.gitignore

@@ -1,10 +0,0 @@
-# ignore the R studio docker image needed by hyak 
-rstudio_latest.sif
-
-
-# do not need to include any R items 
-.Rhistory
-.cache/
-.config/
-.local/
-

bot_activity_analysis/bot_activity_exploration.R

@@ -1,37 +0,0 @@
-library(tidyverse)
-# data directory: /gscratch/comdata/users/mjilg/mw-repo-lifecycles/bot_activity_counts
-# load in the paritioned directories
-library(dplyr)
-monthly_file_dir = "/gscratch/comdata/users/mjilg/mw-repo-lifecycles/bot_activity_counts/011625_dab_monthly/"
-yearly_file_dir = "/gscratch/comdata/users/mjilg/mw-repo-lifecycles/bot_activity_counts/011625_dab_yearly/"
-single_file_dir = "/gscratch/comdata/users/mjilg/mw-repo-lifecycles/bot_activity_counts/011625_dab_single/"
-column_names <- c("wiki_db", "date", "event_entity", "event_action", "count")
-# define a function to combing the multiple csv files in each directory
-consolidate_csv <- function(directory, column_names) {
-  file_list <- list.files(path = directory, pattern = "*.csv", full.names = TRUE)
-  df_list <- lapply(file_list, function(file){
-    df = read.csv(file, header = FALSE) 
-    colnames(df) <- column_names
-    return(df)
-    })
-  combined_df <- do.call(rbind, df_list)
-  return(combined_df)
-}
-#apply the function to our three directories of data 
-monthly_df <- consolidate_csv(monthly_file_dir, column_names)
-yearly_df <- consolidate_csv(yearly_file_dir, column_names)
-single_df <- consolidate_csv(single_file_dir, column_names)
-#rbind 
-combined_df <- rbind(monthly_df, yearly_df, single_df)
-rm(monthly_df)
-rm(yearly_df)
-rm(single_df)
-#making sure data columns are of the right type
-combined_df <- combined_df |> 
-  mutate( 
-    wiki_db = as.factor(wiki_db),
-    date = as.Date(date),
-    event_entity = as.factor(event_entity),
-    event_action = as.factor(event_action),
-    count = as.numeric(count)
-  )

rstudio-server.job

@@ -1,100 +0,0 @@
-#!/bin/sh
-
-#SBATCH --job-name=mgaughan-rstudio-server
-#SBATCH --partition=cpu-g2-mem2x
-
-#SBATCH --time=02:00:00
-#SBATCH --nodes=1
-#SBATCH --ntasks=4
-#SBATCH --mem=20G
-
-#SBATCH --signal=USR2
-#SBATCH --output=%x_%j.out
-
-# This script will request a single CPU with four threads with 20GB of RAM for 2 hours. 
-# You can adjust --time, --nodes, --ntasks, and --mem above to adjust these settings for your session.
-
-# --output=%x_%j.out creates a output file called rstudio-server_XXXXXXXX.out 
-# where the %x is short hand for --job-name above and the X's are an 8-digit 
-# jobID assigned by SLURM when our job is submitted.
-
-RSTUDIO_CWD="/mmfs1/home/mjilg/git/mw-lifecycle-analysis"
-RSTUDIO_SIF="rstudio_latest.sif"
-
-# Create temp directory for ephemeral content to bind-mount in the container
-RSTUDIO_TMP=$(/usr/bin/python3 -c 'import tempfile; print(tempfile.mkdtemp())')
-
-mkdir -p -m 700 \
-        ${RSTUDIO_TMP}/run \
-        ${RSTUDIO_TMP}/tmp \
-        ${RSTUDIO_TMP}/var/lib/rstudio-server
-
-cat > ${RSTUDIO_TMP}/database.conf <<END
-provider=sqlite
-directory=/var/lib/rstudio-server
-END
-
-# Set OMP_NUM_THREADS to prevent OpenBLAS (and any other OpenMP-enhanced
-# libraries used by R) from spawning more threads than the number of processors
-# allocated to the job.
-#
-# Set R_LIBS_USER to a path specific to rocker/rstudio to avoid conflicts with
-# personal libraries from any R installation in the host environment
-
-cat > ${RSTUDIO_TMP}/rsession.sh <<END
-#!/bin/sh
-
-export OMP_NUM_THREADS=${SLURM_JOB_CPUS_PER_NODE}
-export R_LIBS_USER=/gscratch/scrubbed/mjilg/R
-exec /usr/lib/rstudio-server/bin/rsession "\${@}"
-END
-
-chmod +x ${RSTUDIO_TMP}/rsession.sh
-
-export APPTAINER_BIND="${RSTUDIO_CWD}:${RSTUDIO_CWD},/gscratch:/gscratch,${RSTUDIO_TMP}/run:/run,${RSTUDIO_TMP}/tmp:/tmp,${RSTUDIO_TMP}/database.conf:/etc/rstudio/database.conf,${RSTUDIO_TMP}/rsession.sh:/etc/rstudio/rsession.sh,${RSTUDIO_TMP}/var/lib/rstudio-server:/var/lib/rstudio-server"
-
-# Do not suspend idle sessions.
-# Alternative to setting session-timeout-minutes=0 in /etc/rstudio/rsession.conf
-export APPTAINERENV_RSTUDIO_SESSION_TIMEOUT=0
-
-export APPTAINERENV_USER=$(id -un)
-export APPTAINERENV_PASSWORD=$(openssl rand -base64 15)
-
-# get unused socket per https://unix.stackexchange.com/a/132524
-# tiny race condition between the python & apptainer commands
-readonly PORT=$(/mmfs1/sw/pyenv/versions/3.9.5/bin/python -c 'import socket; s=socket.socket(); s.bind(("", 0)); print(s.getsockname()[1]); s.close()')
-cat 1>&2 <<END
-1. SSH tunnel from your workstation using the following command:
-
-   ssh -N -L 8787:${HOSTNAME}:${PORT} ${APPTAINERENV_USER}@klone.hyak.uw.edu
-
-   and point your web browser to http://localhost:8787
-
-2. log in to RStudio Server using the following credentials:
-
-   user: ${APPTAINERENV_USER}
-   password: ${APPTAINERENV_PASSWORD}
-
-When done using RStudio Server, terminate the job by:
-
-1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
-2. Issue the following command on the login node:
-
-      scancel -f ${SLURM_JOB_ID}
-END
-
-source /etc/bashrc
-module load apptainer
-
-apptainer exec --cleanenv --home ${RSTUDIO_CWD} ${RSTUDIO_CWD}/${RSTUDIO_SIF} \
-    rserver --www-port ${PORT} \
-            --auth-none=0 \
-            --auth-pam-helper-path=pam-helper \
-            --auth-stay-signed-in-days=30 \
-            --auth-timeout-minutes=0 \
-            --rsession-path=/etc/rstudio/rsession.sh \
-            --server-user=${APPTAINERENV_USER}
-
-APPTAINER_EXIT_CODE=$?
-echo "rserver exited $APPTAINER_EXIT_CODE" 1>&2
-exit $APPTAINER_EXIT_CODE