anchor.diff:  depends on your preference (or curiosity) only. If you want to analyze
   the **descriptive vocabulary**, type:
   
  -[source, python]
  +[source, r]
   ----
   setwd("./dv/")
   ----
   
   For working with **part-of-speech tags**, type:
   
  -[source, python]
  +[source, r]
   ----
   setwd("./pos/")
   ----
   
   Once one of the folders is chosen, you can *start Stylo* by typing
   
  -[source, python]
  +[source, r]
   ----
   stylo()
   ----
  
positive.code: 
  The easiest step is to extract the *number of sentences*: The only thing you need to do is to find the highest sentence ID number using the function max() on the column containing the sentence IDs (df$sentenceId).
  
  [source, python]
  ----
  sentences = max(df$SentenceId, na.rm = T)
  ----
  
  To compute the *number of words*, we simply take the length of the Token column in the data frame (df$Token), again excluding all entries the POS-tagger has identified as punctuation symbols.
  
  [source, python]
  ----
  words = length(df$Token[df$CPOS != "PUNC"])
  ----
  
  The *number of periods* requires a somewhat more complicated excluding condition. Our POS tag set only marks punctuation in general, the LIX Readability Index specifically defines full stop period, colon, exclamation mark and question mark as periods. Hence, we want to exclude comma and semicolon from the selection. We will once more rely on the function length() to count elements. This time, we want to count only the elements tagged as punctuation (dfCPOS == "PUNC") and to exclude commas and semicolons.
  
  [source, python]
  ----
  periods = length(df$CPOS[df$CPOS == "PUNC" & df$Token != "," & df$Token != ";"])
  ----
  
  To calculate the remaining features, we begin by counting the *characters in each word*. The words themselves can be found in the column 'df$Token'. The function nchar() counts the characters in a string. The function lapply() can be used to apply nchar() upon each single element of df$Token. nchar() Can only be applied on character strings. To ensure that df$Token is of that type and has not accidentally interpreted as a factor when the data frame was loaded, we use the function as.character(), that can transform a factor into a vector of strings.
  
  [source, python]
  ----
  word_length = lapply(as.character(df$Token), nchar)
  ----
  
  As lapply() returns a list, we must convert the results into vector format (with unlist()), then we can get rid of the punctuation tokens.
  
  [source, python]
  ----
  word_length = unlist(word_length)
  word_length = word_length[df$CPOS != "PUNC"]
  
negative.code: the working directory can be changed using the "setwd()" command in the
  R console, like in
  
  [source, r]
  ----
  setwd("~/DKPro/")
  ----
  