xtable {xtable} | R Documentation |
Function converting an R object to an xtable
object, which can then be printed as a LaTeX or HTML table.
xtable(x, caption=NULL, label=NULL, align=NULL, vsep=NULL, digits=NULL, display=NULL, ...)
x |
An R object of class found among methods(xtable) . See below on how to write additional method functions
for xtable . |
caption |
Character vector of length 1 containing the table's caption or title.
Set to NULL to suppress the caption. Default value is NULL . |
label |
Character vector of length 1 containing the LaTeX label or HTML anchor.
Set to NULL to suppress the label. Default value is NULL . |
align |
Character vector of length equal to the number of columns of the resulting
table indicating the alignment of the corresponding columns.
Since the row names are printed in the first column, the length of align
is one greater than ncol(x) if x is a data.frame .
Use "l" , "r" , and "c" to denote left, right, and
center alignment, respectively. Default depends on the class of x . |
vsep |
Character vector with length equal to one or the number of columns in the resulting table + 2 (one for left and one for right margin). These may be any column separators acceptable to LaTeX. Default depends on the class of argument. Ignored in HTML mode. |
digits |
Numeric vector of length equal to the number of columns of the resulting
table indicating the number of digits to display in the corresponding columns.
Since the row names are printed in the first column, the length of align
is one greater than ncol(x) if x is a data.frame .
Default depends of class of x . |
display |
Character vector of length equal to the number of columns of the resulting
table indicating the format for the corresponding columns.
Since the row names are printed in the first column, the length of align
is one greater than ncol(x) if x is a data.frame .
These values are passed to the formatC function. Use "d" (for integers),
"f" , "e" , "E" , "g" , "G" , "fg" (for
reals), or "s" (for strings).
"f" gives numbers in the usual xxx.xxx format; "e" and
"E" give n.ddde+nn or n.dddE+nn (scientific format);
"g" and "G" put x[i] into scientific format only if it saves
space to do so. "fg" uses fixed format as "f" , but digits as
number of significant digits. Note that this can lead to
quite long result strings. Default depends on the class of x . |
... |
Additional arguments. (Currently ignored.) |
This function extracts tabular information from x
and returns an object of class "xtable"
.
The nature of the table generated depends on the class of x
.
For example, aov
objects produce
ANOVA tables while data.frame
objects produce a table of the entire data.frame. One can optionally provide a
caption (called a title in HTML) or label (called an anchor in HTML),
as well as formatting specifications. Default
values for align
, vsep
, digits
, and display
are
class dependent.
The available method functions for xtable
are given by methods(xtable)
.
Users can extend the list of available classes by writing methods for the generic function xtable
.
These methods functions should have x
as their first argument
with additional arguments to
specify caption
, label
, align
, vsep
,
digits
, and
display
. Optionally, other arguments
may be present to specify how the object x
should be manipulated.
All method functions should return an object whose class if given by c("xtable","data.frame")
.
The resulting object can have attributes caption
and
label
, but must have attributes align
,
digits
, and display
. It is strongly recommened that you set these attributes through the
provided replacement functions as they perform validity checks.
An object of class "xtable"
which inherits the data.frame
class and contains several additional attributes
specifying the table formatting options.
David Dahl dbdahl@stat.wisc.edu
print.xtable
, caption
, label
,
align
, digits
, display
, formatC
, methods
## Load example dataset data(tli) ## Demonstrate data.frame tli.table <- xtable(tli[1:20,]) digits(tli.table)[c(2,6)] <- 0 print(tli.table) print(tli.table,type="html") ## Demonstrate matrix design.matrix <- model.matrix(~ sex*grade, data=tli[1:20,]) design.table <- xtable(design.matrix) print(design.table) print(design.table,type="html") ## Demonstrate aov fm1 <- aov(tlimth ~ sex + ethnicty + grade + disadvg, data=tli) fm1.table <- xtable(fm1) print(fm1.table) print(fm1.table,type="html") ## Demonstrate lm fm2 <- lm(tlimth ~ sex*ethnicty, data=tli) fm2.table <- xtable(fm2) print(fm2.table) print(fm2.table,type="html") print(xtable(anova(fm2))) print(xtable(anova(fm2)),type="html") fm2b <- lm(tlimth ~ ethnicty, data=tli) print(xtable(anova(fm2b,fm2))) print(xtable(anova(fm2b,fm2)),type="html") ## Demonstrate glm fm3 <- glm(disadvg ~ ethnicty*grade, data=tli, family=binomial()) fm3.table <- xtable(fm3) print(fm3.table) print(fm3.table,type="html") print(xtable(anova(fm3))) print(xtable(anova(fm3)),type="html") ## Demonstrate aov ## Taken from help(aov) in R 1.1.1 ## From Venables and Ripley (1997) p.210. N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), K=factor(K), yield=yield) npk.aov <- aov(yield ~ block + N*P*K, npk) op <- options(contrasts=c("contr.helmert", "contr.treatment")) npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) options(op) summary(npk.aov) print(xtable(npk.aov)) print(xtable(anova(npk.aov))) print(xtable(summary(npk.aov))) summary(npk.aovE) print(xtable(npk.aovE),type="html") print(xtable(summary(npk.aovE)),type="html") ## Demonstrate lm ## Taken from help(lm) in R 1.1.1 ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. 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) print(xtable(lm.D9)) print(xtable(anova(lm.D9))) ## Demonstrate glm ## Taken from help(glm) in R 1.1.1 ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 93: Randomized Controlled Trial : 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()) print(xtable(glm.D93)) print(xtable(anova(glm.D93))) if(require(stats,quietly=TRUE)) { ## Demonstrate prcomp ## Taken from help(prcomp) in mva package of R 1.1.1 data(USArrests) pr1 <- prcomp(USArrests) print(xtable(pr1)) print(xtable(summary(pr1))) # ## Demonstrate princomp # ## Taken from help(princomp) in mva package of R 1.1.1 # pr2 <- princomp(USArrests) # print(xtable(pr2)) }