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|
<!--
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{Solr search}
%\VignetteEncoding{UTF-8}
-->
Solr search
===========
**A general purpose R interface to [Apache Solr](http://lucene.apache.org/solr/)**
## Solr info
+ [Solr home page](http://lucene.apache.org/solr/)
+ [Highlighting help](http://wiki.apache.org/solr/HighlightingParameters)
+ [Faceting help](http://wiki.apache.org/solr/SimpleFacetParameters)
+ [Install and Setup SOLR in OSX, including running Solr](http://risnandar.wordpress.com/2013/09/08/how-to-install-and-setup-apache-lucene-solr-in-osx/)
## Installation
Stable version from CRAN
```r
install.packages("solrium")
```
Or the development version from GitHub
```r
install.packages("devtools")
devtools::install_github("ropensci/solrium")
```
Load
```r
library("solrium")
```
## Setup connection
You can setup for a remote Solr instance or on your local machine.
```r
(conn <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL))
#> <Solr Client>
#> host: api.plos.org
#> path: search
#> port:
#> scheme: http
#> errors: simple
#> proxy:
```
## Rundown
`solr_search()` only returns the `docs` element of a Solr response body. If `docs` is
all you need, then this function will do the job. If you need facet data only, or mlt
data only, see the appropriate functions for each of those below. Another function,
`solr_all()` has a similar interface in terms of parameter as `solr_search()`, but
returns all parts of the response body, including, facets, mlt, groups, stats, etc.
as long as you request those.
## Search docs
`solr_search()` returns only docs. A basic search:
```r
conn$search(params = list(q = '*:*', rows = 2, fl = 'id'))
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0058099/materials_and_methods
#> 2 10.1371/journal.pone.0030394/introduction
```
__Search in specific fields with `:`__
Search for word ecology in title and cell in the body
```r
conn$search(params = list(q = 'title:"ecology" AND body:"cell"', fl = 'title', rows = 5))
#> # A tibble: 5 x 1
#> title
#> <chr>
#> 1 The Ecology of Collective Behavior
#> 2 Ecology's Big, Hot Idea
#> 3 Chasing Ecological Interactions
#> 4 Spatial Ecology of Bacteria at the Microscale in Soil
#> 5 Biofilm Formation As a Response to Ecological Competition
```
__Wildcards__
Search for word that starts with "cell" in the title field
```r
conn$search(params = list(q = 'title:"cell*"', fl = 'title', rows = 5))
#> # A tibble: 5 x 1
#> title
#> <chr>
#> 1 Cancer Stem Cell-Like Side Population Cells in Clear Cell Renal Cell Car…
#> 2 Tumor Cell Recognition Efficiency by T Cells
#> 3 Enhancement of Chemotactic Cell Aggregation by Haptotactic Cell-To-Cell …
#> 4 Cell-Cell Contact Preserves Cell Viability via Plakoglobin
#> 5 Dcas Supports Cell Polarization and Cell-Cell Adhesion Complexes in Deve…
```
__Proximity search__
Search for words "sports" and "alcohol" within four words of each other
```r
conn$search(params = list(q = 'everything:"stem cell"~7', fl = 'title', rows = 3))
#> # A tibble: 3 x 1
#> title
#> <chr>
#> 1 Effect of Dedifferentiation on Time to Mutation Acquisition in Stem Cell…
#> 2 A Mathematical Model of Cancer Stem Cell Driven Tumor Initiation: Implic…
#> 3 Phenotypic Evolutionary Models in Stem Cell Biology: Replacement, Quiesc…
```
__Range searches__
Search for articles with Twitter count between 5 and 10
```r
conn$search(params = list(q = '*:*', fl = c('alm_twitterCount', 'id'), fq = 'alm_twitterCount:[5 TO 50]', rows = 10))
#> # A tibble: 10 x 2
#> id alm_twitterCount
#> <chr> <int>
#> 1 10.1371/journal.pbio.0030378/title 8
#> 2 10.1371/journal.pbio.0030378/abstract 8
#> 3 10.1371/journal.pbio.0030378/references 8
#> 4 10.1371/journal.pone.0184491 10
#> 5 10.1371/journal.pone.0184491/title 10
#> 6 10.1371/journal.pone.0184491/abstract 10
#> 7 10.1371/journal.pone.0184491/references 10
#> 8 10.1371/journal.pone.0184491/body 10
#> 9 10.1371/journal.pone.0184491/introduction 10
#> 10 10.1371/journal.pone.0184491/results_and_discussion 10
```
__Boosts__
Assign higher boost to title matches than to body matches (compare the two calls)
```r
conn$search(params = list(q = 'title:"cell" abstract:"science"', fl = 'title', rows = 3))
#> # A tibble: 3 x 1
#> title
#> <chr>
#> 1 I Want More and Better Cells! – An Outreach Project about Stem Cells and…
#> 2 Globalization of Stem Cell Science: An Examination of Current and Past C…
#> 3 Virtual Reconstruction and Three-Dimensional Printing of Blood Cells as …
```
```r
conn$search(params = list(q = 'title:"cell"^1.5 AND abstract:"science"', fl = 'title', rows = 3))
#> # A tibble: 3 x 1
#> title
#> <chr>
#> 1 I Want More and Better Cells! – An Outreach Project about Stem Cells and…
#> 2 Virtual Reconstruction and Three-Dimensional Printing of Blood Cells as …
#> 3 Globalization of Stem Cell Science: An Examination of Current and Past C…
```
## Search all
`solr_all()` differs from `solr_search()` in that it allows specifying facets, mlt, groups,
stats, etc, and returns all of those. It defaults to `parsetype = "list"` and `wt="json"`,
whereas `solr_search()` defaults to `parsetype = "df"` and `wt="csv"`. `solr_all()` returns
by default a list, whereas `solr_search()` by default returns a data.frame.
A basic search, just docs output
```r
conn$all(params = list(q = '*:*', rows = 2, fl = 'id'))
#> $search
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0058099/materials_and_methods
#> 2 10.1371/journal.pone.0030394/introduction
#>
#> $facet
#> list()
#>
#> $high
#> # A tibble: 0 x 0
#>
#> $mlt
#> $mlt$docs
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0058099/materials_and_methods
#> 2 10.1371/journal.pone.0030394/introduction
#>
#> $mlt$mlt
#> list()
#>
#>
#> $group
#> numFound start id
#> 1 2113280 0 10.1371/journal.pone.0058099/materials_and_methods
#> 2 2113280 0 10.1371/journal.pone.0030394/introduction
#>
#> $stats
#> NULL
```
Get docs, mlt, and stats output
```r
conn$all(params = list(q = 'ecology', rows = 2, fl = 'id', mlt = 'true', mlt.count = 2, mlt.fl = 'abstract', stats = 'true', stats.field = 'counter_total_all'))
#> $search
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0001248
#> 2 10.1371/journal.pone.0059813
#>
#> $facet
#> list()
#>
#> $high
#> # A tibble: 0 x 0
#>
#> $mlt
#> $mlt$docs
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0001248
#> 2 10.1371/journal.pone.0059813
#>
#> $mlt$mlt
#> $mlt$mlt$`10.1371/journal.pone.0001248`
#> # A tibble: 2 x 3
#> numFound start id
#> <int> <int> <chr>
#> 1 221302 0 10.1371/journal.pbio.1002448
#> 2 221302 0 10.1371/journal.pone.0155843
#>
#> $mlt$mlt$`10.1371/journal.pone.0059813`
#> # A tibble: 2 x 3
#> numFound start id
#> <int> <int> <chr>
#> 1 213642 0 10.1371/journal.pone.0204749
#> 2 213642 0 10.1371/journal.pone.0175014
#>
#>
#>
#> $group
#> numFound start id
#> 1 45873 0 10.1371/journal.pone.0001248
#> 2 45873 0 10.1371/journal.pone.0059813
#>
#> $stats
#> $stats$data
#> min max count missing sum sumOfSquares
#> counter_total_all 0 1113107 45873 0 244364194 9.57388e+12
#> mean stddev
#> counter_total_all 5326.972 13428.75
#>
#> $stats$facet
#> NULL
```
## Facet
```r
conn$facet(params = list(q = '*:*', facet.field = 'journal', facet.query = c('cell', 'bird')))
#> $facet_queries
#> # A tibble: 2 x 2
#> term value
#> <chr> <int>
#> 1 cell 171895
#> 2 bird 18193
#>
#> $facet_fields
#> $facet_fields$journal
#> # A tibble: 9 x 2
#> term value
#> <fct> <fct>
#> 1 plos one 1754116
#> 2 plos genetics 65482
#> 3 plos pathogens 58879
#> 4 plos neglected tropical diseases 55287
#> 5 plos computational biology 51118
#> 6 plos biology 36181
#> 7 plos medicine 25913
#> 8 plos clinical trials 521
#> 9 plos medicin 9
#>
#>
#> $facet_pivot
#> NULL
#>
#> $facet_dates
#> NULL
#>
#> $facet_ranges
#> NULL
```
## Highlight
```r
conn$highlight(params = list(q = 'alcohol', hl.fl = 'abstract', rows = 2))
#> # A tibble: 2 x 2
#> names abstract
#> <chr> <chr>
#> 1 10.1371/journal.pone… "\nAcute <em>alcohol</em> administration can lead …
#> 2 10.1371/journal.pone… Objectives: <em>Alcohol</em>-related morbidity and…
```
## Stats
```r
out <- conn$stats(params = list(q = 'ecology', stats.field = c('counter_total_all', 'alm_twitterCount'), stats.facet = c('journal', 'volume')))
```
```r
out$data
#> min max count missing sum sumOfSquares
#> counter_total_all 0 1113107 45873 0 244364194 9.57388e+12
#> alm_twitterCount 0 3437 45873 0 297821 7.89253e+07
#> mean stddev
#> counter_total_all 5326.972162 13428.74994
#> alm_twitterCount 6.492294 40.96833
```
```r
out$facet
#> $counter_total_all
#> $counter_total_all$volume
#> # A tibble: 16 x 9
#> volume min max count missing sum sumOfSquares mean stddev
#> <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 11 0 287793 5264 0 18005610 316118998184 3421. 6954.
#> 2 12 0 516798 5066 0 12958645 425618775537 2558. 8803.
#> 3 13 0 133733 4271 0 4607026 65316779956 1079. 3759.
#> 4 14 0 104122 389 0 1869920 33268717178 4807. 7911.
#> 5 15 0 45500 111 0 933611 15842926657 8411. 8523.
#> 6 16 0 18492 124 0 352043 2210563125 2839. 3138.
#> 7 1 2053 264368 81 0 1836798 163415872138 22677. 39013.
#> 8 2 2100 150921 482 0 7149303 253950042209 14833. 17536.
#> 9 3 1588 136426 741 0 9458178 286252040290 12764. 14956.
#> 10 4 866 399549 1010 0 12097742 517132809490 11978. 19207.
#> 11 5 125 218447 1539 0 15724071 402125818643 10217. 12530.
#> 12 6 95 396324 2948 0 23618894 670045150068 8012. 12773.
#> 13 7 62 222011 4825 0 33643746 758715844502 6973. 10424.
#> 14 8 34 579345 6360 0 38939478 1162223832632 6123. 12053.
#> 15 9 57 1113107 6620 0 36705411 2774620446321 5545. 19709.
#> 16 10 428 878333 6042 0 26463718 1727021174586 4380. 16331.
#>
#> $counter_total_all$journal
#> # A tibble: 9 x 9
#> journal min max count missing sum sumOfSquares mean stddev
#> <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1226 216898 1249 0 8518485 211216111507 6820. 11077.
#> 2 2 0 344164 1157 0 19856612 1017916307738 17162. 24202.
#> 3 3 0 216934 311 0 6019228 336436112198 19354. 26636.
#> 4 4 9142 18132 2 0 27274 412345588 13637 6357.
#> 5 5 0 878333 38352 0 174567149 6106563200525 4552. 11769.
#> 6 6 0 137398 833 0 7591498 139525368974 9113. 9195.
#> 7 7 0 146147 1020 0 9497241 179862111621 9311. 9473.
#> 8 8 0 316872 2059 0 10721687 221814167179 5207. 8981.
#> 9 9 0 1113107 890 0 7565020 1360134066186 8500. 38179.
#>
#>
#> $alm_twitterCount
#> $alm_twitterCount$volume
#> # A tibble: 16 x 9
#> volume min max count missing sum sumOfSquares mean stddev
#> <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 11 0 2138 5264 0 52016 11337738 9.88 45.3
#> 2 12 0 1877 5066 0 38209 10648781 7.54 45.2
#> 3 13 0 565 4271 0 15400 2332646 3.61 23.1
#> 4 14 0 984 389 0 12925 3815533 33.2 93.4
#> 5 15 0 451 111 0 5168 868780 46.6 75.6
#> 6 16 0 170 124 0 1054 90480 8.5 25.7
#> 7 1 0 47 81 0 202 6176 2.49 8.42
#> 8 2 0 125 482 0 1105 62759 2.29 11.2
#> 9 3 0 503 741 0 1396 270608 1.88 19.0
#> 10 4 0 311 1010 0 1631 153541 1.61 12.2
#> 11 5 0 165 1539 0 2671 141665 1.74 9.44
#> 12 6 0 968 2948 0 5646 1629158 1.92 23.4
#> 13 7 0 860 4825 0 21690 2546258 4.50 22.5
#> 14 8 0 2029 6360 0 39800 9467280 6.26 38.1
#> 15 9 0 1880 6620 0 55061 16814671 8.32 49.7
#> 16 10 0 3437 6042 0 43847 18739223 7.26 55.2
#>
#> $alm_twitterCount$journal
#> # A tibble: 9 x 9
#> journal min max count missing sum sumOfSquares mean stddev
#> <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 451 1249 0 7345 569821 5.88 20.5
#> 2 2 0 2138 1157 0 36580 14375218 31.6 107.
#> 3 3 0 830 311 0 5529 1201645 17.8 59.7
#> 4 4 0 3 2 0 3 9 1.5 2.12
#> 5 5 0 3437 38352 0 209648 59279156 5.47 38.9
#> 6 6 0 248 833 0 8439 412835 10.1 19.8
#> 7 7 0 229 1020 0 8584 419352 8.42 18.5
#> 8 8 0 968 2059 0 11517 1543659 5.59 26.8
#> 9 9 0 558 890 0 10176 1123602 11.4 33.7
```
## More like this
`solr_mlt` is a function to return similar documents to the one
```r
out <- conn$mlt(params = list(q = 'title:"ecology" AND body:"cell"', mlt.fl = 'title', mlt.mindf = 1, mlt.mintf = 1, fl = 'counter_total_all', rows = 5))
out$docs
#> # A tibble: 5 x 2
#> id counter_total_all
#> <chr> <int>
#> 1 10.1371/journal.pbio.1001805 22378
#> 2 10.1371/journal.pbio.0020440 25560
#> 3 10.1371/journal.pbio.1002559 11150
#> 4 10.1371/journal.pone.0087217 11502
#> 5 10.1371/journal.pbio.1002191 24483
```
```r
out$mlt
#> $`10.1371/journal.pbio.1001805`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 4319 0 10.1371/journal.pone.0098876 3857
#> 2 4319 0 10.1371/journal.pone.0082578 3140
#> 3 4319 0 10.1371/journal.pone.0102159 2329
#> 4 4319 0 10.1371/journal.pcbi.1002915 11767
#> 5 4319 0 10.1371/journal.pcbi.1003408 10700
#>
#> $`10.1371/journal.pbio.0020440`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 1254 0 10.1371/journal.pone.0162651 3311
#> 2 1254 0 10.1371/journal.pone.0003259 3310
#> 3 1254 0 10.1371/journal.pntd.0003377 4406
#> 4 1254 0 10.1371/journal.pone.0068814 9416
#> 5 1254 0 10.1371/journal.pone.0101568 5600
#>
#> $`10.1371/journal.pbio.1002559`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 5962 0 10.1371/journal.pone.0023086 8442
#> 2 5962 0 10.1371/journal.pone.0041684 24475
#> 3 5962 0 10.1371/journal.pone.0155028 2662
#> 4 5962 0 10.1371/journal.pone.0155989 2519
#> 5 5962 0 10.1371/journal.pone.0129394 2111
#>
#> $`10.1371/journal.pone.0087217`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 5111 0 10.1371/journal.pone.0204743 0
#> 2 5111 0 10.1371/journal.pone.0175497 1088
#> 3 5111 0 10.1371/journal.pone.0159131 4937
#> 4 5111 0 10.1371/journal.pcbi.0020092 25551
#> 5 5111 0 10.1371/journal.pone.0133941 1336
#>
#> $`10.1371/journal.pbio.1002191`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 13747 0 10.1371/journal.pbio.1002232 3055
#> 2 13747 0 10.1371/journal.pone.0070448 2203
#> 3 13747 0 10.1371/journal.pone.0191705 800
#> 4 13747 0 10.1371/journal.pone.0131700 3051
#> 5 13747 0 10.1371/journal.pone.0121680 4980
```
## Groups
`solr_groups()` is a function to return similar documents to the one
```r
conn$group(params = list(q = 'ecology', group.field = 'journal', group.limit = 1, fl = c('id', 'alm_twitterCount')))
#> groupValue numFound start
#> 1 plos one 38352 0
#> 2 plos computational biology 890 0
#> 3 plos biology 1157 0
#> 4 plos neglected tropical diseases 2059 0
#> 5 plos pathogens 833 0
#> 6 plos genetics 1020 0
#> 7 none 1249 0
#> 8 plos medicine 311 0
#> 9 plos clinical trials 2 0
#> id alm_twitterCount
#> 1 10.1371/journal.pone.0001248 0
#> 2 10.1371/journal.pcbi.1003594 21
#> 3 10.1371/journal.pbio.0060300 0
#> 4 10.1371/journal.pntd.0004689 13
#> 5 10.1371/journal.ppat.1005780 19
#> 6 10.1371/journal.pgen.1005860 135
#> 7 10.1371/journal.pone.0043894 0
#> 8 10.1371/journal.pmed.1000303 1
#> 9 10.1371/journal.pctr.0020010 0
```
## Parsing
`solr_parse()` is a general purpose parser function with extension methods for parsing outputs from functions in `solr`. `solr_parse()` is used internally within functions to do parsing after retrieving data from the server. You can optionally get back raw `json`, `xml`, or `csv` with the `raw=TRUE`, and then parse afterwards with `solr_parse()`.
For example:
```r
(out <- conn$highlight(params = list(q = 'alcohol', hl.fl = 'abstract', rows = 2), raw = TRUE))
#> [1] "{\n \"response\":{\"numFound\":29215,\"start\":0,\"maxScore\":4.6769786,\"docs\":[\n {\n \"id\":\"10.1371/journal.pone.0201042\",\n \"journal\":\"PLOS ONE\",\n \"eissn\":\"1932-6203\",\n \"publication_date\":\"2018-07-26T00:00:00Z\",\n \"article_type\":\"Research Article\",\n \"author_display\":[\"Graeme Knibb\",\n \"Carl. A. Roberts\",\n \"Eric Robinson\",\n \"Abi Rose\",\n \"Paul Christiansen\"],\n \"abstract\":[\"\\nAcute alcohol administration can lead to a loss of control over drinking. Several models argue that this ‘alcohol priming effect’ is mediated by the effect of alcohol on inhibitory control. Alternatively, beliefs about how alcohol affects behavioural regulation may also underlie alcohol priming and alcohol-induced inhibitory impairments. Here two studies examine the extent to which the alcohol priming effect and inhibitory impairments are moderated by beliefs regarding the effects of alcohol on the ability to control behaviour. In study 1, following a priming drink (placebo or .5g/kg of alcohol), participants were provided with bogus feedback regarding their performance on a measure of inhibitory control (stop-signal task; SST) suggesting that they had high or average self-control. However, the bogus feedback manipulation was not successful. In study 2, before a SST, participants were exposed to a neutral or experimental message suggesting acute doses of alcohol reduce the urge to drink and consumed a priming drink and this manipulation was successful. In both studies craving was assessed throughout and a bogus taste test which measured ad libitum drinking was completed. Results suggest no effect of beliefs on craving or ad lib consumption within either study. However, within study 2, participants exposed to the experimental message displayed evidence of alcohol-induced impairments of inhibitory control, while those exposed to the neutral message did not. These findings do not suggest beliefs about the effects of alcohol moderate the alcohol priming effect but do suggest beliefs may, in part, underlie the effect of alcohol on inhibitory control.\\n\"],\n \"title_display\":\"The effect of beliefs about alcohol’s acute effects on alcohol priming and alcohol-induced impairments of inhibitory control\",\n \"score\":4.6769786},\n {\n \"id\":\"10.1371/journal.pone.0185457\",\n \"journal\":\"PLOS ONE\",\n \"eissn\":\"1932-6203\",\n \"publication_date\":\"2017-09-28T00:00:00Z\",\n \"article_type\":\"Research Article\",\n \"author_display\":[\"Jacqueline Willmore\",\n \"Terry-Lynne Marko\",\n \"Darcie Taing\",\n \"Hugues Sampasa-Kanyinga\"],\n \"abstract\":[\"Objectives: Alcohol-related morbidity and mortality are significant public health issues. The purpose of this study was to describe the prevalence and trends over time of alcohol consumption and alcohol-related morbidity and mortality; and public attitudes of alcohol use impacts on families and the community in Ottawa, Canada. Methods: Prevalence (2013–2014) and trends (2000–2001 to 2013–2014) of alcohol use were obtained from the Canadian Community Health Survey. Data on paramedic responses (2015), emergency department (ED) visits (2013–2015), hospitalizations (2013–2015) and deaths (2007–2011) were used to quantify the acute and chronic health effects of alcohol in Ottawa. Qualitative data were obtained from the “Have Your Say” alcohol survey, an online survey of public attitudes on alcohol conducted in 2016. Results: In 2013–2014, an estimated 595,300 (83%) Ottawa adults 19 years and older drank alcohol, 42% reported binge drinking in the past year. Heavy drinking increased from 15% in 2000–2001 to 20% in 2013–2014. In 2015, the Ottawa Paramedic Service responded to 2,060 calls directly attributable to alcohol. Between 2013 and 2015, there were an average of 6,100 ED visits and 1,270 hospitalizations per year due to alcohol. Annually, alcohol use results in at least 140 deaths in Ottawa. Men have higher rates of alcohol-attributable paramedic responses, ED visits, hospitalizations and deaths than women, and young adults have higher rates of alcohol-attributable paramedic responses. Qualitative data of public attitudes indicate that alcohol misuse has greater repercussions not only on those who drink, but also on the family and community. Conclusions: Results highlight the need for healthy public policy intended to encourage a culture of drinking in moderation in Ottawa to support lower risk alcohol use, particularly among men and young adults. \"],\n \"title_display\":\"The burden of alcohol-related morbidity and mortality in Ottawa, Canada\",\n \"score\":4.676675}]\n },\n \"highlighting\":{\n \"10.1371/journal.pone.0201042\":{\n \"abstract\":[\"\\nAcute <em>alcohol</em> administration can lead to a loss of control over drinking. Several models argue\"]},\n \"10.1371/journal.pone.0185457\":{\n \"abstract\":[\"Objectives: <em>Alcohol</em>-related morbidity and mortality are significant public health issues\"]}}}\n"
#> attr(,"class")
#> [1] "sr_high"
#> attr(,"wt")
#> [1] "json"
```
Then parse
```r
solr_parse(out, 'df')
#> # A tibble: 2 x 2
#> names abstract
#> <chr> <chr>
#> 1 10.1371/journal.pone… "\nAcute <em>alcohol</em> administration can lead …
#> 2 10.1371/journal.pone… Objectives: <em>Alcohol</em>-related morbidity and…
```
[Please report any issues or bugs](https://github.com/ropensci/solrium/issues).
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