How to visualize change in a distribution with a few outliers that account for a very large percent of the...
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I'm working on an edtech product where some of our traffic lands on webpages about textbooks.
Textbooks belong to subjects like Algebra, Calculus and Spanish. In each of our subjects, we have "whales" - individual books that account for a large percent (~20%) of total subject traffic.
Year over year, these whales grow or shrink (sometimes books are replaced in a school, or schools drop textbooks altogether). This change in whale traffic contributes to a big change in overall subject traffic.
I'm trying to figure out how to visualize this change, given a dataset that looks something like the table attached below (it shows only 2 months traffic per book, but I have access to all the months).
I've tried overlapping histograms (and boxplots), where each histogram is a month. But this visualization doesn't indicate how huge my whales (outliers) are, and how much influence they have.
Any help on chart types or how to otherwise tell this story would be much appreciated.
visualization outlier distribution
asked yesterday
samthebrandsamthebrand
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samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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I'm working on an edtech product where some of our traffic lands on webpages about textbooks.
Textbooks belong to subjects like Algebra, Calculus and Spanish. In each of our subjects, we have "whales" - individual books that account for a large percent (~20%) of total subject traffic.
Year over year, these whales grow or shrink (sometimes books are replaced in a school, or schools drop textbooks altogether). This change in whale traffic contributes to a big change in overall subject traffic.
I'm trying to figure out how to visualize this change, given a dataset that looks something like the table attached below (it shows only 2 months traffic per book, but I have access to all the months).
I've tried overlapping histograms (and boxplots), where each histogram is a month. But this visualization doesn't indicate how huge my whales (outliers) are, and how much influence they have.
Any help on chart types or how to otherwise tell this story would be much appreciated.
visualization outlier distribution
asked yesterday
samthebrandsamthebrand
1063
New contributor
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm working on an edtech product where some of our traffic lands on webpages about textbooks.
Textbooks belong to subjects like Algebra, Calculus and Spanish. In each of our subjects, we have "whales" - individual books that account for a large percent (~20%) of total subject traffic.
Year over year, these whales grow or shrink (sometimes books are replaced in a school, or schools drop textbooks altogether). This change in whale traffic contributes to a big change in overall subject traffic.
I'm trying to figure out how to visualize this change, given a dataset that looks something like the table attached below (it shows only 2 months traffic per book, but I have access to all the months).
I've tried overlapping histograms (and boxplots), where each histogram is a month. But this visualization doesn't indicate how huge my whales (outliers) are, and how much influence they have.
Any help on chart types or how to otherwise tell this story would be much appreciated.
visualization outlier distribution
asked yesterday
samthebrandsamthebrand
1063
New contributor
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm working on an edtech product where some of our traffic lands on webpages about textbooks.
Textbooks belong to subjects like Algebra, Calculus and Spanish. In each of our subjects, we have "whales" - individual books that account for a large percent (~20%) of total subject traffic.
Year over year, these whales grow or shrink (sometimes books are replaced in a school, or schools drop textbooks altogether). This change in whale traffic contributes to a big change in overall subject traffic.
I'm trying to figure out how to visualize this change, given a dataset that looks something like the table attached below (it shows only 2 months traffic per book, but I have access to all the months).
I've tried overlapping histograms (and boxplots), where each histogram is a month. But this visualization doesn't indicate how huge my whales (outliers) are, and how much influence they have.
Any help on chart types or how to otherwise tell this story would be much appreciated.
visualization outlier distribution
visualization outlier distribution
asked yesterday
samthebrandsamthebrand
1063
New contributor
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
samthebrandsamthebrand
1063
New contributor
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
samthebrandsamthebrand
1063
New contributor
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
samthebrandsamthebrand
1063
asked yesterday
samthebrandsamthebrand
1063
1063
New contributor
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
samthebrand is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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To visualize change in the size of multiple entities that are contributing to a total through time, e.g. total (t) = book_1 (t) + book_2 (t) + ..., we can use Stacked Area Plot. This plot can be used for normalized and un-normalized (absolute) values.
Preprocessing
For large number of entities, to avoid cognitive load, we can keep only
those entities that become significant (whale) at some point in the plot, and group all the others under an "ordinary" entity. This way, cognitive load is minimized and only those entities that matter at some point are distinguished. For example, distinguishing books that have more than 10% of traffic at some point in the plotted time span.If total fluctuation is very high, logarithm of values can be plugged into the plot.
answered yesterday
EsmailianEsmailian
1,591114
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$begingroup$
This is helpful. But these charts get very noisy with hundreds of observations per period. (We have hundreds of books per subject.) And it takes a lot of cognitive load, at a glance, to tell what's growing and what's shrinking. I do appreciate the suggestion!
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– samthebrand
yesterday
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@samthebrand I updated the answer for this problem
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– Esmailian
yesterday
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Thanks @esmailian. This is a good answer, but I don't think it is THE answer, given that I'm interested in visualizing YOY change. For a stacked area chart I would have to look at latest period and then compare its relative size to the period twelve months prior. This feels hard. There's got to be a better way.
$endgroup$
– samthebrand
yesterday
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
oldest
votes
$begingroup$
To visualize change in the size of multiple entities that are contributing to a total through time, e.g. total (t) = book_1 (t) + book_2 (t) + ..., we can use Stacked Area Plot. This plot can be used for normalized and un-normalized (absolute) values.
Preprocessing
For large number of entities, to avoid cognitive load, we can keep only
those entities that become significant (whale) at some point in the plot, and group all the others under an "ordinary" entity. This way, cognitive load is minimized and only those entities that matter at some point are distinguished. For example, distinguishing books that have more than 10% of traffic at some point in the plotted time span.If total fluctuation is very high, logarithm of values can be plugged into the plot.
answered yesterday
EsmailianEsmailian
1,591114
$endgroup$
$begingroup$
This is helpful. But these charts get very noisy with hundreds of observations per period. (We have hundreds of books per subject.) And it takes a lot of cognitive load, at a glance, to tell what's growing and what's shrinking. I do appreciate the suggestion!
$endgroup$
– samthebrand
yesterday
$begingroup$
@samthebrand I updated the answer for this problem
$endgroup$
– Esmailian
yesterday
$begingroup$
Thanks @esmailian. This is a good answer, but I don't think it is THE answer, given that I'm interested in visualizing YOY change. For a stacked area chart I would have to look at latest period and then compare its relative size to the period twelve months prior. This feels hard. There's got to be a better way.
$endgroup$
– samthebrand
yesterday
add a comment |
$begingroup$
To visualize change in the size of multiple entities that are contributing to a total through time, e.g. total (t) = book_1 (t) + book_2 (t) + ..., we can use Stacked Area Plot. This plot can be used for normalized and un-normalized (absolute) values.
Preprocessing
For large number of entities, to avoid cognitive load, we can keep only
those entities that become significant (whale) at some point in the plot, and group all the others under an "ordinary" entity. This way, cognitive load is minimized and only those entities that matter at some point are distinguished. For example, distinguishing books that have more than 10% of traffic at some point in the plotted time span.If total fluctuation is very high, logarithm of values can be plugged into the plot.
answered yesterday
EsmailianEsmailian
1,591114
$endgroup$
$begingroup$
This is helpful. But these charts get very noisy with hundreds of observations per period. (We have hundreds of books per subject.) And it takes a lot of cognitive load, at a glance, to tell what's growing and what's shrinking. I do appreciate the suggestion!
$endgroup$
– samthebrand
yesterday
$begingroup$
@samthebrand I updated the answer for this problem
$endgroup$
– Esmailian
yesterday
$begingroup$
Thanks @esmailian. This is a good answer, but I don't think it is THE answer, given that I'm interested in visualizing YOY change. For a stacked area chart I would have to look at latest period and then compare its relative size to the period twelve months prior. This feels hard. There's got to be a better way.
$endgroup$
– samthebrand
yesterday
add a comment |
$begingroup$
To visualize change in the size of multiple entities that are contributing to a total through time, e.g. total (t) = book_1 (t) + book_2 (t) + ..., we can use Stacked Area Plot. This plot can be used for normalized and un-normalized (absolute) values.
Preprocessing
For large number of entities, to avoid cognitive load, we can keep only
those entities that become significant (whale) at some point in the plot, and group all the others under an "ordinary" entity. This way, cognitive load is minimized and only those entities that matter at some point are distinguished. For example, distinguishing books that have more than 10% of traffic at some point in the plotted time span.If total fluctuation is very high, logarithm of values can be plugged into the plot.
answered yesterday
EsmailianEsmailian
1,591114
$endgroup$
To visualize change in the size of multiple entities that are contributing to a total through time, e.g. total (t) = book_1 (t) + book_2 (t) + ..., we can use Stacked Area Plot. This plot can be used for normalized and un-normalized (absolute) values.
Preprocessing
For large number of entities, to avoid cognitive load, we can keep only
those entities that become significant (whale) at some point in the plot, and group all the others under an "ordinary" entity. This way, cognitive load is minimized and only those entities that matter at some point are distinguished. For example, distinguishing books that have more than 10% of traffic at some point in the plotted time span.If total fluctuation is very high, logarithm of values can be plugged into the plot.
answered yesterday
EsmailianEsmailian
1,591114
edited 1 hour ago
answered yesterday
EsmailianEsmailian
1,591114
answered yesterday
EsmailianEsmailian
1,591114
answered yesterday
EsmailianEsmailian
1,591114
1,591114
$begingroup$
This is helpful. But these charts get very noisy with hundreds of observations per period. (We have hundreds of books per subject.) And it takes a lot of cognitive load, at a glance, to tell what's growing and what's shrinking. I do appreciate the suggestion!
$endgroup$
– samthebrand
yesterday
$begingroup$
@samthebrand I updated the answer for this problem
$endgroup$
– Esmailian
yesterday
$begingroup$
Thanks @esmailian. This is a good answer, but I don't think it is THE answer, given that I'm interested in visualizing YOY change. For a stacked area chart I would have to look at latest period and then compare its relative size to the period twelve months prior. This feels hard. There's got to be a better way.
$endgroup$
– samthebrand
yesterday
add a comment |
$begingroup$
This is helpful. But these charts get very noisy with hundreds of observations per period. (We have hundreds of books per subject.) And it takes a lot of cognitive load, at a glance, to tell what's growing and what's shrinking. I do appreciate the suggestion!
$endgroup$
– samthebrand
yesterday
$begingroup$
@samthebrand I updated the answer for this problem
$endgroup$
– Esmailian
yesterday
$begingroup$
Thanks @esmailian. This is a good answer, but I don't think it is THE answer, given that I'm interested in visualizing YOY change. For a stacked area chart I would have to look at latest period and then compare its relative size to the period twelve months prior. This feels hard. There's got to be a better way.
$endgroup$
– samthebrand
yesterday
$begingroup$
This is helpful. But these charts get very noisy with hundreds of observations per period. (We have hundreds of books per subject.) And it takes a lot of cognitive load, at a glance, to tell what's growing and what's shrinking. I do appreciate the suggestion!
$endgroup$
– samthebrand
yesterday
$begingroup$
This is helpful. But these charts get very noisy with hundreds of observations per period. (We have hundreds of books per subject.) And it takes a lot of cognitive load, at a glance, to tell what's growing and what's shrinking. I do appreciate the suggestion!
$endgroup$
– samthebrand
yesterday
$begingroup$
@samthebrand I updated the answer for this problem
$endgroup$
– Esmailian
yesterday
$begingroup$
@samthebrand I updated the answer for this problem
$endgroup$
– Esmailian
yesterday
$begingroup$
Thanks @esmailian. This is a good answer, but I don't think it is THE answer, given that I'm interested in visualizing YOY change. For a stacked area chart I would have to look at latest period and then compare its relative size to the period twelve months prior. This feels hard. There's got to be a better way.
$endgroup$
– samthebrand
yesterday
$begingroup$
Thanks @esmailian. This is a good answer, but I don't think it is THE answer, given that I'm interested in visualizing YOY change. For a stacked area chart I would have to look at latest period and then compare its relative size to the period twelve months prior. This feels hard. There's got to be a better way.
$endgroup$
– samthebrand
yesterday
add a comment |
samthebrand is a new contributor. Be nice, and check out our Code of Conduct.
samthebrand is a new contributor. Be nice, and check out our Code of Conduct.
samthebrand is a new contributor. Be nice, and check out our Code of Conduct.
samthebrand is a new contributor. Be nice, and check out our Code of Conduct.
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