assignment3_new

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(ggplot2)
library(tidycensus)

df <- fread("C:/Users/عمر/Desktop/Fall 2024/Rstudio/Week 6/CA_MSA.csv")



# Q1

total_population_for_ma <- df %>%
  group_by(NAME) %>%
  summarize(total_population = sum(tpop))



#Q2

df$aw <- abs(df$nhasn / df$nhasnc - df$nhwhite / df$nhwhitec)

Result <- df %>%
  group_by(NAME) %>%
  summarize(aw_diss = 0.5 * sum(aw))



#Q 3

HOLC <- fread("C:/Users/عمر/Desktop/Fall 2024/Rstudio/Week 6/holc_census_tracts.csv")


ave_pct <- HOLC %>%
  group_by(st_name) %>%
  summarize(avg = mean(holc_area))



#Q4

ggplot(HOLC, aes(x = st_name, y = holc_area)) + 
  geom_boxplot() 

#Q5

TX_HOLC <- HOLC[HOLC$state == "TX" & HOLC$holc_grade == "D", ]

holc_grade_d_Texas_city <- TX_HOLC %>%
  group_by(st_name) %>%
  summarize(count = n())



#Q6
census_api_key("0d539976d5203a96fa55bbf4421110d4b3db3648", 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') 


st <-"TX"
ct <-"Bexar"

ct <- get_acs(geography = "tract", variables = var, count=ct,
              state = st,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



#Q7

merged_data <- merge(ct, HOLC, by.x = "GEOID", by.y = "geoid")

# merged_data2 <- merge(ct, HOLC)     This is the code for all  merged data but when i run it my computer doesn't process the data 


avg_black_pct_for_HOLC_grades <- merged_data %>%
  group_by(holc_grade) %>%
  summarize(avg_black_pct = mean(black_pct))




ggplot(avg_black_pct_for_HOLC_grades, aes(x = holc_grade, y = avg_black_pct)) +
  geom_bar(stat = "identity")

#Q8


ggplot(merged_data, aes(x = holc_grade, y = holc_area)) + 
  geom_boxplot() +
  theme_minimal()