This report lists the candidate variable for DataScheme variables of the construct smoking.

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/meta/meta-data-map.csv",
    filter  = "top",
    options = list(pageLength = 6, autoWidth = TRUE)
  )

ALSA

SMOKER

dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="SMOKER") %>% dplyr::select(name,label)
    name                              label
1 SMOKER Do you currently smoke cigarettes?
dto[["unitData"]][["alsa"]] %>% histogram_discrete("SMOKER")

PIPCIGAR

dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="PIPCIGAR") %>% dplyr::select(name,label)
      name                                 label
1 PIPCIGAR Do you regularly smoke pipe or cigar?
dto[["unitData"]][["alsa"]] %>% histogram_discrete("PIPCIGAR")

LBSL

SMK94

dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="SMK94") %>% dplyr::select(name,label)
   name            label
1 SMK94 Currently smoke?
dto[["unitData"]][["lbsl"]] %>% histogram_discrete("SMK94")

SMOKE

dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="SMOKE") %>% dplyr::select(name,label)
   name              label
1 SMOKE Smoke, tobacco use
dto[["unitData"]][["lbsl"]] %>% histogram_discrete("SMOKE")

SATSA

GSMOKNOW

dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GSMOKNOW") %>% dplyr::select(name,label)
      name                                                                                                 label
1 GSMOKNOW Have you smoked more than 6 cigarettes, 4 cigars or used pipe tobacco or snuff during the last month?
dto[["unitData"]][["satsa"]] %>% histogram_discrete("GSMOKNOW")

GEVRSMK

dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GEVRSMK") %>% dplyr::select(name,label)
     name                                            label
1 GEVRSMK Do you smoke cigarettes, cigars or a pipe? - Yes
dto[["unitData"]][["satsa"]] %>% histogram_discrete("GEVRSMK")

GEVRSNS

dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GEVRSNS") %>% dplyr::select(name,label)
     name                    label
1 GEVRSNS Do you take snuff? - Yes
dto[["unitData"]][["satsa"]] %>% histogram_discrete("GEVRSNS")

SHARE

BR0010

dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="BR0010") %>% dplyr::select(name,label)
    name             label
1 BR0010 ever smoked daily
dto[["unitData"]][["share"]] %>% histogram_discrete("BR0010")

BR0020

dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="BR0020") %>% dplyr::select(name,label)
    name                     label
1 BR0020 smoke at the present time
dto[["unitData"]][["share"]] %>% histogram_discrete("BR0020")

BR0030

dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="BR0030") %>% dplyr::select(name,label)
    name                 label
1 BR0030 how many years smoked
dto[["unitData"]][["share"]] %>% dplyr::filter(!BR0030 == 9999) %>% histogram_continuous("BR0030")

TILDA

BH001

dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="BH001") %>% dplyr::select(name,label)
   name                                                                                 label
1 BH001 bh001  Have you ever smoked cigarettes, cigars, cigarillos or a pipe daily for a per?
dto[["unitData"]][["tilda"]] %>% histogram_discrete("BH001")

BH002

dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="BH002") %>% dplyr::select(name,label)
   name                                    label
1 BH002 bh002  Do you smoke at the present time?
dto[["unitData"]][["tilda"]] %>% histogram_discrete("BH002")

BH003

dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="BH003") %>% dplyr::select(name,label)
   name                                             label
1 BH003 bh003  How old were you when you stopped smoking?
dto[["unitData"]][["tilda"]] %>% dplyr::filter(!BH003==-1) %>% histogram_continuous("BH003")

BEHSMOKER

dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="BEHSMOKER") %>% dplyr::select(name,label)
       name             label
1 BEHSMOKER BEHsmoker  Smoker
dto[["unitData"]][["tilda"]] %>% histogram_discrete("BEHSMOKER")

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