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10 Steps For Data Exploration in R

Data Exploration is an important part of how companies and Brands can gain insight into their raw data and findings. An integral important of data exploration is data visualization, a method through which data is presented in graphical or picture format. This method enables the decision makers to grasp and understand analytics in an easier manner as it is presented in a graphic manners. Additionally, this makes it simple for individuals to understand difficult concepts and identify new patterns as well. Interactive visualization is being adopted by many brands where the concept of visualisation is taken a step further with the use of technology. Through the use of drills and charts, interactive visualisation helps brands to understand data and insights in a much more intricate and personal manner than before.

How data visualisation evolve data exploration?

The concept of using pictures in order to understand pictures has been in use for quite some period of time. Since the 17th century maps and graphs were used by explorers and inventors to find new lands and countries. Later the invention of pie charts in the early 1800s also helped to expand the area of data visualisation as well. Many decades later,Charles Minard mapped Napoleon’s invasion of Russia, which was another step in data visualisation. The map depicted the size of the army as well the path that Napoleon retreat from Moscow. By tying the same information to time and temperature, he provided a more detailed and better understanding of this historic event.

However, all these developments were nothing compared to the advancements that took place with the rise of technology. Data visualisation evolved and grew in leaps and bounds with the rise of technology. The advancement and growth of computers and smartphones made it possible for brands to process large amounts of data in a fast and real manner on one hand and gain insights faster on the other hand. With so much advancements in technology, data visualisation is growing at such a rapid pace that it changing the face of brands and companies around the world in a drastic manner.

Data Exploration

Why does the future of brands lies in data visualisation?

Big Data is growing every single day and is impacting almost every sector and economy around the world. It has created almost limitless opportunities for brands to expand and grow their network in a comprehensive and successful manner. Finding value in big data is therefore one of the most important investment that any brand can focus on in the current times. Take for example the retail sector which can go a long way through the various applications that are being developed within the big data sector.

For example insights about how big data can improve customer relationships can help brands unlock better and new opportunities that did not exist before. Like wise, other industries can also create tangible benefit in the improvement of their customer and client experience through the use of Big data and this will eventually help to boost the enterprise’s growth and development.

Everyone knows that visual communication is one of the simplest and easiest way to communicate. This is because the human brain according to research, process visuals 60,000 times faster than text, making it one of the best ways in which brands can communicate their story to customers, clients and stakeholders. That is why charts and graphs are simple ways in which brands can make sense important insights that in other ways might be more complex and much more easier than reading reports and spreadsheets as well. Data visualisation is therefore a quick and simple manner in which complicated concepts can be understood by people around the company.

Further, data visualisation can help brands in the following ways: 1. Data visualisation can help brands to focus on areas that need special attention or improvement 2. Data visualisation can help brands to understand customer behavior in a better manner, thereby ensuring better brand loyalty and empowerment 3. Data visualisation can help brands to understand the market and brand functioning in an intimate manner 4. Data visualisation is a great way to understand and predict future market trends, thereby helping brands to adapt to these changes in a better manner.

Data visualisation and exploration is today helping companies to go beyond their boundaries and explore new opportunities, regardless of their industry and size. Here are some ways in which data visualisation can help companies:

  • Data exploration can help companies to comprehend data in a quick and swift manner: Graphical data allows brands to make sense of large amounts of data in a simple and strategic manner. This helps companies to gain insights and draw conclusions on various topics and thereby take strategic decisions that can empower themselves, both internally and externally. And since graphical data are easier to make sense, brands can address problems even before they arise.
  • Data exploration helps companies to identify patterns and relationships among large amounts of data: Large amounts of data when presented in graphic form can make more sense and are much more easy to understand. Business when they understand the links between these data can take better choices and adopt strategies that will help them reach both their short term and long tern goals in a fast and swift manner.
  • Data exploration can help brands to adapt to changing times and even predict the future in a better manner: The economy and companies across all sectors are extremely competitive. In order to be successful, brands have to understand the dynamics of market and adapt to the trends in a successful manner. In fact, when brands can successfully predict market trends, their chances of success automatically becomes higher. In short, data visualisation is one of the best ways in which brands can predict the market trends and thereby gain a competitive edge as well. By addressing problems that affect the quality of product or customer experience, brands can prevent problems before they become major hurdles in the growth and development of companies.
  • Data visualisation can help companies to communicate their brand story in an effective fashion: Like mentioned before, visual communication is an effective medium to share stories not just with clients but with the customer base as well. When brands communicate their message and story to the wider audience they can create effective engagement and empowerment, both within the company as well as outside it.

data exploration

With so many benefits and advantages, it is important that brands build a predictive model that will help them in the task of understanding data. A good predictive model is not dependent on machine learning or programming language but must be able to perform data exploration in a comprehensive manner. It is important that data scientists learn how to explore data in a comprehensive manner before they understand the process of creating algorithms. Data exploration has one of the most important function that is performed with the help of predictive modeling, that is why they are of critical importance for the growth and development of any company.

Data exploration helps companies to gain deeper and better insights and thereby helping companies to create a better model. Considering the popularity of R programming and its expansive use in data science, here are certain steps that can help in the creation of data exploration in R. While these are generic steps, it is possible to customise codes as well after their  creation.  Here are the eleven main steps involved in the creation of data exploration in R.

  • Step 1 : The process of loading data files:

Data sets can be inputted in various formats which include .XLS, .TXT, .CSV and JSON among others. In R, it is easy to load data from any of the above source, mainly due to the simple syntax and availability of predefined libraries. By reading the code, the user can load the file in a simple manner.

  • Step 2 : The process of converting a variable into a different data type:

The type conversions in R works by adding a character string to a numeric vector, which then in turn converts all the elements in the vector to character.  At this point, it is important to remember that conversion of data structure is extremely critical than the process of format transformation.

  • Step 3 : Transpose a data set is the next step in data exploration:

Sometimes, a dataset is needed to transpose from a wide structure to much narrow structure. There is a code available for users to do this in an effective manner.

  • Step 4 : The next step in data exploration is sorting of DataFrame

Sorting of data is done by using order as an index. This index is based on multiple variables which are either ascending or descending in nature.

  • Step 5 : Creation of plots or histogram is the next step in data exploration

Data visualisation on R is extremely simple and helps to create effective graphs.

  • Step 6 : Generate frequency tables with R

The most basic and effective way to understand the distribution across categories is through the use of frequency tables.

  • Step 7 : Sample data set in R

A few random indices are needed in order to generate a sample dataset in R. This will help to create a sample data set in R.

  • Step 8 : Remove duplicate values of a variable

An extremely simple process, it is easy to remove duplicates on R.

  • Step 9 : Find class level count average and sum in R:

This is done by apply functions that are present in data exploration techniques.

  • Step 10 : Recognise and treat missing values and outliers

Missing value can be inputed with the mean of other numbers and this allows for creation of better values as well.

  • Step 11 : Merge and join data sets is the final step for data exploration

Joining two data frames is the final function and they are done by combining two data frames of common variables. In addition, appending datasets is another function that is used in a frequent manner. In order to join two data frames in a vertical manner, the rbind function is used. So while two data frames must have the same  variables but do not have the same order.

Data exploration is therefore an emerging technology trend but it requires some level of wisdom and understanding before it can be implemented in companies and brands. It is important that brands have a solid grasp on data on one hand and understand the goals, needs and audience on the other hand. Preparing data visualisation technology requires brands to understand a few things so that they can implement data exploration in a better manner. Here are some things that brands must try to implement before they finally begin to use data exploration:

  1. Understand the data that brands are trying to visualise including the uniqueness and size of the data concerned
  2. Determine the medium of visualisation and the kind of information that you want to show to the rest of the world
  3. Try to understand your audience in a better manner, so that brands can make use of visual information in a better manner
  4. Learn how to Use visual communication in such a manner that you can connect with your audience in a simple and effective manner

Once brands have understood and answered these questions, they can explore data in a much better and sophisticated manner than before. Big data brings with itself new challenges and opportunities and at the same time the challenges need to be addressed in a simple manner.  In conclusion, there are many ways in which companies can achieve faster data exploration and this process starts by taking better and informed decisions. There is a reason why data exploration is such an important catchphrase and term. It is an incredible tool that can not just improve connections within but also outside the organisation. At the same time it is important that brand managers understand the strategic importance of data exploration and realise that these insights are delivered  in a manner that is profitable and helpful. Otherwise, it becomes very simple for brands to get lost in the world of big data without being able to gain any important insight or value.

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10 Steps For Data Exploration in R


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