In kgilds/ACLOlderAmericansProfile: ACL Profle of Older Americans

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(httr)
library(readxl)
library(dplyr)
library(esquisse)

profile_2019 <- ("https://acl.gov/sites/default/files/Aging%20and%20Disability%20in%20America/2019OlderAmericansData.xlsx")

#url_district <- ("http://www.fldoe.org/core/fileparse.php/18534/urlt/DistrictGrades19.xls")



httr::GET(profile_2019, write_disk(profile_2019 <- tempfile(fileext = ".xlsx")))

p_2019 <- readxl::read_xlsx(profile_2019)

excel_sheets(profile_2019)

read_dat <- function(df, sheet){
  readxl::read_excel(df, sheet = sheet, skip =2 )
}

person_65_p <- read_excel(profile_2019, sheet = 1, skip =2) %>%


dplyr::glimpse()

esquisse::esquisser(person_65_p)

maritial_status <- read_excel(profile_2019, sheet = 2, skip =2)

maritial_status

living_arrangement <- read_excel(profile_2019, sheet = 3, skip = 2)

living_arrangement

state_percent_65 <- read_excel(profile_2019, sheet =4, skip = 2)

state_percent_65

state_increas <- read_excel(profile_2019, sheet = 5, skip = 2)

state_increas

pop_state <- read_excel(profile_2019, sheet = 6, skip =  2)

pop_state

family_income <- read_excel(profile_2019, sheet = 7, skip = 2)

family_income

personal_income <- read_dat(profile_2019, 8)

personal_income

poverty <- read_dat(profile_2019, 9)

poverty

employment <- read_dat(profile_2019, 10)

employment

health_insurance <- read_dat(profile_2019, 11)

health_insurance

disability <- read_dat(profile_2019, 12)

disability 

Aerobic <- read_dat(profile_2019, 13)

Aerobic

overweight <- read_dat(profile_2019, 14)

overweight

Overweight_2008_2016 <- read_dat(profile_2019, 15)

Overweight_2008_2016

kgilds/ACLOlderAmericansProfile documentation built on May 16, 2021, 7:45 a.m.