I learned something new today and the reason I write this is that I’m somewhat confused as to how I didn’t know it already.

Let us start by creating a data frame.

```
temp = data.frame(x = c(1:3), y = c(10:12))
temp
```

```
x y
1 1 10
2 2 11
3 3 12
```

We can select rows

`temp[1,]`

```
x y
1 1 10
```

and columns

`temp$x`

`[1] 1 2 3`

`temp[,2]`

`[1] 10 11 12`

What I hadn’t realised before is that there is another way to select columns, using `[[`

notation that I’m used to using with lists.

`temp[[1]]`

`[1] 1 2 3`

Let’s see if there is any difference in what is returned with these variations.

`class(temp[1,])`

`[1] "data.frame"`

`class(temp[,1])`

`[1] "integer"`

`class(temp$x)`

`[1] "integer"`

`class(temp[[1]])`

`[1] "integer"`

So selecting a row returns a *data frame*, whereas selecting a column in any way seems to return a *vector*.

Let’s do the same things with a tibble

```
library(tibble)
temp2 = tibble(x = c(1:3), y = c(10:12))
temp2
```

```
# A tibble: 3 x 2
x y
<int> <int>
1 1 10
2 2 11
3 3 12
```

We can select rows

`temp2[1,]`

```
# A tibble: 1 x 2
x y
<int> <int>
1 1 10
```

and columns

`temp2$x`

`[1] 1 2 3`

`temp2[,2]`

```
# A tibble: 3 x 1
y
<int>
1 10
2 11
3 12
```

`temp2[[1]]`

`[1] 1 2 3`

You’ve probably noticed a difference already but if we now look at what is returned we see some differences to the behaviour of `data.frame()`

.

`class(temp2[1,])`

`[1] "tbl_df" "tbl" "data.frame"`

`class(temp2[,1])`

`[1] "tbl_df" "tbl" "data.frame"`

`class(temp2$x)`

`[1] "integer"`

`class(temp2[[1]])`

`[1] "integer"`

There is now consistency using `[,]`

notation - both `temp2[1,]`

and `temp2[,1]`

return a *tibble*. Using either `$`

or `[[]]`

returns a vector as it did for a *data.frame*.

What happened in my case was that I’d written a function that had a *data frame* as one of the arguments. In this function I’d used the `$`

notation to select a column and then treated this as a *vector*. Not a problem.

I then wrote a similar function and used the `[,]`

notation to select a column and then treated it as a *vector*. This wasn’t a problem when I gave it a *data frame* but then I gave it a **tibble**. This actually threw an error for me but this sort of thing could result in just the wrong value being returned. A quick demo of this could be using the `length()`

function.

```
# data frame
length(temp[,1])
```

`[1] 3`

```
# tibble
length(temp2[,1])
```

`[1] 1`

You may be wondering what sort of error I got, well as an example lets do something slightly more meaningful.

```
temp = data.frame(x = rnorm(100))
temp2 = tibble(x = rnorm(100, 2, 0.5))
```

Then something like this will work fine

`t.test(temp$x, temp2$x)`

```
Welch Two Sample t-test
data: temp$x and temp2$x
t = -17.078, df = 143.46, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.131615 -1.689357
sample estimates:
mean of x mean of y
0.1093995 2.0198855
```

but this throws an error.

`t.test(temp[,1], temp2[,1])`

```
Welch Two Sample t-test
data: temp[, 1] and temp2[, 1]
t = -17.078, df = 143.46, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.131615 -1.689357
sample estimates:
mean of x mean of y
0.1093995 2.0198855
```

Now I know about `[[]]`

I will definitely being using it with *tibbles*.