similar to: help in merging

Note the inconsistency in the names in these two examples. X.Time in the first case and Time.1 in the second case. > transform(BOD, X = BOD[1:2] * seq(6)) Time demand X.Time X.demand 1 1 8.3 1 8.3 2 2 10.3 4 20.6 3 3 19.0 9 57.0 4 4 16.0 16 64.0 5 5 15.6 25 78.0 6 7 19.8 42 118.8 >

Hi, R-users, data is a matrix like this AMR BS GE HR MO UK SP500 1974 -0.3505 -0.1154 -0.4246 -0.2107 -0.0758 0.2331 -0.2647 1975 0.7083 0.2472 0.3719 0.2227 0.0213 0.3569 0.3720 1976 0.7329 0.3665 0.2550 0.5815 0.1276 0.0781 0.2384 1977 -0.2034 -0.4271 -0.0490 -0.0938 0.0712 -0.2721 -0.0718 1978 0.1663 -0.0452 -0.0573 0.2751 0.1372 -0.1346

The idea is that one wants to write the line of code below in a general way which works the same whether you specify ix as one column or multiple columns but the naming entirely changes when you do this and BOD[, 1] and transform(BOD, X=..., Y=...) or other hard coding solutions still require writing multiple cases. ix <- 1:2 transform(BOD, X = BOD[ix] * seq(6)) On Tue, Jul 24, 2018 at

Dear All, Is sqldf equipped with delete queries? I have tried delete queries but with no success. Thanks in advance, Paul

Am 27.08.24 um 11:55 schrieb peter dalgaard: > Yes. A quirk, rather than a bug I'd say. One issue is that the internal logic of transform() relies on > > e <- eval(substitute(list(...)), `_data`, parent.frame()) > tags <- names(e) > > so untagged entries in ... will not be included. ... unless at least one is tagged: R> transform(BOD, 0:5, 1:6) Time

Hi, I am new to R and trying to migrate from SAS. I am trying to copy data from one table to another table which have same columns using sqldf. but not working and showing "NULL" I wrote statement as sqldf("INSERT INTO new select * from data") but showing NULL Please help me in this regard. Thank you -- View this message in context:

Hi, I post this message to the general r-help list hoping anyone within a wider range have suggestions: There are three ways to integration R and postgres, especially on 64bit Microsoft windows Platform, 1. via RODBC package, which has 32 bit and 64 bit version for windows 2. via RPostgres interface, which only has 32bit version currently 3. via plr for Greenplum, which only supports a

Hi, With reference to the mail below, I have large datasets, coming from various different sources, which I can read into filebacked big.matrix using library bigmemory. I want to merge them all into one 'big.matrix' object. (Later, I want to run regression using library 'biglm'). I am unsuccessfully trying to do this from quite some time now. Can you please

Hello everyone, I want to import data from huge sas files with 100s of columns. The good thing is that I am only interested in a few selected columns. Is there any way to do that without loading the full dataset. I have tried two functions: (1) read.sas7bdat *[from library 'sas7bdat']*, and (2) read_sas *[from library 'haven']. *But couldn't find what I am looking for. Best

Hi R, I have an excel file in which the third column is "date" and others are "character" and "numeric". Number of columns are 12 If I use this to read the file in R: x = read.xls("D:\\file.xls") The problem is that my date column is read in julian dates. So I am using: x = read.xls("D:\\file.xls",

I am reading regressors from an excel file (I have no control over the file) and some of the element names have spaces: i.e. "Small Bank Aquired" but I have found that lm(SourceData ~ . - "Small Bank Aquired", mcReg) doesn't work (mcReg = modelCurrentRegressors) As they are toggles I have ran them through factor() to be treated propertly as 0 or 1 but due to the fact I

Hi all, I am performing a stepwise regression by running the "step" function on an "lm" object. Now I want to save the intermediate iterations. I know the argument trace=T will print it on the console, but I rather want to assign it to some R object or may be output it in a CSV or text file. Any help will be appreciated. Regards Utkarsh

I use sqldf to join 2 dataframes from 2 distinct databases : a and b come from old sqldf's. sqldf("select a.*, b.*, case a.QTY when null then b.QTY else a.QTY end as NEW_QTY" from a inner join b on a.OBJECT=b.OBJECT") R doesn't understand "when null". I tried with "when NA", "when '' ", "when ' ' " but it doesn't

Hi All, I have vector of length 52, say, x=sample(30,52,replace=T). I want to sort x and split into five *nearly equal groups*. Note that the observations are repeated in x so in case of a tie I want both the observations to fall in same group. This seems a very common task to do, but still I couldn't find an R function to do this. Any help would be highly appreciated. Regards Utkarsh

Hi, Please could someone explain how this element of predict.lm works? From the help file ` newdata An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. ' Does this dataframe (newdata) need to have the same variable names as was used in the original data frame used to fit the model? Or will R just look across consecutive

I am using the library(bigmemory) to handle large datasets, say 1 GB, and facing following problems. Any hints from anybody can be helpful. _Problem-1: _ I am using "read.big.matrix" function to create a filebacked big matrix of my data and get the following warning: > x = read.big.matrix("/home/utkarsh.s/data.csv",header=T,type="double",shared=T,backingfile

hi, the sas universal viewer might be a free, non-R way to convert a sas7bdat file to non-proprietary formats, not sure if it's windows-only. those other formats should be easier to import only a subset of columns into R.. https://support.sas.com/downloads/browse.htm?fil=&cat=74 On Thu, Aug 10, 2017 at 7:42 AM, peter dalgaard <pdalgd at gmail.com> wrote: > I had a look at this

Dear R family, I am a relative newbie and have been dabbling with R for a little while. Simple things really, but my employers are beginning to see the benefits of using R instead of excel. We have a remote monitoring station measuring groundwater levels. We download the date as a .csv file and up until now, we have been using excel to analyse the data. It?s been a hassle trying to wrestle

I've narrowed my scope problems with predict.coxph further. Here is a condensed example: fcall3 <- as.formula("time ~ age") dfun3 <- function(dcall) { fit <- lm(dcall, data=lung, model=FALSE) model.frame(fit) } dfun3(fcall3) The final call fails: it can't find 'dcall'. The relevant code in model.frame.lm is: env <- environment(formula$terms)

I had a look at this a while back and it didn't seem to be easy. The path of least resistance would seem to be to use SAS itself to create a data set with fewer columns, but of course that requires you to get access to SAS. Otherwise, I think you'd have to modify sas7bdat::read.sas7bdat to drop unselected columns. That function is pure R code, so it might not be quite as hard as it