From 090333be5bc5088490eeb1b21518b0ec2e805eed Mon Sep 17 00:00:00 2001 From: Hadley Wickham Date: Mon, 21 Mar 2022 09:24:42 -0500 Subject: [PATCH] Iterate on logicals chapter Also figure out how to get figures sizes aligned nicely. --- README.md | 25 +++- diagrams/transform-logical.png | Bin 158968 -> 0 bytes diagrams/transform.graffle | Bin 3111 -> 279697 bytes diagrams/transform.png | Bin 0 -> 84815 bytes logicals.Rmd | 258 +++++++++++++++++++++++---------- missing-values.Rmd | 30 ---- 6 files changed, 204 insertions(+), 109 deletions(-) delete mode 100644 diagrams/transform-logical.png create mode 100644 diagrams/transform.png diff --git a/README.md b/README.md index 89eb312..b22056a 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,9 @@ # R for Data Science + [![Travis build status](https://travis-ci.org/hadley/r4ds.svg?branch=master)](https://travis-ci.org/hadley/r4ds) -This repository contains the source of [R for Data Science](http://r4ds.had.co.nz) -book. The book is built using [bookdown](https://github.com/rstudio/bookdown). +This repository contains the source of [R for Data Science](http://r4ds.had.co.nz) book. +The book is built using [bookdown](https://github.com/rstudio/bookdown). The R packages used in this book can be installed via @@ -10,6 +11,24 @@ The R packages used in this book can be installed via devtools::install_github("hadley/r4ds") ``` +## Images + +### Omnigraffle drawings + +- Font: 12pt Ubuntu mono +- Export as 300 dpi png. +- Website font is 18 px = 13.5 pt, so scale dpi to match font sizes: 270 = 300 \* 12 / 13.5 +- Verified sizes are visually equivalent by screenshotting. + +```{=html} + +``` + #| echo: FALSE + #| out.width: NULL + #| fig.retina: 1.5 + knitr::include_graphics("diagrams/transform.png", dpi = 270) + ## Code of Conduct -Please note that r4ds uses a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this book, you agree to abide by its terms. +Please note that r4ds uses a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). +By contributing to this book, you agree to abide by its terms. diff --git a/diagrams/transform-logical.png b/diagrams/transform-logical.png deleted file mode 100644 index 18162f5a85e6142ca64fa2560d0edda712ee5314..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 158968 zcmd43cQl;cyFNZM2BSsq!DvCWQ6hTpy+!n1qnC&lMkjhFh#KXMC{ZKA=p-b9AV`8p zln^~3O1^vK{hV{wS?hQH{$LkW zKyV@vFz`;ff|dgK2K6^oQ-svNxw-*ylUcL{3m_Ql->$NqM`@fHmcZ0zHJ;oV)hd|T*6hff4ZvE#w1g8}G-)Ag^{pV=> zqEejyyvB*de)5Fu=R@#~z(?K89|93z$Nq-~J(dfEKwv~}#-;(LI@*#B-kt(>j^6f8 z0zsZW*iS*EgCxOAPp1GoWRRzam%n6?4BO=qlHfJ=Z9z8VTy?X-pLa-wlvqW5ZtPzyAHb&j2^) z|Gtu!|G$p~9#9bb8$lrfl;D4z8+=q6`>v#+lfSpeUF_*^dASA13QJ!e{J-D+@2ma& zl%AiP6S!aO$7F^6{mB1(?|&b!=j88?J^seO$N$gw{`c|vuHFIOV66Sz95lQF 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Logical vectors are the simplest type of vector because each element can only be one of three possible values: `TRUE`, `FALSE`, and `NA`. -Despite that simplicity, they're an extremely powerful tool. +You'll find logical vectors directly in data relatively rarely, but despite that they're extremely powerful because you'll frequently create them during data analysis. + +We'll begin with the most common way of creating logical vectors: numeric comparisons. +Then we'll talk about using Boolean algebra to combine different logical vectors, and some useful summaries for logical vectors. +We'll finish off with some other tool for making conditional changes ### Prerequisites -```{r, message = FALSE} +In this chapter, we'll continue to draw inspiration from the nyclights13 dataset. + +```{r setup, message = FALSE} library(tidyverse) library(nycflights13) ``` +But as we start to discuss more tools, there won't always be a perfect example. +So from this chapter on we'll start to use more abstract examples where we create a vector with `c()`, and then manipulate it in various ways. +This will make it easier to explain the general point without having to construct a full example. +It does make it a little harder to apply directly to your data problems, but remember that you can do these same manipulations with a vector inside a data frame using `mutate()` and friends. + +```{r} +x <- c(1, 2, 3, 5, 7, 11, 13) +x * 2 + +df <- tibble( + x = c(1, 2, 3, 5, 7, 11, 13) +) +df |> + mutate(y = x * 2) +``` + ## Comparisons -Some times you'll get data that already includes logical vectors but in most cases you'll create them by using a comparison, like `<`, `<=`, `>`, `>=`, `!=`, and `==`. - -### In `mutate()` - -So far, you've mostly created these new variables implicitly within `filter()`: +A very common way to create a logical vector is via a numeric comparison with `<`, `<=`, `>`, `>=`, `!=`, and `==`. +You'll learn other ways to create them in later chapters dealing with strings and dates. +So far, we've mostly created logical variables implicitly within `filter()` --- they are computed, used, and then throw away. +For example, the following filter finds all day time departures that leave roughly on time: ```{r} flights |> filter(dep_time > 600 & dep_time < 2000 & abs(arr_delay) < 20) ``` -But it's useful to know that this is a shortcut and you can explicitly create perform these operations inside a `mutate()` +But it's useful to know that this is a shortcut and you can explicitly create the underlying logical variables with `mutate()`: ```{r} flights |> @@ -41,9 +62,12 @@ flights |> ) ``` -So the filter above could also be written as: +This is useful because it allows you to name components, which can made the code easier to read, and it allows you to double-check the intermediate steps. +This is a particularly useful technique when you're doing more complicated Boolean algebra, as you'll learn about in the next section. -```{r} +So the initial filter could also be written as: + +```{r, results = FALSE} flights |> mutate( daytime = dep_time > 600 & dep_time < 2000, @@ -52,57 +76,123 @@ flights |> filter(daytime & approx_ontime) ``` -This is an important technique when you're are doing complicated subsetting because it allows you to double-check the intermediate steps. - ### Floating point comparison Beware when using `==` with numbers as results might surprise you! -You might think that the following two computations yield 1 and 2: +It looks like this vector contains the numbers 1 and 2: ```{r} -(1 / 49 * 49) -sqrt(2) ^ 2 +x <- c(1 / 49 * 49, sqrt(2) ^ 2) +x ``` -But if you test them for equality, you'll discover that they're not what you expect! +But if you test them for equality, you surprisingly get `FALSE`: ```{r} -(1 / 49 * 49) == 1 -(sqrt(2) ^ 2) == 2 +x == c(1, 2) ``` That's because computers use finite precision arithmetic (they obviously can't store an infinite number of digits!) so in most cases, the number number you see is an actually approximation. R usually rounds these numbers to avoid displaying a bunch of usually unimportant digits. -You can use the `digits` argument to `format()` to force R to display more: + +To see the details you can call `print()` with the the `digits`[^logicals-1] argument. +R normally calls print automatically for you (i.e. `x` is a shortcut for `print(x)`), but calling it explicitly is useful if you want to provide other arguments: + +[^logicals-1]: A floating point number can hold roughly 16 decimal digits; the precise number is surprisingly complicated and depends on the number. ```{r} -format(1 / 49 * 49, digits = 20) -format(sqrt(2) ^ 2, digits = 20) +print(x, digits = 16) ``` -Instead of relying on `==`, you can use `dplyr::near()`, which does the comparison with a small amount of tolerance: +Now that you've seen why `==` is failing, what can you do about it? +One option is to use `round()` to round to any number of digits, or instead of `==`, use `dplyr::near()`, which does the comparison with a small amount of tolerance: ```{r} -near(sqrt(2) ^ 2, 2) -near(1 / 49 * 49, 1) +near(x, c(1, 2)) ``` +### Missing values {#na-comparison} + +Missing values represent the unknown so they missing values are "contagious": almost any operation involving an unknown value will also be unknown: + +```{r} +NA > 5 +10 == NA +``` + +The most confusing result is this one: + +```{r} +NA == NA +``` + +It's easiest to understand why this is true with a bit more context: + +```{r} +# Let x be Mary's age. We don't know how old she is. +x <- NA + +# Let y be John's age. We don't know how old he is. +y <- NA + +# Are John and Mary the same age? +x == y +# We don't know! +``` + +So if you want to find all flights with `dep_time` is missing, the following code won't work because `dep_time == NA` will yield a `NA` for every single row, and `filter()` automatically drops missing values: + +```{r} +flights |> + filter(dep_time == NA) +``` + +Instead we'll need a new too: `is.na()`. + ### `is.na()` -Another common way to create logical vector is with `is.na()`. -This is particularly important in conjunction with `filter()` because filter only selects rows where the value is `TRUE`; rows where the value is `FALSE` are automatically dropped. +There's one other very useful way to create logical vectors: `is.na()`. +This takes any type of vector and returns `TRUE` is the value is `NA`, and `FALSE` otherwise: ```{r} -flights |> filter(is.na(dep_delay) | is.na(arr_delay)) -flights |> filter(is.na(dep_delay) != is.na(arr_delay)) +is.na(c(TRUE, NA, FALSE)) +is.na(c(1, NA, 3)) +is.na(c("a", NA, "b")) ``` +We can use `is.na()` to find all the rows with a missing `dep_time`: + +```{r} +flights |> + filter(is.na(dep_time)) +``` + +It can also be useful in `arrange()`, because by default, `arrange()` puts all the missing values at the end. +You can override this default by first sorting by `is.na()`: + +```{r} +flights |> + arrange(arr_delay) + +flights |> + arrange(desc(is.na(arr_delay)), arr_delay) +``` + +### Exercises + +1. How does `dplyr::near()` work? Read the source code to find out. +2. Use `mutate()`, `is.na()`, and `count()` together to describe how the missing values in `dep_time`, `sched_dep_time` and `dep_delay` are connected. + ## Boolean algebra -Once you have multiple logical vectors, you can combine them together using Boolean algebra: `&` is "and", `|` is "or", and `!` is "not". -`xor()` provides one final useful operation: exclusive or. +Once you have multiple logical vectors, you can combine them together using Boolean algebra. +In R, `&` is "and", `|` is "or", and `!` is "not", and `xor()` is exclusive or[^logicals-2]. Figure \@ref(fig:bool-ops) shows the complete set of Boolean operations and how they work. +[^logicals-2]: That is, `xor(x, y)` is true if x is true, or y is true, but not both. + This is how we usually use "or" In English. + Both is not usually an acceptable answer to the question "would you like ice cream or cake?". + ```{r bool-ops} #| echo: false #| out.width: NULL @@ -117,7 +207,7 @@ Figure \@ref(fig:bool-ops) shows the complete set of Boolean operations and how #| x but none of y, x is all of x none of y, xor(x, y) is everything #| except the intersection of x and y, y is all of y none of x, and #| x | y is everything. -knitr::include_graphics("diagrams/transform-logical.png") +knitr::include_graphics("diagrams/transform.png", dpi = 270) ``` As well as `&` and `|`, R also has `&&` and `||`. @@ -143,6 +233,11 @@ Since month is numeric, this is equivalent to `month == 1`, so `flights |> filte An easy way to avoid this issue is to use `%in%`. `x %in% y` returns a logical vector the same length as `x` that is `TRUE` whenever a value in `x` is anywhere in `y` . + +```{r} +letters[1:10] %in% c("a", "e", "i", "o", "u") +``` + So we could instead write: ```{r, eval = FALSE} @@ -150,17 +245,14 @@ flights |> filter(month %in% c(11, 12)) ``` -Sometimes you can simplify complicated subsetting by remembering De Morgan's law: `!(x & y)` is the same as `!x | !y`, and `!(x | y)` is the same as `!x & !y`. -For example, if you wanted to find flights that weren't delayed (on arrival or departure) by more than two hours, you could use either of the following two filters: +Note the `%in%` obeys different rules for `NA` to `==`. -```{r, eval = FALSE} +```{r} flights |> - filter(!(arr_delay > 120 | dep_delay > 120)) -flights |> - filter(arr_delay <= 120 & dep_delay <= 120) + filter(dep_time %in% c(NA, 0800)) ``` -### Missing values {#logical-missing} +### Missing values {#na-boolean} The rules for missing values in Boolean algebra are a little tricky to explain because they seem inconsistent at first glance: @@ -169,10 +261,14 @@ NA & c(TRUE, FALSE, NA) NA | c(TRUE, FALSE, NA) ``` - +To understand what's going on, think about `NA | TRUE`. +If a logical is `NA`, than means it could either be `TRUE` or `FALSE`. +`TRUE | TRUE` and `FALSE | TRUE` are both `TRUE`, so `NA | TRUE` must also be `TRUE`. +Similar reasoning applies with `NA & FALSE`. -To understand what's going on you need to think about `x | TRUE`, because regardless of whether `x` is `TRUE` or `FALSE` the result is still `TRUE`. -That means even if you don't know what `x` is (i.e. it's missing), the result must still be `TRUE`. +### Exercises + +1. Find all flights where `arr_delay` is missing but `dep_delay` is not. Find all flights where neither `arr_time` nor `sched_arr_time` are missing, but `arr_delay` is. ## Summaries @@ -199,7 +295,6 @@ not_cancelled |> group_by(year, month, day) |> summarise(prop_delayed = mean(arr_delay > 0)) |> arrange(desc(prop_delayed)) - ``` Or we could ask how many flights left before 5am, which usually are flights that were delayed from the previous day: @@ -211,6 +306,17 @@ not_cancelled |> arrange(desc(n_early)) ``` +You can also use logical vectors inside summaries: + +```{r} +not_cancelled |> + group_by(year, month, day) |> + summarise( + ahead = mean(arr_delay[arr_delay > 0]), + behind = mean(arr_delay[arr_delay < 0]), + ) +``` + ### Exercises 1. For each plane, count the number of flights before the first delay of greater than 1 hour. @@ -218,41 +324,11 @@ not_cancelled |> ## Transformations -### Cumulative functions - -Another useful pair of functions are cumulative any, `cumany()`, and cumulative all, `cumall()`. -`cumany()` will be `TRUE` after it encounters the first `TRUE`, and `cumall()` will be `FALSE` after it encounters its first `FALSE`. - -```{r} -cumany(c(FALSE, FALSE, TRUE, TRUE, FALSE, TRUE)) -cumall(c(TRUE, FALSE, TRUE, TRUE, FALSE, TRUE)) -``` - -These are particularly useful in conjunction with `filter()` because they allow you to select rows: - -- Before the first `FALSE` with `cumall(x)`. -- Before the first `TRUE` with `cumall(!x)`. -- After the first `TRUE` with `cumany(x)`. -- After the first `FALSE` with `cumany(!x)`. - -If you imagine some data about a bank balance, then these functions allow you t - -```{r} -df <- data.frame( - date = as.Date("2020-01-01") + 0:6, - balance = c(100, 50, 25, -25, -50, 30, 120) -) -# all rows after first overdraft -df |> filter(cumany(balance < 0)) -# all rows until first overdraft -df |> filter(cumall(!(balance < 0))) -``` - ### Conditional outputs -If you want to use one value when a condition is true and another value when it's `FALSE`, you can use `if_else()`[^logicals-1]. +If you want to use one value when a condition is true and another value when it's `FALSE`, you can use `if_else()`[^logicals-3]. -[^logicals-1]: This is equivalent to the base R function `ifelse`. +[^logicals-3]: This is equivalent to the base R function `ifelse`. There are two main advantages of `if_else()`over `ifelse()`: you can choose what should happen to missing values, and `if_else()` is much more likely to give you a meaningful error message if you use the wrong type of variable. ```{r} @@ -307,6 +383,36 @@ case_when( ) ``` -## +### Cumulative functions + +Another useful pair of functions are cumulative any, `cumany()`, and cumulative all, `cumall()`. +`cumany()` will be `TRUE` after it encounters the first `TRUE`, and `cumall()` will be `FALSE` after it encounters its first `FALSE`. + +```{r} +cumany(c(FALSE, FALSE, TRUE, TRUE, FALSE, TRUE)) +cumall(c(TRUE, FALSE, TRUE, TRUE, FALSE, TRUE)) +``` + +These are particularly useful in conjunction with `filter()` because they allow you to select rows: + +- Before the first `FALSE` with `cumall(x)`. +- Before the first `TRUE` with `cumall(!x)`. +- After the first `TRUE` with `cumany(x)`. +- After the first `FALSE` with `cumany(!x)`. + +If you imagine some data about a bank balance, then these functions allow you t + +```{r} +df <- data.frame( + date = as.Date("2020-01-01") + 0:6, + balance = c(100, 50, 25, -25, -50, 30, 120) +) +# all rows after first overdraft +df |> filter(cumany(balance < 0)) +# all rows until first overdraft +df |> filter(cumall(!(balance < 0))) +``` + +### ## diff --git a/missing-values.Rmd b/missing-values.Rmd index f03066a..a74fc95 100644 --- a/missing-values.Rmd +++ b/missing-values.Rmd @@ -22,36 +22,6 @@ Missing topics: ### Missing values {#missing-values-filter} -One important feature of R that can make comparison tricky is missing values, or `NA`s ("not availables"). -`NA` represents an unknown value so missing values are "contagious": almost any operation involving an unknown value will also be unknown. - -```{r} -NA > 5 -10 == NA -NA + 10 -NA / 2 -``` - -The most confusing result is this one: - -```{r} -NA == NA -``` - -It's easiest to understand why this is true with a bit more context: - -```{r} -# Let x be Mary's age. We don't know how old she is. -x <- NA - -# Let y be John's age. We don't know how old he is. -y <- NA - -# Are John and Mary the same age? -x == y -# We don't know! -``` - If you want to determine if a value is missing, use `is.na()`: ```{r}