The document discusses descriptive statistics and how to calculate them in R. It introduces common summary statistics like the mean, median, percentiles, range, and measures of normality like skewness and kurtosis. It demonstrates how to use functions like mean(), sd(), quantile(), range(), skewness(), kurtosis(), summary(), describe(), and describeBy() to calculate these statistics on data frames and vectors in R. Examples are provided to showcase calculating these statistics on variables from a bone data set.

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()