Note: There is a 40-minute video tutorial on YouTube that walks through this document in detail.
filter, select, arrange, mutate, summarise (plus group_by)# load packages
library(hflights)
library(nycflights13)
library(plyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
# explore data
data(hflights)
head(hflights)## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## 5424 2011 1 1 6 1400 1500 AA
## 5425 2011 1 2 7 1401 1501 AA
## 5426 2011 1 3 1 1352 1502 AA
## 5427 2011 1 4 2 1403 1513 AA
## 5428 2011 1 5 3 1405 1507 AA
## 5429 2011 1 6 4 1359 1503 AA
## FlightNum TailNum ActualElapsedTime AirTime ArrDelay DepDelay Origin
## 5424 428 N576AA 60 40 -10 0 IAH
## 5425 428 N557AA 60 45 -9 1 IAH
## 5426 428 N541AA 70 48 -8 -8 IAH
## 5427 428 N403AA 70 39 3 3 IAH
## 5428 428 N492AA 62 44 -3 5 IAH
## 5429 428 N262AA 64 45 -7 -1 IAH
## Dest Distance TaxiIn TaxiOut Cancelled CancellationCode Diverted
## 5424 DFW 224 7 13 0 0
## 5425 DFW 224 6 9 0 0
## 5426 DFW 224 5 17 0 0
## 5427 DFW 224 9 22 0 0
## 5428 DFW 224 9 9 0 0
## 5429 DFW 224 6 13 0 0tbl_df creates a “local data frame”# convert to local data frame
flights <- tbl_df(hflights)
# printing only shows 10 rows and as many columns as can fit on your screen
flights## Source: local data frame [227,496 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 1 6 1400 1500 AA
## 2 2011 1 2 7 1401 1501 AA
## 3 2011 1 3 1 1352 1502 AA
## 4 2011 1 4 2 1403 1513 AA
## 5 2011 1 5 3 1405 1507 AA
## 6 2011 1 6 4 1359 1503 AA
## 7 2011 1 7 5 1359 1509 AA
## 8 2011 1 8 6 1355 1454 AA
## 9 2011 1 9 7 1443 1554 AA
## 10 2011 1 10 1 1443 1553 AA
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int).# you can specify that you want to see more rows
print(flights, n=20)
# convert to a normal data frame to see all of the columns
data.frame(head(flights))# base R approach to view all flights on January 1
flights[flights$Month==1 & flights$DayofMonth==1, ]# dplyr approach
# note: you can use comma or ampersand to represent AND condition
filter(flights, Month==1, DayofMonth==1)## Source: local data frame [552 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 1 6 1400 1500 AA
## 2 2011 1 1 6 728 840 AA
## 3 2011 1 1 6 1631 1736 AA
## 4 2011 1 1 6 1756 2112 AA
## 5 2011 1 1 6 1012 1347 AA
## 6 2011 1 1 6 1211 1325 AA
## 7 2011 1 1 6 557 906 AA
## 8 2011 1 1 6 1824 2106 AS
## 9 2011 1 1 6 654 1124 B6
## 10 2011 1 1 6 1639 2110 B6
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int).# use pipe for OR condition
filter(flights, UniqueCarrier=="AA" | UniqueCarrier=="UA")## Source: local data frame [5,316 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 1 6 1400 1500 AA
## 2 2011 1 2 7 1401 1501 AA
## 3 2011 1 3 1 1352 1502 AA
## 4 2011 1 4 2 1403 1513 AA
## 5 2011 1 5 3 1405 1507 AA
## 6 2011 1 6 4 1359 1503 AA
## 7 2011 1 7 5 1359 1509 AA
## 8 2011 1 8 6 1355 1454 AA
## 9 2011 1 9 7 1443 1554 AA
## 10 2011 1 10 1 1443 1553 AA
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int).# you can also use %in% operator
filter(flights, UniqueCarrier %in% c("AA", "UA"))filter# base R approach to select DepTime, ArrTime, and FlightNum columns
flights[, c("DepTime", "ArrTime", "FlightNum")]# dplyr approach
select(flights, DepTime, ArrTime, FlightNum)## Source: local data frame [227,496 x 3]
##
## DepTime ArrTime FlightNum
## (int) (int) (int)
## 1 1400 1500 428
## 2 1401 1501 428
## 3 1352 1502 428
## 4 1403 1513 428
## 5 1405 1507 428
## 6 1359 1503 428
## 7 1359 1509 428
## 8 1355 1454 428
## 9 1443 1554 428
## 10 1443 1553 428
## .. ... ... ...# use colon to select multiple contiguous columns, and use `contains` to match columns by name
# note: `starts_with`, `ends_with`, and `matches` (for regular expressions) can also be used to match columns by name
select(flights, Year:DayofMonth, contains("Taxi"), contains("Delay"))## Source: local data frame [227,496 x 7]
##
## Year Month DayofMonth TaxiIn TaxiOut ArrDelay DepDelay
## (int) (int) (int) (int) (int) (int) (int)
## 1 2011 1 1 7 13 -10 0
## 2 2011 1 2 6 9 -9 1
## 3 2011 1 3 5 17 -8 -8
## 4 2011 1 4 9 22 3 3
## 5 2011 1 5 9 9 -3 5
## 6 2011 1 6 6 13 -7 -1
## 7 2011 1 7 12 15 -1 -1
## 8 2011 1 8 7 12 -16 -5
## 9 2011 1 9 8 22 44 43
## 10 2011 1 10 6 19 43 43
## .. ... ... ... ... ... ... ...%>% infix operator (which can be pronounced as “then”)# nesting method to select UniqueCarrier and DepDelay columns and filter for delays over 60 minutes
filter(select(flights, UniqueCarrier, DepDelay), DepDelay > 60)# chaining method
flights %>%
select(UniqueCarrier, DepDelay) %>%
filter(DepDelay > 60)## Source: local data frame [10,242 x 2]
##
## UniqueCarrier DepDelay
## (chr) (int)
## 1 AA 90
## 2 AA 67
## 3 AA 74
## 4 AA 125
## 5 AA 82
## 6 AA 99
## 7 AA 70
## 8 AA 61
## 9 AA 74
## 10 AS 73
## .. ... ...# create two vectors and calculate Euclidian distance between them
x1 <- 1:5; x2 <- 2:6
sqrt(sum((x1-x2)^2))# chaining method
(x1-x2)^2 %>% sum() %>% sqrt()## [1] 2.236068# base R approach to select UniqueCarrier and DepDelay columns and sort by DepDelay
flights[order(flights$DepDelay), c("UniqueCarrier", "DepDelay")]# dplyr approach
flights %>%
select(UniqueCarrier, DepDelay) %>%
arrange(DepDelay)## Source: local data frame [227,496 x 2]
##
## UniqueCarrier DepDelay
## (chr) (int)
## 1 OO -33
## 2 MQ -23
## 3 XE -19
## 4 XE -19
## 5 CO -18
## 6 EV -18
## 7 XE -17
## 8 CO -17
## 9 XE -17
## 10 MQ -17
## .. ... ...# use `desc` for descending
flights %>%
select(UniqueCarrier, DepDelay) %>%
arrange(desc(DepDelay))# base R approach to create a new variable Speed (in mph)
flights$Speed <- flights$Distance / flights$AirTime*60
flights[, c("Distance", "AirTime", "Speed")]# dplyr approach (prints the new variable but does not store it)
flights %>%
select(Distance, AirTime) %>%
mutate(Speed = Distance/AirTime*60)## Source: local data frame [227,496 x 3]
##
## Distance AirTime Speed
## (int) (int) (dbl)
## 1 224 40 336.0000
## 2 224 45 298.6667
## 3 224 48 280.0000
## 4 224 39 344.6154
## 5 224 44 305.4545
## 6 224 45 298.6667
## 7 224 43 312.5581
## 8 224 40 336.0000
## 9 224 41 327.8049
## 10 224 45 298.6667
## .. ... ... ...# store the new variable
flights <- flights %>% mutate(Speed = Distance/AirTime*60)group_by creates the groups that will be operated onsummarise uses the provided aggregation function to summarise each group# base R approaches to calculate the average arrival delay to each destination
head(with(flights, tapply(ArrDelay, Dest, mean, na.rm=TRUE)))
head(aggregate(ArrDelay ~ Dest, flights, mean))# dplyr approach: create a table grouped by Dest, and then summarise each group by taking the mean of ArrDelay
flights %>%
group_by(Dest) %>%
summarise(avg_delay = mean(ArrDelay, na.rm=TRUE))## Source: local data frame [116 x 2]
##
## Dest avg_delay
## (chr) (dbl)
## 1 ABQ 7.226259
## 2 AEX 5.839437
## 3 AGS 4.000000
## 4 AMA 6.840095
## 5 ANC 26.080645
## 6 ASE 6.794643
## 7 ATL 8.233251
## 8 AUS 7.448718
## 9 AVL 9.973988
## 10 BFL -13.198807
## .. ... ...summarise_each allows you to apply the same summary function to multiple columns at oncemutate_each is also available# for each carrier, calculate the percentage of flights cancelled or diverted
flights %>%
group_by(UniqueCarrier) %>%
summarise_each(funs(mean), Cancelled, Diverted)## Source: local data frame [15 x 3]
##
## UniqueCarrier Cancelled Diverted
## (chr) (dbl) (dbl)
## 1 AA 0.018495684 0.001849568
## 2 AS 0.000000000 0.002739726
## 3 B6 0.025899281 0.005755396
## 4 CO 0.006782614 0.002627370
## 5 DL 0.015903067 0.003029156
## 6 EV 0.034482759 0.003176044
## 7 F9 0.007159905 0.000000000
## 8 FL 0.009817672 0.003272557
## 9 MQ 0.029044750 0.001936317
## 10 OO 0.013946828 0.003486707
## 11 UA 0.016409266 0.002413127
## 12 US 0.011268986 0.001469868
## 13 WN 0.015504047 0.002293629
## 14 XE 0.015495599 0.003449550
## 15 YV 0.012658228 0.000000000# for each carrier, calculate the minimum and maximum arrival and departure delays
flights %>%
group_by(UniqueCarrier) %>%
summarise_each(funs(min(., na.rm=TRUE), max(., na.rm=TRUE)), matches("Delay"))## Source: local data frame [15 x 5]
##
## UniqueCarrier ArrDelay_min DepDelay_min ArrDelay_max DepDelay_max
## (chr) (int) (int) (int) (int)
## 1 AA -39 -15 978 970
## 2 AS -43 -15 183 172
## 3 B6 -44 -14 335 310
## 4 CO -55 -18 957 981
## 5 DL -32 -17 701 730
## 6 EV -40 -18 469 479
## 7 F9 -24 -15 277 275
## 8 FL -30 -14 500 507
## 9 MQ -38 -23 918 931
## 10 OO -57 -33 380 360
## 11 UA -47 -11 861 869
## 12 US -42 -17 433 425
## 13 WN -44 -10 499 548
## 14 XE -70 -19 634 628
## 15 YV -32 -11 72 54n() counts the number of rows in a groupn_distinct(vector) counts the number of unique items in that vector# for each day of the year, count the total number of flights and sort in descending order
flights %>%
group_by(Month, DayofMonth) %>%
summarise(flight_count = n()) %>%
arrange(desc(flight_count))## Source: local data frame [365 x 3]
## Groups: Month [12]
##
## Month DayofMonth flight_count
## (int) (int) (int)
## 1 1 3 702
## 2 1 2 678
## 3 1 20 663
## 4 1 27 663
## 5 1 13 662
## 6 1 7 661
## 7 1 14 661
## 8 1 21 661
## 9 1 28 661
## 10 1 6 660
## .. ... ... ...# rewrite more simply with the `tally` function
flights %>%
group_by(Month, DayofMonth) %>%
tally(sort = TRUE)## Source: local data frame [365 x 3]
## Groups: Month [12]
##
## Month DayofMonth n
## (int) (int) (int)
## 1 1 3 702
## 2 1 2 678
## 3 1 20 663
## 4 1 27 663
## 5 1 13 662
## 6 1 7 661
## 7 1 14 661
## 8 1 21 661
## 9 1 28 661
## 10 1 6 660
## .. ... ... ...# for each destination, count the total number of flights and the number of distinct planes that flew there
flights %>%
group_by(Dest) %>%
summarise(flight_count = n(), plane_count = n_distinct(TailNum))## Source: local data frame [116 x 3]
##
## Dest flight_count plane_count
## (chr) (int) (int)
## 1 ABQ 2812 716
## 2 AEX 724 215
## 3 AGS 1 1
## 4 AMA 1297 158
## 5 ANC 125 38
## 6 ASE 125 60
## 7 ATL 7886 983
## 8 AUS 5022 1015
## 9 AVL 350 142
## 10 BFL 504 70
## .. ... ... ...# for each destination, show the number of cancelled and not cancelled flights
flights %>%
group_by(Dest) %>%
select(Cancelled) %>%
table() %>%
head()## Cancelled
## Dest 0 1
## ABQ 2787 25
## AEX 712 12
## AGS 1 0
## AMA 1265 32
## ANC 125 0
## ASE 120 5mean) takes n inputs and returns 1 valuemin_rank), offset functions (lead and lag), and cumulative aggregates (like cummean).# for each carrier, calculate which two days of the year they had their longest departure delays
# note: smallest (not largest) value is ranked as 1, so you have to use `desc` to rank by largest value
flights %>%
group_by(UniqueCarrier) %>%
select(Month, DayofMonth, DepDelay) %>%
filter(min_rank(desc(DepDelay)) <= 2) %>%
arrange(UniqueCarrier, desc(DepDelay))# rewrite more simply with the `top_n` function
flights %>%
group_by(UniqueCarrier) %>%
select(Month, DayofMonth, DepDelay) %>%
top_n(2) %>%
arrange(UniqueCarrier, desc(DepDelay))## Selecting by DepDelay## Source: local data frame [30 x 4]
## Groups: UniqueCarrier [15]
##
## UniqueCarrier Month DayofMonth DepDelay
## (chr) (int) (int) (int)
## 1 AA 12 12 970
## 2 AA 11 19 677
## 3 AS 2 28 172
## 4 AS 7 6 138
## 5 B6 10 29 310
## 6 B6 8 19 283
## 7 CO 8 1 981
## 8 CO 1 20 780
## 9 DL 10 25 730
## 10 DL 4 5 497
## .. ... ... ... ...# for each month, calculate the number of flights and the change from the previous month
flights %>%
group_by(Month) %>%
summarise(flight_count = n()) %>%
mutate(change = flight_count - lag(flight_count))## Source: local data frame [12 x 3]
##
## Month flight_count change
## (int) (int) (int)
## 1 1 18910 NA
## 2 2 17128 -1782
## 3 3 19470 2342
## 4 4 18593 -877
## 5 5 19172 579
## 6 6 19600 428
## 7 7 20548 948
## 8 8 20176 -372
## 9 9 18065 -2111
## 10 10 18696 631
## 11 11 18021 -675
## 12 12 19117 1096# rewrite more simply with the `tally` function
flights %>%
group_by(Month) %>%
tally() %>%
mutate(change = n - lag(n))## Source: local data frame [12 x 3]
##
## Month n change
## (int) (int) (int)
## 1 1 18910 NA
## 2 2 17128 -1782
## 3 3 19470 2342
## 4 4 18593 -877
## 5 5 19172 579
## 6 6 19600 428
## 7 7 20548 948
## 8 8 20176 -372
## 9 9 18065 -2111
## 10 10 18696 631
## 11 11 18021 -675
## 12 12 19117 1096# randomly sample a fixed number of rows, without replacement
flights %>% sample_n(5)## Source: local data frame [5 x 22]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum
## (int) (int) (int) (int) (int) (int) (chr) (int)
## 1 2011 3 29 2 617 714 WN 1636
## 2 2011 12 12 1 1252 1635 CO 1068
## 3 2011 12 10 6 1144 1247 OO 5160
## 4 2011 10 1 6 1741 1854 CO 1689
## 5 2011 3 23 3 2128 9 CO 209
## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime
## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),
## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int), Speed (dbl).# randomly sample a fraction of rows, with replacement
flights %>% sample_frac(0.25, replace=TRUE)## Source: local data frame [56,874 x 22]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 5 3 2 610 908 FL
## 2 2011 10 10 1 1606 1908 DL
## 3 2011 4 15 5 918 1047 OO
## 4 2011 8 14 7 1813 1938 CO
## 5 2011 11 4 5 1730 1821 WN
## 6 2011 5 26 4 1921 2310 CO
## 7 2011 12 6 2 1929 2043 WN
## 8 2011 1 12 3 905 1028 CO
## 9 2011 7 19 2 1537 1638 XE
## 10 2011 8 10 3 1446 1548 MQ
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int), Speed (dbl).# base R approach to view the structure of an object
str(flights)## Classes 'tbl_df', 'tbl' and 'data.frame': 227496 obs. of 22 variables:
## $ Year : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
## $ Month : int 1 1 1 1 1 1 1 1 1 1 ...
## $ DayofMonth : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DayOfWeek : int 6 7 1 2 3 4 5 6 7 1 ...
## $ DepTime : int 1400 1401 1352 1403 1405 1359 1359 1355 1443 1443 ...
## $ ArrTime : int 1500 1501 1502 1513 1507 1503 1509 1454 1554 1553 ...
## $ UniqueCarrier : chr "AA" "AA" "AA" "AA" ...
## $ FlightNum : int 428 428 428 428 428 428 428 428 428 428 ...
## $ TailNum : chr "N576AA" "N557AA" "N541AA" "N403AA" ...
## $ ActualElapsedTime: int 60 60 70 70 62 64 70 59 71 70 ...
## $ AirTime : int 40 45 48 39 44 45 43 40 41 45 ...
## $ ArrDelay : int -10 -9 -8 3 -3 -7 -1 -16 44 43 ...
## $ DepDelay : int 0 1 -8 3 5 -1 -1 -5 43 43 ...
## $ Origin : chr "IAH" "IAH" "IAH" "IAH" ...
## $ Dest : chr "DFW" "DFW" "DFW" "DFW" ...
## $ Distance : int 224 224 224 224 224 224 224 224 224 224 ...
## $ TaxiIn : int 7 6 5 9 9 6 12 7 8 6 ...
## $ TaxiOut : int 13 9 17 22 9 13 15 12 22 19 ...
## $ Cancelled : int 0 0 0 0 0 0 0 0 0 0 ...
## $ CancellationCode : chr "" "" "" "" ...
## $ Diverted : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Speed : num 336 299 280 345 305 ...# dplyr approach: better formatting, and adapts to your screen width
glimpse(flights)## Observations: 227,496
## Variables: 22
## $ Year (int) 2011, 2011, 2011, 2011, 2011, 2011, 2011, 20...
## $ Month (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ DayofMonth (int) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1...
## $ DayOfWeek (int) 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6,...
## $ DepTime (int) 1400, 1401, 1352, 1403, 1405, 1359, 1359, 13...
## $ ArrTime (int) 1500, 1501, 1502, 1513, 1507, 1503, 1509, 14...
## $ UniqueCarrier (chr) "AA", "AA", "AA", "AA", "AA", "AA", "AA", "A...
## $ FlightNum (int) 428, 428, 428, 428, 428, 428, 428, 428, 428,...
## $ TailNum (chr) "N576AA", "N557AA", "N541AA", "N403AA", "N49...
## $ ActualElapsedTime (int) 60, 60, 70, 70, 62, 64, 70, 59, 71, 70, 70, ...
## $ AirTime (int) 40, 45, 48, 39, 44, 45, 43, 40, 41, 45, 42, ...
## $ ArrDelay (int) -10, -9, -8, 3, -3, -7, -1, -16, 44, 43, 29,...
## $ DepDelay (int) 0, 1, -8, 3, 5, -1, -1, -5, 43, 43, 29, 19, ...
## $ Origin (chr) "IAH", "IAH", "IAH", "IAH", "IAH", "IAH", "I...
## $ Dest (chr) "DFW", "DFW", "DFW", "DFW", "DFW", "DFW", "D...
## $ Distance (int) 224, 224, 224, 224, 224, 224, 224, 224, 224,...
## $ TaxiIn (int) 7, 6, 5, 9, 9, 6, 12, 7, 8, 6, 8, 4, 6, 5, 6...
## $ TaxiOut (int) 13, 9, 17, 22, 9, 13, 15, 12, 22, 19, 20, 11...
## $ Cancelled (int) 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ CancellationCode (chr) "", "", "", "", "", "", "", "", "", "", "", ...
## $ Diverted (int) 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ Speed (dbl) 336.0000, 298.6667, 280.0000, 344.6154, 305....# connect to an SQLite database containing the hflights data
my_db <- src_sqlite("my_db.sqlite3")
# connect to the "hflights" table in that database
flights_tbl <- tbl(my_db, "hflights")
# example query with our data frame
flights %>%
select(UniqueCarrier, DepDelay) %>%
arrange(desc(DepDelay))
# identical query using the database
flights_tbl %>%
select(UniqueCarrier, DepDelay) %>%
arrange(desc(DepDelay))# send SQL commands to the database
tbl(my_db, sql("SELECT * FROM hflights LIMIT 100"))
# ask dplyr for the SQL commands
flights_tbl %>%
select(UniqueCarrier, DepDelay) %>%
arrange(desc(DepDelay)) %>%
explain()Although my last tutorial used data from the hflights package, Hadley Wickham has rewritten the dplyr vignettes to use the nycflights13 package instead, and so I’m also using nycflights13 for the sake of consistency.
# remove flights data if you just finished my previous tutorial
rm(flights)# load packages
suppressMessages(library(dplyr))
library(nycflights13)
# print the flights dataset from nycflights13
data(flights)
names(flights)## [1] "year" "month" "day" "dep_time" "dep_delay"
## [6] "arr_time" "arr_delay" "carrier" "tailnum" "flight"
## [11] "origin" "dest" "air_time" "distance" "hour"
## [16] "minute"# besides just using select() to pick columns...
flights %>% select(carrier, flight)## Source: local data frame [336,776 x 2]
##
## carrier flight
## (chr) (int)
## 1 UA 1545
## 2 UA 1714
## 3 AA 1141
## 4 B6 725
## 5 DL 461
## 6 UA 1696
## 7 B6 507
## 8 EV 5708
## 9 B6 79
## 10 AA 301
## .. ... ...# ...you can use the minus sign to hide columns
flights %>% select(-month, -day)## Source: local data frame [336,776 x 14]
##
## year dep_time dep_delay arr_time arr_delay carrier tailnum flight
## (int) (int) (dbl) (int) (dbl) (chr) (chr) (int)
## 1 2013 517 2 830 11 UA N14228 1545
## 2 2013 533 4 850 20 UA N24211 1714
## 3 2013 542 2 923 33 AA N619AA 1141
## 4 2013 544 -1 1004 -18 B6 N804JB 725
## 5 2013 554 -6 812 -25 DL N668DN 461
## 6 2013 554 -4 740 12 UA N39463 1696
## 7 2013 555 -5 913 19 B6 N516JB 507
## 8 2013 557 -3 709 -14 EV N829AS 5708
## 9 2013 557 -3 838 -8 B6 N593JB 79
## 10 2013 558 -2 753 8 AA N3ALAA 301
## .. ... ... ... ... ... ... ... ...
## Variables not shown: origin (chr), dest (chr), air_time (dbl), distance
## (dbl), hour (dbl), minute (dbl).# hide a range of columns
flights %>% select(-(dep_time:arr_delay))
# hide any column with a matching name
flights %>% select(-contains("time"))# pick columns using a character vector of column names
cols <- c("carrier", "flight", "tailnum")
flights %>% select(one_of(cols))## Source: local data frame [336,776 x 3]
##
## carrier flight tailnum
## (chr) (int) (chr)
## 1 UA 1545 N14228
## 2 UA 1714 N24211
## 3 AA 1141 N619AA
## 4 B6 725 N804JB
## 5 DL 461 N668DN
## 6 UA 1696 N39463
## 7 B6 507 N516JB
## 8 EV 5708 N829AS
## 9 B6 79 N593JB
## 10 AA 301 N3ALAA
## .. ... ... ...# select() can be used to rename columns, though all columns not mentioned are dropped
flights %>% select(tail = tailnum)## Source: local data frame [336,776 x 1]
##
## tail
## (chr)
## 1 N14228
## 2 N24211
## 3 N619AA
## 4 N804JB
## 5 N668DN
## 6 N39463
## 7 N516JB
## 8 N829AS
## 9 N593JB
## 10 N3ALAA
## .. ...# rename() does the same thing, except all columns not mentioned are kept
flights %>% rename(tail = tailnum)## Source: local data frame [336,776 x 16]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tail
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 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
## 5 2013 1 1 554 -6 812 -25 DL N668DN
## 6 2013 1 1 554 -4 740 12 UA N39463
## 7 2013 1 1 555 -5 913 19 B6 N516JB
## 8 2013 1 1 557 -3 709 -14 EV N829AS
## 9 2013 1 1 557 -3 838 -8 B6 N593JB
## 10 2013 1 1 558 -2 753 8 AA N3ALAA
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# filter() supports the use of multiple conditions
flights %>% filter(dep_time >= 600, dep_time <= 605)## Source: local data frame [2,460 x 16]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 1 2013 1 1 600 0 851 -7 B6 N595JB
## 2 2013 1 1 600 0 837 12 MQ N542MQ
## 3 2013 1 1 601 1 844 -6 B6 N644JB
## 4 2013 1 1 602 -8 812 -8 DL N971DL
## 5 2013 1 1 602 -3 821 16 MQ N730MQ
## 6 2013 1 2 600 0 814 25 EV N13914
## 7 2013 1 2 600 -5 751 -27 EV N760EV
## 8 2013 1 2 600 0 819 4 9E N8946A
## 9 2013 1 2 600 0 846 0 B6 N529JB
## 10 2013 1 2 600 0 737 12 WN N8311Q
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# between() is a concise alternative for determing if numeric values fall in a range
flights %>% filter(between(dep_time, 600, 605))
# side note: is.na() can also be useful when filtering
flights %>% filter(!is.na(dep_time))# slice() filters rows by position
flights %>% slice(1000:1005)## Source: local data frame [6 x 16]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 1 2013 1 2 809 -1 950 2 B6 N304JB
## 2 2013 1 2 810 10 1008 -6 DL N358NW
## 3 2013 1 2 811 -4 1100 4 DL N328NW
## 4 2013 1 2 811 -4 1126 -5 DL N305DQ
## 5 2013 1 2 811 -9 944 -11 MQ N509MQ
## 6 2013 1 2 815 0 1109 -19 DL N335NW
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# keep the first three rows within each group
flights %>% group_by(month, day) %>% slice(1:3)## Source: local data frame [1,095 x 16]
## Groups: month, day [365]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 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 2 42 43 518 36 B6 N580JB
## 5 2013 1 2 126 156 233 154 B6 N636JB
## 6 2013 1 2 458 -2 703 13 US N162UW
## 7 2013 1 3 32 33 504 22 B6 N763JB
## 8 2013 1 3 50 185 203 172 B6 N329JB
## 9 2013 1 3 235 156 700 143 B6 N618JB
## 10 2013 1 4 25 26 505 23 B6 N554JB
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# sample three rows from each group
flights %>% group_by(month, day) %>% sample_n(3)## Source: local data frame [1,095 x 16]
## Groups: month, day [365]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 1 2013 1 1 1716 91 2140 61 B6 N651JB
## 2 2013 1 1 1025 5 1356 26 AA N3FXAA
## 3 2013 1 1 1603 16 1720 12 EV N14168
## 4 2013 1 2 1228 103 1354 102 EV N16919
## 5 2013 1 2 1513 15 1827 11 UA N79521
## 6 2013 1 2 1740 -5 2035 15 MQ N723MQ
## 7 2013 1 3 1250 -10 1455 5 MQ N719MQ
## 8 2013 1 3 2105 5 2240 5 MQ N837MQ
## 9 2013 1 3 1533 3 1846 8 UA N77258
## 10 2013 1 4 1640 -5 1831 -35 9E N8828D
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# keep three rows from each group with the top dep_delay
flights %>% group_by(month, day) %>% top_n(3, dep_delay)## Source: local data frame [1,108 x 16]
## Groups: month, day [365]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 1 2013 1 1 848 853 1001 851 MQ N942MQ
## 2 2013 1 1 1815 290 2120 338 EV N17185
## 3 2013 1 1 2343 379 314 456 EV N21197
## 4 2013 1 2 1412 334 1710 323 UA N474UA
## 5 2013 1 2 1607 337 2003 368 AA N324AA
## 6 2013 1 2 2131 379 2340 359 UA N593UA
## 7 2013 1 3 2008 268 2339 270 DL N338NW
## 8 2013 1 3 2012 252 2314 257 B6 N558JB
## 9 2013 1 3 2056 291 2239 285 9E N928XJ
## 10 2013 1 4 2058 208 2 172 B6 N523JB
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# also sort by dep_delay within each group
flights %>% group_by(month, day) %>% top_n(3, dep_delay) %>% arrange(desc(dep_delay))## Source: local data frame [1,108 x 16]
## Groups: month, day [365]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 1 2013 1 1 848 853 1001 851 MQ N942MQ
## 2 2013 1 1 2343 379 314 456 EV N21197
## 3 2013 1 1 1815 290 2120 338 EV N17185
## 4 2013 1 2 2131 379 2340 359 UA N593UA
## 5 2013 1 2 1607 337 2003 368 AA N324AA
## 6 2013 1 2 1412 334 1710 323 UA N474UA
## 7 2013 1 3 2056 291 2239 285 9E N928XJ
## 8 2013 1 3 2008 268 2339 270 DL N338NW
## 9 2013 1 3 2012 252 2314 257 B6 N558JB
## 10 2013 1 4 2123 288 2332 276 EV N29917
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# unique rows can be identified using unique() from base R
flights %>% select(origin, dest) %>% unique()## Source: local data frame [224 x 2]
##
## origin dest
## (chr) (chr)
## 1 EWR IAH
## 2 LGA IAH
## 3 JFK MIA
## 4 JFK BQN
## 5 LGA ATL
## 6 EWR ORD
## 7 EWR FLL
## 8 LGA IAD
## 9 JFK MCO
## 10 LGA ORD
## .. ... ...# dplyr provides an alternative that is more "efficient"
flights %>% select(origin, dest) %>% distinct()
# side note: when chaining, you don't have to include the parentheses if there are no arguments
flights %>% select(origin, dest) %>% distinct# mutate() creates a new variable (and keeps all existing variables)
flights %>% mutate(speed = distance/air_time*60)## Source: local data frame [336,776 x 17]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 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
## 5 2013 1 1 554 -6 812 -25 DL N668DN
## 6 2013 1 1 554 -4 740 12 UA N39463
## 7 2013 1 1 555 -5 913 19 B6 N516JB
## 8 2013 1 1 557 -3 709 -14 EV N829AS
## 9 2013 1 1 557 -3 838 -8 B6 N593JB
## 10 2013 1 1 558 -2 753 8 AA N3ALAA
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl), speed (dbl).# transmute() only keeps the new variables
flights %>% transmute(speed = distance/air_time*60)## Source: local data frame [336,776 x 1]
##
## speed
## (dbl)
## 1 370.0441
## 2 374.2731
## 3 408.3750
## 4 516.7213
## 5 394.1379
## 6 287.6000
## 7 404.4304
## 8 259.2453
## 9 404.5714
## 10 318.6957
## .. ...# example data frame with row names
mtcars %>% head()## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1# add_rownames() turns row names into an explicit variable
mtcars %>% add_rownames("model") %>% head()## Source: local data frame [6 x 12]
##
## model mpg cyl disp hp drat wt qsec vs am
## (chr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1
## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1
## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1
## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0
## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0
## 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0
## Variables not shown: gear (dbl), carb (dbl).# side note: dplyr no longer prints row names (ever) for local data frames
mtcars %>% tbl_df()## Source: local data frame [32 x 11]
##
## mpg cyl disp hp drat wt qsec vs am gear carb
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## .. ... ... ... ... ... ... ... ... ... ... ...# summarise() can be used to count the number of rows in each group
flights %>% group_by(month) %>% summarise(cnt = n())## Source: local data frame [12 x 2]
##
## month cnt
## (int) (int)
## 1 1 27004
## 2 2 24951
## 3 3 28834
## 4 4 28330
## 5 5 28796
## 6 6 28243
## 7 7 29425
## 8 8 29327
## 9 9 27574
## 10 10 28889
## 11 11 27268
## 12 12 28135# tally() and count() can do this more concisely
flights %>% group_by(month) %>% tally()
flights %>% count(month)# you can sort by the count
flights %>% group_by(month) %>% summarise(cnt = n()) %>% arrange(desc(cnt))## Source: local data frame [12 x 2]
##
## month cnt
## (int) (int)
## 1 7 29425
## 2 8 29327
## 3 10 28889
## 4 3 28834
## 5 5 28796
## 6 4 28330
## 7 6 28243
## 8 12 28135
## 9 9 27574
## 10 11 27268
## 11 1 27004
## 12 2 24951# tally() and count() have a sort parameter for this purpose
flights %>% group_by(month) %>% tally(sort=TRUE)
flights %>% count(month, sort=TRUE)# you can sum over a specific variable instead of simply counting rows
flights %>% group_by(month) %>% summarise(dist = sum(distance))## Source: local data frame [12 x 2]
##
## month dist
## (int) (dbl)
## 1 1 27188805
## 2 2 24975509
## 3 3 29179636
## 4 4 29427294
## 5 5 29974128
## 6 6 29856388
## 7 7 31149199
## 8 8 31149334
## 9 9 28711426
## 10 10 30012086
## 11 11 28639718
## 12 12 29954084# tally() and count() have a wt parameter for this purpose
flights %>% group_by(month) %>% tally(wt = distance)
flights %>% count(month, wt = distance)# group_size() returns the counts as a vector
flights %>% group_by(month) %>% group_size()## [1] 27004 24951 28834 28330 28796 28243 29425 29327 27574 28889 27268
## [12] 28135# n_groups() simply reports the number of groups
flights %>% group_by(month) %>% n_groups()## [1] 12# group by two variables, summarise, arrange (output is possibly confusing)
flights %>% group_by(month, day) %>% summarise(cnt = n()) %>% arrange(desc(cnt)) %>% print(n = 40)## Source: local data frame [365 x 3]
## Groups: month [12]
##
## month day cnt
## (int) (int) (int)
## 1 1 2 943
## 2 1 7 933
## 3 1 10 932
## 4 1 11 930
## 5 1 14 928
## 6 1 31 928
## 7 1 17 927
## 8 1 24 925
## 9 1 18 924
## 10 1 28 923
## 11 1 25 922
## 12 1 4 915
## 13 1 3 914
## 14 1 21 912
## 15 1 9 902
## 16 1 16 901
## 17 1 30 900
## 18 1 8 899
## 19 1 23 897
## 20 1 15 894
## 21 1 22 890
## 22 1 29 890
## 23 1 1 842
## 24 1 6 832
## 25 1 13 828
## 26 1 27 823
## 27 1 20 786
## 28 1 5 720
## 29 1 12 690
## 30 1 26 680
## 31 1 19 674
## 32 2 28 964
## 33 2 21 961
## 34 2 25 961
## 35 2 22 957
## 36 2 14 956
## 37 2 15 954
## 38 2 20 949
## 39 2 18 948
## 40 2 27 945
## .. ... ... ...# ungroup() before arranging to arrange across all groups
flights %>% group_by(month, day) %>% summarise(cnt = n()) %>% ungroup() %>% arrange(desc(cnt))## Source: local data frame [365 x 3]
##
## month day cnt
## (int) (int) (int)
## 1 11 27 1014
## 2 7 11 1006
## 3 7 8 1004
## 4 7 10 1004
## 5 12 2 1004
## 6 7 18 1003
## 7 7 25 1003
## 8 7 12 1002
## 9 7 9 1001
## 10 7 17 1001
## .. ... ... ...data_frame() is a better way than data.frame() for creating data frames. Benefits of data_frame():
# data_frame() example
data_frame(a = 1:6, b = a*2, c = 'string', 'd+e' = 1) %>% glimpse()## Observations: 6
## Variables: 4
## $ a (int) 1, 2, 3, 4, 5, 6
## $ b (dbl) 2, 4, 6, 8, 10, 12
## $ c (chr) "string", "string", "string", "string", "string", "string"
## $ d+e (dbl) 1, 1, 1, 1, 1, 1# data.frame() example
data.frame(a = 1:6, c = 'string', 'd+e' = 1) %>% glimpse()## Observations: 6
## Variables: 3
## $ a (int) 1, 2, 3, 4, 5, 6
## $ c (fctr) string, string, string, string, string, string
## $ d.e (dbl) 1, 1, 1, 1, 1, 1# create two simple data frames
(a <- data_frame(color = c("green","yellow","red"), num = 1:3))## Source: local data frame [3 x 2]
##
## color num
## (chr) (int)
## 1 green 1
## 2 yellow 2
## 3 red 3(b <- data_frame(color = c("green","yellow","pink"), size = c("S","M","L")))## Source: local data frame [3 x 2]
##
## color size
## (chr) (chr)
## 1 green S
## 2 yellow M
## 3 pink L# only include observations found in both "a" and "b" (automatically joins on variables that appear in both tables)
inner_join(a, b)## Joining by: "color"## Source: local data frame [2 x 3]
##
## color num size
## (chr) (int) (chr)
## 1 green 1 S
## 2 yellow 2 M# include observations found in either "a" or "b"
full_join(a, b)## Joining by: "color"## Source: local data frame [4 x 3]
##
## color num size
## (chr) (int) (chr)
## 1 green 1 S
## 2 yellow 2 M
## 3 red 3 NA
## 4 pink NA L# include all observations found in "a"
left_join(a, b)## Joining by: "color"## Source: local data frame [3 x 3]
##
## color num size
## (chr) (int) (chr)
## 1 green 1 S
## 2 yellow 2 M
## 3 red 3 NA# include all observations found in "b"
right_join(a, b)## Joining by: "color"## Source: local data frame [3 x 3]
##
## color num size
## (chr) (int) (chr)
## 1 green 1 S
## 2 yellow 2 M
## 3 pink NA L# right_join(a, b) is identical to left_join(b, a) except for column ordering
left_join(b, a)## Joining by: "color"## Source: local data frame [3 x 3]
##
## color size num
## (chr) (chr) (int)
## 1 green S 1
## 2 yellow M 2
## 3 pink L NA# filter "a" to only show observations that match "b"
semi_join(a, b)## Joining by: "color"## Source: local data frame [2 x 2]
##
## color num
## (chr) (int)
## 1 green 1
## 2 yellow 2# filter "a" to only show observations that don't match "b"
anti_join(a, b)## Joining by: "color"## Source: local data frame [1 x 2]
##
## color num
## (chr) (int)
## 1 red 3# sometimes matching variables don't have identical names
b <- b %>% rename(col = color)
# specify that the join should occur by matching "color" in "a" with "col" in "b"
inner_join(a, b, by=c("color" = "col"))## Source: local data frame [2 x 3]
##
## color num size
## (chr) (int) (chr)
## 1 green 1 S
## 2 yellow 2 M# specify that you want to see more rows
flights %>% print(n = 15)## Source: local data frame [336,776 x 16]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 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
## 5 2013 1 1 554 -6 812 -25 DL N668DN
## 6 2013 1 1 554 -4 740 12 UA N39463
## 7 2013 1 1 555 -5 913 19 B6 N516JB
## 8 2013 1 1 557 -3 709 -14 EV N829AS
## 9 2013 1 1 557 -3 838 -8 B6 N593JB
## 10 2013 1 1 558 -2 753 8 AA N3ALAA
## 11 2013 1 1 558 -2 849 -2 B6 N793JB
## 12 2013 1 1 558 -2 853 -3 B6 N657JB
## 13 2013 1 1 558 -2 924 7 UA N29129
## 14 2013 1 1 558 -2 923 -14 UA N53441
## 15 2013 1 1 559 -1 941 31 AA N3DUAA
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight (int), origin (chr), dest (chr), air_time
## (dbl), distance (dbl), hour (dbl), minute (dbl).# specify that you want to see ALL rows (don't run this!)
flights %>% print(n = Inf)# specify that you want to see all columns
flights %>% print(width = Inf)## Source: local data frame [336,776 x 16]
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## (int) (int) (int) (int) (dbl) (int) (dbl) (chr) (chr)
## 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
## 5 2013 1 1 554 -6 812 -25 DL N668DN
## 6 2013 1 1 554 -4 740 12 UA N39463
## 7 2013 1 1 555 -5 913 19 B6 N516JB
## 8 2013 1 1 557 -3 709 -14 EV N829AS
## 9 2013 1 1 557 -3 838 -8 B6 N593JB
## 10 2013 1 1 558 -2 753 8 AA N3ALAA
## .. ... ... ... ... ... ... ... ... ...
## flight origin dest air_time distance hour minute
## (int) (chr) (chr) (dbl) (dbl) (dbl) (dbl)
## 1 1545 EWR IAH 227 1400 5 17
## 2 1714 LGA IAH 227 1416 5 33
## 3 1141 JFK MIA 160 1089 5 42
## 4 725 JFK BQN 183 1576 5 44
## 5 461 LGA ATL 116 762 5 54
## 6 1696 EWR ORD 150 719 5 54
## 7 507 EWR FLL 158 1065 5 55
## 8 5708 LGA IAD 53 229 5 57
## 9 79 JFK MCO 140 944 5 57
## 10 301 LGA ORD 138 733 5 58
## .. ... ... ... ... ... ... ...# show up to 1000 rows and all columns
flights %>% View()
# set option to see all columns and fewer rows
options(dplyr.width = Inf, dplyr.print_min = 6)
# reset options (or just close R)
options(dplyr.width = NULL, dplyr.print_min = 10)