1. R Studio Download
2. Web Scraping R Studio Download
3. Web Scraping R Studio
4. Web Scraping Applications

By Ajay Ohri, DecisionStats.

RStudio includes an editor with many R specific features, a console to execute your code, and other useful panes, including one to show figures. Most web-based R consoles also provide a pane to edit scripts, but not all permit you to save the scripts for later use. All the R scripts used to generate this book can be found on GitHub 11. Web scraping data for use in R-Studio. Ask Question Asked 2 years, 7 months ago. Active 2 years, 6 months ago. Viewed 386 times 2. I am wanting to pull the data out of this server site and into R-Studio. I am new to R so not at all sure what is possible. Web scraping is one of the most robust and reliable ways of getting web data from the internet. It is increasingly used in price intelligence because it is an efficient way of getting the product data from e-commerce sites. You may not have access to the first and second option. Hence, web scraping can come to your rescue.

Why would anyone want to use R and Python in the same software? Have we not seen enough debates on the internet on which language is better. Even on this website ( an industry bellwether) I could count four such articles here by Datacamp ,Stichfix, by a debate and by a KDnuggets poll. Well if Data Science and Data Scientists can not decide on what data to choose to help them decide which language to use, here is an article on how to use BOTH.

1. Here we show you how you can import data from the web into a tool called R. Reasons why R has become so popular, and continues to grow, are that it’s free, open source, with state-of-the-art practices and a fantastic community. R, and its IDE RStudio, is a statistical software and data.
2. Web scraping is the process of collecting the data from the World Wide Web and transforming it into a structured format. Typically web scraping is referred to an automated procedure, even though formally it includes a manual human scraping. We distinguish several techniques of web scraping.

Data Scientists:
Think Python AND R
and not just PYTHON OR R

Here are some reasons to do so:

1. Both are good stable languages with interesting complementary qualities. You can get much better packages in one and then stich them with some data from the other. An example is using time series forecasting (forecast::auto.arima) and decision trees in R (http://www.statmethods.net/advstats/cart.html) and doing data munging in Python.
2. Both languages borrow from each other. Even seasoned package developers like Hadley Wickham (Rstudio) borrows from Beautiful Soup (python) to make rvest for web scraping. Yhat borrows from sqldf to make pandasql. Rather than reinvent the wheel in the other language developers can focus on innovation
3. The customer does not care which language the code was written, the customer cares for insights.
4. You are less likely to be tied down due to a bug or feature request or a version compatibility issue
5. It is sexier for data scientists to be skilled on (or cooler as we older guys liked to say before Harvard Business Review proclaimed data scientist as the sexiest job)
6. There are only four main languages within Data Science (~91% by KDnuggets Poll) and everyone can use SQL from their own language. There is no debate on SQL.

So how to do it? Never back down no surrender watch online. As of December 2015 there are three principal ways to use BOTH Python an R

1. Use a Python package rpy2 to use R within Python . You can see examples here You can also use Python from within R using the rPython package
2. Use Jupyter with the IR Kernel – The Jupyter project is named after Julia Python and R and makes the interactivity of iPython available to other languages
3. Use Beaker notebook -Inspired by Jupyter, Beaker Notebook allows you to switch from one language in one code block to another language in another code block in a streamlined way to pass shared objects (data)

I wish I could say all these methods are simple and streamlined but they are not. I enjoy Python’s power in data munging and I enjoy R’s huge library of packages and functions for statistics. Will using R and Python together grow in the future. Let’s see. But in case you did not know, you can also use SAS language with R and Python(using Java). Now that is cool for sure.

Bio: Ajay Ohri is the founder of analytics startup DECISIONSTATS. He is the author of ” R for Business Analytics” and “R for Cloud Computing”.

Related:

Pipes are an extremely useful tool from the magrittr package¹ that allow you to express a sequence of multiple operations. They can greatly simplify your code and make your operations more intuitive. However they are not the only way to write your code and combine multiple operations. In fact, for many years the pipe did not exist in R. How else did people write their code?

Suppose we have the following assignment:

Using the penguins dataset, calculate the average body mass for Adelie penguins on different islands.

Okay, first let’s load our libraries and check out the data frame.

We can decompose the problem into a series of discrete steps:

1. Filter penguins to only keep observations where the species is “Adelie”
2. Group the filtered penguins data frame by island
3. Summarize the grouped and filtered penguins data frame by calculating the average body mass

R Studio Download

But how do we implement the code?

Intermediate steps

One option is to save each step as a new object:

Why do we not like doing this? We have to name each intermediate object. Here I just append a number to the end, but this is not good self-documentation. What should we expect to find in penguins_2? It would be nicer to have an informative name, but there isn’t a natural one. Then we have to remember how the data exists in each intermediate step and remember to reference the correct one. What happens if we misidentify the data frame?

We don’t get the correct answer. Worse, we don’t get an explicit error message because the code, as written, works. R can execute this command for us and doesn’t know to warn us that we used penguins_1 instead of penguins_2.

Web Scraping R Studio Download

Overwrite the original

Hologram background apk mod. Instead of creating intermediate objects, let’s just replace the original data frame with the modified form.

Web Scraping R Studio

This works, but still has a couple of problems. Vengeance essential clubsounds download. What happens if I make an error in the middle of the operation? I need to rerun the entire operation from the beginning. With your own data sources, this means having to read in the .csv file all over again to restore a fresh copy.

Web Scraping Applications

Function composition

We could string all the function calls together into a single object and forget assigning it anywhere.

But now we have to read the function from the inside out. Even worse, what happens if we cram it all into a single line?

This is not intuitive for humans. Again, the computer will handle it just fine, but if you make a mistake debugging it will be a pain.

Back to the pipe

Piping is the clearest syntax to implement, as it focuses on actions, not objects. Or as Hadley would say:

[I]t focuses on verbs, not nouns.

magrittr automatically passes the output from the first line into the next line as the input.

This is how I explain the 'pipe' to #rstats newbies.. pic.twitter.com/VdAFTLzijy

— We are R-Ladies (@WeAreRLadies) September 13, 2019

This is why tidyverse functions always accept a data frame as the first argument.

Important tips for piping

• Remember though that you don’t assign anything within the pipes - that is, you should not use <- inside the piped operation. Only use this at the beginning if you want to save the output
• Remember to add the pipe %>% at the end of each line involved in the piped operation. A good rule of thumb: RStudio will automatically indent lines of code that are part of a piped operation. If the line isn’t indented, it probably hasn’t been added to the pipe. If you have an error in a piped operation, always check to make sure the pipe is connected as you expect.

Session Info

1. The basic %>% pipe is automatically imported as part of the tidyverse library. If you wish to use any of the extra tools from magrittr as demonstrated in R for Data Science, you need to explicitly load magrittr. ^{^}