Story2

Data sources:
https://fred.stlouisfed.org/
CPI: https://fred.stlouisfed.org/series/CORESTICKM159SFRBATL
Unemployment rate: https://fred.stlouisfed.org/series/UNRATE
Fed Funds: https://fred.stlouisfed.org/series/FEDFUNDS

library(tidyverse)
library(dplyr)

#Bring in the Data
ff <- as.data.frame(read_csv ("https://raw.githubusercontent.com/dianaplunkett/608/main/FEDFUNDS.csv"))

cp <- as.data.frame( read_csv ("https://raw.githubusercontent.com/dianaplunkett/608/main/FRED-CPI.csv"))

unr <- as.data.frame(read_csv ("https://raw.githubusercontent.com/dianaplunkett/608/main/FRED-UNRATE.csv"))

Filter to last 25 years

ff<- filter(ff, year(DATE)>1998)
cp<- filter(cp, year(DATE)>1998)
unr<- filter(unr, year(DATE)>1998)

cp <- cp%>%
  rename(cpi=CORESTICKM159SFRBATL)

Overview

ggplot() + 
     geom_line(data=unr, 
              color="#4682B4",
                linetype=2,
                linewidth =1,
              aes(x=DATE, y=UNRATE) )+
   geom_line(data=ff, 
              color="#E69F00",linewidth=1.5,
            aes(x=DATE, y=FEDFUNDS))+
   geom_line(data=cp, 
              color="#145a32",linetype=5,linewidth=1,
              aes(x=DATE, y=cpi))+
   theme_classic()+
   theme(axis.title = element_blank())

Calculate the Month over Month Change

Doing this for all 3 data sets (FedFunds, CPI and Unemployment) allows us to highlight the “big” changes.

ff <- ff %>%
  mutate(
    ffMoM = (FEDFUNDS - lag(FEDFUNDS))/lag(FEDFUNDS)
  ) %>% 
  mutate(ffMoM = ifelse(is.na(ffMoM), 0, ffMoM))

cp <- cp%>%
  mutate(
    cpMoM = (cpi - lag(cpi))/lag(cpi)
  ) %>% 
  mutate(cpMoM = ifelse(is.na(cpMoM), 0, cpMoM))

unr <- unr %>%
  mutate(
    unrMoM = (UNRATE - lag(UNRATE))/lag(UNRATE)
  ) %>% 
  mutate(unrMoM = ifelse(is.na(unrMoM), 0, unrMoM))

And plot it..

Whoa, that’s a mess.

ggplot() + 
     geom_line(data=unr, 
              color="#4682B4",
                linetype=2,
                linewidth =.5,
              aes(x=DATE, y=unrMoM) )+
   geom_line(data=ff, 
              color="#E69F00",linewidth=.5,
            aes(x=DATE, y=ffMoM))+
   geom_line(data=cp, 
              color="#145a32",linetype=5,linewidth=.5,
              aes(x=DATE, y=cpMoM))+
   theme_classic()+
   theme(axis.title = element_blank())

Plot Big changes MoM on the Overview

First, make indicators for Big MoM changes.
For Fed Funds, use +/- 0.5 as big moves.
CPI, use +/- 0.15.
Unemployment, use +/- 0.07.

ff <- ff %>% mutate(ffbig = abs(ff$ffMoM)>0.5)

cp <- cp %>% mutate(cpbig=abs(cp$cpMoM)>0.15)

unr <- unr %>% mutate(unrbig=abs(unr$unrMoM)>0.07)
# and sep data sets for just big
ffbig<- filter(ff, ffbig)
cpbig<- filter(cp, cpbig)
unrbig<- filter(unr, unrbig)

ggplot() + 
  geom_line(data=ff,
            color='#FFEFD5',
            linewidth =.5,
            aes(x=DATE,
                y=FEDFUNDS))+
  geom_line(data=cp,
            color='#8FBC8B',
            linetype=5,
            linewidth=.5,
            aes(x=DATE,
                y=cpi))+
  geom_line(data=unr,
            color='#B0C4DE',
            linetype=2,
            linewidth=.5,
            aes(x=DATE,
                y=UNRATE))+
  geom_point(data=ffbig, 
             color="#E69F00",
             size=2,
             aes(x=DATE, 
                 y=FEDFUNDS)) +
  geom_point(data=cpbig,
             color="#145a32",
             size=2,
             aes(x=DATE,
                 y=cpi))+
  geom_point(data=unrbig,
             color="#4682B4",
             size=2,
             aes(x=DATE,
                 y=UNRATE))+
  theme_classic()+
  theme(axis.title = element_blank())

Areas to dive in

First Area - 2007 - 2016

ff.1<- filter(ff, 
              year(DATE)>2007 & year(DATE)<=2016)
ffbig.1<- filter(ffbig, 
              year(DATE)>2007 & year(DATE)<=2016)

cp.1<- filter(cp, 
              year(DATE)>2007 & year(DATE)<=2016)
cpbig.1<- filter(cpbig, 
              year(DATE)>2007 & year(DATE)<=2016)

unr.1<- filter(unr, 
               year(DATE)>2007 & year(DATE)<=2016)
unrbig.1<- filter(unrbig, 
              year(DATE)>2007 & year(DATE)<=2016)

ggplot() + 
  geom_line(data=ff.1,
            color='#FFEFD5',
            linewidth =1,
            aes(x=DATE,
                y=FEDFUNDS))+
  geom_line(data=cp.1,
            color='#8FBC8B',
            linetype=5,
            linewidth=1,
            aes(x=DATE,
                y=cpi))+
  geom_line(data=unr.1,
            color='#B0C4DE',
            linetype=2,
            aes(x=DATE,
                y=UNRATE))+
  geom_point(data=ffbig.1, 
             color="#E69F00",
             size=2,
             aes(x=DATE, 
                 y=FEDFUNDS)) +
  geom_point(data=cpbig.1,
             color="#145a32",
             size=2,
             aes(x=DATE,
                 y=cpi))+
  geom_point(data=unrbig.1,
             color="#4682B4",
             size=2,
             aes(x=DATE,
                 y=UNRATE))+
  theme_classic()+
  theme(axis.title = element_blank())

Second Area - 2020 - 2024

ff.2<- filter(ff, 
              year(DATE)>=2020)
ffbig.2<- filter(ffbig, 
                year(DATE)>=2020)

cp.2<- filter(cp, 
              year(DATE)>=2020)
cpbig.2<- filter(cpbig, 
              year(DATE)>=2020)

unr.2<- filter(unr, 
               year(DATE)>=2020)
unrbig.2<- filter(unrbig, 
              year(DATE)>=2020)

ggplot() + 
  geom_line(data=ff.2,
            color='#FFEFD5',
            linewidth =1,
            aes(x=DATE,
                y=FEDFUNDS))+
  geom_line(data=cp.2,
            color='#8FBC8B',
            linetype=5,
            linewidth=1,
            aes(x=DATE,
                y=cpi))+
  geom_line(data=unr.2,
            color='#B0C4DE',
            linetype=2,
            aes(x=DATE,
                y=UNRATE))+
  geom_point(data=ffbig.2, 
             color="#E69F00",
             size=2,
             aes(x=DATE, 
                 y=FEDFUNDS)) +
  geom_point(data=cpbig.2,
             color="#145a32",
             size=2,
             aes(x=DATE,
                 y=cpi))+
  geom_point(data=unrbig.2,
             color="#4682B4",
             size=2,
             aes(x=DATE,
                 y=UNRATE))+
  theme_classic()+
  theme(axis.title = element_blank())