ggplot2包|创建美丽有用的折线图

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我对于R语言做数据可视化，甚是喜欢。我也一直学习和实践着如何用R语言创建一系列美丽而有用的可视化图形。ggplot2包和以其为基础的扩展包，我都喜欢去尝试，同时，也会阅读这方面相关的资料（书籍、博客和代码等），其目的就是为了指导自己创建能够表达信息、带来价值、又具有美学的可视化图形。本文分享利用ggplot2包及相关包创建美丽而有用的折线图。折线图是一种常用的图形，可以直观展示出一种变化趋势（上升、下降、波动和保持不变等），比方说，我们经常看到用折线图表示时间序列关系，反映某个指标或者不同组下某个指标在时间轴上面的变化动态。

本文逐步地说明，如何用ggplot2包做出美丽而可用的折线图。

首先，加载所需R包

if (!require("pacman")) install.packages("pacman")p_load(ggplot2, ggthemes, dplyr, readr, showtext)

然后，数据准备工作，我们以一份出口数据集为例

# 准备数据集chilean_exports <- "year,product,export,percentage2006,copper,4335009500,812006,others,1016726518,192007,copper,9005361914,862007,others,1523085299,142008,copper,6907056354,802008,others,1762684216,202009,copper,10529811075,812009,others,2464094241,192010,copper,14828284450,852010,others,2543015596,152011,copper,15291679086,822011,others,3447972354,182012,copper,14630686732,802012,others,3583968218,202013,copper,15244038840,792013,others,4051281128,212014,copper,14703374241,782014,others,4251484600,222015,copper,13155922363,782015,others,3667286912,22"exports_data <- read_csv(chilean_exports)# 数据结构理解str(exports_data)

第三，根据这份数据，逐步绘制和完善折线图，用于表示不同产品类型，在不同年份里，出口量的变化趋势情况

3.1 绘制基本的折线图

p1 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line()p1

大家可以思考，基于这个基础图，如何做修改和完善，从而让它更美丽和有用，以达成可以公布的目标。

3.2 调整线条宽度

图中每条折线图的宽度太细，有必要放大一些。

p2 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5)p2

3.3 修改图例的标签和布局位置

exports_data <- exports_data %>%  mutate(product = factor(product, levels = c("copper","others"),                          labels = c("Copper ","Pulp wood, Fruit, Salmon & Others")))
p3 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  theme(legend.position = "bottom",         legend.direction = "horizontal",        legend.title = element_blank()) # 用于控制图例 底部 水平 不显示标题p3

这样做的目的，让图例更有效地表达要传递的信息。

3.4 修改x轴的刻度

观察发现,x轴的刻度不是我们所希望表现的，我们需要它呈现一串年份序列。

p4 <- p3 + scale_x_continuous(breaks = seq(2006, 2015, 1))p4

3.5 设置坐标轴的标签和标题

一幅图形x轴和y轴分别表示什么含义，以及图像所要表示的主题是什么，这可以通过坐标轴的标签和标题来说明。

p5 <- p4 +   labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million")p5

3.6 调整直线的配色

配色是一门艺术，可以参照和学习其它优秀图形的配色。

colour <- c("#5F9EA0", "#E1B378")p6 <- p5 + scale_color_manual(values = colour)p6

3.7 字体设置

font_add("Tahoma","Tahoma.ttf")font_add("Roboto Condensed", "RobotoCondensed-Regular.ttf")showtext_auto()

3.8 主题设置

1）白色主题

p7_1 <- ggplot(data = exports_data,               aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  scale_x_continuous(breaks = seq(2006, 2015, 1)) +  labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million") +  scale_colour_manual(values = colour) +  theme_bw() +  theme(legend.position = "bottom",        legend.direction = "horizontal",        legend.title = element_blank())p7_1

2）使用经济学杂志的主题

p7_2 <- ggplot(data = exports_data,               aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  scale_x_continuous(breaks = seq(2006,2015,1)) +  labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million") +  theme_economist() + scale_colour_economist() +  theme(axis.line.x = element_line(size = .5, colour = "black"),        legend.position = "bottom",        legend.direction = "horizontal",        legend.title = element_blank(),        plot.title = element_text(family = "Roboto Condensed"),        text = element_text(family = "Roboto Condensed"))p7_2

3）使用Five Thirty Eight网站风格的主题

p7_3 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  scale_x_continuous(breaks = seq(2006,2015,1)) +  labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million") +  theme_fivethirtyeight() + scale_colour_fivethirtyeight() +  theme(axis.title = element_text(family = "Roboto Condensed"),        legend.position = "bottom", legend.direction = "horizontal",        legend.title = element_blank(),        plot.title = element_text(family = "Roboto Condensed"),        legend.text = element_text(family = "Roboto Condensed"),        text = element_text(family = "Roboto Condensed"))p7_3

4）自定义主题

完整代码：

##################################ggplot2包画出美丽而有用的点线图#RUser#2021-03-06################################## 加载所需R包if (!require("pacman")) install.packages("pacman")p_load(ggplot2, ggthemes, dplyr, readr, showtext)
# 准备数据集chilean_exports <- "year,product,export,percentage2006,copper,4335009500,812006,others,1016726518,192007,copper,9005361914,862007,others,1523085299,142008,copper,6907056354,802008,others,1762684216,202009,copper,10529811075,812009,others,2464094241,192010,copper,14828284450,852010,others,2543015596,152011,copper,15291679086,822011,others,3447972354,182012,copper,14630686732,802012,others,3583968218,202013,copper,15244038840,792013,others,4051281128,212014,copper,14703374241,782014,others,4251484600,222015,copper,13155922363,782015,others,3667286912,22"exports_data <- read_csv(chilean_exports)
# 数据结构理解str(exports_data)
# 绘制基础的折线图p1 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line()p1
# 调整线条的宽度p2 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5)p2
# 修改图例变量的标签和布局的位置exports_data <- exports_data %>%  mutate(product = factor(product, levels = c("copper","others"),                          labels = c("Copper ","Pulp wood, Fruit, Salmon & Others")))
p3 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  theme(legend.position = "bottom",         legend.direction = "horizontal",        legend.title = element_blank()) # 用于控制图例 底部 水平 不显示标题p3
# 修改x轴的刻度# 使用scale_x_continuous函数p4 <- p3 + scale_x_continuous(breaks = seq(2006, 2015, 1))p4

# 设置坐标轴标签和图形标题p5 <- p4 +   labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million")p5
# 调整配色colour <- c("#5F9EA0", "#E1B378")p6 <- p5 + scale_color_manual(values = colour)p6
# 字体设置font_add("Tahoma","Tahoma.ttf")font_add("Roboto Condensed", "RobotoCondensed-Regular.ttf")showtext_auto()
# 使用主题# 1）白色主题p7_1 <- ggplot(data = exports_data,               aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  scale_x_continuous(breaks = seq(2006, 2015, 1)) +  labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million") +  scale_colour_manual(values = colour) +  theme_bw() +  theme(legend.position = "bottom",        legend.direction = "horizontal",        legend.title = element_blank())p7_1
# 2) 使用经济学角度的图片可视化p7_2 <- ggplot(data = exports_data,               aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  scale_x_continuous(breaks = seq(2006,2015,1)) +  labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million") +  theme_economist() + scale_colour_economist() +  theme(axis.line.x = element_line(size = .5, colour = "black"),        legend.position = "bottom",        legend.direction = "horizontal",        legend.title = element_blank(),        plot.title = element_text(family = "Roboto Condensed"),        text = element_text(family = "Roboto Condensed"))p7_2
# 3) Five Thirty Eightp7_3 <- ggplot(data = exports_data,             aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  scale_x_continuous(breaks = seq(2006,2015,1)) +  labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million") +  theme_fivethirtyeight() + scale_colour_fivethirtyeight() +  theme(axis.title = element_text(family = "Roboto Condensed"),        legend.position = "bottom", legend.direction = "horizontal",        legend.title = element_blank(),        plot.title = element_text(family = "Roboto Condensed"),        legend.text = element_text(family = "Roboto Condensed"),        text = element_text(family = "Roboto Condensed"))p7_3
# 4) 创建自己的主题colour <- c("#40b8d0", "#b2d183")p7_4 <- ggplot(data = exports_data,                aes(x = year, y = export, colour = product)) +  geom_line(size = 1.5) +  scale_x_continuous(breaks = seq(2006,2015,1)) +  labs(title = "Composition of Exports to China ($)",       subtitle = "Source: The Observatory of Economic Complexity") +  labs(x = "Year", y = "USD million") +  scale_colour_manual(values = colour) +  theme(panel.border = element_rect(colour = "black", fill = NA, size = .5),        axis.text.x = element_text(colour = "black", size = 10),        axis.text.y = element_text(colour = "black", size = 10),        legend.key = element_rect(fill = "white", colour = "white"),        legend.position = "bottom", legend.direction = "horizontal",        legend.title = element_blank(),        panel.grid.major = element_line(colour = "#d3d3d3"),        panel.grid.minor = element_blank(),        panel.background = element_blank(),        plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),        text = element_text(family = "Tahoma"))p7_4