The jsonlite package is a JSON 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 JSON format.
One of the main strengths of jsonlite is that it implements a bidirectional mapping 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:
library(jsonlite)
mydata <- fromJSON("http://projects.propublica.org/forensics/geos.json", flatten = TRUE)
View(mydata)
The mydata object is a data frame which can be used directly for modeling or visualization, without the need for any further complicated data manipulation.
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 page parameter in the API. Below an example from the ProPublica Nonprofit Explorer API where we retrieve the first 3 pages of tax-exempt organizations in the USA, ordered by revenue:
baseurl <- "http://projects.propublica.org/nonprofits/api/v1/search.json?order=revenue&sort_order=desc"
mydata0 <- fromJSON(paste0(baseurl, "&page=0"), flatten = TRUE)
mydata1 <- fromJSON(paste0(baseurl, "&page=1"), flatten = TRUE)
mydata2 <- fromJSON(paste0(baseurl, "&page=2"), flatten = TRUE)
#The actual data is in the filings element
mydata0$filings[1:10, c("organization.sub_name", "organization.city", "totrevenue")]
organization.sub_name organization.city totrevenue
1 KAISER FOUNDATION HEALTH PLAN INC PORTLAND 4.015e+10
2 KAISER FOUNDATION HEALTH PLAN INC PORTLAND 3.779e+10
3 KAISER FOUNDATION HOSPITALS PORTLAND 1.854e+10
4 KAISER FOUNDATION HOSPITALS PORTLAND 1.798e+10
5 PARTNERS HEALTHCARE SYSTEM INC BOSTON 1.045e+10
6 PARTNERS HEALTHCARE SYSTEM INC BOSTON 9.637e+09
7 DIGNITY HEALTH PHOENIX 9.212e+09
8 THRIVENT FINANCIAL FOR LUTHERANS MINNEAPOLIS 8.507e+09
9 UPMC GROUP PITTSBURGH 7.698e+09
10 CLEVELAND CLINIC FOUNDATION CLEVELAND 7.011e+09
To analyze or visualize these data, we need to combine the pages into a single dataset. We can do this with the rbind.pages function. Note that in this example, the actual data is contained by the filings field:
#Rows per data frame
nrow(mydata0$filings)
[1] 25
#Combine data frames
filings <- rbind.pages(
list(mydata0$filings, mydata1$filings, mydata2$filings)
)
#Total number of rows
nrow(filings)
[1] 75
We can write a simple loop that automatically downloads and combines many pages. For example to retrieve the first 20 pages with non-profits from the example above:
#store all pages in a list first
baseurl <- "http://projects.propublica.org/nonprofits/api/v1/search.json?order=revenue&sort_order=desc"
pages <- list()
for(i in 0:20){
mydata <- fromJSON(paste0(baseurl, "&page=", i))
message("Retrieving page ", i)
pages[[i+1]] <- mydata$filings
}
#combine all into one
filings <- rbind.pages(pages)
#check output
nrow(filings)
[1] 525
colnames(filings)
[1] "tax_prd" "tax_prd_yr"
[3] "formtype" "pdf_url"
[5] "updated" "totrevenue"
[7] "totfuncexpns" "totassetsend"
[9] "totliabend" "pct_compnsatncurrofcr"
[11] "tax_pd" "subseccd"
[13] "unrelbusinccd" "initiationfees"
[15] "grsrcptspublicuse" "grsincmembers"
[17] "grsincother" "totcntrbgfts"
[19] "totprgmrevnue" "invstmntinc"
[21] "txexmptbndsproceeds" "royaltsinc"
[23] "grsrntsreal" "grsrntsprsnl"
[25] "rntlexpnsreal" "rntlexpnsprsnl"
[27] "rntlincreal" "rntlincprsnl"
[29] "netrntlinc" "grsalesecur"
[31] "grsalesothr" "cstbasisecur"
[33] "cstbasisothr" "gnlsecur"
[35] "gnlsothr" "netgnls"
[37] "grsincfndrsng" "lessdirfndrsng"
[39] "netincfndrsng" "grsincgaming"
[41] "lessdirgaming" "netincgaming"
[43] "grsalesinvent" "lesscstofgoods"
[45] "netincsales" "miscrevtot11e"
[47] "compnsatncurrofcr" "othrsalwages"
[49] "payrolltx" "profndraising"
[51] "txexmptbndsend" "secrdmrtgsend"
[53] "unsecurednotesend" "retainedearnend"
[55] "totnetassetend" "nonpfrea"
[57] "gftgrntsrcvd170" "txrevnuelevied170"
[59] "srvcsval170" "grsinc170"
[61] "grsrcptsrelated170" "totgftgrntrcvd509"
[63] "grsrcptsadmissn509" "txrevnuelevied509"
[65] "srvcsval509" "subtotsuppinc509"
[67] "totsupp509" "ein"
[69] "organization" "eostatus"
[71] "tax_yr" "operatingcd"
[73] "assetcdgen" "transinccd"
[75] "subcd" "grscontrgifts"
[77] "intrstrvnue" "dividndsamt"
[79] "totexcapgn" "totexcapls"
[81] "grsprofitbus" "otherincamt"
[83] "compofficers" "contrpdpbks"
[85] "totrcptperbks" "totexpnspbks"
[87] "excessrcpts" "totexpnsexempt"
[89] "netinvstinc" "totaxpyr"
[91] "adjnetinc" "invstgovtoblig"
[93] "invstcorpstk" "invstcorpbnd"
[95] "totinvstsec" "fairmrktvalamt"
[97] "undistribincyr" "cmpmininvstret"
[99] "sec4940notxcd" "sec4940redtxcd"
[101] "infleg" "contractncd"
[103] "claimstatcd" "propexchcd"
[105] "brwlndmnycd" "furngoodscd"
[107] "paidcmpncd" "trnsothasstscd"
[109] "agremkpaycd" "undistrinccd"
[111] "dirindirintcd" "invstjexmptcd"
[113] "propgndacd" "excesshldcd"
[115] "grntindivcd" "nchrtygrntcd"
[117] "nreligiouscd" "grsrents"
[119] "costsold" "totrcptnetinc"
[121] "trcptadjnetinc" "topradmnexpnsa"
[123] "topradmnexpnsb" "topradmnexpnsd"
[125] "totexpnsnetinc" "totexpnsadjnet"
[127] "othrcashamt" "mrtgloans"
[129] "othrinvstend" "fairmrktvaleoy"
[131] "mrtgnotespay" "tfundnworth"
[133] "invstexcisetx" "sect511tx"
[135] "subtitleatx" "esttaxcr"
[137] "txwithldsrc" "txpaidf2758"
[139] "erronbkupwthld" "estpnlty"
[141] "balduopt" "crelamt"
[143] "tfairmrktunuse" "distribamt"
[145] "adjnetinccola" "adjnetinccolb"
[147] "adjnetinccolc" "adjnetinccold"
[149] "adjnetinctot" "qlfydistriba"
[151] "qlfydistribb" "qlfydistribc"
[153] "qlfydistribd" "qlfydistribtot"
[155] "valassetscola" "valassetscolb"
[157] "valassetscolc" "valassetscold"
[159] "valassetstot" "qlfyasseta"
[161] "qlfyassetb" "qlfyassetc"
[163] "qlfyassetd" "qlfyassettot"
[165] "endwmntscola" "endwmntscolb"
[167] "endwmntscolc" "endwmntscold"
[169] "endwmntstot" "totsuprtcola"
[171] "totsuprtcolb" "totsuprtcolc"
[173] "totsuprtcold" "totsuprttot"
[175] "pubsuprtcola" "pubsuprtcolb"
[177] "pubsuprtcolc" "pubsuprtcold"
[179] "pubsuprttot" "grsinvstinca"
[181] "grsinvstincb" "grsinvstincc"
[183] "grsinvstincd" "grsinvstinctot"
From here, we can go straight to analyzing the filings data without any further tedious data manipulation.