similar to: acf for a univariate time series in a data frame

I am new to the world of R/programming so this may be a really easy question. I thank you for your patience and help in advance I would like the characters km^2 to be displayed on the plot subtitle as km squared - two as a superscript. I would also like to have the numbers from the data set for longitude and latitude to be rounded to four decimal places. Thank you. plot (

Dear R community, I'm hoping someone out there has perhaps done this and can share their code and/or expertise with me. I need to pull recession periods out of a hydrograph - can anyone help me with this? I want to create a subset from streamflow data that consists of just the recession curves - the decreasing runoff after the passage of a peak flow. would really appreciate any help on

Hi all, I am attempting to run PCA on a matrix (nrow=66, ncol=84) using 'prcomp' (stats package). My data (referred to as 'Q' in the code below) are separate river streamflow gaging stations (columns) and peak instantaneous discharge (rows). I am attempting to use PCA to identify regions of that vary together. I am entering the following command:

I don't even know if this is the right place to ask this question. I would like to download USGS stream gauging data for a couple of gauges on a daily basis- save the files to .csv files and append the nest days time series to this. Is there a way to do this automatically in R? thanks, -- Stephen Sefick Let's not spend our time and resources thinking about things that are so little or

Hi, I'm new to the whole R-thing as a replacement for Matlab, not disappointed sofar ;) I found out how to make nice looking boxplots, but i also would like the make a boxplot with 5% and 95% instead of the standard 25 and 75% quantiles. My csv input looks something like: LOCATION FILTER NR DATE VALUE MONTH Peelhorst01 1 14-Jan-94 23.07 1 Peelhorst01 1 28-Jan-94 23.68 1 Peelhorst01 1

i have a loop which looks likes this: > data.info <- rbind(data.info, cbind(station.id, year, date, > max.discharge)) + y <- split(data.info, data.info[station.id]) + for (i in names(y)) {write.csv(y[[i]], file=paste(i, ".csv", sep=","))} i am wondering, where the file (which i am about to write in .csv format) will be saved? i looked at ?write.csv and

hi everyone! I have a 40-year monthy streamflow record. And i want to fit a neural network. I already fitted an autoregressive model and found out that an AR(3) model fits my time series (time.series) the best. I am currently having problems on how to express the argument "formula" for my neural network.. I am aware that my model should be Q(t) ~ Q(t-1) + Q(t-2) + Q(t-3). But I

Dear all, After being archived on CRAN on 2023-10-1, hydroTSM is finally back on CRAN since January 18th: https://cran.r-project.org/package=hydroTSM. This new version 0.7-0 has several new functions, improvements, bugfixes, and a new dataset, mostly devoted to work with sub-daily and sub-hourly time series. *) New functions: baseflow, plot_pq, calendarHeatmap, subhourly2hourly,

Dear all, After being archived on CRAN on 2023-10-1, hydroTSM is finally back on CRAN since January 18th: https://cran.r-project.org/package=hydroTSM. This new version 0.7-0 has several new functions, improvements, bugfixes, and a new dataset, mostly devoted to work with sub-daily and sub-hourly time series. *) New functions: baseflow, plot_pq, calendarHeatmap, subhourly2hourly,

i have the following constructed and running very well,, thanks to Gabor Grothendieck for his help. >data.info <- c("station.id", "year", "date", "max.discharge") > > for(i in 1:num.files) { + station.id <- substring(data[i], 1,8) + DF <- read.table(data[i], sep=",", blank.lines.skip = TRUE) + z <- zoo(DF[,4],

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

hi everyone! hope you can help me here. i am a new R user. what i am trying to do is to find the maximum annual discharge from a daily record. i have a data.frame which includes date and the discharge. somewhat like this.. 10/1/1989 2410 10/2/1989 2460 10/3/1989 2890 ... ... ... 12/31/2005 5730 i have been browsing through the archives and fount out about the aggregate

Hi: I am using a subset of the below dataset to predict PRED_SUIT for the whole dataset but I am having trouble with 'newdata'. The model was created with 153 records and want to predict for 208 records. wolf2 <- structure(list(gridcell = c(367L, 444L, 533L, 587L, 598L, 609L, 620L, 629L, 641L, 651L, 662L, 674L, 684L, 695L, 738L, 748L, 804L, 805L, 872L, 919L, 929L, 938L, 950L, 958L,

Can anyone show me how to add a log Pearson type III plot using the evdistq() command to an extreme value plot using the lmom package? Attached sample code below... Thanks in advance, Dave library(lmom) # annual maximum daily streamflows Mackenzie River mackenzieRiver = c(26600, 30300, 34000, 32000, 29200, 28300, 28600, 26400, 28300, 28800, 29000, 22100, 32900, 31800, 21600, 32100, 27000,

Hello, I'm trying to develop a box plot of time series data to look at the range in the data values over the entire period of record. My data initially starts out as a list of hourly data, and then I've been using this code to make this data into the final ts array. # Read in the station list stn.list <- read.csv("/home/kbennett/fews/stnlist3", as.is=T, header=F) # Read in

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()

I have an external data (.txt) for annual peak flood. The first column is the year, second column is the observation date, and the last is the observed discharge. My task is to calculate the mean, skewness and kurtosis of the said data. I was advised to use read.table() to read the entire data. Please help me on how to perform the required computation. I am obviously a new user of this statistical

http://waterdata.usgs.gov/nwis/uv?format=rdb&period=7&site_no=021973269 I would like to be able to parse this file up: I can do this x <- read.table("http://waterdata.usgs.gov/nwis/uv?format=rdb&period=7&site_no=021973269", skip=26) but If I add another gauge to this x <-

Hello all, A really noddy question for you all: I''m trying without success to do some subhypothesis testing. Using simple anova model, with a toy dataset from a book. I have four factors A,B,C,D, and wish to test mu_C = mu_D. This is what I have tried: > contrasts(infants$group,how.many=1) <- c(0,0,1,-1) > contrasts(infants$group) [,1] A 0 B 0 C 1

Hi all Well, first: happy new year... Second: I write a function in R that might interest some other people. On the other hand, I am closer to beginners than experts; I don't know how valuable my code is. I don't know how long it will take to me to create a library and I don't know if it's worth to. So before starting this long process, I would like some advices, both on the