The Tampa Feasibility Report featuring R based Visualizations

 

Visualizations for Professional Writing's Final Deliverable

These visualizations were taken from FDOT data for our local district on the subject of transit usage, and crash data that was munged into relative ratios that had a regression performed on these variables within. 

Pie Chart



The above figure is a pie chart using data collected that estimates the higher end limits of hourly numbers of passengers that could utilize each method of public transportation during peak hours

Transit Regression


This figure here shows the monthly totals for crashes, fatalities and serious injuries from accidents recorded within FDOT data and the regression line method was used against the 'free_y' inputs which allowed there to be a 95 percent confidence regression on the other two variables from the one in question showing certain trends within and between variables.

Bus versus Pubic Transport with 95 percent regression line fitted

The same style of regression was sued here but this time it was a proportionality histogram showing monthly counts for bus riders compared to the total ridership data which collected data from our TECO Streetcar downtown (Whiting Street to Centennial Park, Ybor), HART bus lines that operate within the city limits as well as unincorporated Hillsborough county, Rail Lines and other methods of publicly sponsored transportation. Busses make up the largest proportion of public transit here and it has a direct influence because of the weight of its entries in the dataset. The trendlines became closer after the pandemic which is what constituted such a sharp drop around January 2020, and as a result began to converge on each other because of such a large logistical interruption that caused many employees to leave, and leaving many bay area residents with lesser of an option matched with equal demand for these public bus services.


Below, is the R script used to generate all of the visuals which were based within ggplot, scales and other packages used for advanced visualization

##Professional Writing Visualizations

library(dplyr)

library(tidyverse)

library(ggplot2)

library(ggthemes)

library(ggbreak)

library(RColorBrewer)

library(caret)

library(ISLR)

library(glmnet)

library(base)

library(reshape2)

library(rpart)

library(rpart.plot)

library(scales)

library(zoo)


##Crash Data


setwd("C:\\Users\\tyler\\Desktop\\ProfessionalWritingData")

crash.df <- read.csv("crashdata.csv")

statecrashdata <- crash.df[crash.df$FDOT.District == 'Statewide', c("Year","Month","Measure.Names","Measure.Values")]

district7crash <- crash.df[crash.df$FDOT.District == '7', c("Year","Month","Measure.Names","Measure.Values")]


##Transit Data

setwd("C:\\Users\\tyler\\Desktop\\ProfessionalWritingData")

transit.df <- read.csv("transitdata.csv")

d7transit <- transit.df[transit.df$FDOT.District == "7",c("Year","Month","Measure.Names","Measure.Values")]



#Transit Hist Data Cleaning and Pull Frame

d7transitclean <- d7transit[d7transit$Measure.Names == "Bus" | d7transit$Measure.Names == "Total Ridership", c("Year","Month","Measure.Names","Measure.Values")]

d7transitclean$Date <- as.yearmon(paste(d7transitclean$Year, d7transitclean$Month), "%Y %m")

colnames(d7transitclean) <- c("Year","Month","Mode of Transportation","Number of Users","Date")


###Subsetting for the Composite data

##Crashes

d7c2017 <- district7crash[district7crash$Year == '2017' & district7crash$Measure.Names == "Crashes", c("Year","Month","Measure.Names","Measure.Values")]

d7c2018 <- district7crash[district7crash$Year == '2018' & district7crash$Measure.Names == "Crashes", c("Year","Month","Measure.Names","Measure.Values")]

d7c2019 <- district7crash[district7crash$Year == '2019' & district7crash$Measure.Names == "Crashes", c("Year","Month","Measure.Names","Measure.Values")]

d7c2020 <- district7crash[district7crash$Year == '2020' & district7crash$Measure.Names == "Crashes", c("Year","Month","Measure.Names","Measure.Values")]

d7cT <- rbind(d7c2017,d7c2018,d7c2019,d7c2020)

##Fatalities

d7f2017 <- district7crash[district7crash$Year == '2017' & district7crash$Measure.Names == "Fatalities", c("Year","Month","Measure.Names","Measure.Values")]

d7f2018 <- district7crash[district7crash$Year == '2018' & district7crash$Measure.Names == "Fatalities", c("Year","Month","Measure.Names","Measure.Values")]

d7f2019 <- district7crash[district7crash$Year == '2019' & district7crash$Measure.Names == "Fatalities", c("Year","Month","Measure.Names","Measure.Values")]

d7f2020 <- district7crash[district7crash$Year == '2020' & district7crash$Measure.Names == "Fatalities", c("Year","Month","Measure.Names","Measure.Values")]

d7fT <- rbind(d7f2017,d7f2018,d7f2019,d7f2020)

##Serious Injuries

d7si2017 <- district7crash[district7crash$Year == '2017' & district7crash$Measure.Names == "Serious Injuries", c("Year","Month","Measure.Names","Measure.Values")]

d7si2018 <- district7crash[district7crash$Year == '2018' & district7crash$Measure.Names == "Serious Injuries", c("Year","Month","Measure.Names","Measure.Values")]

d7si2019 <- district7crash[district7crash$Year == '2019' & district7crash$Measure.Names == "Serious Injuries", c("Year","Month","Measure.Names","Measure.Values")]

d7si2020 <- district7crash[district7crash$Year == '2020' & district7crash$Measure.Names == "Serious Injuries", c("Year","Month","Measure.Names","Measure.Values")]

d7siT <- rbind(d7si2017,d7si2018,d7si2019,d7si2020)

##Aggregate Data

#Aggregate Serious Injuries 

si2017 <- sum(d7si2017$Measure.Values)

si2018 <- sum(d7si2018$Measure.Values)

si2019 <- sum(d7si2019$Measure.Values)

si2020 <- sum(d7si2020$Measure.Values)


#Aggregate Fatalities

f2017 <- sum(d7f2017$Measure.Values)

f2018 <- sum(d7f2018$Measure.Values)

f2019 <- sum(d7f2019$Measure.Values)

f2020 <- sum(d7f2020$Measure.Values)


#Aggregate Crashes

c2017 <- sum(d7c2017$Measure.Values)

c2018 <- sum(d7c2018$Measure.Values)

c2019 <- sum(d7c2019$Measure.Values)

c2020 <- sum(d7c2020$Measure.Values)


#Merged Data

Year <- c(2017,2018,2019,2020)

TotalCrashes <- c(c2017,c2018,c2019,c2020)

TotalFatalities <- c(f2017,f2018,f2019,f2020)

TotalSeriousInjuries <- c(si2017,si2018,si2019,si2020)

compositedata <- cbind(Year,TotalCrashes,TotalFatalities,TotalSeriousInjuries)

finalcomposite <- as.data.frame(compositedata)

view(finalcomposite)


#Visuals 

district7crash$Date <- as.yearmon(paste(district7crash$Year, district7crash$Month), "%Y %m")

crashgraph <- ggplot(district7crash, aes(x = Date, y = Measure.Values , fill = Measure.Names)) +

    geom_col() + 

    geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95) +

    facet_wrap("Measure.Names", scales = "free_y", ncol = 1) +

    scale_y_continuous(labels = label_comma()) +

    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +

    ggtitle("FDOT District 7 Accidents and Outcomes 2017-2020") +

    ylab("Number of Occurances")


###Transit Visuals

transitgraph <- ggplot(d7transit, aes(x = Year, y = Measure.Values , fill = Measure.Names)) +

  geom_bar(stat = "identity") + 

  facet_wrap("Measure.Names", scales = "free_y", ncol = 1) +

  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +

  ggtitle("FDOT District 7 Transit Usage by Method 2017-2020") +

  ylab("Number of Occurances") +

  scale_y_continuous(labels = scales::label_number_si())


#Transit Histogram

transithistogram <- ggplot(d7transitclean, aes(x = Date, y = `Number of Users` , fill = `Mode of Transportation`)) +

  geom_bar(position = "dodge", stat = "identity") +

  ggtitle("Regression over Bus versus Total Public Transport Ridership District 7") +

  geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95) +

  scale_y_continuous(labels = label_comma())


#Transit Regression

d7trandate <- d7transit

d7trandate$Date <- as.yearmon(paste(d7trandate$Year, d7trandate$Month), "%Y %m")

colnames(d7trandate) <- c("Year","Month","Mode of Transportation","Number of Users","Date")


transitgraphregression <- ggplot(d7trandate, aes(x = Date, y = `Number of Users` , fill = `Mode of Transportation`)) +

  geom_bar(stat = "identity") + 

  geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95) +

  facet_wrap("`Mode of Transportation`", scales = "free_y", ncol = 1) +

  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +

  ggtitle("FDOT District 7 Transit Use Method 2017-2020; with Regression") +

  ylab("Number of Occurances") +

  scale_y_continuous(labels = label_comma())

transitgraphregression


##Ratio Visuals 

fatalitiestocrash <- (finalcomposite$TotalFatalities / finalcomposite$TotalCrashes) 

fatalitiestoinjury <- (finalcomposite$TotalFatalities / finalcomposite$TotalSeriousInjuries) 

injurytocrash <- (finalcomposite$TotalSeriousInjuries / finalcomposite$TotalCrashes) 

Year <- c(2017,2018,2019,2020)

ratio.df <- data.frame(Year,fatalitiestoinjury,fatalitiestocrash,injurytocrash)

view(ratio.df)


##Pie chart

#max limits


`Private Motor Vehichles` <- 1600

`Mixed Traffic` <- 2800

`Two Way Protected Bike Lane` <- 7500

`Dedicated Transit Lines` <- 8000

Sidewalk <- 9000

`On Street Transitway (Bus & Rail)` <- 25000

Total <- 53900


`Mode of Transport` <- c(`Private Motor Vehichles`, `Mixed Traffic`, `Two Way Protected Bike Lane`, `Dedicated Transit Lines`, Sidewalk, `On Street Transitway (Bus & Rail)`)

piedata <- as.data.frame(`Mode of Transport`)

piedata$Method <- c("Private Motor Vehichles", "Mixed Traffic", "Two Way Protected Bike Lane", "Dedicated Transit Lines", "Sidewalk", "On Street Transitway (Bus & Rail)")

row.names(piedata) <- c("Private Motor Vehichles", "Mixed Traffic", "Two Way Protected Bike Lane", "Dedicated Transit Lines", "Sidewalk", "On Street Transitway (Bus & Rail)")


custom.col <- c("#FFDB6D", "#C4961A", "#F4EDCA", 

                "#D16103", "#C3D7A4", "#52854C")


piechart <- ggplot(piedata, aes(x="", y = `Mode of Transport`, fill = Method)) +

  geom_bar(stat = "identity", width = 1) +

  coord_polar("y", start = 0) +

  geom_text(aes(label = paste0(`Mode of Transport`)), position = position_stack(vjust=0.5)) +

  theme(text=element_text(size=6)) +

  scale_fill_manual(values=custom.col) +

  theme_void() +

  ggtitle("Predicted Max Limit of People That Could Use Transit in Peak Hours")


## Visual Calls

piechart

transitgraph

transithistogram

crashgraph

transitgraphregression


Comments

Post a Comment

Popular posts from this blog

R Package: pfStat

Image
 Final Project: pfStat, a personal finance mini-suite in R GitHub URL: https://github.com/tahousex/pfStat What is pfStat? This package is entitled pfStat, and is a small package with a few functions dedicated to helping a user calculate different personal finance metrics. The functions include the ability to derive Returns on Interest, Tax Rates based on Income, Taxable Income and Home Equity, and the purpose behind choosing these kinds of metrics was to emphasize the importance of knowing where one puts their wallet. It goes without being entirely sanctimonious that the dollar does not go as far as it once did, and knowing where one stands amongst their own finances and parameters in the financial world will assist them in making more calculated decisions.  The Functions: The taxrate() income receives input from the user income and state tax percentage represented as a decimal percentage (0-1)   and gives the user output of a dollar amount of the taxes owed based on...
Read more

Module 7 Assignment

Module 7 Assignment  R Code: # Module 7 Assignment #1.1 #First define the data set required, I/O values x <- c(16, 17, 13, 18, 12, 14, 19, 11, 11, 10) y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48) #Then we must create the relationship (correlation) cor(x,y) #This value 0.728 that is in the console tells us a few things #This relationship is correlated and is increasing linearly #I may choose to plot these findings using plot() plot(x,y) #See the lower righthand window for a composite plot for this data, as the regression trednline would positively increase through the points #1.2 #This settion of code will get the value for the coefficients of the linear regression for this data Regression_model=lm(y~x) Regression_model$coefficients #2.1 #This problem will go over the aquisition of similar values with a different data set discharge <- c(3.600,1.800,3.333,2.283,4.533,2.883) waiting <- c(79,54,74,62,85,55) plot(discharge,waiting) #2.1 Regression_model_1=lm(waiting~discha...
Read more

Visual Analytics Final Project: Portuguese Wine and Which Components Correlate to Higher Quality

Image
  Visual Analytics Final Project Tyler House 2023-04-27 library (ggplot2) library (ggcorrplot) library (ggmap) library (ggraph) library (ggpubr) library (tidyverse) library (ggthemes) library (dplyr) library (readr) library (tinytex) library (latexpdf) library (stats) winedata <- read_csv ( "C: \\ Users \\ tyler \\ Desktop \\ Spring 2023 \\ Visual Analytics \\ Final Project \\ winequality-red.csv" ) str (winedata) ## spc_tbl_ [1,599 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame) ##   $ fixed acidity        : num [1:1599] 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ... ##   $ volatile acidity     : num [1:1599] 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ... ##   $ citric acid          : num [1:1599] 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ... ##   $ residual sugar       : num [1:1599] 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ... ## ...
Read more