Project 1_HQ
HQ
2023-10-18
# Q1
library(tidycensus)
#https://api.census.gov/data/2020/acs/acs5/variables.html
#ctrl+F to look for variables
#median household income= B19013_001E
#Hispanic population= B03002_001E
#Non-Hispanic African American population= B03002_004E
#male=B01001_002E
#female= B01001_026E
#total population=B01003_001E
#median age= B01002_001E Estimate!!Median age --!!Total: MEDIAN AGE BY SEX
var=c('B19013_001E','B03002_001E','B03002_004E','B01001_002E','B01001_026E','B01003_001E','B01002_001E')
Travis_segregation <- get_acs(geography = "tract", variables = var, county = "Travis",
state = "TX",output="wide", geometry = TRUE)## Getting data from the 2017-2021 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|======================================================================| 100%names(Travis_segregation)[3] <- 'MHHincome'
names(Travis_segregation)[5] <- 'Hispanic'
names(Travis_segregation)[7] <- 'Black or African American'
names(Travis_segregation)[9] <- 'Male'
names(Travis_segregation)[11] <- 'Female'
names(Travis_segregation)[13] <- 'TotalPop'
names(Travis_segregation)[15] <- 'MAge'
#Q2
Travis_segregation$B19013_001M<- NULL
Travis_segregation$B03002_001M<- NULL
Travis_segregation$B03002_004M<- NULL
Travis_segregation$B01001_002M<- NULL
Travis_segregation$B01001_026M<- NULL
Travis_segregation$B01003_001M<- NULL
Travis_segregation$B01002_001M<- NULL
#Q3
write.csv(Travis_segregation,"C:/Users/haomi/Desktop/Urban Planning Methods I URP-5363-901-Fall 2023/Oct 17 - Week 9/Travis_seg.csv")
#Q4
Travis_segregation$pct_Black <- 100*Travis_segregation$Black/Travis_segregation$TotalPop
library(ggplot2)
ggplot(data = Travis_segregation, aes(x = pct_Black, y = MHHincome)) + geom_point()## Warning: Removed 4 rows containing missing values (`geom_point()`).
#Q5 Make a histogram to visualize the age distribution of the county
AgeDistrbution <- ggplot(Travis_segregation, aes(x = MAge))
AgeDistrbution + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).
#Q6 Make a PDF (probability density function) chart to show the distribution of median household income
MHHincome <- ggplot(Travis_segregation, aes(x = MHHincome))
MHHincome + geom_density()## Warning: Removed 4 rows containing non-finite values (`stat_density()`).
#Q7 Make a CDF (cumulative density function) chart to show the distribution of median household income
MHHincome + stat_ecdf()## Warning: Removed 4 rows containing non-finite values (`stat_ecdf()`).
#Q8 Make a boxplot to visualize the median household income
MHHincome + geom_boxplot()## Warning: Removed 4 rows containing non-finite values (`stat_boxplot()`).
#Q9 Make a map to show the spatial distribution of percentage of Hispanic population
library(sf)## Linking to GEOS 3.11.2, GDAL 3.6.2, PROJ 9.2.0; sf_use_s2() is TRUElibrary(tmap)## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
## (status 2 uses the sf package in place of rgdal)## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')Travis_segregation$pct_Hispanic <- 100*Travis_segregation$Hispanic/Travis_segregation$TotalPop
tm_shape(Travis_segregation) +tm_fill(col = "pct_Hispanic")+ tm_layout(title = "Hispanic Percent")
#Q10 Calculate and map the difference between female and male population to show what census tract has more female population
Travis_segregation$Difference <- Travis_segregation$Female - Travis_segregation$Male
tm_shape(Travis_segregation) +tm_fill(col = "Difference")+ tm_layout(title = "Difference between female and male")## Variable(s) "Difference" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
#Q11 Find the population of the county (or the major city within the county) from 2010 to 2023, and predict the population for the next five years (2024-2028)
x <- c(2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023) #year
y <- c(1.03,1.06,1.09,1.12,1.15,1.18,1.21,1.23,1.25,1.27,1.30,1.31,1.33,1.34) #(million persons) find in https://fred.stlouisfed.org/series/TXTRAV3POP
poly.lm1 <- lm(y ~ poly(x, 1))
new.x <- c(2024, 2025, 2026, 2027,2028)
new.df <- data.frame(x=new.x)
new.y <- predict(poly.lm1, newdata=new.df)
print(new.y)## 1 2 3 4 5
## 1.387473 1.411802 1.436132 1.460462 1.484791#Q12 Publish your project to Rpubs using Rmarkdown and submit the link to Canvas