Fix typos

This commit is contained in:
Bill Behrman 2016-07-11 18:29:17 -07:00
parent 081f0c1e39
commit 59803d3921
1 changed files with 8 additions and 8 deletions

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@ -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.