{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Kernel\n", "\n", "The issue seems to be that R is not found in your current PATH, which is necessary for running R from the terminal. This might be because R is not installed in the virtual environment or the path to the system-wide R installation is not included in your PATH.\n", "\n", "Here are the steps to resolve this:\n", "\n", "### Step 1: Ensure R is Installed and Accessible\n", "1. **Check if R is installed system-wide**:\n", " ```bash\n", " which R\n", " ```\n", " This should return the path to the R executable, such as `/usr/local/bin/R`. If it doesn’t, you may need to install R.\n", "\n", "2. **Install R** (if not already installed):\n", " - Download and install R from [CRAN](https://cran.r-project.org/mirrors.html).\n", "\n", "### Step 2: Add R to the PATH`\n", "1. **Find the path to the R executable**:\n", " ```bash\n", " which R\n", " ```\n", " Assume it returns `/usr/local/bin/R`.\n", "\n", "2. **Add the R path to your PATH environment variable**:\n", " ```bash\n", " export PATH=/usr/local/bin:$PATH`\n", " ```\n", "\n", "3. **Add the R path to your shell configuration file** (`~/.zshrc` if you are using zsh):\n", " ```bash\n", " echo 'export PATH=/usr/local/bin:$PATH' >> ~/.zshrc\n", " ```\n", "\n", "4. **Reload the shell configuration file**:\n", " ```bash\n", " source ~/.zshrc\n", " ```\n", "\n", "### Step 3: Register the IRKernel Again\n", "1. **Activate your virtual environment** (if not already active):\n", " ```bash\n", " source /Users/apollo/documents/athena/myenv/bin/activate\n", " ```\n", "\n", "2. **Ensure Jupyter is installed**:\n", " ```bash\n", " pip install jupyter jupyter-client\n", " ```\n", "\n", "3. **Open R from the terminal**:\n", " ```bash\n", " R\n", " ```\n", "\n", "4. **Run the IRKernel installation command**:\n", " ```R\n", " IRkernel::installspec(user = TRUE)\n", " ```\n", "\n", "### Verify Kernel Registration\n", "1. **Check available Jupyter kernels**:\n", " ```bash\n", " jupyter kernelspec list\n", " ```\n", "\n", "These steps should ensure that R is accessible from the terminal and allow you to properly register the IRKernel for use in Jupyter Notebooks within VSCode." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [ "hide-input" ], "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Hello, Jupyter!\"\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 x 2
xy
<dbl><dbl>
1-0.5371955-0.0541714
2-0.1065361-0.9117403
3-1.1484578 0.5542809
4 0.3696978 0.4301771
5 0.1404496-1.7273244
6 0.7060422-0.4229898
\n" ], "text/latex": [ "A data.frame: 6 x 2\n", "\\begin{tabular}{r|ll}\n", " & x & y\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & -0.5371955 & -0.0541714\\\\\n", "\t2 & -0.1065361 & -0.9117403\\\\\n", "\t3 & -1.1484578 & 0.5542809\\\\\n", "\t4 & 0.3696978 & 0.4301771\\\\\n", "\t5 & 0.1404496 & -1.7273244\\\\\n", "\t6 & 0.7060422 & -0.4229898\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 x 2\n", "\n", "| | x <dbl> | y <dbl> |\n", "|---|---|---|\n", "| 1 | -0.5371955 | -0.0541714 |\n", "| 2 | -0.1065361 | -0.9117403 |\n", "| 3 | -1.1484578 | 0.5542809 |\n", "| 4 | 0.3696978 | 0.4301771 |\n", "| 5 | 0.1404496 | -1.7273244 |\n", "| 6 | 0.7060422 | -0.4229898 |\n", "\n" ], "text/plain": [ " x y \n", "1 -0.5371955 -0.0541714\n", "2 -0.1065361 -0.9117403\n", "3 -1.1484578 0.5542809\n", "4 0.3696978 0.4301771\n", "5 0.1404496 -1.7273244\n", "6 0.7060422 -0.4229898" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean of x: -0.2240241 \n", "Mean of y: 0.07943484 \n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# Install necessary packages if not already installed\n", "if (!requireNamespace(\"IRkernel\", quietly = TRUE)) {\n", " install.packages(\"IRkernel\", repos = \"http://cran.us.r-project.org\")\n", " IRkernel::installspec()\n", "}\n", "\n", "if (!requireNamespace(\"ggplot2\", quietly = TRUE)) {\n", " install.packages(\"ggplot2\", repos = \"http://cran.us.r-project.org\")\n", "}\n", "\n", "if (!requireNamespace(\"dplyr\", quietly = TRUE)) {\n", " install.packages(\"dplyr\", repos = \"http://cran.us.r-project.org\")\n", "}\n", "\n", "# Load libraries with suppressed messages\n", "suppressPackageStartupMessages(library(ggplot2))\n", "suppressPackageStartupMessages(library(dplyr))\n", "\n", "# Print a simple message\n", "print(\"Hello, Jupyter!\")\n", "\n", "# Create a simple data frame\n", "data <- data.frame(\n", " x = rnorm(100),\n", " y = rnorm(100)\n", ")\n", "\n", "# Display the first few rows of the data frame\n", "head(data)\n", "\n", "# Create a simple plot\n", "ggplot(data, aes(x = x, y = y)) +\n", " geom_point() +\n", " ggtitle(\"Scatter Plot of Random Data\")\n", "\n", "# Perform a simple calculation\n", "mean_x <- mean(data$x)\n", "mean_y <- mean(data$y)\n", "\n", "# Print the results\n", "cat(\"Mean of x:\", mean_x, \"\\n\")\n", "cat(\"Mean of y:\", mean_y, \"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "2" ], "text/latex": [ "2" ], "text/markdown": [ "2" ], "text/plain": [ "[1] 2" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "1 + 1" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.4.0" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }