challenge_4
Author

Young Soo Choi

Published

August 18, 2022

Code 
library(tidyverse)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in data

Read Fed Funds Rate

Code 
ffr<-read_csv("_data/FedFundsRate.csv",
              show_col_types = FALSE)
ffr
# A tibble: 904 × 10
    Year Month   Day Federal F…¹ Feder…² Feder…³ Effec…⁴ Real …⁵ Unemp…⁶ Infla…⁷
   <dbl> <dbl> <dbl>       <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
 1  1954     7     1          NA      NA      NA    0.8      4.6     5.8      NA
 2  1954     8     1          NA      NA      NA    1.22    NA       6        NA
 3  1954     9     1          NA      NA      NA    1.06    NA       6.1      NA
 4  1954    10     1          NA      NA      NA    0.85     8       5.7      NA
 5  1954    11     1          NA      NA      NA    0.83    NA       5.3      NA
 6  1954    12     1          NA      NA      NA    1.28    NA       5        NA
 7  1955     1     1          NA      NA      NA    1.39    11.9     4.9      NA
 8  1955     2     1          NA      NA      NA    1.29    NA       4.7      NA
 9  1955     3     1          NA      NA      NA    1.35    NA       4.6      NA
10  1955     4     1          NA      NA      NA    1.43     6.7     4.7      NA
# … with 894 more rows, and abbreviated variable names
#   ¹​`Federal Funds Target Rate`, ²​`Federal Funds Upper Target`,
#   ³​`Federal Funds Lower Target`, ⁴​`Effective Federal Funds Rate`,
#   ⁵​`Real GDP (Percent Change)`, ⁶​`Unemployment Rate`, ⁷​`Inflation Rate`
# ℹ Use `print(n = ...)` to see more rows

Briefly describe the data

This data set is regarding various national economic variables from July of 1954 to March of 2017. It has 904 rows and 10 columns. But it also has many missing values and the recorded date is not even constant.

Tidy Data (as needed)

Year, Month, Day are seperated. So I mutated Date column newly.(thanks to Nayan)

Code 
library(lubridate)

ffr2 <- ffr %>% 
  mutate(Date = make_datetime(Year, Month, Day))

ffr2
# A tibble: 904 × 11
    Year Month   Day Federal F…¹ Feder…² Feder…³ Effec…⁴ Real …⁵ Unemp…⁶ Infla…⁷
   <dbl> <dbl> <dbl>       <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
 1  1954     7     1          NA      NA      NA    0.8      4.6     5.8      NA
 2  1954     8     1          NA      NA      NA    1.22    NA       6        NA
 3  1954     9     1          NA      NA      NA    1.06    NA       6.1      NA
 4  1954    10     1          NA      NA      NA    0.85     8       5.7      NA
 5  1954    11     1          NA      NA      NA    0.83    NA       5.3      NA
 6  1954    12     1          NA      NA      NA    1.28    NA       5        NA
 7  1955     1     1          NA      NA      NA    1.39    11.9     4.9      NA
 8  1955     2     1          NA      NA      NA    1.29    NA       4.7      NA
 9  1955     3     1          NA      NA      NA    1.35    NA       4.6      NA
10  1955     4     1          NA      NA      NA    1.43     6.7     4.7      NA
# … with 894 more rows, 1 more variable: Date <dttm>, and abbreviated variable
#   names ¹​`Federal Funds Target Rate`, ²​`Federal Funds Upper Target`,
#   ³​`Federal Funds Lower Target`, ⁴​`Effective Federal Funds Rate`,
#   ⁵​`Real GDP (Percent Change)`, ⁶​`Unemployment Rate`, ⁷​`Inflation Rate`
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Then, I selected date, unemployment rate, and inflation rate to checked Phillip Curve’s validity. To check it I created scatter plot of unemployment and inflation.(thanks again Nayan)

Code 
pc<-select(ffr2, "Date", "Inflation Rate", "Unemployment Rate")
pc
# A tibble: 904 × 3
   Date                `Inflation Rate` `Unemployment Rate`
   <dttm>                         <dbl>               <dbl>
 1 1954-07-01 00:00:00               NA                 5.8
 2 1954-08-01 00:00:00               NA                 6  
 3 1954-09-01 00:00:00               NA                 6.1
 4 1954-10-01 00:00:00               NA                 5.7
 5 1954-11-01 00:00:00               NA                 5.3
 6 1954-12-01 00:00:00               NA                 5  
 7 1955-01-01 00:00:00               NA                 4.9
 8 1955-02-01 00:00:00               NA                 4.7
 9 1955-03-01 00:00:00               NA                 4.6
10 1955-04-01 00:00:00               NA                 4.7
# … with 894 more rows
# ℹ Use `print(n = ...)` to see more rows
Code 
ggplot(pc ,mapping=aes(x=ffr$`Unemployment Rate`, y=ffr$`Inflation Rate`)) + geom_point()

It maybe too long period so I cannot see Phillip Curve clearly.