similar to: Extract time from irregular date and time data records

Hi All, I have a data set of daily measurements of river flow. I would like to create a "ts" object from this data. Here's a sample data set: date <- as.Date(c(1:300), format="%Y") year=as.numeric(format(date, format = "%Y")) month=as.numeric(format(date, format = "%m")) julianday=as.numeric(format(date, format = "%j"))

I've recently stumbled across data.table, Matthew Dowle's package. I'm impressed by the speed of the package in handling operations with large data.frames, but am a bit overwhelmed with the syntax. I'd like to express the SQL statement below using data.table operations rather than sqldf (which was incredibly slow for a small subset of my financial data) or import/export with a

I am very new to R and programming and thank you in advance for your patience and help with a complete novice! I am working with a large multivariate data set that has 10 explanatory environmental variables (e.g. temp, depth) and over 60 response variables (each is a separate species). My data frame is set up like the simplified version below: JulianDay Temperature Salinity Depth Copepod

I am working on a simple pilot project comparing the capability of SQL, SAS and R to perform a rolling mean per the following instructions. I have completed the SQL and SAS analysis, so now it's R's turn. Calculate mean values of x (x=count) for each date in the dataset where mean = the average count of days [t-9] through day [t-3] for each date/illness combination. Dataset aggpilot

Necesito algo de programación básica para resolver un problema, un inconveniente es que intente "if", cuándo se lo aplico digamos a x <- 5 no hay drama, pero si es a leche$litros me dice que solo se toma el primer elemento. Como pueden ver estoy re perdido. Mi problema: los datos están en un data.frame como el siguiente animal inicio fin control idlactancias

Hello everyone, I would like to test two regression slopes:do they differ significantly?The data and commands I've used so far: x<-8.5:32.5 #Vektor x y<-c( NA , NA , 5.67 , 6.53 , 6.83, 7.41 , 7.93 , 8.5 , 8.86, 9.46 , 9.82 , 10 ,10.35 , 10.7 ,11.03 ,11.37 ,11.61 ,11.84, 12.12, 12.39 ,12.67 ,12.96, 13.28 ,13.47, 13.65) #Vektor y (regression<-lm(y~x)) summary(regression)

Dear arun and all R users, I will first of all try to simply define my issue.. I have data in the following format Year Discharge dd/mm/yyyy x .. … … … There are some NA values in the discharge which I would like to predict by using “predict command”. I cant figure out the way to write the coding for that. Could you please help me on that??? I have also ,written

Hey, i have a question about how to reorganize a data frame in the easiest way. my example: what would be the easiest way to bring a data.frame such like this: ---- nr height age Seed 1 1 4.51 3 301 2 15 10.89 5 301 3 29 28.72 10 301 4 43 41.74 15 301 5 57 52.70 20 301 6 71 60.92 25 301 7 2 4.55 3 303 8 16 10.92 5 303 9 30 29.07 10 303 10 44

Confirm there is no change in IR if the hack is disabled in the sources. David wrote that these instructions are created by SCEV. Are other targets affected by the changes, e.g. X86? Kind regards, Evgeny Astigeevich Senior Compiler Engineer Compilation Tools ARM From: Sanjay Patel [mailto:spatel at rotateright.com] Sent: Sunday, January 22, 2017 10:45 PM To: Evgeny Astigeevich Cc: llvm-dev; nd

Thank you for information. I’ll build clang without the hack and re-run the benchmark tomorrow. -Evgeny From: Sanjay Patel [mailto:spatel at rotateright.com] Sent: Sunday, January 22, 2017 8:00 PM To: Evgeny Astigeevich Cc: llvm-dev; nd Subject: Re: [InstCombine] rL292492 affected LoopVectorizer and caused 17.30%/11.37% perf regressions on Cortex-A53/Cortex-A15 LNT machines > Do you mean to

Hi Sanjay, Thank you for your analysis. It’s interesting why the x86 machine is not affected. Maybe the x86 backend is smarter than the AArch64 backend, or it might be micro-architectural differences. I don’t mind to keep the changes on trunk. What I’d like to see is who will/should be involved in solving the issue. What kind of help/support is needed? Should we (ARM Compilation Tools) start

> On Jan 23, 2017, at 3:48 PM, Sanjay Patel via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > All targets are likely affected in some way by the icmp+shl fold introduced with r292492. It's a basic pattern that occurs in lots of code. Did you see any perf wins on your targets with this commit? > > Sadly, it is also likely that many (all?) targets are negatively

Hi Sanjay, The benchmark source file: http://www.llvm.org/viewvc/llvm-project/test-suite/trunk/SingleSource/Benchmarks/Shootout/sieve.c?view=markup Clang options used to produce the initial IR: clang -DNDEBUG -O3 -DNDEBUG -mcpu=cortex-a53 -fomit-frame-pointer -O3 -DNDEBUG -w -Werror=date-time -c sieve.c -S -emit-llvm -mllvm -disable-llvm-optzns --target=aarch64-arm-linux Opt options: opt -O3

> On Jan 24, 2017, at 7:18 AM, Sanjay Patel <spatel at rotateright.com> wrote: > > > > On Mon, Jan 23, 2017 at 10:53 PM, Mehdi Amini <mehdi.amini at apple.com <mailto:mehdi.amini at apple.com>> wrote: > >> On Jan 23, 2017, at 3:48 PM, Sanjay Patel via llvm-dev <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> wrote: >>

I computed "system.time(diag(30000))" with R 2.12.0 on Fedora 13 Linux with 4 GB RAM and with R 2.14.1 on Windows 7 with 8 GB RAM: Linux (4 GB RAM): 0, 0.21, 0.21 -- a fifth of a second Windows 7 (8 GB RAM): 11.37 7.47 93.19 -- over 1.5 minutes. Moreover, during most of that time, I could not switch windows or get any response from the system. When I first encountered this, I

Hello, I would like to get a correlation coefficient (R-squared) for my model. I don't know how to calculate it in R. What I've done so far: x<-8.5:32.5 #Vektor x y<-c(NA ,5.88 , 6.95 , 7.2 , 7.66 , 8.02 , 8.44 , 9.06, 9.65, 10.22 , 10.63 ,11.06, 11.37, 11.91 ,12.28, 12.69 ,13.07 , 13.5 , 13.3 ,14.14 , NA , NA , NA , NA , NA) #Vektor y

Hi, I'm attempting to add a "Julian Day" column to a data frame. Here is my code and the resulting data frame: vic.data <- read.table("C:/VIC/data/vic.data.csv", header=F) names(vic.data) <- c("year", "month", "day", "precip", "evap", "runoff", "baseflow", "Tsup",

Hi, We found that today's 17.30%/11.37% performance regressions in LNT SingleSource/Benchmarks/Shootout/sieve on LNT-AArch64-A53-O3__clang_DEV__aarch64 and LNT-Thumb2v7-A15-O3__clang_DEV__thumbv7 (http://llvm.org/perf/db_default/v4/nts/daily_report/2017/1/20?filter-machine-regex=aarch64%7Carm%7Cthumb%7Cgreen) are caused by changes [rL292492] in InstCombine: https://reviews.llvm.org/D28406

Hi to all I hope you can help me. # I'm trying to plot the following ratio data with standard error bars (horizontal). ratioBiomass <- c(1.327, 0.865, 1.900, 0.992, 1.469, 1.381, 1.230,1.269, 2.411, 1.288, 1.861, 0.714, 1.341, 1.362, 1.065, 2.374) SEratio <- c(19.28, 5.04, 0.01, 0.01, 0.90, 0.02, 0.002, 11.37, 0.004, 0.29, 0.003, 0.13, 0.21, 0.52, 1.66, 14.57) Species <-

Hey, i want to define 3 ideal breaks (bin) for each variable one of those variables is attached in the previous email, i don't want to consider quartile method because quartile is not working ideally for that data set because data distribution is non normal. so i want you to suggest another method so that i can define 3 breaks with the ideal interval for Recency, frequency and monetary to