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Merge branch 'master' of github.com:hadley/r4ds

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hadley 2015-12-18 09:53:22 -06:00
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intro.Rmd
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@ -76,7 +76,7 @@ Another class of big data problem consists of many small data problems. Each ind
### Python
In this book, you won't learn anything about Python, Juli, or any other programming language useful for data science. This isn't because we think these tools are bad. They're not! And in practice, most data science teams use a mix of languages, often at least R and Python.
In this book, you won't learn anything about Python, Julia, or any other programming language useful for data science. This isn't because we think these tools are bad. They're not! And in practice, most data science teams use a mix of languages, often at least R and Python.
However, we strongly believe that it's best to master one tool at a time. You will get better faster if you dive deep, rather than spreading yourself thinly over many topics. This doesn't mean you should be only know one thing, just that you'll generally learn faster if you stick to one thing at a time.

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lists.Rmd
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@ -49,7 +49,7 @@ x <- list(1, 2, 3)
str(x)
x_named <- list(a = 1, b = 2, c = 3)
str(x)
str(x_named)
```
Unlike atomic vectors, `lists()` can contain a mix of objects:
@ -541,7 +541,7 @@ You'll see an example of this in the next section, as `transpose()` is particula
It's called transpose by analogy to matrices. When you subset a transposed matrix, you switch indices: `x[i, j]` is the same as `t(x)[j, i]`. It's the same idea when transposing a list, but the subsetting looks a little different: `x[[i]][[j]]` is equivalent to `transpose(x)[[j]][[i]]`. Similarly, a transpose is its own inverse so `transpose(transpose(x))` is equal to `x`.
Tranpose is also useful when working with JSON apis. Many JSON APIs represent data frames in a row-based format, rather than R's column-based format. `transpose()` makes it easy to switch between the two:
Transpose is also useful when working with JSON apis. Many JSON APIs represent data frames in a row-based format, rather than R's column-based format. `transpose()` makes it easy to switch between the two:
```{r}
df <- dplyr::data_frame(x = 1:3, y = c("a", "b", "c"))