tbl_summary(data,by=NULL,label=NULL,statistic=list(all_continuous()~"{median} ({p25}, {p75})",all_categorical()~"{n} ({p}%)"),digits=NULL,type=NULL,value=NULL,missing=c("ifany","no","always"),missing_text="Unknown",missing_stat="{N_miss}",sort=all_categorical(FALSE)~"alpha...
您可以创建单独的表,然后合并它们: library(gtsummary) df |> subset(group != "C") |> tbl_summary( by = group, statistic = all_continuous() ~ "{mean} ({sd})" ) -> gts1 df |> subset(group == "C") |> tbl_summary( by = group, statistic = all_continuous() ~ "{mean}" ) -...
使用gtsummary版本1.7.0,我正在制作一个包含连续变量的一些统计数据的表: library(gtsummary) library(dplyr) dat <- data.frame(x = 1:100, y = c(rep("A", 50), rep("B", 50))) dat %>% tbl_summary( by = y, type = x ~ "continuous2", statistic = x ~ c("{mean}", "{min}", ...
Example modifyingtbl_summary()arguments. trial|>tbl_summary(by=trt,include=c(age,grade),statistic=list(all_continuous()~"{mean} ({sd})",all_categorical()~"{n} / {N} ({p}%)"),digits=all_continuous()~2,label=list(grade="Tumor Grade"),missing_text="(Missing)") CharacteristicDrug A...
...2.2 小修小改 1)添加一下参数,完成上述需求 table2 <- tbl_summary( trial2, by = trt, # 分组 statistic = list...table3 <- tbl_summary( trial2, by = trt, # 分组 statistic = list(all_continuous() ~ "{mean 2.2K30 compareGroups包,超级超级强大的临床基线特征表绘制包...
corr… categor… Fold level Neit… 18 (7… 6 (5.… 12 (1… 4 Fold chisq.test.no.corr… categor… Fold level R on… 120 (… 64 (5… 56 (4… # ℹ 4 more variables: test_result<list>, statistic<dbl>, parameter<int>, # p.value<dbl> ...
如何在汇总表中使用tbl_summary()有一个gtsummary扩展函数gtreg::tbl_listing()，它可以从数据框中创建...
statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~"{n} / {N} ({p})"), missing = "no" ) 在这个例子中,我们改变了连续变量和分类变量的统计量显示格式,并且去掉了缺失值的显示。 五、结论 总的来说,tbl_svysummary函数为复杂抽样设计的数据提供了方便、灵活的摘要统计...
在chrome65以前，我们可以打开目标网页的开发者工具—source选项卡—目标JS/CSS文件，然后在相关位置写入...
statistic = all_continuous() ~ c("{N_nonmiss}", "{median} ({p25}, {p75})", "{min}, {max}"), missing = "no" ) %>% add_p(pvalue_fun = ~style_pvalue(.x, digits = 2)) #修改P值小数点 1. 2. 3. 4. 5. 6. ...