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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>chapter_11.knit</title>
<meta charset="utf-8" />
<meta name="author" content="" />
<script src="libs/header-attrs-2.20/header-attrs.js"></script>
<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
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<body>
<textarea id="source">
class: center, middle
<span style="font-size: 60px;">第十一章</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: 45px;">ggplot2</span> <br>
<br>
<br>
<span style="font-size: 30px;">胡传鹏</span> <br>
<span style="font-size: 30px;">2023/05/15</span> <br>
---
# 为什么要作图？
- “一图胜千言”
- 直觉地传达信息
- “信达雅”
- 信息量
- 逻辑性
- 美观（简洁、对称、和谐）
---
# 用什么做图？
- ggplot 2
---
class: center, middle
<span style="font-size: 60px;">11.1 Layers and Facets</span> <br>
---
<br>
<div style="display:flex; align-items:center; justify-content:center;">
<img src="https://setscholars.net/wp-content/uploads/2022/02/ggplot.png" style="width:140%; height: 140%; margin-right: -15px;">
<img src="https://pngimg.com/d/photoshop_PNG7.png" style="width:60%; height: 60%; margin-left: -40px;">
</div>
---
# <h1 lang="en">Layers</h1>
# <h4 lang="en"> </h4>
<div style="display:flex; align-items:center;">
<img src="https://cdn.educba.com/academy/wp-content/uploads/2019/09/Layers-in-Photoshop.jpg.webp" style="width:110%; height: 110%; margin-top: 20px;">
</div>
<br>
_<h4 lang="zh-CN">相信大家都用过PhotoShop，对图层这个概念一定已经非常熟悉了 </h4>_
---
# <h1 lang="en">Layers</h1>
# <h4 lang="en"> </h4>
<div style="display:flex; align-items:center; justify-content:center;">
<img src="https://static.vecteezy.com/system/resources/previews/000/363/962/original/vector-plus-sign-line-black-icon.jpg" style="width:60%; height: 60%;">
</div>
<br>
_<h4 lang="zh-CN">在ggplot2中，用 + 加号来表示一个个图层 </h4>_
---
# <h1 lang="en">Layers</h1>
<div style="display:flex; align-items:center; justify-content:center;">
<img src="https://r.qcbs.ca/workshop03/book-en/images/gglayers.png" style="width:90%; height: 90%;">
</div>
<br>
_<h4 lang="zh-CN">和PS类似，ggplot2也是通过修改每个图层的信息来美化图片 </h4>_
---
# <h1 lang="en">Facets</h1>
<div style="display:flex; align-items:center; justify-content:center;">
<img src="https://i.stack.imgur.com/EQ2sk.jpg" style="width:80%; height: 80%;">
</div>
<br>
_<h4 lang="zh-CN">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 </h4>_
---
# <h1 lang="zh-CN">载入包</h1>
```r
# 检查是否已安装 pacman
if (!requireNamespace("pacman", quietly = TRUE)) {
install.packages("pacman") } # 如果未安装，则安装包
# 使用p_load来载入需要的包
pacman::p_load("tidyverse", "ggplot2", "ggdist", "patchwork", "papaja")
# ggdist: plot distribution
# patchwork: 拼接图片
# papaja: APA的主题
```
---
# 传统的图
- 条形图
- 点图 + 折线图
---
# <h1 lang="zh-CN">读取数据</h1>
```r
df.mt.raw <- read.csv('./data/match/match_raw.csv',
header = T, sep=",", stringsAsFactors = FALSE)
```
---
# <h1 lang="zh-CN">清洗数据</h1>
```r
# get the mean RT of correct trials for each participants for each condition
df.mt.rt.subj <- df.mt.raw %>%
dplyr::filter(ACC == 1 & RT > 0.2) %>%
tidyr::extract(Shape, into = c("Valence", "Identity"),
regex = "(moral|immoral)(Self|Other)", remove = FALSE) %>%
dplyr::mutate(Valence = case_when(Valence == "moral" ~ "Good",
Valence == "immoral" ~ "Bad"),
RT_ms = RT * 1000) %>%
dplyr::mutate(Valence = factor(Valence, levels = c("Good", "Bad")),
Identity = factor(Identity, levels = c("Self", "Other"))) %>%
dplyr::group_by(Sub, Match, Identity, Valence) %>%
dplyr::summarise(RT_mean = mean(RT_ms)) %>%
dplyr::ungroup()
# get the overall mean RT
df.mt.rt.sum <- df.mt.rt.subj %>%
dplyr::group_by(Match, Identity, Valence) %>%
dplyr::summarise(grand_mean_RT = mean(RT_mean),
SD_RT = sd(RT_mean),
SE_RT = SD_RT/sqrt(n()-1),
n = n()) %>%
dplyr::ungroup()
```
---
# <h1 lang="zh-CN">清洗数据</h1>
```r
# get the accuracy for each participants for each condition
df.mt.acc.subj <- df.mt.raw %>%
tidyr::extract(Shape, into = c("Valence", "Identity"),
regex = "(moral|immoral)(Self|Other)", remove = FALSE) %>%
dplyr::mutate(Valence = case_when(Valence == "moral" ~ "Good",
Valence == "immoral" ~ "Bad"),
) %>%
dplyr::mutate(Valence = factor(Valence, levels = c("Good", "Bad")),
Identity = factor(Identity, levels = c("Self", "Other"))) %>%
dplyr::group_by(Sub, Match, Identity, Valence) %>%
dplyr::summarise(ACC_mean = mean(ACC)) %>%
dplyr::ungroup()
# get the overall mean acc
df.mt.acc.sum <- df.mt.acc.subj %>%
dplyr::group_by(Match, Identity, Valence) %>%
dplyr::summarise(grand_mean_ACC = mean(ACC_mean),
SD_ACC = sd(ACC_mean),
SE_ACC = SD_ACC/sqrt(n()-1),
n = n()) %>%
dplyr::ungroup()
df.mt.acc.sum.tmp <- df.mt.acc.sum %>%
dplyr::rename(grand_mean = grand_mean_ACC,
SD = SD_ACC,
SE = SE_ACC) %>%
dplyr::mutate(DV = "ACC")
df.mt.rt.sum.tmp <- df.mt.rt.sum %>%
dplyr::rename(grand_mean = grand_mean_RT,
SD = SD_RT,
SE = SE_RT) %>%
dplyr::mutate(DV = "RT")
df.mt.sum.tmp <- rbind(df.mt.acc.sum.tmp, df.mt.rt.sum.tmp)
```
---
# <h1 lang="zh-CN">查看数据</h1>
```r
df.mt.sum <- df.mt.rt.sum %>%
dplyr::left_join(., df.mt.acc.sum)
df.mt.sum
```
<img src="./picture/chp11/df.mt.sum.png" width="100%">
---
## First plot (old-fashion)
```r
p1 <- df.mt.sum %>%
dplyr::filter(Match == "match") %>%
ggplot2::ggplot(.,
aes(x = Identity,
y = grand_mean_RT,
fill = Valence)) +
ggplot2::geom_bar(stat = "identity",
position=position_dodge(),
) +
ggplot2::geom_errorbar(aes(ymin=grand_mean_RT-SE_RT,
ymax=grand_mean_RT+SE_RT),
width=.1,
position = position_dodge(.9)) +
papaja::theme_apa()
ggsave(filename = "./picture/chp11/p1.png", plot = p1, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p1.png" width="60%">
---
## <h1 lang="en">修改图题</h1>
```r
# 修改标题和坐标的题目
p2 <- p1 +
labs(title="Mean RT for different conditions",
x="Identity",
y="Reaction times (ms)")
ggsave(filename = "./picture/chp11/p2.png", plot = p2, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p2.png" width="100%">
---
## <h1 lang="en">定义y轴的range</h1>
```r
p3 <- p2 +
coord_cartesian(ylim = c(500, 800)) # +
# scale_y_continuous(limits = c(500,800))
ggsave(filename = "./picture/chp11/p3.png", plot = p3, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p3.png" width="100%">
---
## <h1 lang="en">定义y轴的range</h1>
```r
df.mt.sum %>%
dplyr::filter(Match == "match") %>%
ggplot2::ggplot(.,
aes(x = Identity,
y = grand_mean_ACC,
fill = Valence)) +
ggplot2::geom_bar(stat = "identity",
position=position_dodge(),
) +
ggplot2::geom_errorbar(aes(ymin=grand_mean_ACC-1.96*SE_ACC,
ymax=grand_mean_ACC+1.96*SE_ACC),
width=.1,
position = position_dodge(.9)) +
papaja::theme_apa()+
labs(title="Mean ACC for different conditions",
x="Identity",
y="Accuracy")+
coord_cartesian(ylim = c(0.5, 1))
ggsave(filename = "./picture/chp11/p3.1.png", plot = last_plot(), height = 5, width = 10, dpi = 300)
```
---
## <h1 lang="en">定义y轴的range</h1>
<img src="./picture/chp11/p3.1.png" width="100%">
---
## <h1 lang="en">定义y轴的range</h1>
```r
df.mt.sum.tmp %>%
dplyr::filter(Match == "match") %>%
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()+
facet_wrap(~DV, scales = "free_y")
# labs(title="Mean ACC for different conditions",
# x="Identity",
# y="Accuracy")+
# coord_cartesian(ylim = c(0.5, 1))
ggsave(filename = "./picture/chp11/p3.2.png",plot = last_plot(), height = 5, width = 10, dpi = 300)
```
---
## <h1 lang="en">定义y轴的range</h1>
<img src="./picture/chp11/p3.2.png" width="100%">
---
## <h1 lang="en">点加上error bars</h1>
```r
p4 <- df.mt.sum %>%
dplyr::filter(Match == "match") %>%
ggplot2::ggplot(.,
aes(x = Identity,
y = grand_mean_RT,
group = Valence,
color = Valence)) +
ggplot2::geom_line() +
ggplot2::geom_point(size = 5) +
ggplot2::geom_errorbar(aes(ymin=grand_mean_RT-SE_RT,
ymax=grand_mean_RT+SE_RT),
width=.1,
) +
ggplot2::scale_y_continuous(limits = c(450, 900)) +
papaja::theme_apa()
ggsave(filename = "./picture/chp11/p4.png", plot = p4, height = 5, width = 10, dpi = 300)
```
---
## <h1 lang="en">点加上error bars</h1>
<img src="./picture/chp11/p4.png" width="100%">
---
## <h1 lang="en">加入个体的数据</h1>
```r
p5 <- p4 +
ggplot2::geom_point(data = df.mt.rt.subj,
aes(x=Identity,
y=RT_mean,
group = Valence))
ggsave(filename = "./picture/chp11/p5.png", plot = p5, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p5.png" width="100%">
---
## <h1 lang="en">jitter points' position</h1>
```r
# 太拥挤了，三种图应该错开一定的距离, 以及调整每个图形的宽度
p6 <- p4 +
ggplot2::geom_point(data = df.mt.rt.subj,
aes(x=Identity,
y=RT_mean,
group = Valence),
position = position_jitter(width = 0.1),
alpha = 0.5)
ggsave(filename = "./picture/chp11/p6.png", plot = p6, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p6.png" width="90%">
---
## <h1 lang="en">jitter points' position</h1>
```r
p7 <- df.mt.sum %>%
dplyr::filter(Match == "match") %>%
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 = 5,
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(450, 900)) +
papaja::theme_apa()
ggsave(filename = "./picture/chp11/p7.png", plot = p7, height = 5, width = 10, dpi = 300)
```
---
## <h1 lang="en">jitter points' position</h1>
<img src="./picture/chp11/p7.png" width="100%">
---
## <h1 lang="en">jitter points' position</h1>
```r
df.mt.rt.subj.m.plot <- df.mt.rt.subj %>%
dplyr::mutate(conds = case_when(Identity == "Self" & Valence == "Good" ~ "0.88",
Identity == "Self" & Valence == "Bad" ~ "1.12",
Identity == "Other" & Valence == "Good" ~ "1.88",
Identity == "Other" & Valence == "Bad" ~ "2.12"),
conds = as.numeric(conds)) %>%
dplyr::filter(Match == "match")
```
---
## <h1 lang="en">jitter points' position</h1>
```r
p8 <- p7 +
ggplot2::geom_point(data = df.mt.rt.subj.m.plot,
aes(x=conds,
y=RT_mean,
group = Valence),
position = position_jitter(width = 0.08),
alpha = 0.5)
ggsave(filename = "./picture/chp11/p8.png", plot = p8, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p8.png" width="100%">
---
## <h1 lang="en">显示个体趋势</h1>
```r
p9 <- p8 +
ggplot2::geom_line(data = df.mt.rt.subj.m.plot,
aes(x=conds,
y=RT_mean,
group=Sub),
linetype = 1,
size=0.8,
color="#000000",
alpha=0.1)
ggsave(filename = "./picture/chp11/p9.png", plot = p9, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p9.png" width="90%">
---
## <h1 lang="en">显示个体趋势</h1>
```r
pd <- position_dodge(0.08)
p10 <- p7 +
ggplot2::geom_point(data = df.mt.rt.subj.m.plot,
aes(x=conds,
y=RT_mean,
group=as.factor(Sub)),
position = pd,
color="#000000",
alpha = 0.05) +
ggplot2::geom_line(data = df.mt.rt.subj.m.plot,
aes(x=conds,
y=RT_mean,
group=as.factor(Sub)),
position = pd,
linetype = 1,
size=0.8,
color="#000000",
alpha=0.05)
ggsave(filename = "./picture/chp11/p10.png", plot = p10, height = 5, width = 10, dpi = 300)
```
---
## <h1 lang="en">显示个体趋势</h1>
<img src="./picture/chp11/p10.png" width="100%">
---
## <h1 lang="en">显示个体趋势</h1>
```r
library(patchwork)
p11 <- p9 + p10 +
plot_layout(guides = 'collect')
ggsave(filename = "./picture/chp11/p11.png", plot = p11, height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/p11.png" width="100%">
---
class: center, middle
<span style="font-size: 60px;">11.2 APA theme</span> <br>
---
## <h1 lang="en">APA theme: theme_bw()</h1>
```r
# theme_bw() 会改变很多地方的颜色，让主题变成黑白，方便接下来的细节修改
p[[17]] <- p[[16]] +
ggplot2::theme_bw()
names(p)[17] <- "APA theme: theme_bw"; p[[17]]
ggsave(filename = "./picture/chp11/plot17.png", plot = p[[17]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot17.png" width="100%">
---
## <h1 lang="en">APA theme: legend.position</h1>
```r
# APA默认图例局右
p[[18]] <- p[[17]] +
ggplot2::theme(legend.position = "right",) # 也可以去掉图例
names(p)[18] <- "APA theme: legend.position"; p[[18]]
ggsave(filename = "./picture/chp11/plot18.png", plot = p[[18]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot18.png" width="100%">
---
## <h1 lang="en">APA theme: font_size</h1>
```r
# 除了字体大小，标题粗体居中
p[[19]] <- p[[18]] +
ggplot2::theme(axis.text = element_text(size = 12), # 坐标轴字体大小
axis.title = element_text(size = 12), # 坐标轴标题字体大小
strip.text = element_text(size = 12), # facet标题字体大小
plot.title = element_text(size = 12, face = "bold", hjust = 0.5)) # 图片整体标题字体大小 title粗体，居中
names(p)[19] <- "APA theme: font_size"; p[[19]]
ggsave(filename = "./picture/chp11/plot19.png", plot = p[[19]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot19.png" width="90%">
---
## <h1 lang="en">APA theme: strip.background()</h1>
```r
# 去掉facet标题底色
p[[20]] <- p[[19]] +
ggplot2::theme(strip.background = element_blank())
names(p)[20] <- "APA theme: facet_title_background"; p[[20]]
ggsave(filename = "./picture/chp11/plot20.png", plot = p[[20]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot20.png" width="100%">
---
## <h1 lang="en">APA theme: panel.grid.major</h1>
```r
# 隐藏水平网格线
p[[21]] <- p[[20]] +
ggplot2::theme(panel.grid.major=element_blank())
names(p)[21] <- "APA theme: panel.grid.major"; p[[21]]
ggsave(filename = "./picture/chp11/plot21.png", plot = p[[21]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot21.png" width="100%">
---
## <h1 lang="en">APA theme: panel.grid.minor</h1>
```r
# 隐藏垂直网格线
p[[22]] <- p[[21]] +
ggplot2::theme(panel.grid.minor=element_blank())
names(p)[22] <- "APA theme: panel.grid.minor"; p[[22]]
ggsave(filename = "./picture/chp11/plot22.png", plot = p[[22]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot22.png" width="100%">
---
## <h1 lang="en">APA theme: panel.border</h1>
```r
# 隐藏边框
p[[23]] <- p[[22]] +
ggplot2::theme(panel.border=element_blank()) # 也可以不去除边框，保留坐标轴
names(p)[23] <- "APA theme: panel.border"; p[[23]]
ggsave(filename = "./picture/chp11/plot23.png", plot = p[[23]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot23.png" width="100%">
---
## <h1 lang="en">APA theme: font_family</h1>
```r
# 改变图上字体family
p[[24]] <- p[[23]] +
ggplot2::theme(text=element_text(family='Times New Roman'))
names(p)[24] <- "APA theme: font_family"; p[[24]]
ggsave(filename = "./picture/chp11/plot24.png", plot = p[[24]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot24.png" width="100%">
---
## <h1 lang="en">APA theme: font_family</h1>
```r
# 使用windowsFonts()查看字体对应的名字是什么, 运气好，里面就有times new roman
windowsFonts()
# 如果没有，可以library(extrafont)
library(extrafont)
# 这个包很笨，只能添加一个文件夹内的字体，
# 默认添加"C:/WINDOWS/FONT/"，这里面有你电脑里所有的字体
# 因此建议你把需要的字体放到桌面，
# 然后设置导入路径font_import(path = "C:/user/desktop/")
font_import(path = "C:/user/desktop/")
# 最后使用 fonttable()，查看字体是否被导入了，
# 记住times new roman对应的FamilyName
fonttable()
# 填在上面的text=element_text(family='Times New Roman')
```
---
## <h1 lang="en">Save APA theme</h1>
```r
# 将刚刚制作的apa格式保存
theme_apa <- ggplot2::theme_bw() +
ggplot2::theme(#legend.position = "none",
axis.text = element_text(size = 12), # 坐标轴字体大小
axis.title = element_text(size = 12), # 坐标轴标题字体大小
strip.text = element_text(size = 12), # facet标题字体大小
plot.title = element_text(size = 12, face = "bold", hjust = 0.5), # 图片整体标题字体大小
strip.background = element_blank(), # facet标题的背景
panel.grid.major=element_blank(), # 水平参考线
panel.grid.minor=element_blank(), # 垂直参考线
panel.border=element_blank(), # 边框
text=element_text(family='Times New Roman'), #字体family,
# papaja::theme_apa()会warning找不到Windows中字体，但其实如果你载入了，就可以在R中找到
)
# 你可以在载入了papaja(jtools)包之后，在画完图后加入 theme_apa()，获得一样的效果
```
---
## <h1 lang="en">APA theme: font_family</h1>
```r
# 和直接用papaja::theme_apa()制作的对比一下,坐标轴更好看了，其他几乎一样
p[[24]] <- p[[16]] +
papaja::theme_apa(base_family = "Times New Roman")
names(p)[24] <- "APA theme: font_family"; p[[24]]
ggsave(filename = "./picture/chp11/plot24_apa.png", plot = p[[24]], height = 5, width = 10, dpi = 300)
```
<img src="./picture/chp11/plot24_apa.png" width="100%">
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