This report combines harmonized variables from all studies into a single data set with study_name as a factor.
This report is a record of interaction with a data transfer object (dto) produced by
./manipulation/0-ellis-island.R.
The next section recaps this script, exposes the architecture of the DTO, and demonstrates the language of interacting with it.
All data land on Ellis Island.
The script 0-ellis-island.R is the first script in the analytic workflow. It accomplished the following:
./data/shared/derived/meta-data-live.csv, which is updated every time Ellis Island script is executed../data/shared/meta-data-map.csv. They are used by automatic scripts in later harmonization and analysis.# load the product of 0-ellis-island.R, a list object containing data and metadata
dto <- readRDS("./data/unshared/derived/dto.rds")# the list is composed of the following elements
names(dto)[1] "studyName" "filePath" "unitData" "metaData" # 1st element - names of the studies as character vector
dto[["studyName"]][1] "alsa" "lbsl" "satsa" "share" "tilda"# 2nd element - file paths of the data files for each study as character vector
dto[["filePath"]][1] "./data/unshared/raw/ALSA-Wave1.Final.sav" "./data/unshared/raw/LBSL-Panel2-Wave1.Final.sav"
[3] "./data/unshared/raw/SATSA-Q3.Final.sav" "./data/unshared/raw/SHARE-Israel-Wave1.Final.sav"
[5] "./data/unshared/raw/TILDA-Wave1.Final.sav" # 3rd element - is a list object containing the following elements
names(dto[["unitData"]])[1] "alsa" "lbsl" "satsa" "share" "tilda"# each of these elements is a raw data set of a corresponding study, for example
dplyr::tbl_df(dto[["unitData"]][["lbsl"]]) Source: local data frame [656 x 39]
id AGE94 SEX94 MSTAT94 EDUC94 NOWRK94 SMK94 SMOKE
(int) (int) (int) (fctr) (int) (fctr) (fctr) (fctr)
1 4001026 68 1 divorced 16 no, retired no never smoked
2 4012015 94 2 widowed 12 no, retired no never smoked
3 4012032 94 2 widowed 20 no, retired no don't smoke at present but smoked in the past
4 4022004 93 2 NA NA NA NA never smoked
5 4022026 93 2 widowed 12 no, retired no never smoked
6 4031031 92 1 married 8 no, retired no don't smoke at present but smoked in the past
7 4031035 92 1 widowed 13 no, retired no don't smoke at present but smoked in the past
8 4032201 92 2 NA NA NA NA don't smoke at present but smoked in the past
9 4041062 91 1 widowed 7 NA no don't smoke at present but smoked in the past
10 4042057 91 2 NA NA NA NA NA
.. ... ... ... ... ... ... ... ...
Variables not shown: ALCOHOL (fctr), WINE (int), BEER (int), HARDLIQ (int), SPORT94 (int), FIT94 (int), WALK94 (int),
SPEC94 (int), DANCE94 (int), CHORE94 (int), EXCERTOT (int), EXCERWK (int), HEIGHT94 (int), WEIGHT94 (int), HWEIGHT
(int), HHEIGHT (int), SRHEALTH (fctr), smoke_now (lgl), smoked_ever (lgl), year_of_wave (dbl), age_in_years (dbl),
year_born (dbl), female (lgl), marital (chr), single (lgl), educ3 (chr), current_work_2 (lgl), current_drink (lgl),
sedentary (lgl), poor_health (lgl), bmi (dbl)# 4th element - a dataset names and labels of raw variables + added metadata for all studies
# dto[["metaData"]] %>% dplyr::select(study_name, name, item, construct, type, categories, label_short, label) %>%
# DT::datatable(
# class = 'cell-border stripe',
# caption = "This is the primary metadata file. Edit at `./data/shared/meta-data-map.csv",
# filter = "top",
# options = list(pageLength = 6, autoWidth = TRUE)
# )dmls <- list() # dummy list
for(s in dto[["studyName"]]){
ds <- dto[["unitData"]][[s]] # get study data from dto
(varnames <- names(ds)) # see what variables there are
(get_these_variables <- c("id",
"year_of_wave","age_in_years","year_born",
"female",
"marital", "single",
"educ3",
"smoke_now","smoked_ever",
"current_work_2",
"current_drink",
"sedentary",
"poor_health",
"bmi"))
(variables_present <- varnames %in% get_these_variables) # variables on the list
dmls[[s]] <- ds[,variables_present] # keep only them
}
lapply(dmls, names) # view the contents of the list object$alsa
[1] "id" "smoke_now" "smoked_ever" "year_of_wave" "age_in_years" "year_born"
[7] "female" "marital" "single" "educ3" "current_work_2" "current_drink"
[13] "sedentary" "poor_health" "bmi"
$lbsl
[1] "id" "smoke_now" "smoked_ever" "year_of_wave" "age_in_years" "year_born"
[7] "female" "marital" "single" "educ3" "current_work_2" "current_drink"
[13] "sedentary" "poor_health" "bmi"
$satsa
[1] "id" "smoke_now" "smoked_ever" "year_of_wave" "age_in_years" "year_born"
[7] "female" "marital" "single" "educ3" "current_work_2" "current_drink"
[13] "sedentary" "poor_health" "bmi"
$share
[1] "id" "smoke_now" "smoked_ever" "year_of_wave" "year_born" "age_in_years"
[7] "female" "marital" "single" "educ3" "current_work_2" "current_drink"
[13] "sedentary" "poor_health" "bmi"
$tilda
[1] "id" "smoke_now" "smoked_ever" "year_of_wave" "age_in_years" "year_born"
[7] "female" "marital" "single" "educ3" "current_work_2" "current_drink"
[13] "sedentary" "poor_health" "bmi" ds <- plyr::ldply(dmls,data.frame,.id = "study_name")
ds$id <- 1:nrow(ds) # some ids values might be identical, replace
head(ds) study_name id smoke_now smoked_ever year_of_wave age_in_years year_born female marital single educ3
1 alsa 1 FALSE FALSE 1992 86 1906 FALSE mar_cohab FALSE more than high school
2 alsa 2 FALSE FALSE 1992 78 1914 TRUE mar_cohab FALSE high school
3 alsa 3 FALSE FALSE 1992 89 1903 TRUE widowed TRUE high school
4 alsa 4 FALSE FALSE 1992 78 1914 FALSE widowed TRUE high school
5 alsa 5 FALSE FALSE 1992 85 1907 FALSE widowed TRUE more than high school
6 alsa 6 FALSE FALSE 1992 92 1900 TRUE widowed TRUE high school
current_work_2 current_drink sedentary poor_health bmi
1 FALSE TRUE FALSE FALSE NA
2 FALSE TRUE FALSE FALSE NA
3 FALSE TRUE TRUE FALSE NA
4 TRUE TRUE FALSE TRUE NA
5 FALSE TRUE FALSE FALSE NA
6 FALSE TRUE TRUE FALSE NAsaveRDS(ds, "./data/unshared/derived/ds_harmonized.rds")We test the harmonized data with a simple logistic regression
#
#
# m1 <- glm(
# formula = smoke_now ~ 1 + study_name + age_in_years + female + marital + educ3,
# data = ds, family = "binomial"
# )
# m1
#
# m2 <- glm(
# formula = smoke_now ~ -1 + age_in_years + female + marital + educ3,
# data = ds_sub_share, family = "binomial"
# )
# m2
# # useful functions working with GLM model objects
# summary(m1) # model summary
# summary(m2)
# coefficients(mdl) # point estimates of model parameters (aka "model solution")
# knitr::kable(vcov(mdl)) # covariance matrix of model parameters (inspect for colliniarity)
# knitr::kable(cov2cor(vcov(mdl))) # view the correlation matrix of model parameters
# confint(mdl, level=0.95) # confidence intervals for the estimated parameters
#
# # predict(mdl); fitted(mld) # generate prediction of the full model (all effects)
# # residuals(mdl) # difference b/w observed and modeled values
# anova(mdl) # put results into a familiary ANOVA table
# # influence(mdl) # regression diagnostics
#
#
# # create a model summary object to query
# (summod <- summary(mdl))
# str(summod)
# Export data to be used by Mplus
convert_to_numeric <- c("smoke_now","smoked_ever")
for(v in convert_to_numeric){
ds[,v] <- as.integer(ds[,v])
}
# ds$marital_n <- car::recode(ds$marital,"
# 'single' = 0;
# 'mar_cohab' = 1;
# 'sep_divorced'= 2;
# 'widowed' = 3
# ", as.numeric.result=TRUE )
# ds$educ3_n <- car::recode(ds$educ3,"
# 'less than high school'= 0;
# 'high school' = 1;
# 'more than high school'= 2
#
# ", as.numeric.result=TRUE )
str(ds)'data.frame': 15342 obs. of 16 variables:
$ study_name : Factor w/ 5 levels "alsa","lbsl",..: 1 1 1 1 1 1 1 1 1 1 ...
$ id : int 1 2 3 4 5 6 7 8 9 10 ...
$ smoke_now : int 0 0 0 0 0 0 0 0 0 0 ...
$ smoked_ever : int 0 0 0 0 0 0 0 0 0 0 ...
$ year_of_wave : num 1992 1992 1992 1992 1992 ...
$ age_in_years : num 86 78 89 78 85 92 74 80 99 85 ...
$ year_born : num 1906 1914 1903 1914 1907 ...
$ female : logi FALSE TRUE TRUE FALSE FALSE TRUE ...
$ marital : chr "mar_cohab" "mar_cohab" "widowed" "widowed" ...
$ single : logi FALSE FALSE TRUE TRUE TRUE TRUE ...
$ educ3 : chr "more than high school" "high school" "high school" "high school" ...
$ current_work_2: logi FALSE FALSE FALSE TRUE FALSE FALSE ...
$ current_drink : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ sedentary : logi FALSE FALSE TRUE FALSE FALSE TRUE ...
$ poor_health : logi FALSE FALSE FALSE TRUE FALSE FALSE ...
$ bmi :Classes 'labelled', 'numeric' atomic [1:15342] NA NA NA NA NA NA NA NA NA NA ...
.. ..- attr(*, "label")= Named chr "Weight in kilograms"
.. .. ..- attr(*, "names")= chr "WEIGHT"# write.table(ds,"./data/unshared/derived/combined-harmonized-data-set.dat", row.names=F, col.names=F)
# write(names(ds), "./data/unshared/derived/variable-names.txt", sep=" ")