Describe: work status
This report lists the candidate variable for DataScheme variables of the construct work status.
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
RETIRED
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="RETIRED") %>% dplyr::select(name,label) name label
1 RETIRED Are you retired from your last job?dto[["unitData"]][["alsa"]]%>% histogram_discrete("RETIRED")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("RETIRED") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
RETIRED n
(fctr) (int)
1 Yes 1767
2 No 134
3 NA 186CURRWORK
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="CURRWORK") %>% dplyr::select(name,label) name label
1 CURRWORK Currently workingdto[["unitData"]][["alsa"]]%>% histogram_discrete("CURRWORK")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("CURRWORK") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
CURRWORK n
(fctr) (int)
1 Yes 31
2 No 2038
3 NA 18LBSL
NOWRK94
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="NOWRK94") %>% dplyr::select(name,label) name label
1 NOWRK94 Working at present time?dto[["unitData"]][["lbsl"]]%>% histogram_discrete("NOWRK94")
dto[["unitData"]][["lbsl"]]%>% dplyr::group_by_("NOWRK94") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
NOWRK94 n
(fctr) (int)
1 yes, full time 105
2 yes, part time 64
3 yes, more than one job 2
4 no, retired 318
5 no, homemaker 34
6 no, unemployed 7
7 no, not seeking work 7
8 no, disabled 14
9 NA 105SATSA
GAMTWORK
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GAMTWORK") %>% dplyr::select(name,label) name label
1 GAMTWORK Which of the following alternatives best describes your current work/retirement situation?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GAMTWORK")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GAMTWORK") %>% dplyr::summarize(n=n())Source: local data frame [11 x 2]
GAMTWORK n
(fctr) (int)
1 old-age pensioner 778
2 pension due to sickness 77
3 On leave of absence from work 2
4 work half-time 112
5 work full-time 407
6 Unemployed (looking for a job) 13
7 Unemployed (not looking for job) 3
8 full time student 3
9 housewife/houseman 22
10 other' 58
11 NA 22SHARE
EP0050
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="EP0050") %>% dplyr::select(name,label) name label
1 EP0050 current job situationdto[["unitData"]][["share"]]%>% histogram_discrete("EP0050")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("EP0050") %>% dplyr::summarize(n=n())Source: local data frame [10 x 2]
EP0050 n
(fctr) (int)
1 Retired 1071
2 Employed or self-employed (including working for family busi 932
3 Unemployed, seeking job 64
4 Unemployed, not seeking job 64
5 Temporarily sick or disabled 46
6 Permanently sick or disabled 89
7 Homemaker 289
8 Other (specify) 34
9 Don't know 1
10 NA 8TILDA
WE001
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="WE001") %>% dplyr::select(name,label) name label
1 WE001 Which one of these would you say best describes your current situation?dto[["unitData"]][["tilda"]]%>% histogram_discrete("WE001")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("WE001") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
WE001 n
(fctr) (int)
1 Retired 3048
2 Employed 2218
3 Self-employed (including farming) 923
4 Unemployed 413
5 Permanently sick or disabled 395
6 Looking after home or family 1346
7 In education or training 55
8 Other (Specify) 104
9 NA 2WE003
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="WE003") %>% dplyr::select(name,label) name label
1 WE003 Did you, nevertheless, do any paid work during the last week, either as an em?dto[["unitData"]][["tilda"]]%>% histogram_discrete("WE003")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("WE003") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
WE003 n
(fctr) (int)
1 UNDOCUMENTED CODE 3141
2 Yes 256
3 No 5107sessionInfo()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