Describe: physique
This report lists the candidate variable for DataScheme variables of the construct physique.
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
WEIGHT
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="WEIGHT")%>%dplyr::select(name,label) name label
1 WEIGHT Weight in kilogramsdto[["unitData"]][["alsa"]]%>%histogram_continuous("WEIGHT")
LBSL
HEIGHT94
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="HEIGHT94") %>% dplyr::select(name,label) name label
1 HEIGHT94 Height in Inchesdto[["unitData"]][["lbsl"]]%>% histogram_continuous("HEIGHT94")
WEIGHT94
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="WEIGHT94") %>% dplyr::select(name,label) name label
1 WEIGHT94 Weight in Poundsdto[["unitData"]][["lbsl"]]%>% histogram_continuous("WEIGHT94")
HHEIGHT
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="HHEIGHT") %>% dplyr::select(name,label) name label
1 HHEIGHT Self-reported height in inchesdto[["unitData"]][["lbsl"]]%>% histogram_continuous("HHEIGHT")
HWEIGHT
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="HWEIGHT") %>% dplyr::select(name,label) name label
1 HWEIGHT Self-reported weight in poundsdto[["unitData"]][["lbsl"]]%>% histogram_continuous("HWEIGHT")
SATSA
GHTCM
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GHTCM") %>% dplyr::select(name,label) name label
1 GHTCM How tall are you? (cm)dto[["unitData"]][["satsa"]]%>% histogram_continuous("GHTCM")
GWTKG
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GWTKG") %>% dplyr::select(name,label) name label
1 GWTKG How much do you weigh? (kg)dto[["unitData"]][["satsa"]]%>% histogram_continuous("GWTKG")
GPI
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GPI") %>% dplyr::select(name,label) name label
1 GPI BMI ((htcm/100)^2)dto[["unitData"]][["satsa"]]%>% histogram_continuous("GPI")
SHARE
PH0130
dto[["metaData"]]%>%dplyr::filter(study_name=="share", name=="PH0130")%>%dplyr::select(name,label) name label
1 PH0130 how tall are you?dto[["unitData"]][["share"]]%>%
dplyr::filter(!PH0130 %in% c(9999999,9999998)) %>%
histogram_continuous("PH0130")
PH0120
dto[["metaData"]]%>%dplyr::filter(study_name=="share", name=="PH0120")%>%dplyr::select(name,label) name label
1 PH0120 weight of respondentdto[["unitData"]][["share"]]%>%
dplyr::filter(!PH0120 == 1000000) %>% histogram_continuous("PH0120")
TILDA
HEIGHT
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="HEIGHT")%>%dplyr::select(name,label) name label
1 HEIGHT Respondent heightdto[["unitData"]][["tilda"]]%>%histogram_continuous("HEIGHT")
WEIGHT
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="WEIGHT")%>%dplyr::select(name,label) name label
1 WEIGHT Respondent weightdto[["unitData"]][["tilda"]] %>%histogram_continuous("WEIGHT")
SR.HEIGHT.CENTIMETRES
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="SR.HEIGHT.CENTIMETRES")%>%dplyr::select(name,label) name label
1 SR.HEIGHT.CENTIMETRES SR_Height_Centimetresdto[["unitData"]][["tilda"]] %>%histogram_continuous("SR.HEIGHT.CENTIMETRES")
SR.WEIGHT.KILOGRAMMES
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="SR.WEIGHT.KILOGRAMMES")%>%dplyr::select(name,label) name label
1 SR.WEIGHT.KILOGRAMMES SR_Weight_Kilogrammesdto[["unitData"]][["tilda"]]%>%histogram_continuous("SR.WEIGHT.KILOGRAMMES")
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