Examples for the `stratified` function.

stratified_examples.R

# library(devtools)
# source_gist("https://gist.github.com/mrdwab/6424112")


# Generate a couple of sample data.frames to play with
set.seed(1)
dat1 <- data.frame(ID = 1:100, A = sample(c("AA", "BB", "CC", "DD", "EE"), 100, 
    replace = TRUE), B = rnorm(100), C = abs(round(rnorm(100), digits = 1)), 
    D = sample(c("CA", "NY", "TX"), 100, replace = TRUE), E = sample(c("M", 
        "F"), 100, replace = TRUE))

# What do the data look like in general?
summary(dat1)
##        ID         A            B                 C          D      E     
##  Min.   :  1.0   AA:13   Min.   :-1.9144   Min.   :0.000   CA:23   F:54  
##  1st Qu.: 25.8   BB:25   1st Qu.:-0.6141   1st Qu.:0.300   NY:42   M:46  
##  Median : 50.5   CC:19   Median :-0.1176   Median :0.650   TX:35         
##  Mean   : 50.5   DD:26   Mean   :-0.0176   Mean   :0.825                 
##  3rd Qu.: 75.2   EE:17   3rd Qu.: 0.5382   3rd Qu.:1.200                 
##  Max.   :100.0           Max.   : 2.4016   Max.   :2.900

# Let's take a 10% sample from all -A- groups in dat1
stratified(dat1, "A", 0.1)
##    ID  A       B   C  D E
## 92 92 AA  1.1766 1.0 CA F
## 86 86 BB -1.5364 0.1 NY F
## 74 74 BB -0.1796 0.3 CA F
## 60 60 CC  1.6822 0.5 TX F
## 58 58 CC  0.9102 0.5 CA F
## 9   9 DD  0.5697 1.4 TX F
## 42 42 DD  1.2079 0.4 TX M
## 46 46 DD  0.5585 1.0 CA M
## 99 99 EE -1.2863 0.2 NY F
## 72 72 EE  1.3430 0.0 NY F

# Let's take a 10% sample from only 'AA' and 'BB' groups from -A- in dat1
stratified(dat1, "A", 0.1, select = list(A = c("AA", "BB")))
##    ID  A      B   C  D E
## 69 69 AA 0.4942 0.6 CA M
## 54 54 BB 0.1580 0.3 TX M
## 5   5 BB 1.4330 1.5 NY F

# Let's take 5 samples from all -D- groups in dat1, specified by column
# number
stratified(dat1, group = 5, size = 5)
##    ID  A       B   C  D E
## 73 73 BB -0.2146 0.6 CA F
## 45 45 CC  1.5868 1.2 CA F
## 87 87 DD -0.3010 0.6 CA M
## 13 13 DD  0.6897 1.1 CA F
## 24 24 AA -0.9341 0.1 CA M
## 40 40 CC  0.2671 0.9 NY F
## 83 83 BB  0.5315 0.6 NY M
## 1   1 BB  0.3981 0.5 NY F
## 56 56 AA  1.7673 2.5 NY M
## 88 88 AA -0.5283 1.2 NY M
## 66 66 BB -0.3928 1.5 TX F
## 22 22 BB -0.7099 0.1 TX M
## 79 79 DD -0.6817 0.2 TX M
## 27 27 AA -0.4433 0.8 TX M
## 51 51 CC -0.6204 0.4 TX F

# Use a two-column strata: -E- and -D- -E- varies more slowly, so it is
# better to put that first
stratified(dat1, c("E", "D"), size = 0.15)
##    ID  A         B   C  D E
## 84 84 BB -1.518394 0.6 CA F
## 34 34 AA -1.523567 1.5 CA F
## 53 53 CC -0.910922 1.6 CA M
## 36 36 DD  0.332950 0.2 CA M
## 97 97 CC  2.087167 0.5 NY F
## 49 49 DD -1.224613 0.4 NY F
## 5   5 BB  1.433024 1.5 NY F
## 37 37 DD  1.063100 1.5 NY M
## 17 17 DD -1.804959 0.3 NY M
## 33 33 CC  1.178087 0.2 NY M
## 14 14 BB  0.028002 0.9 TX F
## 90 90 AA -0.056897 0.0 TX F
## 28 28 BB  0.001105 2.1 TX F
## 80 80 EE -0.324270 0.3 TX M
## 22 22 BB -0.709946 0.1 TX M


# Use a two-column strata (-E- and -D-) but only interested in cases where
# -E- == 'M'
stratified(dat1, c("E", "D"), 0.15, select = list(E = "M"))
##      ID  A        B   C  D E
## 69   69 AA  0.49419 0.6 CA M
## 32   32 CC -0.13518 1.0 CA M
## 12   12 AA -0.03924 0.2 NY M
## 100 100 DD -1.64061 1.0 NY M
## 56   56 AA  1.76729 2.5 NY M
## 79   79 DD -0.68166 0.2 TX M
## 80   80 EE -0.32427 0.3 TX M

## As above, but where -E- == 'M' and -D- == 'CA' or 'TX'
stratified(dat1, c("E", "D"), 0.15, select = list(E = "M", D = c("CA", "TX")))
##    ID  A       B   C  D E
## 53 53 CC -0.9109 1.6 CA M
## 36 36 DD  0.3330 0.2 CA M
## 80 80 EE -0.3243 0.3 TX M
## 8   8 DD -1.0441 0.6 TX M

# Use a three-column strata: -E-, -D-, and -A-
s.out <- stratified(dat1, c("E", "D", "A"), size = 2)
## Some groups ---M.CA.BB, M.CA.EE--- contain fewer observations than desired
## number of samples. All observations have been returned from those groups.

list(head(s.out), tail(s.out))
## [[1]]
##    ID  A       B   C  D E
## 92 92 AA  1.1766 1.0 CA F
## 34 34 AA -1.5236 1.5 CA F
## 24 24 AA -0.9341 0.1 CA M
## 69 69 AA  0.4942 0.6 CA M
## 56 56 AA  1.7673 2.5 NY M
## 10 10 AA -0.1351 1.9 NY M
## 
## [[2]]
##    ID  A        B   C  D E
## 61 61 EE -0.63574 0.2 NY M
## 18 18 EE  1.46555 1.4 NY M
## 21 21 EE  0.47551 2.3 TX F
## 70 70 EE -0.17733 0.0 TX F
## 80 80 EE -0.32427 0.3 TX M
## 77 77 EE -0.07356 0.3 TX M

# How many samples were taken from each strata?
table(interaction(s.out[c("E", "D", "A")]))
## 
## F.CA.AA M.CA.AA F.NY.AA M.NY.AA F.TX.AA M.TX.AA F.CA.BB M.CA.BB F.NY.BB 
##       2       2       0       2       2       2       2       1       2 
## M.NY.BB F.TX.BB M.TX.BB F.CA.CC M.CA.CC F.NY.CC M.NY.CC F.TX.CC M.TX.CC 
##       2       2       2       2       2       2       2       2       0 
## F.CA.DD M.CA.DD F.NY.DD M.NY.DD F.TX.DD M.TX.DD F.CA.EE M.CA.EE F.NY.EE 
##       2       2       2       2       2       2       0       1       2 
## M.NY.EE F.TX.EE M.TX.EE 
##       2       2       2

# Can we verify the message about group sizes?
names(which(table(interaction(dat1[c("E", "D", "A")])) < 2))
## [1] "F.NY.AA" "M.CA.BB" "M.TX.CC" "F.CA.EE" "M.CA.EE"

names(which(table(interaction(s.out[c("E", "D", "A")])) < 2))
## [1] "F.NY.AA" "M.CA.BB" "M.TX.CC" "F.CA.EE" "M.CA.EE"