如何计算分配给类别中个人的变量的平均值? [英] How to make an average of a variable assigned to individuals within a category?

问题描述

我有一个大数据集，可以这样表示:

I have a big data set which could be represented something like this:

plot 1 2 3 3 3 4 4 5 5 5 5 6 7
fate S M S S M S S S M S S M M

其中地块是一个地点，而命运是生存"或死亡"(植物生存或死亡.)植物的地块编号与其下的命运相对应.因此，在地块5中有4种植物.其中3个幸存，1个死亡.

where plot is a location, and fate is either "survivorship" or "mortality" ( a plant lives or dies.) The plot number of a plant corresponds to the fate under it. Thus in plot 5 there are 4 plants. 3 of them survive, 1 dies.

我想找出一种方法，使R计算所有这些情节在每个地块中幸存的个体的比例.事实证明，这非常具有挑战性.

I want to figure out a way to make R calculate the fraction of individuals that survive in each plot for all of these. It is proving very challenging.

示例:情节5将返回3/4或75％的生存值 情节3将返回2/3或66％的生存值

Example: Plot 5 would return a survivorship value of 3/4 or 75% Plot 3 would return a survivorship value of 2/3 or 66%

任何帮助将不胜感激. 谢谢

Any help would be much appreciated. Thank you

structure(list(plot = c(1, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5, 6, 7
), fate = structure(c(2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 
2L, 1L, 1L), .Label = c("M", "S"), class = "factor")), .Names = c("plot", 
"fate"), row.names = c(NA, -13L), class = "data.frame")

推荐答案

这里是dplyr的一种解决方案；我创建了valu列，如果幸存则为1，否则为0.之后，仅需对1进行求和，然后将其除以图的元素总数即可.

Here is one solution with dplyr; I've created valu column with 1 if survived and 0 if not. After that it is only a matter of sum the 1's and divide them by the total number of elements of plot.

library(dplyr)
df %>% group_by(plot) %>%
       mutate(valu = ifelse(fate == "S", 1, 0)) %>%
       mutate(perce = (sum(valu)/n() )*100 )

Source: local data frame [13 x 4]
Groups: plot

   plot fate valu     perce
1     1    S    1 100.00000
2     2    M    0   0.00000
3     3    S    1  66.66667
4     3    S    1  66.66667
5     3    M    0  66.66667
6     4    S    1 100.00000
7     4    S    1 100.00000
8     5    S    1  75.00000
9     5    M    0  75.00000
10    5    S    1  75.00000
11    5    S    1  75.00000
12    6    M    0   0.00000
13    7    M    0   0.00000

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