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Let’s examine how to sort the contents of a data frame by the value of a column

```> numPeople = 10
> sex=sample(c("male","female"),numPeople,replace=T)
> age = sample(14:102, numPeople, replace=T)
> income = sample(20:150, numPeople, replace=T)
> minor = age<18
```

This last statement might look surprising if you’re used to Java or a traditional programming language. Rather than becoming a single boolean/truth value, minor actually becomes a vector of truth values, one per row in the age column.  It’s equivalent to the much more verbose code in Java:

```int[] age= ...;
for (int i = 0; i < income.length; i++) {
minor[i] = age[i] < 18;
}
```

Just as expected, the value of minor is a vector:

```> mode(minor)
[1] "logical"
> minor
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSENext we create a data frame, which groups together our various vectors into the columns of a data structure:> population = data.frame(sex=sex, age=age, income=income, minor=minor)
> population
sex age income minor
1    male  68    150 FALSE
2    male  48     21 FALSE
3  female  68     58 FALSE
4  female  27    124 FALSE
5  female  84    103 FALSE
6    male  92    112 FALSE
7    male  35     65 FALSE
8  female  15    117  TRUE
9    male  89     95 FALSE
10   male  26     54 FALSE
The arguments (sex=sex, age=age, income=income, minor=minor) assign the same names to the columns as I originally named the vectors; I could just as easily call them anything.  For instance,> data.frame(a=sex, b=age, c=income, minor=minor)
a  b   c minor
1    male 68 150 FALSE
2    male 48  21 FALSE
3  female 68  58 FALSE
4  female 27 124 FALSE
5  female 84 103 FALSE
6    male 92 112 FALSE
7    male 35  65 FALSE
8  female 15 117  TRUE
9    male 89  95 FALSE
10   male 26  54 FALSE
But I prefer the more descriptive labels I gave previously.> population
sex   age income minor
1    male  68    150 FALSE
2    male  48     21 FALSE
3  female  68     58 FALSE
4  female  27    124 FALSE
5  female  84    103 FALSE
6    male  92    112 FALSE
7    male  35     65 FALSE
8  female  15    117  TRUE
9    male  89     95 FALSE
10   male  26     54 FALSE
Now let's say we want to order by the age of the people.  To do that is a one liner:> population[order(population\$age),]
sex age income minor
8  female  15    117  TRUE
10   male  26     54 FALSE
4  female  27    124 FALSE
7    male  35     65 FALSE
2    male  48     21 FALSE
1    male  68    150 FALSE
3  female  68     58 FALSE
5  female  84    103 FALSE
9    male  89     95 FALSE
6    male  92    112 FALSE
This is not magic; you can select arbitrary rows from any data frame  with the same syntax:> population[c language="(1,2,3),"][/c]
sex age income minor
1   male  68    150 FALSE
2   male  48     21 FALSE
3 female  68     58 FALSE
The order function merely returns the indices of the rows in sorted order.> order(population\$age)
[1]  8 10  4  7  2  1  3  5  9  6
Note the \$ syntax; you select columns of a data frame by using a dollar sign and the name of the column.  You can retrieve the names of the columns of a data frame with the names function.> names(population)
[1] "sex"    "age"    "income" "minor"

> population\$income
[1] 150  21  58 124 103 112  65 117  95  54
> income
[1] 150  21  58 124 103 112  65 117  95  54
As you can see, they are exactly the same.So what we're really doing with the commandpopulation[order(population\$age),]
ispopulation[c language="(8,10,4,7,2,1,3,5,9,6),"][/c]
Note the trailing comma; what this means is to take all the columns.  If we only wanted certain columns, we could specify after this comma.> population[order(population\$age),c(1,2)]
sex age
8  female  15
10   male  26
4  female  27
7    male  35
2    male  48
1    male  68
3  female  68
5  female  84
9    male  89
6    male  92
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