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導讀查看文件、獲取ID讀取數據表讀取數據表一、查看文件、獲取ID

關鍵參數：

list.files(="條件") # 根據條件獲取文件名

(向量, split="部件" # 切除每個向量值的部件

() # list返回值轉成

dir()  # 查看文件
     [1] "bin.25.tsv" "bin.33.tsv" "bin.36.tsv" "bin.37.tsv" "bin.38.tsv"
     [6] "bin.39.tsv" "bin.43.tsv" "bin.46.tsv" "bin.50.tsv" "bin.56.tsv"
    [11] "bin.58.tsv" "bin.61.tsv" "bin.63.tsv" "bin.65.tsv" "bin.66.tsv"
    [16] "bin.67.tsv" "bin.71.tsv" "bin.81.tsv" "bin.89.tsv" "bin.91.tsv"
    [21] "bin.94.tsv"
files=list.files(pattern="bin.*.tsv")  # 讀取所有文件名
files
     [1] "bin.25.tsv" "bin.33.tsv" "bin.36.tsv" "bin.37.tsv" "bin.38.tsv"
     [6] "bin.39.tsv" "bin.43.tsv" "bin.46.tsv" "bin.50.tsv" "bin.56.tsv"
    [11] "bin.58.tsv" "bin.61.tsv" "bin.63.tsv" "bin.65.tsv" "bin.66.tsv"
    [16] "bin.67.tsv" "bin.71.tsv" "bin.81.tsv" "bin.89.tsv" "bin.91.tsv"
    [21] "bin.94.tsv"
Bin_ID=vector()
for(i in 1:length(files))
{
    Bin_ID[i]=as.character(strsplit(files[i], split=".tsv"))
    # 提取所有文件名
}
Bin_ID
     [1] "bin.25" "bin.33" "bin.36" "bin.37" "bin.38" "bin.39" "bin.43" "bin.46"
     [9] "bin.50" "bin.56" "bin.58" "bin.61" "bin.63" "bin.65" "bin.66" "bin.67"
    [17] "bin.71" "bin.81" "bin.89" "bin.91" "bin.94"

二、讀取數據表

關鍵參數：

read.table()使用多參數

ml=list()  # 定義列表
for(i in 1:length(files))
{
    ml[[i]]=read.table(files[i], sep='\t', na.string="", stringsAsFactors=F, header=T, quote="", comment.char="")
    # 讀取所有數據框到列表ml
}
summary(ml)  # ml列表信息
          Length Class      Mode
     [1,] 7      data.frame list
     [2,] 7      data.frame list
     [3,] 7      data.frame list
     [4,] 7      data.frame list
     [5,] 7      data.frame list
     [6,] 7      data.frame list
     [7,] 7      data.frame list
     [8,] 7      data.frame list
     [9,] 7      data.frame list
    [10,] 7      data.frame list
    [11,] 7      data.frame list
    [12,] 7      data.frame list
    [13,] 7      data.frame list
    [14,] 7      data.frame list
    [15,] 7      data.frame list
    [16,] 7      data.frame list
    [17,] 7      data.frame list
    [18,] 7      data.frame list
    [19,] 7      data.frame list
    [20,] 7      data.frame list
    [21,] 7      data.frame list
head(ml[[1]])  # 打開1#列表，查看基本信息
           locus_tag ftype length_bp   gene EC_number     COG
    1 LBILEGMC_00001   CDS       324             
    2 LBILEGMC_00002   CDS      2589 tmoS_1  2.7.13.3    
    3 LBILEGMC_00003   CDS       852             
    4 LBILEGMC_00004   CDS      1164             
    5 LBILEGMC_00005   CDS      1356             
    6 LBILEGMC_00006   CDS       975     2.5.1.10 COG0142
                                    product
    1                  hypothetical protein
    2          Sensor histidine kinase TmoS
    3                  hypothetical protein
    4                  hypothetical protein
    5                  hypothetical protein
    6 (2E,6E)-farnesyl diphosphate synthase

三、統計“ftype”的各值頻數

CDS_num=vector()
rRNA_num=vector()
tRNA_num=vector()
tmRNA_num=vector()
# 新建向量，用來存儲各值在各個數據框中的頻數
# 預先已知ftype分類變量有四個值
for(i in 1:length(files))
{
    CDS_num[i]=0
    rRNA_num[i]=0
    tRNA_num[i]=0
    tmRNA_num[i]=0
    # 給每個向量賦初值
    
    for(j in 1:length(ml[[i]][,"ftype"]))
    {
        if(ml[[i]][j, "ftype"]=="CDS")
        {
            CDS_num[i]=CDS_num[i]+1
        }
         else if(ml[[i]][j, "ftype"]=="rRNA")
        {
            rRNA_num[i]=rRNA_num[i]+1
        }
        else if(ml[[i]][j, "ftype"]=="tRNA")
        {
            tRNA_num[i]=tRNA_num[i]+1
        }
        else if(ml[[i]][j, "ftype"]=="tmRNA")
        {
            tmRNA_num[i]=tmRNA_num[i]+1
        }
    }
}
prokka_result=data.frame(Bin_ID, CDS_num, tRNA_num, rRNA_num, tmRNA_num)
# 結果匯總成表
write.table(prokka_result, file="prokka_result.txt", sep="\t", quote=F, row.names=F)
# 結果保存

查看結果文件：

圖片.png