tarea_#1

Comenzando con este dataframe

dfFires <- read.csv("C:\\Users\\loren\\OneDrive\\Escritorio\\RDataSets\\RDataSets\\StudyArea.csv")


head(dfFires)

##   FID ORGANIZATI  UNIT SUBUNIT                               SUBUNIT2
## 1   0        FWS 81682 USCADBR San Diego Bay National Wildlife Refuge
## 2   1        FWS 81682 USCADBR San Diego Bay National Wildlife Refuge
## 3   2        FWS 81682 USCADBR San Diego Bay National Wildlife Refuge
## 4   3        FWS 81682 USCADBR San Diego Bay National Wildlife Refuge
## 5   4        FWS 81682 USCADBR San Diego Bay National Wildlife Refuge
## 6   5        FWS 81682 USCADBR San Diego Bay National Wildlife Refuge
##     FIRENAME CAUSE YEAR_   STARTDATED   CONTRDATED OUTDATED      STATE
## 1 PUMP HOUSE Human  2001  1/1/01 0:00  1/1/01 0:00          California
## 2         I5 Human  2002  5/3/02 0:00  5/3/02 0:00          California
## 3   SOUTHBAY Human  2002  6/1/02 0:00  6/1/02 0:00          California
## 4     MARINA Human  2001 7/12/01 0:00 7/12/01 0:00          California
## 5       HILL Human  1994 9/13/94 0:00 9/13/94 0:00          California
## 6 IRRIGATION Human  1994 4/22/94 0:00 4/22/94 0:00          California
##   STATE_FIPS TOTALACRES
## 1          6        0.1
## 2          6        3.0
## 3          6        0.5
## 4          6        0.1
## 5          6        1.0
## 6          6        0.1

idaho_fires <- subset(dfFires, STATE == "Idaho")

# Paso 2: Seleccionar y renombrar las columnas
idaho_fires <- idaho_fires[, c("YEAR_", "CAUSE", "TOTALACRES")]
colnames(idaho_fires) <- c("Year", "Cause", "Total_Acres")

idaho_fires$Total_Acres <- as.numeric(idaho_fires$Total_Acres)

# Paso 3: Agrupar por causa y año, y resumir el total de acres quemados

idaho_summary <- idaho_fires %>%
  group_by(Cause, Year) %>%
  summarise(Total_Acres_Burned = sum(Total_Acres))

## `summarise()` has grouped output by 'Cause'. You can override using the
## `.groups` argument.

idaho_summary$Total_Acres_Burned<- as.numeric(idaho_summary$Total_Acres_Burned)

idaho_summary <- idaho_summary %>%
  filter( Cause != " ")

# Imprimir el DataFrame filtrado
print(idaho_summary)

## # A tibble: 79 × 3
## # Groups:   Cause [3]
##    Cause  Year Total_Acres_Burned
##    <chr> <int>              <dbl>
##  1 Human  1980             71975.
##  2 Human  1981            219362.
##  3 Human  1982             34016.
##  4 Human  1983             48242.
##  5 Human  1984             36838.
##  6 Human  1985             68035.
##  7 Human  1986             43181.
##  8 Human  1987             35128.
##  9 Human  1988            810403.
## 10 Human  1989             28022.
## # ℹ 69 more rows

ggplot(idaho_summary, aes(x = factor(Year), fill = factor(Cause), y = Total_Acres_Burned)) +
  geom_dotplot(binaxis = "y", stackdir = "center")

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

Podemos observar que, dependiendo del año, puede haber una gran variabilidad en los valores. Además, se aprecia un aumento en la cantidad total de acres quemados debido a causas naturales, en contraste con las causas humanas, que parecen mantenerse controladas.