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

# Conflicts:
#	communicate.Rmd

communicate-plots.Rmd

@@ -111,7 +111,7 @@ ggplot(mpg, aes(displ, hwy)) +
   geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.5)
 ```
 
-That helps a bit, but if you look closely in the top-left hand corner, you'll notice that there are two labels practically on top of each other. This happens because the highway mileage and displacement for the best cars in the compact and subcompact categories are exactly the same.There's no way that we can fix these by applying the same transformation for every label. Instead, we can use the __ggrepel__ package by Kamil Slowikowski. This useful package will automatically adjust labels so that they don't overlap:
+That helps a bit, but if you look closely in the top-left hand corner, you'll notice that there are two labels practically on top of each other. This happens because the highway mileage and displacement for the best cars in the compact and subcompact categories are exactly the same. There's no way that we can fix these by applying the same transformation for every label. Instead, we can use the __ggrepel__ package by Kamil Slowikowski. This useful package will automatically adjust labels so that they don't overlap:
 
 ```{r}
 ggplot(mpg, aes(displ, hwy)) +

communicate.Rmd

@@ -2,7 +2,7 @@
 
 # Introduction {#communicate-intro}
 
-So far, you've learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand you data through transformation, visualisation and modelling. Next you'll learn how to __communicate__ your results because it doesn't matter how great your analysis is unless you can explain it to someone else.
+So far, you've learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, visualisation and modelling. However, it doesn't matter how great your analysis is unless you can explain it to others: you need to __communicate__ your results.
 
 ```{r echo = FALSE, out.width = "75%"}
 knitr::include_graphics("diagrams/data-science-communicate.png")

datetimes.Rmd

@@ -10,7 +10,7 @@ This chapter will show you how to work with dates and times in R. At first glanc
 
 I'm sure you know that not every year has 365 days, but do you know the full rule for determining if a year is a leap year? (It has three parts.) You might have remembered that many parts of the world use daylight savings time (DST), so that some days have 23 hours, and others have 25. You might not have known that some minutes have 61 seconds because every now and then leap seconds are added because the Earth's rotation is gradually slowing down.
 
-Dates and times are hard because they have to reconcile two physical phenomenon (the rotation of the Earth and its orbit around the sun) with a whole raft of geopolitical phenomenon including months, time zones, and DST. This chapter won't teach you every last detail about dates and times, but it will give you a solid grounding of practical skills that will help you with common data analysis challenges.
+Dates and times are hard because they have to reconcile two physical phenomena (the rotation of the Earth and its orbit around the sun) with a whole raft of geopolitical phenomena including months, time zones, and DST. This chapter won't teach you every last detail about dates and times, but it will give you a solid grounding of practical skills that will help you with common data analysis challenges.
 
 ### Prerequisites
 
@@ -238,7 +238,7 @@ sched_dep <- flights_dt %>%
     avg_delay = mean(arr_delay, na.rm = TRUE),
     n = n())
 
-ggplot(sched_dep , aes(minute, avg_delay)) +
+ggplot(sched_dep, aes(minute, avg_delay)) +
   geom_line()
 ```
 
@@ -329,7 +329,7 @@ Setting larger components of a date to a constant is a powerful technique that a
 
 ## Time spans
 
-Next you'll learn about how arithmetic with dates works, including substraction, addition, and division. Along the way, you'll learn about three important classes that represent time spans:
+Next you'll learn about how arithmetic with dates works, including subtraction, addition, and division. Along the way, you'll learn about three important classes that represent time spans:
 
 * __durations__, which represent an exact number of seconds.
 * __periods__, which represent human units like weeks and months.
@@ -507,7 +507,7 @@ knitr::include_graphics("diagrams/datetimes-arithmetic.png")
 
 Time zones are an enormously complicated topic because of their interaction with geopolitical entities. Fortunately we don't need to dig into all the details as they're not all important for data analysis, but there are a few challenges we'll need to tackle head on.
 
-The first challange is that everyday names of time zones tend to be ambiguous. For example, if you're American you're probably familiar with EST, or Eastern Standard Time. However, both Australia and Canada also have EST! To avoid confusion, R uses the international standard IANA time zones. These use a consistent naming scheme "<area>/<location>", typically in the form  "\<continent\>/\<city\>" (there are a few exceptions because not every country lies on a continent). Examples include "America/New_York", "Europe/Paris", and "Pacific/Auckland".
+The first challange is that everyday names of time zones tend to be ambiguous. For example, if you're American you're probably familiar with EST, or Eastern Standard Time. However, both Australia and Canada also have EST! To avoid confusion, R uses the international standard IANA time zones. These use a consistent naming scheme "<area>/<location>", typically in the form "\<continent\>/\<city\>" (there are a few exceptions because not every country lies on a continent). Examples include "America/New_York", "Europe/Paris", and "Pacific/Auckland".
 
 You might wonder why the time zone uses a city, when typically you think of time zones as associated with a country or region within a country. This is because the IANA database has to record decades worth of time zone rules. In the course of decades, countries change names (or break apart) fairly frequently, but city names tend to stay the same. Another problem is that name needs to reflect not only to the current behaviour, but also the complete history. For example, there are time zones for both "America/New_York" and "America/Detroit". These cities both currently use Eastern Standard Time but in 1969-1972 Michigan (the state in which Detroit is located), did not follow DST, so it needs a different name. It's worth reading the raw time zone database (available at <http://www.iana.org/time-zones>) just to read some of these stories!