1.4 Reading Web-Based Data
The learning objectives for this section are to:
- Read in web data via web scraping tools and APIs
Not only can you read in data locally stored on your computer, with R it is also fairly easy to read in data stored on the web.
1.4.1 Flat files online
The simplest way to do this is if the data is available online as a flat file (see note below). For example, the “Extended Best Tracks” for the North Atlantic are hurricane tracks that include both the best estimate of the central location of each storm and also gives estimates of how far winds of certain speeds extended from the storm’s center in four quadrants of the storm (northeast, northwest, southeast, southwest) at each measurement point. You can see this file online here.
How can you tell if you’ve found a flat file online? Here are a couple of clues:
- It will not have any formatting. Instead, it will look online as if you opened a file in a text editor on your own computer.
It will often have a web address that ends with a typical flat file extension (
“.fwf”, for example).
Here are a couple of examples of flat files available online:
If you copy and paste the web address for this file, you’ll see that the url for this example hurricane data file is non-secure (starts with
http:) and that it ends with a typical flat file extension (
.txt, in this case). You can read this file into your R session using the same
readr function that you would use to read it in if the file were stored on your computer.
First, you can create an R object with the filepath to the file. In the case of online files, that’s the url. To fit the long web address comfortably in an R script window, you can use the
paste0 function to paste pieces of the web address together:
ext_tracks_file <- paste0("http://rammb.cira.colostate.edu/research/", "tropical_cyclones/tc_extended_best_track_dataset/", "data/ebtrk_atlc_1988_2015.txt")
Next, since this web-based file is a fixed width file, you’ll need to define the width of each column, so that R will know where to split between columns. You can then use the
read_fwf function from the
readr package to read the file into your R session. This data, like a lot of weather data, uses the string
"-99" for missing data, and you can specify that missing value character with the
na argument in
read_fwf. Also, the online file does not include column names, so you’ll have to use the data documentation file for the dataset to determine and set those yourself.
library(readr) # Create a vector of the width of each column ext_tracks_widths <- c(7, 10, 2, 2, 3, 5, 5, 6, 4, 5, 4, 4, 5, 3, 4, 3, 3, 3, 4, 3, 3, 3, 4, 3, 3, 3, 2, 6, 1) # Create a vector of column names, based on the online documentation for this data ext_tracks_colnames <- c("storm_id", "storm_name", "month", "day", "hour", "year", "latitude", "longitude", "max_wind", "min_pressure", "rad_max_wind", "eye_diameter", "pressure_1", "pressure_2", paste("radius_34", c("ne", "se", "sw", "nw"), sep = "_"), paste("radius_50", c("ne", "se", "sw", "nw"), sep = "_"), paste("radius_64", c("ne", "se", "sw", "nw"), sep = "_"), "storm_type", "distance_to_land", "final") # Read the file in from its url ext_tracks <- read_fwf(ext_tracks_file, fwf_widths(ext_tracks_widths, ext_tracks_colnames), na = "-99") ext_tracks[1:3, 1:9]
# A tibble: 3 x 9 storm_id storm_name month day hour year latitude longitude max_wind <chr> <chr> <chr> <chr> <chr> <int> <dbl> <dbl> <int> 1 AL0188 ALBERTO 08 05 18 1988 32.0 77.5 20 2 AL0188 ALBERTO 08 06 00 1988 32.8 76.2 20 3 AL0188 ALBERTO 08 06 06 1988 34.0 75.2 20
For some fixed width files, you may be able to save the trouble of counting column widths by using the
fwf_empty function in the
readr package. This function guesses the widths of columns based on the positions of empty columns. However, the example hurricane dataset we are using here is a bit too messy for this– in some cases, there are values from different columns that are not separated by white space. Just as it is typically safer for you to specify column types yourself, rather than relying on R to correctly guess them, it is also safer when reading in a fixed width file to specify column widths yourself.
You can use some
dplyr functions to check out the dataset once it’s in R (there will be much more about
dplyr in the next section). For example, the following call prints a sample of four rows of data from Hurricane Katrina, with, for each row, the date and time, maximum wind speed, minimum pressure, and the radius of maximum winds of the storm for that observation:
library(dplyr) ext_tracks %>% filter(storm_name == "KATRINA") %>% select(month, day, hour, max_wind, min_pressure, rad_max_wind) %>% sample_n(4)
# A tibble: 4 x 6 month day hour max_wind min_pressure rad_max_wind <chr> <chr> <chr> <int> <int> <int> 1 10 31 18 25 1009 90 2 10 29 06 30 1001 40 3 11 01 12 20 1011 90 4 08 24 18 40 1003 55
With the functions in the
readr package, you can also read in flat files from secure urls (ones that starts with
https:). (This is not true with the
read.table family of functions from base R.) One example where it is common to find flat files on secure sites is on GitHub. If you find a file with a flat file extension in a GitHub repository, you can usually click on it and then choose to view the “Raw” version of the file, and get to the flat file version of the file.
For example, the CDC Epidemic Prediction Initiative has a GitHub repository with data on Zika cases, including files on cases in Brazil. When we wrote this, the most current file was available here, with the raw version (i.e., a flat file) available by clicking the “Raw” button on the top right of the first site.
zika_file <- paste0("https://raw.githubusercontent.com/cdcepi/zika/master/", "Brazil/COES_Microcephaly/data/COES_Microcephaly-2016-06-25.csv") zika_brazil <- read_csv(zika_file) zika_brazil %>% select(location, value, unit)
# A tibble: 210 x 3 location value unit <chr> <int> <chr> 1 Brazil-Acre 2 cases 2 Brazil-Alagoas 75 cases 3 Brazil-Amapa 7 cases 4 Brazil-Amazonas 8 cases 5 Brazil-Bahia 263 cases 6 Brazil-Ceara 124 cases 7 Brazil-Distrito_Federal 5 cases 8 Brazil-Espirito_Santo 13 cases 9 Brazil-Goias 14 cases 10 Brazil-Maranhao 131 cases # ... with 200 more rows
1.4.2 Requesting data through a web API
Web APIs are growing in popularity as a way to access open data from government agencies, companies, and other organizations. “API” stands for “Application Program Interface”“; an API provides the rules for software applications to interact. In the case of open data APIs, they provide the rules you need to know to write R code to request and pull data from the organization’s web server into your R session. Usually, some of the computational burden of querying and subsetting the data is taken on by the source’s server, to create the subset of requested data to pass to your computer. In practice, this means you can often pull the subset of data you want from a very large available dataset without having to download the full dataset and load it locally into your R session.
As an overview, the basic steps for accessing and using data from a web API when working in R are:
- Figure out the API rules for HTTP requests
- Write R code to create a request in the proper format
- Send the request using GET or POST HTTP methods
- Once you get back data from the request, parse it into an easier-to-use format if necessary
To get the data from an API, you should first read the organization’s API documentation. An organization will post details on what data is available through their API(s), as well as how to set up HTTP requests to get that data– to request the data through the API, you will typically need to send the organization’s web server an HTTP request using a GET or POST method. The API documentation details will typically show an example GET or POST request for the API, including the base URL to use and the possible query parameters that can be used to customize the dataset request.
For example, the National Aeronautics and Space Administration (NASA) has an API for pulling the Astronomy Picture of the Day. In their API documentation, they specify that the base URL for the API request should be “https://api.nasa.gov/planetary/apod” and that you can include parameters to specify the date of the daily picture you want, whether to pull a high-resolution version of the picture, and a NOAA API key you have requested from NOAA.
Many organizations will require you to get an API key and use this key in each of your API requests. This key allows the organization to control API access, including enforcing rate limits per user. API rate limits restrict how often you can request data (e.g., an hourly limit of 1,000 requests per user for NASA APIs).
API keys should be kept private, so if you are writing code that includes an API key, be very careful not to include the actual key in any code made public (including any code in public GitHub repositories). One way to do this is to save the value of your key in a file named
.Renviron in your home directory. This file should be a plain text file and must end in a blank line. Once you’ve saved your API key to a global variable in that file (e.g., with a line added to the
.Renviron file like
NOAA_API_KEY="abdafjsiopnab038"), you can assign the key value to an R object in an R session using the
Sys.getenv function (e.g.,
noaa_api_key <- Sys.getenv("NOAA_API_KEY")), and then use this object (
noaa_api_key) anywhere you would otherwise have used the character string with your API key.
To find more R packages for accessing and exploring open data, check out the Open Data CRAN task view. You can also browse through the ROpenSci packages, all of which have GitHub repositories where you can further explore how each package works. ROpenSci is an organization with the mission to create open software tools for science. If you create your own package to access data relevant to scientific research through an API, consider submitting it for peer-review through ROpenSci.
riem package, developed by Maelle Salmon and an ROpenSci package, is an excellent and straightforward example of how you can use R to pull open data through a web API. This package allows you to pull weather data from airports around the world directly from the Iowa Environmental Mesonet. To show you how to pull data into R through an API, in this section we will walk you through code in the
riem package or code based closely on code in the package.
To get a certain set of weather data from the Iowa Environmental Mesonet, you can send an HTTP request specifying a base URL, “https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py/”, as well as some parameters describing the subset of dataset you want (e.g., date ranges, weather variables, output format). Once you know the rules for the names and possible values of these parameters (more on that below), you can submit an HTTP GET request using the
GET function from the
When you are making an HTTP request using the
POST functions from the
httr package, you can include the key-value pairs for any query parameters as a list object in the
query argurment of the function. For example, suppose you want to get wind speed in miles per hour (
data = "sped") for Denver, CO, (
station = "DEN") for the month of June 2016 (
year1 = "2016",
month1 = "6", etc.) in Denver’s local time zone (
tz = "America/Denver") and in a comma-separated file (
format = "comma"). To get this weather dataset, you can run:
library(httr) meso_url <- "https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py/" denver <- GET(url = meso_url, query = list(station = "DEN", data = "sped", year1 = "2016", month1 = "6", day1 = "1", year2 = "2016", month2 = "6", day2 = "30", tz = "America/Denver", format = "comma")) %>% content() %>% read_csv(skip = 5, na = "M") denver %>% slice(1:3)
# A tibble: 3 x 3 station valid sped <chr> <dttm> <dbl> 1 DEN 2016-06-01 00:00:00 9.2 2 DEN 2016-06-01 00:05:00 9.2 3 DEN 2016-06-01 00:10:00 6.9
content call in this code extracts the content from the response to the HTTP request sent by the
GET function. The Iowa Environmental Mesonet API offers the option to return the requested data in a comma-separated file (
format = “comma” in the GET request), so here
read_csv are used to extract and read in that csv file. Usually, data will be returned in a JSON format instead. We include more details later in this section on parsing data returned in a JSON format.
The only tricky part of this process is figuring out the available parameter names (e.g.,
station) and possible values for each (e.g.,
"DEN" for Denver). Currently, the details you can send in an HTTP request through Iowa Environmental Mesonet’s API include:
- A four-character weather station identifier (
- The weather variables (e.g., temperature, wind speed) to include (
- Starting and ending dates describing the range for which you’d like to pull data (
- The time zone to use for date-times for the weather observations (
- Different formatting options (e.g., delimiter to use in the resulting data file [
format], whether to include longitude and latitude)
Typically, these parameter names and possible values are explained in the API documentation. In some cases, however, the documentation will be limited. In that case, you may be able to figure out possible values, especially if the API specifies a GET rather than POST method, by playing around with the website’s point-and-click interface and then looking at the url for the resulting data pages. For example, if you look at the Iowa Environmental Mesonet’s page for accessing this data, you’ll notice that the point-and-click web interface allows you the options in the list above, and if you click through to access a dataset using this interface, the web address of the data page includes these parameter names and values.
riem package implements all these ideas in three very clean and straightforward functions. You can explore the code behind this package and see how these ideas can be incorporated into a small R package, in the
/R directory of the package’s GitHub page.
R packages already exist for many open data APIs. If an R package already exists for an API, you can use functions from that package directly, rather than writing your own code using the API protocols and
httr functions. Other examples of existing R packages to interact with open data APIs include:
rnoaa: National Oceanic and Atmospheric Administration
Quandl: Quandl (financial data)
RGoogleAnalytics: Google Analytics
acs: United States Census
wbstats: World Bank
rdian: The Guardian Media Group
blsAPI: Bureau of Labor Statistics
rtimes: New York Times
waterData: United States Geological Survey
If an R package doesn’t exist for an open API and you’d like to write your own package, find out more about writing API packages with this vignette for the httr package. This document includes advice on error handling within R code that accesses data through an open API.
1.4.3 Scraping web data
You can also use R to pull and clean web-based data that is not accessible through a web API or as an online flat file. In this case, the strategy will often be to pull in the full web page file (often in HTML or XML) and then parse or clean it within R.
rvest package is a good entry point for handling more complex collection and cleaning of web-based data. This package includes functions, for example, that allow you to select certain elements from the code for a web page (e.g., using the
xml_node functions), to parse tables in an HTML document into R data frames (
html_table), and to parse, fill out, and submit HTML forms (
submit_form). Further details on web scraping with R are beyond the scope of this course, but if you’re interested, you can find out more through the rvest GitHub README.
1.4.4 Parsing JSON, XML, or HTML data
Often, data collected from the web, including the data returned from an open API or obtained by scraping a web page, will be in JSON, XML, or HTML format. To use data in a JSON, XML, or HTML format in R, you need to parse the file from its current format and convert it into an R object more useful for analysis.
Typically, JSON-, XML-, or HTML-formatted data is parsed into a list in R, since list objects allow for a lot of flexibility in the structure of the data. However, if the data is structured appropriately, you can often parse data into another type of object (a data frame, for example, if the data fits well into a two-dimensional format of rows and columns). If the data structure of the data that you are pulling in is complex but consistent across different observations, you may alternatively want to create a custom object type to parse the data into.
There are a number of packages for parsing data from these formats, including
xml2. To find out more about parsing data from typical web formats, and for more on working with web-based documents and data, see the CRAN task view for Web Technologies and Services