这是可以参照这篇博文(写得非常的帮!!!)--How to Easily Create Descriptive Summary Statistics Tables in R Studio – By Group,因此分享一下,该文章总共归纳了9个用于创建汇总表的R包:* arsenal * qwraps2 * amisc * table1 * tangram * furniture * tableone * compareGroups...
1 how to build a summary table without reshape 1 how to get summary table with by () into a table or data.frame 1 R - create summary table of means and counts by group for multiple columns 1 pivottabler: Producing grouped summary statistics for total 1 How to create m...
> mytable <- xtabs(~ Treatment+Improved, data=Arthritis) > margin.table(mytable, 1)#生成边际频数 Treatment Placebo Treated 43 41 > prop.table(mytable,1)#生成边际比例 1代表了生成第一行的比例 Improved Treatment None Some Marked Placebo 0.6744186 0.1627907 0.1627907 Treated 0.3170732 0.1707317 0.51...
CrossTable()函数有很多选项,可以做许多事情:计算(行、列、单元格)的百分比;指定小数位数;进行卡方、Fisher和McNemar独立性检验;计算期望和的残差;将缺失值作为一种有效值;进行行和列标题的标注;生成SPSS风格的输出: >library(gmodels)>CrossTable(mtcars$cyl,mtcars$am) Cell Contents|---| | N | | Chi-squar...
(female,starts_with("paren"),letter,summerschool,test_score)%>% datasummary_skim( fmt="%.2f", histogram=FALSE, output="tab_summary_statistics.tex") # load modelsummary library("modelsummary") # create a summary stat table datasummary(female+parental_schooling+parental_lincome+ letter+test_...
by(ma[3:4],ma$group,function(x)stat.desc(x,norm=TRUE))#对数据框ma中的第3列和第4列变量按group分组,分别进行stat.desc获取基本描述统计量和正态分布的统计量后输出结果 关于函数stat.desc(): stat.desc{pastecs}:Descriptive statistics on a data frame or time series。Compute a table giving variou...
group: 1 vars n mean sd median trimmed mad min max range skew kurtosis se X1 1 14 24.56 5.38 22.8 24.34 6 17.8 33.9 16.1 0.41 -1.4 1.44 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. summaryBy() 上面的两个函数均是用来统计一个单数的数据变量,但很多时候我们需要针对一个分组同时统...
describeBy(mtcars[myvars], list(am=mtcars$am))#summary statistics by group via the reshape packagelibrary(reshape) dstats<- function(x)(c(n=length(x), mean=mean(x), sd=sd(x))) dfm<- melt(mtcars, measure.vars=c("mpg","hp","wt"), ...
您可以使用tbl_strata()函数通过变量将tbl_summary()按秒分层。下面的例子！
skew wt.kurtoside 1 3.768895 0.7774001 0.9759294 0.1415676 2 2.411000 0.6169816 0.2103128 -1.1737358 > describeBy(mtcars[myvars], list(am = mtcars$am)) Descriptive statistics by group am: 0 vars n mean sd median trimmed mad min max range skew kurtosis se mpg 1 19 17.15 3.83 17.30 17.12 ...