GitXplorerGitXplorer
t

DMV_Capstone

public
0 stars
2 forks
0 issues

Commits

List of commits on branch master.
Unverified
e176e9e549288e8a3d620b3c009a1cdfb5ba02be

commented out code for plots we don't need for presentation on 12/8

ttylerhutcherson committed 8 years ago
Unverified
c9200f4737f1abee4de4c5018c1f0212428d4fe1

fixed error in propertyDamage repair cost variable

ttylerhutcherson committed 8 years ago
Unverified
9671d13ecd6ceb4cc7e7ef0403fc5ced02bd728f

cleaned up code to merge

ttylerhutcherson committed 8 years ago
Unverified
93d32b051fe73f36738f4152d7f8dba8b115df9f

cleaned up formatting through pedestrians

ttylerhutcherson committed 8 years ago
Unverified
566ae067ac7f91fd33de75ab912f1ba13159ba87

cleaned up formatting through property damage

ttylerhutcherson committed 8 years ago
Unverified
75c44f0f94c4892eff193345994af55c718a7b31

read in csv files instead of RData

ttylerhutcherson committed 8 years ago

README

The README file for this repository.

DMV_Capstone

Collaborative work for 2016-2017 UVA DSI Capstone Project.

  • DataCleaning.R: Merge data with same attributes but in different years into a single data frame. Variable name and type checking have been done before merging raw text files. Nine data frames will be created including:

    1. crash location
    2. vehicle
    3. driver
    4. passenger
    5. pedestrain
    6. license
    7. property damage
    8. vehicle commercial
    9. uva-vt

  • merge_fatal&injury_only.R Merge all the text files into a single data frame. Here the data contains only fatal and injury crashes caused by unrestraint fatalities, which is a subset of the old data. Data created will be used to analyze any correlation between unrestraint fatalities and serious car crashes.

  • mergeOldData.R
    Merge all the nine data frames (from DataCleaning.R) together into a huge data frame. Any observations that are mistyped will be removed. After this, we will obtain the cleaned data for analysis.

  • plots.R Created different plots to help us understand the problems. There are two types of plots:

    1. plot of number of car crashes by month for each year from 2010 to 2015 (different plots for fatal, injury and property damage crashes)
    2. plot of car crash by location for each year from 2010 to 2015 (different plots for fatal, injury and property damage crashes)