anchor.diff:  
   Now that we have a vector at hand that contains the length of every single word in the text as a number, we can simply sum it up to calculate *text length in characters*.
   
  -[source, python]
  +[source, r]
   ----
   characters = sum(word_length)
   ----
   
   And we can now compute the number *long words*, i.e. the number of words longer than six characters.
   
  -[source, python]
  +[source, r]
   ----
   longwords = length(word_length[word_length > 6])
   ----
   
   Now all necessary features have been computed and stored in variables. To *calculate the ARI* for the text, we just need to put the feature values into the ARI formula,
   
  -[source, python]
  +[source, r]
   ----
   ARI = 4.71 * (characters / words) + 0.5 * (words / sentences) - 21.43
   ----
   
   and into another formula for *calculating the LIX*.
   
  -[source, python]
  +[source, r]
   ----
   LIX = (words / periods) + (100 * longwords / words)
   ----
  
positive.code: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  Extracting certain linguistic metrics using the output format of the NLP pipeline as a data frame in R or Python Pandas works strait forward. The following recipe is mainly aimed at demonstrating how to access, address, and use data in an R data frame. As already shown above, the output file can be loaded into the R environment with the following command:
  
  [source, python]
  ----
  df = read.table("PathToFile", header = T, fill = T)
  ----
  
  To compute, for example, the *type token ratio* (TTR) of the text, we take the column containing the tokens that can be addressed as 'df$Token'. We remove the punctuation by subsetting that vector formulating a logical condition that refers to the column containing the part-of-speech tags (df$Token[df$CPOS != "PUNC"]). The function unique() and length() allow us to generate a vector of unique types, and to measure the lengths of vectors.
  
  [source, python]
  ----
  types = length(unique(df$Token[df$CPOS != "PUNC"]))
  tokens = length(df$Token[df$CPOS != "PUNC"])
  
negative.code: 
  In R, a simple reader can be written as follows:
  
  [source, r]
  ----
  df = read.table("./data/Effi_Briest_Short.txt.csv", # or whatever file you want to read
                  header = T,                         # first line as headers
  