在将此问题的逻辑与dplyr的逻辑进行匹配时，我遇到了一些麻烦。通常，如果要将一组减少为一个组，请使用summarise，而如果要为每行计算一个单独的编号，请使用mutate。但是，如果要对每行的组进行计算怎么办？
在下面的示例中，mloc包含一个指向pnum的指针，目标是添加一个新列nm_child，该列针对每一行计算{{1 }}指向pnum中组中行索引的值（即具有与该值相同的值）。如果使用嵌套循环，或者使用mloc（如果我知道如何迭代1）每个组，＆2）每个元素和3）返回映射输出作为组中的列，则很容易做到。
map上面的library(tidyverse)
ser <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2)
pnum <- c(1:5, 1:6)
mloc <- c(0, 2, 2, 0, 3, 1, 1, 0, 0, 3, 4)
tb1 <- tibble(ser,pnum, mloc)
tb2 <- tb1 %>%
group_by(ser) %>%
mutate(nm_child = sum(pnum == mloc))
总是=1。我明白了为什么它不起作用，但我看不出为什么它能做到这一点。
我也尝试过
nm_child（返回
mutate(nm_child = count(pnum == mloc))
和其他各种东西。我确实通过添加几列中间值并使用一堆嵌套的ifelse（）来完成一件事，但是要在我的900万行上运行需要20多分钟的时间-相比之下，例如回归和大多数简单的dplyr操作，它们之间的变化介于几秒钟之间，太快而无法注意到。
所需的输出：
no applicable method for 'groups' applied to an object of class "logical")
答案 0 :(得分：4)
这是使用 <dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-tx</artifactId>
<version>3.1.2.RELEASE</version>
</dependency>
-
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.study</groupId>
<artifactId>test_component</artifactId>
<version>1.0-SNAPSHOT</version>
</parent>
<properties>
<start-class>com.study.TestStarter</start-class>
<java.version>1.8</java.version>
<source>1.8</source>
<target>1.8</target>
<assembly_build_version>2.3.523</assembly_build_version>
<descriptorRef>${svc_install_pkg}</descriptorRef>
<!-- Additionally, Please make sure that your JAVA_HOME is pointing to
1.8 when building on commandline -->
<skip_tomcat_bundle>false</skip_tomcat_bundle>
<tomcat_bundle>tomcat85_24.tgz</tomcat_bundle>
</properties>
<dependencies>
<!-- Add typical dependencies for a web application -->
<!-- Adds Tomcat and Spring MVC, along others -->
<!--dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-parent</artifactId>
<version>1.4.3.RELEASE</version> </dependency -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
<version>1.4.3.RELEASE</version>
<!-- <exclusions> <exclusion> <groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-tomcat</artifactId> </exclusion> </exclusions> -->
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-tomcat</artifactId>
<version>1.4.3.RELEASE</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.tomcat.embed</groupId>
<artifactId>tomcat-embed-el</artifactId>
<version>8.5.6</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>javax.servlet</groupId>
<artifactId>jstl</artifactId>
<version>1.2</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.dataformat</groupId>
<artifactId>jackson-dataformat-xml</artifactId>
<version>2.9.8</version>
</dependency>
<dependency>
<groupId>it.unimi.dsi</groupId>
<artifactId>fastutil</artifactId>
<version>7.0.11</version>
</dependency>
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>19.0</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.12</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-test</artifactId>
<version>5.1.0.RELEASE</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-test-autoconfigure</artifactId>
<version>1.4.4.RELEASE</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.mockito</groupId>
<artifactId>mockito-all</artifactId>
<version>1.10.19</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>2.5</version>
<scope>test</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin><!-- Include if you want to make an executable jar[FAT JAR which
includes all dependencies along with sprinboot loader] that you can run on
commandline using java -jar NAME -->
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.12.4</version>
</plugin>
<plugin>
<groupId>org.jacoco</groupId>
<artifactId>jacoco-maven-plugin</artifactId>
<version>0.7.7.201606060606</version>
<executions>
<execution>
<goals>
<goal>prepare-agent</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-war-plugin</artifactId>
<version>2.2</version>
<configuration>
<failOnMissingWebXml>false</failOnMissingWebXml>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
</plugin>
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<version>3.0.0</version>
<executions>
<execution>
<id>attach-artifacts</id>
<phase>package</phase>
<goals>
<goal>attach-artifact</goal>
</goals>
<configuration>
<artifacts>
<artifact>
<file>target/hgvs-${project.version}.jar</file>
<type>jar</type>
</artifact>
</artifacts>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-source-plugin</artifactId>
<version>3.0.1</version>
<executions>
<execution>
<id>attach-sources</id>
<phase>verify</phase>
<goals>
<goal>jar-no-fork</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<artifactId>maven-assembly-plugin</artifactId>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-deploy-plugin</artifactId>
<executions>
<execution>
<phase>deploy</phase>
</execution>
</executions>
</plugin>
</plugins>
</build>
这是另一种方式，感谢@RonakShah-
sapply更新：查看其他答案中的基准，@ thelatemail的答案肯定是最好的。
答案 1 :(得分：4)
这是ser + mloc的聚合，然后左联接返回原始数据。不必遍历每个值：
tb1 %>%
group_by(ser, mloc) %>%
summarise(nm_child=n()) %>%
left_join(tb1, ., by=c("ser"="ser","pnum"="mloc"))
## A tibble: 11 x 4
# ser pnum mloc nm_child
# <dbl> <dbl> <dbl> <int>
# 1 1.00 1.00 0 NA
# 2 1.00 2.00 2.00 2
# 3 1.00 3.00 2.00 1
# 4 1.00 4.00 0 NA
# 5 1.00 5.00 3.00 NA
# 6 2.00 1.00 1.00 2
# 7 2.00 2.00 1.00 NA
# 8 2.00 3.00 0 1
# 9 2.00 4.00 0 1
#10 2.00 5.00 3.00 NA
#11 2.00 6.00 4.00 NA
这将更加有效：
# big example
tb1 <- tb1[rep(1:11,5e4),]
tb1$ser <- rep(1:1e5, rep(5:6,5e4))
system.time({
tb1 %>%
group_by(ser) %>%
mutate(
nm_child = sapply(pnum, function(x) sum(x == mloc))
)
})
# user system elapsed
# 8.83 0.06 8.97
system.time({
tb1 %>%
group_by(ser, mloc) %>%
summarise(nm_child=n()) %>%
left_join(tb1, ., by=c("ser"="ser","pnum"="mloc"))
})
# user system elapsed
# 0.67 0.02 0.69
在基本R逻辑中，这类似于：
tabu <- aggregate(cbind(nm_child=mloc) ~ ser + mloc, tb1, FUN=length)
merge(tb1, tabu, by.x=c("ser","pnum"), by.y=c("ser","mloc"), all.x=TRUE)
并在data.table中将其四舍五入，这将再快一个数量级：
tb1[tb1[, .N, by=.(ser,mloc)], on=c("ser","pnum"="mloc"), nm_child := N]
答案 2 :(得分：3)
您可以使用outer和rowSums
tb1 %>%
group_by(ser) %>%
mutate(nm_child = rowSums(outer(pnum, mloc, `==`)))
# # A tibble: 11 x 4
# # Groups: ser [2]
# ser pnum mloc nm_child
# <dbl> <int> <dbl> <dbl>
# 1 1 1 0 0
# 2 1 2 2 2
# 3 1 3 2 1
# 4 1 4 0 0
# 5 1 5 3 0
# 6 2 1 1 2
# 7 2 2 1 0
# 8 2 3 0 1
# 9 2 4 0 1
# 10 2 5 3 0
# 11 2 6 4 0
使用最新邮件的示例数据进行基准测试
tb1 <- tb1[rep(1:11,5e4),]
tb1$ser <- rep(1:1e5, rep(5:6,5e4))
tb2 <- as.data.table(tb1)
library(microbenchmark)
microbenchmark(
sapply = {
tb1 %>%
group_by(ser) %>%
mutate(
nm_child = sapply(pnum, function(x) sum(x == mloc))
)
},
join = {
tb1 %>%
group_by(ser, mloc) %>%
summarise(nm_child=n()) %>%
left_join(tb1, ., by=c("ser"="ser","pnum"="mloc"))
},
outer1 = {
tb1 %>%
group_by(ser) %>%
mutate(nm_child = rowSums(outer(pnum, mloc, `==`)))
},
outer2 = {
tb1 %>%
group_by(ser) %>%
mutate(nm_child = colSums(outer(mloc, pnum, `==`)))
},
data.table = {
tb2[tb2[, .N, by=.(ser,mloc)], on=c("ser","pnum"="mloc"), nm_child := N][]
},
times = 10)
基准输出
# Unit: milliseconds
# expr min lq mean median uq max neval
# sapply 8233.5740 8297.7331 8939.9369 8647.5935 8956.3364 10706.3362 10
# join 889.6682 899.0483 935.7493 908.1441 932.2827 1135.8424 10
# outer1 4551.0428 4631.1605 5184.9359 4986.7327 5160.0109 7563.4190 10
# outer2 4495.9134 4552.1169 4763.5954 4723.7783 4893.2190 5198.4556 10
# data.table 108.7449 115.7866 124.4453 120.6742 125.7591 171.8111 10