Working with existing data is a great way to learn the tools of data science, but at some point you want to stop learning and start working with your own data. In this chapter, you'll learn how to use the readr package for reading plain-text rectangular files into R.
This chapter will only scratch surface of data import, but many of the principles will translate to the other forms of data import. The chapter concludes with a few pointers to packages that you might find useful for loading other types of data.
These functions all have similar syntax: once you've mastered one, you can use the others with ease. For the rest of this chapter we'll focus on `read_csv()`. Once you understand `read_csv()`, it will be straightforward to apply your knowledge to all the other functions in readr.
When you run `read_csv()` it prints out a column specification that gives the name and type of each column. That's an important part of readr, which we'll come back to in [parsing a file].
In both cases `read_csv()` uses the first line of the data for the column names, which is a very common convention. There are two cases where you might want to tweak this behaviour:
This is all you need to know to read ~75% of csv files that you'll encounter in practice. You can also easily adapt what you've learned to read tab separated files with `read_tsv()` and fixed width files with `read_fwf()`. To read in more challenging files, you'll need to learn more about how readr parses each column, turning strings into the most appropriate type. That's up next.
If you've used R before, you might wonder why we're not using `read.csv()`. There are a few good reasons to favour readr functions over the base equivalents:
Before we get into the details of how readr reads files from disk, we need to take a little detour to talk about the `parse_*()` functions. These functions take a character vector and return a more specialised vector like a logical, integer, or date:
These functions are useful in their own right, but are also an important building block for readr. Once you've learned how the individual parsers work in this section, we'll circle back and see how they fit together to parse a complete file in the next section.
Like all functions in the tidyverse, the `parse_*()` functions are uniform: the first argument is a character vector to parse, and the `na` argument specifies which strings should be treated as missing:
If there are many parsing failures, you'll need to use `problems()` to get the complete set. This returns a tibble which you can then manipulate with dplyr.
Using parsers is mostly a matter of understanding what's available and how they deal with different types of input. There are eight particularly important parsers:
To address the first problem, readr has the notion of a "locale", an object that specifies parsing options that differ from place to place. When parsing numbers, the most important option is the character you use for the decimal mark. You can override the default value of `.` by creating a new locale:
readr's default locale is US-centric, because generally R is US-centric (i.e. the documentation of base R is written in American English). An alternative approach would be to try and guess the defaults from your operating system. This is hard to do well, but, more importantly, makes your code fragile: even if it works on your computer, it might fail when you email it to a colleague in another country.
`parse_number()` addresses the second problem: it ignores non-numeric characters before and after the number. This is particularly useful for currencies and percentages, but also works to extract numbers embedded in text.
It seems like `parse_character()` should be really simple - it could just return its input. Unfortunately life isn't so simple, as there are multiple ways to represent the same string. To understand what's going on, we need to dive into the details of how computers represent strings. In R, we can get at the underlying representation of a string using `charToRaw()`:
Each hexadecimal number represents a byte of information: `48` is H, `61` is a, and so on. The mapping from hexadecimal number to character is called the encoding, and in this case the encoding is called ASCII. ASCII does a great job of representing English characters, because it's the __American__ Standard Code for Information Interchange.
Things get more complicated for languages other than English. In the early days of computing there were many competing standards for encoding non-English characters, and to correctly interpret a string you need to know both the values and the encoding. For example, two common encodings are Latin1 (aka ISO-8859-1, used for Western European languages) and Latin2 (aka ISO-8859-2, used for Eastern European languages). In Latin1, the byte `b1` is "±", but in Latin2, it's "ą"! Fortunately, today there is one standard that is supported almost everywhere: UTF-8. UTF-8 can encode just about every character used by humans today, as well as many extra symbols (like emoji!).
readr uses UTF-8 everywhere: it assumes your data is UTF-8 encoded when you read it, and always uses it when writing. This is a good default, but will fail for data produced by older systems that don't understand UTF-8. If this happens to you, your strings will look weird when you print them. Sometimes just one or two characters might be messed up; other times you'll get complete gibberish. For example:
How do you find the correct encoding? If you're lucky, it'll be included somewhere in the data documentation. Unfortunately, that's rarely the case, so readr provides `guess_encoding()` to help you figure it out. It's not foolproof, and it works better when you have lots of text (unlike here), but it's a reasonable place to start. Expect to try a fewdifferent encodings before you find the right one.
Encodings are a rich and complex topic, and I've only scratched the surface here. If you'd like to learn more I'd recommend reading the detailed explanation at <http://kunststube.net/encoding/>.
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). When called without any additional arguments:
The best way to figure out the correct format is to create a few examples in a character vector, and test with one of the parsing functions. For example:
If you're using `%b` or `%B` with non-English month names, you'll need to set the `lang` argument to `locale()`. See the list of built-in languages in `date_names_langs()`, or if your language is not already included, create your own with `date_names()`.
Now that you've learned how to parse an individual vector, it's time to return to the beginning and explore how readr parses a file. There are two new things that you'll learn about in this section:
readr uses a heuristic to figure out the type of each column: it reads the first 1000 rows and uses some (moderately conservative) heuristics to figure out the type of each column. You can emulate this process with a character vector using `guess_parser()`, which returns readr's best guess, and `parse_guess()` which uses that guess to parse the column:
There are two printed 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:
A good strategy is to work column by column until there are no problems remaining. Here we can see that there are a lot of parsing problems with the `x` column - there are trailing characters after the integer value. That suggests we need to use a double parser instead.
Every `parse_xyz()` function has a corresponding `col_xyz()` function. You use `parse_xyz()` when the data is in a character vector in R already; you use `col_xyz()` when you want to tell readr how to load the data.
I highly recommend always supplying the `col_types` argument, building from printout provided by readr. This ensures that you have a consistent, reproducible, data import script. If you rely on the default guesses and your data changes, readr will continue to read it in. If you want to be really strict, use `stop_for_problems()`: that function throws an error and stops your script if there are any parsing problems.
readr also comes with two useful functions for writing data back to disk: `write_csv()` and `write_tsv()`. Both functions increase the chances of the output file being read back in correctly by:
The most important arguments are `x` (the data frame to save), and `path` (the location to save it). You can also specify how missing values are written with `na`, and if you want to `append` to an existing file.
This makes csvs a little unreliable for caching interim results---you need to recreate the column specification every time you load in. There are two alternatives:
Feather tends to be faster than RDS and is usable outside of R. RDS supports list-columns (which you'll learn about in [many models]), which feather currently does not.
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 rectangular data:
For more exotic file types, try the [R data import/export manual](https://cran.r-project.org/doc/manuals/r-release/R-data.html) and the [rio](https://github.com/leeper/rio) package.
