基于规则的R采样

本文介绍了基于规则的R采样的处理方法，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 data1 = data.frame("id" = c("f","e","b","a","e","d","e","f","c","d","d","c","d","b","e","b","d","b","e","e","b","a","b","e","a","d","a","d","b","f","b","e","b","d","e","d","b","e","f","a","b","b","f","e","c","a","b","d","c","d","e","e","f","e","a","b","b","c","b","a","b","f","a","b","c","e","d","a","e","d","a","f","b","d","e","b","f","e","f","f","c","b","f","c","b","e","e","f","e","b","f","f","b","e","c","a","e","c","d","b"), "class" = c(4,1,2,4,1,4,3,2,1,1,2,4,2,2,3,1,4,1,2,4,2,2,1,1,1,3,4,4,4,3,3,2,3,2,2,2,3,4,1,2,4,1,1,3,3,2,2,2,4,4,3,3,1,1,4,2,3,2,4,1,4,3,2,3,4,3,3,2,3,4,4,1,4,1,2,3,4,1,2,1,3,4,4,3,1,2,4,2,3,2,4,2,2,1,1,4,1,3,1,1), "score" = c(59,65,61,64,91,91,70,90,64,87,51,54,92,76,75,78,55,99,66,57,88,89,77,66,100,92,80,84,52,66,59,71,56,88,51,97,65,89,65,67,52,57,51,63,67,79,51,90,79,54,90,55,90,72,64,52,95,61,87,54,91,75,80,93,53,81,87,85,84,84,81,93,100,51,70,64,51,54,83,96,65,61,53,80,68,73,52,57,96,55,63,97,94,77,63,98,85,97,65,77)) data2 = data.frame("class" = c(1,2,3,4), "s" = c(2,5,3,1))

我有数据集"data1".我想通过查看"data2"中的"class" col并随机采样"count"行来创建"data2".因此，在 data2 中，"c"的"count"为3；因此，请为 data1 中的类'c'中的每个'id'采样3行.

I have dataset 'data1'. I want to create 'data2' by looking at the 'class' col in 'data2' and sampling randomly 'count' rows. So then: in data2 the 'count' for "c" is 3; so then sample 3 rows for each 'id' in class 'c' in data1.

推荐答案

使用 dplyr ，一种方法是加入 df 和 df.1 和基于 count 列在 df.1 中.

Using dplyr, one way would be to join df and df.1 based on class, group_by class and sample rows from df.1 based on count column in df.1.

library(dplyr) set.seed(123) df.1 %>% rename_at(ncol(.), ~'sample') %>% right_join(df, by = 'class') %>% group_by(class) %>% sample_n(sample) %>% select(names(df)) # id class count # <int> <fct> <dbl> #1 3 a 8 #2 3 b 5 #3 4 b 6 #4 2 b 9 #5 1 b 4 #6 2 c 4 #7 4 c 3 #8 3 c 3

因此，我们从A类获得1行，从B类获得4行，从C获得3行.

Hence, we get 1 row from class A, 4 from B and 3 from C.