# Loading
library(ggplot2)
# Load the data
data(tract_covariates)
## Warning in data(tract_covariates): data set 'tract_covariates' not found
tract_covariates<-read.csv("C:/Users/dejmo/OneDrive/Documents/URP Cert/URP 5363- Planning Methods I/Assignment 1/tract_covariates.csv")

#------------2. pick rows and columns------------------------
# Select relevant columns from mtcars
df <- tract_covariates[, c("czname", "hhinc_mean2000", "popdensity2000")] 

df1 <- tract_covariates[, c(5,6,13)]

city <- tract_covariates[tract_covariates$czname == 'San Antonio', c(5,6,13)]

ggplot(city, aes(x = hhinc_mean2000, )) + 
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(city, aes(x = hhinc_mean2000, fill = popdensity2000)) + 
  geom_histogram(binwidth = 1)
## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

ggplot(city, aes(x= popdensity2000, y=))+
  geom_boxplot()

ggplot(city, aes(x = hhinc_mean2000)) +
  geom_density()

ggplot(city, aes(x = hhinc_mean2000, color = popdensity2000)) + 
  stat_ecdf(geom = "step")
## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

ggplot(city, aes(x = hhinc_mean2000, y = popdensity2000)) + 
  geom_point()+
   labs(x = "Household Income", y = "Population Density")

library(plotly)
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
p <- ggplot(city, aes(x = hhinc_mean2000, y = popdensity2000)) + 
  geom_point()
ggplotly(p)

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