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Typos in transform & EDA (#209)

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* little different to -> a little differently from

* reorder to match order in following code/table

* capitalization

* capitalization + punctuation

* replace / with or since it's hard to see between tt formatted code

* missing pronoun

* they're been -> they've been

* lets need apostrophe

* Instead of display -> Instead of displaying

* missing comma

* add mapping in front of aes in a bunch of locations

* adding mapping before aes for sections before the last section where it explicitly says from here on out we'll omit them to make calls simpler
This commit is contained in:
Mine Cetinkaya-Rundel
2016-07-29 23:05:08 -04:00
committed by Hadley Wickham
parent 8f087e8ce3
commit fe73722b0a
2 changed files with 33 additions and 35 deletions
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6
transform.Rmd
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@@ -24,7 +24,7 @@ To explore the basic data manipulation verbs of dplyr, we'll use `nycflights13::
flights
```
You might notice that this data frame prints little differently to other data frames you might have used in the past: it only shows the first few rows and all the columns that fit on one screen. (To see the whole dataset, you can run `View(flights)` which will open the dataset in the RStudio viewer). It prints differently because it's a __tibble__. Tibbles are data frames, but slightly tweaked to work better in the tidyverse. For now, you don't need to worry about the differences; we'll come back to tibbles in more detail in [wrangle](#wrangle-intro).
You might notice that this data frame prints a little differently from other data frames you might have used in the past: it only shows the first few rows and all the columns that fit on one screen. (To see the whole dataset, you can run `View(flights)` which will open the dataset in the RStudio viewer). It prints differently because it's a __tibble__. Tibbles are data frames, but slightly tweaked to work better in the tidyverse. For now, you don't need to worry about the differences; we'll come back to tibbles in more detail in [wrangle](#wrangle-intro).
You might also have noticed the row of three letter abbreviations under the column names. These describe the type of each variable:
@@ -426,7 +426,7 @@ There are many functions for creating new variables that you can use with `mutat
(e.g. 1st, 2nd, 2nd, 4th). The default gives smallest values the small
ranks; use `desc(x)` to give the largest values the smallest ranks.
If `min_rank()` doesn't do what you need, look at the variants
`row_number()`, `dense_rank()`, `cume_dist()`, `percent_rank()`,
`row_number()`, `dense_rank()`, `percent_rank()`, `cume_dist()`,
`ntile()`.
```{r}
@@ -481,7 +481,7 @@ The last key verb is `summarise()`. It collapses a data frame to a single row:
summarise(flights, delay = mean(dep_delay, na.rm = TRUE))
```
(we'll come back to what that `na.rm = TRUE` means very shortly.)
(We'll come back to what that `na.rm = TRUE` means very shortly.)
`summarise()` is not terribly useful unless we pair it with `group_by()`. This changes the unit of analysis from the complete dataset to individual groups. Then, when you use the dplyr verbs on a grouped data frame they'll be automatically applied "by group". For example, if we applied exactly the same code to a data frame grouped by date, we get the average delay per date: