IS428 2016-17 Term1 Assign2 Gwendoline Tan Wan Xin

Theme Of Interest

The analysis will be conducted using the 101 Innovations – Research Tools Survey Dataset from Kaggle. With new tools and sites appearing to assist people in the entire research process, the types of tools/sites that people get to choose from are enormous. However, one could not have the ability to use all available tools at the same time. As such, I’m interested to identify the factors that could affect people’s choice of tools/sites in supporting their research process.

Analytical/Investigation Questions

The analysis attempts to answer one main question:

• What factors could affect people’s choice of tools/sites used throughout the research process?
  
  

However, as the analysis process evolves, two additional questions arises:

• In which phase of the research do people use the most number of tools?
• Are there different patterns of the type of tools used to search data based on different roles or disciplines or across different regions of the world?

The "Analysis & Visualization Construction Process" section will further elaborate on the evolution of questions from one to the other.

Selected Data Attributes for Analysis

The following data attributes are selected for analysis:

• Participant’s Role
• Country
• Discipline
• Date of Publication
• Tools/Sites to Search Literature/Data

Data Transformation Process

Before the analysis began, the dataset is analysed to identify its format and attributes. The dataset comes from a survey conducted across 20,663 researchers, librarians and other groups. In the survey, participants are presented with questions that provide them with the option to choose multiple answers. For example, one of the question in the survey asked participants to check all tools that they use during the process of researching for data. They could select multiple tools, such as a combination of Google Scholar and Web of Science. Each tool is a column in the dataset and if users indicate that they use the tool, the column will be filled. However, if they did not indicate it, the column will be left blank. An example of a row in the dataset is as follows:

After understanding the format of the data, the following is performed to transform the data into a format that could be used for analysis.

1. Firstly, the columns in the initial dataset is grouped into its own respective questions. Each question will have its own sheet. For all demographics questions (e.g. role, country etc.), it will be grouped together into a single sheet. To identify the user who answered each question, the ID column will be present in all the spreadsheets.

2. For each column, the response is either a “Yes” (user use the tool) or a “No” (user don’t use the tool). To allow better visualization, empty fields are filled with “No” and fields that are filled are changed to “Yes”. This is done using Excel’s Replace All feature. The resulting dataset will then look like the following:
   
   
   
3. It is difficult to perform analysis and processing on the data even though they have now been grouped into its own sheet. To make the data readable by Tableau, the data needs to be reshaped. This is done using Tableau’s Excel Add-In. The usage of the tool is as shown in the following steps:
   
   1. Install the Tableau Add-In in Excel and make use of the “Reshape Data” Feature. Enter the cell to begin the data reshaping process.
      
      
      
   2. A progress bar will be shown as the data is been reshaped. The end result of the reshaped data will look like this:
      
      
      
4. The dataset is now transformed and it will be imported into the tools (e.g. Tableau, PowerBI) for analysis and processing.

Analysis & Visualization Construction Process

The following section elaborates on the analysis and visualization construction process. It details how various graphs are generated, analyzed and how the dataset are further transformed/rearranged to lead to the final visualization constructed.

1. [Analysis] To facilitate better understanding in the data collected, a chart was created to show an overview of the survey participants. As there were multiple attributes (career stage, discipline and role), a treemap was plotted to view the distribution of the research participants’ demographics. These 3 attributes could serve as factors that affect the way in which how people use tools throughout the research process.
   
   
   
   Other than using Tableau, I also attempted to import the data into PowerBI to perform analysis. A good function that PowerBI supports is the ability to drill down into each category. As such, it won’t be directly confusing for a user when they look at the treemap directly. As they drill up and down, they can view information clearer as compared to the one in Tableau. However, the way in which data is segmented is not as clear as compared to a similar one generated in Tableau when it has been broken down into its respective levels. The following shows a chart generated in PowerBI. Given the same data, the chart above (generated by Tableau) allows one to view information clearer. As such, I have preferred to display the chart in Tableau for better visualization.
   
   
   
2. [Analysis & Intermediate Visualization] Another possible factor to look at is the distribution of research participants across the world. This aims to identify whether there are any countries that have a lot of research participants in one discipline but not the other. Based on the chart, majority of the research participants are congregated in the United States & Europe region across all disciplines. This does not provide much useful information as to whether countries may affect the way in which how people use tools. As such, I have decided to continue with the analysis and look back at the region later on to decide if it could be a factor that influence participant’s choice of tools/sites during their research process.
   
   
   
3. [Data Rearrangement] After knowing possible factors that could affect people’s choice of tools/sites, I aim to identify which phases of the research process requires the most number of tools. In order to retrieve this value, there is a need to identify the number of tools that each participant uses during each stage of the research process. This data is currently not available and therefore, new data has to be added into the transformed dataset. This is done by using Excel’s COUNTIF function to count the number of “Yes” in each row. This is done for all the questions and the data column is imported into the “Demographics” sheet. The following illustrates the addition of new data into the transformed dataset.
   
   
   
4. [Analysis & Intermediate Visualization] The data source from Step 3 is then, imported into JMP Pro to perform analysis. Initially, I attempted to plot the number of tools that each person used throughout the entire research process. The following diagram illustrates the chart generated in JMP Pro. However, the graph looks messy and because the data values are almost similar, they pass through similar points. This is not useful when displayed in a parallel coordinates plot. I attempted to use Parallel Sets to display the data instead but the tool crashed repeatedly.
   
   
   
5. [Analysis] To find other alternatives, I attempted to plot an average of the number of search tools/sites used across disciplines. The data source is refreshed in Tableau to reflect the new columns added from Step 3. Tableau’s average function is then used to calculate the average number of tools used for each discipline across the research process. A parallel coordinates plot (as shown below) is then created to show the distribution of number of tools used during each stage of the research process according to their research discipline.

6. [Analysis & Question Evolution] From the parallel coordinates plot, we can see that the majority of the users are using a lot more tools in the phase of searching for literature and data. However, for phases like archiving codes or posters/presentations, they typically don’t use any or use at most 1 tool to do so. As such, the question narrows down to look at which tools are actually the most popular in helping users to search data.This aims to answer the main question in identifying possible factors that could affect one's choice of tools, by first narrowing the analysis process into one main phase - search literature/data.
   
   
   
7. [Analysis & Question Evolution] To identify which search tools/sites are the most popular, a bar chart was plotted to show the total number of users using each of the search tool/site. Based on the chart plotted (as shown below), we can see that majority of the users are using Google Scholar. However, there were tools/sites like Web of Science, PubMed or Other Tools/Sites that participants used commonly. From this chart, the question is further evolved into identifying what are the factors that may affect people’s choice of search tools/sites they use throughout the research process.
   
   
   
8. Previously, it has been identified that 4 factors (career stage, role, discipline, country) could affect people’s choice of tools/sites throughout the research process. As such, the following analysis targets at each of these 4 factors to identify which will affect people’s choice of search tools/sites.

9. [Analysis] A chart was plotted to show the total number of people using each search tool/site based on their roles. From the colours of the circle view chart (as shown below), we can see that they go in almost the same order. This shows that despite its role, there is not much difference in the way they use these tools/sites. However, minor differences do exist. From the chart, we can see that librarian use WorldCat more often than a PhD student. Such minor differences can only be spotted if one analyse closes enough or interact with the filters repeatedly. As such, we can conclude that the role of the user does play a part but it's rather insignificant, in determining the choice of search tools/sites that people will use.
   
   
   
10. [Analysis & Intermediate Visualization] Looking at the second factor – career stage, a chart was plotted to show the total number of people using each tool/site based on their career stages. From the chart, we can see that people in the intermediate and advanced levels used almost similar tools as compared to people in the beginner level. However, it does not provide much information as to what kinds of people use these tools other than its career stage.
    
    
    
11. [Analysis] To provide more in-depth comparison, I attempted to look at the discipline of participants in addition to its career stages. From this chart, we can see that across all disciplines, the choice of search tools/sites used by people are different. For example, participants in the Medicine field use PubMed more than they use Google Scholar. This was not the case for the other disciplines. However, when we look at people in the same discipline with different career stages, the patterns on the type of tools they used to assist them in searching for literature/data were relatively similar. As such, we could potentially identify that discipline is one factor that affect participant's choice of tools/sites used.
    
    
    
12. [Data Rearrangement] Previously, we have identified that countries may not play a huge factor in determining the way how people use search tool/site. We would like to look further into this and identify whether it is indeed true. However, there were too many countries and by plotting them individually will produce an ultimately huge chart. As such, I decided to group countries into their continents. To do so, I retrieved a list of UN countries and continents and convert it into a form of “.csv” file format. This new data is imported into Tableau. In order to group the countries into its continent, it is performed via a left outer join.
    
    
    
13. [Analysis] With the users grouped into their continents, a chart can be plotted to show whether the region or discipline will affect the choice of search tools/sites that people use. From the chart, we can see that people’s choice of search tools/sites varies across disciplines. However, within the same discipline, different regions use the same tools/sites to search for literature/data. Supporting our analysis in Step 11, discipline is a main factor in determining participant's choice of tools/sites used.
    
    
    

From the entire analysis, we will then be able to answer the question on the factors that influence people’s choice of tools/sites used during their research process.

Final Visualization

Based on the entire analysis process, multiple charts were combined to create a dashboard to form a final story on the analysis. The final visualization can be accessed via this URL: https://public.tableau.com/views/Assignment2_v1_5/Story1?:embed=y&:display_count=yes

Caption for Final Visualization: The analysis process aims to identify possible factors that could influence the choice of tools/sites that people use throughout the research process. To do so, the interactive visualization focuses on tools/sites to search literature and data. The rationale for focusing on search tools/sites is due to the fact that most research participants use the most number of tools/sites in this phase throughout the entire research process. As such, more data will be available to perform an analysis. By making use of the interactive visualization, one can identify that a participant’s discipline is a main factor that affects its choice of tools/sites throughout the research process.

Using The Interactive Visualization To Answer Questions Posed
Using the final visualization constructed, one could perform the visualization process in the following steps to answer the questions posed:

1. The analysis begins with the illustration of the demographics of the research participants who took part in the 101 Innovations Research Survey. These are possible factors that could affect participants' choice of tools/site usage throughout the research process.
2. After looking at participants’ demographics, we move on to look at the average number of tools used by participants in each stage of the research process based on their discipline. We conclude that people use the most number of tools in searching for literature and data. As such, the next part of the analysis will be focused on search tools/sites to identify what are the factors that could affect the way how people use tools/sites.
3. The analysis process begins by looking at participants’ roles, career stages, regions and disciplines. By interacting with the 3 charts, we can identify that the main factor which affects the way participants' choice of tools/sites usage during the research process is based on their discipline. Their role may play a slight role in determining their choice of tools/sites used but the level of influence was not significant. In addition, participants’ career stages and region does not impact their choices on the type of tools/sites used during their research process.

Application of Interactive Analytical Techniques
The application of interactive analytical techniques will be elaborated based on each story point of the final visualization.

Story Point 1

The purpose of this story point is to allow users to understand more about the research group that has been surveyed by looking at their demographics (role, discipline, career stages). At the same time, these also serve as potential factors that could influence one’s choice of tool/site usage in a research process. By understanding the demographics, one can then move on to perform further analysis. Moving on, we explore the average number of tools that participants use in each phase of the research process. This is of interest for us so that we can zoom into one of the phase and analyse factors that influence participant’s choice of tools.

Some of the interactive techniques that aim to achieve this purpose includes:

• Use of filters – Users can make use of the filters to explore the distribution of career stages, roles or disciplines. This aims to allow users to understand more about the demographic distribution of the surveyed participants so that they can identify possible explanations to future analysis, when required.
• Use of highlighting – When a user selects a discipline on either the treemap or the parallel coordinates, the corresponding discipline will be highlighted on the respective chart. This allows users to have a close analysis of the selected disciplines that they are interested in.
• Use of brushing – On the parallel coordinates, users could perform brushing to areas or points that they are interested in. This allows them to zoom in to interact/view the average number of tools/sites that users use in each phase of the research process.

Story Point 2

The purpose of this story point is for users to identify what type of tools/sites are majority of the users using to search for literature/data. It is meant to be an intermediate visualization so that users can appreciate and further analyse possible factors that could affect the type of tools/sites which one chooses.

Some of the interactive techniques that aim to achieve this purpose includes:

• Use of brushing – Allow users to identify areas of interest and make use of tooltips to find out more information about each tool/site

Story Point 3

The purpose of this story point is to allow users to identify potential factors that could affect one’s choice of tools/site usage in the search for literature/data – a phase in the research process.

To better illustrate and identify major factors that affect one’s choice of tool/sites used, the data has been filtered (by default) to show clear differences between the usage of tools in each discipline. At the same time, only the top 3 tools/sites were being shown due to the limited size of the dashboard. The region were also filtered to show the United States & Europe region as majority of the sample population are congregated in these regions. The Asia region is also displayed as it might be of relevance/interest for us, who are staying in Asia, to analyze. Of course, users could further filter the data in order to perform a comparison analysis for all other tools/sites that they may be interested in.

Some of the interactive techniques that aim to achieve this purpose includes:

• Use of brushing – This technique applies to the first chart that depicts the relationships between the usage of tools and participant’s roles. By brushing interested data points, users can find out more information about the number of participants who are using the selected tool/site based on their role. This allows users to analyse and identify if participant’s roles are a factor that could determine their choice of tools/sites used in the research process.
• Use of filtering – Filtering is applied to all 3 charts in the dashboard on both the discipline and tool/site fields. By implementing the filter function, it allows selective comparison of each discipline and the respective tool/site selected. For example, a user might only be interested to analyse usage of Google Scholar and Web of Science. He will be able to filter other tools/sites away while only focusing his comparison on the 2 selected tools. This allows one to focus better so as to analyse how the tool is being used based on the participant’s role, career stages and their discipline. With this, it hopes to achieve the main purpose of allowing users to identify potential factors that could affect one’s choice of tool/site usage in the research process.
• Use of highlighting – With multiple tools/sites in view, users can select a tool/site that they are interested in and corresponding charts will also highlight the selected tool/site while keeping other tool/site in view. This allow users to focus their attention on a specific tool/site that they are interested in and at the same time, allow comparison between other tool/site. The use of highlighting aims to complement the filter functions so that comparison can be made without the need to filter and remove other tools/sites that users might want to compare against.

Tools & References

The following tools were used throughout the data transformation, analysis and visualization construction process:

• Microsoft Excel 2016
• Tableau
• Microsoft PowerBI
• JMP Pro

The following websites provided additional guidance on the completion of this assignment:

• Dataset for 101 Innovations - Research Tools Survey (https://www.kaggle.com/bmkramer/101-innovations-research-tools-survey)
• Using Tableau To Visualize Survey Data Part 1 (http://www.datarevelations.com/using-tableau-to-visualize-survey-data-part-1.html)
• Using Tableau To Visualize Survey Data Part 2 (http://www.datarevelations.com/using-tableau-to-visualize-survey-data-part-2.html)
• Creating Parallel Coordinates In Tableau (https://www.youtube.com/watch?v=kD1iEQYtMYo)