950 lines
15 KiB
Plaintext
950 lines
15 KiB
Plaintext
---
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title: "Data Transform"
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subtitle: 《区域水环境污染数据分析实践》<br>Data analysis practice of regional water environment pollution
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author: 苏命、王为东<br>中国科学院大学资源与环境学院<br>中国科学院生态环境研究中心
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date: today
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lang: zh
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format:
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revealjs:
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theme: dark
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slide-number: true
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chalkboard:
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buttons: true
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preview-links: auto
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lang: zh
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toc: true
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toc-depth: 1
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toc-title: 大纲
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logo: ./_extensions/inst/img/ucaslogo.png
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css: ./_extensions/inst/css/revealjs.css
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pointer:
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key: "p"
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color: "#32cd32"
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pointerSize: 18
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revealjs-plugins:
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- pointer
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filters:
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- d2
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---
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```{r}
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#| echo: false
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knitr::opts_chunk$set(echo = TRUE)
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source("../../coding/_common.R")
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library(nycflights13)
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library(tidyverse)
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```
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## 计数
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```{r}
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flights |>
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count(origin, dest, sort = TRUE)
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```
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## 计数-练习
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统计每月的航班数量。
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```{r}
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#| echo: false
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flights |>
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count(year, month, sort = TRUE)
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```
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## 计算新变量
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```{r}
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flights |>
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mutate(
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gain = dep_delay - arr_delay,
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speed = distance / air_time * 60
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)
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```
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## 计算新变量
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```{r}
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flights |>
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mutate(
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gain = dep_delay - arr_delay,
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speed = distance / air_time * 60,
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.before = 1
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)
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```
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## 计算新变量
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```{r}
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flights |>
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mutate(
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gain = dep_delay - arr_delay,
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speed = distance / air_time * 60,
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.after = day
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)
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```
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## 计算新变量
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```{r}
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flights |>
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mutate(
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gain = dep_delay - arr_delay,
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hours = air_time / 60,
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gain_per_hour = gain / hours,
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.keep = "used"
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)
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```
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## 列排序
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```{r}
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flights |>
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relocate(time_hour, air_time)
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```
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## 列排序
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```{r}
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#| results: false
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flights |>
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relocate(year:dep_time, .after = time_hour)
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flights |>
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relocate(starts_with("arr"), .before = dep_time)
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flights |>
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select(starts_with("arr"), everything())
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```
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## 练习
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计算目的地为IAH，按飞行速度排序的表格，保留year:day, `dep_time`, carrier, flight与speed列。
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```{r}
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flights |>
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filter(dest == "IAH") |>
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mutate(speed = distance / air_time * 60) |>
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select(year:day, dep_time, carrier, flight, speed) |>
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arrange(desc(speed))
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```
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## 练习
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计算目的地为IAH，按飞行速度排序的表格，保留year:day, `dep_time`, carrier, flight与speed列。
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```{r}
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#| results: false
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flights1 <- filter(flights, dest == "IAH")
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flights2 <- mutate(flights1, speed = distance / air_time * 60)
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flights3 <- select(flights2, year:day, dep_time, carrier, flight, speed)
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arrange(flights3, desc(speed))
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```
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## 练习
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计算目的地为IAH，按飞行速度排序的表格，保留year:day, `dep_time`, carrier, flight与speed列。
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```{r}
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flights |>
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filter(dest == "IAH") |>
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mutate(speed = distance / air_time * 60) |>
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select(year:day, dep_time, carrier, flight, speed) |>
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arrange(desc(speed))
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```
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## 分组统计
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```{r}
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library(tidyverse)
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mtcars %>%
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group_by(cyl) %>%
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summarize(n = n())
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```
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## 分组统计
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```{r}
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flights |>
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group_by(month)
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```
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## 分组统计
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```{r}
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flights |>
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group_by(month) |>
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summarize(
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avg_delay = mean(dep_delay)
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)
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```
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## 分组统计
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```{r}
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flights |>
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group_by(month) |>
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summarize(
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avg_delay = mean(dep_delay, na.rm = TRUE)
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)
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```
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## 分组统计
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```{r}
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flights |>
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group_by(month) |>
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summarize(
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avg_delay = mean(dep_delay, na.rm = TRUE),
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n = n()
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)
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```
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## 分组统计
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```{r}
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flights |>
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group_by(dest) |>
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slice_max(arr_delay, n = 1) |>
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relocate(dest)
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```
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## 分组统计
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```{r}
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flights |>
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filter(dest == "IAH") |>
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group_by(year, month, day) |>
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summarize(
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arr_delay = mean(arr_delay, na.rm = TRUE)
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)
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```
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## 分组
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```{r}
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daily <- flights |>
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group_by(year, month, day)
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daily
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```
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## 分组统计
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```{r}
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daily_flights <- daily |>
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summarize(n = n())
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```
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## 分组统计
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```{r}
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#| results: false
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daily_flights <- daily |>
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summarize(
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n = n(),
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.groups = "drop_last"
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)
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```
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## 删除分组
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```{r}
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daily |> ungroup()
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```
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## 删除分组
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```{r}
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daily |>
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ungroup() |>
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summarize(
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avg_delay = mean(dep_delay, na.rm = TRUE),
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flights = n()
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)
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```
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## 分组统计
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```{r}
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flights |>
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summarize(
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delay = mean(dep_delay, na.rm = TRUE),
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n = n(),
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.by = month
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)
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```
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## 分组统计
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```{r}
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flights |>
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summarize(
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delay = mean(dep_delay, na.rm = TRUE),
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n = n(),
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.by = c(origin, dest)
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)
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```
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## 练习
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```{r}
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df <- tibble(
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x = 1:5,
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y = c("a", "b", "a", "a", "b"),
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z = c("K", "K", "L", "L", "K")
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)
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df
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```
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```{r}
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#| eval: false
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df |> arrange(y)
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```
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## 练习
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```{r}
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#| echo: false
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df
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```
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```{r}
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#| eval: false
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df |>
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group_by(y) |>
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summarize(mean_x = mean(x))
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```
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## 练习
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```{r}
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#| echo: false
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df
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```
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```{r}
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#| eval: false
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df |>
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group_by(y, z) |>
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summarize(mean_x = mean(x))
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```
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## 练习
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```{r}
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#| echo: false
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df
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```
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```{r}
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#| eval: false
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df |>
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group_by(y, z) |>
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summarize(mean_x = mean(x), .groups = "drop")
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```
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## 练习
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```{r}
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#| echo: false
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df
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```
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```{r}
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#| eval: false
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df |>
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group_by(y, z) |>
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summarize(mean_x = mean(x))
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df |>
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group_by(y, z) |>
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mutate(mean_x = mean(x))
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```
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## 练习
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- 计算不同采样点的平均CO浓度、最大CO浓度、最小CO浓度、中位数CO浓度（`CO_mg/m3`）。
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- 计算各小时全国的平均CO浓度、最大CO浓度、最小CO浓度、中位数CO浓度（`CO_mg/m3`）。
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- 计算不同采样点各小时的平均CO浓度、最大CO浓度、最小CO浓度、中位数CO浓度（`CO_mg/m3`）。
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- 计算各采样点中CO浓度小于全国平均CO浓度的占比。
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- 找出全国各采样点中CO浓度小于全国平均CO浓度的占比最高的10个采样点。
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```{r}
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airqualitydf <- readxl::read_xlsx("../../data/airquality.xlsx",
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sheet = 2)
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```
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## 练习
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按月统计dep_delay最大的3个航班的航班号（flight），用逗号连接。
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```{r}
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#| echo: false
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flights |>
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group_by(year, month) |>
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slice_max(dep_delay, n = 3) |>
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summarize(flight = paste(paste0(carrier, flight), collapse = ", ")) |>
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knitr::kable()
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```
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## 数据变形示意图
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```{r}
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billboard
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knitr::include_graphics("../../image/tidy-data/variables.png", dpi = 270)
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```
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## 数据变形
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```{r}
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billboard |>
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pivot_longer(
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cols = starts_with("wk"),
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names_to = "week",
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values_to = "rank",
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values_drop_na = TRUE
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)
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```
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## 数据变形
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```{r}
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billboard_longer <- billboard |>
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pivot_longer(
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cols = starts_with("wk"),
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names_to = "week",
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values_to = "rank",
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values_drop_na = TRUE
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) |>
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mutate(
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week = parse_number(week)
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)
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billboard_longer
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```
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## 练习
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```{r}
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#| echo: false
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df <- tribble(
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~id, ~bp1, ~bp2,
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"A", 100, 120,
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"B", 140, 115,
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"C", 120, 125
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)
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df
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```
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将以上数据(`df`)转换为如下形式。
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```{r}
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#| echo: false
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df |>
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pivot_longer(
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cols = bp1:bp2,
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names_to = "measurement",
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values_to = "value"
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)
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```
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## 练习
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请转换如下`iris`数据。
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```{r}
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#| echo: false
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as_tibble(head(iris, n = 3))
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cat("转为如下形式：")
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iris |>
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pivot_longer(cols = c(Sepal.Length,
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Sepal.Width,
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Petal.Length,
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Petal.Width),
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names_to = "flower_attr",
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values_to = "attr_value") |>
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head()
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```
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## 数据变形示意图2
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```{r}
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who2
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knitr::include_graphics("../../image/tidy-data/multiple-names.png", dpi = 270)
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```
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## 数据变形
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```{r}
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who2 |>
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pivot_longer(
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cols = !(country:year),
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names_to = c("diagnosis", "gender", "age"),
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names_sep = "_",
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values_to = "count"
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)
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```
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## 数据变形示意图
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```{r}
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household
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knitr::include_graphics("../../image/tidy-data/names-and-values.png", dpi = 270)
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```
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## 数据变形
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```{r}
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household |>
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pivot_longer(
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cols = !family,
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names_to = c(".value", "child"),
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names_sep = "_",
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values_drop_na = TRUE
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)
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```
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## 查看数据
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```{r}
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cms_patient_experience
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```
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## 查看数据
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```{r}
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cms_patient_experience |>
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distinct(measure_cd, measure_title)
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```
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## 数据变形（变宽）
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```{r}
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cms_patient_experience |>
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pivot_wider(
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names_from = measure_cd,
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values_from = prf_rate
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)
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```
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## 数据变形（变宽）
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```{r}
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cms_patient_experience |>
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pivot_wider(
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id_cols = starts_with("org"),
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names_from = measure_cd,
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values_from = prf_rate
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)
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```
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## 练习
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```{r}
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df <- tribble(
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~id, ~measurement, ~value,
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"A", "bp1", 100,
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"B", "bp1", 140,
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"B", "bp2", 115,
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"A", "bp2", 120,
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"A", "bp3", 105
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)
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```
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变形成如下形式：
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```{r}
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#| echo: false
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df |>
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pivot_wider(
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names_from = measurement,
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values_from = value
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)
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```
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## 练习：变宽
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```{r}
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df <- tribble(
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~id, ~measurement, ~value,
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"A", "bp1", 100,
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"A", "bp1", 102,
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"A", "bp2", 120,
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"B", "bp1", 140,
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"B", "bp2", 115
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)
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```
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## 练习
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```{r}
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df |>
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pivot_wider(
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names_from = measurement,
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values_from = value
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)
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```
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## 练习
|
||
|
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```{r}
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df |>
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group_by(id, measurement) |>
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summarize(n = n(), .groups = "drop") |>
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filter(n > 1)
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```
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## nest，套嵌数据框
|
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|
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```{r}
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#| echo: false
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df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)
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df
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```
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```{r}
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df %>% nest(data = c(y, z))
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```
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## nest，套嵌数据框
|
||
|
||
Specify variables to nest by (rather than variables to nest) using `.by`
|
||
|
||
```{r}
|
||
df %>% nest(.by = x)
|
||
```
|
||
|
||
|
||
## nest，套嵌数据框
|
||
|
||
In this case, since `...` isn't used you can specify the resulting column name with `.key`
|
||
|
||
```{r}
|
||
df %>% nest(.by = x, .key = "cols")
|
||
```
|
||
|
||
|
||
## nest，套嵌数据框
|
||
|
||
Use tidyselect syntax and helpers, just like in `dplyr::select()`
|
||
|
||
```{r}
|
||
df %>% nest(data = any_of(c("y", "z")))
|
||
```
|
||
|
||
## nest，套嵌数据框
|
||
|
||
`...` and `.by` can be used together to drop columns you no longer need,
|
||
or to include the columns you are nesting by in the inner data frame too.
|
||
This drops `z`:
|
||
|
||
```{r}
|
||
df %>% nest(data = y, .by = x)
|
||
```
|
||
|
||
## nest，套嵌数据框
|
||
|
||
This includes `x` in the inner data frame:
|
||
|
||
```{r}
|
||
df %>% nest(data = everything(), .by = x)
|
||
```
|
||
|
||
## nest，套嵌数据框
|
||
|
||
Multiple nesting structures can be specified at once
|
||
|
||
```{r}
|
||
iris %>%
|
||
nest(petal = starts_with("Petal"), sepal = starts_with("Sepal"))
|
||
```
|
||
|
||
|
||
## nest，套嵌数据框
|
||
|
||
```{r}
|
||
iris %>%
|
||
nest(width = contains("Width"), length = contains("Length"))
|
||
```
|
||
|
||
|
||
## nest，套嵌数据框
|
||
|
||
Nesting a grouped data frame nests all variables apart from the group vars
|
||
|
||
```{r}
|
||
fish_encounters
|
||
fish_encounters %>%
|
||
dplyr::group_by(fish) %>%
|
||
nest()
|
||
```
|
||
|
||
## nest，套嵌数据框
|
||
|
||
That is similar to `nest(.by = )`, except here the result isn't grouped
|
||
|
||
```{r}
|
||
fish_encounters %>%
|
||
nest(.by = fish)
|
||
```
|
||
|
||
## nest，套嵌数据框
|
||
|
||
Nesting is often useful for creating per group models
|
||
|
||
```{r}
|
||
mtcars %>%
|
||
nest(.by = cyl) %>%
|
||
dplyr::mutate(models = lapply(data, function(df) lm(mpg ~ wt, data = df)))
|
||
```
|
||
|
||
|
||
## 练习
|
||
|
||
```{r}
|
||
#| echo: false
|
||
(airqualitydf <- readxl::read_xlsx("../../data/airquality.xlsx",
|
||
sheet = 2))
|
||
```
|
||
|
||
```{r}
|
||
airqualitydf
|
||
airqualitynestdf <- airqualitydf |>
|
||
nest(sitedf = -site)
|
||
```
|
||
|
||
## `nest`与`group_by`联用
|
||
|
||
```{r}
|
||
#| echo: false
|
||
iris %>%
|
||
group_by(Species) %>%
|
||
nest(.key = "spdf")
|
||
```
|
||
|
||
## unnest
|
||
|
||
```{r}
|
||
airqualitynestdf |> unnest(sitedf)
|
||
```
|
||
|
||
|
||
|
||
## `purrr`包
|
||
|
||
- map()：依次应用一元函数到一个序列的每个元素上，基本等同 lapply()
|
||
- map2()：依次应用二元函数到两个序列的每对元素上
|
||
- pmap()：应用多元函数到多个序列的每组元素上，可以实现对数据框逐行迭代
|
||
- map 系列默认返回列表型，可根据想要的返回类型添加后缀：_int, _dbl, _lgl, _chr, _df, 甚至可以接着对返回的数据框df做行/列合并：_dfr, _dfc
|
||
- 如果只想要函数依次作用的过程，而不需要返回结果，改用 walk 系列即可
|
||
- 所应用的函数，有 purrr公式风格简写（匿名函数），支持一元，二元，多元函数
|
||
- purrr 包中的其它有用函数
|
||
|
||
## `purrr`包
|
||
|
||
- `map_chr(.x, .f)`: 返回字符型向量
|
||
- `map_lgl(.x, .f)`: 返回逻辑型向量
|
||
- `map_dbl(.x, .f)`: 返回实数型向量
|
||
- `map_int(.x, .f)`: 返回整数型向量
|
||
- `map_dfr(.x, .f)`: 返回数据框列表，再 bind_rows 按行合并为一个数据框
|
||
- `map_dfc(.x, .f)`: 返回数据框列表，再 bind_cols 按列合并为一个数据框
|
||
|
||
|
||
## `purrr`包-cheatsheet
|
||
|
||
```{r}
|
||
dwfun::ggsavep("../../image/cheatsheet/purrr.svg", loadit = TRUE)
|
||
```
|
||
|
||
[purrr](../../image/cheatsheet/purrr.pdf)
|
||
|
||
|
||
## `purrr`包
|
||
|
||
生成从1到10的10组随机数，每组随机数个数为100，均值依次为1到10，标准差为1，并存储在数据框中。
|
||
|
||
```{r}
|
||
res <- list()
|
||
for (i in 1:10) {
|
||
res[[i]] <- tibble(随机数 = rnorm(n = 100, mean = i, sd = 1))
|
||
}
|
||
res
|
||
|
||
```
|
||
|
||
|
||
|
||
|
||
## `purrr`包
|
||
|
||
生成从1到10的10组随机数，每组随机数个数为100，均值依次为1到10，标准差为1，并存储在数据框中。
|
||
|
||
```{r}
|
||
1:10 |>
|
||
purrr::map(~tibble(随机数 = rnorm(n = 100, mean = .x, sd = 1)))
|
||
```
|
||
|
||
## purrr
|
||
|
||
```{r}
|
||
library(purrr)
|
||
mtcars |>
|
||
split(mtcars$cyl) |> # from base R
|
||
map(\(df) lm(mpg ~ wt, data = df)) |>
|
||
map(summary) %>%
|
||
map_dbl("r.squared")
|
||
```
|
||
|
||
|
||
## 练习：
|
||
|
||
计算每月最后一个周六的航班数：
|
||
|
||
```{r}
|
||
flights
|
||
```
|
||
|
||
## `tidyr` + `purrr`包
|
||
|
||
任务：展示不同城市间的大气指标散点图
|
||
|
||
```{r}
|
||
(airqualitydf <- readxl::read_xlsx("../../data/airquality.xlsx",
|
||
sheet = 2))
|
||
```
|
||
|
||
## join
|
||
|
||
Perform left join
|
||
|
||
```{r}
|
||
(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))
|
||
(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))
|
||
left_join(df1, df2, by = "id")
|
||
```
|
||
|
||
## left join
|
||
|
||
Create sample data frames with non-matching rows
|
||
|
||
```{r}
|
||
(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))
|
||
(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))
|
||
left_join(df2, df1, by = "id")
|
||
```
|
||
|
||
## left join
|
||
|
||
|
||
Create sample data frames with multiple columns
|
||
|
||
```{r}
|
||
df1 <- data.frame(id1 = c(1, 2, 3), id2 = c("A", "B", "C"), value1 = letters[1:3])
|
||
df2 <- data.frame(id1 = c(2, 3, 4), id2 = c("B", "C", "D"), value2 = LETTERS[1:3])
|
||
# Perform left join
|
||
left_join(df1, df2, by = c("id1", "id2"))
|
||
```
|
||
|
||
## right join
|
||
|
||
|
||
```{r}
|
||
(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))
|
||
(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))
|
||
|
||
# Perform right join
|
||
right_join(df1, df2, by = "id")
|
||
```
|
||
|
||
|
||
## inner join
|
||
|
||
```{r}
|
||
# Create sample data frames
|
||
(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))
|
||
(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))
|
||
# Perform inner join
|
||
inner_join(df1, df2, by = "id")
|
||
```
|
||
|
||
|
||
## full join
|
||
|
||
```{r}
|
||
# Create sample data frames
|
||
(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))
|
||
(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))
|
||
# Perform inner join
|
||
full_join(df1, df2, by = "id")
|
||
```
|
||
|
||
## semi join
|
||
|
||
Create sample data frames
|
||
|
||
```{r}
|
||
(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))
|
||
(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))
|
||
# Perform semi join
|
||
semi_join(df1, df2, by = "id")
|
||
```
|
||
|
||
|
||
## 练习
|
||
|
||
合并`airquality.xlsx`中的数据。
|
||
|
||
|
||
|
||
## 练习
|
||
|
||
统计各城市白天与晚上的大气质量差异，计算不同指标差异最大的10个城市。
|
||
|
||
|
||
|
||
|
||
|
||
|
||
## 欢迎讨论！{.center}
|
||
|
||
|
||
`r rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "https://drwater.rcees.ac.cn/course/public/RWEP/@PUB/SD/")`
|
||
|