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<meta name="date" content="2022-01-16" />
<title>Combining pages of JSON data with jsonlite</title>
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<div id="header">
<h1 class="title toc-ignore">Combining pages of JSON data with
jsonlite</h1>
<h4 class="date">2022-01-16</h4>
</div>
<p>The <a href="https://cran.r-project.org/package=jsonlite">jsonlite</a> package
is a <code>JSON</code> parser/generator for R which is optimized for
pipelines and web APIs. It is used by the OpenCPU system and many other
packages to get data in and out of R using the <code>JSON</code>
format.</p>
<div id="a-bidirectional-mapping" class="section level2">
<h2>A bidirectional mapping</h2>
<p>One of the main strengths of <code>jsonlite</code> is that it
implements a bidirectional <a href="https://arxiv.org/abs/1403.2805">mapping</a> between JSON and data
frames. Thereby it can convert nested collections of JSON records, as
they often appear on the web, immediately into the appropriate R
structure. For example to grab some data from ProPublica we can simply
use:</p>
<pre class="r"><code>library(jsonlite)
mydata &lt;- fromJSON(&quot;https://projects.propublica.org/forensics/geos.json&quot;, flatten = TRUE)
View(mydata)</code></pre>
<p>The <code>mydata</code> object is a data frame which can be used
directly for modeling or visualization, without the need for any further
complicated data manipulation.</p>
</div>
<div id="paging-with-jsonlite" class="section level2">
<h2>Paging with jsonlite</h2>
<p>A question that comes up frequently is how to combine pages of data.
Most web APIs limit the amount of data that can be retrieved per
request. If the client needs more data than what can fits in a single
request, it needs to break down the data into multiple requests that
each retrieve a fragment (page) of data, not unlike pages in a book. In
practice this is often implemented using a <code>page</code> parameter
in the API. Below an example from the <a href="https://projects.propublica.org/nonprofits/api">ProPublica
Nonprofit Explorer API</a> where we retrieve the first 3 pages of
tax-exempt organizations in the USA, ordered by revenue:</p>
<pre class="r"><code>baseurl &lt;- &quot;https://projects.propublica.org/nonprofits/api/v2/search.json?order=revenue&amp;sort_order=desc&quot;
mydata0 &lt;- fromJSON(paste0(baseurl, &quot;&amp;page=0&quot;), flatten = TRUE)
mydata1 &lt;- fromJSON(paste0(baseurl, &quot;&amp;page=1&quot;), flatten = TRUE)
mydata2 &lt;- fromJSON(paste0(baseurl, &quot;&amp;page=2&quot;), flatten = TRUE)
#The actual data is in the organizations element
mydata0$organizations[1:10, c(&quot;name&quot;, &quot;city&quot;, &quot;strein&quot;)]</code></pre>
<pre><code> name city strein
1 0 DEBT EDUCATION INC SANTA ROSA 46-4744976
2 0 TOLERANCE INC SUWANEE 27-2620044
3 00 MOVEMENT INC PENSACOLA 82-4704419
4 00006 LOCAL MEDIA 22-6062777
5 0003 POSTAL FAMILY CINCINNATI 31-0240910
6 0005 GA HEPHZIBAH 58-1514574
7 0005 WRIGHT-PATT CREDIT UNION BEAVERCREEK 31-0278870
8 0009 DE GREENWOOD 26-4507405
9 0011 CALIFORNIA REDWAY 36-4654777
10 00141 LOCAL MEDIA 94-0507697</code></pre>
<p>To analyze or visualize these data, we need to combine the pages into
a single dataset. We can do this with the <code>rbind_pages</code>
function. Note that in this example, the actual data is contained by the
<code>organizations</code> field:</p>
<pre class="r"><code>#Rows per data frame
nrow(mydata0$organizations)</code></pre>
<pre><code>[1] 100</code></pre>
<pre class="r"><code>#Combine data frames
organizations &lt;- rbind_pages(
list(mydata0$organizations, mydata1$organizations, mydata2$organizations)
)
#Total number of rows
nrow(organizations)</code></pre>
<pre><code>[1] 300</code></pre>
</div>
<div id="automatically-combining-many-pages" class="section level2">
<h2>Automatically combining many pages</h2>
<p>We can write a simple loop that automatically downloads and combines
many pages. For example to retrieve the first 20 pages with non-profits
from the example above:</p>
<pre class="r"><code>#store all pages in a list first
baseurl &lt;- &quot;https://projects.propublica.org/nonprofits/api/v2/search.json?order=revenue&amp;sort_order=desc&quot;
pages &lt;- list()
for(i in 0:20){
mydata &lt;- fromJSON(paste0(baseurl, &quot;&amp;page=&quot;, i))
message(&quot;Retrieving page &quot;, i)
pages[[i+1]] &lt;- mydata$organizations
}
#combine all into one
organizations &lt;- rbind_pages(pages)
#check output
nrow(organizations)</code></pre>
<pre><code>[1] 2100</code></pre>
<pre class="r"><code>colnames(organizations)</code></pre>
<pre><code> [1] &quot;ein&quot; &quot;strein&quot; &quot;name&quot; &quot;sub_name&quot; &quot;city&quot; &quot;state&quot; &quot;ntee_code&quot; &quot;raw_ntee_code&quot;
[9] &quot;subseccd&quot; &quot;has_subseccd&quot; &quot;have_filings&quot; &quot;have_extracts&quot; &quot;have_pdfs&quot; &quot;score&quot; </code></pre>
<p>From here, we can go straight to analyzing the organizations data
without any further tedious data manipulation.</p>
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