We are going to visualize data with the help of Tableau.
we have a data of GSS-PremaritalSex which contains 7 columns name as YEAR, ID, AGE , SEX, MARITAL, PREMARSX, BIBLE, SEXFREQ.
Case 1:
first we are going to compare SEX with Year. i:e; we are comparing sex with year and check the trends.
We have an interesting result Over the past 20 years, those opposed to premarital sex have been having more and more sex on average each year.
we can see that some times wrong goes down constanlty after 2002.
Almost Always wrong is goes upward.
Not Wrong at All goes down every year.
Case 2:
Now we are going to compare Average AGE with Year. i:e; we are comparing Average AGE with year and check the trends.
with this graph we can clearly see that younger people have sex much more frequently than older people (create a new worksheet plotting SexPerYear against AGE if you want to see for yourself), we can see that the puzzling trends in sexual activity are likely linked to the changing average age of these difference groups.
Case 3:
At Last we are going to compare Number of Records with Year. i:e; we are comparing Number of Records with year and check the trends.
for this we have to convert into the %.
Now we can see clearly that segment of population into under-40 and over-40 age brackets. In the under-40 age bracket, beliefs have remained remarkably consistent over the past twenty years. In the over-40 age bracket, on the other hand, there has been a clear shift towards increased acceptance of premarital sex
We can now see how many respondents each year had each opinion on premarital sex. However, the total number of respondents each year vary by quite a bit, so looking at percentages will make the graph much more understandable.
This is total about the visualization.
Though on-the-fly data visualization, we were able to rapidly pull new insights out of our date. Rather than simply creating a chart to report results.
we have a data of GSS-PremaritalSex which contains 7 columns name as YEAR, ID, AGE , SEX, MARITAL, PREMARSX, BIBLE, SEXFREQ.
Case 1:
first we are going to compare SEX with Year. i:e; we are comparing sex with year and check the trends.
We have an interesting result Over the past 20 years, those opposed to premarital sex have been having more and more sex on average each year.
we can see that some times wrong goes down constanlty after 2002.
Almost Always wrong is goes upward.
Not Wrong at All goes down every year.
Case 2:
Now we are going to compare Average AGE with Year. i:e; we are comparing Average AGE with year and check the trends.
with this graph we can clearly see that younger people have sex much more frequently than older people (create a new worksheet plotting SexPerYear against AGE if you want to see for yourself), we can see that the puzzling trends in sexual activity are likely linked to the changing average age of these difference groups.
Case 3:
At Last we are going to compare Number of Records with Year. i:e; we are comparing Number of Records with year and check the trends.
for this we have to convert into the %.
Now we can see clearly that segment of population into under-40 and over-40 age brackets. In the under-40 age bracket, beliefs have remained remarkably consistent over the past twenty years. In the over-40 age bracket, on the other hand, there has been a clear shift towards increased acceptance of premarital sex
We can now see how many respondents each year had each opinion on premarital sex. However, the total number of respondents each year vary by quite a bit, so looking at percentages will make the graph much more understandable.
This is total about the visualization.
Though on-the-fly data visualization, we were able to rapidly pull new insights out of our date. Rather than simply creating a chart to report results.
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