Visualisation is an important tool for insight generation, but it is rare that you get the data in exactly the right form you need for visualisation. Often you'll need to create some new variables or summaries, or maybe you just want to rename the variables or reorder the observations in order 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 teach you how to transform your data using the dplyr package.
`nycflights13` data frame. This dataset contains all `r format(nrow(nycflights13::flights), big.mark = ",")` flights that departed from New York City in 2013. The data comes from the US [Bureau of Transportation Statistics](http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=120&Link=0), and is documented in `?nycflights13`.
The first important thing to notice about this dataset is that it prints a little differently to most data frames: it only shows the first ten rows and all the columns that fit on one screen. If you want to see the whole dataset, use `View()` which will open the dataset in the RStudio viewer.
It also prints an abbreviated description of the column type:
* int: integer
* dbl: double (real)
* chr: character
* lgl: logical
* date: dates
* time: times
It prints differently because it has a different "class" to usual data frames:
```{r}
class(flights)
```
This is called a `tbl_df` (prounced tibble diff) or a `data_frame` (pronunced "data underscore frame"; cf. `data dot frame`)
You'll learn more about how that works in data structures. If you want to convert your own data frames to this special case, use `as.data_frame()`. I recommend it for large data frames as it makes interactive exploration much less painful.
To create your own new tbl\_df from individual vectors, use `data_frame()`:
These can all be used in conjunction with `group_by()` which changes the scope of each function from operating on the entire dataset to operating on it group-by-group. These six functions verbs for a language of data manipulation.
`filter()` allows you to subset observations. The first argument is the name of the data frame. The second and subsequent arguments are the expressions that filter the data frame. For example, we can select all flights on January 1st with:
When you run this line of code, dplyr executes the filtering operation and returns a new data frame. dplyr functions never modify their inputs, so if you want to save the results, you'll need to use the assignment operator `<-`:
(Although `filter()` will also drop missings). `filter()` works similarly to `subset()` except that you can give it any number of filtering conditions, which are joined together with `&`.
R provides the standard suite of numeric comparison operators: `>`, `>=`, `<`, `<=`, `!=` (not equal), and `==` (equal). When you're starting out with R, the easiest mistake to make is to use `=` instead of `==` when testing for equality. When this happens you'll get a somewhat uninformative error:
Sometimes you can simplify complicated subsetting by remembering De Morgan's law: `!(x & y)` is the same as `!x | !y`, and `!(x | y)` is the same as `!x & !y`. For example, if you wanted to find flights that weren't delayed (on arrival or departure) by more than two hours, you could use either of the following two filters:
Note that R has both `&` and `|` and `&&` and `||`. `&` and `|` are vectorised: you give them two vectors of logical values and they return a vector of logical values. `&&` and `||` are scalar operators: you give them individual `TRUE`s or `FALSE`s. They're used if `if` statements when programming. You'll learn about that later on.
Sometimes you want to find all rows after the first `TRUE`, or all rows until the first `FALSE`. The cumulative functions `cumany()` and `cumall()` allow you to find these values:
```{r}
df <- data_frame(
x = c(FALSE, TRUE, FALSE),
y = c(TRUE, FALSE, TRUE)
)
filter(df, cumany(x)) # all rows after first TRUE
filter(df, cumall(y)) # all rows until first FALSE
```
Whenever you start using multipart expressions in your `filter()`, it's typically a good idea to make them explicit variables with `mutate()` so that you can more easily check your work. You'll learn about `mutate()` in the next section.
One important feature of R that can make comparison tricky is the missing value, `NA`. `NA` represents an unknown value so missing values are "infectious": any operation involving an unknown value will also be unknown.
`filter()` only includes rows where the condition is `TRUE`; it excludes both `FALSE` and `NA` values. If you want to preserve missing values, ask for them explicitly:
`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:
```{r}
arrange(flights, year, month, day)
```
Use `desc()` to order a column in descending order:
It's not uncommon to get datasets with hundreds or even thousands of variables. In this case, the first challenge is often narrowing in on the variables you're actually interested in. `select()` allows you to rapidly zoom in on a useful subset using operations based on the names of the variables:
But because `select()` drops all the variables not explicitly mentioned, it's not that useful. Instead, use `rename()`, which is a variant of `select()` that keeps variables by default:
This function works similarly to the `select` argument in `base::subset()`. Because the dplyr philosophy is to have small functions that do one thing well, it's its own function in dplyr.
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()` always adds new columns at the end so we'll start by creating a narrower dataset so we can see the new variables. Remember that when you're in RStudio, the easiest way to see all the columns is `View()`
There are many functions for creating new variables. The key property is that the function must be vectorised: it needs to return the same number of outputs as inputs. There's no way to list every possible function that you might use, but here's a selection of the functions that I use most often:
However, that's not terribly useful until we pair it with `group_by()`. This changes the unit of analysis from the complete dataset to individual groups. When you the dplyr verbs on a grouped data frame they'll be automatically applied "by group".
Imagine we want to explore the relationship between the distance and average delay for each location. Using what you already know about dplyr, you might write code like this:
```{r, fig.width = 6}
by_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE))
delay <- filter(delay, count > 20, dest != "HNL")
# Interesting it looks like delays increase with distance up to
# ~750 miles and then decrease. Maybe as flights get longer there's
* Summarise to compute distance, average delay, and number of flights.
* Filter to remove noisy points and Honolulu airport which is almost
twice as far away as the next closest airport.
This code is a little frustraing to write because we have to give each intermediate data frame a name, even though we don't care about it. Naming things well is hard, so this slows us down. There's another way to tackle the same problem with the pipe, `%>%`:
This focuses on the transformations, not what's being transformed, which makes the code easier to read. You can read it as a series of imperative statements: group, then summarise, then filter. As suggested by this reading, a good way to pronounce `%>%` when reading code is "then".
Behind the scenes, `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. We'll use piping frequently from now on because it considerably improves the readability of code, and we'll come back to it in more detail in Chapter XYZ.
The pipe makes it easier to solve complex problems by joining together simple pieces. Each dplyr function does one thing well, helping you advance to your goal with one small step. You can check your work frequently, and if you get stuck, you just need to think: "what's one small thing I could do to advance towards a solution".
Whenever you do any aggregation, it's always a good idea to include either a count (`n()`), or a count of non-missing values (`sum(!is.na(x))`). That way you can check that you're not drawing conclusions based on very small amounts of data amount of non-missing data.
You'll see that most of the very delayed flight numbers happen very rarely. The shape of this plot is very characteristic: whenever you plot a mean (or many other summaries) vs number of observations, you'll see that the variation decreases as the sample size increases.
There's another variation on this type of plot as shown below. Here I use the Lahman package to compute the batting average (number of hits / number of attempts) of every major league baseball player. When I plot the skill of the batter against the number of times batted, you see two patterns:
1. As above, the variation in our aggregate decreases as we get more
data points.
2. There's a correlation between skill and n. This is because baseball
teams controls who gets to try and hit the ball, and obviously they'll
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, and it's not possible to do it exactly for medians.
### Grouped mutates (and filters)
* `mutate()` and `filter()` are most useful in conjunction with window
functions (like `rank()`, or `min(x) == x`). They are described in detail in
the windows function vignette `vignette("window-functions")`.
A grouped filter is basically like a grouped mutate followed by a regular filter. I generally avoid them except for quick and dirty manipulations. Otherwise it's too hard to check that you've done the manipulation correctly.
It's rare that a data analysis involves only a single table of data. In practice, you'll normally have many tables that contribute to an analysis, and you need flexible tools to combine them. In dplyr, there are three families of verbs that work with two tables at a time:
* Mutating joins, which add new variables to one table from matching rows in
another.
* Filtering joins, which filter observations from one table based on whether or
not they match an observation in the other table.
* Set operations, which combine the observations in the data sets as if they
were set elements.
(This discussion assumes that you have [tidy data](http://www.jstatsoft.org/v59/i10/), where the rows are observations and the columns are variables. If you're not familiar with that framework, I'd recommend reading up on it first.)
All two-table verbs work similarly. The first two arguments are `x` and `y`, and provide the tables to combine. The output is always a new table with the same type as `x`.
### Mutating joins
Mutating joins allow you to combine variables from multiple tables. For example, take the nycflights13 data. In one table we have flight information with an abbreviation for carrier, and in another we have a mapping between abbreviations and full names. You can use a join to add the carrier names to the flight data:
```{r, warning = FALSE}
library("nycflights13")
# Drop unimportant variables so it's easier to understand the join results.
As well as `x` and `y`, each mutating join takes an argument `by` that controls which variables are used to match observations in the two tables. There are a few ways to specify it, as I illustrate below with various tables from nycflights13:
* `NULL`, the default. dplyr will will use all variables that appear in
both tables, a __natural__ join. For example, the flights and
weather tables match on their common variables: year, month, day, hour and
origin.
```{r}
flights2 %>% left_join(weather)
```
* A character vector, `by = "x"`. Like a natural join, but uses only
some of the common variables. For example, `flights` and `planes` have
`year` columns, but they mean different things so we only want to join by
`tailnum`.
```{r}
flights2 %>% left_join(planes, by = "tailnum")
```
Note that the year columns in the output are disambiguated with a suffix.
* A named character vector: `by = c("x" = "a")`. This will
match variable `x` in table `x` to variable `a` in table `b`. The
variables from use will be used in the output.
Each flight has an origin and destination `airport`, so we need to specify
There are four types of mutating join, which differ in their behaviour when a match is not found. We'll illustrate each with a simple example:
```{r}
(df1 <- data_frame(x = c(1, 2), y = 2:1))
(df2 <- data_frame(x = c(1, 3), a = 10, b = "a"))
```
* `inner_join(x, y)` only includes observations that match in both `x` and `y`.
```{r}
df1 %>% inner_join(df2) %>% knitr::kable()
```
* `left_join(x, y)` includes all observations in `x`, regardless of whether
they match or not. This is the most commonly used join because it ensures
that you don't lose observations from your primary table.
```{r}
df1 %>% left_join(df2)
```
* `right_join(x, y)` includes all observations in `y`. It's equivalent to
`left_join(y, x)`, but the columns will be ordered differently.
```{r}
df1 %>% right_join(df2)
df2 %>% left_join(df1)
```
* `full_join()` includes all observations from `x` and `y`.
```{r}
df1 %>% full_join(df2)
```
The left, right and full joins are collectively know as __outer joins__. When a row doesn't match in an outer join, the new variables are filled in with missing values.
#### Observations
While mutating joins are primarily used to add new variables, they can also generate new observations. If a match is not unique, a join will add all possible combinations (the Cartesian product) of the matching observations:
Filtering joins match obserations in the same way as mutating joins, but affect the observations, not the variables. There are two types:
* `semi_join(x, y)` __keeps__ all observations in `x` that have a match in `y`.
* `anti_join(x, y)` __drops__ all observations in `x` that have a match in `y`.
These are most useful for diagnosing join mismatches. For example, there are many flights in the nycflights13 dataset that don't have a matching tail number in the planes table:
```{r}
library("nycflights13")
flights %>%
anti_join(planes, by = "tailnum") %>%
count(tailnum, sort = TRUE)
```
If you're worried about what observations your joins will match, start with a `semi_join()` or `anti_join()`. `semi_join()` and `anti_join()` never duplicate; they only ever remove observations.
The final type of two-table verb is set operations. These expect the `x` and `y` inputs to have the same variables, and treat the observations like sets:
* `intersect(x, y)`: return only observations in both `x` and `y`
* `union(x, y)`: return unique observations in `x` and `y`
* `setdiff(x, y)`: return observations in `x`, but not in `y`.
Given this simple data:
```{r}
(df1 <- data_frame(x = 1:2, y = c(1L, 1L)))
(df2 <- data_frame(x = 1:2, y = 1:2))
```
The four possibilities are:
```{r}
intersect(df1, df2)
# Note that we get 3 rows, not 4
union(df1, df2)
setdiff(df1, df2)
setdiff(df2, df1)
```
### Databases
Each two-table verb has a straightforward SQL equivalent:
| R | SQL
|------------------|--------
| `inner_join()` | `SELECT * FROM x JOIN y ON x.a = y.a`
| `left_join()` | `SELECT * FROM x LEFT JOIN y ON x.a = y.a`
| `right_join()` | `SELECT * FROM x RIGHT JOIN y ON x.a = y.a`
| `full_join()` | `SELECT * FROM x FULL JOIN y ON x.a = y.a`
| `semi_join()` | `SELECT * FROM x WHERE EXISTS (SELECT 1 FROM y WHERE x.a = y.a)`
| `anti_join()` | `SELECT * FROM x WHERE NOT EXISTS (SELECT 1 FROM y WHERE x.a = y.a)`
| `intersect(x, y)`| `SELECT * FROM x INTERSECT SELECT * FROM y`
| `union(x, y)` | `SELECT * FROM x UNION SELECT * FROM y`
| `setdiff(x, y)` | `SELECT * FROM x EXCEPT SELECT * FROM y`
`x` and `y` don't have to be tables in the same database. If you specify `copy = TRUE`, dplyr will copy the `y` table into the same location as the `x` variable. This is useful if you've downloaded a summarised dataset and determined a subset of interest that you now want the full data for. You can use `semi_join(x, y, copy = TRUE)` to upload the indices of interest to a temporary table in the same database as `x`, and then perform a efficient semi join in the database.
If you're working with large data, it maybe also be helpful to set `auto_index = TRUE`. That will automatically add an index on the join variables to the temporary table.
### Coercion rules
When joining tables, dplyr is a little more conservative than base R about the types of variable that it considers equivalent. This is mostly likely to surprise if you're working factors:
* Factors with different levels are coerced to character with a warning:
```{r}
df1 <- data_frame(x = 1, y = factor("a"))
df2 <- data_frame(x = 2, y = factor("b"))
full_join(df1, df2) %>% str()
```
* Factors with the same levels in a different order are coerced to character
