Last updated: 2021-05-18

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File Version Author Date Message
Rmd 1bc6f84 Antonio J Perez-Luque 2021-05-18 genera temporal evolution using new sp data
html 2a68381 Antonio J Perez-Luque 2021-05-18 Build site.
Rmd 7ffc8f1 Antonio J Perez-Luque 2021-05-18 genera graficos temporal evolution

Evolución temporal por grupo de elevación

library("tidyverse")
library("here")
library("flextable")
library("DT")
library("broom")
coplas2019 <- read_csv(here::here("data/coplas2019sn.csv"))
  • Exploramos la evolución temporal de la infestación (solo los datos de Sierra Nevada) agrupando por especies de pino y por nivel de elevación. Calculamos los valores medios por nivel de elevación y año.
df <- coplas2019 %>% 
  dplyr::select(code, elevF, `1993`:`2019`) %>% 
  pivot_longer(names_to = "year", values_to = "infestacion", `1993`:`2019`) 

df.avg <- 
  df %>% 
  group_by(elevF, year) %>% 
  summarise(mean = mean(infestacion, na.rm=TRUE),
            sd = sd(infestacion, na.rm=TRUE),
            se = sd/sqrt(length(infestacion)), 
            size = length(infestacion)) %>% 
  mutate(year.date = lubridate::ymd(year, truncated = 2L),
         elevF_label = recode(elevF, "0verylow" = "< 600 m",
                           "1low" = "600 - 1200 m",
                           "2medium" = "1200 - 1700 m",
                           "3high" = "> 1700 m"))


colores_elevacion <- c(
  "0verylow" = "#d7191c",
  "1low" = "#fdae61",
  "2medium" = "#abd9e9",
  "3high" = "#2c7bb6"
)

colores_elevacion_label <- c(
  "< 600 m" = "#d7191c",
  "600 - 1200 m" = "#fdae61",
  "1200 - 1700 m" = "#abd9e9",
  "> 1700 m" = "#2c7bb6"
)


evol_temporal_elevacion <- 
df.avg %>% 
  ggplot(aes(x=year.date, y=mean, group=elevF_label, color = elevF_label, fill=elevF_label)) +
  scale_color_manual(values = colores_elevacion_label) + 
  theme_bw() +
  theme(
    panel.grid = element_blank(),
    legend.title = element_blank(), 
    legend.position = c(0.5,.9), 
    legend.background = element_blank()) + 
  scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
  xlab("Year") + ylab("Mean defoliation") + ylim(0, NA) +
  geom_point(size=1.5) + 
  geom_line() +
  geom_errorbar(aes(ymin=mean - se, ymax=mean + se), width = .1) +
  guides(colour = guide_legend(nrow = 2))

Version Author Date
2a68381 Antonio J Perez-Luque 2021-05-18 
null device 
          1

Evolución temporal por especie

Número de parcelas por especie

DT::datatable(coplas2019 %>% group_by(sp_abrev) %>% count())
  • Seleccionamos P. nigra, P. halpensis, P. sylvestris, P. pinaster
dfspraw <- coplas2019 %>% filter(especie %in% c("P. nigra", "P. halpensis", "P. sylvestris","P. pinaster")) 

dfsp <- dfspraw %>% 
  dplyr::select(code, especie, elev_mean, `1993`:`2019`) %>% 
  pivot_longer(names_to = "year", values_to = "infestacion", `1993`:`2019`) 

dfsp.avg <- 
  dfsp %>% 
  group_by(especie, year) %>% 
  summarise(mean = mean(infestacion, na.rm=TRUE),
            sd = sd(infestacion, na.rm=TRUE),
            se = sd/sqrt(length(infestacion)), 
            size = length(infestacion)) %>% 
  mutate(year.date = lubridate::ymd(year, truncated = 2L)) 

colores_pinos <- c("P. halpensis" = "#809c13", 
                   "P. nigra" = "#45492a", 
                   "P. pinaster" = "gray", 
                   "P. sylvestris" = "#ffa64d")

evol_temporal_especies <- 
  dfsp.avg %>% 
  ggplot(aes(x=year.date, y=mean, group=especie, color = especie, fill=especie)) +
  scale_color_manual(values = colores_pinos) +
  theme_bw() +
  theme(
    panel.grid = element_blank(), 
    legend.title = element_blank(), 
    legend.position = c(0.5,.9), 
    legend.background = element_blank()
  ) + 
  scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
  xlab("Year") + ylab("Mean defoliation") + ylim(0, NA) +
    geom_point(size=1.5) + 
  geom_line() +
  geom_errorbar(aes(ymin=mean - se, ymax=mean + se), width = .1) +
  guides(colour = guide_legend(nrow = 2))

Version Author Date
2a68381 Antonio J Perez-Luque 2021-05-18 
null device 
          1

Tendencia últimos años

  • Explorar la tendendia desde 2005 (solo fines exploratorios). Sabemos que este modelo estadísticamte es inválido.

Version Author Date
2a68381 Antonio J Perez-Luque 2021-05-18 
null device 
          1 
ft1 <- do(df2005 %>% group_by(especie), 
   glance(lm(mean~year, data=.))) %>%
  dplyr::select(especie, r.squared, p.value) %>% 
  mutate_if(is.numeric, round, 4) %>% 
  flextable() 
ft1 
ft2 <- do(df2005 %>% group_by(especie), 
   tidy(lm(mean~year, data=.))) %>% 
  mutate_if(is.numeric, round, 3) %>% 
  flextable() 
ft2

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] broom_0.7.4     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] httr_1.4.2        sass_0.3.1        jsonlite_1.7.2    splines_4.0.2    
 [5] modelr_0.1.8      bslib_0.2.4       assertthat_0.2.1  highr_0.8        
 [9] cellranger_1.1.0  yaml_2.2.1        gdtools_0.2.3     pillar_1.4.7     
[13] backports_1.2.1   lattice_0.20-41   glue_1.4.2        uuid_0.1-4       
[17] digest_0.6.27     promises_1.2.0.1  rvest_0.3.6       colorspace_2.0-0 
[21] Matrix_1.3-2      htmltools_0.5.1.1 httpuv_1.5.5      pkgconfig_2.0.3  
[25] haven_2.3.1       scales_1.1.1      whisker_0.4       later_1.1.0.1    
[29] officer_0.3.16    git2r_0.28.0      mgcv_1.8-33       generics_0.1.0   
[33] farver_2.0.3      ellipsis_0.3.1    withr_2.4.1       cli_2.3.0        
[37] magrittr_2.0.1    crayon_1.4.1      readxl_1.3.1      evaluate_0.14    
[41] fs_1.5.0          nlme_3.1-152      xml2_1.3.2        tools_4.0.2      
[45] data.table_1.13.6 hms_1.0.0         lifecycle_1.0.0   munsell_0.5.0    
[49] reprex_1.0.0      zip_2.1.1         compiler_4.0.2    jquerylib_0.1.3  
[53] systemfonts_1.0.0 rlang_0.4.10      grid_4.0.2        rstudioapi_0.13  
[57] htmlwidgets_1.5.3 crosstalk_1.1.1   base64enc_0.1-3   labeling_0.4.2   
[61] rmarkdown_2.6.6   gtable_0.3.0      DBI_1.1.1         R6_2.5.0         
[65] lubridate_1.7.10  knitr_1.31        rprojroot_2.0.2   stringi_1.5.3    
[69] Rcpp_1.0.6        vctrs_0.3.6       dbplyr_2.1.0      tidyselect_1.1.0 
[73] xfun_0.20