Its my second blog on Data Visualization. Basically this work for the class assigment.
I am going to use sales data set for the analysing of Category and Profit , Ship mode by different different way.
Case: 1
So let's start first we are going to analyse Category and Profit.
for this we use "Category" , "Sub-category" and "profit ".
From this figure we can see clearly that before selecting 3 negative and absolute values the result was Technology category performed the best in terms of Profit with Furniture making the least profit. We can determine this by observing the largest and the smallest sized rectangles respectively as we have placed Profit on the Size card and after seclecting 3 negative and absolute values the result is
Technology is the most profit-making category while the Furniture department incurs the most losses
Case: 2
Ship mode by different different way
A) Ship mode with city
for this we use "Ship mode" , "City" by their co-ordinates .
As evident here, First class was the most favored and popular on the West coast and Midwest. Same day ship mode is sporadic and Second class is popular in the south. While not absolutely accurate, these basic inferences help us probe further and determine which hypothesis to investigate further.
B) Ship mode with Figures
In this sales data, our key performance indicators or KPIs are Profit, Sales and Discount. Our goal is to scrutinize these parameters by looking at each Measure on an overall basis and then drilled down by the Ship Modes. For this, we will create bar plots for the holistic look and area charts to make observations on the aforementioned KPIs, categorized by the shipment modes.
for this we use "Ship mode" , "Sales", "Profit"," Discuount"
If you want to see them on the same scale, we can use the Dual Axis and Synchronize Axis options
C) Ship Mode Figures area
for this we use "Year" , "Sales", "Profit"," Discuount"
As evident, this sheet shows which Ship Mode had the highest Sales and Profit and which had the least. We also infer that Same Day and Second Class Ship modes generated almost the same revenue and profit since the difference in these two areas is slim.
Conclusion:
Now we will actually create our dashboard that showcases these KPIS in one single visualization. We begin by adding a new Dashboard sheet. Tableau provides special worksheets called Dashboards that automatically display the visualizations created until then
This is the complete dashboard of the given dataset.
Application of joins:
Understanding how LEFT, RIGHT, INNER, and OUTER Joins Work
Joining Data in Tableau
Joins With Duplicate Values
Joining on Multiple Fields
Joining Data v.s. Blending Data in Tableau
Data Blending in Tableau
Creating Calculated Fields in a Blend (Advanced Topic)
Creating Table Calculations
Creating Bins and Distributions
Leveraging the Power of Parameters
For this i am using the data set of Coffee and sales data
Case: 1
we are using "Person", "Sub-Category" and "profit"
Here we perform join Coffee data on Sales Data. This is basic Example of Join.
Case: 2
for performing this full Join i am using "State" , "Profit" from both the dataset and apply some calculation( profit*100/sales).
We create a variable that calculates the Profit over Sales metric. Observe that we used the reversed scale to allow easy comparison of the two images.
Case: 3
Applying join over "State" and "Profit" .
Application of Scatterpolt over Sales data set:
Applying Scatter plot over "Discount" and "profit".
It is clear here that we have too many points in the mid range that are profits and losses.
A few points do overlap on the trend line but it is more representative of revenue than the previous version. A trend line can provide a statistical definition of the relationship between two numerical values. To add trend lines to a view, both axes must contain a field that can be interpreted as a number by definition, that is always the case with a scatter plot. In our example, the trend line can showcase the relationship between Profit and Discount.
Application of Line Chart
Applying Line Chart over "Discount" and "Month".
Case: 1
From this plot we can easly see the trends and in which year or month , how much is Discount .
Case:2
from this Line Chart i am showing here profit and loss . In this chart, we will highlight the profit-loss aspect using colors. As in the scatterplot visualization .
Application of Area Graph:
An area chart is a line chart where the area between the line and the axis is shaded with a color. These charts are typically used to represent accumulated totals over time and are the conventional way to display relative proportions of totals or percentage relationships. The area chart is a combination of a line graph and a stacked bar chart. By stacking the volume beneath the line, the chart shows the total of the fields as well as their relative size to each other
for this we are using "Month" and "profit"
Case:1
Case:2
it will be useful to study the relative sizing of discounts and Profits for all the Ship Modes. So, we shall create area charts utilizing these line charts we created.
Conclusion:
I am going o apply Line and Area Chart by dashboard. which gives complete information about this analysis.
Tableau Data Dashboards provide an overview of the different metrics or performance indicators that you want to measure. They can provide snapshots of individual elements as well a summary of the entire system as a whole.
In our charts, we will see the effect of Ship Mode and Date Range on Profit and Discount. We have different methods called Actions to do this. We will use highlighting to see the effect of each Ship Mode and Filters to focus on date ranges of interest
As observed here, our dashboard gives us some interesting insights.
As can be seen here, dashboards help you monitor, analyze, and further improve analysis by providing conclusions and additional exploratory paths.
I am going to use sales data set for the analysing of Category and Profit , Ship mode by different different way.
Case: 1
So let's start first we are going to analyse Category and Profit.
for this we use "Category" , "Sub-category" and "profit ".
From this figure we can see clearly that before selecting 3 negative and absolute values the result was Technology category performed the best in terms of Profit with Furniture making the least profit. We can determine this by observing the largest and the smallest sized rectangles respectively as we have placed Profit on the Size card and after seclecting 3 negative and absolute values the result is
Technology is the most profit-making category while the Furniture department incurs the most losses
Case: 2
Ship mode by different different way
A) Ship mode with city
for this we use "Ship mode" , "City" by their co-ordinates .
As evident here, First class was the most favored and popular on the West coast and Midwest. Same day ship mode is sporadic and Second class is popular in the south. While not absolutely accurate, these basic inferences help us probe further and determine which hypothesis to investigate further.
B) Ship mode with Figures
In this sales data, our key performance indicators or KPIs are Profit, Sales and Discount. Our goal is to scrutinize these parameters by looking at each Measure on an overall basis and then drilled down by the Ship Modes. For this, we will create bar plots for the holistic look and area charts to make observations on the aforementioned KPIs, categorized by the shipment modes.
for this we use "Ship mode" , "Sales", "Profit"," Discuount"
If you want to see them on the same scale, we can use the Dual Axis and Synchronize Axis options
C) Ship Mode Figures area
for this we use "Year" , "Sales", "Profit"," Discuount"
As evident, this sheet shows which Ship Mode had the highest Sales and Profit and which had the least. We also infer that Same Day and Second Class Ship modes generated almost the same revenue and profit since the difference in these two areas is slim.
Conclusion:
Now we will actually create our dashboard that showcases these KPIS in one single visualization. We begin by adding a new Dashboard sheet. Tableau provides special worksheets called Dashboards that automatically display the visualizations created until then
This is the complete dashboard of the given dataset.
Application of joins:
Understanding how LEFT, RIGHT, INNER, and OUTER Joins Work
Joining Data in Tableau
Joins With Duplicate Values
Joining on Multiple Fields
Joining Data v.s. Blending Data in Tableau
Data Blending in Tableau
Creating Calculated Fields in a Blend (Advanced Topic)
Creating Table Calculations
Creating Bins and Distributions
Leveraging the Power of Parameters
For this i am using the data set of Coffee and sales data
Case: 1
we are using "Person", "Sub-Category" and "profit"
Here we perform join Coffee data on Sales Data. This is basic Example of Join.
Case: 2
for performing this full Join i am using "State" , "Profit" from both the dataset and apply some calculation( profit*100/sales).
We create a variable that calculates the Profit over Sales metric. Observe that we used the reversed scale to allow easy comparison of the two images.
Case: 3
Applying join over "State" and "Profit" .
Application of Scatterpolt over Sales data set:
Applying Scatter plot over "Discount" and "profit".
It is clear here that we have too many points in the mid range that are profits and losses.
A few points do overlap on the trend line but it is more representative of revenue than the previous version. A trend line can provide a statistical definition of the relationship between two numerical values. To add trend lines to a view, both axes must contain a field that can be interpreted as a number by definition, that is always the case with a scatter plot. In our example, the trend line can showcase the relationship between Profit and Discount.
Application of Line Chart
Applying Line Chart over "Discount" and "Month".
Case: 1
From this plot we can easly see the trends and in which year or month , how much is Discount .
Case:2
from this Line Chart i am showing here profit and loss . In this chart, we will highlight the profit-loss aspect using colors. As in the scatterplot visualization .
Application of Area Graph:
An area chart is a line chart where the area between the line and the axis is shaded with a color. These charts are typically used to represent accumulated totals over time and are the conventional way to display relative proportions of totals or percentage relationships. The area chart is a combination of a line graph and a stacked bar chart. By stacking the volume beneath the line, the chart shows the total of the fields as well as their relative size to each other
for this we are using "Month" and "profit"
Case:1
Case:2
it will be useful to study the relative sizing of discounts and Profits for all the Ship Modes. So, we shall create area charts utilizing these line charts we created.
Conclusion:
I am going o apply Line and Area Chart by dashboard. which gives complete information about this analysis.
Tableau Data Dashboards provide an overview of the different metrics or performance indicators that you want to measure. They can provide snapshots of individual elements as well a summary of the entire system as a whole.
In our charts, we will see the effect of Ship Mode and Date Range on Profit and Discount. We have different methods called Actions to do this. We will use highlighting to see the effect of each Ship Mode and Filters to focus on date ranges of interest
As observed here, our dashboard gives us some interesting insights.
- By being able to drill down to single elements, we can see that First Class had the highest profit and the greatest fluctuations while Standard class, with least profits also was the most stable source of revenue.
- We also get a holistic view of the company's financial health. Profits show a steady upward trend with the period around Jan 2014 showing slight fluctuations for Same Day ship Mode and wide fluctuations for all the other Ship Modes. As seen on the Profit by Ship mode graph, we can see the differences between totals in each following intervals and how much the totals vary. These could be followed up to understand if it was due to extraneous factors like economy, mergers, executive changes etc. or more controllable factors like policy or pricing changes.
As can be seen here, dashboards help you monitor, analyze, and further improve analysis by providing conclusions and additional exploratory paths.
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