Week 5: Apply 4

Import data

# excel file
fire <- read_excel("myData.xlsx") %>%
    mutate(YEAR = as.numeric(YEAR))
fire

## # A tibble: 83 × 7
##     YEAR NUMBER_FIRES ACRES_BURNED DAMAGE_COSTS SEVERITY Damage_Cost_Per_Acre
##    <dbl>        <dbl>        <dbl>        <dbl> <chr>                   <dbl>
##  1  1933         1994       129210       318636 Moderate                 2.47
##  2  1934         2338       363052       563710 Severe                   1.55
##  3  1935         1447       127262       165543 Moderate                 1.30
##  4  1936         3805       756696      1877147 Severe                   2.48
##  5  1937         2907        71312       151584 Mild                     2.13
##  6  1938         4150       221061       404225 Moderate                 1.83
##  7  1939         2491       513620       847579 Severe                   1.65
##  8  1940         4497       156015       272178 Moderate                 1.74
##  9  1941         5460       278599       515737 Moderate                 1.85
## 10  1942         5236       573597      1484864 Moderate                 2.59
## # ℹ 73 more rows
## # ℹ 1 more variable: Average_Acres_Burned_Per_Fire <dbl>

Plot data

fire %>%
    
    ggplot(aes(SEVERITY)) +
    geom_bar()

State one question

As years go on, fire damage costs will increase

Plot the data

# simple scatterplot
ggplot(fire, 
       aes(x = DAMAGE_COSTS, y = YEAR)) +
  geom_point(color = "cornflowerblue",
             size = 2,
             alpha = .8) +
    scale_x_continuous(label = scales::dollar)

Interpret

After 1975, fire damage costs begin to increase as time goes on. There appears to be a positive relationship between years and the damage costs of fire. The progression of years and costs seem to coincide with new development and settlement in California which may be part of the reason as to why cost increases.