汇总同一调用中的所有组值和条件子集

本文介绍了汇总同一调用中的所有组值和条件子集的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我将通过一个例子来说明我的问题.

I'll illustrate my question with an example.

样本数据:

df <- data.frame(ID = c(1, 1, 2, 2, 3, 5), A = c("foo", "bar", "foo", "foo", "bar", "bar"), B = c(1, 5, 7, 23, 54, 202)) df ID A B 1 1 foo 1 2 1 bar 5 3 2 foo 7 4 2 foo 23 5 3 bar 54 6 5 bar 202

我想做的是通过ID总结B的总和以及当A为"foo"时B的总和.我可以通过几个步骤来做到这一点，例如:

What I want to do is to summarize, by ID, the sum of B and the sum of B when A is "foo". I can do this in a couple steps like:

require(magrittr) require(dplyr) df1 <- df %>% group_by(ID) %>% summarize(sumB = sum(B)) df2 <- df %>% filter(A == "foo") %>% group_by(ID) %>% summarize(sumBfoo = sum(B)) left_join(df1, df2) ID sumB sumBfoo 1 1 6 1 2 2 30 30 3 3 54 NA 4 5 202 NA

但是，我正在寻找一种更优雅/更快的方法，因为我正在处理10gb +的sqlite内存不足数据.

However, I'm looking for a more elegant/faster way, as I'm dealing with 10gb+ of out-of-memory data in sqlite.

require(sqldf) my_db <- src_sqlite("my_db.sqlite3", create = T) df_sqlite <- copy_to(my_db, df)

我想到了使用 mutate 定义一个新的 Bfoo 列:

I thought of using mutate to define a new Bfoo column:

df_sqlite %>% mutate(Bfoo = ifelse(A=="foo", B, 0))

不幸的是，这不适用于数据库.

Unfortunately, this doesn't work on the database end of things.

Error in sqliteExecStatement(conn, statement, ...) : RS-DBI driver: (error in statement: no such function: IFELSE)

推荐答案

写出@hadley的评论作为答案

Writing up @hadley's comment as an answer

df_sqlite %>% group_by(ID) %>% mutate(Bfoo = if(A=="foo") B else 0) %>% summarize(sumB = sum(B), sumBfoo = sum(Bfoo)) %>% collect