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Why use 50 meters as your cut point? Is this a common metric in the literature?
Nice use of ggmapto visualize the spatial data
Rather than
print(str_c("The ",
nrow(DivvyData %>% filter(from_prox==1))/nrow(DivvyData)*100,
"% of all Divvy trips are made from stations in proximity with one CTA stop or more."))
which prints the text as an R comment, directly integrate using in-line R code, like this:
`r nrow(DivvyData %>% filter(from_prox == 1))/nrow(DivvyData)*100`% of all Divvy trips are made from stations in proximity with one CTA stop or more.
Great use of R to collect and visualize the data, but I'm missing the research question. What am I learning from this analysis? There are multi-modal trips and non-multimodal trips, but how is that important? Do we know what causes individuals to take multimodal trips? What is the value of the research? That isn't established here
Well-documented code, and the readme is very helpful in describing the project and how all the files work together. Nice work!
The text was updated successfully, but these errors were encountered:
Evaluation of final project by Benjamin Soltoff
Remarks:
ggmap
to visualize the spatial datawhich prints the text as an R comment, directly integrate using in-line R code, like this:
The text was updated successfully, but these errors were encountered: