1library(reshape2)
2library(ggplot2)
3library(tidyr)
4library(dplyr)

1# Import each text file
2savedrecs.orig <- readLines(con <- file("savedrecs_full_mfm_20181008.txt"))
3
4# Remove the first two rows of each original file
5savedrecs <- as.character(data.frame(savedrecs.orig)[-c(1:2), ])

 1# Select fields pertaining to the desired WOS tag. Use general pattern of '*XX *|* XX*' to select
 2# multiple fields with a grep statement
 3which <- grep("*PT *|*AU *|*AB *|*TI *|*SO *|*DI *|*PY *|*SC *|*WC *", savedrecs)
 4# Filter saved recs by the indexed WOS fields in 'which'
 5savedrecs <- savedrecs[which]
 6# Make sure savedrecs is all characters
 7savedrecs <- as.character(savedrecs)
 8# Isolate the savedrecs WOS field
 9bibtype <- as.character(substr(savedrecs, 1, 2))
10# Isolate the descriptions for each of WOS tags
11desc <- substr(savedrecs, 4, nchar(savedrecs))
12# Build an index for each item
13index <- rep(0, length(savedrecs))
14# Create a df of index, WOS tag, and description
15df <- data.frame(index, bibtype, desc)
16# Find the index for differing publications
17new <- which(df$bibtype == "PT")
18newindex <- 0
19# Now, we build a new index where all indices are for a given pub
20for (i in 1:length(df[, 1])) {
21    if (i %in% new) {
22        newindex <- newindex + 1
23    }
24    df$index[i] <- newindex
25}
26
27# Change bibtype to character, remove rows where bibtype is blank
28df$bibtype <- as.character(df$bibtype)
29df <- df[which(df$bibtype != "  "), ]
30
31# Pivot the long table format to wide format
32df.wide <- dcast(df, index ~ bibtype)

 1focal_samples <- c("fish", "mussel", "plant", "invertebrate", "clam", "plankton", "oyster")
 2
 3systems <- c("marine", "ocean", "estuary", "gulf", "sea", "gyre", "freshwater", "river", "stream", "lake",
 4    "soil", "terrestrial", "forest")
 5
 6marine <- c("marine", "ocean", "estuary", "gulf", "sea", "gyre")
 7
 8freshwater <- c("freshwater", "river", "stream", "lake")
 9
10terrestrial <- c("soil", "terrestrial", "forest")

 1## Proportion of systems across all discplines . First copy the wide dataframe
 2df_system <- df.wide
 3# Create an empty character vector where we will put focal terms
 4df_system$term_system <- ""
 5# Create an empty numeric column to count number of times a term appears
 6df_system$ABcontains <- 0
 7# Convert Publication Year to a numeric
 8df_system$PY <- as.numeric(df_system$PY)
 9# Copy this dataframe to a 'final data frame version'
10df_system_final <- df_system
11# For loop to iterate through each focal system in character vector
12for (i in 1:length(systems)) {
13    # Assign temporary variable for a particular system
14    termi <- systems[i]
15    # Create a temporary dataframe
16    dfi <- df_system
17    # Search for instances of focal system in the abstract
18    whichi <- grep(termi, dfi$AB, ignore.case = TRUE, fixed = TRUE)
19    # Create a reference to which system is present within the abstract
20    dfi$term <- termi
21    # Assign a value of one to abstracts that contain a given system
22    dfi$ABcontains[whichi] <- 1
23    # Start the final dataframe if this is the first iteration through the for loop
24    if (i == 1) {
25        df_system_final <- dfi
26    }
27    # Combine the final data frame with the temporary after the first for loop iteration
28    if (i > 1) {
29        df_system_final <- rbind(df_system_final, dfi)
30    }
31}
32# Set factor levels for the focal system
33df_system_final$term <- factor(df_system_final$term, levels = systems)
34
35# I added a step that makes sure ABcontains only has a value of 1 or 0.
36df.system.binary <- df_system_final
37# In most, if not all cases, this step is redundant. It is included as a fail-safe.
38for (i in length(df.system.binary$ABcontains)) {
39    if (df.system.binary$ABcontains[i] != 0) {
40        df.system.binary$ABcontains[i] = 1
41    }
42}

1df.system.binary$SYSTEM <- NA
2df.system.binary <- df.system.binary %>% mutate(SYSTEM = ifelse(term %in% marine, "marine", NA), SYSTEM = ifelse(term %in%
3    freshwater, "freshwater", SYSTEM), SYSTEM = ifelse(term %in% terrestrial, "terrestrial", SYSTEM))

1df.system.wo.na.part1 <- df.system.binary %>% # Group by abstract Year, then Title, then Pharmaceutical Class
2group_by(PY, TI, SYSTEM) %>% # Sum the number of times a class was references per abstract
3summarize(TOTAL = sum(ABcontains)) %>% # If greater than one, replace with one, so we have a presence/absence
4mutate(TOTAL = ifelse(TOTAL > 1, 1, TOTAL))
5factor(x$var1, levels = c("high", "med", "low"))
6
7## Error in factor(x$var1, levels = c("high", "med", "low")): object 'x' not found

1df.system.wo.na.part2 <- df.system.wo.na.part1 %>% # Select studies that have at least one system referenced
2filter(TOTAL == 1) %>% # Group by abstract year and system type
3group_by(PY, SYSTEM) %>% # Count the number of titles that reference a given system type
4summarize(COUNT.PUBS = n_distinct(TI))

1DI.count.total <- df.system.wo.na.part1 %>%
2  #Select all studies that reference at least one system type
3  filter(TOTAL == 1) %>%
4  #Group by abstract year
5  group_by(PY) %>%
6  #Sum the number of titles in a given year
7  summarize(TOTAL.TI = n_distinct(TI))

1dataplot.system <- full_join(df.system.wo.na.part2, DI.count.total)
2dataplot.system <- dataplot.system %>%
3  #Group by abstract year and pharmaceutical class
4  group_by(PY, SYSTEM) %>%
5  #Calculate proportions for analysis
6 summarize(PROP.COUNT = COUNT.PUBS/TOTAL.TI) %>%
7  as.data.frame()

1dataplot.system <- dataplot.system %>% spread(SYSTEM, PROP.COUNT) %>% gather(SYSTEM, PROP.COUNT, -PY) %>%
2    mutate(PROP.COUNT = ifelse(is.na(PROP.COUNT), 0, PROP.COUNT))

 1### The lines below build the final plot
 2dataplot.system$SYSTEM <- factor(dataplot.system$SYSTEM, levels = c("marine", "freshwater", "terrestrial"))
 3
 4yearplot.systems <- ggplot(dataplot.system, aes(x = as.factor(PY), y = PROP.COUNT, group = SYSTEM)) +
 5    geom_point(size = 4, color = "grey60") + ylab("Proportion of Abstracts") + xlab("Publication Year") +
 6    theme_minimal() + facet_wrap(~SYSTEM, drop = FALSE) + # scale_x_continuous(limits = c(1997, 2017)) +
 7theme(legend.position = "none") + theme(plot.title = element_text(size = 20), strip.text.x = element_text(size = 20),
 8    strip.background = element_rect(fill = "white"), panel.background = element_rect(color = "black"),
 9    axis.title = element_text(size = 20), axis.text.x = element_text(size = 20), axis.text.y = element_text(size = 20),
10    axis.title.y = element_text(margin = margin(0, 20, 0, 0)), axis.title.x = element_text(margin = margin(20,
11        0, 0, 0)))
12yearplot.systems

 1richness.orig <- df.system.binary %>% # Group by abstract year, then title, then system type
 2group_by(PY, TI, SYSTEM) %>% # Sum the number of systems for a given system type
 3summarize(TOTAL = sum(ABcontains)) %>% # Replace summed values with a 1 so we have presence/absence data of the system type
 4mutate(TOTAL = ifelse(TOTAL > 1, 1, TOTAL)) %>% # Filter for studies that mention at least one system type
 5filter(TOTAL >= 1) %>% # Remove grouping
 6ungroup() %>% # Group by abstract year and title
 7group_by(PY, TI) %>% # Sum the number of system types for a given abstract
 8summarize(RICHNESS = sum(TOTAL)) %>% # Remove grouping
 9ungroup() %>% # Group by abstract year and RICHNESS
10group_by(PY, RICHNESS) %>% # Sum the number of unique titles
11mutate(UNIQUE_TI = n_distinct(TI))
12
13system.div <- ggplot(richness.orig, aes(as.factor(PY), as.factor(RICHNESS), size = UNIQUE_TI)) + geom_point(shape = 21,
14    colour = "grey40", fill = "grey80", stroke = 2) + scale_size("Number of Abstracts", range = c(10,
15    35), breaks = c(25, 50, 75, 100, 125, 150, 175, 200)) + geom_text(aes(label = UNIQUE_TI), size = 6,
16    color = "black") + ylab("Number of Systems in an Abstract") + xlab("Publication Year") + theme_minimal() +
17    theme(legend.position = "none", panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
18        panel.background = element_rect(color = "black", size = 1), plot.title = element_text(size = 20),
19        strip.text.x = element_text(size = 20), axis.title = element_text(size = 20), axis.text.x = element_text(size = 20,
20            angle = 45, vjust = 0.5), axis.text.y = element_text(size = 20), axis.title.y = element_text(margin = margin(0,
21            20, 0, 0)), axis.title.x = element_text(margin = margin(20, 0, 0, 0)))
22system.div

 1library(bibliometrix)
 2
 3### The following lines replicate what we did above within base R. These data frames though are not
 4### necessarily reconcilable. We will bring in the full records with references for this example.
 5df.orig <- readFiles("savedrecs_withRefs_mfm_20181008.txt")
 6df.current <- convert2df(df.orig, dbsource = "isi", format = "plaintext")
 7
 8##
 9## Converting your isi collection into a bibliographic dataframe
10##
11## Articles extracted   100
12## Articles extracted   200
13## Articles extracted   300
14## Articles extracted   307
15## Done!
16##
17##
18## Generating affiliation field tag AU_UN from C1:  Done!
19
20plot(x = results, k = 10, pause = FALSE)

1### Bibliometrix can help us look at author collaboration networks. Just put in the Bibliometrix
2### formatted dataframe... Then the type of analysis you want to perform... The network you are trying
3### to build... And what the separator is in your dataframe.
4NetMatrix <- biblioNetwork(df.current, analysis = "collaboration", network = "authors", sep = ";")
5
6### Bibliometrix 2.0.0 offers more flexibility with respect to plotting and metrix.
7
8net <- networkPlot(NetMatrix, normalize = "salton", weighted = NULL, n = 100, Title = "Authors' Coupling",
9    type = "kamada", size = 5, size.cex = T, remove.multiple = TRUE, labelsize = 0.8, label.n = 10, label.cex = F)

1### Similar formulas can be used to build co-citatin networks Two papers are considerd to be co-cited
2### if a third paper cites both of them. Remember to change the network to the references.
3NetMatrix <- biblioNetwork(df.current, analysis = "co-citation", network = "references", sep = ";")
4
5net <- networkPlot(NetMatrix, normalize = "salton", weighted = NULL, n = 10, Title = "Co-citation network",
6    type = "fruchterman", size = 5, size.cex = T, remove.multiple = TRUE, labelsize = 0.8, label.n = 10,
7    label.cex = F)

1### We can also look at a conceptual structure plot, where we are basically looking at associations
2### between common terms.
3CS <- conceptualStructure(df.current, field = "ID", method = "MCA", minDegree = 15, k.max = 8, stemming = FALSE,
4    labelsize = 10, documents = 10)

1### We can also look at the direct historical association with a given publication by creating a
2### historical direct citation network.
3histResults <- histNetwork(df.current, min.citations = 20, sep = ".  ")
4
5## Articles analysed   88
6
7net <- histPlot(histResults, n = 10, size = 10, labelsize = 5, size.cex = TRUE, arrowsize = 0.5, color = TRUE)

 1##
 2##  Legend
 3##
 4##                                              Paper                             DOI Year LCS GCS
 5## 2008 - 1      BROWNE MA, 2008, ENVIRON SCI TECHNOL               10.1021/ES800249A 2008  75 393
 6## 2012 - 5     VON MOOS N, 2012, ENVIRON SCI TECHNOL               10.1021/ES302332W 2012  58 216
 7## 2013 - 7                BROWNE MA, 2013, CURR BIOL       10.1016/J.CUB.2013.10.012 2013  41 192
 8## 2013 - 10 VAN CAUWENBERGHE L, 2013, ENVIRON POLLUT    10.1016/J.ENVPOL.2013.08.013 2013  45 198
 9## 2013 - 14        COLE M, 2013, ENVIRON SCI TECHNOL               10.1021/ES400663F 2013  85 325
10## 2014 - 24           FREE CM, 2014, MAR POLLUT BULL 10.1016/J.MARPOLBUL.2014.06.001 2014  46 153
11## 2014 - 26          LECHNER A, 2014, ENVIRON POLLUT    10.1016/J.ENVPOL.2014.02.006 2014  35 124
12## 2015 - 34        COLE M, 2015, ENVIRON SCI TECHNOL         10.1021/ACS.EST.5B04099 2015  41  49
13## 2015 - 43       KLEIN S, 2015, ENVIRON SCI TECHNOL         10.1021/ACS.EST.5B00492 2015  32  93
14## 2015 - 45            AVIO CG, 2015, ENVIRON POLLUT    10.1016/J.ENVPOL.2014.12.021 2015  33 124