similar to: convert into numeric variable

How would I find the Percent of FuelTypeNum within the Band given AvailableMW? example: type 1 is 1% of PB0 type 2 is 54% of PB0 type 4 is 4% of PB0 type 5 is 42% of PB0 Note: the Bands and fuel types are not always constant. Data: FuelTypeNum Bands AvailableMW AvailableMWNewFormat 1 PB0 185319 185.319 2 PB0 18352000 18352 4 PB0 1338785 1338.785 5 PB0 14189756 14189.756 2 PB1

Hi, I am trying to parse XML files and read them into R as a data frame, but have been unable to find examples which I could apply successfully. I'm afraid I don't know much about XML, which makes this all the more difficult. If someone could point me in the right direction to a resource (preferably with an example or two), it would be greatly appreciated. Here is a snippet from one of

Hi Step 1: I create a data.frame called iolm. Step 2: I create a conditional subset i_wtr. Step 3: In this subset I add 0.3 to all values in the IOLM_AST column. Step 4: Now I am looking for the best way to Œmerge¹ the altered subset back into the original iolm data.frame ## STEP 1 >iolm ID IOLM_AST IOLM_AXIS 1 1 1.15 165.33 2 2 1.20 79.00 3 3 0.40

Hi All, I have a data.frame: >pop consumed ind recruits gonad cell 1 516.74 1 0.02 20.21 0.25 2 1143.20 1 0.02 20.21 0.50 3 250.00 1 0.02 20.21 0.25 4 251.98 1 0.02 18.69 0.25 5 598.08 1 0.02 18.69 0.25 6 437.38 1 0.02 18.69 0.25 7 250.00 1 0.02 17.39 0.25 8 250.00 1 0.02 17.39 0.25 9

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

R-Helpers, I'm sorry to have to ask this -- I've not used R very much in the last 8 or 10 months, and I've gotten rusty. I have the following (ff2 is a subset of a much, much larger dataset): > ff2 hostName user sys idle obsTime 10142 fred 0.4 0.5 98.0 2007-11-01 02:02:18 16886 barney 0.5 0.2 94.6 2007-10-25 19:12:12 8795 fred 0.0 0.1 99.8

Hi [macOSX 10.4.6; R-2.3.0] I have encountered a difference in behaviour between R-2.2.1 and R-2.3.0 when performing a linear model. Transcript follows for R-2.3.0 (R-2.2.1 worked as expected). How to make R-2.3.0 perform as R-2.2.1 did? > dput(x) c(29.13, 29.88, 30.09, 29.99, 29.74, 29.64, 29.65, 29.7, 30.04, 29.89, 29.96, 29.65, 28.76, 28.41, 28.38, 29.55, 29.76, 29.75, 29.84,

Probably this is an old question, but i''m not able to find nothing about... So, i''ve just started to play with tc to limit the transfer speed to my hdsl connection. I''m using the tbf and the command # tc qdisc add dev eth0 root tbf rate 10kbit latency 50ms burst 1000 Then i''ve tried to transfer a big (20 Mbyte) file onto my lan, using ftp and the client

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

Hello R users, For the last few days I am struggling with the following task: my data.frame: A1 A2 A3 B1 B2 B3 58.81 53.292 54.501 13.013 17.39 19.407 56.02 56.251 54.033 20.099 13.15 10.411 55.376 53.099 57.625 13.396 21.031 13.22 58.584 53.194 54.218 13.038 16.854 19.289 55.7 55.921 53.847 19.942 13.153 9.828 55.093 52.934

Hello, Using the following lines of code, I created the following graph:

Hello, Using the following lines of code, I created the following graph:

Hi All, I have the following text file (mytextfile.txt) 738307 527178 714456 557955 #N/A 17.42 6.22 4.73 #N/A 17.3 6.23 4.75 #N/A 17.29 6.17 4.7 #N/A 17.07 6.12 4.6 #N/A 17.27 6.19 4.7 #N/A 17.72 6.4 4.78 #N/A 17.12 6.19 4.75 #N/A 17.07 6.15 4.65 #N/A 17.03 6.07 4.64 #N/A 17.38 6.13 4.7 #N/A 17.38 6.13 4.7 #N/A 17.38 6.13 4.7 #N/A 17.38 6.13 4.7 #N/A

I've only begun investigating R as a substitute for SPSS. I have a need to identify for each CASE the closest (or most similar) 5 other CASES (not including itself as it is automatically the closest). I have a fairly large matrix (50000 cases by 50 vars). In SPSS, I can use Correlate > Distances to generate a matrix of similarity, but only on a small sample. The entire matrix can not

On 3 Oct 2018, at 14.09, Berindeie Avram-Teodor <berindeie.teo at gmail.com> wrote: > > > > I do not have downloaded the source from GitHub. The patch modifies configure.ac, so unless you run autogen.sh the configure script isn't modified and that patch doesn't work. Or as an alternative you could simply manually append to config.h: #define HAVE_CRYPT_H > >

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Dear list, The below dataset and code creates a new dataset with the results from the function cor.test being performed on each individual ('Individual_ID') from my original dataset. How do I convert each variable from the cor.test results to a numeric data type, as it is passed into the new dataframe? For example, 'estimate', 'p.value', and 'conf.int' should be

I know in v1.6 its part of logger.c but I noticed this: https://issues.asterisk.org/bug_view_advanced_page.php?bug_id=11625 However, it doesn't seem to ever been applied to any version of 1.4.x branch.. Nor can I figure out what it was applied to? This is over 3 years old, you would of figured it would have been applied to 1.4 at some point in time.. Any ideas?

HI Philippos, Try this: dat1<- read.csv("Validation_data_set3.csv",sep=",",stringsAsFactors=FALSE) #converted to csv str(dat1) #'data.frame':??? 12573 obs. of? 17 variables: # $ Removed.AGC????????????????????????????? : num? 65.67 46.17 41.26 14.09 5.38 ... # $ Removed.SST????????????????????????????? : chr? "" "46.1658" "41.2566"

Hello everybody. In my case, calculating multiple comparisons (Tukey) after lmer produced the following two errors: > sv.mc <- glht(model.sv,linfct=mcp(comp="Tukey")) Error in x$terms : $ operator not defined for this S4 class Error in factor_contrasts(model) : no 'model.matrix' method for 'model' found! What I have done before: > sv.growth <-