Challenge 6

challenge_6

fed_rate

Visualizing Time and Relationships

Author

Nick Boonstrsa

Published

August 24, 2022

library(tidyverse)
library(summarytools)
library(lubridate)
library(ggplot2)

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

Challenge Overview

Today’s challenge is to:

read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
tidy data (as needed, including sanity checks)
mutate variables as needed (including sanity checks)
create at least one graph including time (evolution)

try to make them “publication” ready (optional)
Explain why you choose the specific graph type

Create at least one graph depicting part-whole or flow relationships

try to make them “publication” ready (optional)
Explain why you choose the specific graph type

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.

(be sure to only include the category tags for the data you use!)

Read in data

fed_orig<-read_csv("_data/FedFundsRate.csv",
                   skip=1,
                   col_names=c("year","month","day","fedfunds_target","fedfunds_upper","fedfunds_lower","fedfunds_eff","realgdp_change","unemploy_rate","inflation_rate"))

fed_orig

# A tibble: 904 × 10
    year month   day fedfunds_…¹ fedfu…² fedfu…³ fedfu…⁴ realg…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 ¹fedfunds_target,
#   ²fedfunds_upper, ³fedfunds_lower, ⁴fedfunds_eff, ⁵realgdp_change,
#   ⁶unemploy_rate, ⁷inflation_rate
# ℹ Use `print(n = ...)` to see more rows

Briefly describe the data

print(dfSummary(fed_orig, varnumbers = FALSE,
                        plain.ascii  = FALSE, 
                        style        = "grid", 
                        graph.magnif = 0.70, 
                        valid.col    = FALSE),
      method = 'render',
      table.classes = 'table-condensed')

Data Frame Summary

fed_orig

Dimensions: 904 x 10
Duplicates: 0

Variable

Stats / Values

Freqs (% of Valid)

Graph

Missing

year [numeric]

Mean (sd) : 1986.7 (17.2)

min ≤ med ≤ max:

1954 ≤ 1987.5 ≤ 2017

IQR (CV) : 28 (0)

64 distinct values

0 (0.0%)

month [numeric]

Mean (sd) : 6.6 (3.5)

min ≤ med ≤ max:

1 ≤ 7 ≤ 12

IQR (CV) : 6 (0.5)

12 distinct values

0 (0.0%)

day [numeric]

Mean (sd) : 3.6 (6.8)

min ≤ med ≤ max:

1 ≤ 1 ≤ 31

IQR (CV) : 0 (1.9)

29 distinct values

0 (0.0%)

fedfunds_target [numeric]

Mean (sd) : 5.7 (2.6)

min ≤ med ≤ max:

1 ≤ 5.5 ≤ 11.5

IQR (CV) : 4 (0.5)

63 distinct values

442 (48.9%)

fedfunds_upper [numeric]

Mean (sd) : 0.3 (0.1)

min ≤ med ≤ max:

0.2 ≤ 0.2 ≤ 1

IQR (CV) : 0 (0.5)

4 distinct values

801 (88.6%)

fedfunds_lower [numeric]

Mean (sd) : 0.1 (0.1)

min ≤ med ≤ max:

0 ≤ 0 ≤ 0.8

IQR (CV) : 0 (2.4)

4 distinct values

801 (88.6%)

fedfunds_eff [numeric]

Mean (sd) : 4.9 (3.6)

min ≤ med ≤ max:

0.1 ≤ 4.7 ≤ 19.1

IQR (CV) : 4.2 (0.7)

466 distinct values

152 (16.8%)

realgdp_change [numeric]

Mean (sd) : 3.1 (3.6)

min ≤ med ≤ max:

-10 ≤ 3.1 ≤ 16.5

IQR (CV) : 3.5 (1.1)

113 distinct values

654 (72.3%)

unemploy_rate [numeric]

Mean (sd) : 6 (1.6)

min ≤ med ≤ max:

3.4 ≤ 5.7 ≤ 10.8

IQR (CV) : 2.1 (0.3)

71 distinct values

152 (16.8%)

inflation_rate [numeric]

Mean (sd) : 3.7 (2.6)

min ≤ med ≤ max:

0.6 ≤ 2.8 ≤ 13.6

IQR (CV) : 2.7 (0.7)

106 distinct values

194 (21.5%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-08-28

Tidy Data (as needed)

There was just a little bit of tidying needed, to clean up the date.

fed<-fed_orig %>% 
  mutate(date=make_date(year,month,day)) %>% 
  select(-c(year,month,day))

fed

# A tibble: 904 × 8
   fedfunds_target fedfunds…¹ fedfu…² fedfu…³ realg…⁴ unemp…⁵ infla…⁶ date      
             <dbl>      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <date>    
 1              NA         NA      NA    0.8      4.6     5.8      NA 1954-07-01
 2              NA         NA      NA    1.22    NA       6        NA 1954-08-01
 3              NA         NA      NA    1.06    NA       6.1      NA 1954-09-01
 4              NA         NA      NA    0.85     8       5.7      NA 1954-10-01
 5              NA         NA      NA    0.83    NA       5.3      NA 1954-11-01
 6              NA         NA      NA    1.28    NA       5        NA 1954-12-01
 7              NA         NA      NA    1.39    11.9     4.9      NA 1955-01-01
 8              NA         NA      NA    1.29    NA       4.7      NA 1955-02-01
 9              NA         NA      NA    1.35    NA       4.6      NA 1955-03-01
10              NA         NA      NA    1.43     6.7     4.7      NA 1955-04-01
# … with 894 more rows, and abbreviated variable names ¹fedfunds_upper,
#   ²fedfunds_lower, ³fedfunds_eff, ⁴realgdp_change, ⁵unemploy_rate,
#   ⁶inflation_rate
# ℹ Use `print(n = ...)` to see more rows

Time Dependent Visualization

I was curious to analyze some of these macroeconomic indicators around the 2008 economic crisis, so I created another new data set that narrows the date range down to the five years before and after 2008.

fed_crisis<-fed %>% 
  filter(date>=ymd("2003-01-01")) %>% 
  filter(date<ymd("2013-01-01")) 

fed_crisis

# A tibble: 147 × 8
   fedfunds_target fedfunds…¹ fedfu…² fedfu…³ realg…⁴ unemp…⁵ infla…⁶ date      
             <dbl>      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <date>    
 1            1.25         NA      NA    1.24     2.1     5.8     1.9 2003-01-01
 2            1.25         NA      NA    1.26    NA       5.9     1.7 2003-02-01
 3            1.25         NA      NA    1.25    NA       5.9     1.7 2003-03-01
 4            1.25         NA      NA    1.26     3.8     6       1.5 2003-04-01
 5            1.25         NA      NA    1.26    NA       6.1     1.6 2003-05-01
 6            1.25         NA      NA    1.22    NA       6.3     1.5 2003-06-01
 7            1            NA      NA   NA       NA      NA      NA   2003-06-25
 8            1            NA      NA    1.01     6.9     6.2     1.5 2003-07-01
 9            1            NA      NA    1.03    NA       6.1     1.3 2003-08-01
10            1            NA      NA    1.01    NA       6.1     1.2 2003-09-01
# … with 137 more rows, and abbreviated variable names ¹fedfunds_upper,
#   ²fedfunds_lower, ³fedfunds_eff, ⁴realgdp_change, ⁵unemploy_rate,
#   ⁶inflation_rate
# ℹ Use `print(n = ...)` to see more rows

I wanted to plot Inflation, Unemployment, and Effective Federal Funds Rate on top of each other for this time period (2003-2013). I was able to plot these, but I was not able to successfully create a legend that clearly identifies which one is which.

fed_crisis %>% 
  ggplot(aes(x=date)) + 
  geom_line(aes(y=unemploy_rate,color="maroon")) +
  geom_line(aes(y=inflation_rate,color="blue")) +
  geom_line(aes(y=fedfunds_eff,color="dark green")) +
  expand_limits(y=0) +
  theme_bw() +
  labs(title="Unemployment, Inflation, and the Federal Funds Rate Around 2008",
       x="Date",
       y="Rate (%)")

Visualizing Part-Whole Relationships

After the 2008 crisis, the Fed adjusted the way they dealt with the Federal Funds Rate, setting upper and lower targets for the effective rate. I wanted to plot these upper, lower, and effective rates from 2009 onwards. Once again, I was not sure how to create a legend for this graph; however, the upper-lower-effective nature of the data does make it somewhat intuitive to interpret, at least for someone familiar with the ideas of the data.

fed_post <- fed %>% 
  filter(date>=ymd("2009-01-01")) 

fed_post

# A tibble: 102 × 8
   fedfunds_target fedfunds…¹ fedfu…² fedfu…³ realg…⁴ unemp…⁵ infla…⁶ date      
             <dbl>      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <date>    
 1              NA       0.25       0    0.15    -5.4     7.8     1.7 2009-01-01
 2              NA       0.25       0    0.22    NA       8.3     1.8 2009-02-01
 3              NA       0.25       0    0.18    NA       8.7     1.8 2009-03-01
 4              NA       0.25       0    0.15    -0.5     9       1.9 2009-04-01
 5              NA       0.25       0    0.18    NA       9.4     1.8 2009-05-01
 6              NA       0.25       0    0.21    NA       9.5     1.7 2009-06-01
 7              NA       0.25       0    0.16     1.3     9.5     1.5 2009-07-01
 8              NA       0.25       0    0.16    NA       9.6     1.4 2009-08-01
 9              NA       0.25       0    0.15    NA       9.8     1.5 2009-09-01
10              NA       0.25       0    0.12     3.9    10       1.7 2009-10-01
# … with 92 more rows, and abbreviated variable names ¹fedfunds_upper,
#   ²fedfunds_lower, ³fedfunds_eff, ⁴realgdp_change, ⁵unemploy_rate,
#   ⁶inflation_rate
# ℹ Use `print(n = ...)` to see more rows

fed_post %>% 
  ggplot(aes(x=date)) + 
  geom_line(aes(y=fedfunds_upper)) +
  geom_line(aes(y=fedfunds_lower)) +
  geom_line(aes(y=fedfunds_eff)) +
  expand_limits(y=0) +
  theme_bw() +
  labs(title="Upper, Lower, and Effective Federal Funds Rates from 2009",
       x="Date",
       y="Rate (%)")