Content
A count of the number of bicycles on each of the bridges in question is provided on a day-by-day basis, along with information on maximum and minimum temperature and precipitation.
NYC Bicycle Data Set
Ahsen Ceren Karasu Dec,2017
About Data Set
This dataset is a daily record of the number of bicycles crossing into or out of Manhattan via one of the East River bridges (that is, excluding Bronx thruways and the non-bikeable Hudson River tunnels) for a stretch of 9 months.
Purpose
Cycling in New York City is associated with mixed cycling conditions that include dense urban proximities, relatively flat terrain, congested roadways with “stop-and-go” traffic, and streets with heavy pedestrian activity. The city’s large cycling population includes utility cyclists, such as delivery and messenger services; cycling clubs for recreational cyclists; and, increasingly, commuters.
In this study we will see NYC cycling density and effect of wheather on it.
Analysis
#packages:
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.4.3library(rmarkdown)## Warning: package 'rmarkdown' was built under R version 3.4.3#Import Data
setwd('C:/Users/pc/Documents/Group Project/')
mydata = read.csv("nyc_bicy.csv")
head(mydata)## X Date Day High.Temp..Â.F. Low.Temp..Â.F. Precipitation Brooklyn.Bridge
## 1 0 1 1 78.1 66.0 0.01 1704
## 2 1 2 2 55.0 48.9 0.15 827
## 3 2 3 3 39.9 34.0 0.09 526
## 4 3 4 4 44.1 33.1 0.47 (S) 521
## 5 4 5 5 42.1 26.1 0 1416
## 6 5 6 6 45.0 30.0 0 1885
## Manhattan.Bridge Williamsburg.Bridge Queensboro.Bridge Total
## 1 3126 4115 2552 11497
## 2 1646 2565 1884 6922
## 3 1232 1695 1306 4759
## 4 1067 1440 1307 4335
## 5 2617 3081 2357 9471
## 6 3329 3856 2849 11919#Summary for checking objective types.
glimpse(mydata)## Observations: 210
## Variables: 11
## $ X <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ...
## $ Date <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
## $ Day <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
## $ High.Temp..Â.F. <dbl> 78.1, 55.0, 39.9, 44.1, 42.1, 45.0, 57.0, ...
## $ Low.Temp..Â.F. <dbl> 66.0, 48.9, 34.0, 33.1, 26.1, 30.0, 53.1, ...
## $ Precipitation <fctr> 0.01, 0.15, 0.09, 0.47 (S), 0, 0, 0.09, 0...
## $ Brooklyn.Bridge <dbl> 1704, 827, 526, 521, 1416, 1885, 1276, 198...
## $ Manhattan.Bridge <int> 3126, 1646, 1232, 1067, 2617, 3329, 2581, ...
## $ Williamsburg.Bridge <dbl> 4115, 2565, 1695, 1440, 3081, 3856, 3282, ...
## $ Queensboro.Bridge <dbl> 2552, 1884, 1306, 1307, 2357, 2849, 2457, ...
## $ Total <int> 11497, 6922, 4759, 4335, 9471, 11919, 9596...#Summary for checking objective types. Here i saw that temperature is fahrenheit and date same with day
glimpse(mydata)## Observations: 210
## Variables: 11
## $ X <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ...
## $ Date <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
## $ Day <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
## $ High.Temp..Â.F. <dbl> 78.1, 55.0, 39.9, 44.1, 42.1, 45.0, 57.0, ...
## $ Low.Temp..Â.F. <dbl> 66.0, 48.9, 34.0, 33.1, 26.1, 30.0, 53.1, ...
## $ Precipitation <fctr> 0.01, 0.15, 0.09, 0.47 (S), 0, 0, 0.09, 0...
## $ Brooklyn.Bridge <dbl> 1704, 827, 526, 521, 1416, 1885, 1276, 198...
## $ Manhattan.Bridge <int> 3126, 1646, 1232, 1067, 2617, 3329, 2581, ...
## $ Williamsburg.Bridge <dbl> 4115, 2565, 1695, 1440, 3081, 3856, 3282, ...
## $ Queensboro.Bridge <dbl> 2552, 1884, 1306, 1307, 2357, 2849, 2457, ...
## $ Total <int> 11497, 6922, 4759, 4335, 9471, 11919, 9596...#For starting to mutate i will convert fahreneit values to celsius(it's hard) and remove date
newdata=mydata%>% select(Date, Day, High.Temp..Â.F.,Low.Temp..Â.F. ,Precipitation,Brooklyn.Bridge,Manhattan.Bridge,Williamsburg.Bridge,Queensboro.Bridge,Total)%>% mutate(HighDegree=High.Temp..Â.F.-32)%>%mutate (HighDegree2=HighDegree*5)%>%mutate (HighDegree3=HighDegree2/9)%>% mutate(LowDegree=Low.Temp..Â.F.-32)%>%mutate (LowDegree2=LowDegree*5)%>%mutate (LowDegree3=LowDegree2/9)## Warning: package 'bindrcpp' was built under R version 3.4.3nycbic=newdata%>% select(Day,Precipitation,Brooklyn.Bridge,Manhattan.Bridge,Williamsburg.Bridge,Queensboro.Bridge,Total,LowDegree3,HighDegree3)%>%rename(HighDegree=HighDegree3)%>%rename(LowDegree=LowDegree3)
glimpse(nycbic)## Observations: 210
## Variables: 9
## $ Day <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
## $ Precipitation <fctr> 0.01, 0.15, 0.09, 0.47 (S), 0, 0, 0.09, 0...
## $ Brooklyn.Bridge <dbl> 1704, 827, 526, 521, 1416, 1885, 1276, 198...
## $ Manhattan.Bridge <int> 3126, 1646, 1232, 1067, 2617, 3329, 2581, ...
## $ Williamsburg.Bridge <dbl> 4115, 2565, 1695, 1440, 3081, 3856, 3282, ...
## $ Queensboro.Bridge <dbl> 2552, 1884, 1306, 1307, 2357, 2849, 2457, ...
## $ Total <int> 11497, 6922, 4759, 4335, 9471, 11919, 9596...
## $ LowDegree <dbl> 18.8888889, 9.3888889, 1.1111111, 0.611111...
## $ HighDegree <dbl> 25.611111, 12.777778, 4.388889, 6.722222, ...#Finally i will add average temperature and round the numbers.
nnycbic=nycbic%>%mutate(Average.Temp=(LowDegree+HighDegree)/2)
head(nnycbic)## Day Precipitation Brooklyn.Bridge Manhattan.Bridge Williamsburg.Bridge
## 1 1 0.01 1704 3126 4115
## 2 2 0.15 827 1646 2565
## 3 3 0.09 526 1232 1695
## 4 4 0.47 (S) 521 1067 1440
## 5 5 0 1416 2617 3081
## 6 6 0 1885 3329 3856
## Queensboro.Bridge Total LowDegree HighDegree Average.Temp
## 1 2552 11497 18.8888889 25.611111 22.250000
## 2 1884 6922 9.3888889 12.777778 11.083333
## 3 1306 4759 1.1111111 4.388889 2.750000
## 4 1307 4335 0.6111111 6.722222 3.666667
## 5 2357 9471 -3.2777778 5.611111 1.166667
## 6 2849 11919 -1.1111111 7.222222 3.055556#Here we can see in NYC temperature changes between 0 to 25 celsius degree when data collected.
library(ggplot2)
ggplot(nnycbic, aes(nnycbic$Average.Temp)) +
geom_histogram(fill= ('pink'), color='black', binwidth=1) +
scale_x_continuous(limit=c(0.0, 25.0), breaks=seq(0.0, 25.0, by = 5)) +
labs(x= 'Temperature', y= 'Frequency') +
ggtitle('Temperature Frequency of Data')
#When we analyze number of cycling on Manhattan Bridge and temperature. Here we can see average people merly use bicycle when temperature under 10 degrees also they don't choose bicycle to transport when temperature is high.
library(ggplot2)
ggplot(data=nnycbic, aes(x=Average.Temp, y=Manhattan.Bridge)) +
geom_bar(stat="identity")
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