Dynamically switch to next tab in flexdashboard

Question

I have around 20 sample for which I need to plot graphics such as histograms, boxplots, etc... I would like to organise all these plots in a flexdashboard where I would have one tab per sample. So each tab has one histogram, one boxplot, etc.

The below template produces only one tab. I doubled the dataset and add a column so it has two type, "first_sample" & "second_sample" (first chunk of code).

Is there an easy way to loop on these types so it generates the plots on seperated tabs for each sample ?

Thanks !

Edit : I also found this post but I couldn't make it work : 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)

```

Answer 1

To do this I had to combine (and I am citing some of this post) :

Use loop to generate section of text in rmarkdown

• sprintf to prepare template text and name tabs by the types of data
• results = "asis", rmarkdown chunk parameter "to prevent knitr from adding formatting to the output"
• cat to prevent R from adding additional stuff like quotes and element numbers"
• print to plot in for loops

The following code produces a flexdashboard with two tabs and two plots for each sample in dat

---
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)
}
```

It still needs tuning but it does what I wanted to do.