When working with table data from the scientific or patent literature, it is extremely common to find that columns contain concatenated data. That is, they contain multiple entries with a semicolon as a separator. Data of this type is not tidy (Wickham 2014). What we commonly want to do is to separate the data out as the basis for counting. However, extra white space can have a major impact on any counts of this data if it is not recognised and dealt with. We will go through this step by step using a simple example and then scale up to a real world example.

Here is a simple example of a table containing a column with concatenated data.

library(tidyverse)
messy <- tibble::tibble(messy = c("this is not the; messiest", 
                                  "messy data column; in the world", 
                                  "it's just; a; tribute"))

If we try to count this:

messy %>% 
  count(messy) %>% 
  kable() %>% 
  kable_styling()

We get three results when of course actually what we want is a count of the concatenated data points in the column. We can handle this easily with the tidyr::separate_rows function in R.

messy %>% 
  separate_rows(messy, sep = ";") %>% 
  count(messy) %>% 
  kable() %>% 
  kable_styling()

Simples… as our friendly local meerkat might say. But let’s look at what happens if we double up our entries and imagine that two different people had written out the same thing.

messy <- tibble::tibble(messy = c("this is not the; messiest", 
                                  "messy data column; in the world", 
                                  "it's just; a; tribute", 
                                  " this is not the; messiest", 
                                  " messy data column;  in the world", 
                                  "it's just; a; tribute"))

Now let’s try again. We are innocently expecting a count of 2 for the repeated words and phrases.

messy %>% 
  separate_rows(messy, sep = ";") %>% 
  count(messy) %>% 
  kable() %>% 
  kable_styling()

This is not adding up correctly because while the two versions appear to be identical there are subtle differences… involving spaces introduced by our mysterious second person. This is a simple case so maybe you spotted them. When using R you will often want to try using str(), the equivalent in a language such as Python, or stare very hard at the screen in Excel.

str(messy)
## Classes 'tbl_df', 'tbl' and 'data.frame':    6 obs. of  1 variable:
##  $ messy: chr  "this is not the; messiest" "messy data column; in the world" "it's just; a; tribute" " this is not the; messiest" ...

It’s hard to read but we can see a white space at the start of " this is not the messiest". This though helps makes the point that when dealing with thousands of data points extra white space can be really hard to spot in R, Excel or anything else.

We can understand this more clearly by using a quick logical test in R to test whether two strings are identical or not.

"this is messy" == " this is messy"
## [1] FALSE

The reason for this is that in testing whether the strings are identical R (and anything else) will match all characters, including white space.

If you work with text based data extra white space appears in the data all the time after separation, mainly in the form of leading white space as we will see below.

The solution is simple, we trim the white space on both sides. In R we can do this using either the stringr function str_trim or the base R function trimws. We’ll use stringr here because it is a reminder of how useful this tidyverse package is.

messy %>% separate_rows(messy, sep = ";") %>%
  mutate(messy = str_trim(messy, side = "both")) %>% 
  count(messy) %>% 
  kable() %>% 
  kable_styling()

We use a call to dplyr::mutate and then a call to stringr str_trim to trim the white space on both sides of the separated strings and then overwrite the column in place.

Both str_trim and the base R trimws have arguments for where to trim white space. In the case of stringr it is side = and with trimws it is which =. As these are general functions there may be situations where you will want to trim either the leading (left) or the trailing (right) spaces. If you are working with metadata from the scientific literature (such as Web of Science or Crossref) or with patent data my recommendation is to always trim on both sides unless you have a good reason not to.

We now have a piece of code that will work for just about anything where white space is left over. We normally want to turn that into a function that we can use over and over again. One reason the tidyverse set of packages are so popular is that they are so easy to use. But, if we try and put the code above into a function it won’t work.

fun <- function(df, col, sep){
  df %>% tidyr::separate_rows(col, sep = sep) %>% 
    dplyr::mutate(col = stringr::str_trim(col, side = "both")) %>% 
    dplyr::count(col)
  }
fun(messy, messy, sep = ";")

We will get a message that

Error: col must evaluate to column positions or names, not a list

If we try quoting the “messy” col it appears to work but instead counts the number of entries. We can go around the houses… and go slightly bananas in the process… trying to fix this only to run into mysterious problem after problem. The reason for this is that dplyr and tidyr use non standard evaluation (tidy evaluation) with the result that R does not know how to evaluate it. We need to start getting to grips with tidy evaluation to get our code to work in a function. A whole bunch of very useful resources on that have been compiled by Mara Averick here. One solution, bearing in mind that there may well be a better one, is this.

separate_rows_trim <- function(df, col, sep){
  col <- enquo(col)
  df %>% tidyr::separate_rows(!!col, sep = sep) %>% 
    dplyr::mutate(!!col := stringr::str_trim(!!col, side = "both")) %>% 
    dplyr::count(!!col := !!col, sort = TRUE) %>% 
    tidyr::drop_na(!!col)
}

In this case we use bang bang !! to tell R when to evaluate col with a bit of help from := from rlang. To actually get to grips with tidy evaluation I recommend Mara Avericks compilation here. For a much deeper dive and highly illuminating read try the metaprogramming chapter of Hadley Wickham’s forthcoming 2nd edition of Advanced R. For the moment we can move on.

Let’s try again.

separate_rows_trim(messy, messy, sep = ";") %>% 
  kable() %>% 
  kable_styling()

We now have a reusable function.

This toy example introduces the importance of trimming white space when working with data that has been separated out. Otherwise bad things will happen when you start to count. To finish off let’s use some real world data from a patent dataset to illustrate this.

install.packages("devtools")

devtools::install_github("poldham/drones")

library(drones)
applicants %>% 
  select(applicants_cleaned)
## # A tibble: 18,970 x 1
##    applicants_cleaned                         
##    <chr>                                      
##  1 SHENZHEN HUBSAN TECHNOLOGY CO. LTD.        
##  2 Intel Corporation                          
##  3 YOKOGAWA ELECTRIC CORPORATION              
##  4 NETWORK PERFORMANCE RESEARCH GROUP LLC     
##  5 NETWORK PERFORMANCE RESEARCH GROUP LLC     
##  6 Choi Hae-Yong                              
##  7 Ziva Corporation                           
##  8 WiTricity Corporation                      
##  9 Thales                                     
## 10 International Business Machines Corporation
## # ... with 18,960 more rows

applicants %>% 
  select(applicants_cleaned) %>% 
  str_count(., pattern = ";") %>% 
  kable()

applicants %>% 
  select(applicants_cleaned) %>% 
  mutate(sepcount = str_count(applicants_cleaned, ";")) %>% 
  drop_na(applicants_cleaned) %>% 
  filter(sepcount == max(sepcount)) %>% 
  select(sepcount) %>% 
  kable()

library(drones)
df1 <- applicants %>% 
  separate_rows(applicants_cleaned, sep = ";") %>%
  drop_na(applicants_cleaned) %>% 
  count(applicants_cleaned, sort = TRUE) %>% 
  rename(messy = n) %>% 
  .[1:10,] %>% 
  mutate(applicants_cleaned = str_trim(applicants_cleaned, side = "both"))

df2 <- separate_rows_trim(applicants, applicants_cleaned, sep = ";") %>% 
  drop_na(applicants_cleaned) %>% 
  rename(tidy = n) %>% 
  .[1:10,]

df3 <- merge(df1, df2, by = "applicants_cleaned") %>% 
  arrange(desc(tidy)) %>%
  mutate(percent = (tidy - messy) / tidy * 100) %>% 
  mutate(percent = formatC(percent, digits = 2))

df3 %>% 
  kable("html", escape = F) %>% 
  kable_styling(position = "center") %>% 
  column_spec(4, width = 8)