「R」如何汇总数据

2020-07-06 17:05:25 浏览数 (3)

问题

你想要按照组别总结你的数据(均值、标准差等等)。

方案

有三种方法描述基于一些特定变量的分组数据,然后对每一组使用总结函数(像均值、标准差等等)。

  • ddply()函数:它比较容易使用,但需要载入plyr包。这种方法可能就是你要找的(说明很多人用呗,好用呗)。
  • summaryBy()函数:它也比较容易使用,然而它需要载入doBy包。
  • aggregate()函数,它比较难使用一点但内置于R中。

假设你有以下数据并想求得每一组样本大小、均值的改变、标准差以及均值的标准误,而这里的组别是根据性别和条件指定的:F-placebo, F-aspirin, M-placeboM-aspirin

代码语言:javascript复制
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
 ')

使用 ddply

代码语言:javascript复制
library(plyr)

# 给每一组运行长度、均值、标准差等函数
# 每一组依据性别 条件划分
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
处理缺失值

如果数据中存在NA值,需要给每个函数添加na.rm=TRUE标记去除缺失值。因为函数length()没有na.rm选项,所以可以使用sum(!is.na(...))的方式对非缺失值进行计数。

代码语言:javascript复制
# 给数据加些NA值
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
自动总结函数

不像我们刚才手动地指定想要的值然后计算标准误,这个函数可以自动处理所有的细节。它可以干以下的事情:

  • 寻找均值、标准差和计数
  • 寻找均值的标准误(强调,如果你处理的是被试内变量这可能不是你想要的
  • 寻找95%的置信区间(也可以指定其他值)
  • 重命令结果数据集的变量名,这样更方便后续处理

要使用的话,把函数放你的代码中然后像下面一样调用它。

代码语言:javascript复制
## 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)
}

举个例子使用它(用95%的置信区间)。与之前手动计算这些步骤相反,summarySE函数一步搞定:

代码语言:javascript复制
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
用零填满空组合

有时候总结的数据框中存在因子的空组合 - 这意思是,因子组合可能存在,但原始数据框里又没有实际出现。它在自动填满有NA值的数据框时有用。要做到这一点,当调用ddplysummarySE时设置.drop=FALSE。(这里我翻译的不是很如意,大家可以查看原文)

例子:

代码语言:javascript复制
# 首先移除所有 Male Placebo 条目
dataSub <- subset(data, !(sex=="M" & condition=="placebo"))

# 如果我们总结数据,在本来有 Male Placebo 的地方会存在空行
# 因为这个组合已经被我们删除了
summarySE(dataSub, 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

# 设置 .drop=FALSE 指定不要扔掉这个组合
summarySE(dataSub, measurevar="change", groupvars=c("sex", "condition"), .drop=FALSE)
#> Warning in qt(conf.interval/2   0.5, datac$N - 1): NaNs produced
#>   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  0       NaN        NA        NA        NA

使用summaryBy

使用summarizeBy()函数瓦解数据:

代码语言:javascript复制
library(doBy)

# 给每一组运行长度、均值、标准差等函数
# 每一组依据性别 条件划分
cdata <- summaryBy(change ~ sex   condition, data=data, FUN=c(length,mean,sd))
cdata
#>   sex condition change.length change.mean change.sd
#> 1   F   aspirin             5   -3.420000 0.8642916
#> 2   F   placebo            12   -2.058333 0.5247655
#> 3   M   aspirin             9   -5.411111 1.1307569
#> 4   M   placebo             4   -0.975000 0.7804913

# Rename column change.length to just N
names(cdata)[names(cdata)=="change.length"] <- "N"

# Calculate standard error of the mean
cdata$change.se <- cdata$change.sd / sqrt(cdata$N)
cdata
#>   sex condition  N change.mean change.sd change.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

注意如果你有任何被试内变量,这些标准误值在比对组别差异时就没用了

处理缺失值

如果数据中存在NA值,你需要添加na.rm=TRUE选项。通常你可以在summaryBy()函数中设置,但length()函数识别不了这个选项。一种解决方式是根据length()函数定义一个新的取长度函数去处理NA值。

代码语言:javascript复制
# 新版的length函数可以处理NA值,如果na.rm=T,则不对NA计数
length2 <- function (x, na.rm=FALSE) {
    if (na.rm) sum(!is.na(x))
    else       length(x)
}

# 给数据添加一些NA值
dataNA <- data
dataNA$change[11:14] <- NA

cdataNA <- summaryBy(change ~ sex   condition, data=dataNA,
                     FUN=c(length2, mean, sd), na.rm=TRUE)
cdataNA
#>   sex condition change.length2 change.mean change.sd
#> 1   F   aspirin              4   -3.425000 0.9979145
#> 2   F   placebo             12   -2.058333 0.5247655
#> 3   M   aspirin              7   -5.142857 1.0674848
#> 4   M   placebo              3   -1.300000 0.5291503

# Now, do the same as before
自动总结函数

(注意这里的自动总结函数与之前的不同,它是通过summaryBy实现的)

不像我们刚才手动地指定想要的值然后计算标准误,这个函数可以自动处理所有的细节。它可以干以下的事情:

  • 寻找均值、标准差和计数
  • 寻找均值的标准误(强调,如果你处理的是被试内变量这可能不是你想要的
  • 寻找95%的置信区间(也可以指定其他值)
  • 重命令结果数据集的变量名,这样更方便后续处理

要使用的话,把函数放你的代码中然后像下面一样调用它。

代码语言:javascript复制
## 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) {
    library(doBy)

    # 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)
    }

    # Collapse the data
    formula <- as.formula(paste(measurevar, paste(groupvars, collapse="   "), sep=" ~ "))
    datac <- summaryBy(formula, data=data, FUN=c(length2,mean,sd), na.rm=na.rm)

    # Rename columns
    names(datac)[ names(datac) == paste(measurevar, ".mean",    sep="") ] <- measurevar
    names(datac)[ names(datac) == paste(measurevar, ".sd",      sep="") ] <- "sd"
    names(datac)[ names(datac) == paste(measurevar, ".length2", sep="") ] <- "N"

    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)
}

举个例子使用它(用95%的置信区间)。与之前手动计算这些步骤相反,summarySE函数一步搞定:

代码语言:javascript复制
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
用零填满空组合

有时候总结的数据框中存在因子的空组合 - 这意思是,因子组合可能存在,但原始数据框里又没有实际出现。它在自动填满有NA值的数据框时有用。

这个例子将会用0填满缺失的组合:

代码语言:javascript复制
fillMissingCombs <- function(df, factors, measures) {

    # Make a list of the combinations of factor levels
    levelList <- list()
    for (f in factors) {  levelList[[f]] <- levels(df[,f])  }

    fullFactors <- expand.grid(levelList)

    dfFull <- merge(fullFactors, df, all.x=TRUE)

    # Wherever there is an NA in the measure vars, replace with 0
    for (m in measures) {
      dfFull[is.na(dfFull[,m]), m] <- 0
    }

    return(dfFull)
}

使用例子:

代码语言:javascript复制
# First remove some all Male Placebo entries from the data
dataSub <- subset(data, !(sex=="M" & condition=="placebo"))

# If we summarize the data, there will be a missing row for Male Placebo,
# since there were no cases with this combination.
cdataSub <- summarySE(dataSub, measurevar="change", groupvars=c("sex", "condition"))
cdataSub
#>   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

# This will fill in the missing combinations with zeros
fillMissingCombs(cdataSub, factors=c("sex","condition"), measures=c("N","change","sd","se","ci"))
#>   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  0  0.000000 0.0000000 0.0000000 0.0000000

使用 aggregate

aggregate函数比较难用,但它内置于R,所以不需要按照其他包。

代码语言:javascript复制
# 对每个目录 (sex*condition) 中的对象计数
cdata <- aggregate(data["subject"], by=data[c("sex","condition")], FUN=length)
cdata
#>   sex condition subject
#> 1   F   aspirin       5
#> 2   M   aspirin       9
#> 3   F   placebo      12
#> 4   M   placebo       4

# 重命名 "subject" 列为 "N"
names(cdata)[names(cdata)=="subject"] <- "N"
cdata
#>   sex condition  N
#> 1   F   aspirin  5
#> 2   M   aspirin  9
#> 3   F   placebo 12
#> 4   M   placebo  4

# 按性别排序
cdata <- cdata[order(cdata$sex),]
cdata
#>   sex condition  N
#> 1   F   aspirin  5
#> 3   F   placebo 12
#> 2   M   aspirin  9
#> 4   M   placebo  4

# 我们也保留 before 和 after列:
# 得到性别和条件下的平均影响大小
# Get the average effect size by sex and condition
cdata.means <- aggregate(data[c("before","after","change")], 
                         by = data[c("sex","condition")], FUN=mean)
cdata.means
#>   sex condition   before     after    change
#> 1   F   aspirin 11.06000  7.640000 -3.420000
#> 2   M   aspirin 11.26667  5.855556 -5.411111
#> 3   F   placebo 10.13333  8.075000 -2.058333
#> 4   M   placebo 11.47500 10.500000 -0.975000

# 融合数据框
cdata <- merge(cdata, cdata.means)
cdata
#>   sex condition  N   before     after    change
#> 1   F   aspirin  5 11.06000  7.640000 -3.420000
#> 2   F   placebo 12 10.13333  8.075000 -2.058333
#> 3   M   aspirin  9 11.26667  5.855556 -5.411111
#> 4   M   placebo  4 11.47500 10.500000 -0.975000

# 得到标准差
cdata.sd <- aggregate(data["change"],
                      by = data[c("sex","condition")], FUN=sd)
# 重命名列
names(cdata.sd)[names(cdata.sd)=="change"] <- "change.sd"
cdata.sd
#>   sex condition change.sd
#> 1   F   aspirin 0.8642916
#> 2   M   aspirin 1.1307569
#> 3   F   placebo 0.5247655
#> 4   M   placebo 0.7804913

# 融合
cdata <- merge(cdata, cdata.sd)
cdata
#>   sex condition  N   before     after    change change.sd
#> 1   F   aspirin  5 11.06000  7.640000 -3.420000 0.8642916
#> 2   F   placebo 12 10.13333  8.075000 -2.058333 0.5247655
#> 3   M   aspirin  9 11.26667  5.855556 -5.411111 1.1307569
#> 4   M   placebo  4 11.47500 10.500000 -0.975000 0.7804913

# 计算标准误
cdata$change.se <- cdata$change.sd / sqrt(cdata$N)
cdata
#>   sex condition  N   before     after    change change.sd change.se
#> 1   F   aspirin  5 11.06000  7.640000 -3.420000 0.8642916 0.3865230
#> 2   F   placebo 12 10.13333  8.075000 -2.058333 0.5247655 0.1514867
#> 3   M   aspirin  9 11.26667  5.855556 -5.411111 1.1307569 0.3769190
#> 4   M   placebo  4 11.47500 10.500000 -0.975000 0.7804913 0.3902456

如果你有NA值想要跳过,设置 na.rm=TRUE:

代码语言:javascript复制
cdata.means <- aggregate(data[c("before","after","change")], 
                         by = data[c("sex","condition")],
                         FUN=mean, na.rm=TRUE)
cdata.means
#>   sex condition   before     after    change
#> 1   F   aspirin 11.06000  7.640000 -3.420000
#> 2   M   aspirin 11.26667  5.855556 -5.411111
#> 3   F   placebo 10.13333  8.075000 -2.058333
#> 4   M   placebo 11.47500 10.500000 -0.975000

0 人点赞