library(compareGroups)
data("regicor")
dim(regicor)
## [1] 2294 25
# str(regicor) # 太长不显示，大家可以自己运行看看结果8 三线表绘制
本章对应课本中的第十章，统计表主要是三线表的绘制，其实这是Word的功能，R语言中的三线表绘制绘制R包通常都是用于绘制各类SCI文献中的第一张表，比如各种基线资料表、人口统计学资料表等。
主要是各种三线表的绘制，但是目前的临床研究的三线表制作还是离不开Word、Excel等传统软件，目前没有任何一款R包可以做到：输出结果无需修改即可直接发表。或多或少都需要在Word中修改一下的。
目前在R语言中绘制三线表的常见R包有：compareGroups、tableone、table1、gtSummary、gt、gtExtras等，我已写过多篇推文进行介绍（公众号后台回复三线表即可获取合集）：
compareGroups：compareGroups包1行代码生成基线资料表tableone:使用R语言快速绘制三线表table1:tableone？table1？傻傻分不清楚gtSummary:超棒的三线表绘制工具，总有一款适合你gt:gt包绘制表格详细介绍gtExtras:使用gtExtras美化表格
我使用下来在多数情况下还是compareGroups最方便，所以下面将会详细介绍compareGroups的用法，其他R包的用法请参考以上链接。
8.1 数据简介
compareGroups可用一句代码生成基线资料表、单因素分析表、多因素分析表等，可直接把结果导出为csv、Excel、Word、Markdown、LaTeX、PDF，而且十分美观，大大提高工作效率。
之前也做过介绍，但是最近发现R包更新后自带数据换了，之前的predimed数据没有了，变成了regicor，所以重新再整理一遍。之前的介绍：使用compareGroups包1行代码生成基线资料表
官方文档的名字就叫：分组描述（Descriptives-by-groups），充分说明该包的强项就是分组描述，特别适合基线资料表、各类SCI中的table 1的绘制。支持生存数据，可以计算HR、OR、P-for-trend、多重检验的P值等。
使用方法和之前介绍的基本一样，主要就是3个函数：
compareGroups：计算createTable：构建表格export2xxx导出表格
之前R包内置的predimed已经没有了，现在默认演示数据变成了regicor。
为了说明这个软件包是如何工作的，我们从REGICOR研究中取了一部分数据。REGICOR是一个 对来自西班牙东北部的参与者进行的横断面研究，包括：人口统计学信息（年龄、性别、身高、体重、腰围等）、血脂特征（总胆固醇和胆固醇、甘油三酯等）、问卷调查信息（体格，活动，生活质量，…）等。此外，心血管事件和 死亡信息来自医院和官方登记处。
查看数据:
各个变量的信息如下：
| Name | Label | Codes |
|---|---|---|
| id | Individual id | |
| year | Recruitment year | 1995; 2000; 2005 |
| age | Age | |
| sex | Sex | Male; Female |
| smoker | Smoking status | Never smoker; Current or former < 1y; Former $\geq$ 1y |
| sbp | Systolic blood pressure | |
| dbp | Diastolic blood pressure | |
| histhtn | History of hypertension | Yes; No |
| txhtn | Hypertension treatment | No; Yes |
| chol | Total cholesterol | |
| hdl | HDL cholesterol | |
| triglyc | Triglycerides | |
| ldl | LDL cholesterol | |
| histchol | History of hyperchol. | Yes; No |
| txchol | Cholesterol treatment | No; Yes |
| height | Height (cm) | |
| weight | Weight (Kg) | |
| bmi | Body mass index | |
| phyact | Physical activity (Kcal/week) | |
| pcs | Physical component | |
| mcs | Mental component | |
| cv | Cardiovascular event | No; Yes |
| tocv | Days to cardiovascular event or end of follow-up | |
| death | Overall death | No; Yes |
| todeath | Days to overall death or end of follow-up |
使用该R包的一些注意点：
- 该包的重点是描述数据，不是对数据进行质量控制或其他目的。
- 强烈建议数据框中只包含需要描述的数据，对于不需要的数据建议不要包含在数据框中。
- 分类变量需要进行因子化。
- 可以给各个变量增加
label属性以展示其详细信息，该包默认会展示各个变量的label。
如果是生存数据（time-to-event），必须使用Surv()包装数据。比如：
library(survival)
regicor$tmain <- with(regicor, Surv(tocv, cv == "Yes"))
attr(regicor$tmain, "label") <- "Time to CV event or censoring"这里新建的tmain是生存时间。
8.2 详细介绍
以year为分组变量，统计各个变量的差异：
# 不要id这个变量
compareGroups(year ~ . - id, data=regicor)
##
##
## -------- Summary of results by groups of 'Recruitment year'---------
##
##
## var N p.value
## 1 Age 2294 0.078*
## 2 Sex 2294 0.506
## 3 Smoking status 2233 <0.001**
## 4 Systolic blood pressure 2280 <0.001**
## 5 Diastolic blood pressure 2280 <0.001**
## 6 History of hypertension 2286 <0.001**
## 7 Hypertension treatment 2251 0.002**
## 8 Total cholesterol 2193 <0.001**
## 9 HDL cholesterol 2225 0.208
## 10 Triglycerides 2231 0.582
## 11 LDL cholesterol 2126 <0.001**
## 12 History of hyperchol. 2273 <0.001**
## 13 Cholesterol treatment 2239 <0.001**
## 14 Height (cm) 2259 0.003**
## 15 Weight (Kg) 2259 0.150
## 16 Body mass index 2259 <0.001**
## 17 Physical activity (Kcal/week) 2206 <0.001**
## 18 Physical component 2054 0.032**
## 19 Mental component 2054 <0.001**
## 20 Cardiovascular event 2163 0.161
## 21 Days to cardiovascular event or end of follow-up 2163 0.099*
## 22 Overall death 2148 <0.001**
## 23 Days to overall death or end of follow-up 2148 0.252
## 24 Time to CV event or censoring 2163 0.157
## method selection
## 1 continuous normal ALL
## 2 categorical ALL
## 3 categorical ALL
## 4 continuous normal ALL
## 5 continuous normal ALL
## 6 categorical ALL
## 7 categorical ALL
## 8 continuous normal ALL
## 9 continuous normal ALL
## 10 continuous normal ALL
## 11 continuous normal ALL
## 12 categorical ALL
## 13 categorical ALL
## 14 continuous normal ALL
## 15 continuous normal ALL
## 16 continuous normal ALL
## 17 continuous normal ALL
## 18 continuous normal ALL
## 19 continuous normal ALL
## 20 categorical ALL
## 21 continuous normal ALL
## 22 categorical ALL
## 23 continuous normal ALL
## 24 Surv [Tmax=1718] ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1比如第一个变量age，其实使用的方差分析，给出了age在不同year中的pvalue，method显示了该包把age这个变量看做连续型变量且符合正态分布。
我们可以自己使用方差分析看下结果：
summary(aov(age ~ year, data = regicor))
## Df Sum Sq Mean Sq F value Pr(>F)
## year 2 623 311.6 2.556 0.0778 .
## Residuals 2291 279320 121.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1可以看到结果是一样的哈~
sex这个变量是用的卡方检验：
chisq.test(regicor$sex, regicor$year)
##
## Pearson's Chi-squared test
##
## data: regicor$sex and regicor$year
## X-squared = 1.364, df = 2, p-value = 0.5056结果也是一样的。
支持之使用其中一部分数据，比如只看一下age、smoker、bmi这3个变量在不同的year中的差异，并且只选择性别为女性，对于bmi这个变量，只选择年龄大于50岁的：
compareGroups(year ~ age + smoker + bmi, data=regicor,
selec = list(bmi=age>50),
subset = sex=="Female")
##
##
## -------- Summary of results by groups of 'year'---------
##
##
## var N p.value method
## 1 Age 1193 0.351 continuous normal
## 2 Smoking status 1162 <0.001** categorical
## 3 Body mass index 709 0.308 continuous normal
## selection
## 1 sex == "Female"
## 2 sex == "Female"
## 3 (sex == "Female") & (age > 50)
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1连续型变量支持选择不同的统计方法（选择有限），通过method指定即可：
compareGroups(year ~ age + smoker + triglyc, data=regicor,
method = c(triglyc=NA),
alpha= 0.01)
## Warning in cor.test.default(x, as.integer(y), method = "spearman"): Cannot
## compute exact p-value with ties
##
##
## -------- Summary of results by groups of 'Recruitment year'---------
##
##
## var N p.value method selection
## 1 Age 2294 0.078* continuous normal ALL
## 2 Smoking status 2233 <0.001** categorical ALL
## 3 Triglycerides 2231 0.762 continuous non-normal ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1method：统计检验方法- 1：数值型变量，正态分布，
- 2：数值型变量，非正态分布
- 3：分类变量
- NA：使用
shapiro.test()决定是否正态分布
alpha：正态性检验的阈值min.dis：所有非因子型向量都会被认为是连续型的，除非某个变量的取值少于5个，可通过此参数更改这个标准。
regicor$age7gr <- as.integer(cut(regicor$age,
breaks = c(-Inf, 40, 45, 50, 55, 65, 70, Inf),
right = TRUE))
compareGroups(year ~ age7gr, data = regicor,
method = c(age7gr = NA), min.dis = 8)
## Warning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
## variable 'age7gr' converted to factor since few different values contained
##
##
## -------- Summary of results by groups of 'Recruitment year'---------
##
##
## var N p.value method selection
## 1 age7gr 2294 0.012** categorical ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1max.ylev：分组变量的最大组数
compareGroups(age7gr ~ sex + bmi + smoker, data = regicor, max.ylev = 7)
##
##
## -------- Summary of results by groups of 'age7gr'---------
##
##
## var N p.value method selection
## 1 Sex 2294 0.950 categorical ALL
## 2 Body mass index 2259 <0.001** continuous normal ALL
## 3 Smoking status 2233 <0.001** categorical ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1显示列的名字，而不是label：
compareGroups(year ~ age + smoker + bmi, data = regicor, include.label = FALSE)
##
##
## -------- Summary of results by groups of 'year'---------
##
##
## var N p.value method selection
## 1 age 2294 0.078* continuous normal ALL
## 2 smoker 2233 <0.001** categorical ALL
## 3 bmi 2259 <0.001** continuous normal ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1Q1/Q3：非正态分布的变量默认显示中位数（25%，75%），这个百分位数可以更改，设置为0和1则是最小值和最大值
resu2 <- compareGroups(year ~ age + triglyc, data = regicor,
method = c(triglyc = 2),
Q1 = 0.025, Q3 = 0.975)
## Warning in cor.test.default(x, as.integer(y), method = "spearman"): Cannot
## compute exact p-value with ties
createTable(resu2)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## _______________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Triglycerides 94.0 [47.0;292] 98.0 [47.0;278] 98.0 [42.0;293] 0.762
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯simplify：有时某个变量在某个分组中可能样本数为0，这时候要设置为TRUE来排除这样的亚组，因为卡方检验和Fisher精确概率法的计算不能为0。
下面新建一个变量smk，其中unknown这个分组的样本数为0：
regicor$smk <- regicor$smoker
levels(regicor$smk) <- c("Never smoker", "Current or former < 1y",
"Former >= 1y", "Unknown")
attr(regicor$smk, "label") <- "Smoking 4 cat."
cbind(table(regicor$smk))
## [,1]
## Never smoker 1201
## Current or former < 1y 593
## Former >= 1y 439
## Unknown 0如果不加simplify = FALSE会给出warning：
compareGroups(year ~ age + smk + bmi, data = regicor)
## Warning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
## Some levels of 'smk' are removed since no observation in that/those levels
##
##
## -------- Summary of results by groups of 'Recruitment year'---------
##
##
## var N p.value method selection
## 1 Age 2294 0.078* continuous normal ALL
## 2 Smoking 4 cat. 2233 <0.001** categorical ALL
## 3 Body mass index 2259 <0.001** continuous normal ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1加simplify = FALSE之后就不再对这个变量进行统计检验了，也就没有P值显示了：
compareGroups(year ~ age + smk + bmi, data = regicor, simplify = FALSE)
##
##
## -------- Summary of results by groups of 'Recruitment year'---------
##
##
## var N p.value method selection
## 1 Age 2294 0.078* continuous normal ALL
## 2 Smoking 4 cat. 2233 . categorical ALL
## 3 Body mass index 2259 <0.001** continuous normal ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1支持更新对象：
res <- compareGroups(year ~ age + sex + smoker + bmi, data = regicor)
res
##
##
## -------- Summary of results by groups of 'Recruitment year'---------
##
##
## var N p.value method selection
## 1 Age 2294 0.078* continuous normal ALL
## 2 Sex 2294 0.506 categorical ALL
## 3 Smoking status 2233 <0.001** categorical ALL
## 4 Body mass index 2259 <0.001** continuous normal ALL
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1res <- update(res, . ~ . - sex + triglyc + cv + tocv,
subset = sex == "Female",
method = c(triglyc = 2, tocv = 2),
selec = list(triglyc = txchol == "No"))
## Warning in cor.test.default(x, as.integer(y), method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in cor.test.default(x, as.integer(y), method = "spearman"): Cannot
## compute exact p-value with ties
res
##
##
## -------- Summary of results by groups of 'year'---------
##
##
## var N p.value
## 1 Age 1193 0.351
## 2 Smoking status 1162 <0.001**
## 3 Body mass index 1169 0.084*
## 4 Triglycerides 1020 0.993
## 5 Cardiovascular event 1121 0.139
## 6 Days to cardiovascular event or end of follow-up 1121 0.427
## method selection
## 1 continuous normal sex == "Female"
## 2 categorical sex == "Female"
## 3 continuous normal sex == "Female"
## 4 continuous non-normal (sex == "Female") & (txchol == "No")
## 5 categorical sex == "Female"
## 6 continuous non-normal sex == "Female"
## -----
## Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1支持提取其中的某些信息，比如P值、均值等，方便后续的操作：
data(SNPs)
tab <- createTable(compareGroups(casco ~ snp10001 + snp10002 + snp10005 +
snp10008 + snp10009, SNPs))
## Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be
## incorrect
## Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be
## incorrect
## Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be
## incorrect
# 提取P值，方便进行校正
pvals <- getResults(tab, "p.overall")
p.adjust(pvals, method = "BH")
## snp10001 snp10002 snp10005 snp10008 snp10009
## 0.7051300 0.7072158 0.7583432 0.7583432 0.70721584.6.0以后的版本还增加了计算调整P值的方法，和p.adjust的方法一样，比如Bonferroni/False Discovery Rate等。
cg <- compareGroups(casco ~ snp10001+snp10002+snp10005+snp10008+snp10009,
SNPs)
## Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be
## incorrect
## Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be
## incorrect
## Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be
## incorrect
createTable(padjustCompareGroups(cg, method = "BH"))
##
## --------Summary descriptives table by 'casco'---------
##
## _________________________________________
## 0 1 p.overall
## N=47 N=110
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## snp10001: 0.705
## CC 2 (4.26%) 10 (9.09%)
## CT 21 (44.7%) 32 (29.1%)
## TT 24 (51.1%) 68 (61.8%)
## snp10002: 0.707
## AA 0 (0.00%) 5 (4.55%)
## AC 25 (53.2%) 53 (48.2%)
## CC 22 (46.8%) 52 (47.3%)
## snp10005: 0.758
## AA 0 (0.00%) 3 (2.73%)
## AG 22 (46.8%) 48 (43.6%)
## GG 25 (53.2%) 59 (53.6%)
## snp10008: 0.758
## CC 30 (63.8%) 74 (67.3%)
## CG 15 (31.9%) 29 (26.4%)
## GG 2 (4.26%) 7 (6.36%)
## snp10009: 0.707
## AA 21 (45.7%) 51 (46.4%)
## AG 25 (54.3%) 54 (49.1%)
## GG 0 (0.00%) 5 (4.55%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯支持计算OR值和HR值，如果是二分类会计算OR值，如果是time-to-event数据会计算HR值：
# show.ratio = TRUE展示p.ratio和OR值
res1 <- compareGroups(cv ~ age + sex + bmi + smoker, data = regicor, ref = 1)
createTable(res1, show.ratio = TRUE)
##
## --------Summary descriptives table by 'Cardiovascular event'---------
##
## ______________________________________________________________________________________
## No Yes OR p.ratio p.overall
## N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.6 (11.1) 57.5 (11.0) 1.02 [1.00;1.04] 0.017 0.018
## Sex: 0.801
## Male 996 (48.1%) 46 (50.0%) Ref. Ref.
## Female 1075 (51.9%) 46 (50.0%) 0.93 [0.61;1.41] 0.721
## Body mass index 27.6 (4.56) 28.1 (4.48) 1.02 [0.98;1.07] 0.313 0.307
## Smoking status: <0.001
## Never smoker 1099 (54.3%) 37 (40.2%) Ref. Ref.
## Current or former < 1y 506 (25.0%) 47 (51.1%) 2.75 [1.77;4.32] <0.001
## Former >= 1y 419 (20.7%) 8 (8.70%) 0.58 [0.25;1.19] 0.142
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ref可以更改参考水平，比如smoker以第一个水平为参考，sex以第二个水平为参考：
res2 <- compareGroups(cv ~ age + sex + bmi + smoker, data = regicor,
ref = c(smoker = 1, sex = 2))
createTable(res2, show.ratio = TRUE)
##
## --------Summary descriptives table by 'Cardiovascular event'---------
##
## ______________________________________________________________________________________
## No Yes OR p.ratio p.overall
## N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.6 (11.1) 57.5 (11.0) 1.02 [1.00;1.04] 0.017 0.018
## Sex: 0.801
## Male 996 (48.1%) 46 (50.0%) 1.08 [0.71;1.64] 0.721
## Female 1075 (51.9%) 46 (50.0%) Ref. Ref.
## Body mass index 27.6 (4.56) 28.1 (4.48) 1.02 [0.98;1.07] 0.313 0.307
## Smoking status: <0.001
## Never smoker 1099 (54.3%) 37 (40.2%) Ref. Ref.
## Current or former < 1y 506 (25.0%) 47 (51.1%) 2.75 [1.77;4.32] <0.001
## Former >= 1y 419 (20.7%) 8 (8.70%) 0.58 [0.25;1.19] 0.142
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯这里的p.overall是通过t检验计算的：(如果不符合正态分布是通过Kruskall-Wallis检验计算)
t.test(age ~ cv, data = regicor)
##
## Welch Two Sample t-test
##
## data: age by cv
## t = -2.4124, df = 99.303, p-value = 0.01768
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## -5.1660205 -0.5031794
## sample estimates:
## mean in group No mean in group Yes
## 54.62192 57.45652这个表格中的p.ratio和OR值就是做个逻辑回归得到的：
aa <- glm(cv ~ age, data = regicor,family = binomial())
broom::tidy(aa,exponentiate = T,conf.int = T)
## # A tibble: 2 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.0120 0.572 -7.74 1.01e-14 0.00377 0.0357
## 2 age 1.02 0.00980 2.39 1.69e- 2 1.00 1.04age的p.value就是表格中的p.ratio，estimate是OR值，conf.low和conf.high是可信区间。
ref.no表示，如果某个变量中有No（或者NO，no，不区分大小写）这个类别，那就以这个类别为参考，这是一个可适用于所有变量的参数。
res <- compareGroups(cv ~ age + sex + bmi + histhtn + txhtn, data = regicor,
ref.no = "NO")
createTable(res, show.ratio = TRUE)
##
## --------Summary descriptives table by 'Cardiovascular event'---------
##
## ____________________________________________________________________________________
## No Yes OR p.ratio p.overall
## N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.6 (11.1) 57.5 (11.0) 1.02 [1.00;1.04] 0.017 0.018
## Sex: 0.801
## Male 996 (48.1%) 46 (50.0%) Ref. Ref.
## Female 1075 (51.9%) 46 (50.0%) 0.93 [0.61;1.41] 0.721
## Body mass index 27.6 (4.56) 28.1 (4.48) 1.02 [0.98;1.07] 0.313 0.307
## History of hypertension: 0.058
## Yes 647 (31.3%) 38 (41.3%) 1.54 [1.00;2.36] 0.049
## No 1418 (68.7%) 54 (58.7%) Ref. Ref.
## Hypertension treatment: 0.270
## No 1657 (81.3%) 70 (76.1%) Ref. Ref.
## Yes 382 (18.7%) 22 (23.9%) 1.37 [0.82;2.21] 0.223
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯sex/histhtn/txhtn这几个变量中都有No这个分类，所以都以这个类别为参考。
对于连续性变量来说，OR值和HR值的解释是自变量每增加一个单位，因变量变化OR倍或HR倍，参数fact.ratio可以控制一个单位具体是多少。比如设置bmi的一个单位是2，age的一个单位是10：
res <- compareGroups(cv ~ age + bmi, data = regicor,
fact.ratio = c(age = 10, bmi = 2))
createTable(res, show.ratio = TRUE)
##
## --------Summary descriptives table by 'Cardiovascular event'---------
##
## __________________________________________________________________________
## No Yes OR p.ratio p.overall
## N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.6 (11.1) 57.5 (11.0) 1.26 [1.04;1.53] 0.017 0.018
## Body mass index 27.6 (4.56) 28.1 (4.48) 1.05 [0.96;1.14] 0.313 0.307
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯通常对于OR值和HR值来说，是相对于因变量的某个类别来说的，比如自变量每增加一个单位，患癌症的风险增加OR倍，可以通过ref.y改成不患癌症的风险增加xx倍。
res <- compareGroups(cv ~ age + sex + bmi + txhtn, data = regicor, ref.y = 2)
createTable(res, show.ratio = TRUE)
##
## --------Summary descriptives table by 'Cardiovascular event'---------
##
## ___________________________________________________________________________________
## No Yes OR p.ratio p.overall
## N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.6 (11.1) 57.5 (11.0) 0.98 [1.00;0.96] 0.017 0.018
## Sex: 0.801
## Male 996 (48.1%) 46 (50.0%) Ref. Ref.
## Female 1075 (51.9%) 46 (50.0%) 1.08 [0.71;1.64] 0.721
## Body mass index 27.6 (4.56) 28.1 (4.48) 0.98 [1.02;0.93] 0.313 0.307
## Hypertension treatment: 0.270
## No 1657 (81.3%) 70 (76.1%) Ref. Ref.
## Yes 382 (18.7%) 22 (23.9%) 0.73 [0.45;1.22] 0.223
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯createTable函数用于把compareGroups计算的结果转换为表格，打印在屏幕上或者导出为CSV、LaTeX、HTML、Word、Excel等格式。
res <- compareGroups(year ~ age + sex + smoker + bmi + sbp, data = regicor,
selec = list(sbp = txhtn == "No"))
restab <- createTable(res)which.table = "descr"给出描述性三线表：
print(restab, which.table = "descr")
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ________________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Sex: 0.506
## Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
## Smoking status: <0.001
## Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
## Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
## Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
## Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
## Systolic blood pressure 129 (17.4) 130 (20.1) 124 (16.9) <0.001
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯which.table = "avail"给出可用数据和方法表：
print(restab, which.table = "avail")
##
##
##
## ---Available data----
##
## ____________________________________________________________________________
## [ALL] 1995 2000 2005 method select
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 2294 431 786 1077 continuous-normal ALL
## Sex 2294 431 786 1077 categorical ALL
## Smoking status 2233 415 758 1060 categorical ALL
## Body mass index 2259 423 771 1065 continuous-normal ALL
## Systolic blood pressure 1810 357 649 804 continuous-normal txhtn == "No"
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯某些二分类的变量，比如男性/女性这种，可以通过hide不展示其中某个类别，比如不展示sex中的male：
update(restab, hide = c(sex = "Male"))
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ________________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Sex: Female 225 (52.2%) 396 (50.4%) 572 (53.1%) 0.506
## Smoking status: <0.001
## Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
## Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
## Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
## Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
## Systolic blood pressure 129 (17.4) 130 (20.1) 124 (16.9) <0.001
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯hide.no和ref.no的含义类似，如果某个变量含有no这个类别，可以全部隐藏：
res <- compareGroups(year ~ age + sex + histchol + histhtn, data = regicor)
createTable(res, hide.no = "no", hide = c(sex = "Male"))
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## _____________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Sex: Female 225 (52.2%) 396 (50.4%) 572 (53.1%) 0.506
## History of hyperchol. 97 (22.5%) 256 (33.2%) 356 (33.2%) <0.001
## History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%) <0.001
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯digits用于控制表格中小数点的位数：
createTable(res, digits = c(age = 2, sex = 3))
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ____________________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.10 (11.72) 54.34 (11.22) 55.28 (10.63) 0.078
## Sex: 0.506
## Male 206 (47.796%) 390 (49.618%) 505 (46.890%)
## Female 225 (52.204%) 396 (50.382%) 572 (53.110%)
## History of hyperchol.: <0.001
## Yes 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 334 (77.5%) 515 (66.8%) 715 (66.8%)
## History of hypertension: <0.001
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯对于分类变量，默认情况下表格中会给出计数和比例，可通过type参数更改只显示计数或比例：
# 1只显示比例，默认是2都显示，3只显示计数
createTable(res, type = 1)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ______________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Sex: 0.506
## Male 47.8% 49.6% 46.9%
## Female 52.2% 50.4% 53.1%
## History of hyperchol.: <0.001
## Yes 22.5% 33.2% 33.2%
## No 77.5% 66.8% 66.8%
## History of hypertension: <0.001
## Yes 25.8% 29.6% 35.5%
## No 74.2% 70.4% 64.5%
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯show.n = TRUE展示每个变量的所有可用数量：
# 注意最后一列
createTable(res, show.n = TRUE)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ___________________________________________________________________________
## 1995 2000 2005 p.overall N
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078 2294
## Sex: 0.506 2294
## Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
## History of hyperchol.: <0.001 2273
## Yes 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 334 (77.5%) 515 (66.8%) 715 (66.8%)
## History of hypertension: <0.001 2286
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯show.descr = FALSE表示不展示描述统计部分，只显示P值：
createTable(res, show.descr = FALSE)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## __________________________________
## p.overall
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 0.078
## Sex:
## Male 0.506
## Female
## History of hyperchol.:
## Yes <0.001
## No
## History of hypertension:
## Yes <0.001
## No
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯show.all = TRUE表示显示所有数量：（注意第一列）
createTable(res, show.all = TRUE)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ___________________________________________________________________________________
## [ALL] 1995 2000 2005 p.overall
## N=2294 N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.7 (11.0) 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Sex: 0.506
## Male 1101 (48.0%) 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 1193 (52.0%) 225 (52.2%) 396 (50.4%) 572 (53.1%)
## History of hyperchol.: <0.001
## Yes 709 (31.2%) 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 1564 (68.8%) 334 (77.5%) 515 (66.8%) 715 (66.8%)
## History of hypertension: <0.001
## Yes 723 (31.6%) 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 1563 (68.4%) 320 (74.2%) 553 (70.4%) 690 (64.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯show.p.overall = FALSE表示不显示P值：
createTable(res, show.p.overall = FALSE)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ____________________________________________________________
## 1995 2000 2005
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6)
## Sex:
## Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
## History of hyperchol.:
## Yes 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 334 (77.5%) 515 (66.8%) 715 (66.8%)
## History of hypertension:
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯如果因变量有2个以上的类别，可通过show.p.trend = TRUE展示p-for-trend，符合正态分布通过pearson计算，不符合通过spearman计算：
# year这个变量是有3个类别的
createTable(res, show.p.trend = TRUE)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ______________________________________________________________________________
## 1995 2000 2005 p.overall p.trend
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078 0.032
## Sex: 0.506 0.544
## Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
## History of hyperchol.: <0.001 <0.001
## Yes 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 334 (77.5%) 515 (66.8%) 715 (66.8%)
## History of hypertension: <0.001 <0.001
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯show.p.mul：分组变量多于两组可以进行两两比较，符合正态分布用Turkey，不符合用Benjamini & Hochberg
createTable(res, show.p.mul = TRUE)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ___________________________________________________________________________________________________________________
## 1995 2000 2005 p.overall p.1995 vs 2000 p.1995 vs 2005 p.2000 vs 2005
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078 0.930 0.143 0.161
## Sex: 0.506 0.794 0.794 0.792
## Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
## History of hyperchol.: <0.001 <0.001 <0.001 1.000
## Yes 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 334 (77.5%) 515 (66.8%) 715 (66.8%)
## History of hypertension: <0.001 0.169 0.001 0.015
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯如果因变量是2分类或者是生存数据，show.ratio = TRUE可以展示Odds Ratios或者Hazard Ratios：
# 展示OR和p.ratio
createTable(update(res, subset = year != 1995), show.ratio = TRUE)
##
## --------Summary descriptives table by 'year'---------
##
## ___________________________________________________________________________________
## 2000 2005 OR p.ratio p.overall
## N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.3 (11.2) 55.3 (10.6) 1.01 [1.00;1.02] 0.064 0.066
## Sex: 0.264
## Male 390 (49.6%) 505 (46.9%) Ref. Ref.
## Female 396 (50.4%) 572 (53.1%) 1.12 [0.93;1.34] 0.245
## History of hyperchol.: 1.000
## Yes 256 (33.2%) 356 (33.2%) Ref. Ref.
## No 515 (66.8%) 715 (66.8%) 1.00 [0.82;1.22] 0.988
## History of hypertension: 0.010
## Yes 233 (29.6%) 379 (35.5%) Ref. Ref.
## No 553 (70.4%) 690 (64.5%) 0.77 [0.63;0.93] 0.008
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯生存数据展示HR值：
createTable(compareGroups(tmain ~ year + age + sex, data = regicor),
show.ratio = TRUE)
##
## --------Summary descriptives table by 'Time to CV event or censoring'---------
##
## _____________________________________________________________________________
## No event Event HR p.ratio p.overall
## N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Recruitment year: 0.157
## 1995 388 (18.7%) 10 (10.9%) Ref. Ref.
## 2000 706 (34.1%) 35 (38.0%) 1.95 [0.96;3.93] 0.063
## 2005 977 (47.2%) 47 (51.1%) 1.82 [0.92;3.59] 0.087
## Age 54.6 (11.1) 57.5 (11.0) 1.02 [1.00;1.04] 0.021 0.021
## Sex: 0.696
## Male 996 (48.1%) 46 (50.0%) Ref. Ref.
## Female 1075 (51.9%) 46 (50.0%) 0.92 [0.61;1.39] 0.696
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯这里的p.overall是logrank检验的P值，比如用sex这个变量演示下计算方法：
# P=0.7（四舍五入了），和上面一样的
survdiff(Surv(tocv,cv=="Yes")~sex,data = regicor)
## Call:
## survdiff(formula = Surv(tocv, cv == "Yes") ~ sex, data = regicor)
##
## n=2163, 131 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## sex=Male 1042 46 44.1 0.0793 0.152
## sex=Female 1121 46 47.9 0.0731 0.152
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.7同理，p.ratio是做cox回归得到的（和上面的分类数据类似，t检验和逻辑回归）：
aa <- coxph(Surv(tocv,cv=="Yes")~sex,data = regicor)
broom::tidy(aa)
## # A tibble: 1 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 sexFemale -0.0814 0.209 -0.390 0.696digits.ratio控制小数点位数：
createTable(compareGroups(tmain ~ year + age + sex, data = regicor),
show.ratio = TRUE,
digits.ratio = 3)
##
## --------Summary descriptives table by 'Time to CV event or censoring'---------
##
## ________________________________________________________________________________
## No event Event HR p.ratio p.overall
## N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Recruitment year: 0.157
## 1995 388 (18.7%) 10 (10.9%) Ref. Ref.
## 2000 706 (34.1%) 35 (38.0%) 1.946 [0.964;3.930] 0.063
## 2005 977 (47.2%) 47 (51.1%) 1.816 [0.918;3.593] 0.087
## Age 54.6 (11.1) 57.5 (11.0) 1.022 [1.003;1.041] 0.021 0.021
## Sex: 0.696
## Male 996 (48.1%) 46 (50.0%) Ref. Ref.
## Female 1075 (51.9%) 46 (50.0%) 0.922 [0.613;1.387] 0.696
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯在print或者导出表格时，header.labels可以修改某些关键列的名称：
tab <- createTable(compareGroups(tmain ~ year + age + sex, data = regicor),
show.all = TRUE)
print(tab, header.labels = c(p.overall = "p-value", all = "All"))
##
## --------Summary descriptives table by 'Time to CV event or censoring'---------
##
## _______________________________________________________________
## All No event Event p-value
## N=2163 N=2071 N=92
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Recruitment year: 0.157
## 1995 398 (18.4%) 388 (18.7%) 10 (10.9%)
## 2000 741 (34.3%) 706 (34.1%) 35 (38.0%)
## 2005 1024 (47.3%) 977 (47.2%) 47 (51.1%)
## Age 54.7 (11.1) 54.6 (11.1) 57.5 (11.0) 0.021
## Sex: 0.696
## Male 1042 (48.2%) 996 (48.1%) 46 (50.0%)
## Female 1121 (51.8%) 1075 (51.9%) 46 (50.0%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯支持按照行合并表格：
restab1 <- createTable(compareGroups(year ~ age + sex, data = regicor))
restab2 <- createTable(compareGroups(year ~ bmi + smoker, data = regicor))
rbind(`Non-modifiable risk factors`=restab1,`Modifiable risk factors`=restab2)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ____________________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Non-modifiable risk factors:
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Sex: 0.506
## Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
## Modifiable risk factors:
## Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
## Smoking status: <0.001
## Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
## Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
## Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯按照列合并表格，也就是分层表格：
res <- compareGroups(year ~ age + smoker + bmi + histhtn, data = regicor)
alltab <- createTable(res, show.p.overall = FALSE)
femaletab <- createTable(update(res, subset = sex == "Female"),
show.p.overall = FALSE)
maletab <- createTable(update(res, subset = sex == "Male"),
show.p.overall = FALSE)
cbind(ALL = alltab, FEMALE = femaletab, MALE = maletab)
##
## --------Summary descriptives table ---------
##
## ________________________________________________________________________________________________________________________________________
## ALL FEMALE MALE
## ___________________________________ ___________________________________ ___________________________________
## 1995 2000 2005 1995 2000 2005 1995 2000 2005
## N=431 N=786 N=1077 N=225 N=396 N=572 N=206 N=390 N=505
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 54.1 (11.7) 54.4 (11.2) 55.2 (10.6) 54.1 (11.8) 54.3 (11.2) 55.4 (10.7)
## Smoking status:
## Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%) 182 (83.1%) 302 (79.3%) 416 (74.0%) 52 (26.5%) 112 (29.7%) 137 (27.5%)
## Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%) 32 (14.6%) 68 (17.8%) 83 (14.8%) 77 (39.3%) 199 (52.8%) 134 (26.9%)
## Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%) 5 (2.28%) 11 (2.89%) 63 (11.2%) 67 (34.2%) 66 (17.5%) 227 (45.6%)
## Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) 27.2 (4.57) 28.0 (5.25) 27.3 (5.39) 26.9 (3.64) 28.2 (3.89) 27.9 (3.58)
## History of hypertension:
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%) 61 (27.1%) 123 (31.1%) 198 (34.8%) 50 (24.3%) 110 (28.2%) 181 (36.2%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%) 164 (72.9%) 273 (68.9%) 371 (65.2%) 156 (75.7%) 280 (71.8%) 319 (63.8%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯4.0版本以后提供了strataTable用于快速创建分层表格：
res <- compareGroups(year ~ age + bmi + smoker + histchol + histhtn, regicor)
restab <- createTable(res, hide.no = "no")
strataTable(restab, "sex")
##
## --------Summary descriptives table ---------
##
## _______________________________________________________________________________________________________________________
## Male Female
## _____________________________________________ _____________________________________________
## 1995 2000 2005 p.overall 1995 2000 2005 p.overall
## N=206 N=390 N=505 N=225 N=396 N=572
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.8) 54.3 (11.2) 55.4 (10.7) 0.212 54.1 (11.7) 54.4 (11.2) 55.2 (10.6) 0.351
## Body mass index 26.9 (3.64) 28.2 (3.89) 27.9 (3.58) <0.001 27.2 (4.57) 28.0 (5.25) 27.3 (5.39) 0.084
## Smoking status: <0.001 <0.001
## Never smoker 52 (26.5%) 112 (29.7%) 137 (27.5%) 182 (83.1%) 302 (79.3%) 416 (74.0%)
## Current or former < 1y 77 (39.3%) 199 (52.8%) 134 (26.9%) 32 (14.6%) 68 (17.8%) 83 (14.8%)
## Former >= 1y 67 (34.2%) 66 (17.5%) 227 (45.6%) 5 (2.28%) 11 (2.89%) 63 (11.2%)
## History of hyperchol. 48 (23.3%) 138 (35.8%) 167 (33.2%) 0.007 49 (21.8%) 118 (30.6%) 189 (33.3%) 0.006
## History of hypertension 50 (24.3%) 110 (28.2%) 181 (36.2%) 0.002 61 (27.1%) 123 (31.1%) 198 (34.8%) 0.097
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯以上介绍的创建表格基本上是两步，首先compareGroups，然后createTable，为了方便，作者提供了一个descrTable，直接完成以上两步：
descrTable(year ~ age + bmi + smoker + histchol + histhtn, data = regicor)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ________________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
## Smoking status: <0.001
## Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
## Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
## Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
## History of hyperchol.: <0.001
## Yes 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 334 (77.5%) 515 (66.8%) 715 (66.8%)
## History of hypertension: <0.001
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯上面介绍的所有参数descrTable都是支持的。
支持的格式非常丰富：
export2csv(restab, file='table1.csv'), 导出为CSVexport2html(restab, file='table1.html'), 导出为HTMLexport2latex(restab, file='table1.tex'), 导出为LaTeXexport2pdf(restab, file='table1.pdf'), 导出为PDFexport2md(restab, file='table1.md'), 导出为Markdownexport2word(restab, file='table1.docx'), 导出为Wordexport2xls(restab, file='table1.xlsx'), 导出为Excel
导出时还支持各种格式调整，比如添加阴影等。