parameter_input_opts.Rmd

---
title: "My Document"
output: html_document
params:
  minimum: 
    label: "Minimum:"
    value: 100
    input: slider
    min: 0
    max: 1000
  region:
    label: "Region:"
    value: east
    input: select
    choices: [east, west, north, south]
  data: 
    label: "Input dataset:"
    value: results.csv
    input: file
---

## My parameters

The minimum is: `r params$minimum` in region `r params$region` using the dataset ...

stock_param.Rmd

---
output: html_document
params:
  symbol: AAPL
---
  
## `r params$symbol` 

```{r, echo=FALSE, message=FALSE}

library(quantmod)

prices <- round(getSymbols(params$symbol, auto.assign = FALSE), 2)
close <- Cl(last(prices))
open <- Op(last(prices))
```

The stock closed `r ifelse(close>open,'up','down')` at `r close` dollars per share yesterday.

### Price History

The chart below is made with the `quantmod` R package, a widely used package for collecting and visualizing financial data in R. You can learn more about `quantmod` at the website 
***
  
```{r echo=FALSE}
chartSeries(prices, theme = chartTheme("white", bg.col = "white"))
```

### Raw Data

The table below displays the daily price data for the stock. In the next section, we will learn how to make a concise, interactive table with the `DT` package, a new package for making searchable data tables. You can learn more about the `DT` package at the website 
***
  
```{r echo=FALSE}
DT::datatable(data.frame(prices[, 1:4], 2))
```

stock_param_new_html_features.Rmd

---
output: 
  html_document:
    toc: true
    toc_float: true
    code_folding: hide
params:
  symbol: TSLA
---
  
# `r params$symbol` 

## Summary{.tabset}

```{r, echo=FALSE, message=FALSE}

library(quantmod)
library(dygraphs)

prices <- round(getSymbols(params$symbol, auto.assign = FALSE), 2)
close <- Cl(last(prices))
open <- Op(last(prices))
```

The stock closed `r ifelse(close>open,'up','down')` at `r close` dollars per share yesterday.

### Price History

The chart below is made with the `quantmod` and `dygraphs` R packages.
***
  
```{r echo=FALSE}
dygraphs::dygraph(prices) %>% dyRangeSelector()
```

### Raw Data

The table below displays the daily price data for the stock. We made a concise, interactive table with the `DT` package, a new package for making searchable data tables. 
***
  
```{r echo=TRUE}
DT::datatable(data.frame(prices[, 1:4], 2))
```

## Model{.tabset}

### Plot

This model is fit with the auto.arima function in the forecast package.

```{r, warning=FALSE, message=FALSE}
library(forecast)
m <- auto.arima(prices[,1])
plot(forecast(m,12))
```

### Forecasts
```{r, warning=FALSE, message=FALSE}
f <- forecast(m,12)
```

The forecast for tomorrow is `r as.numeric(f$mean)[1]`.