README.Rmd

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output: github_document
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# ralger <a><img src='man/figures/logo.png' align="right" height="200" /></a>



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The goal of **ralger** is to facilitate web scraping in R. For a quick video tutorial, I gave a talk at useR2020, which you can find [here](https://www.youtube.com/watch?v=OHi6E8jegQg)

## Installation

You can install the `ralger` package from [CRAN](https://cran.r-project.org/) with:

```{r eval=FALSE}
install.packages("ralger")

```

or you can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("feddelegrand7/ralger")
```
## `scrap()`

This is an example which shows how to extract [top ranked universities' names](http://www.shanghairanking.com/rankings/arwu/2021) according to the ShanghaiRanking Consultancy:


```{r example}
library(ralger)

my_link <- "http://www.shanghairanking.com/rankings/arwu/2021"

my_node <- "a span" # The element ID , I recommend SelectorGadget if you're not familiar with CSS selectors

clean <- TRUE # Should the function clean the extracted vector or not ? Default is FALSE

best_uni <- scrap(link = my_link, node = my_node, clean = clean)

head(best_uni, 10)

```

Thanks to the [robotstxt](https://github.com/ropensci/robotstxt), you can set `askRobot = TRUE` to ask the `robots.txt` file if it's permitted to scrape a specific web page.

If you want to scrap multiple list pages, just use `scrap()` in conjunction with `paste0()`.
Suppose that you want to scrape all `RStudio::conf 2021` speakers:

```{r}

base_link <- "https://global.rstudio.com/student/catalog/list?category_ids=1796-speakers&page="

links <- paste0(base_link, 1:3) # the speakers are listed from page 1 to 3

node <- ".pr-1"


head(scrap(links, node), 10) # printing the first 10 speakers

```

## `attribute_scrap()`

If you need to scrape some elements' attributes, you can use the `attribute_scrap()` function as in the following example:


```{r}
# Getting all classes' names from the anchor elements
# from the ropensci website

attributes <- attribute_scrap(link = "https://ropensci.org/",
                node = "a", # the a tag
                attr = "class" # getting the class attribute
                )

head(attributes, 10) # NA values are a tags without a class attribute
```

Another example, let's we want to get all javascript dependencies within the same web page:

```{r}

js_depend <- attribute_scrap(link = "https://ropensci.org/",
                             node = "script",
                             attr = "src")

js_depend

```

## `table_scrap()`

If you want to extract an __HTML Table__, you can use the `table_scrap()` function. Take a look at this [webpage](https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW) which lists the highest gross revenues in the cinema industry. You can extract the HTML table as follows:

```{r}


data <- table_scrap(link ="https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW")

head(data)


```

__When you deal with a web page that contains many HTML table you can use the `choose` argument to target a specific table__


## `tidy_scrap()`

Sometimes you'll find some useful information on the internet that you want to extract in a tabular manner however these information are not provided in an HTML format. In this context, you can use the `tidy_scrap()` function which returns a tidy data frame according to the arguments that you introduce. The function takes four arguments:

- **link** : the link of the website you're interested for;
- **nodes**: a vector of CSS elements that you want to extract. These elements will form the columns of your data frame;
- **colnames**: this argument represents the vector of names you want to assign to your columns. Note that you should respect the same order as within the **nodes** vector;
- **clean**: if true the function will clean the tibble's columns;
- **askRobot**: ask the robots.txt file if it's permitted to scrape the web page.

### Example

We'll work on the famous [IMDb website](https://www.imdb.com/). Let's say we need a data frame composed of:

- The title of the 50 best ranked movies of all time
- Their release year
- Their rating

We will need to use the `tidy_scrap()` function as follows:

```{r example3, message=FALSE, warning=FALSE}

my_link <- "https://www.imdb.com/search/title/?groups=top_250&sort=user_rating"

my_nodes <- c(
  ".lister-item-header a", # The title
  ".text-muted.unbold", # The year of release
  ".ratings-imdb-rating strong" # The rating)
  )

names <- c("title", "year", "rating") # respect the nodes order


tidy_scrap(link = my_link, nodes = my_nodes, colnames = names)


```

Note that all columns will be of *character* class. you'll have to convert them according to your needs.



## `titles_scrap()`

Using `titles_scrap()`, one can efficiently scrape titles which correspond to the _h1, h2 & h3_ HTML tags.



### Example

If we go to the [New York Times](https://www.nytimes.com/), we can easily extract the titles displayed within a specific web page :


```{r example4}


titles_scrap(link = "https://www.nytimes.com/")



```

Further, it's possible to filter the results using the `contain` argument:


```{r}

titles_scrap(link = "https://www.nytimes.com/", contain = "TrUMp", case_sensitive = FALSE)



```


## `paragraphs_scrap()`


In the same way, we can use the `paragraphs_scrap()` function to extract paragraphs. This function relies on the `p` HTML tag.

Let's get some paragraphs from the lovely [ropensci.org](https://ropensci.org/) website:


```{r}

paragraphs_scrap(link = "https://ropensci.org/")

```

If needed, it's possible to collapse the paragraphs into one bag of words:


```{r}

paragraphs_scrap(link = "https://ropensci.org/", collapse = TRUE)

```


## `weblink_scrap()`

`weblink_scrap()` is used to srape the web links available within a web page. Useful in some cases, for example, getting a list of the available PDFs:


```{r}

weblink_scrap(link = "https://www.worldbank.org/en/access-to-information/reports/",
              contain = "PDF",
              case_sensitive = FALSE)


```

## `images_scrap() ` and `images_preview()`

`images_preview()` allows you to scrape the URLs of the images available within a web page so that you can choose which images __extension__ (see below) you want to focus on.

Let's say we want to list all the images from the official [RStudio](https://rstudio.com/) website:


```{r}

images_preview(link = "https://rstudio.com/")

```

`images_scrap()` on the other hand download the images. It takes the following arguments:

+ **link**: The URL of the web page;

+ **imgpath**: The destination folder of your images. It defaults to `getwd()`

+ **extn**: the extension of the image: jpg, png, jpeg ... among others;

+ **askRobot**: ask the robots.txt file if it's permitted to scrape the web page.


In the following example we extract all the `png` images from [RStudio](https://rstudio.com/)  :


```{r, eval=FALSE}

# Suppose we're in a project which has a folder called my_images:

images_scrap(link = "https://rstudio.com/",
             imgpath = here::here("my_images"),
             extn = "png") # without the .

```


# Accessibility related functions


## `images_noalt_scrap()`


`images_noalt_scrap()` can be used to get the images within a specific web page that don't have an `alt` attribute which can be annoying for people using a screen reader:


```{r}

images_noalt_scrap(link = "https://www.r-consortium.org/")

```
If no images without `alt` attributes are found, the function returns `NULL` and displays an indication message:


```{r}
# WebAim is the reference website for web accessibility

images_noalt_scrap(link = "https://webaim.org/techniques/forms/controls")
```



## Code of Conduct

Please note that the ralger project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.