How to use the fread function in R [part of data.tables]

A fread function is a useful tool for reading files of the same column and row dimensions. Similar to the read. table function, fread is a simpler function for reading a specified byte of data and then being able to call it back from our dataframe in a flash. Though simpler, there are still parameters to work inside to fully utilize the search tool and its multifunctionality, so let’s see how we can fully utilize fread.

Often times we face the challenge of importing a large data file from our library to our interface. We use to fopen command to search for the specific column name of the byte we want to recall, and the read function searches through the stored files until it hits on the specific string of code we tell it. Now, at this point the fopen command will bring up the resource it searched out, but if for some reason the file isn’t formatted properly it won’t open, so make sure your syntax is correct before proceeding.
If the file is able to open, then we can make use of other utilities included in the function. The read function comes with a remarkable level of precision in its recall of a csv file, and the more data you list the more specific information you can recall from the library. Let’s say we have a dataset
We want to specifically call out cities with a below-average rate of reported inhabitants eating out. With the cities names entered, we can narrow down the columns listed and get the exact data we want shown.
mydt fread(“citiesthatdineout.csv”,
select = c(“date”, “county”, “state”, “percent”))
and if I want a separate record of these cities, I can put them in my own column to recall later.
my_cols c(“date”, “county”, “state”, “percents”)
mydt fread(“us-counties.csv”, select = my_cols)
Having this level of precision at your disposal, it’s easy to see how read is the first pick by programmers for faster data sortation.
Customizing columns is just the start of fread() usability. Let’s say you’re looking to narrow down the columns to just include the data you want. We can use the command tool grep to pick out lines with targeted words in them. Inputting ca fread() with each word will call up more specific strings of information, but that still leaves a lot of information if not formatted properly.
Other functions can be added to specify columns and more than one header.
CitiesSpecify fread(cmd = “grep -E ‘Urban| Downtown|Metro|BusinessDistrict’ us-cities.csv”,
col.names = names(mydt10))
All this data can seem chaotic, but with full utilization of the reading function and its tools, a programmer can call up a specific header from a library in no more than a few clicks.
There will be glitches given some characters that a function can’t comprehend. Some code texts contain specific NUL characters involving many low values that can’t be readily interpreted by our software reader. Faced with a command running up against these NUL values, the read function can be conflicted in returning results from commands like these:
n – 1
bytes c(charToRaw(“a=b\nA B C\n1 2 3\n”), rep(as.raw(0), n), charToRaw(“4 5 6\n”))
writeBin(bytes, “test.txt”)
Simply changing the value specified to anything above zero will make it perfectly acceptable to the function, or you could force the command to read all NUL with an added character or as the last line in the answer display. There are many ways to work around specific bugs and glitches. Using the read tool more frequently, you will find what works best for your intentions and integrate your own set of commands when unzipping files for your needs.

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