useR!2017 poster

By David Selby and David Firth, Department of Statistics, University of Warwick.

A poster for the useR!2017 conference in Brussels.

Title

Ranking influential communities in networks

Abstract

Which scientific fields export the most intellectual influence, through recent research, to other fields? Citation behaviour varies greatly across disciplines, making inter-field comparisons difficult. Recent approaches to measuring influence in networks, based on the PageRank algorithm, take the source of citations (or recommendations or links) into account.

By aggregating all publications in each Web of Science field into "super-journals", we model the exchange of citations between fields. We then fit a Bradley–Terry paired comparisons model—which can be shown to be equivalent to scaled PageRank—to measure the relative influence of academic communities. Uncertainty intervals are provided to quantify the robustness of the ranking. All analyses were performed in R.

Which field is top of the table? Visit the poster to find out!

Keywords: PageRank, Bradley–Terry model, networks, ranking, bibliometrics

Design inspiration

Alberto Cairo's two-page spread on the Galileo telescope is a good basis for a layout. This features one huge diagram just left of centre, with an introductory paragraph on the far left, a self-contained series of fun facts along the bottom and a set of more detailed diagrams on the right. See also Giants of the Ocean.

See also the diagram in Figure 3 of Fast unfolding of communities in large networks by Blondel et al (2008), where we have a large node-link diagram with communities aggregated into nodes, then a 'zoom' on a selected community, represented as its constituent nodes and links.

Since useR!2017 specifies posters must be A0 in portrait orientation, we can't implement landscape layouts like those mentioned above. While using some of those ideas, we might consider Colin Purrington's results area design for portrait posters.

Another useful resource is the Better Posters blog.

A large dose of inspiration may also be drawn from Dorling Kindersley Eyewitness books.

Method

Unfortunately, some of the raw data is property of Thomson Reuters / Clarivate Analytics so I cannot republish it in this repository. Nonetheless, hopefully you can get a general idea of what I have done from the source code and results.

We will need the following packages.

library(igraph)
library(ggraph)

Data wrangling

First we load the 2013 Web of Science citation data into R.

WoS <- readRDS('../thesis/data/thomsonreuters.Rds')

We are only interested in citations from 2013 to publications from 2003–2012. Furthermore, some miscellaneous journals are unhelpfully classified as ALL OTHERS and we want to omit any reference to these as well. Citations of count (weight) equal to zero should be deleted to avoid connectivity problems later.

WoS <- data.frame(from = WoS$Citing, to = WoS$Cited, weight = WoS$AllYears - WoS$Earlier)
WoS <- WoS[- union(grep('ALL OTHERS', WoS$from), grep('ALL OTHERS', WoS$to)), ]
WoS <- subset(WoS, weight > 0)

It's important to put the citing journal in the first column, the cited journal second and call the citation counts weight, because of how the igraph function graph_from_data_frame works. We will now turn our data into an igraph object.

ig <- graph_from_data_frame(WoS)

We now have a weighted, directed graph.

Before we run our community detection algorithm, there is some other housekeeping to do. A number of journals either gave out or received zero citations during the study period. These will break the graph up into disconnected components, so we want to remove all these singletons.

Firstly, we calculate the strongly-connected components of the graph. To be strongly connected to the rest of the network, a journal must both cite and be cited by others.

strong <- components(ig, mode = 'strong')
core <- which(strong$csize > 1)
if (length(core) > 1) stop('There should be only one core component')

The whole graph contains 12,604 nodes (journals). Of these, 10,713 belong to our "core" strongly-connected component and 1,891 are singletons, either weakly connected or completely disconnected from the rest of the graph. Let's get rid of these singleton nodes.

ig <- induced_subgraph(ig, which(strong$membership == core))

We now have 10,713 journals in our network.

Super journals

We will put our directed, weighted graph through the Infomap algorithm, as implemented in the igraph package. Results can be nondeterministic, so we will fix the random seed for reproducible results. Community detection can take a long time, so you might want to cache this chunk!

set.seed(2017)
infomap <- cluster_infomap(ig)

The algorithm returns 112 communities; the largest contains 971 journals and the smallest contains 2. The mean community size is 96 journals.

The journals of community will be aggregated into a super-journal representing all incoming and outgoing citations for that community. Edge weights are summed and other edge and vertex attributes are ignored.

sj <- contract.vertices(ig, membership(infomap), 'ignore')
sj <- simplify(sj, remove.multiple = TRUE, remove.loops = TRUE,
               edge.attr.comb = list(weight = 'sum', 'ignore'))

For later reference, each super-journal will be assigned a unique ID.

V(sj)$name <- 1:vcount(sj)

Visualisation

A nice way to visualise these graphs is with the ggraph package. Each node will be represented by a point, proportional to its PageRank score, and each edge will be represented by an arc between these points.

There is a problem, however: ggraph, ggplot2 and our PDF reader won't like it if we try to plot 9,145,683 arcs in a single graphic!

(Another approach is to have a single arc for each pair of nodes, with opacity or width proportional to the number of citations, but this doesn't look very good in my opinion.)

Calculating the positions of nodes in our graphic will involve multidimensional scaling of the correlation matrix. Let's extract the weighted adjacency matrix, paying attention to the fact that igraph considers citations to travel from rows to columns.

xtab <- Matrix::t(as_adjacency_matrix(sj, attr = 'weight'))

We will scale the citations counts so no pair of nodes has more than 1000 arcs drawn between them on the final graphic.

small_xtab <- as.matrix(xtab)
diag(small_xtab) <- 0 # ignore self-citations
scalefactor <- 1000 / max(small_xtab)
small_xtab <- ceiling(small_xtab * scalefactor)

Then we can turn this back into an igraph object for visualisation. A useful attribute is the PageRank, which we can use to make nodes with greater total influence appear larger on the plot.

viz_ig <- graph_from_adjacency_matrix(t(small_xtab))
V(viz_ig)$PageRank <- page.rank(sj)$vector

Now let's perform multidimensional scaling to generate some coordinates.

my_mds <- create_layout(viz_ig,
                        layout = 'igraph',
                        algorithm = 'mds',
                        dist = 1 - cor(as.matrix(xtab)))

Let's make some plots!

ggraph(my_mds)  
  geom_edge_fan0(alpha = .01, colour = '#4F94CD')  
  geom_node_point(aes(size = PageRank), fill = '#4F94CD', pch = 21, colour = 'white')  
  coord_fixed()  
  scale_x_reverse()   # flip horizontally
  theme_graph()  
  theme(legend.position = 'none')

On its own, our visualisation does not imply much because we don't know which community is which. We need labels on the communities for that. I have gone through and manually assigned plausible labels to the communities generated by Infomap.

labels <- read.csv('data/cluster_names.csv', stringsAsFactors = FALSE)
V(viz_ig)$field <- V(sj)$field <- labels$field[match(V(viz_ig)$name, labels$community)]

We can then have a labelled plot for reference, or even show a selection of "interesting" labels on the main graph, while omitting most of them to avoid clutter.

# Hide plot labels for very small fields
my_mds$flabel <- ifelse(rank(my_mds$PageRank) > 32, as.character(my_mds$field), NA)

ggraph(my_mds)  
  geom_edge_fan0(alpha = .007, colour = 'tomato2')   #4F94CD #2D7B95
  geom_node_point(aes(size = PageRank), fill = 'tomato2', pch = 21, colour = 'white')  
  geom_node_label(aes(label = flabel),
                  size = 2,
                  repel = TRUE,
                  family = 'Gill Sans MT Condensed',
                  fontface = 'bold',
                  colour = 'tomato2',
                  # Label options
                  segment.alpha = .5,
                  segment.size = 0.2,
                  label.r = unit(0.1, 'lines'),
                  label.size = NA, # no label border
                  label.padding = unit(0.1, 'lines'),
                  fill = rgb(1, 1, 1, .5))  
  coord_fixed()  
  scale_x_reverse()  
  theme_graph()  
  theme(legend.position = 'none')

Within-field analysis

Now, let's take a particular field out of the network and examine its inner structure. Varin et al. (2016) set a precedent to study statistics journals, so let's have a look at statistics.

statistics <- labels$community[labels$field == 'statistics']
stats_ig <- induced_subgraph(ig, which(membership(infomap) == statistics))
V(stats_ig)$PageRank <- page.rank(stats_ig)$vector

There are 77 journals in the statistics subgraph, in 1 strongly-connected component(s).

At the moment we only have Thomson Reuters' abbreviations for the journal names. Some are more obvious than others! We have the full journal titles, but some are a bit too long, so we will use a table of custom short-ish titles.

titles <- read.csv('data/stats_titles.csv', stringsAsFactors = FALSE)
V(stats_ig)$title <- titles$Short[match(V(stats_ig)$name, titles$JCR)]

We can visualise the network as before.

stats_xtab <- Matrix::t(as_adjacency_matrix(stats_ig))
stats_layout <- create_layout(stats_ig,
                              layout = 'igraph',
                              algorithm = 'mds',
                              dist = 1 - cor(as.matrix(stats_xtab)))

ggraph(stats_layout)  
  geom_edge_fan0(alpha = .05, colour = 'tomato2')  
  geom_node_point(aes(size = PageRank), fill = 'tomato2', pch = 21, colour = 'white')  
  geom_node_text(aes(label = title), size = 3,
                 repel = TRUE,
                 family = 'Gill Sans MT Condensed',
                 fontface = 'bold',
                 colour = 'tomato2',
                 segment.alpha = .2)  
  coord_fixed()  
  theme_graph()  
  theme(legend.position = 'none')

And here is a ranking of these statistical journals, by Bradley–Terry score.

(stats_ranks <- dplyr::arrange(
  data.frame(journal = V(stats_ig)$title,
             Scroogefactor = scrooge::Scroogefactor(stats_xtab),
             PageRank = V(stats_ig)$PageRank,
             BradleyTerry = scrooge::BTscores(stats_xtab),
             rank = rank(-scrooge::BTscores(stats_xtab), ties.method = 'first')),
  desc(BradleyTerry)
))

journal	Scroogefactor	PageRank	BradleyTerry	rank
JRSS-A	0.0282376	0.0103465	0.0283302	1
American Stat	0.0270228	0.0057930	0.0270249	2
JRSS-B	0.0256999	0.0454141	0.0259910	3
Canada J Stats	0.0239206	0.0083131	0.0241060	4
Annals	0.0236113	0.1100324	0.0240983	5
Biometrika	0.0228638	0.0498885	0.0230736	6
JRSS-C	0.0210658	0.0089601	0.0213421	7
Technometrics	0.0209600	0.0139015	0.0208967	8
Stat Sci	0.0209032	0.0153774	0.0206936	9
Biostatistics	0.0209463	0.0158200	0.0205330	10
Lifetime Data Analysis	0.0182751	0.0058858	0.0199515	11
J Stat Software	0.0201867	0.0162305	0.0197136	12
JASA	0.0194846	0.0835435	0.0194837	13
Stats & Comp	0.0199332	0.0109201	0.0193991	14
JCGS	0.0198372	0.0190766	0.0193987	15
Scan J Stats	0.0188394	0.0169175	0.0182769	16
Biometrics	0.0184227	0.0371235	0.0181814	17
J Machine Learning Res	0.0178295	0.0302504	0.0172690	18
Machine Learning	0.0164286	0.0057000	0.0171842	19
Stat Meth Med Res	0.0168654	0.0072661	0.0167750	20
Bernoulli	0.0163526	0.0165876	0.0162539	21
Int Stats Rev	0.0161866	0.0050933	0.0162027	22
Stat Neerlandica	0.0157788	0.0038321	0.0161538	23
J Time Series	0.0164238	0.0075043	0.0160605	24
J Stat Plan & Infer	0.0166547	0.0368382	0.0159328	25
ANZ J Stats	0.0156540	0.0046732	0.0158607	26
Stat Model	0.0157907	0.0047046	0.0157993	27
Ann Inst Stat Maths	0.0157902	0.0083345	0.0155305	28
Extremes	0.0149530	0.0057294	0.0153994	29
Bayesian Analysis	0.0154541	0.0114376	0.0153939	30
Test	0.0162304	0.0075401	0.0152143	31
J Qual Tech	0.0133428	0.0078417	0.0144551	32
Prob Eng & Inf Sci	0.0129198	0.0038546	0.0141473	33
Biometrical J	0.0149472	0.0107017	0.0141281	34
Stats & Prob Letters	0.0146409	0.0227675	0.0137136	35
Stats in Medicine	0.0145437	0.0454388	0.0136752	36
J Nonpar Stats	0.0137078	0.0080410	0.0135831	37
Comp Stats & Data Analysis	0.0146516	0.0420382	0.0134674	38
J Multivariate Analysis	0.0142435	0.0318751	0.0132759	39
Envir & Eco Stats	0.0129528	0.0046764	0.0131239	40
Environometrics	0.0120375	0.0072662	0.0129543	41
Scand Actuarial J	0.0119172	0.0049733	0.0124851	42
Stoch Models	0.0112702	0.0041752	0.0123314	43
Ann Appl Stats	0.0135044	0.0192067	0.0120295	44
Methods & Comp in Appl Prob	0.0108873	0.0045467	0.0115274	45
Metrika	0.0094645	0.0055462	0.0102697	46
Stat Method	0.0088005	0.0046021	0.0092631	47
ESAIM Prob & Stats	0.0092176	0.0036076	0.0092150	48
Comm Stats: Sim & Comp	0.0095515	0.0060782	0.0091717	49
J Stats Comp & Sim	0.0092627	0.0082753	0.0091212	50
Comm Stats: Theory & Methods	0.0094081	0.0118277	0.0090699	51
Seq Analysis	0.0088882	0.0036688	0.0089746	52
EJ Stats	0.0097919	0.0154410	0.0089544	53
J Agr Bio & Envir Stats	0.0089445	0.0045417	0.0089017	54
REVSTAT	0.0086771	0.0028658	0.0083682	55
SORT	0.0079263	0.0021687	0.0083124	56
Statistics	0.0079254	0.0050826	0.0082635	57
Stats & Interface	0.0087419	0.0040690	0.0079890	58
J Applied Stats	0.0082399	0.0066011	0.0078485	59
Appl Stoch Models in Business	0.0067881	0.0035229	0.0077174	60
Stats Papers	0.0065067	0.0060219	0.0069310	61
Qual Eng	0.0063973	0.0043052	0.0068593	62
Pakistan J Stats	0.0058241	0.0040568	0.0064576	63
Int J Biostatistics	0.0073174	0.0051220	0.0063488	64
Brazil J Prob & Stats	0.0056185	0.0025517	0.0059057	65
Pharm Stats	0.0055106	0.0043995	0.0058420	66
J Biopharm Stats	0.0086326	0.0077599	0.0057170	67
R Journal	0.0063932	0.0026105	0.0054341	68
Adv Stat Analysis	0.0051192	0.0027805	0.0052610	69
Stat Methods & Appl	0.0044261	0.0028058	0.0050218	70
Qual Rel Eng Int	0.0044372	0.0060060	0.0046339	71
Comp Stats	0.0048448	0.0035324	0.0046084	72
J Korean SS	0.0045534	0.0032268	0.0045248	73
Stats in Biopharm Res	0.0038185	0.0033177	0.0044349	74
Rev Colomb Estad	0.0041089	0.0023813	0.0043742	75
Adv Data Analysis & Class	0.0032452	0.0025590	0.0036667	76
Qual Tech & Quant Mgmt	0.0021391	0.0022255	0.0021176	77

And now for something completely different. What if we actually have a single non-statistics "super-journal" in the statistics network?

mapping <- match(V(ig)$name, V(stats_ig)$name)
mapping[is.na(mapping)] <- max(mapping, na.rm = TRUE)   1
stats_others <- contract.vertices(ig, mapping, list(name = 'first', 'ignore'))
V(stats_others)$name[vcount(stats_others)] <- "(All others)"
others_xtab <- Matrix::t(as_adjacency_matrix(stats_others))
diag(others_xtab) <- 0

dplyr::arrange(
  data.frame(journal = V(stats_others)$name,
             Scroogefactor = scrooge::Scroogefactor(others_xtab),
             PageRank = page.rank(stats_others)$vector,
             BradleyTerry = scrooge::BTscores(others_xtab)),
  desc(BradleyTerry)
)

journal	Scroogefactor	PageRank	BradleyTerry
J STAT SOFTW	0.0597587	0.0159115	0.0662567
J R STAT SOC B	0.0533165	0.0320152	0.0584122
AM STAT	0.0498796	0.0084807	0.0547457
BIOMETRIKA	0.0343575	0.0368557	0.0361016
J COMPUT GRAPH STAT	0.0275142	0.0169518	0.0283282
ANN STAT	0.0266800	0.0694021	0.0278383
STAT COMPUT	0.0268367	0.0123934	0.0275116
BIOMETRICS	0.0250801	0.0277151	0.0266368
J AM STAT ASSOC	0.0237224	0.0560967	0.0251152
MACH LEARN	0.0224210	0.0070761	0.0246029
J MACH LEARN RES	0.0212466	0.0186240	0.0225928
BIOSTATISTICS	0.0214237	0.0136015	0.0218223
CAN J STAT	0.0225656	0.0100984	0.0206261
STAT METHODS MED RES	0.0188755	0.0085729	0.0203618
TECHNOMETRICS	0.0194398	0.0148760	0.0189859
STAT SCI	0.0190649	0.0139816	0.0189028
BIOMETRICAL J	0.0178737	0.0118255	0.0173150
J R STAT SOC C-APPL	0.0179341	0.0102346	0.0172444
J R STAT SOC A STAT	0.0171675	0.0091808	0.0170781
STAT MED	0.0158302	0.0336120	0.0167550
COMPUT STAT DATA AN	0.0161689	0.0384759	0.0164907
J QUAL TECHNOL	0.0141561	0.0111754	0.0154762
SCAND J STAT	0.0165599	0.0157602	0.0147462
ANN I STAT MATH	0.0154795	0.0098950	0.0141590
INT STAT REV	0.0142590	0.0073603	0.0136896
J STAT PLAN INFER	0.0141285	0.0328774	0.0133039
EXTREMES	0.0133718	0.0081363	0.0128179
LIFETIME DATA ANAL	0.0127270	0.0081576	0.0125918
BAYESIAN ANAL	0.0137490	0.0115487	0.0124839
J TIME SER ANAL	0.0137731	0.0093792	0.0124744
STAT MODEL	0.0132021	0.0074011	0.0117373
TEST-SPAIN	0.0128800	0.0098124	0.0102774
J BIOPHARM STAT	0.0096748	0.0098427	0.0101378
J NONPARAMETR STAT	0.0123958	0.0098934	0.0098247
ENVIRONMETRICS	0.0096255	0.0093404	0.0097203
AUST NZ J STAT	0.0108198	0.0074397	0.0093956
PHARM STAT	0.0083334	0.0073263	0.0093387
STAT NEERL	0.0104667	0.0067418	0.0090792
ENVIRON ECOL STAT	0.0088240	0.0071893	0.0089873
PROBAB ENG INFORM SC	0.0081377	0.0066973	0.0086932
J MULTIVARIATE ANAL	0.0097456	0.0262258	0.0086846
SORT-STAT OPER RES T	0.0074250	0.0054547	0.0081761
REVSTAT-STAT J	0.0079691	0.0061630	0.0079729
METRIKA	0.0077236	0.0085714	0.0078223
BERNOULLI	0.0086403	0.0148244	0.0073900
J AGR BIOL ENVIR ST	0.0076118	0.0071660	0.0072628
SCAND ACTUAR J	0.0068412	0.0073625	0.0072348
STAT PROBABIL LETT	0.0082754	0.0212788	0.0071910
METHODOL COMPUT APPL	0.0066574	0.0071288	0.0069508
STOCH MODELS	0.0061108	0.0066631	0.0065910
STATISTICS	0.0068530	0.0081674	0.0064932
STAT METHODOL	0.0068281	0.0076906	0.0063508
(All others)	0.0056965	0.0115052	0.0061721
QUAL ENG	0.0057950	0.0076824	0.0061600
COMMUN STAT-SIMUL C	0.0071682	0.0090134	0.0060598
J STAT COMPUT SIM	0.0063426	0.0112566	0.0058592
SEQUENTIAL ANAL	0.0067499	0.0071786	0.0057831
COMMUN STAT-THEOR M	0.0059140	0.0140319	0.0054953
R J	0.0065317	0.0059580	0.0052631
QUAL RELIAB ENG INT	0.0049446	0.0092773	0.0052533
INT J BIOSTAT	0.0062151	0.0072493	0.0052001
REV COLOMB ESTAD	0.0039183	0.0060033	0.0045831
ANN APPL STAT	0.0057245	0.0152338	0.0045302
ESAIM-PROBAB STAT	0.0048405	0.0063724	0.0045107
STAT BIOPHARM RES	0.0040342	0.0063369	0.0043468
STAT PAP	0.0041720	0.0095298	0.0040877
BRAZ J PROBAB STAT	0.0039294	0.0057803	0.0039361
STAT INTERFACE	0.0054177	0.0067122	0.0038533
APPL STOCH MODEL BUS	0.0035906	0.0064436	0.0038035
ASTA-ADV STAT ANAL	0.0043451	0.0061006	0.0037645
J APPL STAT	0.0038169	0.0094288	0.0035409
ADV DATA ANAL CLASSI	0.0035820	0.0058913	0.0031111
ELECTRON J STAT	0.0049902	0.0129883	0.0030936
QUAL TECHNOL QUANT M	0.0030537	0.0055776	0.0026304
STAT METHOD APPL-GER	0.0025070	0.0061124	0.0025973
J KOREAN STAT SOC	0.0024670	0.0063257	0.0021873
COMPUTATION STAT	0.0025795	0.0064853	0.0021572
PAK J STAT	0.0012715	0.0068723	0.0012420

We can try plotting it, too.

stats_others <- simplify(stats_others, remove.loops = TRUE, remove.multiple = FALSE) # remove self-citations
V(stats_others)$PageRank <- page.rank(stats_others)$vector

other_layout <- create_layout(stats_others,
                              layout = 'igraph',
                              algorithm = 'mds',
                              dist = 1 - cor(as.matrix(others_xtab)))

ggraph(other_layout)  
  geom_edge_fan0(alpha = .01, colour = '#4F94CD')  
  geom_node_point(aes(size = PageRank), fill = '#4F94CD', pch = 21, colour = 'white')  
  geom_node_text(aes(label = name), size = 3,
                 repel = TRUE,
                 family = 'Gill Sans MT Condensed',
                 fontface = 'bold',
                 colour = '#4F94CD',
                 segment.alpha = .2)  
  coord_fixed()  
  theme_graph()  
  theme(legend.position = 'none')

Ranking

We can penalise the Bradley–Terry model by adding a "player zero" who cites/is cited by every player/journal/field at a constant rate (say 1/2). This will help reduce the chance of outliers (such as journals for which we have very little citation data, or fields containing very few journals) from shooting to the top or the bottom of a Bradley–Terry scores league table.

zero_cite <- .15 * sum(xtab) / 2 / nrow(xtab) ## 2ka = .15n
penalised_xtab <- rbind(zero_cite, cbind(zero_cite, xtab)) # add zeroth player

library(scrooge)
field_ranks <- data.frame(
  field = V(sj)$field,
  PageRank = PageRank(penalised_xtab)[-1],
  BradleyTerry = BTscores(penalised_xtab)[-1],
  Scroogefactor = Scroogefactor(penalised_xtab)[-1]
)
field_ranks$rank <- rank(-field_ranks$BradleyTerry, ties.method = 'first')
dplyr::arrange(field_ranks, desc(BradleyTerry))

field	PageRank	BradleyTerry	Scroogefactor	rank
economics	0.0095262	0.0198675	0.0198085	1
statistics	0.0052172	0.0182253	0.0184844	2
rheumatology	0.0078636	0.0154524	0.0149113	3
medicine	0.0785679	0.0147539	0.0140610	4
surgery	0.0187227	0.0147393	0.0140006	5
nephrology	0.0091929	0.0147153	0.0141108	6
psychometrics	0.0030642	0.0146652	0.0153434	7
oncology	0.0298477	0.0142217	0.0134646	8
neuroscience	0.0483922	0.0138453	0.0131796	9
urology	0.0054990	0.0134027	0.0129622	10
politics	0.0050798	0.0129090	0.0134032	11
orthopaedics	0.0081823	0.0127964	0.0125031	12
parasitology	0.0200139	0.0127915	0.0122454	13
biomedical sciences	0.1391158	0.0126568	0.0119706	14
psychiatry	0.0162007	0.0125158	0.0119816	15
psychology	0.0157250	0.0123666	0.0119232	16
management	0.0082283	0.0123863	0.0118723	17
vascular surgery	0.0032286	0.0120399	0.0120587	18
obstetrics	0.0077170	0.0117973	0.0113524	19
opthalmology	0.0049835	0.0117134	0.0113681	20
sports medicine	0.0062347	0.0115786	0.0112158	21
marketing	0.0044868	0.0112366	0.0109821	22
sociology	0.0065198	0.0109829	0.0108478	23
computer graphics	0.0028843	0.0106927	0.0107898	24
plastic surgery	0.0036232	0.0104481	0.0102228	25
radiology	0.0104202	0.0101617	0.0098458	26
dentistry	0.0048420	0.0095513	0.0092427	27
operational research	0.0058023	0.0094057	0.0092595	28
rhinology	0.0048558	0.0093847	0.0091205	29
philosophy	0.0024042	0.0093150	0.0098626	30
zoology	0.0212266	0.0093070	0.0090279	31
communication	0.0032187	0.0090557	0.0091353	32
dermatology	0.0046755	0.0089293	0.0085348	33
social anthropology	0.0027730	0.0088286	0.0090860	34
mathematical finance	0.0023882	0.0087307	0.0091967	35
music	0.0023110	0.0086923	0.0090142	36
hypnosis	0.0022330	0.0086700	0.0091130	37
history of science	0.0025406	0.0086569	0.0088251	38
evaluation	0.0022738	0.0086527	0.0090182	39
toxicology	0.0082432	0.0086072	0.0080802	40
lighting	0.0022472	0.0088635	0.0089657	41
psychoanalysis	0.0022574	0.0085334	0.0088749	42
transfusion	0.0027156	0.0085100	0.0084292	43
social history	0.0025049	0.0084987	0.0088589	44
French sociology	0.0022331	0.0084802	0.0088812	45
rehabilitation	0.0022275	0.0084746	0.0088672	46
onomastics/history of mathematics	0.0022206	0.0084621	0.0088660	47
natural history	0.0022199	0.0084576	0.0088621	48
Russian sociology	0.0022237	0.0084546	0.0088548	49
history of education	0.0022232	0.0084451	0.0088458	50
history of economics	0.0022352	0.0084432	0.0088541	51
religion	0.0022335	0.0084329	0.0087993	52
complexity	0.0031541	0.0084078	0.0086762	53
translation	0.0022264	0.0083900	0.0087861	54
agriculture	0.0139732	0.0083396	0.0081027	55
German sociology	0.0022213	0.0083082	0.0086561	56
linguistics	0.0027522	0.0082932	0.0083216	57
civil engineering	0.0022271	0.0082824	0.0086966	58
petrochemical engineering	0.0023708	0.0082457	0.0085083	59
Brazilian/French philosophy	0.0022209	0.0082120	0.0084957	60
leisure studies	0.0023499	0.0082049	0.0084187	61
Latin American studies	0.0022265	0.0081671	0.0085518	62
Romanian/Italian ethics	0.0022224	0.0081632	0.0084943	63
leather	0.0022236	0.0081602	0.0085846	64
anthropology	0.0032005	0.0081559	0.0080893	65
policy	0.0027347	0.0080814	0.0081088	66
international law	0.0024032	0.0080547	0.0084432	67
occupational therapy	0.0023249	0.0080541	0.0081471	68
informatics	0.0099826	0.0080403	0.0078628	69
electrical engineering	0.0035042	0.0078939	0.0078633	70
automation	0.0022552	0.0078823	0.0082833	71
geotechnology	0.0027123	0.0078614	0.0080759	72
engineering design	0.0024121	0.0078412	0.0081312	73
forensics	0.0026537	0.0078035	0.0076939	74
education	0.0047743	0.0077080	0.0075149	75
mycology	0.0025491	0.0076863	0.0076372	76
logic	0.0024510	0.0076156	0.0078584	77
bibliometrics	0.0027331	0.0075872	0.0077041	78
anatomy	0.0024009	0.0075638	0.0076517	79
information systems	0.0036066	0.0075381	0.0072705	80
electronics	0.0037840	0.0075347	0.0074561	81
Brazilian healthcare	0.0024541	0.0075112	0.0073808	82
social work	0.0028226	0.0074891	0.0074850	83
biomaterials	0.0112502	0.0073331	0.0067864	84
logistics	0.0034656	0.0072290	0.0071273	85
human geography	0.0045769	0.0072145	0.0071259	86
ecology	0.0061912	0.0069880	0.0068437	87
astrophysics	0.0042515	0.0069680	0.0064905	88
nuclear engineering	0.0024137	0.0069329	0.0073273	89
radioactivity	0.0031276	0.0069110	0.0070901	90
law reviews	0.0026354	0.0068964	0.0067880	91
mathematics	0.0086304	0.0068269	0.0069352	92
learning disabilities	0.0031019	0.0067851	0.0066117	93
marine engineering	0.0023518	0.0066475	0.0070585	94
geology	0.0139842	0.0064870	0.0061994	95
social justice	0.0024211	0.0063083	0.0060054	96
veterinary medicine	0.0057741	0.0062613	0.0060098	97
spectroscopy	0.0107509	0.0061040	0.0058136	98
tourism	0.0025631	0.0060081	0.0060159	99
tribology	0.0037282	0.0059347	0.0058167	100
measurement	0.0025076	0.0057733	0.0061204	101
engineering management	0.0025191	0.0054657	0.0055476	102
environmental science	0.0197079	0.0054299	0.0051928	103
food science	0.0125544	0.0054239	0.0051509	104
chemistry & physics	0.0441748	0.0053294	0.0049305	105
robotics	0.0044990	0.0049737	0.0050510	106
material science	0.0073490	0.0049279	0.0049302	107
agronomy	0.0024144	0.0048210	0.0050570	108
energy	0.0072781	0.0047346	0.0046635	109
textiles	0.0023452	0.0047212	0.0052096	110
wood	0.0024436	0.0045605	0.0049897	111
particle physics	0.0051697	0.0039993	0.0039264	112

lastplace <- max(field_ranks$rank)
interestingfields <- c('mathematics',
                       'informatics',
                       'biomedical sciences',
                       'medicine',
                       'chemistry & physics',
                       'zoology',
                       'textiles')

library(ggrepel)
ggplot(field_ranks)  
  aes(rank, 100*BradleyTerry, label = field)  
  geom_point(colour = 'tomato2', size = 1)  
  geom_text_repel(data = subset(field_ranks, rank < 5 | field %in% interestingfields),
                  nudge_y = .15,
                  nudge_x = -5,
                  segment.alpha = .25,
                  family = 'Gill Sans MT Condensed', 
                  fontface = 'bold',
                  colour = 'tomato2',
                  point.padding = unit(0.1, 'lines')
                  )  
  scale_x_reverse(name = NULL,
                  labels = scales::ordinal,
                  breaks = c(1, seq(20, lastplace, by = 20)),
                  minor_breaks = c(1, seq(5, lastplace, by = 5)),
                  limits = c(lastplace, 1),
                  expand = c(0.02, 0))  
  scale_y_continuous(NULL,
                     position = 'right')  
  theme_bw()  
  theme(text = element_text(family = 'Gill Sans MT Condensed', face = 'bold'),
        axis.text = element_text(colour = 'tomato2'),
        panel.grid = element_blank(),
        panel.border = element_blank(),
        axis.ticks = element_line(colour = 'tomato2'),
        axis.line = element_line(colour = 'tomato2'),
        plot.background = element_rect(fill = 'transparent', colour = NA),
        panel.background = element_rect(fill = 'transparent', colour = NA))

interestingjournals <- c('R Journal',
                         'Stat Science',
                         'Biometrika',
                         #'J Stat Software',
                         'Annals',
                         'Stats in Medicine',
                         #'Biostatistics',
                         'Machine Learning',
                         'JRSS-B',
                         'Statistics',
                         #'J Applied Stats',
                         'Metrika',
                         'JCGS')
lastplace2 <- max(stats_ranks$rank)

ggplot(stats_ranks)  
  aes(rank, 100*BradleyTerry, label = journal)  
  geom_point(colour = '#2D7B95', size = 1)  
  geom_text_repel(data = subset(stats_ranks, rank <= 3 | journal %in% interestingjournals),
                  nudge_y = .15,
                  nudge_x = -5,
                  segment.alpha = .25,
                  family = 'Gill Sans MT Condensed',
                  fontface = 'bold',
                  colour = '#2D7B95',
                  point.padding = unit(0.2, 'lines')
                  )  
  scale_x_reverse(name = NULL,
                  labels = scales::ordinal,
                  breaks = c(1, seq(20, lastplace2, by = 20)),
                  minor_breaks = c(1, seq(5, lastplace2, by = 5)),
                  limits = c(lastplace2, 1),
                  expand = c(0.02, 0))  
  scale_y_continuous(NULL,
                     position = 'right')  
  theme_bw()  
  theme(text = element_text(family = 'Gill Sans MT Condensed', face = 'bold'),
        axis.text = element_text(colour = '#2D7B95'),
        panel.grid = element_blank(),
        panel.border = element_blank(),
        axis.ticks = element_line(colour = '#2D7B95'),
        axis.line = element_line(colour = '#2D7B95'),
        plot.background = element_rect(fill = 'transparent', colour = NA),
        panel.background = element_rect(fill = 'transparent', colour = NA))

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
data		data
img		img
.gitignore		.gitignore
README.Rmd		README.Rmd
README.md		README.md
user2017.pdf		user2017.pdf
user2017.sla		user2017.sla

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