通过每行的条件（>）计算列数 [英] Count number of columns by a condition (>) for each row

问题描述

我试图找出矩阵的每一行有多少列的值大于一个指定的值。对不起，我问这个简单的问题，但我不能弄清楚。

I am trying to work out for each row of a matrix how many columns have values greater than a specified value. I am sorry that I am asking this simple question but I wasn't able to figure it out.

我已经从光栅堆栈中提取了多年的最大温度值数据看起来类似于：

I have extracted maximum temperature values from a raster stack, of multiple years of rasters, for some spatial points I am interested in. The data looks similar to:

data <- cbind('1990' = c(25, 22, 35, 42, 44), '1991' = c(23, 28, 33, 40, 45), '1992' = c(20, 20, 30, 41, 43))

    1990   1991   1992
1     25     23     20
2     22     28     20
3     35     33     30
4     42     40     41
5     44     45     43

我想结束每个地点的温度高于30的年数，例如。：

I want to end up with the number of years that the temperature was above 30 for each location, eg.:

    yr.above   
1          0
2          0
3          2
4          3
5          3

我尝试过几件事，但没有工作并且是相当不合逻辑的（例如，尝试长度（数据[1：长度（数据），这（blah blah没有意义））或apply（数据，1，长度（数据）> 30）

I have tried a few things but they didn't work and were pretty illogical (e.g. trying length(data[1:length(data), which(blah blah doesn't make sense)), or apply(data, 1, length(data) > 30), I know these don't make sense but I am a bit stuck.

推荐答案

这将给你正在寻找的向量：

This will give you the vector you are looking for:

rowSums(data > 30)

无论 data 矩阵或数据帧。此外，它使用矢量化函数，因此是使用 apply 的一个首选方法，它比一个（慢）for循环更多。

It will work whether data is a matrix or a data.frame. Also, it uses vectorized functions, hence is a preferred approach over using apply which is little more than a (slow) for loop.

如果 data 是data.frame，可以通过执行以下操作将结果作为列添加：

If data is a data.frame, you can add the result as a column by doing:

data$yr.above <- rowSums(data > 30)

或如果数据是一个矩阵：

data <- cbind(data, yr.above = rowSums(data > 30))

也创建了一个全新的data.frame：

You can also create a whole new data.frame:

data.frame(yr.above = rowSums(data > 30))

或一个全新的矩阵：

cbind(yr.above = rowSums(data > 30))

这篇关于通过每行的条件（>）计算列数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！