Dynamically switch to next tab in flexdashboard - r

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.