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Ming Su
2026-05-21 01:33:30 +08:00
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AC/_extensions
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../_extensions
AC/index.qmd
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---
subtitle: "Acomplishment"
---
# 项目
# 专利
# 软著
# 专著
# 奖励
# 人才培养
MS/.gitignore
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/figure/
/*.spl
/*.log
/*.blg
/*.bbl
/*.toc
/.DS_Store
/*.tex
MS/AC.qmd
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---
format:
dwms-html: default
dwms-docx: default
dwms-pdf: default
filters:
- latex-environment
- authoraffil
---
# CRediT authorship contribution statement {-}
Ming Su: Conceptualization, Methodology, Validation, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization, Project administration, Funding acquisition. Yiping Zhu: Investigation, Resources, Project administration. Michael Burch: Writing - Review & Editing. Jinping Lu, Tengxin Cao, Yufan Ai: Investigation. Jianwei Yu: Resources. Min Yang: Conceptualization, Validation, Formal analysis, Writing - Review & Editing, Project administration, Resources, Funding acquisition, Supervision.
MS/CL.qmd
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---
format:
dwcl-html: default
dwcl-docx: default
dwcl-pdf: default
date: today
author: "Min Yang"
affliction: Key Laboratory of Environmental Aguatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences
from_email: "yangmin@rcees.ac.cn"
from_phone: "+86 (10) 62843038"
from_institution_logo: "_extensions/drwater/dwcl/inst/rceeslonglogo.pdf"
from_sign: "_extensions/drwater/dwcl/inst/signmyen.pdf"
from_position: "Professor"
from_professional_title: "Ph.D."
from_institution: "Chinese Academy of Sciences"
from_department: "Research Center for Eco-Environmental Sciences"
from_address: "Beijing 100085, China"
to_professional_title: ""
to_name: "Editor-in-Chief"
to_journal: "{{< meta submitjournal >}}"
opening_greeting: "Dear Editor,"
closing_greeting: "Yours Sincerely,"
---
Thank you for your consideration our manuscript (`{{< meta submitid >}}`) in publishing in {{< meta submitjournal >}}, and we also thank the reviewers for their kind and valuable comments.
We have carefully revised the manuscript according to each comment from the reviewers. As requested, revision note with point to point responses and actions, change tracking manuscript and revised manuscript have been prepared and included in the revision submission.
We believe that the paper has been greatly improved. Looking forward to your further instruction regarding its publication.
MS/GA.qmd
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---
subtitle: "Graphical Abstract"
format:
dwms-html: default
dwms-docx: default
dwms-pdf: default
filters:
- latex-environment
- authoraffil
---
#
<!-- ![](./figures/GA3.pdf) -->
MS/HL.qmd
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---
subtitle: "Highlights"
date: ""
format:
dwms-html: default
dwms-docx: default
dwms-pdf: default
filters:
- latex-environment
- authoraffil
commands: [clab]
---
#
- A comprehensive model was contructed to evaluate the risk of odor occurrences
- Turbidity distribution determine underwater light and MIB-producing *Planktothrix*
- Increasing flowrate leading high turbidity that can control *Planktothrix* and MIB
- Odor control based on flow regulation in source water is "green" and fundamental
MS/MS.aux
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\relax
\providecommand\zref@newlabel[2]{}
\providecommand \babel@aux [2]{\global \let \babel@toc \@gobbletwo }
\@nameuse{bbl@beforestart}
\providecommand\hyper@newdestlabel[2]{}
\providecommand*\HyPL@Entry[1]{}
\HyPL@Entry{0<</S/D>>}
\providecommand \oddpage@label [2]{}
\babel@aux{english}{}
\newlabel{abstract}{{}{2}{}{Doc-Start}{}}
\@writefile{toc}{\contentsline {section}{Abstract}{2}{Doc-Start}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {1}Main}{3}{section.1}\protected@file@percent }
\newlabel{main}{{1}{3}{\texorpdfstring {Main}{Main}}{section.1}{}}
\newlabel{r:r1q0101}{{This is something useful according to Reviewer 1.}{}}
\newlabel{r1q0101}{{1}{3}{\texorpdfstring {Main}{Main}}{section.1}{}}
\newlabel{r1q0101end}{{1}{3}{\texorpdfstring {Main}{Main}}{section.1}{}}
\newlabel{line:r1q0101}{{12}{3}{}{}{}}
\newlabel{line:r1q0101end}{{12}{3}{}{}{}}
\@writefile{toc}{\contentsline {section}{\numberline {2}Methods}{3}{section.2}\protected@file@percent }
\newlabel{methods}{{2}{3}{\texorpdfstring {Methods}{Methods}}{section.2}{}}
\@writefile{toc}{\contentsline {section}{\numberline {3}Results}{3}{section.3}\protected@file@percent }
\newlabel{results}{{3}{3}{\texorpdfstring {Results}{Results}}{section.3}{}}
\newlabel{references}{{3}{4}{}{section.3}{}}
\@writefile{toc}{\contentsline {section}{References}{4}{section.3}\protected@file@percent }
\newlabel{refs}{{3}{4}{}{section*.1}{}}
\gdef \@abspage@last{4}
MS/MS.qmd
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---
msname: MS
smname: SM
isMS: true
date: ""
latex-clean: false # to keep aux
endfloat: true
doublespacing: true
linenumber: true
format:
dwms-html: default
dwms-docx:
filters:
- remove-spaces
- docx-quotes
- no-first-paragraph
dwms-pdf:
number-sections: true
filters:
- latex-body-quotes
- latex-header-quotes
filters:
- latex-environment
- authoraffil
- localizecnbib
- quotescnbib
- autocorrect
- sortbib
lightbox: auto
commands: [clab]
---
```{r}
#| include: false
#| cache: false
lang <- "en"
isRendering <- isTRUE(getOption("knitr.in.progress"))
require(tidyverse)
require(drwateR)
require(patchwork)
rmdify::rmd_init()
dwfun::init()
```
{{< pagebreak >}}
# Abstract {-}
This is the abstract.
{{< pagebreak >}}
{{< include MS_main.qmd >}}
{{< include MS_methods.qmd >}}
{{< include MS_results.qmd >}}
<!-- {{< include MS_discussion.qmd >}} -->
{{< pagebreak >}}
<!-- {{< include _est.qmd >}} -->
# References {-}
::: {#refs}
:::
{{< pagebreak >}}
<!-- {{< include _nature.qmd >}} -->
MS/MS_main.qmd
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# Main
This is a nice paragraph. [This is something useful according to Reviewer 1.]{.clab options="r1q0101"}
Figure from SM.pdf, see `r zref("sfg-demo")`.
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# Methods
MS/MS_results.qmd
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# Results
MS/RN.qmd
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---
subtitle: "Revision Note"
msname: MS
smname: SM
isRN: true
doublespacing: true
format:
dwms-html: default
dwms-docx: default
dwms-pdf:
number-sections: true
filters:
- latex-environment
- authoraffil
lightbox: auto
execute:
excute: false
---
```{r}
#| include: false
#| cache: false
lang <- "en"
RM <- "F" # global rendermode, L: load pdata; F: fast load pdf
isrendering <- isTRUE(getOption('knitr.in.progress'))
require(tidyverse)
require(patchwork)
require(drwateR)
rmdify::rmd_init()
```
# Response to Editor
> 0. I very much regret to have to tell you that publication in our journal is not recommended. An explanation for this decision is given in the attached review reports (and on https://www.editorialmanager.com/wr/). I hope that the comments contained therein will be of use to you.
**Response/Action:**
Thank you.
# Reviewer #1:
> 0. COMMENT 0
**Response/Action:**
Thank you very much for your revision. We made changes as shown below.
<!-- `r cref("r1q0101")` -->
> 1. COMMENT 1
**Response/Action:**
Thank you very much for your revision. We added a figure like below.
<!-- ::: {#rfg-figr1} -->
<!---->
<!-- ```{r} -->
<!-- plot(1) -->
<!-- ``` -->
<!-- Figure in RN to clarify. -->
<!-- ::: -->
# Reviewer #2
> 1. COMMENT 1
**Response/Action:**
Thank you very much for your revision. We made changes as shown below.
<!-- `r cref("r2q0101")` -->
MS/SM.aux
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\relax
\providecommand\zref@newlabel[2]{}
\providecommand \babel@aux [2]{\global \let \babel@toc \@gobbletwo }
\@nameuse{bbl@beforestart}
\providecommand\hyper@newdestlabel[2]{}
\providecommand*\HyPL@Entry[1]{}
\HyPL@Entry{0<</S/D>>}
\providecommand \oddpage@label [2]{}
\babel@aux{english}{}
\@writefile{toc}{\contentsline {section}{Supplementary Fig.~\ref {sfg-demo}}{3}{Doc-Start}\protected@file@percent }
\newlabel{sfg-demo}{{}{3}{\texorpdfstring {Supplementary Fig.~\ref {sfg-demo}}{Supplementary Fig.~}}{Doc-Start}{}}
\newlabel{r:r2q0101}{{A new supplementary figure.}{}}
\newlabel{r2q0101}{{}{3}{\texorpdfstring {Supplementary Fig.~\ref {sfg-demo}}{Supplementary Fig.~}}{Doc-Start}{}}
\newlabel{r2q0101end}{{}{3}{\texorpdfstring {Supplementary Fig.~\ref {sfg-demo}}{Supplementary Fig.~}}{Doc-Start}{}}
\newlabel{line:r2q0101}{{14}{3}{}{}{}}
\newlabel{line:r2q0101end}{{14}{3}{}{}{}}
\@writefile{lost}{\contentsline {sfg}{\numberline {1}{\ignorespaces Caption of demo figure}}{3}{sfg.1}\protected@file@percent }
\newlabel{sfg-demo}{{1}{3}{Caption of demo figure}{sfg.1}{}}
\gdef \@abspage@last{3}
MS/SM.qmd
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---
msname: MS
smname: SM
isSM: true
subtitle: "Supplementary Information"
date: ""
keep-tex: true
latex-clean: false # to keep aux
newpagesubsection: true
doublespacing: true
linenumber: true
format:
dwms-html: default
dwms-docx: default
dwms-pdf:
toc: false # 手动设置了
filters:
- latex-environment
- authoraffil
commands: [clab]
---
\thispagestyle{empty}
```{r}
#| include: false
#| cache: false
options(tinytex.clean = FALSE)
lang <- "en"
RM <- "F" # global rendermode, L: load pdata; F: fast load pdf
isRendering <- isTRUE(getOption('knitr.in.progress'))
require(tidyverse)
require(patchwork)
require(drwateR)
rmdify::rmd_init()
```
Figures and/or tables are provided below as the supplementary evidences to the main text.
```{=latex}
\clearpage
{
\hypersetup{linkcolor=black}
\setcounter{tocdepth}{3}
\tableofcontents
}
```
# [@sfg-demo]
[A new supplementary figure.]{.clab options="r2q0101"}
::: {#sfg-demo}
```{r}
#| out-width: "40%"
if (!isRendering) {
data.frame(x = 1:10, y = 1:10) |>
ggplot(aes(x, y)) +
geom_point() +
theme_sci(3, 2.0)
ggsavep("../figures/demo.pdf", loadit = FALSE)
}
ggsavep("../figures/demo", loadit = TRUE)
```
Caption of demo figure
:::
MS/_est.qmd
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# FIGURES {-}
- Fig. 1: Caption of Fig. 1
- Fig. 2: Caption of Fig. 2
# ASSOCIATED CONTENT {-}
## Supporting Information {-}
The Supporting Information is available free of charge at https://.
Description of Figure S1 (Fig. S1);
Description of Table S1 (Table S1);
(PDF)
# AUTHOR INFORMATION {-}
## Corresponding Authors {-}
- **Ming Su**: *1. Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China;* ORCID: https://orcid.org/0000-0001-9821-1268; Email: <mingsu@rcees.ac.cn>
- **Min Yang**: *National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;* ORCID: https://orcid.org/0009-0005-4902-7620; Email: <yangmin@rcees.ac.cn>
## Authors
- **First Last**: *1. Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences; 2. School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China;* ORCID: https://orcid.org/0009-0005-4902-7620
## Author contributions {-}
## Notes {-}
The authors declare no competing financial interest.
MS/_extensions
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../_extensions
MS/_nature.qmd
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# Data availability
The data that support the findings of this study are available upon request.
# Code availability
Analysis scripts are available via GitHub at https://git.drwater.net/.
# Acknowledgements
This research was financially supported by the National Natural Science Foundation of China (52030002, W2412156, 51878649) and the National Key R&D Program of China (2022YFC3203603).
# Author contributions
J.F. led data curation, methodology development, formal analysis, contributed to manuscript writing. editing, while supporting visualisation. M.S. (Corresponding Author) led conceptualization, formal analysis, and visualization while supporting methodology and co-leading manuscript writing and editing. Y.G., Y.A., and T.C. supported methodology and formal analysis. S.H. provided methodological and analytical support. M.Y. (Corresponding Author) co-led conceptualization and project administration, providing supervision and validation.
# Competing interests
The authors declare no competing interests.
# Additional information
Correspondence and requests for materials should be addressed to Ming Su or Min Yang.
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MS/index.qmd
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---
subtitle: Graphical abstract
---
```{r}
#| label: fig-GA
#| fig-cap: "Graphical abstract"
#| out-width: "80%"
fig <- dwfun::ggsavep("../figures/GA.pdf", loadit = TRUE)
if (!is.null(fig)) fig
```
# README
Below are the typical links.
- [Cover Letter](CL.html)
- [Highlights](HL.html)
- [Manuscript](MS.html)
- [Supplementary Material](SM.html)
- [Revision Note](RN.html)
- [Author Credits](AC.html)
RP/.gitignore
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/.quarto/
RP/20231231_report/.gitignore
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/.quarto/
RP/20231231_report/index.qmd
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---
subtitle: "Research Progress Report"
author: ""
format:
dwhm-html: default
dwms-docx: default
dwev-pdf: default
---
```{r}
#| include: false
lang <- "cn"
RM <- "F" # global rendermode, L: load pdata; F: fast load pdf
isRendering <- !isTRUE(getOption('knitr.in.progress'))
require(lubridate)
require(tidyverse)
require(patchwork)
require(drwateR)
rmdify::rmd_init()
```
# 摘要
# 研究背景与国内外现状介绍
# 主要进展1
# 主要进展2
# 结论与下一步计划
# 参考文献{-}
RP/_extensions
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../_extensions
RP/_metadata.yml
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date: today
isbook: true
top-level-division: chapter
classoption:
- fntef
- UTF8
- zihao=-4 # 正文字号小四
- lang=cn
# - chinesefont=founder # founder|ctexfont|nofont
- scheme=chinese
- color=blue # blue|green|
- titlestyle=hang
# classoption: [oneside]
titlepage: true
keep-tex: false
titlepage-rule-color: "360049"
titlepage-text-color: "360049"
titlepage-rule-height: 0
titlepage-background: "_extensions/drwater/dwev/inst/cover1.pdf"
titlepage-halign: "center" # flushleft | flushright
title-vskip: -30em
title-size: "Huge"
author-vskip: -4em
date-vskip: -1em
caption-justification: centering
numbersections: true
parindent: true
# titlepage-logo: "inst/rceeslogo.pdf"
# logo-width: 20mm
# logo-vskip: -8em
# logo-hskip: "-6em"
lang: zh-CN # 不能改为cn，否则目录等不是中文
CJK: true
fig-pos: "!t"
colorlinks: true
urlcolor: blue
header-left: "\\leftmark"
header-center: "\\hspace{1cm}"
header-right: "第\\thepage 页"
footer-left: "\\hspace{1cm}" # \\thetitle
footer-center: "\\includegraphics[height=1.5em]{_extensions/drwater/dwev/inst/drwater-gray1.pdf}"
footer-right: "\\hspace{1cm}"
toc: true
toc-own-page: true
fig-toc: true
CJKmainfont: "Noto Serif CJK SC"
CJKmainboldfont: "NotoSerifCJKsc-Bold"
CJKmainitalicfont: "NotoSansCJKsc-Light"
CJKsansfont: "Noto Sans CJK SC"
CJKmonofont: "Noto Sans Mono CJK SC"
CJKoptions: |
AutoFakeBold=true
# include-in-header: header.tex
# watermark: "drwater"
# watermark-color: "gray!3"
RP/index.qmd
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---
subtitle: "Reports"
author: ""
execute:
freeze: false
---
```{r}
#| output: asis
data.frame(fd = dir(".", pattern = "\\d{4}[-_]")) |>
dplyr::mutate(
fn = purrr::map_chr(
fd,
~ file.path(.x, dir(.x, pattern = "index.qmd|index.html"))[1]
)
) |>
dplyr::transmute(
htmlstr = purrr::map2_chr(
fn,
fd,
~ paste0(
"## [",
.y,
"](",
.x,
")\n\n",
ifelse(
!grepl("qmd$", .x),
"",
paste0(
"{{< pdf ",
gsub("qmd$", "pdf", .x),
" width=400 height=510 button=\"Download\" >}}\n\n"
)
)
)
)
) |>
dplyr::pull(htmlstr) |>
paste(collapse = "") |>
cat()
```
SD/1.1_R语言介绍/_demo.qmd
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---
title: "Lesson 6"
format: html
---
```{r}
https://rs1.drwater.net
username:
- ruser01
- ruser02
- ruser03
- ruser04
- ruser05
- ruser06
RWEP2025
```
# 安装包
```{r}
install.packages("tidyverse")
x <- c(1:10, NA)
hist(x)
mean(x, na.rm = TRUE)
median(x, na.rm = TRUE)
sd(x, na.rm = TRUE)
for(i in 1:10){
print(i)
}
x + y + x * y
myfunc <- function(x, y = 3) {
x + y + x * y
}
myfunc(1, 2)
myfunc(10)
c(FALSE, 2, 1:3, 3)
c(FALSE, 2, 1:3, 3) > 1
all(c(FALSE, 2, 1:3, 3) > 1)
c(1L,2L,3L)
any(c(FALSE, 2, 1:3, 3) > 1)
x <- 10
sin(x) = ?
paste("sin(x) = ", sin(x), sep = " ")
paste0("sin(x) = ", sin(x))
substr("Monday", 1, 3)
```
# tidy
```{r}
require(readxl)
aqdf <-readxl::read_xlsx("../../data/airquality.xlsx", sheet = "metadf")
# install.packages("skimr")
aqdf |>
skimr::skim()
# base
# tidyverse
aqdf |>
dplyr::group_by(Area) |>
dplyr::summarize(
n = n(),
lon.mean = mean(lon, na.rm = TRUE),
lon.sd = sd(lat, na.rm = TRUE)
) |>
dplyr::filter(Area %in% c("北京市", "天津市", "上海市", "重庆市")) |>
ggplot(aes(x = n, y = lon.mean)) +
geom_point() +
geom_line() +
geom_errorbar(
aes(ymin = lon.mean - lon.sd,
ymax = lon.mean + lon.sd)
)
readxl::read_xlsx("./airquality.xlsx")
flights|>
filter(dest=="IAH")|>
group_by(year,month,day)|>summarize(n=n(),
delay=mean(arr_delay,na.rm=TRUE))|>filter(n>10)
```
RP/20231231_report/_extensions → SD/1.1_R语言介绍/_extensions
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SD/1.1_R语言介绍/index.qmd
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---
title: "一、R语言介绍"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
```{r}
#| include: false
#| cache: false
lang <- "cn"
require(tidyverse)
require(learnr)
knitr::opts_chunk$set(echo = TRUE)
```
## R的历史
::: columns
::: {.column width="85%"}
- 1976年：
- 前身是S语言，由贝尔实验室John Chambers及其同事开发
- 1993年：
- R语言由Ross Ihaka和Robert Gentleman在奥克兰大学开发
- R的诞生是为了提供一个强大的统计计算和图形显示的平台
:::
::: {.column width="15%"}
![](https://cran.r-project.org/Rlogo.svg)
:::
:::
- 1995年：R发布第一个公开版本
- 2000年代： R语言逐渐成为统计学和数据科学领域重要工具
- 2010年：Hadley Wickham发布了ggplot2包，数据可视化方面更强
- 2016年：创建CRAN（Comprehensive R Archive Network），R包中央库
- 至今
- **R语言已成为数据科学和统计学领域最受欢迎的工具之一，被广泛用于数据分析、机器学习、数据可视化等领域**
## R的特点
### 一般特点
- 免费、开源、支持各个主要计算机系统。
- 完整的程序设计语言，基于函数和对象。
- 支持完善的数据类型，如向量、矩阵、数据框等。
### 技术特点
- 所有存在都是对象。
- 所有动作都是函数调用。
- 支持函数编程和对象类。
- 是动态类型语言，运行速度相对较慢。
## R的参考资料
### 推荐参考书
- Hadley Wickham and Garrett Grolemund(2022). [R for Data Science](https://r4ds.hadley.nz/)
- Hadley Wickham(2019). [Advanced R](https://adv-r.hadley.nz/)
- Hadley Wickham(2016). [ggplot2 Elegant Graphics for Data Analysis](https://ggplot2-book.org/)
- John M. Chambers(2008). [Software for Data Analysis-Programming with R](https://www.springer.com/gp/book/9780387759357)
- Venables, W. N. & Ripley, B. D.(2002). [Modern Applied Statistics with S (MASS)](https://www.springer.com/gp/book/9780387954578)
## R的参考资料
### 中文参考书
- 《R语言编程艺术》
- 《R语言实战》
- 《R语言教程与实践》
- 《R语言数据可视化实战》
- 《R语言从入门到精通》
## R的下载与安装
### R的下载与安装
- R的官方网站：[https://www.r-project.org/](https://www.r-project.org/)
- CRAN镜像网站：[http://mirror.bjtu.edu.cn/cran/](http://mirror.bjtu.edu.cn/cran/)
- 下载官方的R软件后按提示安装
{{< video https://vimeo.com/203516510 width="600" height="400">}}
## RStudio
### 什么是RStudio？
- RStudio官方网站: [https://posit.co/products/open-source/rstudio/](https://posit.co/products/open-source/rstudio/)
- RStudio是一个集成开发环境（IDE），专门用于R语言编程和数据分析。
- 它提供了一个直观的界面，使得编写、调试和运行R代码变得更加容易。
## RStudio
### RStudio的功能
1. **代码编辑器**：提供了语法高亮、自动补全和代码折叠等功能。
2. **控制台**：用于直接执行R代码并查看结果。
3. **环境和历史记录**：可以查看当前加载的数据、函数和变量，以及之前执行过的命令。
4. **图形和可视化**：RStudio内置了绘图设备，可以方便地创建各种统计图表。
5. **文件管理器**：可直接在RStudio中管理文件和项目。
6. **包管理器**：方便地安装、更新和管理R包。
7. **Markdown编辑器**：支持Markdown格式，可以创建美观的文档和报告。
## 如何获取RStudio？
- RStudio可以从官方网站免费下载并安装：[RStudio官网](https://posit.co/download/rstudio-desktop/)
- RStudio是一个强大的R编程环境，为R用户提供了丰富的功能和工具。
- 它简化了R语言的使用，提高了数据分析和可视化的效率。
{{< video https://vimeo.com/203516968 width="600" height="400">}}
## 扩展包：R package
- R有一万多个扩展软件包，提供了各种各样的功能
- 已安装的基本R包，如base, stats, graphics等，启动R时默认载入
- 其它扩展包需要用`library(.)`函数载入运行；或者采用`dplyr::filter(.)`方式
::: panel-tabset
### Code
```{r}
#| echo: true
#| eval: false
#| out-width: 50%
# load a R package
library(ggplot2)
# plot it based on the functions from `ggplot2` package
mtcars |>
dplyr::filter(cyl != 8) |>
ggplot(aes(hp, mpg, color = am)) +
geom_point() +
geom_smooth(formula = y ~ x, method = "loess")
```
### Output
```{r}
#| echo: false
#| out-width: 80%
#| fig-width: 6
#| fig-height: 3
library(ggplot2)
mtcars |>
dplyr::filter(cyl != 8) |>
ggplot(aes(hp, mpg, color = am)) +
geom_point() +
geom_smooth(formula = y ~ x, method = "loess")
```
:::
## 安装R包
- 以安装sos包为例。
- 在RStudio中调用“Tools”菜单的“Install Packages”，输入或选择sos即可安装。
- R图形界面安装
- 如果不用RStudio， 在R图形界面选菜单“程序包-安装程序包”进行安装。
- 在CRAN镜像选择窗口中选择中国的镜像，如“China (Beijing 2)”，然后选择要安装的扩展软件包名称即可完成下载和安装。
- 程序安装
```r
# 指定镜像网站并安装扩展包
options(repos = c(CRAN = "https://mirror.tuna.tsinghua.edu.cn/CRAN/"))
install.packages("sos")
# 当R包不是CRAN标准包，代码放在[github](https://github.com)
if (!require(devtools)) install.packages('devtools')
devtools::install_github("kjhealy/socviz")
```
## 安装R包
{{< video https://vimeo.com/203516241 width="600" height="400">}}
## 结语
- R语言是一种强大的统计计算和数据分析工具，适用于各种领域。
- 通过学习R语言，可以进行数据处理、统计分析和数据可视化。
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
title: "二、R语言语法基础"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
## 数据类型
### 数值型
R中的数值型数据可以是整数或浮点数。
```{r}
#| echo: true
(x <- 10)
(y <- 1.23e-2)
(z <- pi)
```
## 数据类型
### 字符串
- R 中的字符串用引号括起来，建议用**双引号**。
- 中文编码主要有GBK编码和UTF-8编码， 可能遇到编码错误造成乱码。RStudio软件默认采用UTF-8编码，在R程序运行时字符串一般用UTF-8编码保存。
```{r}
#| echo: true
(str <- "Hello, World!")
(str <- 'Hello, World!')
(str <- 'He was very angry, and shouted: "Stop!"')
```
## 数据类型
### 逻辑
```{r}
c(TRUE, FALSE)
```
## 特殊值
::: columns
::: {.column width="45%"}
- `NA`: 这是最常见的NA类型，表示缺失值
- `NA_integer_`: 这是NA的整数类型
- `NA_real_`: 这是NA的实数类型
- `NA_character_`: 这是NA的字符类型
- `NA_complex_`: 这是NA的复数类型
:::
::: {.column width="5%"}
:::
::: {.column width="45%"}
```{r}
#| echo: true
pi
NA
NA_character_
Inf
```
:::
:::
## 特殊值
在 R 中，`Inf` 代表正无穷大（positive infinity），而 `-Inf` 则代表负无穷大（negative infinity）。这些值通常出现在数学计算中，例如除以零或对负数取对数等操作可能会导致无穷大的结果。
```{r}
#| echo: true
# 正无穷大
(x <- Inf)
# 负无穷大
(y <- -Inf)
# 无穷大的运算
(a <- 5 / 0)
(b <- log(0))
```
## 变量赋值
在 R 中，可以使用 `<-` 或 `=` 运算符将值赋给变量，**建议用`<-`**。
```{r}
#| echo: true
# 使用 `<-` 运算符
(x <- 10)
(y <- "hello")
# 使用 `=` 运算符
(z = c(1, 2, 3))
```
## 变量赋值
```{r}
# 向量赋值
(vec <- c(1, 2, 3, 4, 5))
# 矩阵赋值
(mat <- matrix(1:9, nrow = 3))
```
## 变量赋值
### 数据框赋值
```{r}
#| echo: true
(df <- data.frame(
Name = c("Alice", "Bob", "Charlie"),
Age = c(25, 30, 35),
Married = c(TRUE, FALSE, TRUE)
))
```
## 变量赋值
### 列表赋值
```{r}
(lst <- list(
numbers = c(1, 2, 3),
strings = c("a", "b", "c"),
matrix = matrix(1:9, nrow = 3)
))
```
## 数学函数
```{r}
#| echo: true
round(pi, digits = 3)
log(10)
```
1. `abs(x)`: 返回 `x` 的绝对值
2. `sqrt(x)`: 返回 `x` 的平方根
3. `exp(x)`: 以e为底的指数函数值
4. `log(x, base)`: 以指定底数的对数函数的值，默认底数为e
5. `log10(x)`: 10为底的对数值
6. `log2(x)`: 2为底的对数值
7. `floor(x)`: 不大于`x`的最大整数
8. `ceiling(x)`: 不小于`x`的最小整数
## 数学函数
9. `sin(x)`, `cos(x)`, `tan(x)`: 返回 `x` 的正弦、余弦和正切值，其中 `x` 为弧度
10. `asin(x)`, `acos(x)`, `atan(x)`: `x` 的反正弦、反余弦和反正切值，返回弧度
11. `sinh(x)`, `cosh(x)`, `tanh(x)`: 返回 `x` 的双曲正弦、双曲余弦和双曲正切值
12. `asinh(x)`, `acosh(x)`, `atanh(x)`: 反双曲正弦、反双曲余弦和反双曲正切值
13. `round(x, digits)`: `x` 四舍五入，`digits`指定小数点后位数
14. `trunc(x)`: 返回`x`截断值，即去掉小数部分
15. `sign(x)`: 返回符号
## 统计函数
```{r}
#| echo: true
x <- c(5, 10, 15, 20, 25)
# 计算向量的平均值
mean(x)
# 计算向量的中位数
median(x)
# 计算向量的最小值
min(x)
# 计算向量的最大值
max(x)
# 计算向量的总和
sum(x)
```
## 统计函数
```{r}
#| echo: true
# 计算向量的标准差
sd(x)
# 计算向量的方差
var(x)
# 计算向量的分位数
quantile(x, probs = c(0.25, 0.5, 0.75))
# 统计向量的频数
(frequency <- table(x))
```
## 统计函数
### 执行两样本或单样本 t 检验
```{r}
#| echo: true
y <- c(3, 8, 13, 18, 23)
t.test(x, y)
```
## 统计函数
### Wilcoxon-Mann-Whitney检验
```{r}
#| echo: true
wilcox.test(x, y)
```
### [什么是 Wilcoxon-Mann-Whitney检验？](https://zhuanlan.zhihu.com/p/613524533）
## 统计函数
### 创建向量的直方图
```{r}
#| fig-width: 5
#| fig-height: 3
hist(x)
```
## 函数调用-练习
### 题目：设有一组数据集合 x 包含了一些整数，请编写R语言代码计算并输出以下指标：
- 平均值（mean）
- 中位数（median）
- 最大值（maximum）
- 最小值（minimum）
- 数据集合中所有元素的和（sum）
- 数据集合的标准差（standard deviation）
- 数据集合 x 为：x <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
**要求：使用R语言编写函数，输入参数为数据集合 x，输出为以上指标的值。**
## 控制流程
### if-else 语句
```{r}
#| echo: true
x <- 10
if (x > 10) {
print("x 大于 10")
} else {
print("x 不大于 10")
}
```
## 控制流程
### for 循环
```{r}
#| echo: true
for (i in 1:5) {
print(i)
}
```
## 自定义函数
### 定义函数
使用 `function` 关键字定义函数，并使用 `return` 关键字返回结果。
```{r}
#| echo: true
my_function <- function(x, y) {
return(x + y)
}
```
### 调用函数
```{r}
#| echo: true
result <- my_function(3, 4)
print(result)
```
## 数据结构
### 向量
向量是一维数组，可以包含相同类型的元素。
```{r}
#| echo: true
(v <- c(1, 2, 3, 4, 5))
```
### 列表
列表可以包含不同类型的元素。
```{r}
#| echo: true
(l <- list(a = 1, b = "hello", c = TRUE))
```
## 数值型向量
### 什么是数值型向量？
- 在 R 中，向量是一种基本的数据结构。
- 数值型向量包含相同类型的数值元素。
### 创建数值型向量
```{r}
#| echo: true
# 使用 c() 函数创建数值型向量
(numeric_vector <- c(1, 2, 3, 4, 5))
```
## 数值型向量
### 向量运算
```{r}
#| echo: true
# 创建两个数值型向量
(vector1 <- c(1, 2, 3))
(vector2 <- c(4, 5, 6))
# 执行向量加法
(result <- vector1 + vector2)
# 执行向量乘法
(result <- vector1 * vector2)
```
## 向量运算
### 向量求和
```{r}
#| echo: true
# 创建数值型向量
vector <- c(1, 2, 3, 4, 5)
# 求和
(sum_result <- sum(vector))
```
## 向量运算
### 向量平均值
```{r}
#| echo: true
# 创建数值型向量
vector <- c(1, 2, 3, 4, 5)
# 平均值
(mean_result <- mean(vector))
```
## 运算-数值运算
- a 的平方。
- b 的立方。
- a 除以 b 的商和余数。
**要求：使用R语言编写函数，输入参数为 a 和 b，输出为上述结果。**
## 运算-逻辑运算
```{r}
all(c(FALSE, 2, 1:3, 3) > 1)
any(c(FALSE, 2, 1:3, 3) > 1)
(flag1 <- FALSE)
(flag2 <- (3 > 2))
(flag3 <- TRUE * TRUE)
(flag4 <- TRUE * FALSE)
(flag5 <- TRUE & FALSE)
(flag6 <- TRUE | FALSE)
```
## 运算-逻辑运算
- `which`
```{r}
which(c(FALSE, TRUE, TRUE, FALSE, NA))
which((11:15) > 12)
```
- `identical`
```{r}
identical(c(1, 2, 3), c(1, 2, NA))
identical(c(1L, 2L, 3L), c(1, 2, 3))
```
## 运算-字符型
- 特殊字符
```{r}
c("abc", "", 'a cat', NA, '李明', "\n")
```
- `paste`
```{r}
(users <- paste("ruser", 1:9))
paste(users, collapse = ", ")
```
## 运算-字符型
- 大小写
```{r}
letters[1:5]
toupper(letters[6:9])
tolower(month.abb)
stringr::str_to_title(c("monday", "tuesday"))
```
## 运算-字符型
- 字符串截取
```{r}
substr("Monday", 1, 3)
stringr::str_sub("Monday", 1, 3)
```
## 运算-字符型
- 类型转换
```{r}
100
as.character(100)
as.numeric(c("0100", "0101"))
sprintf('renamedfile%03d.png', c(3, 99, 100))
```
## 运算-字符型
- 字符串替换
```{r}
(mystr <- "He was wrong!")
gsub("wrong", "right", mystr)
```
## 索引
### 向量
```{r}
# 创建一个向量
vector <- c("apple", "banana", "cherry", "date")
# 访问第三个元素
vector[3]
# 访问多个元素
vector[c(2, 4)]
vector[c(2:4)]
```
## 索引
### 向量
```{r}
# 除了第2个元素
vector[-2]
# 超界
vector[100]
# 更新数据
vector[7] <- "New Data"
vector
```
## 索引
```{r}
(x <- 1:10)
x[x > 6]
x[x < 3] <- 99
x
# which
which(x > 10)
which.max(x)
which.min(x)
```
## 索引
### 列表
```{r}
# 创建一个列表
my_list <- list(
fruit = c("apple", "banana", "cherry"),
numbers = c(1, 2, 3, 4, 5)
)
# 访问列表中的第二个元素
my_list[[2]]
```
## 索引
### 数据框
```{r}
# 创建一个数据框
df <- data.frame(fruit = c("apple", "banana", "cherry"), quantity = c(5, 7, 3))
# 访问数据框中的第一个元素
df[1, 1]
# 第2-3行
df[2:3, ]
```
## 日期和时间
### `base` package
```{r}
as.Date("2024-01-01")
as.POSIXct(1)
as.Date(c("12/6/2022", "1/1/2023"), format = "%m/%d/%Y")
```
## 日期和时间
### `lubridate` package
```{r}
lubridate::today()
require(lubridate)
now()
ymd(c(20200321, 240404, "20181231"))
mdy(c("3-10-1998", "01-17-2018", "Feb 3, 2024"))
ymd_hms("1998-03-16 13:15:45", tz = "Asia/Shanghai")
```
## 日期和时间
### `lubridate` package
```{r}
make_date(2028, 1, 30)
as_date("2000-01-01")
as_datetime("2000-01-01", tz = "Asia/Shanghai")
as_datetime("2024-02-01 8:00:00", tz = "Asia/Shanghai")
```
## 日期和时间
### `lubridate` package
```{r}
year(today())
wday(today())
hour(now())
```
## 日期和时间
### `lubridate` package
```{r}
(x <- now())
floor_date(x, unit = "day")
floor_date(x, unit = "hour")
floor_date(x, unit = "10 minutes")
ceiling_date(x, unit = "10 minutes")
```
## 因子（factor）
### Factor是什么？
- 在R中，Factor是用来表示分类数据的特殊数据类型。
- 它将数据分成不同的水平(levels)，每个水平代表了一个类别。
## 因子（factor）
### 创建Factor
```{r}
# 创建一个Factor
gender <- factor(c("Male", "Female", "Female", "Male"))
# 查看Factor的水平
levels(gender)
# 改变Factor的水平顺序
gender <- factor(gender, levels = c("Female", "Male"))
summary(gender) # 使用Factor进行分组
as.numeric(gender) # 因子转换为纯粹的整数值
as.character(gender) # 转为字符
```
## 因子（factor）
### Label of Factor
```{r}
(x <- factor(1:12, label = month.abb))
factor(x, levels = month.abb[c(2:12, 1)])
```
### 分组
```{r}
cut(1:20, breaks = c(0, 5, 10, 15, 18, 20))
```
## 矩阵
```{r}
1:20
(A <- matrix(1:20, nrow = 4, byrow = TRUE))
(B <- matrix(1:20, nrow = 4, byrow = FALSE))
nrow(A)
ncol(B)
```
## 矩阵
### 高维矩阵
```{r}
X <- array(1:12, dim = c(3, 2, 2))
dim(C)
X[1, , ]
X[1, , 1]
```
## 矩阵
### `cbind`、`rbind`
```{r}
cbind(X[1, , ], X[2, , ], X[3, , ])
rbind(X[1, , ], X[2, , ], X[3, , ])
cbind(c(1, 2), c(3, 4), c(5, 6))
```
## 数据框（data frame）
**最主要的数据形式。**
```{r}
# 创建数据框
(df <- data.frame(
Name = c("Alice", "Bob", "Charlie"),
Age = c(25, 30, 35),
Married = c(TRUE, FALSE, TRUE)
))
names(df)
colnames(df)
ncol(df)
nrow(df)
```
## 数据框（data frame）
```{r}
df[1, 1]
df[2, ]
df[, 1]
df$Age
df[["Age"]]
df[, "Age"]
```
## 数据框（data frame）
```{r}
X <- matrix(1:9, nrow = 3)
class(X)
(Y <- as.data.frame(X))
names(Y)
names(Y) <- c("colA", "colB", "colC")
```
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
title: "三、Excel基础"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
## 下载excel文件
[https://git.drwater.net/course/RWEP/raw/branch/main/data/airquality.xlsx](https://git.drwater.net/course/RWEP/raw/branch/main/data/airquality.xlsx)
## Tidy data
由Hadley Wickham提出，tidy data 是一种结构化数据的组织方式，使得数据分析和可视化等操作更加简单、直观和有效。它有助于减少数据的混乱和复杂性，并促进数据的整洁性和易用性。
- 每个变量一列
- 每个观察一行
- 每个表单一个表
## Excel主要学习内容
- 常用函数
- 数据筛选/排序
- 条件格式化
- 数据透视表/图
## 常用函数
### 引用位置
- 相对引用
- 相对引用是默认的引用类型。
- 当公式被复制到其他单元格时，引用的单元格会相对移动。
```
=A1 # 在 B1 单元格中的公式，引用了 A1 单元格的值
=B1+C1 # 在 D1 单元格中的公式，引用了 B1 和 C1 单元格的值
```
### 绝对引用
- 绝对引用固定了单元格的地址，不会随公式的复制而改变。
```
=$A$1 # 在 B1 单元格中的公式，始终引用 A1 单元格的值
```
## 常用函数
- SUM：求和
- AVERAGE：求平均值
- MAX：求最大值
- MIN：求最小值
## 常用函数
- COUNT：计数非空单元格
- COUNTA：计数非空单元格（包括文本）
- COUNTIF：根据条件计数
- SUMIF：根据条件求和
- AVERAGEIF：根据条件求平均值
## 常用函数
- VLOOKUP：垂直查找
- HLOOKUP：水平查找
- INDEX：返回指定位置的值
- MATCH：查找值在区域中的位置
- IF：条件判断函数
## 常用函数
- CONCATENATE：连接多个文本字符串
- LEFT：提取文本左边的字符
- RIGHT：提取文本右边的字符
- MID：提取文本中间的字符
- TEXT：将数值格式化为文本
## 常用函数
- ROUND：四舍五入到指定位数
- ROUNDUP：向上取整
- ROUNDDOWN：向下取整
## 常用函数
- TODAY：返回当前日期
- NOW：返回当前日期和时间
- DATE：返回指定年、月、日的日期
- TIME：返回指定小时、分钟、秒的时间
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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name,age,score
Alice,25,85
Bob,30,92
Charlie,28,89
David,22,95
Eva,35,87
Frank,27,91
Grace,29,88
Helen,26,93
Ivan,31,86
Jack,24,94
Kelly,32,89
Lily,28,90
Mike,33,85
Nancy,27,92
Olivia,34,88
Peter,29,93
Queen,25,89
Ryan,30,94
Samantha,26,91
Tom,31,87
1 name age score
2 Alice 25 85
3 Bob 30 92
4 Charlie 28 89
5 David 22 95
6 Eva 35 87
7 Frank 27 91
8 Grace 29 88
9 Helen 26 93
10 Ivan 31 86
11 Jack 24 94
12 Kelly 32 89
13 Lily 28 90
14 Mike 33 85
15 Nancy 27 92
16 Olivia 34 88
17 Peter 29 93
18 Queen 25 89
19 Ryan 30 94
20 Samantha 26 91
21 Tom 31 87
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---
title: "课后作业6"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
## [第6次课后作业](第6次课后作业_模板.html)
1. 如何在 R 中将数字 10 赋值给变量 x？
2. 在 R 中，如何创建一个包含数字 1 到 5 的向量？
3. 编写一个条件语句，如果变量 age 大于等于 18，则打印 "成年人"，否则打印 "未成年人"。
4. 使用 for 循环打印从 1 到 10 的整数。
5. 编写一个名为 addition 的函数，接受两个参数 a 和 b，返回它们的和。
6. 创建一个列表，包含三个元素：一个数字向量、一个字符向量和一个逻辑向量。
7. 使用 read.csv() 函数读取名为 data.csv 的 CSV 文件，并将数据存储在一个名为 data 的数据框中。
8. 从数据框中选择前五行，并将结果存储在一个新的数据框中。
9. 将字符串 "hello world" 转换为大写。
10. 从数据框中选择 score 列大于等于 90 的行。
## **data.csv**内容
```
name,age,score
Alice,25,85
Bob,30,92
Charlie,28,89
David,22,95
Eva,35,87
Frank,27,91
Grace,29,88
Helen,26,93
Ivan,31,86
Jack,24,94
Kelly,32,89
Lily,28,90
Mike,33,85
Nancy,27,92
Olivia,34,88
Peter,29,93
Queen,25,89
Ryan,30,94
Samantha,26,91
Tom,31,87
```
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
title: 课后作业6
author: 姓名
format: html
---
# 示例问题，在R中输出`1+2+3+...+100`的结果
采用`for`循环完成，代码如下：
```{r}
total <- 0
for (i in 1:100) {
total <- total + i
}
total
```
# 如何在 R 中将数字 10 赋值给变量 x？
# 在 R 中，如何创建一个包含数字 1 到 5 的向量？
# 编写一个条件语句，如果变量 age 大于等于 18，则打印 "成年人"，否则打印 "未成年人"。
# 使用 for 循环打印从 1 到 10 的整数。
# 编写一个名为 addition 的函数，接受两个参数 a 和 b，返回它们的和。
# 创建一个列表，包含三个元素：一个数字向量、一个字符向量和一个逻辑向量。
# 使用 read.csv() 函数读取名为 data.csv 的 CSV 文件，并将数据存储在一个名为 data 的数据框中。
# 从数据框中选择前五行，并将结果存储在一个新的数据框中。
# 将字符串 "hello world" 转换为大写。
# 从数据框中选择 score 列大于等于 90 的行。
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---
title: "代码编写规则"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
## tidy data
```{r}
knitr::include_graphics("../../image/tidy-1.png", dpi = 270)
```
## pipe（管道） |>
```{r}
#| eval: false
require(patchwork)
plot(1:10)
1:10 |> plot()
plot(x = 1:10, y = sin(1:10))
1:10 |> plot(y = sin(1:10))
```
```{r}
#| echo: false
#| layout-nrow: 1
#| fig-width: 4
#| fig-height: 3
#| out-height: 90%
require(patchwork)
plot(1:10)
1:10 |> plot()
plot(x = 1:10, y = sin(1:10))
1:10 |> plot(y = sin(1:10))
```
## pipe（管道）：%>%
```{r}
#| eval: false
#| layout-nrow: 1
#| fig-width: 3
#| fig-height: 4
#| out-height: 125%
require(magrittr)
1:10 %>% plot()
1:10 %>% plot(y = sin(1:10))
sin(1:10) %>% plot(1:10, .)
sin(1:10) |> plot(x = 1:10, y = _)
```
```{r}
#| echo: false
#| layout-nrow: 1
#| fig-width: 3
#| fig-height: 4
#| out-height: 125%
require(magrittr)
1:10 %>% plot()
1:10 %>% plot(y = sin(1:10))
sin(1:10) %>% plot(1:10, .)
sin(1:10) |> plot(x = 1:10, y = _)
```
## 代码编写规则
```{r}
#| eval: false
# Strive for:
short_flights <- flights |> filter(air_time < 60)
# Avoid:
SHORTFLIGHTS <- flights |> filter(air_time < 60)
# Strive for
z <- (a + b)^2 / d
# Avoid
z <- (a + b)^2 / d
# Strive for
mean(x, na.rm = TRUE)
# Avoid
mean(x, na.rm = TRUE)
```
## 练习
```{r}
#| eval: false
flights |>
filter(dest == "IAH") |>
group_by(year, month, day) |>
summarize(n = n(), delay = mean(arr_delay, na.rm = TRUE)) |>
filter(n > 10)
```
## 练习
```{r}
#| eval: false
flights |>
filter(dest == "IAH") |>
group_by(year, month, day) |>
summarize(n = n(), delay = mean(arr_delay, na.rm = TRUE)) |>
filter(n > 10)
```
## quarto
![](../../image/quarto-flow.png)
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
title: "Lesson 7"
format: html
---
```{r}
require(tidyverse)
files <- c(
"../../data/01-sales.csv",
"../../data/02-sales.csv",
"../../data/03-sales.csv"
)
files <- dir("../../data", pattern = "sales.csv", full.names = TRUE)
readr::read_csv(files, id = "file") |>
mutate(file = basename(file))
```
```{r}
require(tidyverse)
library(tidyverse)
files <- dir(
"../../data/gapminder",
pattern = "^[12][09][0-9][0-9].xlsx$",
full.names = TRUE
)
alldf <- tibble::tibble()
for (file in files) {
alldf <- alldf |>
bind_rows(
readxl::read_xlsx(file) |>
mutate(year = parse_number(basename(file)))
)
}
alldf |>
ggplot(aes(x = lifeExp, y = gdpPercap)) +
geom_point(aes(color = factor(year))) +
geom_smooth(method = "lm", se = FALSE) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
) +
facet_wrap(~year, ncol = 4, scale = "fixed")
```
# slope
```{r}
file <- files[1]
lm(y ~ x, data)
m <- lm(log10(gdpPercap) ~ lifeExp, readxl::read_xlsx(file))
summary(m)
coef(m)[2]
slopes <- c()
years <- c()
for (file in files) {
m <- lm(log10(gdpPercap) ~ lifeExp, readxl::read_xlsx(file))
years <- c(years, parse_number(basename(file)))
slopes <- c(slopes, coef(m)[2])
}
years
slopes
plot(years, as.numeric(slopes), type = "b")
```
# purrr
```{r}
require(tidyverse)
df <- tibble(
filename = dir(
"../../data/gapminder",
pattern = "^[12][09][0-9][0-9].xlsx$",
full.names = TRUE
)
) |>
dplyr::mutate(
data = purrr::map(
filename,
\(x) readxl::read_xlsx(x)
)
) |>
mutate(year = parse_number(basename(filename))) |>
mutate(
m = purrr::map(data, \(xxxx) lm(log10(gdpPercap) ~ lifeExp, data = xxxx))
) |>
mutate(slope = purrr::map_dbl(m, \(yyyy) coef(yyyy)[2])) |>
unnest(data)
pvalue <- summary(df$m[[1]])$coefficients[2, 4]
rsq <- summary(df$m[[1]])$r.squared
require(tidymodels)
generics::tidy(df$m[[1]])
df |>
ggplot(aes(x = lifeExp, y = gdpPercap)) +
geom_point(aes(color = factor(year))) +
geom_smooth(method = "lm", se = FALSE) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
) +
facet_wrap(~year, ncol = 4, scale = "fixed")
df |>
ggplot(aes(x = year, y = slope)) +
geom_line() +
geom_point()
df
df$slope[[1]]
coef(df$m[[1]])[2]
lm(y ~ x, data)
df |>
unnest(data) |>
ggplot(aes(x = lifeExp, y = gdpPercap)) +
geom_point(aes(color = factor(year))) +
geom_smooth(method = "lm", se = FALSE) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
) +
facet_wrap(~year, ncol = 4, scale = "fixed")
```
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---
title: "Data import"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
```{r}
#| echo: false
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}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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student_id,full_name,favourite_food,meal_plan,age
1,Sunil Huffmann,Strawberry yoghurt,Lunch only,4
2,Barclay Lynn,French fries,Lunch only,5
3,Jayendra Lyne,NA,Breakfast and lunch,7
4,Leon Rossini,Anchovies,Lunch only,NA
5,Chidiegwu Dunkel,Pizza,Breakfast and lunch,5
6,Güvenç Attila,Ice cream,Lunch only,6
1 student_id full_name favourite_food meal_plan age
2 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
3 2 Barclay Lynn French fries Lunch only 5
4 3 Jayendra Lyne NA Breakfast and lunch 7
5 4 Leon Rossini Anchovies Lunch only NA
6 5 Chidiegwu Dunkel Pizza Breakfast and lunch 5
7 6 Güvenç Attila Ice cream Lunch only 6
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---
title: "Lesson 8"
format: html
---
```{r}
require(tidyverse)
library(nycflights13)
flights |>
select(3:6) |>
head(3) |>
rename_all(~ gsub("_", "", .))
flights |>
select(3:6) |>
head(3) |>
rename_with(toupper, .cols = 2:4)
# 每月10号-15号，dep_delay > 100 的航班
# 每月哪个出发地origin的 dep_delay总时长最长
flights |>
group_by(month, origin) |>
summarize(n = n(), total_dep_delay = mean(dep_delay, na.rm = TRUE)) |>
slice_max(total_dep_delay)
# arrange(month, origin, desc(total_dep_delay))
# 每月随机抽取一天，随机抽取三个航班
slice_sample(n = 1)
flights |>
tidyr::nest(ymddf = -c(year, month, day)) |>
group_by(year, month) |>
slice_sample(n = 5) |>
unnest(ymddf) |>
group_by(year, month, day) |>
slice_sample(n = 3)
flights |>
tidyr::nest(ymddf = -c(year, month, day)) |>
group_by(year, month) |>
slice_sample(n = 1) |>
mutate(
ymddf = purrr::map(ymddf, \(x) {
x |>
slice_sample(n = 3)
})
)
# 每月 每个出发地 周末的平均dep_delay 与 工作日的平均dep_delay 差值最大的3个航班
flights |>
mutate(date = ymd(paste(year, month, day))) |>
mutate(weekday = wday(date)) |>
mutate(isworkday = if_else(between(weekday, 2, 6), "Yes", "No")) |>
group_by(year, month, origin, flight, isworkday) |>
summarize(mean_delay = mean(dep_delay, na.rm = TRUE)) |>
tidyr::nest(diffdelaydf = c(isworkday, mean_delay)) |>
filter(
purrr::map(diffdelaydf, \(x) {
nrow(x)
}) >
1
) |>
mutate(
diffdelay = purrr::map_dbl(diffdelaydf, \(x) {
x |>
arrange(isworkday) |>
pull(mean_delay) |>
diff()
})
) |>
group_by(year, month, origin) |>
slice_max(diffdelay, n = 3)
wday(today())
weekday()
slice_sample(n = 1)
flights |>
group_by(month) |>
slice_sample(n = 1)
```
```{r}
p1 <- flights |>
group_by(year, month) |>
summarize(n = n()) |>
ggplot(aes(month, n)) +
geom_point(shape = 21, size = 6, color = "black", fill = "red") +
geom_line()
ggsave("./a.pdf")
ggsave("./a.png")
require(patchwork)
p1 / p1
```
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---
title: "Data Transform"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
```{r}
#| echo: false
source("../../coding/_common.R")
library(nycflights13)
library(tidyverse)
```
## `tidyverse`风格数据分析总体流程
![](../../image/data-science/transform.png)
## [dplyr cheatsheet](../../image/cheatsheet/data-transformation.pdf)
```{r}
#| echo: false
dwfun::ggsavep("../../image/cheatsheet/data-transformation.svg", loadit = TRUE)
```
## 查看数据
```{r}
flights
```
## 选择列
```{r}
#| results: false
flights |>
select(year, month, day)
```
## 选择列
```{r}
#| results: false
flights |>
select(year:day)
```
## 选择列
```{r}
flights |>
select(3:5)
```
## 选择列
```{r}
flights |>
select(!year:day)
```
## 选择列
```{r}
flights |>
select(-(year:day))
```
## 选择列
```{r}
flights |>
select(where(is.character))
```
## 选择列
```{r}
flights |>
select(!where(is.character)) |>
select(contains("_"))
```
## 选择列
```{r}
flights |>
select(tail_num = tailnum)
```
## 选择列
```{r}
flights |>
select(air_time, everything())
```
## 重命名
```{r}
flights |>
rename(tail_num = tailnum)
```
## 重命名
```{r}
flights |>
rename(年份 = 1) |>
rename(月份 = 2)
```
## 重命名
```{r}
flights |> select(1:4) |> head(n = 3)
# 重命名
flights |> select(1:4) |> head(n = 3) |> rename_all(~ c("c1", "c2", "c3", "c4"))
```
## 重命名
```{r}
flights |> select(1:4) |> head(n = 3)
# 重命名
flights |> select(1:4) |> head(n = 3) |> rename_all(toupper)
```
## 重命名
```{r}
flights |> select(1:4) |> head(n = 3)
# 重命名
flights |>
select(1:4) |>
head(n = 3) |>
rename_all(~ paste0(toupper(.), "_NEW"))
```
## 练习
将含有下划线的列名中的下划线去掉。
```{r}
flights |> select(1:4) |> head(n = 3)
```
## 练习
将`airqualitydf`中列名的单位信息去除（前5列）。
```{r}
airqualitydf <- readxl::read_xlsx("../../data/airquality.xlsx", sheet = 2)
airqualitydf |> select(1:5)
```
## `filter`
```{r}
flights |>
filter(dep_delay > 120)
```
## filter 练习
Flights that departed on January 1.
```{r}
#| echo: false
flights |>
filter(month == 1 & day == 1)
```
## filter 练习
Select flights that departed in January or February
```{r}
#| echo: false
flights |>
filter(month %in% c(1, 2))
```
## filter 练习
```{r}
jan1 <- flights |>
filter(month == 1 & day == 1)
```
## filter
```{r}
#| error: true
#| eval: false
flights |>
filter(month = 1)
```
## filter
```{r}
flights |>
filter(month == 1 | 2)
```
## 排序
```{r}
flights |>
arrange(year, month, day, dep_time)
```
## 排序
```{r}
flights |>
arrange(desc(dep_delay))
```
## slice
```{r}
flights |> head(n = 5)
flights |> slice(1:5)
```
## slice
```{r}
flights |>
slice_max(dep_delay, n = 5)
```
## slice
```{r}
flights |>
slice_min(dep_delay, prop = 0.005)
```
## 排序练习
根据`origin`、`dest`、`air_time`倒序排序。
```{r}
#| echo: false
flights |>
arrange(origin, dest, desc(air_time)) |>
select(origin, dest, air_time, everything())
```
## 去重
```{r}
# Remove duplicate rows, if any
flights |>
distinct()
```
## 去重
```{r}
# Find all unique origin and destination pairs
flights |>
distinct(origin, dest)
```
## 去重
```{r}
flights |>
distinct(origin, dest, .keep_all = TRUE)
```
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
subtitle: "Outline & Key figures"
author: "AUTHOR"
institute: "RCEES, CAS"
# filters:
# - watermark
---
# Outline
## Introduction
## Results
## Discussions
## Methods & Materials
# Figures
# Thanks for your attention!
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
title: "Data Transform"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
```{r}
#| echo: false
source("../../coding/_common.R")
library(nycflights13)
library(tidyverse)
```
## 计数
```{r}
flights |>
count(origin, dest, sort = TRUE)
```
## 计数-练习
统计每月的航班数量。
```{r}
#| echo: false
flights |>
count(year, month, sort = TRUE)
```
## 计算新变量
```{r}
flights |>
mutate(
gain = dep_delay - arr_delay,
speed = distance / air_time * 60
)
```
## 计算新变量
```{r}
flights |>
mutate(
gain = dep_delay - arr_delay,
speed = distance / air_time * 60,
.before = 1
)
```
## 计算新变量
```{r}
flights |>
mutate(
gain = dep_delay - arr_delay,
speed = distance / air_time * 60,
.after = day
)
```
## 计算新变量
```{r}
flights |>
mutate(
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours,
.keep = "used"
)
```
## 列排序
```{r}
flights |>
relocate(time_hour, air_time)
```
## 列排序
```{r}
#| results: false
flights |>
relocate(year:dep_time, .after = time_hour)
flights |>
relocate(starts_with("arr"), .before = dep_time)
flights |>
select(starts_with("arr"), everything())
```
## 练习
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。
```{r}
flights |>
filter(dest == "IAH") |>
mutate(speed = distance / air_time * 60) |>
select(year:day, dep_time, carrier, flight, speed) |>
arrange(desc(speed))
```
## 练习
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。
```{r}
#| results: false
flights1 <- filter(flights, dest == "IAH")
flights2 <- mutate(flights1, speed = distance / air_time * 60)
flights3 <- select(flights2, year:day, dep_time, carrier, flight, speed)
arrange(flights3, desc(speed))
```
## 练习
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。
```{r}
flights |>
filter(dest == "IAH") |>
mutate(speed = distance / air_time * 60) |>
select(year:day, dep_time, carrier, flight, speed) |>
arrange(desc(speed))
```
## 分组统计
```{r}
library(tidyverse)
mtcars %>%
group_by(cyl) %>%
summarize(n = n())
```
## 分组统计
```{r}
flights |>
group_by(month)
```
## 分组统计
```{r}
flights |>
group_by(month) |>
summarize(
avg_delay = mean(dep_delay)
)
```
## 分组统计
```{r}
flights |>
group_by(month) |>
summarize(
avg_delay = mean(dep_delay, na.rm = TRUE)
)
```
## 分组统计
```{r}
flights |>
group_by(month) |>
summarize(
avg_delay = mean(dep_delay, na.rm = TRUE),
n = n()
)
```
## 分组统计
```{r}
flights |>
group_by(dest) |>
slice_max(arr_delay, n = 1) |>
relocate(dest)
```
## 分组统计
```{r}
flights |>
filter(dest == "IAH") |>
group_by(year, month, day) |>
summarize(
arr_delay = mean(arr_delay, na.rm = TRUE)
)
```
## 分组
```{r}
daily <- flights |>
group_by(year, month, day)
daily
```
## 分组统计
```{r}
daily_flights <- daily |>
summarize(n = n())
```
## 分组统计
```{r}
#| results: false
daily_flights <- daily |>
summarize(
n = n(),
.groups = "drop_last"
)
```
## 删除分组
```{r}
daily |> ungroup()
```
## 删除分组
```{r}
daily |>
ungroup() |>
summarize(
avg_delay = mean(dep_delay, na.rm = TRUE),
flights = n()
)
```
## 分组统计
```{r}
flights |>
summarize(
delay = mean(dep_delay, na.rm = TRUE),
n = n(),
.by = month
)
```
## 分组统计
```{r}
flights |>
summarize(
delay = mean(dep_delay, na.rm = TRUE),
n = n(),
.by = c(origin, dest)
)
```
## 练习
```{r}
df <- tibble(
x = 1:5,
y = c("a", "b", "a", "a", "b"),
z = c("K", "K", "L", "L", "K")
)
df
```
```{r}
#| eval: false
df |> arrange(y)
```
## 练习
```{r}
#| echo: false
df
```
```{r}
#| eval: false
df |>
group_by(y) |>
summarize(mean_x = mean(x))
```
## 练习
```{r}
#| echo: false
df
```
```{r}
#| eval: false
df |>
group_by(y, z) |>
summarize(mean_x = mean(x))
```
## 练习
```{r}
#| echo: false
df
```
```{r}
#| eval: false
df |>
group_by(y, z) |>
summarize(mean_x = mean(x), .groups = "drop")
```
## 练习
```{r}
#| echo: false
df
```
```{r}
#| eval: false
df |>
group_by(y, z) |>
summarize(mean_x = mean(x))
df |>
group_by(y, z) |>
mutate(mean_x = mean(x))
```
## 练习
- 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`）。
- 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`）。
- 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`）。
- 计算各采样点中CO浓度小于全国平均CO浓度的占比。
- 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。
```{r}
airqualitydf <- readxl::read_xlsx("../../data/airquality.xlsx", sheet = 2)
```
## 练习
按月统计dep_delay最大的3个航班的航班号（flight），用逗号连接。
```{r}
#| echo: false
flights |>
group_by(year, month) |>
slice_max(dep_delay, n = 3) |>
summarize(flight = paste(paste0(carrier, flight), collapse = ", ")) |>
knitr::kable()
```
## 数据变形示意图
```{r}
billboard
knitr::include_graphics("../../image/tidy-data/variables.png", dpi = 270)
```
## 数据变形
```{r}
billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
)
```
## 数据变形
```{r}
billboard_longer <- billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
) |>
mutate(
week = parse_number(week)
)
billboard_longer
```
## 练习
```{r}
#| echo: false
df <- tribble(
~id,
~bp1,
~bp2,
"A",
100,
120,
"B",
140,
115,
"C",
120,
125
)
df
```
将以上数据(`df`)转换为如下形式。
```{r}
#| echo: false
df |>
pivot_longer(
cols = bp1:bp2,
names_to = "measurement",
values_to = "value"
)
```
## 练习
请转换如下`iris`数据。
```{r}
#| echo: false
as_tibble(head(iris, n = 3))
cat("转为如下形式：")
iris |>
pivot_longer(
cols = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
names_to = "flower_attr",
values_to = "attr_value"
) |>
head()
```
## 数据变形示意图2
```{r}
who2
knitr::include_graphics("../../image/tidy-data/multiple-names.png", dpi = 270)
```
## 数据变形
```{r}
who2 |>
pivot_longer(
cols = !(country:year),
names_to = c("diagnosis", "gender", "age"),
names_sep = "_",
values_to = "count"
)
```
## 数据变形示意图
```{r}
household
knitr::include_graphics("../../image/tidy-data/names-and-values.png", dpi = 270)
```
## 数据变形
```{r}
household |>
pivot_longer(
cols = !family,
names_to = c(".value", "child"),
names_sep = "_",
values_drop_na = TRUE
)
```
## 查看数据
```{r}
cms_patient_experience
```
## 查看数据
```{r}
cms_patient_experience |>
distinct(measure_cd, measure_title)
```
## 数据变形（变宽）
```{r}
cms_patient_experience |>
pivot_wider(
names_from = measure_cd,
values_from = prf_rate
)
```
## 数据变形（变宽）
```{r}
cms_patient_experience |>
pivot_wider(
id_cols = starts_with("org"),
names_from = measure_cd,
values_from = prf_rate
)
```
## 练习
```{r}
df <- tribble(
~id,
~measurement,
~value,
"A",
"bp1",
100,
"B",
"bp1",
140,
"B",
"bp2",
115,
"A",
"bp2",
120,
"A",
"bp3",
105
)
```
变形成如下形式：
```{r}
#| echo: false
df |>
pivot_wider(
names_from = measurement,
values_from = value
)
```
## 练习：变宽
```{r}
df <- tribble(
~id,
~measurement,
~value,
"A",
"bp1",
100,
"A",
"bp1",
102,
"A",
"bp2",
120,
"B",
"bp1",
140,
"B",
"bp2",
115
)
```
## 练习
```{r}
df |>
pivot_wider(
names_from = measurement,
values_from = value
)
```
## 练习
```{r}
df |>
group_by(id, measurement) |>
summarize(n = n(), .groups = "drop") |>
filter(n > 1)
```
## nest，套嵌数据框
```{r}
#| echo: false
df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)
df
```
```{r}
df %>% nest(data = c(y, z))
```
## 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}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
title: "课后作业8"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
```{r}
#| include: false
#| cache: false
lang <- "cn"
require(tidyverse)
require(learnr)
```
## 第8次课后作业
1. 根据`airqualitydf.xlsx`，按采样点统计白天（8:00-20:00）与夜晚（20:00-8:00）中空气质量指数（AQI）中位数，按城市统计低于所有采样点AQI30%分位值的采样点占比，列出上述占比最高的10个城市（不考虑采样点数低于5个的城市）。
2. 按照不同城市分组，统计白天与夜晚AQI中位数是否具有显著差异。
作业模板：[第8次课后作业_模板.qmd](https://git.drwater.net/course/RWEP/raw/branch/main/SD/20240328_9_课后作业/第8次课后作业_模板.qmd)
## 示例代码
### 基于R的示例结果
- [第8次课后作业R示例代码结果](./第8次课后作业_模板.html)
### 基于SAS的示例结果
- [第8次课后作业SAS示例代码](./第8次课后作业_模板.sas)
- [第8次课后作业SAS示例结果1](./median.pdf)
- [第8次课后作业SAS示例结果2](./freq.pdf)
- [第8次课后作业SAS示例结果3](./airqualitymedianoutrow5.pdf)
- [第8次课后作业SAS示例结果4](./npar1wayConover.pdf)
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
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---
title: 课后作业8
author: 姓名
format: html
---
# 数据
下载airquality.xlsx，并读取数据。
```{r}
#| message: false
#| warning: false
# 下载至临时文件
if (FALSE) {
tmpxlsxpath <- file.path(tempdir(), "airquality.xlsx")
download.file(
"https://git.drwater.net/course/RWEP/raw/branch/PUB/data/airquality.xlsx",
destfile = tmpxlsxpath
)
airqualitydf <- readxl::read_xlsx(tmpxlsxpath, sheet = 2)
metadf <- readxl::read_xlsx(tmpxlsxpath, sheet = 1)
saveRDS(airqualitydf, "./airqualitydf.RDS")
saveRDS(metadf, "./metadf.RDS")
}
airqualitydf <- readRDS("./airqualitydf.RDS")
metadf <- readRDS("./metadf.RDS")
```
# 描述统计
根据`airqualitydf.xlsx`，按采样点统计白天（8:00-20:00）与夜晚（20:00-8:00）中空气质量指数（AQI）中位数，按城市统计低于所有采样点AQI30%分位值的采样点占比，列出上述占比最高的10个城市（不考虑采样点数低于5个的城市）。
```{r}
#| message: false
#| warning: false
require(tidyverse)
airqualitydf |>
select(datetime, site, AQI) |>
filter(!is.na(AQI)) |>
group_by(site) |>
summarize(AQI.median = median(AQI, na.rm = TRUE)) |>
left_join(metadf |> select(site, city = Area)) |>
group_by(city) |>
filter(n() > 5) |>
summarize(
p = sum(
AQI.median < quantile(airqualitydf$AQI, probs = 0.5, na.rm = TRUE)
) /
n()
) |>
top_n(10, p)
airqualitydf |>
select(datetime, site, AQI) |>
filter(!is.na(AQI)) |>
group_by(site) |>
summarize(AQI.median = median(AQI, na.rm = TRUE))
airqualitydf |>
select(datetime, site, AQI) |>
filter(!is.na(AQI)) |>
left_join(metadf |> select(site, city = Area)) |>
group_by(city) |>
filter(length(unique(site)) >= 5) |>
summarize(
p = sum(AQI < quantile(airqualitydf$AQI, probs = 0.2, na.rm = TRUE)) / n()
) |>
slice_max(p, n = 10) |>
knitr::kable()
```
# 统计检验
按照不同城市分组，统计白天与夜晚AQI中位数是否具有显著差异。
```{r}
#| message: false
#| warning: false
if (FALSE) {
require(infer)
require(tidyverse)
testdf <- airqualitydf |>
select(datetime, site, AQI) |>
filter(!is.na(AQI)) |>
left_join(metadf |> select(site, city = Area)) |>
group_by(city) |>
filter(length(unique(site)) >= 5) |>
mutate(
dayornight = factor(
ifelse(between(hour(datetime), 8, 20), "day", "night"),
levels = c("day", "night")
)
) |>
group_by(city) |>
nest(citydf = -city) |>
mutate(
median_diff = purrr::map_dbl(
citydf,
~ .x |>
specify(AQI ~ dayornight) |>
calculate(stat = "diff in medians", order = c("day", "night")) |>
pull(stat)
)
) |>
ungroup() |>
# slice_sample(n = 12) |>
mutate(
null_dist = purrr::map(
citydf,
~ .x |>
specify(AQI ~ dayornight) |>
hypothesize(null = "independence") |>
generate(reps = 1000, type = "permute") |>
calculate(stat = "diff in medians", order = c("day", "night"))
)
) |>
mutate(
p_value = purrr::map2_dbl(
null_dist,
median_diff,
~ get_p_value(.x, obs_stat = .y, direction = "both") |>
pull(p_value)
)
) |>
mutate(sigdiff = ifelse(p_value < 0.01, "显著差异", "无显著差异")) |>
mutate(
fig = purrr::pmap(
list(null_dist, median_diff, city, sigdiff),
~ visualize(..1) +
shade_p_value(obs_stat = ..2, direction = "both") +
ggtitle(paste0(..3, "：", ..4)) +
theme_sci(2, 2)
)
) |>
arrange(p_value)
saveRDS(testdf, "./testdf.RDS")
}
if (FALSE) {
lang <- "cn"
require(dwfun)
require(rmdify)
require(drwateR)
dwfun::init()
rmdify::rmd_init()
testdf <- readRDS("./testdf.RDS")
require(tidyverse)
testdf |>
select(city, median_diff, p_value, sigdiff) |>
knitr::kable()
testdf |>
mutate(grp = (row_number() - 1) %/% 12) |>
group_by(grp) |>
nest(grpdf = -grp) |>
ungroup() |>
# slice(1) |>
mutate(
gp = purrr::map(
grpdf,
~ (.x |>
pull(fig)) |>
patchwork::wrap_plots(ncol = 3) +
dwfun::theme_sci(5, 7)
)
) |>
pull(gp)
}
```
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options ls=256 ps=32767 nodate validmemname=extend validvarname=any;
title 'The SAS System';
%macro print(d);
proc print data=&d;run;
%mend;
%macro printobs(d,obs);
proc print data=&d (obs=&obs);run;
%mend;
%macro printfirstobsobs(d,firstobs,obs);
proc print data=&d (firstobs=&firstobs obs=&obs);run;
%mend;
%macro contents(d);
proc contents data=&d varnum;run;
%mend;
%macro contentsshort(d);
proc contents data=&d varnum short;run;
%mend;
%macro save_dataset(d);
data "d:&d";
set &d;
run;
%mend;
%macro load_dataset(d);
data &d;
set "d:&d";
run;
%mend;
%macro kill;
PROC DATASETS LIB=work KILL;RUN;quit;
%mend;
proc template;
list styles;
run;
%kill;
PROC IMPORT OUT=WORK.raw_metadf
DATAFILE="d:airquality.xlsx"
DBMS=EXCEL REPLACE;
RANGE="metadf$";
GETNAMES=YES;
MIXED=YES;
SCANTEXT=YES;
USEDATE=NO;
SCANTIME=NO;
RUN;
%print(raw_metadf);
%save_dataset(raw_metadf); *原始数据集存盘;
PROC IMPORT OUT=WORK.raw_airqualitydf
DATAFILE="d:airquality.xlsx"
DBMS=EXCEL REPLACE;
RANGE="airqualitydf$";
GETNAMES=YES;
MIXED=YES;
SCANTEXT=YES;
USEDATE=NO; *为YES崩溃闪退;
SCANTIME=NO; *为YES崩溃闪退;
RUN;
%print(raw_airqualitydf);
%save_dataset(raw_airqualitydf); *原始数据集存盘;
%kill;
/*加载硬盘原始数据集*/
%load_dataset(raw_metadf);
%load_dataset(raw_airqualitydf);
/*查看数据集内容*/
%contents(raw_metadf);
%contentsshort(raw_metadf);
/*site name Area lon lat*/
%contents(raw_airqualitydf);
%contentsshort(raw_airqualitydf);
/*datetime site 'CO_mg/m3'n 'CO_24h_mg/m3'n 'NO2_μg/m3'n 'NO2_24h_μg/m3'n 'O3_μg/m3'n 'O3_24h_μg/m3'n 'O3_8h_μg/m3'n 'O3_8h_24h_μg/m3'n 'PM10_μg/m3'n 'PM10_24h_μg/m3'n 'PM2#5_μg/m3'n 'PM2#5_24h_μg/m3'n 'SO2_μg/m3'n 'SO2_24h_μg/m3'n AQI PrimaryPollutant Quality Unheathful*/
%printobs(raw_metadf,10);
%printobs(raw_airqualitydf,10);
proc sort data=raw_metadf out=metadfsorted;
by site;
run;
proc sort data=raw_airqualitydf out=airqualitydfsorted;
by site;
run;
/*合并数据集，数据预处理，提取日期、时间部分，划分day、night*/
data airquality;
retain datetime date time DayNight site name Area AQI lon lat;
length DayNight $ 5;
merge metadfsorted airqualitydfsorted;
by site;
date=datepart(datetime);
time=timepart(datetime);
if '8:00't<=time<'20:00't then DayNight='day';
else DayNight='night';
format datetime e8601dt25. date yymmdd10. time time5.;
keep site name Area lon lat datetime date time DayNight AQI;
run;
%printobs(airquality,100);
%save_dataset(airquality); *合并数据集存盘;
%kill;
/*#################### DATA SET airquality ####################*/
/*加载硬盘合并数据集*/
%load_dataset(airquality);
%printobs(airquality,100);
/*检查site、name数量是否一致，发现不一致，后以site进行统计*/
proc sql;
select count(distinct(site)) as count_site from airquality;
select count(distinct(name)) as count_name from airquality;
quit;
/*
count_site
1714
count_name
1522
*/
/*#########################################################################*/
/*按采样点统计白天（8:00-20:00）与夜晚（20:00-8:00）中空气质量指数（AQI）中位数*/
/*#########################################################################*/
/*@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@*/
ods pdf file='d:means.pdf' style=sapphire dpi=1200;
proc means data=airquality median maxdec=1;
class site DayNight;
var AQI;
where AQI is not missing;
run;
ods pdf close;
/*@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@*/
/*####################################################################################################*/
/*按城市统计低于所有采样点AQI30%分位值的采样点占比，列出上述占比最高的10个城市（不考虑采样点数低于5个的城市）*/
/*####################################################################################################*/
/*输出查看所有采样点AQI30%分位值，为与SQL验证，可略去*/
proc univariate data=airquality noprint;
var AQI;
output out=airqualitystats pctlpts=30 pctlpre=P;
run;
%print(airqualitystats); /*42*/
/*输出所有采样点AQI30%分位值到宏变量*/
proc sql;
select AQI into : xvalues separated by ',' from airquality;
select distinct(pctl(30, &xvalues)) into : P30 from airquality;
quit;
/*查看所有采样点AQI30%分位值的宏变量值，为后续调用*/
%put P30=&P30.;
/*按所有采样点AQI30%分位值对AQI分级，对合并数据集所有采样点所有数据直接分级，后续用各采样点中位数进行统计，可略去*/
data airquality1;
set airquality;
if AQI<&P30. then quality='good';
else quality='fair';
run;
%printobs(airquality1,100);
/*输出所有采样点AQI中位数*/
proc means data=airquality median maxdec=1;
class Area site;
var AQI;
where AQI is not missing;
output out=airqualitymedian median=;
run;
%print(airqualitymedian);
/*按所有采样点AQI30%分位值对AQI中位数分级*/
data airqualitymedian1;
set airqualitymedian;
if AQI<&P30. then quality='good';
else quality='fair';
where _TYPE_=3;
run;
%print(airqualitymedian1);
/*按城市统计低于所有采样点AQI30%分位值的采样点占比，查看结果*/
/*@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@*/
ods pdf file='d:freq.pdf' style=sapphire dpi=1200;
proc freq data=airqualitymedian1;
table Area*quality /nocol nopercent;
run;
ods pdf close;
/*@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@*/
/*按城市统计低于所有采样点AQI30%分位值的采样点占比，输出频数统计结果到数据集*/
proc freq data=airqualitymedian1;
table Area*quality /outpct out=airqualitymedianoutrow(drop=percent pct_col); *保留行列频数与行百分比;
run;
%printobs(airqualitymedianoutrow,100);
/*按城市对采样点数进行统计，查看结果，可略去*/
proc means data=airqualitymedianoutrow sum maxdec=0;
class Area;
var COUNT;
run;
/*输出采样点数不低于5个的城市，查看结果，可略去*/
proc sql;
select *,sum(COUNT) as total_COUNT from airqualitymedianoutrow group by Area having calculated total_COUNT>=5 order by quality desc,PCT_ROW desc,COUNT desc;
quit;
/*将采样点数不低于5个的城市，输出到数据集*/
proc sql;
create table airqualitymedianoutrow5 as select *,sum(COUNT) as total_COUNT from airqualitymedianoutrow group by Area having calculated total_COUNT>=5 order by quality desc,PCT_ROW desc,COUNT desc;
quit;
/*列出上述占比最高的10个城市（不含采样点数低于5个的城市）*/
/*@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@*/
ods pdf file='d:airqualitymedianoutrow5.pdf' style=sapphire dpi=1200;
%printobs(airqualitymedianoutrow5,10);
%printobs(airqualitymedianoutrow5,20);
%printobs(airqualitymedianoutrow5,30);
%print(airqualitymedianoutrow5);
ods pdf close;
/*@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@*/
/*#####################################################*/
/*按照不同城市分组，统计白天与夜晚AQI中位数是否具有显著差异*/
/*#####################################################*/
/*发现有的site没有Area对应*/
proc sql;
select distinct(Area),count(distinct(Area)) as count_Area from airquality;
quit;
proc print data=airquality;
where Area is missing;
run;
proc sql;
select distinct(site),count(distinct(site)) as count_site from airquality where Area is missing;
quit;
/*有4个site（采样点）没有Area（城市）对应
site count_site
2628A 4
3128A 4
4034A 4
4036A 4
*/
proc sort data=airquality out=airqualitysorted;
by Area;
run;
/*按照不同城市分组，统计白天与夜晚AQI中位数，查看结果，可略去*/
proc means data=airqualitysorted median maxdec=1;
by Area;
class DayNight;
var AQI;
where AQI is not missing;
run;
/*笼统地看，白天与夜晚AQI中位数是否具有显著差异*/
proc npar1way data=airquality median;
class DayNight;
var AQI;
run;
/*按照不同城市分组，统计白天与夜晚AQI中位数是否具有显著差异*/
/*@@@@@@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@@@@*/
ods pdf file='d:npar1waymedian.pdf' style=sapphire dpi=1200;
proc npar1way data=airqualitysorted median;
class DayNight;
var AQI;
by Area;
where Area is not missing;
run;
ods pdf close;
/*@@@@@@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@@@@*/
/*
Using Wilcoxon scores in the linear rank statistic for two-sample data produces the rank sum statistic of the Mann-Whitney-Wilcoxon test.
Using Wilcoxon scores in the one-way ANOVA statistic produces the Kruskal-Wallis test.
Wilcoxon scores are locally most powerful for location shifts of a logistic distribution.
*//*
Using median scores in the linear rank statistic for two-sample data produces the two-sample median test.
The one-way ANOVA statistic with median scores is equivalent to the Brown-Mood test.
Median scores are particularly powerful for distributions that are symmetric and heavy-tailed.*/
/*
Scores for Linear Rank and One-Way ANOVA Tests
For each score type that you specify, PROC NPAR1WAY computes a one-way ANOVA statistic and also a linear rank statistic for two-sample data. The following score types are used primarily to test for differences in location: Wilcoxon, median, Van der Waerden (normal), and Savage. The following scores types are used to test for scale differences: Siegel-Tukey, Ansari-Bradley, Klotz, and Mood. Conover scores can be used to test for differences in both location and scale. This section gives formulas for the score types available in PROC NPAR1WAY. For further information about the formulas and the applicability of each score, see Randles and Wolfe (1979), Gibbons and Chakraborti (2010), Conover (1999), and Hollander and Wolfe (1999).
In addition to the score types described in this section, you can specify the SCORES=DATA option to use the input data observations as scores. This enables you to produce a wide variety of tests. You can construct any scores by using the DATA step, and then you can use PROC NPAR1WAY to compute the corresponding linear rank and one-way ANOVA tests for these scores. You can also analyze raw (unscored) data by using the SCORES=DATA option; for two-sample data, the corresponding exact test is a permutation test that is known as Pitman’s test.
*/
/*@@@@@@@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@@@@*/
ods pdf file='d:npar1wayConover.pdf' style=sapphire dpi=1200;
proc npar1way data=airqualitysorted Conover;
class DayNight;
var AQI;
by Area;
where Area is not missing;
run;
/*@@@@@@@@@@@@@@@@@@@@@@@@@@@ PDF @@@@@@@@@@@@@@@@@@@@@@@@@*/
/*Conover scores can be used to test for differences in both location and scale.*/
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---
title: "数据前处理+ggplot2画图实践"
format:
dwsd-revealjs:
logo: _extensions/drwater/dwsd/inst/ucaslogo.png
---
```{r}
#| echo: false
source("../../coding/_common.R")
library(tidyverse)
```
## 数据说明
```{r}
mpg
```
## 完成图1
```{r}
#| echo: false
mpg |>
ggplot(aes(x = displ, y = hwy, color = class)) +
geom_point()
```
## 完成图2
```{r}
#| echo: false
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
geom_point() +
geom_smooth(aes(linetype = drv))
```
## 完成图3
```{r}
#| echo: false
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(color = class)) +
geom_smooth()
```
## 完成图4
```{r}
#| echo: false
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point() +
facet_wrap(~cyl)
```
## 完成图5
```{r}
#| echo: false
ggplot(mpg, aes(x = drv, fill = class)) +
geom_bar(position = "fill")
```
## 综合实践：三维荧光数据处理
## 欢迎讨论！{.center}
```{r}
#| results: 'asis'
rmdify::slideend(
wechat = FALSE,
type = "public",
tel = FALSE,
thislink = "../"
)
```
SD/5.1_model/_demo.qmd
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---
title: "Lesson 9"
format: html
---
```{r}
# install.packages("tidymodels")
require(tidymodels)
taxi
taxisplit <- initial_split(taxi, prop = 0.8)
taxi_train <- training(taxisplit)
taxi_test <- testing(taxisplit)
tree_spec <-
decision_tree(cost_complexity = 0.002) %>%
set_mode("classification")
taxi_fit <- workflow() %>%
add_formula(tip ~ .) %>%
add_model(tree_spec) %>%
fit(data = taxi_train)
```
```{r}
augment(taxi_fit, new_data = taxi_train) %>%
relocate(tip, .pred_class, .pred_yes, .pred_no)
augment(taxi_fit, new_data = taxi_train) %>%
conf_mat(truth = tip, estimate = .pred_class)
augment(taxi_fit, new_data = taxi_train) %>%
accuracy(truth = tip, estimate = .pred_class)
augment(taxi_fit, new_data = taxi_train) %>%
sensitivity(truth = tip, estimate = .pred_class)
augment(taxi_fit, new_data = taxi_train) %>%
specificity(truth = tip, estimate = .pred_class)
taxi_metrics <- metric_set(accuracy, specificity, sensitivity)
augment(taxi_fit, new_data = taxi_train) %>%
taxi_metrics(truth = tip, estimate = .pred_class)
taxi_metrics <- metric_set(accuracy, specificity, sensitivity)
augment(taxi_fit, new_data = taxi_train) %>%
group_by(local) %>%
taxi_metrics(truth = tip, estimate = .pred_class)
augment(taxi_fit, new_data = taxi_train) %>%
roc_curve(truth = tip, .pred_yes) %>%
autoplot()
augment(taxi_fit, new_data = taxi_train)
augment(taxi_fit, new_data = taxi_train) %>%
roc_curve(truth = tip, .pred_yes) |>
ggplot(aes(1 - sensitivity, specificity)) +
geom_point() +
geom_line() +
geom_abline(slope = 1)
```
# Cross Validation
```{r}
vfold_cv(taxi_train, v = 10) |>
pull(splits) |>
nth(1)
taxi_folds <- vfold_cv(taxi_train)
taxi_folds$splits[1:3]
vfold_cv(taxi_train, strata = tip)
set.seed(123)
taxi_folds <- vfold_cv(taxi_train, v = 10, strata = tip)
taxi_folds
taxi_wflow <- workflow() %>%
add_formula(tip ~ .) %>%
add_model(tree_spec)
taxi_res <- fit_resamples(taxi_wflow, taxi_folds)
taxi_res
taxi_res$.metrics[[1]]
taxi_res$splits[[1]]
analysis(taxi_res$splits[[1]])
analysis(taxi_res$splits[[1]])
assessment(taxi_res$splits[[1]])
taxi_res %>%
collect_metrics()
taxi_res %>%
collect_metrics() %>%
select(.metric, mean, n)
# Save the assessment set results
ctrl_taxi <- control_resamples(save_pred = TRUE)
taxi_res <- fit_resamples(taxi_wflow, taxi_folds, control = ctrl_taxi)
taxi_res
```
# NLA2007 cyanophyta model
```{r}
require(tidyverse)
sitedf <- readr::read_csv(
"https://www.epa.gov/sites/default/files/2014-01/nla2007_sampledlakeinformation_20091113.csv"
) |>
select(
SITE_ID,
lon = LON_DD,
lat = LAT_DD,
name = LAKENAME,
area = LAKEAREA,
zmax = DEPTHMAX
) |>
group_by(SITE_ID) |>
summarize(
lon = mean(lon, na.rm = TRUE),
lat = mean(lat, na.rm = TRUE),
name = unique(name),
area = mean(area, na.rm = TRUE),
zmax = mean(zmax, na.rm = TRUE)
)
visitdf <- readr::read_csv(
"https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv"
) |>
select(SITE_ID, date = DATE_PROFILE, year = YEAR, visit = VISIT_NO) |>
distinct()
waterchemdf <- readr::read_csv(
"https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv"
) |>
select(
SITE_ID,
date = DATE_PROFILE,
depth = DEPTH,
temp = TEMP_FIELD,
do = DO_FIELD,
ph = PH_FIELD,
cond = COND_FIELD,
)
sddf <- readr::read_csv(
"https://www.epa.gov/sites/default/files/2014-10/nla2007_secchi_20091008.csv"
) |>
select(
SITE_ID,
date = DATE_SECCHI,
sd = SECMEAN,
clear_to_bottom = CLEAR_TO_BOTTOM
)
trophicdf <- readr::read_csv(
"https://www.epa.gov/sites/default/files/2014-10/nla2007_trophic_conditionestimate_20091123.csv"
) |>
select(SITE_ID, visit = VISIT_NO, tp = PTL, tn = NTL, chla = CHLA) |>
left_join(visitdf, by = c("SITE_ID", "visit")) |>
select(-year, -visit) |>
group_by(SITE_ID, date) |>
summarize(
tp = mean(tp, na.rm = TRUE),
tn = mean(tn, na.rm = TRUE),
chla = mean(chla, na.rm = TRUE)
)
phytodf <- readr::read_csv(
"https://www.epa.gov/sites/default/files/2014-10/nla2007_phytoplankton_softalgaecount_20091023.csv"
) |>
select(
SITE_ID,
date = DATEPHYT,
depth = SAMPLE_DEPTH,
phyta = DIVISION,
genus = GENUS,
species = SPECIES,
tax = TAXANAME,
abund = ABUND
) |>
mutate(phyta = gsub(" .*$", "", phyta)) |>
filter(!is.na(genus)) |>
group_by(SITE_ID, date, depth, phyta, genus) |>
summarize(abund = sum(abund, na.rm = TRUE)) |>
nest(phytodf = -c(SITE_ID, date))
phytodf$phytodf[[1]]
envdf <- waterchemdf |>
filter(depth < 2) |>
select(-depth) |>
group_by(SITE_ID, date) |>
summarise_all(~ mean(., na.rm = TRUE)) |>
ungroup() |>
left_join(sddf, by = c("SITE_ID", "date")) |>
left_join(trophicdf, by = c("SITE_ID", "date"))
nla <- envdf |>
left_join(phytodf, by = c("SITE_ID", "date")) |>
left_join(sitedf, by = "SITE_ID") |>
filter(!purrr::map_lgl(phytodf, is.null)) |>
mutate(
cyanophyta = purrr::map(
phytodf,
\(x) {
x |>
dplyr::filter(phyta == "Cyanophyta") |>
summarize(cyanophyta = sum(abund, na.rm = TRUE))
}
)
) |>
unnest(cyanophyta) |>
select(-phyta) |>
mutate(clear_to_bottom = ifelse(is.na(clear_to_bottom), TRUE, FALSE))
# library(rmdify)
# library(dwfun)
# dwfun::init()
```
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