Some fixes for chapters regexps & factors (#1636)
* regexps.qmd: fix name of 'too_few' arg * regexps.qmd: fix typo * factors.qmd: update argument names to .f, .x, .y * factors.qmd: fix language
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factors.qmd
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factors.qmd
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@ -177,9 +177,9 @@ It is hard to read this plot because there's no overall pattern.
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We can improve it by reordering the levels of `relig` using `fct_reorder()`.
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`fct_reorder()` takes three arguments:
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- `f`, the factor whose levels you want to modify.
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- `x`, a numeric vector that you want to use to reorder the levels.
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- Optionally, `fun`, a function that's used if there are multiple values of `x` for each value of `f`. The default value is `median`.
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- `.f`, the factor whose levels you want to modify.
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- `.x`, a numeric vector that you want to use to reorder the levels.
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- Optionally, `.fun`, a function that's used if there are multiple values of `.x` for each value of `.f`. The default value is `median`.
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```{r}
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#| fig-alt: |
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@ -231,7 +231,7 @@ Reserve `fct_reorder()` for factors whose levels are arbitrarily ordered.
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However, it does make sense to pull "Not applicable" to the front with the other special levels.
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You can use `fct_relevel()`.
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It takes a factor, `f`, and then any number of levels that you want to move to the front of the line.
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It takes a factor, `.f`, and then any number of levels that you want to move to the front of the line.
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```{r}
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#| fig-alt: |
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@ -247,7 +247,7 @@ ggplot(rincome_summary, aes(x = age, y = fct_relevel(rincome, "Not applicable"))
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Why do you think the average age for "Not applicable" is so high?
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Another type of reordering is useful when you are coloring the lines on a plot.
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`fct_reorder2(f, x, y)` reorders the factor `f` by the `y` values associated with the largest `x` values.
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`fct_reorder2(.f, .x, .y)` reorders the factor `.f` by the `.y` values associated with the largest `.x` values.
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This makes the plot easier to read because the colors of the line at the far right of the plot will line up with the legend.
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```{r}
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@ -287,7 +287,7 @@ Combine it with `fct_rev()` if you want them in increasing frequency so that in
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```{r}
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#| fig-alt: |
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#| A bar char of marital status ordered in from least to most common:
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#| A bar char of marital status ordered from least to most common:
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#| no answer (~0), separated (~1,000), widowed (~2,000), divorced
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#| (~3,000), never married (~5,000), married (~10,000).
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gss_cat |>
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@ -265,7 +265,7 @@ df |>
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)
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```
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If the match fails, you can use `too_short = "debug"` to figure out what went wrong, just like `separate_wider_delim()` and `separate_wider_position()`.
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If the match fails, you can use `too_few = "debug"` to figure out what went wrong, just like `separate_wider_delim()` and `separate_wider_position()`.
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### Exercises
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@ -336,7 +336,7 @@ That lets you avoid one layer of escaping:
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str_view(x, r"{\\}")
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```
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If you're trying to match a literal `.`, `$`, `|`, `*`, `+`, `?`, `{`, `}`, `(`, `)`, there's an alternative to using a backslash escape: you can use a character class: `[.]`, `[$]`, `[|]`, \...
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If you're trying to match a literal `.`, `$`, `|`, `*`, `+`, `?`, `{`, `}`, `(`, `)`, there's an alternative to using a backslash escape: you can use a character class: `[.]`, `[$]`, `[|]`, ...
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all match the literal values.
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```{r}
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