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2KFR_summarising_ideas.R
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2KFR_summarising_ideas.R
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## code below is an example of how the data can be split up to show areas burnt
## for different times since last burnt. Shows how a greater than or equal bin
## can be created and the wide format can also be exported to show park and
## vegetype summaries.
## Bart Huntley 21 July 2021
library(tidyverse)
df <- read_csv("./output_stats/tslb_area_stats.csv")
## whole of drysdale example
# makes all possibilities of variables
df_exp <- df %>%
filter(!is.na(tslb_yrs) & park == "Drysdale River NP") %>%
expand(year, tslb_yrs, vegetype)
df_04 <- df %>%
filter(!is.na(tslb_yrs) & park == "Drysdale River NP") %>%
right_join(df_exp, by = c("year", "tslb_yrs", "vegetype")) %>%
arrange(year) %>%
filter(tslb_yrs < 5) %>%
group_by(year, tslb_yrs) %>% ## combines euc and sandstone
summarise(area = sum(burnt_area_ha))
# must be setup to compliment df_exp
df_all <- df %>%
filter(!is.na(tslb_yrs) & park == "Drysdale River NP" & tslb_yrs >= 5) %>%
group_by(year) %>% ## combines euc and sandstone
summarise(area = sum(burnt_area_ha)) %>%
mutate(tslb_yrs = 5) %>%
select(year, tslb_yrs, area) %>%
full_join(df_04, by = c("year", "tslb_yrs", "area")) %>%
arrange(year) %>%
pivot_wider(names_from = year, values_from = area)
## drysdale example
# makes all possibilities of variables
df_exp <- df %>%
filter(!is.na(tslb_yrs) & park == "Drysdale River NP") %>%
expand(year, tslb_yrs, vegetype)
df_04 <- df %>%
filter(!is.na(tslb_yrs) & park == "Drysdale River NP") %>%
right_join(df_exp, by = c("year", "tslb_yrs", "vegetype")) %>%
arrange(year) %>%
filter(tslb_yrs < 5) %>%
group_by(year, tslb_yrs, vegetype) %>%
summarise(area = sum(burnt_area_ha))
# must be setup to compliment df_exp
df_all <- df %>%
filter(!is.na(tslb_yrs) & park == "Drysdale River NP" & tslb_yrs >= 5) %>%
group_by(year, vegetype) %>% ## combines euc and sandstone
summarise(area = sum(burnt_area_ha)) %>%
mutate(tslb_yrs = 5) %>%
select(year, tslb_yrs, vegetype, area) %>%
full_join(df_04, by = c("year", "tslb_yrs", "vegetype", "area")) %>%
arrange(vegetype, year, tslb_yrs) %>%
pivot_wider(names_from = year, values_from = area)
# makes all possibilities of variables
df_exp <- df %>%
filter(!is.na(tslb_yrs)) %>%
expand(park, year, tslb_yrs, vegetype)
df_04 <- df %>%
filter(!is.na(tslb_yrs)) %>%
right_join(df_exp, by = c("park", "year", "tslb_yrs", "vegetype")) %>%
arrange(park, year) %>%
filter(tslb_yrs < 5) %>%
group_by(park, year, tslb_yrs, vegetype) %>%
summarise(area = sum(burnt_area_ha))
# must be setup to compliment df_exp
df_all <- df %>%
filter(!is.na(tslb_yrs) & tslb_yrs >= 5) %>%
group_by(park, year, vegetype) %>% ## combines euc and sandstone
summarise(area = sum(burnt_area_ha)) %>%
mutate(tslb_yrs = 5) %>%
select(park, year, tslb_yrs, vegetype, area) %>%
full_join(df_04, by = c("park", "year", "tslb_yrs", "vegetype", "area")) %>%
arrange(park, vegetype, year, tslb_yrs) %>%
pivot_wider(names_from = year, values_from = area) %>%
write_csv(file = "./output_stats/tslb_area_stats_wide_summary.csv")