A general purpose R interface to Apache Solr
Stable version from CRAN
Or the development version from GitHub
Load
You can setup for a remote Solr instance or on your local machine.
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.
solr_search() returns only docs. A basic search:
conn$search(params = list(q = '*:*', rows = 2, fl = 'id'))
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0020843
#> 2 10.1371/journal.pone.0022257Search in specific fields with :
Search for word ecology in title and cell in the body
conn$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 Chasing Ecological Interactions
#> 3 Ecology's Big, Hot Idea
#> 4 Spatial Ecology of Bacteria at the Microscale in Soil
#> 5 Biofilm Formation As a Response to Ecological CompetitionWildcards
Search for word that starts with “cell” in the title field
conn$search(params = list(q = 'title:"cell*"', fl = 'title', rows = 5))
#> # A tibble: 5 x 1
#> title
#> <chr>
#> 1 Altered Development of NKT Cells, γδ T Cells, CD8 T Cells and NK Cells in a P…
#> 2 Cancer Stem Cell-Like Side Population Cells in Clear Cell Renal Cell Carcinom…
#> 3 Cell-Cell Contact Preserves Cell Viability via Plakoglobin
#> 4 Transgelin-2 in B-Cells Controls T-Cell Activation by Stabilizing T Cell - B …
#> 5 Tetherin Can Restrict Cell-Free and Cell-Cell Transmission of HIV from Primar…Proximity search
Search for words “sports” and “alcohol” within four words of each other
conn$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-Driv…
#> 2 Symmetric vs. Asymmetric Stem Cell Divisions: An Adaptation against Cancer?
#> 3 Phenotypic Evolutionary Models in Stem Cell Biology: Replacement, Quiescence,…Range searches
Search for articles with Twitter count between 5 and 10
conn$search(params = list(q = '*:*', fl = c('alm_twitterCount', 'id'), fq = 'alm_twitterCount:[5 TO 50]', rows = 10))
#> # A tibble: 10 x 2
#> id alm_twitterCount
#> <chr> <int>
#> 1 10.1371/journal.pone.0020913 14
#> 2 10.1371/journal.pone.0023176 12
#> 3 10.1371/journal.pone.0023286 16
#> 4 10.1371/journal.pone.0025390 32
#> 5 10.1371/journal.pone.0013199 24
#> 6 10.1371/journal.pone.0013196 5
#> 7 10.1371/journal.pone.0009019 11
#> 8 10.1371/journal.pone.0008993 6
#> 9 10.1371/journal.pone.0021336 18
#> 10 10.1371/journal.pone.0009042 18Boosts
Assign higher boost to title matches than to body matches (compare the two calls)
conn$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 Its …
#> 2 Virtual Reconstruction and Three-Dimensional Printing of Blood Cells as a Too…
#> 3 Globalization of Stem Cell Science: An Examination of Current and Past Collab…conn$search(params = list(q = 'title:"cell"^1.5 AND abstract:"science"', fl = 'title', rows = 3))
#> # A tibble: 3 x 1
#> title
#> <chr>
#> 1 I Want More and Better Cells! – An Outreach Project about Stem Cells and Its …
#> 2 Virtual Reconstruction and Three-Dimensional Printing of Blood Cells as a Too…
#> 3 Globalization of Stem Cell Science: An Examination of Current and Past Collab…solr_all() differs from solr_search() in that it allows specifying facets, mlt, groups, stats, etc, and returns all of those. It defaults to parsetype = "list" and wt="json", whereas solr_search() defaults to parsetype = "df" and wt="csv". solr_all() returns by default a list, whereas solr_search() by default returns a data.frame.
A basic search, just docs output
conn$all(params = list(q = '*:*', rows = 2, fl = 'id'))
#> $search
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0020843
#> 2 10.1371/journal.pone.0022257
#>
#> $facet
#> list()
#>
#> $high
#> # A tibble: 0 x 0
#>
#> $mlt
#> $mlt$docs
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0020843
#> 2 10.1371/journal.pone.0022257
#>
#> $mlt$mlt
#> list()
#>
#>
#> $group
#> numFound start id
#> 1 2349539 0 10.1371/journal.pone.0020843
#> 2 2349539 0 10.1371/journal.pone.0022257
#>
#> $stats
#> NULLGet docs, mlt, and stats output
conn$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 245661 0 10.1371/journal.pone.0001275
#> 2 245661 0 10.1371/journal.pbio.1002448
#>
#> $mlt$mlt$`10.1371/journal.pone.0059813`
#> # A tibble: 2 x 3
#> numFound start id
#> <int> <int> <chr>
#> 1 237548 0 10.1371/journal.pone.0210707
#> 2 237548 0 10.1371/journal.pone.0204749
#>
#>
#>
#> $group
#> numFound start id
#> 1 51895 0 10.1371/journal.pone.0001248
#> 2 51895 0 10.1371/journal.pone.0059813
#>
#> $stats
#> $stats$data
#> min max count missing sum sumOfSquares mean
#> counter_total_all 0 1532460 51895 0 324414287 1.533069e+13 6251.359
#> stddev
#> counter_total_all 16010.71
#>
#> $stats$facet
#> NULLconn$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 186815
#> 2 bird 20072
#>
#> $facet_fields
#> $facet_fields$journal
#> # A tibble: 9 x 2
#> term value
#> <fct> <fct>
#> 1 plos one 1946213
#> 2 plos genetics 72617
#> 3 plos pathogens 66100
#> 4 plos neglected tropical diseases 65251
#> 5 plos computational biology 59985
#> 6 plos biology 42060
#> 7 plos medicine 29915
#> 8 plos clinical trials 521
#> 9 plos medicin 9
#>
#>
#> $facet_pivot
#> NULL
#>
#> $facet_dates
#> NULL
#>
#> $facet_ranges
#> NULLout <- conn$stats(params = list(q = 'ecology', stats.field = c('counter_total_all', 'alm_twitterCount'), stats.facet = c('journal', 'volume')))out$data
#> min max count missing sum sumOfSquares mean
#> counter_total_all 0 1532460 51895 0 324414287 1.533069e+13 6251.359225
#> alm_twitterCount 0 3439 51895 0 308886 8.194604e+07 5.952134
#> stddev
#> counter_total_all 16010.71374
#> alm_twitterCount 39.28964out$facet
#> $counter_total_all
#> $counter_total_all$volume
#> # A tibble: 18 x 9
#> volume min max count missing sum sumOfSquares mean stddev
#> <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 11 0 300326 5264 0 26131455 498787135805 4964. 8374.
#> 2 12 0 637916 5078 0 23045890 754959012682 4538. 11318.
#> 3 13 0 187650 4772 0 14469852 185727513548 3032. 5453.
#> 4 14 0 202041 4117 0 8965786 135038722582 2178. 5298.
#> 5 15 0 56955 1558 0 3703520 44446597378 2377. 4785.
#> 6 16 0 51981 278 0 1639028 23503754454 5896. 7069.
#> 7 17 0 64350 145 0 957264 12461694744 6602. 6531.
#> 8 18 10 10293 25 0 87761 452935251 3510. 2457.
#> 9 1 2201 394613 81 0 2136280 281140741696 26374. 53009.
#> 10 2 0 160743 483 0 7874872 316645622198 16304. 19763.
#> 11 3 0 140263 743 0 10322036 340678112084 13892. 16306.
#> 12 4 0 421769 1010 0 13320977 619882873519 13189. 20982.
#> 13 5 0 261787 1539 0 17578591 518230843883 11422. 14367.
#> 14 6 0 421511 2949 0 27083643 844617111025 9184. 14217.
#> 15 7 0 337925 4826 0 39377491 1060307559235 8159. 12376.
#> 16 8 0 662892 6363 0 46269702 1567973982796 7272. 13913.
#> 17 9 0 1532460 6622 0 45978003 4777504680537 6943. 25949.
#> 18 10 0 1245813 6042 0 35472136 3348332631936 5871. 22799.
#>
#> $counter_total_all$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 1443 227764 1249 0 9827787 251663612483 7869. 11819.
#> 2 2 0 429864 1329 0 24811193 1527110853031 18669. 28304.
#> 3 3 0 337925 355 0 7588181 509085551899 21375. 31303.
#> 4 4 9559 19071 2 0 28630 455077522 14315 6726.
#> 5 5 0 1245813 43312 0 236610221 9778305707321 5463. 13997.
#> 6 6 0 181861 957 0 9440164 202232267528 9864. 10683.
#> 7 7 0 163569 1153 0 11587785 234170621039 10050. 10108.
#> 8 8 0 343133 2440 0 14360560 292773478918 5885. 9240.
#> 9 9 0 1532460 1098 0 10159766 2534894355612 9253. 47170.
#>
#>
#> $alm_twitterCount
#> $alm_twitterCount$volume
#> # A tibble: 18 x 9
#> volume min max count missing sum sumOfSquares mean stddev
#> <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 11 0 2142 5264 0 52454 11452818 9.96 45.6
#> 2 12 0 1890 5078 0 39451 11059295 7.77 46.0
#> 3 13 0 581 4772 0 17551 2521623 3.68 22.7
#> 4 14 0 984 4117 0 15341 4027537 3.73 31.1
#> 5 15 0 453 1558 0 6235 912739 4.00 23.9
#> 6 16 0 456 278 0 2853 414803 10.3 37.3
#> 7 17 0 44 145 0 224 5628 1.54 6.06
#> 8 18 0 3 25 0 5 11 0.2 0.645
#> 9 1 0 47 81 0 208 6306 2.57 8.49
#> 10 2 0 125 483 0 1119 63677 2.32 11.3
#> 11 3 0 504 743 0 1408 271870 1.90 19.0
#> 12 4 0 313 1010 0 1658 155910 1.64 12.3
#> 13 5 0 165 1539 0 2698 142098 1.75 9.45
#> 14 6 0 972 2949 0 5733 1647077 1.94 23.6
#> 15 7 0 864 4826 0 21873 2587889 4.53 22.7
#> 16 8 0 2029 6363 0 40730 10857672 6.40 40.8
#> 17 9 0 1880 6622 0 55312 17034646 8.35 50.0
#> 18 10 0 3439 6042 0 44033 18784443 7.29 55.3
#>
#> $alm_twitterCount$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 0 456 1249 0 7370 577214 5.90 20.7
#> 2 2 0 2142 1329 0 39113 15093463 29.4 102.
#> 3 3 0 832 355 0 5830 1219358 16.4 56.3
#> 4 4 0 3 2 0 3 9 1.5 2.12
#> 5 5 0 3439 43312 0 216075 61407349 4.99 37.3
#> 6 6 0 250 957 0 8508 415112 8.89 18.8
#> 7 7 0 230 1153 0 9273 461699 8.04 18.3
#> 8 8 0 972 2440 0 11854 1563704 4.86 24.8
#> 9 9 0 581 1098 0 10860 1208134 9.89 31.7solr_mlt is a function to return similar documents to the one
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 25190
#> 2 10.1371/journal.pbio.1002559 13578
#> 3 10.1371/journal.pbio.0020440 26486
#> 4 10.1371/journal.pone.0087217 19956
#> 5 10.1371/journal.pbio.1002191 30074out$mlt
#> $`10.1371/journal.pbio.1001805`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 4900 0 10.1371/journal.pone.0098876 4302
#> 2 4900 0 10.1371/journal.pone.0082578 3542
#> 3 4900 0 10.1371/journal.pone.0193049 2661
#> 4 4900 0 10.1371/journal.pone.0102159 2687
#> 5 4900 0 10.1371/journal.pcbi.1002652 4687
#>
#> $`10.1371/journal.pbio.1002559`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 6461 0 10.1371/journal.pone.0155028 4235
#> 2 6461 0 10.1371/journal.pone.0041684 29479
#> 3 6461 0 10.1371/journal.pone.0023086 10177
#> 4 6461 0 10.1371/journal.pone.0155989 3893
#> 5 6461 0 10.1371/journal.pone.0223982 728
#>
#> $`10.1371/journal.pbio.0020440`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 1428 0 10.1371/journal.pone.0162651 3981
#> 2 1428 0 10.1371/journal.pone.0003259 3543
#> 3 1428 0 10.1371/journal.pone.0102679 5638
#> 4 1428 0 10.1371/journal.pone.0068814 10143
#> 5 1428 0 10.1371/journal.pntd.0003377 4835
#>
#> $`10.1371/journal.pone.0087217`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 5802 0 10.1371/journal.pone.0175497 2448
#> 2 5802 0 10.1371/journal.pone.0204743 377
#> 3 5802 0 10.1371/journal.pone.0159131 6664
#> 4 5802 0 10.1371/journal.pone.0220409 1224
#> 5 5802 0 10.1371/journal.pone.0123774 2454
#>
#> $`10.1371/journal.pbio.1002191`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 15002 0 10.1371/journal.pbio.1002232 0
#> 2 15002 0 10.1371/journal.pone.0131700 3824
#> 3 15002 0 10.1371/journal.pone.0070448 2713
#> 4 15002 0 10.1371/journal.pone.0191705 1971
#> 5 15002 0 10.1371/journal.pone.0160798 4387solr_groups() is a function to return similar documents to the one
conn$group(params = list(q = 'ecology', group.field = 'journal', group.limit = 1, fl = c('id', 'alm_twitterCount')))
#> groupValue numFound start id
#> 1 plos one 43312 0 10.1371/journal.pone.0001248
#> 2 plos computational biology 1098 0 10.1371/journal.pcbi.1003594
#> 3 plos biology 1329 0 10.1371/journal.pbio.0060300
#> 4 plos genetics 1153 0 10.1371/journal.pgen.1005860
#> 5 plos neglected tropical diseases 2440 0 10.1371/journal.pntd.0004689
#> 6 plos pathogens 957 0 10.1371/journal.ppat.1005780
#> 7 none 1249 0 10.1371/journal.pone.0046761
#> 8 plos medicine 355 0 10.1371/journal.pmed.1000303
#> 9 plos clinical trials 2 0 10.1371/journal.pctr.0020010
#> alm_twitterCount
#> 1 0
#> 2 21
#> 3 0
#> 4 135
#> 5 13
#> 6 19
#> 7 0
#> 8 1
#> 9 0solr_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:
(out <- conn$highlight(params = list(q = 'alcohol', hl.fl = 'abstract', rows = 2), raw = TRUE))
#> [1] "{\n \"response\":{\"numFound\":32898,\"start\":0,\"maxScore\":4.737952,\"docs\":[\n {\n \"id\":\"10.1371/journal.pone.0218147\",\n \"journal\":\"PLOS ONE\",\n \"eissn\":\"1932-6203\",\n \"publication_date\":\"2019-12-10T00:00:00Z\",\n \"article_type\":\"Research Article\",\n \"author_display\":[\"Victor M. Jimenez Jr.\",\n \"Erik W. Settles\",\n \"Bart J. Currie\",\n \"Paul S. Keim\",\n \"Fernando P. Monroy\"],\n \"abstract\":[\"Background: Binge drinking, an increasingly common form of alcohol use disorder, is associated with substantial morbidity and mortality; yet, its effects on the immune system’s ability to defend against infectious agents are poorly understood. Burkholderia pseudomallei, the causative agent of melioidosis can occur in healthy humans, yet binge alcohol intoxication is increasingly being recognized as a major risk factor. Although our previous studies demonstrated that binge alcohol exposure increased B. pseudomallei near-neighbor virulence in vivo and increased paracellular diffusion and intracellular invasion, no experimental studies have examined the extent to which bacterial and alcohol dosage play a role in disease progression. In addition, the temporal effects of a single binge alcohol dose prior to infection has not been examined in vivo. Principal findings: In this study, we used B. thailandensis E264 a close genetic relative of B. pseudomallei, as useful BSL-2 model system. Eight-week-old female C57BL/6 mice were utilized in three distinct animal models to address the effects of 1) bacterial dosage, 2) alcohol dosage, and 3) the temporal effects, of a single binge alcohol episode. Alcohol was administered comparable to human binge drinking (≤ 4.4 g/kg) or PBS intraperitoneally before a non-lethal intranasal infection. Bacterial colonization of lung and spleen was increased in mice administered alcohol even after bacterial dose was decreased 10-fold. Lung and not spleen tissue were colonized even after alcohol dosage was decreased 20 times below the U.S legal limit. Temporally, a single binge alcohol episode affected lung bacterial colonization for more than 24 h after alcohol was no longer detected in the blood. Pulmonary and splenic cytokine expression (TNF-α, GM-CSF) remained suppressed, while IL-12/p40 increased in mice administered alcohol 6 or 24 h prior to infection. Increased lung and not intestinal bacterial invasion was observed in human and murine non-phagocytic epithelial cells exposed to 0.2% v/v alcohol in vitro. Conclusions: Our results indicate that the effects of a single binge alcohol episode are tissue specific. A single binge alcohol intoxication event increases bacterial colonization in mouse lung tissue even after very low BACs and decreases the dose required to colonize the lungs with less virulent B. thailandensis. Additionally, the temporal effects of binge alcohol alters lung and spleen cytokine expression for at least 24 h after alcohol is detected in the blood. Delayed recovery in lung and not spleen tissue may provide a means for B. pseudomallei and near-neighbors to successfully colonize lung tissue through increased intracellular invasion of non-phagocytic cells in patients with hazardous alcohol intake. \"],\n \"title_display\":\"Persistence of <i>Burkholderia thailandensis</i> E264 in lung tissue after a single binge alcohol episode\",\n \"score\":4.737952},\n {\n \"id\":\"10.1371/journal.pone.0138021\",\n \"journal\":\"PLOS ONE\",\n \"eissn\":\"1932-6203\",\n \"publication_date\":\"2015-09-16T00:00:00Z\",\n \"article_type\":\"Research Article\",\n \"author_display\":[\"Pavel Grigoriev\",\n \"Evgeny M. Andreev\"],\n \"abstract\":[\"Background and Aim: Harmful alcohol consumption has long been recognized as being the major determinant of male premature mortality in the European countries of the former USSR. Our focus here is on Belarus and Russia, two Slavic countries which continue to suffer enormously from the burden of the harmful consumption of alcohol. However, after a long period of deterioration, mortality trends in these countries have been improving over the past decade. We aim to investigate to what extent the recent declines in adult mortality in Belarus and Russia are attributable to the anti-alcohol measures introduced in these two countries in the 2000s. Data and Methods: We rely on the detailed cause-specific mortality series for the period 1980–2013. Our analysis focuses on the male population, and considers only a limited number of causes of death which we label as being alcohol-related: accidental poisoning by alcohol, liver cirrhosis, ischemic heart diseases, stroke, transportation accidents, and other external causes. For each of these causes we computed age-standardized death rates. The life table decomposition method was used to determine the age groups and the causes of death responsible for changes in life expectancy over time. Conclusion: Our results do not lead us to conclude that the schedule of anti-alcohol measures corresponds to the schedule of mortality changes. The continuous reduction in adult male mortality seen in Belarus and Russia cannot be fully explained by the anti-alcohol policies implemented in these countries, although these policies likely contributed to the large mortality reductions observed in Belarus and Russia in 2005–2006 and in Belarus in 2012. Thus, the effects of these policies appear to have been modest. We argue that the anti-alcohol measures implemented in Belarus and Russia simply coincided with fluctuations in alcohol-related mortality which originated in the past. If these trends had not been underway already, these huge mortality effects would not have occurred. \"],\n \"title_display\":\"The Huge Reduction in Adult Male Mortality in Belarus and Russia: Is It Attributable to Anti-Alcohol Measures?\",\n \"score\":4.7355423}]\n },\n \"highlighting\":{\n \"10.1371/journal.pone.0218147\":{\n \"abstract\":[\"Background: Binge drinking, an increasingly common form of <em>alcohol</em> use disorder, is associated\"]},\n \"10.1371/journal.pone.0138021\":{\n \"abstract\":[\"Background and Aim: Harmful <em>alcohol</em> consumption has long been recognized as being the major\"]}}}\n"
#> attr(,"class")
#> [1] "sr_high"
#> attr(,"wt")
#> [1] "json"Then parse