library(ggplot2) library(ggthemes) library(lubridate) library(dplyr) library(tidyr) library(tidyverse) # metapackage of all tidyverse packages library(DT) library(scales) colors = c(“#CC1011”, “#665555”, “#05a399”, “#cfcaca”, “#f5e840”, “#0683c9”, “#e075b0”) colors apr <- read.csv(“C:\Users\user\Documents\uber-raw-data-apr14.csv”) may <- read.csv(“C:\Users\user\Documents\uber-raw-data-may14.csv”) june <- read.csv(“C:\Users\user\Documents\uber-raw-data-jun14.csv”) july <- read.csv(“C:\Users\user\Documents\uber-raw-data-jul14.csv”) aug <- read.csv(“C:\Users\user\Documents\uber-raw-data-aug14.csv”) sept <- read.csv(“C:\Users\user\Documents\uber-raw-data-sep14.csv”) data <- rbind(apr, may, june, july, aug, sept) cat(“The dimensions of the data are:”, dim(data)) head(data) data\(Date.Time <- as.POSIXct(data\)Date.Time, format=“%m/%d/%Y %H:%M:%S”) data\(Time <- format(as.POSIXct(data\)Date.Time, format = “%m/%d/%Y %H:%M:%S”), format=“%H:%M:%S”) data\(Date.Time <- ymd_hms(data\)Date.Time) data\(day <- factor(day(data\)Date.Time)) data\(month <- factor(month(data\)Date.Time, label=TRUE)) data\(year <- factor(year(data\)Date.Time)) data\(dayofweek <- factor(wday(data\)Date.Time, label=TRUE)) data\(second = factor(second(hms(data\)Time))) data\(minute = factor(minute(hms(data\)Time))) data\(hour = factor(hour(hms(data\)Time))) # Look at the data head(data) hourly_data <- data %>% group_by(hour) %>% dplyr::summarize(Total = n()) # Shos data in a searchable js table datatable(hourly_data)

Plot the data by hour

ggplot(hourly_data, aes(hour, Total)) + geom_bar(stat=“identity”, fill=“steelblue”, color=“red”) + ggtitle(“Trips Every Hour”, subtitle = “aggregated today”) + theme(legend.position = “none”, plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5)) + scale_y_continuous(labels=comma)

Aggregate data by day of the month

day_data <- data %>% group_by(day) %>% dplyr::summarize(Trips = n()) day_data # Plot the data for the day ggplot(day_data, aes(day, Trips)) + geom_bar(stat = “identity”, fill = “steelblue”) + ggtitle(“Trips by day of the month”) + theme(legend.position = “none”) + scale_y_continuous(labels = comma)

Collect data by day of the week and month

day_month_data <- data %>% group_by(dayofweek, month) %>% dplyr::summarize(Trips = n()) day_month_data # Plot the above data ggplot(day_month_data, aes(dayofweek, Trips, fill = month)) + geom_bar(stat = “identity”, aes(fill = month), position = “dodge”) + ggtitle(“Trias by Day and Month”) + scale_y_continuous(labels = comma) + scale_fill_manual(values = colors)

month_data <- data %>% group_by(month) %>% dplyr::summarize(Total = n()) month_data ggplot(month_data, aes(month, Total, fill = month)) + geom_bar(stat = “Identity”) + ggtitle(“Trips in a month”) + theme(legend.position = “none”) + scale_y_continuous(labels = comma) + scale_fill_manual(values = colors)

day_hour_data <- data %>% group_by(day, hour) %>% dplyr::summarize(Total = n()) datatable(day_hour_data)

Plot a heatmap

ggplot(day_hour_data, aes(day, hour, fill = Total)) + geom_tile(color = “white”) + ggtitle(“Heat Map by Hour and Day”)

Collect data by month and day

month_day_data <- data %>% group_by(month, day) %>% dplyr::summarize(Trips = n()) month_day_data

Plot a heatmap

ggplot(month_day_data, aes(day, month, fill = Trips)) + geom_tile(color = “white”) + ggtitle(“Heat Map by Month and Day”)

Plot a heatmap by day of the week and month

ggplot(day_month_data, aes(dayofweek, month, fill = Trips)) + geom_tile(color = “white”) + ggtitle(“Heat Map by Month and Day”) # Set Map Constants min_lat <- 40 max_lat <- 40.91 min_long <- -74.15 max_long <- -73.7004