parent
419e7eaa99
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273a481021
6
tidy.Rmd
6
tidy.Rmd
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@ -406,7 +406,7 @@ treatment <- frame_data(
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)
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```
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You can fill in these missing values with `fill()`. It takes a set of columns where you want missing values to be replaced by the most recent non-missing value (sometimese called last observation carried forward).
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You can fill in these missing values with `fill()`. It takes a set of columns where you want missing values to be replaced by the most recent non-missing value (sometimes called last observation carried forward).
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```{r}
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treatment %>%
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@ -502,7 +502,7 @@ who3 <- who2 %>%
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who3
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```
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Then we might as well drop the `new` colum because it's constant in this dataset. While we're dropping columns, let's also drop `iso2` and `iso3` since they're redundant.
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Then we might as well drop the `new` column because it's constant in this dataset. While we're dropping columns, let's also drop `iso2` and `iso3` since they're redundant.
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```{r}
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who3 %>%
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@ -521,7 +521,7 @@ who5
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The `who` dataset is now tidy!
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I've shown you the code a piece at a time, assinging each interim result to a new variable. This typically isn't how you'd work interactively. Instead, you'd gradually build up a complex pipe:
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I've shown you the code a piece at a time, assigning each interim result to a new variable. This typically isn't how you'd work interactively. Instead, you'd gradually build up a complex pipe:
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```{r, results = "hide"}
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who %>%
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