similar to: pairwise cross tabulation tables

Hello, might be rather easy for R pros, but I've been searching to the dead end to ... twsource.area <- table(twsource, area, useNA="ifany") gives me a nice cross tabulation of frequencies of two factors, but now I want to convert to pecentages of those absolute values. In addition I'd like an extra column and an extra row with absolute sums. I know, Excel or the

Hi R, This is a cross tabulation question. Suppose that, > d=read.table("clipboard",header=F) > d V1 V2 V3 A One Apple A One Cake A One Cake B One Apple B One Apple B One Apple > table(d$V2,d$V3) Apple Cake One 4 2 But, I don't want the count to be like the above. Here, it is counting the

i make me thoughts about a "advanced tabulation" package similar to commercial software products like Quantum or Wincross. Before i'm beginning to fight with coding - is in the mailing-List anybody doing something similar in the past and have a good starting point and/or suggestions for me ? My purpose ist to define for a dataset headers (i.e. sex,age-groupes..) which should

Hi every one, how to get frequency distributions for one variable across other variable. ex: var1=age, var2=height i need frequency distribution of age across height. Thanks in Advance. -- View this message in context: http://www.nabble.com/cross-tabulation-for-frequency-distributions-tp24968531p24968531.html Sent from the R help mailing list archive at Nabble.com.

Hi, I am trying to reproduce some functionalities of Excel pivot table in R, sadly, I couldn't figure out how to do it. I am wondering if this is even possible in R. Does anyone know? Here is an example: year=rep(2003,16) quarter=rep(1:4,each=4) sales=1:16 company=rep(c("a","b","c","d"),4) df=data.frame(year,quarter,sales,company) #this is the

Hi all, I have a data set x1 x2 x3 1 2 1 1 2 3 2 1 2 1 2 1 3 1 1 I want to tabulate in the following way. 1 2 3 x1 3 2 1 x2 2 3 0 x3 3 1 1 It is just like frequency distribution Any help is highly appreciated [[alternative HTML version deleted]]

Hi all, Please, is there any way of controlling factors in row/columns when using ftable/xtabs? As far as I can see, the last cross-clasifing variable in the formula will appear in columns. The previous ones, in rows. For instance, is it possible to make tension and replicate appear in columns? ftable(xtabs(breaks ~ wool + tension + replicate, data = warpbreaks)) After some years using SAS

Dear all, I think this can be interesting for others too. For a while I tried to solve that problem - how to tabulate with ENTER/RETURN key too and not only with TAB. You know the problem - you are entering a lot of numbers from numeric keypad but you need to go far left to press TAB. This prevents one-hand data entry and prohibits web apps to be used by accountants, for instance. Here is a

R-helpers: I have a data frame containing 4 factor variables (let's say A,B,C, and D) and 1 numerical variable (N). I would like to produce a cross-tabulated data frame in which A,B,C are individual columns, each factor of D is its own column, and the field is calculated as a given function of N (I would like to have two output data frames, one with the mean(N) and one with the

Hello, I wonder if someone could send me suggestions on how to solve the following problem: I have a vector of an arbitrary size (ex. data<-c(10,10,11,10,12,11,10,12,11,11,10,11)) and use the table function, which gives the following result 10 11 12 5 5 2 that''s fine, but what I would like to do now is: construct new classes based on the number of classes from table, 10

Hello, I am working on a variable selection problem and I wonder whether there is some function or package in R works similar to the 'PROC REG' in SAS? Thank you. Some facts about 'PROC REG': PROC REG in SAS first composes a crossproducts matrix. The matrix can be calculated from input data, reformed from an input correlation matrix, or read in from an SSCP data set. For each

I have the following relatively simple problem. Say we have three factors, and we want to create a cross-tabulation against each of the other two: x <- factor(rbinom(5, 1, 1/2)) y <- factor(rbinom(5, 1, 1/2)) z <- factor(rbinom(5, 1, 1/2)) table(x,y) table(x,z) This looks like: y x 0 1 0 2 0 1 1 2 z x 0 1 0 1 1 1 2 1 I would like to get (surely this will

I am aware of table(is.na(df$var)) but is there an efficient way of create a table that shows the number of missing values in each variable of a data frame? Right now I am forced to create a new variable, varna<-is.na(df$var), for each variable of the data frame, bind them to a new data frame and tabulate it, but surely there is a simpler way? any help is greatly appreciated, angelo

Hi, I am attempting to tabulate binned data. The '1' represents the appearance of the focal mouse pup, and '2' represents the disappearance of the focal mouse pup. The code written below is intended to calculate the total time spent appeared out of 3600s. For Sample 1, both the hand calculation and R code yield the same result, 50. A problem seems to occur when '1' is the

hi, i'm new in R and i need some help. Please, ¿do you know a function how can process cross tables for many variables and show the result in one table who look like this?: +----------------------------------------------------+ |------------------ | X variable | |----------------- | Xop1 | Xop2 | Xop3|.....| +----------------------------------------------------+ |Yvar1 |

Hi useRs! I would like to know how to make aggregated data.frame with aggregate() tabulated. For example, I run the following command to aggregate re with respect to group1 and group2. > (aggr <- with(final, aggregate(re, group1, group2, mean))) Group.1 Group.2 x 1 1992 15 0.16392 2 1993 15 0.15467 3 1994 15 0.15456 4 1995 15 0.15391

I think this is one for Gabor. I don't seem to be able to find my way to an answer despite numerous rereadings of factor and table. Here is a toy example: ### Some data EthnicCode <- c("European/Other", NA, "European/Other", "European/Other", "Pacific", "European/Other", "European/Other",

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

Hi R users I am trying to get cross-tabulation tables using tables. All variables used are binary ones (0 and 1). Each time I constructed cross-tabluation table using a different variable pair (e.g., variable 1 and variable 2, variable 1 and variable 3 etc) In doing so, I ran into some problems i.V2 i.V1 1 0 17 1 33 For variable 2 (i.V2) there was no one belonging to ZERO

Hi dear R-users I try to reproduce the steps included in a LDA. Concerning the eigenvectors there is a difference to SPSS. In my textbook (Bortz) it says, that the matrix with the eigenvectors V usually are not normalized to the length of 1, but in the way that the following holds (SPSS does the same thing): t(Vstar)%*%Derror%*%Vstar = I where Vstar are the normalized eigenvectors. Derror