- Loading dataset
- Choosing columns select rename
- Choosing rows filter between slice sample_n top_n distinct
- Adding new variables mutate transmute add_rownames
- Grouping and counting summarise tally count group_size n_groups ungroup
- Creating data frames data_frame
- Joining merging tables left_join right_join inner_join full_join semi_join anti_join
- Viewing more output print View
- 参考资料
Loading dataset
# 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
flightsChoosing columns: select, rename
# besides just using select() to pick columns...
flights %>% select(carrier, flight)
# ...you can use the minus sign to hide columns
flights %>% select(-month, -day)
# 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))
# select() can be used to rename columns, though all columns not mentioned are dropped
flights %>% select(tail = tailnum)
# rename() does the same thing, except all columns not mentioned are kept
flights %>% rename(tail = tailnum)
Choosing rows: filter, between, slice, sample_n, top_n, distinct
# filter() supports the use of multiple conditions
flights %>% filter(dep_time >= 600, dep_time <= 605)
# 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)
# keep the first three rows within each group
flights %>% group_by(month, day) %>% slice(1:3)
# sample three rows from each group
flights %>% group_by(month, day) %>% sample_n(3)
# keep three rows from each group with the top dep_delay
flights %>% group_by(month, day) %>% top_n(3, dep_delay)
# also sort by dep_delay within each group
flights %>% group_by(month, day) %>% top_n(3, dep_delay) %>% arrange(desc(dep_delay))
# unique rows can be identified using unique() from base R
flights %>% select(origin, dest) %>% unique()
# 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
Adding new variables: mutate, transmute, add_rownames
# mutate() creates a new variable (and keeps all existing variables)
flights %>% mutate(speed = distance/air_time*60)
# transmute() only keeps the new variables
flights %>% transmute(speed = distance/air_time*60)
# example data frame with row names
mtcars %>% head()
# add_rownames() turns row names into an explicit variable
mtcars %>% add_rownames("model") %>% head()
# side note: dplyr no longer prints row names (ever) for local data frames
mtcars %>% tbl_df()
Grouping and counting: summarise, tally, count, group_size, n_groups, ungroup
# summarise() can be used to count the number of rows in each group
flights %>% group_by(month) %>% summarise(cnt = n())
# 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))
# 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))
# 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()
# n_groups() simply reports the number of groups
flights %>% group_by(month) %>% n_groups()
# group by two variables, summarise, arrange (output is possibly confusing)
flights %>% group_by(month, day) %>% summarise(cnt = n()) %>% arrange(desc(cnt)) %>% print(n = 40)
# ungroup() before arranging to arrange across all groups
flights %>% group_by(month, day) %>% summarise(cnt = n()) %>% ungroup() %>% arrange(desc(cnt))
flights %>% group_by(month, day) %>% summarise(cnt = n()) %>% arrange(desc(cnt)) %>% filter(month==7)
Creating data frames: data_frame
data_frame() is a better way than data.frame() for creating data frames. Benefits of data_frame():
- You can use previously defined columns to compute new columns.
- It never coerces column types.
- It never munges column names.
- It never adds row names.
- It only recycles length 1 input.
- It returns a local data frame (a tbl_df).
# data_frame() example
x = data_frame(a = 1:6, b = a*2, c = 'string', 'd+e' = 1) %>% glimpse()
# data.frame() example
x1 = data.frame(a = 1:6, c = 'string', 'd+e' = 1) %>% glimpse()
Joining (merging) tables: left_join, right_join, inner_join, full_join, semi_join, anti_join
# create two simple data frames
(a <- data_frame(color = c("green","yellow","red"), num = 1:3))
(b <- data_frame(color = c("green","yellow","pink"), size = c("S","M","L")))
# only include observations found in both "a" and "b" (automatically joins on variables that appear in both tables)
inner_join(a, b)
# include observations found in either "a" or "b"
full_join(a, b)
# include all observations found in "a"
left_join(a, b)
# include all observations found in "b"
right_join(a, b)
# right_join(a, b) is identical to left_join(b, a) except for column ordering
left_join(b, a)
# filter "a" to only show observations that match "b"
semi_join(a, b)
# filter "a" to only show observations that don't match "b"
anti_join(a, b)
# 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"))Viewing more output: print, View
# specify that you want to see more rows
flights %>% print(n = 15)
# 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)
# 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)