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本期图片

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数据处理

library('tidyverse')
# install.packages('janitor')
library('janitor')
library('readxl')
library('lubridate')
ppa_price <- read_excel("2021_utility-scale_solar_data_update_0.xlsm", sheet = "PPA Price by Project (PV only)", range = "A24:L357"
)
str(ppa_price)
# 长宽转换
ppa_price_long <- ppa_price %>% pivot_longer(cols = c(CAISO:Hawaii),names_to = "region", values_to = "price", values_drop_na = TRUE)
# 列名清洗
ppa_price_long <- ppa_price_long %>%clean_names()
# Convert Region to Factor
ppa_price_long <- ppa_price_long %>%mutate(region_cat = factor(region, ordered = TRUE))# 提取年份
str(ppa_price_long)
ppa_price_long <- ppa_price_long %>% mutate_if(is.POSIXct, as_date) %>%  # 日期格式为POSIXctmutate(ppa_year = year(ppa_execution_date))

散点图

# 颜色设置
color.pal <- c("#4E79A7", #dark blue"#F28E2B", #orange"#E15759", #red"#76B7B2", #teal"#59A14F", #green"#BAB0AC", #gray"#B07AA1", #purple"#FF9DA7", #pink"#9C755F", #brown"#EDC948" #yellow
)## 散点图
p1 <- ppa_price_long %>% ggplot(aes(x = ppa_execution_date,y = price,size = capacity_mw,color = region))+geom_point(shape = 1, stroke = 1.2)+scale_size(guide = "none")+scale_color_manual(values = color.pal, name = "Region")+#ggthemes::scale_color_tableau()+scale_x_date(date_breaks = "2 years", date_labels = "%Y")+ # x轴日期间距及格式ylab("PPA Price (2020 $/MWh)")+theme_light()+theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank(),panel.grid.minor.y = element_blank(),panel.border = element_blank(),axis.line.x.bottom = element_line(color = "lightgray"),axis.line.y.left = element_line(color = "lightgray"),axis.title.y = element_text(size = 10),axis.title.x = element_blank())+guides(color = guide_legend(override.aes=list(shape = 15)))p1

堆叠柱状图

## 堆叠柱状图
year_totals <- ppa_price_long %>% group_by(ppa_year) %>% arrange(ppa_year, region) %>% summarise(year_total = prettyNum(trunc(sum(capacity_mw)),big.mark = ","),region = first(region))p2_data = ppa_price_long %>% group_by(region, ppa_year) %>% summarise(capacity_mw = sum(capacity_mw)) %>% left_join(year_totals)p2 <- p2_data %>% ggplot(aes(x = ppa_year,y = capacity_mw,color = region,fill = region,label = year_total))+geom_col(width = 0.7)+geom_text(position = position_stack(), vjust = -0.5, size = 3) +scale_fill_manual(values = color.pal)+scale_color_manual(values = color.pal)+scale_x_continuous(breaks = seq(2006, 2021, 1))+scale_y_continuous(limits = c(0, 3500), expand = c(0,0),labels = c("0K", "1K", "2K", "3K"),breaks = c(0, 1000, 2000, 3000))+ylab("Capacity (MW-AC)")+theme_light()+theme(legend.position = "top",axis.title.y = element_text(size = 10),panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank(),panel.grid.minor.y = element_blank(),panel.border = element_blank(),axis.line.x.bottom = element_line(color = "lightgray"),axis.line.y.left = element_line(color = "lightgray"),axis.title.x = element_blank(),axis.text.x = element_text(angle = 90))p2

饼图

cumulative_cap <- ppa_price_long %>% group_by(region) %>% summarise(cumulative_capacity = round(sum(capacity_mw))) %>% mutate(capacity_label = prettyNum(cumulative_capacity, big.mark = ","))p3 <- ggplot(cumulative_cap,aes(x="",y=cumulative_capacity,fill=region))+geom_bar(stat="identity", width=1)+scale_fill_manual(values = color.pal)+coord_polar("y", start=0, direction = -1)+annotate("text", y = 4000, x = 1,label = "West (non-ISO)\n 7,919", size = 3)+annotate("text", y = 18000, x = 1.1,label = "CAISO \n 8,649", color = "white", size = 3)+annotate("text", y = 8800, x = 1.6,label = "Southwest (non-ISO) \n 2,538", size = 3)+annotate("text", y = 13000, x = 1.7,label = "ISO-NE \n 326", size = 3)+annotate("text", y = 11000, x = 1.7,label = "PJM \n 739", size = 3)+theme_void()+labs(caption = "Cumulative Capacity (MW-AC)")+theme(legend.position="top",legend.title = element_blank(),legend.key.size = unit(.5, 'cm'),plot.caption = element_text(hjust = .5),plot.title = element_text(hjust = 0.5, size = 12),plot.subtitle = element_text(hjust = 0.5, size = 10))+guides(fill = guide_legend(nrow = 5, byrow = TRUE))
p3

拼图

library('ggpubr')
p_full <- ggarrange(ggarrange(p1, p2, nrow=2, legend = "none"), p3, widths = c(2, 1), heights = c(1,1))p_fullannotate_figure(p_full,top = text_grob("Utility-Scale Solar \n Power Purchase Agreement Prices for PV", face = "bold",size = 14, color = color.pal[1]),bottom = text_grob("Source: Berkeley Lab", hjust = 1,x = .9,face = "italic",size = 9,color = color.pal[1])
)

参考

• Analytics X3: Tidy Tuesday US Solar | KPress R Blog(https://kpress.dev/blog/analytics-x3-tidytuesday-us-solar/)

往期内容

1. (免费教程+代码领取)|跟着Cell学作图系列合集

2. Q&A | 如何在论文中画出漂亮的插图？

3. Front Immunol 复现 | 1. GEO数据下载及sva批次校正(PCA可视化)

4. R绘图 | 气泡散点图+拟合曲线

5. 跟着 Cell 学作图 | 桑葚图(ggalluvial)

6. R绘图 | 对比条形图+连线

7. R绘图 | 一幅小提琴图的美化之旅