similar to: Modifying Package Data

I don't see what's missing in my statements to add rows to a data frame and someone else will probably see what needs to be added to the statements. The data frame has this structure (without any data): $ PHYLUM : chr $ SUBPHYLUM : chr $ SUPERCLASS : chr $ CLASS : chr $ SUBCLASS : chr $ INFRACLASS : chr $ SUPERORDER : chr $ ORDER : chr $ SUBORDER :

I have a reshaped data frame with value column headings concatenated from two column headings in the melted data frame. I want to change all 56 headings in a single command, but 'names' allows me to change only one at a time. In Hadley's 2007 article on reshape in the Journal of Statistical Software he mentions a 'rename' function, but I cannot find that. Is there a way to

With multiple panels in a lattice trellis plot the sequence is, for example, 1, 10, 11, 12, 2, 3, 4. I want the sequence to be 1, 2, 3, 4, 10, 11, 12. Reading ?strip.default and the appropriate section in the Lattice book I'm not seeing how to specify the 'human' numeric order rather than the computer numeric order. A pointer will be appreciated. TIA, Rich

I have a data frame called chemdata with this structure: > str(chemdata) 'data.frame': 14886 obs. of 4 variables: $ site : Factor w/ 148 levels "BC-0.5","BC-1",..: 104 145 126 115 114 128 124 2 3 3 ... $ sampdate: Date, format: "1996-12-27" "1996-08-22" ... $ param : Factor w/ 8 levels "As","Ca","Cl",..:

I need to understand how and why dcast() adds NAs to a data frame that contained no missing values. The database table of chemical concentrations has all missing values removed because they cannot contribute to data analyses. The structure of the R data frame of these data have no NA values, and neither does the data frame resulting from applying the reshape2 melt() function to it. However,

I need to generate 6 x-y scatter plots (lattice xyplot) for each of 11 data frames. I'm sure the tedium of doing these one at a time can be avoided by writing a script that uses nested for loops or the apply() function. My Web search has not provided me with a tutorial on R programming that will introduce me to the language and allow me to write such a script. Please provide pointers to

All but one of the summaries of multiple linear regressions in this analysis set present the residuals by min, 1Q, median, 3Q, and max. Example: lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4, data = snow.cast) Residuals: Min 1Q Median 3Q Max -277.351 -32.551 -2.621 40.812 245.272 The one that doesn't has only a small number of rows (23) and presents the

One of many scripts to produce 4 lattice plots on one page keeps throwing an error. I've tried manipulating the file to eliminate the error, but have not been able to do so. The error is: > source('bicarb.R') Error in source("bicarb.R") : bicarb.R:15:1: unexpected symbol 14: 15: hco33 ^ The 'h' is in column 0 so the caret would be column -1, but it's

I've a data frame with this structure: 'data.frame': 1987 obs. of 11 variables: $ site : Factor w/ 24 levels "B(W)","BC-1",..: 1 1 2 2 2 1 1 1 ... $ sampdate : Date, format: "2000-07-18" "2000-07-18" ... $ tclass : Factor w/ 8 levels "Annelida","Arachnida",..: 1 5 5 5 5 ... $ torder : Factor

I've looked at the lattice book and the 'R Graphics Cookbook' without seeing how to change the labels along the x axis for groups in a box plot, specifically cenbox(). The attached example has a main and axes labels with default group labels. Please point me to a reference on how I can change 'FALSE' and 'TRUE' to something more easily understood by viewers. TIA,

I have water chemistry data with censored values (i.e., those less than reporting levels) in a data frame with a narrow (i.e., database table) format. The structure is: $ site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "2007-12-12" "2007-12-12" ... $ preeq0 : logi TRUE TRUE TRUE TRUE TRUE

Hello R user, I have a data set from a longitudinal study ( sample below) where subjects are followed over time. Second column (status) contains info about if subject is dead or still in the study and third column is time measured in the week. Here is what I need: if status is not dead or unknown take the last week, if status is dead or unknown I need to have corresponding week. Desired resulst:

Ah.... LOD's, typically LLOD's ("lower limits of detection"). Disclaimer: I am *NOT* in any sense an expert on such matters. What follows are just some comments based on my personal experience. Please filter accordingly. Also, while I kept it on list as Martin suggested it might be useful to do so, most folks probably can safely ignore the rant that follows as off topic and not

Hi Albrecht, I am forwarding your reply to the full group. It's been a while since I did this and I don't remember the details. Maybe someone else can comment. (I am a bit busy at the moment.) If no one supplies the information in a few days I will try to take a look. In the meantime you can start your reading on-line. :-) Regards, Eric Dear Eric, I downloaded the material from

One set of data has censored (less-than detection limits) water chemistry concentrations for 80-100% of all observations. My initial trial-and-error attempts to apply the cenboxplot() method suggests that it has an upper limit to the percentage of censored observations. I do not see this limit in Dennis Helsel's second edition. Has anyone experience plotting censored data and can provide

I read in ?na.omit that it returns the object with incomplete cases removed. I interpret this to mean that any zoo object row where any column shows 'NA' will be removed from the data set. That's not what I need, since the 'NA' represents information in my context. However, what I would like to do is eliminate the rows where every column is 'NA'. When I aggregate

Because of regulatory requirement changes over several decades and weather conditions preventing site access the variables in my data set have different lengths. I'd like guidance on how to perform linear regressions and other models with these variables. For example, there are 2206 rows for the parameter "TDS" but only 1191 rows for the parameter "Cond." Such

I just upgraded from 2.14 to 2.15 and have run into an issue upgrading libraries that I've not had in previous version upgrades. For example, trying to install reshape2 for 2.15 throws errors and I don't see what to change to over come the problem: > install.packages("reshape2") Installing package(s) into ?/home/rshepard/R/i486-slackware-linux-gnu-library/2.15? (as ?lib? is

Data frame has this structure: 'data.frame': 11169 obs. of 4 variables: $ stream : Factor w/ 37 levels "Burns","CIL",..: 1 1 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "1987-07-23" "1987-09-17" ... $ param : Factor w/ 8 levels "As","Ca","Cl",..: 1 1 1 1 1 1 1 1 1 1 ... $ quant : num 0.01 0.01 0.01 0.01

R-3.5.0 is installed here. When I tried to update installed packages errors were reported when their dependencies were built under a prior version of R. Reading ?update.packages() I did not see an option that would automatically update dependences built under an earlier version of R. Is there such an option that I missed? Alternatively, is there a function I can run that will rebuild all