Fix typos
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import.Rmd
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import.Rmd
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@ -18,7 +18,7 @@ library(readr)
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Most of readr's functions are concerned with turning flat files into data frames:
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Most of readr's functions are concerned with turning flat files into data frames:
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* `read_csv()` reads comma delimited files, `read_csv2()` reads semi-colon
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* `read_csv()` reads comma delimited files, `read_csv2()` reads semicolon
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separated files (common in countries where `,` is used as the decimal place),
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separated files (common in countries where `,` is used as the decimal place),
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`read_tsv()` reads tab delimited files, and `read_delim()` reads in files
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`read_tsv()` reads tab delimited files, and `read_delim()` reads in files
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with any delimiter.
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with any delimiter.
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@ -108,7 +108,7 @@ If you've used R before, you might wonder why we're not using `read.csv()`. Ther
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your operating system and environment variables, so import code that works
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your operating system and environment variables, so import code that works
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on your computer might not work on someone else's.
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on your computer might not work on someone else's.
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### Exericses
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### Exercises
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1. What function would you use to read a file where fields were separated with
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1. What function would you use to read a file where fields were separated with
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"|"?
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"|"?
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@ -281,7 +281,7 @@ Encodings are a rich and complex topic, and I've only scratched the surface here
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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):
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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):
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* `parse_datetime()` expects an ISO8601 date time. ISO8691 is an
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* `parse_datetime()` expects an ISO8601 date time. ISO8601 is an
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international standard in which the components of a date are
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international standard in which the components of a date are
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organised from biggest to smallest: year, month, day, hour, minute,
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organised from biggest to smallest: year, month, day, hour, minute,
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second.
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second.
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@ -427,7 +427,7 @@ These defaults don't always work for larger files. There are two basic problems:
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a column of doubles that only contains integers in the first 1000 rows.
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a column of doubles that only contains integers in the first 1000 rows.
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1. The column might contain a lot of missing values. If the first 1000
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1. The column might contain a lot of missing values. If the first 1000
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rows contains on `NA`s, readr will guess that it's a character
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rows contains only `NA`s, readr will guess that it's a character
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vector, whereas you probably want to parse it as something more
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vector, whereas you probably want to parse it as something more
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specific.
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specific.
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@ -439,7 +439,7 @@ challenge <- read_csv(readr_example("challenge.csv"))
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(Note the use of `readr_example()` which finds the path to one of the files included with the package)
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(Note the use of `readr_example()` which finds the path to one of the files included with the package)
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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:
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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:
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```{r}
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```{r}
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problems(challenge)
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problems(challenge)
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@ -543,7 +543,7 @@ There are a few other general strategies to help you parse files:
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readr also comes with two useful functions for writing data back to disk: `write_csv()` and `write_tsv()`. They:
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readr also comes with two useful functions for writing data back to disk: `write_csv()` and `write_tsv()`. They:
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* Are faster than the base R equvalents.
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* Are faster than the base R equivalents.
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* Never write rownames, and quote only when needed.
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* Never write rownames, and quote only when needed.
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@ -610,7 +610,7 @@ file.remove("challenge.rds")
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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.
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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.
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For rectanuglar data:
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For rectangular data:
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* haven reads SPSS, Stata, and SAS files.
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* haven reads SPSS, Stata, and SAS files.
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