similar to: Adding a row at top of dataframe

Hello, I have two dataframes DF1 and DF2 that should be identical but are not (DF1 has some rows that aren't in DF2, and vice versa). I would like to produce a new dataframe DF3 containing rows in DF1 that aren't in DF2 (and similarly DF4 would contain rows in DF2 that aren't in DF1). I have a solution for this problem (see self contained example below) but it's awkward and

Hello, I have a data frame with many rows, and I want to create a column with a name only at every 12th row, starting from 97 to 278. Thanks in advance!

I have 3 columns of numbers, and i want to find the mean of each separately, and then the mean of all of the means. ive tried: av3daysB<- mean(mean(avr1), mean(avr2), mean(avr3)) av3daysB but it only gives me the mean of the first column (avr1) Any suggestions? Thanks, Emilija

I have a data frame with both positive and negative values, and I want to make all the negative values equal zero, so i can eventually take an average. I've tried temp2 <- ifelse(tempr<0, 0, tempr) but it doesn't seem to work. Any suggestions? Thanks!

I'm not sure if this is what you want. But the function coordinates() in sp package gives you the coordinates of SpatialObjects. Regards. Rodrigo. 2010/6/9 Nikhil Kaza <nikhil.list@gmail.com> > You need to execute gpclibPermit() to enable gpclib. > > library(maptools) should have issued a warning to that effect. > > > Nikhil Kaza > Asst. Professor, > City and

Dear all, In my dataset I have 12 columns and 5824 rows. The second column contains information about the height of a claim: it might be zero or positive. I would like to do an analysis on the positive part of this matrix, but I do need the other colums with this. So if there are like 1000 rows where the second column is positive, I would like to extract this 1000x13 matrix into another matrix.

I have a set of data with 12 readings for temperature per day, with 180 days. I want to find the average temperature of each day. I am able to do this one by one, but with that many days to calculate the average for, it will get very long. I'm sure there is a faster way to do this, I just don't know how. What i have so far is: av1 <- subset(ER9r, Day == 98, select = c

Hi! I am trying to implement non-bipartite matching. I have around 500 sites which can be clustered by 10 regions. I am able to calculate pairwise Mahalanobis distances between sites (thanks to another post in the forum). However, I want to constrain my match to sites within the same region. Thus I want to replace elements of the distance matrix with a high value, say 999999, for sites not of the

HI, I am really messing up to make a symmetrical matrix using upper.tri() & lower.tri() function. Here is my code:   > set.seed(1) > mat = matrix(rnorm(25), 5, 5) > mat            [,1]       [,2]       [,3]        [,4]        [,5] [1,] -0.6264538 -0.8204684  1.5117812 -0.04493361  0.91897737 [2,]  0.1836433  0.4874291  0.3898432 -0.01619026  0.78213630 [3,] -0.8356286  0.7383247

Hi all, I am sorry if this is a very basic quesion, but I have no experience with analyzing spatial data and could not find the right function/package quickly. Any hints would be much appreciated. I have a matrix of spatial point patterns like the one below and want to find the number of independent components (if that's the right term) in that matrix (or in that image). x=matrix(c(0,1,0,0,0,

I have a data frame with 2 columns, one for day and one for average. The day starts at 97 all the way to 279. I want to subtract day 98 average- day 97 average, then day99 average -day 98 average and so on down my list, creating another column with the subtracted results. I have: Day DailyAverage 1 97 0.6076782 2 98 0.7121478 3 99 0.8059347 4 100 0.9545806 5

I am having trouble with the recode function that is provided in the CAR package. I trying to create a new factors based on existing factors. E.g. >x <- as.factor(1:20) >y <- recode(x, " 1:5='A'; 6:10='B'; 11:15='C'; 16:20='D' ") >y [1] A A A A A 6 7 8 9 A A A A A A A A A A A Levels: 6 7 8 9 A Could someone point to me, my error?

my programe is below: a=c(1,2,1,1,1); b=c(1,2,3,4,1); c=c(3,4,3,3,3); d=c(1,2,3,5,1); e=c(1,5,3,5,1) data.f=data.frame(a,b,c,d,e) origin.data<-data.f cor.matrix<-cor(origin.data) origin.cor<-cor.matrix m<-0 for(i in 1:(cor.matrix[1]-1)) { for(j in (i+1):(cor.matrix[2])) { if (cor.matrix[i,j]>=0.95) { data.f<-data.f[,-i]; i<-i+1 }

Hello everyone. I am trying to build up understanding in R by trying to develop just some simple scenarios. I would like to explain you what I am trying to do and what I did so far. I would like to put inside a RasterLayer (raster package) a Gaussian field (for given covariance) using grf function (geoR package) 1. First I created a Raster Layer object r <- raster() # Default values are

Dear all, I have a code that generates data vectors within R. For example assume: z <- rlnorm(1000, meanlog = 0, sdlog = 1) Every time a vector has been generated I would like to export it into a csv file. So my idea is something as follows: for (i in 1:100) { z <- rlnorm(1000, meanlog = 0, sdlog = 1) write.csv(z, "c:/z_i.csv") Where "z_i.csv" is a filename that is

Dear list: I have a dataset of geographical data that looks like this example data: dat<- data.frame( lon = c(rnorm(1000, mean=-10), rnorm(1000, mean=10), rnorm(1000, mean=5)), lat = c(rnorm(1000, mean=40), rnorm(1000, mean=30), rnorm(1000, mean=0))) plot(dat$lon, dat$lat) My positions are clearly clustered (in this example there are 3 clusters). Is there any R algorithm that allows me to

Hi, I am trying to execute the following SQL statement using two data frames: tab1, tab2 : Two Tables Select tab1.*, tab2.*, tab1.tobiiTime - tab2.ruiTime as timeDiff, IFNULL(n-m, -9999999) as alwaysIncrement FROM tab1 LEFT OUTER JOIN tab2 On tab1.data1 - tab2.mouseX = 0 And tab1.data2 - tab2.mouseY = 0 I am trying to do the following in R:- *#Getting error here:* data

Hi, I have a global data-frame in my R script. At some point in my script, I want to update certain columns of this data-frame by calling in an update function. The function looks like this: # get events data. This populates a global event data frame in the R-script events <- getEvents(con, eventsFilePath) # events has columns eventid, timeStamp, isSynchronized, timeDiff; with millions of

It is a simple problem in that I simply want to convert the For loop to a more efficient method. It simply loops through a large vector checking if the numeric element is not equal to the index of that element. The following example demonstrates a simplified example: > rows <- 10 > collusionM <- Matrix(0,10,10,sparse=TRUE) > matchM <- matrix(c(1,2,3,4,4,6,7,9,9,10)) > >

Here are what i am going to accomplish: I have 400 files named as xxx.txt. the content of the file looks like the following: name count 1. aaa 100 2. bbb 2000 3. ccc 300 4. ddd 3000 ........ more that 1000 rows in each files. these are the areas i need help: 1. how can i only read in the files with the string patterns ggg or fff as part of the file names? for instance, I