Merge pull request #23 from b-cubed-eu/update

Update

R/data.R

@@ -1,7 +1,7 @@
 #' GBIF occurrences data of acacia in South Africa
 #' An example of occurrence data from GBIF containing required column for impact indicator.
 #'
-#' @format A dataframe object containing 17,279 rows and 6 variables
+#' @format A dataframe object containing 19,100 rows and 6 variables
 #' \describe{
 #'   \item{decimalLatitude}{geographic latitude in decimal}
 #'   \item{decimalLongitude}{geographic longitude in decimal}
@@ -13,7 +13,7 @@
 #' @examples
 #'   head(taxa_Acacia,10)
 #'
-#' @source \url{https://www.gbif.org/occurrence/search?country=ZA&taxon_key=2978223&occurrence_status=present}
+#' @source \url{https://doi.org/10.15468/dl.b6gda5}
 "taxa_Acacia"
 
 
@@ -33,18 +33,17 @@
 
 
 
-#' GBIF occurrences data of acacia in South Africa
-#' An example of occurrence data from GBIF containing required column for impact indicator.
+#' EICAT data of acacia taxa
+#' An example of EICAT data containing species name, impact category and mechanism.
 #'
-#' @format A dataframe object containing 138 observations and 4 variables
+#' @format A dataframe object containing 138 observations and 3 variables
 #' \describe{
 #'   \item{scientific_name}{species scientific name}
 #'   \item{impact_category}{EICAT impact category}
 #'   \item{impact_mechanism}{mecahnism of impact}
-#'   \item{probability}{weight of impact}
 #' }
 #' @examples
-#'   head(eicat_data,10)
+#'   head(eicat_acacia,10)
 #'
 #' @source \url{https://github.com/Oceane-Boulesnane-Guengant/Risk_maps_Acacia_SA/tree/main/data}
-"eicat_data"
+"eicat_acacia"

R/impact_category.R

@@ -22,8 +22,6 @@
 #' @export
 #'
 #' @examples
-#' # read the impact data
-#' # eicat_data<-readRDS("data/eicat_data.rds")
 #' # define species list
 #' species_list <- c(
 #'   "Acacia adunca",
@@ -38,7 +36,7 @@
 #' )
 #'
 #' agg_impact <- impact_cat(
-#'   impact_data = eicat_data,
+#'   impact_data = eicat_acacia,
 #'   species_list = species_list,
 #'   col_category = "impact_category",
 #'   col_species = "scientific_name",

R/impact_indicator.R

@@ -32,7 +32,7 @@
 #' )
 #' impact_value <- impact_indicator(
 #'   cube = acacia_cube,
-#'   impact_data = eicat_data,
+#'   impact_data = eicat_acacia,
 #'   col_category = "impact_category",
 #'   col_species = "scientific_name",
 #'   col_mechanism = "impact_mechanism",

R/plot.R

@@ -28,7 +28,7 @@
 #' # compute impact indicator
 #' impact_value <- impact_indicator(
 #'   cube = acacia_cube,
-#'   impact_data = eicat_data,
+#'   impact_data = eicat_acacia,
 #'   col_category = "impact_category",
 #'   col_species = "scientific_name",
 #'   col_mechanism = "impact_mechanism",
@@ -96,7 +96,7 @@ plot.impact_indicator <- function(x,
 #' # compute species impact
 #' speciesImpact <- species_impact(
 #'   cube = acacia_cube,
-#'   impact_data = eicat_data,
+#'   impact_data = eicat_acacia,
 #'   col_category = "impact_category",
 #'   col_species = "scientific_name",
 #'   col_mechanism = "impact_mechanism",
@@ -187,7 +187,7 @@ plot.species_impact <- function(x,
 #' # compute site impact
 #' siteImpact <- site_impact(
 #'   cube = acacia_cube,
-#'   impact_data = eicat_data,
+#'   impact_data = eicat_acacia,
 #'   col_category = "impact_category",
 #'   col_species = "scientific_name",
 #'   col_mechanism = "impact_mechanism",

R/site_impact.R

@@ -31,7 +31,7 @@
 #'
 #' siteImpact <- site_impact(
 #'   cube = acacia_cube,
-#'   impact_data = eicat_data,
+#'   impact_data = eicat_acacia,
 #'   col_category = "impact_category",
 #'   col_species = "scientific_name",
 #'   col_mechanism = "impact_mechanism",

R/species_impact.R

@@ -31,7 +31,7 @@
 #'
 #' speciesImpact <- species_impact(
 #'   cube = acacia_cube,
-#'   impact_data = eicat_data,
+#'   impact_data = eicat_acacia,
 #'   col_category = "impact_category",
 #'   col_species = "scientific_name",
 #'   col_mechanism = "impact_mechanism",

README.Rmd

@@ -19,9 +19,10 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/b-cubed-eu/impIndicator/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/b-cubed-eu/impIndicator/actions/workflows/R-CMD-check.yaml)
 [![repo status](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
+[![CRAN status](https://www.r-pkg.org/badges/version/impIndicator)](https://CRAN.R-project.org/package=impIndicator)
 <!-- badges: end -->
 
-The goal of **impIndicator** is to allow users to seamlessly calculate and visualise the impact of alien taxa and individual species in a given area. It calculates and visualises impact per site as a map. It takes in GBIF occurrence data and EICAT assessment list. It enables users to choose from various methods 
+The goal of **impIndicator** is to allow users to seamlessly calculate and visualise the impact of alien taxa and individual species in a given area. It calculates and visualises impact per site as a map. It takes in GBIF occurrence data and EICAT assessment data. It enables users to choose from various methods 
 of calculating impact indicators based on different studies.\
 
 The impIndicator produces three main products and can be useful as stated below:\
@@ -59,8 +60,9 @@ library(sf) # Spatial features
 
 
 ## Process occurrence cube
+The Global Biodiversity Information Facility (GBIF) occurrence data is a standardised species information that documents the presence or absence of species at particular locations and times. 
 ```{r}
-# load the GBIF occurrence data for taxa
+# Process cube from GBIF occurrence data in the R studio environment
 
 acacia_cube <- taxa_cube(
   taxa = taxa_Acacia,
@@ -72,12 +74,13 @@ acacia_cube <- taxa_cube(
 acacia_cube
 ```
 
-## EICAT data
-
+## EICAT assessment data
+The Environmental Impact Classification for Alien Taxa (EICAT) assessment data is the reported impact of alien taxa based on EICAT method which is the IUCN standard. An assessed alien taxa with adequate data is classified into massive (MV),
+major (MR), moderate (MO), minor (MN), or minimal concern (MC) depending on the severity of the impact caused on recipient ecosystem. Additional information such as the mechanisms and location of impact are also recorded. 
 An example of an EICAT data is:
 ```{r}
-# view EICAT data. Convert to tibble for a nicer display
-head(as_tibble(eicat_data),10)
+# view EICAT data
+head(eicat_acacia,10)
 ```
 
 ## Compute impact map
@@ -89,7 +92,7 @@ indicators, namely, **precautionary**, **precautionary cumulative**, **mean**, *
 ```{r}
 siteImpact <- site_impact(
   cube = acacia_cube,
-  impact_data = eicat_data,
+  impact_data = eicat_acacia,
   col_category = "impact_category",
   col_species = "scientific_name",
   col_mechanism = "impact_mechanism",
@@ -112,7 +115,7 @@ To compute the impact indicator of alien taxa, we sum all the yearly impact scor
 
 impactIndicator <- impact_indicator(
   cube = acacia_cube,
-  impact_data = eicat_data,
+  impact_data = eicat_acacia,
   col_category = "impact_category",
   col_species = "scientific_name",
   col_mechanism = "impact_mechanism",
@@ -131,7 +134,7 @@ We compute the impact indicator per species by summing the impact risk map per s
 
 species_value <- species_impact(
   cube = acacia_cube,
-  impact_data = eicat_data,
+  impact_data = eicat_acacia,
   col_category = "impact_category",
   col_species = "scientific_name",
   col_mechanism = "impact_mechanism",
@@ -162,7 +165,7 @@ all_impact <- data.frame("year" = unique(acacia_cube$data$year))
 for (type in types) {
   impact_value <- impact_indicator(
     cube = acacia_cube,
-    impact_data = eicat_data,
+    impact_data = eicat_acacia,
     col_category = "impact_category",
     col_species = "scientific_name",
     col_mechanism = "impact_mechanism",

README.md

@@ -8,12 +8,14 @@
 [![R-CMD-check](https://github.com/b-cubed-eu/impIndicator/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/b-cubed-eu/impIndicator/actions/workflows/R-CMD-check.yaml)
 [![repo
 status](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
+[![CRAN
+status](https://www.r-pkg.org/badges/version/impIndicator)](https://CRAN.R-project.org/package=impIndicator)
 <!-- badges: end -->
 
 The goal of **impIndicator** is to allow users to seamlessly calculate
 and visualise the impact of alien taxa and individual species in a given
 area. It calculates and visualises impact per site as a map. It takes in
-GBIF occurrence data and EICAT assessment list. It enables users to
+GBIF occurrence data and EICAT assessment data. It enables users to
 choose from various methods of calculating impact indicators based on
 different studies.  
 
@@ -80,8 +82,12 @@ library(sf) # Spatial features
 
 ## Process occurrence cube
 
+The Global Biodiversity Information Facility (GBIF) occurrence data is a
+standardised species information that documents the presence or absence
+of species at particular locations and times.
+
 ``` r
-# load the GBIF occurrence data for taxa
+# Process cube from GBIF occurrence data in the R studio environment
 
 acacia_cube <- taxa_cube(
   taxa = taxa_Acacia,
@@ -110,42 +116,49 @@ acacia_cube
 #> 
 #> # A tibble: 5,559 × 8
 #>    scientificName   taxonKey minCoordinateUncerta…¹  year cellCode xcoord ycoord
-#>    <chr>               <dbl>                  <dbl> <dbl>    <int>  <dbl>  <dbl>
-#>  1 Acacia implexa    2979232                      1  2010      206   18.4  -33.9
-#>  2 Acacia cyclops    2980425                    122  2010      668   18.4  -32.2
-#>  3 Acacia saligna    2978552                      1  2010      206   18.4  -33.9
-#>  4 Acacia pycnantha  2978604                      1  2010      206   18.4  -33.9
-#>  5 Acacia mearnsii   2979775                    110  2010      215   20.6  -33.9
-#>  6 Acacia mearnsii   2979775                      1  2010      215   20.6  -33.9
-#>  7 Acacia mearnsii   2979775                      8  2010     1376   30.4  -29.7
-#>  8 Acacia saligna    2978552                      1  2011      206   18.4  -33.9
-#>  9 Acacia saligna    2978552                     15  2011     1312   30.9  -29.9
-#> 10 Acacia mearnsii   2979775                      1  2011      230   24.4  -33.9
+#>    <chr>               <dbl>                  <dbl> <dbl> <chr>     <dbl>  <dbl>
+#>  1 Acacia implexa    2979232                      1  2010 206        18.4  -33.9
+#>  2 Acacia cyclops    2980425                    122  2010 668        18.4  -32.2
+#>  3 Acacia saligna    2978552                      1  2010 206        18.4  -33.9
+#>  4 Acacia pycnantha  2978604                      1  2010 206        18.4  -33.9
+#>  5 Acacia mearnsii   2979775                    110  2010 215        20.6  -33.9
+#>  6 Acacia mearnsii   2979775                      1  2010 215        20.6  -33.9
+#>  7 Acacia mearnsii   2979775                      8  2010 1376       30.4  -29.7
+#>  8 Acacia saligna    2978552                      1  2011 206        18.4  -33.9
+#>  9 Acacia saligna    2978552                     15  2011 1312       30.9  -29.9
+#> 10 Acacia mearnsii   2979775                      1  2011 230        24.4  -33.9
 #> # ℹ 5,549 more rows
 #> # ℹ abbreviated name: ¹​minCoordinateUncertaintyInMeters
 #> # ℹ 1 more variable: obs <dbl>
 ```
 
-## EICAT data
+## EICAT assessment data
 
-An example of an EICAT data is:
+The Environmental Impact Classification for Alien Taxa (EICAT)
+assessment data is the reported impact of alien taxa based on EICAT
+method which is the IUCN standard. An assessed alien taxa with adequate
+data is classified into massive (MV), major (MR), moderate (MO), minor
+(MN), or minimal concern (MC) depending on the severity of the impact
+caused on recipient ecosystem. Additional information such as the
+mechanisms and location of impact are also recorded. An example of an
+EICAT data is:
 
 ``` r
-# view EICAT data. Convert to tibble for a nicer display
-head(as_tibble(eicat_data),10)
-#> # A tibble: 10 × 4
-#>    scientific_name   impact_category impact_mechanism                probability
-#>    <chr>             <chr>           <chr>                                 <dbl>
-#>  1 Acacia saligna    MC              (1) Competition                           0
-#>  2 Acacia saligna    MC              (12) Indirect impacts through …           0
-#>  3 Acacia saligna    MC              (1) Competition                           0
-#>  4 Acacia saligna    MC              (1) Competition; (9) Chemical …           0
-#>  5 Acacia mearnsii   MC              (6) Poisoning/toxicity                    0
-#>  6 Acacia longifolia MC              (9) Chemical impact on ecosyst…           0
-#>  7 Acacia dealbata   MC              (9) Chemical impact on ecosyst…           0
-#>  8 Acacia dealbata   MC              (9) Chemical impact on ecosyst…           0
-#>  9 Acacia saligna    MC              (9) Chemical impact on ecosyst…           0
-#> 10 Acacia dealbata   MC              (12) Indirect impacts through …           0
+# view EICAT data
+head(eicat_acacia,10)
+#> # A tibble: 10 × 3
+#>    scientific_name   impact_category impact_mechanism                           
+#>    <chr>             <chr>           <chr>                                      
+#>  1 Acacia saligna    MC              (1) Competition                            
+#>  2 Acacia saligna    MC              (12) Indirect impacts through interaction …
+#>  3 Acacia saligna    MC              (1) Competition                            
+#>  4 Acacia saligna    MC              (1) Competition; (9) Chemical impact on th…
+#>  5 Acacia mearnsii   MC              (6) Poisoning/toxicity                     
+#>  6 Acacia longifolia MC              (9) Chemical impact on ecosystems          
+#>  7 Acacia dealbata   MC              (9) Chemical impact on ecosystems          
+#>  8 Acacia dealbata   MC              (9) Chemical impact on ecosystems          
+#>  9 Acacia saligna    MC              (9) Chemical impact on ecosystems          
+#> 10 Acacia dealbata   MC              (12) Indirect impacts through interaction …
 ```
 
 ## Compute impact map
@@ -163,7 +176,7 @@ cumulative** and **cumulative**.
 ``` r
 siteImpact <- site_impact(
   cube = acacia_cube,
-  impact_data = eicat_data,
+  impact_data = eicat_acacia,
   col_category = "impact_category",
   col_species = "scientific_name",
   col_mechanism = "impact_mechanism",
@@ -191,7 +204,7 @@ year.
 
 impactIndicator <- impact_indicator(
   cube = acacia_cube,
-  impact_data = eicat_data,
+  impact_data = eicat_acacia,
   col_category = "impact_category",
   col_species = "scientific_name",
   col_mechanism = "impact_mechanism",
@@ -214,7 +227,7 @@ map per species and correct for sampling effort by dividing by $N$.
 
 species_value <- species_impact(
   cube = acacia_cube,
-  impact_data = eicat_data,
+  impact_data = eicat_acacia,
   col_category = "impact_category",
   col_species = "scientific_name",
   col_mechanism = "impact_mechanism",
@@ -249,7 +262,7 @@ all_impact <- data.frame("year" = unique(acacia_cube$data$year))
 for (type in types) {
   impact_value <- impact_indicator(
     cube = acacia_cube,
-    impact_data = eicat_data,
+    impact_data = eicat_acacia,
     col_category = "impact_category",
     col_species = "scientific_name",
     col_mechanism = "impact_mechanism",