Hello welcome friends, this is ravindu from mathblog93. Today i am gonna show you how to csv file manipulations and do a very simple data visualization.
Often, we come across instances where we need to extract data from several .csv files. In that case, i advice you to gather those .csv files and put them into one folder.
In my case, i have created a folder named “bankData” in my working directory and put my .csv file into it. There i have 4 .csv files.
First of all, what i am going to do is to read all .csv files in my folder to R. For that, i use ‘list.files’ function.
filenames <- list.files("bankData",full.names=TRUE)Here, what i have done is that i have stores all 4 of my file names to the character vector “filenames”. If you type “str(filenames)” in the console, you will see that “filenames” is a character vector of length 4.
After that, we are going to create a list of 4 data frames using the function “lapply” and an anonymous function. What this does is that this takes the list of file names that was returned before and import those file into R using “read_csv” function.
csv_files <- lapply(filenames,function(i){
read.csv(i, header=FALSE, stringsAsFactors = FALSE, skip=4)
})
Here you can see that we have set the argument “stringsAsFactors” to FALSE. Because in R, character variables that are passed into a data frame are converted into factors. We need to avoid this as we are going to do some manipulations with this data. Hence, we have set that argument to FALSE.
Argument “skip” specifies the no. of lines of the data file to skip before it starts reading the data. The .csv files we have here contains title texts in the first 4 rows, therefore, the argument “skip” is set to 4.
Once this is done we have all our data in our working environment.
Now if you type “csv_files” in the console, you will see that there is a column names “V3” which contains text characters rarely. We need to get rid of this column as this is of no use to our analysis.
Following code does that for us ;
csv_files<-lapply(csv_files, function(x) { x["V3"] <- NULL; x })Here, function lapply is applied with the list of csv files and an anonymous function as arguments. What the anonymous function does here is that it assigns “V3” a null value hence removing it from the list.
Next step is to combine the 4 data frames in the list “csv_files” to a single data-frame.
df <- do.call(rbind.data.frame, csv_files)Above code uses “row bind” to combine the 4 data frames into a single data frame. The function “do.call” constructs and executes a function call from a function and a list of arguments to be passed to it.(Refer R Documentation : https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/do.call)
Since this post is becoming very lengthy, we will discuss the rest of the analysis in the next tutorial.
Thanks for reading this guys! Stay tunes with us for latest updates!
Cheers!
Image credits : http://systemicresult.com/index.php/data-science/data-analysis
ප්රක්ෂිප්ත යනු උසස් පෙළ සංයුක්ත ගණිතය විශය නිර්දේශයේ ව්යවහාරික ගණිතය කොටස යටතේ එන විශය කොටසකි. අද අපි විමසන්නේ ප්රක්ෂිප්තයක මූලික සිද්ධාන්ත පිළිබදවයි.
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