I wrote an R script to make some scientometric analyses of Journal Citation Report data (JCR), which I have been using and updating in the past years.
Today, Clarivate has just introduced some changes in its database and now the exported CSV file contains one last empty column, which spoils my script. Because of this last empty column, read.csv automatically assumes that the first column contains the row names.
As before, there is also one first useless row, which is automatically removed in my script with skip = 1.
One simple solution to this "empty column situation" would be to manually remove this last column in Excel, and then proceed with my script as usual.
However, is there a way to add this removal to my script using base R?
The beginning of my script is:
jcreco = read.csv("data/jcr ecology 2020.csv",
na = "n/a", skip = 1, header = T)
The original CSV file downloaded from JCR is available in my Dropbox.
Could you please help me? Thank you!
The real problem is that empty column doesn't have a header. If they had only had the extra comma at the end of the header line this probably wouldn't be as messy. But you can also do a bit of column shuffling with fill=TRUE. For example
dd <- read.table("~/../Downloads/jcr ecology 2020.csv", sep=",",
skip=2, fill=T, header=T, row.names=NULL)
names(dd)[-ncol(dd)] <- names(dd)[-1]
dd <- dd[,-ncol(dd)]
This reads in the data but puts the rows names in the data.frame and fills the last column with NA. Then you shift all the column names over to the left and drop the last column.