mean

I want to calculate means over several columns for each row in my dataframe containing missing values, and place results in a new column called 'means.' Here's my dataframe: df <- data.frame(A=c(3,4,5),B=c(0,6,8),C=c(9,NA,1)) A B C 1 3 0 9 2 4 6 NA 3 5 8 1 The code below successfully accomplishes the task if columns have no missing values, such as columns A and B. library(dplyr) df %>% rowwise() %>% mutate(means=mean(A:B, na.rm=T)) A B C means <dbl> <dbl> <dbl> <dbl> 1 3 0 9 1.5 2 4 6 NA 5.0 3 5 8 1 6.5 However, if a column has missing values, such as C, then I get an error: > df %>% rowwise()

I have the following matrix of depth and temperature data (855 rows, 2 col) and would like to take the mean of every 3 rows within each column. For example: [1,] -6.7 18.91 [2,] -5.4 18.91 [3,] -4.0 18.59 [4,] -6.7 20.37 [5,] -6.7 20.05 [6,] -2.7 20.21 [7,] -4.0 21.03 [8,] -5.4 20.70 [9,] -4.0 20.87 [10,] -2.7 21.37 [11,] -2.7 21.37 [12,] -2.7 21.37 mean(data[1:3,1]) mean(data[4:6,1]) for the entire matrix. How can I accomplish this without manually writing the code for the mean of every 3 rows? Any ideas or suggestions are greatly appreciated. Use rollapply function from zoo package. See

I need some advices to improve automatic minification with node and gulp. The main objective is generate dynamically the minified files(for JS and LESS) in development mode and change automatically normal files(js and less) to minified files in production mode. The scenario contains: NodeJS and ExpressJS for routing and environment configuration Jade as template engine Gulp (task runner) This is my setup: GULP I'm using nodemon in order to lauch server.js wich starts my node server. In this gulp file i have some tasks (['watch']) for watch the changes on JS and LESS files and minify them in

Is there a way to avoid using specific values when applying sum and mean in numpy? I'd like to avoid, for instance, the -999 value when calculating the result. In [14]: c = np.matrix([[4., 2.],[4., 1.]]) In [15]: d = np.matrix([[3., 2.],[4., -999.]]) In [16]: np.sum([c, d], axis=0) Out[16]: array([[ 7., 4.], [ 8., -998.]]) In [17]: np.mean([c, d], axis=0) Out[17]: array([[ 3.5, 2. ], [ 4. , -499. ]]) Use a masked array: >>> c = np.ma.array([[4., 2.], [4., 1.]]) >>> d = np.ma.masked_values([[3., 2.], [4., -999]], -999) >>> np.ma.array([c, d]).sum(axis=0) masked_array(data = [[7.0 4.0] [8.0 1.0]

Given a dataset of months, how do I calculate the "average" month, taking into account that months are circular? months = c(1,1,1,2,3,5,7,9,11,12,12,12) mean(months) ## [1] 6.333333 In this dummy example, the mean should be in January or December. I see that there are packages for circular statistics, but I'm not sure whether they suit my needs here. I think months <- c(1,1,1,2,3,5,7,9,11,12,12,12) library("CircStats") conv <- 2*pi/12 ## months -> radians Now convert from months to radians, compute the circular mean, and convert back to months. I'm subtracting 1 here assuming that January is

ISODATA聚类算法的matlab程序 作者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/ 参考： Kmeans及ISODATA算法的matlab实现 数据见： MATLAB实例：PCA降维 中的iris数据集，保存为：iris.data，最后一列是类标签。 demo_isodata.m clear clc data_load=dlmread('iris.data'); [~,dim]=size(data_load); x=data_load(:,1:dim-1); K=3; theta_N=1; theta_S=1; theta_c=4; L=1; I=5; ISODATA(x,K,theta_N,theta_S,theta_c,L,I) ISODATA.m function ISODATA(x,K,theta_N,theta_S,theta_c,L,I) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%input parameters%%%%%% % x : data % K : 预期的聚类中心数 % theta_N : 每一聚类中心中最少的样本数，少于此数就不作为一个独立的聚类 % theta_S ：一个聚类中样本距离分布的标准差 % theta_c :