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boB Rudis 5 years ago
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  1. 10
      README.Rmd
  2. 27
      README.md

10
README.Rmd

@ -73,7 +73,7 @@ filter(lowes, !(state %in% c("HI", "AK"))) %>%
) -> pts
```
```{r points-map, fig.width=1000/72, fig.height=500/72}
```{r points-map, fig.width=1000/72, fig.height=500/72, cache=TRUE}
ggplot() +
geom_sf(data = usa_sf("longlat"), fill = ft_cols$gray) +
geom_sf(data = pts, size = 0.5, alpha = 1/2, color = ft_cols$yellow) +
@ -98,5 +98,13 @@ count(lowes, county, state, sort=TRUE) %>%
knitr::kable()
```
Assuming $17/hr (~35K/yr) and no same-store re-hiring, ~**`r scales::dollar(nrow(lowes) * 35380)`** annual wages decrease across the whole list and for the top 10 counties:
```{r sal-sum}
count(lowes, county, state, sort=TRUE) %>%
mutate(wages_lost = scales::dollar(n * 35380)) %>%
top_n(10, wt = n) %>%
knitr::kable(align = "llrr")
```

27
README.md

@ -137,3 +137,30 @@ count(lowes, county, state, sort=TRUE) %>%
| Franklin County | OH | 9 |
| Hillsborough County | FL | 9 |
| Mecklenburg County | NC | 9 |
Assuming $17/hr (\~35K/yr) and no same-store re-hiring,
\~**$61,030,500** annual wages decrease across the whole list and for
the top 10 counties:
``` r
count(lowes, county, state, sort=TRUE) %>%
mutate(wages_lost = scales::dollar(n * 35380)) %>%
top_n(10, wt = n) %>%
knitr::kable(align = "llrr")
```
| county | state | n | wages\_lost |
| :-------------------- | :---- | -: | ----------: |
| Maricopa County | AZ | 21 | $742,980 |
| Los Angeles County | CA | 20 | $707,600 |
| Harris County | TX | 18 | $636,840 |
| Clark County | NV | 13 | $459,940 |
| Riverside County | CA | 12 | $424,560 |
| Tarrant County | TX | 11 | $389,180 |
| Bexar County | TX | 10 | $353,800 |
| San Bernardino County | CA | 10 | $353,800 |
| Allegheny County | PA | 9 | $318,420 |
| Dallas County | TX | 9 | $318,420 |
| Franklin County | OH | 9 | $318,420 |
| Hillsborough County | FL | 9 | $318,420 |
| Mecklenburg County | NC | 9 | $318,420 |

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