similar to: Rounding and printing

Hi, Suppose I have the following vector (data points): > x [1] 36.0 57.3 73.3 92.0 300.4 80.9 19.8 31.4 85.8 44.9 24.6 48.0 [13] 28.0 38.3 85.2 103.6 154.4 128.5 38.3 72.4 122.7 123.1 41.8 21.7 [25] 143.6 120.2 46.6 29.2 44.8 25.0 57.3 96.4 29.4 62.9 66.4 30.0 [37] 24.1 14.8 56.6 102.4 117.5 90.4 37.2 79.6 27.8 17.1 26.6 16.3 [49] 41.4 48.9 24.1

Hi all, I am trying to convert a data set of physician death codings (each individual's cause of death is coded by multiple physicians) from long to wide format, but the "reshape" function doesn't seem to work because it requires a "time" variable to identify the sequence among the repeated observations within individuals. My data set has no order, and different

hello, I'd like know how to sort a data frame in R for example how I should do to sort by Catholic with swiss data frame like below thanks Fertility Agriculture Examination Education Catholic Infant.Mortality Courtelary 80.2 17.0 15 12 9.96 22.2 Delemont 83.1 45.1 6 9 84.84 22.2

Hi all! I tried to perform Shapiro-Wilk test for my sample of 243 values. > Us [1] -10.4 -13.1 -12.2 38.1 -18.8 -13.3 -11.7 29.3 49.7 6.8 12.7 16.3 [13] 5.8 -0.7 -29.4 4.1 38.8 -1.4 8.8 15.6 32.9 -5.3 19.1 35.8 [25] 4.0 -1.5 0.6 -4.2 -10.0 -4.0 1.1 48.9 -21.0 -5.3 5.8 -10.8 [37] 21.9 8.2 -3.2 -3.9 -2.3 12.6 -4.7 -8.0 11.8 27.4 -9.5 -20.8 [49]

Dear all, I have the following data frame: > dat V1 V2 V3 V4 V5 V6 V7 V8 V9 1 1 AAAACACCCACCCCCCCCCCCCCCCCCCCCCCCC 9.0 18 12.00 18.0 15.0 12.0 6.0 2 1 ACGATACGGCGACCACCGAGATCTACACTCTTCC 18.0 8 12.00 18.0 15.0 12.0 18.0 3 1 ACTACTGCTCCCCCCCCACTCCCCCCCCCCCCCC 15.0 8 12.00 12.0 18.0 12.0 12.0 4 1 ACTTATACGGCGACCACCGAGATCTACACTCTTT 15.0

Hi Every one, I have a dataframe "class" with "name", "sex", "age", "height", "Weight". if i caluclate summary statistics with the below code numSummary(class[,c("Height", "Weight")], groups=class$Name, statistics=c("mean", "sd", "quantiles"), quantiles=c(0, .25,.5,.75,1)) iam getting

Hello, I have several very large data sets (1-7 million observations, sometimes hundreds of variables) that I'm trying to work with in R, and memory seems to be a big issue. I'm currently using a 2 GB Windows setup, but might have the option to run R on a server remotely. Windows R seems basically limited to 2 GB memory if I'm right; is there the possibility to go much beyond that

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

Hi all, I would like to do a barplot of a dataframe like this one: alfa beta gamma delta qwert 56.5 58.5 56.5 58.5 asdfg 73.0 73.0 43.0 73.0 zxcvb 63.0 63.0 43.0 63.0 yuiop 63.0 63.0 43.0 63.0 with the labels of the rows and columns. I would like to have something that works for dataframes with varying dimensions, and so far I haven't found any way to do it.

Hi, I ran an experiment with 3 factors, 2 levels and 200 replications and as I want to test for residuals independence, I used Durbin-Watson in R. I found two functions (durbin.watson and dwtest) and while both are giving the same rho, the p-values are greatly differ: > durbin.watson(mod1) lag Autocorrelation D-W Statistic p-value 1 -0.04431012 2.088610 0.012 Alternative

Hi, I'm a novice of R. I want to read the following table into R: names mpg cyl disp hp drat Mazda RX4 21.0 6 160.0 110 3.90 Mazda RX4 Wag 21.0 6 160.0 110 3.90 The command I used is: > test <- read.table(file.choose(),header=T) The result is: Error in read.table(file.choose(), header = T) : more columns than column names

Hi, I'm just starting out with R and came across R_inferno.pdf by Patrick Burns just yesterday - I recommend it! His description of how 'growing' objects (e.g. obj <- c(obj, additionalValue) eats up memory prompted me to rewrite a function (which made such calls ~210 times) so that it used indexing into a dimensioned object instead (i.e. obj[i, ] <- additionalValue). This

Dear list, I want the 1st, 2nd, 5th, and 6th columns of mtcars. After copying them, the columns become numeric class rather than data frame. But, when I copy rows, they data frame retains its class. Why is this? I don't see why copying rows vs columns is so different. > class(mtcars) [1] "data.frame" > head(mtcars) mpg cyl disp hp drat wt qsec vs

Dear all, I have been given a data something like below: Dat = "2 3 28.3 3.05 8 3 3 22.5 1.55 0 1 1 26.0 2.30 9 3 3 24.8 2.10 0 3 3 26.0 2.60 4 2 3 23.8 2.10 0 3 2 24.7 1.90 0 2 1 23.7 1.95 0 3 3 25.6 2.15 0 3 3 24.3 2.15 0 2 3 25.8 2.65 0 2 3 28.2 3.05 11 4 2 21.0 1.85 0 2 1 26.0 2.30 14 1 1 27.1 2.95 8 2 3 25.2 2.00 1 2 3 29.0 3.00 1 4 3 24.7 2.20 0 2 3 27.4 2.70 5 2 2 23.2 1.95

Hi ! Kenrel 2.6.21 (kernel.org) Patch lguest-2.6.21-254.patch Distro Slackware 11.0 GCC 3.4.6 GLIBC 2.3.6 HW model name : AMD Duron(tm) procu{s{ Module Size Used by tun 7680 0 lg 54600 0 just started playing with lguest - patching, compiling and booting the host-kernel goes ok - compiling lguest is ok as well after

Hi ! Kenrel 2.6.21 (kernel.org) Patch lguest-2.6.21-254.patch Distro Slackware 11.0 GCC 3.4.6 GLIBC 2.3.6 HW model name : AMD Duron(tm) procu{s{ Module Size Used by tun 7680 0 lg 54600 0 just started playing with lguest - patching, compiling and booting the host-kernel goes ok - compiling lguest is ok as well after

Should I be able to use axis() on a barplot? i have a data.frame, the first 3 values of which are: > c[1:3,] median mean A1 56.5 58.50000 A61 73.0 73.00000 A62 63.0 63.00000 > str(c) `data.frame': 19 obs. of 2 variables: $ median: num 56.5 73 63 161 51 55 44.5 22 54 49 ... $ mean : num 58.5 73.0 63.0 161.0 47.5 ... if I do barplot(median) and then try

I have some doubts about the validity of my procedure to estimeate linear contrasts ina a factorial design. For sake of semplicity, let's imagine a one way ANOVA with three levels. I am interested to test the significance of the difference between the first and third level (called here contrast C1) and between the first and the seconda level (called here contrast C2). I used the following

I am using R-2.10.1 binary from CRAN on a WinXP Pro system. I also use SAS v9.2 on the same box. I just started using the SAS ODBC driver that comes with version 9 of SAS. I have been able to set up an ODBC source for SAS datasets using the driver, and then with RODBC I am able to read a sample SAS dataset. > library(RODBC) > ch <- odbcConnect('sasodbc', believeNRows=FALSE)

Hola!! tengo un data.frame donde cada fila corresponde a un año y cada columna a un mes (De enero a diciembre) > head(valT) V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 1941 18.0 16.3 15.2 10.1 8.1 8.3 8.8 9.2 7.9 12.2 11.9 14.6 1942 17.2 15.9 13.6 11.6 8.7 6.2 6.4 7.2 9.7 12.0 14.1 16.7 1943 17.6 17.3 13.5 12.5 10.5 7.0 8.2 7.9 -999.9 -999.9