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<!DOCTYPE html>
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<title>chapter_12.knit</title>
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<textarea id="source">
class: center, middle
<span style="font-size: 60px;">**第十二章**</span> <br>
<span style="font-size: 50px;">如何得到可发表的图像</span> <br>
<span style="font-size: 50px;">数据可视化进阶</span> <br>
<span style="font-size: 30px;">胡传鹏</span> <br>
<span style="font-size: 20px;"> </span> <br>
<span style="font-size: 30px;">2024-05-22</span> <br>
<span style="font-size: 20px;"> Made with Rmarkdown</span> <br>
<style type="text/css">
/* ---- extra.css ---- */
.bigfont {
font-size: 30px;
}
.size5{
font-size: 20px;
}
.tit_font{
font-size: 60px;
}
</style>
---
<br>
<br>
<br>
<br>
.pull-left[
# 为什么要作图？
- “一图胜千言”
- 信息传递的直观性
# 用什么做图？
- ggplot 2
]
.pull-right[
# 怎么画好一张图？
- 明确想要得到的图像
- “信达雅”
- 信息量
- 逻辑性
- 美观（简洁、对称、和谐）
]
---
.panelset[
.panel[.panel-name[Packages]
```r
if (!requireNamespace('pacman', quietly = TRUE)) {
install.packages('pacman')
}
pacman::p_load(
# 本节课需要用到的 packages
here, tidyverse, reshape, bruceR, ggplot2, patchwork, magick,
# 生成课件
xaringan, xaringanthemer, xaringanExtra, knitr)
options(scipen=99999,digits = 5)
```
.panel[.panel-name[trial data]
```r
df.match.trial <- bruceR::import(here::here('data','match','match_raw.csv')) %>%
tidyr::extract(Shape,
into = c('Valence', 'Identity'),
regex = '(moral|immoral)(Self|Other)',
remove = FALSE) %>% #将Shape列分为两列
dplyr::mutate(Valence = factor(Valence, levels = c('moral','immoral'), labels = c('moral','immoral')),
Identity = factor(Identity, levels = c('Self','Other'), labels = c('Self','Other'))) %>%
dplyr::filter(ACC == 0 | ACC == 1,
RT >= 0.2 & RT <= 1.5,
Match == 'match',
(!Sub %in% c(7302,7303,7338)))
```
<div class="datatables html-widget html-fill-item" id="htmlwidget-a51952170b90a44611db" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-a51952170b90a44611db">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4"],["06-May-2018_14:45:42","06-May-2018_14:45:44","06-May-2018_14:45:50","06-May-2018_14:45:53"],["Exp","Exp","Exp","Exp"],[7304,7304,7304,7304],[25,25,25,25],["female","female","female","female"],["R","R","R","R"],[1,1,1,1],[1,1,1,1],[3,4,7,8],["moralOther","immoralOther","moralSelf","immoralOther"],["moral","immoral","moral","immoral"],["Other","Other","Self","Other"],["moralOther","immoralOther","moralSelf","immoralOther"],["match","match","match","match"],["n","n","n","n"],["n","m","n","m"],[1,0,1,0],[0.8167,0.8728,0.4951,0.8713]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Date<\/th>\n <th>Prac<\/th>\n <th>Sub<\/th>\n <th>Age<\/th>\n <th>Sex<\/th>\n <th>Hand<\/th>\n <th>Block<\/th>\n <th>Bin<\/th>\n <th>Trial<\/th>\n <th>Shape<\/th>\n <th>Valence<\/th>\n <th>Identity<\/th>\n <th>Label<\/th>\n <th>Match<\/th>\n <th>CorrResp<\/th>\n <th>Resp<\/th>\n <th>ACC<\/th>\n <th>RT<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[3,4,7,8,9,17,18]},{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"Date","targets":1},{"name":"Prac","targets":2},{"name":"Sub","targets":3},{"name":"Age","targets":4},{"name":"Sex","targets":5},{"name":"Hand","targets":6},{"name":"Block","targets":7},{"name":"Bin","targets":8},{"name":"Trial","targets":9},{"name":"Shape","targets":10},{"name":"Valence","targets":11},{"name":"Identity","targets":12},{"name":"Label","targets":13},{"name":"Match","targets":14},{"name":"CorrResp","targets":15},{"name":"Resp","targets":16},{"name":"ACC","targets":17},{"name":"RT","targets":18}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
.panel[.panel-name[subj data]
```r
df.match.subj <- df.match.trial %>%
dplyr::group_by(Sub, Identity, Valence) %>%
dplyr::summarise(RT_mean = mean(RT),
ACC_mean = mean(ACC)) %>%
dplyr::ungroup()
```
<div class="datatables html-widget html-fill-item" id="htmlwidget-d3983644fdf2df6ccfe9" style="width:100%;height:auto;"></div>
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.panel[.panel-name[sum data]
```r
df.match.sum <- df.match.subj %>%
dplyr::group_by(Identity, Valence) %>%
dplyr::summarise(grand_mean_RT = mean(RT_mean),
SD_RT = sd(RT_mean),
SE_RT = SD_RT/sqrt(n()-1),
grand_mean_ACC = mean(ACC_mean),
SD_ACC = sd(ACC_mean),
SE_ACC = SD_ACC/sqrt(n()-1),
n = n()) %>%
dplyr::ungroup()
```
<div class="datatables html-widget html-fill-item" id="htmlwidget-be879c504cf1844fed20" style="width:100%;height:auto;"></div>
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]]]]]
---
<br>
<br>
<h1 lang="en" style="font-size: 60px;">Contents</h1>
<br>
<br>
<br>
<span style="font-size: 45px;">12.1 ggplot2基础</span></center> <br>
<br>
<span style="font-size: 45px;">12.2 进阶——细节调整</span></center> <br>
<br>
<span style="font-size: 45px;">12.3 高级图片处理——magick</span></center> <br>
<br>
---
class: center, middle
<span style="font-size: 60px;">12.1 ggplot2基础</span> <br>
---
# 12.1 ggplot2基础
## 什么是ggplot
<img src="./picture/chp12/ggplot.png" width="70%" style="display: block; margin: auto;" />
---
# 12.1 ggplot2基础
## 图层叠加
<img src="./picture/chp12/gramma.png" width="65%" style="display: block; margin: auto;" />
- 和PS类似，采用图层的设计方式；
- 图层之间的叠加是靠 “+” 实现的，越往后，其图层越在上方；
- 有明确的起始（ggplot()开始）与终止；
- 有必须的图层
---
# 12.1 ggplot2基础
## 必须图层
```r
# 以柱状图为例
p1 <- ggplot2::ggplot(data = df.match.sum, aes(x = Identity, y = grand_mean_RT, fill = Valence)) +
ggplot2::geom_bar(stat = "Identity",
position = "dodge") +
ggplot2::geom_errorbar(data = df.match.sum,
aes(ymin = grand_mean_RT - SE_RT, ymax = grand_mean_RT + SE_RT),
width=.1,
position = position_dodge(.9))
```
---
# 12.1 ggplot2基础
## 必须图层
```r
p1
```
<img src="chapter_12_files/figure-html/unnamed-chunk-11-1.png" width="100%" />
---
# 12.1 ggplot2基础
## 可选图层
<img src="./picture/chp12/adjust.png" width="70%" style="display: block; margin: auto;" />
```r
# 以柱状图为例
p2 <- p1 +
ggplot2::scale_y_continuous(expand=c(0, 0),
breaks = seq(0, 0.75, 0.25),
limits = c(0, 0.75)) +
ggplot2::labs(title = "Mean RT for match trials", y = "RT") +
papaja::theme_apa()
```
---
# 12.1 ggplot2基础
## 可选图层
```r
p2
```
<img src="chapter_12_files/figure-html/unnamed-chunk-14-1.png" width="100%" />
---
# 12.1 ggplot2基础
## 同理可以得到ACC的图
<img src="chapter_12_files/figure-html/unnamed-chunk-15-1.png" width="100%" />
---
# 12.1 ggplot2基础
## 可选图层
## 同时呈现两张图——方法一：分面(Facet)
.panelset[
.panel[.panel-name[Facet]
- Facet 也可以被认为是图层的一种，也是通过"+"加号叠加在原始图片上
- 可以分为一维(facet_wrap)和二维(facet_grid)两种，图示为二维
<img src="./picture/chp12/facet.png" width="40%" style="display: block; margin: auto;" />
.panel[.panel-name[Data preprocessing]
```r
df1 <- df.match.sum[,-c(6, 7, 8)]%>%
dplyr::rename(grand_mean = grand_mean_RT,
SD = SD_RT,
SE = SE_RT) %>%
dplyr::mutate(DV = "RT")
df.match.sum.long <- df.match.sum[,-c(3, 4, 5)] %>%
dplyr::rename(grand_mean = grand_mean_ACC,
SD = SD_ACC,
SE = SE_ACC) %>%
dplyr::mutate(DV = "ACC") %>%
rbind(df1,.)
rm(df1)
```
<div class="datatables html-widget html-fill-item" id="htmlwidget-cda03d1e36524a69ce81" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-cda03d1e36524a69ce81">{"x":{"filter":"none","vertical":false,"data":[["1","2","3"],["Self","Self","Other"],["moral","immoral","moral"],[0.6366372764212784,0.7150271256631987,0.6850132435574973],[0.06334254097700114,0.06957103122069196,0.07816459938618592],[0.01001533511349354,0.01100014589120355,0.01235890832274734],[41,41,41],["RT","RT","RT"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Identity<\/th>\n <th>Valence<\/th>\n <th>grand_mean<\/th>\n <th>SD<\/th>\n <th>SE<\/th>\n <th>n<\/th>\n <th>DV<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[3,4,5,6]},{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"Identity","targets":1},{"name":"Valence","targets":2},{"name":"grand_mean","targets":3},{"name":"SD","targets":4},{"name":"SE","targets":5},{"name":"n","targets":6},{"name":"DV","targets":7}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
.panel[.panel-name[figure code]
```r
p4 <- df.match.sum.long %>%
ggplot2::ggplot(.,
aes(x = Identity,
y = grand_mean,
fill = Valence)) +
ggplot2::geom_bar(stat = "identity",
position=position_dodge(),
) +
ggplot2::geom_errorbar(aes(ymin = grand_mean-1.96*SE,
ymax = grand_mean+1.96*SE),
width = .1,
position = position_dodge(.9)) +
papaja::theme_apa() +
ggplot2::facet_wrap(~DV, scales = "free_y") +
ggplot2::labs(title = "Summary data for matching trials",
x="Identity",
y="mean")
```
.panel[.panel-name[figure]
<img src="chapter_12_files/figure-html/unnamed-chunk-20-1.png" width="85%" />
]]]]]
---
# 12.1 ggplot2基础
## 可选图层
## 同时呈现两张图——方法二：patchwork
```r
p2 + p3 + plot_layout(guides = "collect")
```
<img src="chapter_12_files/figure-html/unnamed-chunk-21-1.png" width="85%" />
---
# 12.1 ggplot2基础
## 总结
<img src="./picture/chp12/basic.png" width="80%" style="display: block; margin: auto;" />
---
# 12.1 ggplot2基础
## 总结
<img src="./picture/chp12/summary.png" width="80%" style="display: block; margin: auto;" />
.footnote[
----------------
.footfont[
注：本图来自《R语言数据可视化之美：专业图表绘制指南》
]]
---
class: center, middle
<span style="font-size: 60px;">12.2 进阶——细节调整</span> <br>
---
# 12.2 进阶——细节调整
## 问题1：可视化RT, ACC的总体趋势与个体趋势
## 目标图片1
<img src="./picture/chp12/target1.png" width="70%" style="display: block; margin: auto;" />
---
# 12.2 进阶——细节调整(目标图片1)
## 画出总体均值
.panelset[
.panel[.panel-name[code]
```r
s1 <- df.match.sum %>%
ggplot2::ggplot(.,
aes(x = Identity,
y = grand_mean_RT,
group = Valence,
color = Valence)) +
ggplot2::geom_line(position = position_dodge(0.5)) +
ggplot2::geom_point(size = 3,
position = position_dodge(0.5)) +
ggplot2::geom_errorbar(aes(ymin=grand_mean_RT-SE_RT,
ymax=grand_mean_RT+SE_RT),
width=.1,
position = position_dodge(0.5)) +
ggplot2::scale_y_continuous(limits = c(0.4, 0.9)) + #选取能纳入全部散点的范围
papaja::theme_apa()
```
.panel[.panel-name[figure]
<img src="chapter_12_files/figure-html/unnamed-chunk-26-1.png" width="80%" style="display: block; margin: auto;" />
]]]
---
# 12.2 进阶——细节调整(目标图片1)
## 加入个体数据
.panelset[
.panel[.panel-name[直接加入——拥挤]
```r
s2 <- s1 +
ggplot2::geom_point(data = df.match.subj,
aes(x = Identity,
y = RT_mean,
group = Valence))
```
<img src="chapter_12_files/figure-html/unnamed-chunk-28-1.png" width="70%" style="display: block; margin: auto;" />
.panel[.panel-name[加入抖动——混乱]
```r
s3 <- s1 +
ggplot2::geom_point(data = df.match.subj,
aes(x = Identity, y = RT_mean, group = Valence),
position = position_jitter(width = 0.1),
alpha = 0.5)
```
<img src="chapter_12_files/figure-html/unnamed-chunk-30-1.png" width="70%" style="display: block; margin: auto;" />
]]]
---
# 12.2 进阶——细节调整(目标图片1)
## 加入个体数据——如何得到规则的抖动？
.panelset[
.panel[.panel-name[将不同条件点的位置作为新变量]
```r
df.match.plot <- df.match.subj %>%
dplyr::mutate(conds = case_when(Identity == "Self" & Valence == "moral" ~ "0.88",
Identity == "Self" & Valence == "immoral" ~ "1.12",
Identity == "Other" & Valence == "moral" ~ "1.88",
Identity == "Other" & Valence == "immoral" ~ "2.12"),
conds = as.numeric(conds))
```
.panel[.panel-name[以conds为基础抖动]
```r
s4 <- s1 +
ggplot2::geom_point(data = df.match.plot,
aes(x = conds, y = RT_mean, group = Valence),
position = position_jitter(width = 0.08),
alpha = 0.5)
```
<img src="chapter_12_files/figure-html/unnamed-chunk-33-1.png" width="70%" style="display: block; margin: auto;" />
]]]
---
# 12.2 进阶——细节调整(目标图片1)
## 显示个体趋势
```r
s5 <- s4 +
ggplot2::geom_line(data = df.match.plot,
aes(x = conds, y = RT_mean, group = Sub),
linetype = 1,
size=0.8,
color="#000000",
alpha=0.1)
```
<img src="chapter_12_files/figure-html/unnamed-chunk-35-1.png" width="70%" style="display: block; margin: auto;" />
---
# 12.2 进阶——细节调整(目标图片1)
## 显示个体趋势——如何使点和线正确连接？
.panelset[
.panel[.panel-name[code]
```r
s6 <- s1 +
ggplot2::geom_point(data = df.match.plot,
aes(x = conds,
y = RT_mean,
group = as.factor(Sub)),
position = position_dodge(0.08),
color="#000000",
alpha = 0.05) +
ggplot2::geom_line(data = df.match.plot,
aes(x = conds,
y = RT_mean,
group = as.factor(Sub)),
position = position_dodge(0.08),
linetype = 1,
size=0.8,
color="#000000",
alpha=0.05) +
ggplot2::labs(y = "RT")
```
.panel[.panel-name[figure]
<img src="chapter_12_files/figure-html/unnamed-chunk-37-1.png" width="80%" style="display: block; margin: auto;" />
]]]
---
# 12.2 进阶——细节调整(目标图片1)
## 同理可得ACC的图
<br>
<br>
<img src="chapter_12_files/figure-html/unnamed-chunk-38-1.png" width="80%" style="display: block; margin: auto;" />
---
# 12.2 进阶——细节调整(目标图片1)
## 合并图片
```r
s9 <- s6 + s8 + plot_layout(guides = "collect")
s9
```
<img src="chapter_12_files/figure-html/unnamed-chunk-39-1.png" width="100%" />
---
# 12.2 进阶——细节调整(目标图片1)
## 保存图片
```r
# 保存为pdf更加清晰
ggplot2::ggsave(filename = "./picture/chp12/p1.pdf",
plot = s9,
width = 5,
height = 4)
```
---
# 12.2 进阶——细节调整
## 问题2：可视化层级模型的random effect
## 目标图片2
<img src="picture/chp12/target2.png" width="80%" style="display: block; margin: auto;" />
---
# 12.2 进阶——细节调整(目标图片2)
## 模型拟合
.panelset[
.panel[.panel-name[使用一个简单的模型]
```r
#此处选择12个被试是为了在展示的时候更清晰
sublist <- unique(df.match.trial$Sub)
target2 <- df.match.trial %>%
dplyr::filter(Sub == sublist[1:12]) %>%
dplyr::mutate(Label = factor(Label, levels = c("moralSelf", "moralOther", "immoralSelf", "immoralOther")),
Sub = factor(Sub))
model <- lme4::lmer(data = target2,
RT ~ Identity * Valence + (1 |Sub))
```
]]
---
# 12.2 进阶——细节调整(目标图片2)
## 随机效应森林图
.panelset[
.panel[.panel-name[data preprocessing]
```r
# 提取随机效应
ranef_df <- as.data.frame(ranef(model)$Sub) %>%
dplyr::mutate(Sub = row.names(.)) %>%
dplyr::rename(Intercept = "(Intercept)") %>%
dplyr::mutate(se = sqrt(diag(vcov(model))[1]),
lower = Intercept - 1.96 *se,
upper = Intercept + 1.96 *se) %>%
dplyr::arrange(Intercept) %>%
dplyr::mutate(Sub = factor(Sub, levels = .$Sub))
```
.panel[.panel-name[forest]
```r
# 绘制森林图
ranef_df %>%
ggplot2::ggplot(., aes(x=Intercept, y=Sub)) +
ggplot2::geom_point(size = 2) +
ggplot2::geom_errorbarh(aes(xmax = upper, xmin = lower),
height = .2, color = 'grey') +
ggplot2::geom_vline(xintercept = 0, linetype = 2) +
# ggplot2::facet_wrap(~ variable, nrow = 1) + # 按照对象分面
papaja::theme_apa()
```
<img src="chapter_12_files/figure-html/unnamed-chunk-44-1.png" width="65%" />
]]]
---
# 12.2 进阶——细节调整
## 问题3：使用雨云图(Raincloud plot)呈现数据的分布
## 雨云图将不在PPT中出现，感兴趣的同学可以自行回到rmd文件运行chunk
## 目标图片3
<img src="picture/chp12/target3.png" width="80%" style="display: block; margin: auto;" />
---
class: center, middle
<span style="font-size: 60px;">12.3 高级图片处理——magick</span> <br>
---
# 12.3 高级图片处理——magick
.pull-left[
.size6[
<br>
<br>
<br>
ggplot生成的图像有时需要进一步手动修改（如修改图片格式、图片拼接等），也可能需要批量修改。
R仍然可以处理。
magick包可以应用于所有常见图片操作(甚至包括PDF)，具体功能可以参考相关文档 `\(^*\)`，在这里我们仅以图片剪裁与拼接为例。
]]
.pull-right[
<br>
<br>
<br>
<img src="picture/chp12/r.jpg" width="80%" />
]
.footnote[
----------------
.footfont[
注：[https://search.r-project.org/CRAN/refmans/magick/html/magick.html](https://search.r-project.org/CRAN/refmans/magick/html/magick.html)
]]
---
# 12.3 高级图片处理——magick
## 图片剪裁与拼接
.panelset[
.panel[.panel-name[查看图片]
假设我们希望这两张图变为横向排版，那么首先需要对图片进行剪裁，然后进行横向拼接。
<img src="picture/chp6/pr1.png" width="40%" />
.panel[.panel-name[读取图片]
```r
## 读取图片；图片可以是本地，也可以是图片的网址链接
img = magick::image_read('picture/chp6/pr1.png')
## 查看图片相关信息
img %>% magick::image_info()
```
```
## # A tibble: 1 × 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 870 977 sRGB FALSE 92033 72x72
```
.panel[.panel-name[图片剪裁--语法]
.pull-left[
下面需要根据图片的width 和 height ，使用`magick::image_crop()`进行裁剪，geometry参数接受一个字符串，来对剪裁区域进行定位，比如`"850x480+10+10"`。
这个字符串包含两个部分:
- 第一部分：包含图片剪裁的长和宽（单位可以是百分比，但下面会使用像素），即`"850x480"`（注意：其中连接符为小写字母x），大概指右图中红色线条;
- 第二部分：包含起始点位置，即`"+10+10"`，意思是从左上角顶点向右10个像素，向下10个像素，大概对应右图中灰色点的位置，如果不写默认使用`+0+0`（即左上角顶点）。
]
.pull-right[
<img src="picture/chp12/crop_gram.jpg" width="100%" />
]
.panel[.panel-name[图片剪裁--结果]
```r
img %>% magick::image_crop('850x480+10+10')
```
<img src="chapter_12_files/figure-html/unnamed-chunk-51-1.png" width="80%" />
.panel[.panel-name[图片剪裁与合并]
```r
img1 = img %>% magick::image_crop('870x488')
img2 = img %>% magick::image_crop('870x488+0+485')
## 使用image_append进行拼接，令stack = F进行横向拼接（T为竖向）
*img3 = image_append(c(img1,img2),stack = F)
img3 %>% print()
```
```
## # A tibble: 1 × 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1740 488 sRGB FALSE 0 72x72
```
<img src="chapter_12_files/figure-html/unnamed-chunk-52-1.png" width="80%" />
.panel[.panel-name[其他]
```r
#### NOT RUN ####
# 保存图片到本地
image_write(image = img3,path = 'your path')
# 修改尺寸(可以以像素为单位，这里以百分比为例)
image_scale(img1,'40%')
# 旋转
image_rotate(img1,90)
# OCR(这里以英文为例，中文的识别率经测验确实不太行😢)
image_read("http://jeroen.github.io/images/testocr.png") %>%
image_ocr() %>%
cat()
```
]]]]]]]
---
# 网络资源
- ggplot2常用参数与函数汇总：https://zhuanlan.zhihu.com/p/637483028
- ggplot2位置调整参数：https://zhuanlan.zhihu.com/p/409489632
- ggplot2主题总结：https://zhuanlan.zhihu.com/p/463041897
- ggplot2分面总结：https://zhuanlan.zhihu.com/p/225852640
- patchwork常用功能：https://zhuanlan.zhihu.com/p/384456335
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