### Reference

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:

### Syntax

```
dplyr::tbl_df(iris)
```

Converts data to tbl class. tbl’s are easier to examine than data frames. R displays only the data that fits onscreen:

```
dplyr::glimpse(iris)
```

Information dense summary of tbl data.

```
utils::View(iris)
```

View data set in the spreadsheet-like display (note capital V).

```
dplyr::%>%
```

Passes object on left hand side as the first argument (or .argument) of function on the righthand side.

```
x %>% f(y) is the same as f(x, y)
y %>% f(x, ., z) is the same as f(x, y, z )
```

"Piping" with %>% makes code more readable, e.g.

```
iris %>%
group_by(Species) %>%
summarise(avg = mean(Sepal.Width)) %>%
arrange(avg)
```

### Reshaping Data

```
tidyr::gather(cases, "year", "n", 2:4)
```

Gather columns into rows.

```
tidyr::unite(data, col, ..., sep)
```

Unite several columns into one.

```
dplyr::data_frame(a = 1:3, b = 4:6)
```

Combine vectors into data frame (optimized).

```
dplyr::arrange(mtcars, mpg)
```

Order rows by values of a column (low to high).

```
dplyr::arrange(mtcars, desc(mpg))
```

Order rows by values of a column (high to low).

```
dplyr::rename(tb, y = year)
```

Rename the columns of a data frame.

```
tidyr::spread(pollution, size, amount)
```

Spread rows into columns.

```
tidyr::separate(storms, date, c("y", "m", "d"))
```

Separate one column into several.

### Subset Observations (Rows)

```
dplyr::filter(iris, Sepal.Length > 7)
```

Extract rows that meet logical criteria.

```
dplyr::distinct(iris)
```

Remove duplicate rows.

```
dplyr::sample_frac(iris, 0.5, replace = TRUE)
```

Randomly select fraction of rows.

```
dplyr::sample_n(iris, 10, replace = TRUE)
```

Randomly select n rows.

```
dplyr::slice(iris, 10:15)
```

Select rows by position.

```
dplyr::top_n(storms, 2, date)
```

Select and order top n entries (by group if grouped data).

#### Logic in R

< | Less than | != | Not equal to |

> | Greater than | %in% | Group membership |

== | Equal to | is.na | Is NA |

<= | Less than or equal to | !is.na | Is not NA |

>= | Greater than or equal to | &,\ | ,!,xor,any,all |

### Subset Variables (Columns)

```
dplyr::select(iris, Sepal.Width, Petal.Length, Species)
```

Select columns by name or helper function.

#### Helper functions for select

```
select(iris, contains("."))
```

Select columns whose name contains a character string.

```
select(iris, ends_with("Length"))
```

Select columns whose name ends with a character string.

```
select(iris, everything())
```

Select every column.

```
select(iris, matches(".t."))
```

Select columns whose name matches a regular expression.

```
select(iris, num_range("x", 1:5))
```

Select columns named x1, x2, x3, x4, x5.

```
select(iris, one_of(c("Species", "Genus")))
```

Select columns whose names are in a group of names.

```
select(iris, starts_with("Sepal"))
```

Select columns whose name starts with a character string.

```
select(iris, Sepal.Length:Petal.Width)
```

Select all columns between Sepal.Length and Petal.Width (inclusive).

```
select(iris, -Species)
```

Select all columns except Species.

### Make New Variables

```
dplyr::mutate(iris, sepal = Sepal.Length + Sepal. Width)
```

Compute and append one or more new columns.

```
dplyr::mutate_each(iris, funs(min_rank))
```

Apply window function to each column.

```
dplyr::transmute(iris, sepal = Sepal.Length + Sepal. Width)
```

Compute one or more new columns. Drop original columns.

#### Window function

Mutate uses window functions, functions that take a vector of values and return another vector of values, such as:

```
dplyr::lead
```

Copy with values shifted by 1.

```
dplyr::lag
```

Copy with values lagged by 1.

```
dplyr::dense_rank
```

Ranks with no gaps.

```
dplyr::min_rank
```

Ranks. Ties get min rank.

```
dplyr::percent_rank
```

Ranks rescaled to [0, 1].

```
dplyr::row_number
```

Ranks. Ties got to first value.

```
dplyr::ntile
```

Bin vector into n buckets.

```
dplyr::between
```

Are values between a and b?

```
dplyr::cume_dist
```

Cumulative distribution.

```
dplyr::cumall
```

Cumulative all

```
dplyr::cumany
```

Cumulative any

```
dplyr::cummean
```

Cumulative mean

```
cumsum
```

Cumulative sum

```
cummax
```

Cumulative max

```
cummin
```

Cumulative min

```
cumprod
```

Cumulative prod

```
pmax
```

Element-wise max

```
pmin
```

Element-wise min

### Summarise Data

```
dplyr::summarise(iris, avg = mean(Sepal.Length))
```

Summarise data into single row of values.

```
dplyr::summarise_each(iris, funs(mean))
```

Apply summary function to each column.

```
dplyr::count(iris, Species, wt = Sepal.Length)
```

Count the number of rows with each unique value of a variable (with or without weights).

#### Summary function

Summarise uses summary functions, functions that take a vector of values and return a single value, such as:

```
dplyr::first
```

The first value of a vector.

```
dplyr::last
```

Last value of a vector.

```
dplyr::nth
```

Nth value of a vector.

```
dplyr::n
```

# of values in a vector.

```
dplyr::n_distinct
```

# of distinct values in a vector.

```
IQR
```

IQR of a vector.

```
min
```

The minimum value in a vector.

```
max
```

The maximum value in a vector.

```
mean
```

Mean value of a vector.

```
median
```

The median value of a vector.

```
var
```

The variance of a vector.

```
sd
```

The standard deviation of a vector

### Combine Data Sets

#### Mutating Joins

```
dplyr::lef_join(a, b, by = "x1")
```

Join matching rows from b to a.

```
dplyr::right_join(a, b, by = "x1")
```

Join matching rows from a to b.

```
dplyr::inner_join(a, b, by = "x1")
```

Join data. Retain only rows in both sets.

```
dplyr::full_join(a, b, by = "x1")
```

Join data. Retain all values, all rows.

#### Filtering Joins

```
dplyr::semi_join(a, b, by = "x1")
```

All rows in a that have a match in b.

```
dplyr::anti_join(a, b, by = "x1")
```

All rows in a that do not have a match in b.

#### Set Operations

```
dplyr::intersect(y, z)
```

Rows that appear in both y and z.

```
dplyr::union(y, z)
```

Rows that appear in either or both y and z.

```
dplyr::setdiff(y, z)
```

Rows that appear in y but not z.

#### Binding

```
dplyr::bind_rows(y, z)
```

Append z to y as new rows.

```
dplyr::bind_cols(y, z)
```

Append z to y as new columns.

Caution: matches rows by position.

### Group Data

null

#### Group data into rows with the same value of Species.

```
dplyr::group_by(iris, Species)
```

#### Remove grouping information from data frame.

```
dplyr::ungroup(iris)
```

#### Compute separate summary row for each group.

```
iris %>% group_by(Species) %>% summarise(…)
```

#### Compute new variables by group.

```
iris %>% group_by(Species) %>% mutate(…)
```

### Usage

```
library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)
```

### Installation

null

#### You can install from CRAN with

```
install.packages("dtplyr")
```

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