columnmean <- function(x, removeNA=TRUE){ ## removeNA=TRUE表示默认去掉缺失值NA nc <-ncol(x) ## 用ncol找到x中的列数,赋值给nc means <- numeric(nc) ## 设置一个储存均值的向量叫means,means的长度等于nc for(i in 1:nc){ ## 开始循环,从第一列循环到最后一列 means[i]<- mean(x[,i]...
above<-function(x,n = 10) { use<-x>n x[use] } ## 求每一列均值 columnmean<-function(y, removeNA = TRUE) { ## 去除所有空值 nc<-ncol(y) means<-numeric(nc) for (i in 1:nc) { means[i]<-mean(y[,i], na.rm = removeNA) } means } 1. 2. 3. 4. 5. 6. 7. 8. 9...
需要用到的函数代码如下: group_data <- function(data, by, group) { library(tidyverse)#load needed package data[,1:length(data)] %>% scale() %>% apply(1,mean) %>% #calculate mean for column data.frame(data) ->df2 #calculate scores group_by = quantile(df2[[1]],probs = by)#cal...
#那么我们也可以用for循环完成相似的事情data <- data.frame(ID = colnames(df)[1:4])for(i in1:4) {print(i)data[i,2] <- mean(df[,i])}#那么如果我们用map函数输出的格式默认是列表,我们应该如何整理成数据框呢?x<- unlist(map(df[,1:4],mean))data.frame(x) %>%rownames_to_column %>...
#那么我们也可以用for循环完成相似的事情data <- data.frame(ID = colnames(df)[1:4])for(i in1:4) {print(i)data[i,2] <- mean(df[,i])}#那么如果我们用map函数输出的格式默认是列表,我们应该如何整理成数据框呢?x<- unlist(map(df[...
mystats <- function(x, na.omit = FALSE) { if (na.omit) x <- x[!is.na(x)] m <- mean(x) n <- length(x) s <- sd(x) skew <- sum((x - m)^3/s^3)/n kurt <- sum((x - m)^4/s^4)/n - 3 return(c(n = n, mean = m, stdev = s, skew = skew, kurtosis ...
1.columnmean <- function(x){ ## 给函数命名,设置参数x,用于储存矩阵 2.nc <- ncol(x) ## 计算矩阵中有多少列 3.means <- numeric(nc) ## 设置数值向量储存列数,长度等于列数,它是一个空向量,每个元素的初始值为0,数值在循环中填满。 4.for (i in 1:nc){ ## 设置循环,循环参数在整数向量1...
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You can interpret the first command as, “Store into an object named model the result of the lm (linear model) function analysis where the dependent variable to predict is the Rate column in the table object (data$Rate), and the independent predictor variables are Color...