similar to: Apparent bug in summary.data.frame() with columns of Date class and NA's present

Hello, eveRybody, I've been trying to find the origin for the following formatting-"inconsistency": E. g., look at the number of digits in summary.defaults()'s output when NAs occur: in my example below the number of NA's is displayed as an integer, the rest as numeric (floating point numbers): > summary.default( c( 1:2, NA)) Min. 1st Qu. Median Mean 3rd Qu.

(summary.default): show the vector length in addition to quantiles diff -u -i -p -F '^(def' -b -w -B /home/sds/src/R-3.0.1/src/library/base/R/summary.R.old /home/sds/src/R-3.0.1/src/library/base/R/summary.R --- /home/sds/src/R-3.0.1/src/library/base/R/summary.R.old 2013-03-05 18:02:33.000000000 -0500 +++ /home/sds/src/R-3.0.1/src/library/base/R/summary.R 2013-09-10 10:19:02.682946339

Hello all! I have a problem to extract values greater that for example 1820. I try this code: x[x[,1]>1820,]->x1 Please help me! Thank you! The data structure is: structure(c(2.576, 1.728, 3.434, 2.187, 1.928, 1.886, 1.2425, 1.23, 1.075, 1.1785, 1.186, 1.165, 1.732, 1.517, 1.4095, 1.074, 1.618, 1.677, 1.845, 1.594, 1.6655, 1.1605, 1.425, 1.099, 1.007, 1.1795, 1.3855, 1.4065, 1.138, 1.514,

Here's a bug one of my students noticed. When you call plot() on a glm object, plot.lm gets called. The second plot it shows is supposed to give a normal QQ plot of the standard deviance residuals, but it doesn't. The glm object created by glm.fit returns something (the IRLS weights?) in fit$weights which plot.lm takes as observation weights, so you get strange residuals in the QQ

Hello all! I have a problem with R. I try to merge data like this: structure(c(2.1785, 1.868, 2.1855, 2.5175, 2.025, 2.435, 1.809, 1.628, 1.327, 1.3485, 1.4335, 2.052, 2.2465, 2.151, 1.7945, 1.79, 1.6055, 1.616, 1.633, 1.665, 2.002, 2.152, 1.736, 1.7985, 1.9155, 1.7135, 1.548, 1.568, 1.713, 2.079, 1.875, 2.12, 2.072, 1.906, 1.4645, 1.3025, 1.407, 1.5445, 1.437, 1.463, 1.5235, 1.609, 1.738, 1.478,

Hello All, Using the standard "summary" function in 'R', I ran across some odd behavior that I cannot understand. Easy to reproduce: Typing: summary(c(6,207936)) Yields:: Min. *1st Qu. Median Mean 3rd Qu. Max.* 6 *51990 104000 104000 156000 207900* None of these values are correct except for the minimum. If I perform "quantile(c(6,

Hello all! I have a problem to group my data (years) in 10 years classes. For example for year year decade 1598 1590-1600 1599 1590-1600 1600 1590-1600 1601 1600-1610 --- my is like this> [1] 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 [16] 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 [31] 1628 1629 1630 1631 1632 1633

Hi, again i am stuck in my presentation, and i have never learn R before in my life but need this to be done, so please help me out for a favour: http://www.nabble.com/file/p20333155/kew.dat kew.dat run this in R and these comes up: Month Year Rain 1 Jan 1900 74.400000 2 Feb 1900 80.500000 3 Mar 1900 23.600000 4 Apr 1900 23.600000 5 May 1900 25.100000 6

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

Consider > summary(1:5) Min. 1st Qu. Median Mean 3rd Qu. Max. 1 2 3 3 4 5 > summary(c(1:5,NA)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 1 2 3 3 4 5 1 Wouldn't it be more stringent if "NA's" was also reported in the first case?? Regards S?ren

Hello all! I have a problem with my data in R. When I want to plot the following data, I have a problem with y scale. The maximum value is cc. 10 degrees and in R is about 100. I use this code: fasy<-read.table("gridd1.txt",sep="\t",dec=",",header=T,row.names=1) # here are the years: x <- as.numeric(rownames(fasy)) # extract a series that you want to plot: y

Dear all I am happy that summary.default() no longer rounds since R 3.4.0. However, in R 3.4.0, in a few cases, print.summaryDefault() rounds the mean value (and the median value) differently on my GNU/Linux machine and on my colleague's MS-Windows machine. Here is a small (simplified) reproducible example: R> a <- 1234568.01 + c(0:1) R> summary(a) Output on MS-Windows (expected

Might this be related to the Linux version? I'm testing on one of our university servers, and they tend to be deprived of regular updates sometimes... (Dirk, sorry for sending you this twice.) > Sys.info() sysname "Linux" release

Dear R-helpers, Happy New Year to all the helpful members of the list. Here is the behavior I'm looking for: > v1 <- c("a","b","c") > n1 <- c(0, 1, 2) > v2 <- c("c", "a", "b") > n2 <- c(0, 1 , 2) > (f1 <- data.frame(v1, n1)) v1 n1 1 a 0 2 b 1 3 c 2 > (f2 <- data.frame(v2, n2))

Greetings Everybody: I generated a 1.2MB dta file based on the general social survey with Stata8 for linux. The file can be re-opened with Stata, but when I bring it into R, it says all the values are missing for most of the variables. This dataset is called "morgen.dta" and I dropped a copy online in case you are interested http://www.ku.edu/~pauljohn/R/morgen.dta looks like this

I find it's just great to be able to say: library(tseries) x <- get.hist.quote(instrument="ongc.ns") and it gets a full time-series of the stock price of the symbol ongc.ns from Yahoo quote. However, once my hopes have been raised by such beauty :-) I get disappointed when I do > plot(x) and the annotation is horrible! The x axis is not labelled as dates. The default

I have a case where I would like to change multiple columns containing numbers to factors. I can change each column one at a time as in: TEMP.FACT$EXPOS01<-factor(TEMP.FACT$EXPOS01,levels=c(1,2,3),labels=c("No ne","Low Impact","MedHigh Imp")) TEMP.FACT$EXPOS02<-factor(TEMP.FACT$EXPOS02,levels=c(1,2,3),labels=c("No ne","Low

Working with 5 subset streams from my source data frame, three of them successfully call dcast(), but two fail: jerritt.cast <- dcast(jerritt.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length and winters.cast <- dcast(winters.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length Yet both data frames have the values in their

An Sweave file, 'test.Rnw': \documentclass{article} \title{Sweave minimal} \author{MK} \begin{document} \maketitle We try Sweave: <<1>>= data(airquality) summary(airquality) x <- airquality[1, 1] @ I try Sexpr: \Sexpr{x} We plot: \begin{center} <<2, fig=TRUE, echo=FALSE >>= boxplot(Ozone ~ Month, data = airquality) @ \end{center} \end{document} I check the

Greetings all, Ran into a strange problem with the krige function from geoR. The problem that I am having is that while the krige function seems to work well, the resulting predicted values are all NAs. Given the size of the datasets I am working with can't attach it, but I can provide snippets of the datasets. > casedata station year month day obs mpe bias type