plyr包的特点

其基础函数有以下特点：

第一个参数df

返回df

没有数据更改in place

正是因为有这些特点，才可以使用%>%操作符，方便逻辑式编程。

载入数据

library(plyr)library(dplyr)# load packagessuppressMessages(library(dplyr))install.packages("hflights") library(hflights) # explore data data(hflights) head(hflights) # 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 # 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))

filter

keep rows matching criteria

# base R approach to view all flights on January 1flights[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) # use pipe for OR condition filter(flights, UniqueCarrier=="AA" | UniqueCarrier=="UA") # you can also use %in% operator filter(flights, UniqueCarrier %in% c("AA", "UA"))

select

pick columns by name

# base R approach to select DepTime, ArrTime, and FlightNum columnsflights[, c("DepTime", "ArrTime", "FlightNum")]# dplyr approach select(flights, DepTime, ArrTime, FlightNum) # 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"))

“chaining” or “pipelining”

# nesting method to select UniqueCarrier and DepDelay columns and filter for delays over 60 minutesfilter(select(flights, UniqueCarrier, DepDelay), DepDelay > 60)# chaining methodflights %>%    select(UniqueCarrier, DepDelay) %>%    filter(DepDelay > 60)# 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()

arrange

reorder rows

# base R approach to select UniqueCarrier and DepDelay columns and sort by DepDelayflights[order(flights$DepDelay), c("UniqueCarrier", "DepDelay")]# dplyr approachflights %>%    select(UniqueCarrier, DepDelay) %>%    arrange(DepDelay)# use `desc` for descending flights %>% select(UniqueCarrier, DepDelay) %>% arrange(desc(DepDelay))

mutate

add new variable
create new variables that are functions of exciting variables
which is d
ifferent form transform

# base R approach to create a new variable Speed (in mph)flights$Speed <- flights$Distance / flights$AirTime*60flights[, c("Distance", "AirTime", "Speed")] # dplyr approach (prints the new variable but does not store it) flights %>% select(Distance, AirTime) %>% mutate(Speed = Distance/AirTime*60) # store the new variable flights <- flights %>% mutate(Speed = Distance/AirTime*60)

summarise

reduce variables to values

# base R approaches to calculate the average arrival delay to each destinationhead(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 ArrDelayflights %>%    group_by(Dest) %>%    summarise(avg_delay = mean(ArrDelay, na.rm=TRUE))#summarise_each allows you to apply the same summary function to multiple columns at once #Note: mutate_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) # 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")) #Helper function n() counts the number of rows in a group #Helper function n_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)) # rewrite more simply with the `tally` function flights %>% group_by(Month, DayofMonth) %>% tally(sort = TRUE) # 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)) # Grouping can sometimes be useful without summarising # for each destination, show the number of cancelled and not cancelled flights flights %>% group_by(Dest) %>% select(Cancelled) %>% table() %>% head()

Window Functions

Aggregation function (like mean) takes n inputs and returns 1 value

Window function takes n inputs and returns n values
Includes ranking and ordering functions (like min_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 valueflights %>%    group_by(UniqueCarrier) %>%    select(Month, DayofMonth, DepDelay) %>%    filter(min_rank(desc(DepDelay)) <= 2) %>%    arrange(UniqueCarrier, desc(DepDelay))# rewrite more simply with the `top_n` functionflights %>%    group_by(UniqueCarrier) %>%    select(Month, DayofMonth, DepDelay) %>%    top_n(2,DepDelay) %>% arrange(UniqueCarrier, desc(DepDelay)) # 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)) # rewrite more simply with the `tally` function flights %>% group_by(Month) %>% tally() %>% mutate(change = n - lag(n))

Other functions

# randomly sample a fixed number of rows, without replacementflights %>% sample_n(5)# randomly sample a fraction of rows, with replacementflights %>% sample_frac(0.25, replace=TRUE) # base R approach to view the structure of an object str(flights) # dplyr approach: better formatting, and adapts to your screen width glimpse(flights)

Connecting Databases

dplyr can connect to a database as if the data was loaded into a data frame

Use the same syntax for local data frames and databases

Only generates SELECT statements

Currently supports SQLite, PostgreSQL/Redshift, MySQL/MariaDB, BigQuery, MonetDB

Example below is based upon an SQLite database containing the hflights data

Instructions for creating this database are in the databases vignette

# connect to an SQLite database containing the hflights datamy_db <- src_sqlite("my_db.sqlite3")# connect to the "hflights" table in that databaseflights_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))

You can write the SQL commands yourself

dplyr can tell you the SQL it plans to run and the query execution plan

# send SQL commands to the databasetbl(my_db, sql("SELECT * FROM hflights LIMIT 100"))# ask dplyr for the SQL commandsflights_tbl %>%    select(UniqueCarrier, DepDelay) %>%    arrange(desc(DepDelay)) %>%    explain()