Stratified random sampling in R

r

Solution

You could first separate into bins e.g. <100, between 100 and 300, etc using the `cut` function.

data$cut <- cut(data$FARMS92, breaks = c(0,100,300,600, 1E7), labels = c("A","B","C", "D"), right = TRUE)

Then use the `stratify` function (https://gist.github.com/mrdwab/6424112).

stratified(data, "cut", size = c(2,2,2,2))

For this particular example I used `size = c(2,2,2,2)` that will return 2 from each bin. Since you want a sample size = 100 then adjust the size accordingly. For instance, for proportional allocation you could use for your original dataset something like: `size = round(100 * prop.table(table(data$cut)), 0)`.

Output:

     COUNTY STATE ACRES92 ACRES87 FARMS92 cut
7   BALDWIN    AL  167832  192082      22   A
6   AUTAUGA    AL  107259  116050      14   A
4    JUNEAU    AK     210     214     127   B
12   BUTLER    AL   96427   99997     124   B
11  BULLOCK    AL  144799  156332     385   C
15 CHEROKEE    AL  121504  119956     436   C
9      BIBB    AL   48022   50818   49630   D
8   BARBOUR    AL  177189  207906  222066   D

I modified your dataset to produce a better working example. Data:

data <- read.table(text= "COUNTY   STATE  ACRES92 ACRES87 FARMS92
1   ALEUTIAN  AK    683533  726596  76
2   ANCHORAGE AK    47146   59297   2
3   FAIRBANKS AK    141338  154913  204
4   JUNEAU    AK    210     214     127
5   KENAI     AK    50810   85712   480
6   AUTAUGA   AL    107259  116050  14
7   BALDWIN   AL    167832  192082  22
8   BARBOUR   AL    177189  207906  222066
9   BIBB      AL    48022   50818   49630
10  BLOUNT    AL    137426  140107  163638
11  BULLOCK   AL    144799  156332  385
12  BUTLER    AL    96427   99997   124
13  CALHOUN   AL    73841   90474   93248
14  CHAMBERS  AL    109555  102153  121
15  CHEROKEE  AL    121504  119956  436 ", stringsAsFactors=FALSE, header = TRUE)   

Problem

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