Tables that are one way, two way, or three way in structure may often be helpfully represented as multiple bar charts. The one, two, or three variables that define the structure of the table thus determine rows, columns, and panels in which bars are arranged. The merits of this design include easy focus on individual values or groups of values; leaving space for numeric information to be shown as in a table; and convenient axis or panel labeling through text rather than through a key or legend. A Stata command for these purposes, tabplot, is discussed systematically.
AitkinM., AndersonD., FrancisB., and HindeJ.1989. Statistical Modelling in GLIM.Oxford: Oxford University Press.
2.
BarberD.2012. Bayesian Reasoning and Machine Learning.Cambridge: Cambridge University Press.
3.
BeckerR. A., ChambersJ. M., and WilksA. R.1988. The New S Language: A Programming Environment for Data Analysis and Graphics.Pacific Grove, CA: Wadsworth and Brooks/Cole.
4.
BertinJ.1981. Graphics and Graphic Information Processing.Berin: De Gruyter.
5.
BertinJ.1983. Semiology of Graphics: Diagrams, Networks, Maps.Madison: University of Wisconsin Press.
6.
BishopC. M.2006. Pattern Recognition and Machine Learning.New York: Springer.
7.
BradstreetT. E.2012. Grables: Visual displays that combine the best attributes of graphs and tables. In A Picture is Worth a Thousand Tables: Graphics in Life Sciences, ed. KrauseA., and O'ConnellM., 41–69. New York: Springer.
ChauchatJ.-H., and RissonA.1998. Bertin's graphics and multidimensional data analysis. In Visualization of Categorical Data, ed. BlasiusJ., and GreenacreM., 37–45. San Diego, CA: Academic Press.
10.
CoxN. J.2003. Speaking Stata: Problems with tables, Part I. Stata Journal3: 309–324.
11.
CoxN. J.2004. Speaking Stata: Graphing categorical and compositional data. Stata Journal4: 190–215.
12.
CoxN. J.2007. Stata tip 52: Generating composite categorical variables. Stata Journal7: 582–583.
13.
CoxN. J.2008a. Speaking Stata: Between tables and graphs. Stata Journal8: 269–289.
14.
CoxN. J.2008b. Speaking Stata: Spineplots and their kin. Stata Journal8: 105–121.
15.
CoxN. J.2012. Speaking Stata: Axis practice, or what goes where on a graph. Stata Journal12: 549–561.
16.
CoxN. J.2016. Software Updates: Spineplots and their kin. Stata Journal16: 521–522.
17.
CoxN. J., and BarlowN. L. M.2008. Stata tip 62: Plotting on reversed scales. Stata Journal8: 295–298.
18.
de FalguerollesA., FriedrichF., and SawitzkiG.1997. A tribute to J. Bertin's graphical data analysis. In Softstat ‘97: Advances in Statistical Software 6: The 9th Conference on the Scientific Use of Statistical Software, March 3–6, 1997, ed. BandillaW., and FaulbaumF., 11–20. Stuttgart: Lucius & Lucius.
19.
DoranJ. E., and HodsonF. R.1975. Mathematics and Computers in Archaeology.Edinburgh: Edinburgh University Press.
20.
EmenyB.1934. The Strategy of Raw Materials: A Study of America in Peace and War.New York: Macmillan.
21.
FewS.2012. Show Me the Numbers: Designing Tables and Graphs to Enlighten. 2nd ed. Burlingame, CA: Analytics Press.
GrinsteinG. G., HoffmanP. E., PickettR. M., and LaskowskiS. J.2002. Benchmark development for the evaluation of visualization for data mining. In Information Visualization in Data Mining and Knowledge Discovery, ed. FayyadU., GrinsteinG. G., and WierseA., 129–176. San Diego, CA: Academic Press.
24.
HahslerM., HornikK., and BuchtaC.2008. Getting things in order: An introduction to the R package seriation. Journal of Statistical Software25(3): 1–34. https://www.jstatsoft.org/article/view/v025i03.
25.
HinkJ. K., EustaceJ. K., and WogalterM. S.1998. Do grables enable the extraction of quantitative information better than pure graphs or tables?International Journal of Industrial Ergonomics22: 439–447.
26.
HinkJ. K., WogalterM. S., and EustaceJ. K.1996. Display of quantitative information: Are grables better than plain graphs or tables?Proceedings of the Human Factors and Ergonomics Society Annual Meeting40: 1155–1159.
27.
HoffmanP. E., and GrinsteinG. G.2002. A survey of visualizations for high-dimensional data mining. In Information Visualization in Data Mining and Knowledge Discovery, ed. FayyadU., GrinsteinG. G., and WierseA., 47–82. San Diego, CA: Academic Press.
28.
HofmannH.2008. Mosaic plots and their variants. In Handbook of Data Visualization, ed. ChenC., HärdleW., and UnwinA., 617–642. Berlin: Springer.
29.
LohningerH.1994. INSPECT: A program system to visualize and interpret chemical data. Chemometrics and Intelligent Laboratory Systems22: 147–153.
30.
LohningerH.1996. INSPECT: A Program System for Scientific and Engineering Data.Berlin: Springer.
31.
MacKayD. J. C.2003. Information Theory, Inference, and Learning Algorithms.Cambridge: Cambridge University Press.
32.
MacKayD. J. C.2009. Sustainable Energy—Without the Hot Air.Cambridge: UIT Cambridge.
33.
MackinlayJ. D.1986. Automating the design of graphical presentations of relational information. ACM Transactions on Graphics5: 110–141.
34.
McDanielE., and McDanielS.2012a. The Accidental Analyst: Show Your Data Who's Boss.Seattle, WA: Freakalytics.
35.
McDanielS., and McDanielE.2012b. Rapid Graphs with Tableau Software 7: Create Intuitive, Actionable Insights in Just 15 Days.Seattle, WA: Freakalytics.
36.
MorrisonP. S.1985. Symbolic representation of tabular data. New Zealand Journal of Geography79: 11–18.
37.
MurphyK. P.2012. Machine Learning: A Probabilistic Perspective.Cambridge, MA: MIT Press.
38.
PirolliP., and RaoR.1996. Table lens as a tool for making sense of data. In AVI ‘96: Proceedings of the Workshop on Advanced Visual Interfaces, ed. CatarciT., CostabileM. F., LevialdiS., and SantucciG., 67–80. New York: Association for Computing Machinery.
39.
PlayfairW. H.1786. The Commercial and Political Atlas.London: Robinson, Sewell, and Debrett.
40.
PlayfairW. H.2005. The Commercial and Political Atlas and Statistical Breviary.Cambridge University Press: Cambridge. Edited by H. Wainer and I. Spence.
41.
RaoR., and CardS. K.1994. The table lens: Merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In CHI ‘94: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ed. AdelsonB., DumaisS., and OlsonJ. S., 318–322. New York: Association for Computing Machinery.
42.
RobertsD. H., EvansD. J. A., LodwickJ., and CoxN. J.2013. The subglacial and ice-marginal signature of the North Sea Lobe of the British–Irish ice sheet during the last glacial maximum at Upgang, North Yorkshire, UK. Proceedings of the Geologists’ Association124: 503–519.
43.
RumelhartD. E., HintonG. E., and WilliamsR. J.1986. Learning representations by back-propagating errors. Nature323: 533–536.
44.
SearsP. B.1933. Climatic change as a factor in forest succession. Journal of Forestry31: 934–942.
45.
SearsP. B.1935. Types of North American pollen profiles. Ecology16: 488–499.
46.
SpenceR.2007. Information Visualization: Design for Interaction. 2nd ed. Harlow, UK: Pearson.
47.
TheusM., and UrbanekS.2009. Interactive Graphics for Data Analysis: Principles and Examples.Boca Raton, FL: Chapman & Hall/CRC.
48.
UnwinA.2015. Graphical Data Analysis with R.Boca Raton, FL: Taylor & Francis.
49.
UnwinA., TheusM., and HofmannH.2006. Graphics of Large Datasets: Visualizing a Million.New York: Springer.
50.
WainerH.2005. Graphic Discovery: A Trout in the Milk and Other Visual Adventures.Princeton, NJ: Princeton University Press.
51.
WainerH.2009. Picturing the Uncertain World: How to Understand, Communicate, and Control Uncertainty through Graphical Display.Princeton, NJ: Princeton University Press.
52.
WardM., GrinsteinG., and KeimD.2010. Interactive Data Visualization: Foundations, Techniques, and Applications.Natick, MA: AK Peters.