---
title: "Data import"
subtitle: 《区域水环境污染数据分析实践》<br>Data analysis practice of regional water environment pollution
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
date: today
lang: zh
format:
  revealjs:
    theme: dark
    slide-number: true
    chalkboard:
      buttons: true
    preview-links: auto
    lang: zh
    toc: true
    toc-depth: 1
    toc-title: 大纲
    logo: ./_extensions/inst/img/ucaslogo.png
    css: ./_extensions/inst/css/revealjs.css
    pointer:
      key: "p"
      color: "#32cd32"
      pointerSize: 18
revealjs-plugins:
  - pointer
filters:
  - d2
---


```{r}
#| echo: false
knitr::opts_chunk$set(echo = TRUE)
source("../../coding/_common.R")
library(tidyverse)
```


## tidyverse风格数据分析总体流程


![](../../image/data-science/import.png) 


## 导入csv数据

```{r}
read_lines("../../data/students.csv") |> cat(sep = "\n")
```

## 导入csv数据


```{r}
read_csv("../../data/students.csv") |>
  knitr::kable()
```


## 读取数据

```{r}
(students <- read_csv("../../data/students.csv"))
```


## 读取数据

```{r}
#| message: false
(students <- read_csv("../../data/students.csv", na = c("N/A", "")))
```

## 列名不要有空格

```{r}
students |> 
  rename(
    student_id = `Student ID`,
    full_name = `Full Name`
  )
```

## `janitor`处理空格

```{r}
#| message: false

students |> janitor::clean_names()
```

## `janitor`处理空格

```{r}
students |>
  janitor::clean_names() |>
  mutate(meal_plan = factor(meal_plan))
```

## `janitor`处理空格

```{r}
students <- students |>
  janitor::clean_names() |>
  mutate(
    meal_plan = factor(meal_plan),
    age = parse_number(if_else(age == "five", "5", age))
  )
students
```


## 直接录入

```{r}
#| message: false

read_csv(
  "The first line of metadata
  The second line of metadata
  x,y,z
  1,2,3",
  skip = 2
)
```


## 直接录入

```{r}
#| message: false
read_csv(
  "# A comment I want to skip
  x,y,z
  1,2,3",
  comment = "#"
)
```


## 指定列名

```{r}
#| message: false

read_csv(
  "1,2,3
  4,5,6",
  col_names = c("x", "y", "z")
)
```

## 指定列的类型

```{r}
another_csv <- "
x,y,z
1,2,3"

read_csv(
  another_csv, 
  col_types = cols(.default = col_character())
)
read_csv(
  another_csv,
  col_types = cols_only(x = col_character())
)
```


## 练习

```{r}
#| eval: false

read_csv("a,b\n1,2,3\n4,5,6")
read_csv("a,b,c\n1,2\n1,2,3,4")
read_csv("a,b\n\"1")
read_csv("a,b\n1,2\na,b")
read_csv("a;b\n1;3")
```

## 练习

```{r}
#| eval: false
annoying <- tibble(
  `1` = 1:10,
  `2` = `1` * 2 + rnorm(length(`1`))
)
```



## 批量读取

```{r}
#| message: false

sales_files <- c("../../data/01-sales.csv",
  "../../data/02-sales.csv",
  "../../data/03-sales.csv")
read_csv(sales_files, id = "file")
```




## 读取Excel，建议用`readxl`包

```{r}
(surveydf <- readxl::read_xlsx("../../data/survey.xlsx"))
```

## 读取Excel

```{r}
(airqualitydf <- readxl::read_xlsx("../../data/airquality.xlsx", sheet = 2))
```



## 批量读取

```{r}
sales_files <- list.files("../../data",
  pattern = "sales\\.csv$", full.names = TRUE)
sales_files
```

## 写入csv

```{r}
#| warning: false
#| message: false
students
write_csv(students, "students-2.csv")
read_csv("students-2.csv")
```

## 写入Excel

```{r}
writexl::write_xlsx(students, "../../data/writexldemo.xlsx")
```

## 读取数据库，以MySQL为例

```{r}
if (FALSE) {
  conn <- cctdb::get_dbconn("nationalairquality")
  DBI::dbListTables(conn)
}
```



## 读取数据库，以MySQL为例

```{r}
if (FALSE) {
  conn <- cctdb::get_dbconn("nationalairquality")
  metadf <- tbl(conn, "metadf") |> 
    head(100) |>
    collect()
  DBI::dbDisconnect(conn)
  saveRDS(metadf, file = "../../data/metadfdemo.RDS")
}
metadf <- readRDS(file = "../../data/metadfdemo.RDS")
lang <- "cn"
metadf |>
  ggplot(aes(lon, lat)) +
geom_point(aes(fill = Area)) +
dwfun::theme_sci()
```


## 练习

```{r}
#| eval: false
metadf <- readxl::read_xlsx("../../data/airquality.xlsx")
dir.create("../../data/metacity2/")
metadf |>
  nest(sitedf = -site) |>
  mutate(flag = purrr::map2(site, sitedf,
    ~ writexl::write_xlsx(.y, paste0("../../data/metacity2/", .x, ".xlsx"))))
```


## 练习

```{r}
#| include: false
#| eval: false
if (FALSE) {
  require(tidyverse)
  conn <- cctdb::get_dbconn("nationalairquality")
  metadf <- tbl(conn, "metadf") |> 
    collect()
  DBI::dbDisconnect(conn)
  metanestdf <- metadf |>
    nest(citydf = -Area)
  names(metanestdf$citydf) <- metanestdf$Area
  writexl::write_xlsx(metanestdf$citydf, path = "../../data/meta_city.xlsx")
  dir.create("../../data/metacity/")
  metanestdf |>
    mutate(flag = purrr::map2(Area, citydf,
      ~ writexl::write_xlsx(.y,
        path = paste0("../../data/metacity/", .x, ".xlsx")
      )))
}
```

1. 从“../../data/sales.xlsx”读取第9到13行的数据
2. 从“../../data/meta_city.xlsx”读取所有的数据，并保存至“../../data/meta_city_onetable1.xlsx”
3. 从“../../data/metacity/”读取所有的数据，并保存至“../../data/meta_city_onetable2.xlsx”



## 欢迎讨论！{.center}


`r rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "../")`

