计算双因子 table 的均值向量

Question

我正在尝试计算变量 RBC、WBC 和 hemoglobin 的平均试剂向量。我是 R 的新手，所以我的问题是：你能告诉我一种更简单的方法来在 R 中进行以下计算吗？数据来自Rencher的Table6.19。我正在按照 Rencher 中的示例练习在 R 中进行计算。

reagent.dat <- read.table("https://dl.dropboxusercontent.com/u/28713619/reagent.dat")
colnames(reagent.dat) <- c("reagent", "subject", "RBC", "WBC", "hemoglobin")
reagent.dat$reagent <- factor(reagent.dat$reagent)
reagent.dat$subject <- factor(reagent.dat$subject)
library(plyr) 
library(dplyr)
library(reshape2)
# Calculate the means per variable, across reagents
reagent.datm <- melt(reagent.dat)
group.means <- ddply(reagent.datm, c("variable","reagent"), summarise,mean=mean(value))
group.means <- tbl_df(group.means)
newdata <- group.means %>% select(reagent, mean)
# Store the group means into a matrix
y_bar <- matrix(c(rep(NA, times=12)), ncol=4)
for (i in 1:4)
  y_bar[,i] <- as.matrix(filter(newdata, reagent == i)$mean, ncol=1)
y_bar

Answer 1

dplyr 包实际上可以很容易地简化您的代码，绝对值得学习，因为它非常强大。例如：

reagent.dat <- read.table("https://dl.dropboxusercontent.com/u/28713619/reagent.dat")
colnames(reagent.dat) <- c("reagent", "subject", "RBC", "WBC", "hemoglobin")

#Using dplyr
library(dplyr)
reagentmeans <- reagent.dat  %>% select(reagent, RBC, WBC, hemoglobin)  %>% 
group_by(reagent)  %>% 
summarize(mean_RBC = mean(RBC), mean_WBC = mean(WBC),
 mean_hemoglobin = mean(hemoglobin))

> reagentmeans
Source: local data frame [4 x 4]

  reagent mean_RBC mean_WBC mean_hemoglobin
   (fctr)    (dbl)    (dbl)           (dbl)
1       1    7.290   4.9535          15.310
2       2    7.210   4.8985          15.725
3       3    7.055   4.8810          15.595
4       4    7.025   4.8915          15.765

Answer 2

您可以使用data.table,

library(data.table)
setDT(reagent.dat)[, lapply(.SD, mean), by = reagent, .SDcols = c('RBC', 'WBC', 'hemoglobin')]
#   reagent   RBC    WBC hemoglobin
#1:       1 7.290 4.9535     15.310
#2:       2 7.210 4.8985     15.725
#3:       3 7.055 4.8810     15.595
#4:       4 7.025 4.8915     15.765

计算双因子 table 的均值向量

Calculating the mean vectors for a two-factorial table

r

plyr

dplyr