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In order for most data analysis to be viable, the data set in question usually must be cleaned and formatted first. This point can be especially clear in web scraped data that generally comes in the form of raw character strings. The variables were systematically extracted from the raw text files in an iterative process as new issues presented themselves.

Deborah Nolan

Once cleaned, the data was combined into a dataframe that can easily be used in further analysis. Data cleaning is a very important step in the data science process. In the modern age of big data, it is very rare to find a dataset that is in a state that is conducive to effective analysis without any pre-work. It is not uncommon for a raw data set to have missing values, formatting errors disparate layouts, and a host of other issues that need to be dealt with.

Web scraping can introduce even more issues that need to be cleaned before a data set can be worked with. When scraping data from a website, it is not necessarily returned in an easy to work with format such as a table or JSON array.

The data could simply be a large string of characters. This is the case in the data set being utilized in this case study. The data is from through and was obtained from www. The data comes in the form of text files. There is one text file for each year. These files contains variables such as name, hometown, age, gun time, and net time. Not all files adhere to the same format or contain the same set of variables.

The objective of this case study is to answer Question 7 in that chapter. The code in this case study is based on the code in the Nolan and Lang book. The text files containing race data are not formatted the same way from year to year. Some years have additional columns and the headers differ from year to year. We will need to develop a way to programatically determine where the data begins and what columns each file contains.

We will start by reading in one of the files and looking at the first 10 rows to see what it looks like. Comparing these two files, we can see that they both contain similar headers. The column names are separated from the data by a row of equal signs. The equal signs have a space in between them where a new column begins.

We will now begin to determine how to write a function that can read in all of the files and account for their differences.

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The first step is to determine where the line containing the equals signs is in the file. This can be accomplished using the grep command and searching for a string of 3 equals signs at the beginning of a line.

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With this information, the column headers can be extracted from the previous row and the data can be pulled from the rows following this row. In order to make the header row easier to parse, we will convert the column names to lower case format. Each column will need to be extracted individually. In order to determine how to do this, we will begin with the age column. We will attempt to locate the age data by finding the starting position of the age column in the header row. We will attempt to pull the ages of runners by taking positions 49 and 50 of the data.

This appears to have worked. The youngest female runner in was 12 and the oldest was There are also no null values, which is a positive sign. Since the column widths can change from year to year, an easier method for determining where columns begin and end is to search for the breaks in the spacer row. This can be done using a global regular expression.

This returns the locations of all of the spaces in the row of equals signs, but since there is not a space at the beginning of the row, a 0 can be appended to the output to specify the starting position of the first column. The locations in the searchLocs variable can now be used to extract all of the values from the data with the substr function. This logic can now be encapsulated in a function to make it easier to run on all of the text files.

In this function, we will add additional logic to deal with cases where the last character in a row is not a blank space. The data contains lots of columns, but they are not all necessary. We will right a function to select only the name, age, hometown, gun time, net time and time columns. The summary statistics using this new function match the more manual extraction method that was used earlier. This will make it easier to clean up all of the files instead of just the data.

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Next, the disparity in column names from file will need to be accounted for. The column names vary from file to file, so we will create a list of the first few characters of each of the desired columns. Since some columns are not present in some files, this will need to be accounted for by setting the values for those missing columns to NA.

This results in a matrix of strings. Now we will take a closer look at this data to see what it looks like.


Since the data contains a time column, but no gun time or net time columns, the time column is populated and the other two columns contain all NA values. All of these helper functions can now be wrapped up into a larger function that can be used to extract the column data from all of the data files. This function accepts a character vector as an argument, not an actual file.

So in order for the files to be parsed, they will first need to be loaded into R.

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Now that the files have been read, the extractVariables function can be run to pull out all of the desired variables. This results in an error. Now, the files have been read in successfully and the number of rows in each year looks reasonable. With the data read into R, we can now convert the convert them into a format that lends itself to analysis more easily and perform more data cleaning as necessary.

To do this, we will convert the character matrix into a dataframe and then convert each column to a data type that makes sense for each respective variable. We will start by converting the age column from the data to numeric values and checking its validity. This small subset of ages look correct, but further investigation is needed. Python for data analysis. Kevin Sheppard - Lecture Notes. Harrington P. Machine learning in action. Shelter Island, N. Zumel N, Mount J. Practical data science with R.

Shelter Island, New York: : Manning Data science in R: a case studies approach to computational reasoning and problem solving. Kabacoff R. R in action: data analysis and graphics with R. Shelter Island, NY: : Manning Wickham H. Advanced R. Sarkar D. Lattice: multivariate data visualization with R. Lantz B. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications.

Birmingham: : Packt Publishing Limited R packages.

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