plotrix: Interfaces for plotrix package for data science pipelines.
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to plotrix functions that can be used
in a pipeline implemented by magrittr.
Usage
1
2
3
4
5
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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## Not run:
library(intubate)
library(magrittr)
library(plotrix)
## ntbt_barNest: Display a nested breakdown of numeric values
titanic<-data.frame(
class=c(rep("1st",325),rep("2nd",285),rep("3rd",706),rep("Crew",885)),
age=c(rep("Adult",319),rep("Child",6),rep("Adult",261),rep("Child",24),
rep("Adult",627),rep("Child",79),rep("Adult",885)),
sex=c(rep("M",175),rep("F",144),rep("M",5),rep("F",1),
rep("M",168),rep("F",93),rep("M",11),rep("F",13),
rep("M",462),rep("F",165),rep("M",48),rep("F",31),
rep("M",862),rep("F",23)),
survived=c(rep("Yes",57),rep("No",118),rep("Yes",140),rep("No",4),rep("Yes",6),
rep("Yes",14),rep("No",154),rep("Yes",80),rep("No",13),rep("Yes",24),
rep("Yes",75),rep("No",387),rep("Yes",76),rep("No",89),
rep("Yes",13),rep("No",35),rep("Yes",14),rep("No",17),
rep("Yes",192),rep("No",670),rep("Yes",20),rep("No",3)))
titanic.colors<-list("gray90",c("#0000ff","#7700ee","#aa00cc","#dd00aa"),
c("#ddcc00","#ee9900"),c("pink","lightblue"))
## Original function to interface
barNest(survived ~ class + age + sex, titanic, col = titanic.colors,
showall = TRUE, main = "Titanic survival by class, age and sex",
ylab = "Proportion surviving", FUN = c("propbrk","binciWu","binciWl","valid.n"),
shrink = 0.15, trueval = "Yes")
## The interface puts data as first parameter
ntbt_barNest(titanic, survived ~ class + age + sex, col = titanic.colors,
showall = TRUE, main = "Titanic survival by class, age and sex",
ylab = "Proportion surviving", FUN = c("propbrk","binciWu","binciWl","valid.n"),
shrink = 0.15, trueval = "Yes")
## so it can be used easily in a pipeline.
titanic %>%
ntbt_barNest(survived ~ class + age + sex, col = titanic.colors,
showall = TRUE, main = "Titanic survival by class, age and sex",
ylab = "Proportion surviving", FUN = c("propbrk","binciWu","binciWl","valid.n"),
shrink = 0.15, trueval = "Yes")
## ntbt_brkdn.plot: A point/line plotting routine
test.df<-data.frame(a=rnorm(80)+4,b=rnorm(80)+4,c=rep(LETTERS[1:4],each=20),
d=rep(rep(letters[1:4],each=4),5))
## Original function to interface
brkdn.plot("a", "c", "d", test.df, pch = 1:4, col = 1:4)
## The interface puts data as first parameter
ntbt_brkdn.plot(test.df, "a", "c", "d", pch = 1:4, col = 1:4)
## so it can be used easily in a pipeline.
test.df %>%
ntbt_brkdn.plot("a", "c", "d", pch = 1:4, col = 1:4)
## ntbt_brkdnNest: Perform a nested breakdown of numeric values
brkdntest <- data.frame(Age=rnorm(100,25,10),
Sex=sample(c("M","F"),100,TRUE),
Marital=sample(c("M","X","S","W"),100,TRUE),
Employ=sample(c("FT","PT","NO"),100,TRUE))
## Original function to interface
brkdnNest(Age ~ Sex + Marital + Employ, data = brkdntest)
## The interface puts data as first parameter
ntbt_brkdnNest(brkdntest, Age ~ Sex + Marital + Employ)
## so it can be used easily in a pipeline.
brkdntest %>%
ntbt_brkdnNest(Age ~ Sex + Marital + Employ)
## ntbt_histStack: Histogram "stacked" by categories
set.seed(409)
df <- data.frame(len=rnorm(100)+5,
grp=sample(c("A","B","C","D"),100,replace=TRUE))
## Original function to interface
histStack(len ~ grp, data = df, main = "Default (rainbow) colors",
xlab = "Length category")
## The interface puts data as first parameter
ntbt_histStack(df, len ~ grp, main = "Default (rainbow) colors",
xlab = "Length category")
## so it can be used easily in a pipeline.
df %>%
ntbt_histStack(len ~ grp, main = "Default (rainbow) colors",
xlab = "Length category")
## ntbt_plotH: Scatterplot with histogram-like bars
d <- data.frame(x=c(1,5,10:20),y=runif(13)+1,
g=factor(sample(c("A","B","C"),13,replace=TRUE)))
## Original function to interface
plotH(y ~ x, data = d)
## The interface puts data as first parameter
ntbt_plotH(d, y ~ x)
## so it can be used easily in a pipeline.
d %>%
ntbt_plotH(y ~ x)
## End(Not run)
Example output
propbrk
Overall 0.323035
1st 0.6246154
Adult 0.6175549
F 0.9722222
M 0.3257143
Child 1
F 1
M 1
2nd 0.4140351
Adult 0.3601533
F 0.8602151
M 0.08333333
Child 1
F 1
M 1
3rd 0.2521246
Adult 0.2408293
F 0.4606061
M 0.1623377
Child 0.3417722
F 0.4516129
M 0.2708333
Crew 0.239548
Adult 0.239548
F 0.8695652
M 0.2227378
Child NA
F NA
M NA
binciWu
Overall 0.3428652
1st 0.6755158
Adult 0.6691867
F 0.9891459
M 0.3982436
Child 1
F 1
M 1
2nd 0.4719931
Adult 0.4200318
F 0.9164528
M 0.1350082
Child 1
F 1
M 1
3rd 0.2854383
Adult 0.2758115
F 0.5366919
M 0.1987244
Child 0.451509
F 0.6222783
M 0.4099696
Crew 0.268755
Adult 0.268755
F 0.9546234
M 0.2517099
Child NA
F NA
M NA
binciWl
Overall 0.3038214
1st 0.5708035
Adult 0.5631254
F 0.9307585
M 0.2606721
Child 0.6096657
F 0.2065493
M 0.5655175
2nd 0.3583637
Adult 0.3043316
F 0.7753997
M 0.05028734
Child 0.8620238
F 0.7719046
M 0.741167
3rd 0.2214939
Adult 0.2090036
F 0.3863128
M 0.1315198
Child 0.2467097
F 0.2916174
M 0.1656596
Crew 0.2125924
Adult 0.2125924
F 0.6787252
M 0.196226
Child NA
F NA
M NA
valid.n
Overall 2201
1st 325
Adult 319
F 144
M 175
Child 6
F 1
M 5
2nd 285
Adult 261
F 93
M 168
Child 24
F 13
M 11
3rd 706
Adult 627
F 165
M 462
Child 79
F 31
M 48
Crew 885
Adult 885
F 23
M 862
Child 0
F 0
M 0
Warning message:
In is.na(x) : is.na() applied to non-(list or vector) of type 'NULL'
propbrk
Overall 0.323035
1st 0.6246154
Adult 0.6175549
F 0.9722222
M 0.3257143
Child 1
F 1
M 1
2nd 0.4140351
Adult 0.3601533
F 0.8602151
M 0.08333333
Child 1
F 1
M 1
3rd 0.2521246
Adult 0.2408293
F 0.4606061
M 0.1623377
Child 0.3417722
F 0.4516129
M 0.2708333
Crew 0.239548
Adult 0.239548
F 0.8695652
M 0.2227378
Child NA
F NA
M NA
binciWu
Overall 0.3428652
1st 0.6755158
Adult 0.6691867
F 0.9891459
M 0.3982436
Child 1
F 1
M 1
2nd 0.4719931
Adult 0.4200318
F 0.9164528
M 0.1350082
Child 1
F 1
M 1
3rd 0.2854383
Adult 0.2758115
F 0.5366919
M 0.1987244
Child 0.451509
F 0.6222783
M 0.4099696
Crew 0.268755
Adult 0.268755
F 0.9546234
M 0.2517099
Child NA
F NA
M NA
binciWl
Overall 0.3038214
1st 0.5708035
Adult 0.5631254
F 0.9307585
M 0.2606721
Child 0.6096657
F 0.2065493
M 0.5655175
2nd 0.3583637
Adult 0.3043316
F 0.7753997
M 0.05028734
Child 0.8620238
F 0.7719046
M 0.741167
3rd 0.2214939
Adult 0.2090036
F 0.3863128
M 0.1315198
Child 0.2467097
F 0.2916174
M 0.1656596
Crew 0.2125924
Adult 0.2125924
F 0.6787252
M 0.196226
Child NA
F NA
M NA
valid.n
Overall 2201
1st 325
Adult 319
F 144
M 175
Child 6
F 1
M 5
2nd 285
Adult 261
F 93
M 168
Child 24
F 13
M 11
3rd 706
Adult 627
F 165
M 462
Child 79
F 31
M 48
Crew 885
Adult 885
F 23
M 862
Child 0
F 0
M 0
Warning message:
In is.na(x) : is.na() applied to non-(list or vector) of type 'NULL'
$mean
[,1] [,2] [,3] [,4]
[1,] 4.377175 3.754622 4.771959 4.767052
[2,] 3.351871 3.887376 3.194279 3.769258
[3,] 4.246710 3.562883 4.152460 4.811665
[4,] 4.641343 4.358878 4.803150 4.214318
$std.error
[,1] [,2] [,3] [,4]
[1,] 0.5404524 0.3269474 0.3473906 0.5115113
[2,] 0.3358804 0.2721583 0.4884998 0.4167576
[3,] 0.7895857 0.3206563 0.2764176 0.2977193
[4,] 0.2352755 0.4134742 0.5380608 0.3661268
$mean
[,1] [,2] [,3] [,4]
[1,] 4.377175 3.754622 4.771959 4.767052
[2,] 3.351871 3.887376 3.194279 3.769258
[3,] 4.246710 3.562883 4.152460 4.811665
[4,] 4.641343 4.358878 4.803150 4.214318
$std.error
[,1] [,2] [,3] [,4]
[1,] 0.5404524 0.3269474 0.3473906 0.5115113
[2,] 0.3358804 0.2721583 0.4884998 0.4167576
[3,] 0.7895857 0.3206563 0.2764176 0.2977193
[4,] 0.2352755 0.4134742 0.5380608 0.3661268
$mean
[,1] [,2] [,3] [,4]
[1,] 4.377175 3.754622 4.771959 4.767052
[2,] 3.351871 3.887376 3.194279 3.769258
[3,] 4.246710 3.562883 4.152460 4.811665
[4,] 4.641343 4.358878 4.803150 4.214318
$std.error
[,1] [,2] [,3] [,4]
[1,] 0.5404524 0.3269474 0.3473906 0.5115113
[2,] 0.3358804 0.2721583 0.4884998 0.4167576
[3,] 0.7895857 0.3206563 0.2764176 0.2977193
[4,] 0.2352755 0.4134742 0.5380608 0.3661268
mean
Overall 26.15243
F 26.10056
M 23.63269
FT 26.48875
NO 24.83714
PT 17.16326
S 29.05608
FT 29.46636
NO 35.49662
PT 22.43969
W 24.23464
FT 27.69297
NO 18.94979
PT 26.06115
X 27.01712
FT 27.92369
NO 24.22592
PT 29.83214
M 26.21333
M 25.62356
FT 23.24873
NO 28.92033
PT 23.51422
S 25.059
FT 20.46589
NO 29.47261
PT 16.50105
W 30.77633
FT 36.67576
NO 27.30087
PT 29.51085
X 24.36472
FT 18.05092
NO 33.41934
PT 23.43482
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
valid.n
Overall 100
F 54
M 15
FT 3
NO 9
PT 3
S 17
FT 3
NO 7
PT 7
W 12
FT 4
NO 4
PT 4
X 10
FT 3
NO 4
PT 3
M 46
M 10
FT 2
NO 4
PT 4
S 12
FT 3
NO 7
PT 2
W 10
FT 3
NO 4
PT 3
X 14
FT 5
NO 4
PT 5
mean
Overall 26.15243
F 26.10056
M 23.63269
FT 26.48875
NO 24.83714
PT 17.16326
S 29.05608
FT 29.46636
NO 35.49662
PT 22.43969
W 24.23464
FT 27.69297
NO 18.94979
PT 26.06115
X 27.01712
FT 27.92369
NO 24.22592
PT 29.83214
M 26.21333
M 25.62356
FT 23.24873
NO 28.92033
PT 23.51422
S 25.059
FT 20.46589
NO 29.47261
PT 16.50105
W 30.77633
FT 36.67576
NO 27.30087
PT 29.51085
X 24.36472
FT 18.05092
NO 33.41934
PT 23.43482
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
valid.n
Overall 100
F 54
M 15
FT 3
NO 9
PT 3
S 17
FT 3
NO 7
PT 7
W 12
FT 4
NO 4
PT 4
X 10
FT 3
NO 4
PT 3
M 46
M 10
FT 2
NO 4
PT 4
S 12
FT 3
NO 7
PT 2
W 10
FT 3
NO 4
PT 3
X 14
FT 5
NO 4
PT 5
mean
Overall 26.15243
F 26.10056
M 23.63269
FT 26.48875
NO 24.83714
PT 17.16326
S 29.05608
FT 29.46636
NO 35.49662
PT 22.43969
W 24.23464
FT 27.69297
NO 18.94979
PT 26.06115
X 27.01712
FT 27.92369
NO 24.22592
PT 29.83214
M 26.21333
M 25.62356
FT 23.24873
NO 28.92033
PT 23.51422
S 25.059
FT 20.46589
NO 29.47261
PT 16.50105
W 30.77633
FT 36.67576
NO 27.30087
PT 29.51085
X 24.36472
FT 18.05092
NO 33.41934
PT 23.43482
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
valid.n
Overall 100
F 54
M 15
FT 3
NO 9
PT 3
S 17
FT 3
NO 7
PT 7
W 12
FT 4
NO 4
PT 4
X 10
FT 3
NO 4
PT 3
M 46
M 10
FT 2
NO 4
PT 4
S 12
FT 3
NO 7
PT 2
W 10
FT 3
NO 4
PT 3
X 14
FT 5
NO 4
PT 5
intubate documentation built on May 2, 2019, 2:46 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to plotrix functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 3 4 5 |
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | ## Not run:
library(intubate)
library(magrittr)
library(plotrix)
## ntbt_barNest: Display a nested breakdown of numeric values
titanic<-data.frame(
class=c(rep("1st",325),rep("2nd",285),rep("3rd",706),rep("Crew",885)),
age=c(rep("Adult",319),rep("Child",6),rep("Adult",261),rep("Child",24),
rep("Adult",627),rep("Child",79),rep("Adult",885)),
sex=c(rep("M",175),rep("F",144),rep("M",5),rep("F",1),
rep("M",168),rep("F",93),rep("M",11),rep("F",13),
rep("M",462),rep("F",165),rep("M",48),rep("F",31),
rep("M",862),rep("F",23)),
survived=c(rep("Yes",57),rep("No",118),rep("Yes",140),rep("No",4),rep("Yes",6),
rep("Yes",14),rep("No",154),rep("Yes",80),rep("No",13),rep("Yes",24),
rep("Yes",75),rep("No",387),rep("Yes",76),rep("No",89),
rep("Yes",13),rep("No",35),rep("Yes",14),rep("No",17),
rep("Yes",192),rep("No",670),rep("Yes",20),rep("No",3)))
titanic.colors<-list("gray90",c("#0000ff","#7700ee","#aa00cc","#dd00aa"),
c("#ddcc00","#ee9900"),c("pink","lightblue"))
## Original function to interface
barNest(survived ~ class + age + sex, titanic, col = titanic.colors,
showall = TRUE, main = "Titanic survival by class, age and sex",
ylab = "Proportion surviving", FUN = c("propbrk","binciWu","binciWl","valid.n"),
shrink = 0.15, trueval = "Yes")
## The interface puts data as first parameter
ntbt_barNest(titanic, survived ~ class + age + sex, col = titanic.colors,
showall = TRUE, main = "Titanic survival by class, age and sex",
ylab = "Proportion surviving", FUN = c("propbrk","binciWu","binciWl","valid.n"),
shrink = 0.15, trueval = "Yes")
## so it can be used easily in a pipeline.
titanic %>%
ntbt_barNest(survived ~ class + age + sex, col = titanic.colors,
showall = TRUE, main = "Titanic survival by class, age and sex",
ylab = "Proportion surviving", FUN = c("propbrk","binciWu","binciWl","valid.n"),
shrink = 0.15, trueval = "Yes")
## ntbt_brkdn.plot: A point/line plotting routine
test.df<-data.frame(a=rnorm(80)+4,b=rnorm(80)+4,c=rep(LETTERS[1:4],each=20),
d=rep(rep(letters[1:4],each=4),5))
## Original function to interface
brkdn.plot("a", "c", "d", test.df, pch = 1:4, col = 1:4)
## The interface puts data as first parameter
ntbt_brkdn.plot(test.df, "a", "c", "d", pch = 1:4, col = 1:4)
## so it can be used easily in a pipeline.
test.df %>%
ntbt_brkdn.plot("a", "c", "d", pch = 1:4, col = 1:4)
## ntbt_brkdnNest: Perform a nested breakdown of numeric values
brkdntest <- data.frame(Age=rnorm(100,25,10),
Sex=sample(c("M","F"),100,TRUE),
Marital=sample(c("M","X","S","W"),100,TRUE),
Employ=sample(c("FT","PT","NO"),100,TRUE))
## Original function to interface
brkdnNest(Age ~ Sex + Marital + Employ, data = brkdntest)
## The interface puts data as first parameter
ntbt_brkdnNest(brkdntest, Age ~ Sex + Marital + Employ)
## so it can be used easily in a pipeline.
brkdntest %>%
ntbt_brkdnNest(Age ~ Sex + Marital + Employ)
## ntbt_histStack: Histogram "stacked" by categories
set.seed(409)
df <- data.frame(len=rnorm(100)+5,
grp=sample(c("A","B","C","D"),100,replace=TRUE))
## Original function to interface
histStack(len ~ grp, data = df, main = "Default (rainbow) colors",
xlab = "Length category")
## The interface puts data as first parameter
ntbt_histStack(df, len ~ grp, main = "Default (rainbow) colors",
xlab = "Length category")
## so it can be used easily in a pipeline.
df %>%
ntbt_histStack(len ~ grp, main = "Default (rainbow) colors",
xlab = "Length category")
## ntbt_plotH: Scatterplot with histogram-like bars
d <- data.frame(x=c(1,5,10:20),y=runif(13)+1,
g=factor(sample(c("A","B","C"),13,replace=TRUE)))
## Original function to interface
plotH(y ~ x, data = d)
## The interface puts data as first parameter
ntbt_plotH(d, y ~ x)
## so it can be used easily in a pipeline.
d %>%
ntbt_plotH(y ~ x)
## End(Not run)
|
Example output
propbrk
Overall 0.323035
1st 0.6246154
Adult 0.6175549
F 0.9722222
M 0.3257143
Child 1
F 1
M 1
2nd 0.4140351
Adult 0.3601533
F 0.8602151
M 0.08333333
Child 1
F 1
M 1
3rd 0.2521246
Adult 0.2408293
F 0.4606061
M 0.1623377
Child 0.3417722
F 0.4516129
M 0.2708333
Crew 0.239548
Adult 0.239548
F 0.8695652
M 0.2227378
Child NA
F NA
M NA
binciWu
Overall 0.3428652
1st 0.6755158
Adult 0.6691867
F 0.9891459
M 0.3982436
Child 1
F 1
M 1
2nd 0.4719931
Adult 0.4200318
F 0.9164528
M 0.1350082
Child 1
F 1
M 1
3rd 0.2854383
Adult 0.2758115
F 0.5366919
M 0.1987244
Child 0.451509
F 0.6222783
M 0.4099696
Crew 0.268755
Adult 0.268755
F 0.9546234
M 0.2517099
Child NA
F NA
M NA
binciWl
Overall 0.3038214
1st 0.5708035
Adult 0.5631254
F 0.9307585
M 0.2606721
Child 0.6096657
F 0.2065493
M 0.5655175
2nd 0.3583637
Adult 0.3043316
F 0.7753997
M 0.05028734
Child 0.8620238
F 0.7719046
M 0.741167
3rd 0.2214939
Adult 0.2090036
F 0.3863128
M 0.1315198
Child 0.2467097
F 0.2916174
M 0.1656596
Crew 0.2125924
Adult 0.2125924
F 0.6787252
M 0.196226
Child NA
F NA
M NA
valid.n
Overall 2201
1st 325
Adult 319
F 144
M 175
Child 6
F 1
M 5
2nd 285
Adult 261
F 93
M 168
Child 24
F 13
M 11
3rd 706
Adult 627
F 165
M 462
Child 79
F 31
M 48
Crew 885
Adult 885
F 23
M 862
Child 0
F 0
M 0
Warning message:
In is.na(x) : is.na() applied to non-(list or vector) of type 'NULL'
propbrk
Overall 0.323035
1st 0.6246154
Adult 0.6175549
F 0.9722222
M 0.3257143
Child 1
F 1
M 1
2nd 0.4140351
Adult 0.3601533
F 0.8602151
M 0.08333333
Child 1
F 1
M 1
3rd 0.2521246
Adult 0.2408293
F 0.4606061
M 0.1623377
Child 0.3417722
F 0.4516129
M 0.2708333
Crew 0.239548
Adult 0.239548
F 0.8695652
M 0.2227378
Child NA
F NA
M NA
binciWu
Overall 0.3428652
1st 0.6755158
Adult 0.6691867
F 0.9891459
M 0.3982436
Child 1
F 1
M 1
2nd 0.4719931
Adult 0.4200318
F 0.9164528
M 0.1350082
Child 1
F 1
M 1
3rd 0.2854383
Adult 0.2758115
F 0.5366919
M 0.1987244
Child 0.451509
F 0.6222783
M 0.4099696
Crew 0.268755
Adult 0.268755
F 0.9546234
M 0.2517099
Child NA
F NA
M NA
binciWl
Overall 0.3038214
1st 0.5708035
Adult 0.5631254
F 0.9307585
M 0.2606721
Child 0.6096657
F 0.2065493
M 0.5655175
2nd 0.3583637
Adult 0.3043316
F 0.7753997
M 0.05028734
Child 0.8620238
F 0.7719046
M 0.741167
3rd 0.2214939
Adult 0.2090036
F 0.3863128
M 0.1315198
Child 0.2467097
F 0.2916174
M 0.1656596
Crew 0.2125924
Adult 0.2125924
F 0.6787252
M 0.196226
Child NA
F NA
M NA
valid.n
Overall 2201
1st 325
Adult 319
F 144
M 175
Child 6
F 1
M 5
2nd 285
Adult 261
F 93
M 168
Child 24
F 13
M 11
3rd 706
Adult 627
F 165
M 462
Child 79
F 31
M 48
Crew 885
Adult 885
F 23
M 862
Child 0
F 0
M 0
Warning message:
In is.na(x) : is.na() applied to non-(list or vector) of type 'NULL'
$mean
[,1] [,2] [,3] [,4]
[1,] 4.377175 3.754622 4.771959 4.767052
[2,] 3.351871 3.887376 3.194279 3.769258
[3,] 4.246710 3.562883 4.152460 4.811665
[4,] 4.641343 4.358878 4.803150 4.214318
$std.error
[,1] [,2] [,3] [,4]
[1,] 0.5404524 0.3269474 0.3473906 0.5115113
[2,] 0.3358804 0.2721583 0.4884998 0.4167576
[3,] 0.7895857 0.3206563 0.2764176 0.2977193
[4,] 0.2352755 0.4134742 0.5380608 0.3661268
$mean
[,1] [,2] [,3] [,4]
[1,] 4.377175 3.754622 4.771959 4.767052
[2,] 3.351871 3.887376 3.194279 3.769258
[3,] 4.246710 3.562883 4.152460 4.811665
[4,] 4.641343 4.358878 4.803150 4.214318
$std.error
[,1] [,2] [,3] [,4]
[1,] 0.5404524 0.3269474 0.3473906 0.5115113
[2,] 0.3358804 0.2721583 0.4884998 0.4167576
[3,] 0.7895857 0.3206563 0.2764176 0.2977193
[4,] 0.2352755 0.4134742 0.5380608 0.3661268
$mean
[,1] [,2] [,3] [,4]
[1,] 4.377175 3.754622 4.771959 4.767052
[2,] 3.351871 3.887376 3.194279 3.769258
[3,] 4.246710 3.562883 4.152460 4.811665
[4,] 4.641343 4.358878 4.803150 4.214318
$std.error
[,1] [,2] [,3] [,4]
[1,] 0.5404524 0.3269474 0.3473906 0.5115113
[2,] 0.3358804 0.2721583 0.4884998 0.4167576
[3,] 0.7895857 0.3206563 0.2764176 0.2977193
[4,] 0.2352755 0.4134742 0.5380608 0.3661268
mean
Overall 26.15243
F 26.10056
M 23.63269
FT 26.48875
NO 24.83714
PT 17.16326
S 29.05608
FT 29.46636
NO 35.49662
PT 22.43969
W 24.23464
FT 27.69297
NO 18.94979
PT 26.06115
X 27.01712
FT 27.92369
NO 24.22592
PT 29.83214
M 26.21333
M 25.62356
FT 23.24873
NO 28.92033
PT 23.51422
S 25.059
FT 20.46589
NO 29.47261
PT 16.50105
W 30.77633
FT 36.67576
NO 27.30087
PT 29.51085
X 24.36472
FT 18.05092
NO 33.41934
PT 23.43482
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
valid.n
Overall 100
F 54
M 15
FT 3
NO 9
PT 3
S 17
FT 3
NO 7
PT 7
W 12
FT 4
NO 4
PT 4
X 10
FT 3
NO 4
PT 3
M 46
M 10
FT 2
NO 4
PT 4
S 12
FT 3
NO 7
PT 2
W 10
FT 3
NO 4
PT 3
X 14
FT 5
NO 4
PT 5
mean
Overall 26.15243
F 26.10056
M 23.63269
FT 26.48875
NO 24.83714
PT 17.16326
S 29.05608
FT 29.46636
NO 35.49662
PT 22.43969
W 24.23464
FT 27.69297
NO 18.94979
PT 26.06115
X 27.01712
FT 27.92369
NO 24.22592
PT 29.83214
M 26.21333
M 25.62356
FT 23.24873
NO 28.92033
PT 23.51422
S 25.059
FT 20.46589
NO 29.47261
PT 16.50105
W 30.77633
FT 36.67576
NO 27.30087
PT 29.51085
X 24.36472
FT 18.05092
NO 33.41934
PT 23.43482
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
valid.n
Overall 100
F 54
M 15
FT 3
NO 9
PT 3
S 17
FT 3
NO 7
PT 7
W 12
FT 4
NO 4
PT 4
X 10
FT 3
NO 4
PT 3
M 46
M 10
FT 2
NO 4
PT 4
S 12
FT 3
NO 7
PT 2
W 10
FT 3
NO 4
PT 3
X 14
FT 5
NO 4
PT 5
mean
Overall 26.15243
F 26.10056
M 23.63269
FT 26.48875
NO 24.83714
PT 17.16326
S 29.05608
FT 29.46636
NO 35.49662
PT 22.43969
W 24.23464
FT 27.69297
NO 18.94979
PT 26.06115
X 27.01712
FT 27.92369
NO 24.22592
PT 29.83214
M 26.21333
M 25.62356
FT 23.24873
NO 28.92033
PT 23.51422
S 25.059
FT 20.46589
NO 29.47261
PT 16.50105
W 30.77633
FT 36.67576
NO 27.30087
PT 29.51085
X 24.36472
FT 18.05092
NO 33.41934
PT 23.43482
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
sd
Overall 9.804057
F 10.05387
M 11.15727
FT 11.46795
NO 11.84025
PT 9.877733
S 12.07827
FT 4.999623
NO 12.50273
PT 11.17248
W 7.776787
FT 3.493515
NO 8.97697
PT 8.445657
X 6.009485
FT 5.581986
NO 5.916309
PT 7.081605
M 9.612789
M 7.439582
FT 0.5665087
NO 10.75509
PT 5.106798
S 11.45859
FT 15.15037
NO 9.836001
PT 7.65617
W 8.527774
FT 8.773616
NO 9.52192
PT 5.95917
X 9.945279
FT 8.543445
NO 10.07242
PT 6.282922
valid.n
Overall 100
F 54
M 15
FT 3
NO 9
PT 3
S 17
FT 3
NO 7
PT 7
W 12
FT 4
NO 4
PT 4
X 10
FT 3
NO 4
PT 3
M 46
M 10
FT 2
NO 4
PT 4
S 12
FT 3
NO 7
PT 2
W 10
FT 3
NO 4
PT 3
X 14
FT 5
NO 4
PT 5
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