动态切换到 flexdashboard 中的下一个选项卡

Posted 2023-02-14

【中文标题】动态切换到 flexdashboard 中的下一个选项卡

【英文标题】：Dynamically switch to next tab in flexdashboard

【发布时间】：2022-01-14 09:54:37

【问题描述】：

我有大约 20 个样本，我需要为这些样本绘制图形，例如直方图、箱线图等……我想将所有这些图组织在一个 flexdashboard 中，每个样本都有一个选项卡。所以每个选项卡都有一个直方图、一个箱线图等。

以下模板仅生成一个选项卡。我将数据集翻了一番并添加了一列，因此它有两个 type、“first_sample”和“second_sample”（第一块代码）。

有没有一种简单的方法可以循环这些类型，以便在每个样本的单独选项卡上生成图？

谢谢！

编辑：我也找到了这篇文章，但我无法让它工作：Dynamicly increasing amount of tabs and pages in flexdashboards

---
title: "ggplotly geoms"
author: "Carson Sievert"
output: 
  flexdashboard::flex_dashboard:
  orientation: rows
social: menu
source_code: embed
---
  
```r setup, include=FALSE
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)

# Make some noisily increasing data
set.seed(955)
dat1 <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))

dat1$type <- "first_sample"

dat2 <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))

dat2$type <- "second_sample"

dat <- rbind(dat1, dat2)

```

geom_point
=======================================================================
  
Row
-----------------------------------------------------------------------
  
### Scatter Chart with geom_point
  
```r
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1)      # Use hollow circles
ggplotly(p)
```


### geom_smooth Linear Regression

```r
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth(method=lm)   # Add linear regression line
ggplotly(p)
```

Row
-----------------------------------------------------------------------
  
### geom_smooth with Loess Smoothed Fit
  
```r
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p)
```

### Constraining Slope with stat_smooth

```r
n <- 20
x1 <- rnorm(n); x2 <- rnorm(n)
y1 <- 2 * x1 + rnorm(n)
y2 <- 3 * x2 + (2 + rnorm(n))
A <- as.factor(rep(c(1, 2), each = n))
df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
fm <- lm(y ~ x + A, data = df)

p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
p <- p + geom_point() + geom_line(aes(y = pred))
ggplotly(p)

```

【参考方案1】：

要做到这一点，我必须结合起来（我引用了这篇文章的一些内容）：

Use loop to generate section of text in rmarkdown

sprintf 通过数据的types 准备模板文本和名称选项卡 results = "asis"，rmarkdown 块参数“防止 knitr 向输出添加格式” cat 防止 R 添加额外的东西，如引号和元素编号” print 在for 循环中绘图

以下代码为dat 中的每个样本生成一个带有两个选项卡和两个绘图的 flexdashboard

---
title: "test"
author: "Paul Endymion"
output: 
  flexdashboard::flex_dashboard:
  orientation: rows
social: menu
source_code: embed
---

  
```r setup, include=FALSE
library(ggplot2)
library(flexdashboard)
library(data.table)

# Make some noisily increasing data
set.seed(955)
dat1 <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))

dat1$type <- "first_sample"
    
dat2 <- data.frame(cond = rep(c("A", "B"), each=10),
                      xvar = 1:20 + rnorm(20,sd=3),
                      yvar = 1:20 + rnorm(20,sd=3))
    
dat2$type <- "second_sample"
    
dat <- rbind(dat1, dat2)

setDT(dat)
```

```r echo = FALSE, results = "asis"

template <- "

%s
=======================================================================
  
### Scatter Chart with geom_point

" # dont't forget the newline

template2 <- "

Row
-----------------------------------------------------------------------

### geom_smooth Linear Regression

"

for (i in unique(dat$type)) 
  cat(sprintf(template, i))
  
  p<-ggplot(dat[type == i], aes(x=xvar, y=yvar)) +
    geom_point(shape=1)      # Use hollow circles
  print(p)
  
  cat(template2)
  
  p2 <- ggplot(dat[type == i], aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth(method=lm)   # Add linear regression line
  print(p2)

```

它仍然需要调整，但它做了我想做的事情。