bev curve

gistfile1.txt

```{r}
library(tidyverse)
library(tidyquant)
library(timetk)
library(readxl)
library(plotly)
library(scales)
library(formattable)
```

Let's get job openings rate and unemployment rate data from FRED, and combine into one data frame. 

```{r}
job_op_unrate <- 
"JTSJOR" %>% 
  get_series_from_fred(start_date = "2000-01-01") %>% 
  select(title, date, value = Rate) %>% 
  mutate(title = str_replace(title, ": Total Nonfarm", " Rate") ) %>% 
  bind_rows(
    "UNRATE" %>% 
  get_series_from_fred(start_date = "2000-01-01") %>% 
  select(title, date, value = Percent)
  ) %>% 
  pivot_wider(
    names_from = title, 
    values_from = value
  ) %>% 
  arrange(date) %>% 
  drop_na()

job_op_unrate %>% 
  tail()
```

Next we  take `USREC` data from FRED and turn into data ranges. `USREC` is a timeseries from NBER that flags each recessionary month with a 1, else with a 0.

```{r}
recession_range <- 
  "USREC" %>% 
  tq_get(get = "economic.data", from = "2000-12-01") %>% 
  select(date, recession_flag = price) %>% 
  filter(date <= max(job_op_unrate$date)) %>% 
  mutate(recession_label = 
           case_when(recession_flag == 1 & lag(recession_flag == 0) ~ str_glue("{year(date)} recession"), 
         TRUE ~ NA_character_)) %>% 
  filter(recession_flag == 1) %>% 
  fill(recession_label, .direction = "down")  %>% 
    bind_rows(
    "USREC" %>% 
  tq_get(get = "economic.data", from = "2000-12-01") %>% 
  select(date, recession_flag = price) %>% 
    filter(date <= max(job_op_unrate$date)) %>% 
  mutate(recession_label = 
           case_when(recession_flag == 1 & lag(recession_flag == 0) ~ str_glue("{year(date)} recession"), 
         TRUE ~ NA_character_)) %>% 
  filter(recession_flag == 0) 
  ) %>% 
  arrange(date) %>% 
  mutate(recession_label = 
           case_when(recession_flag == 0 & lead(recession_flag == 1) ~ str_glue("{year(date)} non recession"), 
         TRUE ~ recession_label)) %>% 
  fill(recession_label, .direction = "up")  %>% 
  group_by(recession_label) %>% 
  mutate(recession_range =  
           case_when(recession_flag == 1 ~ str_glue("{month(min(date), label = T, abbr = T)} {year(min(date))} to {month(max(date), label = T, abbr = T)}  {year(max(date))}*"),
                                     TRUE ~ str_glue("{month(min(date), label = T, abbr = T)} {year(min(date))} to {month(max(date), label = T, abbr = T)}  {year(max(date))}"))
         )%>% 
  ungroup() %>% 
  select(date, recession_range)

recession_range
```
Have a look at the recession range object we just created and imagine how we could put it to use. It allows us to sort time series into different date ranges, bounded by the beginnings and ends to recessionary periods. 

Let's `left_join()` that data to our Beveridge Curve object `job_op_unrate` and take a peek at the results. 

```{r}
job_op_unrate %>% 
  left_join(recession_range) %>% 
  drop_na() %>% 
  mutate(recession_range = as_factor(recession_range)) %>% 
  group_by(recession_range) %>% 
  slice(1, n())
```

Notice how we have our `date` column if we wish to reference a specific month, and also a `recession_range` column, a factor label for the date range of each observation. Now we can plot the relationship between the `Job Openings Rate` column and the `Unemploymnent Rate` column, and highlight how that relationship, or curve, has changed with different date ranges.

```{r}
job_op_unrate %>% 
  left_join(recession_range) %>% 
  drop_na() %>% 
  mutate(recession_range = as_factor(recession_range)) %>% 
  ggplot(aes(y = `Job Openings Rate`, x = `Unemployment Rate`, color = recession_range)) +
  geom_line() +
  geom_point() +
  theme_minimal() +
  ggrepel::geom_text_repel(aes(label = str_glue("{month(date, label = T, abbr = F)}
                                                {year(date)}")), 
                data = . %>% ungroup() %>% filter(date == max(date)),
                nudge_x = -.1,
                nudge_y = -.6,
                show.legend = F,
                color = "black") +
  scale_y_continuous(labels = function(x) str_glue("{x}%")) +
  scale_x_continuous(labels = function(x) str_glue("{x}%"), 
                     breaks = pretty_breaks(10)) +
  labs(color = "", caption = "* is recssionary period", title = "Beveridge Curve: Job Openings Rate v. Unemployment Rate")
```