我想使用ddply总结来自多个变量，多个因素的数据。
我有以下测试数据:
site block plot rep name weight height dtf
Alberta 1 2 1 A 43 139 54
Alberta 2 5 2 A 46 139 46
Alberta 4 10 3 A 49 136 54
Nunavut 1 1 1 A 49 136 59
Nunavut 2 4 2 A 51 135 50
Nunavut 3 8 3 A 52 133 56
Alberta 5 13 1 B 55 132 50
Alberta 4 12 2 B 55 125 46
Alberta 5 15 3 B 56 120 46
Nunavut 5 14 1 B 57 119 54
Nunavut 5 13 2 B 58 119 55
Nunavut 4 11 3 B 59 118 51
...
等等。
我想采用变量“weight”，“height”，“dtf”，并根据“site”和“name”因素对它们进行汇总。
我从列名的向量开始:
data.factors <- NULL
data.variables <- NULL
for(n in 1:length(data)){if(is.factor(data[[n]])){ data.factors <- c(data.factors,colnames(data[n]))} else next}
for(n in 1:length(data)){if(is.numeric(data[[n]]) || is.integer(data[[n]])){ data.variables <- c(data.variables,colnames(data[n]))} else next}
这适用于执行多个单因素方差分析:
for(variables in data.variables){
for(factors in data.factors){
output1 <- aov(lm(data[[variables]]~data[[factors]]))
cat(variables)
cat(" by ")
cat(factors)
cat("\n")
print(summary(output1))
}}
但是我无法使其与ddply一起使用。
for (x in data.variables){
variable.summary <- ddply(data, .(site,name), summarise,
N = sum(!is.na(x[1])),
min = min(x[1], na.rm=TRUE),
max = max(x[1], na.rm=TRUE),
mean = mean(x[1], na.rm=TRUE),
sd = sd(x[1], na.rm=TRUE),
se = sd / sqrt(N)
)
print(variable.summary)
}
我所得到的是以下内容:
site name N min max mean sd se
1 Alberta A 1 weight weight NA NA NA
2 Alberta B 1 weight weight NA NA NA
3 Alberta C 1 weight weight NA NA NA
4 Alberta D 1 weight weight NA NA NA
5 Alberta E 1 weight weight NA NA NA
6 Nunavut A 1 weight weight NA NA NA
7 Nunavut B 1 weight weight NA NA NA
8 Nunavut C 1 weight weight NA NA NA
9 Nunavut D 1 weight weight NA NA NA
10 Nunavut E 1 weight weight NA NA NA
....
如果我使用单个变量(直接键入而不是通过“x”引用的变量)来测试ddply，那么它将正常工作。
获取功能以识别引用的列ID是否有技巧？我已经习惯了PERL，它的$ Scalars可以在任何地方引用，并且希望R中可以使用类似的系统。
最佳答案
ddply的后继者dplyr可以使用group_by()和summarise_each()真正轻松地做到这一点，而无需循环任何内容:
df <- data.frame(site = c("Alberta", "Alberta", "Alberta", "Nunavut", "Nunavut", "Nunavut", "Alberta", "Alberta", "Alberta", "Nunavut", "Nunavut", "Nunavut"),
block = c(1, 2, 4, 1, 2, 3, 5, 4, 5, 5, 5, 4),
plot = c(2, 5, 10, 1, 4, 8, 13, 12, 15, 14, 13, 11),
rep = c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3),
name = c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"),
weight = c(43, 46, 49, 49, 51, 52, 55, 55, 56, 57, 58, 59),
height = c(139, 139, 136, 136, 135, 133, 132, 125, 120, 119, 119, 118),
dtf = c(54, 46, 54, 59, 50, 56, 50, 46, 46, 54, 55, 51))
library(dplyr)
df.summary <- df %>%
group_by(site, name) %>%
summarise_each(funs(sum, min, max, mean, sd), weight, height, dtf)
结果是这样的数据帧:
> df.summary
Source: local data frame [4 x 17]
Groups: site
site name weight_length height_length dtf_length weight_min height_min dtf_min
1 Alberta A 3 3 3 43 136 46
2 Alberta B 3 3 3 55 120 46
3 Nunavut A 3 3 3 49 133 50
4 Nunavut B 3 3 3 57 118 51
Variables not shown: weight_max (dbl), height_max (dbl), dtf_max (dbl), weight_mean (dbl),
height_mean (dbl), dtf_mean (dbl), weight_sd (dbl), height_sd (dbl), dtf_sd (dbl)
您可以将所需的任何函数传递给
funs()内的summarise_each，因此，如果您想要一列标准错误，只需首先创建该函数:se <- function(x) {
N <- sum(!is.na(x[1]))
return(sd / sqrt(N))
}
并通过:
summarise_each(funs(sum, min, max, mean, sd, se)...)关于R ddply循环;多因素，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/27414068/