Describe: physical activity
This report lists the candidate variable for DataScheme variables of the construct physact.
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
EXRTHOUS
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="EXRTHOUS")%>%dplyr::select(name,label) name label
1 EXRTHOUS Exertion around housedto[["unitData"]][["alsa"]]%>%histogram_discrete("EXRTHOUS")
dto[["unitData"]][["alsa"]]%>%dplyr::group_by_("EXRTHOUS")%>%dplyr::summarize(n=n())Source: local data frame [3 x 2]
EXRTHOUS n
(fctr) (int)
1 Yes 290
2 No 1773
3 NA 24HWMNWK2W
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="HWMNWK2W") %>% dplyr::select(name,label) name label
1 HWMNWK2W Times walked in past two weeksdto[["unitData"]][["alsa"]]%>% histogram_continuous("HWMNWK2W", bin_width=1)
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("HWMNWK2W") %>% dplyr::summarize(n=n())Source: local data frame [24 x 2]
HWMNWK2W n
(int) (int)
1 1 58
2 2 117
3 3 46
4 4 96
5 5 36
6 6 67
7 7 34
8 8 33
9 9 4
10 10 53
.. ... ...LSVEXC2W
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="LSVEXC2W") %>% dplyr::select(name,label) name label
1 LSVEXC2W Less vigor sessions last 2 weeksdto[["unitData"]][["alsa"]]%>% histogram_continuous("LSVEXC2W", bin_width=1)
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("LSVEXC2W") %>% dplyr::summarize(n=n())Source: local data frame [20 x 2]
LSVEXC2W n
(int) (int)
1 1 25
2 2 69
3 3 14
4 4 70
5 5 15
6 6 52
7 7 12
8 8 11
9 9 1
10 10 14
11 11 1
12 12 9
13 14 76
14 18 2
15 22 1
16 24 1
17 28 10
18 30 1
19 42 5
20 NA 1698LSVIGEXC
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="LSVIGEXC")%>%dplyr::select(name,label) name label
1 LSVIGEXC Less vigor past 2 weeksdto[["unitData"]][["alsa"]]%>%histogram_discrete("LSVIGEXC")
dto[["unitData"]][["alsa"]]%>%dplyr::group_by_("LSVIGEXC")%>%dplyr::summarize(n=n())Source: local data frame [3 x 2]
LSVIGEXC n
(fctr) (int)
1 Yes 389
2 No 1675
3 NA 23TMHVYEXR
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="TMHVYEXR")%>%dplyr::select(name,label) name label
1 TMHVYEXR Time heavy physical exertiondto[["unitData"]][["alsa"]]%>%histogram_continuous("TMHVYEXR", bin_width=5)
dto[["unitData"]][["alsa"]]%>%dplyr::group_by_("TMHVYEXR")%>%dplyr::summarize(n=n())Source: local data frame [27 x 2]
TMHVYEXR n
(int) (int)
1 1 77
2 2 71
3 3 15
4 4 30
5 5 11
6 6 8
7 7 4
8 8 7
9 10 11
10 11 1
.. ... ...TMVEXC2W
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="TMVEXC2W")%>%dplyr::select(name,label) name label
1 TMVEXC2W Vigor Time past 2 weeksdto[["unitData"]][["alsa"]]%>%histogram_continuous("TMVEXC2W", bin_width=60)
dto[["unitData"]][["alsa"]]%>%dplyr::group_by_("TMVEXC2W")%>%dplyr::summarize(n=n())Source: local data frame [37 x 2]
TMVEXC2W n
(int) (int)
1 1 1
2 2 1
3 4 3
4 5 2
5 8 4
6 10 1
7 11 1
8 14 1
9 18 1
10 21 1
.. ... ...VIGEXC2W
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="VIGEXC2W")%>%dplyr::select(name,label) name label
1 VIGEXC2W Vigor Sessions in past 2 weeksdto[["unitData"]][["alsa"]]%>%histogram_continuous("VIGEXC2W", bin_width=1)
dto[["unitData"]][["alsa"]]%>%dplyr::group_by_("VIGEXC2W")%>%dplyr::summarize(n=n())Source: local data frame [13 x 2]
VIGEXC2W n
(int) (int)
1 1 10
2 2 24
3 3 2
4 4 10
5 5 1
6 6 8
7 7 1
8 8 1
9 9 1
10 10 7
11 12 1
12 14 19
13 NA 2002VIGEXCS
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="VIGEXCS")%>%dplyr::select(name,label) name label
1 VIGEXCS Vigorous exercisedto[["unitData"]][["alsa"]]%>%histogram_discrete("VIGEXCS")
dto[["unitData"]][["alsa"]]%>%dplyr::group_by_("VIGEXCS")%>%dplyr::summarize(n=n())Source: local data frame [3 x 2]
VIGEXCS n
(fctr) (int)
1 Yes 85
2 No 1979
3 NA 23WALK2WKS
dto[["metaData"]]%>%dplyr::filter(study_name=="alsa", name=="WALK2WKS")%>%dplyr::select(name,label) name label
1 WALK2WKS Walking past 2 weeksdto[["unitData"]][["alsa"]]%>%histogram_discrete("WALK2WKS")
dto[["unitData"]][["alsa"]]%>%dplyr::group_by_("WALK2WKS")%>%dplyr::summarize(n=n())Source: local data frame [3 x 2]
WALK2WKS n
(fctr) (int)
1 Yes 973
2 No 1091
3 NA 23LBSL
CHORE94
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="CHORE94")%>%dplyr::select(name,label) name label
1 CHORE94 Doing household chores, number of hours spent per weekdto[["unitData"]][["lbsl"]]%>%histogram_continuous("CHORE94", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("CHORE94")%>%dplyr::summarize(n=n())Source: local data frame [30 x 2]
CHORE94 n
(int) (int)
1 0 21
2 1 70
3 2 67
4 3 56
5 4 44
6 5 41
7 6 31
8 7 32
9 8 26
10 9 4
.. ... ...DANCE94
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="DANCE94")%>%dplyr::select(name,label) name label
1 DANCE94 Dancingdto[["unitData"]][["lbsl"]]%>%histogram_continuous("DANCE94", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("DANCE94")%>%dplyr::summarize(n=n())Source: local data frame [13 x 2]
DANCE94 n
(int) (int)
1 0 280
2 1 18
3 2 8
4 3 8
5 4 8
6 5 3
7 6 3
8 8 1
9 10 4
10 12 1
11 15 1
12 20 1
13 NA 320EXCERTOT
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="EXCERTOT")%>%dplyr::select(name,label) name label
1 EXCERTOT Number of total hours in an average week exercising for shape/fun (not housework)dto[["unitData"]][["lbsl"]]%>%histogram_continuous("EXCERTOT", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("EXCERTOT")%>%dplyr::summarize(n=n())Source: local data frame [24 x 2]
EXCERTOT n
(int) (int)
1 0 131
2 1 43
3 2 63
4 3 67
5 4 50
6 5 38
7 6 33
8 7 37
9 8 16
10 9 4
.. ... ...EXCERWK
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="EXCERWK")%>%dplyr::select(name,label) name label
1 EXCERWK Number of times in past week exercised or played sportsdto[["unitData"]][["lbsl"]]%>%histogram_continuous("EXCERWK", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("EXCERWK")%>%dplyr::summarize(n=n())Source: local data frame [21 x 2]
EXCERWK n
(int) (int)
1 0 171
2 1 36
3 2 52
4 3 52
5 4 53
6 5 54
7 6 31
8 7 63
9 8 4
10 9 2
.. ... ...FIT94
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="FIT94")%>%dplyr::select(name,label) name label
1 FIT94 Physical fitness, number of hours each weekdto[["unitData"]][["lbsl"]]%>%histogram_continuous("FIT94", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("FIT94")%>%dplyr::summarize(n=n())Source: local data frame [17 x 2]
FIT94 n
(int) (int)
1 0 137
2 1 50
3 2 44
4 3 51
5 4 30
6 5 14
7 6 20
8 7 8
9 8 5
10 10 8
11 12 5
12 15 1
13 18 1
14 20 1
15 21 1
16 25 1
17 NA 279SPEC94
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="SPEC94")%>%dplyr::select(name,label) name label
1 SPEC94 Spectator sports, number of hours spent per weekdto[["unitData"]][["lbsl"]]%>%histogram_continuous("SPEC94", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("SPEC94")%>%dplyr::summarize(n=n())Source: local data frame [9 x 2]
SPEC94 n
(int) (int)
1 0 248
2 1 21
3 2 15
4 3 21
5 4 12
6 5 3
7 8 2
8 9 1
9 NA 333SPORT94
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="SPORT94")%>%dplyr::select(name,label) name label
1 SPORT94 Participant sports, number of hoursdto[["unitData"]][["lbsl"]]%>%histogram_continuous("SPORT94", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("SPORT94")%>%dplyr::summarize(n=n())Source: local data frame [16 x 2]
SPORT94 n
(int) (int)
1 0 251
2 1 11
3 2 11
4 3 11
5 4 14
6 5 3
7 6 8
8 7 3
9 8 9
10 9 1
11 10 9
12 12 3
13 15 3
14 16 1
15 20 4
16 NA 314WALK94
dto[["metaData"]]%>%dplyr::filter(study_name=="lbsl", name=="WALK94")%>%dplyr::select(name,label) name label
1 WALK94 Walking, number of hours per weekdto[["unitData"]][["lbsl"]]%>%histogram_continuous("WALK94", bin_width=1)
dto[["unitData"]][["lbsl"]]%>%dplyr::group_by_("WALK94")%>%dplyr::summarize(n=n())Source: local data frame [17 x 2]
WALK94 n
(int) (int)
1 0 75
2 1 72
3 2 78
4 3 57
5 4 40
6 5 33
7 6 28
8 7 24
9 8 8
10 10 10
11 11 1
12 12 5
13 14 1
14 15 1
15 20 2
16 30 1
17 NA 220SATSA
GEXERCIS
dto[["metaData"]]%>%dplyr::filter(study_name=="satsa", name=="GEXERCIS")%>%dplyr::select(name,label) name
1 GEXERCIS
label
1 Here are seven different options concerning exercise during your leisure time. Which one of these options best fits how you yourself exercise on a yearly basis?dto[["unitData"]][["satsa"]]%>%histogram_discrete("GEXERCIS")
dto[["unitData"]][["satsa"]]%>%dplyr::group_by_("GEXERCIS")%>%dplyr::summarize(n=n())Source: local data frame [8 x 2]
GEXERCIS n
(fctr) (int)
1 I hardly get any exercise at all 169
2 I get very little exercise 181
3 I get little exercise 193
4 I don't get very much exercise 394
5 I get quite a lot of exercise 430
6 I get a lot of exercise 88
7 I get very much exercise 17
8 NA 25SHARE
BR0150
dto[["metaData"]]%>%dplyr::filter(study_name=="share", name=="BR0150")%>%dplyr::select(name,label) name label
1 BR0150 sports or activities that are vigorousdto[["unitData"]][["share"]]%>%histogram_discrete("BR0150")
dto[["unitData"]][["share"]]%>%dplyr::group_by_("BR0150")%>%dplyr::summarize(n=n())Source: local data frame [6 x 2]
BR0150 n
(fctr) (int)
1 more than once a week 1092
2 once a week 322
3 one to three times a month 131
4 hardly ever, or never 1046
5 don't know 3
6 NA 4BR0160
dto[["metaData"]]%>%dplyr::filter(study_name=="share", name=="BR0160")%>%dplyr::select(name,label) name label
1 BR0160 activities requiring a moderate level of energydto[["unitData"]][["share"]]%>%histogram_discrete("BR0160")
dto[["unitData"]][["share"]]%>%dplyr::group_by_("BR0160")%>%dplyr::summarize(n=n())Source: local data frame [6 x 2]
BR0160 n
(fctr) (int)
1 more than once a week 1524
2 once a week 294
3 one to three times a month 116
4 hardly ever, or never 657
5 don't know 3
6 NA 4TILDA
BH101
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH101")%>%dplyr::select(name,label) name label
1 BH101 bh101 During the last 7 days, on how many days did you do vigorous physical activit?dto[["unitData"]][["tilda"]]%>%histogram_discrete("BH101")
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH101")%>%dplyr::summarize(n=n())Source: local data frame [9 x 2]
BH101 n
(int) (int)
1 0 6303
2 1 454
3 2 409
4 3 346
5 4 208
6 5 272
7 6 97
8 7 403
9 NA 12BH102
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH102")%>%dplyr::select(name,label) name label
1 BH102 bh102 How much time did you usually spend doing vigorous physical activities on one?dto[["unitData"]][["tilda"]] %>% dplyr::filter(!BH102==-1)%>%histogram_discrete("BH102")
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH102")%>%dplyr::summarize(n=n())Source: local data frame [13 x 2]
BH102 n
(int) (int)
1 -1 6314
2 0 408
3 1 621
4 2 404
5 3 231
6 4 216
7 5 145
8 6 60
9 7 23
10 8 53
11 9 4
12 10 16
13 NA 9BH102A
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH102A")%>%dplyr::select(name,label) name label
1 BH102A bh102a How much time did you usually spend doing vigorous physical activities on one?dto[["unitData"]][["tilda"]]%>% dplyr::filter(!BH102A==-1)%>%histogram_continuous("BH102A", bin_width=5)
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH102A")%>%dplyr::summarize(n=n())Source: local data frame [16 x 2]
BH102A n
(int) (int)
1 -1 6314
2 0 1418
3 10 45
4 14 1
5 15 44
6 20 81
7 24 1
8 25 7
9 28 1
10 30 441
11 35 8
12 40 33
13 45 69
14 50 23
15 55 6
16 NA 12BH103
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH103")%>%dplyr::select(name,label) name label
1 BH103 bh103 During the last 7 days, on how many days did you do moderate physical activit?dto[["unitData"]][["tilda"]]%>%histogram_discrete("BH103")
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH103")%>%dplyr::summarize(n=n())Source: local data frame [9 x 2]
BH103 n
(int) (int)
1 0 4378
2 1 409
3 2 589
4 3 521
5 4 338
6 5 493
7 6 210
8 7 1549
9 NA 17BH104
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH104")%>%dplyr::select(name,label) name label
1 BH104 bh104 How much time did you usually spend doing moderate physical activities on one?dto[["unitData"]][["tilda"]]%>% dplyr::filter(!BH104==-1)%>%histogram_continuous("BH104", bin_width=1)
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH104")%>%dplyr::summarize(n=n())Source: local data frame [13 x 2]
BH104 n
(int) (int)
1 -1 4395
2 0 776
3 1 1078
4 2 840
5 3 468
6 4 416
7 5 261
8 6 94
9 7 46
10 8 68
11 9 5
12 10 34
13 NA 23BH104A
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH104A")%>%dplyr::select(name,label) name label
1 BH104A bh104a How much time did you usually spend doing moderate physical activities on one?dto[["unitData"]][["tilda"]]%>% dplyr::filter(!BH104A==-1)%>%histogram_continuous("BH104A", bin_width=5)
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH104A")%>%dplyr::summarize(n=n())Source: local data frame [20 x 2]
BH104A n
(int) (int)
1 -1 4395
2 0 2666
3 10 118
4 12 3
5 13 2
6 14 1
7 15 77
8 16 1
9 19 1
10 20 198
11 25 14
12 30 864
13 35 2
14 36 1
15 40 42
16 45 66
17 50 24
18 55 10
19 56 1
20 NA 18BH105
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH105")%>%dplyr::select(name,label) name label
1 BH105 bh105 During the last 7 days, on how many days did you walk for at least 10 minutes?dto[["unitData"]][["tilda"]]%>%histogram_discrete("BH105")
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH105")%>%dplyr::summarize(n=n())Source: local data frame [9 x 2]
BH105 n
(int) (int)
1 0 1369
2 1 349
3 2 499
4 3 668
5 4 557
6 5 746
7 6 328
8 7 3974
9 NA 14BH106
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH106")%>%dplyr::select(name,label) name label
1 BH106 bh106 How much time did you usually spend walking on one of those days? HOURSdto[["unitData"]][["tilda"]]%>%histogram_discrete("BH106")
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH106")%>%dplyr::summarize(n=n())Source: local data frame [8 x 2]
BH106 n
(int) (int)
1 -1 1383
2 0 3166
3 1 2434
4 2 704
5 3 320
6 4 267
7 5 205
8 NA 25BH106A
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH106A")%>%dplyr::select(name,label) name label
1 BH106A bh106a How much time did you usually spend walking on one of those days? MINSdto[["unitData"]][["tilda"]]%>% dplyr::filter(!BH106A==-1)%>%histogram_continuous("BH106A", bin_width=1)
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH106A")%>%dplyr::summarize(n=n())Source: local data frame [24 x 2]
BH106A n
(int) (int)
1 -1 1382
2 0 2966
3 10 302
4 12 6
5 13 2
6 15 305
7 18 2
8 20 615
9 24 2
10 25 65
.. ... ...BH107
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH107")%>%dplyr::select(name,label) name label
1 BH107 bh107 During the last 7 days, how much time did you spend sitting on a week day? HO?dto[["unitData"]][["tilda"]]%>%histogram_continuous("BH107")
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH107")%>%dplyr::summarize(n=n())Source: local data frame [21 x 2]
BH107 n
(int) (int)
1 0 115
2 1 325
3 2 980
4 3 1491
5 4 1601
6 5 1090
7 6 951
8 7 608
9 8 551
10 9 128
.. ... ...BH107A
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="BH107A")%>%dplyr::select(name,label) name label
1 BH107A bh107a During the last 7 days, how much time did you spend sitting on a week day? MINSdto[["unitData"]][["tilda"]]%>%histogram_continuous("BH107A")
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("BH107A")%>%dplyr::summarize(n=n())Source: local data frame [14 x 2]
BH107A n
(int) (int)
1 0 7033
2 10 67
3 15 19
4 16 1
5 20 120
6 23 1
7 25 4
8 30 1079
9 35 5
10 40 23
11 45 31
12 50 19
13 55 7
14 NA 95IPAQEXERCISE3
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="IPAQEXERCISE3")%>%dplyr::select(name,label) name label
1 IPAQEXERCISE3 IPAQmetminutes Phsyical activity met-minutesdto[["unitData"]][["tilda"]]%>%histogram_continuous("IPAQEXERCISE3", bin_width = 500)
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("IPAQEXERCISE3")%>%dplyr::summarize(n=n())Source: local data frame [1,707 x 2]
IPAQEXERCISE3 n
(dbl) (int)
1 0.0 827
2 33.0 12
3 40.0 2
4 49.5 13
5 60.0 1
6 66.0 43
7 80.0 5
8 89.5 1
9 99.0 63
10 113.0 1
.. ... ...IPAQMETMINUTES
dto[["metaData"]]%>%dplyr::filter(study_name=="tilda", name=="IPAQMETMINUTES")%>%dplyr::select(name,label) name label
1 IPAQMETMINUTES IPAQmetminutes Phsyical activity met-minutesdto[["unitData"]][["tilda"]]%>%histogram_continuous("IPAQMETMINUTES", bin_width = 500)
dto[["unitData"]][["tilda"]]%>%dplyr::group_by_("IPAQMETMINUTES")%>%dplyr::summarize(n=n())Source: local data frame [1,707 x 2]
IPAQMETMINUTES n
(dbl) (int)
1 0.0 827
2 33.0 12
3 40.0 2
4 49.5 13
5 60.0 1
6 66.0 43
7 80.0 5
8 89.5 1
9 99.0 63
10 113.0 1
.. ... ...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