如何向量化长度-频率计算? [英] How to vectorize length-frequency calculation?
问题描述
目前，我有一个很长的代码，带有for循环，用于计算数据集不同成熟度下各种长度的频率，我想对代码进行矢量化处理/找到一个更优雅的解决方案，但是到目前为止，我已经无法解决该怎么做.频率计算是一个相对简单的方法:
(count of occurances of a specific length at a certain maturity/total number of females or males)*100
At the moment I have a quite long code with a for loop calculating the frequency of the various lengths at different maturities of a dataset, I would like to vectorize the code/find a more elegant solution, however so far I've not been able to work out how to do that. The frequency calculation is a relatively simple one:
(count of occurances of a specific length at a certain maturity/total number of females or males)*100
示例数据:
Species Sex Maturity Length
1 HAK M 1 7
2 HAK M 2 24
3 HAK F 2 10
4 HAK M 3 25
5 HAK F 5 25
6 HAK F 4 12
我当前正在使用的代码:
Code that I'm currently using:
reps <- seq(min(Length), max(Length), by = 1)
m1 <- m2 <- m3 <- m4 <- m5 <- rep(NA, length(reps))
f1 <- f2 <- f3 <- f4 <- f5 <- rep(NA, length(reps))
# Makes vectors for each maturity stage for both sexes
# same length as the reps vector filled with NA for the loop:
# Loop:
for (i in 1:length(reps)) # repeats for each value of the x axis
{
m1[i]<- length(Length[Length == reps[i] & Sex == "M" & Maturity == 1])/total.m*100
m2[i]<- length(Length[Length == reps[i] & Sex == "M" & Maturity == 2])/total.m*100
m3[i]<- length(Length[Length == reps[i] & Sex == "M" & Maturity == 3])/total.m*100
m4[i]<- length(Length[Length == reps[i] & Sex == "M" & Maturity == 4])/total.m*100
m5[i]<- length(Length[Length == reps[i] & Sex == "M" & Maturity == 5])/total.m*100
f1[i]<- length(Length[Length == reps[i] & Sex == "F" & Maturity == 1])/total.f*100
f2[i]<- length(Length[Length == reps[i] & Sex == "F" & Maturity == 2])/total.f*100
f3[i]<- length(Length[Length == reps[i] & Sex == "F" & Maturity == 3])/total.f*100
f4[i]<- length(Length[Length == reps[i] & Sex == "F" & Maturity == 4])/total.f*100
f5[i]<- length(Length[Length == reps[i] & Sex == "F" & Maturity == 5])/total.f*100
}
#Stitching together the output of the loop.
males_all<-rbind(m1, m2, m3, m4, m5)
females_all<-rbind(f1, f2, f3, f4, f5)
这是我通常从循环中获得的输出:
This is the output I usually get from the loop:
mat X8 X9 X10 X11 X12 X14 X15
1 m1 0.104712 0.104712 0.6282723 1.3612565 1.884817 0.1047120 0.2094241
2 m2 0.000000 0.000000 0.3141361 0.8376963 2.198953 2.4083770 1.3612565
3 m3 0.000000 0.000000 0.0000000 0.0000000 0.104712 0.2094241 0.1047120
4 m4 0.000000 0.000000 0.0000000 0.0000000 0.000000 0.0000000 0.0000000
5 m5 0.000000 0.000000 0.0000000 0.0000000 0.000000 0.0000000 0.2094241
mat之后的列是长度，为了简洁起见，我没有全部包括在内，它们最多可以增加30个左右. females_all看起来与mat列中的f1, f2等相同.
The columns after mat are the lengths, for the sake of brevity I've not included all of them, they would go up to 30 or so. The females_all looks the same, just with f1, f2 etc. in the mat column.
推荐答案
据我所知，这就是您想要的:
Near as I can tell, this is what you want:
library(dplyr)
counts = count(df, Sex, Maturity, Length)
totals = count(df, Sex, name = "total")
counts = counts %>% left_join(totals) %>%
mutate(prop = n / total)
# # Joining, by = "Sex"
# # A tibble: 6 x 6
# Sex Maturity Length n total prop
# <fct> <int> <int> <int> <int> <dbl>
# 1 F 2 10 1 3 0.333
# 2 F 4 12 1 3 0.333
# 3 F 5 25 1 3 0.333
# 4 M 1 7 1 3 0.333
# 5 M 2 24 1 3 0.333
# 6 M 3 25 1 3 0.333
counts %>% select(Sex, Maturity, Length, prop) %>%
tidyr::spread(key = Length, value = prop, fill = 0)
# # A tibble: 6 x 7
# Sex Maturity `7` `10` `12` `24` `25`
# <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 F 2 0 0.333 0 0 0
# 2 F 4 0 0 0.333 0 0
# 3 F 5 0 0 0 0 0.333
# 4 M 1 0.333 0 0 0 0
# 5 M 2 0 0 0 0.333 0
# 6 M 3 0 0 0 0 0.333
使用此数据:
df = read.table(text = " Species Sex Maturity Length
1 HAK M 1 7
2 HAK M 2 24
3 HAK F 2 10
4 HAK M 3 25
5 HAK F 5 25
6 HAK F 4 12", header = T)
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