Accessing apis from r a tutorial

You can report issue about the content on this page here Want to share your content on R-bloggers? APIs are the driving force behind data mash-ups. It is APIs that allow machines to access data programmatically — that is automatically from within a program — to make use of API provided functionalities and data. This post is about using APIs with R. As an example. Of course, there are the APIs of large vendors, like Google or Facebook, that are thought out and well documented.

But then there is the vast majority of smaller APIs for special applications that often lack in structure or documentation. Nevertheless, these APIs often provide access to valuable ressources. Basically, it means a way of accessing the functionality of a program from inside another program. So instead of performing an action using an interface that was made for humans, a point and click GUI for instance, an API allows a program to perform that action automatically.

The power of this concept becomes only visible, when you imagine that you can mesh the calling of an API in the program with anything else that program might want to do. Some examples from data science:. Indeed, one might consider browsing the web as using APIs: a program the browser uses a defined set of commands and conventions to retrieve data the webpage from a remote server the website and renders it locally in the browser the thing you see.

By specifying the parameter year the API returns not all articles, but only those that were written in a specific year. We already know which part is the URL and which part is the path to the endpoint. In this example, year is the name of the parameter, and is its value.

Upon receiving the API call, the remote system crafts an answer. The answer can be in any format. It could be a image file, or a movie, or text, or … In recent years, JSON has become the most common answer format by far.

JSON is a simple text file that uses special characters and conventions to bring structure into its contents. For now it suffices to know that is a popular format to store data, that can potentially be nested and delivered together with metadata. And that R can process it quite easily. The big problem with APIs is that they are always designed by humans. So APIs vary wildly in logical structure and the quality of documentation.

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This unfortunately means, that there is no simple catch-all solution for working with APIs and all programs will need to be custom tailored to the API used.

This also means that using an API almost always requires programming to some degree. More specifically, we are interested in which week day is most popular for EU energy legislative documents to go into force. There are many facilities in R that can be used to access APIs.

Working with JSON data is facilitated a lot by the jsonlite package. Well, most of the time, anyway. EurLex documents all bear a document classifier directory code in EurLex parlance that can be used to single out documents that relate to a specific topic. The EurLex classifiers are always four dot separated pairs of digits. For instance, the classifier We will use the appropriate classifiers to retrieve the data on energy related documents.

Steps 1 and 3 will involve calling the API and 2 and 4 are just local data processing chores. Attaching package: 'jsonlite' The following object is masked from 'package:utils': View. This is great, when doing actual statistical work. It is this magic that allows R to turn multinomial variables into dummy variables in regression models and produce nice cross tables.

Note: this call only affects the current session; when you restart R, all settings will be back to normal. A query is not required at this point, as the API provides the answer directly.This tutorial will walk through the basics of using the R language to obtain data from a web API. I will explain the basic concepts and demonstrate getting data from a handful of publicly accessible web APIs. An Application Programming Interface API is a set of defined methods that allow various software to communicate with each other.

Many R packages leafletdygraphsplotlyetc. In a nutshell, accessing data from a web API is very similar to visiting a website; in both cases, you specify a url and information is sent to your machine.

Many APIs e. More on API keys later. Most APIs should have some form of documentation online to direct you and explain what type of information can be requested. Looks like our request worked! By httr magic, those 98, raw values have been transformed into an 18 element list with each element itself being a 69 element list! At this stage, we can start wrangling data out of the API response in the same way we would handle any other list object in R.

I previously mentioned that there were 69 elements For each repo in the list. A majority of these list elements are actually addtional API endpoint urlsmeaning they tell us where we can request additional data on a given repo. I had to do a little url processing in order to get a successful request. If we look at the new response from the GitHub API we see that it looks very similar to our original request.

However, we quickly end up dealing with rather complex nested lists that can be a pain to wrangle. Earlier, when discussing API requests, I skimmed over a useful aspect of constructing your query.

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This query returns a data frame very similar to my sfg-aqua repo except that there are multiple people committing to the eco-data-science. Instead, however, suppose we were only interested in seeing the commits authored by GitHub user afflerbach nceas. Now if we look at our new response data frame we can see that it looks just like the previous response but only includes commits by the GitHub user afflerbach nceas.This article was prepared by a guest contributor to ProgrammableWeb.

The opinions expressed in this article are the author's own and do not necessarily reflect the view of ProgrammableWeb or its editorial staff. For this API request, one of the pieces of information you received in the original GET request was the number of pages.

So, let's initialize the pages variable with that value:. The following code is a for loop that gets each page of data:.

Figure 6. In this example, there are 93 pages of data, so you'll make 93 API calls to get each page. Then it repeats until all of the pages have been returned.

This leaves you with a nice neat data frame with all of the data you requested that R can then analyze. Figure 7. Notice that the data frame has grown from rows to more than 9, as each page of data has been added. Intercom's APIfor example, has a scroll parameter. Your first API call will return this as a character that you can add to subsequent calls to get more data. Instead of a for loop, you can write a while loop:.

As long as you can adapt the paging methodology of the API you'd like to use, you can use these techniques to access just about any API in R. Glad you liked it. It makes me happy that other R users are learning from this. When I was learning, articles and posts were supper helpful, so its nice to give back. Each and every time if I run the code it retrieves all the data from API and feed it to database but I want to pull only newly added data in API and feed those into database to avoid API transaction overflow.

Great post but you have to update it a bit.Enroll now! Learn more. In the previous lessons, you learned how to access human readable text files data programmatically using:. In this lesson, you will learn about API interfaces. An API allows us to access data stored on a computer or server using a specific query.

APIs are powerful ways to access data and more specifically the specific type and subset of data that you need for your analysis, programmatically. You will also explore the machine readable JSON data structure. Machine readable data structures are more efficient - particularly for larger data that contain hierarchical structures. You explored the concept of a request and then a subsequent response.

An endpoint refers to a dataset that you can access and query against. Every Socrata dataset, and even every individual data record, has its own endpoint. Read more about endpoints. These data include population estimates for males and females for every county in Colorado for every year from to for multiple age groups.

Using URL parameters, you can define a more specific request to limit what data you get back in response to your API request. For example, if you only want data for Boulder, Colorado, you can query just that subset of the data using the RESTful call. In the link below, note that the?

Parameters associated with accessing data using this API are documented here. Click here to view data. JSON format. The data that are returned from an API request are called the response. The first thing that you need to do is create your API request string. Remember that this is a URL with parameters parameters that specify which subset of the data that you want to access. Note that you are using a new function - paste0 - to paste together a complex URL string.

This is useful because you may want to iterate over different subsets of the same data ie reuse the base url or the endpoint but request different subsets using different URL parameters. There are a few ways to access the data however the most direct way is to. Then, you import the data directly into a data.

You are not going to learn this in this class however it is a good option that results in code that is a bit cleaner given the various parameters are passed to the function via argument like syntax. Also note that if you wanted to use getURLyou could do so as follows:.APIs, or application program interfacesare a way for people to access data in a plain text format using multiple programming languages.

Most APIs require you to provide an email address for a key and some even require a justification for requesting and using their data. We will start with an example from the DataUSA.


Read the documentation here. More on this later. This is a high-level overview of what is contained in the request, but we will have to dig deeper to understand more about extracting the data. The message tells me the response is using the default text encoding UTF The good news is that the data look like college degrees, so we can be confident the API request is working! How it works: The httr::content function uses the Content-Type data from httr::GET to determine the best way to parse the incoming data.

The JSON format is beneficial because 1 it's a plain text file, and 2 it doesn't need to be structured in a tabular data frame i.

Now we've converted the contents of the API request to a data frame! Let's repeat this process, but with a slightly more complicated request from a different API. You can send more specific requests using API requests, too. To demonstrate this, we'll be using the opensecrets.

This requires you to sign up for an access key here. After you've signed up and have an API access key, you'll need to read up on the documentation for the available data. For this example, I'll be downloading the data from the candContrib table, which contains information on the "top contributors to specified candidate for a House or Senate seat or member of Congress.

In the documentation, an example API query is presented and I've represented each component in the figure below:. API queries follow a general syntax called query parameters for accessing various resources on the web server. We will build a new request using the same method as above, but with a few additional specifications.

How to access APIs in R

The first portion of this should be familiar from the previous request we built--it contains the http and domain information. Next we will add the data source we are interested in candContrib. We will add the cycle information cycle to limit the amount of data to the year This API key should be stored in a separate file, so it doesn't get unintentionally shared or distributed. The api-key. Finally, I'll include the cid which is the unique identifier for candidates. These are available for download here in the data documentation.

We'll import the candidates sheet from this file below. I am interested in Cory Booker, so we'll use this lookup table to try and find his cid. This shows a Tykiem Bookerbut this isn't who I am looking for. Fortunately, I just learned a bit about how url's get built. I will return to the opensecrets website and search for Cory Booker. This gives us the following search results, and I want to choose the second result down titled, "Sen.

I chose this option because I can see cid is listed in the url. After clicking on the link, I can see that the url contains syntax that looks like the query parameters I've been building. We can see this is a list of one, and each object inside the list has data on Cory Booker. If I start investigating the contents of this list, I can see the actual data are embedded inside a few layers.

This is where RStudio comes in handy. The first contains information on our candidate and is stored in the object below.This article was prepared by a guest contributor to ProgrammableWeb. The opinions expressed in this article are the author's own and do not necessarily reflect the view of ProgrammableWeb or its editorial staff.

R is an excellent language for data analytics, but it's uncommon to use it for serious development. This is a how-to guide for connecting to an API to receive stock prices as a data frame when the API doesn't have a specific package for R. For those of you not familiar with R, a data frame is like a spreadsheet, with data arranged in rows in columns. You can then use these same techniques to pull data into R from other APIs.

An API can automate your data collection, so it's well worth the effort. This tutorial assumes you have a basic working knowledge of R and are comfortable scripting with RStudio or working with the Rstudio console. These examples will work on Mac or PC as long as you have an internet connection and an up to date version of R installed on your computer 3.

A good way to follow along with this how-to guide is to copy each line of code into a script in RStudio. This will enable you to run each line of code individually so you can see it working and then to run them all at once at the end. You can also enter them line by line from the R console.

This package makes requesting data from just about any API easier by formatting your GET requests with the proper headers and authentications. Next, install jsonlite in your script:.

If you're like most R users, you'll want to convert the JSON from its native nested form to a flat form like a data frame so it's easier to work with. The jsonlite package makes this easy. Figure 2. The headers are often used to negotiate other parameters that enable the application to communicate with the API successfully.

For example, they may describe the formatting of the data payload. Glad you liked it. It makes me happy that other R users are learning from this.

When I was learning, articles and posts were supper helpful, so its nice to give back. Each and every time if I run the code it retrieves all the data from API and feed it to database but I want to pull only newly added data in API and feed those into database to avoid API transaction overflow. Great post but you have to update it a bit.

accessing apis from r a tutorial

They change policy about the username and password so I had to contact them and they provided a username and password for me that is not in my account originally and only then the whole thing started to work. I think there is a need to provide the best SQL practices to the people so that we can get to know more about it in depth and hence provide for the best ongoing SEO practices.

How do you handle this in authenticate? An update from the Intrinio on this blog. There was a change in the authentication method. Skip to main content. Add Your Api.

accessing apis from r a tutorial

Write for us Become member Login. Become member Login.Here's an example of all the aggregated reviews on Yelp that you could do on a smaller scale: 6. Create comparison charts Run a poll across your site to get customer feedback and then add this data into a comparison so people can see how you stack up against competitors.

A good example is: Anti-Spyware Reviews 7. Add reviews to your website An obvious step, but one that is missed a lot. Link to external reviews from your website I've written reviews before simply because I wanted to get either a tweet or a link from a major company to my own blog and I'm sure I can't be the first person to do this.

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If you create something interesting it naturally encourages debate and reviews. Give me what I want If people ask your company to make changes to a product or service then compile the results and add the most requested features.

accessing apis from r a tutorial

If customers feel their input has been acknowledged they are more likely to tell others. And my favourite: 20. Published 10 October, 2011 by Mike Essex Mike Essex is Online Marketing Manager at Koozai Ltd and a contributor to Econsultancy. Email Password Email Email address Your name First name Last name Your work Company Job title Country Please select a country. Wallis and Futuna Western Sahara Yemen Zambia Zimbabwe Password Receive the Daily Pulse Send me notifications of follow up comments Save or Cancel Nick Stamoulis Allowing consumers to post reviews directly to your site is both good and bad.

Liz Broomfield Good ideas here. Laura Galyer, Marketing Director, EMEA, APAC, South America at Sensus This is a really interesting post Mike.