Merge branch 'master' of github.com:hadley/r4ds

transform.Rmd

@@ -64,7 +64,7 @@ It prints differently because it has a different "class" to usual data frames:
 class(flights)
 ```
 
-This is called a `tbl_df` (prounced tibble diff) or a `data_frame` (pronunced "data underscore frame"; cf. `data dot frame`). Generally, however, we want worry about this relatively minor difference and will refer to everything as data frames.
+This is called a `tbl_df` (pronounced tibble diff) or a `data_frame` (pronounced "data underscore frame"; cf. `data dot frame`). Generally, however, we want worry about this relatively minor difference and will refer to everything as data frames.
 
 You'll learn more about how that works in data structures. If you want to convert your own data frames to this special case, use `as.data_frame()`. I recommend it for large data frames as it makes interactive exploration much less painful.
 
@@ -299,7 +299,7 @@ filter(df, is.na(x) | x > 1)
     * There were operated by United, American, or Delta.
     * That were delayed by more two hours.
     * That arrived more than two hours late, but didn't leave late.
-    * We delayed by at least an hour, but made up over 30 minutes in flight.
+    * Were delayed by at least an hour, but made up over 30 minutes in flight.
     * Departed between midnight and 6am.
 
 1.  How many flights have a missing `dep_time`? What other variables are 
@@ -399,7 +399,7 @@ This function works similarly to the `select` argument in `base::subset()`. Beca
 1.  Brainstorm as many ways as possible to select `dep_time`, `dep_delay`,
     `arr_time`, and `arr_delay` from `flights`.
 
-## Add new variable with `mutate()`
+## Add new variables with `mutate()`
 
 Besides selecting sets of existing columns, it's often useful to add new columns that are functions of existing columns. This is the job of `mutate()`. 
 
@@ -459,7 +459,7 @@ There are many functions for creating new variables. The key property is that th
     the proportion of a total and `y - mean(y)` computes the difference from 
     the mean, and so on.
     
-*   Modular arithmetic: `%/%` (integer divison) and `%%` (remainder), where
+*   Modular arithmetic: `%/%` (integer division) and `%%` (remainder), where
     `x == y * (x %/% y) + (x %% y)`. Modular arithmetic is a handy tool because 
     it allows you to break integers up into pieces. For example, in the 
     flights dataset, you can compute `hour` and `minute` from `dep_time` with:
@@ -563,7 +563,7 @@ by_day <- group_by(flights, year, month, day)
 summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))
 ```
 
-Together `group_by()` and `summarise()` provide one of tools that you'll use most commonly when working with dplyr: groued summaries. But before we go any further with this idea, we need to introduce a powerful new idea: the pipe.
+Together `group_by()` and `summarise()` provide one of tools that you'll use most commonly when working with dplyr: grouped summaries. But before we go any further with this idea, we need to introduce a powerful new idea: the pipe.
 
 ### Combining multiple operations with the pipe
 
@@ -594,7 +594,7 @@ There are three steps:
 * Filter to remove noisy points and Honolulu airport which is almost
   twice as far away as the next closest airport.
 
-This code is a little frustraing to write because we have to give each intermediate data frame a name, even though we don't care about it. Naming things well is hard, so this slows us down. 
+This code is a little frustrating to write because we have to give each intermediate data frame a name, even though we don't care about it. Naming things well is hard, so this slows us down. 
 
 There's another way to tackle the same problem with the pipe, `%>%`:
 
@@ -744,12 +744,10 @@ Just using means, counts, and sum can get you a long way, but R provides many ot
         avg_delay2 = mean(arr_delay[arr_delay > 0])
       )
     ```
-    
-     mean(arr_delay[arr_delay > 0])
 
 *   Measure 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)`
+    The interquartile range `IQR()` and median absolute deviation `mad(x)`
     are robust equivalents that maybe more useful if you have outliers.
     
     ```{r}
@@ -778,7 +776,7 @@ Just using means, counts, and sum can get you a long way, but R provides many ot
     group that only has two elements). 
     
     These functions are complementary to filtering on ranks. Filtering gives
-    you all variables, which each observation in a separate row. Summarising
+    you all variables, with each observation in a separate row. Summarising
     gives you one row per group, with multiple variables:
     
     ```{r}
@@ -849,7 +847,7 @@ daily <- group_by(flights, year, month, day)
 (per_year  <- summarise(per_month, flights = sum(flights)))
 ```
 
-Becareful when progressively rolling up summaries: it's ok for sums and counts, but you need to think about weighting for means and variances, and it's not possible to do it exactly for rank-based statistics like the median (i.e. the sum of groupwise sums is the overall sum, but the median of groupwise medians is not the overall median).
+Be careful when progressively rolling up summaries: it's OK for sums and counts, but you need to think about weighting for means and variances, and it's not possible to do it exactly for rank-based statistics like the median (i.e. the sum of groupwise sums is the overall sum, but the median of groupwise medians is not the overall median).
 
 ### Ungrouping
 
@@ -857,7 +855,7 @@ If you need to remove grouping, and return to operations on ungrouped data, use
 
 ### Exercises
 
-1.  Brainstorm at least 5 different ways to assess the typically delay 
+1.  Brainstorm at least 5 different ways to assess the typical delay 
     characteristics of a group of flights. Consider the following scenarios:
     
     * A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of 
@@ -921,7 +919,7 @@ Functions that work most naturally in grouped mutates and filters are known as
 1.  What time of day should you fly if you want to avoid delays as much
     as possible?
     
-1.  Delays are typically temporarily correlated: even once the problem that
+1.  Delays are typically temporally correlated: even once the problem that
     caused the initial delay has been resolved, later flights are delayed 
     to allow earlier flights to leave. Using `lag()` explore how the delay
     of a flight is related to the delay of the flight that left just