similar to: Entering Multiline Commands With ESS

I don't find how to do what I need to do in Dalgaard or 'R Cookbook', so I'm asking here. I have a data frame with water chemistry data and I want to start exploring these data. There are three factors (site, date, chemical) associated with each measurement. The data frame looks like this: > summary(chemdata) site_id.sample_date.param.quant

The data set (called 'chemdata') has 6 columns (4 factors, 1 date, 1 numeric) and I need to create subsets for each of one of the factors ('stream'). This has worked flawlessly for all but two streams which were created yesterday. The command I use to create the subsets is like this: > rnchH <- subset(chemdata, stream == 'RanchSpgsH', select = c(site, sampdate,

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 want to create individual data.frames for each of the 8 param factors in chemdata. The syntax I tried (based on Teetor's book, page 132) and R's response are: > ars <- subset(chemdata, select=c(site,sampdate,param,quant), subset=(param = "As")) Error in subset.data.frame(chemdata, select = c(site, sampdate, param, : 'subset' must evaluate to logical

I've tried various commands. ?plot, Teetor's book, "R Cookbook", and Mittal's book, "R Graphs Cookbook" without seeing how to write the command to create scatterplots from my data.frame. The structure is: > str(chemdata) 'data.frame': 14886 obs. of 4 variables: $ site : Factor w/ 148 levels "BC-0.5","BC-1",..: 104 145 126 115

This is my first excursion into using reshape2 and I want to ensure that the melt() function call is syntactically correct. The unmodifed data frame is organized this way: head(tds.anal) site sampdate param quant 1 UDS-O 2006-12-06 TDS 10800 4 STC-FS 1996-06-14 Cond 280 7 UDS-O 2007-10-04 Mg 1620 9 UDS-O 2007-10-04 SO4 7580 19 JCM-10B 2007-06-21 Ca 79 20

Thanks to David's help I subset my large data set and produced a smaller one for a single stream and 7 factors of interest. The structure of this data frame is: str(burns.tds.anal) 'data.frame': 718 obs. of 4 variables: $ site : Factor w/ 143 levels "BC-0.5","BC-1",..: 1 1 4 6 4 4 4 5 5 5 $ sampdate: Date, format: "1996-06-02"

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

Before reading water chemistry into a data frame I removed all missing data. Yet when I try to run cenros() to summarize a specific chemical I get an error that I do not understand: with( subset(chem, param=='Ag'), cenros(quant,ceneq1) ) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in 'y' I would like to learn what I did incorrectly

I'm not able to create the proper syntax to specify a lattice bwplot() for only one of two conditioning factors. The syntax that produces a box plot of each of the two conditioning factors is: bwplot(quant ~ param | era, data=mg.d, main='Dissolved Magnesium', ylab='Concentration (mg/L)') What I've tried unsuccessfully are: bwplot(quant ~ param |

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 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

I have read ?zoo but am not sure how to relate the parameters (x, order.by, frequency, and style) to my data.frame. The structure of the data.frame is '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/

Working with 5 subset streams from my source data frame, three of them successfully call dcast(), but two fail: jerritt.cast <- dcast(jerritt.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length and winters.cast <- dcast(winters.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length Yet both data frames have the values in their

The sample data sets that come with the NADA package are limited to one or two variables and a censored measurement indicator column. I try to mimic examples using my data but keep missing the target. My water chemistry data is available in two formats: long (as seen in a database table) and wide (as seen in a spreadsheet). The two structures are: str(chem) 'data.frame': 65349 obs. of

I got two questions on factors in regression: Q1. In a table, there a few categorical/factor variables, a few numerical variables and the response variable is numeric. Some factors are important but others not. How to determine which categorical variables are significant to the response variable? Q2. As we knew, lm can deal with categorical variables. I thought, when there is a categorical

Dear all, We want to test if the invasiveStatus is predicted by the amount (quant) of animals arriving to a country of a certain species (taxonid). We are using lmer to perform the model. The model is: lmer(invasiveStatus~I(log(quant+1))+I(log(inDegree+1))+(1|taxonid)+(1|country), family=binomial,data=td), where invasiveStatus is a binary variable, quant and inDegree are integer variables, and

Hello Everyone, I'm currently learning about quantile regressions. I've been using an optimizer to compare with the rq() command for quantile regression. When I run the code, the results show that my coefficients are consistent with rq(), but the intercept term can vary by a lot. I don't think my optimizer code is wrong and suspects it has something to do with the starting

Dear, I want to make an application to calculate quantile within a For() I tried the following without success: ej. date p_val <- matrix(sample(10, 1000, replace=TRUE), 200,5) test 1 rr <- paste("p_val$",names(p_val[1]), sep="") quant <- quantile(rr, probs = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)/100, na.rm=FALSE, type=1) test 2 rr <-

I set up a local repo for testing packages. My packages are not showing up from the repository when viewed by Linux clients. I suspect this is a web administrator/firewall issue, but it could be I created the repo wrongly. I am supposed to run write_PACKAGES separately in each R-version folder. Right? Maybe other novices can use these scripts, if they are not wrong :) Here's the file