not run)