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

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hadley
2016-07-14 10:57:58 -05:00
parent 3c5838dcbd fd2453dd58
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import.Rmd
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@@ -18,7 +18,7 @@ library(readr)
Most of readr's functions are concerned with turning flat files into data frames: 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), separated files (common in countries where `,` is used as the decimal place),
`read_tsv()` reads tab delimited files, and `read_delim()` reads in files `read_tsv()` reads tab delimited files, and `read_delim()` reads in files
with any delimiter. 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 your operating system and environment variables, so import code that works
on your computer might not work on someone else's. 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 1. What function would you use to read a file where fields were separated with
"|"? "|"?
@@ -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): 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 international standard in which the components of a date are
organised from biggest to smallest: year, month, day, hour, minute, organised from biggest to smallest: year, month, day, hour, minute,
second. 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. 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 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 vector, whereas you probably want to parse it as something more
specific. 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) (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} ```{r}
problems(challenge) 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: 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. * 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. 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. * haven reads SPSS, Stata, and SAS files.

2
relational-data.Rmd
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@@ -383,7 +383,7 @@ dplyr | SQL
Note that "INNER" and "OUTER" are optional, and often omitted. 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} ## Filtering joins {#filtering-joins}