More work on O'Reilly book
* Make width narrower * Convert deps to table * Strip chapter status
This commit is contained in:
Hadley Wickham
@@ -1,13 +1,5 @@
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<section data-type="chapter" id="chp-joins">
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<h1><span id="sec-joins" class="quarto-section-identifier d-none d-lg-block"><span class="chapter-title">Joins</span></span></h1><div data-type="note"><div class="callout-body d-flex">
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<div class="callout-icon-container">
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<i class="callout-icon"/>
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</div>
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</div>
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<p>You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at <a href="https://r4ds.had.co.nz" class="uri">https://r4ds.had.co.nz</a>.</p></div>
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<h1><span id="sec-joins" class="quarto-section-identifier d-none d-lg-block"><span class="chapter-title">Joins</span></span></h1><p>::: status callout-note You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at <a href="https://r4ds.had.co.nz" class="uri">https://r4ds.had.co.nz</a>. :::</p>
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<section id="introduction" data-type="sect1">
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<h1>
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Introduction</h1>
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@@ -57,14 +49,14 @@ Primary and foreign keys</h2>
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<div class="cell">
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<pre data-type="programlisting" data-code-language="downlit">airports
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#> # A tibble: 1,458 × 8
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#> faa name lat lon alt tz dst tzone
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#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
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#> 1 04G Lansdowne Airport 41.1 -80.6 1044 -5 A America/Ne…
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#> 2 06A Moton Field Municipal Airport 32.5 -85.7 264 -6 A America/Ch…
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#> 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/Ch…
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#> 4 06N Randall Airport 41.4 -74.4 523 -5 A America/Ne…
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#> 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America/Ne…
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#> 6 0A9 Elizabethton Municipal Airport 36.4 -82.2 1593 -5 A America/Ne…
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#> faa name lat lon alt tz dst tzone
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#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
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#> 1 04G Lansdowne Airport 41.1 -80.6 1044 -5 A America…
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#> 2 06A Moton Field Municipal Airport 32.5 -85.7 264 -6 A America…
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#> 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America…
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#> 4 06N Randall Airport 41.4 -74.4 523 -5 A America…
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#> 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America…
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#> 6 0A9 Elizabethton Municipal Airport 36.4 -82.2 1593 -5 A America…
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#> # … with 1,452 more rows</pre>
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</div>
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</li>
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@@ -73,14 +65,14 @@ Primary and foreign keys</h2>
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<div class="cell">
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<pre data-type="programlisting" data-code-language="downlit">planes
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#> # A tibble: 3,322 × 9
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#> tailnum year type manuf…¹ model engines seats speed engine
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#> <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
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#> 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-… 2 55 NA Turbo…
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#> 2 N102UW 1998 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
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#> 3 N103US 1999 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
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#> 4 N104UW 1999 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
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#> 5 N10575 2002 Fixed wing multi engine EMBRAER EMB-… 2 55 NA Turbo…
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#> 6 N105UW 1999 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
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#> tailnum year type manuf…¹ model engines seats speed engine
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#> <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
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#> 1 N10156 2004 Fixed wing multi en… EMBRAER EMB-… 2 55 NA Turbo…
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#> 2 N102UW 1998 Fixed wing multi en… AIRBUS… A320… 2 182 NA Turbo…
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#> 3 N103US 1999 Fixed wing multi en… AIRBUS… A320… 2 182 NA Turbo…
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#> 4 N104UW 1999 Fixed wing multi en… AIRBUS… A320… 2 182 NA Turbo…
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#> 5 N10575 2002 Fixed wing multi en… EMBRAER EMB-… 2 55 NA Turbo…
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#> 6 N105UW 1999 Fixed wing multi en… AIRBUS… A320… 2 182 NA Turbo…
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#> # … with 3,316 more rows, and abbreviated variable name ¹manufacturer</pre>
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</div>
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</li>
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@@ -89,16 +81,17 @@ Primary and foreign keys</h2>
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<div class="cell">
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<pre data-type="programlisting" data-code-language="downlit">weather
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#> # A tibble: 26,115 × 15
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#> origin year month day hour temp dewp humid wind_dir wind_speed wind_gust
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#> <chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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#> 1 EWR 2013 1 1 1 39.0 26.1 59.4 270 10.4 NA
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#> 2 EWR 2013 1 1 2 39.0 27.0 61.6 250 8.06 NA
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#> 3 EWR 2013 1 1 3 39.0 28.0 64.4 240 11.5 NA
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#> 4 EWR 2013 1 1 4 39.9 28.0 62.2 250 12.7 NA
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#> 5 EWR 2013 1 1 5 39.0 28.0 64.4 260 12.7 NA
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#> 6 EWR 2013 1 1 6 37.9 28.0 67.2 240 11.5 NA
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#> # … with 26,109 more rows, and 4 more variables: precip <dbl>, pressure <dbl>,
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#> # visib <dbl>, time_hour <dttm></pre>
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#> origin year month day hour temp dewp humid wind_dir wind_sp…¹ wind_…²
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#> <chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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#> 1 EWR 2013 1 1 1 39.0 26.1 59.4 270 10.4 NA
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#> 2 EWR 2013 1 1 2 39.0 27.0 61.6 250 8.06 NA
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#> 3 EWR 2013 1 1 3 39.0 28.0 64.4 240 11.5 NA
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#> 4 EWR 2013 1 1 4 39.9 28.0 62.2 250 12.7 NA
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#> 5 EWR 2013 1 1 5 39.0 28.0 64.4 260 12.7 NA
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#> 6 EWR 2013 1 1 6 37.9 28.0 67.2 240 11.5 NA
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#> # … with 26,109 more rows, 4 more variables: precip <dbl>, pressure <dbl>,
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#> # visib <dbl>, time_hour <dttm>, and abbreviated variable names
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#> # ¹wind_speed, ²wind_gust</pre>
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</div>
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</li>
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</ul><p>A <strong>foreign key</strong> is a variable (or set of variables) that corresponds to a primary key in another table. For example:</p>
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@@ -147,8 +140,8 @@ weather |>
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filter(is.na(tailnum))
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#> # A tibble: 0 × 9
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#> # … with 9 variables: tailnum <chr>, year <int>, type <chr>,
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#> # manufacturer <chr>, model <chr>, engines <int>, seats <int>, speed <int>,
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#> # engine <chr>
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#> # manufacturer <chr>, model <chr>, engines <int>, seats <int>,
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#> # speed <int>, engine <chr>
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weather |>
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filter(is.na(time_hour) | is.na(origin))
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@@ -189,18 +182,19 @@ Surrogate keys</h2>
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mutate(id = row_number(), .before = 1)
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flights2
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#> # A tibble: 336,776 × 20
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#> id year month day dep_time sched_dep_t…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵
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#> <int> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl>
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#> 1 1 2013 1 1 517 515 2 830 819 11
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#> 2 2 2013 1 1 533 529 4 850 830 20
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#> 3 3 2013 1 1 542 540 2 923 850 33
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#> 4 4 2013 1 1 544 545 -1 1004 1022 -18
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#> 5 5 2013 1 1 554 600 -6 812 837 -25
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#> 6 6 2013 1 1 554 558 -4 740 728 12
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#> id year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵
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#> <int> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl>
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#> 1 1 2013 1 1 517 515 2 830 819 11
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#> 2 2 2013 1 1 533 529 4 850 830 20
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#> 3 3 2013 1 1 542 540 2 923 850 33
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#> 4 4 2013 1 1 544 545 -1 1004 1022 -18
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#> 5 5 2013 1 1 554 600 -6 812 837 -25
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#> 6 6 2013 1 1 554 558 -4 740 728 12
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#> # … with 336,770 more rows, 10 more variables: carrier <chr>, flight <int>,
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#> # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
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#> # hour <dbl>, minute <dbl>, time_hour <dttm>, and abbreviated variable names
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#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
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#> # hour <dbl>, minute <dbl>, time_hour <dttm>, and abbreviated variable
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#> # names ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time,
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#> # ⁵arr_delay</pre>
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</div>
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<p>Surrogate keys can be particular useful when communicating to other humans: it’s much easier to tell someone to take a look at flight 2001 than to say look at UA430 which departed 9am 2013-01-03.</p>
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</section>
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@@ -247,14 +241,14 @@ flights2
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left_join(airlines)
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#> Joining with `by = join_by(carrier)`
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#> # A tibble: 336,776 × 7
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#> year time_hour origin dest tailnum carrier name
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA United Air Lines Inc.
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA United Air Lines Inc.
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA American Airlines Inc.
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 JetBlue Airways
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL Delta Air Lines Inc.
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA United Air Lines Inc.
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#> year time_hour origin dest tailnum carrier name
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA United Air Lines In…
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA United Air Lines In…
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA American Airlines I…
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 JetBlue Airways
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL Delta Air Lines Inc.
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA United Air Lines In…
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#> # … with 336,770 more rows</pre>
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</div>
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<p>Or we could find out the temperature and wind speed when each plane departed:</p>
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@@ -279,14 +273,14 @@ flights2
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left_join(planes |> select(tailnum, type, engines, seats))
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#> Joining with `by = join_by(tailnum)`
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#> # A tibble: 336,776 × 9
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#> year time_hour origin dest tailnum carrier type engines seats
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <int> <int>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA Fixed wi… 2 149
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA Fixed wi… 2 149
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA Fixed wi… 2 178
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 Fixed wi… 2 200
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL Fixed wi… 2 178
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA Fixed wi… 2 191
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#> year time_hour origin dest tailnum carrier type engines seats
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <int> <int>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA Fixed… 2 149
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA Fixed… 2 149
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA Fixed… 2 178
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 Fixed… 2 200
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL Fixed… 2 178
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA Fixed… 2 191
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#> # … with 336,770 more rows</pre>
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</div>
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<p>When <code><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html">left_join()</a></code> fails to find a match for a row in <code>x</code>, it fills in the new variables with missing values. For example, there’s no information about the plane with tail number <code>N3ALAA</code> so the <code>type</code>, <code>engines</code>, and <code>seats</code> will be missing:</p>
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@@ -318,14 +312,14 @@ Specifying join keys</h2>
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left_join(planes)
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#> Joining with `by = join_by(year, tailnum)`
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#> # A tibble: 336,776 × 13
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#> year time_hour origin dest tailnum carrier type manufactu…¹ model
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA <NA> <NA> <NA>
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA <NA> <NA> <NA>
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA <NA> <NA> <NA>
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 <NA> <NA> <NA>
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL <NA> <NA> <NA>
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA <NA> <NA> <NA>
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#> year time_hour origin dest tailnum carrier type manufa…¹ model
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA <NA> <NA> <NA>
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA <NA> <NA> <NA>
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA <NA> <NA> <NA>
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 <NA> <NA> <NA>
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL <NA> <NA> <NA>
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA <NA> <NA> <NA>
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#> # … with 336,770 more rows, 4 more variables: engines <int>, seats <int>,
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#> # speed <int>, engine <chr>, and abbreviated variable name ¹manufacturer</pre>
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</div>
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@@ -334,17 +328,16 @@ Specifying join keys</h2>
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<pre data-type="programlisting" data-code-language="downlit">flights2 |>
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left_join(planes, join_by(tailnum))
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#> # A tibble: 336,776 × 14
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#> year.x time_hour origin dest tailnum carrier year.y type manuf…¹
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#> <int> <dttm> <chr> <chr> <chr> <chr> <int> <chr> <chr>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA 1999 Fixed … BOEING
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA 1998 Fixed … BOEING
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA 1990 Fixed … BOEING
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 2012 Fixed … AIRBUS
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL 1991 Fixed … BOEING
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA 2012 Fixed … BOEING
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#> # … with 336,770 more rows, 5 more variables: model <chr>, engines <int>,
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#> # seats <int>, speed <int>, engine <chr>, and abbreviated variable name
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#> # ¹manufacturer</pre>
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#> year.x time_hour origin dest tailnum carrier year.y type
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#> <int> <dttm> <chr> <chr> <chr> <chr> <int> <chr>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA 1999 Fixed wing …
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA 1998 Fixed wing …
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA 1990 Fixed wing …
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 2012 Fixed wing …
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL 1991 Fixed wing …
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA 2012 Fixed wing …
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#> # … with 336,770 more rows, and 6 more variables: manufacturer <chr>,
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#> # model <chr>, engines <int>, seats <int>, speed <int>, engine <chr></pre>
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</div>
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<p>Note that the <code>year</code> variables are disambiguated in the output with a suffix (<code>year.x</code> and <code>year.y</code>), which tells you whether the variable came from the <code>x</code> or <code>y</code> argument. You can override the default suffixes with the <code>suffix</code> argument.</p>
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<p><code>join_by(tailnum)</code> is short for <code>join_by(tailnum == tailnum)</code>. It’s important to know about this fuller form for two reasons. Firstly, it describes the relationship between the two tables: the keys must be equal. That’s why this type of join is often called an <strong>equi-join</strong>. You’ll learn about non-equi-joins in <a href="#sec-non-equi-joins" data-type="xref">#sec-non-equi-joins</a>.</p>
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@@ -353,30 +346,30 @@ Specifying join keys</h2>
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<pre data-type="programlisting" data-code-language="downlit">flights2 |>
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left_join(airports, join_by(dest == faa))
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#> # A tibble: 336,776 × 13
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#> year time_hour origin dest tailnum carrier name lat lon alt
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA Geor… 30.0 -95.3 97
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA Geor… 30.0 -95.3 97
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA Miam… 25.8 -80.3 8
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 <NA> NA NA NA
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL Hart… 33.6 -84.4 1026
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA Chic… 42.0 -87.9 668
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#> # … with 336,770 more rows, and 3 more variables: tz <dbl>, dst <chr>,
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#> # tzone <chr>
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#> year time_hour origin dest tailnum carrier name lat lon
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
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#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA George … 30.0 -95.3
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#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA George … 30.0 -95.3
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#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA Miami I… 25.8 -80.3
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#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 <NA> NA NA
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#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL Hartsfi… 33.6 -84.4
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#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA Chicago… 42.0 -87.9
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#> # … with 336,770 more rows, and 4 more variables: alt <dbl>, tz <dbl>,
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#> # dst <chr>, tzone <chr>
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flights2 |>
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left_join(airports, join_by(origin == faa))
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#> # A tibble: 336,776 × 13
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#> year time_hour origin dest tailnum carrier name lat lon alt
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#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
|
||||
#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA Newa… 40.7 -74.2 18
|
||||
#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA La G… 40.8 -73.9 22
|
||||
#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA John… 40.6 -73.8 13
|
||||
#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 John… 40.6 -73.8 13
|
||||
#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL La G… 40.8 -73.9 22
|
||||
#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA Newa… 40.7 -74.2 18
|
||||
#> # … with 336,770 more rows, and 3 more variables: tz <dbl>, dst <chr>,
|
||||
#> # tzone <chr></pre>
|
||||
#> year time_hour origin dest tailnum carrier name lat lon
|
||||
#> <int> <dttm> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
|
||||
#> 1 2013 2013-01-01 05:00:00 EWR IAH N14228 UA Newark … 40.7 -74.2
|
||||
#> 2 2013 2013-01-01 05:00:00 LGA IAH N24211 UA La Guar… 40.8 -73.9
|
||||
#> 3 2013 2013-01-01 05:00:00 JFK MIA N619AA AA John F … 40.6 -73.8
|
||||
#> 4 2013 2013-01-01 05:00:00 JFK BQN N804JB B6 John F … 40.6 -73.8
|
||||
#> 5 2013 2013-01-01 06:00:00 LGA ATL N668DN DL La Guar… 40.8 -73.9
|
||||
#> 6 2013 2013-01-01 05:00:00 EWR ORD N39463 UA Newark … 40.7 -74.2
|
||||
#> # … with 336,770 more rows, and 4 more variables: alt <dbl>, tz <dbl>,
|
||||
#> # dst <chr>, tzone <chr></pre>
|
||||
</div>
|
||||
<p>In older code you might see a different way of specifying the join keys, using a character vector:</p>
|
||||
<ul><li>
|
||||
@@ -405,14 +398,14 @@ Filtering joins</h2>
|
||||
<pre data-type="programlisting" data-code-language="downlit">airports |>
|
||||
semi_join(flights2, join_by(faa == dest))
|
||||
#> # A tibble: 101 × 8
|
||||
#> faa name lat lon alt tz dst tzone
|
||||
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
|
||||
#> 1 ABQ Albuquerque International Sunport 35.0 -107. 5355 -7 A Americ…
|
||||
#> 2 ACK Nantucket Mem 41.3 -70.1 48 -5 A Americ…
|
||||
#> 3 ALB Albany Intl 42.7 -73.8 285 -5 A Americ…
|
||||
#> 4 ANC Ted Stevens Anchorage Intl 61.2 -150. 152 -9 A Americ…
|
||||
#> 5 ATL Hartsfield Jackson Atlanta Intl 33.6 -84.4 1026 -5 A Americ…
|
||||
#> 6 AUS Austin Bergstrom Intl 30.2 -97.7 542 -6 A Americ…
|
||||
#> faa name lat lon alt tz dst tzone
|
||||
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
|
||||
#> 1 ABQ Albuquerque International Sunpo… 35.0 -107. 5355 -7 A Amer…
|
||||
#> 2 ACK Nantucket Mem 41.3 -70.1 48 -5 A Amer…
|
||||
#> 3 ALB Albany Intl 42.7 -73.8 285 -5 A Amer…
|
||||
#> 4 ANC Ted Stevens Anchorage Intl 61.2 -150. 152 -9 A Amer…
|
||||
#> 5 ATL Hartsfield Jackson Atlanta Intl 33.6 -84.4 1026 -5 A Amer…
|
||||
#> 6 AUS Austin Bergstrom Intl 30.2 -97.7 542 -6 A Amer…
|
||||
#> # … with 95 more rows</pre>
|
||||
</div>
|
||||
<p><strong>Anti-joins</strong> are the opposite: they return all rows in <code>x</code> that don’t have a match in <code>y</code>. They’re useful for finding missing values that are <strong>implicit</strong> in the data, the topic of <a href="#sec-missing-implicit" data-type="xref">#sec-missing-implicit</a>. Implicitly missing values don’t show up as <code>NA</code>s but instead only exist as an absence. For example, we can find rows that as missing from <code>airports</code> by looking for flights that don’t have a matching destination airport:</p>
|
||||
@@ -664,14 +657,14 @@ Allow multiple rows</h2>
|
||||
|
||||
plane_flights
|
||||
#> # A tibble: 284,170 × 9
|
||||
#> tailnum type engines seats year time_hour origin dest carrier
|
||||
#> <chr> <chr> <int> <int> <int> <dttm> <chr> <chr> <chr>
|
||||
#> 1 N10156 Fixed wi… 2 55 2013 2013-01-10 06:00:00 EWR PIT EV
|
||||
#> 2 N10156 Fixed wi… 2 55 2013 2013-01-10 10:00:00 EWR CHS EV
|
||||
#> 3 N10156 Fixed wi… 2 55 2013 2013-01-10 15:00:00 EWR MSP EV
|
||||
#> 4 N10156 Fixed wi… 2 55 2013 2013-01-11 06:00:00 EWR CMH EV
|
||||
#> 5 N10156 Fixed wi… 2 55 2013 2013-01-11 11:00:00 EWR MCI EV
|
||||
#> 6 N10156 Fixed wi… 2 55 2013 2013-01-11 18:00:00 EWR PWM EV
|
||||
#> tailnum type engines seats year time_hour origin dest carrier
|
||||
#> <chr> <chr> <int> <int> <int> <dttm> <chr> <chr> <chr>
|
||||
#> 1 N10156 Fixed… 2 55 2013 2013-01-10 06:00:00 EWR PIT EV
|
||||
#> 2 N10156 Fixed… 2 55 2013 2013-01-10 10:00:00 EWR CHS EV
|
||||
#> 3 N10156 Fixed… 2 55 2013 2013-01-10 15:00:00 EWR MSP EV
|
||||
#> 4 N10156 Fixed… 2 55 2013 2013-01-11 06:00:00 EWR CMH EV
|
||||
#> 5 N10156 Fixed… 2 55 2013 2013-01-11 11:00:00 EWR MCI EV
|
||||
#> 6 N10156 Fixed… 2 55 2013 2013-01-11 18:00:00 EWR PWM EV
|
||||
#> # … with 284,164 more rows</pre>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
Reference in New Issue
Block a user