franperezlopez

```{r include=F}
options(width=120)
set.seed(3874)
options(error=function() {save.image(file="error.rmd.RData")})
```

franperezlopez

---
title: "Untitled"
output: html_document
---

franperezlopez

const Nightmare = require ("nightmare");
const vo = require("vo");

function scrape (url: string, dataScraper: Function, urlScraper: Function, 
                 injectJQuery: boolean = true, validate: Function = null)
{
    var item : any = null, urls: any = null;
    // do not show window, do not load images (reduce time to ready())
    var nightmare = Nightmare({show: false, pollInterval: 800, webPreferences: {images: false}});

franperezlopez

```{r}
# save document's environment (replaces existing one)
save.image(file="housePrices.rmd.RData")
# archive (optional)
save.image(file=paste0("housePrices_", format(Sys.time(),"%d%m%y_%H%M"), ".rmd.RData"))
```

franperezlopez

# reset current environment (optional)
rm(list=ls())
# load environment from file
load("housePrices.rmd.RData")

franperezlopez

# reset current environment (optional)
rm(list=ls())
# load environment from file
load("housePrices.rmd.RData")

franperezlopez

```{r}
options(error=function() {save.image(file="error.rmd.RData")})
```

franperezlopez

```{r}
eval.trainModels=T
eval.pcaOptimization=T
```

```{r eval=eval.pcaOptimization, include=eval.pcaOptimization}
# code applying PCA optimization
...

franperezlopez

```{r rf, cache=T}
# train random forest model
```

```{r cache=T, dependson=”rf”}
# predict using random forest model
```

franperezlopez

unlink('housePrices_cache', recursive = TRUE)