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

import.Rmd

@@ -18,7 +18,7 @@ library(readr)
 
 Most of readr's functions are concerned with turning flat files into data frames:
 
-* `read_csv()` reads comma delimited files, `read_csv2()` reads semi-colon
+* `read_csv()` reads comma delimited files, `read_csv2()` reads semicolon
   separated files (common in countries where `,` is used as the decimal place),
   `read_tsv()` reads tab delimited files, and `read_delim()` reads in files
   with any delimiter.
@@ -108,7 +108,7 @@ If you've used R before, you might wonder why we're not using `read.csv()`. Ther
   your operating system and environment variables, so import code that works 
   on your computer might not work on someone else's.
 
-### Exericses
+### Exercises
 
 1.  What function would you use to read a file where fields were separated with  
     "|"?
@@ -119,7 +119,7 @@ If you've used R before, you might wonder why we're not using `read.csv()`. Ther
 1.  What is the most important argument to `read_fwf()` that we haven't already
     discussed?
 
-1.  Some times strings in a csv file contain commas. To prevent them from
+1.  Sometimes strings in a csv file contain commas. To prevent them from
     causing problems they need to be surrounded by a quoting character, like
     `"` or `'`. By convention, `read_csv()` assumes that the quoting
     character will be `"`, and if you want to change it you'll need to
@@ -281,7 +281,7 @@ Encodings are a rich and complex topic, and I've only scratched the surface here
 
 You pick between three parsers depending on whether you want a date (the number of days since 1970-01-01), a date time (the number of seconds since midnight 1970-01-01), or a time (the number of seconds since midnight):
 
-*   `parse_datetime()` expects an ISO8601 date time. ISO8691 is an
+*   `parse_datetime()` expects an ISO8601 date time. ISO8601 is an
     international standard in which the components of a date are
     organised from biggest to smallest: year, month, day, hour, minute, 
     second.
@@ -427,7 +427,7 @@ These defaults don't always work for larger files. There are two basic problems:
     a column of doubles that only contains integers in the first 1000 rows. 
 
 1.  The column might contain a lot of missing values. If the first 1000
-    rows contains on `NA`s, readr will guess that it's a character 
+    rows contains only `NA`s, readr will guess that it's a character 
     vector, whereas you probably want to parse it as something more
     specific.
 
@@ -439,7 +439,7 @@ challenge <- read_csv(readr_example("challenge.csv"))
 
 (Note the use of `readr_example()` which finds the path to one of the files included with the package)
 
-There are two outputs: the column specification generated by looking at the first 1000 rows, and the first five parsing failures. It's always a good idea to explicitly pull out the `problems()` so you can explore them in more depth:
+There are two outputs: the column specification generated by looking at the first 1000 rows, and the first five parsing failures. It's always a good idea to explicitly pull out the `problems()`, so you can explore them in more depth:
 
 ```{r}
 problems(challenge)
@@ -543,7 +543,7 @@ There are a few other general strategies to help you parse files:
 
 readr also comes with two useful functions for writing data back to disk: `write_csv()` and `write_tsv()`. They:
 
-* Are faster than the base R equvalents.
+* Are faster than the base R equivalents.
 
 * Never write rownames, and quote only when needed. 
 
@@ -610,7 +610,7 @@ file.remove("challenge.rds")
 
 To get other types of data into R, we recommend starting with the tidyverse packages listed below. They're certainly not perfect, but they are a good place to start.
 
-For rectanuglar data:
+For rectangular data:
 
 * haven reads SPSS, Stata, and SAS files.
 

relational-data.Rmd

@@ -235,7 +235,7 @@ Graphically, that looks like:
 knitr::include_graphics("diagrams/join-outer.png")
 ```
 
-The most commonly used join is the left join: you use this whenever you lookup additional data out of another table, because it preserves the original observations even when there isn't a match. The left join should be your default join: use it unless you have a strong reason to prefer one of the others.
+The most commonly used join is the left join: you use this whenever you look up additional data out of another table, because it preserves the original observations even when there isn't a match. The left join should be your default join: use it unless you have a strong reason to prefer one of the others.
 
 Another way to depict the different types of joins is with a Venn diagram:
 
@@ -383,7 +383,7 @@ dplyr                        | SQL
 
 Note that "INNER" and "OUTER" are optional, and often omitted.
 
-Joining different variables between the tables, e.g. `inner_join(x, y, by = c("a" = "b"))` uses a slightly different syntax in SQL: `SELECT * FROM x INNER JOIN y ON x.a = y.b`. As this syntax suggests SQL supports a wide range of join types than dplyr because you can connect the tables using constraints other than equality (sometimes called non-equijoins).
+Joining different variables between the tables, e.g. `inner_join(x, y, by = c("a" = "b"))` uses a slightly different syntax in SQL: `SELECT * FROM x INNER JOIN y ON x.a = y.b`. As this syntax suggests SQL supports a wider  range of join types than dplyr because you can connect the tables using constraints other than equality (sometimes called non-equijoins).
 
 ## Filtering joins {#filtering-joins}