# 【3.12】Summarizing data

1. ddply() 需要 plyr包
2. summarizeBy() 需要doBy包
3. aggregate() R内置的函数，但用起来麻烦

## 一、测试数据：

data <- read.table(header=TRUE, text='
subject sex condition before after change
1   F   placebo   10.1   6.9   -3.2
2   F   placebo    6.3   4.2   -2.1
3   M   aspirin   12.4   6.3   -6.1
4   F   placebo    8.1   6.1   -2.0
5   M   aspirin   15.2   9.9   -5.3
6   F   aspirin   10.9   7.0   -3.9
7   F   aspirin   11.6   8.5   -3.1
8   M   aspirin    9.5   3.0   -6.5
9   F   placebo   11.5   9.0   -2.5
10   M   placebo   11.9  11.0   -0.9
11   F   aspirin   11.4   8.0   -3.4
12   M   aspirin   10.0   4.4   -5.6
13   M   aspirin   12.5   5.4   -7.1
14   M   placebo   10.6  10.6    0.0
15   M   aspirin    9.1   4.3   -4.8
16   F   placebo   12.1  10.2   -1.9
17   F   placebo   11.0   8.8   -2.2
18   F   placebo   11.9  10.2   -1.7
19   M   aspirin    9.1   3.6   -5.5
20   M   placebo   13.5  12.4   -1.1
21   M   aspirin   12.0   7.5   -4.5
22   F   placebo    9.1   7.6   -1.5
23   M   placebo    9.9   8.0   -1.9
24   F   placebo    7.6   5.2   -2.4
25   F   placebo   11.8   9.7   -2.1
26   F   placebo   11.8  10.7   -1.1
27   F   aspirin   10.1   7.9   -2.2
28   M   aspirin   11.6   8.3   -3.3
29   F   aspirin   11.3   6.8   -4.5
30   F   placebo   10.3   8.3   -2.0
')


## 二、ddply

library(plyr)

# Run the functions length, mean, and sd on the value of "change" for each group,
# broken down by sex + condition
cdata <- ddply(data, c("sex", "condition"), summarise,
N    = length(change),
mean = mean(change),
sd   = sd(change),
se   = sd / sqrt(N)
)
cdata
#>   sex condition  N      mean        sd        se
#> 1   F   aspirin  5 -3.420000 0.8642916 0.3865230
#> 2   F   placebo 12 -2.058333 0.5247655 0.1514867
#> 3   M   aspirin  9 -5.411111 1.1307569 0.3769190
#> 4   M   placebo  4 -0.975000 0.7804913 0.3902456


# Put some NA's in the data
dataNA <- data
dataNA$change[11:14] <- NA cdata <- ddply(dataNA, c("sex", "condition"), summarise, N = sum(!is.na(change)), mean = mean(change, na.rm=TRUE), sd = sd(change, na.rm=TRUE), se = sd / sqrt(N) ) cdata #> sex condition N mean sd se #> 1 F aspirin 4 -3.425000 0.9979145 0.4989572 #> 2 F placebo 12 -2.058333 0.5247655 0.1514867 #> 3 M aspirin 7 -5.142857 1.0674848 0.4034713 #> 4 M placebo 3 -1.300000 0.5291503 0.3055050  升级版的ddply ## Summarizes data. ## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%). ## data: a data frame. ## measurevar: the name of a column that contains the variable to be summariezed ## groupvars: a vector containing names of columns that contain grouping variables ## na.rm: a boolean that indicates whether to ignore NA's ## conf.interval: the percent range of the confidence interval (default is 95%) summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) { library(plyr) # New version of length which can handle NA's: if na.rm==T, don't count them length2 <- function (x, na.rm=FALSE) { if (na.rm) sum(!is.na(x)) else length(x) } # This does the summary. For each group's data frame, return a vector with # N, mean, and sd datac <- ddply(data, groupvars, .drop=.drop, .fun = function(xx, col) { c(N = length2(xx[[col]], na.rm=na.rm), mean = mean (xx[[col]], na.rm=na.rm), sd = sd (xx[[col]], na.rm=na.rm) ) }, measurevar ) # Rename the "mean" column datac <- rename(datac, c("mean" = measurevar)) datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1) datac$ci <- datac\$se * ciMult

return(datac)
}


summarySE的使用

summarySE(data, measurevar="change", groupvars=c("sex", "condition"))
#>   sex condition  N    change        sd        se        ci
#> 1   F   aspirin  5 -3.420000 0.8642916 0.3865230 1.0731598
#> 2   F   placebo 12 -2.058333 0.5247655 0.1514867 0.3334201
#> 3   M   aspirin  9 -5.411111 1.1307569 0.3769190 0.8691767
#> 4   M   placebo  4 -0.975000 0.7804913 0.3902456 1.2419358

# With a data set with NA's, use na.rm=TRUE
summarySE(dataNA, measurevar="change", groupvars=c("sex", "condition"), na.rm=TRUE)
#>   sex condition  N    change        sd        se        ci
#> 1   F   aspirin  4 -3.425000 0.9979145 0.4989572 1.5879046
#> 2   F   placebo 12 -2.058333 0.5247655 0.1514867 0.3334201
#> 3   M   aspirin  7 -5.142857 1.0674848 0.4034713 0.9872588
#> 4   M   placebo  3 -1.300000 0.5291503 0.3055050 1.3144821


## 参考资料：

http://www.cookbook-r.com/Manipulating_data/Summarizing_data/