Data Analytics and Computational Social Science: HW2blog

Henry Mulvey

HW2 try 2

library(“tidyverse”) library(“dplyr”)

library(readxl) MBTA_bus_all2 <- read_excel(“MBTA_bus_all2.xlsx”) View(MBTA_bus_all2)

mode, type of transit vehicle (bus), numeral

season, text

route_id and route_name, number of transit route

stop_sequence, not sure, numeral

direction_id, not sure, numeral

day_type_id, numerical representation of day_type_name, numeral

day_type_nam, weekday or weekend, text

time_period_id, not sure, text

stop_name, name of transit stop, text

stop_id, numerical id of transit stop, numeral

average_ons, average of boardings at that transit stop, numeral

average_offs, average of people getting off at that transit stop, numeral

average_load, I think average percentage of bus occupancy at that transit stop, percentage

num_trips, I think number of trips that day that serviced that stop, numeral

rdm_all <- filter(MBTA_bus_all2, route_name > 13 & route_name < 31) view(rdm_all)

rdm_all isolates all of the bus stops in the Boston neighborhoods of Roxbury, Dorchester, and Mattapan

popular_stops_rdm <- arrange(rdm_all, desc(average_ons)) view(popular_stops_rdm)

popular_stops_rdm ranks all of the stops in rdm_all by the average number of people that get on the bus at that stop

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For attribution, please cite this work as

Mulvey (2022, Feb. 13). Data Analytics and Computational Social Science: HW2blog. Retrieved from https://github.com/DACSS/dacss_course_website/posts/httpsrpubscomhmuleyumass865375/

BibTeX citation

@misc{mulvey2022hw2blog,
  author = {Mulvey, Henry},
  title = {Data Analytics and Computational Social Science: HW2blog},
  url = {https://github.com/DACSS/dacss_course_website/posts/httpsrpubscomhmuleyumass865375/},
  year = {2022}
}