Challenge6

Read in Data

fed_rates <- read_csv("../challenge_datasets/FedFundsRate.csv")

## Rows: 904 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (10): Year, Month, Day, Federal Funds Target Rate, Federal Funds Upper T...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(fed_rates)

## # A tibble: 6 × 10
##    Year Month   Day `Federal Funds Target Rate` `Federal Funds Upper Target`
##   <dbl> <dbl> <dbl>                       <dbl>                        <dbl>
## 1  1954     7     1                          NA                           NA
## 2  1954     8     1                          NA                           NA
## 3  1954     9     1                          NA                           NA
## 4  1954    10     1                          NA                           NA
## 5  1954    11     1                          NA                           NA
## 6  1954    12     1                          NA                           NA
## # ℹ 5 more variables: `Federal Funds Lower Target` <dbl>,
## #   `Effective Federal Funds Rate` <dbl>, `Real GDP (Percent Change)` <dbl>,
## #   `Unemployment Rate` <dbl>, `Inflation Rate` <dbl>

Describe and tidy data

There wasn’t much to tidy other than removing some NA values for ease of work later on.

fed_rates <- fed_rates %>%
  mutate(Month = paste0(Year, "-", Month),
         Date = as_date(Month, format = "%Y-%m")) %>%
  select(-Month) 

fed_rates_plot <- fed_rates %>%   
  filter(!is.na(`Effective Federal Funds Rate`))

fed_rates <- fed_rates %>%
  mutate(across(c(`Real GDP (Percent Change)`, 
                  `Unemployment Rate`,  
                  `Inflation Rate`), 
                as.numeric))

fed_rates

## # A tibble: 904 × 10
##     Year   Day `Federal Funds Target Rate` `Federal Funds Upper Target`
##    <dbl> <dbl>                       <dbl>                        <dbl>
##  1  1954     1                          NA                           NA
##  2  1954     1                          NA                           NA
##  3  1954     1                          NA                           NA
##  4  1954     1                          NA                           NA
##  5  1954     1                          NA                           NA
##  6  1954     1                          NA                           NA
##  7  1955     1                          NA                           NA
##  8  1955     1                          NA                           NA
##  9  1955     1                          NA                           NA
## 10  1955     1                          NA                           NA
## # ℹ 894 more rows
## # ℹ 6 more variables: `Federal Funds Lower Target` <dbl>,
## #   `Effective Federal Funds Rate` <dbl>, `Real GDP (Percent Change)` <dbl>,
## #   `Unemployment Rate` <dbl>, `Inflation Rate` <dbl>, Date <date>

Time evolution

We can take a loop at federal funds target over time! This is important as it would be easy to observe the change of rates over time and could be a part of further analysis on maybe why the rates fluctuate.

ggplot(data = fed_rates) + 
  geom_smooth(mapping = aes(x = Year, 
                            y = `Effective Federal Funds Rate`),
              method="loess", formula=y~x, se=FALSE) +

  labs(
    title = "Effective Federal Funds Rate Over Time",
    subtitle = "Interest rates",
    x = "Year",
    y = "Effective Fed Funds Rate (%)"
  ) + 
  
  theme_minimal() +
  theme(plot.title = element_text(size=14))

## Warning: Removed 152 rows containing non-finite values (`stat_smooth()`).

Unemployment vs inflation

ggplot(fed_rates, aes(Year, `Unemployment Rate`)) +
  geom_smooth(aes(color="Unemployment"),
              method="loess", se=FALSE, span=0.25) +
  
  geom_smooth(aes(y = `Inflation Rate`, color="Inflation"), 
              method="loess", se=FALSE, span=0.25) +

  scale_y_continuous(sec.axis = sec_axis(~.*1, name="Inflation")) +
  
  labs(title="Inflation and Unemployment over Time",
       color="") +

  theme_classic()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 152 rows containing non-finite values (`stat_smooth()`).

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 194 rows containing non-finite values (`stat_smooth()`).

Reading about the Philips Curve on the news, I was actually pretty curious on how this relationship holds and it seems to hold in some cases. We can see that in most cases it holds this relationship!