Related
I try to get the gene names out of a binding analysis of the 5'UTR. Therefore I have this little code. Until the vmatchPattern everything works fine. At least I hope so.
library(biomaRt)
library(GenomicFeatures)
library(XVector)
library(Biostrings)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(BSgenome.Mmusculus.UCSC.mm10)
fUTR <- fiveUTRsByTranscript(TxDb.Mmusculus.UCSC.mm10.knownGene)
Mmusculus <- BSgenome.Mmusculus.UCSC.mm10
seqlevelsStyle(Mmusculus) <- 'ensembl'
seqlevelsStyle(fUTR) <- 'ensembl'
Seq <- getSeq(Mmusculus, fUTR)
Pbind <- RNAString('UGUGUGAAHAA')
Match <- vmatchPattern(Pbind, unlist2(Seq), max.mismatch = 0, min.mismatch = 0, with.indels = F, fixed = T, algorithm = 'auto')
Afterwards however I want to get the gene names to create a list in the end and use this in Python for further analysis of a RNAseq experiment. There comes a problem, I think I found so far three different ways on how to potentially do this. However none of them are working for me.
##How to get gene names from the match Pattern
#1
matches <- unlist(Match, recursive = T, use.names = T)
m <- as.matrix(matches)
subseq(genes[rownames(m),], start = m[rownames(m),1], width = 20)
#2
transcripts(TxDb.Mmusculus.UCSC.mm10.knownGene, columns = c('tx_id', 'tx_name', 'gene_id'))
#3
count_index <- countIndex(Match)
wh <- which(count_index > 0)
result_list = list()
for(i in 1: length(wh))
{
result_list[[i]] = Views(subject[[wh[i]]], mindex[[wh[i]]])
}
names(result_listF) = nm[wh]
I am happy to hear some suggestions and get some help or solution for this problem. I am no Bioinformation by training, so this took me already quite a while to figure this out.
So I found an answer, I hope this helps someone, and there is no mistake somewhere.
library(BSgenome.Mmusculus.UCSC.mm10)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(org.Mm.eg.db)
##get all 5’ UTR sequences
fUTR <- fiveUTRsByTranscript(TxDb.Mmusculus.UCSC.mm10.knownGene)
utr_ul <- unlist(fUTR, use.names = F)
mcols(utr_ul)$tx_id <- rep(as.integer(names(fUTR)), lengths(fUTR))
utr_ul
tx2gene <- mcols(transcripts(TxDb.Mmusculus.UCSC.mm10.knownGene, columns = c('tx_id', 'tx_name', 'gene_id')))
tx2gene$gene_id <- as.character(tx2gene$gene_id)
m <- match(mcols(utr_ul)$tx_id, tx2gene$tx_id)
mcols(utr_ul) <- cbind(mcols(utr_ul), tx2gene[m, -1L, drop = F])
utr5_by_gene <- split(utr_ul, mcols(utr_ul)$gene_id)
seqs <- getSeq(Mmusculus, utr5_by_gene)
##search with motif UGUGUGAAHAA
motif <- DNAString('TGTGTGAAHAA')
x <- vmatchPattern(motif, unlist(seqs), fixed = F)
matches <- unlist(x, recursive = T, use.names = T)
##list all genes with matches
hits <- mapIds(org.Mm.eg.db, keys = unique(names(matches)), keytype = 'ENTREZID',
column = 'SYMBOL', multiVals = 'first')
I have a dataframe e.g.
df_reprex <- data.frame(id = rep(paste0("S",round(runif(100, 1000000, 9999999),0)), each=10),
date = rep(seq.Date(today(), by=-7, length.out = 10), 100),
var1 = runif(1000, 10, 20),
var2 = runif(1000, 20, 50),
var3 = runif(1000, 2, 5),
var250 = runif(1000, 100, 200),
var1_baseline = rep(runif(100, 5, 10), each=10),
var2_baseline = rep(runif(100, 50, 80), each=10),
var3_baseline = rep(runif(100, 1, 3), each=10),
var250_baseline = rep(runif(100, 20, 70), each=10))
I want to write a function containing a for loop that for each row in the dataframe will subtract every "_baseline" column from the non-baseline column with the same name.
I have created a script that automatically creates a character string containing the code I would like to run:
df <- df_reprex
# get only numeric columns
df_num <- df %>% dplyr::select_if(., is.numeric)
# create a version with no baselines
df_nobaselines <- df_num %>% select(-contains("baseline"))
#extract names of non-baseline columns
numeric_cols <- names(df_nobaselines)
#initialise empty string
mutatestring <- ""
#write loop to fill in string:
for (colname in numeric_cols) {
mutatestring <- paste(mutatestring, ",", paste0(colname, "_change"), "=", colname, "-", paste0(colname, "_baseline"))
# df_num <- df_num %>%
# mutate(paste0(col, "_change") = col - paste0(col, "_baseline"))
}
mutatestring <- substr(mutatestring, 4, 9999999) # remove stuff at start (I know it's inefficient)
mutatestring2 <- paste("df %>% mutate(", mutatestring, ")") # add mutate call
but when I try to call "mutatestring2" it just prints the character string e.g.:
[1] "df %>% mutate( var1_change = var1 - var1_baseline , var2_change = var2 - var2_baseline , var3_change = var3 - var3_baseline , var250_change = var250 - var250_baseline )"
I thought that this part would be relatively easy and I'm sure I've missed something obvious, but I just can't get the text inside that string to run!
I've tried various slightly ridiculous methods but none of them return the desired output (i.e. the result returned by the character string if it was entered into the console as a command):
call(mutatestring2)
eval(mutatestring2)
parse(mutatestring2)
str2lang(mutatestring2)
mget(mutatestring2)
diff_func <- function() {mutatestring2}
diff_func1 <- function() {
a <-mutatestring2
return(a)}
diff_func2 <- function() {str2lang(mutatestring2)}
diff_func3 <- function() {eval(mutatestring2)}
diff_func4 <- function() {parse(mutatestring2)}
diff_func5 <- function() {call(mutatestring2)}
diff_func()
diff_func1()
diff_func2()
diff_func3()
diff_func4()
diff_func5()
I'm sure there must be a very straightforward way of doing this, but I just can't work it out!
How do I convert a character string to something that I can run or pass to a magrittr pipe?
You need to use the text parameter in parse, then eval the result. For example, you can do:
eval(parse(text = "print(5)"))
#> [1] 5
However, using eval(parse()) is normally a very bad idea, and there is usually a more sensible alternative.
In your case you can do this without resorting to eval(parse()), for example in base R you could subtract all the appropriate variables from each other like this:
baseline <- grep("_baseline$", names(df_reprex), value = TRUE)
non_baseline <- gsub("_baseline", "", baseline)
df_new <- cbind(df_reprex, as.data.frame(setNames(mapply(
function(i, j) df_reprex[[i]] - df_reprex[[j]],
baseline, non_baseline, SIMPLIFY = FALSE),
paste0(non_baseline, "_corrected"))))
Or if you want to keep the whole thing in a single pipe without storing intermediate variables, you could do:
mapply(function(i, j) df_reprex[[i]] - df_reprex[[j]],
grep("_baseline$", names(df_reprex), value = TRUE),
gsub("_baseline", "", grep("_baseline$", names(df_reprex), value = TRUE)),
SIMPLIFY = FALSE) %>%
setNames(gsub("_baseline", "_corrected",
grep("_baseline$", names(df_reprex), value = TRUE))) %>%
as.data.frame() %>%
{cbind(df_reprex, .)}
Good evening,
I asked a question earlier and found it hard to implement the solution so I am gonna reask it in a more clear way.
I have the problem, that I want to add a column to a dataframe of daily returns of a stock. Lets say its normally distributed and I would like to add a column that contains the value at risk (hist) whose function I wrote myself.
The restriction is that each observation should be assigned to my function and take the last 249 observations as well.
So when the next observation is calculated it should also take only the last 249 observations of the das before. So the input values should move as the time goes on. In other words I want values from 251 days ago to be excluded. Hopefully I explained myself well enough. If not maybe the code speaks for me:
df<- data.frame(Date=seq(ISOdate(2000,1,1), by = "days", length.out = 500), Returns=rnorm(500))
#function
VaR.hist<- function(x, n=250, hd=20, q=0.05){
width<-nrow(x)
NA.x<-na.omit(x)
quantil<-quantile(NA.x[(width-249):width],probs=q)
VaR<- quantil*sqrt(hd)%>%
return()
}
# Run the function on the dataframe
df$VaR<- df$Returns%>%VaR.hist()
Error in (width - 249):width : argument of length 0
This is the Error code that I get and not my new Variable...
Thanks !!
As wibom wrote in the comment nrow(x) does not work for vectors. What you need is length() instead. Also you do not need return() in the last line as R automatically returns the last line of a function if there is no early return() before.
library(dplyr)
df<- data.frame(Date=seq(ISOdate(2000,1,1), by = "days", length.out = 500), Returns=rnorm(500))
#function
VaR.hist <- function(x, n=250, hd=20, q=0.05){
width <- length(x) # here you need length as x is a vector, nrow only works for data.frames/matrixes
NA.x <- na.omit(x)
quantil <- quantile(NA.x[(width-249):width], probs = q)
quantil*sqrt(hd)
}
# Run the function on the dataframe
df$VaR <- df$Returns %>% VaR.hist()
It's a bit hard to understand what you want to do exactly.
My understanding is that you wish to compute a new variable VarR, calculated based on the current and previous 249 observations of df$Returns, right?
Is this about what you wish to do?:
library(tidyverse)
set.seed(42)
df <- tibble(
Date = seq(ISOdate(2000, 1, 1), by = "days", length.out = 500),
Returns=rnorm(500)
)
the_function <- function(i, mydata, hd = 20, q = .05) {
r <-
mydata %>%
filter(ridx <= i, ridx > i - 249) %>%
pull(Returns)
quantil <- quantile(r, probs = q)
VaR <- quantil*sqrt(hd)
}
df <-
df %>%
mutate(ridx = row_number()) %>%
mutate(VaR = map_dbl(ridx, the_function, mydata = .))
If you are looking for a base-R solution:
set.seed(42)
df <- data.frame(
Date = seq(ISOdate(2000, 1, 1), by = "days", length.out = 500),
Returns = rnorm(500)
)
a_function <- function(i, mydata, hd = 20, q = .05) {
r <- mydata$Returns[mydata$ridx <= i & mydata$ridx > (i - 249)]
quantil <- quantile(r, probs = q)
VaR <- quantil*sqrt(hd)
}
df$ridx <- 1:nrow(df) # add index
df$VaR <- sapply(df$ridx, a_function, mydata = df)
I am working on a credit card prospect identification case study. I have to replace values of all columns with its corresponding WOE values. I can do it in 2-3 steps. However, I want to know whether there is a way to do it in 1 shot.
Use scorecard package and it is simple to use woebin(),woebin_plot(),woebin_ply(),iv() function.
temp <- credit_data
library(scorecard)
bins <- woebin(dt = temp,y = "targetvariable")
woebin_plot(bins$Income)
WOE_temp <- woebin_ply(temp,bins)
View(WOE_temp)
View(temp[is.na(temp$No.of.dependents),])
IV_values <- iv(dt = temp,y = "target variable")
(IV_values)
You might want to take a look at the woe package (in case WOE stands for Weight of Evidence).
Here's the relevant code snippet from the documentation:
library(woe)
res_woe <- woe(Data = mtcars, Independent = "cyl", Continuous = FALSE, Dependent = "am", C_Bin = 10, Bad = 0, Good = 1)
Hi please follow the following steps :-
Step 1: Calculate woe and iv using information package:-
library(fuzzyjoin)
library(Information)
IV <-
Information::create_infotables(data = test_df,
y = "label_column",
parallel =
TRUE)
Where in 'y' we need to assign label and 'data' we need to assign a dataframe.
Step 2: Use the following function:-
This is my own custom written function to replace actual values in a dataframe with woe calculated using information package:-
woe_replace <- function(df_orig, IV) {
df <- cbind(df_orig)
df_clmtyp <- data.frame(clmtyp = sapply(df, class))
df_col_typ <-
data.frame(clmnm = colnames(df), clmtyp = df_clmtyp$clmtyp)
for (rownm in 1:nrow(df_col_typ)) {
colmn_nm <- toString(df_col_typ[rownm, "clmnm"])
if(colmn_nm %in% names(IV$Tables)){
column_woe_df <- cbind(data.frame(IV$Tables[[toString(df_col_typ[rownm, "clmnm"])]]))
if (df_col_typ[rownm, "clmtyp"] == "factor" | df_col_typ[rownm, "clmtyp"] == "character") {
df <-
dplyr::inner_join(
df,
column_woe_df[,c(colmn_nm,"WOE")],
by = colmn_nm,
type = "inner",
match = "all"
)
df[colmn_nm]<-NULL
colnames(df)[colnames(df)=="WOE"]<-colmn_nm
} else if (df_col_typ[rownm, "clmtyp"] == "numeric" | df_col_typ[rownm, "clmtyp"] == "integer") {
column_woe_df$lv<-as.numeric(str_sub(
column_woe_df[,colmn_nm],
regexpr("\\[", column_woe_df[,colmn_nm]) + 1,
regexpr(",", column_woe_df[,colmn_nm]) - 1
))
column_woe_df$uv<-as.numeric(str_sub(
column_woe_df[,colmn_nm],
regexpr(",", column_woe_df[,colmn_nm]) + 1,
regexpr("\\]", column_woe_df[,colmn_nm]) - 1
))
column_woe_df[colmn_nm]<-NULL
column_woe_df<-column_woe_df[,c("lv","uv","WOE")]
colnames(df)[colnames(df)==colmn_nm]<-"WOE_temp2381111111111111697"
df <-
fuzzy_inner_join(
df,
column_woe_df[,c("lv","uv","WOE")],
by = c("WOE_temp2381111111111111697"="lv","WOE_temp2381111111111111697"="uv"),
match_fun=list(`>=`,`<=`)
)
df["WOE_temp2381111111111111697"]<-NULL
df["lv"]<-NULL
df["uv"]<-NULL
colnames(df)[colnames(df)=="WOE"]<-colmn_nm
}}
}
return(df)
}
Function Call:-
test_df_woe <- woe_replace(test_df, IV)
OR Super one Shot:-
test_df_woe <- woe_replace(test_df,Information::create_infotables(data = test_df, y = "label_column",parallel =TRUE))
Edit: Building off of aL3xa's answer below, I've modified his syntax below. Not perfect, but getting closer. I still haven't found a way to make xtable accept \multicolumn{} arguments for columns or rows. It also appears that Hmisc handles some of these type of tasks behind the scenes, but it looks like a bit of an undertaking to understand what's going on there. Does anyone have experience with the latex function in Hmisc?
ctab <- function(tab, dec = 2, margin = NULL) {
tab <- as.table(tab)
ptab <- paste(round(prop.table(tab, margin = margin) * 100, dec), "%", sep = "")
res <- matrix(NA, nrow = nrow(tab) , ncol = ncol(tab) * 2, byrow = TRUE)
oddc <- 1:ncol(tab) %% 2 == 1
evenc <- 1:ncol(tab) %% 2 == 0
res[,oddc ] <- tab
res[,evenc ] <- ptab
res <- as.table(res)
colnames(res) <- rep(colnames(tab), each = 2)
rownames(res) <- rownames(tab)
return(res)
}
I would like to create a table formatted for LaTeX output that contains both the counts and percentages for each column or variable. I have not found a ready made solution to this problem, but feel I must be recreating the wheel to some extent.
I have developed a solution for straight tabulations, but am struggling with adopting something for a cross tabulation.
First some sample data:
#Generate sample data
dow <- sample(1:7, 100, replace=TRUE)
purp <- sample(1:4, 100, replace=TRUE)
dow <- factor(dow, 1:7, c("Mon", "Tues", "Wed", "Thurs", "Fri", "Sat", "Sun"))
purp <- factor(purp, 1:4, c("Business", "Commute", "Vacation", "Other"))
And now the working straight tab function:
customTable <- function(var, capt = NULL){
counts <- table(var)
percs <- 100 * prop.table(counts)
print(
xtable(
cbind(
Count = counts
, Percent = percs
)
, caption = capt
, digits = c(0,0,2)
)
, caption.placement="top"
)
}
#Usage
customTable(dow, capt="Day of Week")
customTable(purp, capt="Trip Pupose")
Does anyone have any suggestions for adopting this for cross tabulations (i.e. day of week BY trip purpose)? Here is what I've currently written, which does NOT use the xtable library and ALMOST works, but is not dynamic and is quite ugly to work with:
#Create table and percentages
a <- table(dow, purp)
b <- round(prop.table(a, 1),2)
#Column bind all of the counts & percentages together, this SHOULD become dynamic in future
d <- cbind( cbind(Count = a[,1],Percent = b[,1])
, cbind(Count = a[,2], Percent = b[,2])
, cbind(Count = a[,3], Percent = b[,3])
, cbind(Count = a[,4], Percent = b[,4])
)
#Ugly function that needs help, or scrapped for something else
crossTab <- function(title){
cat("\\begin{table}[ht]\n")
cat("\\begin{center}\n")
cat("\\caption{", title, "}\n", sep="")
cat("\\begin{tabular}{rllllllll}\n")
cat("\\hline\n")
cat("", cat("", paste("&\\multicolumn{2}{c}{",colnames(a), "}"), sep = ""), "\\\\\n", sep="")
c("&", cat("", colnames(d), "\\\\\n", sep=" & "))
cat("\\hline\n")
c("&", write.table(d, sep = " & ", eol="\\\\\n", quote=FALSE, col.names=FALSE))
cat("\\hline\n")
cat("\\end{tabular}\n")
cat("\\end{center}\n")
cat("\\end{table}\n")
}
crossTab(title = "Day of week BY Trip Purpose")
In the Tables-package it is one line:
# data:
dow <- sample(1:7, 100, replace=TRUE)
purp <- sample(1:4, 100, replace=TRUE)
dow <- factor(dow, 1:7, c("Mon", "Tues", "Wed", "Thurs", "Fri", "Sat", "Sun"))
purp <- factor(purp, 1:4, c("Business", "Commute", "Vacation", "Other"))
dataframe <- data.frame( dow, purp)
# The packages
library(tables)
library(Hmisc)
# The table
tabular( (Weekday=dow) ~ (Purpose=purp)*(Percent("row")+ 1) ,data=dataframe )
# The latex table
latex( tabular( (Weekday=dow) ~ (Purpose=purp)*(Percent("col")+ 1) ,data=dataframe ))
Using booktabs, you get this (can be further customised):
Great question, this one's bothering me for a while (it's not that hard, it's just me being lazy as hell... as usual). However... though the question's great, your approach, I'm afraid, isn't. There's priceless package called xtable that you can (mis)use. Besides, this issue is too common - there's a great chance that there's already some ready-made solution sitting somewhere on the Internets.
One of these days I'm about to work it out once and for all (I'll post the code on GitHub). The main idea goes a little bit like this: would you like frequency and/or percentage values within one cell (separated by \) or rows with absolute and relative frequencies (or %) in succession? I'd go with the 2nd one, so I'll post a "first-aid" solution for now:
ctab <- function(tab, dec = 2, ...) {
tab <- as.table(tab)
ptab <- paste(round(prop.table(tab) * 100, dec), "%", sep = "")
res <- matrix(NA, nrow = nrow(tab) * 2, ncol = ncol(tab), byrow = TRUE)
oddr <- 1:nrow(tab) %% 2 == 1
evenr <- 1:nrow(tab) %% 2 == 0
res[oddr, ] <- tab
res[evenr, ] <- ptab
res <- as.table(res)
colnames(res) <- colnames(tab)
rownames(res) <- rep(rownames(tab), each = 2)
return(res)
}
Now try something like:
data(HairEyeColor) # load an appropriate dataset
tb <- HairEyeColor[, , 1] # choose only male respondents
ctab(tb)
Brown Blue Hazel Green
Black 32 11 10 3
Black 11.47% 3.94% 3.58% 1.08%
Brown 53 50 25 15
Brown 19% 17.92% 8.96% 5.38%
Red 10 10 7 7
Red 3.58% 3.58% 2.51% 2.51%
Blond 3 30 5 8
Blond 1.08% 10.75% 1.79% 2.87%
Make sure you loaded xtable package and use print (it's a generic function, so you must pass a xtable classed object). It's important that you suppress the row names. I'll optimize this one tomorrow - it should be xtable compatible. It's 3AM in my time zone, so with these lines I'll end my answer:
print(xtable(ctab(tb)), include.rownames = FALSE)
Cheers!
I wasn't able to figure out how to generate a multi column header using xtable, but I did realize that i could concatenate my counts & percentages into the same column for printing purposes. Not ideal, but seems to get the job done. Here's the function I've written:
ctab3 <- function(row, col, margin = 1, dec = 2, percs = FALSE, total = FALSE, tex = FALSE, caption = NULL){
tab <- as.table(table(row,col))
ptab <- signif(prop.table(tab, margin = margin), dec)
if (percs){
z <- matrix(NA, nrow = nrow(tab), ncol = ncol(tab), byrow = TRUE)
for (i in 1:ncol(tab)) z[,i] <- paste(tab[,i], ptab[,i], sep = " ")
rownames(z) <- rownames(tab)
colnames(z) <- colnames(tab)
if (margin == 1 & total){
rowTot <- paste(apply(tab, 1, sum), apply(ptab, 1, sum), sep = " ")
z <- cbind(z, Total = rowTot)
} else if (margin == 2 & total) {
colTot <- paste(apply(tab, 2, sum), apply(ptab, 2, sum), sep = " ")
z <- rbind(z,Total = colTot)
}
} else {
z <- table(row, col)
}
ifelse(tex, return(xtable(z, caption)), return(z))
}
Probably not the final product, but does allow for some flexibility in parameters. At the most basic level, is only a wrapper of table() but can also generate LaTeX formatted output as well. Here is what I ended up using in a Sweave document:
<<echo = FALSE>>=
for (i in 1:ncol(df)){
print(ctab3(
col = df[,1]
, row = df[,i]
, margin = 2
, total = TRUE
, tex = TRUE
, caption = paste("Dow by", colnames(df[i]), sep = " ")
))
}
#
Using multicolumn with latex from the Hmisc package isn't too bad. This minimal Sweave document:
\documentclass{article}
\begin{document}
<<echo = FALSE,results = tex>>=
library(Hmisc)
dow <- sample(1:7, 100, replace=TRUE)
purp <- sample(1:4, 100, replace=TRUE)
dow <- factor(dow, 1:7, c("Mon", "Tues", "Wed", "Thurs", "Fri", "Sat", "Sun"))
purp <- factor(purp, 1:4, c("Business", "Commute", "Vacation", "Other"))
tbl <- table(dow,purp)
tbl_prop <- round(100 * prop.table(tbl,1),2)
tbl_df <- as.data.frame.matrix(tbl)
tbl_prop_df <- as.data.frame.matrix(tbl_prop)
colnames(tbl_prop_df) <- paste(colnames(tbl_prop_df),"1",sep = "")
df <- cbind(tbl_df,tbl_prop_df)[,ggplot2:::interleave(1:4,5:8)]
colnames(df) <- rep(c('n','\\%'),times = 4)
latex(object=df,file="",cgroup = colnames(tbl_df),
colheads = NULL,rowlabel = "",
center = "centering",collabel.just = rep("r",8))
#
\end{document}
Produces this for me:
Obviously, I've hard-coded a fair bit of stuff, and there could be slicker ways to produce the data frame that you end up passing to latex, but this should at least give a start using multicolum.
Also, a slight gotcha, I've used ggplot2's interleave function when combining the counts and percentages to alternate the columns. That's just cause I'm lazy.
How would this work for you?
library(reshape)
library(plyr)
df <- data.frame(dow = dow, purp = purp)
df.count <- count(df)
df.count <- ddply(df.count, .(dow), transform, p = round(freq / sum(freq),2))
df.m <- melt(df.count)
df.print <- cast(df.m, dow ~ purp + variable)
library(xtable)
xtable(df.print)
It doesn't give you nice multicolumns, and I don't have enough experience with xtable to figure out if that's possible. However, if you're going to be writing custom functions, you might try one which operates over the column names of df.print. You might be even able to write one sufficiently general to take all manner of recast data frames as input.
Edit:
Just thought of a good solution to get you closer. After creating df.m
df.preprint <- ddply(df.m, .(dow, purp), function(x){
x <- cast(x, dow ~ variable)
x$value <- paste(x$freq, x$p, sep = " / ")
return(c(value = x$value))
}
)
df.print <- cast(df.preprint, dow ~ purp)
print(xtable(df.print), include.rownames = F)
Now, every cell will contain N / percent values
I realize this thread is a bit old, but the tableNominal() function in the reporttools package may provide the functionality you are looking for.
tab<-table(row, col)
ctab<-round(100*prop.table(tab,2), 2) # for column percents (see the args for prop.table)
for (i in 1:length(tab)) {
ctab[i]<-paste(tab[i]," (", ctab[i], "%", ")", sep="")
}
require(xtable);
k<-xtable(ctab,digits=1) # make latex table