xingpel

filter <- function(threshold,zeros,countData){
        #1,threshold is the critical mean value of each row;
        #2,countdata is the input original countdata that is going to filter;
        #3,zeros is the critical number of zero reads that each row contained that would filtered out,
        #every row that contain more than this number of zero reads will be deleted. 
        Means <- rowMeans(countData, na.rm = FALSE)
        fullcountdata <- cbind(countData,Means)
        filcountdata <- subset(fullcountdata,Means>threshold)
        filcountdata$Means <- NULL
        #delete the rows that have zero reads less than "zeros" number.

xingpel

corr <- function(directory, threshold = 0) {
        result <- NULL
        #for each csv file, use function complete() to obtain the complete number and
        #compare with threshold
        for (i in 1:332) {
                # get the number into 000 format, exp: 1 -> 001
                num <- sprintf("%03d",i)
                # read the file and saved to data
                path <- paste(directory,num,".csv",sep="")
                data <- read.csv(path)

xingpel

complete <- function(directory, id = 1:332) {
        #1,create an output matrix
        output <- NULL
        #2,for each id file, calculate NA numbers
        for (i in id){
                num1 <- sprintf("%03d",i)
                path <- paste(directory,num1,".csv",sep="")
                data <- read.csv(path)
                nobs <- 0
                #read each line

xingpel

pollutantmean <- function(directory, pollutant, id = 1:332) {
        #1, read id csv files and merge into one datafile called data.
        data <- NULL
        for (i in id) {
                count <- sprintf("%03d",i)
                path <- paste(directory,count,".csv",sep="")
                data <- rbind(data, read.csv(path))
        }
        #2, return the mean of the specific pollutant, 
        #with variable precision = 4

xingpel

data_file = open("sampleData.txt")
result_file = open ("result.txt","w")
        for line in data_file:
                if "abc" in line: 
                        key=line.split(' ')[0]
                        key=key.split('>')[1]
                        for line2 in open("dictionary.txt"):
                                if key in line2:
                                        result_file.write(">"+line2)
                else:

xingpel

with open ("anotation.txt", "r") as annotation:
anotation_dict = {}
for line in annotation:
line = line.split()
if line: #test whether it is an empty line
anotation_dict[line[0]]=line[1:]
else:
continue
 
# really should not parse the fasta file by myself. there are