## New names:
## Rows: 395 Columns: 122
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Park Name, Park Type dbl (119): 2022, 2021, 2020, 2019, 2018, 2017, 2016,
## 2015, 2014, 2013, 2012,... lgl (1): ...5
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...5`
wf_for_camp =read_csv('wf.csv')
## Rows: 36 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (6): Year, Fires, Acres, ForestService, DOI, Total
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
camp <- camp[,-5]
# Filter years from 2019 to 1999years_camp <- camp %>%select(`Park Name`, `2019`:`1999`)filtered_camp <-na.omit(years_camp)df <-data.frame(colSums(Filter(is.numeric, filtered_camp)))df$Year =c(2019:1999)colnames(df)[1] ="Visitation"
ggplot(df, aes(x = Year, y = Visitation)) +geom_point(col ='red') +geom_smooth(formula = y ~ x, method = lm, col ='orange') +xlab("Year") +ylab('Visitation Number') +scale_y_continuous(labels = scales::comma)
wf2 <- wf_for_camp %>%filter(Year >=1999& Year <2020)df_final <-merge(wf2, df, by ='Year')
ggplot(df_final, aes(x = Visitation, y = Fires)) +geom_point(col ='red') +geom_smooth(formula = y ~ x, method = lm, col ='orange') +xlab("Visitation") +ylab('Fires') +scale_y_continuous(labels = scales::comma)
ggplot(df_final, aes(x = Visitation, y = Acres)) +geom_point(col ='red') +geom_smooth(formula = y ~ x, method = lm, col ='orange') +xlab("Visitation") +ylab('Acres') +scale_y_continuous(labels = scales::comma)
ggplot(df_final, aes(x = Visitation, y = Total)) +geom_point(col ='red') +geom_smooth(formula = y ~ x, method = lm, col ='orange') +xlab("Visitation") +ylab('Total Money') +scale_y_continuous(labels = scales::comma)