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Fixes from @schuess

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Closes #409
This commit is contained in:
hadley
2016-10-03 08:36:51 -05:00
parent 74cb7d57f6
commit a0eba42266
13 changed files with 51 additions and 51 deletions
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8
transform.Rmd
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@@ -106,7 +106,7 @@ near(1 / 49 * 49, 1)
Multiple arguments to `filter()` are combined with "and": every expression must be true in order for a row to be included in the output. For other types of combinations, you'll need to use Boolean operators yourself: `&` is "and", `|` is "or", and `!` is "not". Figure \@ref(fig:bool-ops) shows the complete set of Boolean operations.
```{r bool-ops, echo = FALSE, fig.cap = "Complete set of boolean operations. `x` is the left-hand circle, `y` is the right hand circle, and the shaded region show which parts each operator selects."}
```{r bool-ops, echo = FALSE, fig.cap = "Complete set of boolean operations. `x` is the left-hand circle, `y` is the right-hand circle, and the shaded region show which parts each operator selects."}
knitr::include_graphics("diagrams/transform-logical.png")
```
@@ -192,7 +192,7 @@ filter(df, is.na(x) | x > 1)
1. Find all flights that
1. Had an arrival delay of two or more hours.
1. Had an arrival delay of two or more hours
1. Flew to Houston (`IAH` or `HOU`)
1. Were operated by United, American, or Delta
1. Departed in summer (July, August, and September)
@@ -675,7 +675,7 @@ Just using means, counts, and sum can get you a long way, but R provides many ot
* Measures of spread: `sd(x)`, `IQR(x)`, `mad(x)`. The mean squared deviation,
or standard deviation or sd for short, is the standard measure of spread.
The interquartile range `IQR()` and median absolute deviation `mad(x)`
are robust equivalents that maybe more useful if you have outliers.
are robust equivalents that may be more useful if you have outliers.
```{r}
# Why is distance to some destinations more variable than to others?
@@ -772,7 +772,7 @@ Just using means, counts, and sum can get you a long way, but R provides many ot
### Grouping by multiple variables
When you group by multiple variables, each summary peels off one level of the grouping. That makes it easy to progressively roll-up a dataset:
When you group by multiple variables, each summary peels off one level of the grouping. That makes it easy to progressively roll up a dataset:
```{r}
daily <- group_by(flights, year, month, day)