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Using RStudio to Prepare and Clean Data

There is now more data available than previously, the depth and scope is increasing daily. The explosion of the internet and connected devices has increased this and big data is now big business. With the increase in data open to us, so has the need for research of this data. Many companies use this data to anticipate future fads. Also, what has improved is the tools we use to analysis and present this data in a significant way.

In the past statistical software was very expensive and often without graphical capabilities. Enter the R programming language an instrument that supports both, first released in 1995 with the first secure build in 2000, now on version 3 that was released in 2013. R is a free of charge open source task with over 7000 increase deals available. Many companies such as Google and Facebook are employing R for their data evaluation.

In this laboratory book we can look at cleaning and getting ready data so that it can analysed. We use R Studio room which can be an IDE (Integrated development environment) for the R programming language. R Studio can be found as an open source or commercial version, it has two editions R Studio room desktop and R Studio room Server and operates on Glass windows, macOS and Linux os's.

The dataset we have is from the united kingdom government, and is based on MOT shops in Great britain, Scotland, and Wales, it includes data such as name, address, post codes, telephone amounts and categories of vehicles analyzed. On quick analysis of the dataset there are a great number of blank areas, extra white spots, typo's in calling column as well as second mobile phone numbers segregated by the "/" image.

Using R Studio we will try to tidy and clean the dataset. On this lab e book we will make clear the various commands and techniques used to get ready the data for analytical examination.

  1. Make a copy of the info to utilize:

Method: Here we make a copy of the initial dataset x2016motsitelist and call it MotList, this is good practice as you will not contaminate the initial dataset.


Result: From the above display screen shot you can view we have renamed our dataset to MotList, utilizing the name of the dataset in R studio room it lists the dataset in a display screen dump on the unit.

  1. Get the Stucture of your Data Frame

Method: by using the str() order in the unit we get the framework in our data.


Result: by using the composition command word str() we can easily see that our dataset has 22, 980 things and 14 different factors. The next lines which contain $ indicate column headings and screen a few of the components contained in these columns. This command just offers a list with components and titles.

  1. View the data

Method: Using the top command to view the data.


Result: by using this command word the first 6 details are displayed in the unit window.

  1. ID titles of columns

Method: We use the Labels command to display column labels.


Result: this displays the names of our own columns in the unit window.

  1. Summary of what is contained in the columns

Method: we use the Summation command word to get an overview of the info in our columns.


Result: the conclusion command provides us an overview for each vector in the data frame, explains to us inside our case that the distance is 22980 rows, that all vectors are identity classes.

  1. Missing values

Method: we will use the is. na demand, blend of is. na with the any control and lastly the sum command line to check on for missing ideals in the info.


Result: the result of the is. na demand profits a Boolean true or false result on the data set to tell us if a missing value exists or not.


Result: with the utilization of the any order we find that there surely is indeed absent data in the dataset.


Result: with the utilization of the sum demand we get the amount of missing files, which is 149097 in this case.

  1. Rename columns inside our data arranged.

Method: we use the colnames order to improve the columns inside our data establish that are numbered 1, 2, 3, 4, 5 and 7.


Result: with the utilization of these commands we change the name of the columns using the name to recognize which column to apply the name change to. We use the names(MotList) to confirm the result.


  1. Remove NA from the various categories of vehicle that are Mot tested

Method: we create another backup of our dataset and call it MotListMod, upon this dataset we will change the NA beliefs in the columns that people renamed earlier so that the several types of vehicles analyzed will have complete values and no absent data. We do that giving the dataset name and then your $ column name, we then use the which command which is. na to improve the worthiness to the desired result.


Result: As is seen from the display shot above, we've improved the NA beliefs in the six columns of our own dataset, our dataset now tells us in case a Mot test centre provides out tests on the various vehicle categories Y or N, were as before it only advised us the if the centre do Y with a empty field for N. Again, we run the sum is. na command word on both datasets we've, now the MotListMod dataset has far less Na's in the dataset.

  1. Remove and tidy up VTS Telephone column.

Method: Firstly, using the GSUB command line we removed cases of "Tel. " and "TEL. " from our column, subsequently, we divide the column in two areas #1 1 and 2 with the Independent command as a few of the test centres have two mobile phone numbers segregated by "/" in the dataset, thirdly we tidy up the white space.


Using GSUB wrongly above didn't produce the required outcome, however in the two monitors below we get the required outcome.

The above screen shows were the VTS Cell phone column is put into different areas.

Trimming white space from leading of calling numbers.

Removing the NA's from the VTS Phone number2

Result: By using GSUB and discovering the column we wished to target, we substituted the cases of "Tel. " and "TEL. " inside our dataset with " " whitespace, we then proceeded to split the column into two different areas, when we have this it created a lot of NA's in the next column because don't assume all test centre has two phone numbers, to counter work this we replace the NA's with the value 0. We then tidy up the white space at the start of the two columns.

  1. Write to CSV file in R studio

Method: We will write the MotListMod3 dataset to a CSV file with the WRITE. CSV command line.


Result: The above order writes the dataset to a csv document and can be viewed or shared with others, see above screen shot of the data file in excel.

  1. Outliers and story function.

Method: using the HIST command line we create a histogram of the automobiles column, the columns school had to be changed to a factor form to help make the function work, also we used the Desk command to rely the amount of "Y" and "N" in this column.


In the display screen shot above you can see a histogram of the autos column.

Result: No outliers can be found as our columns only have a "Y" or "N" within the different type of vehicles analyzed columns. Also, our data was of class personality, this had to be converted to one factor form in order we're able to use the histogram function on the column automobiles. We used the desk demand on the column to display a numeric end result for N = 1054 and Y=21926.

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