challenge_6
Author

Young Soo Choi

Published

August 23, 2022

Code 
library(tidyverse)
library(ggplot2)

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

Read in data

Read Fedral Funds Rate data. I changed column names and added date column to make analyse easy.

Code 
ffr<-read_csv("_data/FedFundsRate.csv",
              show_col_types = FALSE,
              col_names = c("year","month","day","federal_funds_target_rate", "federal_funds_upper_target", "federal_funds_lower_target","effective_federal_funds_rate", "real_gdp", "unemployment_rate","inflation_rate"),
              skip=1)

library(lubridate)

ffr <- mutate(ffr, date = make_datetime(year, month, day))

ffr
# 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,
#   ⁶​unemployment_rate, ⁷​inflation_rate
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
Code 
colnames(ffr)
 [1] "year"                         "month"                       
 [3] "day"                          "federal_funds_target_rate"   
 [5] "federal_funds_upper_target"   "federal_funds_lower_target"  
 [7] "effective_federal_funds_rate" "real_gdp"                    
 [9] "unemployment_rate"            "inflation_rate"              
[11] "date"

Tidy Data

I’d like to check the time series of Real GDP and relation between Real GDP and Unemployment Rate after 2000. So I filtered after 2000 then selected Real GDP column and Unemployment column.

Code 
ffr2<-filter(ffr, year>=2000)
ffr2<-select(ffr2, year, real_gdp, unemployment_rate, date)
ffr2
# A tibble: 252 × 4
    year real_gdp unemployment_rate date               
   <dbl>    <dbl>             <dbl> <dttm>             
 1  2000      1.2               4   2000-01-01 00:00:00
 2  2000     NA                 4.1 2000-02-01 00:00:00
 3  2000     NA                NA   2000-02-02 00:00:00
 4  2000     NA                 4   2000-03-01 00:00:00
 5  2000     NA                NA   2000-03-21 00:00:00
 6  2000      7.8               3.8 2000-04-01 00:00:00
 7  2000     NA                 4   2000-05-01 00:00:00
 8  2000     NA                NA   2000-05-16 00:00:00
 9  2000     NA                 4   2000-06-01 00:00:00
10  2000      0.5               4   2000-07-01 00:00:00
# … with 242 more rows
# ℹ Use `print(n = ...)` to see more rows

Time Dependent Visualization

To make time series line graph, I removed the rows that has NA.

Code 
ffr2<-filter(ffr2, !is.na(real_gdp))
ffr2
# A tibble: 68 × 4
    year real_gdp unemployment_rate date               
   <dbl>    <dbl>             <dbl> <dttm>             
 1  2000      1.2               4   2000-01-01 00:00:00
 2  2000      7.8               3.8 2000-04-01 00:00:00
 3  2000      0.5               4   2000-07-01 00:00:00
 4  2000      2.3               3.9 2000-10-01 00:00:00
 5  2001     -1.1               4.2 2001-01-01 00:00:00
 6  2001      2.1               4.4 2001-04-01 00:00:00
 7  2001     -1.3               4.6 2001-07-01 00:00:00
 8  2001      1.1               5.3 2001-10-01 00:00:00
 9  2002      3.7               5.7 2002-01-01 00:00:00
10  2002      2.2               5.9 2002-04-01 00:00:00
# … with 58 more rows
# ℹ Use `print(n = ...)` to see more rows

Then I drawed time line graph of real gdp.

Code 
ggplot(ffr2, aes(x=date, y=real_gdp)) +
  geom_line()

The line is usually between zero and five points during the period, but it hit a low during the financial crisis in the late 2000s.

Visualizing Part-Whole Relationships

What I concerned is that the relation of real gdp growth and umemployment rate. So I drawed scatter plot of two variable. But according to the scatter plot, it looks like there are no clear relation between those two.

Code 
ggplot(ffr2, aes(x=real_gdp, y=unemployment_rate))+
  geom_point()