1. Tabulating
data with
2012-10-22 @HSPH
Kazuki Yoshida, M.D.
MPH-CLE student
FREEDOM
TO
KNOW

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

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

4. Menu
n Categorical data
n How to tabulate
n Get sums and proportions

5. Ingredients
Epi/Stat Programming
n Tables n data()
n Cross tables n table(), summary()
n Stratified tables n prop.table()
n addmargins()
n xtabs(), ftable()
n gmodels::CrossTable()
n epiR::epi.2by2()
n Creating categorical variables

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

7. Open
R Studio

8. Install and Load
vcd epiR

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

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

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

12. summary of whole
dataset
summary
summary(Arthritis)

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

14. Accessing a single variable in data set
dataset name variable name
Arthritis$Treatment

15. Arthritis$Treatment
factor levels (categories)

16. Check factor levels of a vector
levels
levels(Arthritis$Treatment)

17. Your turn adopted from Hadley Wickham
n Arthritis$Improved
n levels(Arthritis$Improved)

18. This is an ordered factor

19. factor

20. factor is categorical
variable in R

21. Create a single
variable summary
table
table(Arthritis$Improved)

22. Your turn adopted from Hadley Wickham
n table(Arthritis$Improved)

23. Convert tables to proportions
prop.table
table(table.object)

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

25. Create cross tables
xtabs
xtabs(formula = ~ , data = Arthritis)

26. Your turn adopted from Hadley Wickham
n xtabs(~ Treatment +Improved, Arthritis)
n xtabs(~ Treatment +Improved +Sex, Arthritis)

27. 2nd dimention
1st
dimention
3rd dimention

28. Add margins to tables
addmargins
addmargins(table.object)

29. Your turn adopted from Hadley Wickham
n tab1 <- xtabs(~ Treatment +Improved, Arthritis)
n addmargins(tab1)

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

31. Your turn adopted from Hadley Wickham
n tab2 <- xtabs(~ Treatment +Improved +Sex, Arthritis)
n ftable(tab2)

32. Proportions again
prop.table
table(cross.table.object)

33. Your turn adopted from Hadley Wickham
n tab3 <- xtabs(~ Treatment +Improved, Arthritis)
n prop.table(tab3) # proportion to total
n prop.table(tab3, 1) # proportion to row sum
1st dimension
n prop.table(tab3, 2) # proportion to2nd dimension sum
column

34. Chi-squared test
chisq.test
chisq.test(cross.table.object)

35. Fisher’s exact test
fisher.test
fisher.test(cross.table.object)

36. Your turn adopted from Hadley Wickham
n tab3 <- xtabs(~ Treatment +Improved, Arthritis)
n chisq.test(tab3)
n fisher.test(tab3)

37. SAS-like cross tables available in
gmodels package
CrossTable
CrossTable(tab.2d)

38. Your turn adopted from Hadley Wickham
n tab3 <- xtabs(~ Treatment +Improved, Arthritis)
n CrossTable(tab3)

40. 2x2 table with RR RD OR available in
epiR package
epi.2x2
epi.2x2(tab.2by2)

41. Your turn adopted from Hadley Wickham
n tab.2by2 <- xtabs(~ Sex +Treatment, Arthritis)
n epi.2by2(tab.2by2, units = 1)

42. Creating factor

43. Data in Excel
factor factor Integer

44. To convert number vector to factor vector
dat$Stage <- factor(dat$Stage)

45. To convert back to number
dat$Stage <- as.numeric(as.character(dat$Stage))