inst/doc/biosurvey_preparing_data.R

In biosurvey: Tools for Biological Survey Planning

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", echo = FALSE)

## ----prepare_master_matrix, eval=FALSE, echo=TRUE-----------------------------
#  # Data
#  data("mx", package = "biosurvey")
#  variables <- raster::stack(system.file("extdata/variables.tif",
#                                        package = "biosurvey"))
#  names(variables) <- c("Mean_temperature", "Max_temperature", "Min_temperature",
#                        "Annual_precipitation", "Prec_wettest_month",
#                        "Prec_driest_month" )
#  
#  # Create master matrix object
#  m_matrix <- prepare_master_matrix(region = mx, variables = variables,
#                                    do_pca = TRUE, center = TRUE, scale = TRUE)
#  #> Processing raster layers, please wait...
#  #> Performing PCA analysis
#  
#  summary(m_matrix)
#  #>
#  #>                      Summary of a master_matrix object
#  #> ---------------------------------------------------------------------------
#  #>
#  #> Data matrix summary:
#  #>    Longitude          Latitude     Mean_temperature Max_temperature
#  #>  Min.   :-116.92   Min.   :14.58   Min.   : 85.0    Min.   :180
#  #>  1st Qu.:-106.79   1st Qu.:19.92   1st Qu.:174.0    1st Qu.:309
#  #>  Median :-102.58   Median :24.25   Median :203.0    Median :335
#  #>  Mean   :-102.52   Mean   :23.95   Mean   :203.6    Mean   :330
#  #>  3rd Qu.: -98.75   3rd Qu.:27.92   3rd Qu.:236.0    3rd Qu.:354
#  #>  Max.   : -86.92   Max.   :32.58   Max.   :291.0    Max.   :425
#  #>  Min_temperature  Annual_precipitation Prec_wettest_month Prec_driest_month
#  #>  Min.   :-60.00   Min.   :  53.0       Min.   :  9.0      Min.   :  0.00
#  #>  1st Qu.: 30.00   1st Qu.: 352.0       1st Qu.: 78.0      1st Qu.:  3.00
#  #>  Median : 60.00   Median : 619.0       Median :144.5      Median :  6.00
#  #>  Mean   : 70.66   Mean   : 768.8       Mean   :164.7      Mean   : 10.67
#  #>  3rd Qu.:112.00   3rd Qu.:1046.2       3rd Qu.:224.0      3rd Qu.: 12.00
#  #>  Max.   :213.00   Max.   :4103.0       Max.   :750.0      Max.   :140.00
#  #>       PC1               PC2
#  #>  Min.   :-2.5770   Min.   :-5.9018
#  #>  1st Qu.:-1.3957   1st Qu.:-0.7861
#  #>  Median :-0.6003   Median : 0.1946
#  #>  Mean   : 0.0000   Mean   : 0.0000
#  #>  3rd Qu.: 1.0825   3rd Qu.: 0.9541
#  #>  Max.   : 9.1506   Max.   : 3.1070
#  #>
#  #>
#  #> No preselected sites were defined
#  #>
#  #>
#  #> Region of interest:
#  #> class       : SpatialPolygonsDataFrame
#  #> features    : 1
#  #> extent      : -118.4042, -86.7014, 14.55055, 32.71846  (xmin, xmax, ymin, ymax)
#  #> crs         : +proj=longlat +datum=WGS84 +no_defs
#  #> variables   : 11
#  #> names       : FIPS, ISO2, ISO3,  UN,   NAME,   AREA,   POP2005, REGION, SUBREGION,      LON,    LAT
#  #> value       :   MX,   MX,  MEX, 484, Mexico, 190869, 104266392,     19,        13, -102.535, 23.951
#  

## ----prepare_master_matrix1, eval=FALSE, echo=TRUE----------------------------
#  # preselected site example
#  data("preselected", package = "biosurvey")
#  
#  # Create master matrix object
#  m_matrix_pre <- prepare_master_matrix(region = mx, variables = variables,
#                                        preselected_sites = preselected,
#                                        do_pca = TRUE, center = TRUE, scale = TRUE)
#  #> Processing raster layers, please wait...
#  #> Performing PCA analysis
#  
#  summary(m_matrix_pre)
#  #>
#  #>                      Summary of a master_matrix object
#  #> ---------------------------------------------------------------------------
#  #>
#  #> Data matrix summary:
#  #>    Longitude          Latitude     Mean_temperature Max_temperature
#  #>  Min.   :-116.92   Min.   :14.58   Min.   : 85.0    Min.   :180
#  #>  1st Qu.:-106.79   1st Qu.:19.92   1st Qu.:174.0    1st Qu.:309
#  #>  Median :-102.58   Median :24.25   Median :203.0    Median :335
#  #>  Mean   :-102.52   Mean   :23.95   Mean   :203.6    Mean   :330
#  #>  3rd Qu.: -98.75   3rd Qu.:27.92   3rd Qu.:236.0    3rd Qu.:354
#  #>  Max.   : -86.92   Max.   :32.58   Max.   :291.0    Max.   :425
#  #>  Min_temperature  Annual_precipitation Prec_wettest_month Prec_driest_month
#  #>  Min.   :-60.00   Min.   :  53.0       Min.   :  9.0      Min.   :  0.00
#  #>  1st Qu.: 30.00   1st Qu.: 352.0       1st Qu.: 78.0      1st Qu.:  3.00
#  #>  Median : 60.00   Median : 619.0       Median :144.5      Median :  6.00
#  #>  Mean   : 70.66   Mean   : 768.8       Mean   :164.7      Mean   : 10.67
#  #>  3rd Qu.:112.00   3rd Qu.:1046.2       3rd Qu.:224.0      3rd Qu.: 12.00
#  #>  Max.   :213.00   Max.   :4103.0       Max.   :750.0      Max.   :140.00
#  #>       PC1               PC2
#  #>  Min.   :-2.5770   Min.   :-5.9018
#  #>  1st Qu.:-1.3957   1st Qu.:-0.7861
#  #>  Median :-0.6003   Median : 0.1946
#  #>  Mean   : 0.0000   Mean   : 0.0000
#  #>  3rd Qu.: 1.0825   3rd Qu.: 0.9541
#  #>  Max.   : 9.1506   Max.   : 3.1070
#  #>
#  #>
#  #> Sites preselected by user:
#  #>                Site  Longitude Latitude
#  #> 1           Chamela -105.04479 19.50090
#  #> 2       Los Tuxtlas  -95.07419 18.58489
#  #> 3            Chajul  -90.94067 16.17000
#  #> 4 Parque de Tlalpan  -99.19778 19.29139
#  #> 5  Parque Chipinque -100.35940 25.61750
#  #>
#  #>
#  #> Region of interest:
#  #> class       : SpatialPolygonsDataFrame
#  #> features    : 1
#  #> extent      : -118.4042, -86.7014, 14.55055, 32.71846  (xmin, xmax, ymin, ymax)
#  #> crs         : +proj=longlat +datum=WGS84 +no_defs
#  #> variables   : 11
#  #> names       : FIPS, ISO2, ISO3,  UN,   NAME,   AREA,   POP2005, REGION, SUBREGION,      LON,    LAT
#  #> value       :   MX,   MX,  MEX, 484, Mexico, 190869, 104266392,     19,        13, -102.535, 23.951
#  

## ----explore_data_EG, eval=FALSE, echo=TRUE-----------------------------------
#  # Plot using environmental variables
#  explore_data_EG(m_matrix, variable_1 = "Mean_temperature",
#                  variable_2 = "Annual_precipitation")

## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V1f1.png")

## ----explore_data_EG1, eval=FALSE, echo=TRUE----------------------------------
#  # Plot using Principal Components resulted
#  explore_data_EG(m_matrix, variable_1 = "PC1", variable_2 = "PC2")

## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V1f2.png")

## ----make_blocks, eval=FALSE, echo=TRUE---------------------------------------
#  # Creating blocks
#  m_blocks <- make_blocks(m_matrix, variable_1 = "PC1",
#                         variable_2 = "PC2", n_cols = 10, n_rows = 10,
#                         block_type = "equal_area")
#  unique(m_blocks$data_matrix$Block)
#  #>  [1]   7   9  32   6  31  10   5   8  30   4  29  28   3  42  27  41  40  39  26
#  #> [20]  43  38  52  25  51  37  36  53  50  62  61  73  71  72  63  49  59  60  48
#  #> [39]  70  64  58  81  80  91  47  68  35  54  24   2  23  82  74  83  92  93  84
#  #> [58]  69 102 103  94 104 114 113  65
#  

## ----plot_blocks_EG, eval=FALSE, echo=TRUE------------------------------------
#  # plotting all blocks
#  plot_blocks_EG(master = m_blocks, block_ID = TRUE)
#  

## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V1f3.png")