1. Descriptive
Statistics with
2012-10-12 @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 to R
n Reading Data into R (1)
n Reading Data into R (2)
Group Website: http://rpubs.com/kaz_yos/useR_at_HSPH

4. Menu
n mean and sd
n median, quantiles, IQR, max, min, and range
n skewness and kurtosis
n smarter ways of doing these

5. Ingredients
Statistics Programming
n Summary statistics for n vector and data frame
continuous data
n DATA$VAR extraction
n Normal data
n Indexing by [row,col]
n Non-normal data
n Various functions
n Normality check
n skewness(), kurtosis()
n summary()
n describe(), describeBy()

6. Data loaded
What’s next?
http://echrblog.blogspot.com/2011/04/statistics-on-states-with-systemic-or.html

7. Descriptive
Statistics
http://www.ehow.com/info_8650637_descriptive-statistical-methods.html

8. Descriptive statistics is the
describing
discipline of quantitatively the
main features of a collection of data
http://en.wikipedia.org/wiki/Descriptive_statistics

9. Open
R Studio

10. Download comma-separated and Excel
Put them in folder
BONEDEN.DAT.txt
http://www.cengage.com/cgi-wadsworth/course_products_wp.pl?
fid=M20bI&product_isbn_issn=9780538733496

11. Read in BONEDEN.DAT.txt
Name it bone

12. Accessing a single variable in data set
dataset name variable name
DATA$VAR
e.g., mean(bone$age)

13. vector

14. ?
http://healthy-india.org/enviromentalhealth/
direct_indirect2.html

15. DATA$VAR is a vector
1 2 3 4 5 6 7 8
OR
“A” “B” “C” “D” “E” “F” “G” “H”
like strings with
values attached

16. Multiple vectors
of same length
tied together
Tied here
DATA is a data frame
1 2 3 4 5 6 7 8
“A” “B” “C” “D” “E” “F” “G” “H
1 2 3 4 5 6 7 8
“A” “B” “C” “D” “E” “F” “G” “H
1 2 3 4 5 6 7 8
“A” “B” “C” “D” “E” “F” “G” “H

17. Indexing: extraction of data from
data frame
Extract 1st to 15th rows Extract 1st to 12th columns
bone[1:15 , 1:12]
Colon in between
Don’t forget comma

18. age vector within bone data frame

19. bone$age
Extracted as a vector

20. mean
mean(x, trim = 0, na.rm = FALSE)

21. Your turn adopted from Hadley Wickham
n What is the mean of age?

22. sd
sd(x, na.rm = FALSE)

23. Your turn adopted from Hadley Wickham
n What is the sd of age?

24. median
median(x, na.rm = FALSE)

25. Your turn adopted from Hadley Wickham
n What is the median of age?

26. 0th, 25th, 50th, 75th, and 100th percentiles by defaults
quantile
quantile(x, probs = seq(0, 1, 0.25), na.rm = FALSE,
names = TRUE, type = 7)

27. Your turn adopted from Hadley Wickham
n What is the 25th and 75th percentiles of age?

28. 75th percentile - 25th percentile
IQR
IQR(x, na.rm = FALSE, type = 7)

29. Your turn adopted from Hadley Wickham
n What is the IQR of age?

30. max
max(..., na.rm = FALSE)

31. min
min(..., na.rm = FALSE)

32. Your turn adopted from Hadley Wickham
n What are the minimum and maximum of age?

33. range
range(..., na.rm = FALSE)

34. Your turn adopted from Hadley Wickham
n What the range of age?

35. We now
resort to
external
packages

36. Install and Load
e1071, psych

37. To load a package by command
package name here
library(package)
double quote “” can be omitted

38. Assessment of
normality

39. Load e1071 package

40. library(e1071)
skewness
skewness(x, na.rm = FALSE, type = 3)
type = 2 SAS
type = 1 Stata

41. library(e1071)
kurtosis
kurtosis(x, na.rm = FALSE, type = 3)
type = 2 SAS
type = 1 Stata

42. Your turn adopted from Hadley Wickham
n What are the skewness and kurtosis of age by the
Stata-method?

43. Multiple
variables
at once

44. summary
summary(object, ...)

45. Your turn adopted from Hadley Wickham
n Try summary on the dataset (data frame).

46. Various summary
measures library(psych)
describe
describe(x, na.rm = TRUE, interp = FALSE, skew =
TRUE, ranges = TRUE,trim = .1, type = 3)
type = 2 SAS
type = 1 Stata

47. Your turn adopted from Hadley Wickham
n describe(bone[,-1], type = 2)

48. Groupwise
summary library(psych)
describeBy
describeBy(x, group=NULL,mat=FALSE,type=3,...)
type = 2 SAS
type = 1 Stata

49. Your turn adopted from Hadley Wickham
bone data frame
without 1st columns zyg vector for grouping
n describeBy(bone[ , c(-1)] , bone$zyg , type = 2)
SAS method for
skewness and kurtosis

50. Ingredients
Statistics Programming
n Summary statistics for n vector and data frame
continuous data
n DATA$VAR extraction
n Normal data
n Indexing by [row,col]
n Non-normal data
n Various functions
n Normality check
n skewness(), kurtosis()
n summary()
n describe(), describeBy()