2019-08-26用ggplot2畫“美圖”

A ggplot2 Tutorial for Beautiful Plotting in R

原文見https://cedricscherer.netlify.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/

引言

從2016年開始，我不得不準備博士工作介紹的講演锈锤，因為非常討厭R的基礎(chǔ)繪圖包的語法和樣式驯鳖，從那時起開始用ggplot2 來可視化我的數(shù)據(jù)。因為時間不夠久免，我試了n次錯用于繪制這些圖形浅辙，還用了大量的谷歌搜索的幫助。我經(jīng)逞掷眩回顧的資源是篇博客文章Beautiful plotting in R: A ggplot2 cheatsheet 记舆，作者是 Zev Ross, 2014年8月4日發(fā)布，最后更新于2016年1月呼巴。歸功于這個博客文章泽腮，我的演講中包含了很多非常優(yōu)美的圖形。我決定把這篇教程逐步予以介紹衣赶。我從中獲益良多诊赊，從直接修改代碼開始，與時俱進府瞄，增加了很多代碼段碧磅、圖表類型和資源。

由于Zev Ross 的博客很多年沒有更新了摘能，我在自己的Github上有跟新版（最后更新于2017年1月）。在此主頁是最合適的地方（另外敲街，我添加了多處更新团搞，如精彩絕倫的patchwork和ggforce包。還有餅圖多艇，因為人人都愛looooves餅圖Ｂ呖帧）

image.png

以下是我做的主要修改:

遵循R的規(guī)范格式，如 Hadley Wickham, Google 或 Coding Club 指導(dǎo)格式),
改變了圖形的樣式和外觀(如不僅僅做了些許改變峻黍，我改變了所有繪圖的軸標題复隆、圖例和漂亮的顏色),
更新的版本在 ggplot2 中記錄了這些變化,
修改了輸入的數(shù)據(jù) (GitHub 資源),
為實戰(zhàn)練習(xí)和研討會準備了可執(zhí)行的R腳本，
加入了額外的小提示姆涩，如:
- 另外的繪圖類型 (例如輪廓圖挽拂、地毯圖、屋脊圖)
- 如何和為什么使用Viridis調(diào)色板
- 使用 Tufte 打印樣式創(chuàng)建最小繪圖
- 如何調(diào)整繪圖標題骨饿、副標題和標題
- 如何為圖形加入不同的線型
- 如何在圖例和圖例項名稱改變順序
- 如何為數(shù)據(jù)添加標簽（如何做的更美觀）

Preparation
The Dataset
The ggplot2 Package
A Default ggplot
Working with Axes
Working with Titles
Working with Legends
Working with Backgrounds & Grid Lines
Working with Margins
Working with Multi-Panel Plots
Working with Themes
Working with Colors
Working with Lines
Working with Text
Working with Coordinates
Working with Chart Types
Working with Ribbons (AUC, CI, etc.)
Working with Smoothings
Working with Interactive Plots

Preparation（準備）

用著這個鏈接下載數(shù)據(jù) 這里.
帶有可執(zhí)行代碼的 Rmarkdwon 腳本在下載 here.
以下是需要安裝的包:
- ggplot2
- ggthemes
- tidyverse
- extrafont
- patchwork
- cowplot
- grid
- gridExtra
- ggrepel
- reshape2
- ggforce
- ggridges
- shiny

install.packages(c("ggplot2", "ggthemes", "tidyverse", "extrafont", 
                   "cowplot", "grid", "gridExtra", "ggrepel", 
                   "reshape2", "ggforce", "ggridges", "shiny"))

devtools::install_github("thomasp85/patchwork")

(出于教學(xué)目的黎侈，在任何一個繪圖章節(jié)中，除了個ggplot2之外闷游，在相應(yīng)的代碼塊中都加載了必要的R包）

The Dataset（數(shù)據(jù)集）

我們采用的數(shù)據(jù)來源于 National Morbidity and Mortality Air Pollution Study (NMMAPS)峻汉。為了使圖形可控，我們把數(shù)據(jù)限定于芝加哥和1997-2000脐往。更多的數(shù)據(jù)細節(jié)休吠，參考Roger Peng的圖書 Statistical Methods in Environmental Epidemiology with R.

chic <- readr::read_csv("https://raw.githubusercontent.com/Z3tt/R-Tutorials/master/ggplot2/chicago-nmmaps.csv")
tibble::glimpse(chic)

## Observations: 1,461
## Variables: 10
## $ city     <chr> "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic"...
## $ date     <date> 1997-01-01, 1997-01-02, 1997-01-03, 1997-01-04, 1997-01-05, 1997-01-06, 1997-01-07, 1997-01-08, 1997-01-09, 1997-01-10, 1997-01-11, 1997-01-12, 1997-01-13, 1997-01-14, 1997-01-15, 1997-01-16, 1997-01-17, 1997-01-18, 1997-01-19, 1997-01-20, 1997-01-21, 1997-01-22, 1997-01-23, 1997-01-24, 1997-01-25, 1997-01-26, 1997-01-27, 1997-01-28, 1997-01-29, 1997-01-30, 1997-01-31, 1997-02-01, 1997-02-02, 1997-02-03, 1997-02-04, 1997-02-05, 1997-02-06, 1997-02-07, 1997-02-08, 1997-02-09, 1997-02-10, 1997-02-11, 1997-02-12, 1997-02-13, 1997-02-14, 1997-02-15, 1997-02-16, 1997-02-17, 1997-02-18, 1997-02-19, 1997-02-20, 1997-02-21, 1997-02-22, 1997-02-23, 1997-02-24, 1997-02-25, 1997-...
## $ death    <dbl> 137, 123, 127, 146, 102, 127, 116, 118, 148, 121, 110, 127, 129, 151, 128, 132, 116, 142, 124, 124, 127, 121, 134, 120, 109, 109, 115, 105, 114, 120, 117, 126, 97, 96, 119, 125, 116, 118, 121, 114, 111, 107, 127, 98, 104, 122, 124, 120, 106, 103, 139, 133, 109, 121, 111, 105, 107, 123, 124, 125, 108, 114, 104, 120, 134, 101, 102, 125, 119, 115, 121, 112, 127, 99, 125, 115, 113, 105, 113, 120, 105, 119, 147, 123, 108, 117, 110, 106, 96, 119, 119, 99, 120, 130, 97, 105, 102, 104, 137, 111, 108, 96, 100, 105, 128, 120, 98, 118, 94, 117, 121, 110, 110, 108, 121, 114, 116, 109, 123, 115, 101, 118, 100, 126, 126, 121, 114, 112, 111, 111, 107, 124, 104, 107, 109, 133, 108, 109...
## $ temp     <dbl> 36.0, 45.0, 40.0, 51.5, 27.0, 17.0, 16.0, 19.0, 26.0, 16.0, 1.5, 1.0, 3.0, 10.0, 19.0, 9.5, -3.0, 0.0, 14.0, 31.0, 35.0, 36.5, 26.0, 32.0, 14.5, 11.0, 17.0, 2.0, 8.0, 16.5, 31.5, 35.0, 36.5, 30.0, 34.5, 30.0, 26.0, 25.5, 25.5, 26.0, 27.0, 23.5, 21.0, 20.5, 25.5, 20.0, 18.5, 30.0, 48.5, 37.5, 35.5, 36.0, 26.0, 28.0, 21.5, 25.5, 36.5, 34.5, 37.5, 45.5, 35.0, 33.5, 38.0, 33.0, 26.5, 35.5, 39.0, 37.0, 44.0, 37.0, 33.5, 37.5, 26.5, 19.0, 24.5, 45.0, 33.5, 35.5, 46.0, 53.5, 37.5, 32.5, 33.0, 40.5, 44.0, 60.5, 55.5, 43.5, 37.5, 38.5, 44.5, 53.0, 59.5, 62.5, 60.5, 45.0, 34.0, 28.5, 30.0, 30.5, 33.5, 33.5, 38.5, 41.5, 49.0, 43.0, 40.5, 40.0, 45.5, 49.0, 45.0, 43.0, 48.5, 47.5, 4...
## $ dewpoint <dbl> 37.50000, 47.25000, 38.00000, 45.50000, 11.25000, 5.75000, 7.00000, 17.75000, 24.00000, 5.37500, -6.62500, -8.87500, 1.50000, 11.50000, 23.25000, -9.75000, -10.37500, -4.12500, 22.62500, 27.25000, 41.62500, 20.75000, 18.75000, 29.50000, -1.37500, 17.12500, 8.37500, -6.37500, 11.00000, 16.37500, 33.75000, 29.66667, 29.62500, 28.00000, 32.00000, 24.25000, 21.87500, 23.37500, 22.50000, 21.00000, 21.75000, 19.50000, 11.60000, 16.37500, 23.00000, 15.25000, 8.12500, 32.62500, 41.37500, 27.50000, 44.12500, 29.62500, 24.25000, 14.62500, 10.87500, 27.12500, 35.00000, 30.25000, 36.00000, 44.00000, 27.37500, 29.37500, 28.87500, 28.62500, 13.37500, 35.25000, 28.25000, 32.62500, 33....
## $ pm10     <dbl> 13.052268, 41.948600, 27.041751, 25.072573, 15.343121, 9.364655, 20.228428, 33.134819, 12.118381, 24.761534, 18.126151, 16.013770, 34.991079, 64.945403, 26.941955, 27.022906, 18.837025, 31.859740, 30.923168, 19.894566, 27.882017, 18.508762, 11.845698, 26.687346, 16.612825, 21.641455, 22.672498, 28.101180, 51.776607, 48.741462, 24.686329, 23.784943, 27.762150, 21.600928, 17.050900, 10.157749, 15.943086, 33.010704, 14.955909, 30.410449, 23.914813, 22.972347, 12.712336, 22.719836, 35.676001, 28.373076, 15.662430, 38.744847, 27.597166, 17.612211, 29.768805, 7.340321, 7.856717, 7.908915, 17.834350, 41.124012, 34.052583, 19.749350, 26.126759, 28.129506, 9.940940, 15.980970, 2...
## $ o3       <dbl> 5.659256, 5.525417, 6.288548, 7.537758, 20.760798, 14.940874, 11.920985, 8.678477, 13.355892, 10.448264, 15.866094, 15.115290, 9.381068, 8.029508, 7.066111, 20.113023, 15.363898, 12.713223, 9.616133, 16.840369, 12.758676, 21.024213, 18.665072, 7.131938, 17.167861, 9.960118, 9.167350, 13.613967, 7.945009, 7.660619, 11.882608, 16.676182, 12.032368, 21.849559, 10.887549, 14.894031, 15.957824, 14.391243, 19.749645, 12.397635, 14.193562, 20.492388, 23.091993, 20.171005, 15.453240, 19.526661, 20.019234, 17.297562, 27.013275, 19.055436, 6.890252, 16.313610, 23.015853, 24.990318, 18.939318, 12.526243, 7.962753, 13.194153, 15.178614, 13.860717, 30.992349, 29.260852, 15.413875, 1...
## $ time     <dbl> 3654, 3655, 3656, 3657, 3658, 3659, 3660, 3661, 3662, 3663, 3664, 3665, 3666, 3667, 3668, 3669, 3670, 3671, 3672, 3673, 3674, 3675, 3676, 3677, 3678, 3679, 3680, 3681, 3682, 3683, 3684, 3685, 3686, 3687, 3688, 3689, 3690, 3691, 3692, 3693, 3694, 3695, 3696, 3697, 3698, 3699, 3700, 3701, 3702, 3703, 3704, 3705, 3706, 3707, 3708, 3709, 3710, 3711, 3712, 3713, 3714, 3715, 3716, 3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730, 3731, 3732, 3733, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741, 3742, 3743, 3744, 3745, 3746, 3747, 3748, 3749, 3750, 3751, 3752, 3753, 3754, 3755, 3756, 3757, 3758, 3759, 3760, 3761, 3762, 3763, 3764, 3765, 3766, ...
## $ season   <chr> "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter"...
## $ year     <dbl> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...

head(chic, 10)

## # A tibble: 10 x 10
##    city  date       death  temp dewpoint  pm10    o3  time season  year
##    <chr> <date>     <dbl> <dbl>    <dbl> <dbl> <dbl> <dbl> <chr>  <dbl>
##  1 chic  1997-01-01   137  36      37.5  13.1   5.66  3654 Winter  1997
##  2 chic  1997-01-02   123  45      47.2  41.9   5.53  3655 Winter  1997
##  3 chic  1997-01-03   127  40      38    27.0   6.29  3656 Winter  1997
##  4 chic  1997-01-04   146  51.5    45.5  25.1   7.54  3657 Winter  1997
##  5 chic  1997-01-05   102  27      11.2  15.3  20.8   3658 Winter  1997
##  6 chic  1997-01-06   127  17       5.75  9.36 14.9   3659 Winter  1997
##  7 chic  1997-01-07   116  16       7    20.2  11.9   3660 Winter  1997
##  8 chic  1997-01-08   118  19      17.8  33.1   8.68  3661 Winter  1997
##  9 chic  1997-01-09   148  26      24    12.1  13.4   3662 Winter  1997
## 10 chic  1997-01-10   121  16       5.38 24.8  10.4   3663 Winter  1997

The `ggplot2` Package(ggplot2包）

ggplot2 是個以聲明方式創(chuàng)建圖形的系統(tǒng)，基于此書 The Grammar of Graphics钙勃。你需要提供數(shù)據(jù)蛛碌，告訴 ggplot2 如何把變量映射到美學(xué)外觀、使用何種圖形屬性辖源，細節(jié)問題由ggplot2處理蔚携。

隨后，一幅ggplot圖被從幾個基本原件所創(chuàng)建:

1.數(shù)據(jù):
用于繪圖的原始數(shù)據(jù)The raw data that you want to plot.
2.幾何形狀 geom_:
表示數(shù)據(jù)的幾何圖形克饶。
3.美學(xué)外觀 aes()_:
幾何和統(tǒng)計對象的美學(xué)外觀酝蜒，如顏色、大小矾湃、形狀霉咨、透明度和位置顽腾。
4.標度 scale_:
在數(shù)據(jù)和美學(xué)維度間進行映射锅风，如數(shù)據(jù)范圍到繪圖寬度或賦予顏色的因數(shù)。
5.統(tǒng)計變換 stat_:
數(shù)據(jù)的統(tǒng)計匯總工育，分位數(shù)、擬合曲線和總和坞生。
6.坐標系 coord_:
用于將數(shù)據(jù)坐標映射到數(shù)據(jù)矩形平面的轉(zhuǎn)換。
7分面 facet_:
把數(shù)據(jù)分列成圖形網(wǎng)格卒废。
8.視覺主題 theme():
圖形的總體視覺默認效果沛厨，如背景、網(wǎng)格升熊、坐標軸俄烁、默認字體、大小和顏色级野。

A Default ggplot（默認的ggplot)

首先页屠，我們加載 ggplot2包 (也能通過 tidyverse包加載):

library(ggplot2)
#library(tidyverse)

ggplot2 的語法體系有別于基本R繪圖包。如前所示蓖柔，我們經(jīng)常開始通過 ggplot(data = df, aes(x = 變量1, y = 變量2)) 定義繪圖原件辰企，它告訴ggplot2我們用此數(shù)據(jù)開始工作。因而况鸣，運行這個命令牢贸，只創(chuàng)建了一個繪圖板，因為ggplot2不知道我們想怎樣用這個數(shù)據(jù)進行繪圖镐捧。

(g <- ggplot(chic, aes(x = date, y = temp)))

image.png

提示: 在創(chuàng)建圖形時潜索，用括號創(chuàng)建一個對象，將即刻輸出對象（而不是寫作g <- ggplot(...) 懂酱，然后 g)竹习。

讓我們告訴 ggplot想用的樣式：

g + geom_point()

image.png

別擔(dān)心，我們將在后面學(xué)習(xí)幾個繪圖類型列牺。

Change Color of Points（改變點的顏色）

用這個命令整陌，可以改變美學(xué)外觀，如改變點的顏色：

g + geom_point(color = "firebrick")

image.png

把其用之于我們的繪圖原件，以下基于g的圖形就有了紅色點泌辫。

通過設(shè)置一個不同內(nèi)置的主題随夸，如theme_bw，我們就去除了默認的灰色系的外觀震放。

theme_set(theme_bw())

g + geom_point(color = "firebrick")

image.png

(能在 “Working with Themes”部分找到怎樣使用內(nèi)置主題和怎樣定制主題宾毒。）

Working with Axes（坐標軸）

加入坐標軸標簽

讓我們?yōu)樽鴺溯S加入寫好的標簽

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = expression(paste("Temperature (", degree ~ F, ")")))

image.png

Move Labels Away from the Plot & Change Color(從圖形中移動標簽和改變標簽顏色）

theme() 是一個必要的命令用于修改各種主題元素（文本和標題，框線殿遂、符號伍俘、背景等）。我們將大量使用它勉躺，參見可能的外觀here.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(axis.title.x = element_text(color = "sienna", 
                                    size = 15, vjust = -0.35),
        axis.title.y = element_text(color = "orangered", 
                                    size = 15, vjust = 0.35))

image.png

Change Size & Angle of Tick Text（改變文本的大小和角度）

用angle和vjust調(diào)整文本的位置（0=左對齊癌瘾，0.5=中心對齊，1=右對齊）:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.text.x = element_text(angle = 50, size = 16, 
                                   vjust = 0.5))

image.png

Remove Axis Ticks & Tick Text（去除坐標軸刻度和刻度文本）

很少有理由去這么做-但是要這么做就需要這樣來：

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.ticks.y = element_blank(), 
        axis.text.y = element_blank())

image.png

如果你想去掉主體元素饵溅，參數(shù)的元素就是element_blank()妨退。

Limit Axis Range(限制坐標軸范圍）

有時候想要縮放你的數(shù)據(jù)，可以這樣做蜕企，而不需要從數(shù)據(jù)取子集咬荷。

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)") +
  ylim(c(0, 50))

image.png

另外，可用g + scale_x_continuous(limits = c(0, 50))或者g + coord_cartesian(xlim = c(0, 50))轻掩。前者移除了范圍以外的所有的數(shù)據(jù)點幸乒，而第二個調(diào)整了可視的范圍。

Force Plot to Start at Origin（強制繪圖開始于原點）

與之相關(guān)唇牧，可以強制R繪圖開始于原點：

library(tidyverse)

chic %>% 
  dplyr::filter(temp > 25, o3 > 20) %>% 
  ggplot(aes(x = temp, y = o3)) + 
    geom_point() + 
    labs(x = expression(paste("Temperature higher than 25 ", degree ~ F, "")), 
         y = "Ozone higher than 20 ppb") + 
   expand_limits(x = 0, y = 0)

image.png

用coord_cartesian(xlim = c(0, max(chic_red$temp)), ylim = c(0, max(chic_red$o3)))能得到相同的結(jié)果罕扎。
也能真正的強制繪圖開始于原點。

chic %>% 
  dplyr::filter(temp > 25, o3 > 20) %>% 
  ggplot(aes(x = temp, y = o3)) + 
    geom_point() + 
    labs(x = expression(paste("Temperature higher than 25 ", degree ~ F, "")), 
         y = "Ozone higher than 20 ppb") + 
    expand_limits(x = 0, y = 0) + 
    scale_x_continuous(expand = c(0, 0)) + 
    scale_y_continuous(expand = c(0, 0)) +
    coord_cartesian(clip = "off")

image.png

Axes with Same Scaling（帶有相同標度的坐標）

出于展示的目的丐重，我們用溫度和帶有隨機噪聲的溫度繪圖：

ggplot(chic, aes(x = temp, y = temp + rnorm(nrow(chic), sd = 20))) +
   geom_point() +
   labs(x = "Temperature (°F)") +
   xlim(c(0, 100)) + ylim(c(0, 150)) +
   coord_equal()

image.png

Use a Function to Alter Labels(用函數(shù)改變標簽）

有時要稍稍改變標簽腔召，或者加入單位或百分比符號，而不把他們加入你的數(shù)據(jù)扮惦。這種情況下臀蛛，可以用一個函數(shù)來實現(xiàn)。這是一個例子：

ggplot(chic, aes(x = date, y = temp)) +
   geom_point(color = "firebrick") +
   labs(x = "Year", y = "Temperature (°F)") +
   scale_y_continuous(label = function(x) {return(paste(x, "Degrees Fahrenheit"))})

image.png

Working with Titles(標題）

Add a Title（加入標題）

能用ggtitle()函數(shù)加入標題:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)") + 
  ggtitle("Temperatures in Chicago")

image.png

此外崖蜜，可以用g + labs(tite = "Temperatures in Chicago")浊仆。這里可以加入幾個參數(shù)，如加入副標題和一個標題：

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)",
       title = "Temperatures in Chicago", 
       subtitle = "Seasonal pattern of daily temperatures from 1997 to 2001", 
       caption = "Data: NMMAPS")

image.png

Make Title Bold & Add a Space at the Baseline(將標題加粗和基線加入空格）

face參數(shù)能用于使字體加粗或斜體豫领。margin參數(shù)用 margin函數(shù)提供上下左右的邊界(默認單位是點）抡柿。

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)", 
       title = "Temperatures in Chicago") + 
  theme(plot.title = element_text(size = 15, face = "bold", 
                                  margin = margin(10, 0, 10, 0)))

image.png

(記住邊界參數(shù)的好方法是“trouble”的trbl字母與四個邊很像）宴卖。

Adjust Position of Titles（調(diào)整標題的位置）

用hjust控制對齊（代表水平調(diào)整）:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +  
  labs(x = "Year", y = "Temperature (°F)", 
       title = "Temperatures in Chicago") + 
  theme(plot.title = element_text(size = 15, face = 4, hjust = 1))

image.png

當(dāng)然埃仪，用vjust可以調(diào)節(jié)垂直對齊。

Use a Non-Traditional Font in Your Title（在題目中用非傳統(tǒng)的字體）

值得注意的是可以用不同的字體亚再。為了用安裝到你機器上的字體（可能你在用office程序）鼠锈，我們從extrafont包獲取幫助闪檬。在加載包后，需要輸入和加載安裝到你機器上的:

library(extrafont)
extrafont::font_import()

## Importing fonts may take a few minutes, depending on the number of fonts and the speed of the system.
## Continue? [y/n]

## extrafont::loadfonts(device = "win")

可以先看一下你輸入的字體庫购笆，用fonts()或 fonttable().

現(xiàn)在粗悯，我們使用這些字體家族的一種：

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)", title = "Temperatures in Chicago") + 
  theme(plot.title = element_text(size = 18, family = "Merriweather"))

image.png

(你也可以為你的圖形設(shè)置一種非默認的字體，詳見“Working with Themes”部分同欠。我用Roboto Condensed作為圖形的新的默認字體)

theme_set(theme_bw(base_size = 12, base_family = "Roboto Condensed"))

Change Spacing in Multi-Line Text(在多行文本中改變間隔）

能用 lineheight 參數(shù)改變行的間隔样傍。在本例中，我把行壓縮了一點（行高小于1）铺遂。

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") + 
  labs(x = "Year", y = "Temperature (°F)") + 
  ggtitle("Temperatures in Chicago\nfrom 1997 to 2001") + 
  theme(plot.title = element_text(size = 16, face = "bold", 
                                  vjust = 1, lineheight = 0.75))

image.png

Working with Legends（圖例）

我們將按照季節(jié)為圖形著色衫哥。你會看到默認情況下，圖例的標題正是我們在顏色參數(shù)所定義的:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)")

image.png

Turn Off the Legend（關(guān)閉圖例）

常被問及的第一個問題是：“我怎樣才能去掉圖例襟锐？”撤逢。
很容易就能用legend.position = "none"實現(xiàn)：

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.position = "none")

image

取決于特定的情況，也可以用guides(fill = F)或者用scale_fill_discrete(guide = F)粮坞。

Turn Off Legend Titles（關(guān)閉圖例標題）

正如我們所學(xué)蚊荣，用 element_blank() 來繪制無圖（nothing）:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.title = element_blank())

image

Change Legend Position(改變圖例位置）

如不想把圖例置于右側(cè)，可在用theme中legend.position 參數(shù) :

gplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.position = "bottom")

image

可能的位置包括：上莫杈，右互例，下，左筝闹。

Change Style of Legend Titles（改變圖例標題的樣式）

通過調(diào)節(jié)主題元素legend.title修改圖例題目的外觀:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.title = element_text(color = "chocolate", 
                                    size = 14, face = "bold"))

image

Change Legend Title(改變圖例標題）

改變圖例標題最簡便的方式是使用labs參數(shù)：

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)", color = "Seasons\nindicated\nby colors:") + 
  theme(legend.title = element_text(color = "chocolate", 
                                    size = 14, face = "bold"))

image.png

取決于展示變量的類型媳叨，圖例的細節(jié)可通過scale_color_discrete 或 scale_color_continuous修改。

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.title = element_text(color = "chocolate", 
                                    size = 14, face = "bold")) +
  scale_color_discrete(name = "Seasons\nindicated\nby colors:")

Change Order of Legend Keys(改變圖例的排序）

通過改變season的水平達到目的 :

chic$season <- factor(chic$season, levels = c("Spring", "Summer", 
                                              "Autumn", "Winter"))

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)")

Change Legend Labels(改變圖例標簽）

我們實現(xiàn)以其涵蓋的月份替換季節(jié)標簽:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.title = element_text(color = "chocolate", 
                                    size = 14, face = 2)) +
  scale_color_discrete("Seasons:", labels = c("Mar - May", "Jun - Aug", 
                                              "Sep - Nov", "Dec - Feb"))

image.png

Change Background Boxes in the Legend(改變圖例的背景邊框）

要改變（填充）圖例項的背景色关顷，我們需要調(diào)整主題元素legend.key的設(shè)置:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.key = element_rect(fill = "darkgoldenrod1"),
        legend.title = element_text(color = "chocolate", 
                                    size = 14, face = 2)) +
  scale_color_discrete("Seasons:")

image

如果要全部去除背景肩杈，使用fill = NA。

Change Size of Legend Symbols（改變圖例符號）

圖例中的點有點損失解寝，特別是沒有邊框時扩然。覆蓋默認的點，試試以下代碼:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(legend.key = element_rect(fill = NA),
        legend.title = element_text(color = "chocolate", 
                                    size = 14, face = 2)) +
  scale_color_discrete("Seasons:") +
  guides(color = guide_legend(override.aes = list(size = 6))

image

Leave a Layer Off the Legend(只在圖例中保留一個圖層）

假如你有一個點狀圖層聋伦，接著你又為同樣的數(shù)據(jù)添加了一個邊際圖夫偶。默認情況下，點圖和線圖使得圖例變成這樣：

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  geom_rug() +
  theme(legend.title = element_text(color = "chocolate", 
                                    size = 14, face = 2)) +
  scale_color_discrete("Seasons:")

image.png

可用show.legend = F來關(guān)閉圖例的一個圖層:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  geom_rug(show.legend = F) +
  theme(legend.title = element_text(color = "chocolate", size = 14, face = 2)) +
  scale_color_discrete("Seasons:")

image.png

Manually Adding Legend Items(手動添加圖例項）

ggplot2 不能自動添加圖例觉增，除非你來把美學(xué)參數(shù)（顏色兵拢、大小等）映射到一個變量。我曾不止一次的想有一個圖例逾礁，使得所繪之圖更加清晰说铃。
這是默認圖形:

ggplot(chic, aes(x = date, y = o3)) +
  geom_line(color = "gray") +
  geom_point(color = "darkorange2") +
  labs(x = "Year", y = "Ozone")

image

我們可以強制映射一個圖例到一個變量访惜。用 aes() 映射線和點。在此我們的數(shù)據(jù)集中腻扇，不映射到一個變量债热，而是映射到一個代碼串（以便為每一個線或點繪制一種顏色）。

ggplot(chic, aes(x = date, y = o3)) +
  geom_line(aes(color = "line")) +
  geom_point(aes(color = "points")) +
  labs(x = "Year", y = "Ozone") +
  scale_color_discrete("Type:")

image

已經(jīng)接近幼苛，但是這不是我們想要的窒篱。我們想要灰色和紅色。要改變顏色舶沿，我們使用scale_color_manual()墙杯。另外，使用 guide()函數(shù)覆蓋圖例外觀括荡。

Voila! 現(xiàn)在高镐，我們繪出了灰色的線和紅色的點，還有帶有灰色線和紅色點的圖例符號:

ggplot(chic, aes(x = date, y = o3)) + 
  geom_line(aes(color = "line")) +  
  geom_point(aes(color = "points")) +
  labs(x = "Year", y = "Ozone") +
  scale_color_manual("", guide = "legend", 
                     values = c("points" = "darkorange2", 
                                "line" = "gray")) +
  guides(color = guide_legend(override.aes = list(linetype = c(1, 0), 
                                                  shape = c(NA, 16))))

image

Working with Backgrounds & Grid Lines（背景和網(wǎng)格線）

有很多種方法用一個函數(shù)（如下）改變繪圖的整體外觀畸冲，但是如果你只想改變部分元素的顏色避消，你可以這么做。

Change the Panel Color（改變面板顏色）

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(panel.background = element_rect(fill = "moccasin"))

image

Change Grid Lines(改變網(wǎng)格線）

有兩種網(wǎng)格線：主要網(wǎng)格線指示刻度和在主要網(wǎng)格線之間的次要網(wǎng)格線召夹。

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(panel.background = element_rect(fill = "grey90"),
        panel.grid.major = element_line(color = "gray10", size = 0.5),
        panel.grid.minor = element_line(color = "gray70", size = 0.25))

image

進一步岩喷，你可以在主要網(wǎng)格線和次要網(wǎng)格線間自定義間隔：

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") + 
  scale_y_continuous(breaks = seq(0, 100, 10),
                     minor_breaks = seq(0, 100, 2.5))

image.png

Change the Plot Background Color(改變圖形的背景色）

要改變（填充）圖形區(qū)域的背景色，需要調(diào)節(jié)主題元素的 plot.background:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(plot.background = element_rect(fill = "gray60"))

image

Working with Margins(邊界）

有時候為繪圖邊界加入一點空隙是有用的监憎。同前面的例子相似纱意，我們用theme()函數(shù)的參數(shù)實現(xiàn)。在此使用參數(shù) plot.margin. 如前鲸阔，我們通過plot.background改變背景色偷霉，已經(jīng)展示了默認的邊際。

現(xiàn)在褐筛，讓我們?yōu)樽笥姨砑宇~外的空間类少。參數(shù)plot.margin可以處理多種不同的單位（厘米、英寸等）渔扎，但是需要使用 grid 包的函數(shù)單位來定義單位硫狞。在此我在左右使用用5厘米的邊際。

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") + 
  theme(plot.background = element_rect(fill = "gray60"),
        plot.margin = unit(c(1, 5, 1, 5), "cm"))

image

邊際的順序是上右下左晃痴。一個容易記憶的方法是 "trouble 分別代表四個邊的首字母残吩。

Working with Multi-Panel Plots（分面繪圖）

ggplot2 包有兩個很好的函數(shù)用于創(chuàng)建分面繪圖。他們相互有聯(lián)系倘核，但是有所不同泣侮，facet_wrap 本質(zhì)上基于一個變量創(chuàng)建了一個繪圖帶，而facet_grid 可以接受兩個變量紧唱。

Create a Single Row of Plots Based on One Variable（基于一個變量創(chuàng)建一列圖形）

facet_wrap 創(chuàng)建一個變量的分面圖活尊，前面用一個破折號: facet_wrap(~ variable)隶校。子圖的外觀用 ncol 和 nrow參數(shù)進行控制:

g <- ggplot(chic, aes(x = date, y = temp)) +
       geom_point(color = "chartreuse4") +
       labs(x = "Year", y = "Temperature (°F)")

g + facet_wrap(~ year, nrow = 1) +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

image

Create a Matrix of Plots Based on One Variable(基于一個變量創(chuàng)建繪圖矩陣）

g + facet_wrap(~ year, nrow = 2)

image.png

Allow Scales to Roam Free(自由展示標度）

ggplot2分面圖的默認效果是為各自分面圖使用相同的標度。但有時你想讓自己的數(shù)據(jù)的每個分面有自己的標度蛹锰。這不是個好主意深胳，因為這會給用戶對數(shù)據(jù)以錯誤的印象。你可以設(shè)置scales = "free" 來實現(xiàn):

g + facet_wrap(~ year, nrow = 2, scales = "free")

image

注意x和y軸的范圍是不同的宁仔。

Create a Grid of Plots Based on Two Variables（基于兩個變量創(chuàng)建一個繪圖矩陣）

在兩個變量的情況下，facet_grid 可以完成此任務(wù)峦睡。在此翎苫，變量的順序決定了行數(shù)和列數(shù):

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "orangered") +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  labs(x = "Year", y = "Temperature (°F)") +
  facet_grid(year ~ season)

image

要交換行對列的排布，可以改變 facet_grid(year ~ season) 為 facet_grid(season ~ year)榨了。

Put Two (Different) Plots Side by Side（把兩張圖肩靠肩排列）

有好幾種方法來組合繪圖煎谍。以我愚見，最簡便的方法是 patchwork 包龙屉，
Thomas Lin Pedersen寫的:

p1 <- ggplot(chic, aes(x = date, y = temp, 
                       color = factor(season))) + 
        geom_point() + 
        geom_rug() +
        labs(x = "Year", y = "Temperature (°F)")

p2 <- ggplot(chic, aes(x = date, y = o3)) + 
        geom_line(color = "gray") + 
        geom_point(color = "darkorange2") + 
        labs(x = "Year", y = "Ozone")

library(patchwork)
p1 + p2

image

通過兩圖相除呐粘，可以改變圖的順序（注意此排列方式，一個有圖例而另一個沒有圖例）:

p1 / p2

image.png

而且嵌套作圖也是可以滴转捕！

(g + p2) / p1

image.png

(注意圖的排布作岖，甚至只有一行包含圖例）
另外，Claus Wilke寫的 cowplot 包提供了同樣的功能（以及其它許多好的應(yīng)用）:

library(cowplot)
plot_grid(p1, p2)

… gridExtra包也可以:

library(gridExtra)
grid.arrange(p1, p2, ncol = 2)

Working with Themes（主題）

Change the Overall Plotting Style（改變整體的繪圖格式）

通過應(yīng)用主題可以改變整體外觀五芝。例如痘儡，Jeffrey Arnold把 ggthemes 庫和幾個用戶定義的主題放到一起。清單參見 ggthemes 網(wǎng)站. 不用任何代碼枢步，你就可以調(diào)節(jié)數(shù)種樣式沉删，其中一些樣式和美學(xué)外觀為大眾所熟知。

這里是一個例子復(fù)制了《經(jīng)濟學(xué)人》雜志的繪圖樣式：

library(ggthemes)

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") + 
  ggtitle("Ups and Downs of Chicago's Daily Temperatures") +
  theme_economist() + 
  scale_color_economist(name = "Seasons:") +
  theme(legend.title = element_text(size = 12, face = "bold"))

image.png

另外一個例子是Tufte的繪圖樣式醉途，這是一個基于 Edward Tufte的書 The Visual Display of Quantitative Information的最少筆墨主題矾瑰。此書以Minard’s chart depicting Napoleon’s march on Russia 作為有史以來人類所創(chuàng)建的最好的統(tǒng)計學(xué)繪圖得以廣為人知。Tuftes 圖變的知名是由于純粹的樣式隘擎。請看你自己的圖:

set.seed(2019)
chic.red <- chic[sample(nrow(chic), 50), ]

ggplot(chic.red, aes(x = temp, y = o3)) +
  geom_point() +
  labs(x = "Temperature (°F)", y = "Ozone") + 
  ggtitle("Temperature and Ozone Levels in Chicago") +
  theme_tufte() +
  stat_smooth(method = "lm", col = "black", size = 0.7, 
              fill = "gray60", alpha = 0.2)

image.png

由于Tufte’s樣式是極簡主義殴穴，我們首先減少用于展示的數(shù)據(jù)點的數(shù)目以貼合這條規(guī)則。（不關(guān)心`stat_smooth() '命令货葬，稍后我會解釋）推正。只是添加它以使繪圖更具趣味。

ggplot(chic.red, aes(x = temp, y = o3)) +
  geom_point() +
  labs(x = "Temperature (°F)", y = "Ozone") + 
  ggtitle("Temperature and Ozone Levels in Chicago") +
  theme_tufte() +
  stat_smooth(method = "lm", col = "black", size = 0.7, 
              fill = "gray60", alpha = 0.2) + 
  geom_rangeframe()

image.png

如果你想如此繪圖宝惰，請參見此博客植榕，用R重繪幾個Tufte 圖。

Change the Size of All Plot Text Elements（改變所有繪圖文本元素的大心岫帷）

很容易就一次性改變所有的文本元素的大小尊残。如果你仔細研究默認的主題（見如下“Create and Use Your Custom Theme”章節(jié)）炒瘸，就會注意到所有的元素的大小都相對于基本的大小。所以寝衫，你可以簡單改變基本大小顷扩，如果你想增加繪圖的可讀性：

theme_set(theme_gray(base_size = 30, base_family = "Roboto Condensed"))

ggplot(chic, aes(x = date, y = temp, color = factor(season))) + 
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") + 
  guides(color = F)

image.png

Create and Use Your Custom Theme（用戶自定義的主題的創(chuàng)建和使用）

如果想要改變整體主題，可以用 theme_set,例如theme_set(theme_bw())慰毅。默認的主題是theme_gray隘截。如果要創(chuàng)建自定義的主題，可以從灰度主題提取代碼并修改汹胃。注意 rel()函數(shù)改變了base_size的大小婶芭。

theme_gray

ize = 11, base_family = "", base_line_size = base_size/22, 
##     base_rect_size = base_size/22) 
## {
##     half_line <- base_size/2
##     theme(line = element_line(colour = "black", size = base_line_size, 
##         linetype = 1, lineend = "butt"), rect = element_rect(fill = "white", 
##         colour = "black", size = base_rect_size, linetype = 1), 
##         text = element_text(family = base_family, face = "plain", 
##             colour = "black", size = base_size, lineheight = 0.9, 
##             hjust = 0.5, vjust = 0.5, angle = 0, margin = margin(), 
##             debug = FALSE), axis.line = element_blank(), axis.line.x = NULL, 
##         axis.line.y = NULL, axis.text = element_text(size = rel(0.8), 
##             colour = "grey30"), axis.text.x = element_text(margin = margin(t = 0.8 * 
##             half_line/2), vjust = 1), axis.text.x.top = element_text(margin = margin(b = 0.8 * 
##             half_line/2), vjust = 0), axis.text.y = element_text(margin = margin(r = 0.8 * 
##             half_line/2), hjust = 1), axis.text.y.right = element_text(margin = margin(l = 0.8 * 
##             half_line/2), hjust = 0), axis.ticks = element_line(colour = "grey20"), 
##         axis.ticks.length = unit(half_line/2, "pt"), axis.ticks.length.x = NULL, 
##         axis.ticks.length.x.top = NULL, axis.ticks.length.x.bottom = NULL, 
##         axis.ticks.length.y = NULL, axis.ticks.length.y.left = NULL, 
##         axis.ticks.length.y.right = NULL, axis.title.x = element_text(margin = margin(t = half_line/2), 
##             vjust = 1), axis.title.x.top = element_text(margin = margin(b = half_line/2), 
##             vjust = 0), axis.title.y = element_text(angle = 90, 
##             margin = margin(r = half_line/2), vjust = 1), axis.title.y.right = element_text(angle = -90, 
##             margin = margin(l = half_line/2), vjust = 0), legend.background = element_rect(colour = NA), 
##         legend.spacing = unit(2 * half_line, "pt"), legend.spacing.x = NULL, 
##         legend.spacing.y = NULL, legend.margin = margin(half_line, 
##             half_line, half_line, half_line), legend.key = element_rect(fill = "grey95", 
##             colour = "white"), legend.key.size = unit(1.2, "lines"), 
##         legend.key.height = NULL, legend.key.width = NULL, legend.text = element_text(size = rel(0.8)), 
##         legend.text.align = NULL, legend.title = element_text(hjust = 0), 
##         legend.title.align = NULL, legend.position = "right", 
##         legend.direction = NULL, legend.justification = "center", 
##         legend.box = NULL, legend.box.margin = margin(0, 0, 0, 
##             0, "cm"), legend.box.background = element_blank(), 
##         legend.box.spacing = unit(2 * half_line, "pt"), panel.background = element_rect(fill = "grey92", 
##             colour = NA), panel.border = element_blank(), panel.grid = element_line(colour = "white"), 
##         panel.grid.minor = element_line(size = rel(0.5)), panel.spacing = unit(half_line, 
##             "pt"), panel.spacing.x = NULL, panel.spacing.y = NULL, 
##         panel.ontop = FALSE, strip.background = element_rect(fill = "grey85", 
##             colour = NA), strip.text = element_text(colour = "grey10", 
##             size = rel(0.8), margin = margin(0.8 * half_line, 
##                 0.8 * half_line, 0.8 * half_line, 0.8 * half_line)), 
##         strip.text.x = NULL, strip.text.y = element_text(angle = -90), 
##         strip.placement = "inside", strip.placement.x = NULL, 
##         strip.placement.y = NULL, strip.switch.pad.grid = unit(half_line/2, 
##             "pt"), strip.switch.pad.wrap = unit(half_line/2, 
##             "pt"), plot.background = element_rect(colour = "white"), 
##         plot.title = element_text(size = rel(1.2), hjust = 0, 
##             vjust = 1, margin = margin(b = half_line)), plot.subtitle = element_text(hjust = 0, 
##             vjust = 1, margin = margin(b = half_line)), plot.caption = element_text(size = rel(0.8), 
##             hjust = 1, vjust = 1, margin = margin(t = half_line)), 
##         plot.tag = element_text(size = rel(1.2), hjust = 0.5, 
##             vjust = 0.5), plot.tag.position = "topleft", plot.margin = margin(half_line, 
##             half_line, half_line, half_line), complete = TRUE)
## }
## <bytecode: 0x0000000006c89660>
## <environment: namespace:ggplot2>

現(xiàn)在，讓我們修改默認主題函數(shù)着饥，并看看效果圖：

theme_custom <- function (base_size = 12, base_family = "Roboto Condensed") {
  half_line <- base_size/2
  theme(line = element_line(color = "black", size = 0.5, linetype = 1, lineend = "butt"), 
        rect = element_rect(fill = "white", color = "black", size = 0.5, linetype = 1), 
        text = element_text(family = base_family, face = "plain", color = "black", 
                            size = base_size, lineheight = 0.9, hjust = 0.5, vjust = 0.5, 
                            angle = 0, margin = margin(), debug = F), 
        axis.line = element_blank(), 
        axis.line.x = NULL, 
        axis.line.y = NULL, 
        axis.text = element_text(size = base_size * 1.1, color = "gray30"), 
        axis.text.x = element_text(margin = margin(t = 0.8 * half_line/2), vjust = 1), 
        axis.text.x.top = element_text(margin = margin(b = 0.8 * half_line/2), vjust = 0), 
        axis.text.y = element_text(margin = margin(r = 0.8 * half_line/2), hjust = 1), 
        axis.text.y.right = element_text(margin = margin(l = 0.8 * half_line/2), hjust = 0), 
        axis.ticks = element_line(color = "gray30", size = 0.7), 
        axis.ticks.length = unit(half_line / 1.5, "pt"), 
        axis.title.x = element_text(margin = margin(t = half_line), vjust = 1, 
                                    size = base_size * 1.3, face = "bold"), 
        axis.title.x.top = element_text(margin = margin(b = half_line), vjust = 0), 
        axis.title.y = element_text(angle = 90, margin = margin(r = half_line), 
                                    vjust = 1, size = base_size * 1.3, face = "bold"), 
        axis.title.y.right = element_text(angle = -90, vjust = 0, 
                                          margin = margin(l = half_line)), 
        legend.background = element_rect(color = NA), 
        legend.spacing = unit(0.4, "cm"), 
        legend.spacing.x = NULL, 
        legend.spacing.y = NULL, 
        legend.margin = margin(0.2, 0.2, 0.2, 0.2, "cm"), 
        legend.key = element_rect(fill = "gray95", color = "white"), 
        legend.key.size = unit(1.2, "lines"), 
        legend.key.height = NULL, 
        legend.key.width = NULL, 
        legend.text = element_text(size = rel(0.8)), 
        legend.text.align = NULL, 
        legend.title = element_text(hjust = 0), 
        legend.title.align = NULL, 
        legend.position = "right", 
        legend.direction = NULL, 
        legend.justification = "center", 
        legend.box = NULL, 
        legend.box.margin = margin(0, 0, 0, 0, "cm"), 
        legend.box.background = element_blank(), 
        legend.box.spacing = unit(0.4, "cm"), 
        panel.background = element_rect(fill = "white", color = NA),
        panel.border = element_rect(color = "gray30", 
                                    fill = NA, size = 0.7),
        panel.grid.major = element_line(color = "gray90", size = 1),
        panel.grid.minor = element_line(color = "gray90", size = 0.5, 
                                        linetype = "dashed"),
        panel.spacing = unit(base_size, "pt"), 
        panel.spacing.x = NULL, 
        panel.spacing.y = NULL, 
        panel.ontop = F, 
        strip.background = element_rect(fill = "white", color = "gray30"), 
        strip.text = element_text(color = "black", size = base_size), 
        strip.text.x = element_text(margin = margin(t = half_line, 
                                                    b = half_line)), 
        strip.text.y = element_text(angle = -90, margin = margin(l = half_line, 
                                                                 r = half_line)), 
        strip.placement = "inside", 
        strip.placement.x = NULL, 
        strip.placement.y = NULL, 
        strip.switch.pad.grid = unit(0.1, "cm"), 
        strip.switch.pad.wrap = unit(0.1, "cm"), 
        plot.background = element_rect(color = NA), 
        plot.title = element_text(size = base_size * 1.8, hjust = 0.5, 
                                  vjust = 1, face = "bold", 
                                  margin = margin(b = half_line * 1.2)), 
        plot.subtitle = element_text(size = base_size * 1.3, hjust = 0.5, vjust = 1, 
                                     margin = margin(b = half_line * 0.9)), 
        plot.caption = element_text(size = rel(0.9), hjust = 1, vjust = 1, 
                                    margin = margin(t = half_line * 0.9)),
        plot.tag = element_text(size = rel(1.2), hjust = 0.5, vjust = 0.5), 
        plot.tag.position = "topleft", 
        plot.margin = margin(base_size, base_size, base_size, base_size), complete = T)
}

瀏覽一下修改的外觀犀农，新的面板和網(wǎng)格線，還有坐標軸標度宰掉、文本和標題:

theme_set(theme_custom())

ggplot(chic, aes(x = date, y = temp, color = factor(season))) + 
  geom_point() + labs(x = "Year", y = "Temperature (°F)") + guides(color = F)

image.png

這種改變繪圖設(shè)計的方式值得大力推薦呵哨。因為它讓你能快速一次性改變繪圖的任何元素。在數(shù)秒內(nèi)讓你的結(jié)果符合恰當(dāng)?shù)臉邮讲M足其它任何需求（例如有更大字體大小用于演示或期刊需要）轨奄。

也可以通過theme_update()快速進行設(shè)置：

theme_custom <- theme_update(panel.background = element_rect(fill = "gray60"))

ggplot(chic, aes(x = date, y = temp, color = factor(season))) + 
  geom_point() + labs(x = "Year", y = "Temperature (°F)") + guides(color = F)

image.png

出于聯(lián)系目的孟害，我們用自己的主題，使用白色填充和不帶有白色的小網(wǎng)格線：

theme_custom <- theme_update(panel.background = element_rect(fill = "white"),
                             panel.grid.major = element_line(size = 0.5),
                             panel.grid.minor = element_blank())

Working with Colors(顏色）

對于簡單應(yīng)用顏色在ggplot2中是直接的挪拟，但是當(dāng)你有更高級的需求時纹坐，可能會遇到挑戰(zhàn)。對更高級的處理顏色的主題可以翻閱Hadley’s book 舞丛，這本書涵蓋了很好的內(nèi)容耘子。也有幾個其它的好資源包括 R Cookbook 和ggplot2 online docs。哥倫比亞的 Tian Zheng 創(chuàng)建了實用性的 PDF of R colors.

為了用你的數(shù)據(jù)使用顏色球切，對重要的是需要指導(dǎo)你是否正在處理分類或連續(xù)性的變量谷誓。

Categorical Variables: Manually Select Colors（分類變量：手動選擇顏色）

(g <- ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
        geom_point() + 
        labs(x = "Year", y = "Temperature (°F)") +
        theme(legend.title = element_blank()) +
        scale_color_manual(values = c("dodgerblue4", "darkolivegreen4", 
                                      "darkorchid3", "goldenrod1")))

image.png

Categorical Variables: Use Built-In Palettes(分類變量：使用內(nèi)置的調(diào)色板）

可以用ColorBrewer palettes ，通過 scale_*_brewer吨凑，這是ggplot2包內(nèi)置的函數(shù)：

g + scale_color_brewer(palette = "Set1")

image.png

可以忽略程序前臺的信息捍歪，替換已有的標度為我們想要的。

Categorical Variables: Use Tableau colors（分類變量鸵钝，使用Tableau顏色）

Tableau是著名的可視化軟件糙臼，帶有為人熟知的調(diào)色板。對R的用戶恩商，可以通過 ggthemes的命令 scale_color_tableau()獲得:

library(ggthemes)
g + scale_color_tableau()

image.png

Continuous Variables: Default Color Schemes(連續(xù)性變量：默認的顏色方案）

本例中变逃，我們要改變變量，把顏色賦予ozone怠堪，它是一個連續(xù)性的變量揽乱，同溫度強相關(guān)(higher temperature = higher ozone)名眉。函數(shù)scale_color_gradient()是一個連續(xù)性的梯度，而scale_color_gradient2()是另一個分支凰棉。

這是默認的ggplot2連續(xù)顏色方案 (sequential color scheme):

ggplot(chic, aes(x = date, y = temp, color = o3)) + 
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") +
  scale_color_continuous("Ozone:")

image.png

此代碼繪同樣的圖：

ggplot(chic, aes(x = date, y = temp, color = o3)) +  
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") +
  scale_color_gradient()

這里是另一個默認的顏色方案:

ggplot(chic, aes(x = date, y = temp, color = o3)) +  
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") +
  scale_color_gradient2()

image.png

Continuous Variables: Manually Set a Sequential Color Scheme(連續(xù)性變量：手動設(shè)置連續(xù)顏色方案）

梯度改變的顏色板被用于連續(xù)性變量损拢，可以通過手動設(shè)置scale_*_gradient:

ggplot(chic, aes(x = date, y = temp, color = o3)) + 
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") +
  scale_color_gradient(low = "darkkhaki", high = "darkgreen", "Ozone:")

image.png

溫度數(shù)據(jù)是正態(tài)分布的，因此使用對比性大的顏色方案會怎樣呢（不是連續(xù)性的顏色）撒犀。使用
scale_color_gradient2函數(shù)用于diverging顏色:

mid <- max(chic$o3) / 2  # or mid <- mean(chic$o3)

ggplot(chic, aes(x = date, y = temp, color = o3)) + 
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") + 
  scale_color_gradient2(midpoint = mid, low = "blue4", 
                        mid = "white", high = "red4", "Ozone:")

image.png

Continuous Variables: The Beautiful Viridis Color Palette（連續(xù)性變量：美麗的翡翠調(diào)色板）

viridis color palettes 不僅是你的圖形更漂亮和易于理解福压，還可以使色盲者易于閱讀和以灰色的進行打印。你可以用 dichromate測試在色盲適用的各種形式下你繪圖是怎樣的外觀或舞。
如下多面板的圖形展示了4個翡翠調(diào)色板中的兩個:

g <- ggplot(chic, aes(x = date, y = temp, color = o3)) + 
       geom_point() + 
       labs(x = "Year", y = "Temperature (°F)")

library(viridis)
p1 <- g + scale_color_viridis("Ozone:") + ggtitle("'viridis' (default)")
p2 <- g + scale_color_viridis(option = "inferno", "Ozone:") + ggtitle("'inferno'")
p3 <- g + scale_color_viridis(option = "cividis", "Ozone:") + ggtitle("'cividis'")

library(patchwork)
(p1 + p2 + p3) * theme(legend.position = "bottom")

image.png

將翡翠調(diào)色板用于非連續(xù)的變量也是可能的荆姆。

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.title = element_blank()) +
  scale_color_viridis(discrete = T, end = 1)

image.png

Working with Lines(線條）

Add Horizonal or Vertical Lines to a Plot（為圖形添加水平或垂直的線）

你可能想強調(diào)給定的范圍或閾值，使用geom_hline()(對于水平線) 或者 geom_vline()(用于垂直線)嚷那，在給定的坐標位置繪制一條線 :

ggplot(chic, aes(x = date, y = temp, color = o3)) + 
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") + 
  geom_hline(yintercept = c(0, 73))

image.png

如果你想非0或1位置添加一條斜線胞枕，需用用到'geom_abline()`杆煞。這里是一個例子魏宽，用于添加回歸線。

reg <- lm(o3 ~ temp, data = chic)
ggplot(chic, aes(x = temp, y = o3)) +
  geom_point(alpha = 0.5) +
  labs(caption = paste0("y = ", round(coefficients(reg)[2], 2), 
                        " * x + ", round(coefficients(reg)[1], 2)), 
       x = "Temperature (°F)", y = "Ozone") + 
  geom_abline(intercept = coefficients(reg)[1], slope = coefficients(reg)[2], 
              color = "darkorange2", size = 1.5)

image.png

隨后决乎，我們將學(xué)習(xí)怎樣用命令添加線性擬合線队询，用`stat_smooth(method = "lm")。但是构诚，可能有其它原因讓我們添加一條給定斜率的的線蚌斩。

Working with Text(文本）

Add Labels to Your Data（為你的數(shù)據(jù)添加標簽)

有時，我們想為我們的數(shù)據(jù)點添加標簽范嘱。為避免文本標簽重疊或擁擠送膳，使用1%原始數(shù)據(jù)抽樣，均等代表四個季節(jié)丑蛤。

set.seed(1)

library(tidyverse)
sample <- chic %>% 
  dplyr::group_by(season) %>% 
  dplyr::sample_frac(0.01)

## code without pipes: 
## sample <- sample_frac(group_by(chic, season), 0.01)

chic %>% 
  group_by(season) %>% 
  sample_frac(0.01) %>% 
  ggplot(aes(x = date, y = temp, label = season)) +
    geom_point() + 
    geom_text(aes(color = factor(temp)), hjust = 0.5, vjust = -0.5) +
    labs(x = "Year", y = "Temperature (°F)") +
    xlim(as.Date(c('1997-01-01', '2000-12-31'))) + 
    ylim(c(0, 90)) +
    theme(legend.position = "none")

image.png

好吧叠聋，避免標簽擁擠看起來不work。別擔(dān)心受裹，我們馬上就解決碌补。
可以用'geom_label '框圖:

ggplot(sample, aes(x = date, y = temp, label = season)) +
  geom_point() + 
  geom_label(aes(fill = factor(temp)), color = "white", 
             fontface = "bold", hjust = 0.5, vjust = -0.25) +
  labs(x = "Year", y = "Temperature (°F)") +
  xlim(as.Date(c('1997-01-01', '2000-12-31'))) +
  ylim(c(0, 90)) +
  theme(legend.position = "none")

image.png

很酷的一個包是ggrepel，它為ggplot2提供了geoms以強制以上例子中重疊的標簽分離棉饶。這里厦章，我們展示了兩者，原始的數(shù)據(jù)和我們帶標簽的抽樣數(shù)據(jù):

library(ggrepel)
ggplot(chic, aes(x = date, y = temp, label = season)) +
  geom_point(alpha = 0.5) +
  geom_point(data = sample, aes(color = factor(temp)), size = 2.5) +
  geom_label_repel(data = sample, aes(fill = factor(temp)), 
                   color = "white", fontface = "bold") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.position = "none")

image.png

對于純文本標簽照藻，使用 geom_text_repel同樣奏效袜啃。看一下所有的例子 usage examples.

Add Text Annotation in the Top-Right, Top-Left etc.（在上右邊幸缕，上左等加入文本注釋）

用 ggplot2 可以為 Inf 設(shè)置注釋坐標囊骤，但是用處有限晃择。這里是一個例子 (基于代碼this Google group) 用grid 基于坐標標度來指定位置。0是最低也物，1是最高的位置宫屠。

grobTree函數(shù)來自于 grid 包，可以創(chuàng)建網(wǎng)格圖形對象滑蚯，textGrob 創(chuàng)建文本圖形對象。 annotation_custom() 函數(shù)來自于 ggplot2 滑绒，設(shè)計用grob作為輸入管钳。

library(grid)
my_grob <- grobTree(textGrob("This text stays in place!", 
                             x = 0.1, y = 0.9, hjust = 0, 
                             gp = gpar(col = "black", 
                                       fontsize = 15, 
                                       fontface = "bold")))

ggplot(chic, aes(x = temp, y = o3)) +
  geom_point(color = "tan", alpha = 0.5) + 
  labs(x = "Temperature (°F)", y ="Ozone") +
  annotation_custom(my_grob)

image.png

在你有很多圖有不同的標度時焰手，這個函數(shù)的作用尤其冥想。如下你看到的圖中史侣，坐標軸的范圍有很大變化，上面同樣的代碼能用于在每個分圖上同樣的位置添加注釋。

ggplot(chic, aes(x = temp, y = o3)) +
  geom_point(color = "tan") + 
  labs(x = "Temperature (°F)", y ="Ozone") +
  facet_wrap(~ season, scales = "free") +
  annotation_custom(my_grob)

image.png

Working with Coordinates(標度）

Flip a Plot（翻轉(zhuǎn)圖像）

以圖形的邊翻轉(zhuǎn)圖像非常容易實現(xiàn)钝吮。在此我們引入coord_flip()，需要它來翻轉(zhuǎn)圖像（順便轴踱，我們用 geom_boxpot()來繪制新的圖形嘁傀。

ggplot(chic, aes(x = season, y = o3)) +
  geom_boxplot(fill = "indianred") + 
  labs(x = "Season", y = "Ozone") +
  coord_flip()

image.png

Reverse an Axis（翻轉(zhuǎn)坐標軸）

分別使用scale_x_reverse()或scale_y_reverse()很容易實現(xiàn)坐標軸翻轉(zhuǎn):

ggplot(chic, aes(x = date, y = temp, color = o3)) + 
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") + 
  scale_y_reverse()

image.png

Transform an Axis（坐標軸轉(zhuǎn)換）

… 使用scale_y_log10()或scale_y_sqrt()轉(zhuǎn)換默認的線性繪圖瓤狐。例如如贷，在此用 log10-轉(zhuǎn)換坐標軸(注意婿屹，會引入NA）:

ggplot(chic, aes(x = date, y = temp, color = o3)) + 
  geom_point() + 
  labs(x = "Year", y = "Temperature (°F)") + 
  scale_y_log10(lim = c(0.1, 100))

image.png

Circularize a Plot（環(huán)形繪圖）

通過 coord_polar讓坐標系統(tǒng)成環(huán)形是可以滴（極性圖）溢十。

library(tidyverse)

chic %>% 
  dplyr::group_by(season) %>% 
  dplyr::summarize(o3 = median(o3)) %>% 
  ggplot(aes(x = season, y = o3)) +
    geom_col(aes(fill = factor(season))) + 
    labs(x = "", y = "Median Ozone Level") +
    coord_polar() +
    guides(fill = F)

image.png

此坐標系統(tǒng)可以畫餅圖：

chic %>% 
  dplyr::mutate(o3_avg = median(o3)) %>% 
  dplyr::filter(o3 > o3_avg) %>% 
  dplyr::mutate(n_all = n()) %>% 
  dplyr::group_by(season) %>% 
  dplyr::summarize(rel = n() / unique(n_all)) %>% 
  ggplot(aes(x = "", y = rel)) +
    geom_col(aes(fill = factor(season)), width = 1) + 
    labs(x = "", 
         y = "Proportion of Days Exceeding\nthe Median Ozone Level") +
    coord_polar("y") +
    scale_fill_brewer(palette = "Set1", name = "Season:") +
    theme(axis.ticks = element_blank())

image.png

Working with Chart Types(圖形）

Alternatives to a Box Plot（方框圖的替代圖）

方框圖是很好的，但是可能特讓人厭煩羡铲。有好幾種替代的圖形磕昼，但首先讓我們畫一個普通的方框圖：

image

有效性? Yes.
趣味性? No.

1. Alternative: Plot of Points（替代圖形：繪制點圖）

讓我們用原始數(shù)據(jù)繪制每個數(shù)據(jù)點：

g + geom_point(color = "firebrick")

image

不僅讓人煩鸟廓，而且沒有提供有用的信息。為了改進圖形贮预，可以增加透明度來處理重疊點：

g + geom_point(color = "firebrick", alpha = 0.1)

image.png

然而贝室，這里設(shè)了透明度也不行，因為重疊依然嚴重仿吞，高值或極值仍然不可見滑频。糟糕了，來試試其它方法唤冈。

2. Alternative: Jitter the Points（替代圖形：點的抖動）

試著為數(shù)據(jù)添加一些抖動峡迷。我超愛這個內(nèi)部的可視化，但需要注意你虹，人為添加的抖動增加了數(shù)據(jù)的噪音绘搞，可能導(dǎo)致數(shù)據(jù)的誤導(dǎo)。

g + geom_jitter(aes(color = season), alpha = 0.25, 
                position = position_jitter(width = 0.3)) +
    theme(legend.position = "none")

image.png

3. Alternative: Violin Plots(替代圖形：小提琴圖）

小提琴圖通過方框圖類似傅物，其突出優(yōu)勢是用核密度來展示最多的數(shù)據(jù)夯辖，這是一種有益的可視化圖形。

g + geom_violin(color = "sienna", fill = "red", alpha = 0.4)

image.png

4. Alternative: Combining Violin Plots with Jitter(替代圖形：小提琴圖和抖動合體）

當(dāng)然可以組合使用兩者董饰，同時估計密度和有原始數(shù)據(jù)點：

g + geom_violin(aes(color = season), fill = "gray80", alpha = 0.5) +
    geom_jitter(aes(color = season), alpha = 0.25, 
                position = position_jitter(width = 0.3)) +
    theme(legend.position = "none") +
    coord_flip()

image.png

ggforce 包提供了所謂的sina 函數(shù)蒿褂，抖動的寬度可以通過數(shù)據(jù)的密度來控制圆米，可是讓抖動圖更具視覺吸引力：

library(ggforce)

g + geom_violin(aes(color = season), fill = "gray80", alpha = 0.5) +
    geom_sina(aes(color = season), alpha = 0.25) +
    theme(legend.position = "none") +
    coord_flip()

image.png

5. Alternative: Combining Violin Plots with Box Plots(替代圖形：小提琴圖和框型圖合體）

為了易于估計分位數(shù)，可以為小提琴圖內(nèi)部添加框型圖啄栓，用于指示25%分位數(shù)榨咐、中位數(shù)和75%分位數(shù)：

g + geom_violin(aes(fill = season), color = "transparent", alpha = 0.5) +
    geom_boxplot(outlier.alpha = 0, coef = 0, 
                 color = "gray40", width = 0.1) +
    theme(legend.position = "none") +
    coord_flip()

image.png

Create a Rug Representation to a Plot(為圖形添加地毯圖形）

地毯表示單一定量變量的數(shù)據(jù)，表現(xiàn)形式是沿著坐標軸的標記谴供。多數(shù)情況下块茁，用于散點圖或熱圖來展示一個或兩個變量的整體分布:

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  geom_rug() +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.position = "none")

image.png

ggplot(chic, aes(x = date, y = temp, color = factor(season))) +
  geom_point() +
  geom_rug(sides = "r", alpha = 0.3) +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.position = "none")

image.png

Create a Tiled Correlation Plot(繪制瓦片相關(guān)圖）

第一步是建立相關(guān)矩陣。我們用Pearson相關(guān)性桂肌，因為所有的變量都是相當(dāng)?shù)恼龖B(tài)分布（但是可以用Spearman系數(shù)数焊，如果變量遵循不同的分布模式）。注意因為相關(guān)矩陣有冗余的信息崎场，我們設(shè)置它的一半為 NA佩耳。

corm <- round(cor(chic[ , sort(c("death", "temp", "dewpoint", "pm10", "o3"))], 
                  method = "pearson", use = "pairwise.complete.obs"), 2)
corm[lower.tri(corm)] <- NA
corm

##          death dewpoint    o3 pm10  temp
## death        1    -0.47 -0.24 0.00 -0.49
## dewpoint    NA     1.00  0.45 0.33  0.96
## o3          NA       NA  1.00 0.21  0.53
## pm10        NA       NA    NA 1.00  0.37
## temp        NA       NA    NA   NA  1.00

現(xiàn)在我們用 reshape2包的melt函數(shù)，把矩陣變?yōu)?long 形式谭跨，丟掉 NA 值的記錄：

library(reshape2)
corm <- melt(corm)
corm$Var1 <- as.character(corm$Var1)
corm$Var2 <- as.character(corm$Var2)
corm <- na.omit(corm)
head(corm, 10)

##        Var1     Var2 value
## 1     death    death  1.00
## 6     death dewpoint -0.47
## 7  dewpoint dewpoint  1.00
## 11    death       o3 -0.24
## 12 dewpoint       o3  0.45
## 13       o3       o3  1.00
## 16    death     pm10  0.00
## 17 dewpoint     pm10  0.33
## 18       o3     pm10  0.21
## 19     pm10     pm10  1.00

畫圖我們用 geom_tile干厚，但是如你有很多數(shù)據(jù)，可以考慮用geom_raster螃宙，它會更快蛮瞄。

ggplot(corm, aes(x = Var2, y = Var1)) +
   geom_tile(data = corm, aes(fill = value), color = "white") +
   labs(x = "Variable 2", y = "Variable 1") +
   scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                        midpoint = 0, limit = c(-1, 1), 
                        name = "Correlation\n(Pearson)") +
   theme(axis.text.x = element_text(angle = 45, size = 11, 
                                    vjust = 1, hjust = 1)) +
   coord_equal()

image

Create a Contour Plot(畫登高線圖）

等高線圖是展示三維數(shù)據(jù)的好方法，可以指明數(shù)值的末端閾值谆扎。在此挂捅，我們用露點數(shù)據(jù)畫圖（也就是空氣中的水蒸氣凝結(jié)成液態(tài)錄水的溫度)，與溫度和臭氧的濃度有關(guān):

## interpolate data
library(akima)
fld <- with(chic, interp(x = temp, y = o3, z = dewpoint))

## prepare data in long format
library(reshape2)
df <- melt(fld$z, na.rm = T)
names(df) <- c("x", "y", "Dewpoint")
df$Temperature <- fld$x[df$x]
df$Ozone <- fld$y[df$y]

g <- ggplot(data = df, aes(x = Temperature, y = Ozone, z = Dewpoint)) +
         theme(panel.background = element_rect(fill = "white"),
               panel.border = element_rect(color = "black", fill = NA),
               legend.title = element_text(size = 15),
               axis.text = element_text(size = 12),
               axis.title.x = element_text(size = 15, vjust = -0.5),
               axis.title.y = element_text(size = 15, vjust = 0.2),
               legend.text = element_text(size = 12))
         
g + stat_contour(aes(color = ..level.., fill = Dewpoint))

image.png

震驚了! 如其定義堂湖，露點多數(shù)情況下等于測量溫度闲先。

這些線表示的是不同的露點水平，但這不是一個靚圖无蜂，由于缺少邊界也難以閱讀伺糠。讓我們試著畫一個瓦片圖，用 viridis調(diào)色板編碼露點的臭氧水平和溫度組合：

g + geom_tile(aes(fill = Dewpoint)) +
    scale_fill_viridis(option = "inferno")

image.png

讓等高線圖和瓦片圖合體斥季，在登高線下填充這些區(qū)域會怎樣呢训桶？

g + geom_tile(aes(fill = Dewpoint)) + 
    stat_contour(color = "white", size = 0.7, bins = 5) + 
    scale_fill_viridis()

image.png

Create a Joyplot aka Ridge Plot(一種波濤洶涌,哦不對,是山峰疊巒的可視化方式)

山峰疊巒Joyplots (亦稱之為 屋脊(線)圖)是一種新型的圖形，此刻很流行泻肯。 (有趣的事實: 名字參考Joy Division’s “Unknown Pleasures” LP.封面)

你可以用基本 ggplot 命令繪圖渊迁，此圖的流行的后果就是有人寫了一個包，更加容易繪制此類圖形: ggridges. 這里我們用一下這個包灶挟。

library(ggridges)
ggplot(chic, aes(x = temp, y = factor(year))) + 
   geom_density_ridges(fill = "gray90") +
   labs(x = "Temperature (°F)", y = "Year")

image.png

使用參數(shù) rel_min_height和scale`分別易于區(qū)分重疊和拖尾。這個包帶有自己的主題（但是我更愿意自己造個輪子毒租，見“Create and Use Your Custom Theme”)稚铣。另外箱叁，我們基于年度來變更顏色，使圖形更具吸引力惕医。

ggplot(chic, aes(x = temp, y = factor(year), fill = year)) + 
  geom_density_ridges(alpha = 0.8, color = "white", 
                      scale = 2.5, rel_min_height = 0.01) + 
  labs(x = "Temperature (°F)", y = "Year") + 
  guides(fill = F) + 
  theme_ridges()

image.png

也能用scaling參數(shù)值小于1耕漱，來去除重疊（但是這個有悖于山峰疊巒的本意）。這是一個例子抬伺，用viridis調(diào)色板:

ggplot(chic, aes(x = temp, y = season, fill = ..x..)) + 
  geom_density_ridges_gradient(scale = 0.9, gradient_lwd = 0.5, 
                               color = "black") + 
  scale_fill_viridis(option = "plasma", name = "") + 
  labs(x = "Temperature (°F)", y = "Season:") +
  theme_ridges(font_family = "Roboto Condensed", grid = F)

image.png

也可以比較每個山峰疊巒線的幾個組螟够，根據(jù)他們的組別來上色。這個剽竊了 Marc Belzunces的思路峡钓。

library(tidyverse)

## only plot extreme season using dplyr from the tidyverse
ggplot(data = filter(chic, season %in% c("Summer", "Winter")), 
         aes(x = temp, y = year, fill = paste(year, season))) +
  geom_density_ridges(alpha = 0.7, rel_min_height = 0.01, 
                      color = "white", from = -5, to = 95) +
  scale_fill_cyclical(breaks = c("1997 Summer", "1997 Winter"),
                      labels = c(`1997 Summer` = "Summer", 
                                 `1997 Winter` = "Winter"),
                      values = c("tomato", "dodgerblue"),
                      name = "Season:", guide = "legend") +
  theme_ridges(font_family = "Roboto Condensed") + 
  labs(x = "Temperature (°F)", y = "Year")

image.png

ggridges包在geom_density_ridges命令中用 stat = "binline" 創(chuàng)建直方圖是有用的：

ggplot(chic, aes(x = temp, y = factor(year), fill = year)) + 
  geom_density_ridges(stat = "binline", bins = 25, scale = 0.9, 
                      draw_baseline = F, show.legend = F) + 
  theme_ridges(font_family = "Roboto Condensed") +
  labs(x = "Temperature (°F)", y = "Season")

image.png

Working with Ribbons (AUC, CI, etc.)(絲帶圖）

展示絲帶圖的數(shù)據(jù)不是非常理想妓笙，但是絲帶圖非常有用。此例中能岩，我們將用filter（）函數(shù)繪制一個30天的動態(tài)平均寞宫，以便讓我們的絲帶的噪音不會太多。

chic$o3run <- as.numeric(stats::filter(chic$o3, rep(1/30, 30), sides = 2))

ggplot(chic, aes(x = date, y = o3run)) +
   geom_line(color = "chocolate", lwd = 0.8) +
   labs(x = "Year", y = "Temperature (°F)")

image

如果我們用geom_ribbon()函數(shù)為曲線下的區(qū)域添充拉鹃，看起來會如何呢辈赋？

ggplot(chic, aes(x = date, y = o3run)) +
   geom_ribbon(aes(ymin = 0, ymax = o3run), fill = "orange", 
               color = "orange", alpha = 0.4) +
   geom_line(color = "chocolate", lwd = 0.8) +
   labs(x = "Year", y = "Temperature (°F)")

image.png

很好表明了曲線下面積 area under the curve (AUC)，但是這不是 geom_ribbon()的經(jīng)典實用方式膏燕。替代方法是钥屈，我們繪制一條絲帶，在我們數(shù)據(jù)的上下添加標準差：

chic$mino3 <- chic$o3run - sd(chic$o3run, na.rm = T)
chic$maxo3 <- chic$o3run + sd(chic$o3run, na.rm = T)

ggplot(chic, aes(x = date, y = o3run)) +
   geom_ribbon(aes(ymin = mino3, ymax = maxo3), alpha = 0.5, 
               fill = "darkseagreen3", color = "transparent") +
   geom_line(color = "aquamarine4", lwd = 0.7) +
   labs(x = "Year", y = "Temperature (°F)")

image.png

Working with Smoothings(平滑線）

實用ggplot2為數(shù)據(jù)添加平滑線易如反掌坝辫。

Default: Adding a LOESS or GAM Smoothing（默認方式：添加LOESS或GAM平滑線）

簡單實用stat_smooth() – 甚至都不用公式焕蹄。如果數(shù)據(jù)少于1000個點，這個添加了LOESS 線(局部權(quán)重的散點平滑線阀溶，method = "loess") 或者 GAM線 (廣義加法模型, method = "gam") 腻脏。由于我們點多于1000個，平滑線基于GAM银锻。

ggplot(chic, aes(x = date, y = temp)) + 
  geom_point(color = "gray40", alpha = 0.5)+
  labs(x = "Year", y = "Temperature (°F)") +
  stat_smooth()

image.png

Specifying the Formula for Smoothing(為平滑線定義公式）

ggplot2 讓你可以想用的方式定義模型永品。比如增加GAM的維度（為平滑線加入一些其他額外的起伏）:

ggplot(chic, aes(x = date, y = temp)) + 
   geom_point(color = "gray40", alpha = 0.3) +
   labs(x = "Year", y = "Temperature (°F)") +
   stat_smooth(method = "gam", formula = y ~ s(x, k = 1000), 
               se = F, size = 1.3, aes(col = "1000")) +
   stat_smooth(method = "gam", formula = y ~ s(x, k = 100), 
               se = F, size = 1, aes(col = "100")) +
   stat_smooth(method = "gam", formula = y ~ s(x, k = 10), 
               se = F, size = 0.8, aes(col = "10")) +
   scale_color_manual(name = "k", values = c("darkorange2", 
                                             "firebrick", 
                                             "dodgerblue3"))

image.png

Adding a Linear Fit(加入線性擬合）

雖然默認的是 LOESS或GAM平滑線，添加標準的線性擬合也相當(dāng)容易 :

ggplot(chic, aes(x = temp, y = death)) +
   geom_point(color = "gray40", alpha = 0.5) +
   labs(x = "Temperature (°F)", y = "Deaths") +
   stat_smooth(method = "lm", col = "firebrick", se = F, size = 1.3)

image

Working with Interactive Plots(交互繪圖）

Shiny

Shiny是RStudio的一個包击纬，使得用R創(chuàng)建交互網(wǎng)絡(luò)應(yīng)用非常便捷鼎姐，介紹和實例，見 Shiny homepage.

瀏覽一下潛在用途更振，可以看Hello Shiny例子炕桨。這是第一個:

Plot.ly

Plot.ly 可以用你的ggplot2很容易創(chuàng)建在線的交互圖形。過程相當(dāng)容易肯腕，用R就可以實現(xiàn)献宫。

Remarks, Tipps & Tricks（備注、提示和技巧）

Using `ggplot2` in Loops and Functions（在循環(huán)和函數(shù)中用ggplot2）

基于網(wǎng)格的圖形函數(shù)lattice和ggplot2中創(chuàng)建圖形對象实撒。當(dāng)你在命令行交互使用這些函數(shù)時姊途，結(jié)果就會自動打印涉瘾，但是在 source()或你自己的函數(shù)中，需要明確聲明print(), i.e. 在我們的例子中的print(g) 捷兰。也見 Q&A page of R.

其它資源

“Fundamentals of Data Visualization”, an online book by Claus Wilke about data visualization in general but using ggplot2. (You can find the codes on his GitHub profile.)
“Cookbook for R”, an online and printed book by Winston Chang with reciped to quickly produce the desired plot.

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2019-08-26用ggplot2畫“美圖”

A ggplot2 Tutorial for Beautiful Plotting in R

引言

目錄

Preparation（準備）

The Dataset（數(shù)據(jù)集）

The ggplot2 Package(ggplot2包）

A Default ggplot（默認的ggplot)

Change Color of Points（改變點的顏色）

Working with Axes（坐標軸）

加入坐標軸標簽

Move Labels Away from the Plot & Change Color(從圖形中移動標簽和改變標簽顏色）

Change Size & Angle of Tick Text（改變文本的大小和角度）

Remove Axis Ticks & Tick Text（去除坐標軸刻度和刻度文本）

Limit Axis Range(限制坐標軸范圍）

Force Plot to Start at Origin（強制繪圖開始于原點）

Axes with Same Scaling（帶有相同標度的坐標）

Use a Function to Alter Labels(用函數(shù)改變標簽）

Working with Titles(標題）

Add a Title（加入標題）

Make Title Bold & Add a Space at the Baseline(將標題加粗和基線加入空格）

Adjust Position of Titles（調(diào)整標題的位置）

Use a Non-Traditional Font in Your Title（在題目中用非傳統(tǒng)的字體）

Change Spacing in Multi-Line Text(在多行文本中改變間隔）

Working with Legends（圖例）

Turn Off the Legend（關(guān)閉圖例）

Turn Off Legend Titles（關(guān)閉圖例標題）

Change Legend Position(改變圖例位置）

Change Style of Legend Titles（改變圖例標題的樣式）

Change Legend Title(改變圖例標題）

Change Order of Legend Keys(改變圖例的排序）

Change Legend Labels(改變圖例標簽）

Change Background Boxes in the Legend(改變圖例的背景邊框）

Change Size of Legend Symbols（改變圖例符號）

Leave a Layer Off the Legend(只在圖例中保留一個圖層）

Manually Adding Legend Items(手動添加圖例項）

Working with Backgrounds & Grid Lines（背景和網(wǎng)格線）

Change the Panel Color（改變面板顏色）

Change Grid Lines(改變網(wǎng)格線）

Change the Plot Background Color(改變圖形的背景色）

Working with Margins(邊界）

Working with Multi-Panel Plots（分面繪圖）

Create a Single Row of Plots Based on One Variable（基于一個變量創(chuàng)建一列圖形）

Create a Matrix of Plots Based on One Variable(基于一個變量創(chuàng)建繪圖矩陣）

Allow Scales to Roam Free(自由展示標度）

Create a Grid of Plots Based on Two Variables（基于兩個變量創(chuàng)建一個繪圖矩陣）

Put Two (Different) Plots Side by Side（把兩張圖肩靠肩排列）

Working with Themes（主題）

Change the Overall Plotting Style（改變整體的繪圖格式）

Change the Size of All Plot Text Elements（改變所有繪圖文本元素的大心岫帷）

Create and Use Your Custom Theme（用戶自定義的主題的創(chuàng)建和使用）

Working with Colors(顏色）

Categorical Variables: Manually Select Colors（分類變量：手動選擇顏色）

Categorical Variables: Use Built-In Palettes(分類變量：使用內(nèi)置的調(diào)色板）

Categorical Variables: Use Tableau colors（分類變量鸵钝，使用Tableau顏色）

Continuous Variables: Default Color Schemes(連續(xù)性變量：默認的顏色方案）

Continuous Variables: Manually Set a Sequential Color Scheme(連續(xù)性變量：手動設(shè)置連續(xù)顏色方案）

Continuous Variables: The Beautiful Viridis Color Palette（連續(xù)性變量：美麗的翡翠調(diào)色板）

Working with Lines(線條）

Add Horizonal or Vertical Lines to a Plot（為圖形添加水平或垂直的線）

Working with Text(文本）

Add Labels to Your Data（為你的數(shù)據(jù)添加標簽)

Add Text Annotation in the Top-Right, Top-Left etc.（在上右邊幸缕，上左等加入文本注釋）

Working with Coordinates(標度）

Flip a Plot（翻轉(zhuǎn)圖像）

Reverse an Axis（翻轉(zhuǎn)坐標軸）

Transform an Axis（坐標軸轉(zhuǎn)換）

Circularize a Plot（環(huán)形繪圖）

Working with Chart Types(圖形）

Alternatives to a Box Plot（方框圖的替代圖）

1. Alternative: Plot of Points（替代圖形：繪制點圖）

2. Alternative: Jitter the Points（替代圖形：點的抖動）

3. Alternative: Violin Plots(替代圖形：小提琴圖）

4. Alternative: Combining Violin Plots with Jitter(替代圖形：小提琴圖和抖動合體）

5. Alternative: Combining Violin Plots with Box Plots(替代圖形：小提琴圖和框型圖合體）

Create a Rug Representation to a Plot(為圖形添加地毯圖形）

Create a Tiled Correlation Plot(繪制瓦片相關(guān)圖）

Create a Contour Plot(畫登高線圖）

Create a Joyplot aka Ridge Plot(一種波濤洶涌,哦不對,是山峰疊巒的可視化方式)

Working with Ribbons (AUC, CI, etc.)(絲帶圖）

The `ggplot2` Package(ggplot2包）

Using `ggplot2` in Loops and Functions（在循環(huán)和函數(shù)中用ggplot2）