Small edits to import.Rmd (typos and mistakes)

import.Rmd

@@ -7,7 +7,7 @@ library(readr)
 
 ## Overview
 
-You can't apply any of the tools you've applied so far to your own work, unless you can get your own data into R. In this chapter, you'll learn how to:
+You can't apply any of the tools you've learned so far to your own work, unless you can get your own data into R. In this chapter, you'll learn how to:
 
 * Import flat files (like csv) with readr.
 * 
@@ -20,16 +20,16 @@ The common link between all these packages is they all aim to take your data and
 There are many ways to read flat files into R. If you've be using R for a while, you might be familiar with `read.csv()`, `read.fwf()` and friends. We're not going to use these base functions. Instead we're going to use `read_csv()`, `read_fwf()`, and friends from the readr package. Because:
 
 * These functions are typically much faster (~10x) than the base equivalents.
-  Long run running jobs also have a progress bar, so you can see what's
+  Long running jobs also have a progress bar, so you can see what's
   happening. (If you're looking for raw speed, try `data.table::fread()`,
   it's slightly less flexible than readr, but can be twice as fast.)
 
 * They have more flexible parsers: they can read in dates, times, currencies,
   percentages, and more.
 
-* They fail to do some annoying things like converting character vectors to
-  factors, munging the column headers to make sure they're valid R
-  variable names, and using row names.
+* They do not do some annoying things that base R functions do, like converting 
+character vectors to factors, munging the column headers to make sure they're 
+valid R variable names, and using row names.
 
 * They return objects with class `tbl_df`. As you saw in the dplyr chapter,
   this provides a nicer printing method, so it's easier to work with large
@@ -67,15 +67,17 @@ readr also provides a number of functions for reading files off disk into simple
 
 These might be useful for other programming tasks.
 
-As well as reading data from disk, readr also provides tools for working with data frames and character vectors in R:
+readr also provides tools for working with data frames and character vectors in R:
 
 * `type_convert()` applies the same parsing heuristics to the character columns
   in a data frame. You can override its choices using `col_types`.
 
-For the rest of this chapter we'll focus on `read_csv()`. If you understand how to use this function, it will be straightforward to your knowledge to all the other functions in readr.
+For the rest of this chapter we'll focus on `read_csv()`. If you understand how to use this function, it will be straightforward to apply your knowledge to all the other functions in readr.
 
 ### Basics
 
+EXAMPLE
+
 The first two arguments of `read_csv()` are:
 
 * `file`: path (or URL) to the file you want to load. Readr can automatically
@@ -93,8 +95,6 @@ The first two arguments of `read_csv()` are:
     * A character vector, used as column names. If these don't match up
       with the columns in the data, you'll get a warning message.
 
-EXAMPLE
-
 ### Column types
 
 Readr uses a heuristic to figure out the types of your columns: it reads the first 1000 rows and uses some (moderately conservative) heuristics to figure out the type of each column. This is fast, and fairly robust. If readr detects the wrong type of data, you'll get warning messages. Readr prints out the first five, and you can access them all with `problems()`:
@@ -120,7 +120,7 @@ You can use the following types of columns
   `col_logical()` (l) parses TRUE, T, FALSE and F into a logical vector.
   `col_character()` (c) leaves strings as is.
 
-* `col_number()` (n) is a more flexible parsed for numbers embedded in other
+* `col_number()` (n) is a more flexible parser for numbers embedded in other
   strings. It will look for the first number in a string, ignoring non-numeric
   prefixes and suffixes. It will also ignore the grouping mark specified by
   the locale (see below for more details).
@@ -142,9 +142,9 @@ read_csv("mypath.csv", col_types = cols(
 ))
 ```
 
-(If you just have a few columns you supply a single string giving the type for each column: `i__dc`. See the documentation for more details. It's not as easy to understand as the `cols()` specification, so I'm not going to describe it further here.)
+(If you just have a few columns, you can supply a single string that gives the type for each column: `i__dc`. See the documentation for more details. It's not as easy to understand as the `cols()` specification, so I'm not going to describe it further here.)
 
-By default, any column not mentioned in `cols` will be guessed. If you'd rather those columns are simply not read in, use `cols_only()`. In that case, you can use `col_guess()` (?) if you want to guess the type of a column.
+By default, any column not mentioned in `cols` will be guessed. If you'd rather those columns are simply not read in, use `cols_only()`. In that case, you can use `col_guess()` (?) if you want to guess the type of a column and include it to be read.
 
 Each `col_XYZ()` function also has a corresponding `parse_XYZ()` that you can use on a character vector. This makes it easier to explore what each of the parsers does interactively.
 
@@ -163,7 +163,7 @@ parse_logical(c("TRUE ", " ."), na = ".")
 
 #### Datetimes
 
-Readr provides three options depending on where 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 (i.e. the number of seconds since midnight). The defaults read:
+Readr provides three options 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 (i.e. the number of seconds since midnight). The defaults read:
 
 * Date times: an [ISO8601](https://en.wikipedia.org/wiki/ISO_8601) date time.
 * Date: a year, optional separator, month, optional separator, day.