如何编写一个简短的脚本来创建一个新的数据框,该数据框为以下调查的每一列连续数据报告以下描述性统计数据:平均值、标准差、中位数、最小值、最大值、样本量?
Distance Age Height Coning
1 21.4 18 3.3 Yes
2 13.9 17 3.4 Yes
3 23.9 16 2.9 Yes
4 8.7 18 3.6 No
5 241.8 6 0.7 No
6 44.5 17 1.3 Yes
7 30.0 15 2.5 Yes
8 32.3 16 1.8 Yes
9 31.4 17 5.0 No
10 32.8 13 1.6 No
11 53.3 12 2.0 No
12 54.3 6 0.9 No
13 96.3 11 2.6 No
14 133.6 4 0.6 No
15 32.1 15 2.3 No
16 57.9 12 2.4 Yes
17 30.8 17 1.8 No
18 59.9 7 0.8 No
19 42.7 15 2.0 Yes
20 20.6 18 1.7 Yes
21 62.0 8 1.3 No
22 53.1 7 1.6 No
23 28.9 16 2.2 Yes
24 177.4 5 1.1 No
25 24.8 14 1.5 Yes
26 75.3 14 2.3 Yes
27 51.6 7 1.4 No
28 36.1 9 1.1 No
29 116.1 6 1.1 No
30 28.1 16 2.5 Yes
31 8.7 19 2.2 Yes
32 105.1 6 0.8 No
33 46.0 15 3.0 Yes
34 102.6 7 1.2 No
35 15.8 15 2.2 No
36 60.0 7 1.3 No
37 96.4 13 2.6 No
38 24.2 14 1.7 No
39 14.5 15 2.4 No
40 36.6 14 1.5 No
41 65.7 5 0.6 No
42 116.3 7 1.6 No
43 113.6 8 1.0 No
44 16.7 15 4.3 Yes
45 66.0 7 1.0 No
46 60.7 7 1.0 No
47 90.6 7 0.7 No
48 91.3 7 1.3 No
49 14.4 18 3.1 Yes
50 72.8 14 3.0 Yes
你可以编写自己的函数,将这样的摘要放入 data.frame 中:
# Defining the function
my.summary <- function(x, na.rm=TRUE){
result <- c(Mean=mean(x, na.rm=na.rm),
SD=sd(x, na.rm=na.rm),
Median=median(x, na.rm=na.rm),
Min=min(x, na.rm=na.rm),
Max=max(x, na.rm=na.rm),
N=length(x))
}
# identifying numeric columns
ind <- sapply(df, is.numeric)
# applying the function to numeric columns only
sapply(df[, ind], my.summary)
Distance Age Height
Mean 58.67200 11.840000 1.9160000
SD 45.48137 4.604168 0.9796626
Median 48.80000 13.500000 1.7000000
Min 8.70000 4.000000 0.6000000
Max 241.80000 19.000000 5.0000000
N 50.00000 50.000000 50.0000000
或者,您可以使用 fBasics 包中的内置函数basicStats
来获得更详细的摘要:
> library(fBasics)
> basicStats(df[, ind])
Distance Age Height
nobs 50.000000 50.000000 50.000000
NAs 0.000000 0.000000 0.000000
Minimum 8.700000 4.000000 0.600000
Maximum 241.800000 19.000000 5.000000
1. Quartile 28.300000 7.000000 1.125000
3. Quartile 74.675000 15.750000 2.475000
Mean 58.672000 11.840000 1.916000
Median 48.800000 13.500000 1.700000
Sum 2933.600000 592.000000 95.800000
SE Mean 6.432037 0.651128 0.138545
LCL Mean 45.746337 10.531510 1.637583
UCL Mean 71.597663 13.148490 2.194417
Variance 2068.555118 21.198367 0.959739
Stdev 45.481371 4.604168 0.979663
Skewness 1.711028 -0.158853 0.905415
Kurtosis 3.753948 -1.574527 0.578684
下面对do.call
、rbind
和sapply
的使用为具有类"numeric"的每一列提供了摘要。如果您需要与summary
不同的统计,您可以编写自己的统计函数(请参阅 @Jilber 的答案)。
mtcars$carb = as.factor(mtcars$carb) # Forcing one column to a factor
do.call('rbind', sapply(mtcars, function(x) if(is.numeric(x)) summary(x)))
Min. 1st Qu. Median Mean 3rd Qu. Max.
mpg 10.400 15.420 19.200 20.0900 22.80 33.900
cyl 4.000 4.000 6.000 6.1880 8.00 8.000
disp 71.100 120.800 196.300 230.7000 326.00 472.000
hp 52.000 96.500 123.000 146.7000 180.00 335.000
drat 2.760 3.080 3.695 3.5970 3.92 4.930
wt 1.513 2.581 3.325 3.2170 3.61 5.424
qsec 14.500 16.890 17.710 17.8500 18.90 22.900
vs 0.000 0.000 0.000 0.4375 1.00 1.000
am 0.000 0.000 0.000 0.4062 1.00 1.000
gear 3.000 3.000 4.000 3.6880 4.00 5.000
一些使用 data.table
的示例。我正在使用前面答案中定义的函数。
my.summary <- function(x, na.rm=TRUE){
result <- c(Mean=mean(x, na.rm=na.rm),
SD=sd(x, na.rm=na.rm),
Median=median(x, na.rm=na.rm),
Min=min(x, na.rm=na.rm),
Max=max(x, na.rm=na.rm),
N=length(x))
}
set.seed(123)
df <- data.frame(id = 1:1000,
Distance = rnorm(1000, 50, 100),
Age = rnorm(1000, 50, 100),
Height = rnorm(1000, 50, 100)
)
df$Coning <- as.factor(ifelse(df$Distance > 0, "Yes", "No"))
library(fBasics)
library(data.table)
DT <- data.table(df)
setkey(DT, id)
按因子变量"圆锥"分组
DT[,lapply(.SD,my.summary),by="Coning"]
使用 my.summary() 和 basicStats()只是数字变量
DT[,lapply(.SD, my.summary),, .SDcols = names(DT)[2:4]]
BS <- DT[,sapply(.SD, basicStats),, .SDcols = names(DT)[2:4]]
BS[, summary := znames]
setnames(BS, 1:3, names(DT)[2:4])
BS
DT[,lapply(.SD, summary),, .SDcols = names(DT)[2:4]]
使用摘要()数值变量使用
DT[,sapply(.SD, function(x) if(is.numeric(x)) summary(x)),, .SDcols = names(DT)[2:4]]
因子变量
DT[,sapply(.SD, function(x) if(is.factor(x)) summary(x)),, .SDcols = names(DT)[5]]
使用分位数函数也非常有用:
DT[,sapply(.SD, function(x) if(is.numeric(x)) quantile(x)),, .SDcols = names(DT)[2:4]]
> 包collapse
提供快速高效的汇总统计生成器,qsu
。我一直在寻找类似于 STATA su
的 R 函数,这个函数对我来说是最好的。
https://sebkrantz.github.io/collapse/articles/collapse_intro.html