Re-render book for O'Reilly
This commit is contained in:
Hadley Wickham
@@ -4,7 +4,7 @@
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<h1>
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Introduction</h1>
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<p>Visualisation is an important tool for generating insight, but it’s rare that you get the data in exactly the right form you need for it. Often you’ll need to create some new variables or summaries to see the most important patterns, or maybe you just want to rename the variables or reorder the observations to make the data a little easier to work with. You’ll learn how to do all that (and more!) in this chapter, which will introduce you to data transformation using the <strong>dplyr</strong> package and a new dataset on flights that departed New York City in 2013.</p>
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<p>The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. We’ll come back these functions in more detail in later chapters, as we start to dig into specific types of data (e.g. numbers, strings, dates).</p>
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<p>The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. We’ll start with functions that operate on rows and then columns of a data frame. We will then introduce the ability to work with groups. We will end the chapter with a case study that showcases these functions in action and we’ll come back to the functions in more detail in later chapters, as we start to dig into specific types of data (e.g. numbers, strings, dates).</p>
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<section id="prerequisites" data-type="sect2">
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<h2>
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@@ -13,14 +13,16 @@ Prerequisites</h2>
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<div class="cell">
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<pre data-type="programlisting" data-code-language="r">library(nycflights13)
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library(tidyverse)
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#> ── Attaching packages ──────────────────────────────────── tidyverse 1.3.2 ──
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#> ✔ ggplot2 3.4.0.9000 ✔ purrr 0.9000.0.9000
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#> ✔ tibble 3.1.8 ✔ dplyr 1.0.99.9000
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#> ✔ tidyr 1.2.1.9001 ✔ stringr 1.4.1.9000
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#> ✔ readr 2.1.3 ✔ forcats 0.5.2
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#> ── Attaching core tidyverse packages ──────────────── tidyverse 1.3.2.9000 ──
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#> ✔ dplyr 1.0.99.9000 ✔ readr 2.1.3
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#> ✔ forcats 0.5.2.9000 ✔ stringr 1.5.0.9000
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#> ✔ ggplot2 3.4.0.9000 ✔ tibble 3.1.8
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#> ✔ lubridate 1.9.0 ✔ tidyr 1.2.1.9001
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#> ✔ purrr 1.0.1
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#> ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
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#> ✖ dplyr::filter() masks stats::filter()
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#> ✖ dplyr::lag() masks stats::lag()</pre>
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#> ✖ dplyr::lag() masks stats::lag()
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#> ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors</pre>
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</div>
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<p>Take careful note of the conflicts message that’s printed when you load the tidyverse. It tells you that dplyr overwrites some functions in base R. If you want to use the base version of these functions after loading dplyr, you’ll need to use their full names: <code><a href="https://rdrr.io/r/stats/filter.html">stats::filter()</a></code> and <code><a href="https://rdrr.io/r/stats/lag.html">stats::lag()</a></code>. So far we’ve mostly ignored which package a function comes from because most of the time it doesn’t matter. However, knowing the package can help you find help and find related functions, so when we need to be precise about which function a package comes from, we’ll use the same syntax as R: <code>packagename::functionname()</code>.</p>
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</section>
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@@ -32,21 +34,45 @@ nycflights13</h2>
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<div class="cell">
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<pre data-type="programlisting" data-code-language="r">flights
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#> # A tibble: 336,776 × 19
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#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
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#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
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#> 1 2013 1 1 517 515 2 830 819 11 UA
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#> 2 2013 1 1 533 529 4 850 830 20 UA
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#> 3 2013 1 1 542 540 2 923 850 33 AA
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#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
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#> 5 2013 1 1 554 600 -6 812 837 -25 DL
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#> 6 2013 1 1 554 558 -4 740 728 12 UA
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#> # … with 336,770 more rows, 9 more variables: flight <int>, tailnum <chr>,
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#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
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#> # 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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#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
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#> <int> <int> <int> <int> <int> <dbl> <int> <int>
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#> 1 2013 1 1 517 515 2 830 819
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#> 2 2013 1 1 533 529 4 850 830
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#> 3 2013 1 1 542 540 2 923 850
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#> 4 2013 1 1 544 545 -1 1004 1022
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#> 5 2013 1 1 554 600 -6 812 837
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#> 6 2013 1 1 554 558 -4 740 728
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#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
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#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
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#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
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#> # time_hour <dttm></pre>
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</div>
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<p>If you’ve used R before, you might notice that this data frame prints a little differently to other data frames you’ve seen. That’s because it’s a <strong>tibble</strong>, a special type of data frame used by the tidyverse to avoid some common gotchas. The most important difference is the way it prints: tibbles are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen. To see everything you can use <code>print(flights, width = Inf)</code> to show everything in the console, but it’s generally more convenient to instead use <code>View(flights)</code> to open the dataset in the scrollable RStudio viewer.</p>
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<p>You might have noticed the short abbreviations that follow each column name. These tell you the type of each variable: <code><int></code> is short for integer, <code><dbl></code> is short for double (aka real numbers), <code><chr></code> for character (aka strings), and <code><dttm></code> for date-time. These are important because the operations you can perform on a column depend so much on its “type”, and these types are used to organize the chapters in the next section of the book.</p>
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<p>If you’ve used R before, you might notice that this data frame prints a little differently to other data frames you’ve seen. That’s because it’s a <strong>tibble</strong>, a special type of data frame used by the tidyverse to avoid some common gotchas. The most important difference is the way it prints: tibbles are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen. There are a few options to see everything. If you’re using RStudio, the most convenient is probably <code>View(flights)</code>, which will open an interactive scrollable and filterable view. Otherwise you can use <code>print(flights, width = Inf)</code> to show all columns, or use call <code><a href="https://pillar.r-lib.org/reference/glimpse.html">glimpse()</a></code>:</p>
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<div class="cell">
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<pre data-type="programlisting" data-code-language="r">glimpse(flights)
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#> Rows: 336,776
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#> Columns: 19
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#> $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013…
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#> $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
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#> $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
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#> $ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 55…
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#> $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 60…
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#> $ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2,…
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#> $ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 8…
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#> $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 8…
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#> $ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7,…
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#> $ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6"…
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#> $ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301…
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#> $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N…
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#> $ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LG…
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#> $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IA…
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#> $ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149…
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#> $ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 73…
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#> $ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6…
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#> $ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59…
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#> $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-0…</pre>
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</div>
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<p>In both views, the variables names are followed by abbreviations that tell you the type of each variable: <code><int></code> is short for integer, <code><dbl></code> is short for double (aka real numbers), <code><chr></code> for character (aka strings), and <code><dttm></code> for date-time. These are important because the operations you can perform on a column depend so much on its “type”, and these types are used to organize the chapters in the next section of the book.</p>
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</section>
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<section id="dplyr-basics" data-type="sect2">
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@@ -66,14 +92,14 @@ dplyr basics</h2>
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)</pre>
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</div>
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<p>The code starts with the <code>flights</code> dataset, then filters it, then groups it, then summarizes it. We’ll come back to the pipe and its alternatives in <a href="#sec-pipes" data-type="xref">#sec-pipes</a>.</p>
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<p>dplyr’s verbs are organised into four groups based on what they operate on: <strong>rows</strong>, <strong>columns</strong>, <strong>groups</strong>, or <strong>tables</strong>. In the following sections you’ll learn the most important verbs for rows, columns, and groups, then we’ll come back to verb that work on tables in <a href="#chp-joins" data-type="xref">#chp-joins</a>. Let’s dive in!</p>
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<p>dplyr’s verbs are organised into four groups based on what they operate on: <strong>rows</strong>, <strong>columns</strong>, <strong>groups</strong>, or <strong>tables</strong>. In the following sections you’ll learn the most important verbs for rows, columns, and groups, then we’ll come back to verbs that work on tables in <a href="#chp-joins" data-type="xref">#chp-joins</a>. Let’s dive in!</p>
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</section>
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</section>
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<section id="rows" data-type="sect1">
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<h1>
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Rows</h1>
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<p>The most important verbs that operate on rows are <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code>, which changes which rows are present without changing their order, and <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code>, which changes the order of the rows without changing which are present. Both functions only affect the rows, and the columns are left unchanged.</p>
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<p>The most important verbs that operate on rows are <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code>, which changes which rows are present without changing their order, and <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code>, which changes the order of the rows without changing which are present. Both functions only affect the rows, and the columns are left unchanged. We’ll also discuss <code><a href="https://dplyr.tidyverse.org/reference/distinct.html">distinct()</a></code> which finds rows with unique values but unlike <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> it can also optionally modify the columns.</p>
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<section id="filter" data-type="sect2">
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<h2>
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@@ -84,18 +110,18 @@ Rows</h1>
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<pre data-type="programlisting" data-code-language="r">flights |>
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filter(arr_delay > 120)
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#> # A tibble: 10,034 × 19
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#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
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#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
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#> 1 2013 1 1 811 630 101 1047 830 137 MQ
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#> 2 2013 1 1 848 1835 853 1001 1950 851 MQ
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#> 3 2013 1 1 957 733 144 1056 853 123 UA
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#> 4 2013 1 1 1114 900 134 1447 1222 145 UA
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#> 5 2013 1 1 1505 1310 115 1638 1431 127 EV
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#> 6 2013 1 1 1525 1340 105 1831 1626 125 B6
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#> # … with 10,028 more rows, 9 more variables: flight <int>, tailnum <chr>,
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#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
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#> # 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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#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
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#> <int> <int> <int> <int> <int> <dbl> <int> <int>
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#> 1 2013 1 1 811 630 101 1047 830
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#> 2 2013 1 1 848 1835 853 1001 1950
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#> 3 2013 1 1 957 733 144 1056 853
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#> 4 2013 1 1 1114 900 134 1447 1222
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#> 5 2013 1 1 1505 1310 115 1638 1431
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#> 6 2013 1 1 1525 1340 105 1831 1626
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#> # … with 10,028 more rows, and 11 more variables: arr_delay <dbl>,
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#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
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#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
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#> # time_hour <dttm></pre>
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</div>
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<p>As well as <code>></code> (greater than), you can use <code>>=</code> (greater than or equal to), <code><</code> (less than), <code><=</code> (less than or equal to), <code>==</code> (equal to), and <code>!=</code> (not equal to). You can also use <code>&</code> (and) or <code>|</code> (or) to combine multiple conditions:</p>
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<div class="cell">
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@@ -103,35 +129,35 @@ Rows</h1>
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flights |>
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filter(month == 1 & day == 1)
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#> # A tibble: 842 × 19
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#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
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#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
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#> 1 2013 1 1 517 515 2 830 819 11 UA
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#> 2 2013 1 1 533 529 4 850 830 20 UA
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#> 3 2013 1 1 542 540 2 923 850 33 AA
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#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
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#> 5 2013 1 1 554 600 -6 812 837 -25 DL
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#> 6 2013 1 1 554 558 -4 740 728 12 UA
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#> # … with 836 more rows, 9 more variables: flight <int>, tailnum <chr>,
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#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
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#> # 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
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#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
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#> <int> <int> <int> <int> <int> <dbl> <int> <int>
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#> 1 2013 1 1 517 515 2 830 819
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#> 2 2013 1 1 533 529 4 850 830
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#> 3 2013 1 1 542 540 2 923 850
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#> 4 2013 1 1 544 545 -1 1004 1022
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#> 5 2013 1 1 554 600 -6 812 837
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#> 6 2013 1 1 554 558 -4 740 728
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#> # … with 836 more rows, and 11 more variables: arr_delay <dbl>,
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#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
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#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
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#> # time_hour <dttm>
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# Flights that departed in January or February
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flights |>
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filter(month == 1 | month == 2)
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#> # A tibble: 51,955 × 19
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#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
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#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
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#> 1 2013 1 1 517 515 2 830 819 11 UA
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#> 2 2013 1 1 533 529 4 850 830 20 UA
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#> 3 2013 1 1 542 540 2 923 850 33 AA
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#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
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#> 5 2013 1 1 554 600 -6 812 837 -25 DL
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#> 6 2013 1 1 554 558 -4 740 728 12 UA
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#> # … with 51,949 more rows, 9 more variables: flight <int>, tailnum <chr>,
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#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
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#> # 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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#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
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#> <int> <int> <int> <int> <int> <dbl> <int> <int>
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#> 1 2013 1 1 517 515 2 830 819
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#> 2 2013 1 1 533 529 4 850 830
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#> 3 2013 1 1 542 540 2 923 850
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#> 4 2013 1 1 544 545 -1 1004 1022
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#> 5 2013 1 1 554 600 -6 812 837
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#> 6 2013 1 1 554 558 -4 740 728
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#> # … with 51,949 more rows, and 11 more variables: arr_delay <dbl>,
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#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
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#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
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#> # time_hour <dttm></pre>
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</div>
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<p>There’s a useful shortcut when you’re combining <code>|</code> and <code>==</code>: <code>%in%</code>. It keeps rows where the variable equals one of the values on the right:</p>
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<div class="cell">
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@@ -139,18 +165,18 @@ flights |>
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flights |>
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filter(month %in% c(1, 2))
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#> # A tibble: 51,955 × 19
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#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
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#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
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#> 1 2013 1 1 517 515 2 830 819 11 UA
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#> 2 2013 1 1 533 529 4 850 830 20 UA
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#> 3 2013 1 1 542 540 2 923 850 33 AA
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#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
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#> 5 2013 1 1 554 600 -6 812 837 -25 DL
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#> 6 2013 1 1 554 558 -4 740 728 12 UA
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#> # … with 51,949 more rows, 9 more variables: flight <int>, tailnum <chr>,
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#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
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#> # 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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#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
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#> <int> <int> <int> <int> <int> <dbl> <int> <int>
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#> 1 2013 1 1 517 515 2 830 819
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#> 2 2013 1 1 533 529 4 850 830
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#> 3 2013 1 1 542 540 2 923 850
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#> 4 2013 1 1 544 545 -1 1004 1022
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#> 5 2013 1 1 554 600 -6 812 837
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#> 6 2013 1 1 554 558 -4 740 728
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#> # … with 51,949 more rows, and 11 more variables: arr_delay <dbl>,
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#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
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#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
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#> # time_hour <dttm></pre>
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</div>
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<p>We’ll come back to these comparisons and logical operators in more detail in <a href="#chp-logicals" data-type="xref">#chp-logicals</a>.</p>
|
||||
<p>When you run <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> dplyr executes the filtering operation, creating a new data frame, and then prints it. It doesn’t modify the existing <code>flights</code> dataset because dplyr functions never modify their inputs. To save the result, you need to use the assignment operator, <code><-</code>:</p>
|
||||
@@ -189,36 +215,36 @@ Common mistakes</h2>
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
arrange(year, month, day, dep_time)
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 1 1 517 515 2 830 819 11 UA
|
||||
#> 2 2013 1 1 533 529 4 850 830 20 UA
|
||||
#> 3 2013 1 1 542 540 2 923 850 33 AA
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
|
||||
#> 5 2013 1 1 554 600 -6 812 837 -25 DL
|
||||
#> 6 2013 1 1 554 558 -4 740 728 12 UA
|
||||
#> # … with 336,770 more rows, 9 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 1 517 515 2 830 819
|
||||
#> 2 2013 1 1 533 529 4 850 830
|
||||
#> 3 2013 1 1 542 540 2 923 850
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022
|
||||
#> 5 2013 1 1 554 600 -6 812 837
|
||||
#> 6 2013 1 1 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
<p>You can use <code><a href="https://dplyr.tidyverse.org/reference/desc.html">desc()</a></code> to re-order by a column in descending order. For example, this code shows the most delayed flights:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
arrange(desc(dep_delay))
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 1 9 641 900 1301 1242 1530 1272 HA
|
||||
#> 2 2013 6 15 1432 1935 1137 1607 2120 1127 MQ
|
||||
#> 3 2013 1 10 1121 1635 1126 1239 1810 1109 MQ
|
||||
#> 4 2013 9 20 1139 1845 1014 1457 2210 1007 AA
|
||||
#> 5 2013 7 22 845 1600 1005 1044 1815 989 MQ
|
||||
#> 6 2013 4 10 1100 1900 960 1342 2211 931 DL
|
||||
#> # … with 336,770 more rows, 9 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 9 641 900 1301 1242 1530
|
||||
#> 2 2013 6 15 1432 1935 1137 1607 2120
|
||||
#> 3 2013 1 10 1121 1635 1126 1239 1810
|
||||
#> 4 2013 9 20 1139 1845 1014 1457 2210
|
||||
#> 5 2013 7 22 845 1600 1005 1044 1815
|
||||
#> 6 2013 4 10 1100 1900 960 1342 2211
|
||||
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
<p>You can combine <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> to solve more complex problems. For example, we could look for the flights that were most delayed on arrival that left on roughly on time:</p>
|
||||
<div class="cell">
|
||||
@@ -226,21 +252,61 @@ Common mistakes</h2>
|
||||
filter(dep_delay <= 10 & dep_delay >= -10) |>
|
||||
arrange(desc(arr_delay))
|
||||
#> # A tibble: 239,109 × 19
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 11 1 658 700 -2 1329 1015 194 VX
|
||||
#> 2 2013 4 18 558 600 -2 1149 850 179 AA
|
||||
#> 3 2013 7 7 1659 1700 -1 2050 1823 147 US
|
||||
#> 4 2013 7 22 1606 1615 -9 2056 1831 145 DL
|
||||
#> 5 2013 9 19 648 641 7 1035 810 145 UA
|
||||
#> 6 2013 4 18 655 700 -5 1213 950 143 AA
|
||||
#> # … with 239,103 more rows, 9 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 11 1 658 700 -2 1329 1015
|
||||
#> 2 2013 4 18 558 600 -2 1149 850
|
||||
#> 3 2013 7 7 1659 1700 -1 2050 1823
|
||||
#> 4 2013 7 22 1606 1615 -9 2056 1831
|
||||
#> 5 2013 9 19 648 641 7 1035 810
|
||||
#> 6 2013 4 18 655 700 -5 1213 950
|
||||
#> # … with 239,103 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section id="distinct" data-type="sect2">
|
||||
<h2>
|
||||
<code>distinct()</code>
|
||||
</h2>
|
||||
<p><code><a href="https://dplyr.tidyverse.org/reference/distinct.html">distinct()</a></code> finds all the unique rows in a dataset, so in a technical sense, it primarily operates on the rows. Most of the time, however, you’ll want to the distinct combination of some variables, so you can also optionally supply column names:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r"># This would remove any duplicate rows if there were any
|
||||
flights |>
|
||||
distinct()
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 1 517 515 2 830 819
|
||||
#> 2 2013 1 1 533 529 4 850 830
|
||||
#> 3 2013 1 1 542 540 2 923 850
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022
|
||||
#> 5 2013 1 1 554 600 -6 812 837
|
||||
#> 6 2013 1 1 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm>
|
||||
|
||||
# This finds all unique origin and destination pairs.
|
||||
flights |>
|
||||
distinct(origin, dest)
|
||||
#> # A tibble: 224 × 2
|
||||
#> origin dest
|
||||
#> <chr> <chr>
|
||||
#> 1 EWR IAH
|
||||
#> 2 LGA IAH
|
||||
#> 3 JFK MIA
|
||||
#> 4 JFK BQN
|
||||
#> 5 LGA ATL
|
||||
#> 6 EWR ORD
|
||||
#> # … with 218 more rows</pre>
|
||||
</div>
|
||||
<p>Note that if you want to find the number of duplicates, or rows that weren’t duplicated, you’re better off swapping <code><a href="https://dplyr.tidyverse.org/reference/distinct.html">distinct()</a></code> for <code><a href="https://dplyr.tidyverse.org/reference/count.html">count()</a></code> and then filtering as needed.</p>
|
||||
</section>
|
||||
|
||||
<section id="exercises" data-type="sect2">
|
||||
<h2>
|
||||
Exercises</h2>
|
||||
@@ -255,15 +321,16 @@ Exercises</h2>
|
||||
</ol></li>
|
||||
<li><p>Sort <code>flights</code> to find the flights with longest departure delays. Find the flights that left earliest in the morning.</p></li>
|
||||
<li><p>Sort <code>flights</code> to find the fastest flights (Hint: try sorting by a calculation).</p></li>
|
||||
<li><p>Which flights traveled the farthest? Which traveled the shortest?</p></li>
|
||||
<li><p>Does it matter what order you used <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code> in if you’re using both? Why/why not? Think about the results and how much work the functions would have to do.</p></li>
|
||||
<li><p>Was there a flight on every day of 2013?</p></li>
|
||||
<li><p>Which flights traveled the farthest distance? Which traveled the least distance?</p></li>
|
||||
<li><p>Does it matter what order you used <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code> if you’re using both? Why/why not? Think about the results and how much work the functions would have to do.</p></li>
|
||||
</ol></section>
|
||||
</section>
|
||||
|
||||
<section id="columns" data-type="sect1">
|
||||
<h1>
|
||||
Columns</h1>
|
||||
<p>There are four important verbs that affect the columns without changing the rows: <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rename.html">rename()</a></code>, and <code><a href="https://dplyr.tidyverse.org/reference/relocate.html">relocate()</a></code>. <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> creates new columns that are functions of the existing columns; <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rename.html">rename()</a></code>, and <code><a href="https://dplyr.tidyverse.org/reference/relocate.html">relocate()</a></code> change which columns are present, their names, or their positions.</p>
|
||||
<p>There are four important verbs that affect the columns without changing the rows: <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rename.html">rename()</a></code>, and <code><a href="https://dplyr.tidyverse.org/reference/relocate.html">relocate()</a></code>. <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> creates new columns that are functions of the existing columns; <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rename.html">rename()</a></code>, and <code><a href="https://dplyr.tidyverse.org/reference/relocate.html">relocate()</a></code> change which columns are present, their names, or their positions. We’ll also discuss <code><a href="https://dplyr.tidyverse.org/reference/pull.html">pull()</a></code> since it allows you to get a column out of data frame.</p>
|
||||
|
||||
<section id="sec-mutate" data-type="sect2">
|
||||
<h2>
|
||||
@@ -277,19 +344,18 @@ Columns</h1>
|
||||
speed = distance / air_time * 60
|
||||
)
|
||||
#> # A tibble: 336,776 × 21
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 1 1 517 515 2 830 819 11 UA
|
||||
#> 2 2013 1 1 533 529 4 850 830 20 UA
|
||||
#> 3 2013 1 1 542 540 2 923 850 33 AA
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
|
||||
#> 5 2013 1 1 554 600 -6 812 837 -25 DL
|
||||
#> 6 2013 1 1 554 558 -4 740 728 12 UA
|
||||
#> # … with 336,770 more rows, 11 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, gain <dbl>, speed <dbl>, and abbreviated
|
||||
#> # variable names ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time,
|
||||
#> # ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 1 517 515 2 830 819
|
||||
#> 2 2013 1 1 533 529 4 850 830
|
||||
#> 3 2013 1 1 542 540 2 923 850
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022
|
||||
#> 5 2013 1 1 554 600 -6 812 837
|
||||
#> 6 2013 1 1 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, and 13 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm>, gain <dbl>, speed <dbl></pre>
|
||||
</div>
|
||||
<p>By default, <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> adds new columns on the right hand side of your dataset, which makes it difficult to see what’s happening here. We can use the <code>.before</code> argument to instead add the variables to the left hand side<span data-type="footnote">Remember that in RStudio, the easiest way to see a dataset with many columns is <code><a href="https://rdrr.io/r/utils/View.html">View()</a></code>.</span>:</p>
|
||||
<div class="cell">
|
||||
@@ -300,21 +366,20 @@ Columns</h1>
|
||||
.before = 1
|
||||
)
|
||||
#> # A tibble: 336,776 × 21
|
||||
#> gain speed year month day dep_time sched_dep_…¹ dep_d…² arr_t…³ sched…⁴
|
||||
#> <dbl> <dbl> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 -9 370. 2013 1 1 517 515 2 830 819
|
||||
#> 2 -16 374. 2013 1 1 533 529 4 850 830
|
||||
#> 3 -31 408. 2013 1 1 542 540 2 923 850
|
||||
#> 4 17 517. 2013 1 1 544 545 -1 1004 1022
|
||||
#> 5 19 394. 2013 1 1 554 600 -6 812 837
|
||||
#> 6 -16 288. 2013 1 1 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm>, and abbreviated variable names ¹sched_dep_time,
|
||||
#> # ²dep_delay, ³arr_time, ⁴sched_arr_time</pre>
|
||||
#> gain speed year month day dep_time sched_dep_time dep_delay arr_time
|
||||
#> <dbl> <dbl> <int> <int> <int> <int> <int> <dbl> <int>
|
||||
#> 1 -9 370. 2013 1 1 517 515 2 830
|
||||
#> 2 -16 374. 2013 1 1 533 529 4 850
|
||||
#> 3 -31 408. 2013 1 1 542 540 2 923
|
||||
#> 4 17 517. 2013 1 1 544 545 -1 1004
|
||||
#> 5 19 394. 2013 1 1 554 600 -6 812
|
||||
#> 6 -16 288. 2013 1 1 554 558 -4 740
|
||||
#> # … with 336,770 more rows, and 12 more variables: sched_arr_time <int>,
|
||||
#> # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm></pre>
|
||||
</div>
|
||||
<p>The <code>.</code> is a sign that <code>.before</code> is an argument to the function, not the name of a new variable. You can also use <code>.after</code> to add after a variable, and in both <code>.before</code> and <code>.after</code> you can the name of a variable name instead of a position. For example, we could add the new variables after <code>day:</code></p>
|
||||
<p>The <code>.</code> is a sign that <code>.before</code> is an argument to the function, not the name of a new variable. You can also use <code>.after</code> to add after a variable, and in both <code>.before</code> and <code>.after</code> you can use the variable name instead of a position. For example, we could add the new variables after <code>day:</code></p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
mutate(
|
||||
@@ -323,19 +388,18 @@ Columns</h1>
|
||||
.after = day
|
||||
)
|
||||
#> # A tibble: 336,776 × 21
|
||||
#> year month day gain speed dep_time sched_dep_…¹ dep_d…² arr_t…³ sched…⁴
|
||||
#> <int> <int> <int> <dbl> <dbl> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 1 -9 370. 517 515 2 830 819
|
||||
#> 2 2013 1 1 -16 374. 533 529 4 850 830
|
||||
#> 3 2013 1 1 -31 408. 542 540 2 923 850
|
||||
#> 4 2013 1 1 17 517. 544 545 -1 1004 1022
|
||||
#> 5 2013 1 1 19 394. 554 600 -6 812 837
|
||||
#> 6 2013 1 1 -16 288. 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm>, and abbreviated variable names ¹sched_dep_time,
|
||||
#> # ²dep_delay, ³arr_time, ⁴sched_arr_time</pre>
|
||||
#> year month day gain speed dep_time sched_dep_time dep_delay arr_time
|
||||
#> <int> <int> <int> <dbl> <dbl> <int> <int> <dbl> <int>
|
||||
#> 1 2013 1 1 -9 370. 517 515 2 830
|
||||
#> 2 2013 1 1 -16 374. 533 529 4 850
|
||||
#> 3 2013 1 1 -31 408. 542 540 2 923
|
||||
#> 4 2013 1 1 17 517. 544 545 -1 1004
|
||||
#> 5 2013 1 1 19 394. 554 600 -6 812
|
||||
#> 6 2013 1 1 -16 288. 554 558 -4 740
|
||||
#> # … with 336,770 more rows, and 12 more variables: sched_arr_time <int>,
|
||||
#> # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm></pre>
|
||||
</div>
|
||||
<p>Alternatively, you can control which variables are kept with the <code>.keep</code> argument. A particularly useful argument is <code>"used"</code> which allows you to see the inputs and outputs from your calculations:</p>
|
||||
<div class="cell">
|
||||
@@ -397,18 +461,17 @@ flights |>
|
||||
flights |>
|
||||
select(!year:day)
|
||||
#> # A tibble: 336,776 × 16
|
||||
#> dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier flight tailnum
|
||||
#> <int> <int> <dbl> <int> <int> <dbl> <chr> <int> <chr>
|
||||
#> 1 517 515 2 830 819 11 UA 1545 N14228
|
||||
#> 2 533 529 4 850 830 20 UA 1714 N24211
|
||||
#> 3 542 540 2 923 850 33 AA 1141 N619AA
|
||||
#> 4 544 545 -1 1004 1022 -18 B6 725 N804JB
|
||||
#> 5 554 600 -6 812 837 -25 DL 461 N668DN
|
||||
#> 6 554 558 -4 740 728 12 UA 1696 N39463
|
||||
#> # … with 336,770 more rows, 7 more variables: origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm>, and abbreviated variable names ¹sched_dep_time,
|
||||
#> # ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay
|
||||
#> dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
|
||||
#> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 517 515 2 830 819 11 UA
|
||||
#> 2 533 529 4 850 830 20 UA
|
||||
#> 3 542 540 2 923 850 33 AA
|
||||
#> 4 544 545 -1 1004 1022 -18 B6
|
||||
#> 5 554 600 -6 812 837 -25 DL
|
||||
#> 6 554 558 -4 740 728 12 UA
|
||||
#> # … with 336,770 more rows, and 9 more variables: flight <int>,
|
||||
#> # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
|
||||
#> # hour <dbl>, minute <dbl>, time_hour <dttm>
|
||||
|
||||
# Select all columns that are characters
|
||||
flights |>
|
||||
@@ -433,7 +496,7 @@ flights |>
|
||||
<code>contains("ijk")</code>: matches names that contain “ijk”.</li>
|
||||
<li>
|
||||
<code>num_range("x", 1:3)</code>: matches <code>x1</code>, <code>x2</code> and <code>x3</code>.</li>
|
||||
</ul><p>See <code><a href="https://dplyr.tidyverse.org/reference/select.html">?select</a></code> for more details. Once you know regular expressions (the topic of <a href="#chp-regexps" data-type="xref">#chp-regexps</a>) you’ll also be use <code><a href="https://tidyselect.r-lib.org/reference/starts_with.html">matches()</a></code> to select variables that match a pattern.</p>
|
||||
</ul><p>See <code><a href="https://dplyr.tidyverse.org/reference/select.html">?select</a></code> for more details. Once you know regular expressions (the topic of <a href="#chp-regexps" data-type="xref">#chp-regexps</a>) you’ll also be able to use <code><a href="https://tidyselect.r-lib.org/reference/starts_with.html">matches()</a></code> to select variables that match a pattern.</p>
|
||||
<p>You can rename variables as you <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code> them by using <code>=</code>. The new name appears on the left hand side of the <code>=</code>, and the old variable appears on the right hand side:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
@@ -460,18 +523,18 @@ flights |>
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
rename(tail_num = tailnum)
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 1 1 517 515 2 830 819 11 UA
|
||||
#> 2 2013 1 1 533 529 4 850 830 20 UA
|
||||
#> 3 2013 1 1 542 540 2 923 850 33 AA
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
|
||||
#> 5 2013 1 1 554 600 -6 812 837 -25 DL
|
||||
#> 6 2013 1 1 554 558 -4 740 728 12 UA
|
||||
#> # … with 336,770 more rows, 9 more variables: flight <int>, tail_num <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 1 517 515 2 830 819
|
||||
#> 2 2013 1 1 533 529 4 850 830
|
||||
#> 3 2013 1 1 542 540 2 923 850
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022
|
||||
#> 5 2013 1 1 554 600 -6 812 837
|
||||
#> 6 2013 1 1 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tail_num <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
<p>It works exactly the same way as <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code>, but keeps all the variables that aren’t explicitly selected.</p>
|
||||
<p>If you have a bunch of inconsistently named columns and it would be painful to fix them all by hand, check out <code><a href="https://rdrr.io/pkg/janitor/man/clean_names.html">janitor::clean_names()</a></code> which provides some useful automated cleaning.</p>
|
||||
@@ -486,51 +549,51 @@ flights |>
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
relocate(time_hour, air_time)
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> time_hour air_time year month day dep_time sched_dep…¹ dep_d…²
|
||||
#> <dttm> <dbl> <int> <int> <int> <int> <int> <dbl>
|
||||
#> 1 2013-01-01 05:00:00 227 2013 1 1 517 515 2
|
||||
#> 2 2013-01-01 05:00:00 227 2013 1 1 533 529 4
|
||||
#> 3 2013-01-01 05:00:00 160 2013 1 1 542 540 2
|
||||
#> 4 2013-01-01 05:00:00 183 2013 1 1 544 545 -1
|
||||
#> 5 2013-01-01 06:00:00 116 2013 1 1 554 600 -6
|
||||
#> 6 2013-01-01 05:00:00 150 2013 1 1 554 558 -4
|
||||
#> # … with 336,770 more rows, 11 more variables: arr_time <int>,
|
||||
#> # sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
|
||||
#> # tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, and abbreviated variable names ¹sched_dep_time, ²dep_delay</pre>
|
||||
#> time_hour air_time year month day dep_time sched_dep_time
|
||||
#> <dttm> <dbl> <int> <int> <int> <int> <int>
|
||||
#> 1 2013-01-01 05:00:00 227 2013 1 1 517 515
|
||||
#> 2 2013-01-01 05:00:00 227 2013 1 1 533 529
|
||||
#> 3 2013-01-01 05:00:00 160 2013 1 1 542 540
|
||||
#> 4 2013-01-01 05:00:00 183 2013 1 1 544 545
|
||||
#> 5 2013-01-01 06:00:00 116 2013 1 1 554 600
|
||||
#> 6 2013-01-01 05:00:00 150 2013 1 1 554 558
|
||||
#> # … with 336,770 more rows, and 12 more variables: dep_delay <dbl>,
|
||||
#> # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
|
||||
#> # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
|
||||
#> # hour <dbl>, minute <dbl></pre>
|
||||
</div>
|
||||
<p>But you can use the same <code>.before</code> and <code>.after</code> arguments as <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> to choose where to put them:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
relocate(year:dep_time, .after = time_hour)
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> sched…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier flight tailnum origin dest
|
||||
#> <int> <dbl> <int> <int> <dbl> <chr> <int> <chr> <chr> <chr>
|
||||
#> 1 515 2 830 819 11 UA 1545 N14228 EWR IAH
|
||||
#> 2 529 4 850 830 20 UA 1714 N24211 LGA IAH
|
||||
#> 3 540 2 923 850 33 AA 1141 N619AA JFK MIA
|
||||
#> 4 545 -1 1004 1022 -18 B6 725 N804JB JFK BQN
|
||||
#> 5 600 -6 812 837 -25 DL 461 N668DN LGA ATL
|
||||
#> 6 558 -4 740 728 12 UA 1696 N39463 EWR ORD
|
||||
#> # … with 336,770 more rows, 9 more variables: air_time <dbl>,
|
||||
#> # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>, year <int>,
|
||||
#> # month <int>, day <int>, dep_time <int>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay
|
||||
#> sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight
|
||||
#> <int> <dbl> <int> <int> <dbl> <chr> <int>
|
||||
#> 1 515 2 830 819 11 UA 1545
|
||||
#> 2 529 4 850 830 20 UA 1714
|
||||
#> 3 540 2 923 850 33 AA 1141
|
||||
#> 4 545 -1 1004 1022 -18 B6 725
|
||||
#> 5 600 -6 812 837 -25 DL 461
|
||||
#> 6 558 -4 740 728 12 UA 1696
|
||||
#> # … with 336,770 more rows, and 12 more variables: tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, year <int>, month <int>, day <int>,
|
||||
#> # dep_time <int>
|
||||
flights |>
|
||||
relocate(starts_with("arr"), .before = dep_time)
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> year month day arr_time arr_de…¹ dep_t…² sched…³ dep_d…⁴ sched…⁵ carrier
|
||||
#> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <int> <chr>
|
||||
#> 1 2013 1 1 830 11 517 515 2 819 UA
|
||||
#> 2 2013 1 1 850 20 533 529 4 830 UA
|
||||
#> 3 2013 1 1 923 33 542 540 2 850 AA
|
||||
#> 4 2013 1 1 1004 -18 544 545 -1 1022 B6
|
||||
#> 5 2013 1 1 812 -25 554 600 -6 837 DL
|
||||
#> 6 2013 1 1 740 12 554 558 -4 728 UA
|
||||
#> # … with 336,770 more rows, 9 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹arr_delay, ²dep_time, ³sched_dep_time, ⁴dep_delay, ⁵sched_arr_time</pre>
|
||||
#> year month day arr_time arr_delay dep_time sched_dep_time dep_delay
|
||||
#> <int> <int> <int> <int> <dbl> <int> <int> <dbl>
|
||||
#> 1 2013 1 1 830 11 517 515 2
|
||||
#> 2 2013 1 1 850 20 533 529 4
|
||||
#> 3 2013 1 1 923 33 542 540 2
|
||||
#> 4 2013 1 1 1004 -18 544 545 -1
|
||||
#> 5 2013 1 1 812 -25 554 600 -6
|
||||
#> 6 2013 1 1 740 12 554 558 -4
|
||||
#> # … with 336,770 more rows, and 11 more variables: sched_arr_time <int>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
@@ -574,27 +637,27 @@ Groups</h1>
|
||||
group_by(month)
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> # Groups: month [12]
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 1 1 517 515 2 830 819 11 UA
|
||||
#> 2 2013 1 1 533 529 4 850 830 20 UA
|
||||
#> 3 2013 1 1 542 540 2 923 850 33 AA
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
|
||||
#> 5 2013 1 1 554 600 -6 812 837 -25 DL
|
||||
#> 6 2013 1 1 554 558 -4 740 728 12 UA
|
||||
#> # … with 336,770 more rows, 9 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 1 517 515 2 830 819
|
||||
#> 2 2013 1 1 533 529 4 850 830
|
||||
#> 3 2013 1 1 542 540 2 923 850
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022
|
||||
#> 5 2013 1 1 554 600 -6 812 837
|
||||
#> 6 2013 1 1 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
<p><code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> doesn’t change the data but, if you look closely at the output, you’ll notice that it’s now “grouped by” month. This means subsequent operations will now work “by month”.</p>
|
||||
<p><code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> doesn’t change the data but, if you look closely at the output, you’ll notice that it’s now “grouped by” month. This means subsequent operations will now work “by month”. <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> doesn’t do anything by itself; instead it changes the behavior of the subsequent verbs.</p>
|
||||
</section>
|
||||
|
||||
<section id="sec-summarize" data-type="sect2">
|
||||
<h2>
|
||||
<code>summarize()</code>
|
||||
</h2>
|
||||
<p>The most important grouped operation is a summary. It collapses each group to a single row<span data-type="footnote">This is a slightly simplification; later on you’ll learn how to use <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarize()</a></code> to produce multiple summary rows for each group.</span>. Here we compute the average departure delay by month:</p>
|
||||
<p>The most important grouped operation is a summary, which collapses each group to a single row. In dplyr, this is operation is performed by <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarize()</a></code><span data-type="footnote">Or <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code>, if you prefer British English.</span>, as shown by the following example, which computes the average departure delay by month:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
group_by(month) |>
|
||||
@@ -665,7 +728,7 @@ The<code>slice_</code> functions</h2>
|
||||
<li>
|
||||
<code>df |> slice_max(x, n = 1)</code> takes the row with the largest value of <code>x</code>.</li>
|
||||
<li>
|
||||
<code>df |> slice_sample(x, n = 1)</code> takes one random row.</li>
|
||||
<code>df |> slice_sample(n = 1)</code> takes one random row.</li>
|
||||
</ul><p>You can vary <code>n</code> to select more than one row, or instead of <code>n =</code>, you can use <code>prop = 0.1</code> to select (e.g.) 10% of the rows in each group. For example, the following code finds the most delayed flight to each destination:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">flights |>
|
||||
@@ -673,18 +736,18 @@ The<code>slice_</code> functions</h2>
|
||||
slice_max(arr_delay, n = 1)
|
||||
#> # A tibble: 108 × 19
|
||||
#> # Groups: dest [105]
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 7 22 2145 2007 98 132 2259 153 B6
|
||||
#> 2 2013 7 23 1139 800 219 1250 909 221 B6
|
||||
#> 3 2013 1 25 123 2000 323 229 2101 328 EV
|
||||
#> 4 2013 8 17 1740 1625 75 2042 2003 39 UA
|
||||
#> 5 2013 7 22 2257 759 898 121 1026 895 DL
|
||||
#> 6 2013 7 10 2056 1505 351 2347 1758 349 UA
|
||||
#> # … with 102 more rows, 9 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 7 22 2145 2007 98 132 2259
|
||||
#> 2 2013 7 23 1139 800 219 1250 909
|
||||
#> 3 2013 1 25 123 2000 323 229 2101
|
||||
#> 4 2013 8 17 1740 1625 75 2042 2003
|
||||
#> 5 2013 7 22 2257 759 898 121 1026
|
||||
#> 6 2013 7 10 2056 1505 351 2347 1758
|
||||
#> # … with 102 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
<p>This is similar to computing the max delay with <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarize()</a></code>, but you get the whole row instead of the single summary:</p>
|
||||
<div class="cell">
|
||||
@@ -692,7 +755,7 @@ The<code>slice_</code> functions</h2>
|
||||
group_by(dest) |>
|
||||
summarize(max_delay = max(arr_delay, na.rm = TRUE))
|
||||
#> Warning: There was 1 warning in `summarize()`.
|
||||
#> ℹ In argument `max_delay = max(arr_delay, na.rm = TRUE)`.
|
||||
#> ℹ In argument: `max_delay = max(arr_delay, na.rm = TRUE)`.
|
||||
#> ℹ In group 52: `dest = "LGA"`.
|
||||
#> Caused by warning in `max()`:
|
||||
#> ! no non-missing arguments to max; returning -Inf
|
||||
@@ -719,18 +782,18 @@ Grouping by multiple variables</h2>
|
||||
daily
|
||||
#> # A tibble: 336,776 × 19
|
||||
#> # Groups: year, month, day [365]
|
||||
#> year month day dep_time sched_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
|
||||
#> 1 2013 1 1 517 515 2 830 819 11 UA
|
||||
#> 2 2013 1 1 533 529 4 850 830 20 UA
|
||||
#> 3 2013 1 1 542 540 2 923 850 33 AA
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022 -18 B6
|
||||
#> 5 2013 1 1 554 600 -6 812 837 -25 DL
|
||||
#> 6 2013 1 1 554 558 -4 740 728 12 UA
|
||||
#> # … with 336,770 more rows, 9 more variables: flight <int>, tailnum <chr>,
|
||||
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
|
||||
#> # minute <dbl>, time_hour <dttm>, and abbreviated variable names
|
||||
#> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay</pre>
|
||||
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
|
||||
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
|
||||
#> 1 2013 1 1 517 515 2 830 819
|
||||
#> 2 2013 1 1 533 529 4 850 830
|
||||
#> 3 2013 1 1 542 540 2 923 850
|
||||
#> 4 2013 1 1 544 545 -1 1004 1022
|
||||
#> 5 2013 1 1 554 600 -6 812 837
|
||||
#> 6 2013 1 1 554 558 -4 740 728
|
||||
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
|
||||
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
|
||||
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
|
||||
#> # time_hour <dttm></pre>
|
||||
</div>
|
||||
<p>When you summarize a tibble grouped by more than one variable, each summary peels off the last group. In hindsight, this wasn’t great way to make this function work, but it’s difficult to change without breaking existing code. To make it obvious what’s happening, dplyr displays a message that tells you how you can change this behavior:</p>
|
||||
<div class="cell">
|
||||
@@ -779,6 +842,66 @@ Exercises</h2>
|
||||
<li><p>How do delays vary over the course of the day. Illustrate your answer with a plot.</p></li>
|
||||
<li><p>What happens if you supply a negative <code>n</code> to <code><a href="https://dplyr.tidyverse.org/reference/slice.html">slice_min()</a></code> and friends?</p></li>
|
||||
<li><p>Explain what <code><a href="https://dplyr.tidyverse.org/reference/count.html">count()</a></code> does in terms of the dplyr verbs you just learn. What does the <code>sort</code> argument to <code><a href="https://dplyr.tidyverse.org/reference/count.html">count()</a></code> do?</p></li>
|
||||
<li>
|
||||
<p>Suppose we have the following tiny data frame:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">df <- tibble(
|
||||
x = 1:5,
|
||||
y = c("a", "b", "a", "a", "b"),
|
||||
z = c("K", "K", "L", "L", "K")
|
||||
)</pre>
|
||||
</div>
|
||||
<ol type="a"><li>
|
||||
<p>What does the following code do? Run it, analyze the result, and describe what <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> does.</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">df |>
|
||||
group_by(y)</pre>
|
||||
</div>
|
||||
</li>
|
||||
<li>
|
||||
<p>What does the following code do? Run it, analyze the result, and describe what <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code> does. Also comment on how it’s different from the <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> in part (a)?</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">df |>
|
||||
arrange(y)</pre>
|
||||
</div>
|
||||
</li>
|
||||
<li>
|
||||
<p>What does the following code do? Run it, analyze the result, and describe what the pipeline does.</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">df |>
|
||||
group_by(y) |>
|
||||
summarize(mean_x = mean(x))</pre>
|
||||
</div>
|
||||
</li>
|
||||
<li>
|
||||
<p>What does the following code do? Run it, analyze the result, and describe what the pipeline does. Then, comment on what the message says.</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">df |>
|
||||
group_by(y, z) |>
|
||||
summarize(mean_x = mean(x))</pre>
|
||||
</div>
|
||||
</li>
|
||||
<li>
|
||||
<p>What does the following code do? Run it, analyze the result, and describe what the pipeline does. How is the output different from the one in part (d).</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">df |>
|
||||
group_by(y, z) |>
|
||||
summarize(mean_x = mean(x), .groups = "drop")</pre>
|
||||
</div>
|
||||
</li>
|
||||
<li>
|
||||
<p>What do the following pipelines do? Run both, analyze the results, and describe what each pipeline does. How are the outputs of the two pipelines different?</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">df |>
|
||||
group_by(y, z) |>
|
||||
summarize(mean_x = mean(x))
|
||||
|
||||
df |>
|
||||
group_by(y, z) |>
|
||||
mutate(mean_x = mean(x))</pre>
|
||||
</div>
|
||||
</li>
|
||||
</ol></li>
|
||||
</ol></section>
|
||||
</section>
|
||||
|
||||
@@ -795,18 +918,18 @@ Case study: aggregates and sample size</h1>
|
||||
n = n()
|
||||
)
|
||||
|
||||
ggplot(delays, aes(delay)) +
|
||||
ggplot(delays, aes(x = delay)) +
|
||||
geom_freqpoly(binwidth = 10)</pre>
|
||||
<div class="cell-output-display">
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-36-1.png" class="img-fluid" alt="A frequency histogram showing the distribution of flight delays. The distribution is unimodal, with a large spike around 0, and asymmetric: very few flights leave more than 30 minutes early, but flights are delayed up to 5 hours." width="576"/></p>
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-45-1.png" class="img-fluid" alt="A frequency histogram showing the distribution of flight delays. The distribution is unimodal, with a large spike around 0, and asymmetric: very few flights leave more than 30 minutes early, but flights are delayed up to 5 hours." width="576"/></p>
|
||||
</div>
|
||||
</div>
|
||||
<p>Wow, there are some planes that have an <em>average</em> delay of 5 hours (300 minutes)! That seems pretty surprising, so lets draw a scatterplot of number of flights vs. average delay:</p>
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">ggplot(delays, aes(n, delay)) +
|
||||
<pre data-type="programlisting" data-code-language="r">ggplot(delays, aes(x = n, y = delay)) +
|
||||
geom_point(alpha = 1/10)</pre>
|
||||
<div class="cell-output-display">
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-37-1.png" class="img-fluid" alt="A scatterplot showing number of flights versus after delay. Delays for planes with very small number of flights have very high variability (from -50 to ~300), but the variability rapidly decreases as the number of flights increases." width="576"/></p>
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-46-1.png" class="img-fluid" alt="A scatterplot showing number of flights versus after delay. Delays for planes with very small number of flights have very high variability (from -50 to ~300), but the variability rapidly decreases as the number of flights increases." width="576"/></p>
|
||||
</div>
|
||||
</div>
|
||||
<p>Not surprisingly, there is much greater variation in the average delay when there are few flights for a given plane. The shape of this plot is very characteristic: whenever you plot a mean (or other summary) vs. group size, you’ll see that the variation decreases as the sample size increases<span data-type="footnote">*cough* the central limit theorem *cough*.</span>.</p>
|
||||
@@ -814,11 +937,11 @@ ggplot(delays, aes(delay)) +
|
||||
<div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">delays |>
|
||||
filter(n > 25) |>
|
||||
ggplot(aes(n, delay)) +
|
||||
ggplot(aes(x = n, y = delay)) +
|
||||
geom_point(alpha = 1/10) +
|
||||
geom_smooth(se = FALSE)</pre>
|
||||
<div class="cell-output-display">
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-38-1.png" class="img-fluid" alt="Now that the y-axis (average delay) is smaller (-20 to 60 minutes), we can see a more complicated story. The smooth line suggests an initial decrease in average delay from 10 minutes to 0 minutes as number of flights per plane increases from 25 to 100. This is followed by a gradual increase up to 10 minutes for 250 flights, then a gradual decrease to ~5 minutes at 500 flights." width="576"/></p>
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-47-1.png" class="img-fluid" alt="Now that the y-axis (average delay) is smaller (-20 to 60 minutes), we can see a more complicated story. The smooth line suggests an initial decrease in average delay from 10 minutes to 0 minutes as number of flights per plane increases from 25 to 100. This is followed by a gradual increase up to 10 minutes for 250 flights, then a gradual decrease to ~5 minutes at 500 flights." width="576"/></p>
|
||||
</div>
|
||||
</div>
|
||||
<p>Note the handy pattern for combining ggplot2 and dplyr. It’s a bit annoying that you have to switch from <code>|></code> to <code>+</code>, but it’s not too much of a hassle once you get the hang of it.</p>
|
||||
@@ -848,11 +971,11 @@ batters
|
||||
</ol><div class="cell">
|
||||
<pre data-type="programlisting" data-code-language="r">batters |>
|
||||
filter(n > 100) |>
|
||||
ggplot(aes(n, perf)) +
|
||||
ggplot(aes(x = n, y = perf)) +
|
||||
geom_point(alpha = 1 / 10) +
|
||||
geom_smooth(se = FALSE)</pre>
|
||||
<div class="cell-output-display">
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-40-1.png" class="img-fluid" alt="A scatterplot of number of batting opportunites vs batting performance overlaid with a smoothed line. Average performance increases sharply from 0.2 at when n is 1 to 0.25 when n is ~1000. Average performance continues to increase linearly at a much shallower slope reaching ~0.3 when n is ~15,000." width="576"/></p>
|
||||
<p><img src="data-transform_files/figure-html/unnamed-chunk-49-1.png" class="img-fluid" alt="A scatterplot of number of batting opportunites vs. batting performance overlaid with a smoothed line. Average performance increases sharply from 0.2 at when n is 1 to 0.25 when n is ~1000. Average performance continues to increase linearly at a much shallower slope reaching ~0.3 when n is ~15,000." width="576"/></p>
|
||||
</div>
|
||||
</div>
|
||||
<p>This also has important implications for ranking. If you naively sort on <code>desc(ba)</code>, the people with the best batting averages are clearly lucky, not skilled:</p>
|
||||
@@ -876,7 +999,7 @@ batters
|
||||
<section id="summary" data-type="sect1">
|
||||
<h1>
|
||||
Summary</h1>
|
||||
<p>In this chapter, you’ve learned the tools that dplyr provides for working with data frames. The tools are roughly grouped into three categories: those that manipulate the rows (like <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code>, those that manipulate the columns (like <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code>), and those that manipulate groups (like <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code>). In this chapter, we’ve focused on these “whole data frame” tools, but you haven’t yet learned much about what you can do with the individual variable. We’ll come back to that in the Transform part of the book, where each chapter will give you tools for a specific type of variable.</p>
|
||||
<p>In this chapter, you’ve learned the tools that dplyr provides for working with data frames. The tools are roughly grouped into three categories: those that manipulate the rows (like <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/arrange.html">arrange()</a></code>, those that manipulate the columns (like <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code>), and those that manipulate groups (like <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarize()</a></code>). In this chapter, we’ve focused on these “whole data frame” tools, but you haven’t yet learned much about what you can do with the individual variable. We’ll come back to that in the Transform part of the book, where each chapter will give you tools for a specific type of variable.</p>
|
||||
<p>For now, we’ll pivot back to workflow, and in the next chapter you’ll learn more about the pipe, <code>|></code>, why we recommend it, and a little of the history that lead from magrittr’s <code>%>%</code> to base R’s <code>|></code>.</p>
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user