df_sum<-apply(df[,2:ncol(df)],2,sum)#+apply(df[,2:ncol(df)],1,sum) order<-sort(df_sum,index.return=TRUE,decreasing =FALSE) df_melt <- melt(df,id.vars = 'Region') colnames(df_melt)<-c("from","to","value") df_melt$to<-gsub("\\.", " ",df_melt$to) df_melt$to<...
将此处换成你自己电脑里的文件 # 绘图 ggplot(df,aes(x = Ratio, y = reorder(Term,Enrichment,sum), # 按照富集度大小排序 size = Size, colour=Enrichment)) + geom_point(shape = 16) + # 设置点的形状 labs(x
tidy data: 1.Each variable forms a column 2.Each observation forms a row 3.Each type of observational unit forms a table install.packages(c("tidyr","dplyr")) library(tidyr) 1. 2. df <- data.frame(x=c(NA,"a.b","a.d","b.c")) # x #1 <NA> #2 a.b #3 a.d #4 b.c...
b = 2 c = 1 # apply along each row: row_fn <- apply(data, 1, fn, x2 = b, x3 = c) # apply along each column: col_fn <- apply(data, 2, fn, x2 = b, x3 = c) 让我们检查一下第fn行和第fn列 代码语言:javascript 复制 row_fn 代码语言:javascript 复制 col_fn apply()系列...
group_by(cyl) %>%summarise(mean = mean(disp), n = n())#> # A tibble: 3 × 3#> cyl mean n#> <dbl> <dbl> <int>#> 1 4 105. 11#> 2 6 183. 7#> 3 8 353. 14# Each summary call removes one grouping level (since that group# is now just a single row)mtcars %>% ...
### Field map identification (name for each Plot). 'fieldPlot' argument can be a number or name. DataTable<-read.csv("DataTable.csv",header = T) fieldMap<-fieldMap(fieldPlot=DataTable$Plot, fieldColumn=DataTable$Row, fieldRow=DataTable$Range, decreasing=T) fieldMap # The new column...
拆分后的y需要赋值,不然还是原来的y 3.按位置提取字符串 str_sub(x,5,9) #空格也要算上 x字符串里5-9位置 4.字符检测 str_detect(x2,"h") # h类似于地雷,扫雷探测,含有h的就会变成TURE,生成长度相等的逻辑值向量,可以用来取子集,可以提出来含有h的地雷 ...
The argumentweightsallows to adjust the contribution of each sample to the log-likelihood. This allows for instance to balance different group sizes: library(zeroSum) # load the R package x <- log2(exampleData$x) # load example data and use log transformation y <- exampleData$ylogistic #...
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