World Crocodile Species_final
Karigirwa Narcisse | The Analyst
2025-10-14
Introduction
This report provides an exploratory analysis of the global crocodile species dataset. It includes data loading, summary statistics, and visualizations.
Overall Insights
The dataset provides a comprehensive view of species diversity, ecological distribution, and population characteristics of crocodiles.
Certain species and families dominate observations, indicating either higher abundance or observer focus.
Quantitative traits such as length and weight align with biological expectations, while temporal and habitat-based patterns reveal ecological and observational trends.
Observer activity analysis highlights the contribution of a few key individuals in collecting most of the data.
##Install necessary packages
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## not free for commercial and Governmental use1. Load World Crocodile Species Dataset
crocodile_species <- read.csv("crocodile_dataset.csv")2. Dataset Overview: Glimpes of the Data, Column names and overall summary
glimpse(crocodile_species)## Rows: 1,000
## Columns: 15
## $ Observation.ID <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
## $ Common.Name <chr> "Morelet's Crocodile", "American Crocodile", "Ori…
## $ Scientific.Name <chr> "Crocodylus moreletii", "Crocodylus acutus", "Cro…
## $ Family <chr> "Crocodylidae", "Crocodylidae", "Crocodylidae", "…
## $ Genus <chr> "Crocodylus", "Crocodylus", "Crocodylus", "Crocod…
## $ Observed.Length..m. <dbl> 1.90, 4.09, 1.08, 2.42, 3.75, 2.64, 2.85, 0.35, 3…
## $ Observed.Weight..kg. <dbl> 62.0, 334.5, 118.2, 90.4, 269.4, 137.4, 157.7, 4.…
## $ Age.Class <chr> "Adult", "Adult", "Juvenile", "Adult", "Adult", "…
## $ Sex <chr> "Male", "Male", "Unknown", "Male", "Unknown", "Ma…
## $ Date.of.Observation <chr> "31-03-2018", "28-01-2015", "07-12-2010", "01-11-…
## $ Country.Region <chr> "Belize", "Venezuela", "Venezuela", "Mexico", "In…
## $ Habitat.Type <chr> "Swamps", "Mangroves", "Flooded Savannas", "River…
## $ Conservation.Status <chr> "Least Concern", "Vulnerable", "Critically Endang…
## $ Observer.Name <chr> "Allison Hill", "Brandon Hall", "Melissa Peterson…
## $ Notes <chr> "Cause bill scientist nation opportunity.", "Ago …colnames(crocodile_species)## [1] "Observation.ID" "Common.Name" "Scientific.Name"
## [4] "Family" "Genus" "Observed.Length..m."
## [7] "Observed.Weight..kg." "Age.Class" "Sex"
## [10] "Date.of.Observation" "Country.Region" "Habitat.Type"
## [13] "Conservation.Status" "Observer.Name" "Notes"summary(crocodile_species)## Observation.ID Common.Name Scientific.Name Family
## Min. : 1.0 Length:1000 Length:1000 Length:1000
## 1st Qu.: 250.8 Class :character Class :character Class :character
## Median : 500.5 Mode :character Mode :character Mode :character
## Mean : 500.5
## 3rd Qu.: 750.2
## Max. :1000.0
## Genus Observed.Length..m. Observed.Weight..kg. Age.Class
## Length:1000 Min. :0.140 Min. : 4.40 Length:1000
## Class :character 1st Qu.:1.637 1st Qu.: 53.23 Class :character
## Mode :character Median :2.430 Median : 100.60 Mode :character
## Mean :2.415 Mean : 155.77
## 3rd Qu.:3.010 3rd Qu.: 168.88
## Max. :6.120 Max. :1139.70
## Sex Date.of.Observation Country.Region Habitat.Type
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Conservation.Status Observer.Name Notes
## Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## head(crocodile_species)## Observation.ID Common.Name Scientific.Name
## 1 1 Morelet's Crocodile Crocodylus moreletii
## 2 2 American Crocodile Crocodylus acutus
## 3 3 Orinoco Crocodile Crocodylus intermedius
## 4 4 Morelet's Crocodile Crocodylus moreletii
## 5 5 Mugger Crocodile (Marsh Crocodile) Crocodylus palustris
## 6 6 Mugger Crocodile (Marsh Crocodile) Crocodylus palustris
## Family Genus Observed.Length..m. Observed.Weight..kg. Age.Class
## 1 Crocodylidae Crocodylus 1.90 62.0 Adult
## 2 Crocodylidae Crocodylus 4.09 334.5 Adult
## 3 Crocodylidae Crocodylus 1.08 118.2 Juvenile
## 4 Crocodylidae Crocodylus 2.42 90.4 Adult
## 5 Crocodylidae Crocodylus 3.75 269.4 Adult
## 6 Crocodylidae Crocodylus 2.64 137.4 Adult
## Sex Date.of.Observation Country.Region Habitat.Type
## 1 Male 31-03-2018 Belize Swamps
## 2 Male 28-01-2015 Venezuela Mangroves
## 3 Unknown 07-12-2010 Venezuela Flooded Savannas
## 4 Male 01-11-2019 Mexico Rivers
## 5 Unknown 15-07-2019 India Rivers
## 6 Male 08-06-2023 India Reservoirs
## Conservation.Status Observer.Name
## 1 Least Concern Allison Hill
## 2 Vulnerable Brandon Hall
## 3 Critically Endangered Melissa Peterson
## 4 Least Concern Edward Fuller
## 5 Vulnerable Donald Reid
## 6 Vulnerable Randy Brown
## Notes
## 1 Cause bill scientist nation opportunity.
## 2 Ago current practice nation determine operation speak according.
## 3 Democratic shake bill here grow gas enough analysis least by two.
## 4 Officer relate animal direction eye bag do.
## 5 Class great prove reduce raise author play move each left establish understand read detail.
## 6 Source husband at tree note responsibility defense.3. Data Cleaning
3.1. Check if there are any NAs at all
any(is.na(crocodile_species))## [1] FALSE3.2. Replace all dots with underscores in column names and Check if all names has been chenged
# Use==>> colnames(crocodile_species) <- gsub("\\.", "_", colnames(crocodile_species))
# OR use clean_names(): my favorite function because it replaces both . by _ and uppercase to undercase letters
crocodile_species <- crocodile_species %>%
clean_names()
# Check if all names has been changed accordingly
colnames(crocodile_species)## [1] "observation_id" "common_name" "scientific_name"
## [4] "family" "genus" "observed_length_m"
## [7] "observed_weight_kg" "age_class" "sex"
## [10] "date_of_observation" "country_region" "habitat_type"
## [13] "conservation_status" "observer_name" "notes"3.3. Change/Convert char columns into numeric columns
crocodile_species <- crocodile_species %>%
mutate(observed_length_m = as.numeric(observed_length_m),
observed_weight_kg = as.numeric(observed_weight_kg))3.4. Change/Convert Date order from DD-MM-YYY to YYY-MM-DD standard
#Convert Date
crocodile_species <- crocodile_species %>%
mutate( date_of_observation = as.Date(date_of_observation, format("%d-%m-%y")))
# Check the changes
head(crocodile_species)## observation_id common_name scientific_name
## 1 1 Morelet's Crocodile Crocodylus moreletii
## 2 2 American Crocodile Crocodylus acutus
## 3 3 Orinoco Crocodile Crocodylus intermedius
## 4 4 Morelet's Crocodile Crocodylus moreletii
## 5 5 Mugger Crocodile (Marsh Crocodile) Crocodylus palustris
## 6 6 Mugger Crocodile (Marsh Crocodile) Crocodylus palustris
## family genus observed_length_m observed_weight_kg age_class
## 1 Crocodylidae Crocodylus 1.90 62.0 Adult
## 2 Crocodylidae Crocodylus 4.09 334.5 Adult
## 3 Crocodylidae Crocodylus 1.08 118.2 Juvenile
## 4 Crocodylidae Crocodylus 2.42 90.4 Adult
## 5 Crocodylidae Crocodylus 3.75 269.4 Adult
## 6 Crocodylidae Crocodylus 2.64 137.4 Adult
## sex date_of_observation country_region habitat_type
## 1 Male 2020-03-31 Belize Swamps
## 2 Male 2020-01-28 Venezuela Mangroves
## 3 Unknown 2020-12-07 Venezuela Flooded Savannas
## 4 Male 2020-11-01 Mexico Rivers
## 5 Unknown 2020-07-15 India Rivers
## 6 Male 2020-06-08 India Reservoirs
## conservation_status observer_name
## 1 Least Concern Allison Hill
## 2 Vulnerable Brandon Hall
## 3 Critically Endangered Melissa Peterson
## 4 Least Concern Edward Fuller
## 5 Vulnerable Donald Reid
## 6 Vulnerable Randy Brown
## notes
## 1 Cause bill scientist nation opportunity.
## 2 Ago current practice nation determine operation speak according.
## 3 Democratic shake bill here grow gas enough analysis least by two.
## 4 Officer relate animal direction eye bag do.
## 5 Class great prove reduce raise author play move each left establish understand read detail.
## 6 Source husband at tree note responsibility defense.4. Data Visualization
4.1. Categorical Distributions Analysis
4.1.1. Most Observed Crocodile Species by Country of it’s origin
# 📦Load necessary libraries for visualization
library(dplyr)
library(rnaturalearth)
library(rnaturalearthdata)##
## Attaching package: 'rnaturalearthdata'## The following object is masked from 'package:rnaturalearth':
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## countries110library(sf)## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUElibrary(ggplot2)
library(stringr)
#Remove the word "crocodile" from common_name (case insensitive)
#Count how many times each species appears per country (n = n()).
#.groups = "drop" ungroups the result after summarizing.
#Regroup by country only to find the top species per country.
#slice_max(order_by = n, n = 1)
#Within each country, pick the species with the highest count (n).
top_species <- crocodile_species %>%
group_by(country_region, common_name) %>%
summarize(n = n(), .groups = "drop") %>%
group_by(country_region) %>%
slice_max(order_by = n, n = 1) %>%
mutate(common_name = str_replace_all(common_name, regex("crocodile", ignore_case = TRUE), "") %>%
str_trim())
## Load world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Join top species data with world map
map_data <- left_join(world, top_species, by = c("name" = "country_region"))
# Plot the map
ggplot(map_data) +
geom_sf(aes(fill = common_name), color = " white ") +
labs(title = "Most Observed Crocodile Species by Country of Origin", fill = "") +
theme_minimal() +
theme(legend.position = "bottom")
### Libraries explanations - dplyr: for data manipulation (grouping,
summarizing, filtering). - rnaturalearth & rnaturalearthdata: to
load Natural Earth world map data. - sf: for handling spatial (map) data
using simple features. - ggplot2: for plotting. - stringr: for
string/text manipulation (used to remove “crocodile” from common
names).
4.1.2. Common Name distribution (top 10 species)
library(ggplot2)
ggplot(top_species, aes(x = reorder(common_name, n), y = n)) +
geom_col(fill = " darkcyan ", color = " black ") +
coord_flip() +
labs(title = "Top Most Observed Species", x = "Species", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
## 4.1.3. Age Class Distribution
ggplot(crocodile_species, aes(x = age_class)) +
geom_bar(fill = " darkcyan ", color = " black ") +
labs(title = "Age Class Distribution", x = "age class", y = "count") +
theme_minimal()
4.1.4. Sex Distribution
ggplot(crocodile_species, aes(x = sex)) +
geom_bar(fill = " darkcyan ", color = " black ") +
labs(title = "Sex Distribution", x = "sex", y = "count")
4.1.5. Habitat type distribution
ggplot(crocodile_species, aes(x = habitat_type)) +
geom_bar(fill = " darkcyan ", color = " black ") +
labs(title = "Habitat Type Distribution", x = "habitat type", y = "count") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
4.1.6. Conservation status
ggplot(crocodile_species, aes(x = conservation_status)) +
geom_bar(fill = " darkcyan ", color = " black ") +
labs(title = "Conservation Status", x = "conservation status", y = "count")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
5.2. Quantitative Distributions Analysis
5.2.1. Length distribution
ggplot(crocodile_species, aes(x = observed_length_m)) +
geom_histogram(fill = " darkcyan ", bins = 20, color = " black ") +
labs(title = "Distribution of Length Observation (m)", x = "length observed (m)" , y = "frequency")
5.2.2. Weight distribution
ggplot(crocodile_species, aes(x = observed_weight_kg)) +
geom_histogram(fill = " darkcyan ", bins = 20, color = " black ")+
labs(title = "Distribution of weight observed (kg)", x = "weight observed (kg)", y ="frequency")
5.2.3. Length vs Weight Analysis
ggplot(crocodile_species, aes(x = observed_length_m, y = observed_weight_kg)) +
geom_point(alpha = 0.6, color = " darkcyan ") +
geom_smooth(method = "lm", se = FALSE, color = " red ") +
labs(title = "Observed Length vs Weight", x = "Length (m)", y = "Weight (kg)")## `geom_smooth()` using formula = 'y ~ x'
5.2.4. Crocodiles Weight by Age Class
ggplot(crocodile_species, aes(x = reorder(age_class, observed_weight_kg, median), y = observed_weight_kg, fill = age_class)) +
geom_boxplot() +
labs(title = "Crocodile Weight By Age Class (Ascending Order)", x = "Age Class", y = "Weight (kg)") +
theme_minimal() +
theme(legend.position = "right")
5.2.4. Crocodile Length by sex
ggplot(crocodile_species, aes(x = sex,y = observed_length_m, fill = sex)) +
geom_boxplot() +
labs(title = "Crocodile Length by Sex", x = "Sex", y = "Length (m)") +
theme_minimal() +
theme(legend.position = "none")
5.3. Temporal Analysis
5.3.1 Crocodile Species Observations over time
ggplot(crocodile_species, aes(x = date_of_observation)) +
geom_histogram(fill = " darkcyan ", binwidth = 30, color = " black ") +
labs(title = "Number of Crocodile Observation Overtime", x = "date", y = "count")
5.3.2. Relationships Between Categorical Variables
##sorts the habitats by total number of observations (highest to lowest)
ggplot(crocodile_species, aes(x = reorder(habitat_type, -table(habitat_type)[habitat_type]), fill = conservation_status)) +
geom_bar(position = "stack") +
labs(title = "Habitat Type vs Conservation Type", x = "habitat", y = "count") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
5.3.3. Crocodile Age Class vs Sex
##orders from most frequent to least frequent.
ggplot(crocodile_species, aes(x = reorder(age_class, -table(age_class)[age_class]), fill = sex)) +
geom_bar(position = "dodge") +
labs(title = "Age Class vs Sex", x = "age class", y = "count")
## END