Description Usage Arguments Details Value Author(s) Examples
Interfaces to plotrix
functions that can be used
in a pipeline implemented by magrittr
.
1 2 3 4 5 |
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Interfaces call their corresponding interfaced function.
Object returned by interfaced function.
Roberto Bertolusso
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)
|
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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