similar to: problem with strptime and 2010 dates

I have the following data exported as a .txt file on Windows. Everything is working fine, except that the the data in the 10th column is treated as a factor. Date Week Time Completed Work_Delta Mean_Delta Balance Total Total_Delta Work Index Open_Bugs Bug_Delta Bug_Delta2 8/17/2009 4 11.8% 64 64 16 611 675 675 9.5% 0.81 333 31 -31 8/30/2009 6 17.6% 104 40 20 801 905 230 11.5% 0.65 269 32 -32

Hello, I am trying to find a direct way to write a nested if of sorts to find data for a specific time range for a specific day (across a range of days) and have exhausted my abilities with the manuals I have at hand. I have a good deal of data of this approximate form: day time price 1 1am 5 1 2am 7 1 3am 9 1 4am 12 2 1am 5 2 2am 7 2

I have two xyplots that i want to superimpose (code below). By default they are displayed on slightly different y scales (one runs from 10 to 25, the other from 10 to 30). I would like to force them both onto the same scale (10 to 30) so the relation between the two is clear. Is there a way to do this? thanks much pct_compl_chart <- xyplot(pct_compl ~ date, col="red",

Hi, I have a datetime select in my form like this: DATE/TIME REPORTED: <%= datetime_select ''report'', ''rep_date'' %> This works beautifully for creating a new record. The data is saved as: 2006-08-07 11:33:00 Now, the problem is when I try to load the form with saved values to do an update. The above code works and loads the saved data in another one

Hi Shakeel, Assuming that you are starting with a bunch of dates: # make a vector of character strings that can be converted to dates rep_dates<-paste(sample(1:30,500,TRUE),sample(1:12,500,TRUE), sample(2013:2017,500,TRUE),sep="/") # if this isn't your format, change it date_format<-"%d/%m/%Y" # create a data frame with a column of dates

Hi, I am fairly new to 'R' and would like advice on the following. I want to calculate a weekly average number of reports (e.g. of flu, norovirus) based on the same weeks for the last five years. I will then use this to plot a chart with 52 points for the average based on the last five years; another line will then plot the current year, enabling a comparison of current weekly counts

Dear *, I have to perform an ANOVA analysis to study the effects on the performance of some algorithmic components in the design of my algorithm. I have done a full factorial design for the following 5 factors (my response variable is "best"): $ model :'data.frame': 3360 obs. of 6 variables: ..$ best : num [1:3360] 0.108 0.573 0.625 1.057 0.451 ...

# Can anyone suggest why this works datafilename <- "http://personality-project.org/r/datasets/maps.mixx.epi.bfi.data" person.data <- read.table(datafilename,header=TRUE) # but this does not? dd <- "https://sites.google.com/site/jrkrideau/home/general-stores/trees.txt" treedata <- read.table(dd, header=TRUE)

> datafilename="E:/my documents/r/sex/bysex1.csv" > data.sex=read.table(datafilename,header=T) > data.sex y.sex.age.region.c.n 1 1980,F,A,N,-18.15,13.61 2 1980,F,A,N,-18.61,13.04 3 1980,F,A,N,-18.81,12.32 4 1990,F,A,N,-21.12,11.7 5 1990,F,A,N,-20.77,11.58 6 1990,F,A,N,-21.6,13.34 7 1990,F,A,N,-21.78,12.6 > model.anova<-aov(c~age*sex,data=data.sex)

Hi, I am trying to reconcile anova table in R (summary(lm)) with individual t.test. datafilename="http://personality-project.org/R/datasets/R.appendix1.data" data.ex1=read.table(datafilename,header=T) #read the data into a table summary(lm(Alertness~Dosage,data=data.ex1)) gives: Call: lm(formula = Alertness ~ Dosage, data = data.ex1) Residuals:    Min     1Q Median     3Q    Max

I've benefited from this list with input on how to build up a symmetrical matrix. The purpose of that query was to work with the output from the MINE routine posted at www.exploredata.net To the extent it helps others, here is the script that I was working on an which turns a given MINE output column (in the case below, the third column corresponding to "MIC") into a matrix.

Hi there, I checked google for aov. usually one uses summary to see whether the p-value is small. but I want to put aov in my script. how can I get the p-value, (0.1115, 0.6665, 0.6665 in the following example)? thanks YU > datafilename="http://personality-project.org/r/datasets/R.appendix2.data" > data.example2=read.table(datafilename,header=T) > aov.ex2 =

Dear all I am a newbie to R and I would appreciate it very much if someone can give me some advice on this. Please note that I am not a programmer so some of the questions might sound really stupid. I would like to compute McDonald's omega calculation using R, I'm aware I can use the omega function in the psych package. But I'm really not sure how to do it. I have read these two

Hello, I'm new to R and have been following many guides including the two-way anova (http://www.personality-project.org/r/r.anova.html). Using that walkthrough including the supplied data I do str(data.ex2) and receive the appropriate types of data as follows: > str(data.ex2) 'data.frame': 16 obs. of 4 variables: $ Observation: int 1 2 3 4 5 6 7 8 9 10 ... $ Gender :

I have an elementary programming question. Could someone please point me in the right direction? I have a function which will run for thousands of companies. At each invocation, it returns 2 numbers. I plan to do something like: think_one_firm <- function(filename) { # Do stuff return(list(x=x,y=y)) } So for each of the firms in my dataset, I will call

Using a recent build of wine (20011023 and previous) I've gotten heroes of might and magic 3 working, minus networking :/ After doing a full install in WINE (with fake windows), attempting to run the game came up with the 'unable to initialize resources - possible disk problem'. Examination of the output of wine --debugmsg +relay,+text,+string Heroes3.exe indicates that the game is

To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood,

I currently have an instance of R running on Solaris 8 Version 2.2.0 (2005-10-06 r35749) Package: its Version: 1.0.9 Package: Hmisc Version: 3.0-1 I decided to build a new install on Linux RedHat As4.0 32 Bit running R version 2.4.0 (2006-10-03) Package: its Version: 1.1.4 Package: Hmisc Version: 3.1-1 Package: chron Version: 2.3-8 I am using the library(its) on both machines and

Hi all, I am creating an X1.RData file using the R 4.2.2 library. x1.R save(datafilename, file="X1.RData") When I am trying to load this file using another script X2.R load("X1.RData") I am getting this error message: Error in load("X1.RData", : bad restore file magic number (file may be corrupted) .. no data loaded. I am using the same R library (R

Here is the first few bytes, xxd -l 128 X1.RData 00000000: 8d5a 35f8 1ac5 cc14 a04e be5c 572f a3ad .Z5......N.\W/.. 00000010: 6210 7024 9b58 93c7 34d0 acb7 7a82 3f99 b.p$.X..4...z.?. 00000020: 66ce 0ebb 2057 ec36 55b4 0ece a036 695a f... W.6U....6iZ 00000030: 258b 3493 b661 f620 f7fe ada7 158a 15f7 %.4..a. ........ 00000040: e016 a548 6fcb 20c8 6fb4 493d adc9 ea4a ...Ho. .o.I=...J 00000050: