Describe: alcohol
This report lists the candidate variable for DataScheme variables of the construct alcohol.
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
FR6ORMOR
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="FR6ORMOR") %>% dplyr::select(name,label) name label
1 FR6ORMOR Frequency six or more drinksdto[["unitData"]][["alsa"]]%>% histogram_discrete("FR6ORMOR")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("FR6ORMOR") %>% dplyr::summarize(n=n())Source: local data frame [6 x 2]
FR6ORMOR n
(fctr) (int)
1 Never 1064
2 Less than monthly 134
3 Monthly 39
4 Weekly 32
5 Daily or almost daily 23
6 NA 795FREQALCH
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="FREQALCH") %>% dplyr::select(name,label) name label
1 FREQALCH Frequency alcoholdto[["unitData"]][["alsa"]]%>% histogram_discrete("FREQALCH")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("FREQALCH") %>% dplyr::summarize(n=n())Source: local data frame [6 x 2]
FREQALCH n
(fctr) (int)
1 Never 774
2 Monthly or less 368
3 Two to four times a month 167
4 Two to three times a week 214
5 Four or more times a week 544
6 NA 20NOSTDRNK
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="NOSTDRNK") %>% dplyr::select(name,label) name label
1 NOSTDRNK Number of standard drinksdto[["unitData"]][["alsa"]]%>% histogram_discrete("NOSTDRNK")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("NOSTDRNK") %>% dplyr::summarize(n=n())Source: local data frame [6 x 2]
NOSTDRNK n
(fctr) (int)
1 One or two 1033
2 Three or four 195
3 Five or six 46
4 Seven to nine 16
5 Ten or more 2
6 NA 795LBSL
ALCOHOL
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="ALCOHOL") %>% dplyr::select(name,label) name label
1 ALCOHOL Alcohol usedto[["unitData"]][["lbsl"]]%>% histogram_discrete("ALCOHOL")
dto[["unitData"]][["lbsl"]]%>% dplyr::group_by_("ALCOHOL") %>% dplyr::summarize(n=n())Source: local data frame [8 x 2]
ALCOHOL n
(fctr) (int)
1 never drank 92
2 not in last year 92
3 few times a year 143
4 once or twice per month 59
5 once a week 35
6 two or three times weekly 59
7 daily or almost daily 82
8 NA 94BEER
# requires categorization
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="BEER") %>% dplyr::select(name,label) name label
1 BEER Number of cans/bottles of beer last weekdto[["unitData"]][["lbsl"]]%>% histogram_continuous("BEER")
dto[["unitData"]][["lbsl"]]%>% dplyr::group_by_("BEER") %>% dplyr::summarize(n=n())Source: local data frame [18 x 2]
BEER n
(int) (int)
1 0 242
2 1 31
3 2 22
4 3 8
5 4 6
6 5 4
7 6 5
8 7 5
9 8 2
10 9 2
11 10 7
12 12 2
13 14 1
14 15 1
15 18 1
16 25 1
17 30 1
18 NA 315HARDLIQ
# requires categorization
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="HARDLIQ") %>% dplyr::select(name,label) name label
1 HARDLIQ Number of drinks containing hard liquor last weekdto[["unitData"]][["lbsl"]]%>% histogram_continuous("HARDLIQ")
dto[["unitData"]][["lbsl"]]%>% dplyr::group_by_("HARDLIQ") %>% dplyr::summarize(n=n())Source: local data frame [17 x 2]
HARDLIQ n
(int) (int)
1 0 231
2 1 23
3 2 34
4 3 8
5 4 7
6 5 7
7 6 10
8 7 13
9 8 2
10 9 1
11 10 1
12 12 1
13 14 9
14 15 1
15 21 1
16 25 1
17 NA 306WINE
# requires categorization
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="WINE") %>% dplyr::select(name,label) name label
1 WINE Number of glasses of wine last weekdto[["unitData"]][["lbsl"]]%>% histogram_continuous("WINE")
dto[["unitData"]][["lbsl"]]%>% dplyr::group_by_("WINE") %>% dplyr::summarize(n=n())Source: local data frame [16 x 2]
WINE n
(int) (int)
1 0 189
2 1 45
3 2 28
4 3 17
5 4 15
6 5 6
7 6 10
8 7 10
9 8 3
10 9 2
11 10 7
12 12 3
13 14 4
14 15 4
15 21 1
16 NA 312SATSA
GALCOHOL
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GALCOHOL") %>% dplyr::select(name,label) name label
1 GALCOHOL Do you ever drink alcoholic beverages?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GALCOHOL")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GALCOHOL") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
GALCOHOL n
(fctr) (int)
1 No 529
2 Yes 934
3 NA 34GBEERX
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GBEERX") %>% dplyr::select(name,label) name label
1 GBEERX How much beer do you usually drink at a time?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GBEERX")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GBEERX") %>% dplyr::summarize(n=n())Source: local data frame [8 x 2]
GBEERX n
(fctr) (int)
1 1 glass or less 351
2 1 bottle (33 cl) 358
3 2 bottles 56
4 3 bottles (two 45 cl cans) 28
5 4 bottles 5
6 5 bottles 6
7 6 bottles or more 9
8 NA 684GBOTVIN
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GBOTVIN") %>% dplyr::select(name,label) name label
1 GBOTVIN ..more than 1 bottle, i.e.____bottles (state number of bottles): GBOTVINdto[["unitData"]][["satsa"]]%>% histogram_discrete("GBOTVIN")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GBOTVIN") %>% dplyr::summarize(n=n())Source: local data frame [5 x 2]
GBOTVIN n
(int) (int)
1 0 1
2 1 1
3 2 3
4 4 1
5 NA 1491GDRLOTS
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GDRLOTS") %>% dplyr::select(name,label) name
1 GDRLOTS
label
1 How often do you consume more than five bottles of beer or more than one bottle of wine or more than 1/2 bottle liquot at one occasion?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GDRLOTS")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GDRLOTS") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
GDRLOTS n
(fctr) (int)
1 Never 1002
2 1-3 times a year 110
3 4-6 times a year 41
4 approx. once a month 33
5 a few times a month 12
6 approx. once a week 8
7 a few times a week 6
8 almost daily 1
9 NA 284GEVRALK
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GEVRALK") %>% dplyr::select(name,label) name label
1 GEVRALK Do you ever drink alcoholic drinks? - Yesdto[["unitData"]][["satsa"]]%>% histogram_discrete("GEVRALK")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GEVRALK") %>% dplyr::summarize(n=n())Source: local data frame [4 x 2]
GEVRALK n
(fctr) (int)
1 Yes 961
2 No, I have never drunk alcoholic drinks 376
3 No, I quit. 78
4 NA 82GFREQBER
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GFREQBER") %>% dplyr::select(name,label) name label
1 GFREQBER How often do you drink beer (not light beer)?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GFREQBER")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GFREQBER") %>% dplyr::summarize(n=n())Source: local data frame [10 x 2]
GFREQBER n
(fctr) (int)
1 Never 300
2 Once a year or less 77
3 2-6 times a year 212
4 Once a month 122
5 2 times a month 117
6 Once a week 127
7 2 times a week 100
8 every other day 34
9 every day 29
10 NA 379GFREQLIQ
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GFREQLIQ") %>% dplyr::select(name,label) name
1 GFREQLIQ
label
1 How often do you usually drink hard liquor? (e.g. aquavit, whiskey, gin, brandy, punsch. Also liquot in cocktails and long drinks)dto[["unitData"]][["satsa"]]%>% histogram_discrete("GFREQLIQ")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GFREQLIQ") %>% dplyr::summarize(n=n())Source: local data frame [10 x 2]
GFREQLIQ n
(fctr) (int)
1 Never 278
2 Once a year or less 100
3 2-6 times a year 349
4 Once a month 138
5 2 times a month 131
6 Once a week 103
7 2 times a week 60
8 every other day 10
9 every day 5
10 NA 323GFREQVIN
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GFREQVIN") %>% dplyr::select(name,label) name label
1 GFREQVIN How often do you usually drink wine (red or white)?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GFREQVIN")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GFREQVIN") %>% dplyr::summarize(n=n())Source: local data frame [10 x 2]
GFREQVIN n
(fctr) (int)
1 Never 261
2 Once a year or less 111
3 2-6 times a year 304
4 Once a month 123
5 2 times a month 126
6 Once a week 112
7 2 times a week 72
8 every other day 8
9 every day 4
10 NA 376GLIQX
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GLIQX") %>% dplyr::select(name,label) name label
1 GLIQX How much hard liquot do you usually drink at time?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GLIQX")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GLIQX") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
GLIQX n
(fctr) (int)
1 4 cl (approx. a small shot or equivalent) 328
2 6 cl (a big shot or equivalent) 190
3 8 cl 135
4 12 cl 106
5 18 cl 65
6 37 cl (half a bottle) 57
7 60 cl 3
8 75 cl (1 whole bottle) 7
9 NA 606GSTOPALK
# requires categorization? maybe, maybe not
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GSTOPALK") %>% dplyr::select(name,label) name label
1 GSTOPALK Do you ever drink alcoholic drinks? -No I quit. When? 19__dto[["unitData"]][["satsa"]]%>% histogram_continuous("GSTOPALK")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GSTOPALK") %>% dplyr::summarize(n=n())Source: local data frame [33 x 2]
GSTOPALK n
(int) (int)
1 34 1
2 40 2
3 41 1
4 44 1
5 46 1
6 47 1
7 50 3
8 58 2
9 59 1
10 60 2
.. ... ...GVINX
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="GVINX") %>% dplyr::select(name,label) name label
1 GVINX How much wine do you usually drink at a time?dto[["unitData"]][["satsa"]]%>% histogram_discrete("GVINX")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("GVINX") %>% dplyr::summarize(n=n())Source: local data frame [7 x 2]
GVINX n
(fctr) (int)
1 10 cl (1 wine glass) 330
2 20 cl 308
3 37 cl (half a bottle) 184
4 60 cl 14
5 75 cl (one whole bottle) 18
6 More than 1 whole bottle 2
7 NA 641SHARE
BR0100
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="BR0100") %>% dplyr::select(name,label) name label
1 BR0100 beverages consumed last 6 monthsdto[["unitData"]][["share"]]%>% histogram_discrete("BR0100")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("BR0100") %>% dplyr::summarize(n=n())Source: local data frame [8 x 2]
BR0100 n
(fctr) (int)
1 almost every day 76
2 five or six days a week 14
3 three or four days a week 43
4 once or twice a week 240
5 once or twice a month 146
6 less than once a month 220
7 not at all in the last 6 months 1855
8 NA 4BR0110
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="BR0110") %>% dplyr::select(name,label) name label
1 BR0110 freq more than 2 glasses beer in a daydto[["unitData"]][["share"]]%>% histogram_discrete("BR0110")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("BR0110") %>% dplyr::summarize(n=n())Source: local data frame [8 x 2]
BR0110 n
(fctr) (int)
1 almost every day 5
2 five or six days a week 4
3 three or four days a week 13
4 once or twice a week 57
5 once or twice a month 56
6 less than once a month 127
7 not at all in the last 6 months 477
8 NA 1859BR0120
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="BR0120") %>% dplyr::select(name,label) name label
1 BR0120 freq more than 2 glasses wine in a daydto[["unitData"]][["share"]]%>% histogram_discrete("BR0120")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("BR0120") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
BR0120 n
(fctr) (int)
1 almost every day 9
2 five or six days a week 1
3 three or four days a week 11
4 once or twice a week 77
5 once or twice a month 95
6 less than once a month 166
7 not at all in the last 6 months 379
8 don't know 1
9 NA 1859BR0130
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="BR0130") %>% dplyr::select(name,label) name label
1 BR0130 freq more than 2 hard liquor in a daydto[["unitData"]][["share"]]%>% histogram_discrete("BR0130")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("BR0130") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
BR0130 n
(fctr) (int)
1 almost every day 3
2 five or six days a week 3
3 three or four days a week 7
4 once or twice a week 21
5 once or twice a month 38
6 less than once a month 95
7 not at all in the last 6 months 571
8 don't know 1
9 NA 1859TILDA
DRINKSPERDAY
# requires categorization
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="BEHALC.DRINKSPERDAY") %>% dplyr::select(name,label) name label
1 BEHALC.DRINKSPERDAY BEHalc_drinksperday Standard drinks per daydto[["unitData"]][["tilda"]]%>% histogram_continuous("BEHALC.DRINKSPERDAY")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("BEHALC.DRINKSPERDAY") %>% dplyr::summarize(n=n())Source: local data frame [36 x 2]
BEHALC.DRINKSPERDAY n
(dbl) (int)
1 0.0 1835
2 0.5 8
3 0.7 1
4 1.0 631
5 1.5 139
6 2.0 1212
7 2.5 199
8 3.0 823
9 3.5 152
10 4.0 731
.. ... ...DRINKSPERWEEK
# requires categorization
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="BEHALC.DRINKSPERWEEK") %>% dplyr::select(name,label) name label
1 BEHALC.DRINKSPERWEEK BEHalc_drinksperweek Standard drinks a weekdto[["unitData"]][["tilda"]]%>% histogram_continuous("BEHALC.DRINKSPERWEEK")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("BEHALC.DRINKSPERWEEK") %>% dplyr::summarize(n=n())Source: local data frame [121 x 2]
BEHALC.DRINKSPERWEEK n
(dbl) (int)
1 0.000 1893
2 0.060 3
3 0.084 1
4 0.120 181
5 0.180 19
6 0.240 171
7 0.300 11
8 0.350 152
9 0.360 76
10 0.420 6
.. ... ...BEHALC.FREQ.WEEK
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="BEHALC.FREQ.WEEK") %>% dplyr::select(name,label) name label
1 BEHALC.FREQ.WEEK BEHalc_freq_week Average times drinking per weekdto[["unitData"]][["tilda"]]%>% histogram_discrete("BEHALC.FREQ.WEEK")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("BEHALC.FREQ.WEEK") %>% dplyr::summarize(n=n())Source: local data frame [8 x 2]
BEHALC.FREQ.WEEK n
(dbl) (int)
1 0.00 1935
2 0.12 667
3 0.35 865
4 1.50 2043
5 3.50 841
6 5.50 269
7 6.50 445
8 NA 1439SCQALCOFREQ
# requires labelling factor levels
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="SCQALCOFREQ") %>% dplyr::select(name,label) name label
1 SCQALCOFREQ SCQalcofreq frequency of drinking alcoholdto[["unitData"]][["tilda"]]%>%
dplyr::filter(!SCQALCOFREQ %in% c(-867,-856,-845,-823,-812)) %>% histogram_discrete("SCQALCOFREQ")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("SCQALCOFREQ") %>% dplyr::summarize(n=n())Source: local data frame [13 x 2]
SCQALCOFREQ n
(int) (int)
1 -856 2
2 -845 1
3 -823 2
4 -812 5
5 -1 1806
6 1 440
7 2 267
8 3 841
9 4 2042
10 5 863
11 6 667
12 7 129
13 NA 1439SCQALCOHOL
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="SCQALCOHOL") %>% dplyr::select(name,label) name label
1 SCQALCOHOL SCQalcohol drink alcoholdto[["unitData"]][["tilda"]]%>% histogram_discrete("SCQALCOHOL")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("SCQALCOHOL") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
SCQALCOHOL n
(fctr) (int)
1 yes 5349
2 no 1812
3 NA 1343SCQALCONO1
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="SCQALCONO1") %>% dplyr::select(name,label) name label
1 SCQALCONO1 SCQalcono1 more than two drinks in a single daydto[["unitData"]][["tilda"]]%>%
dplyr::filter(!SCQALCONO1 %in% c(-867,-856,-845,-823,-812)) %>% histogram_discrete("SCQALCONO1")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("SCQALCONO1") %>% dplyr::summarize(n=n())Source: local data frame [12 x 2]
SCQALCONO1 n
(int) (int)
1 -867 1
2 -856 2
3 -812 1
4 -1 1806
5 1 272
6 2 207
7 3 664
8 4 1810
9 5 905
10 6 857
11 7 552
12 NA 1427SCQALCONO2
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="SCQALCONO2") %>% dplyr::select(name,label) name label
1 SCQALCONO2 SCQalcono2 How many drinks consumed on days drink takendto[["unitData"]][["tilda"]]%>%
dplyr::filter(!SCQALCONO2 %in% c(-99, -1 )) %>% histogram_continuous("SCQALCONO2")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("SCQALCONO2") %>% dplyr::summarize(n=n())Source: local data frame [21 x 2]
SCQALCONO2 n
(dbl) (int)
1 -99.0 405
2 -1.0 1803
3 0.0 41
4 1.0 631
5 1.5 139
6 2.0 1212
7 2.5 199
8 3.0 823
9 3.5 152
10 4.0 731
.. ... ...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