getting_started.RMD

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

#############
load(file = "C:/CodigoR/Mammal_Col/MammalCol/data/taxon.rda") # , taxon
load(file = "C:/CodigoR/Mammal_Col/MammalCol/data/colmap.rda") # , colmap
############

search_mammalcol <- function(splist, max_distance = 0.2) {
  # Defensive function here, check for user input errors
  if (is.factor(splist)) {
    splist <- as.character(splist)
  }
  # Fix species name
  splist_st <- standardize_names(splist)
  dupes_splist_st <- find_duplicates(splist_st)

  if (length(dupes_splist_st) != 0) {
    message(
      "The following names are repeated in the 'splist': ",
      paste(dupes_splist_st, collapse = ", ")
    )
  }
  splist_std <- unique(splist_st)

  # create an output data container
  output_matrix <- matrix(nrow = length(splist_std), ncol = 20) # two more
  colnames(output_matrix) <- c(
    "name_submitted",
    names(taxon),
    "Distance"
  )

  # loop code to find the matching string

  for (i in seq_along(splist_std)) {
    # Standardise max distance value
    if (max_distance < 1 & max_distance > 0) {
      max_distance_fixed <- ceiling(nchar(splist_std[i]) * max_distance)
    } else {
      max_distance_fixed <- max_distance
    }

    # fuzzy and exact match
    matches <- agrep(splist_std[i],
      taxon$scientificName, # base data column
      max.distance = max_distance_fixed,
      value = TRUE
    )

    # check non matching result
    if (length(matches) == 0) {
      row_data <- rep("nill", 18) # number of columns
    } else if (length(matches) != 0) { # match result
      dis_value <- as.numeric(utils::adist(splist_std[i], matches))
      matches1 <- matches[dis_value <= max_distance_fixed]
      dis_val_1 <- dis_value[dis_value <= max_distance_fixed]

      if (length(matches1) == 0) {
        row_data <- rep("nill", 18) # number of columns
      } else if (length(matches1) != 0) {
        row_data <- as.matrix(taxon[taxon$scientificName %in% matches1, ])
      }
    }

    # distance value
    if (is.null(nrow(row_data))) {
      dis_value_1 <- "nill"
    } else {
      dis_value_1 <- utils::adist(splist_std[i], row_data[, 2])
    }

    output_matrix[i, ] <-
      c(splist_std[i], row_data, dis_value_1)
  }

  # Output
  output <- as.data.frame(output_matrix)
  # rownames(output) <- NULL
  output <- output[, -2] # delete the id column
  return(output[output$scientificName != "nill", ])
}




standardize_names <- function(splist) {
  fixed1 <- simple_cap(trimws(splist)) # all up
  fixed2 <- gsub("cf\\.", "", fixed1)
  fixed3 <- gsub("aff\\.", "", fixed2)
  fixed4 <- trimws(fixed3) # remove trailing and leading space
  fixed5 <- gsub("_", " ", fixed4) # change names separated by _ to space

  # Hybrids
  fixed6 <- gsub("(^x )|( x$)|( x )", " ", fixed5)
  hybrids <- fixed5 == fixed6
  if (!all(hybrids)) {
    sp_hybrids <- splist[!hybrids]
    warning(
      paste(
        "The 'x' sign indicating hybrids have been removed in the",
        "following names before search:",
        paste(paste0("'", sp_hybrids, "'"), collapse = ", ")
      ),
      immediate. = TRUE, call. = FALSE
    )
  }
  # Merge multiple spaces
  fixed7 <- gsub("(?<=[\\s])\\s*|^\\s+|\\s+$", "", fixed6, perl = TRUE)
  return(fixed7)
}



simple_cap <- function(x) {
  # Split each string into words, remove unnecessary white spaces, and convert to lowercase
  words <- sapply(strsplit(x, "\\s+"), function(words) paste(tolower(words), collapse = " "))

  # Capitalize the first letter of each word
  capitalized <- sapply(strsplit(words, ""), function(word) {
    if (length(word) > 0) {
      word[1] <- toupper(word[1])
    }
    paste(word, collapse = "")
  })

  return(capitalized)
}



find_duplicates <- function(vector) {
  # Count the frequency of each word
  word_counts <- table(vector)
  # Find words with a frequency greater than 1
  duplicated_words <- names(word_counts[word_counts > 1])
  return(duplicated_words)
}


################
mammalmap <- function(species) {
  if (!requireNamespace("ggplot2", quietly = TRUE)) {
    install.packages("ggplot2")
  }
  if (!requireNamespace("sf", quietly = TRUE)) {
    install.packages("sf")
  }

  # require("ggplot2")
  # require("sf")

  # load("data/colmap.rda")
  # load("data/taxon.rda")

  distribution_list <-
    strsplit(taxon$distribution, "\\|") # trimws () removes spaces

  deptos <- as.data.frame(cbind(Depto = unique(colmap$NAME_1), fill = "white"))
  sp_id <- which(taxon$scientificName == species)
  unos <- trimws(distribution_list[[sp_id]]) # species number

  # nested loop to get deptos
  for (i in 1:length(deptos[, 1])) {
    for (j in 1:length(unos)) {
      if (deptos$Depto[i] == unos[j]) {
        deptos$fill[i] <- "blue"
      }
    }
  }

  # make the map
  ggplot2::ggplot(colmap) +
    ggplot2::geom_sf(ggplot2::aes(fill = NAME_1)) +
    ggplot2::scale_fill_manual(values = deptos$fill) +
    # ggtitle(taxon$scientificName[25]) + #species name number
    ggplot2::labs(subtitle = taxon$scientificName[sp_id]) +
    ggplot2::theme(
      legend.position = "right", # locatio legend
      legend.title = ggplot2::element_blank(), # element_text(size=7),#,
      legend.text = ggplot2::element_text(size = 7, ), # text depto size
      plot.subtitle = ggplot2::element_text(face = "italic") # italica
    ) # ,
}


```



# mammalcol <img src="man/figures/logo.png" align="right" height="139" alt="" />
<!-- badges: start -->
[![R-CMD-check](https://github.com/dlizcano/mammalcol/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/dlizcano/mammalcol/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/dlizcano/mammalcol/branch/main/graph/badge.svg)](https://app.codecov.io/gh/dlizcano/mammalcol?branch=main)
[![CRAN status](https://www.r-pkg.org/badges/version/mammalcol)](https://CRAN.R-project.org/package=mammalcol)
<!-- badges: end -->

The goal of mammalcol is to allow easy access to the List of Mammal Species of Colombia.

## Installation

You can install the development version of mammalcol from [GitHub](https://github.com/dlizcano/mammalcol) with:

``` r
# install.packages("devtools")
devtools::install_github("dlizcano/mammalcol")
```