Difference between revisions of "G1-Group01 Datacleaning"
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== Data Cleaning with R == | == Data Cleaning with R == | ||
As mentioned above, our team used R to clean up data for our analysis. Thus, we use one of age group as example. | As mentioned above, our team used R to clean up data for our analysis. Thus, we use one of age group as example. | ||
| − | [[File:1.loading.jpg| | + | [[File:1.loading.jpg|600px]] <br/> |
| − | [[File:2..jpg| | + | [[File:2..jpg|600px]] <br/> |
| − | [[File:Outputa1.jpg| | + | [[File:Outputa1.jpg|600px]] <br/> |
| − | [[File:Outputb1.jpg| | + | '''Data Frame: a1''' <br/> |
| − | [[File:Outputc1.jpg| | + | [[File:Outputb1.jpg|600px]] <br/> |
| − | [[File:Outputd1.jpg| | + | '''Data Frame: b1''' <br/> |
| − | [[File:Outpute1.jpg| | + | [[File:Outputc1.jpg|600px]] <br/> |
| + | '''Data Frame: c1''' <br/> | ||
| + | [[File:Outputd1.jpg|600px]] <br/> | ||
| + | '''Data Frame d1''' <br/> | ||
| + | [[File:Outpute1.jpg|600px]] <br/> | ||
| + | '''Data Frame e1''' <br/> | ||
Revision as of 15:21, 16 November 2019
Due to some of files were not in the proper format for our analysis.
Thus, our team used R and tableau for our data cleaning and data Visualization. However, in this section, we will only share how we use the R code to "Geocode" and "data clean".
Geocode
Our group used R package "ggmap" and the function "geocode". And the coordinate it has generated will be base on projection 4326. Thus we need to export the file again to become projection 3414 for our further analysis. Requirement of using ggmap:
1. "ggmap" must be installed in your R studio
2. Must have google API in order to require information from google
Data Cleaning with R
As mentioned above, our team used R to clean up data for our analysis. Thus, we use one of age group as example.
Data Frame: a1
Data Frame: b1
Data Frame: c1
Data Frame d1
Data Frame e1
