dataNAO-SierraNevada
Antonio J Perez-Luque
2021-05-17
Last updated: 2021-05-27
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Knit directory: booksn_ppm/
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| Rmd | d5f92fb | Antonio J Perez-Luque | 2021-05-27 | repeat NAO analysis for SN |
library("tidyverse")
library("here")
library("flextable")
library("DT")
library("ggpubr")Objetivo
El objetivo es generar un dataset con los datos de la NAO y los datos de infestación. - Utilizmaos el índice NAO de invierno (Hurrell’s winter NAO) cuyos datos podemos obtener en el National Center for Atmospheric Research. Descargamos los datos y los guardamos en data/nao_station_djfm.txt
- Genero el dataset de NAO (con lag 1, 2, y 3) desde 1989 en adelante
nao <- read_csv(here::here("data/nao_station_djfm.csv"))
nao <- nao %>% filter(year > 1988) %>%
mutate(nao1 = lag(nao, n=1),
nao2 = lag(nao, n=2),
nao3 = lag(nao, n=3))Siguiendo la aproximación de Hódar, Zamora, and Cayuela (2012) vamos a calcular el porcentaje de parcleas con infestación >= 3 para cada año.
Leer los datos para Sierra Nevada
Agrupar en high infestacion (infestacion >= 3) y low infestacion (infestacion < 3)
Computar valor para cada año
Filtramos para las especies: pinaster, halepensis, sylvetris, nigra
coplas2019 <- read_csv(here::here("data/coplas2019sn.csv"))
df <- coplas2019 %>%
filter(!is.na(sp)) %>%
dplyr::select(code, especie, `1993`:`2019`) %>%
pivot_longer(`1993`:`2019`, names_to = "year", values_to = "infesta") %>%
mutate(type = case_when(
infesta >= 3 ~ "high",
TRUE ~ "low"
)) %>%
mutate(year = as.numeric(year))
genera.dfnao <- function(x) {
n_teorico <- x %>% group_by(year) %>% summarise(ntotal = length(infesta))
n_real <- x %>%
filter(!is.na(infesta)) %>%
group_by(year) %>%
summarise(nreal = length(infesta)) %>%
mutate(year = as.numeric(year))
ppm <- x %>%
filter(!is.na(infesta)) %>%
group_by(year, type) %>%
summarise(n = n()) %>%
group_by(year) %>%
mutate(pct = n /sum(n, na.rm = TRUE)*100)
ppm_year <- ppm %>%
pivot_wider(names_from = type,
values_from = c(n, pct))
out <- ppm_year %>%
left_join(n_real) %>%
left_join(n_teorico)
return(out)
}
nao_all <- genera.dfnao(df) %>%
mutate(especie = "All")
nao_halepensis <- df %>%
filter(especie == "P. halepensis") %>%
genera.dfnao() %>%
mutate(especie = "P. halepensis")
nao_pinaster <- df %>%
filter(especie == "P. pinaster") %>%
genera.dfnao() %>%
mutate(especie = "P. pinaster")
nao_nigra <- df %>%
filter(especie == "P. nigra") %>%
genera.dfnao() %>%
mutate(especie = "P. nigra")
nao_sylvestris <- df %>%
filter(especie == "P. sylvestris") %>%
genera.dfnao() %>%
mutate(especie = "P. sylvestris")
dfnaos <- bind_rows(nao_all, nao_halepensis, nao_nigra,
nao_pinaster, nao_sylvestris) %>%
mutate(pct_high = replace_na(pct_high, 0))
nao_ppm <- nao %>% left_join(dfnaos)- Repetir figura 1 del trabajo de Hódar, Zamora, and Cayuela (2012), ampliando la serie hasta 2019
df_plot <- nao_ppm %>%
filter(!is.na(especie)) %>%
dplyr::select(year, nao:nao3, pct_high, especie) %>%
pivot_longer(nao:nao3, names_to = "nao_index")
pearson_plot <- df_plot %>%
ggplot(aes(x=value, y=pct_high)) +
geom_point() +
geom_smooth(method = "lm", se=FALSE, size=.4, color="black") +
theme_bw() +
ylim(c(0,40)) +
xlab("") +
ylab("Percent of stands with defoliation >= 3") +
theme(panel.grid = element_blank(),
strip.background = element_blank()) +
stat_cor(aes(label = ..r.label..), color = "black", geom = "text") + facet_grid(fct_relevel(especie,"All", "P. pinea",
"P. pinaster", "P. halepensis",
"P. nigra", "P. sylvestris")~nao_index,
labeller = labeller(nao_index = toupper))
null device
1 Explorar patron temporal
df_plot_ts <- nao_ppm %>%
dplyr::select(year, nao, pct_high, especie)
df_plot_ts$especie <- factor(df_plot_ts$especie,
levels = c("NA","All","P. sylvestris", "P. nigra",
"P. pinaster","P. halepensis"))
ylim.prim <- c(0,40)
ylim.sec <- c(-5,5)
b <- diff(ylim.prim)/diff(ylim.sec)
a <- ylim.prim[1] - b*ylim.sec[1]
patron_nao_ppm <- df_plot_ts %>%
filter(!is.na(especie)) %>%
filter(especie != "All") %>%
ggplot(aes(x=year, y=pct_high)) +
geom_hline(yintercept = a, color="gray") +
geom_point() +
facet_wrap(~especie, nrow=4) +
geom_line(stat="identity") +
geom_line(data = (
df_plot_ts %>% select(year, nao)
), aes(x=year, y= a + nao*b), color="red") +
scale_y_continuous(sec.axis = sec_axis(name="NAO", ~(.-a)/b)) +
scale_x_continuous(limits=c(1989,2019),
breaks = seq(1989,2019, by=1)) +
theme_bw() +
theme(panel.grid = element_blank(),
axis.text.x = element_text(size=8, angle=90, vjust = 0.5),
axis.title.y.right = element_text(color = "red"),
strip.background = element_blank()) +
xlab("") +
ylab("Percent of stands with defoliation >= 3") 
null device
1 Referencias
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.4.0 DT_0.17 flextable_0.6.3 here_1.0.1
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.1.2 tibble_3.0.6 ggplot2_3.3.3
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-152 fs_1.5.0 lubridate_1.7.10 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.0.2 backports_1.2.1 bslib_0.2.4
[9] R6_2.5.0 DBI_1.1.1 mgcv_1.8-33 colorspace_2.0-0
[13] withr_2.4.1 tidyselect_1.1.0 curl_4.3 compiler_4.0.2
[17] git2r_0.28.0 cli_2.3.0 rvest_0.3.6 xml2_1.3.2
[21] officer_0.3.16 labeling_0.4.2 sass_0.3.1 scales_1.1.1
[25] systemfonts_1.0.0 digest_0.6.27 foreign_0.8-81 rmarkdown_2.6.6
[29] rio_0.5.16 base64enc_0.1-3 pkgconfig_2.0.3 htmltools_0.5.1.1
[33] highr_0.8 dbplyr_2.1.0 htmlwidgets_1.5.3 rlang_0.4.10
[37] readxl_1.3.1 rstudioapi_0.13 farver_2.0.3 jquerylib_0.1.3
[41] generics_0.1.0 jsonlite_1.7.2 zip_2.1.1 car_3.0-10
[45] magrittr_2.0.1 Matrix_1.3-2 Rcpp_1.0.6 munsell_0.5.0
[49] abind_1.4-5 gdtools_0.2.3 lifecycle_1.0.0 stringi_1.5.3
[53] whisker_0.4 yaml_2.2.1 carData_3.0-4 grid_4.0.2
[57] promises_1.2.0.1 crayon_1.4.1 lattice_0.20-41 haven_2.3.1
[61] splines_4.0.2 hms_1.0.0 knitr_1.31 pillar_1.4.7
[65] uuid_0.1-4 ggsignif_0.6.0 reprex_1.0.0 glue_1.4.2
[69] evaluate_0.14 data.table_1.13.6 modelr_0.1.8 vctrs_0.3.6
[73] httpuv_1.5.5 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[77] xfun_0.20 openxlsx_4.2.3 broom_0.7.4 rstatix_0.6.0
[81] later_1.1.0.1 ellipsis_0.3.1