From 59803d39219248ed1a52dbdabc904dca472ab330 Mon Sep 17 00:00:00 2001 From: Bill Behrman Date: Mon, 11 Jul 2016 18:29:17 -0700 Subject: [PATCH] Fix typos --- import.Rmd | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/import.Rmd b/import.Rmd index 8c20b26..1b7210d 100644 --- a/import.Rmd +++ b/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.