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:

    1. Reads in raw data files from the candidate studies
    1. 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.
    1. 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.
    1. 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 status
dto[["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     1

LBSL

MSTAT94

dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="MSTAT94") %>% dplyr::select(name,label)
     name                  label
1 MSTAT94 Marital Status in 1994
dto[["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    97

SATSA

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    15

SHARE

DN0140

dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN0140") %>% dplyr::select(name,label)
    name          label
1 DN0140 marital status
dto[["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     1

TILDA

SOCMARRIED

dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="SOCMARRIED") %>% dplyr::select(name,label)
        name                         label
1 SOCMARRIED SOCmarried  Currently married
dto[["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  2538

MAR_4

dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="MAR_4") %>% dplyr::select(name,label)
   name                label
1 MAR_4 mar4  Marital Status
dto[["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  1195

CS006

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  1195
sessionInfo()
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