similar to: How to do it through 1 step?

Hello all, I have read as many merge issues as I possibly could tonight and although I presume this is a small error, I have not found the solution to my problem. I'm trying to merge two data sets: dat0 and TransTable. As you can see below, dat0 has 8000 rows, whereas TransTable has 47296 rows. I would expect when I merge the two data sets, with all.x=F, and all.y=F, that the

Hi, I am trying to merge data similar to the example data below > dat0 id var1 var2 var3 2 1 0 1 3 1 0 1 4 0 1 1 5 0 1 1 > dat1 id var4 var5 var6 2 1 0 1 3 1 0 1 6 0 1 1 7 0 1 1 > dat2 id

Hi, set.seed(45) test1<-data.frame(columnA=rnorm(7,45),columnB=rnorm(7,10)) #used an example probably similar to your actual data apply(test1,2,function(x) sprintf("%.1f",median(x))) #columnA columnB # "44.5"? "10.2" par(mfrow=c(1,2)) lapply(test1,function(x) {b<-

I have a longitudinal competing risk data of the form: ID COMPL SEX HEREDITY 1 0 1 2 1 0 1 2 1 3 1 2 2 0 0 1 2 1 0 1 2 2 0 1 2 2 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 2 0 1 4 0 1 2 4 0 1

Hi, I am trying to write a loop to recode my data from -999 to NA in R. What's the most efficient way to do this? Below is what I'm presently doing, which is inefficient. Thanks, Chris dat0 <- read.table("time1.dat") colnames(dat0) <- c("e1dq", "e1arcp", "e1dev", "s1prcp", "s1nrcp", "s1ints",

Hi, here i have a Max and Min values Min <-3 Max <-6 and also a matrix like this, ABC XYZ PQR ------ ------- ------- 2 4 3 5 4 8 7 1 3 In this i need to check each particular column values are between Max and Min value. If the coulmn value not coming between Max and

Dear R helpers Suppose I have a data frame as given below mydat = data.frame(x = c(1,1,1, 2, 2, 2, 2, 2, 5, 5, 6), y = c(10, 10, 10, 8, 8, 8, 7, 7, 2, 2, 4)) mydat         x     y 1      1     10 2      1     10 3      1     10 4      2       8 5      2       8 6      2       8 7      2       7 8      2       7 9      5       2 10    5       2 11    6       4 unique(mydat$x) will give me 1,

Hola, Esta es una forma de hacerlo... Mira que lo primero que he modificado es el fichero "x.csv" para sustituir los espacios en los nombres por "_". Y también he quitado los acentos y las eñes... He utilizado el paquete RNNS y la función "mlp()" para ajustar la red. #------------------------------------------- > x <- read.csv("x.csv",

I'm trying to use mars{mda} to model functions that look fairly close to a sequence of straight line segments. Unfortunately, 'mars' seems to totally miss the obvious places for the knots in the apparent first order spline model, and I wonder if someone can suggest a better way to do this. The following example consists of a slight downward trend followed by a jump up after

Hi dear R-users, I have a question about a function I'm trying to improve. How can I stop the function calculation at the last numeric value of my data? The problem is that the end of my data contains missing values (NAs). And the aim of my function is to compare the first numeric value with the next one (till the end). For the moment, It works well when my data doesn't contains any NAs

Dear all, I have several character strings with a high number of different levels. unique(x) gives me values in the range of 100-200. This creates problems as I would like to use them as predictors in a coxph model. I therefore would like to convert each of these strings to a new string (x_new). x_new should be equal to x for the top n categories (i.e. the top n levels with the highest

Hi, folks, Happy work in weekends >_< My question is how to plot the dependent variable against one of the predictors with other predictors as constant. Not for the original data, but after prediction. It means y is the predicted value of the dependent variables. The constane value of the other predictors may be the average or some fixed value. ####### y=1:10 x=10:1 z=2:11

I have some data fit to a smooth.spline object as follows: (x=vector of data for the predictor variable, y=vector of data for the response variable) fit <- smooth.spline(x,y) Now, given a spline fit point y_new, I want to be able to find out what value of x_new yielded this fit value. How to do so? (This problem is the inverse of the predict.smooth.spline function, which takes x_new as input

Hi all, Are there functions in R that could help me do the following? We have a special type of regression which is called Geometric Mean Regression. We have done some search and found the following: https://stat.ethz.ch/pipermail/r-help/2011-July/285022.html The question is: how to do the statistical inference on GMR results? More specifically, we are looking for the prediction interval:

Dear R Helpers, I'm struggling with a data preparation problem. I feel that it is a quite easy task but I don't get it done. I hope you can help me with that. I have a data frame looking like this: ID <- c(1,1,1,2,2,3,3,3,3) T <- c(1,2,3,1,4,3,5,6,8) x <- rep(1,9) df <- data.frame(ID,T,x) >df ID T x 1 1 1 1 2 1 1 3 1 2 1 1 2 4 1 3 3 1 3 5 1 3 6 1 3 8 1 I want to

Dear all, Please suggest me how can I do it. I have a matrix which look like following: x1 x2 x3 t1 .01 0.3 0 t2 0 0.1 0.01 t3 0 .01 .01 t4 0 0 t5 5 0 0 t6 0 0 0 t7 0 0 0 t8 0 0 0 t9 0.6 0 0 t10 0 0 0.66 t11 0 0.6 0.11 t12 0 0.4 0 I want to sort decreasing order in each column based on rows. and then to display only those rows which has a value. The expected out put matrix will

Hi all: I have a quesion about ANOVA: Is SS(Sum of Square) of a specific factor constant with the number of factors changing? dat1 includes one factor g1,and g1's SS is called SS_g1_dat1. dat2 includes two factors g1,g2,and g1's SS is called SS_g1_dat2. My quesion is: Is SS_g1_dat1 equals to SS_g1_dat2? I have both "yes" and "no" reasons for the quesion,but

Dear all, I have a data frame with 2 columns and 102500 rows. The data looks like below V1 V2 8098 110000....... 9099 001100....... 0023 110011....... 6545 111111....... . . . . . . . . . . I want to split the colums like this ; V1 V2 V3 V4 V5 V6 v7 8098 1 1 0 0 0 0 ...... 9099

I'm studying alone the R language for data preparation. I found a course at MIT for data preparation that uses python but I'm using R to learning. The first exercise is the preparation of data from a database that shows the contributions made to candidates for U.S. president. The database is described in FORMART

Sarah's solutions are good, and here's another, even more basic: tmp1 <- unique(dat1$B) tmp2 <- seq_along(tmp1) dat1$C <- tmp2[ match( dat1$B, tmp1) ] > dat1 N B C 1 1 29_log 1 2 2 29_log 1 3 3 29_log 1 4 4 27_cat 2 5 5 27_cat 2 6 6 1_log 3 7 7 1_log 3 8 8 1_log 3 9 9 1_log 3 10 10 1_log 3 11 11 3_cat 4 12 12 3_cat 4 As a single line