I have a table that summarizes ~100 clinical trials. This table needs to be added to a shiny app and then the user filter the table based on the different variables in the table like endpoint, intervention, etc.

我有一个表格，总结了大约100项临床试验。这个表需要添加到一个闪亮的应用程序中，然后用户根据表中的不同变量(如端点、干预等)来筛选表。

The problem with that the table is that every study, as far as I know, has to be in only one row. But in excel, each study needs more than one row since variables like "Intervention" and "Endpoint" has so many levels, and I will need to subset on my data based on these variables.

据我所知，这张表格的问题是，每一项研究都必须只排在一行。但在EXCEL中，每个研究都需要不止一行，因为像“干预”和“终点”这样的变量有很多级别，我需要根据这些变量对我的数据进行子集。

For example, user choose to subset on studies that included outcome 1 or maybe intervention 1. Many studies has a mix of these, as in the screenshot below.

例如，用户选择包含结果1或干预1的研究的子集。许多研究混合了这两种方法，如下面的截图所示。

This makes it impossible for each study to fit in one row especially that a study can have up to 15 different interventions (there is a long list of interventions that the use will subset based on)

这使得每项研究不可能放在一行中，特别是一项研究可以有多达15种不同的干预措施(有一长串干预措施将基于使用的子集)。

How to approach this?

如何处理这一问题？

It sounds like you need to figure out how to "fill in" all of the empty rows with the preceding row's data. Ignoring the shiny component for now, if I have data (in xlsx, though it doesn't matter if CSV) like the following:

听起来您需要弄清楚如何用前一行的数据“填充”所有空行。现在先忽略闪亮的组件，如果我有如下数据(在xlsx中，但如果是csv并不重要)：

We can read in and fix with zoo::na.locf or tidyr::fill:

我们可以读入并修复zoo：：na.locf或tidyr：：Fill：

dat <- readxl::read_excel("~/Downloads/something.xlsx")
dat
# # A tibble: 6 × 5
#   Author    Year Methodology    `Intervention(s)` `Endpoint(s)`
#   <chr>    <dbl> <chr>          <chr>             <chr>        
# 1 X et al.  2022 Prospective    Intervention 1    Endpoint 1   
# 2 NA          NA NA             Intervention 2    Endpoint 2   
# 3 NA          NA NA             Intervention 3    Endpoint 3   
# 4 Y et al.  2021 Something else Intervention 4    Endpoint 4   
# 5 NA          NA NA             Intervention 5    Endpoint 5   
# 6 NA          NA NA             Intervention 6    Endpoint 6   

and fix with one of the following:

并使用以下选项之一进行修复：

tidyr::fill(dat, Author, Year, Methodology)
# # A tibble: 6 × 5
#   Author    Year Methodology    `Intervention(s)` `Endpoint(s)`
#   <chr>    <dbl> <chr>          <chr>             <chr>        
# 1 X et al.  2022 Prospective    Intervention 1    Endpoint 1   
# 2 X et al.  2022 Prospective    Intervention 2    Endpoint 2   
# 3 X et al.  2022 Prospective    Intervention 3    Endpoint 3   
# 4 Y et al.  2021 Something else Intervention 4    Endpoint 4   
# 5 Y et al.  2021 Something else Intervention 5    Endpoint 5   
# 6 Y et al.  2021 Something else Intervention 6    Endpoint 6   

dat[] <- lapply(dat, zoo::na.locf)
dat
# # A tibble: 6 × 5
#   Author    Year Methodology    `Intervention(s)` `Endpoint(s)`
#   <chr>    <dbl> <chr>          <chr>             <chr>        
# 1 X et al.  2022 Prospective    Intervention 1    Endpoint 1   
# 2 X et al.  2022 Prospective    Intervention 2    Endpoint 2   
# 3 X et al.  2022 Prospective    Intervention 3    Endpoint 3   
# 4 Y et al.  2021 Something else Intervention 4    Endpoint 4   
# 5 Y et al.  2021 Something else Intervention 5    Endpoint 5   
# 6 Y et al.  2021 Something else Intervention 6    Endpoint 6   

From here, you can filter however you need without losing information on the author/year/etc.

在这里，您可以根据需要进行筛选，而不会丢失有关作者/年份/等的信息。