Example of R Markdown documents that read/write data to a top-level persistent storage directory on RStudio Connect

persistent-read-example.Rmd

---
title: "Read Persistent Data on RStudio Connect"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)
```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

```{r}
library(gt)
library(dplyr)

per_data <- read.csv("/tmp_shared/write-data-example.csv")

per_data %>%
  sample_n(10) %>%
  gt() %>%
  tab_header(
    title = "Current Data Sample"
  )
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}

```

### Chart C

```{r}

```

persistent-write-example.Rmd

---
title: "Write Persistent Data to Shared Storage on RStudio Connect"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(reticulate)
```

A common use case for [Schduling on RStudio Connect](https://docs.rstudio.com/connect/user/r-markdown-schedule.html), is to use that feature as part of an R-based process to automate scheduled data updates. 
This example report outputs a CSV file that can be used/consumed by other assets hosted on RStudio Connect.

## R Markdown Writing to Persistent Storage on RStudio Connect

- Reference: [Persistent Storage on RStudio Connect](https://support.rstudio.com/hc/en-us/articles/360007981134-Persistent-Storage-on-RStudio-Connect)

### Create Data

```{r}
df <- data.frame(a=rnorm(50), b=rnorm(50), c=rnorm(50), d=rnorm(50), e=rnorm(50))
```

Every time this report is executed, it creates a new random data frame. _Creating dummy data is not representative of a typical ETL process._ 
You'll likely want to replace this section with code that pulls data from a database or API.

- Best practices for working with databases can be found at [db.rstudio.com](https://db.rstudio.com/)
- The `httr` package is a [good place to start](https://cran.r-project.org/web/packages/httr/vignettes/quickstart.html) when working with REST APIs and the http protocol

### Preview Data (optional)

```{r message=FALSE, warning=FALSE}
library(gt)
library(dplyr)
df %>%
  sample_n(6) %>%
  gt() %>%
  tab_header(
    title = "Current Data Sample"
  )
```

### Write Data 

Absolute references must be concerned about the sandboxed processes on RStudio Connect. 
RStudio Connect protects certain areas on the file system from access by the processes that it executes. 
As a result, when absolute references are in use, it is best to define a top-level directory (like /tmp_shared) that is uniquely named and houses persistent data.

Check for old data and remove from tmp_shared:

```{python echo = TRUE}
import os
if os.path.exists("/tmp_shared/write-data-example.csv"):
  os.remove("/tmp_shared/write-data-example.csv")
```

Write the new data file:

```{r}
write.csv(df, "/tmp_shared/write-data-example.csv", row.names=FALSE)
```

Log a success:

```{r}
system("date | cat - /tmp_shared/write-ex-log.txt > temp && mv temp /tmp_shared/write-ex-log.txt")
```

Test - Read the data back in (to a new variable):

```{r}
per_data <- read.csv("/tmp_shared/write-data-example.csv")
summary(per_data)
```

Hi,
the "/tmp_shared/" is the diretory of r connect? is a standard folder in the server or you create it?