Introduction

Table Show

Package "ztable" make everything possible about table. Basically, An object of "ztable" made from a data.frame. The default output format of ztable is RStudio::viewer or web-browser format(type="viewer"). So if you want to use ztable in a "html" format, you should change the parameter ztable.type to "html". If you want to use ztable in latex format, you should change the parameter ztable.type to "latex".

require(ztable)
options(ztable.type="html")
z=ztable(head(iris))
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

You can change the position of data in each cell by adjusting the parameter "align".

z=ztable(head(iris),align="cccccc")
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

You can add column groups to ztable using addcgroup function. The n.cgroup means how much columns included in each row group.

cgroup=c("Sepal","Petal","Species")
n.cgroup=c(2,2,1)
z=addcgroup(z,cgroup=cgroup,n.cgroup=n.cgroup)
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

You can add row groups to ztable using addrgroup function. The n.rgroup means how much rows included in each row group. The cspan.rgroup means how much columns occupied by row group name.

rgroup=c("OneToThree","Four","FiveToSix")
n.rgroup=c(3,1,2)

z=addrgroup(z,rgroup=rgroup,n.rgroup=n.rgroup,cspan.rgroup=1)
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
OneToThree
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
Four
4	4.60	3.10	1.50	0.20	setosa
FiveToSix
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

print(z,type="latex")

You can add another colname(subcolname), the N count for example. The length of subcolnames should be same with column count of data.frame. You can use "NA" and the column name spans 2 rows.

ncount=c(123,120,123,124)
sub=paste("(N=",ncount,")",sep="")
z=addSubColNames(z,c(sub,NA))
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
	(N=123)	(N=120)	(N=123)	(N=124)	Species
OneToThree
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
Four
4	4.60	3.10	1.50	0.20	setosa
FiveToSix
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

You can merge cells by spanRow or spanCol function.

z=spanRow(z,col=2,from=4,to=7,"orange")
z=spanRow(z,col=3,from=5,to=7,"platinum")
z=spanRow(z,col=4,from=6,to=7,"cyan")
z=spanRow(z,col=5,from=5,to=7,"yellow")
z=spanRow(z,col=6,from=3,to=5,"yellow")
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
	(N=123)	(N=120)	(N=123)	(N=124)	Species
OneToThree
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20
Four
4		3.10	1.50	0.20
FiveToSix
5			1.40		setosa
6			1.40		setosa

z=spanCol(z,row=2,from=3,to=4,"yellow")
z=spanCol(z,row=3,from=4,to=5,"lightblue")
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
	(N=123)	(N=120)	(N=123)	(N=124)	Species
OneToThree
1	5.10	3.50		0.20	setosa
2	4.90	3.00	1.40		setosa
3	4.70	3.20	1.30	0.20
Four
4		3.10	1.50	0.20
FiveToSix
5			1.40		setosa
6			1.40		setosa

You can add or adjust vertical lines of table by vlines function

vlines(z,type="all")       # type=1 gets same result


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
	(N=123)	(N=120)	(N=123)	(N=124)	Species
OneToThree
1	5.10	3.50		0.20	setosa
2	4.90	3.00	1.40		setosa
3	4.70	3.20	1.30	0.20
Four
4		3.10	1.50	0.20
FiveToSix
5			1.40		setosa
6			1.40		setosa

z=vlines(z,type="none")      # type=0 gets same result
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
	(N=123)	(N=120)	(N=123)	(N=124)	Species
OneToThree
1	5.10	3.50		0.20	setosa
2	4.90	3.00	1.40		setosa
3	4.70	3.20	1.30	0.20
Four
4		3.10	1.50	0.20
FiveToSix
5			1.40		setosa
6			1.40		setosa

z=vlines(z,add=c(1,2,5))
z


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	Sepal		Petal		Species
	(N=123)	(N=120)	(N=123)	(N=124)	Species
OneToThree
1	5.10	3.50		0.20	setosa
2	4.90	3.00	1.40		setosa
3	4.70	3.20	1.30	0.20
Four
4		3.10	1.50	0.20
FiveToSix
5			1.40		setosa
6			1.40		setosa

Please note that if you add vertical lines between groups, the space between groups(empty columns) disappeared and vice versa.

Merge two tables

You can bind two or more data.frame by cbind function.

t1=head(iris,10)[,c(1,3,5)]
t2=tail(iris,10)[,c(1,3,5)]
t=cbind(t1,t2)
z=ztable(t,caption="Table 1. Top 10 and Last 10 Data from iris",align="ccccccc")
z

Table 1. Top 10 and Last 10 Data from iris
	Sepal.Length	Petal.Length	Species	Sepal.Length	Petal.Length	Species
1	5.10	1.40	setosa	6.70	5.60	virginica
2	4.90	1.40	setosa	6.90	5.10	virginica
3	4.70	1.30	setosa	5.80	5.10	virginica
4	4.60	1.50	setosa	6.80	5.90	virginica
5	5.00	1.40	setosa	6.70	5.70	virginica
6	5.40	1.70	setosa	6.70	5.20	virginica
7	4.60	1.40	setosa	6.30	5.00	virginica
8	5.00	1.50	setosa	6.50	5.20	virginica
9	4.40	1.40	setosa	6.20	5.40	virginica
10	4.90	1.50	setosa	5.90	5.10	virginica

And then, you can add column groups, row groups, add row colors, add column colors, add cell colors, and merge cells

cgroup=c("Top 10","Last 10")
n.cgroup=c(3,3)
z=addcgroup(z,cgroup=cgroup,n.cgroup=n.cgroup)
z

Table 1. Top 10 and Last 10 Data from iris
	Sepal.Length	Petal.Length	Species	Sepal.Length	Petal.Length	Species
	Top 10			Last 10
1	5.10	1.40	setosa	6.70	5.60	virginica
2	4.90	1.40	setosa	6.90	5.10	virginica
3	4.70	1.30	setosa	5.80	5.10	virginica
4	4.60	1.50	setosa	6.80	5.90	virginica
5	5.00	1.40	setosa	6.70	5.70	virginica
6	5.40	1.70	setosa	6.70	5.20	virginica
7	4.60	1.40	setosa	6.30	5.00	virginica
8	5.00	1.50	setosa	6.50	5.20	virginica
9	4.40	1.40	setosa	6.20	5.40	virginica
10	4.90	1.50	setosa	5.90	5.10	virginica

rgroup=c("Top 1-3","Top 4-6"," Top 7-10")
n.rgroup=c(3,3,4)
z=addrgroup(z,rgroup=rgroup,n.rgroup=n.rgroup,cspan.rgroup=1)
z

Table 1. Top 10 and Last 10 Data from iris
	Sepal.Length	Petal.Length	Species	Sepal.Length	Petal.Length	Species
	Top 10			Last 10
Top 1-3
1	5.10	1.40	setosa	6.70	5.60	virginica
2	4.90	1.40	setosa	6.90	5.10	virginica
3	4.70	1.30	setosa	5.80	5.10	virginica
Top 4-6
4	4.60	1.50	setosa	6.80	5.90	virginica
5	5.00	1.40	setosa	6.70	5.70	virginica
6	5.40	1.70	setosa	6.70	5.20	virginica
Top 7-10
7	4.60	1.40	setosa	6.30	5.00	virginica
8	5.00	1.50	setosa	6.50	5.20	virginica
9	4.40	1.40	setosa	6.20	5.40	virginica
10	4.90	1.50	setosa	5.90	5.10	virginica

z=addRowColor(z,c(5,10),"pink")
z=addColColor(z,4,"amber")
z=addCellColor(z,rows=c(5,10),cols=4,"orange")
z

Table 1. Top 10 and Last 10 Data from iris
	Sepal.Length	Petal.Length	Species	Sepal.Length	Petal.Length	Species
	Top 10			Last 10
Top 1-3
1	5.10	1.40	setosa	6.70	5.60	virginica
2	4.90	1.40	setosa	6.90	5.10	virginica
3	4.70	1.30	setosa	5.80	5.10	virginica
Top 4-6
4	4.60	1.50	setosa	6.80	5.90	virginica
5	5.00	1.40	setosa	6.70	5.70	virginica
6	5.40	1.70	setosa	6.70	5.20	virginica
Top 7-10
7	4.60	1.40	setosa	6.30	5.00	virginica
8	5.00	1.50	setosa	6.50	5.20	virginica
9	4.40	1.40	setosa	6.20	5.40	virginica
10	4.90	1.50	setosa	5.90	5.10	virginica

z=spanCol(z,row=2,from=2,to=3,color="lightcyan")
z=spanRow(z,col=7,from=7,to=8,color="cyan")
z

Table 1. Top 10 and Last 10 Data from iris
	Sepal.Length	Petal.Length	Species	Sepal.Length	Petal.Length	Species
	Top 10			Last 10
Top 1-3
1	5.10		setosa	6.70	5.60	virginica
2	4.90	1.40	setosa	6.90	5.10	virginica
3	4.70	1.30	setosa	5.80	5.10	virginica
Top 4-6
4	4.60	1.50	setosa	6.80	5.90	virginica
5	5.00	1.40	setosa	6.70	5.70	virginica
6	5.40	1.70	setosa	6.70	5.20	virginica
Top 7-10
7	4.60	1.40	setosa	6.30	5.00
8	5.00	1.50	setosa	6.50	5.20	virginica
9	4.40	1.40	setosa	6.20	5.40	virginica
10	4.90	1.50	setosa	5.90	5.10	virginica

And you can adjust vertical lines, too.

vlines(z,type=0)  # No vertical lines

Table 1. Top 10 and Last 10 Data from iris
	Sepal.Length	Petal.Length	Species	Sepal.Length	Petal.Length	Species
	Top 10			Last 10
Top 1-3
1	5.10		setosa	6.70	5.60	virginica
2	4.90	1.40	setosa	6.90	5.10	virginica
3	4.70	1.30	setosa	5.80	5.10	virginica
Top 4-6
4	4.60	1.50	setosa	6.80	5.90	virginica
5	5.00	1.40	setosa	6.70	5.70	virginica
6	5.40	1.70	setosa	6.70	5.20	virginica
Top 7-10
7	4.60	1.40	setosa	6.30	5.00
8	5.00	1.50	setosa	6.50	5.20	virginica
9	4.40	1.40	setosa	6.20	5.40	virginica
10	4.90	1.50	setosa	5.90	5.10	virginica

vlines(z,type=1)  # Vertical lines for all column

Table 1. Top 10 and Last 10 Data from iris
	Sepal.Length	Petal.Length	Species	Sepal.Length	Petal.Length	Species
	Top 10			Last 10
Top 1-3
1	5.10		setosa	6.70	5.60	virginica
2	4.90	1.40	setosa	6.90	5.10	virginica
3	4.70	1.30	setosa	5.80	5.10	virginica
Top 4-6
4	4.60	1.50	setosa	6.80	5.90	virginica
5	5.00	1.40	setosa	6.70	5.70	virginica
6	5.40	1.70	setosa	6.70	5.20	virginica
Top 7-10
7	4.60	1.40	setosa	6.30	5.00
8	5.00	1.50	setosa	6.50	5.20	virginica
9	4.40	1.40	setosa	6.20	5.40	virginica
10	4.90	1.50	setosa	5.90	5.10	virginica

Basic Use

Package "ztable" consist of one function: ztable. It's main function is creating zebra zebra striping tables(tables with alternating row colors) in both Latex and html formats easily from mainly data.frame or an R object such as matrix, lm, aov, anova, glm and coxph objects. It is fully customizable and you can get similar tables in both latex and html format without changing source. The default output is Rstudio::viewer, but you can get html format by adding just one sentence.

options(ztable.type="html")

It's usage is somewhat similar to xtable, but very simple.

data.frame

Basic Use

It's use is very simple. Just use 'ztable()' function. You can get the zebra stripig table by set the parameter zebra=1 (default value is NULL)

require(ztable)
options(ztable.type="html")
options(ztable.zebra=1)
options(ztable.zebra.color="platinum")
options(ztable.colnames.bold=TRUE)
ztable(head(mtcars))


	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.00	6.00	160.00	110.00	3.90	2.62	16.46	0.00	1.00	4.00	4.00
Mazda RX4 Wag	21.00	6.00	160.00	110.00	3.90	2.88	17.02	0.00	1.00	4.00	4.00
Datsun 710	22.80	4.00	108.00	93.00	3.85	2.32	18.61	1.00	1.00	4.00	1.00
Hornet 4 Drive	21.40	6.00	258.00	110.00	3.08	3.21	19.44	1.00	0.00	3.00	1.00
Hornet Sportabout	18.70	8.00	360.00	175.00	3.15	3.44	17.02	0.00	0.00	3.00	2.00
Valiant	18.10	6.00	225.00	105.00	2.76	3.46	20.22	1.00	0.00	3.00	1.00

Tailoring zebra striping

You can get non-zebra table by change parameter zebra=NULL or change zebra striping on even rows by zebra=2.

ztable(head(mtcars),zebra=NULL,size=3,
       caption="Table 1. Non-zebra Table with small size")

Table 1. Non-zebra Table with small size
	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.00	6.00	160.00	110.00	3.90	2.62	16.46	0.00	1.00	4.00	4.00
Mazda RX4 Wag	21.00	6.00	160.00	110.00	3.90	2.88	17.02	0.00	1.00	4.00	4.00
Datsun 710	22.80	4.00	108.00	93.00	3.85	2.32	18.61	1.00	1.00	4.00	1.00
Hornet 4 Drive	21.40	6.00	258.00	110.00	3.08	3.21	19.44	1.00	0.00	3.00	1.00
Hornet Sportabout	18.70	8.00	360.00	175.00	3.15	3.44	17.02	0.00	0.00	3.00	2.00
Valiant	18.10	6.00	225.00	105.00	2.76	3.46	20.22	1.00	0.00	3.00	1.00

Customize the caption and the font size

You can change the position of table by using parameter position. You can use "r" for right position, "l" for left position and "c" for center position(default). You can change the color of zebra striping by change the parameter zebra.color. You can also change the size of font from 1 to 10(default is 5). You can change the caption.placement("top" or "bottom") and caption.position("c" for center / "r" for right/ "l" for left).

ztable(head(mtcars[c(1:7)]),zebra=2,zebra.color="lightcyan",size=7,
       caption="Table 2. Left-sided caption at botom with large font",
       caption.placement="bottom",caption.position="l")

Table 2. Left-sided caption at botom with large font
	mpg	cyl	disp	hp	drat	wt	qsec
Mazda RX4	21.00	6.00	160.00	110.00	3.90	2.62	16.46
Mazda RX4 Wag	21.00	6.00	160.00	110.00	3.90	2.88	17.02
Datsun 710	22.80	4.00	108.00	93.00	3.85	2.32	18.61
Hornet 4 Drive	21.40	6.00	258.00	110.00	3.08	3.21	19.44
Hornet Sportabout	18.70	8.00	360.00	175.00	3.15	3.44	17.02
Valiant	18.10	6.00	225.00	105.00	2.76	3.46	20.22

aov object

'ztable()' can be used for 'aov' object. When used for 'aov' object, the function call is added as footer to the table. The parameter 'show.footer' can be used whether or not include footer in the table. Dafault value is TRUE.

out <- aov(mpg ~ ., data=mtcars)
ztable(out)


	Df	Sum Sq	Mean Sq	F value	Pr(>F)
cyl	1	817.71	817.71	116.42	0.0000
disp	1	37.59	37.59	5.35	0.0309
hp	1	9.37	9.37	1.33	0.2610
drat	1	16.47	16.47	2.34	0.1406
wt	1	77.48	77.48	11.03	0.0032
qsec	1	3.95	3.95	0.56	0.4617
vs	1	0.13	0.13	0.02	0.8932
am	1	14.47	14.47	2.06	0.1659
gear	1	0.97	0.97	0.14	0.7137
carb	1	0.41	0.41	0.06	0.8122
Residuals	21	147.49	7.02
Call: aov(formula = mpg ~ ., data = mtcars)

Linear model : 'lm' object

'ztable()' can be used for 'lm' object. When used for 'lm' object, the function call is added as footer to the table, too.

fit <- lm(mpg ~ cyl + disp + wt + drat + am, data=mtcars)
ztable(fit)


	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	41.2964	7.5384	5.48	0.0000
cyl	-1.7940	0.6505	-2.76	0.0105
disp	0.0074	0.0123	0.60	0.5546
wt	-3.5870	1.2105	-2.96	0.0064
drat	-0.0936	1.5488	-0.06	0.9523
am	0.1730	1.5300	0.11	0.9109
Call: lm(formula = mpg ~ cyl + disp + wt + drat + am, data = mtcars)

Analysis of Variance Table : 'anova' object

'ztable()' can be used for 'anova' object to show the anova table. When used for 'anova' object, headings of anova are added as headings to the table. The parameter 'show.footer' can be used whether or not include footer in the table. Dafault value is TRUE.

a=anova(fit)
ztable(a)


	Df	Sum Sq	Mean Sq	F value	Pr(>F)
Analysis of Variance Table
Response: mpg
cyl	1	817.71	817.71	112.85	0.0000
disp	1	37.59	37.59	5.19	0.0312
wt	1	82.25	82.25	11.35	0.0024
drat	1	0.00	0.00	0.00	0.9939
am	1	0.09	0.09	0.01	0.9109
Residuals	26	188.40	7.25

This is examples of another 'anova' object. The models in this anova tables showed as table headings. You can decide whether or not include the headings in the tableby using parameter 'show.heading'(default: TRUE).

fit2 <- lm(mpg ~ cyl+wt, data=mtcars)
b=anova(fit2,fit)
ztable(b)


	Res.Df	RSS	Df	Sum of Sq	F	Pr(>F)
Analysis of Variance Table
Model 1: mpg ~ cyl + wt
Model 2: mpg ~ cyl + disp + wt + drat + am
1	29.0	191.17
2	26.0	188.40	3.0	2.77	0.13	0.9429

ztable(b,show.heading=FALSE)


	Res.Df	RSS	Df	Sum of Sq	F	Pr(>F)
1	29.0	191.17
2	26.0	188.40	3.0	2.77	0.13	0.9429

Generalized linear model ; 'glm' object

'ztable()' can be used for 'glm'(generalized linear model) object. In this time, 'ztable()' shows the odds ratio(OR) and 95% confidence interval as well as atandard R output.

require(survival)

## Loading required package: survival
## Loading required package: splines

data(colon)
attach(colon)
out <- glm(status ~ rx+obstruct+adhere+nodes+extent, data=colon, family=binomial)
ztable(out)


	Estimate	Std. Error	z value	Pr(>\|z\|)	OR	lcl	ucl
(Intercept)	-2.3642	0.3426	-6.90	0.0000	0.09	0.05	0.18
rxLev	-0.0712	0.1203	-0.59	0.5538	0.93	0.74	1.18
rxLev+5FU	-0.6135	0.1231	-4.98	0.0000	0.54	0.42	0.69
obstruct	0.2320	0.1251	1.85	0.0636	1.26	0.99	1.61
adhere	0.4164	0.1429	2.91	0.0036	1.52	1.15	2.01
nodes	0.1845	0.0183	10.06	0.0000	1.20	1.16	1.25
extent	0.6238	0.1142	5.46	0.0000	1.87	1.50	2.34
Call: glm(formula = status ~ rx + obstruct + adhere + nodes + extent, family = binomial, data = colon)

Again, 'ztable()' also shows the anova table of this model.

ztable(anova(out))


	Df	Deviance	Resid. Df	Resid. Dev
Analysis of Deviance Table
Model: binomial, link: logit
Response: status
Terms added sequentially (first to last)
NULL			1821	2525.40
rx	2	34.84	1819	2490.56
obstruct	1	3.66	1818	2486.90
adhere	1	11.74	1817	2475.16
nodes	1	145.01	1816	2330.15
extent	1	32.59	1815	2297.55

More 'aov' object

op <- options(contrasts = c("contr.helmert", "contr.poly"))
npk.aov <- aov(yield ~ block + N*P*K, npk) 
ztable(npk.aov,zebra=1)


	Df	Sum Sq	Mean Sq	F value	Pr(>F)
block	5	343.29	68.66	4.45	0.0159
N	1	189.28	189.28	12.26	0.0044
P	1	8.40	8.40	0.54	0.4749
K	1	95.20	95.20	6.17	0.0288
N:P	1	21.28	21.28	1.38	0.2632
N:K	1	33.14	33.14	2.15	0.1686
P:K	1	0.48	0.48	0.03	0.8628
Residuals	12	185.29	15.44
Call: aov(formula = yield ~ block + N * P * K, data = npk)

More 'lm' object

ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
ztable(lm.D9)


	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	4.8465	0.1557	31.12	0.0000
group1	-0.1855	0.1557	-1.19	0.2490
Call: lm(formula = weight ~ group)

ztable(anova(lm.D9),align="|c|rrrr|r|")


	Df	Sum Sq	Mean Sq	F value	Pr(>F)
Analysis of Variance Table
Response: weight
group	1	0.69	0.69	1.42	0.2490
Residuals	18	8.73	0.48

More 'glm' object

counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
d.AD <- data.frame(treatment, outcome, counts)
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
ztable(glm.D93)


	Estimate	Std. Error	z value	Pr(>\|z\|)	OR	lcl	ucl
(Intercept)	2.7954	0.0831	33.64	0.0000	16.37	13.84	19.18
outcome1	-0.2271	0.1011	-2.25	0.0246	0.80	0.65	0.97
outcome2	-0.0220	0.0592	-0.37	0.7106	0.98	0.87	1.10
treatment1	-0.0000	0.1000	-0.00	1.0000	1.00	0.82	1.22
treatment2	-0.0000	0.0577	-0.00	1.0000	1.00	0.89	1.12
Call: glm(formula = counts ~ outcome + treatment, family = poisson())

Principal Components Analysis : 'prcomp' object

'ztable()' can be used in principal components analysis. Followings are examples of ztable() of 'prcomp' object.

data(USArrests)
pr1 <- prcomp(USArrests) 
ztable(pr1)


	PC1	PC2	PC3	PC4
Rotation:
Murder	0.0417	-0.0448	0.0799	-0.9949
Assault	0.9952	-0.0588	-0.0676	0.0389
UrbanPop	0.0463	0.9769	-0.2005	-0.0582
Rape	0.0752	0.2007	0.9741	0.0723

ztable(summary(pr1))


	PC1	PC2	PC3	PC4
Importance of components:
Standard deviation	83.7324	14.2124	6.4894	2.4828
Proportion of Variance	0.9655	0.0278	0.0058	0.0008
Cumulative Proportion	0.9655	0.9933	0.9991	1.0000

Survival Analysis : 'coxph' object

'ztable()' can be used in survival analysis. When used for Cox proportional hazard model, 'ztable()' showed the hazard ratio and 95% confidence interval ready for publication to medical journal.

colon$TS = Surv(time,status==1) 
out=coxph(TS~rx+obstruct+adhere+differ+extent+surg+node4,data=colon)
ztable(out)


	HR	lcl	ucl	se(coef)	z	Pr(>\|z\|)
rx1	0.999	0.925	1.079	0.039	-0.030	0.9764
rx2	0.871	0.829	0.915	0.025	-5.464	0.0000
obstruct	1.267	1.079	1.489	0.082	2.885	0.0039
adhere	1.181	0.991	1.409	0.090	1.856	0.0634
differ	1.219	1.067	1.394	0.068	2.906	0.0037
extent	1.523	1.298	1.787	0.082	5.152	0.0000
surg	1.274	1.104	1.469	0.073	3.319	0.0009
node4	2.359	2.059	2.702	0.069	12.383	0.0000
Call: coxph(formula = TS ~ rx + obstruct + adhere + differ + extent + surg + node4, data = colon)

Nonlinear Least Squares: 'nls' obbject

'ztable()' can be used to determine the nonlinear (weighted) least-squares estimates of the parameters of a nonlinear model. Followings are examples of ztable() of 'nls' object.

require(graphics)

DNase1 <- subset(DNase, Run == 1)

## using a selfStart model
fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal),DNase1)
summary(fm1DNase1)


Formula: density ~ SSlogis(log(conc), Asym, xmid, scal)

Parameters:
     Estimate Std. Error t value Pr(>|t|)    
Asym  2.34518    0.07815   30.01 2.17e-13 ***
xmid  1.48309    0.08135   18.23 1.22e-10 ***
scal  1.04146    0.03227   32.27 8.51e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.01919 on 13 degrees of freedom

Number of iterations to convergence: 0 
Achieved convergence tolerance: 3.281e-06

ztable(fm1DNase1)


	Estimate	Std. Error	t value	Pr(>\|t\|)
Nonlinear regression model
model: density ~ SSlogis(log(conc), Asym, xmid, scal)
data: DNase1
Asym	2.3452	0.0782	30.01	0.0000
xmid	1.4831	0.0814	18.23	0.0000
scal	1.0415	0.0323	32.27	0.0000

Maximum-likelihood Fitting of Univariate Distributions

'ztable()' can be used in maximum-likelihood fitting of univariate distributions. Followings are examples of ztable() of 'fitdistr' object.

require(MASS)
set.seed(123)
x <- rgamma(100, shape = 5, rate = 0.1)
a=fitdistr(x, "gamma")
ztable(a)


	shape	rate
estimate	6.487	0.137
sd	0.895	0.020
N=100, The log-likelihood=-429.18

x3 <- rweibull(100, shape = 4, scale = 100)
b=fitdistr(x3, "weibull")
ztable(b)


	shape	scale
estimate	3.855	102.462
sd	0.305	2.794
N=100, The log-likelihood=-471.68

Customize the zebra striping colors

If you wanted to use several colors for zebra striping, you can set the parameter 'zebra' to zero(e.g. zebra=0) and set the 'zebra.color' parameter with vector of your favorite colors. Your favorite colors are used to zebra striping. For your convienience, ten colors are predifned for this purpose. The predefined colors are: c("peach","peach-orange","peachpuff","peach-yellow","pear","pearl","peridot","periwinkle","pastelred", "pastelgray").

ztable(head(mtcars,15),zebra=0,zebra.color=NULL)


	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.00	6.00	160.00	110.00	3.90	2.62	16.46	0.00	1.00	4.00	4.00
Mazda RX4 Wag	21.00	6.00	160.00	110.00	3.90	2.88	17.02	0.00	1.00	4.00	4.00
Datsun 710	22.80	4.00	108.00	93.00	3.85	2.32	18.61	1.00	1.00	4.00	1.00
Hornet 4 Drive	21.40	6.00	258.00	110.00	3.08	3.21	19.44	1.00	0.00	3.00	1.00
Hornet Sportabout	18.70	8.00	360.00	175.00	3.15	3.44	17.02	0.00	0.00	3.00	2.00
Valiant	18.10	6.00	225.00	105.00	2.76	3.46	20.22	1.00	0.00	3.00	1.00
Duster 360	14.30	8.00	360.00	245.00	3.21	3.57	15.84	0.00	0.00	3.00	4.00
Merc 240D	24.40	4.00	146.70	62.00	3.69	3.19	20.00	1.00	0.00	4.00	2.00
Merc 230	22.80	4.00	140.80	95.00	3.92	3.15	22.90	1.00	0.00	4.00	2.00
Merc 280	19.20	6.00	167.60	123.00	3.92	3.44	18.30	1.00	0.00	4.00	4.00
Merc 280C	17.80	6.00	167.60	123.00	3.92	3.44	18.90	1.00	0.00	4.00	4.00
Merc 450SE	16.40	8.00	275.80	180.00	3.07	4.07	17.40	0.00	0.00	3.00	3.00
Merc 450SL	17.30	8.00	275.80	180.00	3.07	3.73	17.60	0.00	0.00	3.00	3.00
Merc 450SLC	15.20	8.00	275.80	180.00	3.07	3.78	18.00	0.00	0.00	3.00	3.00
Cadillac Fleetwood	10.40	8.00	472.00	205.00	2.93	5.25	17.98	0.00	0.00	3.00	4.00

The color names used for this purpose are predefined in the data 'zcolors' included in 'ztable' package. Please type '?zcolors' in R console for help file or just type 'zcolors'. You can see 749 color names defined in data 'zcolors'.

Vertical striping

If you wanted to vertical striping table, you can get it by set the parameter zebra.type 2. You can change the ztables parameters when printing.

z1=ztable(head(iris),zebra=2)
z1


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

print(z1,zebra.type=2)


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

print(z1,zebra=1,zebra.type=2,zebra.colnames=TRUE)


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

More tailoring zebra striping

You can update parameters of ztable with 'update_ztable' function.

options(ztable.zebra.color=NULL)
(z1=ztable(head(iris),zebra=0,zebra.type=2))


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

You can change the background color of colnames rows by setting zebra.colnames=TRUE.

update_ztable(z1,colnames.bold=TRUE,zebra.colnames=TRUE)


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

You can customize the striping when printing.

print(z1,zebra.color=c(rep("white",5),"peach"),zebra.colnames=TRUE)


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

Change the background color of all cells

You can change the background color of all cells by setting the zebra.type=0.

ztable(head(iris),zebra=0,zebra.type=0)


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

ztable(head(iris),zebra=0,zebra.type=0,zebra.color=zcolors$name,zebra.colnames=TRUE)


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

Diagonal striping

You can make diagonal striping with use of zebra.color greater/lesser than column length by 1.

ztable(head(iris),zebra=0,zebra.type=0,zebra.color=1:7,zebra.colnames=TRUE)


	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa

ztable(head(mtcars[,1:9]),zebra=0,zebra.type=0,zebra.color=1:9,zebra.colnames=TRUE)


	mpg	cyl	disp	hp	drat	wt	qsec	vs	am
Mazda RX4	21.00	6.00	160.00	110.00	3.90	2.62	16.46	0.00	1.00
Mazda RX4 Wag	21.00	6.00	160.00	110.00	3.90	2.88	17.02	0.00	1.00
Datsun 710	22.80	4.00	108.00	93.00	3.85	2.32	18.61	1.00	1.00
Hornet 4 Drive	21.40	6.00	258.00	110.00	3.08	3.21	19.44	1.00	0.00
Hornet Sportabout	18.70	8.00	360.00	175.00	3.15	3.44	17.02	0.00	0.00
Valiant	18.10	6.00	225.00	105.00	2.76	3.46	20.22	1.00	0.00

All background colors

This is demonstration of All background colors. All 749 colors are available in package ztable. Please type ?zcolors.

mycolor=rep("white",6)
for(i in 1:149){
    mycolor=c(mycolor,"white",zcolors$name[((i-1)*5+1):((i-1)*5+5)])
}
mycolor=c(mycolor,"white",zcolors$name[c(746:749,1)])
a=c(zcolors$name[1:5])
for(i in 2:149){
    a=rbind(a,zcolors$name[((i-1)*5+1):((i-1)*5+5)])
}
a=rbind(a,zcolors$name[c(746:749,1)])
a=data.frame(a,stringsAsFactors=FALSE,row.names=NULL)
ztable(a,zebra=0,zebra.type=0,zebra.color=mycolor,include.rownames=FALSE,
       include.colnames=FALSE,longtable=TRUE)


airforceblue	aliceblue	alizarin	almond	amaranth
amber	ambersaeece	americanrose	amethyst	anti-flashwhite
antiquebrass	antiquefuchsia	antiquewhite	ao	aoenglish
applegreen	apricot	aqua	aquamarine	armygreen
arsenic	arylideyellow	ashgrey	asparagus	atomictangerine
auburn	aureolin	aurometalsaurus	awesome	azurecolorwheel
azurewebazuremist	babyblue	babyblueeyes	babypink	ballblue
bananamania	bananayellow	battleshipgrey	bazaar	beaublue
beaver	beige	bisque	bistre	bittersweet
black	blanchedalmond	bleudefrance	blizzardblue	blond
blue	bluemunsell	bluencs	bluepigment	blueryb
bluebell	bluegray	blue-green	blue-violet	blush
bole	bondiblue	bostonuniversityred	brandeisblue	brass
brickred	brightcerulean	brightgreen	brightlavender	brightmaroon
brightpink	brightturquoise	brightube	brilliantlavender	brilliantrose
brinkpink	britishracinggreen	bronze	browntraditional	brownweb
bubblegum	bubbles	buff	bulgarianrose	burgundy
burlywood	burntorange	burntsienna	burntumber	byzantine
byzantium	cadet	cadetblue	cadetgrey	cadmiumgreen
cadmiumorange	cadmiumred	cadmiumyellow	calpolypomonagreen	cambridgeblue
camel	camouflagegreen	canaryyellow	candyapplered	candypink
capri	caputmortuum	cardinal	caribbeangreen	carmine
carminepink	carminered	carnationpink	carnelian	carolinablue
carrotorange	ceil	celadon	celestialblue	cerise
cerisepink	cerulean	ceruleanblue	chamoisee	champagne
charcoal	chartreusetraditional	chartreuseweb	cherryblossompink	chestnut
chocolatetraditional	chocolateweb	chromeyellow	cinereous	cinnabar
cinnamon	citrine	classicrose	cobalt	cocoabrown
columbiablue	coolblack	coolgrey	copper	copperrose
coquelicot	coral	coralpink	coralred	cordovan
corn	cornellred	cornflowerblue	cornsilk	cosmiclatte
cottoncandy	cream	crimson	crimsonglory	cyan
cyanprocess	daffodil	dandelion	darkblue	darkbrown
darkbyzantium	darkcandyapplered	darkcerulean	darkchampagne	darkchestnut
darkcoral	darkcyan	darkelectricblue	darkgoldenrod	darkgray
darkgreen	darkjunglegreen	darkkhaki	darklava	darklavender
darkmagenta	darkmidnightblue	darkolivegreen	darkorange	darkorchid
darkpastelblue	darkpastelgreen	darkpastelpurple	darkpastelred	darkpink
darkpowderblue	darkraspberry	darkred	darksalmon	darkscarlet
darkseagreen	darksienna	darkslateblue	darkslategray	darkspringgreen
darktan	darktangerine	darktaupe	darkterracotta	darkturquoise
darkviolet	dartmouthgreen	debianred	deepcarmine	deepcarminepink
deepcarrotorange	deepcerise	deepchampagne	deepchestnut	deepfuchsia
deepjunglegreen	deeplilac	deepmagenta	deeppeach	deeppink
deepsaffron	deepskyblue	denim	desert	desertsand
dimgray	dodgerblue	dogwoodrose	dollarbill	drab
dukeblue	earthyellow	ecru	eggplant	eggshell
egyptianblue	electricblue	electriccrimson	electriccyan	electricgreen
electricindigo	electriclavender	electriclime	electricpurple	electricultramarine
electricviolet	electricyellow	emerald	etonblue	fallow
falured	fandango	fashionfuchsia	fawn	feldgrau
ferngreen	ferrarired	fielddrab	firebrick	fireenginered
flame	flamingopink	flavescent	flax	floralwhite
fluorescentorange	fluorescentpink	fluorescentyellow	folly	forestgreentraditional
forestgreenweb	frenchbeige	frenchblue	frenchlilac	frenchrose
fuchsia	fuchsiapink	fulvous	fuzzywuzzy	gainsboro
gamboge	ghostwhite	ginger	glaucous	goldmetallic
goldwebgolden	goldenbrown	goldenpoppy	goldenyellow	goldenrod
grannysmithapple	gray	grayhtmlcssgray	grayx11gray	gray-asparagus
greencolorwheelx11green	greenhtmlcssgreen	greenmunsell	greenncs	greenpigment
greenryb	green-yellow	grullo	guppiegreen	halayaube
hanblue	hanpurple	hansayellow	harlequin	harvardcrimson
harvestgold	heartgold	heliotrope	hollywoodcerise	honeydew
hotmagenta	hotpink	huntergreen	iceberg	icterine
inchworm	indiagreen	indianred	indianyellow	indigodye
indigoweb	internationalkleinblue	internationalorange	iris	isabelline
islamicgreen	ivory	jade	jasper	jazzberryjam
jonquil	junebud	junglegreen	kellygreen	khakihtmlcsskhaki
khakix11lightkhaki	lasallegreen	languidlavender	lapislazuli	laserlemon
lava	lavenderfloral	lavenderweb	lavenderblue	lavenderblush
lavendergray	lavenderindigo	lavendermagenta	lavendermist	lavenderpink
lavenderpurple	lavenderrose	lawngreen	lemon	lemonchiffon
lightapricot	lightblue	lightbrown	lightcarminepink	lightcoral
lightcornflowerblue	lightcyan	lightfuchsiapink	lightgoldenrodyellow	lightgray
lightgreen	lightkhaki	lightmauve	lightpastelpurple	lightpink
lightsalmon	lightsalmonpink	lightseagreen	lightskyblue	lightslategray
lighttaupe	lightthulianpink	lightyellow	lilac	limecolorwheel
limewebx11green	limegreen	lincolngreen	linen	liver
lust	macaroniandcheese	magenta	magentadye	magentaprocess
magicmint	magnolia	mahogany	maize	majorelleblue
malachite	manatee	mangotango	maroonhtmlcss	maroonx11
mauve	mauvetaupe	mauvelous	mayablue	meatbrown
mediumaquamarine	mediumblue	mediumcandyapplered	mediumcarmine	mediumelectricblue
mediumjunglegreen	mediumlavendermagenta	mediumorchid	mediumpersianblue	mediumpurple
mediumred-violet	mediumseagreen	mediumslateblue	mediumspringbud	mediumspringgreen
mediumtaupe	mediumtealblue	mediumturquoise	mediumviolet-red	melon
midnightblue	midnightgreeneaglegreen	mikadoyellow	mint	mintcream
mintgreen	mistyrose	moccasin	modebeige	moonstoneblue
mordantred19	mossgreen	mountainmeadow	mountbattenpink	mulberry
mustard	myrtle	msugreen	nadeshikopink	napiergreen
naplesyellow	navajowhite	navyblue	neoncarrot	neonfuchsia
neongreen	non-photoblue	oceanboatblue	ochre	officegreen
oldgold	oldlace	oldlavender	oldmauve	oldrose
olive	olivine	onyx	operamauve	orangecolorwheel
orangeryb	orangewebcolor	orangepeel	orange-red	orchid
otterbrown	outerspace	outrageousorange	oxfordblue	oucrimsonred
pakistangreen	palatinateblue	palatinatepurple	paleaqua	paleblue
palebrown	palecarmine	palecerulean	palechestnut	palecopper
palecornflowerblue	palegold	palegoldenrod	palegreen	palemagenta
palepink	paleplum	palered-violet	palerobineggblue	palesilver
palespringbud	paletaupe	paleviolet-red	pansypurple	papayawhip
parisgreen	pastelblue	pastelbrown	pastelgray	pastelgreen
pastelmagenta	pastelorange	pastelpink	pastelpurple	pastelred
pastelviolet	pastelyellow	patriarch	peach	peach-orange
peachpuff	peach-yellow	pear	pearl	peridot
periwinkle	persianblue	persiangreen	persianindigo	persianorange
peru	persianpink	persianplum	persianred	persianrose
persimmon	phlox	phthaloblue	phthalogreen	piggypink
pinegreen	pink	pink-orange	pinkpearl	pinksherbet
pistachio	platinum	plumtraditional	plumweb	portlandorange
powderblueweb	princetonorange	prune	prussianblue	psychedelicpurple
puce	pumpkin	purplehtmlcss	purplemunsell	purplex11
purpleheart	purplemountainmajesty	purplepizzazz	purpletaupe	radicalred
raspberry	raspberryglace	raspberrypink	raspberryrose	rawumber
razzledazzlerose	razzmatazz	red	redmunsell	redncs
redpigment	redryb	red-brown	red-violet	redwood
regalia	richblack	richbrilliantlavender	richcarmine	richelectricblue
richlavender	richlilac	richmaroon	riflegreen	robineggblue
rose	rosebonbon	roseebony	rosegold	rosemadder
rosepink	rosequartz	rosetaupe	rosevale	rosewood
rossocorsa	rosybrown	royalazure	royalbluetraditional	royalblueweb
royalfuchsia	royalpurple	ruby	ruddy	ruddybrown
ruddypink	rufous	russet	rust	sacramentostategreen
saddlebrown	safetyorangeblazeorange	saffron	salmon	salmonpink
sand	sanddune	sandstorm	sandybrown	sandytaupe
sangria	sapgreen	sapphire	satinsheengold	scarlet
schoolbusyellow	screamingreen	seagreen	sealbrown	seashell
selectiveyellow	sepia	shadow	shamrockgreen	shockingpink
sienna	silver	sinopia	skobeloff	skyblue
skymagenta	slateblue	slategray	smaltdarkpowderblue	smokeytopaz
smokyblack	snow	spirodiscoball	splashedwhite	springbud
springgreen	steelblue	stildegrainyellow	straw	sunglow
sunset	tan	tangelo	tangerine	tangerineyellow
taupe	taupegray	teagreen	tearoseorange	tearoserose
teal	tealblue	tealgreen	terracotta	thistle
thulianpink	ticklemepink	tiffanyblue	timberwolf	titaniumyellow
tomato	toolbox	tractorred	trolleygrey	tropicalrainforest
trueblue	tuftsblue	tumbleweed	turkishrose	turquoise
turquoiseblue	turquoisegreen	tuscanred	twilightlavender	tyrianpurple
uablue	uared	ube	uclablue	uclagold
ufogreen	ultramarine	ultramarineblue	ultrapink	umber
unitednationsblue	unmellowyellow	upforestgreen	upmaroon	upsdellred
urobilin	usccardinal	uscgold	utahcrimson	vanilla
vegasgold	venetianred	verdigris	vermilion	veronica
violet	violetcolorwheel	violetryb	violetweb	viridian
vividauburn	vividburgundy	vividcerise	vividtangerine	vividviolet
warmblack	wenge	wheat	white	whitesmoke
wildblueyonder	wildstrawberry	wildwatermelon	wisteria	xanadu
yaleblue	yellow	yellowmunsell	yellowncs	yellowprocess
yellowryb	yellow-green	zaffre	zinnwalditebrown	airforceblue

Place two or more ztables or figures side by side

If you wanted to place two or more ztables or figures side by side, you can use function parallelTables(). Function parallelTables() takes three parameters. The first parameter width is a numeric vector specifies the width to which the tables or figures should be scaled. The second parameter is a list of ztable or names of valid figure. The 3rd parameter 'type' is the type of table to produce. Possible values for type are "latex" or "html". Default value is "latex". See the examples.

z=ztable(head(mtcars[1:3]),tabular=TRUE,zebra.color="peach-orange")
z1=ztable(head(iris[1:3]),tabular=TRUE,zebra=2)

parallelTables(width=c(0.5,0.5),list(z,z1),type="html")


	mpg	cyl	disp
Mazda RX4	21.00	6.00	160.00
Mazda RX4 Wag	21.00	6.00	160.00
Datsun 710	22.80	4.00	108.00
Hornet 4 Drive	21.40	6.00	258.00
Hornet Sportabout	18.70	8.00	360.00
Valiant	18.10	6.00	225.00


	Sepal.Length	Sepal.Width	Petal.Length
1	5.10	3.50	1.40
2	4.90	3.00	1.40
3	4.70	3.20	1.30
4	4.60	3.10	1.50
5	5.00	3.60	1.40
6	5.40	3.90	1.70

parallelTables(width=c(0.5,0.5),list(z,"vignettes/figures/ztable3.png"),type="html")


	mpg	cyl	disp
Mazda RX4	21.00	6.00	160.00
Mazda RX4 Wag	21.00	6.00	160.00
Datsun 710	22.80	4.00	108.00
Hornet 4 Drive	21.40	6.00	258.00
Hornet Sportabout	18.70	8.00	360.00
Valiant	18.10	6.00	225.00

mytable object from "moonBook" package

'ztable()' can be used for 'mytable' object made by "mytable" function from "moonBook" package.

require(moonBook)

## Loading required package: moonBook

res=mytable(Dx~.,data=acs)
options(ztable.zebra=NULL)
z=ztable(res)
z


	NSTEMI	STEMI	Unstable Angina	p
	(N=153)	(N=304)	(N=400)	p
age	64.3 ± 12.3	62.1 ± 12.1	63.8 ± 11.0	0.073
sex				0.012
Female	50 (32.7%)	84 (27.6%)	153 (38.2%)
Male	103 (67.3%)	220 (72.4%)	247 (61.8%)
cardiogenicShock				0.000
No	149 (97.4%)	256 (84.2%)	400 (100.0%)
Yes	4 ( 2.6%)	48 (15.8%)	0 ( 0.0%)
entry				0.001
Femoral	58 (37.9%)	133 (43.8%)	121 (30.2%)
Radial	95 (62.1%)	171 (56.2%)	279 (69.8%)
EF	55.0 ± 9.3	52.4 ± 9.5	59.2 ± 8.7	0.000
height	163.3 ± 8.2	165.1 ± 8.2	161.7 ± 9.7	0.000
weight	64.3 ± 10.2	65.7 ± 11.6	64.5 ± 11.6	0.361
BMI	24.1 ± 3.2	24.0 ± 3.3	24.6 ± 3.4	0.064
obesity				0.186
No	106 (69.3%)	209 (68.8%)	252 (63.0%)
Yes	47 (30.7%)	95 (31.2%)	148 (37.0%)
TC	193.7 ± 53.6	183.2 ± 43.4	183.5 ± 48.3	0.057
LDLC	126.1 ± 44.7	116.7 ± 39.5	112.9 ± 40.4	0.004
HDLC	38.9 ± 11.9	38.5 ± 11.0	37.8 ± 10.9	0.501
TG	130.1 ± 88.5	106.5 ± 72.0	137.4 ± 101.6	0.000
DM				0.209
No	96 (62.7%)	208 (68.4%)	249 (62.2%)
Yes	57 (37.3%)	96 (31.6%)	151 (37.8%)
HBP				0.002
No	62 (40.5%)	150 (49.3%)	144 (36.0%)
Yes	91 (59.5%)	154 (50.7%)	256 (64.0%)
smoking				0.000
Ex-smoker	42 (27.5%)	66 (21.7%)	96 (24.0%)
Never	50 (32.7%)	97 (31.9%)	185 (46.2%)
Smoker	61 (39.9%)	141 (46.4%)	119 (29.8%)

vlines(z,type="all")


	NSTEMI	STEMI	Unstable Angina	p
	(N=153)	(N=304)	(N=400)	p
age	64.3 ± 12.3	62.1 ± 12.1	63.8 ± 11.0	0.073
sex				0.012
Female	50 (32.7%)	84 (27.6%)	153 (38.2%)
Male	103 (67.3%)	220 (72.4%)	247 (61.8%)
cardiogenicShock				0.000
No	149 (97.4%)	256 (84.2%)	400 (100.0%)
Yes	4 ( 2.6%)	48 (15.8%)	0 ( 0.0%)
entry				0.001
Femoral	58 (37.9%)	133 (43.8%)	121 (30.2%)
Radial	95 (62.1%)	171 (56.2%)	279 (69.8%)
EF	55.0 ± 9.3	52.4 ± 9.5	59.2 ± 8.7	0.000
height	163.3 ± 8.2	165.1 ± 8.2	161.7 ± 9.7	0.000
weight	64.3 ± 10.2	65.7 ± 11.6	64.5 ± 11.6	0.361
BMI	24.1 ± 3.2	24.0 ± 3.3	24.6 ± 3.4	0.064
obesity				0.186
No	106 (69.3%)	209 (68.8%)	252 (63.0%)
Yes	47 (30.7%)	95 (31.2%)	148 (37.0%)
TC	193.7 ± 53.6	183.2 ± 43.4	183.5 ± 48.3	0.057
LDLC	126.1 ± 44.7	116.7 ± 39.5	112.9 ± 40.4	0.004
HDLC	38.9 ± 11.9	38.5 ± 11.0	37.8 ± 10.9	0.501
TG	130.1 ± 88.5	106.5 ± 72.0	137.4 ± 101.6	0.000
DM				0.209
No	96 (62.7%)	208 (68.4%)	249 (62.2%)
Yes	57 (37.3%)	96 (31.6%)	151 (37.8%)
HBP				0.002
No	62 (40.5%)	150 (49.3%)	144 (36.0%)
Yes	91 (59.5%)	154 (50.7%)	256 (64.0%)
smoking				0.000
Ex-smoker	42 (27.5%)	66 (21.7%)	96 (24.0%)
Never	50 (32.7%)	97 (31.9%)	185 (46.2%)
Smoker	61 (39.9%)	141 (46.4%)	119 (29.8%)

cbind.mytable object

'ztable()' can be used for 'cbind.mytable' object made by "mytable" function from "moonBook" package.

res1=mytable(sex+DM~.,data=acs)
z=ztable(res1)
z

Descriptive Statistics Stratified by 'SEX' and 'DM'
	No	Yes	p	No	Yes	p
	Male			Female
	(N=380)	(N=190)	p	(N=173)	(N=114)	p
age	60.9 ± 11.5	60.1 ± 10.6	0.472	69.3 ± 11.4	67.8 ± 9.7	0.257
cardiogenicShock			0.685			0.296
No	355 (93.4%)	175 (92.1%)		168 (97.1%)	107 (93.9%)
Yes	25 ( 6.6%)	15 ( 7.9%)		5 ( 2.9%)	7 ( 6.1%)
entry			0.552			0.665
Femoral	125 (32.9%)	68 (35.8%)		74 (42.8%)	45 (39.5%)
Radial	255 (67.1%)	122 (64.2%)		99 (57.2%)	69 (60.5%)
Dx			0.219			0.240
NSTEMI	71 (18.7%)	32 (16.8%)		25 (14.5%)	25 (21.9%)
STEMI	154 (40.5%)	66 (34.7%)		54 (31.2%)	30 (26.3%)
Unstable Angina	155 (40.8%)	92 (48.4%)		94 (54.3%)	59 (51.8%)
EF	56.5 ± 8.3	53.9 ± 11.0	0.007	56.0 ± 10.1	56.6 ± 10.0	0.655
height	168.1 ± 5.8	167.5 ± 6.7	0.386	153.9 ± 6.5	153.6 ± 5.8	0.707
weight	68.1 ± 10.4	69.8 ± 10.2	0.070	56.5 ± 8.7	58.4 ± 10.0	0.106
BMI	24.0 ± 3.1	24.9 ± 3.5	0.005	23.8 ± 3.2	24.8 ± 4.0	0.046
obesity			0.027			0.359
No	261 (68.7%)	112 (58.9%)		121 (69.9%)	73 (64.0%)
Yes	119 (31.3%)	78 (41.1%)		52 (30.1%)	41 (36.0%)
TC	184.1 ± 46.7	181.8 ± 44.5	0.572	186.0 ± 43.1	193.3 ± 60.8	0.274
LDLC	117.9 ± 41.8	112.1 ± 39.4	0.115	116.3 ± 35.2	119.8 ± 48.6	0.519
HDLC	38.4 ± 11.4	36.8 ± 9.6	0.083	39.2 ± 10.9	38.8 ± 12.2	0.821
TG	115.2 ± 72.2	153.4 ± 130.7	0.000	114.2 ± 82.4	128.4 ± 65.5	0.112
HBP			0.000			0.356
No	205 (53.9%)	68 (35.8%)		54 (31.2%)	29 (25.4%)
Yes	175 (46.1%)	122 (64.2%)		119 (68.8%)	85 (74.6%)
smoking			0.386			0.093
Ex-smoker	101 (26.6%)	54 (28.4%)		34 (19.7%)	15 (13.2%)
Never	77 (20.3%)	46 (24.2%)		118 (68.2%)	91 (79.8%)
Smoker	202 (53.2%)	90 (47.4%)		21 (12.1%)	8 ( 7.0%)

vlines(z,type="all")

Descriptive Statistics Stratified by 'SEX' and 'DM'
	No	Yes	p	No	Yes	p
	Male			Female
	(N=380)	(N=190)	p	(N=173)	(N=114)	p
age	60.9 ± 11.5	60.1 ± 10.6	0.472	69.3 ± 11.4	67.8 ± 9.7	0.257
cardiogenicShock			0.685			0.296
No	355 (93.4%)	175 (92.1%)		168 (97.1%)	107 (93.9%)
Yes	25 ( 6.6%)	15 ( 7.9%)		5 ( 2.9%)	7 ( 6.1%)
entry			0.552			0.665
Femoral	125 (32.9%)	68 (35.8%)		74 (42.8%)	45 (39.5%)
Radial	255 (67.1%)	122 (64.2%)		99 (57.2%)	69 (60.5%)
Dx			0.219			0.240
NSTEMI	71 (18.7%)	32 (16.8%)		25 (14.5%)	25 (21.9%)
STEMI	154 (40.5%)	66 (34.7%)		54 (31.2%)	30 (26.3%)
Unstable Angina	155 (40.8%)	92 (48.4%)		94 (54.3%)	59 (51.8%)
EF	56.5 ± 8.3	53.9 ± 11.0	0.007	56.0 ± 10.1	56.6 ± 10.0	0.655
height	168.1 ± 5.8	167.5 ± 6.7	0.386	153.9 ± 6.5	153.6 ± 5.8	0.707
weight	68.1 ± 10.4	69.8 ± 10.2	0.070	56.5 ± 8.7	58.4 ± 10.0	0.106
BMI	24.0 ± 3.1	24.9 ± 3.5	0.005	23.8 ± 3.2	24.8 ± 4.0	0.046
obesity			0.027			0.359
No	261 (68.7%)	112 (58.9%)		121 (69.9%)	73 (64.0%)
Yes	119 (31.3%)	78 (41.1%)		52 (30.1%)	41 (36.0%)
TC	184.1 ± 46.7	181.8 ± 44.5	0.572	186.0 ± 43.1	193.3 ± 60.8	0.274
LDLC	117.9 ± 41.8	112.1 ± 39.4	0.115	116.3 ± 35.2	119.8 ± 48.6	0.519
HDLC	38.4 ± 11.4	36.8 ± 9.6	0.083	39.2 ± 10.9	38.8 ± 12.2	0.821
TG	115.2 ± 72.2	153.4 ± 130.7	0.000	114.2 ± 82.4	128.4 ± 65.5	0.112
HBP			0.000			0.356
No	205 (53.9%)	68 (35.8%)		54 (31.2%)	29 (25.4%)
Yes	175 (46.1%)	122 (64.2%)		119 (68.8%)	85 (74.6%)
smoking			0.386			0.093
Ex-smoker	101 (26.6%)	54 (28.4%)		34 (19.7%)	15 (13.2%)
Never	77 (20.3%)	46 (24.2%)		118 (68.2%)	91 (79.8%)
Smoker	202 (53.2%)	90 (47.4%)		21 (12.1%)	8 ( 7.0%)

You can use all ztable related function in this table.

z=addRowColor(z,c(13,16),"platinum")
z=addColColor(z,c(5,8),"pink")
z=addCellColor(z,rows=16,cols=c(5,8),color="orange")
z=addCellColor(z,rows=13,cols=5,color="orange")
z

Descriptive Statistics Stratified by 'SEX' and 'DM'
	No	Yes	p	No	Yes	p
	Male			Female
	(N=380)	(N=190)	p	(N=173)	(N=114)	p
age	60.9 ± 11.5	60.1 ± 10.6	0.472	69.3 ± 11.4	67.8 ± 9.7	0.257
cardiogenicShock			0.685			0.296
No	355 (93.4%)	175 (92.1%)		168 (97.1%)	107 (93.9%)
Yes	25 ( 6.6%)	15 ( 7.9%)		5 ( 2.9%)	7 ( 6.1%)
entry			0.552			0.665
Femoral	125 (32.9%)	68 (35.8%)		74 (42.8%)	45 (39.5%)
Radial	255 (67.1%)	122 (64.2%)		99 (57.2%)	69 (60.5%)
Dx			0.219			0.240
NSTEMI	71 (18.7%)	32 (16.8%)		25 (14.5%)	25 (21.9%)
STEMI	154 (40.5%)	66 (34.7%)		54 (31.2%)	30 (26.3%)
Unstable Angina	155 (40.8%)	92 (48.4%)		94 (54.3%)	59 (51.8%)
EF	56.5 ± 8.3	53.9 ± 11.0	0.007	56.0 ± 10.1	56.6 ± 10.0	0.655
height	168.1 ± 5.8	167.5 ± 6.7	0.386	153.9 ± 6.5	153.6 ± 5.8	0.707
weight	68.1 ± 10.4	69.8 ± 10.2	0.070	56.5 ± 8.7	58.4 ± 10.0	0.106
BMI	24.0 ± 3.1	24.9 ± 3.5	0.005	23.8 ± 3.2	24.8 ± 4.0	0.046
obesity			0.027			0.359
No	261 (68.7%)	112 (58.9%)		121 (69.9%)	73 (64.0%)
Yes	119 (31.3%)	78 (41.1%)		52 (30.1%)	41 (36.0%)
TC	184.1 ± 46.7	181.8 ± 44.5	0.572	186.0 ± 43.1	193.3 ± 60.8	0.274
LDLC	117.9 ± 41.8	112.1 ± 39.4	0.115	116.3 ± 35.2	119.8 ± 48.6	0.519
HDLC	38.4 ± 11.4	36.8 ± 9.6	0.083	39.2 ± 10.9	38.8 ± 12.2	0.821
TG	115.2 ± 72.2	153.4 ± 130.7	0.000	114.2 ± 82.4	128.4 ± 65.5	0.112
HBP			0.000			0.356
No	205 (53.9%)	68 (35.8%)		54 (31.2%)	29 (25.4%)
Yes	175 (46.1%)	122 (64.2%)		119 (68.8%)	85 (74.6%)
smoking			0.386			0.093
Ex-smoker	101 (26.6%)	54 (28.4%)		34 (19.7%)	15 (13.2%)
Never	77 (20.3%)	46 (24.2%)		118 (68.2%)	91 (79.8%)
Smoker	202 (53.2%)	90 (47.4%)		21 (12.1%)	8 ( 7.0%)

Package ztable

Keon-Woong Moon

2015-02-12

Introduction

Table Show

Merge two tables

Basic Use

data.frame

Basic Use

Tailoring zebra striping

Customize the caption and the font size

aov object

Linear model : 'lm' object

Analysis of Variance Table : 'anova' object

Generalized linear model ; 'glm' object

More 'aov' object

More 'lm' object

More 'glm' object

Principal Components Analysis : 'prcomp' object

Survival Analysis : 'coxph' object

Nonlinear Least Squares: 'nls' obbject

Maximum-likelihood Fitting of Univariate Distributions

Customize the zebra striping colors

Vertical striping

More tailoring zebra striping

Change the background color of all cells

Diagonal striping

All background colors

Place two or more ztables or figures side by side

mytable object from "moonBook" package

cbind.mytable object