similar to: cumulative sum of within levels of a dataframe

Using the following lattice plot as an example, I would like to add horizontal lines where y=0: library(lattice) library(grid) fac <- gl(4,12) x <- letters[rep(1:3,16)] y <- runif(48,min=0.0) dotplot(y~x|fac) I've tried it with grid.lines using npc and native units, which works fine unless I change the size of the output device - then the lines are in the wrong place. Is there a

Dear list, I'm trying to do a set of generic functions do make contingency tables from data.frames. It is just running "nice" (I'm learning R), but I think it can be better. I would like to filter the data.frame, i.e, eliminate all not numeric variables. And I don't know how to make it: please, help me. Below one of the my functions ('er' is a mention to EasieR,

Hi, Maybe I have not been looking in the right spot, but, I have not been able to fine a command to automatically calculate the running cumulative sum of a vector. Is there such a command? Example of current code: > eig$values [1] 678.365651 6.769697 2.853783 > prop<-eig$values/sum(eig$values) > prop [1] 0.986012163 0.009839832 0.004148005 >

Dear R-Help List, I have a question about data manipulation. I tried to make code myself but too much for me. I would greatly appreciate your help. I have data set consisting of site (from 1 to N1) and distance and there are several variables (1 to N2) collected from each sampling site. I am interested in looking at cumulative sums of each variable based on site and distance like below. Can

I'm able to do this as follows, but am wondering if anyone knows a simpler way which still avoids explicit loops? > (mystring <- letters[1:5]) [1] "a" "b" "c" "d" "e" > unlist(sapply(mystring[-length(mystring)], + function(x) paste(x,mystring[(grep(x,mystring)+1):length(mystring)],sep=""))) a1 a2 a3

Hello, I have a data frame that looks like this (containing interarrival times): > str(df) 'data.frame': 18233 obs. of 1 variable: $ Interarrival: int 135 806 117 4 14 1 9 104 169 0 ... > head(df) Interarrival 1 135 2 806 3 117 4 4 5 14 6 1 > This corresponds to the time differences (in ms) of a poisson arrival

Dear List, I'll appreciate your help on this. I'm trying to create a variable as in "cumsum_y.cond1" below, which should compute the cumulative sum of "y" conditional on the value of cond==1. set.seed(1) d <- data.frame(y= sample(c(0,1), 10, replace= T), cond= sample(c(0,1), 10, replace= T)) y cond cumsum_y.cond1 1 0 0 0 2 0 0

How can I look up the aspect ratio of a plot, so I can use that to correctly adjust the angle of text which is supposed to be parallel to a line in the plot? The following example code works for a 1:1 aspect ratio, but puts the text at the wrong angle if the plot region is short and wide or tall and narrow. I can't find a par() component containing the plot aspect ratio. It will be for

Dear all Could someone explain me why lapply(split(x,fac),mean) returns means per levels of fac for each column of x whereas lapply(split(x,fac),sum) does not return sums per level of fac and columns of x, but adds all columns together? Hence, how can I get sum to behave as mean in this instance? Thank you very much in advance E. Castella

Hi STATISTICA has a function called "Two-way joining" (see http://www.statsoft.com/TEXTBOOK/stcluan.html#twotwo) and the reference material states that this is based on the method as published by Hartigan (found this paper: http://www.jstor.org/pss/2284710 through wikipedia). What is the relationship (if any) between the "heatmap" function in R and this technique? Is there an

Hi @all, I am biting my nails with the following problem: I have a data set of daily rainfall measurements for the last 20 years. What I want to do is calculate the daily cumulative sum of rainfall but only for every year which means that the cumulative sum has to be reset each year. After the calculations I want to plot each year of cumulative rainfall as a separate line in one graph preferably

Hi All, I would like to make an invitation to make a collective package with all functions related to TABLES. I know that there are many packages with these functions, the original idea is collect all this functions and to make a single package, because is arduous for the user know all this functions broadcast in many packages. So, I think that the original packages can continue with its

Dear R users, I have various vector geometry operations to perform on 3-D coordinate data located on multiple (500+) csv files. The code I have written for the calculations works just fine. I have written a 'for' loop to automate the task of extracting the coordinates from the files and perform the analyses. The loop works reasonable well, but if the number of csv files is greater than

For example, subtracting 1:2 from the rows of a two-column matrix: > t(apply(matrix(1:6,ncol=2),MARGIN=1,function(y) y - 1:2)) [,1] [,2] [1,] 0 2 [2,] 1 3 [3,] 2 4 > sweep(matrix(1:6,ncol=2),MARGIN=2,1:2,FUN="-") [,1] [,2] [1,] 0 2 [2,] 1 3 [3,] 2 4 Is there a logic to this difference, or is it just a quirk of the history of these

I am creating a timeline plot, but running into a problem in positions where the values to plot are too close together to print the text without overlap. The simplest way I can think of to solve this (although there may be other ways?) is to create a new numeric vector whose values are as close as possible to the original vector, but spread out to a given minimum difference. For example, take

I tried searching but I couldn't quite find what I was looking for. Here's a dummy data matrix (with row and column labels): > y 0 1 2 3 4 21 3 4 8 5 5 22 3 6 8 6 NA 23 4 5 11 4 3 24 4 2 1 4 6 25 6 4 4 6 6 I can get cumulative row sums as follows: > cy<-t(apply(y,1,cumsum)) > cy 0 1 2 3 4 21 3 7 15 20 25 22 3 9 17 23 NA 23 4 9 20 24 27 24 4 6 7 11 17

Hello world, I am actually transferring a course in data management for students in biology, geography and agriculture from statistica to R - it works surprisingly well. If anyone is interested in my scratch/notepad (in German language), please see www.hydrology.uni-kiel.de/~schorsch/statistik/statistik_datenauswertung.pdf (pages 40-52) The dataset is:

Suppose I have a dataframe 'd' defined as L3 <- LETTERS[1:3] d0 <- data.frame(cbind(x = 1, y = 1:10), fac = sample(L3, 10, replace = TRUE)) (d <- d0[d0$fac %in% c('A', 'B'),]) x y fac 2 1 2 B 3 1 3 A 4 1 4 A 5 1 5 A 6 1 6 B 8 1 8 A Even though factor 'fac' in 'd' only has 2 levels, but it seems to bear the birthmark

Dear R-helpers, I have a huge data-set so need to avoid for loops as much as possible. Can someone think how I can compute the result in the following example (that uses a for-loop) using some version of apply instead (or any other similarly super-efficient function)? example: #Suppose a matrix: m1=cbind(1:5,1:5,1:5) #The aim is to create a new matrix with every column containing the

What functions exist for differentiating a numeric vector (in my case spectral data)? That is, experimental data without an analytical function. ie, > x <- seq(1,10,0.1) > y=x^3+rnorm(length(x),sd=0.01) #although the real function would be nothing simple like x^3... > derivy <- .... I know I could just use diff(y) but it would be nice to estimate derivatives at the