library(readr)
dfFires <- read_csv("StudyArea.csv", col_types = list(UNIT = col_character()), col_names = TRUE)
library(dplyr)
## 
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
dfFires_subset<-dfFires %>% 
  filter(STATE == 'Idaho') %>% 
  select("YR" = "YEAR_", "CS"  = "CAUSE","ACRES" = "TOTALACRES")
knitr::kable(head(dfFires_subset, 5))
YR CS ACRES
1987 Human 5
1991 Natural 150
1991 Human 800
1990 Natural 2
1985 Human 38 
dfFires_summary<-dfFires_subset %>% 
  group_by(CS,YR) %>% 
  summarise(Total_Acres_Quemados=sum(ACRES))
## `summarise()` has grouped output by 'CS'. You can override using the `.groups`
## argument.
knitr::kable(head(dfFires_summary, 5))
CS YR Total_Acres_Quemados
Human 1980 71974.7
Human 1981 219362.4
Human 1982 34016.2
Human 1983 48242.5
Human 1984 36837.8 
##install.packages("ggplot2")
library(ggplot2)

ggplot(dfFires_summary, aes(x = YR, y = Total_Acres_Quemados, color =CS)) +
  geom_line() +
  theme_minimal() +
  labs(title = 'Total acres quemados por causa y año en Idaho',
       x = 'Año',
       y = 'Total Acres Quemados') +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))