diff --git a/.source_state b/.source_state
index 2564ecf..2d12976 100644
--- a/.source_state
+++ b/.source_state
@@ -1 +1 @@
-1a930ffc3764f8ee50c52c744e9a9258
+e1f98a39611eed5aee06e98abb66ef12
diff --git a/SD/1.1_R语言介绍/_demo.qmd b/SD/1.1_R语言介绍/_demo.qmd
index 14b83f8..68b00ab 100644
--- a/SD/1.1_R语言介绍/_demo.qmd
+++ b/SD/1.1_R语言介绍/_demo.qmd
@@ -86,6 +86,53 @@ 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)
+
+```
diff --git a/SD/1.9_课后作业6/data.csv b/SD/1.9_课后作业6/data.csv
new file mode 100644
index 0000000..3f48e9d
--- /dev/null
+++ b/SD/1.9_课后作业6/data.csv
@@ -0,0 +1,21 @@
+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
diff --git a/SD/3.9_课后作业8/.RData b/SD/3.9_课后作业8/.RData
new file mode 100644
index 0000000..6f7dafd
Binary files /dev/null and b/SD/3.9_课后作业8/.RData differ
diff --git a/SD/3.9_课后作业8/_extensions b/SD/3.9_课后作业8/_extensions
new file mode 120000
index 0000000..74119e3
--- /dev/null
+++ b/SD/3.9_课后作业8/_extensions
@@ -0,0 +1 @@
+../../_extensions
\ No newline at end of file
diff --git a/SD/3.9_课后作业8/airqualitydf.RDS b/SD/3.9_课后作业8/airqualitydf.RDS
new file mode 100644
index 0000000..5d6d693
Binary files /dev/null and b/SD/3.9_课后作业8/airqualitydf.RDS differ
diff --git a/SD/3.9_课后作业8/airqualitymedianoutrow5.pdf b/SD/3.9_课后作业8/airqualitymedianoutrow5.pdf
new file mode 100644
index 0000000..face837
Binary files /dev/null and b/SD/3.9_课后作业8/airqualitymedianoutrow5.pdf differ
diff --git a/SD/3.9_课后作业8/freq.pdf b/SD/3.9_课后作业8/freq.pdf
new file mode 100644
index 0000000..446ee8c
Binary files /dev/null and b/SD/3.9_课后作业8/freq.pdf differ
diff --git a/SD/3.9_课后作业8/index.qmd b/SD/3.9_课后作业8/index.qmd
new file mode 100644
index 0000000..ffd30e0
--- /dev/null
+++ b/SD/3.9_课后作业8/index.qmd
@@ -0,0 +1,66 @@
+---
+title: "课后作业8"
+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
+resources:
+ - "*.pdf"
+ - "*.sas"
+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}
+#| 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 rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "https://drc.drwater.net/course/public/RWEP/PUB/SD/")`
+
diff --git a/SD/3.9_课后作业8/median.pdf b/SD/3.9_课后作业8/median.pdf
new file mode 100644
index 0000000..eafe394
Binary files /dev/null and b/SD/3.9_课后作业8/median.pdf differ
diff --git a/SD/3.9_课后作业8/metadf.RDS b/SD/3.9_课后作业8/metadf.RDS
new file mode 100644
index 0000000..454bc5f
Binary files /dev/null and b/SD/3.9_课后作业8/metadf.RDS differ
diff --git a/SD/3.9_课后作业8/npar1wayConover.pdf b/SD/3.9_课后作业8/npar1wayConover.pdf
new file mode 100644
index 0000000..2cc2998
Binary files /dev/null and b/SD/3.9_课后作业8/npar1wayConover.pdf differ
diff --git a/SD/3.9_课后作业8/testdf.RDS b/SD/3.9_课后作业8/testdf.RDS
new file mode 100644
index 0000000..a21548f
Binary files /dev/null and b/SD/3.9_课后作业8/testdf.RDS differ
diff --git a/SD/3.9_课后作业8/第8次课后作业_模板.qmd b/SD/3.9_课后作业8/第8次课后作业_模板.qmd
new file mode 100644
index 0000000..a6b54d6
--- /dev/null
+++ b/SD/3.9_课后作业8/第8次课后作业_模板.qmd
@@ -0,0 +1,152 @@
+---
+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)
+
+
+}
+
+```
+
diff --git a/SD/3.9_课后作业8/第8次课后作业_模板.sas b/SD/3.9_课后作业8/第8次课后作业_模板.sas
new file mode 100644
index 0000000..9cdaf41
--- /dev/null
+++ b/SD/3.9_课后作业8/第8次课后作业_模板.sas
@@ -0,0 +1,289 @@
+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$";
+ GETNAMES=YES;
+ MIXED=YES;
+ SCANTEXT=YES;
+ USEDATE=NO;
+ SCANTIME=NO;
+RUN;
+ %print(raw_metadf);
+ %save_dataset(raw_metadf); *ԭʼ���ݼ�����;
+
+PROC IMPORT OUT=WORK.raw_airqualitydf
+ DATAFILE="d:airquality.xlsx"
+ DBMS=EXCEL REPLACE;
+ RANGE="airqualitydf$";
+ GETNAMES=YES;
+ MIXED=YES;
+ SCANTEXT=YES;
+ USEDATE=NO; *ΪYES��������;
+ SCANTIME=NO; *ΪYES��������;
+RUN;
+ %print(raw_airqualitydf);
+ %save_dataset(raw_airqualitydf); *ԭʼ���ݼ�����;
+
+
+%kill;
+/*����Ӳ��ԭʼ���ݼ�*/
+%load_dataset(raw_metadf);
+%load_dataset(raw_airqualitydf);
+/*�鿴���ݼ�����*/
+%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_��g/m3'n 'NO2_24h_��g/m3'n 'O3_��g/m3'n 'O3_24h_��g/m3'n 'O3_8h_��g/m3'n 'O3_8h_24h_��g/m3'n 'PM10_��g/m3'n 'PM10_24h_��g/m3'n 'PM2#5_��g/m3'n 'PM2#5_24h_��g/m3'n 'SO2_��g/m3'n 'SO2_24h_��g/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;
+/*�ϲ����ݼ�������Ԥ��������ȡ���ڡ�ʱ�䲿�֣�����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); *�ϲ����ݼ�����;
+
+
+
+%kill;
+/*#################### DATA SET airquality ####################*/
+/*����Ӳ�̺ϲ����ݼ�*/
+%load_dataset(airquality);
+%printobs(airquality,100);
+
+/*���site��name�����Ƿ�һ�£����ֲ�һ�£�����site����ͳ��*/
+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
+*/
+
+
+/*#########################################################################*/
+/*��������ͳ�ư��죨8:00-20:00����ҹ����20:00-8:00���п�������ָ����AQI����λ��*/
+/*#########################################################################*/
+/*@@@@@@@@@@@@@@@@@@@@@ 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 @@@@@@@@@@@@@@@@@@@@@@*/
+
+
+/*####################################################################################################*/
+/*������ͳ�Ƶ������в�����AQI30%��λֵ�IJ�����ռ�ȣ��г�����ռ����ߵ�10�����У������Dz�����������5���ij��У�*/
+/*####################################################################################################*/
+/*����鿴���в�����AQI30%��λֵ��Ϊ��SQL��֤������ȥ*/
+proc univariate data=airquality noprint;
+ var AQI;
+ output out=airqualitystats pctlpts=30 pctlpre=P;
+run;
+ %print(airqualitystats); /*42*/
+
+/*������в�����AQI30%��λֵ�������*/
+proc sql;
+ select AQI into : xvalues separated by ',' from airquality;
+ select distinct(pctl(30, &xvalues)) into : P30 from airquality;
+quit;
+/*�鿴���в�����AQI30%��λֵ�ĺ����ֵ��Ϊ��������*/
+ %put P30=&P30.;
+
+/*�����в�����AQI30%��λֵ��AQI�ּ����Ժϲ����ݼ����в�������������ֱ�ӷּ��������ø���������λ������ͳ�ƣ�����ȥ*/
+data airquality1;
+ set airquality;
+ if AQI<&P30. then quality='good';
+ else quality='fair';
+run;
+ %printobs(airquality1,100);
+
+/*������в�����AQI��λ��*/
+proc means data=airquality median maxdec=1;
+ class Area site;
+ var AQI;
+ where AQI is not missing;
+ output out=airqualitymedian median=;
+run;
+ %print(airqualitymedian);
+
+/*�����в�����AQI30%��λֵ��AQI��λ���ּ�*/
+data airqualitymedian1;
+ set airqualitymedian;
+ if AQI<&P30. then quality='good';
+ else quality='fair';
+ where _TYPE_=3;
+run;
+ %print(airqualitymedian1);
+
+/*������ͳ�Ƶ������в�����AQI30%��λֵ�IJ�����ռ�ȣ��鿴���*/
+/*@@@@@@@@@@@@@@@@@@@@@ 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 @@@@@@@@@@@@@@@@@@@@@*/
+/*������ͳ�Ƶ������в�����AQI30%��λֵ�IJ�����ռ�ȣ����Ƶ��ͳ�ƽ�������ݼ�*/
+proc freq data=airqualitymedian1;
+ table Area*quality /outpct out=airqualitymedianoutrow(drop=percent pct_col); *��������Ƶ�����аٷֱ�;
+run;
+ %printobs(airqualitymedianoutrow,100);
+
+/*�����жԲ�����������ͳ�ƣ��鿴���������ȥ*/
+proc means data=airqualitymedianoutrow sum maxdec=0;
+ class Area;
+ var COUNT;
+run;
+/*�����������������5���ij��У��鿴���������ȥ*/
+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;
+/*����������������5���ij��У���������ݼ�*/
+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;
+/*�г�����ռ����ߵ�10�����У�����������������5���ij��У�*/
+/*@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 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 @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@*/
+
+
+/*#####################################################*/
+/*���ղ�ͬ���з��飬ͳ�ư�����ҹ��AQI��λ���Ƿ������������*/
+/*#####################################################*/
+/*�����е�siteû��Area��Ӧ*/
+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;
+/*��4��site�������㣩û��Area�����У���Ӧ
+site count_site
+2628A 4
+3128A 4
+4034A 4
+4036A 4
+*/
+proc sort data=airquality out=airqualitysorted;
+ by Area;
+run;
+/*���ղ�ͬ���з��飬ͳ�ư�����ҹ��AQI��λ�����鿴���������ȥ*/
+proc means data=airqualitysorted median maxdec=1;
+ by Area;
+ class DayNight;
+ var AQI;
+ where AQI is not missing;
+run;
+/*��ͳ�ؿ���������ҹ��AQI��λ���Ƿ������������*/
+proc npar1way data=airquality median;
+ class DayNight;
+ var AQI;
+run;
+/*���ղ�ͬ���з��飬ͳ�ư�����ҹ��AQI��λ���Ƿ������������*/
+/*@@@@@@@@@@@@@@@@@@@@@@@@@@ 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.*/
diff --git a/SD/4.1_datavisualize/_extensions b/SD/4.1_datavisualize/_extensions
new file mode 120000
index 0000000..74119e3
--- /dev/null
+++ b/SD/4.1_datavisualize/_extensions
@@ -0,0 +1 @@
+../../_extensions
\ No newline at end of file
diff --git a/SD/4.1_datavisualize/index.qmd b/SD/4.1_datavisualize/index.qmd
new file mode 100644
index 0000000..e203666
--- /dev/null
+++ b/SD/4.1_datavisualize/index.qmd
@@ -0,0 +1,3938 @@
+---
+title: "数据可视化"
+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
+knitr:
+ opts_chunk:
+ dev: "svg"
+ retina: 3
+execute:
+ freeze: auto
+ cache: true
+ echo: true
+ fig-width: 5
+ fig-height: 6
+---
+
+```{r}
+#| include: false
+#| cache: false
+knitr::opts_chunk$set(echo = TRUE)
+source("../../coding/_common.R")
+require(learnr)
+library(tidyverse)
+library(palmerpenguins)
+library(ggthemes)
+```
+
+
+
+## {background-image="../../img/concepts/tidyverse-packages-ggplot.png" background-position="center" background-size="100%"}
+
+
+
+## The ggplot2 Package
+
+<br>
+
+... is an **R package to visualize data** created by Hadley Wickham in 2005
+
+```{r}
+#| label: ggplot-package-install-2
+#| eval: false
+# install.packages("ggplot2")
+library(ggplot2)
+```
+
+<br>
+
+::: fragment
+... is part of the [`{tidyverse}`](https://www.tidyverse.org/)
+
+```{r}
+#| label: tidyverse-package-install-2
+#| eval: false
+# install.packages("tidyverse")
+library(tidyverse)
+```
+:::
+
+# The Grammar of {ggplot2}
+
+
+
+## The Grammar of {ggplot2}
+
+<br>
+<table style='width:100%;font-size:14pt;'>
+ <tr>
+ <th>Component</th>
+ <th>Function</th>
+ <th>Explanation</th>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Data</b></td>
+ <td><code>ggplot(data)</code> </td>
+ <td>*The raw data that you want to visualise.*</td>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Aesthetics </b></td>
+ <td><code>aes()</code></td>
+ <td>*Aesthetic mappings between variables and visual properties.*</td>
+ <tr>
+ <td><b style='color:#67676;'>Geometries</b></td>
+ <td><code>geom_*()</code></td>
+ <td>*The geometric shapes representing the data.*</td>
+ </tr>
+</table>
+
+
+
+## The Grammar of {ggplot2}
+
+
+<br>
+<table style='width:100%;font-size:14pt;'>
+ <tr>
+ <th>Component</th>
+ <th>Function</th>
+ <th>Explanation</th>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Data</b></td>
+ <td><code>ggplot(data)</code> </td>
+ <td>*The raw data that you want to visualise.*</td>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Aesthetics </b></td>
+ <td><code>aes()</code></td>
+ <td>*Aesthetic mappings between variables and visual properties.*</td>
+ <tr>
+ <td><b style='color:#67676;'>Geometries</b></td>
+ <td><code>geom_*()</code></td>
+ <td>*The geometric shapes representing the data.*</td>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Statistics</b></td>
+ <td><code>stat_*()</code></td>
+ <td>*The statistical transformations applied to the data.*</td>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Scales</b></td>
+ <td><code>scale_*()</code></td>
+ <td>*Maps between the data and the aesthetic dimensions.*</td>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Coordinate System</b></td>
+ <td><code>coord_*()</code></td>
+ <td>*Maps data into the plane of the data rectangle.*</td>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Facets</b></td>
+ <td><code>facet_*()</code></td>
+ <td>*The arrangement of the data into a grid of plots.*</td>
+ </tr>
+ <tr>
+ <td><b style='color:#67676;'>Visual Themes</b></td>
+ <td><code>theme() / theme_*()</code></td>
+ <td>*The overall visual defaults of a plot.*</td>
+ </tr>
+</table>
+
+
+
+## The Data
+
+<b style='font-size:2.3rem;'>Bike sharing counts in London, UK, powered by [TfL Open Data](https://tfl.gov.uk/modes/cycling/santander-cycles)</b>
+
+::: incremental
+- covers the years 2015 and 2016
+- incl. weather data acquired from [freemeteo.com](https://freemeteo.com)
+- prepared by Hristo Mavrodiev for [Kaggle](https://www.kaggle.com/hmavrodiev/london-bike-sharing-dataset)
+:::
+
+<br>
+
+::: fragment
+```{r}
+#| label: data-import
+bikes <- readr::read_csv("../../data/ggplot2/london-bikes-custom.csv",
+ ## or: "https://raw.githubusercontent.com/z3tt/graphic-design-ggplot2/main/data/london-bikes-custom.csv"
+ col_types = "Dcfffilllddddc"
+)
+
+bikes$season <- forcats::fct_inorder(bikes$season)
+```
+:::
+
+------------------------------------------------------------------------
+
+```{r}
+#| label: data-table
+#| echo: false
+#| purl: false
+library(tidyverse)
+tibble(
+ Variable = names(bikes),
+ Description = c(
+ "Date encoded as `YYYY-MM-DD`", "`day` (6:00am–5:59pm) or `night` (6:00pm–5:59am)", "`2015` or `2016`", "`1` (January) to `12` (December)", "`winter`, `spring`, `summer`, or `autumn`", "Sum of reported bikes rented", "`TRUE` being Monday to Friday and no bank holiday", "`TRUE` being Saturday or Sunday", "`TRUE` being a bank holiday in the UK", "Average air temperature (°C)", "Average feels like temperature (°C)", "Average air humidity (%)", "Average wind speed (km/h)", "Most common weather type"
+ ),
+ Class = c(
+ "date", "character", "factor", "factor", "factor", "integer", "logical", "logical", "logical", "double", "double", "double", "double", "character"
+ )
+ ) %>%
+ kableExtra::kbl(
+ booktabs = TRUE, longtable = TRUE
+ ) %>%
+ kableExtra::kable_styling(
+ font_size = 20
+ ) %>%
+ kableExtra::kable_minimal(
+ "hover", full_width = TRUE, position = "left", html_font = "Spline Sans Mono"
+ )
+```
+
+## `ggplot2::ggplot()`
+
+```{r}
+#| label: ggplot-function
+#| eval: false
+#| echo: false
+#?ggplot
+```
+
+{fig-alt="The help page of the ggplot() function." fig-width="175%"}
+
+## Data
+
+```{r}
+#| label: setup-ggplot-slides
+#| include: false
+#| purl: false
+library(ggplot2)
+theme_set(theme_grey(base_size = 14))
+```
+
+```{r}
+#| label: ggplot-data
+#| output-location: column
+ggplot(data = bikes)
+```
+
+## Aesthetic Mapping（视觉映射）：`aes(.)`
+
+<br>
+
+<b class='simple-highlight-grn' style='font-size:2.6rem;'>= link variables to graphical properties</b><br><br>
+
+::: incremental
+- positions (`x`, `y`)
+- colors (`color`, `fill`)
+- shapes (`shape`, `linetype`)
+- size (`size`)
+- transparency (`alpha`)
+- groupings (`group`)
+:::
+
+## Aesthetic Mapping（视觉映射）：`aes(.)`
+
+```{r}
+#| label: ggplot-aesthetics-outside
+#| output-location: column
+#| code-line-numbers: "2|1,2"
+ggplot(data = bikes) +
+ aes(x = temp_feel, y = count)
+```
+
+## <span style='color:#4758AB;'>aes</span>thetics
+
+`aes()` outside as component
+
+```{r}
+#| label: ggplot-aesthetics-outside-comp
+#| eval: false
+ggplot(data = bikes) +
+ aes(x = temp_feel, y = count)
+```
+
+<br>
+
+::: fragment
+`aes()` inside, explicit matching
+
+```{r}
+#| label: ggplot-aesthetics-inside
+#| eval: false
+ggplot(data = bikes, mapping = aes(x = temp_feel, y = count))
+```
+
+<br>
+:::
+
+::: fragment
+`aes()` inside, implicit matching
+
+```{r}
+#| label: ggplot-aesthetics-inside-implicit
+#| eval: false
+ggplot(bikes, aes(temp_feel, count))
+```
+
+<br>
+:::
+
+::: fragment
+`aes()` inside, mixed matching
+
+```{r}
+#| label: ggplot-aesthetics-inside-mix
+#| eval: false
+ggplot(bikes, aes(x = temp_feel, y = count))
+```
+:::
+
+# Geometrical Layers
+
+## Geometries（几何图层）：geom_*
+
+<br>
+
+<b class='simple-highlight-grn' style='font-size:2.6rem;'>= interpret aesthetics as graphical representations</b><br><br>
+
+::: incremental
+- points
+- lines
+- polygons
+- text labels
+- ...
+:::
+
+## Geometries（几何图层）：geom_*
+
+```{r}
+#| label: geom-point
+#| output-location: column
+#| code-line-numbers: "1,2,3,4|5"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point()
+```
+
+## Visual Properties of Layers（图层属性）
+
+```{r}
+#| label: geom-point-properties
+#| output-location: column
+#| code-line-numbers: "5,6,7,8,9,10,11|6,7,8,9,10"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point(
+ color = "#28a87d",
+ alpha = .5,
+ shape = "X",
+ stroke = 1,
+ size = 4
+ )
+```
+
+## Setting vs Mapping of Visual Properties
+
+::: {layout-ncol="2"}
+```{r}
+#| label: geom-point-properties-set
+#| fig-height: 3.5
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point(
+ color = "#28a87d",
+ alpha = .5
+ )
+```
+
+::: fragment
+```{r}
+#| label: geom-point-properties-map
+#| fig-height: 3.5
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point(
+ aes(color = season),
+ alpha = .5
+ )
+```
+:::
+:::
+
+## Mapping Expressions
+
+```{r}
+#| label: geom-point-aes-expression
+#| output-location: column
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point(
+ aes(color = temp_feel > 20),
+ alpha = .5
+ )
+```
+
+## Filter Data
+
+```{r}
+#| label: geom-point-aes-expression-exercise-na
+#| output-location: column
+#| code-line-numbers: "2"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = temp, y = temp_feel)
+ ) +
+ geom_point(
+ aes(color = weather_type == "clear",
+ size = count),
+ shape = 18,
+ alpha = .5
+ )
+```
+
+## Filter Data
+
+```{r}
+#| label: geom-point-aes-expression-exercise-na-pipe
+#| output-location: column
+#| code-line-numbers: "2"
+ggplot(
+ bikes %>% filter(!is.na(weather_type)),
+ aes(x = temp, y = temp_feel)
+ ) +
+ geom_point(
+ aes(color = weather_type == "clear",
+ size = count),
+ shape = 18,
+ alpha = .5
+ )
+```
+
+```{r}
+#| label: reset-theme
+#| include: false
+#| purl: false
+theme_set(theme_grey(base_size = 14))
+```
+
+## Local vs. Global（应用至当前图层或所有图层）
+
+::: {layout-ncol="2"}
+```{r}
+#| label: geom-point-aes-geom
+#| code-line-numbers: "3,6"
+#| fig-height: 3.2
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point(
+ aes(color = season),
+ alpha = .5
+ )
+```
+
+::: fragment
+```{r}
+#| label: geom-point-aes-global
+#| code-line-numbers: "3,4"
+#| fig-height: 3.2
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count,
+ color = season)
+ ) +
+ geom_point(
+ alpha = .5
+ )
+```
+:::
+:::
+
+## Adding More Layers
+
+```{r}
+#| label: geom-smooth
+#| output-location: column
+#| code-line-numbers: "9,10,11"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count,
+ color = season)
+ ) +
+ geom_point(
+ alpha = .5
+ ) +
+ geom_smooth(
+ method = "lm"
+ )
+```
+
+## Global Color Encoding
+
+```{r}
+#| label: geom-smooth-aes-global
+#| output-location: column
+#| code-line-numbers: "3,4,9,10,11"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count,
+ color = season)
+ ) +
+ geom_point(
+ alpha = .5
+ ) +
+ geom_smooth(
+ method = "lm"
+ )
+```
+
+## Local Color Encoding
+
+```{r}
+#| label: geom-smooth-aes-fixed
+#| output-location: column
+#| code-line-numbers: "6,9,10,11"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point(
+ aes(color = season),
+ alpha = .5
+ ) +
+ geom_smooth(
+ method = "lm"
+ )
+```
+
+## The \`group\` Aesthetic
+
+```{r}
+#| label: geom-smooth-aes-grouped
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point(
+ aes(color = season),
+ alpha = .5
+ ) +
+ geom_smooth(
+ aes(group = day_night),
+ method = "lm"
+ )
+```
+
+## Set Both as Global Aesthetics
+
+```{r}
+#| label: geom-smooth-aes-global-grouped
+#| output-location: column
+#| code-line-numbers: "4,5"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count,
+ color = season,
+ group = day_night)
+ ) +
+ geom_point(
+ alpha = .5
+ ) +
+ geom_smooth(
+ method = "lm"
+ )
+```
+
+## Overwrite Global Aesthetics
+
+```{r}
+#| label: geom-smooth-aes-global-grouped-overwrite
+#| output-location: column
+#| code-line-numbers: "4,12"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count,
+ color = season,
+ group = day_night)
+ ) +
+ geom_point(
+ alpha = .5
+ ) +
+ geom_smooth(
+ method = "lm",
+ color = "black"
+ )
+```
+
+
+
+# Statistical Layers
+
+
+## \`stat_\*()\` and \`geom_\*()\`
+
+::: {layout-ncol="2"}
+```{r}
+#| label: stat-geom
+#| fig-height: 5.1
+#| code-line-numbers: "2"
+ggplot(bikes, aes(x = temp_feel, y = count)) +
+ stat_smooth(geom = "smooth")
+```
+
+```{r}
+#| label: geom-stat
+#| fig-height: 5.1
+#| code-line-numbers: "2"
+ggplot(bikes, aes(x = temp_feel, y = count)) +
+ geom_smooth(stat = "smooth")
+```
+:::
+
+
+## \`stat_\*()\` and \`geom_\*()\`
+
+::: {layout-ncol="2"}
+```{r}
+#| label: stat-geom-2
+#| fig-height: 5.1
+#| code-line-numbers: "2"
+ggplot(bikes, aes(x = season)) +
+ stat_count(geom = "bar")
+```
+
+```{r}
+#| label: geom-stat-2
+#| fig-height: 5.1
+#| code-line-numbers: "2"
+ggplot(bikes, aes(x = season)) +
+ geom_bar(stat = "count")
+```
+:::
+
+
+## \`stat_\*()\` and \`geom_\*()\`
+
+::: {layout-ncol="2"}
+```{r}
+#| label: stat-geom-3
+#| fig-height: 5.1
+#| code-line-numbers: "2"
+ggplot(bikes, aes(x = date, y = temp_feel)) +
+ stat_identity(geom = "point")
+```
+
+```{r}
+#| label: geom-stat-3
+#| fig-height: 5.1
+#| code-line-numbers: "2"
+ggplot(bikes, aes(x = date, y = temp_feel)) +
+ geom_point(stat = "identity")
+```
+:::
+
+## Statistical Summaries
+
+```{r}
+#| label: stat-summary
+#| output-location: column
+#| code-line-numbers: "5|3"
+ggplot(
+ bikes,
+ aes(x = season, y = temp_feel)
+ ) +
+ stat_summary()
+```
+
+
+## Statistical Summaries
+
+```{r}
+#| label: stat-summary-defaults
+#| output-location: column
+#| code-line-numbers: "6,7"
+ggplot(
+ bikes,
+ aes(x = season, y = temp_feel)
+ ) +
+ stat_summary(
+ fun.data = mean_se, ## the default
+ geom = "pointrange" ## the default
+ )
+```
+
+
+## Statistical Summaries
+
+```{r}
+#| label: stat-summary-median
+#| output-location: column
+#| code-line-numbers: "5|5,6,11|6,7,8,9,10,11|7,8"
+ggplot(
+ bikes,
+ aes(x = season, y = temp_feel)
+ ) +
+ geom_boxplot() +
+ stat_summary(
+ fun = mean,
+ geom = "point",
+ color = "#28a87d",
+ size = 3
+ )
+```
+
+
+## Statistical Summaries
+
+```{r}
+#| label: stat-summary-custom
+#| output-location: column
+#| code-line-numbers: "5,6,7,8,9|7,8"
+ggplot(
+ bikes,
+ aes(x = season, y = temp_feel)
+ ) +
+ stat_summary(
+ fun = mean,
+ fun.max = function(y) mean(y) + sd(y),
+ fun.min = function(y) mean(y) - sd(y)
+ )
+```
+
+
+
+# Extending a ggplot
+
+## Store a ggplot as Object
+
+```{r}
+#| label: ggplot-object
+#| code-line-numbers: "1,16"
+g <-
+ ggplot(
+ bikes,
+ aes(x = temp_feel, y = count,
+ color = season,
+ group = day_night)
+ ) +
+ geom_point(
+ alpha = .5
+ ) +
+ geom_smooth(
+ method = "lm",
+ color = "black"
+ )
+
+class(g)
+```
+
+## Inspect a ggplot Object
+
+```{r}
+#| label: ggplot-object-data
+g$data
+```
+
+## Inspect a ggplot Object
+
+```{r}
+#| label: ggplot-object-mapping
+g$mapping
+```
+
+## Extend a ggplot Object: Add Layers
+
+```{r}
+#| label: ggplot-object-extend-geom
+#| output-location: column
+g +
+ geom_rug(
+ alpha = .2
+ )
+```
+
+## Remove a Layer from the Legend
+
+```{r}
+#| label: geom-guide-none
+#| output-location: column
+#| code-line-numbers: "4"
+g +
+ geom_rug(
+ alpha = .2,
+ show.legend = FALSE
+ )
+```
+
+## Extend a ggplot Object: Add Labels
+
+```{r}
+#| label: ggplot-labs-individual
+#| output-location: column
+#| code-line-numbers: "2,3,4"
+g +
+ xlab("Feels-like temperature (°F)") +
+ ylab("Reported bike shares") +
+ ggtitle("TfL bike sharing trends")
+```
+
+## Extend a ggplot Object: Add Labels
+
+```{r}
+#| label: ggplot-labs-bundled
+#| output-location: column
+#| code-line-numbers: "2,3,4,5,6"
+g +
+ labs(
+ x = "Feels-like temperature (°F)",
+ y = "Reported bike shares",
+ title = "TfL bike sharing trends"
+ )
+```
+
+## Extend a ggplot Object: Add Labels
+
+```{r}
+#| label: ggplot-labs-bundled-color
+#| output-location: column
+#| code-line-numbers: "6"
+g <- g +
+ labs(
+ x = "Feels-like temperature (°F)",
+ y = "Reported bike shares",
+ title = "TfL bike sharing trends",
+ color = "Season:"
+ )
+
+g
+```
+
+## Extend a ggplot Object: Add Labels
+
+```{r}
+#| label: ggplot-labs-bundled-extended
+#| output-location: column
+#| code-line-numbers: "6,7,9"
+g +
+ labs(
+ x = "Feels-like temperature (°F)",
+ y = "Reported bike shares",
+ title = "TfL bike sharing trends",
+ subtitle = "Reported bike rents versus feels-like temperature in London",
+ caption = "Data: TfL",
+ color = "Season:",
+ tag = "Fig. 1"
+ )
+```
+
+## Extend a ggplot Object: Add Labels
+
+::: {layout-ncol="2"}
+```{r}
+#| label: ggplot-labs-empty-vs-null-A
+#| fig-height: 3.6
+#| code-line-numbers: "3"
+g +
+ labs(
+ x = "",
+ caption = "Data: TfL"
+ )
+```
+
+```{r}
+#| label: ggplot-labs-empty-vs-null-B
+#| fig-height: 3.6
+#| code-line-numbers: "3"
+g +
+ labs(
+ x = NULL,
+ caption = "Data: TfL"
+ )
+```
+:::
+
+## Extend a ggplot Object: Themes
+
+::: {layout-ncol="2"}
+```{r}
+#| label: ggplot-object-extend-theme-light
+#| fig-height: 5.5
+g + theme_light()
+```
+
+::: fragment
+```{r}
+#| label: ggplot-object-extend-theme-minimal
+#| fig-height: 5.5
+g + theme_minimal()
+```
+:::
+:::
+
+## Change the Theme Base Settings
+
+```{r}
+#| label: ggplot-theme-extend-theme-base
+#| output-location: column
+#| code-line-numbers: "2,3|1,2,3,4"
+g + theme_light(
+ base_size = 14
+)
+```
+
+## Set a Theme Globally
+
+```{r}
+#| label: ggplot-theme-global
+#| output-location: column
+theme_set(theme_light())
+
+g
+```
+
+## Change the Theme Base Settings
+
+```{r}
+#| label: ggplot-theme-global-base
+#| output-location: column
+#| code-line-numbers: "2,3|1,2,3,4"
+theme_set(theme_light(
+ base_size = 14
+))
+
+g
+```
+
+
+## Overwrite Specific Theme Settings
+
+```{r}
+#| label: ggplot-theme-settings-individual-1
+#| output-location: column
+#| code-line-numbers: "2|3"
+g +
+ theme(
+ panel.grid.minor = element_blank()
+ )
+```
+
+## Overwrite Specific Theme Settings
+
+```{r}
+#| label: ggplot-theme-settings-individual-2
+#| output-location: column
+#| code-line-numbers: "4"
+g +
+ theme(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold")
+ )
+```
+
+## Overwrite Specific Theme Settings
+
+```{r}
+#| label: ggplot-theme-settings-individual-3
+#| output-location: column
+#| code-line-numbers: "5"
+g +
+ theme(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "top"
+ )
+```
+
+## Overwrite Specific Theme Settings
+
+```{r}
+#| label: ggplot-theme-settings-individual-legend-none
+#| output-location: column
+#| code-line-numbers: "5"
+g +
+ theme(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "none"
+ )
+```
+
+## Overwrite Specific Theme Settings
+
+```{r}
+#| label: ggplot-theme-settings-individual-4
+#| output-location: column
+#| code-line-numbers: "6|2,3,4,6,7"
+g +
+ theme(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "top",
+ plot.title.position = "plot"
+ )
+```
+
+## Overwrite Theme Settings Globally
+
+```{r}
+#| label: ggplot-theme-settings-global
+#| output-location: column
+#| code-line-numbers: "1|2,3,4,5|1,2,3,4,5,6"
+theme_update(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "top",
+ plot.title.position = "plot"
+)
+
+g
+```
+
+## Save the Graphic
+
+```{r}
+#| label: ggplot-save
+#| eval: false
+ggsave(g, filename = "my_plot.png")
+```
+
+::: fragment
+```{r}
+#| label: ggplot-save-implicit
+#| eval: false
+ggsave("my_plot.png")
+```
+:::
+
+::: fragment
+```{r}
+#| label: ggplot-save-aspect
+#| eval: false
+ggsave("my_plot.png", width = 8, height = 5, dpi = 600)
+```
+:::
+
+::: fragment
+```{r}
+#| label: ggplot-save-vector
+#| eval: false
+ggsave("my_plot.pdf", width = 20, height = 12, unit = "cm", device = cairo_pdf)
+```
+:::
+
+::: fragment
+```{r}
+#| label: ggplot-save-cairo_pdf
+#| eval: false
+grDevices::cairo_pdf("my_plot.pdf", width = 10, height = 7)
+g
+dev.off()
+```
+:::
+
+------------------------------------------------------------------------
+
+<br>
+
+{fig-alt="A comparison of vector and raster graphics." fig-width="150%"}
+
+# Facets（面）
+
+## Facets（面）
+
+<br>
+
+<b class='simple-highlight-grn' style='font-size:2.6rem;'>= split variables to multiple panels</b><br><br>
+
+::: fragment
+Facets are also known as:
+
+- small multiples
+- trellis graphs
+- lattice plots
+- conditioning
+:::
+
+------------------------------------------------------------------------
+
+::: {layout-ncol="2"}
+```{r}
+#| label: facet-types-wrap
+#| echo: false
+#| purl: false
+ggplot(bikes, aes(x = 1, y = 1)) +
+ geom_text(
+ aes(label = paste0("Subset for\n", stringr::str_to_title(season))),
+ size = 5, family = "Cabinet Grotesk", lineheight = .9
+ ) +
+ facet_wrap(~stringr::str_to_title(season)) +
+ ggtitle("facet_wrap()") +
+ theme_bw(base_size = 24) +
+ theme(
+ plot.title = element_text(hjust = .5, family = "Tabular", face = "bold"),
+ strip.text = element_text(face = "bold", size = 18),
+ panel.grid = element_blank(),
+ axis.ticks = element_blank(),
+ axis.text = element_blank(),
+ axis.title = element_blank(),
+ plot.background = element_rect(color = "#f8f8f8", fill = "#f8f8f8"),
+ plot.margin = margin(t = 3, r = 25)
+ )
+```
+
+::: fragment
+```{r}
+#| label: facet-types-grid
+#| echo: false
+#| purl: false
+data <- tibble(
+ x = 1, y = 1,
+ day_night = c("Day", "Day", "Night", "Night"),
+ year = factor(c("2015", "2016", "2015", "2016"), levels = levels(bikes$year)),
+ label = c("Subset for\nDay × 2015", "Subset for\nDay × 2016",
+ "Subset for\nNight × 2015", "Subset for\nNight × 2016")
+)
+
+ggplot(data, aes(x = 1, y = 1)) +
+ geom_text(
+ aes(label = label),
+ size = 5, family = "Cabinet Grotesk", lineheight = .9
+ ) +
+ facet_grid(day_night ~ year) +
+ ggtitle("facet_grid()") +
+ theme_bw(base_size = 24) +
+ theme(
+ plot.title = element_text(hjust = .5, family = "Tabular", face = "bold"),
+ strip.text = element_text(face = "bold", size = 18),
+ panel.grid = element_blank(),
+ axis.ticks = element_blank(),
+ axis.text = element_blank(),
+ axis.title = element_blank(),
+ plot.background = element_rect(color = "#f8f8f8", fill = "#f8f8f8"),
+ plot.margin = margin(t = 3, l = 25)
+ )
+```
+:::
+:::
+
+## Setup
+
+```{r}
+#| label: theme-size-facets
+#| include: false
+#| purl: false
+theme_set(theme_light(base_size = 12))
+
+theme_update(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "top",
+ plot.title.position = "plot"
+)
+```
+
+```{r}
+#| label: facet-setup
+#| output-location: column
+#| code-line-numbers: "1,2,3,4,5,6,7,8,9,10|12"
+g <-
+ ggplot(
+ bikes,
+ aes(x = temp_feel, y = count,
+ color = season)
+ ) +
+ geom_point(
+ alpha = .3,
+ guide = "none"
+ )
+
+g
+```
+
+## Wrapped Facets
+
+```{r}
+#| label: facet-wrap
+#| output-location: column
+#| code-line-numbers: "1,2,3,4|2,4|3"
+g +
+ facet_wrap(
+ vars(day_night)
+ )
+```
+
+## Wrapped Facets
+
+```{r}
+#| label: facet-wrap-circumflex
+#| output-location: column
+#| code-line-numbers: "3"
+g +
+ facet_wrap(
+ ~ day_night
+ )
+```
+
+## Facet Multiple Variables
+
+```{r}
+#| label: facet-wrap-multiple
+#| output-location: column
+#| code-line-numbers: "3"
+g +
+ facet_wrap(
+ ~ is_workday + day_night
+ )
+```
+
+## Facet Options: Cols + Rows
+
+```{r}
+#| label: facet-wrap-options-ncol
+#| output-location: column
+#| code-line-numbers: "4"
+g +
+ facet_wrap(
+ ~ day_night,
+ ncol = 1
+ )
+```
+
+## Facet Options: Free Scaling
+
+```{r}
+#| label: facet-wrap-options-scales
+#| output-location: column
+#| code-line-numbers: "5"
+g +
+ facet_wrap(
+ ~ day_night,
+ ncol = 1,
+ scales = "free"
+ )
+```
+
+## Facet Options: Free Scaling
+
+```{r}
+#| label: facet-wrap-options-freey
+#| output-location: column
+#| code-line-numbers: "5"
+g +
+ facet_wrap(
+ ~ day_night,
+ ncol = 1,
+ scales = "free_y"
+ )
+```
+
+## Facet Options: Switch Labels
+
+```{r}
+#| label: facet-wrap-options-switch
+#| output-location: column
+#| code-line-numbers: "5"
+g +
+ facet_wrap(
+ ~ day_night,
+ ncol = 1,
+ switch = "x"
+ )
+```
+
+## Gridded Facets
+
+```{r}
+#| label: facet-grid
+#| output-location: column
+#| code-line-numbers: "2,5|3,4"
+g +
+ facet_grid(
+ rows = vars(day_night),
+ cols = vars(is_workday)
+ )
+```
+
+## Gridded Facets
+
+```{r}
+#| label: facet-grid-circumflex
+#| output-location: column
+#| code-line-numbers: "3"
+g +
+ facet_grid(
+ day_night ~ is_workday
+ )
+```
+
+## Facet Multiple Variables
+
+```{r}
+#| label: facet-grid-multiple
+#| output-location: column
+#| code-line-numbers: "3"
+g +
+ facet_grid(
+ day_night ~ is_workday + season
+ )
+```
+
+## Facet Options: Free Scaling
+
+```{r}
+#| label: facet-grid-options-scales
+#| output-location: column
+#| code-line-numbers: "4"
+g +
+ facet_grid(
+ day_night ~ is_workday,
+ scales = "free"
+ )
+```
+
+## Facet Options: Switch Labels
+
+```{r}
+#| label: facet-grid-options-switch
+#| output-location: column
+#| code-line-numbers: "5"
+g +
+ facet_grid(
+ day_night ~ is_workday,
+ scales = "free",
+ switch = "y"
+ )
+```
+
+## Facet Options: Proportional Spacing
+
+```{r}
+#| label: facet-grid-options-space
+#| output-location: column
+#| code-line-numbers: "4,5|5"
+g +
+ facet_grid(
+ day_night ~ is_workday,
+ scales = "free",
+ space = "free"
+ )
+```
+
+## Facet Options: Proportional Spacing
+
+```{r}
+#| label: facet-grid-options-space-y
+#| output-location: column
+#| code-line-numbers: "4,5"
+g +
+ facet_grid(
+ day_night ~ is_workday,
+ scales = "free_y",
+ space = "free_y"
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-start
+#| output-location: column
+#| code-line-numbers: "1,2,3,4,5,6,7,8,9,10,11,12|8,9,10"
+ggplot(
+ diamonds,
+ aes(x = carat, y = price)
+ ) +
+ geom_point(
+ alpha = .3
+ ) +
+ geom_smooth(
+ method = "lm",
+ se = FALSE,
+ color = "dodgerblue"
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet
+#| output-location: column
+#| code-line-numbers: "13,14,15,16,17"
+ggplot(
+ diamonds,
+ aes(x = carat, y = price)
+ ) +
+ geom_point(
+ alpha = .3
+ ) +
+ geom_smooth(
+ method = "lm",
+ se = FALSE,
+ color = "dodgerblue"
+ ) +
+ facet_grid(
+ cut ~ clarity,
+ space = "free_x",
+ scales = "free_x"
+ )
+```
+
+## Diamonds Facet (Dark Theme Bonus)
+
+```{r}
+#| label: diamonds-facet-dark
+#| output-location: column
+#| code-line-numbers: "19,20,21,22"
+ggplot(
+ diamonds,
+ aes(x = carat, y = price)
+ ) +
+ geom_point(
+ alpha = .3,
+ color = "white"
+ ) +
+ geom_smooth(
+ method = "lm",
+ se = FALSE,
+ color = "dodgerblue"
+ ) +
+ facet_grid(
+ cut ~ clarity,
+ space = "free_x",
+ scales = "free_x"
+ ) +
+ theme_dark(
+ base_size = 14
+ )
+```
+
+# Scales（尺度）
+
+```{r}
+#| label: theme-size-reset
+#| include: false
+#| purl: false
+theme_set(theme_light(base_size = 14))
+
+theme_update(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "top",
+ plot.title.position = "plot"
+)
+```
+
+## Scales
+
+<br>
+
+<b class='simple-highlight-grn' style='font-size:2.6rem;'>= translate between variable ranges and property ranges</b><br><br>
+
+::: incremental
+- feels-like temperature ⇄ x
+- reported bike shares ⇄ y
+- season ⇄ color
+- year ⇄ shape
+- ...
+:::
+
+## Scales
+
+The `scale_*()` components control the properties of all the<br><b class='simple-highlight-ylw'>aesthetic dimensions mapped to the data.</b>
+
+<br>Consequently, there are `scale_*()` functions for all aesthetics such as:
+
+- **positions** via `scale_x_*()` and `scale_y_*()`
+
+- **colors** via `scale_color_*()` and `scale_fill_*()`
+
+- **sizes** via `scale_size_*()` and `scale_radius_*()`
+
+- **shapes** via `scale_shape_*()` and `scale_linetype_*()`
+
+- **transparency** via `scale_alpha_*()`
+
+## Scales
+
+The `scale_*()` components control the properties of all the<br><b class='simple-highlight-ylw'>aesthetic dimensions mapped to the data.</b>
+
+<br>The extensions (`*`) can be filled by e.g.:
+
+- `continuous()`, `discrete()`, `reverse()`, `log10()`, `sqrt()`, `date()` for positions
+
+- `continuous()`, `discrete()`, `manual()`, `gradient()`, `gradient2()`, `brewer()` for colors
+
+- `continuous()`, `discrete()`, `manual()`, `ordinal()`, `area()`, `date()` for sizes
+
+- `continuous()`, `discrete()`, `manual()`, `ordinal()` for shapes
+
+- `continuous()`, `discrete()`, `manual()`, `ordinal()`, `date()` for transparency
+
+------------------------------------------------------------------------
+
+](../../img/concepts/continuous_discrete.png){fig-size="120%" fig-align="center" fig-alt="Allison Horsts illustration ofthe correct use of continuous versus discrete; however, in {ggplot2} these are interpeted in a different way: as quantitative and qualitative."}
+
+## Continuous vs. Discrete in {ggplot2}
+
+::: {layout-ncol="2"}
+## Continuous:<br>quantitative or numerical data
+
+- height
+- weight
+- age
+- counts
+
+## Discrete:<br>qualitative or categorical data
+
+- species
+- sex
+- study sites
+- age group
+:::
+
+## Continuous vs. Discrete in {ggplot2}
+
+::: {layout-ncol="2"}
+## Continuous:<br>quantitative or numerical data
+
+- height (continuous)
+- weight (continuous)
+- age (continuous or discrete)
+- counts (discrete)
+
+## Discrete:<br>qualitative or categorical data
+
+- species (nominal)
+- sex (nominal)
+- study site (nominal or ordinal)
+- age group (ordinal)
+:::
+
+## Aesthetics + Scales
+
+```{r}
+#| label: scales-default-invisible
+#| output-location: column
+#| code-line-numbers: "3,4"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point()
+```
+
+## Aesthetics + Scales
+
+```{r}
+#| label: scales-default
+#| output-location: column
+#| code-line-numbers: "3,4,7,8,9|7,8,9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_date() +
+ scale_y_continuous() +
+ scale_color_discrete()
+```
+
+## Scales
+
+```{r}
+#| label: scales-overwrite-1
+#| output-location: column
+#| code-line-numbers: "7"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_continuous() +
+ scale_y_continuous() +
+ scale_color_discrete()
+```
+
+## Scales
+
+```{r}
+#| label: scales-overwrite-2
+#| output-location: column
+#| code-line-numbers: "8"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_continuous() +
+ scale_y_log10() +
+ scale_color_discrete()
+```
+
+## Scales
+
+```{r}
+#| label: scales-overwrite-3
+#| output-location: column
+#| code-line-numbers: "9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_continuous() +
+ scale_y_log10() +
+ scale_color_viridis_d()
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-trans
+#| output-location: column
+#| code-line-numbers: "8,9,10|9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ trans = "log10"
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-name
+#| output-location: column
+#| code-line-numbers: "7,8,9|8"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares"
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-breaks-seq
+#| output-location: column
+#| code-line-numbers: "9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = seq(0, 60000, by = 15000)
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-breaks-short
+#| output-location: column
+#| code-line-numbers: "9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = 0:4*15000
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-breaks-irregular
+#| output-location: column
+#| code-line-numbers: "9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = c(0, 2:12*2500, 40000, 50000)
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-labels
+#| output-location: column
+#| code-line-numbers: "8,10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares in thousands",
+ breaks = 0:4*15000,
+ labels = 0:4*15
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-labels-paste
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares in thousands",
+ breaks = 0:4*15000,
+ labels = paste(0:4*15000, "bikes")
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-limits
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = 0:4*15000,
+ limits = c(NA, 60000)
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-expand.no
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = 0:4*15000,
+ expand = c(0, 0)
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-expand
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = -1:5*15000,
+ expand = c(.5, .5) ## c(add, mult)
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-expand-add-explicit
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = -1:5*15000,
+ expand = expansion(add = 2000)
+ )
+```
+
+## \`scale_x\|y_continuous\`
+
+```{r}
+#| label: scales-xy-continuous-guide-none
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_y_continuous(
+ name = "Reported bike shares",
+ breaks = 0:4*15000,
+ guide = "none"
+ )
+```
+
+## \`scale_x\|y_date\`
+
+```{r}
+#| label: scales-xy-date-breaks-months
+#| output-location: column
+#| code-line-numbers: "7,10|7,8,9,10|9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_date(
+ name = NULL,
+ date_breaks = "4 months"
+ )
+```
+
+## \`scale_x\|y_date\`
+
+```{r}
+#| label: scales-xy-date-breaks-weeks
+#| output-location: column
+#| code-line-numbers: "9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_date(
+ name = NULL,
+ date_breaks = "20 weeks"
+ )
+```
+
+## \`scale_x\|y_date\` with \`strftime()\`
+
+```{r}
+#| label: scales-xy-date-labels
+#| output-location: column
+#| code-line-numbers: "9,10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_date(
+ name = NULL,
+ date_breaks = "6 months",
+ date_labels = "%Y/%m/%d"
+ )
+```
+
+## \`scale_x\|y_date\` with \`strftime()\`
+
+```{r}
+#| label: scales-xy-date-labels-special
+#| output-location: column
+#| code-line-numbers: "10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_x_date(
+ name = NULL,
+ date_breaks = "6 months",
+ date_labels = "%b '%y"
+ )
+```
+
+## \`scale_x\|y_discrete\`
+
+```{r}
+#| label: scales-xy-discrete
+#| output-location: column
+#| code-line-numbers: "3,6,9|6,7,8,9|7,8"
+ggplot(
+ bikes,
+ aes(x = season, y = count)
+ ) +
+ geom_boxplot() +
+ scale_x_discrete(
+ name = "Period",
+ labels = c("Dec-Feb", "Mar-May", "Jun-Aug", "Sep-Nov")
+ )
+```
+
+## \`scale_x\|y_discrete\`
+
+```{r}
+#| label: scales-xy-discrete-expand
+#| output-location: column
+#| code-line-numbers: "8"
+ggplot(
+ bikes,
+ aes(x = season, y = count)
+ ) +
+ geom_boxplot() +
+ scale_x_discrete(
+ name = "Season",
+ expand = c(.5, 0) ## add, mult
+ )
+```
+
+## Discrete or Continuous?
+
+```{r}
+#| label: scales-xy-fake-discrete-visible
+#| output-location: column
+#| code-line-numbers: "3,5,6,7"
+ggplot(
+ bikes,
+ aes(x = as.numeric(season), y = count)
+ ) +
+ geom_boxplot(
+ aes(group = season)
+ )
+```
+
+## Discrete or Continuous?
+
+```{r}
+#| label: scales-xy-fake-discrete
+#| output-location: column
+#| code-line-numbers: "9,10,11,12,13|11|12"
+ggplot(
+ bikes,
+ aes(x = as.numeric(season),
+ y = count)
+ ) +
+ geom_boxplot(
+ aes(group = season)
+ ) +
+ scale_x_continuous(
+ name = "Season",
+ breaks = 1:4,
+ labels = levels(bikes$season)
+ )
+```
+
+## Discrete or Continuous?
+
+```{r}
+#| label: scales-xy-fake-discrete-shift
+#| output-location: column
+#| code-line-numbers: "3,4"
+ggplot(
+ bikes,
+ aes(x = as.numeric(season) +
+ as.numeric(season) / 8,
+ y = count)
+ ) +
+ geom_boxplot(
+ aes(group = season)
+ ) +
+ scale_x_continuous(
+ name = "Season",
+ breaks = 1:4,
+ labels = levels(bikes$season)
+ )
+```
+
+## \`scale_color\|fill_discrete\`
+
+```{r}
+#| label: scales-color-discrete-type-vector
+#| output-location: column
+#| code-line-numbers: "7,10|7,8,9,10|8,9"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_color_discrete(
+ name = "Season:",
+ type = c("#69b0d4", "#00CB79", "#F7B01B", "#a78f5f")
+ )
+```
+
+## Inspect Assigned Colors
+
+```{r}
+#| label: scales-color-discrete-type-inspect
+#| output-location: column
+#| code-line-numbers: "1|12|14"
+g <- ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_color_discrete(
+ name = "Season:",
+ type = c("#3ca7d9", "#1ec99b", "#F7B01B", "#bb7e8f")
+ )
+
+gb <- ggplot_build(g)
+
+gb$data[[1]][c(1:5, 200:205, 400:405), 1:5]
+```
+
+## \`scale_color\|fill_discrete\`
+
+```{r}
+#| label: scales-color-discrete-type-vector-named
+#| output-location: column
+#| code-line-numbers: "1,2,3,4,5,6|1,16"
+my_colors <- c(
+ `winter` = "#3c89d9",
+ `spring` = "#1ec99b",
+ `summer` = "#F7B01B",
+ `autumn` = "#a26e7c"
+)
+
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_color_discrete(
+ name = "Season:",
+ type = my_colors
+ )
+```
+
+## \`scale_color\|fill_discrete\`
+
+```{r}
+#| label: scales-color-discrete-type-vector-named-shuffled
+#| output-location: column
+#| code-line-numbers: "2,5|1,16"
+my_colors_alphabetical <- c(
+ `autumn` = "#a26e7c",
+ `spring` = "#1ec99b",
+ `summer` = "#F7B01B",
+ `winter` = "#3c89d9"
+)
+
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_color_discrete(
+ name = "Season:",
+ type = my_colors_alphabetical
+ )
+```
+
+## \`scale_color\|fill_discrete\`
+
+```{r}
+#| label: scales-color-discrete-type-palette
+#| output-location: column
+#| code-line-numbers: "1|11,12,13"
+library(RColorBrewer)
+
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = season)
+ ) +
+ geom_point() +
+ scale_color_discrete(
+ name = "Season:",
+ type = brewer.pal(
+ n = 4, name = "Dark2"
+ )
+ )
+```
+
+## \`scale_color\|fill_manual\`
+
+```{r}
+#| label: scales-color-manual-na
+#| output-location: column
+#| code-line-numbers: "4,9,10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = weather_type)
+ ) +
+ geom_point() +
+ scale_color_manual(
+ name = "Season:",
+ values = brewer.pal(n = 6, name = "Pastel1"),
+ na.value = "black"
+ )
+```
+
+## \`scale_color\|fill_carto_d\`
+
+```{r}
+#| label: scales-color-discrete-carto
+#| output-location: column
+#| code-line-numbers: "7,8,9,10"
+ggplot(
+ bikes,
+ aes(x = date, y = count,
+ color = weather_type)
+ ) +
+ geom_point() +
+ rcartocolor::scale_color_carto_d(
+ name = "Season:",
+ palette = "Pastel",
+ na.value = "black"
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-store
+#| output-location: column
+#| code-line-numbers: "1|10|20"
+facet <-
+ ggplot(
+ diamonds,
+ aes(x = carat, y = price)
+ ) +
+ geom_point(
+ alpha = .3
+ ) +
+ geom_smooth(
+ aes(color = cut),
+ method = "lm",
+ se = FALSE
+ ) +
+ facet_grid(
+ cut ~ clarity,
+ space = "free_x",
+ scales = "free_x"
+ )
+
+facet
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-scales-xy
+#| output-location: column
+facet +
+ scale_x_continuous(
+ breaks = 0:5
+ ) +
+ scale_y_continuous(
+ limits = c(0, 30000),
+ breaks = 0:3*10000,
+ labels = c("$0", "$10,000", "$20,000", "$30,000")
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-scales-y-paste-format
+#| output-location: column
+#| code-line-numbers: "8,9,10,11,12,13,14"
+facet +
+ scale_x_continuous(
+ breaks = 0:5
+ ) +
+ scale_y_continuous(
+ limits = c(0, 30000),
+ breaks = 0:3*10000,
+ labels = paste0(
+ "$", format(
+ 0:3*10000,
+ big.mark = ",",
+ trim = TRUE
+ )
+ )
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-scales-y-function
+#| output-location: column
+#| code-line-numbers: "8,9,10,11,12,13"
+facet +
+ scale_x_continuous(
+ breaks = 0:5
+ ) +
+ scale_y_continuous(
+ limits = c(0, 30000),
+ breaks = 0:3*10000,
+ labels = function(y) paste0(
+ "$", format(
+ y, big.mark = ",",
+ trim = TRUE
+ )
+ )
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-scales-y-dollar-format
+#| output-location: column
+#| code-line-numbers: "8"
+facet +
+ scale_x_continuous(
+ breaks = 0:5
+ ) +
+ scale_y_continuous(
+ limits = c(0, 30000),
+ breaks = 0:3*10000,
+ labels = scales::dollar_format()
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-scales-color
+#| output-location: column
+#| code-line-numbers: "10,11,12,13"
+facet +
+ scale_x_continuous(
+ breaks = 0:5
+ ) +
+ scale_y_continuous(
+ limits = c(0, 30000),
+ breaks = 0:3*10000,
+ labels = scales::dollar_format()
+ ) +
+ scale_color_brewer(
+ palette = "Set2",
+ guide = "none"
+ )
+```
+
+## Diamonds Facet
+
+```{r}
+#| label: diamonds-facet-scales-no-legend
+#| output-location: column
+#| code-line-numbers: "13,14,15"
+facet +
+ scale_x_continuous(
+ breaks = 0:5
+ ) +
+ scale_y_continuous(
+ limits = c(0, 30000),
+ breaks = 0:3*10000,
+ labels = scales::dollar_format()
+ ) +
+ scale_color_brewer(
+ palette = "Set2"
+ ) +
+ theme(
+ legend.position = "none"
+ )
+```
+
+# Coordinate Systems（投影）
+
+## Coordinate Systems
+
+<br>
+
+<b class='simple-highlight-grn' style='font-size:2.6rem;'>= interpret the position aesthetics</b><br><br>
+
+::: incremental
+- **linear coordinate systems:** preserve the geometrical shapes
+ - `coord_cartesian()`
+ - `coord_fixed()`
+ - `coord_flip()`
+- **non-linear coordinate systems:** likely change the geometrical shapes
+ - `coord_polar()`
+ - `coord_map()` and `coord_sf()`
+ - `coord_trans()`
+:::
+
+## Cartesian Coordinate System
+
+```{r}
+#| label: coord-cartesian
+#| output-location: column
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = season, y = count)
+ ) +
+ geom_boxplot() +
+ coord_cartesian()
+```
+
+## Cartesian Coordinate System
+
+```{r}
+#| label: coord-cartesian-zoom
+#| output-location: column
+#| code-line-numbers: "6,7,8"
+ggplot(
+ bikes,
+ aes(x = season, y = count)
+ ) +
+ geom_boxplot() +
+ coord_cartesian(
+ ylim = c(NA, 15000)
+ )
+```
+
+## Changing Limits
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-cartesian-ylim
+#| fig-height: 3.5
+#| code-line-numbers: "6,7,8"
+ggplot(
+ bikes,
+ aes(x = season, y = count)
+ ) +
+ geom_boxplot() +
+ coord_cartesian(
+ ylim = c(NA, 15000)
+ )
+```
+
+```{r}
+#| label: scale-y-limits
+#| fig-height: 3.5
+#| code-line-numbers: "6,7,8"
+ggplot(
+ bikes,
+ aes(x = season, y = count)
+ ) +
+ geom_boxplot() +
+ scale_y_continuous(
+ limits = c(NA, 15000)
+ )
+```
+:::
+
+## Clipping
+
+```{r}
+#| label: coord-clip
+#| output-location: column
+#| code-line-numbers: "8"
+ggplot(
+ bikes,
+ aes(x = season, y = count)
+ ) +
+ geom_boxplot() +
+ coord_cartesian(
+ ylim = c(NA, 15000),
+ clip = "off"
+ )
+```
+
+## Clipping
+
+```{r}
+#| label: coord-clip-text
+#| output-location: column
+#| code-line-numbers: "2,3|6,7,8,9,10|12"
+ggplot(
+ filter(bikes, is_holiday == TRUE),
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point() +
+ geom_text(
+ aes(label = season),
+ nudge_x = .3,
+ hjust = 0
+ ) +
+ coord_cartesian(
+ clip = "off"
+ )
+```
+
+## ... or better use {ggrepel}
+
+```{r}
+#| label: coord-clip-text-repel
+#| output-location: column
+#| code-line-numbers: "6"
+ggplot(
+ filter(bikes, is_holiday == TRUE),
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point() +
+ ggrepel::geom_text_repel(
+ aes(label = season),
+ nudge_x = .3,
+ hjust = 0
+ ) +
+ coord_cartesian(
+ clip = "off"
+ )
+```
+
+## Remove All Padding
+
+```{r}
+#| label: coord-expand-off-clip
+#| output-location: column
+#| code-line-numbers: "7|8"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = count)
+ ) +
+ geom_point() +
+ coord_cartesian(
+ expand = FALSE,
+ clip = "off"
+ )
+```
+
+## Fixed Coordinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-fixed
+#| fig-height: 4.2
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = temp)
+ ) +
+ geom_point() +
+ coord_fixed()
+```
+
+::: fragment
+```{r}
+#| label: coord-fixed-custom
+#| fig-height: 4.2
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp_feel, y = temp)
+ ) +
+ geom_point() +
+ coord_fixed(ratio = 4)
+```
+:::
+:::
+
+## Flipped Coordinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-cartesian-comp-flip
+#| fig-height: 4.1
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = weather_type)
+ ) +
+ geom_bar() +
+ coord_cartesian()
+```
+
+```{r}
+#| label: coord-flip
+#| fig-height: 4.1
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = weather_type)
+ ) +
+ geom_bar() +
+ coord_flip()
+```
+:::
+
+## Flipped Coordinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-cartesian-switch-x-y
+#| fig-height: 4.1
+#| code-line-numbers: "3,6"
+ggplot(
+ bikes,
+ aes(y = weather_type)
+ ) +
+ geom_bar() +
+ coord_cartesian()
+```
+
+```{r}
+#| label: coord-flip-again
+#| fig-height: 4.1
+#| code-line-numbers: "3,6"
+ggplot(
+ bikes,
+ aes(x = weather_type)
+ ) +
+ geom_bar() +
+ coord_flip()
+```
+:::
+
+## Reminder: Sort Your Bars!
+
+```{r}
+#| label: forcats-sort-infreq
+#| output-location: column
+#| code-line-numbers: "3|2"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(y = fct_infreq(weather_type))
+ ) +
+ geom_bar()
+```
+
+## Reminder: Sort Your Bars!
+
+```{r}
+#| label: forcats-sort-infreq-rev
+#| output-location: column
+#| code-line-numbers: "3,4,5"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(y = fct_rev(
+ fct_infreq(weather_type)
+ ))
+ ) +
+ geom_bar()
+```
+
+## Circular Corrdinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-polar
+#| fig-height: 3.9
+#| code-line-numbers: "7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = weather_type,
+ fill = weather_type)
+ ) +
+ geom_bar() +
+ coord_polar()
+```
+
+::: fragment
+```{r}
+#| label: coord-cartesian-comp-polar
+#| fig-height: 3.9
+#| code-line-numbers: "7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = weather_type,
+ fill = weather_type)
+ ) +
+ geom_bar() +
+ coord_cartesian()
+```
+:::
+:::
+
+## Circular Cordinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-polar-coxcomb
+#| fig-height: 3.9
+#| code-line-numbers: "6,7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = fct_infreq(weather_type),
+ fill = weather_type)
+ ) +
+ geom_bar(width = 1) +
+ coord_polar()
+```
+
+```{r}
+#| label: coord-cartesian-comp-polar-no-padding
+#| fig-height: 3.9
+#| code-line-numbers: "6,7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = fct_infreq(weather_type),
+ fill = weather_type)
+ ) +
+ geom_bar(width = 1) +
+ coord_cartesian()
+```
+:::
+
+## Circular Corrdinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-polar-theta-x
+#| fig-height: 3.9
+#| code-line-numbers: "7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = fct_infreq(weather_type),
+ fill = weather_type)
+ ) +
+ geom_bar() +
+ coord_polar(theta = "x")
+```
+
+```{r}
+#| label: coord-polar-theta-y
+#| fig-height: 3.9
+#| code-line-numbers: "7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = fct_infreq(weather_type),
+ fill = weather_type)
+ ) +
+ geom_bar() +
+ coord_polar(theta = "y")
+```
+:::
+
+## Circular Corrdinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-polar-pie
+#| fig-height: 4.1
+#| code-line-numbers: "5"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = 1, fill = weather_type)
+ ) +
+ geom_bar(position = "stack") +
+ coord_polar(theta = "y")
+```
+
+```{r}
+#| label: coord-cartesian-comp-polar-stacked
+#| fig-height: 4.1
+#| code-line-numbers: "5"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = 1, fill = weather_type)
+ ) +
+ geom_bar(position = "stack") +
+ coord_cartesian()
+```
+:::
+
+## Circular Corrdinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: coord-polar-pie-sorted
+#| fig-height: 3.6
+#| code-line-numbers: "4,6,7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = 1,
+ fill = fct_rev(fct_infreq(weather_type)))
+ ) +
+ geom_bar(position = "stack") +
+ coord_polar(theta = "y") +
+ scale_fill_discrete(name = NULL)
+```
+
+```{r}
+#| label: coord-cartesian-comp-polar-stacked-sorted
+#| fig-height: 3.6
+#| code-line-numbers: "4,6,7"
+ggplot(
+ filter(bikes, !is.na(weather_type)),
+ aes(x = 1,
+ fill = fct_rev(fct_infreq(weather_type)))
+ ) +
+ geom_bar(position = "stack") +
+ coord_cartesian() +
+ scale_fill_discrete(name = NULL)
+```
+:::
+
+## Transform a Coordinate System
+
+```{r}
+#| label: coord-trans-log
+#| output-location: column
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp, y = count)
+ ) +
+ geom_point() +
+ coord_trans(y = "log10")
+```
+
+## Transform a Coordinate System
+
+::: {layout-ncol="2"}
+```{r}
+#| label: trans-log-via-coord
+#| fig-height: 3.6
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp, y = count,
+ group = day_night)
+ ) +
+ geom_point() +
+ geom_smooth(method = "lm") +
+ coord_trans(y = "log10")
+```
+
+::: fragment
+```{r}
+#| label: trans-log-via-scale
+#| fig-height: 3.6
+#| code-line-numbers: "6"
+ggplot(
+ bikes,
+ aes(x = temp, y = count,
+ group = day_night)
+ ) +
+ geom_point() +
+ geom_smooth(method = "lm") +
+ scale_y_log10()
+```
+:::
+:::
+
+
+# 图形组合
+
+------------------------------------------------------------------------
+
+](../../img/layout/ah_patchwork.jpg){fig-align="center" fig-alt="Allison Horsts monster illustration of the patchwork extension package."}
+
+::: footer
+:::
+
+------------------------------------------------------------------------
+
+::: panel-tabset
+### Graphic
+
+```{r}
+#| label: patchwork-p1
+#| fig-width: 10
+#| fig-height: 5.8
+#| echo: false
+theme_std <- theme_set(theme_minimal(base_size = 18))
+theme_update(
+ # text = element_text(family = "Pally"),
+ panel.grid = element_blank(),
+ axis.text = element_text(color = "grey50", size = 12),
+ axis.title = element_text(color = "grey40", face = "bold"),
+ axis.title.x = element_text(margin = margin(t = 12)),
+ axis.title.y = element_text(margin = margin(r = 12)),
+ axis.line = element_line(color = "grey80", size = .4),
+ legend.text = element_text(color = "grey50", size = 12),
+ plot.tag = element_text(size = 40, margin = margin(b = 15)),
+ plot.background = element_rect(fill = "white", color = "white")
+)
+
+bikes_sorted <-
+ bikes %>%
+ filter(!is.na(weather_type)) %>%
+ group_by(weather_type) %>%
+ mutate(sum = sum(count)) %>%
+ ungroup() %>%
+ mutate(
+ weather_type = forcats::fct_reorder(
+ str_to_title(str_wrap(weather_type, 5)), sum
+ )
+ )
+
+p1 <- ggplot(
+ bikes_sorted,
+ aes(x = weather_type, y = count, color = weather_type)
+ ) +
+ geom_hline(yintercept = 0, color = "grey80", size = .4) +
+ stat_summary(
+ geom = "point", fun = "sum", size = 12
+ ) +
+ stat_summary(
+ geom = "linerange", ymin = 0, fun.max = function(y) sum(y),
+ size = 2, show.legend = FALSE
+ ) +
+ coord_flip(ylim = c(0, NA), clip = "off") +
+ scale_y_continuous(
+ expand = c(0, 0), limits = c(0, 8500000),
+ labels = scales::comma_format(scale = .0001, suffix = "K")
+ ) +
+ scale_color_viridis_d(
+ option = "magma", direction = -1, begin = .1, end = .9, name = NULL,
+ guide = guide_legend(override.aes = list(size = 7))
+ ) +
+ labs(
+ x = NULL, y = "Sum of reported bike shares", tag = "P1",
+ ) +
+ theme(
+ axis.line.y = element_blank(),
+ axis.text.y = element_text(family = "Pally", color = "grey50", face = "bold",
+ margin = margin(r = 15), lineheight = .9)
+ )
+
+p1
+```
+
+### Code
+
+```{r}
+#| label: patchwork-p1
+#| eval: false
+```
+:::
+
+------------------------------------------------------------------------
+
+::: panel-tabset
+### Graphic
+
+```{r}
+#| label: patchwork-p2
+#| fig-width: 10
+#| fig-height: 5.8
+#| echo: false
+p2 <- bikes_sorted %>%
+ filter(season == "winter", is_weekend == TRUE, day_night == "night") %>%
+ group_by(weather_type, .drop = FALSE) %>%
+ mutate(id = row_number()) %>%
+ ggplot(
+ aes(x = weather_type, y = id, color = weather_type)
+ ) +
+ geom_point(size = 4.5) +
+ scale_color_viridis_d(
+ option = "magma", direction = -1, begin = .1, end = .9, name = NULL,
+ guide = guide_legend(override.aes = list(size = 7))
+ ) +
+ labs(
+ x = NULL, y = "Reported bike shares on\nweekend winter nights", tag = "P2",
+ ) +
+ coord_cartesian(ylim = c(.5, NA), clip = "off")
+
+p2
+```
+
+### Code
+
+```{r}
+#| label: patchwork-p2
+#| eval: false
+```
+:::
+
+------------------------------------------------------------------------
+
+::: panel-tabset
+### Graphic
+
+```{r}
+#| label: patchwork-p3
+#| fig-width: 10
+#| fig-height: 5.8
+#| echo: false
+my_colors <- c("#cc0000", "#000080")
+
+p3 <- bikes %>%
+ group_by(week = lubridate::week(date), day_night, year) %>%
+ summarize(count = sum(count)) %>%
+ group_by(week, day_night) %>%
+ mutate(avg = mean(count)) %>%
+ ggplot(aes(x = week, y = count, group = interaction(day_night, year))) +
+ geom_line(color = "grey65", size = 1) +
+ geom_line(aes(y = avg, color = day_night), stat = "unique", size = 1.7) +
+ annotate(
+ geom = "text", label = c("Day", "Night"), color = my_colors,
+ x = c(5, 18), y = c(125000, 29000), size = 8, fontface = "bold", family = "Pally"
+ ) +
+ scale_x_continuous(breaks = c(1, 1:10*5)) +
+ scale_y_continuous(labels = scales::comma_format()) +
+ scale_color_manual(values = my_colors, guide = "none") +
+ labs(
+ x = "Week of the Year", y = "Reported bike shares\n(cumulative # per week)", tag = "P3",
+ )
+
+p3
+```
+
+### Code
+
+```{r}
+#| label: patchwork-p3
+#| eval: false
+```
+:::
+
+## {patchwork}
+
+```{r}
+#| label: patchwork-composition
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+#| code-line-numbers: "3|2,3"
+# install.packages("patchwork")
+require(patchwork)
+(p1 + p2) / p3
+```
+
+## "Collect Guides"
+
+```{r}
+#| label: patchwork-composition-guides
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+(p1 + p2) / p3 + plot_layout(guides = "collect")
+```
+
+## Apply Theming
+
+```{r}
+#| label: patchwork-composition-guides-just
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+((p1 + p2) / p3 & theme(legend.justification = "top")) +
+plot_layout(guides = "collect")
+```
+
+## Apply Theming
+
+```{r}
+#| label: patchwork-composition-legend-off
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+(p1 + p2) / p3 & theme(legend.position = "none",
+ plot.background = element_rect(color = "black", size = 3))
+```
+
+## Adjust Widths and Heights
+
+```{r}
+#| label: patchwork-composition-heights-widths
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+#| code-line-numbers: "2"
+((p1 + p2) / p3 & theme(legend.position = "none")) +
+ plot_layout(heights = c(.2, .1), widths = c(2, 1))
+```
+
+## Use A Custom Layout
+
+```{r}
+#| label: patchwork-composition-design
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+#| code-line-numbers: "1,2,3,4|5"
+picasso <- "
+AAAAAA#BBBB
+CCCCCCCCC##
+CCCCCCCCC##"
+(p1 + p2 + p3 & theme(legend.position = "none")) +
+plot_layout(design = picasso)
+```
+
+## Add Labels
+
+```{r}
+#| label: patchwork-composition-labs-prep
+pl1 <- p1 + labs(tag = NULL, title = "Plot One") + theme(legend.position = "none")
+pl2 <- p2 + labs(tag = NULL, title = "Plot Two") + theme(legend.position = "none")
+pl3 <- p3 + labs(tag = NULL, title = "Plot Three") + theme(legend.position = "none")
+```
+
+## Add Labels
+
+```{r}
+#| label: patchwork-composition-labs
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+#| code-line-numbers: "2"
+(pl1 + pl2) / pl3 +
+ plot_annotation(tag_levels = "1",
+ tag_prefix = "P",
+ title = "An overarching title for all 3 plots, placed on the very top while all other titles are sitting below the tags.")
+```
+
+## Add Text
+
+::: panel-tabset
+### Graphic
+
+```{r}
+#| label: patchwork-composition-textbox-prep
+#| echo: false
+#| fig-width: 9
+#| fig-height: 4.5
+#| fig-align: "center"
+text <- tibble::tibble(
+ x = 0, y = 0, label = "Lorem ipsum dolor sit amet, **consectetur adipiscing elit**, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation <b style='color:#000080;'>ullamco laboris nisi</b> ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat <b style='color:#cc0000;'>cupidatat non proident</b>, sunt in culpa qui officia deserunt mollit anim id est laborum."
+)
+pt <- ggplot(text, aes(x = x, y = y)) +
+ ggtext::geom_textbox(
+ aes(label = label),
+ box.color = NA, width = unit(23, "lines"),
+ color = "grey40", size = 6.5, lineheight = 1.4
+ ) +
+ coord_cartesian(expand = FALSE, clip = "off") +
+ theme_void()
+pt
+```
+
+### Code
+
+```{r}
+#| label: patchwork-composition-textbox-prep
+#| eval: false
+```
+:::
+
+## Add Text
+
+```{r}
+#| label: patchwork-composition-textbox
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+(p1 + pt) / p3
+```
+
+## Add Inset Plots
+
+```{r}
+#| label: patchwork-composition-inset-1
+#| fig-width: 12
+#| fig-height: 7
+#| fig-align: "center"
+pl1 + inset_element(pl2,
+ l = .6, b = .1, r = 1, t = .6)
+```
+
+## Add Inset Plots
+
+```{r}
+#| label: patchwork-composition-inset-2
+#| fig-width: 12
+#| fig-height: 7
+#| fig-align: "center"
+pl1 + inset_element(pl2,
+ l = .6, b = 0, r = 1, t = .5, align_to = 'full')
+```
+
+## Add Inset Plots
+
+```{r}
+#| label: patchwork-composition-inset-3
+#| fig-width: 15
+#| fig-height: 12
+#| fig-align: "center"
+(pl1 + inset_element(pl2,
+ l = .6, b = .1, r = 1, t = .6) + pt) / pl3
+```
+
+
+## 练习
+
+```{r}
+library(palmerpenguins)
+library(ggthemes)
+penguins
+```
+
+## 效果
+
+```{r}
+#| echo: false
+#| warning: false
+#| fig-width: 8
+#| fig-height: 5
+penguins |>
+ ggplot(aes(x = flipper_length_mm, y = body_mass_g)) +
+geom_point(aes(color = species, shape = species)) +
+geom_smooth(method = "lm") +
+labs(
+ title = "Body mass and flipper length",
+ subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
+ x = "Flipper length (mm)",
+ y = "Body mass (g)",
+ color = "Species",
+ shape = "Species"
+) +
+scale_color_colorblind()
+```
+
+
+```{r}
+#| include: false
+#| fig-alt: |
+#| A blank, gray plot area.
+
+ggplot(data = penguins)
+```
+
+
+```{r}
+#| include: false
+ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g)
+)
+```
+
+
+```{r}
+#| include: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. The plot
+#| displays a positive, linear, and relatively strong relationship between
+#| these two variables.
+
+ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+ geom_point()
+```
+
+
+```{r}
+#| include: false
+#| warning: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. The plot
+#| displays a positive, fairly linear, and relatively strong relationship
+#| between these two variables. Species (Adelie, Chinstrap, and Gentoo)
+#| are represented with different colors.
+
+ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
+) +
+ geom_point()
+```
+
+
+```{r}
+#| include: false
+#| warning: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. Overlaid
+#| on the scatterplot are three smooth curves displaying the
+#| relationship between these variables for each species (Adelie,
+#| Chinstrap, and Gentoo). Different penguin species are plotted in
+#| different colors for the points and the smooth curves.
+
+ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
+) +
+ geom_point() +
+ geom_smooth(method = "lm")
+```
+
+
+```{r}
+#| include: false
+#| warning: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. Overlaid
+#| on the scatterplot is a single line of best fit displaying the
+#| relationship between these variables for each species (Adelie,
+#| Chinstrap, and Gentoo). Different penguin species are plotted in
+#| different colors for the points only.
+
+ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+ geom_point(mapping = aes(color = species)) +
+ geom_smooth(method = "lm")
+```
+
+
+```{r}
+#| include: false
+#| warning: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. Overlaid
+#| on the scatterplot is a single line of best fit displaying the
+#| relationship between these variables for each species (Adelie,
+#| Chinstrap, and Gentoo). Different penguin species are plotted in
+#| different colors and shapes for the points only.
+
+ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+ geom_point(mapping = aes(color = species, shape = species)) +
+ geom_smooth(method = "lm")
+```
+
+## 练习
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+p <- ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+ geom_point(aes(color = species, shape = species)) +
+ geom_smooth(method = "lm") +
+ labs(
+ title = "Body mass and flipper length",
+ subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
+ x = "Flipper length (mm)", y = "Body mass (g)",
+ color = "Species", shape = "Species"
+ ) +
+ scale_color_colorblind()
+```
+
+
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 练习
+
+:::: {.panel-tabset}
+
+### Code
+
+
+```{r}
+#| warning: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins, colored
+#| by bill depth. A smooth curve of the relationship between body mass
+#| and flipper length is overlaid. The relationship is positive,
+#| fairly linear, and moderately strong.
+p <- ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g)) +
+ geom_point(aes(color = bill_depth_mm)) +
+ geom_smooth()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 练习
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+p <- ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
+) +
+ geom_point() +
+ geom_smooth(se = FALSE)
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 分层展示
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+p <- ggplot(
+ data = penguins,
+ mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+ geom_point() +
+ geom_smooth()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 柱状图
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| fig-alt: |
+#| A bar chart of frequencies of species of penguins: Adelie
+#| (approximately 150), Chinstrap (approximately 90), Gentoo
+#| (approximately 125).
+
+p <- ggplot(penguins, aes(x = species)) +
+ geom_bar()
+```
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 柱状图
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| fig-alt: |
+#| A bar chart of frequencies of species of penguins, where the bars are
+#| ordered in decreasing order of their heights (frequencies): Adelie
+#| (approximately 150), Gentoo (approximately 125), Chinstrap
+#| (approximately 90).
+
+p <- ggplot(penguins, aes(x = fct_infreq(species))) +
+ geom_bar()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+
+## 直方图
+
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+#| fig-alt: |
+#| A histogram of body masses of penguins. The distribution is unimodal
+#| and right skewed, ranging between approximately 2500 to 6500 grams.
+
+p <- ggplot(penguins, aes(x = body_mass_g)) +
+ geom_histogram(binwidth = 200)
+```
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 直方图
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+#| layout-ncol: 2
+#| fig-width: 3
+#| fig-alt: |
+#| Two histograms of body masses of penguins, one with binwidth of 20
+#| (left) and one with binwidth of 2000 (right). The histogram with binwidth
+#| of 20 shows lots of ups and downs in the heights of the bins, creating a
+#| jagged outline. The histogram with binwidth of 2000 shows only three bins.
+
+p1 <- ggplot(penguins, aes(x = body_mass_g)) +
+ geom_histogram(binwidth = 20)
+p2 <- ggplot(penguins, aes(x = body_mass_g)) +
+ geom_histogram(binwidth = 2000)
+p <- p1 + p2
+```
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 10
+#| fig-height: 5
+p
+```
+
+::::
+
+## 密度图
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| fig-alt: |
+#| A density plot of body masses of penguins. The distribution is unimodal
+#| and right skewed, ranging between approximately 2500 to 6500 grams.
+
+p <- ggplot(penguins, aes(x = body_mass_g)) +
+ geom_density()
+```
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+
+## 箱图
+
+
+:::: {.panel-tabset}
+
+### Code
+
+
+```{r}
+#| warning: false
+#| fig-alt: |
+#| Side-by-side box plots of distributions of body masses of Adelie,
+#| Chinstrap, and Gentoo penguins. The distribution of Adelie and
+#| Chinstrap penguins' body masses appear to be symmetric with
+#| medians around 3750 grams. The median body mass of Gentoo penguins
+#| is much higher, around 5000 grams, and the distribution of the
+#| body masses of these penguins appears to be somewhat right skewed.
+
+p <- ggplot(penguins,
+ aes(x = species, y = body_mass_g)) +
+ geom_boxplot()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 分组
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+#| fig-alt: |
+#| A density plot of body masses of penguins by species of penguins. Each
+#| species (Adelie, Chinstrap, and Gentoo) is represented with different
+#| colored outlines for the density curves.
+
+p <- ggplot(penguins,
+ aes(x = body_mass_g, color = species)) +
+ geom_density(linewidth = 0.75)
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 分组
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+#| fig-alt: |
+#| A density plot of body masses of penguins by species of penguins. Each
+#| species (Adelie, Chinstrap, and Gentoo) is represented in different
+#| colored outlines for the density curves. The density curves are also
+#| filled with the same colors, with some transparency added.
+
+p <- ggplot(penguins,
+ aes(x = body_mass_g, color = species, fill = species)) +
+ geom_density(alpha = 0.5)
+```
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+
+## 分组
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| fig-alt: |
+#| Bar plots of penguin species by island (Biscoe, Dream, and Torgersen)
+p <- ggplot(penguins,
+ aes(x = island, fill = species)) +
+ geom_bar()
+```
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+
+## 分组
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| fig-alt: |
+#| Bar plots of penguin species by island (Biscoe, Dream, and Torgersen)
+#| the bars are scaled to the same height, making it a relative frequencies
+#| plot
+
+p <- ggplot(penguins,
+ aes(x = island, fill = species)) +
+ geom_bar(position = "fill")
+```
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 分组
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. The plot
+#| displays a positive, linear, relatively strong relationship between
+#| these two variables.
+
+p <- ggplot(penguins,
+ aes(x = flipper_length_mm, y = body_mass_g)) +
+ geom_point()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 分组
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. The plot
+#| displays a positive, linear, relatively strong relationship between
+#| these two variables. The points are colored based on the species of the
+#| penguins and the shapes of the points represent islands (round points are
+#| Biscoe island, triangles are Dream island, and squared are Torgersen
+#| island). The plot is very busy and it's difficult to distinguish the shapes
+#| of the points.
+
+p <- ggplot(penguins,
+ aes(x = flipper_length_mm, y = body_mass_g)) +
+ geom_point(aes(color = species, shape = island))
+```
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+
+## 分面
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+#| fig-width: 8
+#| fig-asp: 0.33
+#| fig-alt: |
+#| A scatterplot of body mass vs. flipper length of penguins. The shapes and
+#| colors of points represent species. Penguins from each island are on a
+#| separate facet. Within each facet, the relationship between body mass and
+#| flipper length is positive, linear, relatively strong.
+
+p <- ggplot(penguins,
+ aes(x = flipper_length_mm, y = body_mass_g)) +
+ geom_point(aes(color = species, shape = species)) +
+ facet_wrap(~island)
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 分面
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| warning: false
+
+p <- ggplot(
+ data = penguins,
+ mapping = aes(
+ x = bill_length_mm, y = bill_depth_mm,
+ color = species, shape = species
+ )
+) +
+ geom_point() +
+ labs(color = "Species")
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 练习
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+#| layout-ncol: 2
+
+p1 <- ggplot(penguins, aes(x = island, fill = species)) +
+ geom_bar(position = "fill")
+p2 <- ggplot(penguins, aes(x = species, fill = island)) +
+ geom_bar(position = "fill")
+p <- p1 + p2
+```
+
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 10
+#| fig-height: 5
+p
+```
+
+::::
+
+## 练习
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+p <- ggplot(penguins,
+ aes(x = flipper_length_mm, y = body_mass_g)) +
+ geom_point()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 练习
+
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+p <- ggplot(mpg, aes(x = class)) +
+geom_bar()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 练习
+
+:::: {.panel-tabset}
+
+### Code
+
+
+```{r}
+p <- ggplot(mpg, aes(x = cty, y = hwy)) +
+geom_point()
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 练习
+
+:::: {.panel-tabset}
+
+### Code
+
+```{r}
+p <- ggplot(data = mpg) +
+geom_point(mapping = aes(x = displ, y = hwy))
+```
+
+### Graphic
+
+```{r}
+#| echo: false
+#| fig-width: 8
+#| fig-height: 5
+p
+```
+
+::::
+
+## 欢迎讨论！{.center}
+
+
+`r rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "https://drc.drwater.net/course/public/RWEP/PUB/SD/")`
+
diff --git a/SD/4.1_datavisualize/mpg-plot.png b/SD/4.1_datavisualize/mpg-plot.png
new file mode 100644
index 0000000..de3439b
Binary files /dev/null and b/SD/4.1_datavisualize/mpg-plot.png differ
diff --git a/SD/4.2_实践部分/_extensions b/SD/4.2_实践部分/_extensions
new file mode 120000
index 0000000..74119e3
--- /dev/null
+++ b/SD/4.2_实践部分/_extensions
@@ -0,0 +1 @@
+../../_extensions
\ No newline at end of file
diff --git a/SD/4.2_实践部分/index.qmd b/SD/4.2_实践部分/index.qmd
new file mode 100644
index 0000000..f07e128
--- /dev/null
+++ b/SD/4.2_实践部分/index.qmd
@@ -0,0 +1,103 @@
+---
+title: "数据前处理+ggplot2画图实践"
+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)
+```
+
+
+## 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 rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "https://drc.drwater.net/course/public/RWEP/PUB/SD/")`
diff --git a/SD/5.1_model/_extensions b/SD/5.1_model/_extensions
new file mode 120000
index 0000000..74119e3
--- /dev/null
+++ b/SD/5.1_model/_extensions
@@ -0,0 +1 @@
+../../_extensions
\ No newline at end of file
diff --git a/SD/5.1_model/images/10-Fold-CV.svg b/SD/5.1_model/images/10-Fold-CV.svg
new file mode 100644
index 0000000..5be8f3f
--- /dev/null
+++ b/SD/5.1_model/images/10-Fold-CV.svg
@@ -0,0 +1,158 @@
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+ <path d="M 1810 2780.1253 L 1810 2732.1253 C 1810 2728.8133 1842.816 2726.1253 1883.25 2726.1253 C 1923.684 2726.1253 1956.5 2728.8133 1956.5 2732.1253 L 1956.5 2780.1253 C 1956.5 2783.4373 1923.684 2786.1253 1883.25 2786.1253 C 1842.816 2786.1253 1810 2783.4373 1810 2780.1253" fill="#fffbf1"/>
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+ <text transform="translate(1815 2749.9013)" fill="black">
+ <tspan font-family="Helvetica Neue" font-size="16" fill="black" x="40.394" y="15">Training</tspan>
+ </text>
+ </g>
+ <g id="Line_674">
+ <line x1="1966.0633" y1="2637.0015" x2="1909.5877" y2="2718.2395" marker-end="url(#FilledArrow_Marker)" stroke="black" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ </g>
+ <g id="Graphic_673">
+ <path d="M 2022.5 2774.5253 L 2022.5 2737.7253 C 2022.5 2735.186 2044.34 2733.1253 2071.25 2733.1253 C 2098.16 2733.1253 2120 2735.186 2120 2737.7253 L 2120 2774.5253 C 2120 2777.0645 2098.16 2779.1253 2071.25 2779.1253 C 2044.34 2779.1253 2022.5 2777.0645 2022.5 2774.5253" fill="#7a81ff" fill-opacity=".08"/>
+ <path d="M 2022.5 2774.5253 L 2022.5 2737.7253 C 2022.5 2735.186 2044.34 2733.1253 2071.25 2733.1253 C 2098.16 2733.1253 2120 2735.186 2120 2737.7253 L 2120 2774.5253 C 2120 2777.0645 2098.16 2779.1253 2071.25 2779.1253 C 2044.34 2779.1253 2022.5 2777.0645 2022.5 2774.5253 M 2022.5 2737.7253 C 2022.5 2740.2645 2044.34 2742.3253 2071.25 2742.3253 C 2098.16 2742.3253 2120 2740.2645 2120 2737.7253" stroke="black" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ <text transform="translate(2027.5 2751.0773)" fill="black">
+ <tspan font-family="Helvetica Neue" font-size="16" fill="black" x="9.078" y="15">Validation</tspan>
+ </text>
+ </g>
+ <g id="Line_672">
+ <line x1="2003.6854" y1="2637.083" x2="2053.4026" y2="2724.68" marker-end="url(#FilledArrow_Marker_2)" stroke="black" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ </g>
+ </g>
+ </g>
+ </g>
+</svg>
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+---
+title: "模型构建"
+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
+knitr:
+ opts_chunk:
+ dev: "svg"
+ retina: 3
+execute:
+ freeze: auto
+ cache: true
+ echo: true
+ fig-width: 5
+ fig-height: 6
+---
+
+# tidymodels主要步骤
+
+```{r}
+#| echo: false
+hexes <- function(..., size = 64) {
+ x <- c(...)
+ x <- sort(unique(x), decreasing = TRUE)
+ right <- (seq_along(x) - 1) * size
+ res <- glue::glue(
+ '{.absolute top=-20 right=<right> width="<size>" height="<size * 1.16>"}',
+ .open = "<",
+ .close = ">"
+ )
+ paste0(res, collapse = " ")
+}
+
+knitr::opts_chunk$set(
+ digits = 3,
+ comment = "#>",
+ dev = 'svglite'
+)
+
+# devtools::install_github("gadenbuie/countdown")
+# library(countdown)
+library(ggplot2)
+theme_set(theme_bw())
+options(cli.width = 70, ggplot2.discrete.fill = c("#7e96d5", "#de6c4e"))
+
+train_color <- "#1a162d"
+test_color <- "#cd4173"
+data_color <- "#767381"
+assess_color <- "#84cae1"
+splits_pal <- c(data_color, train_color, test_color)
+```
+
+
+
+
+
+## 何为tidymodels? {background-image="images/tm-org.png" background-size="80%"}
+
+```{r load-tm}
+#| message: true
+#| echo: true
+#| warning: true
+library(tidymodels)
+```
+
+## 整体思路
+
+```{r diagram-split, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-split.jpg")
+```
+
+## 整体思路
+
+```{r diagram-model-1, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-model-1.jpg")
+```
+
+:::notes
+Stress that we are **not** fitting a model on the entire training set other than for illustrative purposes in deck 2.
+:::
+
+## 整体思路
+
+```{r diagram-model-n, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-model-n.jpg")
+```
+
+## 整体思路
+
+```{r, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-resamples.jpg")
+```
+
+## 整体思路
+
+```{r, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-select.jpg")
+```
+
+## 整体思路
+
+```{r diagram-final-fit, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-final-fit.jpg")
+```
+
+## 整体思路
+
+```{r diagram-final-performance, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-final-performance.jpg")
+```
+
+## 相关包的安装
+
+```{r load-pkgs}
+#| eval: false
+
+# Install the packages for the workshop
+pkgs <-
+ c(
+ "bonsai",
+ "doParallel",
+ "embed",
+ "finetune",
+ "lightgbm",
+ "lme4",
+ "plumber",
+ "probably",
+ "ranger",
+ "rpart",
+ "rpart.plot",
+ "rules",
+ "splines2",
+ "stacks",
+ "text2vec",
+ "textrecipes",
+ "tidymodels",
+ "vetiver",
+ "remotes"
+ )
+
+install.packages(pkgs)
+```
+
+. . .
+
+<br></br>
+
+
+
+
+## Data on Chicago taxi trips
+
+```{r taxi-print}
+library(tidymodels)
+taxi
+```
+
+## 数据分割与使用
+
+对于机器学习，我们通常将数据分成训练集和测试集：
+
+. . .
+
+- 训练集用于估计模型参数。
+- 测试集用于独立评估模型性能。
+
+. . .
+
+在训练过程中不要使用测试集。
+
+
+. . .
+
+```{r test-train-split}
+#| echo: false
+#| fig.width: 12
+#| fig.height: 3
+#|
+set.seed(123)
+library(forcats)
+require(tidymodels)
+require(tidyverse)
+one_split <- taxi |>
+ dplyr::slice(1:30) |>
+ rsample::initial_split() |>
+ generics::tidy() |>
+ tibble::add_row(Row = 1:30, Data = "Original") |>
+ dplyr::mutate(
+ Data = case_when(
+ Data == "Analysis" ~ "Training",
+ Data == "Assessment" ~ "Testing",
+ TRUE ~ Data
+ )
+ ) |>
+ dplyr::mutate(
+ Data = factor(Data, levels = c("Original", "Training", "Testing"))
+ )
+all_split <-
+ ggplot(one_split, aes(x = Row, y = fct_rev(Data), fill = Data)) +
+ geom_tile(color = "white", linewidth = 1) +
+ scale_fill_manual(values = splits_pal, guide = "none") +
+ theme_minimal() +
+ theme(
+ axis.text.y = element_text(size = rel(2)),
+ axis.text.x = element_blank(),
+ legend.position = "top",
+ panel.grid = element_blank()
+ ) +
+ coord_equal(ratio = 1) +
+ labs(x = NULL, y = NULL)
+all_split
+```
+
+## The initial split
+
+```{r taxi-split}
+set.seed(123)
+taxi_split <- initial_split(taxi)
+taxi_split
+```
+
+## Accessing the data
+
+```{r taxi-train-test}
+taxi_train <- training(taxi_split)
+taxi_test <- testing(taxi_split)
+```
+
+## The training set
+
+```{r taxi-train}
+taxi_train
+```
+
+## 练习
+
+```{r taxi-split-prop}
+set.seed(123)
+taxi_split <- initial_split(taxi, prop = 0.8)
+taxi_train <- training(taxi_split)
+taxi_test <- testing(taxi_split)
+
+nrow(taxi_train)
+nrow(taxi_test)
+```
+
+## Stratification
+
+Use `strata = tip`
+
+```{r taxi-split-prop-strata}
+set.seed(123)
+taxi_split <- initial_split(taxi, prop = 0.8, strata = tip)
+taxi_split
+```
+
+## Stratification
+
+Stratification often helps, with very little downside
+
+```{r taxi-tip-pct-by-split, echo = FALSE}
+bind_rows(
+ taxi_train %>% mutate(split = "train"),
+ taxi_test %>% mutate(split = "test")
+) %>%
+ ggplot(aes(x = split, fill = tip)) +
+ geom_bar(position = "fill")
+```
+
+## 模型类型
+
+
+模型多种多样
+
+- `lm` for linear model
+
+- `glm` for generalized linear model (e.g. logistic regression)
+
+- `glmnet` for regularized regression
+
+- `keras` for regression using TensorFlow
+
+- `stan` for Bayesian regression
+
+- `spark` for large data sets
+
+
+## 指定模型
+
+```{r}
+#| echo: false
+library(tidymodels)
+
+set.seed(123)
+
+taxi_split <- initial_split(taxi, prop = 0.8, strata = tip)
+taxi_train <- training(taxi_split)
+taxi_test <- testing(taxi_split)
+```
+
+```{r logistic-reg}
+logistic_reg()
+```
+
+
+:::notes
+Models have default engines
+:::
+
+
+
+## To specify a model
+
+```{r logistic-reg-glmnet}
+logistic_reg() %>%
+ set_engine("glmnet")
+```
+
+. . .
+
+
+```{r logistic-reg-stan}
+logistic_reg() %>%
+ set_engine("stan")
+```
+
+
+::: columns
+::: {.column width="40%"}
+- Choose a model
+- Specify an engine
+- Set the [mode]{.underline}
+:::
+
+::: {.column width="60%"}
+
+:::
+:::
+
+
+## To specify a model
+
+```{r decision-tree}
+decision_tree()
+```
+
+:::notes
+Some models have a default mode
+:::
+
+## To specify a model
+
+```{r decision-tree-classification}
+decision_tree() %>%
+ set_mode("classification")
+```
+
+. . .
+
+<br></br>
+
+::: r-fit-text
+All available models are listed at <https://www.tidymodels.org/find/parsnip/>
+:::
+
+
+## Workflows
+
+```{r good-workflow}
+#| echo: false
+#| out-width: '70%'
+#| fig-align: 'center'
+knitr::include_graphics("images/good_workflow.png")
+```
+
+
+## 为什么要使用 `workflow()`?
+
+
+- 与基本的 R 工具相比，工作流能更好地处理新的因子水平
+
+. . .
+
+- 除了公式之外，还可以使用其他的预处理器（更多关于高级 tidymodels 中的特征工程！）
+
+. . .
+
+- 在使用多个模型时，它们可以帮助组织工作
+
+. . .
+
+- [最重要的是]{.underline}，工作流涵盖了整个建模过程：`fit()` 和 `predict()` 不仅适用于实际的模型拟合，还适用于预处理步骤
+
+::: notes
+工作流比基本的 R 处理水平更好的两种方式：
+
+- 强制要求在预测时不允许出现新的水平（这是一个可选的检查，可以关闭）
+
+- 恢复在拟合时存在但在预测时缺失的水平（例如，“新”数据中没有该水平的实例）
+:::
+
+
+## A model workflow
+
+```{r tree-spec}
+tree_spec <-
+ decision_tree(cost_complexity = 0.002) %>%
+ set_mode("classification")
+
+tree_spec %>%
+ fit(tip ~ ., data = taxi_train)
+```
+
+## A model workflow
+
+```{r tree-wflow}
+tree_spec <-
+ decision_tree(cost_complexity = 0.002) %>%
+ set_mode("classification")
+
+workflow() %>%
+ add_formula(tip ~ .) %>%
+ add_model(tree_spec) %>%
+ fit(data = taxi_train)
+```
+
+## A model workflow
+
+```{r tree-wflow-fit}
+tree_spec <-
+ decision_tree(cost_complexity = 0.002) %>%
+ set_mode("classification")
+
+workflow(tip ~ ., tree_spec) %>%
+ fit(data = taxi_train)
+```
+
+## 预测
+
+How do you use your new `tree_fit` model?
+
+```{r tree-wflow-fit-2}
+tree_spec <-
+ decision_tree(cost_complexity = 0.002) %>%
+ set_mode("classification")
+
+tree_fit <-
+ workflow(tip ~ ., tree_spec) %>%
+ fit(data = taxi_train)
+```
+
+## 练习
+
+*Run:*
+
+`predict(tree_fit, new_data = taxi_test)`
+
+
+
+. . .
+
+
+*Run:*
+
+`augment(tree_fit, new_data = taxi_test)`
+
+*What do you get?*
+
+
+## tidymodels 的预测
+
+- 预测结果始终在一个 **tibble** 内
+- 列名和类型可读性强
+- `new_data` 中的行数和输出中的行数**相同**
+
+## 理解模型
+
+如何 **理解**`tree_fit` 模型？
+
+```{r plot-tree-fit-4}
+#| echo: false
+#| fig-align: center
+#| fig-width: 8
+#| fig-height: 5
+#| out-width: 100%
+library(rpart.plot)
+tree_fit %>%
+ extract_fit_engine() %>%
+ rpart.plot(roundint = FALSE)
+```
+
+## Evaluating models: 预测值
+
+```{r}
+#| echo: false
+library(tidymodels)
+
+set.seed(123)
+taxi_split <- initial_split(taxi, prop = 0.8, strata = tip)
+taxi_train <- training(taxi_split)
+taxi_test <- testing(taxi_split)
+
+tree_spec <- decision_tree(cost_complexity = 0.0001, mode = "classification")
+taxi_wflow <- workflow(tip ~ ., tree_spec)
+taxi_fit <- fit(taxi_wflow, taxi_train)
+```
+
+```{r taxi-fit-augment}
+augment(taxi_fit, new_data = taxi_train) %>%
+ relocate(tip, .pred_class, .pred_yes, .pred_no)
+```
+
+## Confusion matrix
+
+
+
+## Confusion matrix
+
+```{r conf-mat}
+augment(taxi_fit, new_data = taxi_train) %>%
+ conf_mat(truth = tip, estimate = .pred_class)
+```
+
+## Confusion matrix
+
+```{r conf-mat-plot}
+augment(taxi_fit, new_data = taxi_train) %>%
+ conf_mat(truth = tip, estimate = .pred_class) %>%
+ autoplot(type = "heatmap")
+```
+
+## Metrics for model performance
+
+::: columns
+::: {.column width="60%"}
+```{r acc}
+augment(taxi_fit, new_data = taxi_train) %>%
+ accuracy(truth = tip, estimate = .pred_class)
+```
+:::
+
+::: {.column width="40%"}
+
+:::
+:::
+
+## 二分类模型评估
+
+模型的敏感性（Sensitivity）和特异性（Specificity）是评估二分类模型性能的重要指标：
+
+- **敏感性**（Sensitivity），也称为真阳性率，衡量了模型正确识别正类别样本的能力。公式为真阳性数除以真阳性数加上假阴性数：
+
+$$
+\text{Sensitivity} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}}
+$$
+
+
+- **特异性**（Specificity），也称为真阴性率，衡量了模型正确识别负类别样本的能力。公式为真阴性数除以真阴性数加上假阳性数：
+
+$$
+\text{Specificity} = \frac{\text{True Negatives}}{\text{True Negatives} + \text{False Positives}}
+$$
+
+在评估模型时，我们希望敏感性和特异性都很高。高敏感性表示模型能够捕获真正的正类别样本，高特异性表示模型能够准确排除负类别样本。
+
+
+## Metrics for model performance
+
+::: columns
+::: {.column width="60%"}
+```{r sens}
+augment(taxi_fit, new_data = taxi_train) %>%
+ sensitivity(truth = tip, estimate = .pred_class)
+```
+:::
+
+::: {.column width="40%"}
+
+:::
+:::
+
+
+## Metrics for model performance
+
+::: columns
+::: {.column width="60%"}
+```{r sens-2}
+#| code-line-numbers: "3-6"
+augment(taxi_fit, new_data = taxi_train) %>%
+ sensitivity(truth = tip, estimate = .pred_class)
+```
+
+<br>
+
+```{r spec}
+augment(taxi_fit, new_data = taxi_train) %>%
+ specificity(truth = tip, estimate = .pred_class)
+```
+:::
+
+::: {.column width="40%"}
+
+:::
+:::
+
+## Metrics for model performance
+
+We can use `metric_set()` to combine multiple calculations into one
+
+```{r taxi-metrics}
+taxi_metrics <- metric_set(accuracy, specificity, sensitivity)
+
+augment(taxi_fit, new_data = taxi_train) %>%
+ taxi_metrics(truth = tip, estimate = .pred_class)
+```
+
+## Metrics for model performance
+
+```{r taxi-metrics-grouped}
+taxi_metrics <- metric_set(accuracy, specificity, sensitivity)
+
+augment(taxi_fit, new_data = taxi_train) %>%
+ group_by(local) %>%
+ taxi_metrics(truth = tip, estimate = .pred_class)
+```
+
+
+## Varying the threshold
+
+```{r}
+#| label: thresholds
+#| echo: false
+
+augment(taxi_fit, new_data = taxi_train) %>%
+ roc_curve(truth = tip, .pred_yes) %>%
+ filter(is.finite(.threshold)) %>%
+ pivot_longer(
+ c(specificity, sensitivity),
+ names_to = "statistic",
+ values_to = "value"
+ ) %>%
+ rename(`event threshold` = .threshold) %>%
+ ggplot(aes(
+ x = `event threshold`,
+ y = value,
+ col = statistic,
+ group = statistic
+ )) +
+ geom_line() +
+ scale_color_brewer(palette = "Dark2") +
+ labs(y = NULL) +
+ coord_equal() +
+ theme(legend.position = "top")
+```
+
+## ROC 曲线
+
+- ROC（Receiver Operating Characteristic）曲线用于评估二分类模型的性能，特别是在不同的阈值下比较模型的敏感性和特异性。
+- ROC曲线的横轴是假阳性率（False Positive Rate，FPR），纵轴是真阳性率（True Positive Rate，TPR）。在ROC曲线上，每个点对应于一个特定的阈值。通过改变阈值，我们可以观察到模型在不同条件下的表现。
+- ROC曲线越接近左上角（0,1）点，说明模型的性能越好，因为这表示在较低的假阳性率下，模型能够获得较高的真阳性率。ROC曲线下面积（Area Under the ROC Curve，AUC）也是评估模型性能的一种指标，AUC值越大表示模型性能越好。
+
+
+
+## ROC curve plot
+
+```{r roc-curve}
+#| fig-width: 6
+#| fig-height: 6
+#| output-location: "column"
+
+augment(taxi_fit, new_data = taxi_train) %>%
+ roc_curve(truth = tip, .pred_yes) %>%
+ autoplot()
+```
+
+
+## 过度拟合
+
+
+
+## 过度拟合
+
+
+
+
+## Cross-validation {background-color="white" background-image="https://www.tmwr.org/premade/resampling.svg" background-size="80%"}
+
+## Cross-validation
+
+
+
+## Cross-validation
+
+
+
+## Cross-validation
+
+```{r vfold-cv}
+vfold_cv(taxi_train) # v = 10 is default
+```
+
+## Cross-validation
+
+What is in this?
+
+```{r taxi-splits}
+taxi_folds <- vfold_cv(taxi_train)
+taxi_folds$splits[1:3]
+```
+
+::: notes
+Talk about a list column, storing non-atomic types in dataframe
+:::
+
+## Cross-validation
+
+```{r vfold-cv-v}
+vfold_cv(taxi_train, v = 5)
+```
+
+## Cross-validation
+
+```{r vfold-cv-strata}
+vfold_cv(taxi_train, strata = tip)
+```
+
+. . .
+
+Stratification often helps, with very little downside
+
+## Cross-validation
+
+We'll use this setup:
+
+```{r taxi-folds}
+set.seed(123)
+taxi_folds <- vfold_cv(taxi_train, v = 10, strata = tip)
+taxi_folds
+```
+
+. . .
+
+Set the seed when creating resamples
+
+
+## Fit our model to the resamples
+
+```{r fit-resamples}
+taxi_res <- fit_resamples(taxi_wflow, taxi_folds)
+taxi_res
+```
+
+## Evaluating model performance
+
+```{r collect-metrics}
+taxi_res %>%
+ collect_metrics()
+```
+
+::: notes
+collect_metrics() 是一套 collect_*() 函数之一，可用于处理调参结果的列。调参结果中以 . 为前缀的大多数列都有对应的 collect_*() 函数，可以进行常见摘要选项的汇总。
+:::
+
+. . .
+
+We can reliably measure performance using only the **training** data 🎉
+
+## Comparing metrics
+
+How do the metrics from resampling compare to the metrics from training and testing?
+
+```{r calc-roc-auc}
+#| echo: false
+taxi_training_roc_auc <-
+ taxi_fit %>%
+ augment(taxi_train) %>%
+ roc_auc(tip, .pred_yes) %>%
+ pull(.estimate) %>%
+ round(digits = 2)
+
+taxi_testing_roc_auc <-
+ taxi_fit %>%
+ augment(taxi_test) %>%
+ roc_auc(tip, .pred_yes) %>%
+ pull(.estimate) %>%
+ round(digits = 2)
+```
+
+::: columns
+::: {.column width="50%"}
+```{r collect-metrics-2}
+taxi_res %>%
+ collect_metrics() %>%
+ select(.metric, mean, n)
+```
+:::
+
+::: {.column width="50%"}
+The ROC AUC previously was
+
+- `r taxi_training_roc_auc` for the training set
+- `r taxi_testing_roc_auc` for test set
+:::
+:::
+
+. . .
+
+Remember that:
+
+⚠️ the training set gives you overly optimistic metrics
+
+⚠️ the test set is precious
+
+## Evaluating model performance
+
+```{r save-predictions}
+# 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
+```
+
+## Evaluating model performance
+
+```{r collect-predictions}
+# Save the assessment set results
+taxi_preds <- collect_predictions(taxi_res)
+taxi_preds
+```
+
+## Evaluating model performance
+
+```{r taxi-metrics-by-id}
+taxi_preds %>%
+ group_by(id) %>%
+ taxi_metrics(truth = tip, estimate = .pred_class)
+```
+
+## Where are the fitted models?
+
+```{r taxi-res}
+taxi_res
+```
+
+
+## Bootstrapping
+
+
+
+## Bootstrapping
+
+```{r bootstraps}
+set.seed(3214)
+bootstraps(taxi_train)
+```
+
+
+## Monte Carlo Cross-Validation
+
+```{r mc-cv}
+set.seed(322)
+mc_cv(taxi_train, times = 10)
+```
+
+## Validation set
+
+```{r validation-split}
+set.seed(853)
+taxi_val_split <- initial_validation_split(taxi, strata = tip)
+validation_set(taxi_val_split)
+```
+
+
+## Create a random forest model
+
+```{r rf-spec}
+rf_spec <- rand_forest(trees = 1000, mode = "classification")
+rf_spec
+```
+
+## Create a random forest model
+
+```{r rf-wflow}
+rf_wflow <- workflow(tip ~ ., rf_spec)
+rf_wflow
+```
+
+## Evaluating model performance
+
+```{r collect-metrics-rf}
+ctrl_taxi <- control_resamples(save_pred = TRUE)
+
+# Random forest uses random numbers so set the seed first
+
+set.seed(2)
+rf_res <- fit_resamples(rf_wflow, taxi_folds, control = ctrl_taxi)
+collect_metrics(rf_res)
+```
+
+## The whole game - status update
+
+```{r diagram-select, echo = FALSE}
+#| fig-align: "center"
+
+knitr::include_graphics("images/whole-game-transparent-select.jpg")
+```
+
+## The final fit
+
+```{r final-fit}
+# taxi_split has train + test info
+final_fit <- last_fit(rf_wflow, taxi_split)
+
+final_fit
+```
+
+## 何为`final_fit`?
+
+```{r collect-metrics-final-fit}
+collect_metrics(final_fit)
+```
+
+. . .
+
+These are metrics computed with the **test** set
+
+## 何为`final_fit`?
+
+```{r collect-predictions-final-fit}
+collect_predictions(final_fit)
+```
+
+## 何为`final_fit`?
+
+```{r extract-workflow}
+extract_workflow(final_fit)
+```
+
+. . .
+
+Use this for **prediction** on new data, like for deploying
+
+
+
+
+## Tuning models - Specifying tuning parameters
+
+
+```{r}
+#| label: tag-for-tuning
+#| code-line-numbers: "1|"
+
+rf_spec <- rand_forest(min_n = tune()) %>%
+ set_mode("classification")
+
+rf_wflow <- workflow(tip ~ ., rf_spec)
+rf_wflow
+```
+
+## Try out multiple values
+
+`tune_grid()` works similar to `fit_resamples()` but covers multiple parameter values:
+
+```{r}
+#| label: rf-tune_grid
+#| code-line-numbers: "2|3-4|5|"
+
+set.seed(22)
+rf_res <- tune_grid(
+ rf_wflow,
+ taxi_folds,
+ grid = 5
+)
+```
+
+## Compare results
+
+Inspecting results and selecting the best-performing hyperparameter(s):
+
+```{r}
+#| label: rf-results
+
+show_best(rf_res)
+
+best_parameter <- select_best(rf_res)
+best_parameter
+```
+
+`collect_metrics()` and `autoplot()` are also available.
+
+## The final fit
+
+```{r}
+#| label: rf-finalize
+
+rf_wflow <- finalize_workflow(rf_wflow, best_parameter)
+
+final_fit <- last_fit(rf_wflow, taxi_split)
+
+collect_metrics(final_fit)
+```
+
+# 实践部分
+
+
+## 数据
+
+```{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))
+
+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) |>
+ left_join(sitedf, by = "SITE_ID") |>
+ filter(!purrr::map_lgl(phytodf, is.null)) |>
+ mutate(
+ cyanophyta = purrr::map(
+ phytodf,
+ ~ .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()
+```
+
+
+## 数据
+
+```{r}
+skimr::skim(nla)
+```
+
+
+
+## 简单模型
+
+```{r}
+nla |>
+ filter(tp > 1) |>
+ ggplot(aes(tn, tp)) +
+ geom_point() +
+ geom_smooth(method = "lm") +
+ scale_x_log10(
+ breaks = scales::trans_breaks("log10", function(x) 10^x),
+ labels = scales::trans_format("log10", scales::math_format(10^.x))
+ ) +
+ scale_y_log10(
+ breaks = scales::trans_breaks("log10", function(x) 10^x),
+ labels = scales::trans_format("log10", scales::math_format(10^.x))
+ )
+
+m1 <- lm(log10(tp) ~ log10(tn), data = nla)
+
+summary(m1)
+```
+
+## 复杂指标
+
+```{r}
+nla |>
+ filter(tp > 1) |>
+ ggplot(aes(tp, cyanophyta)) +
+ geom_point() +
+ geom_smooth(method = "lm") +
+ scale_x_log10(
+ breaks = scales::trans_breaks("log10", function(x) 10^x),
+ labels = scales::trans_format("log10", scales::math_format(10^.x))
+ ) +
+ scale_y_log10(
+ breaks = scales::trans_breaks("log10", function(x) 10^x),
+ labels = scales::trans_format("log10", scales::math_format(10^.x))
+ )
+
+m2 <- lm(log10(cyanophyta) ~ log10(tp), data = nla)
+
+summary(m2)
+```
+
+
+
+
+## tidymodels - Data split
+
+```{r}
+(nla_split <- rsample::initial_split(nla, prop = 0.7, strata = zmax))
+(nla_train <- training(nla_split))
+(nla_test <- testing(nla_split))
+```
+
+## tidymodels - recipe
+
+```{r}
+nla_formula <- as.formula(
+ "cyanophyta ~ temp + do + ph + cond + sd + tp + tn + chla + clear_to_bottom"
+)
+# nla_formula <- as.formula("cyanophyta ~ temp + do + ph + cond + sd + tp + tn")
+nla_recipe <- recipes::recipe(nla_formula, data = nla_train) |>
+ recipes::step_string2factor(all_nominal()) |>
+ recipes::step_nzv(all_nominal()) |>
+ recipes::step_log(chla, cyanophyta, base = 10) |>
+ recipes::step_normalize(all_numeric_predictors()) |>
+ prep()
+nla_recipe
+```
+
+## tidymodels - cross validation
+
+```{r}
+nla_cv <- recipes::bake(
+ nla_recipe,
+ new_data = training(nla_split)
+) |>
+ rsample::vfold_cv(v = 10)
+nla_cv
+```
+
+## tidymodels - Model specification
+
+```{r}
+xgboost_model <- parsnip::boost_tree(
+ mode = "regression",
+ trees = 1000,
+ min_n = tune(),
+ tree_depth = tune(),
+ learn_rate = tune(),
+ loss_reduction = tune()
+) |>
+ set_engine("xgboost", objective = "reg:squarederror")
+xgboost_model
+```
+
+
+## tidymodels - Grid specification
+
+```{r}
+# grid specification
+xgboost_params <- dials::parameters(
+ min_n(),
+ tree_depth(),
+ learn_rate(),
+ loss_reduction()
+)
+xgboost_params
+```
+
+## tidymodels - Grid specification
+
+```{r}
+xgboost_grid <- dials::grid_max_entropy(
+ xgboost_params,
+ size = 60
+)
+knitr::kable(head(xgboost_grid))
+```
+
+## tidymodels - Workflow
+
+```{r}
+xgboost_wf <- workflows::workflow() |>
+ add_model(xgboost_model) |>
+ add_formula(nla_formula)
+xgboost_wf
+```
+
+
+## tidymodels - Tune
+
+```{r}
+#| cache: true
+# hyperparameter tuning
+if (FALSE) {
+ xgboost_tuned <- tune::tune_grid(
+ object = xgboost_wf,
+ resamples = nla_cv,
+ grid = xgboost_grid,
+ metrics = yardstick::metric_set(rmse, rsq, mae),
+ control = tune::control_grid(verbose = TRUE)
+ )
+ saveRDS(xgboost_tuned, "./xgboost_tuned.RDS")
+}
+xgboost_tuned <- readRDS("./xgboost_tuned.RDS")
+```
+
+## tidymodels - Best model
+
+```{r}
+xgboost_tuned |>
+ tune::show_best(metric = "rmse") |>
+ knitr::kable()
+```
+
+
+## tidymodels - Best model
+
+```{r}
+xgboost_tuned |>
+ collect_metrics()
+```
+
+
+## tidymodels - Best model
+
+```{r}
+#| fig-width: 9
+#| fig-height: 5
+#| out-width: "100%"
+xgboost_tuned |>
+ autoplot()
+```
+
+
+## tidymodels - Best model
+
+
+```{r}
+xgboost_best_params <- xgboost_tuned |>
+ tune::select_best(metric = "rmse")
+
+knitr::kable(xgboost_best_params)
+```
+
+
+## tidymodels - Final model
+
+```{r}
+xgboost_model_final <- xgboost_model |>
+ finalize_model(xgboost_best_params)
+xgboost_model_final
+```
+
+
+## tidymodels - Train evaluation
+
+
+```{r}
+(train_processed <- bake(nla_recipe, new_data = nla_train))
+```
+
+## tidymodels - Train data
+
+```{r}
+train_prediction <- xgboost_model_final |>
+ # fit the model on all the training data
+ fit(
+ formula = nla_formula,
+ data = train_processed
+ ) |>
+ # predict the sale prices for the training data
+ predict(new_data = train_processed) |>
+ bind_cols(
+ nla_train |>
+ mutate(.obs = log10(cyanophyta))
+ )
+xgboost_score_train <-
+ train_prediction |>
+ yardstick::metrics(.obs, .pred) |>
+ mutate(.estimate = format(round(.estimate, 2), big.mark = ","))
+knitr::kable(xgboost_score_train)
+```
+
+## tidymodels - train evaluation
+
+```{r}
+#| fig-width: 5
+#| fig-height: 3
+#| out-width: "80%"
+train_prediction |>
+ ggplot(aes(.pred, .obs)) +
+ geom_point() +
+ geom_smooth(method = "lm")
+```
+
+
+## tidymodels - test data
+
+
+```{r}
+test_processed <- bake(nla_recipe, new_data = nla_test)
+
+test_prediction <- xgboost_model_final |>
+ # fit the model on all the training data
+ fit(
+ formula = nla_formula,
+ data = train_processed
+ ) |>
+ # use the training model fit to predict the test data
+ predict(new_data = test_processed) |>
+ bind_cols(
+ nla_test |>
+ mutate(.obs = log10(cyanophyta))
+ )
+
+# measure the accuracy of our model using `yardstick`
+xgboost_score <- test_prediction |>
+ yardstick::metrics(.obs, .pred) |>
+ mutate(.estimate = format(round(.estimate, 2), big.mark = ","))
+
+knitr::kable(xgboost_score)
+```
+
+
+## tidymodels - evaluation
+
+```{r}
+#| fig-width: 5
+#| fig-height: 3
+#| out-width: "80%"
+cyanophyta_prediction_residual <- test_prediction |>
+ arrange(.pred) %>%
+ mutate(residual_pct = (.obs - .pred) / .pred) |>
+ select(.pred, residual_pct)
+
+cyanophyta_prediction_residual |>
+ ggplot(aes(x = .pred, y = residual_pct)) +
+ geom_point() +
+ xlab("Predicted Cyanophyta") +
+ ylab("Residual (%)")
+```
+
+
+
+
+## tidymodels - test evaluation
+
+```{r}
+#| fig-width: 5
+#| fig-height: 3
+#| out-width: "80%"
+test_prediction |>
+ ggplot(aes(.pred, .obs)) +
+ geom_point() +
+ geom_smooth(method = "lm", colour = "black")
+```
+
+
+
+## 欢迎讨论！{.center}
+
+
+`r rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "https://drc.drwater.net/course/public/RWEP/PUB/SD/")`
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new file mode 120000
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+../../_extensions
\ No newline at end of file
diff --git a/SD/5.2_大数据分析工具/index.qmd b/SD/5.2_大数据分析工具/index.qmd
new file mode 100644
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+++ b/SD/5.2_大数据分析工具/index.qmd
@@ -0,0 +1,166 @@
+---
+title: "大数据分析工具"
+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(nycflights13)
+library(tidyverse)
+
+```
+
+## 匹配数字
+
+### 匹配数字：
+
+- \d：匹配任意数字字符。
+- \d+：匹配一个或多个数字字符。
+- [0-9]: 匹配数字
+
+### 匹配字母：
+
+- \w：匹配任意字母、数字或下划线字符。
+- \w+：匹配一个或多个字母、数字或下划线字符。
+
+## 匹配数字
+
+### 匹配空白字符：
+
+- \s：匹配任意空白字符，包括空格、制表符、换行符等。
+- \s+：匹配一个或多个空白字符。
+
+### 匹配特定字符：
+
+- [abc]：匹配字符 a、b 或 c 中的任意一个。
+- [a-z]：匹配任意小写字母。
+- [A-Z]：匹配任意大写字母。
+- [0-9]：匹配任意数字。
+
+## 匹配数字
+
+### 匹配重复次数：
+
+- {n}：匹配前一个字符恰好 n 次。
+- {n,}：匹配前一个字符至少 n 次。
+- {n,m}：匹配前一个字符至少 n 次，但不超过 m 次。
+
+### 匹配边界：
+
+- ^：匹配字符串的开头。
+- $：匹配字符串的结尾。
+
+## 匹配数字
+
+### 匹配特殊字符：
+
+- \：转义特殊字符，使其按字面意义匹配。
+- .：匹配任意单个字符。
+- |：表示“或”关系，匹配两个或多个表达式之一。
+
+#### 匹配次数：
+
+- *：匹配前一个字符零次或多次。
+- +：匹配前一个字符一次或多次。
+- ?：匹配前一个字符零次或一次。
+
+## 匹配数字
+
+### 分组和捕获：
+
+- ()：将一系列模式组合成一个单元，可与特殊字符一起使用。
+
+### 预定义字符集：
+
+- \d：任意数字，相当于 [0-9]。
+- \w：任意字母、数字或下划线字符，相当于 [a-zA-Z0-9_]。
+- \s：任意空白字符，相当于 [ \t\n\r\f\v]。
+
+
+
+## 实例
+
+```{r}
+library(babynames)
+(x <- c("apple", "apppple", "abc123def"))
+x[str_detect(x, "[0-9]")]
+x[str_detect(x, "abc[0-9]+")]
+x[str_detect(x, "pp")]
+x[str_detect(x, "p{4}")]
+x[str_detect(x, "p{4}")]
+x[str_detect("apple", "ap*")]
+x[str_detect("apple", "app*")]
+x[str_detect("apple", "a..le")]
+```
+
+## 练习
+
+
+找出`babyname`中名字含有ar的行
+
+```{r}
+#| echo: false
+babynames |>
+ filter(str_detect(name, "ar"))
+```
+
+## 练习
+
+
+找出`babyname`中名字含有ar或者以ry结尾的行。
+
+```{r}
+#| echo: false
+babynames |>
+ filter(str_detect(name, "ar"))
+```
+
+
+
+
+## GNU/Linux服务器
+
+- `ssh`, `scp`
+- `bash`
+ - grep
+ - sed
+ - awk
+ - find
+ - xargs
+- `Editor`
+ - `Virtual Studio Code`
+ - `Vim`
+ - `Emacs`
+
+
+
+## 欢迎讨论！{.center}
+
+
+`r rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "https://drc.drwater.net/course/public/RWEP/PUB/SD/")`
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