QUESTION ONE
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
library(writexl)
df<- fread("C:/Users/adrian.gallegos/Downloads/CA_MSA.csv")
results<-df %>% group_by(NAME) %>% summarize(population = sum(tpop))
QUESTION TWO
df$wa<- abs(df$nhasn/df$nhasnc-df$nhwhite/df$nhwhitec)
results_AW<-df %>% group_by(NAME) %>% summarize(diss_a_w= 0.5*sum(wa))
QUESTION THREE
HOLC <- fread("C:/Users/adrian.gallegos/Downloads/holc_census_tracts.csv")
avg_holc_area <-HOLC %>%
group_by(state) %>%
summarize(avg_holc_area =mean(holc_area))
QUESTION FOUR
library(ggplot2)
ggplot(HOLC, aes(x = state, y = holc_area )) +
geom_boxplot()
QUESTION FIVE
TX_HOLC_GRADE_D<- HOLC[HOLC$state=="TX" & HOLC$holc_grade=="D",]
COUNT_TX_HOLC_D<-TX_HOLC_GRADE_D %>%
group_by(st_name , holc_grade) %>%
summarize(count=n())
## `summarise()` has grouped output by 'st_name'. You can override using the
## `.groups` argument.
QUESTION SIX
library(tidycensus)
census_api_key("4156b3413d433f2f6803ff4b60faa2a8bb579da2",overwrite ="TRUE")
## To install your API key for use in future sessions, run this function with `install = TRUE`.
var <- c(poptotal='B03002_001E',
hispanic='B03002_012E',
white='B03002_003E',
black='B03002_004E',
asian='B03002_006E',
poptotal2='B17017_001E',
poverty='B17017_002E')
ct <- get_acs(geography = "tract", variables = var, count= "Bexar",
state = "TX" ,output="wide", year = 2021, geometry = TRUE)
## Getting data from the 2017-2021 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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ct$black_pct <-ct$black/ct$poptotal
ct$poverty_pct <- ct$poverty /ct$poptotal2
QUESTION SEVEN
SA <- HOLC[HOLC$st_name=="San Antonio",]
SA <- SA[,c(2,14,18)]
SA$geoid <- as.character(SA$geoid)
names(SA)[2] <-"GEOID"
SA_HOLC_merge <-merge(SA,ct, by="GEOID")
avg_pct_black_SA <-SA_HOLC_merge %>%
group_by(st_name, holc_grade) %>%
summarize(avg_black_pct=mean(black_pct))
## `summarise()` has grouped output by 'st_name'. You can override using the
## `.groups` argument.
library(ggplot2)
ggplot(avg_pct_black_SA, aes(x=holc_grade, y=avg_black_pct , fill=holc_grade)) +
geom_col()
QUESTION EIGHT
SA2 <- HOLC[HOLC$st_name=="San Antonio",]
ggplot(SA2, aes(x = holc_grade, y = holc_area )) +
geom_boxplot()