Week 9: Apply it to your data 8

Import your data

fires <- read_excel("myData.xlsx")

Chapter 13

What are primary keys in your data?

Year, Number_Fires, Acres_Burned, DAMAGE_Costs, Damage_Costs_Per_Acre, and Avergae_Acres_Burned_Per_Fire

Can you divide your data into two?

Divide it using dplyr::select in a way the two have a common variable, which you could use to join the two.

dplyr::select(fires, SEVERITY, NUMBER_FIRES, DAMAGE_COSTS)

## # A tibble: 83 × 3
##    SEVERITY NUMBER_FIRES DAMAGE_COSTS
##    <chr>           <dbl>        <dbl>
##  1 Moderate         1994       318636
##  2 Severe           2338       563710
##  3 Moderate         1447       165543
##  4 Severe           3805      1877147
##  5 Mild             2907       151584
##  6 Moderate         4150       404225
##  7 Severe           2491       847579
##  8 Moderate         4497       272178
##  9 Moderate         5460       515737
## 10 Moderate         5236      1484864
## # ℹ 73 more rows

fire_stats <- dplyr::select(fires, SEVERITY, NUMBER_FIRES, DAMAGE_COSTS)

Can you join the two together?

Use tidyr::left_join or other joining functions.

left_join(fires, fire_stats)

## Joining with `by = join_by(NUMBER_FIRES, DAMAGE_COSTS, SEVERITY)`

## # A tibble: 83 × 7
##    YEAR  NUMBER_FIRES ACRES_BURNED DAMAGE_COSTS SEVERITY Damage_Cost_Per_Acre
##    <chr>        <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>