Describe: marital
This report lists the candidate variable for DataScheme variables of the construct marital.
Exposition
This report is meant to be compiled after having executed the script
./manipulation/0-ellis-island.R, which prepares the necessary data transfer object (DTO). We begin with a brief recap of this script and the DTO it produces.
Ellis Island
All data land on Ellis Island.
The script 0-ellis-island.R is the first script in the analytic workflow. It accomplished the following:
- Reads in raw data files from the candidate studies
- Reads in raw data files from the candidate studies
- Extract, combines, and exports their metadata (specifically, variable names and labels, if provided) into
./data/shared/derived/meta-data-live.csv, which is updated every time Ellis Island script is executed.
- Extract, combines, and exports their metadata (specifically, variable names and labels, if provided) into
- Augments raw metadata with instructions for renaming and classifying variables. The instructions are provided as manually entered values in
./data/shared/meta-data-map.csv. They are used by automatic scripts in later harmonization and analysis.
- Augments raw metadata with instructions for renaming and classifying variables. The instructions are provided as manually entered values in
- Combines unit and metadata into a single DTO to serve as a starting point to all subsequent analyses.
# 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 - list objects with 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 27]
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)Meta
# 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)
)ALSA
MARITST
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="MARITST") %>% dplyr::select(name,label) name label
1 MARITST Marital statusdto[["unitData"]][["alsa"]]%>% histogram_discrete("MARITST")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("MARITST") %>% dplyr::summarize(n=n())Source: local data frame [7 x 2]
MARITST n
(fctr) (int)
1 Married 1361
2 De facto 6
3 Separated 16
4 Divorced 33
5 Widowed 594
6 Never married 76
7 NA 1LBSL
MSTAT94
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="MSTAT94") %>% dplyr::select(name,label) name label
1 MSTAT94 Marital Status in 1994dto[["unitData"]][["lbsl"]]%>% histogram_discrete("MSTAT94")
dto[["unitData"]][["lbsl"]]%>% dplyr::group_by_("MSTAT94") %>% dplyr::summarize(n=n())Source: local data frame [6 x 2]
MSTAT94 n
(fctr) (int)
1 single 22
2 divorced 73
3 separated 4
4 widowed 134
5 married 326
6 NA 97SATSA
GMARITAL
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GMARITAL") %>% dplyr::select(name,label) name label
1 GMARITAL What is your marital status?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GMARITAL")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GMARITAL") %>% dplyr::summarize(n=n())Source: local data frame [5 x 2]
GMARITAL n
(fctr) (int)
1 Not married 149
2 married /living together with person 961
3 widow/widower 259
4 divorced 113
5 NA 15SHARE
DN0140
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN0140") %>% dplyr::select(name,label) name label
1 DN0140 marital statusdto[["unitData"]][["share"]]%>% histogram_discrete("DN0140")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN0140") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
DN0140 n
(fctr) (int)
1 married and living together with spouse 2039
2 registered partnership 10
3 married, living separated from spouse 19
4 never married 51
5 divorced 140
6 widowed 336
7 refusal 1
8 don't know 1
9 NA 1TILDA
SOCMARRIED
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="SOCMARRIED") %>% dplyr::select(name,label) name label
1 SOCMARRIED SOCmarried Currently marrieddto[["unitData"]][["tilda"]]%>% histogram_discrete("SOCMARRIED")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("SOCMARRIED") %>% dplyr::summarize(n=n())Source: local data frame [2 x 2]
SOCMARRIED n
(fctr) (int)
1 Married 5966
2 Not married 2538MAR_4
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="MAR_4") %>% dplyr::select(name,label) name label
1 MAR_4 mar4 Marital Statusdto[["unitData"]][["tilda"]]%>% histogram_discrete("MAR_4")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("MAR_4") %>% dplyr::summarize(n=n())Source: local data frame [4 x 2]
MAR_4 n
(fctr) (int)
1 Married 5966
2 Never married 791
3 Sep/divorced 552
4 Widowed 1195CS006
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="CS006") %>% dplyr::select(name,label) name label
1 CS006 cs006 Are you...?dto[["unitData"]][["tilda"]]%>% histogram_discrete("CS006")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("CS006") %>% dplyr::summarize(n=n())Source: local data frame [6 x 2]
CS006 n
(fctr) (int)
1 Married 5748
2 Living with a partner as if married 218
3 Single (never married) 791
4 Separated 352
5 Divorced 200
6 Widowed 1195sessionInfo()R version 3.2.5 (2016-04-14)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_2.1.0 knitr_1.12.3 magrittr_1.5
loaded via a namespace (and not attached):
[1] splines_3.2.5 lattice_0.20-33 colorspace_1.2-6 htmltools_0.3.5 mgcv_1.8-12
[6] yaml_2.1.13 chron_2.3-47 survival_2.38-3 nloptr_1.0.4 foreign_0.8-66
[11] DBI_0.4-1 RColorBrewer_1.1-2 plyr_1.8.3 stringr_1.0.0 MatrixModels_0.4-1
[16] munsell_0.4.3 gtable_0.2.0 htmlwidgets_0.6 evaluate_0.9 labeling_0.3
[21] latticeExtra_0.6-28 SparseM_1.7 extrafont_0.17 quantreg_5.21 pbkrtest_0.4-6
[26] parallel_3.2.5 markdown_0.7.7 highr_0.5.1 Rttf2pt1_1.3.3 Rcpp_0.12.5
[31] acepack_1.3-3.3 scales_0.4.0 DT_0.1.40 formatR_1.3 Hmisc_3.17-4
[36] jsonlite_0.9.20 lme4_1.1-12 gridExtra_2.2.1 testit_0.5 digest_0.6.9
[41] stringi_1.0-1 dplyr_0.4.3 grid_3.2.5 tools_3.2.5 lazyeval_0.1.10
[46] dichromat_2.0-0 Formula_1.2-1 cluster_2.0.3 tidyr_0.4.1 extrafontdb_1.0
[51] car_2.1-2 MASS_7.3-45 Matrix_1.2-4 rsconnect_0.4.2.1 data.table_1.9.6
[56] assertthat_0.1 minqa_1.2.4 rmarkdown_0.9.6 R6_2.1.2 rpart_4.1-10
[61] nnet_7.3-12 nlme_3.1-126