Publications by Patrick Ford
GSMNP Compass Declination
# Load necessary libraries pacman::p_load(pacman, readr, tidyverse, ggplot2, dplyr, gridExtra, sf, akima, rnaturalearth, rnaturalearthdata) # Load the dataset data <- read.csv("GRSM_COMPASS_DECLINATION_5646692726181302872.csv") # Convert the data into an sf object coordinates <- st_as_sf(data, coords = c("LON", "LAT"), crs = 4326) # Get natural ...
14 sym 5 img
North & South Pole Movements: IGRF 2D
# Load necessary libraries pacman::p_load(pacman, readr, tidyverse, ggplot2, viridis) # Function to load, process, and split the dataset load_and_process_data <- function(file_path) { # Load the dataset data <- read.csv(file_path, header = FALSE, col.names = "Values") # Split the 'Values' column into Longitude, Latitude, and Year data <- ...
7 sym Python (1885 sym/2 pcs) 2 img
North & South Pole Movements: IGRF Globe
# Load necessary libraries pacman::p_load(pacman, readr, tidyverse, ggplot2, viridis, maps, mapproj) # Function to load, process, and split the dataset load_and_process_data <- function(file_path) { # Load the dataset data <- read.csv(file_path, header = FALSE, col.names = "Values") # Split the 'Values' column into Longitude, Latitude, and ...
7 sym Python (2570 sym/2 pcs) 2 img
Americans Belief of Spiritual Concepts
# Load necessary libraries pacman::p_load(pacman, readr,tidyverse, dplyr, ggplot2, gridExtra, tidyr, lubridate, stringr) # Load the data data_TRrb5 <- read.csv("data-TRrb5.csv", header = TRUE, stringsAsFactors = FALSE) data_1ArwA <- read.csv("data-1ArwA.csv", header = TRUE, stringsAsFactors = FALSE) data_LbYTZ <- read.csv("data-LbYTZ.csv", header ...
10 sym 3 img
Cancer: Bar, Scatter plots and Summary table
# Load necessary libraries pacman::p_load(pacman, readr, dplyr, ggplot2, gridExtra) # Load the data GDO_data_wide <- read_csv("GDO_data_wide.csv") ## Rows: 42664 Columns: 177 ## ── Column specification ───────────────────────────────────────────────�...
22 sym Python (7642 sym/17 pcs) 2 img
Histogram and Box Plots
# Load necessary libraries pacman::p_load(pacman, readr, dplyr, ggplot2, gridExtra, scales) # Load the data GDO_data_wide <- read_csv("GDO_data_wide.csv") ## Rows: 42664 Columns: 177 ## ── Column specification ─────────────────────────────────────────────...
40 sym Python (6412 sym/33 pcs) 4 img
Cancer Bar Plot (side by side(dodge))
# Load necessary libraries pacman::p_load(pacman, readr, dplyr, ggplot2, gridExtra, tidyr) # Load the data GDO_data_wide <- read_csv("GDO_data_wide.csv") ## Rows: 42664 Columns: 177 ## ── Column specification ─────────────────────────────────────────────�...
14 sym 1 img
Mining and Refining
# Load necessary libraries pacman::p_load(pacman, readr, dplyr, ggplot2, tidyr, sf, skimr) # Load the data mining_refining <- read.csv("mining_refining.csv") # Drop NA values in the 'value' column mining_refining <- mining_refining %>% drop_na(value) # Convert Country to a factor with specified levels (excluding "NA") mining_refining$Country <- ...
15 sym 6 img
Time Spent on Social Media
# Load required libraries pacman::p_load(pacman, readr, ggplot2, lubridate, scales, dplyr, gridExtra) # Read the data DT <- read_csv("DT.csv") ## Rows: 13 Columns: 2 ## ── Column specification ──────────────────────────────────────────────────�...
10 sym Python (4330 sym/6 pcs) 1 img
Chilli Heat (SHU) by Variety
# Load necessary libraries pacman::p_load(pacman, ggplot2, dplyr, scales, gridExtra, packcircles) # Load the data chilli_data <- read.csv("chile-variety-heat-levels.csv") # Clean up column names colnames(chilli_data) <- c("Chilli_Name", "Lower_Bound", "Middle", "Upper_Bound") # Plot of Heat Levels p1 <- ggplot(chilli_data, aes(x = reorder(Chil...
12 sym Python (5523 sym/5 pcs) 4 img