今天新学了一个所谓最简单的R包
主要总结如下
![](https://img.haomeiwen.com/i12798714/bab7e4d0e6401c7a.png)
新建表格
![](https://img.haomeiwen.com/i12798714/814913056cba9707.png)
键值对对应关系key-value,如samplename和expression
![](https://img.haomeiwen.com/i12798714/b7dc3850b5a4cb98.png)
reshape data
![](https://img.haomeiwen.com/i12798714/f0c6fc08a2d00e7e.png)
命令生成 a<-data.frame(country = c("A","B","c"),"1999"=paste(c(0.7,23,25),"k"),"2000"=paste(c(2,3,50),"k"))
![](https://img.haomeiwen.com/i12798714/a62d6f2f274ac691.png)
gather(a,X1999,X2000,key = "year",value = "cases")
gather括号里的分别是:,数据框名,需合并的列名(两个),合并后的key列名,value列名。
或者 gather(a,"year","cases",X1999,X2000)
或者排除法gather(a,year,cases,-country)用于列名较多的情况
Handle Missing Values
X<-read.csv('doudou.txt')
![](https://img.haomeiwen.com/i12798714/eac8bd1c3ddfea7c.png)
csv的导入和导出方式
导入:X<-read.csv('doudou.csv')
导出:write.csv(X,'doudou.csv')
drop_na():有空值的,整行删除掉
括号里填数据框名,依据的列名(有空值那一列的列名)
例如drop_na(x,X2)
fill(),根据上一行的数值填充上,例如fill(x,X2)
replace_na(),空值填进去特定的一个数值(还是在应付)
括号里填数据框名,要填的列名=要填的值,例如 replace_na(x, list(X2=3))
实例结果如下
![](https://img.haomeiwen.com/i12798714/3f5eb9a398eedca9.png)
Expand table
complete(x,nesting(bioplanet),fill=list(X2=5))
当用com.csv文件
![](https://img.haomeiwen.com/i12798714/336e124c5267f867.png)
可用命令其中的三个空值,填充上ddd relate
com <- read.csv("com.csv")
complete(com,nesting(geneid,samplename,expression),fill=list(Annotion="ddd relate"))
Expand
据说对a变量输入如下命令,但是好像并没有反应????
pin2<-data.frame(GeneId =rep("gene5",times=3),SampleName =paste("Sample",1:3,sep=""),Expression=c(14,19,18))
split cells(选修)
![](https://img.haomeiwen.com/i12798714/5b80ac7375656f70.png)
今天的内容有些没有领会,还得再研究研究
网友评论
那个命令是新建一个数据框并赋值给pin2,和a没关系哦