# 03.条形图绘制

### 清除当前环境中的变量

``````rm(list=ls())
``````

### 设置工作目录

``````setwd("C:/Users/Dell/Desktop/R_Plots/03barplot/")
``````

## barplot函数绘制条形图

``````# 使用mtcars内置数据集
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

# 使用table函数进行计数
counts <- table(mtcars\$cyl)
counts
##
##  4  6  8
## 11  7 14

# 默认条形图垂直放置
barplot(counts,xlab = "mtcars\$cyl", ylab = "counts",
col = heat.colors(3))
``````
image.png
``````# 设置horiz = T参数进行水平放置
barplot(counts,xlab = "mtcars\$cyl", ylab = "counts",
col = heat.colors(3), horiz = T)
``````
image.png
``````# 绘制分组条形图
counts <- table(mtcars\$cyl,mtcars\$carb)
counts
##
##     1 2 3 4 6 8
##   4 5 6 0 0 0 0
##   6 2 0 0 4 1 0
##   8 0 4 3 6 0 1

# 默认为堆砌条形图
barplot(counts, xlab = "cyl", ylab = "carb", legend = T,
col = c("red","blue","green"), main = "Group of cyl and carb")
``````
image.png
``````# 设置beside = T参数绘制并列分组条形图
barplot(counts, xlab = "cyl", ylab = "carb", beside = T, legend = T,
col = c("red","blue","green"), main = "Group of cyl and carb")
``````
image.png

## ggplot2包绘制分组条形图

``````# 读取示例数据
# 查看数据
##                            Annotation cluster_1 cluster_2 cluster_3
## 1                       Naive B cells         8        96       875
## 2       Mature B cells class switched         7        51       337
## 3 Mature B cells class able to switch         2        22       280
## 4                      Mature B cells         1        12       131
## 5                         Pro B cells       457       196      1044
## 6                       Early B cells        33       204       106
##   cluster_4 cluster_5
## 1      2071      2392
## 2      1187      1590
## 3       856      1079
## 4       294       353
## 5       689       256
## 6       108        37

library(ggplot2)
library(reshape2)
data <- melt(data,variable.name = "Cluster", value.name = "Count")
## Using Annotation as id variables
##                            Annotation   Cluster Count
## 1                       Naive B cells cluster_1     8
## 2       Mature B cells class switched cluster_1     7
## 3 Mature B cells class able to switch cluster_1     2
## 4                      Mature B cells cluster_1     1
## 5                         Pro B cells cluster_1   457
## 6                       Early B cells cluster_1    33

# 设置position = "stack"参数绘制堆砌条形图
ggplot(data, aes(Cluster, Count, fill=Annotation)) +
geom_bar(stat = "identity", position = "stack") + theme_bw() + theme(legend.position = "top")
``````
image.png
``````# 设置position = "dodge"参数绘制并列条形图
ggplot(data, aes(Cluster, Count, fill=Annotation)) +
geom_bar(stat = "identity", position = "dodge") + theme_bw() + theme(legend.position = "top")
``````
image.png
``````# 设置position = "fill"参数绘制填充条形图
ggplot(data, aes(Cluster, Count, fill=Annotation)) +
geom_bar(stat = "identity", position = "fill") + theme_bw() + theme(legend.position = "top")
``````
image.png
``````# 添加coord_flip参数进行水平翻转
ggplot(data, aes(Cluster, Count, fill=Annotation)) +
geom_bar(stat = "identity", position = "fill") +
theme_bw() + theme(legend.position = "top") + coord_flip()
``````
image.png

## ggpubr包绘制带误差棒的条形图

``````# 读取示例数据
data <- read.table("demo2_barplot.txt",header = T,row.names = 1, check.names = F, sep = "\t")
# 查看数据
##            Gender   Stage GeneSymbol      TPM
## GSM1328533 Female 2 weeks       Egfr 1.296193
## GSM1328534 Female 2 weeks       Egfr 1.337968
## GSM1328535 Female 2 weeks       Egfr 1.352709
## GSM1328536 Female 2 weeks       Egfr 1.293170
## GSM1328537 Female 6 weeks       Egfr 1.059719
## GSM1328538 Female 6 weeks       Egfr 1.184652

library(ggpubr)
ggbarplot(data, x = "Stage", y = "TPM",
color = "Gender", fill = "Gender",
add = c("mean_se","dotplot"), width = 0.6,
position = position_dodge())
``````
image.png
``````ggbarplot(data, x = "Stage", y = "TPM", orientation = "horiz",
color = "Gender", fill = "Gender",
add = c("mean_se","jitter"), width = 0.6,
palette = c("#00AFBB", "#E7B800"),
position = position_dodge())
``````
image.png
``````sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## 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] stats     graphics  grDevices utils     datasets  methods   base
##
## other attached packages:
## [1] ggpubr_0.2.1   magrittr_1.5   reshape2_1.4.3 ggplot2_3.2.0
##
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.1       knitr_1.23       tidyselect_0.2.5 munsell_0.5.0
##  [5] colorspace_1.4-1 R6_2.4.0         rlang_0.4.0      plyr_1.8.4
##  [9] stringr_1.4.0    dplyr_0.8.3      tools_3.6.0      grid_3.6.0
## [13] gtable_0.3.0     xfun_0.8         withr_2.1.2      htmltools_0.3.6
## [17] yaml_2.2.0       lazyeval_0.2.2   digest_0.6.20    assertthat_0.2.1
## [21] tibble_2.1.3     ggsignif_0.5.0   crayon_1.3.4     purrr_0.3.2
## [25] glue_1.3.1       evaluate_0.14    rmarkdown_1.13   labeling_0.3
## [29] stringi_1.4.3    compiler_3.6.0   pillar_1.4.2     scales_1.0.0
## [33] pkgconfig_2.0.2
``````