CPS Unemployment.Rmd

---
title: "cps analysis covid"
author: "Corey Sparks, PhD"
date: "5/18/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## 

```{r}
library(ipumsr)
library(dplyr)
library(car)
library(zoo)
library(ggplot2)
library(questionr)
library(tidyquant)
library(fpp)
```

```{r}
ddi<-read_ipums_ddi("/media/corey/extra/cps_00024.xml")
cpsdat<-read_ipums_micro(ddi)
cpsdat<-zap_labels(cpsdat)
```

```{r}

stfips<-data.frame(stfip=unique(tidycensus::fips_codes$state_code), stname=unique(tidycensus::fips_codes$state_name))

cpsdat<-merge(cpsdat, stfips, by.x="STATEFIP", by.y= "stfip")

cpsdat2<-cpsdat%>%
  filter(YEAR >2009,AGE>18& AGE<65, EMPSTAT<30)%>%
  mutate(emp = Recode(EMPSTAT, recodes = "0 = NA; 1='af'; 10='curr work'; 12 = 'recent no job'; 20:22='unemp'"))%>%
  mutate( curremp = ifelse(emp =='curr work' , 1, 0))

cpsdat2$month<- as.Date(as.yearmon(paste(cpsdat2$YEAR,cpsdat2$MONTH, sep="/"), format = "%Y/%m"))


out<-cpsdat2%>%
  group_by(month)%>%
  summarise(emprate =1- wtd.mean(curremp,weights = WTFINL, na.rm=T))%>%
  arrange( month)%>%
  ungroup()

#test<-as.list(tapply(out$emprate, out$stname, FUN = ts, start = c(2017,1), end = c(2020, 3), frequency = 12))
ts1<-ts(out$emprate, start = c(2017,1), end = c(2020, 4), frequency = 12)

#lapply(test, decompose, type="add")

adjust1<-decompose(ts1,type =  "add")
adjust2<-ts1 - adjust1$seasonal

test<-data.frame(month = unique(out$month), unemprate = adjust2)

ggplot(data=test, aes(month, unemprate))+geom_line()+labs(title = "Seasonally Adjusted Unemployment Rate",
       subtitle = "Jan 2017 to April 2020",
       caption = "Source: IPUMS CPS Monthly Data \n Calculations by Corey S. Sparks, Ph.D.",
       x = "Month",
       y = "Unemployment Rate")+theme_minimal()

ggsave(filename="~/emprate.png", height=10, width=12)
```