处理输出对象
面向 SAS 用户的 R
Melinda Higgins, PhD
Research Professor/Senior Biostatistician Emory University
summary 的输出
# 保存 summary 输出，查看 davissmry
davissmry <- daviskeep %>%
select(weight, height, bmi) %>%
summary()
# 显示 summary 输出对象
davissmry
weight height bmi
Min. : 39.0 Min. :148.0 Min. :15.82
1st Qu.: 55.0 1st Qu.:164.0 1st Qu.:20.22
Median : 63.0 Median :170.0 Median :21.80
Mean : 65.3 Mean :170.6 Mean :22.26
3rd Qu.: 73.5 3rd Qu.:177.5 3rd Qu.:23.94
Max. :119.0 Max. :197.0 Max. :36.73
summary 的输出
# davissmry 的类
class(davissmry)
# 检查 davissmry 是否也是矩阵
is.matrix(davissmry)
# 显示 davissmry 的维度
dim(davissmry)
[1] "table"
[1] TRUE
[1] 6 3
从 summary 对象选择元素
# 显示输出的第 1 到 2 列
davissmry[,1:2]
weight height
Min. : 39.0 Min. :148.0
1st Qu.: 55.0 1st Qu.:164.0
Median : 63.0 Median :170.0
Mean : 65.3 Mean :170.6
3rd Qu.: 73.5 3rd Qu.:177.5
Max. :119.0 Max. :197.0
# 显示输出的第 4 到 6 行
davissmry[4:6,]
weight height bmi
Mean : 65.3 Mean :170.6 Mean :22.26
3rd Qu.: 73.5 3rd Qu.:177.5 3rd Qu.:23.94
Max. :119.0 Max. :197.0 Max. :36.73
dplyr summarise 的输出
# 保存 summarise 的输出
davissmall <- daviskeep %>%
select(weight, height) %>%
summarise(across(everything(),
list(mean = ~mean(.x),
sd = ~sd(.x))))
# 显示 davissmall 输出对象
davissmall
weight_mean weight_sd height_mean height_sd
1 65.29648 13.34346 170.5879 8.948848
# 查看 davissmall 的结构
str(davissmall)
'data.frame': 1 obs. of 4 variables:
$ weight_mean: num 65.3
$ weight_sd : num 13.3
$ height_mean: num 171
$ height_sd : num 8.95
显示 summarise 输出的元素
# 显示第 3 列（身高均值）
davissmall[, 3]
[1] 170.5879
# 显示元素名 height_mean
davissmall$height_mean
[1] 170.5879
dplyr group_by 的输出
# 用 group_by 按性别计算 bmi 统计量
davisbmisex <- daviskeep %>%
group_by(sex) %>%
select(bmi, sex) %>%
summarise(across(everything(),
list(mean = ~mean(.x),
sd = ~sd(.x))))
# 显示输出对象
davisbmisex
# A tibble: 2 × 3
sex bmi_mean bmi_sd
<fct> <dbl> <dbl>
1 F 21.0 2.18
2 M 23.9 3.12
# 查看 davisbmisex 的结构
str(davisbmisex)
tibble [2 × 3] (S3: tbl_df/tbl/data.frame)
$ sex : Factor w/ 2 levels "F","M": 1 2
$ bmi_mean: num [1:2] 21 23.9
$ bmi_sd : num [1:2] 2.18 3.12
1 sex 变量被自动编码为 Factor 类型。
显示 group_by 输出的元素
# 显示 davisbmisex 的第 2 列
davisbmisex[, 2]
# A tibble: 2 × 1
bmi_mean
<dbl>
1 21.0
2 23.9
# 显示 davisbmisex 的第 1 行
davisbmisex[1,]
# A tibble: 1 × 3
sex bmi_mean bmi_sd
<fct> <dbl> <dbl>
1 F 21.0 2.18
显示 group_by 输出的元素
# 显示 davisbmisex 的 sd 元素
davisbmisex$bmi_sd
[1] 2.176094 3.120285
# 显示男性行
davisbmisex %>%
filter(sex == "M")
# A tibble: 1 × 3
sex bmi_mean bmi_sd
<fct> <dbl> <dbl>
1 M 23.9 3.12
psych describe 的输出
# 将 psych::describe() 输出保存为 davispsych
davispsych <- daviskeep %>%
select(weight, height, bmi) %>%
psych::describe()
# 显示 davispsych 输出对象
davispsych
vars n mean sd median trimmed mad min max range skew kurtosis se
weight 1 199 65.30 13.34 63.0 64.12 11.86 39.00 119.00 80.00 0.91 0.84 0.95
height 2 199 170.59 8.95 170.0 170.40 10.38 148.00 197.00 49.00 0.21 -0.38 0.63
bmi 3 199 22.26 3.01 21.8 22.08 2.55 15.82 36.73 20.91 0.91 1.91 0.21
psych describe 输出的类与结构
str(davispsych)
Classes 'psych', 'describe' and 'data.frame':
3 obs. of 13 variables:
$ vars : int 1 2 3
$ n : num 199 199 199
$ mean : num 65.3 170.6 22.3
$ sd : num 13.34 8.95 3.01
$ median : num 63 170 21.8
$ trimmed : num 64.1 170.4 22.1
$ mad : num 11.86 10.38 2.55
$ min : num 39 148 15.8
$ max : num 119 197 36.7
$ range : num 80 49 20.9
$ skew : num 0.905 0.21 0.913
$ kurtosis: num 0.843 -0.379 1.914
$ se : num 0.946 0.634 0.213
显示 psych describe 输出的元素
# 显示截尾均值
davispsych$trimmed
[1] 64.12422 170.40373 22.08452
# 显示第 2 行（身高）
davispsych %>%
slice(2)
vars n mean sd median trimmed mad min max range skew kurtosis se
1 2 199 170.59 8.95 170 170.4 10.38 148 197 49 0.21 -0.38 0.63
显示 psych describe 输出的元素
# 显示样本量、中位数、最小值和最大值
davispsych %>%
select(n, median, min, max)
n median min max
weight 199 63.0 39.00 119.00
height 199 170.0 148.00 197.00
bmi 199 21.8 15.82 36.73
让我们为鲍鱼数据的统计输出自定义结果！
面向 SAS 用户的 R
