From 65c6cc790a2e2c61802df3724ed47403b7e950e7 Mon Sep 17 00:00:00 2001 From: hadley Date: Wed, 23 Sep 2015 08:58:16 -0500 Subject: [PATCH 01/20] More on column types --- import.Rmd | 114 ++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 91 insertions(+), 23 deletions(-) diff --git a/import.Rmd b/import.Rmd index d82ea2b..70ab566 100644 --- a/import.Rmd +++ b/import.Rmd @@ -5,6 +5,7 @@ output: bookdown::html_chapter --- ```{r, include = FALSE} +library(dplyr) library(readr) ``` @@ -78,11 +79,6 @@ As well as reading data frame disk, readr also provides tools for working with d * `type_convert()` applies the same parsing heuristics to the character columns in a data frame. You can override its choices using `col_types`. -* `parse_datetime()`, `parse_factor()`, `parse_integer()`, etc. Corresponding - to each `col_XYZ()` function is a `parse_XYZ()` function that takes a - character vector and returns a parsed vector. We'll use these in examples - so you can see how a single piece works at a time. - For the rest of this chapter we'll focus on `read_csv()`. If you understand how to use this function, it will be straightforward to your knowledge to all the other functions in readr. ### Basics @@ -108,31 +104,69 @@ EXAMPLE ### Column types -Readr uses a heuristic to figure out the types of your columns: it reads the first 1000 rows. This is fast, and fairly robust. If readr detects the wrong type of data, you'll get warning messages: +Readr uses a heuristic to figure out the types of your columns: it reads the first 1000 rows and uses some (moderately conservative) heuristics to figure out the type of each column. This is fast, and fairly robust. If readr detects the wrong type of data, you'll get warning messages. Readr prints out the first five, and you can access them all with `problems()`: EXAMPLE -You can fix these by overriding readr's guesses with the `col_type` argument. +Typically, you'll see a lot of warnings if readr has guessed the column type incorrectly. This most often occurs when the first 1000 rows are different to the rest of the data. Perhaps there are a lot of missing data there, or maybe your data is mostly numeric but a few rows have characters. Fortunately, it's easy to fix these problems using the `col_type` argument. (Note that if you have a very large file, you might want to set `n_max` to 10,000 or 100,000. That will speed up iteration while you're finding common problems) -* `col_integer()` and `col_double()` specify integer and doubles. `col_number()` - is a more flexible parsed for numbers embedded in other strings. It will - look for the first number in a string, ignoring non-numeric prefixes and - suffixes. It will also ignoring the grouping mark specified by the locale - (see below for more details). - -* `col_logical()` parses TRUE, T, FALSE and F into a logical vector. - -* `col_character()` leaves strings as is. `col_factor()` allows you to load - data directly into a factor if you know what the levels are. - -* `col_skip()` completely ignores a column. +Specifying the `col_type` looks like this: -* `col_date()`, `col_datetime()` and `col_time()` parse into dates, date times, - and times as described below. +```{r, eval = FALSE} +read_csv("mypath.csv", col_types = col( + x = col_integer(), + treatment = col_character() +)) +``` -Parsing occurs after leading and trailing whitespace has been removed (if not overridden with `trim_ws = FALSE`) and missing values listed in `na` have been removed. +You can use the following types of columns + +* `col_integer()` (i) and `col_double()` (d) specify integer and doubles. + `col_logical()` (l) parses TRUE, T, FALSE and F into a logical vector. + `col_character()` (c) leaves strings as is. + +* `col_number()` (n) is a more flexible parsed for numbers embedded in other + strings. It will look for the first number in a string, ignoring non-numeric + prefixes and suffixes. It will also ignoring the grouping mark specified by + the locale (see below for more details). + +* `col_factor()` (f) allows you to load data directly into a factor if you know + what the levels are. + +* `col_skip()` (_, -) completely ignores a column. + +* `col_date()` (D), `col_datetime()` (T) and `col_time()` (t) parse into dates, + date times, and times as described below. + +You might have noticed that each column parser has a one letter abbreviation, which you can instead of the full function call (assuming you're happy with the default arguments): + +```{r, eval = FALSE} +read_csv("mypath.csv", col_types = cols( + x = "i", + treatment = "c" +)) +``` + +(If you just have a few columns you supply a single string giving the type for each column: `i__dc`. See the documentation for more details. It's not as easy to understand as the `cols()` specification, so I'm not going to describe it further here.) + +By default, any column not mentioned in `cols` will be guessed. If you'd rather those columns are simply not read in, use `cols_only()`. In that case, you can use `col_guess()` (?) if you want to guess the type of a column. + +Each `col_XYZ()` function also has a corresponding `parse_XYZ()` that you can use on a character vector. This makes it easier to explore what each of the parsers does interactively. + +```{r} +parse_integer(c("1", "2", "3")) +parse_logical(c("TRUE", "FALSE", "NA")) +parse_number(c("$1000", "20%", "3,000")) +parse_number(c("$1000", "20%", "3,000")) +``` + +Parsing occurs after leading and trailing whitespace has been removed (if not overridden with `trim_ws = FALSE`) and missing values listed in `na` have been removed: + +```{r} +parse_logical(c("TRUE ", " ."), na = ".") +``` #### Datetimes @@ -149,7 +183,7 @@ parse_date("2010-10-01") parse_time("20:10:01") ``` -If these don't work for your data (common!) you can supply your own date time formats, built up of the following pieces: +If these defaults don't work for your data you can supply your own date time formats, built up of the following pieces: * Year: `%Y` (4 digits). `%y` (2 digits); 00-69 -> 2000-2069, 70-99 -> 1970-1999. @@ -181,8 +215,42 @@ parse_date("01/02/15", "%d/%m/%y") parse_date("01/02/15", "%y/%m/%d") ``` +Then when you read in the data with `read_csv()` you can easily translate to the `col_date()` format. + ### International data +The goal of readr's locales is to encapsulate the common options that vary between languages and different regions of the world. This includes: + +* Names of months and days, used when parsing dates. +* The default time zones, used when parsing date times. +* The character encoding, used when reading non-ASCII strings. +* Default date and time formats, used when guessing column types. +* The decimal and grouping marks, used when reading numbers. + +Readr is designed to be independent of your current locale settings. This makes a bit more hassle in the short term, but makes it much much easier to share your code with others: if your readr code works locally, it will also work for everyone else in the world. The same is not true for base R code, since it often inherits defaults from your system settings. Just because data ingest code works for you doesn't mean that it will work for someone else in another country. + +The settings you are most like to need to change are: + +* The names of days and months: + + ```{r} + locale("fr") + locale("fr", asciify = TRUE) + ``` + +* The character encoding used in the file. If you don't know the encoding + you can use `guess_encoding()`. It's not perfect, but if you have a decent + sample of text, it's likely to be able to figure it out. + + Readr converts all strings into UTF-8 as this is safest to work with across + platforms. (It's also what every stringr operation does.) + +### Exercises + +* Parse these dates (incl. non-English examples). +* Parse these example files. +* Parse this fixed width file. + ## Databases ## Web APIs From f2a8c6bbf76746fbcc1ad89bba2a9f3c1a5ec3a4 Mon Sep 17 00:00:00 2001 From: hadley Date: Wed, 23 Sep 2015 09:57:34 -0500 Subject: [PATCH 02/20] Note about row names --- import.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/import.Rmd b/import.Rmd index 70ab566..14f37b7 100644 --- a/import.Rmd +++ b/import.Rmd @@ -35,8 +35,8 @@ There are many ways to read flat files into R. If you've be using R for a while, percentages, and more. * They fail to do some annoying things like converting character vectors to - factors, and munging the column headers to make sure they're valid R - variable names. + factors, munging the column headers to make sure they're valid R + variable names, and using row names. * They return objects with class `tbl_df`. As you saw in the dplyr chapter, this provides a nicer printing method, so it's easier to work with large From e8d5b0e479cfa42872cbb02e0a1c62173fdeb57f Mon Sep 17 00:00:00 2001 From: hadley Date: Mon, 19 Oct 2015 08:41:33 -0500 Subject: [PATCH 03/20] Brain dump of expressing yourself. --- .travis.yml | 2 +- _includes/package-nav.html | 1 + expressing-yourself.Rmd | 229 +++++++++++++++++++++++++++++++++++++ 3 files changed, 231 insertions(+), 1 deletion(-) create mode 100644 expressing-yourself.Rmd diff --git a/.travis.yml b/.travis.yml index c69e0f6..c381aa4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -22,7 +22,7 @@ install: # Install R packages - ./travis-tool.sh r_binary_install knitr png - - ./travis-tool.sh r_install ggplot2 dplyr tidyr + - ./travis-tool.sh r_install ggplot2 dplyr tidyr pryr - ./travis-tool.sh github_package hadley/bookdown garrettgman/DSR hadley/readr script: jekyll build diff --git a/_includes/package-nav.html b/_includes/package-nav.html index 1cd532a..dd85dad 100644 --- a/_includes/package-nav.html +++ b/_includes/package-nav.html @@ -8,6 +8,7 @@
  • Dates and times
  • -->
  • Tidy
  • +
  • Expressing yourself
  • Import
  • Transform
  • +
  • String manipulation
  • Tidy
  • diff --git a/strings.Rmd b/strings.Rmd new file mode 100644 index 0000000..fd884ad --- /dev/null +++ b/strings.Rmd @@ -0,0 +1,110 @@ +--- +layout: default +title: String manipulation +output: bookdown::html_chapter +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +# String manipulation + +When working with text data, one of the most powerful tools at your disposal is regular expressions. Regular expressions are a very concise language for describing patterns in strings. When you first look at them, you'll think a cat walked across your keyboard, but as you learn more, you'll see how they allow you to express complex patterns very concisely. + +In this chapter, you'll learn the basics of regular expressions using the stringr package. + +The chapter concludes with a brief look at the stringi package. This package is what stringr uses internally: it's more complex than stringr (and includes many many more functions). stringr includes tools to let you tackle the most common 90% of string manipulation challenges; stringi contains functions to let you tackle the last 10%. + +## String basics + +In R, strings are always stored in a character vector. You can create strings with either single quotes or double quotes: there is no different in behaviour. + +To include a literal single or double quote in a string you can use `\` to "escape". Note that when you print a string, you see the escapes. To see the raw contents of the string, use `writeLines()` (or for a length-1 character vector, `cat(x, "\n")`). + +```{r} +x <- c("\"", "\\") +x +writeLines(x) +``` + +Base R contains many functions to work with strings but we'll generally avoid them because they're inconsistent, and hard to remember. A particularly annoying inconsistency is that the function that computes the number of characters in a string, `nchar()`, returns 2 for `NA` (instead of `NA`) + +```{r} +# (Will be fixed in R 3.3.0) +nchar(NA) + +stringr::str_length(NA) +``` + +## Introduction to stringr + +```{r} +library(stringr) +``` + +The stringr package contains functions for working with strings and patterns. We'll focus on three: + +* `str_detect(string, pattern)`: does string match a pattern? +* `str_extract(string, pattern)`: extact matching pattern from string +* `str_replace(string, pattern, replacement)`: replace pattern with replacement +* `str_split(string, pattern)`. + +## Extracting patterns + +## Introduction to regular expressions + +Goal is not to be exhaustive. + +### Character classes and alternative + +* `.`: any character +* `\d`: a digit +* `\s`: whitespace + +* `x|y`: match x or y + +* `[abc]`: match a, b, or c +* `[a-e]`: match any character between a and e +* `[!abc]`: match anything except a, b, or c + +### Escaping + +You may have noticed that since `.` is a special regular expression character, you'll need to escape `.` + +### Repetition + +* `?`: 0 or 1 +* `+`: 1 or more +* `*`: 0 or more + +* `{n}`: exactly n +* `{n,}`: n or more +* `{,m}`: at most m +* `{n,m}`: between n and m + +(By default these matches are "greedy": they will match the longest string possible. You can make them "lazy", matching the shortest string possible by putting a `?` after them.) + +### Anchors + +* `^` match the start of the line +* `*` match the end of the line +* `\b` match boundary between words + +My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). + + +## Detecting matches + + +### Groups + +`str_match()`, `str_match_all()` + +## Replacing patterns + +## Other types of pattern + +* `fixed()` +* `coll()` +* `boundary()` From ec529ef1fae3f80b210b95785a11c760c0f299b0 Mon Sep 17 00:00:00 2001 From: hadley Date: Thu, 22 Oct 2015 13:17:00 -0500 Subject: [PATCH 06/20] More about strings --- strings.Rmd | 213 ++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 189 insertions(+), 24 deletions(-) diff --git a/strings.Rmd b/strings.Rmd index fd884ad..2861eb8 100644 --- a/strings.Rmd +++ b/strings.Rmd @@ -6,6 +6,7 @@ output: bookdown::html_chapter ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) +library(stringr) ``` # String manipulation @@ -14,6 +15,10 @@ When working with text data, one of the most powerful tools at your disposal is In this chapter, you'll learn the basics of regular expressions using the stringr package. +```{r} +library(stringr) +``` + The chapter concludes with a brief look at the stringi package. This package is what stringr uses internally: it's more complex than stringr (and includes many many more functions). stringr includes tools to let you tackle the most common 90% of string manipulation challenges; stringi contains functions to let you tackle the last 10%. ## String basics @@ -28,56 +33,153 @@ x writeLines(x) ``` +### String length + Base R contains many functions to work with strings but we'll generally avoid them because they're inconsistent, and hard to remember. A particularly annoying inconsistency is that the function that computes the number of characters in a string, `nchar()`, returns 2 for `NA` (instead of `NA`) ```{r} # (Will be fixed in R 3.3.0) nchar(NA) - -stringr::str_length(NA) +str_length(NA) ``` -## Introduction to stringr +### Combining strings + +To combine two or more strings, use `str_c()`: ```{r} -library(stringr) +str_c("x", "y") +str_c("x", "y", "z") ``` -The stringr package contains functions for working with strings and patterns. We'll focus on three: +Use the `sep` argument to control how they're separated: -* `str_detect(string, pattern)`: does string match a pattern? -* `str_extract(string, pattern)`: extact matching pattern from string -* `str_replace(string, pattern, replacement)`: replace pattern with replacement -* `str_split(string, pattern)`. +```{r} +str_c("x", "y", sep = ", ") +``` -## Extracting patterns +Like most other functions in R, missing values are infectious. If you want them to print as `NA`, use `str_replace_na()`: -## Introduction to regular expressions +```{r} +str_c("x", NA, "y") +str_c("x", str_replace_na(NA), "y") +``` -Goal is not to be exhaustive. +`str_c()` is vectorised, and it automatically recycles the shortest vectors to the same length as the longest: -### Character classes and alternative +```{r} +str_c("prefix-", c("a", "b", "c"), "-suffix") +``` -* `.`: any character -* `\d`: a digit -* `\s`: whitespace +To collapse vectors into a single string, use `collapse`: -* `x|y`: match x or y +```{r} +str_c(c("x", "y", "z"), collapse = ", ") +``` +When creating strings you might also find `str_pad()` and `str_dup()` useful: + +```{r} +x <- c("apple", "banana", "pear") +str_pad(x, 10) + +str_c("Na ", str_dup("na ", 4), "batman!") +``` + +### Subsetting strings + +You can extract parts of a string using `str_sub()`: + +```{r} +x <- c("apple", "banana", "pear") +str_sub(x, 1, 3) +# negative numbers count backwards from end +str_sub(x, -3, -1) +``` + +You can also use `str_sub()` to modify strings: + +```{r} +str_sub(x, 1, 1) <- str_to_lower(str_sub(x, 1, 1)) +x +``` + +### Exercises + +1. In your own words, describe the difference between `sep` and `collapse`. + +## Regular expressions + +The stringr package contains functions for working with strings and patterns. We'll focus on four main categories + +* What matches the pattern? +* Does a string match a pattern? +* How can you replace a pattern with text? +* How can you split a string into pieces? + +Key to all of these functions are regular expressions. Regular expressions are a very terse language that allow to describe patterns in string. They take a little while to get your head around, but once you've got it you'll find them extremely useful. + +```{r} + +``` + +Goal is not to be exhaustive, but to give you a solid foundation that allows you to solve a wide variety of problems. We'll point you to more resources where you can learn more about regular expresssions. + +### Matching anything and escaping + +Regular expression are not limited to matching fixed string. You can also use special characters that match patterns. For example, `.` allows you to match any character: + +```{r} +str_subset(c("abc", "adc", "bef"), "a.c") +``` + +But if "`.`" matches any character, how do you match an actual "`.`"? You need to use an "escape" to tell the regular expression you want to match it exactly, not use the special behaviour. The escape character used by regular expressions is `\`. Unfortunately, that's also the escape character used by strings, so to match a literal "`.`" you need to use `\\.`. + +```{r} +# To create the regular expression, we need \\ +dot <- "\\." + +# But the expression itself only contains one: +cat(dot, "\n") + +# And this tells R to look for explicit . +str_subset(c("abc", "a.c", "bef"), "a\\.c") +``` + +If `\` is used an escape character, how do you match a literal `\`? Well you need to escape it, creating the regular expression `\\`. And in R that needs to be in a string, so you need to write `"\\\\"` - that's right, you need four backslashes to match one! + +### Character classes and alternatives + +As well as `.` there are a number of other special patterns that match more than one character: + +* `\d`: any digit +* `\s`: any whitespace (space, tab, newline) * `[abc]`: match a, b, or c * `[a-e]`: match any character between a and e * `[!abc]`: match anything except a, b, or c -### Escaping +Remember, to create a regular expression containing `\d` or `\s`, you'll need to escape the `\` for the string, so you'll type `"\\d"` or `"\\s"`. -You may have noticed that since `.` is a special regular expression character, you'll need to escape `.` +A similar idea is alternation: `x|y` matches either x or y. Note that the precedence for `|` is low, so that `abc|xyz` matches either `abc` or `xyz` not `abcyz` or `abxyz`: + +```{r} +str_detect(c("abc", "xyz"), "abc|xyz") +``` + +Like with mathematics, if precedence ever gets confusing, use parentheses to make it clear what you want: + +```{r} +str_detect(c("grey", "gray"), "gr(e|a)y") +str_detect(c("grey", "gray"), "gr(?:e|a)y") +``` + +Unfortunately parentheses have some other side-effects in regular expressions, which we'll learn about later. Technically, the parentheses you should use are `(?:)` which are called non-capturing parentheses. Most of the time this won't make any difference so it's easy to use `()`, but it sometimes helpful to be aware of `(?:)`. ### Repetition * `?`: 0 or 1 * `+`: 1 or more * `*`: 0 or more - * `{n}`: exactly n * `{n,}`: n or more * `{,m}`: at most m @@ -85,17 +187,34 @@ You may have noticed that since `.` is a special regular expression character, y (By default these matches are "greedy": they will match the longest string possible. You can make them "lazy", matching the shortest string possible by putting a `?` after them.) +Note that the precedence of these operators are high, so you write: `colou?r`. That means you'll need to use parentheses for many uses: `bana(na)+` or `ba(na){2,}`. + ### Anchors * `^` match the start of the line * `*` match the end of the line -* `\b` match boundary between words My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). +To force a regular expression to only match a complete string: + +```{r} +str_detect(c("abcdef", "bcd"), "^bcd$") +``` + +You can also match the boundary between words with `\b`. I don't find I often use this in R, but I will sometimes use it when I'm doing a find all in RStudio when I want to find the name of a function that's a component of other functions. For example, I'll search for `\bsum\b` to avoid matching `summarise`, `summary`, `rowsum` and so on. + +### Exercises + +1. Replace all `/` in a string with `\`. ## Detecting matches +`str_detect()`, `str_subset()`, `str_count()` + +## Extracting matches + +`str_extract()`, `str_extract_all()` ### Groups @@ -103,8 +222,54 @@ My favourite mneomic for rememember which is which (from [Evan Misshula](https:/ ## Replacing patterns +`str_replace()`, `str_replace_all()` + +## Splitting + +`str_split()`, `str_split_fixed()`. + ## Other types of pattern -* `fixed()` -* `coll()` -* `boundary()` +When you use a pattern that's a string, it's automatically wrapped into a call to `regex()`. Sometimes it's useful to call it explicitly so you can control the + +* `fixed()`: matches exactly that sequence of characters (i.e. ignored + all special regular expression pattern). + +* `coll()`: compare strings using standard **coll**ation rules. This is + useful for doing case insensitive matching. Note that `coll()` takes a + `locale` parameter that controls which rules are used for comparing + characters. Unfortunately different parts of the world use different rules! + +```{r} +# Turkish has two i's: with and without a dot, and it +# has a different rule for capitalising them: +str_to_upper(c("i", "ı")) +str_to_upper(c("i", "ı"), locale = "tr") + +# That means you also need to be aware of the difference +# when doing case insensitive matches: +i <- c("I", "İ", "i", "ı") +i + +str_subset(i, fixed("i", TRUE)) +str_subset(i, coll("i", TRUE)) +str_subset(i, coll("i", TRUE, locale = "tr")) +``` + +## Other uses of regular expressions + +There are a few other functions in base R that accept regular expressions: + +* `apropos()` searchs all objects avaiable from the global environment. This + is useful if you can't quite remember the name of the function. + +* `ls()` is similar to `apropos()` but only works in the current + environment. However, if you have so many objects in your environment + that you have to use a regular expression to filter them all, you + need to think about what you're doing! (And probably use a list instead). + +* `dir()` lists all the files in a directory. The `pattern` argument takes + a regular expression and only return file names that match the pattern. + For example, you can find all csv files with `dir(pattern = "\\.csv$")`. + (If you're more comfortable with "globs" like `*.csv`, you can convert + them to regular expressions with `glob2rx()`) From 979289c50b4d85e3f8292d7cb8c42bb5ccf1900d Mon Sep 17 00:00:00 2001 From: hadley Date: Mon, 26 Oct 2015 09:52:24 -0500 Subject: [PATCH 07/20] Some string tweaking --- strings.Rmd | 90 +++++++++++++++++++++++++++++++++++------------------ 1 file changed, 59 insertions(+), 31 deletions(-) diff --git a/strings.Rmd b/strings.Rmd index 2861eb8..793e97a 100644 --- a/strings.Rmd +++ b/strings.Rmd @@ -21,9 +21,17 @@ library(stringr) The chapter concludes with a brief look at the stringi package. This package is what stringr uses internally: it's more complex than stringr (and includes many many more functions). stringr includes tools to let you tackle the most common 90% of string manipulation challenges; stringi contains functions to let you tackle the last 10%. +For example, if you have + +```{r} + +``` + +The goal of this chapter is not to teach you every detail of regular expressions. Instead we'll give you a solid foundation that allows you to solve a wide variety of problems and point you to resources where you can learn more. + ## String basics -In R, strings are always stored in a character vector. You can create strings with either single quotes or double quotes: there is no different in behaviour. +In R, strings are always stored in a character vector. You can create strings with either single quotes or double quotes: there is no difference in behaviour. I recommend always using `"`, unless you want to create a string that contains multiple `"`, in which case use `'`. To include a literal single or double quote in a string you can use `\` to "escape". Note that when you print a string, you see the escapes. To see the raw contents of the string, use `writeLines()` (or for a length-1 character vector, `cat(x, "\n")`). @@ -33,6 +41,17 @@ x writeLines(x) ``` +There are a handful of other special characters. The most common used are `"\n"`, new line, and `"\t"`, tab, but you can see the complete list by requesting help on `"`: `?'"'`, or `?"'"`. + +You'll also sometimes strings like `"\u00b5"`, this is a way of writing special characters that works on all platforms: + +```R +x <- "\u00b5" +x +``` + +Remember that the representation of a string is different from the string itself. + ### String length Base R contains many functions to work with strings but we'll generally avoid them because they're inconsistent, and hard to remember. A particularly annoying inconsistency is that the function that computes the number of characters in a string, `nchar()`, returns 2 for `NA` (instead of `NA`) @@ -61,11 +80,12 @@ str_c("x", "y", sep = ", ") Like most other functions in R, missing values are infectious. If you want them to print as `NA`, use `str_replace_na()`: ```{r} -str_c("x", NA, "y") -str_c("x", str_replace_na(NA), "y") +x <- c("abc", NA) +str_c("|-", x, "-|") +str_c("|-", str_replace_na(x), "-|") ``` -`str_c()` is vectorised, and it automatically recycles the shortest vectors to the same length as the longest: +As shown above, `str_c()` is vectorised, automatically recycling the shortest vectors to the same length as the longest: ```{r} str_c("prefix-", c("a", "b", "c"), "-suffix") @@ -108,25 +128,10 @@ x 1. In your own words, describe the difference between `sep` and `collapse`. -## Regular expressions - -The stringr package contains functions for working with strings and patterns. We'll focus on four main categories - -* What matches the pattern? -* Does a string match a pattern? -* How can you replace a pattern with text? -* How can you split a string into pieces? +## Regular expressions basics Key to all of these functions are regular expressions. Regular expressions are a very terse language that allow to describe patterns in string. They take a little while to get your head around, but once you've got it you'll find them extremely useful. -```{r} - -``` - -Goal is not to be exhaustive, but to give you a solid foundation that allows you to solve a wide variety of problems. We'll point you to more resources where you can learn more about regular expresssions. - -### Matching anything and escaping - Regular expression are not limited to matching fixed string. You can also use special characters that match patterns. For example, `.` allows you to match any character: ```{r} @@ -148,6 +153,14 @@ str_subset(c("abc", "a.c", "bef"), "a\\.c") If `\` is used an escape character, how do you match a literal `\`? Well you need to escape it, creating the regular expression `\\`. And in R that needs to be in a string, so you need to write `"\\\\"` - that's right, you need four backslashes to match one! +```{r} +x <- "a\\b" +cat(x, "\n") + +y <- str_replace(x, "\\\\", "-slash-") +cat(y, "\n") +``` + ### Character classes and alternatives As well as `.` there are a number of other special patterns that match more than one character: @@ -166,7 +179,7 @@ A similar idea is alternation: `x|y` matches either x or y. Note that the preced str_detect(c("abc", "xyz"), "abc|xyz") ``` -Like with mathematics, if precedence ever gets confusing, use parentheses to make it clear what you want: +Like with mathematical expression, if precedence ever gets confusing, use parentheses to make it clear what you want: ```{r} str_detect(c("grey", "gray"), "gr(e|a)y") @@ -191,42 +204,57 @@ Note that the precedence of these operators are high, so you write: `colou?r`. T ### Anchors -* `^` match the start of the line -* `*` match the end of the line +Regular expressions can also match things that are not characters. The most important non-character matches are: -My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). +* `^`: the start of the line. +* `*`: the end of the line. -To force a regular expression to only match a complete string: +To force a regular expression to only match a complete string, anchor it with both `^` and `$`.: ```{r} str_detect(c("abcdef", "bcd"), "^bcd$") ``` +My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). + You can also match the boundary between words with `\b`. I don't find I often use this in R, but I will sometimes use it when I'm doing a find all in RStudio when I want to find the name of a function that's a component of other functions. For example, I'll search for `\bsum\b` to avoid matching `summarise`, `summary`, `rowsum` and so on. ### Exercises 1. Replace all `/` in a string with `\`. -## Detecting matches +## Regular expression operations + +The stringr package contains functions for working with strings and patterns. We'll focus on four main categories: + +* What matches the pattern? +* Does a string match a pattern? +* How can you replace a pattern with text? +* How can you split a string into pieces? + +### Detecting matches `str_detect()`, `str_subset()`, `str_count()` -## Extracting matches +### Extracting matches `str_extract()`, `str_extract_all()` -### Groups +### Extracting grouped matches `str_match()`, `str_match_all()` -## Replacing patterns +### Replacing patterns `str_replace()`, `str_replace_all()` -## Splitting +### Splitting -`str_split()`, `str_split_fixed()`. +`str_split()`, `str_split_fixed()`. + +### Finding locations + +`str_locate()`, `str_locate_all()` gives you the starting and ending positions of each match. These are particularly useful when none of the other functions does exactly what you want. You can use `str_locate()` to find the matching pattern, `str_sub()` to extract and/or modify them. ## Other types of pattern From 4ca5cdbaab8fc21c4fb89171971e57dc92996d03 Mon Sep 17 00:00:00 2001 From: hadley Date: Tue, 27 Oct 2015 09:33:41 -0500 Subject: [PATCH 08/20] More on strings --- .travis.yml | 2 +- strings.Rmd | 153 +++++++++++++++++++++++++++++++++++++++++++++------- 2 files changed, 134 insertions(+), 21 deletions(-) diff --git a/.travis.yml b/.travis.yml index eaedd29..35c22e0 100644 --- a/.travis.yml +++ b/.travis.yml @@ -23,7 +23,7 @@ install: # Install R packages - ./travis-tool.sh r_binary_install knitr png - ./travis-tool.sh r_install ggplot2 dplyr tidyr pryr stringr - - ./travis-tool.sh github_package hadley/bookdown garrettgman/DSR hadley/readr + - ./travis-tool.sh github_package hadley/bookdown garrettgman/DSR hadley/readr gaborcsardi/rcorpora script: jekyll build diff --git a/strings.Rmd b/strings.Rmd index 793e97a..33e83ab 100644 --- a/strings.Rmd +++ b/strings.Rmd @@ -7,6 +7,7 @@ output: bookdown::html_chapter ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) library(stringr) +library(stringi) ``` # String manipulation @@ -45,7 +46,7 @@ There are a handful of other special characters. The most common used are `"\n"` You'll also sometimes strings like `"\u00b5"`, this is a way of writing special characters that works on all platforms: -```R +```{r} x <- "\u00b5" x ``` @@ -62,6 +63,8 @@ nchar(NA) str_length(NA) ``` +Every stringr function starts with `str_`. That's particularly useful if you're using RStudio, because by the time you've type `str_`, RStudio will be ready to offer autocomplete for the reminaing characters. That's useful if you can't quite remember the name of the function. + ### Combining strings To combine two or more strings, use `str_c()`: @@ -85,7 +88,7 @@ str_c("|-", x, "-|") str_c("|-", str_replace_na(x), "-|") ``` -As shown above, `str_c()` is vectorised, automatically recycling the shortest vectors to the same length as the longest: +As shown above, `str_c()` is vectorised, automatically recycling shorter vectors to the same length as the longest: ```{r} str_c("prefix-", c("a", "b", "c"), "-suffix") @@ -108,7 +111,7 @@ str_c("Na ", str_dup("na ", 4), "batman!") ### Subsetting strings -You can extract parts of a string using `str_sub()`: +You can extract parts of a string using `str_sub()`. `str_sub()` takes two arguments in addition to the string: the position to start at, and the postion to end at (inclusive): ```{r} x <- c("apple", "banana", "pear") @@ -117,6 +120,12 @@ str_sub(x, 1, 3) str_sub(x, -3, -1) ``` +`str_sub()` returns the longest string possible. If you don't want this behaviour, you'll need to check `str_length()` yourself. + +```{r} +str_sub("a", 1, 5) +``` + You can also use `str_sub()` to modify strings: ```{r} @@ -124,15 +133,47 @@ str_sub(x, 1, 1) <- str_to_lower(str_sub(x, 1, 1)) x ``` +You can use `str_to_lower()`, `str_to_upper()`, and `str_to_title()` to convert the case of a vector. Note that what that means depends on where you are in the world, so these functions all have a locale argument. If left blank it will use the current locale. + ### Exercises 1. In your own words, describe the difference between `sep` and `collapse`. -## Regular expressions basics +1. In code that doesn't use stringr, you'll often see `paste()` and `paste0()`. + What's the difference between the two functions? What's stringr function are + they equivalent too? How do the functions differ in their handling of + `NA`? + +1. Use `str_length()` and `str_sub()` to extract the middle character from + a character vector. + +1. Write a function that turns (e.g.) a vector `c("a", "b", "c")` into + the string `a, b, and c`. Think carefully about what it should do if + given a vector of length 0, 1, or 2. -Key to all of these functions are regular expressions. Regular expressions are a very terse language that allow to describe patterns in string. They take a little while to get your head around, but once you've got it you'll find them extremely useful. +1. What does `str_wrap()` do? When might you want to use it? -Regular expression are not limited to matching fixed string. You can also use special characters that match patterns. For example, `.` allows you to match any character: +1. What does `str_trim()` do? + +## Matching patterns with regular expressions + +Regular expressions, regexps for short, a very terse language that allow to describe patterns in string. They take a little while to get your head around, but once you've got it you'll find them extremely useful. + +To learn regular expressions, we'll use `str_show()` and `str_show_all()`. These functions take a character vector and a regular expression, and shows you how they match. We'll start with very simple regular expressions and then gradually move to more and more complicated. Once you've mastered the basics of pattern matching with regular expression, you'll learn all the stringr functions that use them and learn how to use them to solve real problems. + +### Fixed matches + +The simplest patterns that + +```{r} + +``` + +```{r} +common <- rcorpora::corpora("words/common")$commonWords +``` + +### Match anything and escaping ```{r} str_subset(c("abc", "adc", "bef"), "a.c") @@ -161,6 +202,35 @@ y <- str_replace(x, "\\\\", "-slash-") cat(y, "\n") ``` +Here I'll write a regular expression like `\.` and the string that represents the regular expression as `"\."`. + +Use regular expressions to: + +* Solve this crossword puzzle clue: `a??le` + +### Anchors + +Regular expressions can also match things that are not characters. The most important non-character matches are: + +* `^`: the start of the line. +* `*`: the end of the line. + +To force a regular expression to only match a complete string, anchor it with both `^` and `$`.: + +```{r} +str_detect(c("abcdef", "bcd"), "^bcd$") +``` + +My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). + +You can also match the boundary between words with `\b`. I don't find I often use this in R, but I will sometimes use it when I'm doing a find all in RStudio when I want to find the name of a function that's a component of other functions. For example, I'll search for `\bsum\b` to avoid matching `summarise`, `summary`, `rowsum` and so on. + +Practice these by finding all common words: + +* Start with y. +* End in x. +* That are exactly 4 letters long. Without using `str_length()` + ### Character classes and alternatives As well as `.` there are a number of other special patterns that match more than one character: @@ -183,10 +253,14 @@ Like with mathematical expression, if precedence ever gets confusing, use parent ```{r} str_detect(c("grey", "gray"), "gr(e|a)y") -str_detect(c("grey", "gray"), "gr(?:e|a)y") ``` -Unfortunately parentheses have some other side-effects in regular expressions, which we'll learn about later. Technically, the parentheses you should use are `(?:)` which are called non-capturing parentheses. Most of the time this won't make any difference so it's easy to use `()`, but it sometimes helpful to be aware of `(?:)`. +Practice these by finding: + +* Start with a vowel. +* That only contain constants. +* That don't contain any vowels. + ### Repetition @@ -202,28 +276,31 @@ Unfortunately parentheses have some other side-effects in regular expressions, w Note that the precedence of these operators are high, so you write: `colou?r`. That means you'll need to use parentheses for many uses: `bana(na)+` or `ba(na){2,}`. -### Anchors +Practice these by finding all common words: -Regular expressions can also match things that are not characters. The most important non-character matches are: +* That contain three or more vowels in a row. -* `^`: the start of the line. -* `*`: the end of the line. - -To force a regular expression to only match a complete string, anchor it with both `^` and `$`.: +### Grouping and backreferences ```{r} -str_detect(c("abcdef", "bcd"), "^bcd$") +fruit <- rcorpora::corpora("foods/fruits")$fruits +str_subset(fruit, "(..)\\1") ``` -My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). +Unfortunately `()` in regexps serve two purposes: you usually use them to disambiguate precedence, but you can also use for grouping. If you're using one set for grouping and one set for disambiguation, things can get confusing. You might want to use `(?:)` instead: it only disambiguates, and doesn't modify the grouping. They are called non-capturing parentheses. -You can also match the boundary between words with `\b`. I don't find I often use this in R, but I will sometimes use it when I'm doing a find all in RStudio when I want to find the name of a function that's a component of other functions. For example, I'll search for `\bsum\b` to avoid matching `summarise`, `summary`, `rowsum` and so on. +For example: -### Exercises +```{r} +str_detect(c("grey", "gray"), "gr(e|a)y") +str_detect(c("grey", "gray"), "gr(?:e|a)y") +``` -1. Replace all `/` in a string with `\`. +Describe in words what these expressions will match: -## Regular expression operations +* `str_subset(common, "(.)(.)\\2\\1")` + +## Tools The stringr package contains functions for working with strings and patterns. We'll focus on four main categories: @@ -244,6 +321,8 @@ The stringr package contains functions for working with strings and patterns. We `str_match()`, `str_match_all()` +Note that matches are always non-overlapping. The second match starts after the first is complete. + ### Replacing patterns `str_replace()`, `str_replace_all()` @@ -256,6 +335,10 @@ The stringr package contains functions for working with strings and patterns. We `str_locate()`, `str_locate_all()` gives you the starting and ending positions of each match. These are particularly useful when none of the other functions does exactly what you want. You can use `str_locate()` to find the matching pattern, `str_sub()` to extract and/or modify them. +### Exercises + +1. Replace all `/` in a string with `\`. + ## Other types of pattern When you use a pattern that's a string, it's automatically wrapped into a call to `regex()`. Sometimes it's useful to call it explicitly so you can control the @@ -301,3 +384,33 @@ There are a few other functions in base R that accept regular expressions: For example, you can find all csv files with `dir(pattern = "\\.csv$")`. (If you're more comfortable with "globs" like `*.csv`, you can convert them to regular expressions with `glob2rx()`) + +## Advanced topics + + +### The stringi package + +stringr is built on top of the __stringi__ package. stringr is useful when you're learning because it exposes a minimal set of functions, that have been carefully picked to handle the most common string manipulation functions. stringi on the other hand is designed to be comprehensive. It contains almost every function you might ever need. stringi has `length(ls("package:stringi"))` functions to stringr's `length(ls("package:stringr"))`. + +So if you find yourself struggling to do something that doesn't seem natural in stringr, it's worth taking a look at stringi. The use of the two packages are very similar because stringi was designed to mimic stringi's interface. The main difference is the prefix: `str_` vs `stri_`. + +### Encoding + +Complicated and fraught with difficulty. Best approach is to convert to UTF-8 as soon as possible. All stringr and stringi functions do this. Readr always reads as UTF-8. + +* UTF-8 +* Latin1 +* bytes: everything else + +Generally, you should fix encoding problems during the data import phase. + +Detect encoding operates statistically, by comparing frequency of byte fragments across languages and encodings. Fundamentally heuristic and works better with larger amounts of text (i.e. a whole file, not a single string from that file). + +```{r} +x <- "\xc9migr\xe9 cause c\xe9l\xe8bre d\xe9j\xe0 vu." +x +str_conv(x, "ISO-8859-1") + +as.data.frame(stringi::stri_enc_detect(x)) +str_conv(x, "ISO-8859-2") +``` From 5e65371942d13421bc5925338aea6ba8313ed1cd Mon Sep 17 00:00:00 2001 From: hadley Date: Tue, 27 Oct 2015 11:03:27 -0500 Subject: [PATCH 09/20] Use str_view htmlwidget --- .travis.yml | 2 +- strings.Rmd | 10 +++++----- www/.gitignore | 4 +--- 3 files changed, 7 insertions(+), 9 deletions(-) diff --git a/.travis.yml b/.travis.yml index 35c22e0..2c47a5e 100644 --- a/.travis.yml +++ b/.travis.yml @@ -23,7 +23,7 @@ install: # Install R packages - ./travis-tool.sh r_binary_install knitr png - ./travis-tool.sh r_install ggplot2 dplyr tidyr pryr stringr - - ./travis-tool.sh github_package hadley/bookdown garrettgman/DSR hadley/readr gaborcsardi/rcorpora + - ./travis-tool.sh github_package hadley/bookdown garrettgman/DSR hadley/readr gaborcsardi/rcorpora hadley/stringr script: jekyll build diff --git a/strings.Rmd b/strings.Rmd index 33e83ab..140121c 100644 --- a/strings.Rmd +++ b/strings.Rmd @@ -176,7 +176,7 @@ common <- rcorpora::corpora("words/common")$commonWords ### Match anything and escaping ```{r} -str_subset(c("abc", "adc", "bef"), "a.c") +str_view(c("abc", "adc", "bef"), "a.c") ``` But if "`.`" matches any character, how do you match an actual "`.`"? You need to use an "escape" to tell the regular expression you want to match it exactly, not use the special behaviour. The escape character used by regular expressions is `\`. Unfortunately, that's also the escape character used by strings, so to match a literal "`.`" you need to use `\\.`. @@ -189,7 +189,7 @@ dot <- "\\." cat(dot, "\n") # And this tells R to look for explicit . -str_subset(c("abc", "a.c", "bef"), "a\\.c") +str_view(c("abc", "a.c", "bef"), "a\\.c") ``` If `\` is used an escape character, how do you match a literal `\`? Well you need to escape it, creating the regular expression `\\`. And in R that needs to be in a string, so you need to write `"\\\\"` - that's right, you need four backslashes to match one! @@ -218,7 +218,7 @@ Regular expressions can also match things that are not characters. The most impo To force a regular expression to only match a complete string, anchor it with both `^` and `$`.: ```{r} -str_detect(c("abcdef", "bcd"), "^bcd$") +str_view(c("abcdef", "bcd"), "^bcd$") ``` My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). @@ -246,13 +246,13 @@ Remember, to create a regular expression containing `\d` or `\s`, you'll need to A similar idea is alternation: `x|y` matches either x or y. Note that the precedence for `|` is low, so that `abc|xyz` matches either `abc` or `xyz` not `abcyz` or `abxyz`: ```{r} -str_detect(c("abc", "xyz"), "abc|xyz") +str_view(c("abc", "xyz"), "abc|xyz") ``` Like with mathematical expression, if precedence ever gets confusing, use parentheses to make it clear what you want: ```{r} -str_detect(c("grey", "gray"), "gr(e|a)y") +str_view(c("grey", "gray"), "gr(e|a)y") ``` Practice these by finding: diff --git a/www/.gitignore b/www/.gitignore index 51c709b..72e8ffc 100644 --- a/www/.gitignore +++ b/www/.gitignore @@ -1,3 +1 @@ -bootstrap-2.3.2/ -highlight/ -jquery-1.11.0/ \ No newline at end of file +* From 8a665354a235fe8c04e73189da4e90dd2b9777a9 Mon Sep 17 00:00:00 2001 From: hadley Date: Tue, 27 Oct 2015 11:15:42 -0500 Subject: [PATCH 10/20] Install specific version of jekyll --- .travis.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 2c47a5e..ef787d5 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,7 +18,8 @@ install: - rm pandoc-1.12.3.zip # Install jekyll - - travis_retry gem install jekyll mime-types + - travis_retry gem install mime-types + - travis_retry gem install jekyll -v 2.5.3 # Install R packages - ./travis-tool.sh r_binary_install knitr png From 25989219c72a92a1b5fd86511b2766d248861a06 Mon Sep 17 00:00:00 2001 From: hadley Date: Tue, 27 Oct 2015 13:11:02 -0500 Subject: [PATCH 11/20] Install htmlwidgets and htmltools --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index ef787d5..7e36da7 100644 --- a/.travis.yml +++ b/.travis.yml @@ -23,7 +23,7 @@ install: # Install R packages - ./travis-tool.sh r_binary_install knitr png - - ./travis-tool.sh r_install ggplot2 dplyr tidyr pryr stringr + - ./travis-tool.sh r_install ggplot2 dplyr tidyr pryr stringr htmlwidgets htmltools - ./travis-tool.sh github_package hadley/bookdown garrettgman/DSR hadley/readr gaborcsardi/rcorpora hadley/stringr script: jekyll build From f5740de1e7394eb4b2b58a60002c9384d1095916 Mon Sep 17 00:00:00 2001 From: hadley Date: Wed, 28 Oct 2015 11:03:11 -0500 Subject: [PATCH 12/20] More working on strings --- strings.Rmd | 151 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 87 insertions(+), 64 deletions(-) diff --git a/strings.Rmd b/strings.Rmd index 140121c..600ffae 100644 --- a/strings.Rmd +++ b/strings.Rmd @@ -12,29 +12,29 @@ library(stringi) # String manipulation -When working with text data, one of the most powerful tools at your disposal is regular expressions. Regular expressions are a very concise language for describing patterns in strings. When you first look at them, you'll think a cat walked across your keyboard, but as you learn more, you'll see how they allow you to express complex patterns very concisely. +This chapter introduces you to string manipulation in R. You'll learn the basics of how strings work and how to create them by hand, but the focus of this chapter will be on regular expressions. Character variables typically unstructured or semi-structured data so you need some tools to make order from madness. Regular expressions are a very concise language for describing patterns in strings. When you first look at them, you'll think a cat walked across your keyboard, but as you learn more, you'll see how they allow you to express complex patterns very concisely. The goal of this chapter is not to teach you every detail of regular expressions. Instead we'll give you a solid foundation that allows you to solve a wide variety of problems and point you to resources where you can learn more. -In this chapter, you'll learn the basics of regular expressions using the stringr package. - -```{r} -library(stringr) -``` - -The chapter concludes with a brief look at the stringi package. This package is what stringr uses internally: it's more complex than stringr (and includes many many more functions). stringr includes tools to let you tackle the most common 90% of string manipulation challenges; stringi contains functions to let you tackle the last 10%. - -For example, if you have - -```{r} - -``` - -The goal of this chapter is not to teach you every detail of regular expressions. Instead we'll give you a solid foundation that allows you to solve a wide variety of problems and point you to resources where you can learn more. +This chapter will focus on the __stringr__ package. This package provides a consistent set of functions that all work the same way and are easier to learn than the base R equivalents. We'll also take a brief look at the __stringi__ package. This package is what stringr uses internally: it's more complex than stringr (and includes many many more functions). stringr includes tools to let you tackle the most common 90% of string manipulation challenges; stringi contains functions to let you tackle the last 10%. ## String basics -In R, strings are always stored in a character vector. You can create strings with either single quotes or double quotes: there is no difference in behaviour. I recommend always using `"`, unless you want to create a string that contains multiple `"`, in which case use `'`. +In R, strings are stored in a character vector. You can create strings with either single quotes or double quotes: there is no difference in behaviour. I recommend always using `"`, unless you want to create a string that contains multiple `"`, in which case use `'`. -To include a literal single or double quote in a string you can use `\` to "escape". Note that when you print a string, you see the escapes. To see the raw contents of the string, use `writeLines()` (or for a length-1 character vector, `cat(x, "\n")`). +```{r} +string1 <- "This is a string" +string2 <- 'If I want to include a "quote" inside a string, I use single quotes' +``` + +To include a literal single or double quote in a string you can use `\` to "escape" it: + +```{r} +double_quote <- "\"" # or '"' +single_quote <- '\'' # or "'" +``` + +That means if you want to include a literal `\`, you'll need to double it up: `"\\"`. + +Beware that the printed representation of the string is not the same as string itself, because the printed representation shows the escapes. To see the raw contents of the string, use writeLines()`: ```{r} x <- c("\"", "\\") @@ -42,28 +42,29 @@ x writeLines(x) ``` -There are a handful of other special characters. The most common used are `"\n"`, new line, and `"\t"`, tab, but you can see the complete list by requesting help on `"`: `?'"'`, or `?"'"`. - -You'll also sometimes strings like `"\u00b5"`, this is a way of writing special characters that works on all platforms: +There are a handful of other special characters. The most common used are `"\n"`, new line, and `"\t"`, tab, but you can see the complete list by requesting help on `"`: `?'"'`, or `?"'"`. You'll also sometimes strings like `"\u00b5"`, this is a way of writing non-English characters that works on all platforms: ```{r} x <- "\u00b5" x ``` -Remember that the representation of a string is different from the string itself. - ### String length -Base R contains many functions to work with strings but we'll generally avoid them because they're inconsistent, and hard to remember. A particularly annoying inconsistency is that the function that computes the number of characters in a string, `nchar()`, returns 2 for `NA` (instead of `NA`) +Base R contains many functions to work with strings but we'll generally avoid them because they're inconsistent and hard to remember. Their behaviour is particularly inconsistent when it comes to missing values. For examle, `nchar()`, which gives the length of a string, returns 2 for `NA` (instead of `NA`) ```{r} # (Will be fixed in R 3.3.0) nchar(NA) +``` + +Instead we'll use functions from stringr. These have more evocative names, and all start with `str_`: + +```{r} str_length(NA) ``` -Every stringr function starts with `str_`. That's particularly useful if you're using RStudio, because by the time you've type `str_`, RStudio will be ready to offer autocomplete for the reminaing characters. That's useful if you can't quite remember the name of the function. +The common `str_` prefix is particularly useful if you use RStudio, because typing `str_` trigger autocomplete, so you can easily see all of the stringr functions. ### Combining strings @@ -94,24 +95,28 @@ As shown above, `str_c()` is vectorised, automatically recycling shorter vectors str_c("prefix-", c("a", "b", "c"), "-suffix") ``` +Objects of length 0 are silently dropped. This is particularly useful in conjunction with `if`: + +```{r} +name <- "Hadley" +time_of_day <- "morning" +birthday <- FALSE + +str_c("Good ", time_of_day, " ", name, + if (birthday) " and HAPPY BIRTHDAY", + "." +) +``` + To collapse vectors into a single string, use `collapse`: ```{r} str_c(c("x", "y", "z"), collapse = ", ") ``` -When creating strings you might also find `str_pad()` and `str_dup()` useful: - -```{r} -x <- c("apple", "banana", "pear") -str_pad(x, 10) - -str_c("Na ", str_dup("na ", 4), "batman!") -``` - ### Subsetting strings -You can extract parts of a string using `str_sub()`. `str_sub()` takes two arguments in addition to the string: the position to start at, and the postion to end at (inclusive): +You can extract parts of a string using `str_sub()`. As well as the string, `str_sub()` takes `start` and `end` argument which give the (inclusive) position of the substring: ```{r} x <- c("apple", "banana", "pear") @@ -120,63 +125,79 @@ str_sub(x, 1, 3) str_sub(x, -3, -1) ``` -`str_sub()` returns the longest string possible. If you don't want this behaviour, you'll need to check `str_length()` yourself. +Note that `str_sub()` won't fail if the string is too short: it will just return as much as possible: ```{r} str_sub("a", 1, 5) ``` -You can also use `str_sub()` to modify strings: +You can also use the assignment form of `str_sub()`, `` `str_sub<-()` ``, to modify strings: ```{r} str_sub(x, 1, 1) <- str_to_lower(str_sub(x, 1, 1)) x ``` -You can use `str_to_lower()`, `str_to_upper()`, and `str_to_title()` to convert the case of a vector. Note that what that means depends on where you are in the world, so these functions all have a locale argument. If left blank it will use the current locale. +### Locales + +Above I used`str_to_lower()` to change to lower case. You can also use `str_to_upper()` or `str_to_title()`. However, changing case is more complicated than it might at first seem because different languages have different rules for changing case. You can pick which set of rules to use by specifying a locale: + +```{r} +str_to_upper("i") +# In Turkish, an uppercase i has a dot over it: +str_to_upper("i", locale = "tr") +``` + +The locale is specified as ISO 639 language codes, which are two or three letter abbreviations. If you don't already know the code for your language, [Wikipedia](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) has a good list. If you leave the locale blank, it will use the current locale. + +Another important operation that's affected by the locale is sorting. The base R `order()` and `sort()` functions sort strings using the currect locale. If you want robust behaviour across different computers, you may want to use `str_sort()` and `str_order()` which take an additional `locale` argument: + +```{r} +x <- c("apple", "eggplant", "banana") +str_sort(x, locale = "en") # English +str_sort(x, locale = "haw") # Hawaiian +``` ### Exercises -1. In your own words, describe the difference between `sep` and `collapse`. +1. In your own words, describe the difference between the `sep` and `collapse` + arguments to `str_c()`. 1. In code that doesn't use stringr, you'll often see `paste()` and `paste0()`. - What's the difference between the two functions? What's stringr function are - they equivalent too? How do the functions differ in their handling of + What's the difference between the two functions? What stringr function are + they equivalent to? How do the functions differ in their handling of `NA`? 1. Use `str_length()` and `str_sub()` to extract the middle character from a character vector. - + +1. What does `str_wrap()` do? When might you want to use it? + +1. What does `str_trim()` do? What's the opposite of `str_trim()`? + 1. Write a function that turns (e.g.) a vector `c("a", "b", "c")` into the string `a, b, and c`. Think carefully about what it should do if given a vector of length 0, 1, or 2. -1. What does `str_wrap()` do? When might you want to use it? - -1. What does `str_trim()` do? - ## Matching patterns with regular expressions -Regular expressions, regexps for short, a very terse language that allow to describe patterns in string. They take a little while to get your head around, but once you've got it you'll find them extremely useful. +Regular expressions, regexps for short, are a very terse language that allow to describe patterns in strings. They take a little while to get your head around, but once you've got it you'll find them extremely useful. -To learn regular expressions, we'll use `str_show()` and `str_show_all()`. These functions take a character vector and a regular expression, and shows you how they match. We'll start with very simple regular expressions and then gradually move to more and more complicated. Once you've mastered the basics of pattern matching with regular expression, you'll learn all the stringr functions that use them and learn how to use them to solve real problems. +To learn regular expressions, we'll use `str_show()` and `str_show_all()`. These functions take a character vector and a regular expression, and shows you how they match. We'll start with very simple regular expressions and then gradually get more and more complicated. Once you've mastered pattern matching, you'll learn how to apply those ideas with various stringr functions. -### Fixed matches +### Basics matches -The simplest patterns that +The simplest patterns match exact strings: ```{r} - +x <- c("apple", "banana", "pear") +str_view(x, "an") ``` -```{r} -common <- rcorpora::corpora("words/common")$commonWords -``` - -### Match anything and escaping +The next step up in complexity is `.`, which matches any character: ```{r} -str_view(c("abc", "adc", "bef"), "a.c") +str_view(x, ".a.") ``` But if "`.`" matches any character, how do you match an actual "`.`"? You need to use an "escape" to tell the regular expression you want to match it exactly, not use the special behaviour. The escape character used by regular expressions is `\`. Unfortunately, that's also the escape character used by strings, so to match a literal "`.`" you need to use `\\.`. @@ -186,28 +207,30 @@ But if "`.`" matches any character, how do you match an actual "`.`"? You need t dot <- "\\." # But the expression itself only contains one: -cat(dot, "\n") +writeLines(dot) # And this tells R to look for explicit . str_view(c("abc", "a.c", "bef"), "a\\.c") ``` -If `\` is used an escape character, how do you match a literal `\`? Well you need to escape it, creating the regular expression `\\`. And in R that needs to be in a string, so you need to write `"\\\\"` - that's right, you need four backslashes to match one! +If `\` is used an escape character, how do you match a literal `\`? Well you need to escape it, creating the regular expression `\\`. To create that regular expression, you need to use a string, which also needs to escape `\`. That means to match a literal `\` you need to write `"\\\\"` - you need four backslashes to match one! ```{r} x <- "a\\b" -cat(x, "\n") +writeLines(x) -y <- str_replace(x, "\\\\", "-slash-") -cat(y, "\n") +str_view(x, "\\\\") ``` -Here I'll write a regular expression like `\.` and the string that represents the regular expression as `"\."`. +In this book, I'll write a regular expression like `\.` and the string that represents the regular expression as `"\\."`. -Use regular expressions to: +### Exercises + +* Explain why each of these strings don't match a `\`: `"\"`, `"\\"`, `"\\\"`. * Solve this crossword puzzle clue: `a??le` + ### Anchors Regular expressions can also match things that are not characters. The most important non-character matches are: From 1d430e7e3e4425e0789d8498760eea3b99df2f0a Mon Sep 17 00:00:00 2001 From: hadley Date: Wed, 28 Oct 2015 11:11:46 -0500 Subject: [PATCH 13/20] Try also copyring www dir --- .travis.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.travis.yml b/.travis.yml index 7e36da7..e1b8d52 100644 --- a/.travis.yml +++ b/.travis.yml @@ -30,6 +30,8 @@ script: jekyll build after_success: - cp -r figures/ _site/figures + - ls www/ + - cp -r www/ _site/www deploy: provider: s3 From 96af27e155ee5e8b67c3c19b3583712485308e56 Mon Sep 17 00:00:00 2001 From: hadley Date: Wed, 28 Oct 2015 11:35:25 -0500 Subject: [PATCH 14/20] Didn't help --- .travis.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index e1b8d52..7e36da7 100644 --- a/.travis.yml +++ b/.travis.yml @@ -30,8 +30,6 @@ script: jekyll build after_success: - cp -r figures/ _site/figures - - ls www/ - - cp -r www/ _site/www deploy: provider: s3 From 23908731a6298d691bb1685c6bafd93fa9e54586 Mon Sep 17 00:00:00 2001 From: hadley Date: Thu, 29 Oct 2015 10:13:19 -0500 Subject: [PATCH 15/20] More work on strings --- strings.Rmd | 182 +++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 137 insertions(+), 45 deletions(-) diff --git a/strings.Rmd b/strings.Rmd index 600ffae..bf95518 100644 --- a/strings.Rmd +++ b/strings.Rmd @@ -194,7 +194,7 @@ x <- c("apple", "banana", "pear") str_view(x, "an") ``` -The next step up in complexity is `.`, which matches any character: +The next step up in complexity is `.`, which matches any character (except a new line): ```{r} str_view(x, ".a.") @@ -224,49 +224,73 @@ str_view(x, "\\\\") In this book, I'll write a regular expression like `\.` and the string that represents the regular expression as `"\\."`. -### Exercises +#### Exercises -* Explain why each of these strings don't match a `\`: `"\"`, `"\\"`, `"\\\"`. +1. Explain why each of these strings don't match a `\`: `"\"`, `"\\"`, `"\\\"`. -* Solve this crossword puzzle clue: `a??le` +1. How would you match the sequence `"'\`? +1. What patterns does will this regular expression match `"\..\..\..`? + How would you represent it as a string? ### Anchors -Regular expressions can also match things that are not characters. The most important non-character matches are: +By default, regular expressions will match any part of a string. It's often useful to _anchor_ the regular expression so that it matches from the start or end of the string. You can use: -* `^`: the start of the line. -* `*`: the end of the line. +* `^` to match the start of the string. +* `*` to match the end of the string. + +```{r} +x <- c("apple", "banana", "pear") +str_view(x, "^a") +str_view(x, "a$") +``` + +To remember which is which, try this mneomic which I learned from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): if you begin with power (`^`), you end up with money (`$`). To force a regular expression to only match a complete string, anchor it with both `^` and `$`.: ```{r} -str_view(c("abcdef", "bcd"), "^bcd$") +x <- c("apple pie", "apple", "apple cake") +str_view(x, "apple") +str_view(x, "^apple$") ``` -My favourite mneomic for rememember which is which (from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): begin with power (`^`), end with money (`$`). - You can also match the boundary between words with `\b`. I don't find I often use this in R, but I will sometimes use it when I'm doing a find all in RStudio when I want to find the name of a function that's a component of other functions. For example, I'll search for `\bsum\b` to avoid matching `summarise`, `summary`, `rowsum` and so on. -Practice these by finding all common words: +#### Exercises -* Start with y. -* End in x. -* That are exactly 4 letters long. Without using `str_length()` +1. How would you match the literal string `"$^$"`? + +1. Given this corpus of common words: + + ```{r} + common <- rcorpora::corpora("words/common")$commonWords + ``` + + Create regular expressions that find all words that: + + 1. Start with "y". + 1. End with "x" + 1. Are exactly three letters long. (Don't cheat by using `str_length()`!) + 1. Have seven letters or more. + + Since this list is long, you might want to use the `match` argument to + `str_view()` to show only the matching or non-matching words. ### Character classes and alternatives -As well as `.` there are a number of other special patterns that match more than one character: +There are number of other special patterns that match more than one character: -* `\d`: any digit -* `\s`: any whitespace (space, tab, newline) -* `[abc]`: match a, b, or c -* `[a-e]`: match any character between a and e -* `[!abc]`: match anything except a, b, or c +* `.`: any character apart from a new line. +* `\d`: any digit. +* `\s`: any whitespace (space, tab, newline). +* `[abc]`: match a, b, or c. +* `[!abc]`: match anything except a, b, or c. Remember, to create a regular expression containing `\d` or `\s`, you'll need to escape the `\` for the string, so you'll type `"\\d"` or `"\\s"`. -A similar idea is alternation: `x|y` matches either x or y. Note that the precedence for `|` is low, so that `abc|xyz` matches either `abc` or `xyz` not `abcyz` or `abxyz`: +You can use _alternation_ to pick between one or more alternative patterns. For example, `abc|d..f` will match either '"abc"', or `"deaf"`. Note that the precedence for `|` is low, so that `abc|xyz` matches either `abc` or `xyz` not `abcyz` or `abxyz`: ```{r} str_view(c("abc", "xyz"), "abc|xyz") @@ -278,15 +302,29 @@ Like with mathematical expression, if precedence ever gets confusing, use parent str_view(c("grey", "gray"), "gr(e|a)y") ``` -Practice these by finding: +#### Exercises -* Start with a vowel. -* That only contain constants. -* That don't contain any vowels. +1. Create regular expressions that find all words that: + 1. Start with a vowel. + + 1. That only contain constants. (Hint: thinking about matching + "not"-vowels.) + + 1. End with `ed`, but not with `eed`. + + 1. End with `ing` or `ise`. + +1. Write a regular expression that matches a word if it's probably written + in British English, not American English. + +1. Create a regular expression that will match telephone numbers as commonly + written in your country. ### Repetition +The next step up in power involves control how many times a pattern matches: + * `?`: 0 or 1 * `+`: 1 or more * `*`: 0 or more @@ -297,19 +335,36 @@ Practice these by finding: (By default these matches are "greedy": they will match the longest string possible. You can make them "lazy", matching the shortest string possible by putting a `?` after them.) -Note that the precedence of these operators are high, so you write: `colou?r`. That means you'll need to use parentheses for many uses: `bana(na)+` or `ba(na){2,}`. +Note that the precedence of these operators are high, so you can write: `colou?r` to match either American or British spellings. That means most uses will need parentheses, like `bana(na)+` or `ba(na){2,}`. -Practice these by finding all common words: +#### Exercises -* That contain three or more vowels in a row. +1. Describe in words what these regular expressions match: + (read carefully to see I'm using a regular expression or a string + that defines a regular expression.) + + 1. `^.*$` + 1. `"\\{.+\\}"` + 1. `\d{4}-\d{2}-\d{2}` + 1. `"\\\\{4}"` + +1. Create regular expressions to find all words that: + + 1. Have three or more vowels in a row. + 1. Start with three consonants + 1. Have two or more vowel-consontant pairs in a row. ### Grouping and backreferences +You learned about parentheses earlier as a way to disambiguate complex expression. They do one other special thing: they also define numeric groups that you can refer to with _backreferences_, `\1`, `\2` etc.For example, the following regular expression finds all fruits that have a pair letters that's repeated. + ```{r} fruit <- rcorpora::corpora("foods/fruits")$fruits -str_subset(fruit, "(..)\\1") +str_view(fruit, "(..)\\1", match = TRUE) ``` +(You'll also see how they're useful in conjunction with `str_match()` in a few pages.) + Unfortunately `()` in regexps serve two purposes: you usually use them to disambiguate precedence, but you can also use for grouping. If you're using one set for grouping and one set for disambiguation, things can get confusing. You might want to use `(?:)` instead: it only disambiguates, and doesn't modify the grouping. They are called non-capturing parentheses. For example: @@ -319,44 +374,81 @@ str_detect(c("grey", "gray"), "gr(e|a)y") str_detect(c("grey", "gray"), "gr(?:e|a)y") ``` -Describe in words what these expressions will match: +### Exercises -* `str_subset(common, "(.)(.)\\2\\1")` +1. Describe, in words, what these expressions will match: + + 1. `"(.)(.)\\2\\1"` + 1. `(..)\1` + 1. `"(.)(.)(.).*\\3\\2\\1"` + +1. Construct regular expressions to match words that: + + 1. Start and end with the same character. ## Tools -The stringr package contains functions for working with strings and patterns. We'll focus on four main categories: +Now that you've learned the basics of regular expression, it's time to learn how to apply to real problems. In this section you'll learn a wide array of stringr functions that let you: -* What matches the pattern? -* Does a string match a pattern? -* How can you replace a pattern with text? -* How can you split a string into pieces? +* Determine which elements match a pattern. +* Find the positions of matches. +* Extract the content of matches. +* Replace matches with new values. +* How can you split a string into based on a match. -### Detecting matches +### Detect matches -`str_detect()`, `str_subset()`, `str_count()` +To determine if a character vector matches a pattern, use `str_detect()`. It returns a logical vector: -### Extracting matches +```{r} +# How many common words start with t? +sum(str_detect(common, "^t")) +``` + +When you have complicated logical conditions (e.g. match this or that but not these) combining multiple `str_detect()` calls with logical operators is often easy. A simple example is if you want to find all words that don't contain any vowels: + +```{r} +# Find all words containing at least one vowel, and negate +no_vowels_1 <- !str_detect(common, "[aeiou]") +# Find all words consisting only of consonants (non-vowels) +no_vowels_2 <- str_detect(common, "^[^aeiou]+$") +all.equal(no_vowels_1, no_vowels_2) +``` + +If you find your regular expression is getting hard to understand, trying breaking it up into smaller pieces, giving each piece a name, and then combining with logical operations. + +`str_count()` is similar to `str_detect()` but it returns an integer count of the number of matches, instead of a true/false: + +```{r} +# What's the average number of vowels per word? +mean(str_count(common, "[aeiou]")) +``` + +`str_subset()` is a wrapper for the common pattern `x[str_detect(x, pattern)]`. + +### Find matches + +`str_locate()`, `str_locate_all()` gives you the starting and ending positions of each match. These are particularly useful when none of the other functions does exactly what you want. You can use `str_locate()` to find the matching pattern, `str_sub()` to extract and/or modify them. + +### Extract matches `str_extract()`, `str_extract_all()` -### Extracting grouped matches - `str_match()`, `str_match_all()` Note that matches are always non-overlapping. The second match starts after the first is complete. -### Replacing patterns +### Replacing matches `str_replace()`, `str_replace_all()` +Backreferences. + ### Splitting `str_split()`, `str_split_fixed()`. -### Finding locations - -`str_locate()`, `str_locate_all()` gives you the starting and ending positions of each match. These are particularly useful when none of the other functions does exactly what you want. You can use `str_locate()` to find the matching pattern, `str_sub()` to extract and/or modify them. +`boundary()` ### Exercises From 3b5b47aacdbcef3fa9c992567530b632ee9b6226 Mon Sep 17 00:00:00 2001 From: hadley Date: Thu, 29 Oct 2015 10:15:48 -0500 Subject: [PATCH 16/20] Add htmlwidgets reqs to template --- _layouts/default.html | 5 + www/.gitignore | 2 +- www/htmlwidgets-0.5/htmlwidgets.js | 625 ++++++++++++++++++++ www/jquery-1.11.0/jquery.min.js | 4 + www/str_view-0.1.0/str_view.css | 11 + www/str_view-binding-1.0.0.9000/str_view.js | 17 + 6 files changed, 663 insertions(+), 1 deletion(-) create mode 100644 www/htmlwidgets-0.5/htmlwidgets.js create mode 100644 www/jquery-1.11.0/jquery.min.js create mode 100644 www/str_view-0.1.0/str_view.css create mode 100644 www/str_view-binding-1.0.0.9000/str_view.js diff --git a/_layouts/default.html b/_layouts/default.html index 024634d..38374df 100644 --- a/_layouts/default.html +++ b/_layouts/default.html @@ -8,6 +8,11 @@ + + + + + diff --git a/www/.gitignore b/www/.gitignore index 72e8ffc..c143e03 100644 --- a/www/.gitignore +++ b/www/.gitignore @@ -1 +1 @@ -* +bootstrap-2.3.2 diff --git a/www/htmlwidgets-0.5/htmlwidgets.js b/www/htmlwidgets-0.5/htmlwidgets.js new file mode 100644 index 0000000..0777920 --- /dev/null +++ b/www/htmlwidgets-0.5/htmlwidgets.js @@ -0,0 +1,625 @@ +(function() { + // If window.HTMLWidgets is already defined, then use it; otherwise create a + // new object. This allows preceding code to set options that affect the + // initialization process (though none currently exist). + window.HTMLWidgets = window.HTMLWidgets || {}; + + // See if we're running in a viewer pane. If not, we're in a web browser. + var viewerMode = window.HTMLWidgets.viewerMode = + /\bviewer_pane=1\b/.test(window.location); + + // See if we're running in Shiny mode. If not, it's a static document. + // Note that static widgets can appear in both Shiny and static modes, but + // obviously, Shiny widgets can only appear in Shiny apps/documents. + var shinyMode = window.HTMLWidgets.shinyMode = + typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings; + + // We can't count on jQuery being available, so we implement our own + // version if necessary. + function querySelectorAll(scope, selector) { + if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) { + return scope.find(selector); + } + if (scope.querySelectorAll) { + return scope.querySelectorAll(selector); + } + } + + function asArray(value) { + if (value === null) + return []; + if ($.isArray(value)) + return value; + return [value]; + } + + // Implement jQuery's extend + function extend(target /*, ... */) { + if (arguments.length == 1) { + return target; + } + for (var i = 1; i < arguments.length; i++) { + var source = arguments[i]; + for (var prop in source) { + if (source.hasOwnProperty(prop)) { + target[prop] = source[prop]; + } + } + } + return target; + } + + // IE8 doesn't support Array.forEach. + function forEach(values, callback, thisArg) { + if (values.forEach) { + values.forEach(callback, thisArg); + } else { + for (var i = 0; i < values.length; i++) { + callback.call(thisArg, values[i], i, values); + } + } + } + + // Replaces the specified method with the return value of funcSource. + // + // Note that funcSource should not BE the new method, it should be a function + // that RETURNS the new method. funcSource receives a single argument that is + // the overridden method, it can be called from the new method. The overridden + // method can be called like a regular function, it has the target permanently + // bound to it so "this" will work correctly. + function overrideMethod(target, methodName, funcSource) { + var superFunc = target[methodName] || function() {}; + var superFuncBound = function() { + return superFunc.apply(target, arguments); + }; + target[methodName] = funcSource(superFuncBound); + } + + // Implement a vague facsimilie of jQuery's data method + function elementData(el, name, value) { + if (arguments.length == 2) { + return el["htmlwidget_data_" + name]; + } else if (arguments.length == 3) { + el["htmlwidget_data_" + name] = value; + return el; + } else { + throw new Error("Wrong number of arguments for elementData: " + + arguments.length); + } + } + + // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex + function escapeRegExp(str) { + return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&"); + } + + function hasClass(el, className) { + var re = new RegExp("\\b" + escapeRegExp(className) + "\\b"); + return re.test(el.className); + } + + // elements - array (or array-like object) of HTML elements + // className - class name to test for + // include - if true, only return elements with given className; + // if false, only return elements *without* given className + function filterByClass(elements, className, include) { + var results = []; + for (var i = 0; i < elements.length; i++) { + if (hasClass(elements[i], className) == include) + results.push(elements[i]); + } + return results; + } + + function on(obj, eventName, func) { + if (obj.addEventListener) { + obj.addEventListener(eventName, func, false); + } else if (obj.attachEvent) { + obj.attachEvent(eventName, func); + } + } + + function off(obj, eventName, func) { + if (obj.removeEventListener) + obj.removeEventListener(eventName, func, false); + else if (obj.detachEvent) { + obj.detachEvent(eventName, func); + } + } + + // Translate array of values to top/right/bottom/left, as usual with + // the "padding" CSS property + // https://developer.mozilla.org/en-US/docs/Web/CSS/padding + function unpackPadding(value) { + if (typeof(value) === "number") + value = [value]; + if (value.length === 1) { + return {top: value[0], right: value[0], bottom: value[0], left: value[0]}; + } + if (value.length === 2) { + return {top: value[0], right: value[1], bottom: value[0], left: value[1]}; + } + if (value.length === 3) { + return {top: value[0], right: value[1], bottom: value[2], left: value[1]}; + } + if (value.length === 4) { + return {top: value[0], right: value[1], bottom: value[2], left: value[3]}; + } + } + + // Convert an unpacked padding object to a CSS value + function paddingToCss(paddingObj) { + return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px"; + } + + // Makes a number suitable for CSS + function px(x) { + if (typeof(x) === "number") + return x + "px"; + else + return x; + } + + // Retrieves runtime widget sizing information for an element. + // The return value is either null, or an object with fill, padding, + // defaultWidth, defaultHeight fields. + function sizingPolicy(el) { + var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']"); + if (!sizingEl) + return null; + var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}"); + if (viewerMode) { + return sp.viewer; + } else { + return sp.browser; + } + } + + function initSizing(el) { + var sizing = sizingPolicy(el); + if (!sizing) + return; + + var cel = document.getElementById("htmlwidget_container"); + if (!cel) + return; + + if (typeof(sizing.padding) !== "undefined") { + document.body.style.margin = "0"; + document.body.style.padding = paddingToCss(unpackPadding(sizing.padding)); + } + + if (sizing.fill) { + document.body.style.overflow = "hidden"; + document.body.style.width = "100%"; + document.body.style.height = "100%"; + document.documentElement.style.width = "100%"; + document.documentElement.style.height = "100%"; + if (cel) { + cel.style.position = "absolute"; + var pad = unpackPadding(sizing.padding); + cel.style.top = pad.top + "px"; + cel.style.right = pad.right + "px"; + cel.style.bottom = pad.bottom + "px"; + cel.style.left = pad.left + "px"; + el.style.width = "100%"; + el.style.height = "100%"; + } + + return { + getWidth: function() { return cel.offsetWidth; }, + getHeight: function() { return cel.offsetHeight; } + }; + + } else { + el.style.width = px(sizing.width); + el.style.height = px(sizing.height); + + return { + getWidth: function() { return el.offsetWidth; }, + getHeight: function() { return el.offsetHeight; } + }; + } + } + + // Default implementations for methods + var defaults = { + find: function(scope) { + return querySelectorAll(scope, "." + this.name); + }, + renderError: function(el, err) { + var $el = $(el); + + this.clearError(el); + + // Add all these error classes, as Shiny does + var errClass = "shiny-output-error"; + if (err.type !== null) { + // use the classes of the error condition as CSS class names + errClass = errClass + " " + $.map(asArray(err.type), function(type) { + return errClass + "-" + type; + }).join(" "); + } + errClass = errClass + " htmlwidgets-error"; + + // Is el inline or block? If inline or inline-block, just display:none it + // and add an inline error. + var display = $el.css("display"); + $el.data("restore-display-mode", display); + + if (display === "inline" || display === "inline-block") { + $el.hide(); + if (err.message !== "") { + var errorSpan = $("").addClass(errClass); + errorSpan.text(err.message); + $el.after(errorSpan); + } + } else if (display === "block") { + // If block, add an error just after the el, set visibility:none on the + // el, and position the error to be on top of the el. + // Mark it with a unique ID and CSS class so we can remove it later. + $el.css("visibility", "hidden"); + if (err.message !== "") { + var errorDiv = $("
    ").addClass(errClass).css("position", "absolute") + .css("top", el.offsetTop) + .css("left", el.offsetLeft) + // setting width can push out the page size, forcing otherwise + // unnecessary scrollbars to appear and making it impossible for + // the element to shrink; so use max-width instead + .css("maxWidth", el.offsetWidth) + .css("height", el.offsetHeight); + errorDiv.text(err.message); + $el.after(errorDiv); + + // Really dumb way to keep the size/position of the error in sync with + // the parent element as the window is resized or whatever. + var intId = setInterval(function() { + if (!errorDiv[0].parentElement) { + clearInterval(intId); + return; + } + errorDiv + .css("top", el.offsetTop) + .css("left", el.offsetLeft) + .css("maxWidth", el.offsetWidth) + .css("height", el.offsetHeight); + }, 500); + } + } + }, + clearError: function(el) { + var $el = $(el); + var display = $el.data("restore-display-mode"); + $el.data("restore-display-mode", null); + + if (display === "inline" || display === "inline-block") { + if (display) + $el.css("display", display); + $(el.nextSibling).filter(".htmlwidgets-error").remove(); + } else if (display === "block"){ + $el.css("visibility", "inherit"); + $(el.nextSibling).filter(".htmlwidgets-error").remove(); + } + }, + sizing: {} + }; + + // Called by widget bindings to register a new type of widget. The definition + // object can contain the following properties: + // - name (required) - A string indicating the binding name, which will be + // used by default as the CSS classname to look for. + // - initialize (optional) - A function(el) that will be called once per + // widget element; if a value is returned, it will be passed as the third + // value to renderValue. + // - renderValue (required) - A function(el, data, initValue) that will be + // called with data. Static contexts will cause this to be called once per + // element; Shiny apps will cause this to be called multiple times per + // element, as the data changes. + window.HTMLWidgets.widget = function(definition) { + if (!definition.name) { + throw new Error("Widget must have a name"); + } + if (!definition.type) { + throw new Error("Widget must have a type"); + } + // Currently we only support output widgets + if (definition.type !== "output") { + throw new Error("Unrecognized widget type '" + definition.type + "'"); + } + // TODO: Verify that .name is a valid CSS classname + if (!definition.renderValue) { + throw new Error("Widget must have a renderValue function"); + } + + // For static rendering (non-Shiny), use a simple widget registration + // scheme. We also use this scheme for Shiny apps/documents that also + // contain static widgets. + window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || []; + // Merge defaults into the definition; don't mutate the original definition. + var staticBinding = extend({}, defaults, definition); + overrideMethod(staticBinding, "find", function(superfunc) { + return function(scope) { + var results = superfunc(scope); + // Filter out Shiny outputs, we only want the static kind + return filterByClass(results, "html-widget-output", false); + }; + }); + window.HTMLWidgets.widgets.push(staticBinding); + + if (shinyMode) { + // Shiny is running. Register the definition as an output binding. + + // Merge defaults into the definition; don't mutate the original definition. + // The base object is a Shiny output binding if we're running in Shiny mode, + // or an empty object if we're not. + var shinyBinding = extend(new Shiny.OutputBinding(), defaults, definition); + + // Wrap renderValue to handle initialization, which unfortunately isn't + // supported natively by Shiny at the time of this writing. + + // NB: shinyBinding.initialize may be undefined, as it's optional. + + // Rename initialize to make sure it isn't called by a future version + // of Shiny that does support initialize directly. + shinyBinding._htmlwidgets_initialize = shinyBinding.initialize; + delete shinyBinding.initialize; + + overrideMethod(shinyBinding, "find", function(superfunc) { + return function(scope) { + + var results = superfunc(scope); + + // Only return elements that are Shiny outputs, not static ones + var dynamicResults = results.filter(".html-widget-output"); + + // It's possible that whatever caused Shiny to think there might be + // new dynamic outputs, also caused there to be new static outputs. + // Since there might be lots of different htmlwidgets bindings, we + // schedule execution for later--no need to staticRender multiple + // times. + if (results.length !== dynamicResults.length) + scheduleStaticRender(); + + return dynamicResults; + }; + }); + + overrideMethod(shinyBinding, "renderValue", function(superfunc) { + return function(el, data) { + // Resolve strings marked as javascript literals to objects + if (!(data.evals instanceof Array)) data.evals = [data.evals]; + for (var i = 0; data.evals && i < data.evals.length; i++) { + window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]); + } + if (!this.renderOnNullValue) { + if (data.x === null) { + el.style.visibility = "hidden"; + return; + } else { + el.style.visibility = "inherit"; + } + } + if (!elementData(el, "initialized")) { + initSizing(el); + + elementData(el, "initialized", true); + if (this._htmlwidgets_initialize) { + var result = this._htmlwidgets_initialize(el, el.offsetWidth, + el.offsetHeight); + elementData(el, "init_result", result); + } + } + Shiny.renderDependencies(data.deps); + superfunc(el, data.x, elementData(el, "init_result")); + }; + }); + + overrideMethod(shinyBinding, "resize", function(superfunc) { + return function(el, width, height) { + // Shiny can call resize before initialize/renderValue have been + // called, which doesn't make sense for widgets. + if (elementData(el, "initialized")) { + superfunc(el, width, height, elementData(el, "init_result")); + } + }; + }); + + Shiny.outputBindings.register(shinyBinding, shinyBinding.name); + } + }; + + var scheduleStaticRenderTimerId = null; + function scheduleStaticRender() { + if (!scheduleStaticRenderTimerId) { + scheduleStaticRenderTimerId = setTimeout(function() { + scheduleStaticRenderTimerId = null; + window.HTMLWidgets.staticRender(); + }, 1); + } + } + + // Render static widgets after the document finishes loading + // Statically render all elements that are of this widget's class + window.HTMLWidgets.staticRender = function() { + var bindings = window.HTMLWidgets.widgets || []; + forEach(bindings, function(binding) { + var matches = binding.find(document.documentElement); + forEach(matches, function(el) { + var sizeObj = initSizing(el, binding); + + if (hasClass(el, "html-widget-static-bound")) + return; + el.className = el.className + " html-widget-static-bound"; + + var initResult; + if (binding.initialize) { + initResult = binding.initialize(el, + sizeObj ? sizeObj.getWidth() : el.offsetWidth, + sizeObj ? sizeObj.getHeight() : el.offsetHeight + ); + } + + if (binding.resize) { + var lastSize = {}; + var resizeHandler = function(e) { + var size = { + w: sizeObj ? sizeObj.getWidth() : el.offsetWidth, + h: sizeObj ? sizeObj.getHeight() : el.offsetHeight + }; + if (size.w === 0 && size.h === 0) + return; + if (size.w === lastSize.w && size.h === lastSize.h) + return; + lastSize = size; + binding.resize(el, size.w, size.h, initResult); + }; + + on(window, "resize", resizeHandler); + + // This is needed for cases where we're running in a Shiny + // app, but the widget itself is not a Shiny output, but + // rather a simple static widget. One example of this is + // an rmarkdown document that has runtime:shiny and widget + // that isn't in a render function. Shiny only knows to + // call resize handlers for Shiny outputs, not for static + // widgets, so we do it ourselves. + if (window.jQuery) { + window.jQuery(document).on("shown", resizeHandler); + window.jQuery(document).on("hidden", resizeHandler); + } + + // This is needed for the specific case of ioslides, which + // flips slides between display:none and display:block. + // Ideally we would not have to have ioslide-specific code + // here, but rather have ioslides raise a generic event, + // but the rmarkdown package just went to CRAN so the + // window to getting that fixed may be long. + if (window.addEventListener) { + // It's OK to limit this to window.addEventListener + // browsers because ioslides itself only supports + // such browsers. + on(document, "slideenter", resizeHandler); + on(document, "slideleave", resizeHandler); + } + } + + var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']"); + if (scriptData) { + var data = JSON.parse(scriptData.textContent || scriptData.text); + // Resolve strings marked as javascript literals to objects + if (!(data.evals instanceof Array)) data.evals = [data.evals]; + for (var k = 0; data.evals && k < data.evals.length; k++) { + window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]); + } + binding.renderValue(el, data.x, initResult); + } + }); + }); + } + + // Wait until after the document has loaded to render the widgets. + if (document.addEventListener) { + document.addEventListener("DOMContentLoaded", function() { + document.removeEventListener("DOMContentLoaded", arguments.callee, false); + window.HTMLWidgets.staticRender(); + }, false); + } else if (document.attachEvent) { + document.attachEvent("onreadystatechange", function() { + if (document.readyState === "complete") { + document.detachEvent("onreadystatechange", arguments.callee); + window.HTMLWidgets.staticRender(); + } + }); + } + + + window.HTMLWidgets.getAttachmentUrl = function(depname, key) { + // If no key, default to the first item + if (typeof(key) === "undefined") + key = 1; + + var link = document.getElementById(depname + "-" + key + "-attachment"); + if (!link) { + throw new Error("Attachment " + depname + "/" + key + " not found in document"); + } + return link.getAttribute("href"); + }; + + window.HTMLWidgets.dataframeToD3 = function(df) { + var names = []; + var length; + for (var name in df) { + if (df.hasOwnProperty(name)) + names.push(name); + if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") { + throw new Error("All fields must be arrays"); + } else if (typeof(length) !== "undefined" && length !== df[name].length) { + throw new Error("All fields must be arrays of the same length"); + } + length = df[name].length; + } + var results = []; + var item; + for (var row = 0; row < length; row++) { + item = {}; + for (var col = 0; col < names.length; col++) { + item[names[col]] = df[names[col]][row]; + } + results.push(item); + } + return results; + }; + + window.HTMLWidgets.transposeArray2D = function(array) { + var newArray = array[0].map(function(col, i) { + return array.map(function(row) { + return row[i] + }) + }); + return newArray; + }; + // Split value at splitChar, but allow splitChar to be escaped + // using escapeChar. Any other characters escaped by escapeChar + // will be included as usual (including escapeChar itself). + function splitWithEscape(value, splitChar, escapeChar) { + var results = []; + var escapeMode = false; + var currentResult = ""; + for (var pos = 0; pos < value.length; pos++) { + if (!escapeMode) { + if (value[pos] === splitChar) { + results.push(currentResult); + currentResult = ""; + } else if (value[pos] === escapeChar) { + escapeMode = true; + } else { + currentResult += value[pos]; + } + } else { + currentResult += value[pos]; + escapeMode = false; + } + } + if (currentResult !== "") { + results.push(currentResult); + } + return results; + } + // Function authored by Yihui/JJ Allaire + window.HTMLWidgets.evaluateStringMember = function(o, member) { + var parts = splitWithEscape(member, '.', '\\'); + for (var i = 0, l = parts.length; i < l; i++) { + var part = parts[i]; + // part may be a character or 'numeric' member name + if (o !== null && typeof o === "object" && part in 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