Building from the data set you used in the first deliverable, complete the following tasks:
By creating the ID adjacency list and the names adjacency list of the authors, we are using them to create the edge lists to list out the connections of authorship of each article. It is an essential step for us to clearly see the connections between the authors.
The dimensions of both adjacency lists are [179, 41]. It means that I have 179 articles in total, with at least one of the article having 41 authors.
2.1 Let us assume that you did not have the same dimensions, what would be the reason for this discrepancy and how would you address this issue?
It could be due to some authors’ name changes over time, while it is still the same person, R would not recognize the different names as the same person. And the author’s ID stays the same as the name may change, hence the numbers of ID may not match the numbers of the names. To address this issue, we need to match the ID with its most recent publication’s author’s name.
3.1 What is the meaning of the number of rows of this database?
It means the total number of unique authors we have in the database.
3.2 Does this number match the number of unique actors you have in your first deliverable in the object g (i.e., the graph we created)?
No, it does not match. The number in object g is smaller than the total number of rows in this data set.
3.3 If the number does not match, what do you think is the reason for this mismatch?
The reason for this mismatch could be that solo authors’ IDs are not included in the igraph I printed in the first deliverable. They were excluded because they did not have any coauthors.
By unlisting both author IDs and their names into separate column, it is easier for us to match each ID with the author’s name in the following steps.
There are 10 most prolific authors in my data set because they all published 2 articles within this five-years time span, while all other authors only published one article. Most of these authors’ publications situate around the effects of AI tools (such as Google Assistants) have on second language teaching and learning.
library(splitstackshape)
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## uniongetwd()## [1] "/Users/aihe/Desktop"source <- read.csv("/Users/aihe/Desktop/AI_SLL.csv")
source <- source[source$Author.s..ID!="[No author id available]",]
source$au <- gsub(" Jr.,", "",
gsub(" II.,", "",
gsub(" Jr.", "",
gsub(" M.S.", "",
gsub(" M.S.,", "",
gsub(" II.", "", source[,1]))))))
source$au <- gsub("\\.,", ";", source$au)
source$au <- tolower(source$au)
df_all_authors <- as.data.frame(source[ , ncol(source)])
colnames(df_all_authors) <- "AU"
authornames_split <- cSplit(df_all_authors, splitCols = "AU", sep = ";", direction = "wide", drop = TRUE)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
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## the caller; using TRUEdim(authornames_split)## [1] 179 41df_authors_id <- as.data.frame(source[ , 2])
colnames(df_authors_id) <- "AU"
author_id_split <- cSplit(df_authors_id, splitCols = "AU", sep = ";", direction = "wide", drop = TRUE) ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
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## the caller; using TRUEdim(author_id_split)## [1] 179 41df_authorid_authorname_unlisted <- data.frame(id = unlist(author_id_split), names = unlist(authornames_split))
df_authorid_authorname_unlisted <- df_authorid_authorname_unlisted[!is.na(df_authorid_authorname_unlisted$id),]
dim(df_authorid_authorname_unlisted)## [1] 737 2df_authorid_authorname_unlisted <- df_authorid_authorname_unlisted[!duplicated(df_authorid_authorname_unlisted$id),]
dim(df_authorid_authorname_unlisted)## [1] 727 2pub_count <- as.data.frame(table(unlist(author_id_split)))
df_authorid_authorname_unlisted$pub_count <- pub_count$Freq[match(df_authorid_authorname_unlisted$id, pub_count$Var1)]
head(df_authorid_authorname_unlisted[order(df_authorid_authorname_unlisted$pub_count, decreasing=T),])## id names pub_count
## AU_015 58061860400 moulieswaran n 2
## AU_018 57221813377 sha l 2
## AU_0119 57222872644 kim h.-s 2
## AU_0138 57220797184 kim n.-y. 2
## AU_0196 57219976831 garvin t 2
## AU_028 57063266900 camburu o.-m 2df_authorid_authorname_unlisted<-df_authorid_authorname_unlisted[order(df_authorid_authorname_unlisted$pub_count, decreasing=T),]
head(df_authorid_authorname_unlisted)## id names pub_count
## AU_015 58061860400 moulieswaran n 2
## AU_018 57221813377 sha l 2
## AU_0119 57222872644 kim h.-s 2
## AU_0138 57220797184 kim n.-y. 2
## AU_0196 57219976831 garvin t 2
## AU_028 57063266900 camburu o.-m 2a<-df_authorid_authorname_unlisted
head(a)## id names pub_count
## AU_015 58061860400 moulieswaran n 2
## AU_018 57221813377 sha l 2
## AU_0119 57222872644 kim h.-s 2
## AU_0138 57220797184 kim n.-y. 2
## AU_0196 57219976831 garvin t 2
## AU_028 57063266900 camburu o.-m 2a## id names pub_count
## AU_015 58061860400 moulieswaran n 2
## AU_018 57221813377 sha l 2
## AU_0119 57222872644 kim h.-s 2
## AU_0138 57220797184 kim n.-y. 2
## AU_0196 57219976831 garvin t 2
## AU_028 57063266900 camburu o.-m 2
## AU_0219 57222869163 cha y. 2
## AU_0296 57222104444 kimbleton s. 2
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## AU_0190 57220903146 tran d.c 1
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## AU_01117 57315121800 alnami a 1
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## AU_01121 55806765400 poongodi m 1
## AU_01122 56230086000 pikhart m. 1
## AU_01123 57223898570 doumbouya m 1
## AU_01124 57212303924 williams k.a 1
## AU_01125 56565582500 haruna u 1
## AU_01126 57212346601 dillard l 1
## AU_01127 57188667328 moriuchi e. 1
## AU_01128 57209782796 wang y 1
## AU_01129 7005093287 lawless w.f. 1
## AU_01130 57200090297 mehta s 1
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## AU_01136 56406067400 bhattacharjee a 1
## AU_01137 6504574010 yabloko l. 1
## AU_01138 57202902190 alam m.m 1
## AU_01139 55848921100 choubey s.k 1
## AU_01140 57368537700 almutairi a 1
## AU_01141 55597573100 wang l 1
## AU_01143 57198891988 kumar a 1
## AU_01144 7102746954 tavazzi l. 1
## AU_01145 57191243306 giannopoulou e 1
## AU_01146 57193133189 alotaibi r 1
## AU_01147 57219591833 silva a.c 1
## AU_01148 57218311587 qian m 1
## AU_01149 57216297312 gómez-hernández e.j 1
## AU_01150 6602862826 khemani d. 1
## AU_01151 57188757098 yu f 1
## AU_01152 57195473676 demirer m 1
## AU_01153 57211759490 nakamura k. 1
## AU_01154 57215117931 smuts m 1
## AU_01155 57212103868 reynolds o. 1
## AU_01156 56257339200 pang s 1
## AU_01157 55933624700 shen j 1
## AU_01158 57091199200 osman a 1
## AU_01159 57210321313 yan w. 1
## AU_01160 57073076300 abo khamis m 1
## AU_01161 6701815202 jaakkola h 1
## AU_01162 7101677064 behrens j.t 1
## AU_01163 8310138000 godwin-jones r. 1
## AU_01164 57202790788 benzebouchi n.e 1
## AU_01165 16315433700 freeze r 1
## AU_01166 57201320595 tan h 1
## AU_01167 8590706200 chen j.f 1
## AU_01168 57203284197 khan m.a 1
## AU_01169 57211253293 xu j 1
## AU_01170 57211203593 greco c 1
## AU_01171 57193775331 tafazoli d 1
## AU_01172 56440077800 mohamed mokmin n.a. 1
## AU_01173 57195223484 ehsan u 1
## AU_01174 57140451800 alotaibi b 1
## AU_01175 46461354700 mandal s 1
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## AU_01177 57088207100 jee m.j. 1
## AU_01178 55505341400 kannan j 1
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## AU_0213 55253499700 amrani m 1
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## AU_0230 6504172985 hadrich belguith l. 1
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## AU_0236 57816221100 jiang z 1
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## AU_0240 57215770167 min i.k 1
## AU_0241 57188698154 castonguay a 1
## AU_0242 57191135612 ferretti c. 1
## AU_0243 36063168200 liu b. 1
## AU_0244 57207932854 cooper h 1
## AU_0245 7406967942 lee e.-b. 1
## AU_0246 16230465000 lan y.-j. 1
## AU_0247 7004661333 gloor p.a 1
## AU_0248 57224568937 kabe d 1
## AU_0249 7004296745 willemsen m.c 1
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