Category

Let’s first have a look at all the category of the publications that are in my bibliography. I am expecting a high number of research articles, but I know I also have a few R packages for instance.

count(df, CATEGORY, sort = TRUE)

Without surprise, most of the publications in my PhD bibliography collection are research articles. There is also two books. In my bibliography, the publications in “INCOLLECTION” correspond to book sections and the one in “INPROCEEDINGS” to conference papers. To finish, the “MISC” category corresponds to the R, Matlab and GIMP softwares, and to the major R packages I used. The Fiji image analysis software is cited as a research article.
This data mining is an opportunity for me to remind you that it is important to mention the softwares, but also the packages and plugins used in scientific articles, especially those that are free and open-source. Citations are currently often the only way to ensure continuity of funding for these tools.

filter(df, CATEGORY == "MISC") %>% 
  mutate(TITLE = gsub("\\{|\\}", "", TITLE)) %>% 
  select(TITLE)

## # A tibble: 9 x 1
##   TITLE                                                             
##   <chr>                                                             
## 1 R: A Language and Environment for Statistical Computing           
## 2 Matlab                                                            
## 3 GNU Image Manipulation Program                                    
## 4 moments: Moments, cumulants, skewness, kurtosis and related tests 
## 5 raster: Geographic Data Analysis and Modeling                     
## 6 FactoMineR: Multivariate Exploratory Data Analysis and Data Mining
## 7 RImageJROI: Read 'ImageJ' Region of Interest (ROI) Files          
## 8 snow: Simple Network of Workstations                              
## 9 rgl: 3D visualization device system (OpenGL)

Journal

I am curious to find what is the most common journal in my PhD bibliography. I am doing this exploration for fun, but I could use this information to know better where I am usually finding my bibliography ressources, maybe reinforce this bias by suscribing to newsletters or RSS feeds from these journals (as they are my major source of information) or on the contrary, choose to diversify more my sources. I’ll filter the data to keep only the ones having a journal name, count how many occurences of the journal name there are, and arrange in descending order to see the most common journals first. Let’s see what is the top ten.

top_ten_journals <- df %>%
  filter(!is.na(JOURNAL)) %>%
  group_by(JOURNAL) %>%
  summarize(n = n()) %>%
  arrange(desc(n)) %>% 
  top_n(10, n)

ggplot(top_ten_journals) +
  geom_col(aes(fct_reorder(JOURNAL, n), n, fill = n), 
           colour = "grey30", width = 1) +
  labs(x = "", y = "", title = "Top 10 journals in my bibliography") +
  coord_flip() +
  scale_fill_gradientn("n", colours = greenpal(10)) +
  scale_y_continuous(expand = c(0, 0)) +
  scale_x_discrete(expand = c(0, 0)) +
  custom_plot_theme()

Year

Another interesting metric is the median year of the publications in my PhD bibliography. Did I mostly gathered recent papers? I will compare this median year to the year at which I submitted my PhD thesis and defended (2015) and calculate the median time between 2015 and the year of publication of the paper.

year_pub <- df %>% 
  filter(is.na(YEAR) == FALSE) %>% 
  mutate(YEAR = as.numeric(YEAR)) %>% 
  mutate(age = 2015 - YEAR) %>%
  summarize(median_age = median(age),
            median_year = median(YEAR))
year_pub

## # A tibble: 1 x 2
##   median_age median_year
##        <dbl>       <dbl>
## 1          6        2009

Half of the publications were 6 years old or less at the time of submission of my PhD manuscript, which means that I mainly gathered recent publications in my bibliography.

Let’s now plot the number of publication per year to see how is the distribution, and let’s position the median year of publications with a vertical line.

df %>% 
  filter(is.na(YEAR) == FALSE) %>% 
ggplot(aes(x = as.numeric(YEAR))) +
  geom_bar(width = 1, fill = greenpal(6)[4]) +
  geom_vline(data = year_pub, aes(xintercept = median_year), 
             colour = "grey30", size = 2) +
  labs(x = "", y = "") +
  ggtitle("Number of publication per year") + 
  custom_plot_theme() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

The oldest publications correspond to the cell division rules from the XIX^th century. Then, I have one article from 1947, and all the rest of the publications I have are from after 1950. Half of them were even published in 2009 or after. It is not surprising to see more recent publications, as the number of publications per year continuously increased between the 50s and now, and as the most recent publications are more easily accessible online. However, I am surprised to see that the median year is only 6 years before my PhD manuscript submission.

Authors

I am curious to see who are the most frequent authors in my publication list. The initials of the first name are prone to typos as they were often entered manually. Some might be missing (in the case of multiple names, for instance). We will start using only the family name.

top_ten <- df %>% 
  select(AUTHOR) %>% 
  unnest() %>% 
  extract(AUTHOR, into = "familyName", regex = "(.*)(?=,)") %>% 
  mutate(familyName = tolower(familyName)) %>% 
  count(familyName) %>% 
  arrange(desc(n)) %>% 
  top_n(10, n)

top_ten

## # A tibble: 10 x 2
##    familyName     n
##    <chr>      <int>
##  1 traas         25
##  2 hamant        23
##  3 lloyd         18
##  4 meyerowitz    16
##  5 boudaoud      15
##  6 smith         15
##  7 wasteneys     12
##  8 wang          10
##  9 gibson         9
## 10 li             9

The risk of using only the family name is that common family names such as Smith, Li, Gibson or Wang are all attributed to the same person. To avoid this issue, I can add the initials, keeping in mind that they might be more prone to typos than the family name itself. To limit that risk, I’ll remove all punctuation signs and only keep the first initial.

top_ten_with_initials <- df %>% 
  select(AUTHOR) %>% 
  unnest() %>% 
  extract(AUTHOR, into = c("familyName", "initials"), regex = "(.*)(?=,)(.*)") %>% 
  mutate(familyName = tolower(familyName)) %>% 
  mutate(initials = toupper(initials) %>%
           gsub(pattern = "[[:punct:]]", replacement = " ") %>%
           gsub(initials, pattern = " ", replacement = "") %>% 
           substr(1,1)) %>%
  group_by(familyName, initials) %>% 
  count(familyName) %>% 
  ungroup() %>% 
  mutate(name = 
           glue("{substr(toupper(familyName), 1, 1)}",
                "{substr(familyName, 2, nchar(familyName))}, {initials}.")) %>% 
  arrange(desc(n)) %>% 
  top_n(10, n)

ggplot(top_ten_with_initials) +
  geom_col(aes(fct_reorder(name, n), n, fill = n), 
           colour = "grey30", width = 1) +
  labs(x = "", y = "", title = "Top 10 authors in my bibliography") +
  coord_flip() +
  scale_fill_gradientn("n", colours = greenpal(10)) +
  scale_y_continuous(expand = c(0, 0)) +
  scale_x_discrete(expand = c(0, 0)) +
  custom_plot_theme()

If we add the first initial, we see that “Smith” remains in the top ten but has now only 7 citations (the other 8 belonging to other “Smith” with other initials), while “Li” is not part of the top ten anymore. In the same way as for the journal names, in order to have good quality data, it would be necessary to check the authors names one by one.

Text transformation

To modify a text document, for instance to replace special characters, we can use the function tm_map from the package {tm}. tm_map applies a given transformation to a corpus and give back the modified corpus. To create our own transformations, we can use the function content_transformer from the package {tm}. The syntax is the following (in upper case the part to modify):

CHOOSE MEANINGFUL NAME <- content_transformer(
  function(x , pattern ) gsub(pattern, CHOOSE REPLACEMENT, x))
)

Removing punctuation

toSpace <- content_transformer(
  function(x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "[[:punct:]]")

Removing accents

toNoAccent <- content_transformer(
  function(x , pattern ) gsub(pattern, "a", x))
docs <- tm_map(docs, toNoAccent, "à|ä|â")
toNoAccent <- content_transformer(
  function(x , pattern ) gsub(pattern, "e", x))
docs <- tm_map(docs, toNoAccent, "è|é|ê|ë")
toNoAccent <- content_transformer(
  function(x , pattern ) gsub(pattern, "o", x))
docs <- tm_map(docs, toNoAccent, "ô|ö")
toNoAccent <- content_transformer(
  function(x , pattern ) gsub(pattern, "u", x))
docs <- tm_map(docs, toNoAccent, "û|ü")
toNoAccent <- content_transformer(
  function(x , pattern ) gsub(pattern, "i", x))
docs <- tm_map(docs, toNoAccent, "î|ï")
toNoAccent <- content_transformer(
  function(x , pattern ) gsub(pattern, "oe", x))
docs <- tm_map(docs, toNoAccent, "œ")

Converting the text to lower case

docs <- tm_map(docs, content_transformer(tolower))

Removing alone numbers

docs <- tm_map(docs, toSpace, " [[:digit:]]* ")

Removing english common stopwords

docs <- tm_map(docs, removeWords, stopwords("english"))

Removing my own stop word

rm_words <- c("using")
docs <- tm_map(docs, removeWords, rm_words) 

For a non scientific corpus we could use the package {pluralize}, but here it does not work well with the scientific words such as microtubules/microtubule.

tosingular <- content_transformer(
  function(x, pattern, replacement) gsub(pattern, replacement, x)
  )

docs <- tm_map(docs, tosingular, "plants", "plant")
docs <- tm_map(docs, tosingular, "microtubules", "microtubule")
docs <- tm_map(docs, tosingular, "cells", "cell")
docs <- tm_map(docs, tosingular, "forces", "force")
docs <- tm_map(docs, tosingular, "walls", "wall")
docs <- tm_map(docs, tosingular, "divisions", "division")
docs <- tm_map(docs, tosingular, "models", "model")
docs <- tm_map(docs, tosingular, "proteins", "protein")
docs <- tm_map(docs, tosingular, "dynamics", "dynamic")

Eliminating extra white spaces

docs <- tm_map(docs, stripWhitespace)

Term-document matrix

The term-document matrix is a contigency table with all the words and their counts. Now that the corpus is clean, we can have a look at the top ten words used in the publication titles.

dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m), decreasing = TRUE)
d <- data.frame(word = names(v), freq = v)

d %>% 
  top_n(10, freq) %>% 
ggplot() +
  geom_col(aes(fct_reorder(word, freq), freq, fill = freq), 
           colour = "grey30", width = 1) +
  labs(x = "", y = "", title = "Top 10 words") +
  coord_flip() +
  scale_fill_gradientn("freq", colours = greenpal(10)) +
  scale_y_continuous(expand = c(0, 0)) +
  scale_x_discrete(expand = c(0, 0)) +
  custom_plot_theme()

The most common word in in the corpus is the word “cell”. During my PhD I worked on cell division in plants, i.e. how one plant cell partition its volume to make two daughter cells. More precisely, I tried to understand to which extent the mechanical stress within the growing plant tissue can affect this partitionning. So, to me, it is not surprising to see that the bibliography I gathered during my PhD thesis is mainly centered on (biological) cells, but also on microtubules, which are cytoskeletal elements that play a key role during the plant cell division, and of course, on plant, division and on arabidopsis, the name of the model plant I studied. The other keywords also describe very well my PhD.

Wordclouds

Now that the corpus is cleaned, I’ll redo first a simple wordcloud using the {wordcloud} package. Compared to the one above, it has some colors, and I used set.seed() to always have the same graph appearance.

set.seed(500)
my_wordcloud <- wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words = nrow(d), random.order = FALSE, rot.per = 0.35, 
          scale = c(6, 0.25),
          colors = brewer.pal(8, "Dark2"))

Now, let’s use {wordcloud2} with a background image. I’ll use a leaf image as background as a reference to the plant related topic of my PhD thesis. The leaf image was adapted from this other image by Sébastien Rochette, and can be downloaded here.
The snapshot of the html wordcloud is done manually on my web browser, to the targeted dimensions. I could have used {webshot}, but the use of a figure as a mask requires to refresh the browser, which is not easy with {webshot}.

figPath <- "leaf.png"

l <- nrow(d)

# Change to square root to reduce difference between sizes
d_sqrt <- d %>% 
  mutate(freq = sqrt(freq))

my_graph <- wordcloud2(
  d_sqrt,
  size = 0.5, # sqrt : 0.4
  minSize = 0.1, # sqrt : 0.2
  rotateRatio = 0.6,
  figPath = figPath,
  gridSize = 5, # sqrt : 5
  color = c(rev(tail(greenpal(50), 40)), rep("grey", nrow(d_sqrt) - 40)) # green and grey
  # color = c(rev(tail(greenpal(50), 40)), rep(greenpal(9)[3], nrow(d_sqrt) - 40)) # green
  # color = c(rev(tail(greenpal(50), 40)), rep("#FFAC40", nrow(d_sqrt) - 40)) # green and orange
)

I’ve set the color to green and grey. In the commented lines, one can find two alternative choices of colors: all green, and green and orange.