Last updated: 2022-02-04

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Introduction

  • Genera tabla de descriptivos
library(tidyverse)
library(here)
library(kableExtra)
  • Read data
denraw <- read_csv(here::here("data/densidad_by_year.csv")) 
divraw <- read_csv(here::here("data/diversidad_by_year.csv")) 
riqraw <- read_csv(here::here("data/riqueza_by_year.csv"))
richness <- read_csv(here::here("data/riqueza_by_site.csv"))
transectos <- read_csv(here::here("data/transectos_tabla.csv"))
densidad_avg <- denraw %>%
  group_by(transecto, site, elev) %>%
  summarise(mean = mean(den, na.rm = TRUE),
            sd = sd(den, na.rm = TRUE),
            se = sd/sqrt(n()))

diversidad_avg <- divraw %>%
  group_by(transecto, site, elev) %>%
  summarise(mean = mean(diversidad, na.rm = TRUE),
            sd = sd(diversidad, na.rm = TRUE),
            se = sd/sqrt(n()))

abundancia <- denraw %>%
  group_by(transecto, site) %>%
  summarise(mean = round(mean(abundancia),2),
            sd = sd(abundancia),
            se = round(sd/sqrt(n()),2),
            n_ind_total = sum(abundancia))
descriptivos <- 
  transectos %>% inner_join(densidad_avg) %>%
  dplyr::select(-sd) %>%
  mutate(mean = round(mean, 2),
         se = round(se,2)) %>%
  unite("density", mean,se, sep = " ± ") %>% 
  inner_join(diversidad_avg) %>%
  dplyr::select(-sd) %>%
  mutate(mean = round(mean, 2),
         se = round(se,2)) %>%
  unite("diversity", mean,se, sep = " ± ") %>%
  inner_join(richness) %>%
  rename(richness = riq) %>%
  inner_join(abundancia) %>%
  dplyr::select(-sd) %>%
  unite("abundancia_media", mean,se, sep = " ± ") %>% 
  unite("elev_rango", min_altitu, max_altitu, sep = "-") %>% 
  rowwise() %>% 
  mutate(elev_rango = paste0("(", elev_rango, ")")) %>% 
  unite("Elevation", elev, elev_rango, sep=" ") %>% 
  dplyr::select(
    "Transect" = transecto,
    "Code" = site,
    Elevation,
    "Length" = longitud,
    "Abundance" = density,
    "Diversity (H’)" = diversity,
    "Richness" = richness,
    "Mean number of individuals" = abundancia_media,
    "Total number of individuals" = n_ind_total)

write_csv(descriptivos, here::here("data/tabla_descriptivos.csv"))
descriptivos %>% 
  kbl() %>% 
  kable_styling()
Transect Code Elevation Length Abundance Diversity (H’) Richness Mean number of individuals Total number of individuals
Altas cumbres AC 3126 (3081-3170) 3209 0.47 ± 0.03 1.02 ± 0.17 16 82.12 ± 10.04 657
Barranco de San Juan BSJ 1362 (1347-1377) 453 5.52 ± 0.63 2.92 ± 0.05 73 236.5 ± 38.17 1892
Borreguiles San Juan BOSJ 2545 (2527-2563) 2533 2.38 ± 0.27 2.49 ± 0.04 45 378.38 ± 45.91 3027
Campos de Otero CO 2248 (2178-2317) 2992 4.06 ± 0.48 2.67 ± 0.04 70 986.12 ± 157.42 7889
Carihuela CAR 2808 (2792-2824) 387 0.56 ± 0.1 1.02 ± 0.14 9 9 ± 1.35 72
Catifas CAT 1666 (1658-1673) 483 4.18 ± 0.27 2.75 ± 0.07 63 190.62 ± 22.05 1525
Cauchiles CAU 3203 (3196-3210) 624 1.08 ± 0.37 1.41 ± 0.19 18 28.12 ± 5.59 225
Dúrcal DU 1950 (1913-1987) 3563 9.41 ± 0.99 3.05 ± 0.04 64 3038.86 ± 331.97 21272
Fabriquilla FA 992 (971-1013) 415 2.47 ± 0.25 2.43 ± 0.06 56 88.5 ± 18.88 708
Laguna Seca LS 2282 (2257-2306) 3044 3.15 ± 0.46 1.85 ± 0.1 38 754.5 ± 119 6036
Laguna LP 732 (726-739) 500 3.95 ± 0.35 0.96 ± 0.13 24 193.14 ± 22.83 1352
Matas Verdes MV 1919 (1851-1987) 2672 5.23 ± 0.61 2.87 ± 0.12 78 1277.25 ± 168.91 10218
Loma de Papeles Alto PA 2400 (2338-2463) 3068 2.49 ± 0.34 2.23 ± 0.07 46 592.75 ± 103.02 4742
Loma de Papeles Bajo PB 2121 (2052-2190) 2743 1.95 ± 0.15 2.58 ± 0.05 56 448 ± 49.25 3584
Pitres PI 1379 (1283-1475) 2671 4.51 ± 0.35 2.85 ± 0.04 64 979.14 ± 84.37 6854
Praillos PRA 1894 (1887-1900) 366 4.61 ± 0.45 2.94 ± 0.09 73 156 ± 23.14 1248
Purche PU 1442 (1387-1496) 2758 5.67 ± 0.77 2.83 ± 0.06 78 1303.88 ± 235.67 10431
Robledal de Dilar DI 1720 (1673-1767) 2725 5.78 ± 0.52 2.93 ± 0.13 82 1402.38 ± 150.25 11219
Sabinas SA 2180 (2177-2184) 289 3.28 ± 0.51 2.42 ± 0.06 50 76 ± 11.82 608
Turbera TU 745 (723-767) 2754 1.4 ± 0.31 1.7 ± 0.09 45 364.14 ± 67.13 2549

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] kableExtra_1.3.1 here_1.0.1       forcats_0.5.1    stringr_1.4.0   
 [5] dplyr_1.0.6      purrr_0.3.4      readr_1.4.0      tidyr_1.1.3     
 [9] tibble_3.1.2     ggplot2_3.3.5    tidyverse_1.3.1  workflowr_1.7.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        lubridate_1.7.10  getPass_0.2-2     ps_1.5.0         
 [5] assertthat_0.2.1  rprojroot_2.0.2   digest_0.6.27     utf8_1.1.4       
 [9] R6_2.5.1          cellranger_1.1.0  backports_1.2.1   reprex_2.0.0     
[13] evaluate_0.14     highr_0.8         httr_1.4.2        pillar_1.6.1     
[17] rlang_0.4.12      readxl_1.3.1      rstudioapi_0.13   whisker_0.4      
[21] callr_3.7.0       jquerylib_0.1.3   rmarkdown_2.8     webshot_0.5.2    
[25] munsell_0.5.0     broom_0.7.9       compiler_4.0.2    httpuv_1.5.5     
[29] modelr_0.1.8      xfun_0.23         pkgconfig_2.0.3   htmltools_0.5.2  
[33] tidyselect_1.1.1  viridisLite_0.4.0 fansi_0.4.2       crayon_1.4.1     
[37] dbplyr_2.1.1      withr_2.4.1       later_1.1.0.1     grid_4.0.2       
[41] jsonlite_1.7.2    gtable_0.3.0      lifecycle_1.0.1   DBI_1.1.1        
[45] git2r_0.28.0      magrittr_2.0.1    scales_1.1.1.9000 cli_2.5.0        
[49] stringi_1.7.4     fs_1.5.0          promises_1.2.0.1  xml2_1.3.2       
[53] bslib_0.2.4       ellipsis_0.3.2    generics_0.1.0    vctrs_0.3.8      
[57] tools_4.0.2       glue_1.4.2        hms_1.0.0         processx_3.5.1   
[61] fastmap_1.1.0     yaml_2.2.1        colorspace_2.0-2  rvest_1.0.0      
[65] knitr_1.31        haven_2.3.1       sass_0.3.1