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<!DOCTYPE html>
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<title>Introduction to dplyr</title>
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<!--
%\VignetteEngine{knitr}
%\VignetteIndexEntry{Introduction to dplyr}
-->
<h1>Introduction to dplyr</h1>
R code snippets are matched with corresponding translations into pandas, with and without pandas-ply.
<table>
<tr><td>
<h2>dplyr</h2>
</td><td>
<h2>pandas</h2>
</td><td>
<h2>pandas-ply</h2>
</td></tr>
<tr><td>
<p>When working with data you must:</p>
<ul>
<li><p>Figure out what you want to do.</p></li>
<li><p>Precisely describe what you want in the form of a computer program.</p></li>
<li><p>Execute the code.</p></li>
</ul>
<p>The dplyr package makes each of these steps as fast and easy as possible by:</p>
<ul>
<li><p>Elucidating the most common data manipulation operations, so that your
options are helpfully constrained when thinking about how to tackle a
problem.</p></li>
<li><p>Providing simple functions that correspond to the most common
data manipulation verbs, so that you can easily translate your thoughts
into code.</p></li>
<li><p>Using efficient data storage backends, so that you spend as little time
waiting for the computer as possible.</p></li>
</ul>
<p>The goal of this document is to introduce you to the basic tools that dplyr provides, and show how you to apply them to data frames. Other vignettes provide more details on specific topics:</p>
<ul>
<li><p>databases: as well as in memory data frames, dplyr also connects to
databases. It allows you to work with remote, out-of-memory data, using
exactly the same tools, because dplyr will translate your R code into
the appropriate SQL.</p></li>
<li><p>benchmark-baseball: see how dplyr compares to other tools for data
manipulation on a realistic use case.</p></li>
<li><p>window-functions: a window function is a variation on an aggregation
function. Where an aggregate function uses <code>n</code> inputs to produce 1
output, a window function uses <code>n</code> inputs to produce <code>n</code> outputs.</p></li>
</ul>
<h2>Data: nycflights13</h2>
<p>To explore the basic data manipulation verbs of dplyr, we'll start with the built in
<code>nycflights13</code> data frame. This dataset contains all 336776 flights that departed from New York City in 2013. The data comes from the US <a href="http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=120&Link=0">Bureau of Transporation Statistics</a>, and is documented in <code>?nycflights13</code></p>
<pre><code class="r">library(nycflights13)
</code></pre>
<pre><code>#> Warning: package 'nycflights13' was built under R version 3.1.1
</code></pre>
<pre><code>#>
#> Attaching package: 'nycflights13'
#>
#> The following object is masked _by_ '.GlobalEnv':
#>
#> planes
</code></pre>
<pre><code class="r">dim(flights)
</code></pre>
<pre><code>#> [1] 336776 16
</code></pre>
<pre><code class="r">head(flights)
</code></pre>
<pre><code>#> Source: local data frame [6 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 1 1 517 2 830 11 UA N14228
#> 2 2013 1 1 533 4 850 20 UA N24211
#> 3 2013 1 1 542 2 923 33 AA N619AA
#> 4 2013 1 1 544 -1 1004 -18 B6 N804JB
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
<p>dplyr can work with data frames as is, but if you're dealing with large data, it's worthwhile to convert them to a <code>tbl_df</code>: this is a wrapper around a data frame that won't accidentally print a lot of data to the screen.</p>
<h2>Single table verbs</h2>
<p>Dplyr aims to provide a function for each basic verb of data manipulating:</p>
<ul>
<li><code>filter()</code> (and <code>slice()</code>)</li>
<li><code>arrange()</code></li>
<li><code>select()</code> (and <code>rename()</code>)</li>
<li><code>distinct()</code></li>
<li><code>mutate()</code> (and <code>transmute()</code>)</li>
<li><code>summarise()</code></li>
<li><code>sample_n()</code> and <code>sample_frac()</code></li>
</ul>
<p>If you've used plyr before, many of these will be familar.</p>
<h2>Filter rows with <code>filter()</code></h2>
<p><code>filter()</code> allows you to select a subset of the rows of a data frame. The first argument is the name of the data frame, and the second and subsequent are filtering expressions evaluated in the context of that data frame:</p>
<p>For example, we can select all flights on January 1st with:</p>
</td></tr>
<tr><td>
<pre><code class="r">filter(flights, month == 1, day == 1)
</code></pre>
<pre><code>#> Source: local data frame [842 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 1 1 517 2 830 11 UA N14228
#> 2 2013 1 1 533 4 850 20 UA N24211
#> 3 2013 1 1 542 2 923 33 AA N619AA
#> 4 2013 1 1 544 -1 1004 -18 B6 N804JB
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
<p>This is equivalent to the more verbose:</p>
<pre><code class="r">flights[flights$month == 1 & flights$day == 1, ]
</code></pre>
<p><code>filter()</code> works similarly to <code>subset()</code> except that you can give it any number of filtering conditions which are joined together with <code>&</code> (not <code>&&</code> which is easy to do accidentally!). You can use other boolean operators explicitly:</p>
</td><td>
<pre><code class="python">flights[(flights.month == 1) & (flights.day == 1)]
</code></pre>
<p>This is similar to the "more verbose" notation available in R.</p>
<p>(Note that, due to python's order of precedence, the paretheses here are necessary.)</p>
</td><td>
<pre><code class="python">flights.ply_where(X.month == 1, X.day == 1)
</code></pre>
<p>The benefits of not repeating `flights` in the conditions are minor here, but if this selection were coming towards the end of a long series of transformations, it would be impractical to repeat the input dataframe's definition in the conditions, so the chain of pipes would have to come to some named end before the filtering could happen.</p>
</td></tr>
<tr><td>
<pre><code class="r">filter(flights, month == 1 | month == 2)
</code></pre>
</td><td>
<pre><code class="python">flights[(flights.month == 1) | (flights.day == 1)]
</code></pre>
</td><td>
<pre><code class="python">flights.ply_where((X.month == 1) | (X.day == 1))
</code></pre>
</td></tr>
<tr><td>
<p>To select rows by position, use <code>slice()</code>:</p>
<pre><code class="r">slice(flights, 1:10)
</code></pre>
<pre><code>#> Source: local data frame [10 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 1 1 517 2 830 11 UA N14228
#> 2 2013 1 1 533 4 850 20 UA N24211
#> 3 2013 1 1 542 2 923 33 AA N619AA
#> 4 2013 1 1 544 -1 1004 -18 B6 N804JB
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
</td></tr>
<tr><td>
<h2>Arrange rows with <code>arrange()</code></h2>
<p><code>arrange()</code> works similarly to <code>filter()</code> except that instead of filtering or selecting rows, it reorders them. It takes a data frame, and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns:</p>
</td></tr>
<tr><td>
<pre><code class="r">arrange(flights, year, month, day)
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 1 1 517 2 830 11 UA N14228
#> 2 2013 1 1 533 4 850 20 UA N24211
#> 3 2013 1 1 542 2 923 33 AA N619AA
#> 4 2013 1 1 544 -1 1004 -18 B6 N804JB
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
<p>Use <code>desc()</code> to order a column in descending order:</p>
<pre><code class="r">arrange(flights, desc(arr_delay))
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 1 9 641 1301 1242 1272 HA N384HA
#> 2 2013 6 15 1432 1137 1607 1127 MQ N504MQ
#> 3 2013 1 10 1121 1126 1239 1109 MQ N517MQ
#> 4 2013 9 20 1139 1014 1457 1007 AA N338AA
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
<p><code>dplyr::arrange()</code> works the same way as <code>plyr::arrange()</code>. It's a straighforward wrapper around <code>order()</code> that requires less typing. The previous code is equivalent to:</p>
<pre><code class="r">flights[order(flights$year, flights$month, flights$day), ]
flights[order(desc(flights$arr_delay)), ]
</code></pre>
<h2>Select columns with <code>select()</code></h2>
<p>Often you work with large datasets with many columns where only a few are actually of interest to you. <code>select()</code> allows you to rapidly zoom in on a useful subset using operations that usually only work on numeric variable positions:</p>
</td><td>
<pre><code class="python">flights.sort(['year', 'month', 'day'])
</code></pre>
</td><td>
</td></tr>
<tr><td>
<pre><code class="r"># Select columns by name
select(flights, year, month, day)
</code></pre>
</td><td>
<pre><code class="python"># Select columns by name
flights[['year', 'month', 'day']]
</code></pre>
</td><td>
<pre><code class="py"># Select columns by name
flights.ply_select('year', 'month', 'day')
</code></pre>
</td></tr>
<tr><td>
<pre><code>#> Source: local data frame [336,776 x 3]
#>
#> year month day
#> 1 2013 1 1
#> 2 2013 1 1
#> 3 2013 1 1
#> 4 2013 1 1
#> .. ... ... ...
</code></pre>
<pre><code class="r"># Select all columns between year and day (inclusive)
select(flights, year:day)
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 3]
#>
#> year month day
#> 1 2013 1 1
#> 2 2013 1 1
#> 3 2013 1 1
#> 4 2013 1 1
#> .. ... ... ...
</code></pre>
<pre><code class="r"># Select all columns except those from year to day (inclusive)
select(flights, -(year:day))
</code></pre>
</td></tr>
<tr><td>
<pre><code>#> Source: local data frame [336,776 x 13]
#>
#> dep_time dep_delay arr_time arr_delay carrier tailnum flight origin
#> 1 517 2 830 11 UA N14228 1545 EWR
#> 2 533 4 850 20 UA N24211 1714 LGA
#> 3 542 2 923 33 AA N619AA 1141 JFK
#> 4 544 -1 1004 -18 B6 N804JB 725 JFK
#> .. ... ... ... ... ... ... ... ...
#> dest
#> 1 IAH
#> 2 IAH
#> 3 MIA
#> 4 BQN
#> .. ...
#> Variables not shown: air_time (dbl), distance (dbl), hour (dbl), minute
#> (dbl)
</code></pre>
<p>This function works similarly to the <code>select</code> argument to the <code>base::subset()</code>. It's its own function in dplyr, because the dplyr philosophy is to have small functions that each do one thing well.</p>
<p>There are a number of helper functions you can use within <code>select()</code>, like <code>starts_with()</code>, <code>ends_with()</code>, <code>matches()</code> and <code>contains()</code>. These let you quickly match larger blocks of variable that meet some criterion. See <code>?select</code> for more details.</p>
<p>You can rename variables with <code>select()</code> by using named arguments:</p>
</td></tr>
<tr><td>
<pre><code class="r">select(flights, tail_num = tailnum)
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 1]
#>
#> tail_num
#> 1 N14228
#> 2 N24211
#> 3 N619AA
#> 4 N804JB
#> .. ...
</code></pre>
</td><td>
<pre><code class="python">flights[['tail_num']].rename(columns={tailnum': 'tail_num'})
</code></pre>
<p>or</p>
<pre><code class="python">pd.DataFrame(dict(tail_num = flights.tailnum))
</code></pre>
<p>or something even worse</p>
</td><td>
<pre><code class="python">flights.ply_select(tail_num = X.tailnum)
</code></pre>
</td></tr>
<tr><td>
<p>But because <code>select()</code> drops all the variables not explicitly mentioned, it's not that use. Instead, use <code>rename()</code>:</p>
</td></tr>
<tr><td>
<pre><code class="r">rename(flights, tail_num = tailnum)
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tail_num
#> 1 2013 1 1 517 2 830 11 UA N14228
#> 2 2013 1 1 533 4 850 20 UA N24211
#> 3 2013 1 1 542 2 923 33 AA N619AA
#> 4 2013 1 1 544 -1 1004 -18 B6 N804JB
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
</td><td>
<pre><code class="python">flights.rename(columns={'tailnum': 'tail_num'})
</code></pre>
</td><td>
<p>That's probably fine. But if you like...</p>
<pre><code class="python">flights.ply_select('-tailnum', tail_num = X.tailnum)
</code></pre>
</td></tr>
<tr><td>
<h2>Extract distinct (unique) rows</h2>
<p>A common use of <code>select()</code> is to find out what values are set of variables takes. This is particularly useful in conjunction with the <code>distinct()</code> verb which only returns the unique values in a table.</p>
</td></tr>
<tr><td>
<pre><code class="r">distinct(select(flights, tailnum))
</code></pre>
<pre><code>#> Source: local data frame [4,044 x 1]
#>
#> tailnum
#> 1 N14228
#> 2 N24211
#> 3 N619AA
#> 4 N804JB
#> .. ...
</code></pre>
</td><td>
<pre><code class="python">flights[['tailnum']].drop_duplicates()
</code></pre>
</td></tr>
<tr><td>
<pre><code class="r">distinct(select(flights, origin, dest))
</code></pre>
<pre><code>#> Source: local data frame [224 x 2]
#>
#> origin dest
#> 1 EWR IAH
#> 2 LGA IAH
#> 3 JFK MIA
#> 4 JFK BQN
#> .. ... ...
</code></pre>
</td><td>
<pre><code class="python">flights[['origin', 'dest']].drop_duplicates()
</code></pre>
</td></tr>
<tr><td>
<p>(This is very similar to <code>base::unique()</code> but should be much faster.)</p>
<h2>Add new columns with <code>mutate()</code></h2>
<p>As well as selecting from the set of existing columns, it's often useful to add new columns that are functions of existing columns. This is the job of <code>mutate()</code>:</p>
</td></tr>
<tr><td>
<pre><code class="r">mutate(flights,
gain = arr_delay - dep_delay,
speed = distance / air_time * 60)
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 18]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 1 1 517 2 830 11 UA N14228
#> 2 2013 1 1 533 4 850 20 UA N24211
#> 3 2013 1 1 542 2 923 33 AA N619AA
#> 4 2013 1 1 544 -1 1004 -18 B6 N804JB
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl), gain (dbl), speed (dbl)
</code></pre>
<p><code>dplyr::mutate()</code> works the same way as <code>plyr::mutate()</code> and similarly to <code>base::transform()</code>. The key difference between <code>mutate()</code> and <code>transform()</code> is that mutate allows you to refer to columns that you just created:</p>
</td><td>
<pre><code class="python">output = flights[:]
output['gain'] = flights.arr_delay - flights.dep_delay
ouptut['speed'] = flights.distance / flights.air_time * 60))
output
</code></pre>
<p>(For this to not be awkward, <code>flights</code> must be saved as a short variable name. The mutation step certainly cannot be chained onto previous steps without saving its input as a variable.)</p>
</td><td>
<pre><code class="python">flights.ply_select('*',
gain = X.arr_delay - X.dep_delay,
speed = X.distance / X.air_time * 60)
</code></pre>
</td></tr>
<tr><td>
<pre><code class="r">mutate(flights,
gain = arr_delay - dep_delay,
gain_per_hour = gain / (air_time / 60)
)
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 18]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 1 1 517 2 830 11 UA N14228
#> 2 2013 1 1 533 4 850 20 UA N24211
#> 3 2013 1 1 542 2 923 33 AA N619AA
#> 4 2013 1 1 544 -1 1004 -18 B6 N804JB
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl), gain (dbl),
#> gain_per_hour (dbl)
</code></pre>
<pre><code class="r">transform(flights,
gain = arr_delay - delay,
gain_per_hour = gain / (air_time / 60)
)
#> Error: object 'gain' not found
</code></pre>
<p>If you only want to keep the new variables, use <code>transmute()</code>:</p>
</td><td>
<pre><code class="python">output = flights[:]
output['gain'] = flights.arr_delay - flights.dep_delay
ouptut['gain_per_hour'] = output.gain / (flights.air_time / 60)
output
</code></pre>
</td><td>
<pre><code class="python">(flights
.ply_select('*', gain = X.arr_delay - X.dep_delay)
.ply_select('*', gain_per_hour = X.gain / (X.air_time / 60))
</code></pre>
<p>This is pretty awkward, because we do not have <code>mutate</code>/<code>transmute</code>'s "later fields can depend on earlier fields" support.</p>
<p>It's still better than raw pandas though. :)</p>
</td></tr>
<tr><td>
<pre><code class="r">transmute(flights,
gain = arr_delay - dep_delay,
gain_per_hour = gain / (air_time / 60)
)
</code></pre>
<pre><code>#> Source: local data frame [336,776 x 2]
#>
#> gain gain_per_hour
#> 1 9 2.379
#> 2 16 4.229
#> 3 31 11.625
#> 4 -17 -5.574
#> .. ... ...
</code></pre>
<h2>Summarise values with <code>summarise()</code></h2>
</td><td>
<pre><code class="python">output = pd.DataFrame()
output['gain'] = flights.arr_delay - flights.dep_delay
output['gain_per_hour'] = output.gain / (flights.air_time / 60)
output
</code></pre>
</td><td>
<pre><code class="python">(flights
.ply_select('*', gain = X.arr_delay - X.dep_delay)
.ply_select('gain', gain_per_hour = X.gain / (X.air_time / 60))
)
</code></pre>
<p>or</p>
<pre><code class="python">(flights
.ply_select('*', gain = X.arr_delay - X.dep_delay)
.ply_select('*', gain_per_hour = X.gain / (X.air_time / 60))
.ply_select('gain', 'gain_per_hour')
)
</code></pre>
</td></tr>
<tr><td>
<p>The last verb is <code>summarise()</code>, which collapses a data frame to a single row. It's not very useful yet:</p>
<pre><code class="r">summarise(flights,
delay = mean(dep_delay, na.rm = TRUE))
</code></pre>
<pre><code>#> Source: local data frame [1 x 1]
#>
#> delay
#> 1 12.64
</code></pre>
<p>This is exactly equivalent to <code>plyr::summarise()</code>.</p>
<h2>Randomly sample rows with <code>sample_n()</code> and <code>sample_frac()</code></h2>
<p>You can use <code>sample_n()</code> and <code>sample_frac()</code> to take a random sample of rows, either a fixed number for <code>sample_n()</code> or a fixed fraction for <code>sample_frac()</code>.</p>
<pre><code class="r">sample_n(flights, 10)
</code></pre>
<pre><code>#> Source: local data frame [10 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 9 30 2104 -6 2323 -18 EV N17560
#> 2 2013 11 8 859 -1 1135 -4 DL N3771K
#> 3 2013 3 17 2110 73 2232 77 EV N14148
#> 4 2013 8 23 2013 2 2257 3 UA N87527
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
<pre><code class="r">sample_frac(flights, 0.01)
</code></pre>
<pre><code>#> Source: local data frame [3,368 x 16]
#>
#> year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1 2013 8 17 533 -7 825 -15 AA N5DVAA
#> 2 2013 2 27 1828 28 1949 9 UA N821UA
#> 3 2013 8 15 1654 71 1921 41 UA N504UA
#> 4 2013 4 8 1724 -5 2008 -42 UA N839UA
#> .. ... ... ... ... ... ... ... ... ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#> (dbl), distance (dbl), hour (dbl), minute (dbl)
</code></pre>
<p>Use <code>replace = TRUE</code> to perform a bootstrap sample, and optionally weight the sample with the <code>weight</code> argument.</p>
<h2>Commonalities</h2>
<p>You may have noticed that all these functions are very similar:</p>
<ul>
<li><p>The first argument is a data frame.</p></li>
<li><p>The subsequent arguments describe what to do with it, and you can refer
to columns in the data frame directly without using <code>$</code>.</p></li>
<li><p>The result is a new data frame</p></li>
</ul>
<p>Together these properties make it easy to chain together multiple simple steps to achieve a complex result.</p>
<p>These five functions provide the basis of a language of data manipulation. At the most basic level, you can only alter a tidy data frame in five useful ways: you can reorder the rows (<code>arrange()</code>), pick observations and variables of interest (<code>filter()</code> and <code>select()</code>), add new variables that are functions of existing variables (<code>mutate()</code>) or collapse many values to a summary (<code>summarise()</code>). The remainder of the language comes from applying the five functions to different types of data, like to grouped data, as described next.</p>
<h1>Grouped operations</h1>
<p>These verbs are useful, but they become really powerful when you combine them with the idea of “group by”, repeating the operation individually on groups of observations within the dataset. In dplyr, you use the <code>group_by()</code> function to describe how to break a dataset down into groups of rows. You can then use the resulting object in exactly the same functions as above; they'll automatically work “by group” when the input is a grouped.</p>
<p>The verbs are affected by grouping as follows:</p>
<ul>
<li><p>grouped <code>select()</code> is the same ungrouped <code>select()</code>, excepted that retains
grouping variables are always retained. </p></li>
<li><p>grouped <code>arrange()</code> orders first by grouping variables</p></li>
<li><p><code>mutate()</code> and <code>filter()</code> are most useful in conjunction with window
functions (like <code>rank()</code>, or <code>min(x) == x</code>), and are described in detail in
<code>vignette("window-function")</code>.</p></li>
<li><p><code>sample_n()</code> and <code>sample_frac()</code> sample the specified number/fraction of
rows in each group.</p></li>
<li><p><code>slice()</code> extracts rows within each group.</p></li>
<li><p><code>summarise()</code> is easy to understand and very useful, and is described in
more detail below.</p></li>
</ul>
<p>In the following example, we split the complete dataset into individual planes and then summarise each plane by counting the number of flights (<code>count = n()</code>) and computing the average distance (<code>dist = mean(Distance, na.rm = TRUE)</code>) and delay (<code>delay = mean(ArrDelay, na.rm = TRUE)</code>). We then use ggplot2 to display the output.</p>
</td></tr>
<tr><td>
<pre><code class="r">planes <- group_by(flights, tailnum)
delay <- summarise(planes,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE))
delay <- filter(delay, count > 20, dist < 2000)
# Interestingly, the average delay is only slightly related to the
# average distance flown by a plane.
ggplot(delay, aes(dist, delay)) +
geom_point(aes(size = count), alpha = 1/2) +
geom_smooth() +
scale_size_area()
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-20"/> </p>
</td><td>
<pre><code class="python">planes = flights.groupby('tailnum')
delay = pd.DataFrame(dict(
count = planes.size(),
dist = planes.distance.mean(),
delay = planes.arr_delay.mean()
))
delay_filtered = delay[(delay.count > 20) & (delay.dist < 2000)]
</code></pre>
<p>Note that both of these intermediate values are necessary. <code>planes</code> must be saved since it is used multiple times to construct the new dataframe, and the first version of <code>delay</code> must be saved since it is used in the filter conditions.</p>
<p>Use of <code>.agg</code> and <code>.rename</code> together could keep us from needing to save <code>planes</code>:</p>
<pre><code class="python">delay = (flights
.groupby('tailnum')
.agg({
'tailnum': 'count',
'distance': 'mean',
'arr_delay': 'mean'
})
.rename(columns={
'tailnum': 'count',
'distance': 'dist',
'arr_delay': 'delay'
})
)
delay_filtered = delay[(delay.count > 20) & (delay.dist < 2000)]
</code></pre>
<p>This is verbose and awkward. (Also, it wouldn't work if we wanted multiple aggregates of a single column.)</p>
</td><td>
<pre><code class="python">delay = (flights
.groupby('tailnum')
.ply_select(
count = X.size(),
dist = X.distance.mean(),
delay = X.arr_delay.mean()
)
.ply_where(X.count > 20, X.dist < 2000)
)
</code></pre>
</td></tr>
<tr><td>
<p>You use <code>summarise()</code> with <strong>aggregate functions</strong>, which take a vector of values, and return a single number. There are many useful functions in base R like <code>min()</code>, <code>max()</code>, <code>mean()</code>, <code>sum()</code>, <code>sd()</code>, <code>median()</code>, and <code>IQR()</code>. dplyr provides a handful of others:</p>
<ul>
<li><p><code>n()</code>: number of observations in the current group</p></li>
<li><p><code>n_distinct(x)</code>: count the number of unique values in <code>x</code>.</p></li>
<li><p><code>first(x)</code>, <code>last(x)</code> and <code>nth(x, n)</code> - these work
similarly to <code>x[1]</code>, <code>x[length(x)]</code>, and <code>x[n]</code> but give you more control
of the result if the value isn't present.</p></li>
</ul>
<p>For example, we could use these to find the number of planes and the number of flights that go to each possible destination:</p>
<pre><code class="r">destinations <- group_by(flights, dest)
summarise(destinations,
planes = n_distinct(tailnum),
flights = n()
)
</code></pre>
<pre><code>#> Source: local data frame [105 x 3]
#>
#> dest planes flights
#> 1 ABQ 108 254
#> 2 ACK 58 265
#> 3 ALB 172 439
#> 4 ANC 6 8
#> .. ... ... ...
</code></pre>