Mesoamerican Species Maps
Rokaya A
2024-11-25
library(readr)
species_data <- read_csv("Mesoamerica.csv")## Rows: 8 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Name
## dbl (9): Mammals, Birds, Reptiles*, Amphibians, Fishes*, Molluscs*, Other In...
## num (2): Plants*, Total
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(species_data)## # A tibble: 6 × 12
## Name Mammals Birds `Reptiles*` Amphibians `Fishes*` `Molluscs*`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Belize 10 7 8 2 67 0
## 2 Costa Rica 12 24 16 56 138 2
## 3 El Salvador 6 9 10 8 52 0
## 4 Guatemala 16 18 39 96 98 2
## 5 Honduras 10 17 60 70 89 0
## 6 Mexico 97 67 106 232 305 14
## # ℹ 5 more variables: `Other Inverts*` <dbl>, `Plants*` <dbl>, `Fungi*` <dbl>,
## # `Chromists*` <dbl>, Total <dbl># Clean all of the column names
colnames(species_data)## [1] "Name" "Mammals" "Birds" "Reptiles*"
## [5] "Amphibians" "Fishes*" "Molluscs*" "Other Inverts*"
## [9] "Plants*" "Fungi*" "Chromists*" "Total"colnames(species_data) <- gsub("\\.", "_", colnames(species_data))
colnames(species_data)## [1] "Name" "Mammals" "Birds" "Reptiles*"
## [5] "Amphibians" "Fishes*" "Molluscs*" "Other Inverts*"
## [9] "Plants*" "Fungi*" "Chromists*" "Total"# Remove asterisks from column names
colnames(species_data) <- gsub("\\*", "", colnames(species_data))
# View cleaned column names
colnames(species_data)## [1] "Name" "Mammals" "Birds" "Reptiles"
## [5] "Amphibians" "Fishes" "Molluscs" "Other Inverts"
## [9] "Plants" "Fungi" "Chromists" "Total"library(sf)## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE# Read and view shapefile
map_data <- st_read("/Users/rokayaaqrabawi/Documents/EMES_203_Homework/ne_110m_admin_0_countries.shp")## Reading layer `ne_110m_admin_0_countries' from data source
## `/Users/rokayaaqrabawi/Documents/EMES_203_Homework/ne_110m_admin_0_countries.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 177 features and 168 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84unique(map_data$ADMIN)## [1] "Fiji"
## [2] "United Republic of Tanzania"
## [3] "Western Sahara"
## [4] "Canada"
## [5] "United States of America"
## [6] "Kazakhstan"
## [7] "Uzbekistan"
## [8] "Papua New Guinea"
## [9] "Indonesia"
## [10] "Argentina"
## [11] "Chile"
## [12] "Democratic Republic of the Congo"
## [13] "Somalia"
## [14] "Kenya"
## [15] "Sudan"
## [16] "Chad"
## [17] "Haiti"
## [18] "Dominican Republic"
## [19] "Russia"
## [20] "The Bahamas"
## [21] "Falkland Islands"
## [22] "Norway"
## [23] "Greenland"
## [24] "French Southern and Antarctic Lands"
## [25] "East Timor"
## [26] "South Africa"
## [27] "Lesotho"
## [28] "Mexico"
## [29] "Uruguay"
## [30] "Brazil"
## [31] "Bolivia"
## [32] "Peru"
## [33] "Colombia"
## [34] "Panama"
## [35] "Costa Rica"
## [36] "Nicaragua"
## [37] "Honduras"
## [38] "El Salvador"
## [39] "Guatemala"
## [40] "Belize"
## [41] "Venezuela"
## [42] "Guyana"
## [43] "Suriname"
## [44] "France"
## [45] "Ecuador"
## [46] "Puerto Rico"
## [47] "Jamaica"
## [48] "Cuba"
## [49] "Zimbabwe"
## [50] "Botswana"
## [51] "Namibia"
## [52] "Senegal"
## [53] "Mali"
## [54] "Mauritania"
## [55] "Benin"
## [56] "Niger"
## [57] "Nigeria"
## [58] "Cameroon"
## [59] "Togo"
## [60] "Ghana"
## [61] "Ivory Coast"
## [62] "Guinea"
## [63] "Guinea-Bissau"
## [64] "Liberia"
## [65] "Sierra Leone"
## [66] "Burkina Faso"
## [67] "Central African Republic"
## [68] "Republic of the Congo"
## [69] "Gabon"
## [70] "Equatorial Guinea"
## [71] "Zambia"
## [72] "Malawi"
## [73] "Mozambique"
## [74] "eSwatini"
## [75] "Angola"
## [76] "Burundi"
## [77] "Israel"
## [78] "Lebanon"
## [79] "Madagascar"
## [80] "Palestine"
## [81] "Gambia"
## [82] "Tunisia"
## [83] "Algeria"
## [84] "Jordan"
## [85] "United Arab Emirates"
## [86] "Qatar"
## [87] "Kuwait"
## [88] "Iraq"
## [89] "Oman"
## [90] "Vanuatu"
## [91] "Cambodia"
## [92] "Thailand"
## [93] "Laos"
## [94] "Myanmar"
## [95] "Vietnam"
## [96] "North Korea"
## [97] "South Korea"
## [98] "Mongolia"
## [99] "India"
## [100] "Bangladesh"
## [101] "Bhutan"
## [102] "Nepal"
## [103] "Pakistan"
## [104] "Afghanistan"
## [105] "Tajikistan"
## [106] "Kyrgyzstan"
## [107] "Turkmenistan"
## [108] "Iran"
## [109] "Syria"
## [110] "Armenia"
## [111] "Sweden"
## [112] "Belarus"
## [113] "Ukraine"
## [114] "Poland"
## [115] "Austria"
## [116] "Hungary"
## [117] "Moldova"
## [118] "Romania"
## [119] "Lithuania"
## [120] "Latvia"
## [121] "Estonia"
## [122] "Germany"
## [123] "Bulgaria"
## [124] "Greece"
## [125] "Turkey"
## [126] "Albania"
## [127] "Croatia"
## [128] "Switzerland"
## [129] "Luxembourg"
## [130] "Belgium"
## [131] "Netherlands"
## [132] "Portugal"
## [133] "Spain"
## [134] "Ireland"
## [135] "New Caledonia"
## [136] "Solomon Islands"
## [137] "New Zealand"
## [138] "Australia"
## [139] "Sri Lanka"
## [140] "China"
## [141] "Taiwan"
## [142] "Italy"
## [143] "Denmark"
## [144] "United Kingdom"
## [145] "Iceland"
## [146] "Azerbaijan"
## [147] "Georgia"
## [148] "Philippines"
## [149] "Malaysia"
## [150] "Brunei"
## [151] "Slovenia"
## [152] "Finland"
## [153] "Slovakia"
## [154] "Czechia"
## [155] "Eritrea"
## [156] "Japan"
## [157] "Paraguay"
## [158] "Yemen"
## [159] "Saudi Arabia"
## [160] "Antarctica"
## [161] "Northern Cyprus"
## [162] "Cyprus"
## [163] "Morocco"
## [164] "Egypt"
## [165] "Libya"
## [166] "Ethiopia"
## [167] "Djibouti"
## [168] "Somaliland"
## [169] "Uganda"
## [170] "Rwanda"
## [171] "Bosnia and Herzegovina"
## [172] "North Macedonia"
## [173] "Republic of Serbia"
## [174] "Montenegro"
## [175] "Kosovo"
## [176] "Trinidad and Tobago"
## [177] "South Sudan"# View unique country names in species dataset
unique(species_data$Name)## [1] "Belize" "Costa Rica" "El Salvador" "Guatemala" "Honduras"
## [6] "Mexico" "Nicaragua" "Panama"# Filter map data
filtered_map <- map_data[map_data$ADMIN %in% species_data$Name, ]
# View filtered map data
unique(filtered_map$ADMIN)## [1] "Mexico" "Panama" "Costa Rica" "Nicaragua" "Honduras"
## [6] "El Salvador" "Guatemala" "Belize"# Merge species data and map data
merged_data <- merge(filtered_map, species_data, by.x = "ADMIN", by.y = "Name")
# View merged data
head(merged_data)## Simple feature collection with 6 features and 179 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -117.1278 ymin: 8.225028 xmax: -82.5462 ymax: 32.72083
## Geodetic CRS: WGS 84
## ADMIN featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF
## 1 Belize Admin-0 country 1 6 Belize BLZ 0
## 2 Costa Rica Admin-0 country 1 5 Costa Rica CRI 0
## 3 El Salvador Admin-0 country 1 6 El Salvador SLV 0
## 4 Guatemala Admin-0 country 1 3 Guatemala GTM 0
## 5 Honduras Admin-0 country 1 5 Honduras HND 0
## 6 Mexico Admin-0 country 1 2 Mexico MEX 0
## LEVEL TYPE TLC ADM0_A3 GEOU_DIF GEOUNIT GU_A3 SU_DIF
## 1 2 Sovereign country 1 BLZ 0 Belize BLZ 0
## 2 2 Sovereign country 1 CRI 0 Costa Rica CRI 0
## 3 2 Sovereign country 1 SLV 0 El Salvador SLV 0
## 4 2 Sovereign country 1 GTM 0 Guatemala GTM 0
## 5 2 Sovereign country 1 HND 0 Honduras HND 0
## 6 2 Sovereign country 1 MEX 0 Mexico MEX 0
## SUBUNIT SU_A3 BRK_DIFF NAME NAME_LONG BRK_A3 BRK_NAME
## 1 Belize BLZ 0 Belize Belize BLZ Belize
## 2 Costa Rica CRI 0 Costa Rica Costa Rica CRI Costa Rica
## 3 El Salvador SLV 0 El Salvador El Salvador SLV El Salvador
## 4 Guatemala GTM 0 Guatemala Guatemala GTM Guatemala
## 5 Honduras HND 0 Honduras Honduras HND Honduras
## 6 Mexico MEX 0 Mexico Mexico MEX Mexico
## BRK_GROUP ABBREV POSTAL FORMAL_EN FORMAL_FR NAME_CIAWF
## 1 <NA> Belize BZ Belize <NA> Belize
## 2 <NA> C.R. CR Republic of Costa Rica <NA> Costa Rica
## 3 <NA> El. S. SV Republic of El Salvador <NA> El Salvador
## 4 <NA> Guat. GT Republic of Guatemala <NA> Guatemala
## 5 <NA> Hond. HN Republic of Honduras <NA> Honduras
## 6 <NA> Mex. MX United Mexican States <NA> Mexico
## NOTE_ADM0 NOTE_BRK NAME_SORT NAME_ALT MAPCOLOR7 MAPCOLOR8 MAPCOLOR9
## 1 <NA> <NA> Belize <NA> 1 4 5
## 2 <NA> <NA> Costa Rica <NA> 3 2 4
## 3 <NA> <NA> El Salvador <NA> 1 4 6
## 4 <NA> <NA> Guatemala <NA> 3 3 3
## 5 <NA> <NA> Honduras <NA> 2 5 2
## 6 <NA> <NA> Mexico <NA> 6 1 7
## MAPCOLOR13 POP_EST POP_RANK POP_YEAR GDP_MD GDP_YEAR
## 1 7 390353 10 2019 1879 2019
## 2 2 5047561 13 2019 61801 2019
## 3 8 6453553 13 2019 27022 2019
## 4 6 16604026 14 2019 76710 2019
## 5 5 9746117 13 2019 25095 2019
## 6 3 127575529 17 2019 1268870 2019
## ECONOMY INCOME_GRP FIPS_10 ISO_A2 ISO_A2_EH
## 1 6. Developing region 4. Lower middle income BH BZ BZ
## 2 5. Emerging region: G20 3. Upper middle income CS CR CR
## 3 6. Developing region 4. Lower middle income ES SV SV
## 4 6. Developing region 4. Lower middle income GT GT GT
## 5 6. Developing region 4. Lower middle income HO HN HN
## 6 4. Emerging region: MIKT 3. Upper middle income MX MX MX
## ISO_A3 ISO_A3_EH ISO_N3 ISO_N3_EH UN_A3 WB_A2 WB_A3 WOE_ID WOE_ID_EH
## 1 BLZ BLZ 084 084 084 BZ BLZ 23424760 23424760
## 2 CRI CRI 188 188 188 CR CRI 23424791 23424791
## 3 SLV SLV 222 222 222 SV SLV 23424807 23424807
## 4 GTM GTM 320 320 320 GT GTM 23424834 23424834
## 5 HND HND 340 340 340 HN HND 23424841 23424841
## 6 MEX MEX 484 484 484 MX MEX 23424900 23424900
## WOE_NOTE ADM0_ISO ADM0_DIFF ADM0_TLC ADM0_A3_US ADM0_A3_FR
## 1 Exact WOE match as country BLZ <NA> BLZ BLZ BLZ
## 2 Exact WOE match as country CRI <NA> CRI CRI CRI
## 3 Exact WOE match as country SLV <NA> SLV SLV SLV
## 4 Exact WOE match as country GTM <NA> GTM GTM GTM
## 5 Exact WOE match as country HND <NA> HND HND HND
## 6 Exact WOE match as country MEX <NA> MEX MEX MEX
## ADM0_A3_RU ADM0_A3_ES ADM0_A3_CN ADM0_A3_TW ADM0_A3_IN ADM0_A3_NP ADM0_A3_PK
## 1 BLZ BLZ BLZ BLZ BLZ BLZ BLZ
## 2 CRI CRI CRI CRI CRI CRI CRI
## 3 SLV SLV SLV SLV SLV SLV SLV
## 4 GTM GTM GTM GTM GTM GTM GTM
## 5 HND HND HND HND HND HND HND
## 6 MEX MEX MEX MEX MEX MEX MEX
## ADM0_A3_DE ADM0_A3_GB ADM0_A3_BR ADM0_A3_IL ADM0_A3_PS ADM0_A3_SA ADM0_A3_EG
## 1 BLZ BLZ BLZ BLZ BLZ BLZ BLZ
## 2 CRI CRI CRI CRI CRI CRI CRI
## 3 SLV SLV SLV SLV SLV SLV SLV
## 4 GTM GTM GTM GTM GTM GTM GTM
## 5 HND HND HND HND HND HND HND
## 6 MEX MEX MEX MEX MEX MEX MEX
## ADM0_A3_MA ADM0_A3_PT ADM0_A3_AR ADM0_A3_JP ADM0_A3_KO ADM0_A3_VN ADM0_A3_TR
## 1 BLZ BLZ BLZ BLZ BLZ BLZ BLZ
## 2 CRI CRI CRI CRI CRI CRI CRI
## 3 SLV SLV SLV SLV SLV SLV SLV
## 4 GTM GTM GTM GTM GTM GTM GTM
## 5 HND HND HND HND HND HND HND
## 6 MEX MEX MEX MEX MEX MEX MEX
## ADM0_A3_ID ADM0_A3_PL ADM0_A3_GR ADM0_A3_IT ADM0_A3_NL ADM0_A3_SE ADM0_A3_BD
## 1 BLZ BLZ BLZ BLZ BLZ BLZ BLZ
## 2 CRI CRI CRI CRI CRI CRI CRI
## 3 SLV SLV SLV SLV SLV SLV SLV
## 4 GTM GTM GTM GTM GTM GTM GTM
## 5 HND HND HND HND HND HND HND
## 6 MEX MEX MEX MEX MEX MEX MEX
## ADM0_A3_UA ADM0_A3_UN ADM0_A3_WB CONTINENT REGION_UN SUBREGION
## 1 BLZ -99 -99 North America Americas Central America
## 2 CRI -99 -99 North America Americas Central America
## 3 SLV -99 -99 North America Americas Central America
## 4 GTM -99 -99 North America Americas Central America
## 5 HND -99 -99 North America Americas Central America
## 6 MEX -99 -99 North America Americas Central America
## REGION_WB NAME_LEN LONG_LEN ABBREV_LEN TINY HOMEPART MIN_ZOOM
## 1 Latin America & Caribbean 6 6 6 -99 1 0
## 2 Latin America & Caribbean 10 10 4 -99 1 0
## 3 Latin America & Caribbean 11 11 6 -99 1 0
## 4 Latin America & Caribbean 9 9 5 4 1 0
## 5 Latin America & Caribbean 8 8 5 -99 1 0
## 6 Latin America & Caribbean 6 6 4 -99 1 0
## MIN_LABEL MAX_LABEL LABEL_X LABEL_Y NE_ID WIKIDATAID NAME_AR
## 1 5.0 10.0 -88.71296 17.20207 1159320431 Q242 بليز
## 2 2.5 8.0 -84.07792 10.06510 1159320525 Q800 كوستاريكا
## 3 5.0 10.0 -88.89012 13.68537 1159321253 Q792 السلفادور
## 4 3.0 8.0 -90.49713 14.98213 1159320815 Q774 غواتيمالا
## 5 4.5 9.5 -86.88760 14.79480 1159320827 Q783 هندوراس
## 6 2.0 6.7 -102.28945 23.91999 1159321055 Q96 المكسيك
## NAME_BN NAME_DE NAME_EN NAME_ES NAME_FA NAME_FR
## 1 বেলিজ Belize Belize Belice بلیز Belize
## 2 কোস্টা রিকা Costa Rica Costa Rica Costa Rica کاستاریکا Costa Rica
## 3 এল সালভাদোর El Salvador El Salvador El Salvador السالوادور Salvador
## 4 গুয়াতেমালা Guatemala Guatemala Guatemala گواتمالا Guatemala
## 5 হন্ডুরাস Honduras Honduras Honduras هندوراس Honduras
## 6 মেক্সিকো Mexiko Mexico México مکزیک Mexique
## NAME_EL NAME_HE NAME_HI NAME_HU NAME_ID NAME_IT
## 1 Μπελίζ בליז बेलीज़ Belize Belize Belize
## 2 Κόστα Ρίκα קוסטה ריקה कोस्टा रीका Costa Rica Kosta Rika Costa Rica
## 3 Ελ Σαλβαδόρ אל סלוודור अल साल्वाडोर Salvador El Salvador El Salvador
## 4 Γουατεμάλα גואטמלה ग्वाटेमाला Guatemala Guatemala Guatemala
## 5 Ονδούρα הונדורס हौण्डुरस Honduras Honduras Honduras
## 6 Μεξικό מקסיקו मेक्सिको Mexikó Meksiko Messico
## NAME_JA NAME_KO NAME_NL NAME_PL NAME_PT NAME_RU
## 1 ベリーズ 벨리즈 Belize Belize Belize Белиз
## 2 コスタリカ 코스타리카 Costa Rica Kostaryka Costa Rica Коста-Рика
## 3 エルサルバドル 엘살바도르 El Salvador Salwador El Salvador Сальвадор
## 4 グアテマラ 과테말라 Guatemala Gwatemala Guatemala Гватемала
## 5 ホンジュラス 온두라스 Honduras Honduras Honduras Гондурас
## 6 メキシコ 멕시코 Mexico Meksyk México Мексика
## NAME_SV NAME_TR NAME_UK NAME_UR NAME_VI NAME_ZH
## 1 Belize Belize Беліз بیلیز Belize 伯利兹
## 2 Costa Rica Kosta Rika Коста-Рика کوسٹاریکا Costa Rica 哥斯达黎加
## 3 El Salvador El Salvador Сальвадор ایل سیلواڈور El Salvador 萨尔瓦多
## 4 Guatemala Guatemala Гватемала گواتیمالا Guatemala 危地马拉
## 5 Honduras Honduras Гондурас ہونڈوراس Honduras 洪都拉斯
## 6 Mexiko Meksika Мексика میکسیکو México 墨西哥
## NAME_ZHT FCLASS_ISO TLC_DIFF FCLASS_TLC FCLASS_US FCLASS_FR
## 1 貝里斯 Admin-0 country <NA> Admin-0 country <NA> <NA>
## 2 哥斯大黎加 Admin-0 country <NA> Admin-0 country <NA> <NA>
## 3 薩爾瓦多 Admin-0 country <NA> Admin-0 country <NA> <NA>
## 4 瓜地馬拉 Admin-0 country <NA> Admin-0 country <NA> <NA>
## 5 宏都拉斯 Admin-0 country <NA> Admin-0 country <NA> <NA>
## 6 墨西哥 Admin-0 country <NA> Admin-0 country <NA> <NA>
## FCLASS_RU FCLASS_ES FCLASS_CN FCLASS_TW FCLASS_IN FCLASS_NP FCLASS_PK
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## FCLASS_DE FCLASS_GB FCLASS_BR FCLASS_IL FCLASS_PS FCLASS_SA FCLASS_EG
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## FCLASS_MA FCLASS_PT FCLASS_AR FCLASS_JP FCLASS_KO FCLASS_VN FCLASS_TR
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## FCLASS_ID FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## FCLASS_UA Mammals Birds Reptiles Amphibians Fishes Molluscs Other Inverts
## 1 <NA> 10 7 8 2 67 0 27
## 2 <NA> 12 24 16 56 138 2 57
## 3 <NA> 6 9 10 8 52 0 11
## 4 <NA> 16 18 39 96 98 2 36
## 5 <NA> 10 17 60 70 89 0 36
## 6 <NA> 97 67 106 232 305 14 152
## Plants Fungi Chromists Total geometry
## 1 114 0 0 235 MULTIPOLYGON (((-89.14308 1...
## 2 463 6 0 774 MULTIPOLYGON (((-82.5462 9....
## 3 106 0 0 202 MULTIPOLYGON (((-89.35333 1...
## 4 470 1 0 776 MULTIPOLYGON (((-92.22775 1...
## 5 214 2 0 498 MULTIPOLYGON (((-83.14722 1...
## 6 1632 7 0 2614 MULTIPOLYGON (((-117.1278 3...library(ggplot2)
# Create map
ggplot(data = merged_data) +
geom_sf(aes(fill = Total)) +
scale_fill_gradient(low = "pink", high = "darkblue", name = "Threatened Species") +
labs(title = "Total Threatened Species in Mesoamerica",
caption = "Source: IUCN Red List") +
theme_minimal()
# Define the function
plotspecies <- function(species_data, map_data, column_name) {
# Merge datasets
merged_data <- merge(map_data, species_data, by.x = "ADMIN", by.y = "Name")
# Create map
ggplot(data = merged_data) +
geom_sf(aes_string(fill = column_name)) +
scale_fill_gradient(low = "pink", high = "darkblue", name = paste(column_name, "Species")) +
labs(title = paste("Threatened", column_name, "in Mesoamerica"),
caption = "Source: IUCN Red List") +
theme_minimal()
}
# Test "Mammals"
plotspecies(species_data, filtered_map, "Mammals")## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Bird Plot
plotspecies(species_data, filtered_map, "Birds")
# Fish Plot
plotspecies(species_data, filtered_map, "Fishes")
# Plant Plot
plotspecies(species_data, filtered_map, "Plants")
# Find the country with most threatened species per category
most_total <- species_data[which.max(species_data$Total), "Name"]
most_mammals <- species_data[which.max(species_data$Mammals), "Name"]
most_birds <- species_data[which.max(species_data$Birds), "Name"]
most_fishes <- species_data[which.max(species_data$Fishes), "Name"]
most_plants <- species_data[which.max(species_data$Plants), "Name"]
# Print results
list(
Most_Threatened_Total = most_total,
Most_Threatened_Mammals = most_mammals,
Most_Threatened_Birds = most_birds,
Most_Threatened_Fishes = most_fishes,
Most_Threatened_Plants = most_plants
)## $Most_Threatened_Total
## # A tibble: 1 × 1
## Name
## <chr>
## 1 Mexico
##
## $Most_Threatened_Mammals
## # A tibble: 1 × 1
## Name
## <chr>
## 1 Mexico
##
## $Most_Threatened_Birds
## # A tibble: 1 × 1
## Name
## <chr>
## 1 Mexico
##
## $Most_Threatened_Fishes
## # A tibble: 1 × 1
## Name
## <chr>
## 1 Mexico
##
## $Most_Threatened_Plants
## # A tibble: 1 × 1
## Name
## <chr>
## 1 MexicoObservations
- Most Threatened Total Species: Mexico has the highest total number of threatened species. This might be due to Mexico’s high biodiversity and diverse ecosystems, which are under significant environmental pressure.
- Most Threatened Mammals: Mexico leads in threatened mammals, possibly due to habitat loss and human activities affecting wildlife habitats.
- Most Threatened Birds: Mexico also has the most threatened bird species, likely because of deforestation and climate change affecting migratory patterns and nesting areas.
- Most Threatened Fishes: For fishes, the high threat level in Mexico could be attributed to overfishing, water pollution, and loss of aquatic habitats.
- Most Threatened Plants: The large number of threatened plant species might be due to deforestation, agricultural expansion, and urbanization impacting native plant species.
Conclusion
Mexico is leading in the number of threatened species across all categories shows its role as a biodiversity hotspot, with diverse ecosystems ranging from deserts and forests to coastal regions and marine environments. Habitat loss, pollution, climate change, and urbanization continue to put pressure on its organisms. The findings demonstrate the need for conservation efforts to Mexico’s ecosystems and the other ecosystems in Mesoamerica. Protecting Mexico’s biodiversity is not only essential for preserving its natural heritage but also for maintaining the ecological balance critical to the region’s resilience in the face of global environmental change.
With this, your assignment is nearly complete! Let me know if you need help refining anything or if you’re ready to knit your R Markdown file into an HTML report.