publication – paulvanderlaken.com

TLDR; You can use the corrtable package (see CRAN or Github)!

In most (observational) research papers you read, you will probably run into a correlation matrix. Often it looks something like this:

In Social Sciences, like Psychology, researchers like to denote the statistical significance levels of the correlation coefficients, often using asterisks (i.e., *). Then the table will look more like this:

Table 4 from Family moderators of relation between community ...

Regardless of my personal preferences and opinions, I had to make many of these tables for the scientific (non-)publications of my Ph.D..

I remember that, when I first started using R, I found it quite difficult to generate these correlation matrices automatically.

Yes, there is the cor function, but it does not include significance levels.

Then there the (in)famous Hmisc package, with its rcorr function. But this tool provides a whole new range of issues.

What’s this storage.mode, and what are we trying to coerce again?

Soon you figure out that Hmisc::rcorr only takes in matrices (thus with only numeric values). Hurray, now you can run a correlation analysis on your dataframe, you think…

Yet, the output is all but publication-ready!

You wanted one correlation matrix, but now you have two… Double the trouble?

[UPDATED] To spare future scholars the struggle of the early day R programming, Laura Lambert and I created an R package corrtable, which includes the helpful function correlation_matrix.

This correlation_matrix takes in a dataframe, selects only the numeric (and boolean/logical) columns, calculates the correlation coefficients and p-values, and outputs a fully formatted publication-ready correlation matrix!

You can specify many formatting options in correlation_matrix.

For instance, you can use only 2 decimals. You can focus on the lower triangle (as the lower and upper triangle values are identical). And you can drop the diagonal values:

Or maybe you are interested in a different type of correlation coefficients, and not so much in significance levels:

For other formatting options, do have a look at the source code on github.

Now, to make matters even easier, the package includes a second function (save_correlation_matrix) to directly save any created correlation matrices:

Once you open your new correlation matrix file in Excel, it is immediately ready to be copy-pasted into Word!

If you are looking for ways to visualize your correlations do have a look at the packages corrr, corrplot, or ppsr.

I hope this package is of help to you!

Do reach out if you get to use them in any of your research papers!

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Eiko Fried, researcher at the University of Amsterdam, recently blogged about personal collaborator networks. I came across his post on twitter, discussing how to conduct such analysis in R, and got inspired.

Unfortunately, my own publication record is quite boring to analyse, containing only a handful of papers. However, my promotors – Prof. dr. Jaap Paauwe and Prof. dr. Marc van Veldhoven – have more extensive publication lists. Although I did not manage to retrieve those using the scholarpackage, I was able to scrape Jaap Paauwe’s publication list from his Google Scholar page. Jaap has 141 publications listed with one or more citation on Google Scholar. More than enough for an analysis!

While Eiko uses his colleague Sacha Epskamp’s R package qgraph, I found an alternative in the packages igraph and ggraph.

### PAUL VAN DER LAKEN
### 2017-10-31
### COAUTHORSHIP NETWORK VISUALIZATION

# LOAD IN PACKAGES
library(readxl)
library(dplyr)
library(ggraph)
library(igraph)

# STANDARDIZE VISUALIZATIONS
w = 14
h = 7
dpi = 900

# LOAD IN DATA
pub_history <- read_excel("paauwe_wos.xlsx")

# RETRIEVE AUTHORS
pub_history %>%
  filter(condition == 1) %>%
  select(name) %>%
  .$name %>%
  gsub("[A-Z]{2,}|[A-Z][ ]", "", .) %>%
  strsplit(",") %>%
  lapply(function(x) gsub("\\..*", "", x)) %>%
  lapply(function(x) gsub("^[ ]+","",x)) %>%
  lapply(function(x) x[x != ""]) %>%
  lapply(function(x) tolower(x))->
  authors

# ADD JAAP PAAUWE WHERE MISSING
authors <- lapply(authors, function(x){
  if(!"paauwe" %in% x){
    return(c(x,"paauwe"))
  } else{
    return(x)
  }
})

# EXTRACT UNIQUE AUTHORS
authors_unique <- authors %>% unlist() %>% unique() %>% sort(F)

# FORMAT AUTHOR NAMES 
# CAPATILIZE
simpleCap <- function(x) {
  s <- strsplit(x, " ")[[1]]
  names(s) <- NULL
  paste(toupper(substring(s, 1,1)), substring(s, 2),
        sep="", collapse=" ")
}
authors_unique_names <- sapply(authors_unique, simpleCap)

The above retrieve the names of every unique author from the excel file I got from Google Scholar. Now we need to examine to what extent the author names co-occur. We do that with the below code, storing all co-occurance data in a matrix, which we then transform to an adjacency matrix igraph can deal with. The output graph data looks like this:

# CREATE COAUTHORSHIP MATRIX
coauthorMatrix <- do.call(
  cbind,
  lapply(authors, function(x){
  1*(authors_unique %in% x)
}))

# TRANSFORM TO ADJECENY MATRIX
adjacencyMatrix <- coauthorMatrix %*% t(coauthorMatrix)

# CREATE NETWORK GRAPH
g <- graph.adjacency(adjacencyMatrix, 
                     mode = "undirected", 
                     diag = FALSE)
V(g)$Degree <- degree(g, mode = 'in') # CALCULATE DEGREE
V(g)$Name <- authors_unique_names # ADD NAMES
g # print network

## IGRAPH f1b50a7 U--- 168 631 -- 
## + attr: Degree (v/n), Name (v/c)
## + edges from f1b50a7:
##  [1]  1-- 21  1--106  2-- 44  2-- 52  2--106  2--110  3-- 73  3--106
##  [9]  4-- 43  4-- 61  4-- 78  4-- 84  4--106  5-- 42  5--106  6-- 42
## [17]  6-- 42  6-- 97  6-- 97  6--106  6--106  6--125  6--125  6--127
## [25]  6--127  6--129  6--129  7--106  7--106  7--150  7--150  8-- 24
## [33]  8-- 38  8-- 79  8-- 98  8-- 99  8--106  9-- 88  9--106  9--133
## [41] 10-- 57 10--106 10--128 11-- 76 11-- 85 11--106 12-- 30 12-- 80
## [49] 12--106 12--142 12--163 13-- 16 13-- 16 13-- 22 13-- 36 13-- 36
## [57] 13--106 13--106 13--106 13--166 14-- 70 14-- 94 14--106 14--114
## + ... omitted several edges

This graph data we can now feed into ggraph:

# SET THEME FOR NETWORK VISUALIZATION
theme_networkMap <- theme(
  plot.background = element_rect(fill = "beige"),
  panel.border = element_blank(),
  panel.grid = element_blank(),
  panel.background = element_blank(),
  legend.background = element_blank(),
  legend.position = "none",
  legend.title = element_text(colour = "black"),
  legend.text = element_text(colour = "black"),
  legend.key = element_blank(),
  axis.text = element_blank(), 
  axis.title = element_blank(),
  axis.ticks = element_blank()
)
# VISUALIZE NETWORK
ggraph(g, layout = "auto") +
  # geom_edge_density() +
  geom_edge_diagonal(alpha = 1, label_colour = "blue") +
  geom_node_label(aes(label = Name, size = sqrt(Degree), fill = sqrt(Degree))) +
  theme_networkMap +
  scale_fill_gradient(high = "blue", low = "lightblue") +
  labs(title = "Coauthorship Network of Jaap Paauwe",
       subtitle = "Publications with more than one Google Scholar citation included",
       caption = "paulvanderlaken.com") +
  ggsave("Paauwe_Coauthorship_Network.png", dpi = dpi, width = w, height = h)