导读

查看文件、获取ID

读取数据表

读取数据表

一、查看文件、获取ID

关键参数：

list.files(pattern="条件") # 根据条件获取文件名

strsplit(向量, split="部件" # 切除每个向量值的部件

ascharacter() # list返回值转成character

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") # 读取所有文件名

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、读取数据表

关键参数：

read.table()使用多参数

ml=list() # 定义列表

for(i in 1:length(files))

{

ml[[i]]=read.table(files[i], sep='\t', na.string="", stringsAsFactors=F, header=T, quote="", comment.char="")

# 读取所有数据框到列表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]]) # 打开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、统计“ftype”的各值频数

CDS_num=vector()

rRNA_num=vector()

tRNA_num=vector()

tmRNA_num=vector()

# 新建向量，用来存储各值在各个数据框中的频数

# 预先已知ftype分类变量有四个值

for(i in 1:length(files))

{

CDS_num[i]=0

rRNA_num[i]=0

tRNA_num[i]=0

tmRNA_num[i]=0

# 给每个向量赋初值

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)

# 结果汇总成表

write.table(prokka_result, file="prokka_result.txt", sep="\t", quote=F, row.names=F)

# 结果保存

查看结果文件：

图片.png