Lesson 07 for Plotting in R for Biologists
这节课主要两个知识点，一个是图形分面，一个是图形嵌入。
数据加载及清洗
library(tidyverse)
theme_set(theme_gray())
my_data <- read.csv("variants_from_assembly.bed", sep = "\t", quote = '', stringsAsFactors = TRUE, header = FALSE)
names(my_data) <- c("chrom","start","stop","name","size","strand","type","ref.dist","query.dist")
head(my_data)
## chrom start stop name size strand type ref.dist query.dist
## 1 6 103832058 103832059 SV1 185 + Insertion 0 185
## 2 6 102958468 102958469 SV2 317 + Insertion -14 303
## 3 6 102741692 102741693 SV3 130 + Deletion 130 0
## 4 6 102283759 102283760 SV4 1271 + Insertion -12 1259
## 5 6 101194032 101194033 SV5 2864 + Insertion -13 2851
## 6 6 101056644 101056645 SV6 265 + Insertion 0 265
my_data <- my_data[my_data$chrom %in% c(seq(1:22), "X","Y"), ]
my_data$chrom <- factor(gsub("chr", "",my_data$chrom), levels = c(seq(1:22),"X","Y"))
my_data$type <- factor(my_data$type, levels = c("Insertion","Deletion","Expansion","Contraction"))
可视化&分面
ggplot(my_data, aes(x=size, fill=type))+geom_density(alpha=0.5)+xlim(0,500)
ggplot(my_data, aes(x=size, fill=type))+geom_density(alpha=0.5)+xlim(0,500)+facet_grid(type~.)
ggplot(my_data, aes(x=size, fill=type))+geom_density(alpha=0.5)+xlim(0,500)+facet_grid(.~type)
分面的规则语法是：
plot+facet_grid(rows~columns)
比如下面的图按染色体为行、type为列进行分面
ggplot(my_data, aes(x=size, fill=type))+geom_density()+xlim(0,500)+facet_grid(chrom~type)
#也可以反过来
ggplot(my_data, aes(x=size, fill=type))+geom_density()+xlim(0,500)+facet_grid(type~chrom)
可以根据自己的喜好以及数据的分布进行分面，有的时候数据不是很适合分面操作，需要慎重，不然越分越乱，无法直观地展示数据。
图形嵌入
#先设置主题
theme_set(theme_gray()+
theme(
axis.line = element_line(size=0.5),
panel.background = element_rect(fill=NA, size = rel(20)),
panel.grid.minor = element_line(colour = NA),
axis.text = element_text(size = 16),
axis.title = element_text(size = 16)
)
)
big_plot <- ggplot(my_data, aes(x=size, fill=type))+
geom_bar(binwidth = 100)+
guides(fill=FALSE)+
scale_y_continuous(expand = c(0,0))
big_plot
small_plot <- ggplot(my_data, aes(x=size, fill=type))+
geom_bar(binwidth = 5)+
xlim(0, 500)+
theme(axis.title = element_blank())+
scale_y_continuous(expand = c(0,0))
small_plot
图形嵌入需要使用包grid
library(grid)
#构造画布，这一步需要不断调整位置
vp <- viewport(width = 0.8, height = 0.7, x=0.65, y=0.65)#分别设置需要嵌入的图形的宽度、高度以及坐标位置
png("insert_plot.png")
print(big_plot)
print(small_plot, vp = vp)
dev.off()
SessionInfo
sessionInfo()
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 16299)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Chinese (Simplified)_China.936
## [2] LC_CTYPE=Chinese (Simplified)_China.936
## [3] LC_MONETARY=Chinese (Simplified)_China.936
## [4] LC_NUMERIC=C
## [5] LC_TIME=Chinese (Simplified)_China.936
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] forcats_0.2.0 stringr_1.2.0 dplyr_0.7.4
## [4] purrr_0.2.4 readr_1.1.1 tidyr_0.7.2
## [7] tibble_1.4.2 ggplot2_2.2.1.9000 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.15 cellranger_1.1.0 pillar_1.1.0
## [4] compiler_3.4.3 plyr_1.8.4 bindr_0.1
## [7] tools_3.4.3 digest_0.6.15 lubridate_1.7.1
## [10] jsonlite_1.5 evaluate_0.10.1 nlme_3.1-131
## [13] gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.1
## [16] rlang_0.1.6 psych_1.7.8 cli_1.0.0
## [19] rstudioapi_0.7 yaml_2.1.16 parallel_3.4.3
## [22] haven_1.1.1 bindrcpp_0.2 xml2_1.2.0
## [25] httr_1.3.1 knitr_1.19 hms_0.4.1
## [28] rprojroot_1.3-2 glue_1.2.0 R6_2.2.2
## [31] readxl_1.0.0 foreign_0.8-69 rmarkdown_1.8
## [34] modelr_0.1.1 reshape2_1.4.3 magrittr_1.5
## [37] backports_1.1.2 scales_0.5.0.9000 htmltools_0.3.6
## [40] rvest_0.3.2 assertthat_0.2.0 mnormt_1.5-5
## [43] colorspace_1.3-2 labeling_0.3 stringi_1.1.6
## [46] lazyeval_0.2.1 munsell_0.4.3 broom_0.4.3
## [49] crayon_1.3.4
