# Introduction to dplyr

When working with data you must:

- Figure out what you want to do.

- Describe those tasks in the form of a computer program.

- Execute the program.

The dplyr package makes these steps fast and easy:

- By constraining your options, it helps you think about your data manipulation challenges.

- It provides simple “verbs”, functions that correspond to the most common data manipulation tasks, to help you translate your thoughts into code.

- It uses efficient backends, so you spend less time waiting for the computer.

This document introduces you to dplyr’s basic set of tools, and shows you how to apply them to data frames. dplyr also supports databases via the dbplyr package, once you’ve installed, read `vignette("dbplyr")` to learn more.

## Data: nycflights13

To explore the basic data manipulation verbs of dplyr, we’ll use `nycflights13::flights`. This dataset contains all 336776 flights that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics, and is documented in `?nycflights13`

    library(nycflights13)
    dim(flights)
    #> [1] 336776     19
    flights
    #> # A tibble: 336,776 x 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
    #> # … with 336,772 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>

Note that `nycflights13::flights` is a tibble, a modern reimagining of the data frame. It’s particularly useful for large datasets because it only prints the first few rows. You can learn more about tibbles at http://tibble.tidyverse.org; in particular you can convert data frames to tibbles with `as_tibble()`.

## Single table verbs

Dplyr aims to provide a function for each basic verb of data manipulation:

- `filter()` to select cases based on their values.
- `arrange()` to reorder the cases.
- `select()` and `rename()` to select variables based on their names.
- `mutate()` and `transmute()` to add new variables that are functions of existing variables.
- `summarise()` to condense multiple values to a single value.
- `sample_n()` and `sample_frac()` to take random samples.

### Filter rows with `filter()`

`filter()` allows you to select a subset of rows in a data frame. Like all single verbs, the first argument is the tibble (or data frame). The second and subsequent arguments refer to variables within that data frame, selecting rows where the expression is `TRUE`.

For example, we can select all flights on January 1st with:

    filter(flights, month == 1, day == 1)
    #> # A tibble: 842 x 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
    #> # … with 838 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 is rougly equivalent to this base R code:

    flights[flights$month == 1 & flights$day == 1, ]

### Arrange rows with `arrange()`

`arrange()` works similarly to `filter()` 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:

    arrange(flights, year, month, day)
    #> # A tibble: 336,776 x 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
    #> # … with 336,772 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>

Use `desc()` to order a column in descending order:

    arrange(flights, desc(arr_delay))
    #> # A tibble: 336,776 x 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     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
    #> # … with 336,772 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>

### Select columns with `select()`

Often you work with large datasets with many columns but only a few are actually of interest to you. `select()` allows you to rapidly zoom in on a useful subset using operations that usually only work on numeric variable positions:

    # Select columns by name
    select(flights, year, month, day)
    #> # A tibble: 336,776 x 3
    #>    year month   day
    #>   <int> <int> <int>
    #> 1  2013     1     1
    #> 2  2013     1     1
    #> 3  2013     1     1
    #> 4  2013     1     1
    #> # … with 336,772 more rows
    # Select all columns between year and day (inclusive)
    select(flights, year:day)
    #> # A tibble: 336,776 x 3
    #>    year month   day
    #>   <int> <int> <int>
    #> 1  2013     1     1
    #> 2  2013     1     1
    #> 3  2013     1     1
    #> 4  2013     1     1
    #> # … with 336,772 more rows
    # Select all columns except those from year to day (inclusive)
    select(flights, -(year:day))
    #> # A tibble: 336,776 x 16
    #>   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
    #> # … with 336,772 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>

There are a number of helper functions you can use within `select()`, like `starts_with()`, `ends_with()`, `matches()` and `contains()`. These let you quickly match larger blocks of variables that meet some criterion. See `?select` for more details.

You can rename variables with `select()` by using named arguments:

    select(flights, tail_num = tailnum)
    #> # A tibble: 336,776 x 1
    #>   tail_num
    #>   <chr>
    #> 1 N14228
    #> 2 N24211
    #> 3 N619AA
    #> 4 N804JB
    #> # … with 336,772 more rows

But because `select()` drops all the variables not explicitly mentioned, it’s not that useful. Instead, use `rename()`:

    rename(flights, tail_num = tailnum)
    #> # A tibble: 336,776 x 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
    #> # … with 336,772 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>

### Add new columns with `mutate()`

Besides selecting sets of existing columns, it’s often useful to add new columns that are functions of existing columns. This is the job of `mutate()`:

    mutate(flights,
      gain = arr_delay - dep_delay,
      speed = distance / air_time * 60
    )
    #> # A tibble: 336,776 x 21
    #>    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
    #> # … with 336,772 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>

`dplyr::mutate()` is similar to the base `transform()`, but allows you to refer to columns that you’ve just created:

    mutate(flights,
      gain = arr_delay - dep_delay,
      gain_per_hour = gain / (air_time / 60)
    )
    #> # A tibble: 336,776 x 21
    #>    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
    #> # … with 336,772 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>, gain_per_hour <dbl>

If you only want to keep the new variables, use `transmute()`:

    transmute(flights,
      gain = arr_delay - dep_delay,
      gain_per_hour = gain / (air_time / 60)
    )
    #> # A tibble: 336,776 x 2
    #>    gain gain_per_hour
    #>   <dbl>         <dbl>
    #> 1     9          2.38
    #> 2    16          4.23
    #> 3    31         11.6
    #> 4   -17         -5.57
    #> # … with 336,772 more rows

### Summarise values with `summarise()`

The last verb is `summarise()`. It collapses a data frame to a single row.

    summarise(flights,
      delay = mean(dep_delay, na.rm = TRUE)
    )
    #> # A tibble: 1 x 1
    #>   delay
    #>   <dbl>
    #> 1  12.6

It’s not that useful until we learn the `group_by()` verb below.

### Randomly sample rows with `sample_n()` and `sample_frac()`

You can use `sample_n()` and `sample_frac()` to take a random sample of rows: use `sample_n()` for a fixed number and `sample_frac()` for a fixed fraction.

    sample_n(flights, 10)
    #> # A tibble: 10 x 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     9     9     1440           1429        11     1649           1655
    #> 2  2013     5    15     1427           1429        -2     1728           1738
    #> 3  2013     8    14     1121           1120         1     1228           1233
    #> 4  2013     8     4     1448           1429        19     1657           1659
    #> # … with 6 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>
    sample_frac(flights, 0.01)
    #> # A tibble: 3,368 x 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    11     7     1855           1900        -5     2238           2251
    #> 2  2013     3    10     1930           1935        -5     2213           2242
    #> 3  2013     2    22     1622           1615         7     1820           1831
    #> 4  2013     8     9      602            605        -3      744            749
    #> # … with 3,364 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>

Use `replace = TRUE` to perform a bootstrap sample. If needed, you can weight the sample with the `weight` argument.

### Commonalities

You may have noticed that the syntax and function of all these verbs are very similar:

- The first argument is a data frame.

- The subsequent arguments describe what to do with the data frame. You can refer to columns in the data frame directly without using `$`.

- The result is a new data frame

Together these properties make it easy to chain together multiple simple steps to achieve a complex result.

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 (`arrange()`), pick observations and variables of interest (`filter()` and `select()`), add new variables that are functions of existing variables (`mutate()`), or collapse many values to a summary (`summarise()`). The remainder of the language comes from applying the five functions to different types of data. For example, I’ll discuss how these functions work with grouped data.

## Patterns of operations

The dplyr verbs can be classified by the type of operations they accomplish (we sometimes speak of their semantics, i.e., their meaning). The most important and useful distinction is between grouped and ungrouped operations. In addition, it is helpful to have a good grasp of the difference between select and mutate operations.

### Grouped operations

The dplyr verbs are useful on their own, but they become even more powerful when you apply them to groups of observations within a dataset. In dplyr, you do this with the `group_by()` function. It breaks down a dataset into specified groups of rows. When you then apply the verbs above on the resulting object they’ll be automatically applied “by group”.

Grouping affects the verbs as follows:

- grouped `select()` is the same as ungrouped `select()`, except that grouping variables are always retained.

- grouped `arrange()` is the same as ungrouped; unless you set `.by_group = TRUE`, in which case it orders first by the grouping variables

- `mutate()` and `filter()` are most useful in conjunction with window functions (like `rank()`, or `min(x) == x`). They are described in detail in `vignette("window-functions")`.

- `sample_n()` and `sample_frac()` sample the specified number/fraction of rows in each group.

- `summarise()` computes the summary for each group.

In the following example, we split the complete dataset into individual planes and then summarise each plane by counting the number of flights (`count = n()`) and computing the average distance (`dist = mean(distance, na.rm = TRUE)`) and arrival delay (`delay = mean(arr_delay, na.rm = TRUE)`). We then use ggplot2 to display the output.

    by_tailnum <- group_by(flights, tailnum)
    delay <- summarise(by_tailnum,
      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()

You use `summarise()` with aggregate functions, which take a vector of values and return a single number. There are many useful examples of such functions in base R like `min()`, `max()`, `mean()`, `sum()`, `sd()`, `median()`, and `IQR()`. dplyr provides a handful of others:

- `n()`: the number of observations in the current group

- `n_distinct(x)`:the number of unique values in `x`.

- `first(x)`, `last(x)` and `nth(x, n)` - these work similarly to `x[1]`, `x[length(x)]`, and `x[n]` but give you more control over the result if the value is missing.

For example, we could use these to find the number of planes and the number of flights that go to each possible destination:

    destinations <- group_by(flights, dest)
    summarise(destinations,
      planes = n_distinct(tailnum),
      flights = n()
    )
    #> # A tibble: 105 x 3
    #>   dest  planes flights
    #>   <chr>  <int>   <int>
    #> 1 ABQ      108     254
    #> 2 ACK       58     265
    #> 3 ALB      172     439
    #> 4 ANC        6       8
    #> # … with 101 more rows

When you group by multiple variables, each summary peels off one level of the grouping. That makes it easy to progressively roll-up a dataset:

    daily <- group_by(flights, year, month, day)
    (per_day   <- summarise(daily, flights = n()))
    #> # A tibble: 365 x 4
    #> # Groups:   year, month [12]
    #>    year month   day flights
    #>   <int> <int> <int>   <int>
    #> 1  2013     1     1     842
    #> 2  2013     1     2     943
    #> 3  2013     1     3     914
    #> 4  2013     1     4     915
    #> # … with 361 more rows
    (per_month <- summarise(per_day, flights = sum(flights)))
    #> # A tibble: 12 x 3
    #> # Groups:   year [1]
    #>    year month flights
    #>   <int> <int>   <int>
    #> 1  2013     1   27004
    #> 2  2013     2   24951
    #> 3  2013     3   28834
    #> 4  2013     4   28330
    #> # … with 8 more rows
    (per_year  <- summarise(per_month, flights = sum(flights)))
    #> # A tibble: 1 x 2
    #>    year flights
    #>   <int>   <int>
    #> 1  2013  336776

However you need to be careful when progressively rolling up summaries like this: it’s ok for sums and counts, but you need to think about weighting for means and variances (it’s not possible to do this exactly for medians).

### Selecting operations

One of the appealing features of dplyr is that you can refer to columns from the tibble as if they were regular variables. However, the syntactic uniformity of referring to bare column names hides semantical differences across the verbs. A column symbol supplied to `select()` does not have the same meaning as the same symbol supplied to `mutate()`.

Selecting operations expect column names and positions. Hence, when you call `select()` with bare variable names, they actually represent their own positions in the tibble. The following calls are completely equivalent from dplyr’s point of view:

    # `year` represents the integer 1
    select(flights, year)
    #> # A tibble: 336,776 x 1
    #>    year
    #>   <int>
    #> 1  2013
    #> 2  2013
    #> 3  2013
    #> 4  2013
    #> # … with 336,772 more rows
    select(flights, 1)
    #> # A tibble: 336,776 x 1
    #>    year
    #>   <int>
    #> 1  2013
    #> 2  2013
    #> 3  2013
    #> 4  2013
    #> # … with 336,772 more rows

By the same token, this means that you cannot refer to variables from the surrounding context if they have the same name as one of the columns. In the following example, `year` still represents 1, not 5:

    year <- 5
    select(flights, year)

One useful subtlety is that this only applies to bare names and to selecting calls like `c(year, month, day)` or `year:day`. In all other cases, the columns of the data frame are not put in scope. This allows you to refer to contextual variables in selection helpers:

    year <- "dep"
    select(flights, starts_with(year))
    #> # A tibble: 336,776 x 2
    #>   dep_time dep_delay
    #>      <int>     <dbl>
    #> 1      517         2
    #> 2      533         4
    #> 3      542         2
    #> 4      544        -1
    #> # … with 336,772 more rows

These semantics are usually intuitive. But note the subtle difference:

    year <- 5
    select(flights, year, identity(year))
    #> # A tibble: 336,776 x 2
    #>    year sched_dep_time
    #>   <int>          <int>
    #> 1  2013            515
    #> 2  2013            529
    #> 3  2013            540
    #> 4  2013            545
    #> # … with 336,772 more rows

In the first argument, `year` represents its own position `1`. In the second argument, `year` is evaluated in the surrounding context and represents the fifth column.

For a long time, `select()` used to only understand column positions. Counting from dplyr 0.6, it now understands column names as well. This makes it a bit easier to program with `select()`:

    vars <- c("year", "month")
    select(flights, vars, "day")
    #> Note: Using an external vector in selections is ambiguous.
    #> ℹ Use `all_of(vars)` instead of `vars` to silence this message.
    #> ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
    #> This message is displayed once per session.
    #> # A tibble: 336,776 x 3
    #>    year month   day
    #>   <int> <int> <int>
    #> 1  2013     1     1
    #> 2  2013     1     1
    #> 3  2013     1     1
    #> 4  2013     1     1
    #> # … with 336,772 more rows

Note that the code above is somewhat unsafe because you might have added a column named `vars` to the tibble, or you might apply the code to another data frame containing such a column. To avoid this issue, you can wrap the variable in an `identity()` call as we mentioned above, as this will bypass column names. However, a more explicit and general method that works in all dplyr verbs is to unquote the variable with the `!!` operator. This tells dplyr to bypass the data frame and to directly look in the context:

    # Let's create a new `vars` column:
    flights$vars <- flights$year

    # The new column won't be an issue if you evaluate `vars` in the
    # context with the `!!` operator:
    vars <- c("year", "month", "day")
    select(flights, !! vars)
    #> # A tibble: 336,776 x 3
    #>    year month   day
    #>   <int> <int> <int>
    #> 1  2013     1     1
    #> 2  2013     1     1
    #> 3  2013     1     1
    #> 4  2013     1     1
    #> # … with 336,772 more rows

This operator is very useful when you need to use dplyr within custom functions. You can learn more about it in `vignette("programming")`. However it is important to understand the semantics of the verbs you are unquoting into, that is, the values they understand. As we have just seen, `select()` supports names and positions of columns. But that won’t be the case in other verbs like `mutate()` because they have different semantics.

### Mutating operations

Mutate semantics are quite different from selection semantics. Whereas `select()` expects column names or positions, `mutate()` expects column vectors. Let’s create a smaller tibble for clarity:

    df <- select(flights, year:dep_time)

When we use `select()`, the bare column names stand for ther own positions in the tibble. For `mutate()` on the other hand, column symbols represent the actual column vectors stored in the tibble. Consider what happens if we give a string or a number to `mutate()`:

    mutate(df, "year", 2)
    #> # A tibble: 336,776 x 6
    #>    year month   day dep_time `"year"`   `2`
    #>   <int> <int> <int>    <int> <chr>    <dbl>
    #> 1  2013     1     1      517 year         2
    #> 2  2013     1     1      533 year         2
    #> 3  2013     1     1      542 year         2
    #> 4  2013     1     1      544 year         2
    #> # … with 336,772 more rows

`mutate()` gets length-1 vectors that it interprets as new columns in the data frame. These vectors are recycled so they match the number of rows. That’s why it doesn’t make sense to supply expressions like `"year" + 10` to `mutate()`. This amounts to adding 10 to a string! The correct expression is:

    mutate(df, year + 10)
    #> # A tibble: 336,776 x 5
    #>    year month   day dep_time `year + 10`
    #>   <int> <int> <int>    <int>       <dbl>
    #> 1  2013     1     1      517        2023
    #> 2  2013     1     1      533        2023
    #> 3  2013     1     1      542        2023
    #> 4  2013     1     1      544        2023
    #> # … with 336,772 more rows

In the same way, you can unquote values from the context if these values represent a valid column. They must be either length 1 (they then get recycled) or have the same length as the number of rows. In the following example we create a new vector that we add to the data frame:

    var <- seq(1, nrow(df))
    mutate(df, new = var)
    #> # A tibble: 336,776 x 5
    #>    year month   day dep_time   new
    #>   <int> <int> <int>    <int> <int>
    #> 1  2013     1     1      517     1
    #> 2  2013     1     1      533     2
    #> 3  2013     1     1      542     3
    #> 4  2013     1     1      544     4
    #> # … with 336,772 more rows

A case in point is `group_by()`. While you might think it has select semantics, it actually has mutate semantics. This is quite handy as it allows to group by a modified column:

    group_by(df, month)
    #> # A tibble: 336,776 x 4
    #> # Groups:   month [12]
    #>    year month   day dep_time
    #>   <int> <int> <int>    <int>
    #> 1  2013     1     1      517
    #> 2  2013     1     1      533
    #> 3  2013     1     1      542
    #> 4  2013     1     1      544
    #> # … with 336,772 more rows
    group_by(df, month = as.factor(month))
    #> # A tibble: 336,776 x 4
    #> # Groups:   month [12]
    #>    year month   day dep_time
    #>   <int> <fct> <int>    <int>
    #> 1  2013 1         1      517
    #> 2  2013 1         1      533
    #> 3  2013 1         1      542
    #> 4  2013 1         1      544
    #> # … with 336,772 more rows
    group_by(df, day_binned = cut(day, 3))
    #> # A tibble: 336,776 x 5
    #> # Groups:   day_binned [3]
    #>    year month   day dep_time day_binned
    #>   <int> <int> <int>    <int> <fct>
    #> 1  2013     1     1      517 (0.97,11]
    #> 2  2013     1     1      533 (0.97,11]
    #> 3  2013     1     1      542 (0.97,11]
    #> 4  2013     1     1      544 (0.97,11]
    #> # … with 336,772 more rows

This is why you can’t supply a column name to `group_by()`. This amounts to creating a new column containing the string recycled to the number of rows:

    group_by(df, "month")
    #> # A tibble: 336,776 x 5
    #> # Groups:   "month" [1]
    #>    year month   day dep_time `"month"`
    #>   <int> <int> <int>    <int> <chr>
    #> 1  2013     1     1      517 month
    #> 2  2013     1     1      533 month
    #> 3  2013     1     1      542 month
    #> 4  2013     1     1      544 month
    #> # … with 336,772 more rows

Since grouping with select semantics can be sometimes useful as well, we have added the `group_by_at()` variant. In dplyr, variants suffixed with `_at()` support selection semantics in their second argument. You just need to wrap the selection with `vars()`:

    group_by_at(df, vars(year:day))
    #> # A tibble: 336,776 x 4
    #> # Groups:   year, month, day [365]
    #>    year month   day dep_time
    #>   <int> <int> <int>    <int>
    #> 1  2013     1     1      517
    #> 2  2013     1     1      533
    #> 3  2013     1     1      542
    #> 4  2013     1     1      544
    #> # … with 336,772 more rows

You can read more about the `_at()` and `_if()` variants in the `?scoped` help page.

## Piping

The dplyr API is functional in the sense that function calls don’t have side-effects. You must always save their results. This doesn’t lead to particularly elegant code, especially if you want to do many operations at once. You either have to do it step-by-step:

    a1 <- group_by(flights, year, month, day)
    a2 <- select(a1, arr_delay, dep_delay)
    a3 <- summarise(a2,
      arr = mean(arr_delay, na.rm = TRUE),
      dep = mean(dep_delay, na.rm = TRUE))
    a4 <- filter(a3, arr > 30 | dep > 30)

Or if you don’t want to name the intermediate results, you need to wrap the function calls inside each other:

    filter(
      summarise(
        select(
          group_by(flights, year, month, day),
          arr_delay, dep_delay
        ),
        arr = mean(arr_delay, na.rm = TRUE),
        dep = mean(dep_delay, na.rm = TRUE)
      ),
      arr > 30 | dep > 30
    )
    #> Adding missing grouping variables: `year`, `month`, `day`
    #> # A tibble: 49 x 5
    #> # Groups:   year, month [11]
    #>    year month   day   arr   dep
    #>   <int> <int> <int> <dbl> <dbl>
    #> 1  2013     1    16  34.2  24.6
    #> 2  2013     1    31  32.6  28.7
    #> 3  2013     2    11  36.3  39.1
    #> 4  2013     2    27  31.3  37.8
    #> # … with 45 more rows

This is difficult to read because the order of the operations is from inside to out. Thus, the arguments are a long way away from the function. To get around this problem, dplyr provides the `%>%` operator from magrittr. `x %>% f(y)` turns into `f(x, y)` so you can use it to rewrite multiple operations that you can read left-to-right, top-to-bottom:

    flights %>%
      group_by(year, month, day) %>%
      select(arr_delay, dep_delay) %>%
      summarise(
        arr = mean(arr_delay, na.rm = TRUE),
        dep = mean(dep_delay, na.rm = TRUE)
      ) %>%
      filter(arr > 30 | dep > 30)

## Other data sources

As well as data frames, dplyr works with data that is stored in other ways, like data tables, databases and multidimensional arrays.

### Data table

dplyr also provides data table methods for all verbs through dtplyr. If you’re using data.tables already this lets you to use dplyr syntax for data manipulation, and data.table for everything else.

For multiple operations, data.table can be faster because you usually use it with multiple verbs simultaneously. For example, with data table you can do a mutate and a select in a single step. It’s smart enough to know that there’s no point in computing the new variable for rows you’re about to throw away.

The advantages of using dplyr with data tables are:

- For common data manipulation tasks, it insulates you from the reference semantics of data.tables, and protects you from accidentally modifying your data.

- Instead of one complex method built on the subscripting operator (`[`), it provides many simple methods.

### Databases

dplyr also allows you to use the same verbs with a remote database. It takes care of generating the SQL for you so that you can avoid the cognitive challenge of constantly switching between languages. To use these capabilities, you’ll need to install the dbplyr package and then read `vignette("dbplyr")` for the details.

### Multidimensional arrays / cubes

`tbl_cube()` provides an experimental interface to multidimensional arrays or data cubes. If you’re using this form of data in R, please get in touch so I can better understand your needs.

## Comparisons

Compared to all existing options, dplyr:

- abstracts away how your data is stored, so that you can work with data frames, data tables and remote databases using the same set of functions. This lets you focus on what you want to achieve, not on the logistics of data storage.

- provides a thoughtful default `print()` method that doesn’t automatically print pages of data to the screen (this was inspired by data table’s output).

Compared to base functions:

- dplyr is much more consistent; functions have the same interface. So once you’ve mastered one, you can easily pick up the others

- base functions tend to be based around vectors; dplyr is based around data frames

Compared to plyr, dplyr:

- is much much faster

- provides a better thought out set of joins

- only provides tools for working with data frames (e.g. most of dplyr is equivalent to `ddply()` + various functions, `do()` is equivalent to `dlply()`)

Compared to virtual data frame approaches:

- it doesn’t pretend that you have a data frame: if you want to run lm etc, you’ll still need to manually pull down the data

- it doesn’t provide methods for R summary functions (e.g. `mean()`, or `sum()`)