challenge$categories## color name score_out_of_10
## 1 #f3c32c Housing 1.00000
## 2 #f3d630 Cost of Living 2.61800
## 3 #f4eb33 Startups 10.00000
## 4 #d2ed31 Venture Capital 10.00000
## 5 #7adc29 Travel Connectivity 3.65450
## 6 #36cc24 Commute 4.68725
## 7 #19ad51 Business Freedom 8.67100
## 8 #0d6999 Safety 5.71550
## 9 #051fa5 Healthcare 8.74800
## 10 #150e78 Education 8.62450
## 11 #3d14a4 Environmental Quality 6.48150
## 12 #5c14a1 Economy 6.51450
## 13 #88149f Taxation 4.48800
## 14 #b9117d Internet Access 5.60550
## 15 #d10d54 Leisure & Culture 9.40700
## 16 #e70c26 Tolerance 8.01250
## 17 #f1351b Outdoors 7.01400ggplot(data = challenge$categories, aes(x=score_out_of_10)) + geom_histogram(aes(fill = name))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Based on the table and histogram above we can see the cost of living score for the San Francisco/Bay Area is very low at a 2.5. However, quality of life varies throughout the plot and table since there are many variables that represent quality of life. With this information we can see what variables in the Bay Area need to be fixed and improved upon.