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mentalhealth_project's Introduction

Exploration of Mental health survey Dataset

There is an increase in cases of mental illness on a global scale. Mental health problems are slowly becoming one of the top contributors for disease and disability across the world. Cases of mental illness can be found across developing as well as developed nations. They are present among the across different age groups, social strata as well as gender. Tech companies are rapidly growing in number and size and this means there is more pressure on it's workers to perform. This pressure could have negative implications on a few individuals and before they realize could lead to mental illness.

In this analysis I look at mental health survey data of workers across the globe to determine if the tech companies are doing enough to ensure mental wellbeing of their workers. This includes a positive working environment, right tools to cope with stress and awareness of mental illness and it's symptoms.

Dataset

The dataset is obtained from a survey by OSMI(Open Source Mental Illness). This survey was conducted in 2016 and had 1433 people responding to 63 questions. I was able to download this data from data world repository. Codebook containing variable descriptions can be found here

Hypothesis

The hypothesis that I will be testing in this project is 'Are the tech companies doing enough to ensure mental wellbeing of their employees?'

Why this analysis?

There is a boom in the number of startups in the tech industry, as well as growth of the existing tech giants. They are looking to outdo each other and hire workers who can get them to the top. This sometimes comes at a cost of the mental health of it's employees. As someone who is aspiring to work for a tech company I want to analyze the response of these companies to mental illness.

Visualizing the dataset

There are multiple questions regarding company policy towards mental illness which are simply have Yes/No/Maybe answers. This can be visualized by the use of a bar plots to analyze the general trend. Future implementation could include sentiment analysis on the comments of the employee and geographic aspect to this.

Running this analysis

Steps to reproduce this analysis are -

  1. Clone the github repo or download files from the scripts folder to your local system.
  2. Download image from Docker(you need to have Docker on your system)
docker pull abimurali/mentalhealth_project
  1. Clean to build from scratch
docker run --rm -v <<Location where folder was cloned>>:/home/mentalhealth_project abimurali/mentalhealth_project make -C '/home/mentalhealth_project' clean
  1. Run analysis from scratch
docker run --rm -v <<Location where folder was cloned>>:/home/mentalhealth_project abimurali/mentalhealth_project make -C '/home/mentalhealth_project'

PS: Packrat can be used to install R packages required to run the code on your system. Opening the .Rproj file should do this

mentalhealth_project's People

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mentalhealth_project's Issues

Shiny app review

The layout and functionality of the app are effective for professionals to analyze the visualize the data set, though a minimal amount of description of the data set may help better understanding at first glance.

The interaction functionality between the map and the data table allow users who have specific regions to investigate in mind to better explore the data. Also, the data-exporting action buttons give the flexibility to satisfy the needs for different file types. One aspect I would suggest is to include a button to select and download data for multiple and all the states. Also, highlighting how the map and the data are linked would be helpful (i.e., highlighting the instruction above the map).

For the user input filtering widgets, they were properly chosen based on the data set. The biggest confusion to me is the checkbox group for Size of company. It is not intuitive to see if I have selected all sizes of companies because the checkboxes do not change. Besides, it seems like missing values are not found in the dataset (i.e., the map crash when I only check the Missing).

The Models section is a convenient feature for the analyst, but it seems to a static analysis that the filters in the Exploration section have no impact on it. Specifying this in the Models section would cause less confusion.

Shiny app review

The app is very effective in terms of helping people analyze and visualize mental health data in the tech industry. People who want to gain more insight into how mental illness is perceived in the workplace will benefit greatly from this app.

The reactive functions are quick which allows for a pleasant user experience.

The black, white and red colour scheme is clean and consistent across the user interface. The only issue with the colour scheme, is the black sidebar menu that does not catch the user's eye on initial view. However, once it is discovered it becomes a frequently used feature of the interface. The subtle menu item icons is a nice touch, and makes the compliments the professional look of the app well. I would suggest changing the colour of the sidebar menu to a marginally lighter shade of grey - this will make the user aware of the sidebar menu sooner, but should still blend in with the background when user's attention is fixed on the main white panel.

The user input filtering widgets have been chosen appropriately. Filtering age with a range slider widget makes a lot of sense, due to the large range of discrete age values to choose from. On the other hand, categorical and binary variables, such as gender, can be filtered easily with radio buttons and dropdown menus. The only critique I have for the widget functionality, is that all checkboxes aren't visually selected by default when the "Select all" option is checked for the "Size of company" filter. It would be a more intuitive user experience if a reactive event took place when this option is checked.

The hovering functionality on the map is an elegant solution to displaying summary information per US state. The single click functionality on the map is another user-friendly solution to accessing more detailed information about a state.

The data exporting action buttons is a functionality that sets this app apart from other data analysis apps.

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