This document discusses various methods for summarizing categorical data in R, including table(), summary(), xtabs(), ftable(), prop.table(), and CrossTable(). It provides examples using the built-in Arthritis dataset to demonstrate how to index data frames, check factor levels and lengths, create one-variable and cross tables, calculate proportions, and more. Interactive exercises are included throughout for the reader to practice these methods on the Arthritis data.

1. Categorical
data with
FREEDOM TO KNOW
DRAFT

2. Group Website is at:
http://rpubs.com/kaz_yos/useR_at_HSPH

3. n Introduction
n Reading Data into R (1)
n Reading Data into R (2)
n Descriptive statistics
Previously in this group
Group Website: http://rpubs.com/kaz_yos/useR_at_HSPH

4. Menu
n Categorical data
n How to summarize

5. Ingredients
n n
n table(), summary()
n xtabs(), ftable()
n prop.table()
n margin.table()
n CrossTable()
Statistics
Summary statistics for
categorical data
Programming
Creating categorical var.

6. Categorical
gender
country
race
ethnicity
cancer stage
data
disease severity
education level

7. Open
Studio

8. vcd
Install and Load

9. Load built-in dataset Named “Arthritis”
data(Arthritis)
We will use “Arthritis” dataset in vcd package

10. Arthritis[1:17 , ]
Extract 1st to 17th rows Show all columns
Indexing: extraction of data from
data frame
Don’t forget comma
Colon in between

11. Treatment vector in Arthritis data frame
Five vectors of same
length tied together

12. summary(object)
summary.factor(vector)

13. Your turn
n Do summary() on Arthritis
adopted from Hadley Wickham

14. Accessing a single variable in data set
dataset name variable name
DATA$VAR

15. Arthritis$Treatment
factor levels (categories)

16. levels
levels(x)
Check factor levels of a vector

17. Your turn
n
n
Check levels of Improved vector
Also show contents of Improved vector
adopted from Hadley Wickham

18. This is an ordered factor

20. factor is categorical
variable in

21. length
length(x)
Check length of a vector

22. Your turn
n Check length of Improved vector
adopted from Hadley Wickham

23. table(..., exclude = if (useNA == "no") c(NA, NaN),
useNA = c("no", "ifany","always"), dnn =
list.names(...), deparse.level = 1)

24. Your turn
n Create one-variable table of Improved vector
adopted from Hadley Wickham

25. prop. table
table(..., exclude = if (useNA == "no") c(NA, NaN),
useNA = c("no", "ifany","always"), dnn =
list.names(...), deparse.level = 1)

26. Your turn
n
n
Improved.cat <- table(Arthritis$Improved)
Do prop.table(Improved.cat)
adopted from Hadley Wickham

27. xtabs(formula = ~., data = parent.frame(),
subset, sparse = FALSE, na.action, exclude =
c(NA, NaN), drop.unused.levels = FALSE)
Create cross tables

28. Your turn
n
n
do xtabs(~ Treatment +Improved, Arthritis)
Further stratify by Sex
adopted from Hadley Wickham

29. 1st
dimention
2nd dimension
3rd
dimention

30. Create flat tables
Good for > 3 dimentional
ftable(..., exclude = c(NA, NaN),
row.vars = NULL, col.vars = NULL)

31. Your turn
n table3 <- xtabs(~ Treatment +Improved +Sex,
Arthritis)
n Do ftable(table3)
adopted from Hadley Wickham

32. Your turn
n table2d <- xtabs(~ Treatment +Improved,
Arthritis)
n
n
n
prop.table(table2d)
prop.table(table2d, 1)
prop.table(table2d, 2)
adopted from Hadley Wickham

33. CrossTable
CrossTable(x, y, digits=3, max.width =
5, expected=FALSE, prop.r=TRUE,
prop.c=TRUE, prop.t=TRUE,
prop.chisq=TRUE, chisq = FALSE,
fisher=FALSE, mcnemar=FALSE,
resid=FALSE, sresid=FALSE,
available in
gmodels package
SAS-like cross tables

34. Your turn
n table2d <- xtabs(~ Treatment +Improved,
Arthritis)
n CrossTable(table2d)
adopted from Hadley Wickham