组中所有不适用的投递ID

本文介绍了组中所有不适用的投递ID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

我使用以下代码准备了一些数据：

I have some data prepared using the below code:

# # Data Preparation ---------------------- library(lubridate) start_date <- "2018-10-30 00:00:00" start_date <- as.POSIXct(start_date, origin="1970-01-01") dates <- c(start_date) for(i in 1:287) { dates <- c(dates, start_date + minutes(i * 10)) } dates <- as.POSIXct(dates, origin="1970-01-01") date_val <- format(dates, '%d-%m-%Y') weather.forecast.data <- data.frame(dateTime = dates, date = date_val, id = 'GH1', radiation = runif(288)) weather.forecast.data$radiation[(weather.forecast.data$id == 'GH1') & (weather.forecast.data$date == '30-10-2018')] = NA

我的任务是从 weather.forecast.data 过滤行，其中对于id和date的每个唯一实例，所有辐射值都将丢失。

My task is to filter rows from the weather.forecast.data where all radiation values are missing for each unique instance of id and date.

我有使用 data.table 编写的代码：

library(data.table) setDT(weather.forecast.data) weather.forecast.data[, dateid := paste(date, id, sep = "__")] weather.forecast.data[, is_all_na := all(is.na(radiation)), dateid] weather.forecast.data = weather.forecast.data[!(is_all_na), !c('dateid', 'is_all_na'), with = FALSE]

我正在尝试使用 dplyr 函数和管道操作以提高可读性：

I am trying to use dplyr functions and pipe operations to make it better readable:

library(dplyr) weather.forecast.data %>% mutate(dateid = paste(date, id, sep = "__")) %>% group_by(dateid) %>% summarise(is_all_na = all(is.na(radiation))) %>% filter(is_all_na) %>% select(dateid)

我能够检索 id 都丢失了。但是，我无法从原始数据中删除 id 。

I am able to retrieve the id with all missing. But, I am unable to remove the id from the original data.

推荐答案

无需一起粘贴 列，则可以 group_by 多列

No need to paste columns together, you can group_by multiple columns

library(dplyr) weather.forecast.data %>% group_by(date, id) %>% filter(!all(is.na(radiation)))

这将删除行其中全部辐射 是每个<$ c的 NA $ c>日期和 id 。

This will drop the rows where all the radiation is NA for each date and id.