Fix typos in Chapter 7 (#534)
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EDA.Rmd
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EDA.Rmd
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@ -171,7 +171,7 @@ ggplot(diamonds) +
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geom_histogram(mapping = aes(x = y), binwidth = 0.5)
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geom_histogram(mapping = aes(x = y), binwidth = 0.5)
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```
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```
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There are so many observations in the common bins that the rare bins are so short that you can't see them (although maybe if you stare intently at 0 you'll spot something). To make it easy to see the unusual values, we need to zoom into to small values of the y-axis with `coord_cartesian()`:
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There are so many observations in the common bins that the rare bins are so short that you can't see them (although maybe if you stare intently at 0 you'll spot something). To make it easy to see the unusual values, we need to zoom to small values of the y-axis with `coord_cartesian()`:
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```{r}
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```{r}
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ggplot(diamonds) +
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ggplot(diamonds) +
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@ -262,7 +262,7 @@ ggplot(data = diamonds2, mapping = aes(x = x, y = y)) +
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geom_point(na.rm = TRUE)
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geom_point(na.rm = TRUE)
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```
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```
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Other times you want to understand what makes observations with missing values different to observations with recorded values. For example, in `nycflights13::flights`, missing values in the `dep_time` variable indicate that the flight was cancelled. So you might want to compare the scheduled departure times for cancelled and non-cancelled times. You can do by making a new variable with `is.na()`.
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Other times you want to understand what makes observations with missing values different to observations with recorded values. For example, in `nycflights13::flights`, missing values in the `dep_time` variable indicate that the flight was cancelled. So you might want to compare the scheduled departure times for cancelled and non-cancelled times. You can do this by making a new variable with `is.na()`.
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```{r}
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```{r}
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nycflights13::flights %>%
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nycflights13::flights %>%
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