vignettes/scimeetr.md
In MaximeRivest/scimeetr: Scientometrics Meet R
Introduction
Scimeetr helps explore the scholarly literature. It contains a suit of
function that let someone:
- load bibliometric data into R
- make a map of peer reviewed papers by creating various networks
- find research community
- characterise the research communities
- generate reading list
This tutorial is composed of two self-contained section. The first
section show case the whole process with all the default parameters. The
second section describes each function in more detail by presenting the
rational for the function, the algorithms used and the options.
From data to reading list
You can automatically generate a reading list of seminal papers in a
research litterature by using only those three functions:
ìmport_wos_files, scimap, and scilist. This first section
describes this process in more details.
loading and exploring bibliometric data
The first step in exploring the literature is to retrieve bibliometric
data from the Web of Science or Scopus. In this first tutorial I use
a dataset from the Web of Science about ecological networks.
library(scimeetr)
scimeetr_list <- import_wos_files("path/to/folder/")
Then,summary can be used to get a quick characterisation of the data.
summary(scimeetr_list)
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
From this summary, we see that there is 396 papers in my data set which
overal cites 16567 different elements. On average, each paper cites 53
elements.
Than we learn that, in this research community, 25% of the papers are
cited less than 2 times, 50% are cited less than 9 times and 75% are
cited less than ~23 times. There are papers that are cited up to 1333
times. The average citation per paper is ~25. This is much higher than
the median (9), thus most paper are cited only a few times and a few
papers are profusely cited. When correcting for the age of the paper, we
learn that papers are cited 2 times per year on average.
By looking at the most frequent keyword and journals, we learn that this
community of research is about biodiversity, agriculture, ecosystem
services and policy. Keyword and journal frequency tables efficiently
reveal the theme of a scientific community.
Mapping scientific community
The previous characterisation is great, but it is limited if your
dataset contains many different scientific communities. By detecting the
scientific communities present within a dataset a map of science can be
drawn and each cluster can be characterised on its own. The function
scimap can be used for this task.
scimap_result <- scimap(scimeetr_list)
The function returns all the data that scimeetr_list contained and
more. For example communities have been identified and now if the
function summary is used on scim_result. In addition of the previous
information. The descriminant keywords of each communities constituating
the main community are listed.
summary(scimap_result)
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_1 (202)
## 9 PARTICIPATION
## 10 AGRICULTURE
## 11 FARMERS
## 12 ATTITUDES
## 13 AGRI-ENVIRONMENTAL SCHEMES
## 14 POLICY
## 15 com1_8 (132)
## 16 ADOPTION
## 17 AGRICULTURE
## 18 POLICY
## 19 WATER QUALITY
## 20 AUSTRALIA
## 21 FARMERS
## 22 com1_4 (67)
## 23 LAND-USE
## 24 AGRICULTURE
## 25 SCHEMES
## 26 LANDSCAPE
## 27 POLICY
## 28 COMMON AGRICULTURAL POLICY
## 29 com1_3 (292)
## 30 BIODIVERSITY
## 31 AGRI-ENVIRONMENT SCHEMES
## 32 CONSERVATION
## 33 MANAGEMENT
## 34 DIVERSITY
## 35 AGRICULTURAL LANDSCAPES
Except for the last tables, all of the output is identical to the
summary output above. Those last tables now reveals that the papers in
our database can be clustered in two communities. One that is about x
and the other that is about y.
The function plot can be used on the output of the function summary
for a graphical representation of the sub-communities.
plot(summary(scimap_result, com_size = 30))
Automatically generating a reading list of seminal papers
Now that we have characterise the main community and seen of which
community it is constituted, we can decide if it is the community that
we wish to join / review. If it is, we use the function scilist to get
reading lists. The defaul readin list will find the seminal papers of
each communitiy.
reading_list <- scilist(scimap_result)
reading_list$com1
ID
Frequency.x
Pourcentage
KLEIJN D, 2003, J APPL ECOL, V40, P947, DOI 10.1111/J.1365-2664.2003.00868.X
113
0.0030005
KLEIJN D, 2006, ECOL LETT, V9, P243, DOI 10.1111/J.1461-0248.2005.00869.X
73
0.0019383
KLEIJN D, 2001, NATURE, V413, P723, DOI 10.1038/35099540
57
0.0015135
BENTON TG, 2003, TRENDS ECOL EVOL, V18, P182, DOI 10.1016/S0169-5347(03)00011-9
54
0.0014338
PANNELL DJ, 2006, AUST J EXP AGR, V46, P1407, DOI 10.1071/EA5037
50
0.0013276
MORRIS C, 1995, J RURAL STUD, V11, P51, DOI 10.1016/0743-0167(94)00037-A
47
0.0012480
TSCHARNTKE T, 2005, ECOL LETT, V8, P857, DOI 10.1111/J.1461-0248.2005.00782.X
45
0.0011949
FALCONER K, 2000, J RURAL STUD, V16, P379, DOI 10.1016/S0743-0167(99)00066-2
43
0.0011418
In depth description of each steps
How to get bibliometric data?
Biliometric data can be obtained from either Scopus or the Web of
Science. Most university library have access to either one and some
have access to both.
Retrieving data from Scopus
Scopus home page.
Select all and export
Export as CSV file and select all fields for exportation
Following the previous steps will get you one or several .csv files.
Then, to import this/these file(s) in R, you need to put it/them in a
new folder which contains only the files to import into R
Retrieving data from Web of Science
Web of Science home page. Make sure that Select a database corresponds
to Web of Science Core Collection
Save to Other Files Formats
You can download only 500 items at a time. You should select Full
Record and Cited References. And select the Tab-delimeted (UTF-8) as
file format.
Following the previous steps will get you one or several .txt files.
Then, to import this/these file(s) in R, you need to put it/them in a
new folder which contains only the files to import into R
How to upload bibliometric data into R
The bibliometric data obtained from Scopus or Web of science are either
in .csv or .txt format. These are standard file formats and you most
likely know them. There are built in function in R that let you import
.csv and .txt files. So why does scimeetr provide you with
import_scopus_files and import_wos_files? There are four reasons.
The main one is that bibliometric data contains author names from around
the world, which means that all alphabets are used and this leads to
problems with file encoding. Scimeetr's import functions solves that
problem. Second, Scopus do not provide standard, uniform and consisten
cited reference list. Thus, import_scopus_files has to standardize it
at import. This explains the additional time required to load scopus
files versus wos files. Third, Scopus and Web of Science do not use the
same column names so they have to be homogenized at import. Finally, the
data can be transformed into a scimeetr object so that summary, plot
and print will know what to do with it.
scimeetr_list <- import_wos_files(files_directory = "/path/to/folder/")
scimeetr_list <- import_scopus_files(files_directory = "/path/to/folder/")
Do not forget that this function take in a path to a folder not a file.
Thus, it need a slash at the end of the folder path.
Exploring scimeetr data
Printing scimeetr_list that we just created will provide some
informations about it, but summary will provide more.
scimeetr_list
##
## # A scimeetr object #
## ---------------------
## Number of papers: 742
## Number of communities: 1
## Names of communities: com1
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 BIODIVERSITY CONSERVATION FARMERS
## 2 CONSERVATION AGRICULTURAL CONSERVATION
## 3 MANAGEMENT MANAGEMENT AGRICULTURAL
## 4 AGRICULTURE POLICY MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES FARMERS POLICY
summary(scimeetr_list)
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
A scimeetr object such as scimeetr_list contains more data than what
can be seen with print and summary. A scimeetr object is in fact a
list of communities list which are themselves list of up to 9 elements.
Each communities contain a data.frame called dfsci. This dataframe
contains all the bibliometric data that was importedinto R.
scimeetr_list$com1$dfsci
PT
AU
BA
BE
GP
AF
BF
CA
TI
SO
SE
BS
LA
DT
CT
CY
CL
SP
HO
DE
ID
C1
RP
EM
RI
OI
FU
NR
TC
Z9
U1
U2
PU
PI
PA
SN
EI
BN
J9
JI
PD
PY
VL
IS
PN
SU
SI
MA
BP
EP
AR
DI
D2
PG
WC
SC
GA
UT
PM
RECID
J
Holstead, KL; Kenyon, W; Rouillard, JJ; Hopkins, J; Galan-Diaz, C
NA
NA
NA
Holstead, K. L.; Kenyon, W.; Rouillard, J. J.; Hopkins, J.; Galan-Diaz, C.
NA
Natural flood management from the farmer's perspective: criteria that affect uptake
JOURNAL OF FLOOD RISK MANAGEMENT
NA
NA
English
Article
Natural flood management; catchment management; flood risk management; farmer decision making; land use change
DECISION-MAKING; BEHAVIOR; CONSERVATIONISTS; PARTICIPATION; ATTITUDES; SCHEMES; ENGLAND
[Holstead, K. L.; Kenyon, W.; Hopkins, J.] James Hutton Inst, Social Econ & Geog Sci Grp, Aberdeen AB15 8QH, Scotland; [Rouillard, J. J.] Univ Dundee, Sch Environm, Dundee, Scotland; [Galan-Diaz, C.] Dot Rural Univ Aberdeen, Aberdeen, Scotland
Holstead, KL (reprint author), James Hutton Inst, Social Econ & Geog Sci Grp, Aberdeen AB15 8QH, Scotland.
kirsty.holstead@hutton.ac.uk
Scottish Government's Rural and Environment Science and Analytical Services (RESAS) Division, Work Package 2.4: Methods for mitigating and adapting to flood risk
59
2
2
0
0
WILEY
HOBOKEN
111 RIVER ST, HOBOKEN 07030-5774, NJ USA
1753-318X
NA
J FLOOD RISK MANAG
J. Flood Risk Manag.
JUN
2017
10
2
NA
SI
NA
205
218
10.1111/jfr3.12129
NA
14
Environmental Sciences; Water Resources
Environmental Sciences & Ecology; Water Resources
EU4HB
WOS:000400989300008
NA
HOLSTEAD KL, 2017, J FLOOD RISK MANAG, 10, 205
Making reading lists
If we are confident that the papers contained in scimeetr_list are
those for which we want a reading list we can used the function
scilist to find various lists of papers. The default list given by
scilist contains the seminal papers for the community analysed. That
is, it rank the paper by the number of times they were cited by all the
papers and list them by citation frequency.
scilist(scimeetr_list)
ID
Frequency.x
Pourcentage
KLEIJN D, 2003, J APPL ECOL, V40, P947, DOI 10.1111/J.1365-2664.2003.00868.X
113
0.0030005
KLEIJN D, 2006, ECOL LETT, V9, P243, DOI 10.1111/J.1461-0248.2005.00869.X
73
0.0019383
KLEIJN D, 2001, NATURE, V413, P723, DOI 10.1038/35099540
57
0.0015135
BENTON TG, 2003, TRENDS ECOL EVOL, V18, P182, DOI 10.1016/S0169-5347(03)00011-9
54
0.0014338
PANNELL DJ, 2006, AUST J EXP AGR, V46, P1407, DOI 10.1071/EA5037
50
0.0013276
MORRIS C, 1995, J RURAL STUD, V11, P51, DOI 10.1016/0743-0167(94)00037-A
47
0.0012480
TSCHARNTKE T, 2005, ECOL LETT, V8, P857, DOI 10.1111/J.1461-0248.2005.00782.X
45
0.0011949
FALCONER K, 2000, J RURAL STUD, V16, P379, DOI 10.1016/S0743-0167(99)00066-2
43
0.0011418
With the parameter k, we can control the length of the reading list.
scilist(scimeetr_list, k = 3)
ID
Frequency.x
Pourcentage
KLEIJN D, 2003, J APPL ECOL, V40, P947, DOI 10.1111/J.1365-2664.2003.00868.X
113
0.0030005
KLEIJN D, 2006, ECOL LETT, V9, P243, DOI 10.1111/J.1461-0248.2005.00869.X
73
0.0019383
KLEIJN D, 2001, NATURE, V413, P723, DOI 10.1038/35099540
57
0.0015135
With the parameter reading_list, we can get any of the following 12
reading lists that fits into three categories:
- Core
- core_papers
- core_yr
- core_residual
- Experts
- by_expert_LC
- by_expert_TC
- group_of_experts_TC
- group_of_experts_LC
- Centrality
- cite_most_others
- betweeness
- closeness
- connectness
- page_rank
The default reading list is core_papers.
Core
I categorise the reading lists as core because they are reading
lists of core papers as they are all a variation of the number of times
papers within our community of interest refers to the paper listed.
Although the number of citation is not a perfect measure of a papers
importance for a community it should be a good proxy. A weekness of the
number of citation as a measure of papers importance is that not all
citations are equal. For example, sometimes a paper is cited because it
is criticized or because it contrasts with other findings. This as been
realised by others before and some have attempted to fix it by creating
the concept of influential citation. Influential citation is a great
concept by to be calculated it requires advance text processing and
access to the full text of each papers. As it is notoriosly time
consuming to get full text and even harder to get it in the right
format, we are left with citation count.
Most cited per year
Using scilist with reading_list = "core_yr" will list the most cited
paper for each year from three years before present to ten years before
present. The parameter k controls the number of paper per year to
list.
scilist(scimeetr_list, reading_list = "core_yr", k = 2)
record
Frequency.x
age
PE'ER G, 2014, SCIENCE, V344, P1090, DOI 10.1126/SCIENCE.1253425
9
3
MEICHTRY-STIER KS, 2014, AGR ECOSYST ENVIRON, V189, P101, DOI 10.1016/J.AGEE.2014.02.038
5
3
RIBEIRO PF, 2014, AGR ECOSYST ENVIRON, V183, P138, DOI 10.1016/J.AGEE.2013.11.002
5
3
BURTON RJF, 2013, LAND USE POLICY, V30, P628, DOI 10.1016/J.LANDUSEPOL.2012.05.002
25
4
UTHES S, 2013, ENVIRON MANAGE, V51, P251, DOI 10.1007/S00267-012-9959-6
16
4
BAUMGART-GETZ A, 2012, J ENVIRON MANAGE, V96, P17, DOI 10.1016/J.JENVMAN.2011.10.006
17
5
EMERY SB, 2012, J RURAL STUD, V28, P218, DOI 10.1016/J.JRURSTUD.2012.02.004
12
5
KLEIJN D, 2011, TRENDS ECOL EVOL, V26, P474, DOI 10.1016/J.TREE.2011.05.009
23
6
BATARY P, 2011, P ROY SOC B-BIOL SCI, V278, P1894, DOI 10.1098/RSPB.2010.1923
15
6
SATTLER C, 2010, LAND USE POLICY, V27, P70, DOI 10.1016/J.LANDUSEPOL.2008.02.002
20
7
MATZDORF B, 2010, LAND USE POLICY, V27, P535, DOI 10.1016/J.LANDUSEPOL.2009.07.011
18
7
STOATE C, 2009, J ENVIRON MANAGE, V91, P22, DOI 10.1016/J.JENVMAN.2009.07.005
22
8
RUTO E, 2009, J ENVIRON PLANN MAN, V52, P631, DOI 10.1080/09640560902958172
19
8
BURTON RJF, 2008, SOCIOL RURALIS, V48, P16, DOI 10.1111/J.1467-9523.2008.00452.X
41
9
DEFRANCESCO E, 2008, J AGR ECON, V59, P114, DOI 10.1111/J.1477-9552.2007.00134.X
38
9
KNOWLER D, 2007, FOOD POLICY, V32, P25, DOI 10.1016/J.FOODPOL.2006.01.003
38
10
WHITTINGHAM MJ, 2007, J APPL ECOL, V44, P1, DOI 10.1111/J.1365-2664.2006.01263.X
29
10
More cited than expected
Using scilist with reading_list = "core_residual" will list the
papers that diverge most from the expected number of citation for this
particular paper. This can be visualised in the figure below. The point
that have the biggest difference between their frequency value and the
fitted blue lines are listed in the core_residual reading list.
Here is an example of the code and its result.
scilist(scimeetr_list, reading_list = "core_residual", k = 3)
record
Frequency.x
age
KLEIJN D, 2003, J APPL ECOL, V40, P947, DOI 10.1111/J.1365-2664.2003.00868.X
113
14
MORRIS C, 1995, J RURAL STUD, V11, P51, DOI 10.1016/0743-0167(94)00037-A
47
22
ERVIN CA, 1982, LAND ECON, V58, P277, DOI 10.2307/3145937
15
35
Experts
The reading lists that I categorise as expert are built from authors
information. Experts within a community are identified based on the
number of papers they published and the number of times each of their
papers are cited.
Recent paper of a few experts
Using scilist with reading_list = "by_expert_LC" we will get a list
of recent papers by one or a few experts in the community. For the
option by_expert_LC, authors are ranked based on their harmonic local
H-index. The H-index is a measure of an other productivity and impact.
An author with an H-index of 10 means that he has published at least 10
papers with 10 or more citation each. A local H-index means that only
citations from other papers in the community are counted. A harmonic
local H-index means that authors do not get the full credit for each
citation their paper received. It is corrected depending on the authos
position in the authors list. First authors gets most of the credit,
then the last author gets the second most, and the authors gets credit
as a proportion of their position. Once the authors
harmonic-local-H-index is found they are ranked and the m most recent
publication of the k most 'expert' authors are listed as a reading
list.
scilist(scimeetr_list, reading_list = "by_expert_LC", k = 2, m = 2)
AU
HL
PAPER
Herzog, F
6
SEREKE F, 2015, AGRON SUSTAIN DEV, 35, 759
Herzog, F
6
KELEMEN E, 2013, LAND USE POLICY, 35, 318
Matzdorf, B
5
MEYER C, 2016, LAND USE POLICY, 55, 352
Matzdorf, B
5
SCHOMERS S, 2015, SUSTAINABILITY-BASEL, 7, 13856
Matzdorf, B
5
SCHOMERS S, 2015, LAND USE POLICY, 42, 58
Schupbach, B
5
SCHUPBACH B, 2016, LAND USE POLICY, 53, 27
Schupbach, B
5
AVIRON S, 2011, RESTOR ECOL, 19, 500
Schupbach, B
5
JUNGE X, 2011, BIOL CONSERV, 144, 1430
Using scilist with reading_list = "by_expert_TC" instead of
reading_list = "by_expert_LC", notice the _TC instead of the _LC
will based the ranking calculation on total citation of it's
publications instead of only the local citations.
Paper of experts group
Using scilist with reading_list = "group_of_experts_LC" we will get
a list of papers for which many authors are experts in the community.
For this option, authors are assigned a harmonic local H-index like
described in the previous section. But this time, a weighted sum of the
harmonic-local-H-index of each authors of a paper is calculated.
scilist(scimeetr_list, reading_list = "group_of_experts_LC", k = 5)
RECID
AuS
HERZOG F, 2005, AGR ECOSYST ENVIRON, 108, 189
8.678571
AVIRON S, 2011, RESTOR ECOL, 19, 500
8.383333
AVIRON S, 2007, AGR ECOSYST ENVIRON, 122, 295
8.166667
AVIRON S, 2005, GRASSLAND SCI EUR, 10, 340
7.955952
KAMPMANN D, 2008, J NAT CONSERV, 16, 12
7.926190
Using scilist with reading_list = "group_of_experts_TC" instead of
reading_list = "group_of_experts_LC", notice the _TC instead of the
_LC will based the ranking calculation on total citation of it's
publications instead of only the local citations.
Centrality
Their are several measures of nodes centrality in graph
theory. The most central
papers of a community of papers can be found with scilist.
Betweeness
Betweeness measures the importance of a paper in connecting two clusters
of papers. Papers with a high betweeness would therefore be a paper that
tend to be more interdisciplinary.
scilist(scimeetr_list, reading_list = "betweeness", k = 5)
.x[[i]]
HEJNOWICZ AP, 2016, LAND USE POLICY, 55, 240
UTHES S, 2013, ENVIRON MANAGE, 51, 251
BURTON RJF, 2013, LAND USE POLICY, 30, 628
JARVIS DI, 2011, CRIT REV PLANT SCI, 30, 125
STOATE C, 2001, J ENVIRON MANAGE, 63, 337
Closeness
Closeness measures the average number of link between a paper and all
other papers. Papers with a high closeness would therefore be a paper
that tend to have a large and wide list of citations.
scilist(scimeetr_list, reading_list = "closeness", k = 5)
.x[[i]]
HEJNOWICZ AP, 2016, LAND USE POLICY, 55, 240
UTHES S, 2013, ENVIRON MANAGE, 51, 251
HOLLAND JM, 2016, PEST MANAG SCI, 72, 1638
JARVIS DI, 2011, CRIT REV PLANT SCI, 30, 125
STOATE C, 2001, J ENVIRON MANAGE, 63, 337
Connectness
Connectness measures the number of links a paper has. Papers with a high
connectness would therefore be a paper that tend to have cited what most
other studies cited.
scilist(scimeetr_list, reading_list = "connectness", k = 5)
.x[[i]]
METTEPENNINGEN E, 2013, LAND USE POLICY, 33, 20
GUILLEM EE, 2013, LAND USE POLICY, 31, 565
UTHES S, 2013, ENVIRON MANAGE, 51, 251
BURTON RJF, 2013, LAND USE POLICY, 30, 628
WADE MR, 2008, PHILOS T R SOC B, 363, 831
Page rank
Page rank was developped by
Larry Page at google and it's a way to measure web page importance. The
algorithm was applied to directed graph, so I am not sure of the
consequence of applying it on the undirected graph that we have here.
scilist(scimeetr_list, reading_list = "page_rank", k = 5)
.x[[i]]
METTEPENNINGEN E, 2013, LAND USE POLICY, 33, 20
GUILLEM EE, 2013, LAND USE POLICY, 31, 565
UTHES S, 2013, ENVIRON MANAGE, 51, 251
GABRIEL D, 2009, J APPL ECOL, 46, 323
VAN DER WAL R, 2008, BIOL LETTERS, 4, 256
Cite most others
With the option cite_most_others, the papers that cite most other
papers of the community can be found. This is not a centrality measure
but it is also based on papers connection to each other. It should tend
to find litterature review and recent papers that have an especially
good grasp on the community.
scilist(scimeetr_list, reading_list = "cite_most_others", k = 5)
RECID.x
DOI
Nb_of_ref_within_com
UTHES S, 2013, ENVIRON MANAGE, 51, 251
10.1007/s00267-012-9959-6
24
HEJNOWICZ AP, 2016, LAND USE POLICY, 55, 240
10.1016/j.landusepol.2016.04.005
16
SCHOMERS S, 2015, SUSTAINABILITY-BASEL, 7, 13856
10.3390/su71013856
13
SCHOMERS S, 2015, LAND USE POLICY, 42, 58
10.1016/j.landusepol.2014.06.025
12
DEDEURWAERDERE T, 2015, ECOL ECON, 119, 24
10.1016/j.ecolecon.2015.07.025
11
Finding the main communities of research
In the previous sections we have looked at only the main research
community. But, splitting the main community in sub-communities can
provide a more detail picture of the litterature. It can also help
identify and then remove irrelevant sub-communities. To achieve any of
this, the sub-communities have to be identified and characterized. The
function scimap, as in science map, was developped for this task. By
default, the graph use bibliographic coupling to calculate connections
between papers, but coupling can also be done based on abstract words
(abc), title words (tic) or keywords (kec).
summary(scimap(scimeetr_list, coupling_by = 'bic', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_1 (202)
## 9 PARTICIPATION
## 10 AGRICULTURE
## 11 FARMERS
## 12 ATTITUDES
## 13 AGRI-ENVIRONMENTAL SCHEMES
## 14 POLICY
## 15 com1_8 (132)
## 16 ADOPTION
## 17 AGRICULTURE
## 18 POLICY
## 19 WATER QUALITY
## 20 AUSTRALIA
## 21 FARMERS
## 22 com1_3 (292)
## 23 BIODIVERSITY
## 24 AGRI-ENVIRONMENT SCHEMES
## 25 CONSERVATION
## 26 MANAGEMENT
## 27 DIVERSITY
## 28 AGRICULTURAL LANDSCAPES
summary(scimap(scimeetr_list, coupling_by = 'abc', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_3 (255)
## 9 ADOPTION
## 10 POLICY
## 11 FARMERS
## 12 ECOSYSTEM SERVICES
## 13 AUSTRALIA
## 14 INCENTIVES
## 15 com1_2 (237)
## 16 AGRICULTURE
## 17 PARTICIPATION
## 18 POLICY
## 19 SCHEMES
## 20 COMMON AGRICULTURAL POLICY
## 21 AGRI-ENVIRONMENTAL SCHEMES
## 22 com1_1 (249)
## 23 BIODIVERSITY
## 24 AGRI-ENVIRONMENT SCHEMES
## 25 CONSERVATION
## 26 MANAGEMENT
## 27 DIVERSITY
## 28 AGRICULTURAL LANDSCAPES
summary(scimap(scimeetr_list, coupling_by = 'tic', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_1 (176)
## 9 CONSERVATION
## 10 POLICY
## 11 FARMERS
## 12 ADOPTION
## 13 AUSTRALIA
## 14 LANDSCAPES
## 15 com1_3 (127)
## 16 BIODIVERSITY
## 17 SCHEMES
## 18 POLICY
## 19 AGRICULTURAL LANDSCAPES
## 20 ECOSYSTEM SERVICES
## 21 PARTICIPATION
## 22 com1_5 (128)
## 23 MANAGEMENT
## 24 ECOSYSTEM SERVICES
## 25 AGRICULTURE
## 26 ADOPTION
## 27 BIODIVERSITY CONSERVATION
## 28 POLICY
## 29 com1_4 (160)
## 30 BIODIVERSITY
## 31 CONSERVATION
## 32 AGRI-ENVIRONMENT SCHEMES
## 33 MANAGEMENT
## 34 AGRICULTURAL LANDSCAPES
## 35 AGRICULTURAL INTENSIFICATION
## 36 com1_2 (134)
## 37 BIODIVERSITY
## 38 LAND-USE
## 39 AGRICULTURAL POLICY
## 40 PARTICIPATION
## 41 SCHEMES
## 42 FARMERS
summary(scimap(scimeetr_list, coupling_by = 'kec', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_3 (297)
## 9 AGRICULTURE
## 10 ADOPTION
## 11 POLICY
## 12 PARTICIPATION
## 13 FARMERS
## 14 AGRI-ENVIRONMENTAL SCHEMES
## 15 com1_2 (151)
## 16 BIODIVERSITY
## 17 CONSERVATION
## 18 AGRI-ENVIRONMENT SCHEMES
## 19 MANAGEMENT
## 20 AGRICULTURAL INTENSIFICATION
## 21 DIVERSITY
## 22 com1_5 (112)
## 23 MANAGEMENT
## 24 ECOSYSTEM SERVICES
## 25 BIODIVERSITY CONSERVATION
## 26 LANDSCAPE
## 27 POLICY
## 28 SYSTEMS
## 29 com1_1 (127)
## 30 CONSERVATION
## 31 AGRI-ENVIRONMENT SCHEMES
## 32 AGRICULTURAL LANDSCAPES
## 33 DIVERSITY
## 34 BIRDS
## 35 LAND-USE
Focusing on a sub-community
With the function focus_on, it is possible to change focus on a
sub-community.
scil <- scimap(scimeetr_list)
scil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 742
## Number of communities: 5
## Names of communities: com1 com1_1 com1_8 com1_4 com1_3
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 BIODIVERSITY CONSERVATION FARMERS
## 2 CONSERVATION AGRICULTURAL CONSERVATION
## 3 MANAGEMENT MANAGEMENT AGRICULTURAL
## 4 AGRICULTURE POLICY MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES FARMERS POLICY
subscil <- focus_on(scil, grab = 'com1_1')
subscil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 202
## Number of communities: 1
## Names of communities: com1_1
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 PARTICIPATION FARMERS FARMERS
## 2 AGRICULTURE POLICY POLICY
## 3 CONSERVATION AGRIENVIRONMENTAL ENVIRONMENTAL
## 4 FARMERS AGRICULTURAL AGRICULTURAL
## 5 MANAGEMENT SCHEMES FARM
Dive to a sub-community
With the function dive_to, it is possible to move down to a
sub-community and keep it's sub-communities.
scil <- scimap(scimap(scimeetr_list))
scil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 742
## Number of communities: 13
## Names of communities: com1 com1_1 com1_1_2 com1_1_1 com1_1_3 com1_8 com1_8_3 com1_8_4 com1_4 com1_3 com1_3_4 com1_3_1 com1_3_5
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 BIODIVERSITY CONSERVATION FARMERS
## 2 CONSERVATION AGRICULTURAL CONSERVATION
## 3 MANAGEMENT MANAGEMENT AGRICULTURAL
## 4 AGRICULTURE POLICY MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES FARMERS POLICY
subscil <- dive_to(scil, aim_at = 'com1_1')
subscil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 202
## Number of communities: 4
## Names of communities: com1_1 com1_1_2 com1_1_1 com1_1_3
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 PARTICIPATION FARMERS FARMERS
## 2 AGRICULTURE POLICY POLICY
## 3 CONSERVATION AGRIENVIRONMENTAL ENVIRONMENTAL
## 4 FARMERS AGRICULTURAL AGRICULTURAL
## 5 MANAGEMENT SCHEMES FARM
MaximeRivest/scimeetr documentation built on May 8, 2019, 9:51 a.m.
R Package Documentation
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Introduction
Scimeetr helps explore the scholarly literature. It contains a suit of function that let someone:
- load bibliometric data into R
- make a map of peer reviewed papers by creating various networks
- find research community
- characterise the research communities
- generate reading list
This tutorial is composed of two self-contained section. The first section show case the whole process with all the default parameters. The second section describes each function in more detail by presenting the rational for the function, the algorithms used and the options.
From data to reading list
You can automatically generate a reading list of seminal papers in a
research litterature by using only those three functions:
ìmport_wos_files, scimap, and scilist. This first section
describes this process in more details.
loading and exploring bibliometric data
The first step in exploring the literature is to retrieve bibliometric data from the Web of Science or Scopus. In this first tutorial I use a dataset from the Web of Science about ecological networks.
library(scimeetr)
scimeetr_list <- import_wos_files("path/to/folder/")
Then,summary can be used to get a quick characterisation of the data.
summary(scimeetr_list)
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
From this summary, we see that there is 396 papers in my data set which overal cites 16567 different elements. On average, each paper cites 53 elements.
Than we learn that, in this research community, 25% of the papers are cited less than 2 times, 50% are cited less than 9 times and 75% are cited less than ~23 times. There are papers that are cited up to 1333 times. The average citation per paper is ~25. This is much higher than the median (9), thus most paper are cited only a few times and a few papers are profusely cited. When correcting for the age of the paper, we learn that papers are cited 2 times per year on average.
By looking at the most frequent keyword and journals, we learn that this community of research is about biodiversity, agriculture, ecosystem services and policy. Keyword and journal frequency tables efficiently reveal the theme of a scientific community.
Mapping scientific community
The previous characterisation is great, but it is limited if your
dataset contains many different scientific communities. By detecting the
scientific communities present within a dataset a map of science can be
drawn and each cluster can be characterised on its own. The function
scimap can be used for this task.
scimap_result <- scimap(scimeetr_list)
The function returns all the data that scimeetr_list contained and
more. For example communities have been identified and now if the
function summary is used on scim_result. In addition of the previous
information. The descriminant keywords of each communities constituating
the main community are listed.
summary(scimap_result)
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_1 (202)
## 9 PARTICIPATION
## 10 AGRICULTURE
## 11 FARMERS
## 12 ATTITUDES
## 13 AGRI-ENVIRONMENTAL SCHEMES
## 14 POLICY
## 15 com1_8 (132)
## 16 ADOPTION
## 17 AGRICULTURE
## 18 POLICY
## 19 WATER QUALITY
## 20 AUSTRALIA
## 21 FARMERS
## 22 com1_4 (67)
## 23 LAND-USE
## 24 AGRICULTURE
## 25 SCHEMES
## 26 LANDSCAPE
## 27 POLICY
## 28 COMMON AGRICULTURAL POLICY
## 29 com1_3 (292)
## 30 BIODIVERSITY
## 31 AGRI-ENVIRONMENT SCHEMES
## 32 CONSERVATION
## 33 MANAGEMENT
## 34 DIVERSITY
## 35 AGRICULTURAL LANDSCAPES
Except for the last tables, all of the output is identical to the
summary output above. Those last tables now reveals that the papers in
our database can be clustered in two communities. One that is about x
and the other that is about y.
The function plot can be used on the output of the function summary
for a graphical representation of the sub-communities.
plot(summary(scimap_result, com_size = 30))
Automatically generating a reading list of seminal papers
Now that we have characterise the main community and seen of which
community it is constituted, we can decide if it is the community that
we wish to join / review. If it is, we use the function scilist to get
reading lists. The defaul readin list will find the seminal papers of
each communitiy.
reading_list <- scilist(scimap_result)
reading_list$com1
In depth description of each steps
How to get bibliometric data?
Biliometric data can be obtained from either Scopus or the Web of Science. Most university library have access to either one and some have access to both.
Retrieving data from Scopus
Scopus home page.
Select all and export
Export as CSV file and select all fields for exportation
Following the previous steps will get you one or several .csv files.
Then, to import this/these file(s) in R, you need to put it/them in a
new folder which contains only the files to import into R
Retrieving data from Web of Science
Web of Science home page. Make sure that Select a database corresponds to Web of Science Core Collection
Save to Other Files Formats
You can download only 500 items at a time. You should select Full Record and Cited References. And select the Tab-delimeted (UTF-8) as file format.
Following the previous steps will get you one or several .txt files.
Then, to import this/these file(s) in R, you need to put it/them in a
new folder which contains only the files to import into R
How to upload bibliometric data into R
The bibliometric data obtained from Scopus or Web of science are either
in .csv or .txt format. These are standard file formats and you most
likely know them. There are built in function in R that let you import
.csv and .txt files. So why does scimeetr provide you with
import_scopus_files and import_wos_files? There are four reasons.
The main one is that bibliometric data contains author names from around
the world, which means that all alphabets are used and this leads to
problems with file encoding. Scimeetr's import functions solves that
problem. Second, Scopus do not provide standard, uniform and consisten
cited reference list. Thus, import_scopus_files has to standardize it
at import. This explains the additional time required to load scopus
files versus wos files. Third, Scopus and Web of Science do not use the
same column names so they have to be homogenized at import. Finally, the
data can be transformed into a scimeetr object so that summary, plot
and print will know what to do with it.
scimeetr_list <- import_wos_files(files_directory = "/path/to/folder/")
scimeetr_list <- import_scopus_files(files_directory = "/path/to/folder/")
Do not forget that this function take in a path to a folder not a file. Thus, it need a slash at the end of the folder path.
Exploring scimeetr data
Printing scimeetr_list that we just created will provide some
informations about it, but summary will provide more.
scimeetr_list
##
## # A scimeetr object #
## ---------------------
## Number of papers: 742
## Number of communities: 1
## Names of communities: com1
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 BIODIVERSITY CONSERVATION FARMERS
## 2 CONSERVATION AGRICULTURAL CONSERVATION
## 3 MANAGEMENT MANAGEMENT AGRICULTURAL
## 4 AGRICULTURE POLICY MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES FARMERS POLICY
summary(scimeetr_list)
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
A scimeetr object such as scimeetr_list contains more data than what
can be seen with print and summary. A scimeetr object is in fact a
list of communities list which are themselves list of up to 9 elements.
Each communities contain a data.frame called dfsci. This dataframe
contains all the bibliometric data that was importedinto R.
scimeetr_list$com1$dfsci
Making reading lists
If we are confident that the papers contained in scimeetr_list are
those for which we want a reading list we can used the function
scilist to find various lists of papers. The default list given by
scilist contains the seminal papers for the community analysed. That
is, it rank the paper by the number of times they were cited by all the
papers and list them by citation frequency.
scilist(scimeetr_list)
With the parameter k, we can control the length of the reading list.
scilist(scimeetr_list, k = 3)
With the parameter reading_list, we can get any of the following 12
reading lists that fits into three categories:
- Core
- core_papers
- core_yr
- core_residual
- Experts
- by_expert_LC
- by_expert_TC
- group_of_experts_TC
- group_of_experts_LC
- Centrality
- cite_most_others
- betweeness
- closeness
- connectness
- page_rank
The default reading list is core_papers.
Core
I categorise the reading lists as core because they are reading lists of core papers as they are all a variation of the number of times papers within our community of interest refers to the paper listed. Although the number of citation is not a perfect measure of a papers importance for a community it should be a good proxy. A weekness of the number of citation as a measure of papers importance is that not all citations are equal. For example, sometimes a paper is cited because it is criticized or because it contrasts with other findings. This as been realised by others before and some have attempted to fix it by creating the concept of influential citation. Influential citation is a great concept by to be calculated it requires advance text processing and access to the full text of each papers. As it is notoriosly time consuming to get full text and even harder to get it in the right format, we are left with citation count.
Most cited per year
Using scilist with reading_list = "core_yr" will list the most cited
paper for each year from three years before present to ten years before
present. The parameter k controls the number of paper per year to
list.
scilist(scimeetr_list, reading_list = "core_yr", k = 2)
More cited than expected
Using scilist with reading_list = "core_residual" will list the
papers that diverge most from the expected number of citation for this
particular paper. This can be visualised in the figure below. The point
that have the biggest difference between their frequency value and the
fitted blue lines are listed in the core_residual reading list.
Here is an example of the code and its result.
scilist(scimeetr_list, reading_list = "core_residual", k = 3)
Experts
The reading lists that I categorise as expert are built from authors information. Experts within a community are identified based on the number of papers they published and the number of times each of their papers are cited.
Recent paper of a few experts
Using scilist with reading_list = "by_expert_LC" we will get a list
of recent papers by one or a few experts in the community. For the
option by_expert_LC, authors are ranked based on their harmonic local
H-index. The H-index is a measure of an other productivity and impact.
An author with an H-index of 10 means that he has published at least 10
papers with 10 or more citation each. A local H-index means that only
citations from other papers in the community are counted. A harmonic
local H-index means that authors do not get the full credit for each
citation their paper received. It is corrected depending on the authos
position in the authors list. First authors gets most of the credit,
then the last author gets the second most, and the authors gets credit
as a proportion of their position. Once the authors
harmonic-local-H-index is found they are ranked and the m most recent
publication of the k most 'expert' authors are listed as a reading
list.
scilist(scimeetr_list, reading_list = "by_expert_LC", k = 2, m = 2)
Using scilist with reading_list = "by_expert_TC" instead of
reading_list = "by_expert_LC", notice the _TC instead of the _LC
will based the ranking calculation on total citation of it's
publications instead of only the local citations.
Paper of experts group
Using scilist with reading_list = "group_of_experts_LC" we will get
a list of papers for which many authors are experts in the community.
For this option, authors are assigned a harmonic local H-index like
described in the previous section. But this time, a weighted sum of the
harmonic-local-H-index of each authors of a paper is calculated.
scilist(scimeetr_list, reading_list = "group_of_experts_LC", k = 5)
Using scilist with reading_list = "group_of_experts_TC" instead of
reading_list = "group_of_experts_LC", notice the _TC instead of the
_LC will based the ranking calculation on total citation of it's
publications instead of only the local citations.
Centrality
Their are several measures of nodes centrality in graph
theory. The most central
papers of a community of papers can be found with scilist.
Betweeness
Betweeness measures the importance of a paper in connecting two clusters of papers. Papers with a high betweeness would therefore be a paper that tend to be more interdisciplinary.
scilist(scimeetr_list, reading_list = "betweeness", k = 5)
Closeness
Closeness measures the average number of link between a paper and all other papers. Papers with a high closeness would therefore be a paper that tend to have a large and wide list of citations.
scilist(scimeetr_list, reading_list = "closeness", k = 5)
Connectness
Connectness measures the number of links a paper has. Papers with a high connectness would therefore be a paper that tend to have cited what most other studies cited.
scilist(scimeetr_list, reading_list = "connectness", k = 5)
Page rank
Page rank was developped by Larry Page at google and it's a way to measure web page importance. The algorithm was applied to directed graph, so I am not sure of the consequence of applying it on the undirected graph that we have here.
scilist(scimeetr_list, reading_list = "page_rank", k = 5)
Cite most others
With the option cite_most_others, the papers that cite most other
papers of the community can be found. This is not a centrality measure
but it is also based on papers connection to each other. It should tend
to find litterature review and recent papers that have an especially
good grasp on the community.
scilist(scimeetr_list, reading_list = "cite_most_others", k = 5)
Finding the main communities of research
In the previous sections we have looked at only the main research
community. But, splitting the main community in sub-communities can
provide a more detail picture of the litterature. It can also help
identify and then remove irrelevant sub-communities. To achieve any of
this, the sub-communities have to be identified and characterized. The
function scimap, as in science map, was developped for this task. By
default, the graph use bibliographic coupling to calculate connections
between papers, but coupling can also be done based on abstract words
(abc), title words (tic) or keywords (kec).
summary(scimap(scimeetr_list, coupling_by = 'bic', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_1 (202)
## 9 PARTICIPATION
## 10 AGRICULTURE
## 11 FARMERS
## 12 ATTITUDES
## 13 AGRI-ENVIRONMENTAL SCHEMES
## 14 POLICY
## 15 com1_8 (132)
## 16 ADOPTION
## 17 AGRICULTURE
## 18 POLICY
## 19 WATER QUALITY
## 20 AUSTRALIA
## 21 FARMERS
## 22 com1_3 (292)
## 23 BIODIVERSITY
## 24 AGRI-ENVIRONMENT SCHEMES
## 25 CONSERVATION
## 26 MANAGEMENT
## 27 DIVERSITY
## 28 AGRICULTURAL LANDSCAPES
summary(scimap(scimeetr_list, coupling_by = 'abc', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_3 (255)
## 9 ADOPTION
## 10 POLICY
## 11 FARMERS
## 12 ECOSYSTEM SERVICES
## 13 AUSTRALIA
## 14 INCENTIVES
## 15 com1_2 (237)
## 16 AGRICULTURE
## 17 PARTICIPATION
## 18 POLICY
## 19 SCHEMES
## 20 COMMON AGRICULTURAL POLICY
## 21 AGRI-ENVIRONMENTAL SCHEMES
## 22 com1_1 (249)
## 23 BIODIVERSITY
## 24 AGRI-ENVIRONMENT SCHEMES
## 25 CONSERVATION
## 26 MANAGEMENT
## 27 DIVERSITY
## 28 AGRICULTURAL LANDSCAPES
summary(scimap(scimeetr_list, coupling_by = 'tic', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_1 (176)
## 9 CONSERVATION
## 10 POLICY
## 11 FARMERS
## 12 ADOPTION
## 13 AUSTRALIA
## 14 LANDSCAPES
## 15 com1_3 (127)
## 16 BIODIVERSITY
## 17 SCHEMES
## 18 POLICY
## 19 AGRICULTURAL LANDSCAPES
## 20 ECOSYSTEM SERVICES
## 21 PARTICIPATION
## 22 com1_5 (128)
## 23 MANAGEMENT
## 24 ECOSYSTEM SERVICES
## 25 AGRICULTURE
## 26 ADOPTION
## 27 BIODIVERSITY CONSERVATION
## 28 POLICY
## 29 com1_4 (160)
## 30 BIODIVERSITY
## 31 CONSERVATION
## 32 AGRI-ENVIRONMENT SCHEMES
## 33 MANAGEMENT
## 34 AGRICULTURAL LANDSCAPES
## 35 AGRICULTURAL INTENSIFICATION
## 36 com1_2 (134)
## 37 BIODIVERSITY
## 38 LAND-USE
## 39 AGRICULTURAL POLICY
## 40 PARTICIPATION
## 41 SCHEMES
## 42 FARMERS
summary(scimap(scimeetr_list, coupling_by = 'kec', community_algorithm = 'louvain', min_com_size = 100))
##
## # Summary of Scimeetr #
## -----------------------
## Number of papers: 742
## Number of different reference: 28526
##
## Average number of reference per paper: 51
##
## Quantiles of total citation per paper:
##
## 0% 25% 50% 75% 100%
## 0.00 2.00 7.00 19.75 1333.00
##
## Mean number of citation per paper: 19.81536
##
## Average number of citation per paper per year: 1.666667
##
##
## Table of the 10 most mentionned keywords
##
## Keyword Frequency
## 1 BIODIVERSITY 57
## 2 AGRICULTURE 46
## 3 COMMON AGRICULTURAL POLICY 32
## 4 ECOSYSTEM SERVICES 31
## 5 CONSERVATION 28
## 6 AGRI-ENVIRONMENT SCHEMES 27
## 7 AGRI-ENVIRONMENT SCHEME 20
## 8 AGRI-ENVIRONMENTAL SCHEMES 19
## 9 AGRICULTURAL POLICY 18
## 10 WATER QUALITY 18
##
##
##
## Table of the 10 most productive journal
##
## Journal Frequency
## 1 LAND USE POLICY 84
## 2 AGRICULTURE ECOSYSTEMS & ENVIRONMENT 37
## 3 JOURNAL OF ENVIRONMENTAL MANAGEMENT 33
## 4 BIOLOGICAL CONSERVATION 24
## 5 JOURNAL OF APPLIED ECOLOGY 21
## 6 ECOLOGICAL ECONOMICS 17
## 7 JOURNAL OF RURAL STUDIES 17
## 8 AGRICULTURAL SYSTEMS 14
## 9 JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 14
## 10 LANDSCAPE AND URBAN PLANNING 14
##
##
##
## Table of the most descriminant keywords
##
## comID tag
## 1 com1 (742)
## 2 BIODIVERSITY
## 3 CONSERVATION
## 4 MANAGEMENT
## 5 AGRICULTURE
## 6 AGRI-ENVIRONMENT SCHEMES
## 7 ECOSYSTEM SERVICES
## 8 com1_3 (297)
## 9 AGRICULTURE
## 10 ADOPTION
## 11 POLICY
## 12 PARTICIPATION
## 13 FARMERS
## 14 AGRI-ENVIRONMENTAL SCHEMES
## 15 com1_2 (151)
## 16 BIODIVERSITY
## 17 CONSERVATION
## 18 AGRI-ENVIRONMENT SCHEMES
## 19 MANAGEMENT
## 20 AGRICULTURAL INTENSIFICATION
## 21 DIVERSITY
## 22 com1_5 (112)
## 23 MANAGEMENT
## 24 ECOSYSTEM SERVICES
## 25 BIODIVERSITY CONSERVATION
## 26 LANDSCAPE
## 27 POLICY
## 28 SYSTEMS
## 29 com1_1 (127)
## 30 CONSERVATION
## 31 AGRI-ENVIRONMENT SCHEMES
## 32 AGRICULTURAL LANDSCAPES
## 33 DIVERSITY
## 34 BIRDS
## 35 LAND-USE
Focusing on a sub-community
With the function focus_on, it is possible to change focus on a
sub-community.
scil <- scimap(scimeetr_list)
scil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 742
## Number of communities: 5
## Names of communities: com1 com1_1 com1_8 com1_4 com1_3
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 BIODIVERSITY CONSERVATION FARMERS
## 2 CONSERVATION AGRICULTURAL CONSERVATION
## 3 MANAGEMENT MANAGEMENT AGRICULTURAL
## 4 AGRICULTURE POLICY MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES FARMERS POLICY
subscil <- focus_on(scil, grab = 'com1_1')
subscil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 202
## Number of communities: 1
## Names of communities: com1_1
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 PARTICIPATION FARMERS FARMERS
## 2 AGRICULTURE POLICY POLICY
## 3 CONSERVATION AGRIENVIRONMENTAL ENVIRONMENTAL
## 4 FARMERS AGRICULTURAL AGRICULTURAL
## 5 MANAGEMENT SCHEMES FARM
Dive to a sub-community
With the function dive_to, it is possible to move down to a
sub-community and keep it's sub-communities.
scil <- scimap(scimap(scimeetr_list))
scil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 742
## Number of communities: 13
## Names of communities: com1 com1_1 com1_1_2 com1_1_1 com1_1_3 com1_8 com1_8_3 com1_8_4 com1_4 com1_3 com1_3_4 com1_3_1 com1_3_5
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 BIODIVERSITY CONSERVATION FARMERS
## 2 CONSERVATION AGRICULTURAL CONSERVATION
## 3 MANAGEMENT MANAGEMENT AGRICULTURAL
## 4 AGRICULTURE POLICY MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES FARMERS POLICY
subscil <- dive_to(scil, aim_at = 'com1_1')
subscil
##
## # A scimeetr object #
## ---------------------
## Number of papers: 202
## Number of communities: 4
## Names of communities: com1_1 com1_1_2 com1_1_1 com1_1_3
##
## Table of the 5 most mentionned words
##
## key_words title_words abstract_words
## 1 PARTICIPATION FARMERS FARMERS
## 2 AGRICULTURE POLICY POLICY
## 3 CONSERVATION AGRIENVIRONMENTAL ENVIRONMENTAL
## 4 FARMERS AGRICULTURAL AGRICULTURAL
## 5 MANAGEMENT SCHEMES FARM
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