Manipulating Data - 计算移动平均数

问题

你想要计算移动平均数

解决方案

假设你的数据时带缺失值的噪声正弦波。

set.seed(993)
x <- 1:300
y <- sin(x/20) + rnorm(300,sd=.1)
y[251:255] <- NA

filter()函数可以用来计算移动平均数。

# 绘制未平滑的数据（灰色）
plot(x, y, type="l", col=grey(.5))
# 绘制网格
grid()

# 延迟平滑
# 当前样本和之前19个样本的平均数（红色）
f20 <- rep(1/20, 20)
f20
#>  [1] 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
#> [18] 0.05 0.05 0.05
y_lag <- filter(y, f20, sides=1)
lines(x, y_lag, col="red")


# 对称性平滑：
#计算当前样本，时间上将来样本和过去10个样本的平均数（蓝色）
f21 <- rep(1/21,21)
f21
#>  [1] 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905
#>  [8] 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905
#> [15] 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905 0.04761905
y_sym <- filter(y, f21, sides=2)
lines(x, y_sym, col="blue")

plot of chunk unnamed-chunk-2

filter()会在遭遇缺失值时留下空缺，就像上面图中显示的一样。

一种处理缺失值的方法是简单地忽略它，不把它包含在平均数的计算中。这个功能可以由下面的函数实现：

# x: the vector
# n: the number of samples
# centered: if FALSE, then average current sample and previous (n-1) samples
#           if TRUE, then average symmetrically in past and future. (If n is even, use one more sample from future.)
movingAverage <- function(x, n=1, centered=FALSE) {
    
    if (centered) {
        before <- floor  ((n-1)/2)
        after  <- ceiling((n-1)/2)
    } else {
        before <- n-1
        after  <- 0
    }

    # Track the sum and count of number of non-NA items
    s     <- rep(0, length(x))
    count <- rep(0, length(x))
    
    # Add the centered data 
    new <- x
    # Add to count list wherever there isn't a 
    count <- count + !is.na(new)
    # Now replace NA_s with 0_s and add to total
    new[is.na(new)] <- 0
    s <- s + new
    
    # Add the data from before
    i <- 1
    while (i <= before) {
        # This is the vector with offset values to add
        new   <- c(rep(NA, i), x[1:(length(x)-i)])

        count <- count + !is.na(new)
        new[is.na(new)] <- 0
        s <- s + new
        
        i <- i+1
    }

    # Add the data from after
    i <- 1
    while (i <= after) {
        # This is the vector with offset values to add
        new   <- c(x[(i+1):length(x)], rep(NA, i))
       
        count <- count + !is.na(new)
        new[is.na(new)] <- 0
        s <- s + new
        
        i <- i+1
    }
    
    # return sum divided by count
    s/count
}


# 用比较厚的线条绘制和之前一样的图
plot(x, y, type="l", col=grey(.5))
grid()
y_lag <- filter(y, rep(1/20, 20), sides=1)
lines(x, y_lag, col="red", lwd=4)         # 用红色表示延迟平均
y_sym <- filter(y, rep(1/21,21), sides=2)
lines(x, y_sym, col="blue", lwd=4)        # 用蓝色表示对称平均

# 用上面定义的函数计算延迟平均
y_lag_na.rm <- movingAverage(y, 20)
lines(x, y_lag_na.rm, col="green", lwd=2)

# 用上面定义的函数计算对称性平均
y_sym_na.rm <- movingAverage(y, 21, TRUE)
lines(x, y_sym_na.rm, col="green", lwd=2)

plot of chunk unnamed-chunk-3

最后编辑于：2018.01.20 13:29:26