We have analyzed a corpus of human-authored arguments expressed in text and information graphics, non-pictorial graphics such as bar graphs. The goal of our research is to enable intelligent argument generation systems to make effective use of these media. This paper presents and compares two classification schemes used to analyze the corpus, illustrated by examples from the corpus, and discusses implications for generation systems.