dplyr Getting started with dplyr
Remarks
This section provides an overview of what dplyr is, and why a developer might want to use it.
It should also mention any large subjects within dplyr, and link out to the related topics. Since the Documentation for dplyr is new, you may need to create initial versions of those related topics.
Basic Verbs
library(dplyr) library(nycflights13)
There are several verbs most commonly used in dplyr to modify datasets.
select
Select tailnum , type , model variables from the dataframe planes :
select(planes, tailnum, type, model)
## # A tibble: 3,322 × 3
## tailnum type model
## <chr> <chr> <chr>
## 1 N10156 Fixed wing multi engine EMB-145XR
## 2 N102UW Fixed wing multi engine A320-214
## 3 N103US Fixed wing multi engine A320-214
## 4 N104UW Fixed wing multi engine A320-214
## 5 N10575 Fixed wing multi engine EMB-145LR
## 6 N105UW Fixed wing multi engine A320-214
## 7 N107US Fixed wing multi engine A320-214
## 8 N108UW Fixed wing multi engine A320-214
## 9 N109UW Fixed wing multi engine A320-214
## 10 N110UW Fixed wing multi engine A320-214
## # ... with 3,312 more rows
Rewrite the statement above with the forward-pipe operator (%>% ) from the magrittr package:
planes %>% select(tailnum, type, model)
## # A tibble: 3,322 × 3
## tailnum type model
## <chr> <chr> <chr>
## 1 N10156 Fixed wing multi engine EMB-145XR
## 2 N102UW Fixed wing multi engine A320-214
## 3 N103US Fixed wing multi engine A320-214
## 4 N104UW Fixed wing multi engine A320-214
## 5 N10575 Fixed wing multi engine EMB-145LR
## 6 N105UW Fixed wing multi engine A320-214
## 7 N107US Fixed wing multi engine A320-214
## 8 N108UW Fixed wing multi engine A320-214
## 9 N109UW Fixed wing multi engine A320-214
## 10 N110UW Fixed wing multi engine A320-214
## # ... with 3,312 more rows
filter
filter rows based on crieria.
Return a dataset where manufacturer is "EMBRAER":
planes %>% filter(manufacturer == "EMBRAER")
## # A tibble: 299 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 2 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR 2
## 3 N11106 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 4 N11107 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 5 N11109 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 6 N11113 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 7 N11119 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 8 N11121 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 9 N11127 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 10 N11137 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## # ... with 289 more rows, and 3 more variables: seats <int>, speed <int>,
## # engine <chr>
Return a dataset where manufacturer is "EMBRAER" and model is "EMB-145XR":
planes %>% filter(manufacturer == "EMBRAER", model == "EMB-145XR")
## # A tibble: 104 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 2 N11106 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 3 N11107 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 4 N11109 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 5 N11113 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 6 N11119 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 7 N11121 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 8 N11127 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 9 N11137 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 10 N11140 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## # ... with 94 more rows, and 3 more variables: seats <int>, speed <int>,
## # engine <chr>
The statement above is the same as writing an "AND" condition.
planes %>% filter(manufacturer == "EMBRAER" & model == "EMB-145XR")
## # A tibble: 104 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 2 N11106 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 3 N11107 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 4 N11109 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 5 N11113 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 6 N11119 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 7 N11121 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 8 N11127 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 9 N11137 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 10 N11140 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## # ... with 94 more rows, and 3 more variables: seats <int>, speed <int>,
## # engine <chr>
Use the pipe (|) character for "OR" conditions:
planes %>% filter(manufacturer == "EMBRAER" | model == "EMB-145XR")
## # A tibble: 299 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 2 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR 2
## 3 N11106 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 4 N11107 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 5 N11109 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 6 N11113 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 7 N11119 2002 Fixed wing multi engine EMBRAER EMB-145XR 2
## 8 N11121 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 9 N11127 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## 10 N11137 2003 Fixed wing multi engine EMBRAER EMB-145XR 2
## # ... with 289 more rows, and 3 more variables: seats <int>, speed <int>,
## # engine <chr>
Use grepl in combination with filter for pattern-matching conditions.
planes %>% filter(grepl("^172.", model))
## # A tibble: 3 × 9
## tailnum year type manufacturer model engines seats
## <chr> <int> <chr> <chr> <chr> <int> <int>
## 1 N378AA 1963 Fixed wing single engine CESSNA 172E 1 4
## 2 N621AA 1975 Fixed wing single engine CESSNA 172M 1 4
## 3 N737MQ 1977 Fixed wing single engine CESSNA 172N 1 4
## # ... with 2 more variables: speed <int>, engine <chr>
between
Return all rows where year is between 2004 and 2005:
planes %>% filter(between(year, 2004, 2005))
## # A tibble: 354 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 2 N11155 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 3 N11164 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 4 N11165 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 5 N11176 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 6 N11181 2005 Fixed wing multi engine EMBRAER EMB-145XR 2
## 7 N11184 2005 Fixed wing multi engine EMBRAER EMB-145XR 2
## 8 N11187 2005 Fixed wing multi engine EMBRAER EMB-145XR 2
## 9 N11189 2005 Fixed wing multi engine EMBRAER EMB-145XR 2
## 10 N11191 2005 Fixed wing multi engine EMBRAER EMB-145XR 2
## # ... with 344 more rows, and 3 more variables: seats <int>, speed <int>,
## # engine <chr>
slice
slice returns only rows by the given index.
Return the first five rows of data (same as the base head function):
planes %>% slice(1:5)
## # A tibble: 5 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 2 N102UW 1998 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
## 3 N103US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
## 4 N104UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
## 5 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR 2
## # ... with 3 more variables: seats <int>, speed <int>, engine <chr>
Return the 1st, 3rd, and 5th rows of data:
planes %>% slice(c(1, 3, 5))
## # A tibble: 3 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
## 2 N103US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
## 3 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR 2
## # ... with 3 more variables: seats <int>, speed <int>, engine <chr>
Return the first and last rows:
planes %>% slice(c(1, nrow(planes)))
## # A tibble: 2 × 9
## tailnum year type manufacturer
## <chr> <int> <chr> <chr>
## 1 N10156 2004 Fixed wing multi engine EMBRAER
## 2 N999DN 1992 Fixed wing multi engine MCDONNELL DOUGLAS CORPORATION
## # ... with 5 more variables: model <chr>, engines <int>, seats <int>,
## # speed <int>, engine <chr>
mutate
mutate can add new variables or modify existing variables.
Add a dummy variable, engine.dummy with a default value of 0:
planes %>% mutate(engine.dummy = 0) %>% select(engine, engine.dummy)
## # A tibble: 3,322 × 2
## engine engine.dummy
## <chr> <dbl>
## 1 Turbo-fan 0
## 2 Turbo-fan 0
## 3 Turbo-fan 0
## 4 Turbo-fan 0
## 5 Turbo-fan 0
## 6 Turbo-fan 0
## 7 Turbo-fan 0
## 8 Turbo-fan 0
## 9 Turbo-fan 0
## 10 Turbo-fan 0
## # ... with 3,312 more rows
Using dplyr::if_else , add engine.dummy set to 1 if engine == "Turbo-fan", otherwise set engine.dummy to 0:
planes %>% mutate(engine.dummy = if_else(engine == "Turbo-fan", 1, 0)) %>% select(engine, engine.dummy)
## # A tibble: 3,322 × 2
## engine engine.dummy
## <chr> <dbl>
## 1 Turbo-fan 1
## 2 Turbo-fan 1
## 3 Turbo-fan 1
## 4 Turbo-fan 1
## 5 Turbo-fan 1
## 6 Turbo-fan 1
## 7 Turbo-fan 1
## 8 Turbo-fan 1
## 9 Turbo-fan 1
## 10 Turbo-fan 1
## # ... with 3,312 more rows
Convert planes$engine to a factor.
planes %>% mutate(engine = as.factor(engine)) %>% select(engine)
## # A tibble: 3,322 × 1
## engine
## <fctr>
## 1 Turbo-fan
## 2 Turbo-fan
## 3 Turbo-fan
## 4 Turbo-fan
## 5 Turbo-fan
## 6 Turbo-fan
## 7 Turbo-fan
## 8 Turbo-fan
## 9 Turbo-fan
## 10 Turbo-fan
## # ... with 3,312 more rows
arrange
Use arrange to sort your dataframe.
Arrange planes by year :
planes %>% arrange(year)
## # A tibble: 3,322 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N381AA 1956 Fixed wing multi engine DOUGLAS DC-7BF 4
## 2 N201AA 1959 Fixed wing single engine CESSNA 150 1
## 3 N567AA 1959 Fixed wing single engine DEHAVILLAND OTTER DHC-3 1
## 4 N378AA 1963 Fixed wing single engine CESSNA 172E 1
## 5 N575AA 1963 Fixed wing single engine CESSNA 210-5(205) 1
## 6 N14629 1965 Fixed wing multi engine BOEING 737-524 2
## 7 N615AA 1967 Fixed wing multi engine BEECH 65-A90 2
## 8 N425AA 1968 Fixed wing single engine PIPER PA-28-180 1
## 9 N383AA 1972 Fixed wing multi engine BEECH E-90 2
## 10 N364AA 1973 Fixed wing multi engine CESSNA 310Q 2
## # ... with 3,312 more rows, and 3 more variables: seats <int>,
## # speed <int>, engine <chr>
arrange planes by year desc :
planes %>% arrange(desc(year))
## # A tibble: 3,322 × 9
## tailnum year type manufacturer model engines
## <chr> <int> <chr> <chr> <chr> <int>
## 1 N150UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 2 N151UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 3 N152UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 4 N153UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 5 N154UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 6 N155UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 7 N156UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 8 N157UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 9 N198UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## 10 N199UW 2013 Fixed wing multi engine AIRBUS A321-211 2
## # ... with 3,312 more rows, and 3 more variables: seats <int>,
## # speed <int>, engine <chr>
group_by
group_by allows you to perform operations on a dataframe by subsets without extracting the subset.
df <- planes %>% group_by(manufacturer, model)
The returned dataframe may not appear grouped. However, the class and attributes of the dataframe will confirm it is.
class(df)
## [1] "grouped_df" "tbl_df" "tbl" "data.frame"
attributes(df)$vars
## [[1]]
## manufacturer
##
## [[2]]
## model
head(attributes(df)$labels, n = 5L)
## manufacturer model
## 1 AGUSTA SPA A109E
## 2 AIRBUS A319-112
## 3 AIRBUS A319-114
## 4 AIRBUS A319-115
## 5 AIRBUS A319-131
If you wish to add grouping elements to the dataframe without removing existing grouping elements, use the add parameter set to TRUE (set to FALSE by default):
df <- df %>% group_by(type, year, add = TRUE)
class(df)
## [1] "grouped_df" "tbl_df" "tbl" "data.frame"
attributes(df)$vars
## [[1]]
## manufacturer
##
## [[2]]
## model
##
## [[3]]
## type
##
## [[4]]
## year
head(attributes(df)$labels, n = 5L)
## manufacturer model type year
## 1 AGUSTA SPA A109E Rotorcraft 2001
## 2 AIRBUS A319-112 Fixed wing multi engine 2002
## 3 AIRBUS A319-112 Fixed wing multi engine 2005
## 4 AIRBUS A319-112 Fixed wing multi engine 2006
## 5 AIRBUS A319-112 Fixed wing multi engine 2007
If you want to remove grouping use ungroup .
df <- df %>% ungroup()
class(df)
## [1] "tbl_df" "tbl" "data.frame"
attributes(df)$vars
## NULL
attributes(df)$labels
## NULL
summarise
summarise is used to perform calculations on a dataset either as a whole or by groups.
Find the mean number of of seats per manufacturer ?
planes %>% group_by(manufacturer) %>% summarise(Mean = mean(seats))
## # A tibble: 35 × 2
## manufacturer Mean
## <chr> <dbl>
## 1 AGUSTA SPA 8.0000
## 2 AIRBUS 221.2024
## 3 AIRBUS INDUSTRIE 187.4025
## 4 AMERICAN AIRCRAFT INC 2.0000
## 5 AVIAT AIRCRAFT INC 2.0000
## 6 AVIONS MARCEL DASSAULT 12.0000
## 7 BARKER JACK L 2.0000
## 8 BEECH 9.5000
## 9 BELL 8.0000
## 10 BOEING 175.1877
## # ... with 25 more rows
summarise will not return variables that are not explicitly grouped or included in summary functions. If you want to add another variable you must pass it as a predicate to group_by or summarise .
planes %>% group_by(year, manufacturer) %>% summarise(Mean = mean(seats))
## Source: local data frame [164 x 3]
## Groups: year [?]
##
## year manufacturer Mean
## <int> <chr> <dbl>
## 1 1956 DOUGLAS 102
## 2 1959 CESSNA 2
## 3 1959 DEHAVILLAND 16
## 4 1963 CESSNA 5
## 5 1965 BOEING 149
## 6 1967 BEECH 9
## 7 1968 PIPER 4
## 8 1972 BEECH 10
## 9 1973 CESSNA 6
## 10 1974 CANADAIR LTD 2
## # ... with 154 more rows
rename
rename a variable:
planes %>% rename(Mfr = manufacturer) %>% names()
## [1] "tailnum" "year" "type" "Mfr" "model" "engines" "seats"
## [8] "speed" "engine"
Helper Functions
Helper functions are used in conjunction with select to identify variables to return. Unless otherwise noted, these functions expect a string as the first parameter match . Passing a vector or another object will generate an error.
library(dplyr) library(nycflights13)
starts_with
starts_with allows us to identify variables whose name begins with a string.
Returns all variables that begin with the letter "e".
planes %>% select(starts_with("e"))
## # A tibble: 3,322 × 2
## engines engine
## <int> <chr>
## 1 2 Turbo-fan
## 2 2 Turbo-fan
## 3 2 Turbo-fan
## 4 2 Turbo-fan
## 5 2 Turbo-fan
## 6 2 Turbo-fan
## 7 2 Turbo-fan
## 8 2 Turbo-fan
## 9 2 Turbo-fan
## 10 2 Turbo-fan
## # ... with 3,312 more rows
Set ignore.case parameter to FALSE for strict casing.
planes %>% select(starts_with("E", ignore.case = FALSE))
## # A tibble: 3,322 × 0
ends_with
Return all variables that end with the letter "e".
planes %>% select(ends_with("e"))
## # A tibble: 3,322 × 2
## type engine
## <chr> <chr>
## 1 Fixed wing multi engine Turbo-fan
## 2 Fixed wing multi engine Turbo-fan
## 3 Fixed wing multi engine Turbo-fan
## 4 Fixed wing multi engine Turbo-fan
## 5 Fixed wing multi engine Turbo-fan
## 6 Fixed wing multi engine Turbo-fan
## 7 Fixed wing multi engine Turbo-fan
## 8 Fixed wing multi engine Turbo-fan
## 9 Fixed wing multi engine Turbo-fan
## 10 Fixed wing multi engine Turbo-fan
## # ... with 3,312 more rows
Set ignore.case parameter to FALSE for strict casing.
planes %>% select(ends_with("E", ignore.case = FALSE))
## # A tibble: 3,322 × 0
contains
contains allows you to find any variables that contain a given string.
planes %>% select(contains("ea"))
## # A tibble: 3,322 × 2
## year seats
## <int> <int>
## 1 2004 55
## 2 1998 182
## 3 1999 182
## 4 1999 182
## 5 2002 55
## 6 1999 182
## 7 1999 182
## 8 1999 182
## 9 1999 182
## 10 1999 182
## # ... with 3,312 more rows
Set ignore.case parameter to FALSE for strict casing.
planes %>% select(contains("EA", ignore.case = FALSE))
## # A tibble: 3,322 × 0
matches
matches is the only helper function that allows the use of regular expressions.
Return all variables with a name at least six alpha characters:
planes %>% select(matches("[[:alpha:]]{6,}"))
## # A tibble: 3,322 × 4
## tailnum manufacturer engines engine
## <chr> <chr> <int> <chr>
## 1 N10156 EMBRAER 2 Turbo-fan
## 2 N102UW AIRBUS INDUSTRIE 2 Turbo-fan
## 3 N103US AIRBUS INDUSTRIE 2 Turbo-fan
## 4 N104UW AIRBUS INDUSTRIE 2 Turbo-fan
## 5 N10575 EMBRAER 2 Turbo-fan
## 6 N105UW AIRBUS INDUSTRIE 2 Turbo-fan
## 7 N107US AIRBUS INDUSTRIE 2 Turbo-fan
## 8 N108UW AIRBUS INDUSTRIE 2 Turbo-fan
## 9 N109UW AIRBUS INDUSTRIE 2 Turbo-fan
## 10 N110UW AIRBUS INDUSTRIE 2 Turbo-fan
## # ... with 3,312 more rows
Set ignore.case parameter to FALSE for strict casing.
num_range
For this example I will generate a dummy dataframe with random values and sequential variable names.
set.seed(1)
df <- data.frame(x1 = runif(10),
x2 = runif(10),
x3 = runif(10),
x4 = runif(10),
x5 = runif(10))
num_range can be used to select a range of varaibles given a consistent prefix .
Select the variables 2:4 from df :
df %>% select(num_range('x', range = 2:4))
## x2 x3 x4
## 1 0.2059746 0.93470523 0.4820801
## 2 0.1765568 0.21214252 0.5995658
## 3 0.6870228 0.65167377 0.4935413
## 4 0.3841037 0.12555510 0.1862176
## 5 0.7698414 0.26722067 0.8273733
## 6 0.4976992 0.38611409 0.6684667
## 7 0.7176185 0.01339033 0.7942399
## 8 0.9919061 0.38238796 0.1079436
## 9 0.3800352 0.86969085 0.7237109
## 10 0.7774452 0.34034900 0.4112744
one_of
one_of can take a vector as the match parameter and returns each variable.
planes %>% select(one_of(c("tailnum", "model")))
## # A tibble: 3,322 × 2
## tailnum model
## <chr> <chr>
## 1 N10156 EMB-145XR
## 2 N102UW A320-214
## 3 N103US A320-214
## 4 N104UW A320-214
## 5 N10575 EMB-145LR
## 6 N105UW A320-214
## 7 N107US A320-214
## 8 N108UW A320-214
## 9 N109UW A320-214
## 10 N110UW A320-214
## # ... with 3,312 more rows
everything
everything can be used to reposition variables in the dataframe.
Make manufacturer the first variable followed by all remaining variables.
planes %>% select(manufacturer, everything())
## # A tibble: 3,322 × 9
## manufacturer tailnum year type model
## <chr> <chr> <int> <chr> <chr>
## 1 EMBRAER N10156 2004 Fixed wing multi engine EMB-145XR
## 2 AIRBUS INDUSTRIE N102UW 1998 Fixed wing multi engine A320-214
## 3 AIRBUS INDUSTRIE N103US 1999 Fixed wing multi engine A320-214
## 4 AIRBUS INDUSTRIE N104UW 1999 Fixed wing multi engine A320-214
## 5 EMBRAER N10575 2002 Fixed wing multi engine EMB-145LR
## 6 AIRBUS INDUSTRIE N105UW 1999 Fixed wing multi engine A320-214
## 7 AIRBUS INDUSTRIE N107US 1999 Fixed wing multi engine A320-214
## 8 AIRBUS INDUSTRIE N108UW 1999 Fixed wing multi engine A320-214
## 9 AIRBUS INDUSTRIE N109UW 1999 Fixed wing multi engine A320-214
## 10 AIRBUS INDUSTRIE N110UW 1999 Fixed wing multi engine A320-214
## # ... with 3,312 more rows, and 4 more variables: engines <int>,
## # seats <int>, speed <int>, engine <chr>
Other Helpers
Though the : and - operators are not part of the dplyr package we can still use them to identify variables to return.
:
Define an inclusive range of variables to return.
Return every variable from year to manufacturer :
planes %>% select(year:manufacturer)
## # A tibble: 3,322 × 3
## year type manufacturer
## <int> <chr> <chr>
## 1 2004 Fixed wing multi engine EMBRAER
## 2 1998 Fixed wing multi engine AIRBUS INDUSTRIE
## 3 1999 Fixed wing multi engine AIRBUS INDUSTRIE
## 4 1999 Fixed wing multi engine AIRBUS INDUSTRIE
## 5 2002 Fixed wing multi engine EMBRAER
## 6 1999 Fixed wing multi engine AIRBUS INDUSTRIE
## 7 1999 Fixed wing multi engine AIRBUS INDUSTRIE
## 8 1999 Fixed wing multi engine AIRBUS INDUSTRIE
## 9 1999 Fixed wing multi engine AIRBUS INDUSTRIE
## 10 1999 Fixed wing multi engine AIRBUS INDUSTRIE
## # ... with 3,312 more rows
Return multiple ranges of variables:
planes %>% select(c(year:manufacturer, seats:engine))
## # A tibble: 3,322 × 6
## year type manufacturer seats speed engine
## <int> <chr> <chr> <int> <int> <chr>
## 1 2004 Fixed wing multi engine EMBRAER 55 NA Turbo-fan
## 2 1998 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## 3 1999 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## 4 1999 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## 5 2002 Fixed wing multi engine EMBRAER 55 NA Turbo-fan
## 6 1999 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## 7 1999 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## 8 1999 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## 9 1999 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## 10 1999 Fixed wing multi engine AIRBUS INDUSTRIE 182 NA Turbo-fan
## # ... with 3,312 more rows
-
The - operator will remove a variable from a result set.
Return all variables with the exception of type :
planes %>% select(-type)
## # A tibble: 3,322 × 8
## tailnum year manufacturer model engines seats speed engine
## <chr> <int> <chr> <chr> <int> <int> <int> <chr>
## 1 N10156 2004 EMBRAER EMB-145XR 2 55 NA Turbo-fan
## 2 N102UW 1998 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## 3 N103US 1999 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## 4 N104UW 1999 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## 5 N10575 2002 EMBRAER EMB-145LR 2 55 NA Turbo-fan
## 6 N105UW 1999 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## 7 N107US 1999 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## 8 N108UW 1999 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## 9 N109UW 1999 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## 10 N110UW 1999 AIRBUS INDUSTRIE A320-214 2 182 NA Turbo-fan
## # ... with 3,312 more rows
You can also pass a vector of variable names to exclude from your result set.
planes %>% select(-c(type, engines:engine))
## # A tibble: 3,322 × 4
## tailnum year manufacturer model
## <chr> <int> <chr> <chr>
## 1 N10156 2004 EMBRAER EMB-145XR
## 2 N102UW 1998 AIRBUS INDUSTRIE A320-214
## 3 N103US 1999 AIRBUS INDUSTRIE A320-214
## 4 N104UW 1999 AIRBUS INDUSTRIE A320-214
## 5 N10575 2002 EMBRAER EMB-145LR
## 6 N105UW 1999 AIRBUS INDUSTRIE A320-214
## 7 N107US 1999 AIRBUS INDUSTRIE A320-214
## 8 N108UW 1999 AIRBUS INDUSTRIE A320-214
## 9 N109UW 1999 AIRBUS INDUSTRIE A320-214
## 10 N110UW 1999 AIRBUS INDUSTRIE A320-214
## # ... with 3,312 more rows
Any combination of helper functions
Select all variables between type and speed (inclusive) and exclude manufacturer .
planes %>% select(type:speed, -manufacturer)
## # A tibble: 3,322 × 5
## type model engines seats speed
## <chr> <chr> <int> <int> <int>
## 1 Fixed wing multi engine EMB-145XR 2 55 NA
## 2 Fixed wing multi engine A320-214 2 182 NA
## 3 Fixed wing multi engine A320-214 2 182 NA
## 4 Fixed wing multi engine A320-214 2 182 NA
## 5 Fixed wing multi engine EMB-145LR 2 55 NA
## 6 Fixed wing multi engine A320-214 2 182 NA
## 7 Fixed wing multi engine A320-214 2 182 NA
## 8 Fixed wing multi engine A320-214 2 182 NA
## 9 Fixed wing multi engine A320-214 2 182 NA
## 10 Fixed wing multi engine A320-214 2 182 NA
## # ... with 3,312 more rows
Modify the previous statement to exclude manufacturer and model .
planes %>% select(type:speed, -c(manufacturer, model))
## # A tibble: 3,322 × 4
## type engines seats speed
## <chr> <int> <int> <int>
## 1 Fixed wing multi engine 2 55 NA
## 2 Fixed wing multi engine 2 182 NA
## 3 Fixed wing multi engine 2 182 NA
## 4 Fixed wing multi engine 2 182 NA
## 5 Fixed wing multi engine 2 55 NA
## 6 Fixed wing multi engine 2 182 NA
## 7 Fixed wing multi engine 2 182 NA
## 8 Fixed wing multi engine 2 182 NA
## 9 Fixed wing multi engine 2 182 NA
## 10 Fixed wing multi engine 2 182 NA
## # ... with 3,312 more rows
You can use the same helper function more than once.
planes %>% select(starts_with("m"), starts_with("s"))
## # A tibble: 3,322 × 4
## manufacturer model seats speed
## <chr> <chr> <int> <int>
## 1 EMBRAER EMB-145XR 55 NA
## 2 AIRBUS INDUSTRIE A320-214 182 NA
## 3 AIRBUS INDUSTRIE A320-214 182 NA
## 4 AIRBUS INDUSTRIE A320-214 182 NA
## 5 EMBRAER EMB-145LR 55 NA
## 6 AIRBUS INDUSTRIE A320-214 182 NA
## 7 AIRBUS INDUSTRIE A320-214 182 NA
## 8 AIRBUS INDUSTRIE A320-214 182 NA
## 9 AIRBUS INDUSTRIE A320-214 182 NA
## 10 AIRBUS INDUSTRIE A320-214 182 NA
## # ... with 3,312 more rows
You can use multiple helper functions together:
planes %>% select(starts_with("m"), ends_with("l"))
## # A tibble: 3,322 × 2
## manufacturer model
## <chr> <chr>
## 1 EMBRAER EMB-145XR
## 2 AIRBUS INDUSTRIE A320-214
## 3 AIRBUS INDUSTRIE A320-214
## 4 AIRBUS INDUSTRIE A320-214
## 5 EMBRAER EMB-145LR
## 6 AIRBUS INDUSTRIE A320-214
## 7 AIRBUS INDUSTRIE A320-214
## 8 AIRBUS INDUSTRIE A320-214
## 9 AIRBUS INDUSTRIE A320-214
## 10 AIRBUS INDUSTRIE A320-214
## # ... with 3,312 more rows
Installation or Setup
To install dplyr simply type in the R console.
install.packages("dplyr")
And then to load dplyr , type
library("dplyr")
It's also possible to install the latest development version from Github with:
if (packageVersion("devtools") < 1.6) {
install.packages("devtools")
}
devtools::install_github("hadley/lazyeval")
devtools::install_github("hadley/dplyr")
You may want to install the data packages used in most examples: install.packages(c("nycflights13", "Lahman")) .
