ISSS608 2016-17 T3 Assign APARAJITA SHUKLA DataPrep

From Visual Analytics and Applications
Revision as of 20:35, 7 July 2018 by Aparajitas.2017 (talk | contribs) (dataprep)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Factory-and-water-pollution-illustration.jpg   VAST Challenge: Mini Challenge 2


VAST MINI CHALLENGE 2:

Like a Duck to Water

Introduction

Data Preparation & Methodology

Insight & Conclusion

Back to Dropbox

 


Methodology

We have been provided with three files along with a map of the Boonsong Lekagul preserve area. Where two files are .csv and one is .txt.

Dp1.png

  • The first file, Boonsong Lekagul waterways readings.csv contains 5 columns namely Id, value, location, sample date, measure and have 136924 rows.
  • The second file, chemical units of measure.csv has two columns namely, measure and unit.
  • The third .txt file basically contains all the information about the data present in the other files.
  • The last .jpg file contains the map of Boonsong Lekagul waterways.


Checking Missing Values

To check the missing values, I have used JMP.


Picture2 apa.png


As can be seen from the image above, there were no missing value present in given dataset.


Getting the Map


After importing data file into tableau, we first import the image of the map and using annotate using points, I marked all the locations in the map and manually created a separate .xslx file:

Picture3 apa.png


And then create map by dragging and dropping the coordinates on the worksheet.

Joining the Tables


As we needed to join Location table, measure.csv and Boonsong Lekagul waterways readings.csv tables together, the tool used for this is Tableau:

Picture4 apa.png