table1.jpg
[TOC]
简介
大多数期刊文章的第一个表，即Table 1，是根据暴露程度分层的研究人群基线特征的描述性统计表。table1 包使得使用R生成这样一个表非常简单。结果输出格式是HTML(它的优点是易于复制到Word文档中)。
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Example 1
# 安装和加载包
# install.packages("table1")
# install.packages("boot") 示例数据
library(table1)
library(boot)
melanoma2 <- melanoma # 导入数据
# 修改变量名称
melanoma2$status <-
factor(melanoma2$status,
levels=c(2,1,3),
labels=c("Alive",
"Melanoma death",
"Non-melanoma death"))
head(melanoma2) #查看示例数据
> head(melanoma2)
time status sex age year thickness
1 10 Non-melanoma death 1 76 1972 6.76
2 30 Non-melanoma death 1 56 1968 0.65
3 35 Alive 1 41 1977 1.34
4 99 Non-melanoma death 0 71 1968 2.90
5 185 Melanoma death 1 52 1965 12.08
6 204 Melanoma death 1 28 1971 4.84
ulcer
1 1
2 0
3 0
4 0
5 1
6 1
# 绘制基本的三线表
table1(~ factor(sex) + age + factor(ulcer) + thickness | status, data=melanoma2)
注意，
table1包使用了一个熟悉的公式接口，各变量之间用+分隔，条件符号|的右边为分层变量，参数data指定使用的数据集。
image-20210922153019363
变量和分类的标签可能不适合用来描述结果，可以给分类变量指定标签，给特定的连续变量指定单位。我们可以为分类变量(sex和ulcer)创建带有描述性标签的因素，按照我们想要的方式为每个变量贴上标签，并为连续变量(age和thickness)指定单位，如下所示:
melanoma2$sex <-
factor(melanoma2$sex, levels=c(1,0),
labels=c("Male",
"Female"))
melanoma2$ulcer <-
factor(melanoma2$ulcer, levels=c(0,1),
labels=c("Absent",
"Present"))
# label()添加标签
label(melanoma2$sex) <- "Sex"
label(melanoma2$age) <- "Age"
label(melanoma2$ulcer) <- "Ulceration"
label(melanoma2$thickness) <- "Thickness"
# units()添加单位
units(melanoma2$age) <- "years"
units(melanoma2$thickness) <- "mm"
table1(~ sex + age + ulcer + thickness | status, data=melanoma2, overall="Total")
# overall = F即可不统计全部
image-20210922153642493
Example 2
# 同样的 新建一个数据集
f <- function(x, n, ...) factor(sample(x, n, replace=T, ...), levels=x)
set.seed(427)
n <- 146
dat <- data.frame(id=1:n)
dat$treat <- f(c("Placebo", "Treated"), n, prob=c(1, 2)) # 2:1 randomization
dat$age <- sample(18:65, n, replace=TRUE)
dat$sex <- f(c("Female", "Male"), n, prob=c(.6, .4)) # 60% female
dat$wt <- round(exp(rnorm(n, log(70), 0.23)), 1)
# Add some missing data
dat$wt[sample.int(n, 5)] <- NA
label(dat$age) <- "Age"
label(dat$sex) <- "Sex"
label(dat$wt) <- "Weight"
label(dat$treat) <- "Treatment Group"
units(dat$age) <- "years"
units(dat$wt) <- "kg"
# 查看数据集
head(dat)
> head(dat)
id treat age sex wt
1 1 Treated 18 Female 62.6
2 2 Treated 50 Male 57.4
3 3 Treated 37 Male 104.6
4 4 Treated 25 Female 55.5
5 5 Placebo 60 Female 58.4
6 6 Treated 44 Female 41.9
也可以用两个变量进行分层
table1(~ age + wt | treat*sex, data=dat)
image-20210922160647960
也可以不分层
table1(~ treat + age + sex + wt, data=dat)
image-20210922160813674
# 新建数据集|(治疗组多了个亚组)
dat$dose <- (dat$treat != "Placebo")*sample(1:2, n, replace=T)
dat$dose <- factor(dat$dose, labels=c("Placebo", "5 mg", "10 mg"))
# 查看数据集
head(dat)
> head(dat)
id treat age sex wt dose
1 1 Treated 18 Female 62.6 5 mg
2 2 Treated 50 Male 57.4 5 mg
3 3 Treated 37 Male 104.6 5 mg
4 4 Treated 25 Female 55.5 10 mg
5 5 Placebo 60 Female 58.4 Placebo
6 6 Treated 44 Female 41.9 10 mg
strata <- c(split(dat, dat$dose), list("All treated"=subset(dat, treat=="Treated")), list(Overall=dat))
labels <- list(
variables=list(age=render.varlabel(dat$age),
sex=render.varlabel(dat$sex),
wt=render.varlabel(dat$wt)),
groups=list("", "Treated", ""))
table1(strata, labels, groupspan=c(1, 3, 1))
image-20210922161421964
修改描述性参数
table1(strata, labels, groupspan=c(1, 3, 1),
render.continuous=c(.="Mean (CV%)",
.="Median [Min, Max]",
"Geo. mean (Geo. CV%)"="GMEAN (GCV%)"))
image-20210922162318211
另外还可以根据需要修改为N, NMISS, MEAN, SD, CV, GMEAN, GCV, MEDIAN, MIN, MAX, IQR, Q1, Q2, Q3, T1, T2, FREQ, PCT等参数
改变表的外观
使用内置的样式
该package包括有限数量的内置样式，包括：
- zebra: 交替的阴影行和非阴影行(斑马条纹)
- grid: 显示所有网格线
- shade: 将标题行涂成灰色
- times: 使用serif字体
- center: 将所有列居中，包括第一列（行标签列）
table1(~ age + sex + wt | treat, data=dat, topclass="Rtable1-zebra")
image-20210922162552942
table1(~ age + sex + wt | treat, data=dat, topclass="Rtable1-grid")
image-20210922162623996
table1(~ age + sex + wt | treat, data=dat, topclass="Rtable1-grid Rtable1-shade Rtable1-times")
image-20210922162653296
添加p值
在本例中，分类变量使用独立卡方检验，连续变量使用t检验(如果需要，还可以使用其他检验)。
pvalue <- function(x, ...) {
# Construct vectors of data y, and groups (strata) g
y <- unlist(x)
g <- factor(rep(1:length(x), times=sapply(x, length)))
if (is.numeric(y)) {
# For numeric variables, perform a standard 2-sample t-test
p <- t.test(y ~ g)$p.value
} else {
# For categorical variables, perform a chi-squared test of independence
p <- chisq.test(table(y, g))$p.value
}
# Format the p-value, using an HTML entity for the less-than sign.
# The initial empty string places the output on the line below the variable label.
c("", sub("<", "<", format.pval(p, digits=3, eps=0.001)))
}
table1(~ age + sex + wt | treat, data=dat, overall=F, extra.col=list(`P-value`=pvalue))
image-20210922192714876
Reference
Using the table1 Package to Create HTML Tables of Descriptive Statistics (r-project.org)
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