mean

问题 datetime 2012-01-01 125.5010 2012-01-02 NaN 2012-01-03 125.5010 2013-01-04 NaN 2013-01-05 125.5010 2013-02-28 125.5010 2014-02-28 125.5010 2016-01-02 125.5010 2016-01-04 125.5010 2016-02-28 NaN I would like to fill in the missig values in this dataframe by using a climatology computed from the dataset i.e fill in missing 28th feb 2016 value by averaging values of 28th feb from other years. How do i do this? 回答1: You can use groupby by month and day and transform with fillna mean: print df

问题 Over in the thread "Evenly distributing n points on a sphere" this topic is touched upon: Evenly distributing n points on a sphere. But what I would like to know is: "Is the Fibonacci lattice the very best way to evenly distribute N points on a sphere? So far it seems that it is the best. Does anyone know of a better method?" I have a Ph.D. in physics and may have an application for some of this research in physics. I came across this wonderful paper: http://arxiv.org/pdf/0912.4540.pdf

问题 I have a array with following content: > head(MEAN) 1901DJF 1901JJA 1901MAM 1901SON 1902DJF 1902JJA -0.45451556 -0.72922229 -0.17669396 -1.12095590 -0.86523850 -0.04031273 This should be a time series with seasonal mean values from 1901 to 2009. The problem is that the generated column heads are strictly alphabetically ordered. However, in terms of season this doesn't make to much sense, e.g. JJA (june, july, august) is leading MAM (march, april, may). How could I switch each MAM and JJA

问题 I have been trying to do a program that applies a mean filter over images, and I think I am close to do it correctly, but there still small flaws in the images. For instance: Original racing: http://s72.photobucket.com/user/john_smith140/media/gp4_zpstafhejk5.jpg.html?filters[user]=139318132&filters[recent]=1&sort=1&o=2 Original Triangles: http://s72.photobucket.com/user/john_smith140/media/input_zpsz2cfhrc7.jpeg.html?filters[user]=139318132&filters[recent]=1&sort=1&o=3 Modified racing: http:

问题 I'm stuck with the problem of how to graphically plot the results of an ANCOVA, I would really appreciate it if someone could help me with this. I have two vectors consisting of 23 baseline values (covariate) and 23 values after treatment (independent variable) and I have two factors with both two levels. I created an ANCOVA model and calculated the adjusted means, standard errors and confidence intervals. Example: library(effects) baseline = c(0.7672,1.846,0.6487,0.4517,0.5599,0.2255,0.5946

问题 I have new question related with this my topic deleting outlier in r with account of nominal var. In new case variables x and x1 has different lenght x <- c(-10, 1:6, 50) x1<- c(-20, 1:5, 60) z<- c(1,2,3,4,5,6,7,8) bx <- boxplot(x) bx$out bx1 <- boxplot(x1) bx1$out x<- x[!(x %in% bx$out)] x1 <- x1[!(x1 %in% bx1$out)] x_to_remove<-which(x %in% bx$out) x <- x[!(x %in% bx$out)] x1_to_remove<-which(x1 %in% bx1$out) x1 <- x1[!(x1 %in% bx1$out)] z<-z[-unique(c(x_to_remove,x1_to_remove))] z data

问题 So I am new to working with matrices and functions and I am trying to work out how to apply a function to calculate the column means to multiple matrices. Here is some dummy martices: A <- matrix(c(1,2,3,4,5,6,7,8,9),nrow=3) B <- matrix(c(9,8,7,6,5,4,3,2,1),nrow=3) I have 13 large matrices all different variables, but they all have the same dimensions. I want to get the mean of the columns for each individual matrices. I have worked out how to do this for an individual matrix: AA <- sapply(1

问题 How to use R to calculate row mean ? Sample data: f<- data.frame( name=c("apple","orange","banana"), day1sales=c(2,5,4), day1sales=c(2,8,6), day1sales=c(2,15,24), day1sales=c(22,51,13), day1sales=c(5,8,7) ) Expected Results : Subsequently the table will add more column for example the expected results is only until AverageSales day1sales.4 . After running more data, it will add on to day1sales.6 and so on. So how can I count the average for all the rows? 回答1: with rowMeans > rowMeans(f[-1]) #

问题 I have a barplot where the exact bar heights are in the dataframe. df <- data.frame(x=LETTERS[1:6], y=c(1:6, 1:6 + 1), g=rep(x = c("a", "b"), each=6)) ggplot(df, aes(x=x, y=y, fill=g, group=g)) + geom_bar(stat="identity", position="dodge") Now I want to add two hlines displaying the mean of all bars per group. All I get with ggplot(df, aes(x=x, y=y, fill=g, group=g)) + geom_bar(stat="identity", position="dodge") + stat_summary(fun.y=mean, aes(yintercept=..y.., group=g), geom="hline") is As I

问题 Ok so I have an array <134x106x108>. What I'm trying to do is loop through this array and store the average/standard deviation into another array. So basically there will be 134 <106x108 doubles> that will be in this meanArray and sdArray. %dayv=<134x106x108> sdArray=zeros(1,106,108); meanArray=zeros(1,106,108); for i=1:size(dayv,1) %store means/standard deviation into an array meanArray(i,:,:) = squeeze(mean(dayv(i,:,:))); sdArray(i,:,:) = squeeze(std(dayv(i,:,:))); end 回答1: If you want each