Woocommerce - Problem in Google List Plugin - wordpress

i installed Google List Plugin and go to setting
this come with blank loading and not Display anything in setting to can connect my merchant account
i go to Console Tab and see this following MSG
deprecated.min.js?ver=6c963cb9494ba26b77eb:2 woocommerce_admin_onboarding_task_list is deprecated and will be removed from #woocommerce/data in version 2.10.0. Please use TaskLists::add_task() instead.
i also check my woocommerce status Get this following Report
`
`### WordPress Environment ###
WordPress address (URL): https://totepstore.com
Site address (URL): https://totepstore.com
WC Version: 7.3.0
REST API Version: ✔ 7.3.0
WC Blocks Version: ✔ 9.1.5
Action Scheduler Version: ✔ 3.4.0
Log Directory Writable: ✔
WP Version: 6.1.1
WP Multisite: –
WP Memory Limit: 1 جيجابايت
WP Debug Mode: –
WP Cron: ✔
Language: ar
External object cache: –
### Server Environment ###
Server Info: Apache
PHP Version: 8.0.26
PHP Post Max Size: 1 جيجابايت
PHP Time Limit: 3000
PHP Max Input Vars: 3000
cURL Version: 7.84.0
OpenSSL/1.1.1p
SUHOSIN Installed: –
MySQL Version: 10.3.37-MariaDB-log-cll-lve
Max Upload Size: 1 جيجابايت
Default Timezone is UTC: ✔
fsockopen/cURL: ✔
SoapClient: ✔
DOMDocument: ✔
GZip: ✔
Multibyte String: ✔
Remote Post: ✔
Remote Get: ✔
### Database ###
WC Database Version: 7.3.0
WC Database Prefix: wpzm_
إجمالي حجم قاعدة البيانات: 123.97MB
حجم بيانات قاعدة البيانات: 118.59MB
حجم فهرس قاعدة البيانات: 5.38MB
wpzm_woocommerce_sessions: البيانات: 0.14 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_woocommerce_api_keys: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_woocommerce_attribute_taxonomies: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_downloadable_product_permissions: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_order_items: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_order_itemmeta: البيانات: 0.01 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_woocommerce_tax_rates: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_tax_rate_locations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_shipping_zones: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_shipping_zone_locations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_shipping_zone_methods: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_payment_tokens: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_payment_tokenmeta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_woocommerce_log: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_access_key: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_conversations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_conversation_messages: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_extend_products: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_flash_sale: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_hit_products: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_list_categories: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_list_mini_banner: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_mobile_slider: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_mobile_variable: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_notification: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_popular_categories: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
revo_token_firebase: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_acfw_store_credits: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_actionscheduler_actions: البيانات: 1.92 م.ب. + الفهرس: 0.57 م.ب. + المحرك MyISAM
wpzm_actionscheduler_claims: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_actionscheduler_groups: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_actionscheduler_logs: البيانات: 1.10 م.ب. + الفهرس: 0.85 م.ب. + المحرك MyISAM
wpzm_ac_abandoned_cart_history_lite: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_ac_email_templates_lite: البيانات: 0.01 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_ac_guest_abandoned_cart_history_lite: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_ac_sent_history_lite: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_app_builder_cart: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_abandoned_carts: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_customers: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_automatewoo_customer_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_events: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_guests: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_automatewoo_guest_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_logs: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_log_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_queue: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_automatewoo_queue_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_commentmeta: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_comments: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_dokan_announcement: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك InnoDB
wpzm_dokan_orders: البيانات: 0.02 م.ب. + الفهرس: 0.03 م.ب. + المحرك InnoDB
wpzm_dokan_refund: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك InnoDB
wpzm_dokan_reverse_withdrawal: البيانات: 0.02 م.ب. + الفهرس: 0.08 م.ب. + المحرك InnoDB
wpzm_dokan_shipping_tracking: البيانات: 0.02 م.ب. + الفهرس: 0.03 م.ب. + المحرك InnoDB
wpzm_dokan_shipping_zone_locations: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك InnoDB
wpzm_dokan_shipping_zone_methods: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك InnoDB
wpzm_dokan_vendor_balance: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك InnoDB
wpzm_dokan_withdraw: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك InnoDB
wpzm_ewwwio_images: البيانات: 0.50 م.ب. + الفهرس: 0.32 م.ب. + المحرك MyISAM
wpzm_ewwwio_queue: البيانات: 0.02 م.ب. + الفهرس: 0.02 م.ب. + المحرك MyISAM
wpzm_e_events: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_e_submissions: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_e_submissions_actions_log: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_e_submissions_values: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_gla_attribute_mapping_rules: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_gla_budget_recommendations: البيانات: 0.11 م.ب. + الفهرس: 0.12 م.ب. + المحرك MyISAM
wpzm_gla_merchant_issues: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_gla_shipping_rates: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_gla_shipping_times: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_links: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_options: البيانات: 7.89 م.ب. + الفهرس: 0.25 م.ب. + المحرك MyISAM
wpzm_postmeta: البيانات: 16.99 م.ب. + الفهرس: 1.73 م.ب. + المحرك MyISAM
wpzm_posts: البيانات: 86.79 م.ب. + الفهرس: 0.31 م.ب. + المحرك MyISAM
wpzm_revslider_css: البيانات: 0.09 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_revslider_css_bkp: البيانات: 0.01 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_layer_animations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_layer_animations_bkp: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_navigations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_navigations_bkp: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_sliders: البيانات: 0.01 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_sliders_bkp: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_slides: البيانات: 0.12 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_slides_bkp: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_static_slides: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_revslider_static_slides_bkp: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_scalability_pro_cache: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_termmeta: البيانات: 0.92 م.ب. + الفهرس: 0.04 م.ب. + المحرك MyISAM
wpzm_terms: البيانات: 0.07 م.ب. + الفهرس: 0.11 م.ب. + المحرك MyISAM
wpzm_term_relationships: البيانات: 0.09 م.ب. + الفهرس: 0.17 م.ب. + المحرك MyISAM
wpzm_term_taxonomy: البيانات: 0.05 م.ب. + الفهرس: 0.03 م.ب. + المحرك MyISAM
wpzm_uap_affiliates: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_affiliate_referral_users_relations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_banners: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_campaigns: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_coupons_code_affiliates: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_cpm: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_dashboard_notifications: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_landing_commissions: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_mlm_relations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_notifications: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_offers: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_offers_affiliates_reference: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_payments: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_ranks: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_ranks_history: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_referrals: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_ref_links: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_reports: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_uap_visits: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_usermeta: البيانات: 0.14 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_users: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_wcfm_daily_analysis: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_detailed_analysis: البيانات: 0.01 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_wcfm_enquiries: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_enquiries_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_enquiries_response: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_enquiries_response_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_fbc_chat_rows: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_fbc_chat_sessions: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_fbc_chat_visitors: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_fbc_offline_messages: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_following_followers: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_orders: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_orders_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_product_multivendor: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_refund_request: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_refund_request_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_reverse_withdrawal: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_reverse_withdrawal_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_reviews: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_reviews_response: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_reviews_response_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_review_rating_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_shipping_zone_locations: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_shipping_zone_methods: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_store_taxonomies: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_vendor_ledger: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_withdraw_request: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_marketplace_withdraw_request_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_membership_subscription: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_messages: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_messages_modifier: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_messages_stat: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_support: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_support_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_support_response: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wcfm_support_response_meta: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wc_admin_notes: البيانات: 0.02 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wc_admin_note_actions: البيانات: 0.01 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wc_category_lookup: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wc_customer_lookup: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_wc_download_log: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wc_order_coupon_lookup: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wc_order_product_lookup: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_wc_order_stats: البيانات: 0.00 م.ب. + الفهرس: 0.01 م.ب. + المحرك MyISAM
wpzm_wc_order_tax_lookup: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
wpzm_wc_points_rewards_user_points: البيانات: 0.00 م.ب. + الفهرس: 0.00 م.ب. + المحرك MyISAM
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Related

Minimizing weighted sum of matrix while ensuring distribution of outcomes in R

I am struggeling with an optimization problem involving a simple matrix operation. The task is the following: I have a sqare matrix D containing "damage multipliers" stemming from a prodcuction reduction in producing countries (columns) and felt by "receiving" countries (rows).
AUT BEL BGR CYP CZE DEU DNK ESP EST FIN FRA GBR GRC HRV HUN IRL ITA LTU LUX LVA MLT NLD POL PRT ROU SVK SVN SWE
AUT 1.48 0.15 0.18 0.08 0.19 0.22 0.01 0.01 0.02 0.02 0.05 0.01 0.01 0.02 0.14 0.00 0.02 0.03 0.02 0.02 0.00 0.04 0.10 0.09 0.11 0.16 0.17 0.11
BEL 0.03 2.70 0.34 0.09 0.05 0.03 0.02 0.01 0.04 0.09 0.09 0.02 0.01 0.01 0.03 0.01 0.01 0.03 0.08 0.02 0.00 0.04 0.03 0.37 0.09 0.07 0.15 0.29
BGR 0.01 0.02 9.81 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.12 0.01 0.00 0.01
CYP 0.00 0.01 0.01 9.87 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
CZE 0.19 0.11 0.08 0.07 4.14 0.27 0.01 0.00 0.01 0.01 0.03 0.01 0.00 0.00 0.05 0.00 0.03 0.05 0.01 0.01 0.00 0.02 0.32 0.07 0.03 2.57 0.05 0.05
DEU 0.29 2.54 0.27 0.15 0.19 1.71 0.10 0.04 0.06 0.22 0.22 0.09 0.03 0.02 0.11 0.03 0.08 0.12 0.08 0.07 0.00 0.28 0.28 0.55 0.25 0.26 0.11 1.09
DNK 0.01 0.09 0.02 0.09 0.01 0.14 3.43 0.00 0.02 0.12 0.02 0.02 0.00 0.00 0.01 0.00 0.01 0.02 0.01 0.02 0.00 0.01 0.03 0.05 0.01 0.01 0.01 1.39
ESP 0.02 0.26 0.06 0.05 0.02 0.03 0.02 2.72 0.45 0.04 0.22 0.05 0.04 0.01 0.01 0.05 0.06 0.02 0.01 0.01 0.00 0.02 0.03 1.28 0.05 0.02 0.01 0.32
EST 0.00 0.01 0.00 0.03 0.00 0.00 0.00 0.00 5.03 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05
FIN 0.01 0.09 0.02 0.03 0.01 0.01 0.06 0.00 0.21 5.48 0.01 0.01 0.00 0.00 0.00 0.01 0.00 0.02 0.01 0.02 0.00 0.01 0.02 0.05 0.01 0.01 0.00 1.99
FRA 0.04 0.89 0.11 0.13 0.03 0.08 0.03 0.18 0.04 0.08 5.19 0.05 0.02 0.01 0.03 0.05 0.06 0.06 0.03 0.03 0.00 0.14 0.04 0.54 0.08 0.04 0.03 0.79
GBR 0.03 0.80 0.09 2.13 0.03 0.05 0.12 0.08 0.03 0.30 0.15 3.13 0.02 0.01 0.02 0.41 0.02 0.12 0.02 0.05 0.00 0.19 0.06 0.36 0.05 0.04 0.02 2.28
GRC 0.00 0.04 0.14 0.26 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.00 2.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.00 0.00 0.02
HRV 0.19 0.01 0.01 0.03 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.25 0.03 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.09 0.01
HUN 0.29 0.07 0.08 0.17 0.30 0.08 0.02 0.00 0.01 0.01 0.06 0.00 0.00 0.01 4.83 0.00 0.01 0.09 0.01 0.05 0.00 0.01 0.05 0.04 0.13 0.23 0.06 0.04
IRL 0.00 0.03 0.01 0.06 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.03 0.00 0.00 0.00 1.80 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.02 0.00 0.00 0.00 0.03
ITA 0.76 0.46 0.40 0.20 0.06 0.24 0.02 0.18 0.04 0.05 0.19 0.03 0.14 0.06 0.06 0.06 4.16 0.05 0.02 0.07 0.00 0.14 0.05 0.37 0.15 0.08 0.21 0.34
LTU 0.00 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.02 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.18 0.00 0.03 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.02
LUX 0.00 0.14 0.00 0.02 0.00 0.02 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.04 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.01
LVA 0.00 0.01 0.00 0.03 0.00 0.00 0.00 0.00 0.05 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.16 0.00 6.77 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.03
MLT 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.67 0.00 0.00 0.00 0.00 0.00 0.00 0.01
NLD 0.02 0.86 0.07 0.08 0.02 0.04 0.03 0.01 0.03 0.11 0.08 0.03 0.01 0.01 0.02 0.05 0.01 0.07 0.03 0.02 0.00 2.03 0.03 0.23 0.04 0.03 0.02 0.43
POL 0.02 0.09 0.03 0.19 0.16 0.13 0.01 0.01 0.01 0.02 0.06 0.01 0.00 0.00 0.02 0.00 0.01 0.33 0.00 0.03 0.00 0.01 2.18 0.05 0.02 0.11 0.01 0.11
PRT 0.00 0.05 0.01 0.10 0.00 0.03 0.01 0.07 0.02 0.01 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.53 0.01 0.00 0.00 0.07
ROU 0.04 0.06 0.89 0.13 0.04 0.02 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.00 0.31 0.00 0.02 0.02 0.00 0.01 0.00 0.00 0.03 0.04 10.52 0.06 0.01 0.03
SVK 0.23 0.04 0.02 0.08 1.12 0.60 0.00 0.00 0.00 0.01 0.32 0.00 0.00 0.00 0.11 0.00 0.00 0.07 0.00 0.02 0.00 0.00 0.34 0.03 0.03 7.06 0.02 0.03
SVN 0.13 0.01 0.02 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.00 0.05 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 6.77 0.01
SWE 0.02 0.20 0.05 0.08 0.02 0.03 0.26 0.01 0.12 0.90 0.04 0.03 0.00 0.01 0.01 0.01 0.01 0.03 0.01 0.06 0.00 0.02 0.05 0.12 0.03 0.02 0.02 8.05
The values represent the effect of a unitary shock in production: ie. if country AUT reduces production by one unit, the damage felt in country DEU is 0.29. Hence, the matrix can be seen as a symmetric network of production effects between countries.
My goal is to find the optimal weights of a weighted unitary shock (i.e. weighting the columns so that the total reduction of production summed over all countries is = 1) that:
ensure a certain distribution of damage across receiving (row) countries (i.e. the row sums), lets say an equal distribution
while at the same minimizing the damage in the overall economic system
I've tried solving it as a simple non-linear optimization problem with equality constraints, using the package Rsolnp:
# objective function to be minimized (global damage)
damage <- function(weights) {
D_weighted <- t(t(D)*weights); return(sum(D_weighted))
}
# constraints (combined in one function:
constr <- function(weights) {
# constraint 1: sum of weights needs to be 1
c1 = sum(weights)
# constraint 2: equal distribution in damage outcome
D_weighted <- t(t(D)*weights)
damage_per_country <- rowSums(D_weighted)/sum(D_weighted)
c2 = damage_per_country/sum(D_weighted)
return(c(c1, c2))
}
# target distribution of damage outcome (for example: equal distribution)
targ_dist <- c(rep(1/(ncol(D)), ncol(D)))
# starting weights (sart with same production reduction in every country)
startweights <- rep(1/ncol(D), ncol(D))
# run optimization with Rsolnp
opt_weights <- solnp(pars = startweights, fun = damage, eqfun = constr, eqB = c(1, targ_dist), LB = rep(0, ncol(D)), UB = rep(1, ncol(D)), control=list(outer.iter=1000,trace=0, tol= 0.001))
but it doesn't converge and returns a warning message:
"The linearized problem has no feasible solution. The problem may not be feasible".
Changing the tolerance doesn't solve the problem. It might be that this solver is not suited for this kind of problem or I need to reformulate the problem completely. I'd be thankful for any help!

How to extract value of CPU idle from sar command using AWK

From the outut of a sar command, I want to extract only the lines in which the %iowait value is higher than a set threshold.
I tried using AWK but somehow I'm not able to perform the action.
sar -u -f sa12 | sed 's/\./,/g' | awk -f" " '{ if ( $7 -gt 0 ) print $0 }'
I tried to substitute the . with , and using -gt but still no joy.
Can someone suggest a solution?

If we need entire line output of sar -u with iowait > 0.01 then, we can use this ,
Command
sar -u | grep -v "CPU" | awk '$7 > 0.01'
Output will be similar to
03:40:01 AM all 3.16 0.00 0.05 0.11 0.00 96.68
04:40:01 PM all 0.19 0.00 0.05 0.02 0.00 99.74
if wish to out specific fields, say only iowait, we can use as given below,
Command to out specific field(s),
sar -u | grep -v "CPU" | awk '{if($7 > 0.01 ) print $7}'
Output will be
0.11
0.02
Note : grep -v is used just to remove the headings in the output
Hope this helps,

My sar -u gives several lines similar to the following:
Linux 4.4.0-127-generic (v1) 06/12/2018 _x86_64_ (1 CPU)
12:00:01 AM CPU %user %nice %system %iowait %steal %idle
12:05:01 AM all 0.29 0.00 0.30 0.01 0.00 99.40
12:15:01 AM all 0.33 0.00 0.34 0.00 0.00 99.32
12:25:01 AM all 0.33 0.00 0.30 0.01 0.00 99.36
12:35:01 AM all 0.31 0.00 0.29 0.01 0.00 99.39
12:45:01 AM all 0.33 0.00 0.32 0.01 0.00 99.35
12:55:01 AM all 0.32 0.00 0.30 0.00 0.00 99.38
01:05:01 AM all 0.32 0.00 0.28 0.00 0.00 99.39
01:15:01 AM all 0.33 0.00 0.30 0.01 0.00 99.37
01:25:01 AM all 0.31 0.00 0.30 0.01 0.00 99.39
01:35:01 AM all 0.31 0.00 0.33 0.00 0.00 99.36
01:45:01 AM all 0.31 0.00 0.28 0.01 0.00 99.40
01:55:01 AM all 0.31 0.00 0.30 0.00 0.00 99.38
02:05:01 AM all 0.31 0.00 0.28 0.01 0.00 99.40
02:15:01 AM all 0.32 0.00 0.30 0.01 0.00 99.38
02:25:01 AM all 0.31 0.00 0.30 0.01 0.00 99.38
02:35:01 AM all 0.33 0.00 0.33 0.00 0.00 99.33
02:45:01 AM all 0.35 0.00 0.32 0.01 0.00 99.32
02:55:01 AM all 0.28 0.00 0.30 0.00 0.00 99.42
03:05:01 AM all 0.32 0.00 0.31 0.00 0.00 99.37
03:15:01 AM all 0.34 0.00 0.30 0.01 0.00 99.36
03:25:01 AM all 0.32 0.00 0.29 0.01 0.00 99.38
03:35:01 AM all 0.33 0.00 0.26 0.00 0.00 99.40
03:45:01 AM all 0.34 0.00 0.29 0.00 0.00 99.36
03:55:01 AM all 0.30 0.00 0.28 0.01 0.00 99.41
04:05:01 AM all 0.32 0.00 0.30 0.01 0.00 99.37
04:15:01 AM all 0.37 0.00 0.31 0.01 0.00 99.32
04:25:01 AM all 1.78 2.04 0.59 0.05 0.00 95.55
To filter out those where %iowait is greater than, let's say, 0.01:
sar -u | awk '$7>0.01{print}'
Linux 4.4.0-127-generic (v1) 06/12/2018 _x86_64_ (1 CPU)
04:25:01 AM all 1.78 2.04 0.59 0.05 0.00 95.55
05:15:01 AM all 0.34 0.00 0.32 0.02 0.00 99.32
06:35:01 AM all 0.33 0.22 1.23 4.48 0.00 93.74
06:45:01 AM all 0.16 0.00 0.12 0.02 0.00 99.71
10:35:01 AM all 0.22 0.00 0.13 0.02 0.00 99.63
12:15:01 PM all 0.42 0.00 0.16 0.03 0.00 99.40
01:45:01 PM all 0.17 0.00 0.11 0.02 0.00 99.71
04:05:01 PM all 0.15 0.00 0.12 0.03 0.00 99.70
04:15:01 PM all 0.42 0.00 0.23 0.10 0.00 99.25
Edit:
As correctly pointed out by #Ed Morton, the awk code can be shortened to simply awk '$7>0.01', since the default action is to print the current line.

Boxplot: Need to capture all extreme outliers

I'm trying to capture all my data in a boxplot. I found a neat example in Cross Validated, but it's not entirely working for me and I was hoping that someone could help me out.
My code is:
boxplot(x,horizontal=TRUE,boxwex=.7,axes=FALSE,frame.plot=TRUE)
axis(1,at=xlab,labels=xlab)
opar <- par()
layout(matrix(1:3,nr=1,nc=3),heights=c(1,1,1),widths=c(1,6,1))
par(oma = c(5,4,0,0) + 0.1,mar = c(0,0,1,1) + 0.1)
stripchart(x[x< -400],pch=1,cex=1,xlim=c(-1700000,-400),method="jitter")
boxplot(x[abs(x)<400],horizontal=TRUE,ylim=c(-400,400),at=0,boxwex=.7,cex=1,method="jitter")
stripchart(x[x> 400],pch=1,cex=1,xlim=c(400,60000),method="jitter")
par(opar)
but the jitter doesn't work in the boxplot and the stripcharts shouldn't start at 0. If I can figure out how to paste the output chart I would do it.
[1] -1620000.00 -85000.00 -32672.62 -30963.50 -28335.64 -26531.30 -18305.68 -13964.04 -13500.00
[10] -13248.48 -10975.05 -7410.00 -6034.32 -5629.00 -5349.09 -5125.00 -4994.45 -4973.72
[19] -4404.84 -4063.76 -3632.77 -3118.50 -3056.18 -3000.00 -2774.00 -2699.86 -2541.50
[28] -2327.06 -2238.89 -1750.00 -1548.63 -1343.25 -1271.67 -1187.55 -1114.80 -1087.44
[37] -1084.59 -1080.00 -977.20 -936.00 -900.00 -896.50 -853.60 -850.00 -792.00
[46] -791.44 -773.53 -750.00 -750.00 -710.82 -700.00 -697.68 -678.00 -665.00
[55] -620.00 -578.49 -513.96 -500.00 -474.18 -468.51 -412.47 -334.50 -332.50
[64] -331.20 -305.32 -300.00 -300.00 -244.04 -239.65 -212.30 -210.00 -203.32
[73] -202.15 -199.50 -198.24 -188.64 -177.25 -174.78 -169.80 -168.80 -168.25
[82] -166.75 -144.35 -140.00 -129.98 -126.74 -120.33 -120.00 -115.92 -114.99
[91] -112.45 -108.00 -106.64 -103.40 -100.00 -100.00 -98.28 -95.68 -89.36
[100] -87.84 -86.59 -75.68 -72.16 -72.04 -71.13 -65.52 -51.00 -50.00
[109] -50.00 -44.12 -41.25 -40.00 -35.18 -35.14 -34.41 -33.82 -33.80
[118] -33.60 -32.98 -30.00 -30.00 -29.13 -28.00 -27.44 -26.46 -26.32
[127] -25.92 -25.50 -25.06 -25.00 -21.84 -20.00 -19.63 -19.14 -18.64
[136] -18.60 -18.00 -17.25 -16.72 -16.69 -16.54 -15.50 -15.00 -13.51
[145] -12.16 -11.78 -11.69 -11.56 -11.26 -10.97 -10.88 -10.84 -10.62
[154] -10.45 -10.20 -10.00 -9.83 -9.04 -9.00 -8.75 -8.70 -8.50
[163] -8.28 -8.26 -7.92 -7.88 -7.74 -6.70 -6.44 -6.10 -5.35
[172] -5.04 -4.84 -4.73 -4.65 -4.50 -4.44 -4.40 -4.34 -4.25
[181] -4.00 -3.99 -3.98 -3.96 -3.94 -3.70 -3.08 -2.88 -2.85
[190] -2.75 -2.52 -2.14 -2.06 -2.00 -1.98 -1.96 -1.92 -1.74
[199] -1.68 -1.50 -1.10 -1.08 -0.89 -0.67 -0.60 -0.50 -0.48
[208] -0.42 -0.40 -0.30 -0.14 -0.04 0.00 0.00 0.00 0.00
[217] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[226] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[235] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[244] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[253] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[262] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[271] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[280] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[289] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[298] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[307] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[316] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[325] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[334] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[343] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[352] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[361] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[370] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[379] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[388] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[397] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[406] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[415] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[424] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[433] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[442] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[451] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[460] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[469] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[478] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[487] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[496] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[505] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[514] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[523] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[532] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[541] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[550] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[559] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[568] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[577] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[586] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[595] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[604] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[613] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[622] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[631] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[640] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[649] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[658] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[667] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[676] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[685] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.21
[694] 0.40 0.44 0.46 0.46 0.48 0.59 0.70 1.00 1.14
[703] 1.17 1.25 1.28 1.40 1.42 1.60 1.68 2.10 2.10
[712] 2.16 2.32 2.34 2.37 2.52 2.80 2.88 3.50 3.51
[721] 3.99 4.76 5.00 5.63 5.76 5.85 5.85 6.00 6.20
[730] 6.50 7.36 8.07 8.68 9.25 9.67 9.80 9.82 11.02
[739] 14.00 15.00 15.04 15.27 16.60 17.55 17.68 19.50 20.94
[748] 21.81 23.51 23.86 24.57 24.57 25.96 27.36 27.44 27.81
[757] 29.20 29.59 29.72 30.30 38.50 39.77 47.20 47.92 50.00
[766] 50.59 51.00 54.20 65.02 68.00 71.28 75.00 92.80 95.28
[775] 105.29 110.00 126.84 134.04 134.24 140.00 140.58 147.50 148.78
[784] 152.48 173.80 181.37 181.80 185.60 186.90 188.48 201.30 209.50
[793] 215.27 228.64 240.00 243.68 248.08 250.00 250.00 255.58 277.50
[802] 282.00 285.40 290.80 304.39 325.00 327.76 339.80 362.00 372.93
[811] 373.24 380.70 400.00 440.00 450.00 493.74 508.50 510.64 538.20
[820] 551.37 565.00 570.95 612.22 616.00 653.40 665.24 666.75 667.20
[829] 718.23 770.66 825.26 855.79 884.00 1000.00 1064.00 1064.77 1080.00
[838] 1152.00 1159.62 1177.24 1271.27 1495.52 1590.00 1670.00 1739.79 2075.68
[847] 2496.00 3570.00 3648.64 4152.64 4158.00 4556.44 4594.75 5040.00 5099.40
[856] 5150.67 5926.65 5967.81 6110.64 6144.00 6942.20 7350.00 7525.32 8667.90
[865] 9601.02 11557.20 12360.12 14425.70 15000.00 17962.14 27655.72 34709.96 45430.00
[874] 50000.00 57785.00

OK, rather than go through the cumbersome process I used (and before I get another Tumbleweed "award"), I found a better solution posted by bdemarest in 2015 under the title "Understanding Boxplot with jitter". If my dataframe is called DRP with headings "Cost_Delta" and "Month" (Data for Jan-2017 is in first post), my solution chart can be found here https://i.stack.imgur.com/sSWtr.png. Code is below.
DRP<-read.table("C:\\Projects\\Mat Group\\DRP\\1000_Item_Data\\RFiles\\Cost Delta\\DRP_CostDelta2.txt",header=TRUE)
DRP$Month <- as.character(DRP$Month)
DRP$Month <- factor(DRP$Month, levels=unique(DRP$Month))
library(ggplot2)
p<-ggplot(DRP, aes(x=Month, y=Cost_Delta)) +
geom_point(aes(fill=Month), size=2, shape=21, colour="grey20",
position=position_jitter(width=0.2, height=0.1)) +
geom_boxplot(outlier.colour=NA, fill=NA, colour="grey20")
p + scale_y_continuous(labels=comma,breaks=seq(-300000,350000,50000)) +
labs(x="Month-Year", y="Cost Delta (Demand-DRP Forecast)") +
#*** January Outliers
geom_text(x=1, y=-250000, label="-1,620,000",size=3) +
geom_segment(aes(x=1, xend=1, y=-275000, yend=-276000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
#*** February Outliers
geom_text(x=2, y=300000, label="1,101,786",size=3) +
geom_segment(aes(x=2, xend=2, y=325000, yend=326000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
geom_text(x=2, y=-250000, label="-7,020,000",size=3) +
geom_segment(aes(x=2, xend=2, y=-275000, yend=-276000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
#*** March Outliers
geom_text(x=3, y=-250000, label="-3,780,000",size=3) +
geom_segment(aes(x=3, xend=3, y=-275000, yend=-276000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
#*** August Outliers
geom_text(x=6, y=-225000, label="-484,960",size=3) +
geom_text(x=6, y=-250000, label="-540,000",size=3) +
geom_segment(aes(x=6, xend=6, y=-275000, yend=-276000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
#*** September Outliers
geom_text(x=7, y=300000, label="593,960",size=3) +
geom_segment(aes(x=7, xend=7, y=325000, yend=326000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
geom_text(x=7, y=-250000, label="-484,960",size=3) +
geom_segment(aes(x=7, xend=7, y=-275000, yend=-276000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
#*** October Outliers
geom_text(x=8, y=300000, label="969,920",size=3) +
geom_segment(aes(x=8, xend=8, y=325000, yend=326000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
#*** November Outliers
geom_text(x=9, y=300000, label="2,909,760",size=3) +
geom_segment(aes(x=9, xend=9, y=325000, yend=326000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
#*** December Outliers
geom_text(x=10, y=300000, label="1,080,000",size=3) +
geom_segment(aes(x=10, xend=10, y=325000, yend=326000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red") +
geom_text(x=10, y=-250000, label="-1,939,000",size=3) +
geom_segment(aes(x=10, xend=10, y=-275000, yend=-276000),
arrow = arrow(length = unit(0.3, "cm"),ends="last", type = "closed"),col="red")

corr.test arguments imply differing number of rows

I have seen this error multiple times in different projects and I was wondering if there is a way to tell which line caused the error in general?
My specific case:
http://archive.ics.uci.edu/ml/machine-learning-databases/00275/
#using the bike.csv
data<-read.csv("PATH_HERE\\Bike-Sharing-Dataset\\day.csv",header=TRUE)
require(psych)
corr.test(data)
data<-data[,c("atemp","casual","cnt","holiday","hum","mnth","registered",
"season","temp","weathersit","weekday","windspeed","workingday","yr")]
data[data=='']<-NA
#View(data)
require(psych)
cors<-corr.test(data)
returns the error:
Error in data.frame(lower = lower, r = r[lower.tri(r)], upper = upper, :
arguments imply differing number of rows: 0, 91

It works for me
> #using the bike.csv
> data <- read.csv("day.csv",header=TRUE)
> require(psych)
> corr.test(data)
Error in cor(x, use = use, method = method) : 'x' must be numeric
> data <- data[,c("atemp","casual","cnt","holiday","hum","mnth","registered",
+ "season","temp","weathersit","weekday","windspeed","workingday","yr")]
> data[data==''] <- NA
> #View(data)
>
> require(psych)
> cors <- corr.test(data)
> cors
Call:corr.test(x = data)
Correlation matrix
atemp casual cnt holiday hum mnth registered season temp
atemp 1.00 0.54 0.63 -0.03 0.14 0.23 0.54 0.34 0.99
casual 0.54 1.00 0.67 0.05 -0.08 0.12 0.40 0.21 0.54
cnt 0.63 0.67 1.00 -0.07 -0.10 0.28 0.95 0.41 0.63
holiday -0.03 0.05 -0.07 1.00 -0.02 0.02 -0.11 -0.01 -0.03
hum 0.14 -0.08 -0.10 -0.02 1.00 0.22 -0.09 0.21 0.13
mnth 0.23 0.12 0.28 0.02 0.22 1.00 0.29 0.83 0.22
registered 0.54 0.40 0.95 -0.11 -0.09 0.29 1.00 0.41 0.54
season 0.34 0.21 0.41 -0.01 0.21 0.83 0.41 1.00 0.33
temp 0.99 0.54 0.63 -0.03 0.13 0.22 0.54 0.33 1.00
weathersit -0.12 -0.25 -0.30 -0.03 0.59 0.04 -0.26 0.02 -0.12
weekday -0.01 0.06 0.07 -0.10 -0.05 0.01 0.06 0.00 0.00
windspeed -0.18 -0.17 -0.23 0.01 -0.25 -0.21 -0.22 -0.23 -0.16
workingday 0.05 -0.52 0.06 -0.25 0.02 -0.01 0.30 0.01 0.05
yr 0.05 0.25 0.57 0.01 -0.11 0.00 0.59 0.00 0.05
weathersit weekday windspeed workingday yr
atemp -0.12 -0.01 -0.18 0.05 0.05
casual -0.25 0.06 -0.17 -0.52 0.25
cnt -0.30 0.07 -0.23 0.06 0.57
holiday -0.03 -0.10 0.01 -0.25 0.01
hum 0.59 -0.05 -0.25 0.02 -0.11
mnth 0.04 0.01 -0.21 -0.01 0.00
registered -0.26 0.06 -0.22 0.30 0.59
season 0.02 0.00 -0.23 0.01 0.00
temp -0.12 0.00 -0.16 0.05 0.05
weathersit 1.00 0.03 0.04 0.06 -0.05
weekday 0.03 1.00 0.01 0.04 -0.01
windspeed 0.04 0.01 1.00 -0.02 -0.01
workingday 0.06 0.04 -0.02 1.00 0.00
yr -0.05 -0.01 -0.01 0.00 1.00
Sample Size
[1] 731
Probability values (Entries above the diagonal are adjusted for multiple tests.)
atemp casual cnt holiday hum mnth registered season temp
atemp 0.00 0.00 0.00 1.00 0.01 0.00 0.00 0.00 0.00
casual 0.00 0.00 0.00 1.00 1.00 0.04 0.00 0.00 0.00
cnt 0.00 0.00 0.00 1.00 0.28 0.00 0.00 0.00 0.00
holiday 0.38 0.14 0.06 0.00 1.00 1.00 0.15 1.00 1.00
hum 0.00 0.04 0.01 0.67 0.00 0.00 0.58 0.00 0.03
mnth 0.00 0.00 0.00 0.60 0.00 0.00 0.00 0.00 0.00
registered 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00
season 0.00 0.00 0.00 0.78 0.00 0.00 0.00 0.00 0.00
temp 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00
weathersit 0.00 0.00 0.00 0.35 0.00 0.24 0.00 0.60 0.00
weekday 0.84 0.11 0.07 0.01 0.16 0.80 0.12 0.93 1.00
windspeed 0.00 0.00 0.00 0.87 0.00 0.00 0.00 0.00 0.00
workingday 0.16 0.00 0.10 0.00 0.51 0.87 0.00 0.74 0.15
yr 0.21 0.00 0.00 0.83 0.00 0.96 0.00 0.96 0.20
weathersit weekday windspeed workingday yr
atemp 0.05 1.00 0.00 1.00 1.00
casual 0.00 1.00 0.00 0.00 0.00
cnt 0.00 1.00 0.00 1.00 0.00
holiday 1.00 0.25 1.00 0.00 1.00
hum 0.00 1.00 0.00 1.00 0.13
mnth 1.00 1.00 0.00 1.00 1.00
registered 0.00 1.00 0.00 0.00 0.00
season 1.00 1.00 0.00 1.00 1.00
temp 0.05 1.00 0.00 1.00 1.00
weathersit 0.00 1.00 1.00 1.00 1.00
weekday 0.40 0.00 1.00 1.00 1.00
windspeed 0.29 0.70 0.00 1.00 1.00
workingday 0.10 0.33 0.61 0.00 1.00
yr 0.19 0.88 0.75 0.96 0.00
To see confidence intervals of the correlations, print with the short=FALSE option
>

It works for me:::
rm(list=ls())
# http://archive.ics.uci.edu/ml/machine-learning-databases/00275/
#using the bike.csv
day <- read.csv("Bike-Sharing-Dataset//day.csv")
require(psych)
day<-day[,c("atemp","casual","cnt","holiday","hum","mnth","registered",
"season","temp","weathersit","weekday","windspeed","workingday","yr")]
day[day=='']<-NA
require(psych)
corr.test(day)
# corr.test(day)
# Call:corr.test(x = day)
# Correlation matrix
# atemp casual cnt holiday hum mnth registered season temp weathersit weekday windspeed workingday yr
# atemp 1.00 0.54 0.63 -0.03 0.14 0.23 0.54 0.34 0.99 -0.12 -0.01 -0.18 0.05 0.05
# casual 0.54 1.00 0.67 0.05 -0.08 0.12 0.40 0.21 0.54 -0.25 0.06 -0.17 -0.52 0.25
# cnt 0.63 0.67 1.00 -0.07 -0.10 0.28 0.95 0.41 0.63 -0.30 0.07 -0.23 0.06 0.57
# holiday -0.03 0.05 -0.07 1.00 -0.02 0.02 -0.11 -0.01 -0.03 -0.03 -0.10 0.01 -0.25 0.01
# hum 0.14 -0.08 -0.10 -0.02 1.00 0.22 -0.09 0.21 0.13 0.59 -0.05 -0.25 0.02 -0.11
# mnth 0.23 0.12 0.28 0.02 0.22 1.00 0.29 0.83 0.22 0.04 0.01 -0.21 -0.01 0.00
# registered 0.54 0.40 0.95 -0.11 -0.09 0.29 1.00 0.41 0.54 -0.26 0.06 -0.22 0.30 0.59
# season 0.34 0.21 0.41 -0.01 0.21 0.83 0.41 1.00 0.33 0.02 0.00 -0.23 0.01 0.00
# temp 0.99 0.54 0.63 -0.03 0.13 0.22 0.54 0.33 1.00 -0.12 0.00 -0.16 0.05 0.05
# weathersit -0.12 -0.25 -0.30 -0.03 0.59 0.04 -0.26 0.02 -0.12 1.00 0.03 0.04 0.06 -0.05
# weekday -0.01 0.06 0.07 -0.10 -0.05 0.01 0.06 0.00 0.00 0.03 1.00 0.01 0.04 -0.01
# windspeed -0.18 -0.17 -0.23 0.01 -0.25 -0.21 -0.22 -0.23 -0.16 0.04 0.01 1.00 -0.02 -0.01
# workingday 0.05 -0.52 0.06 -0.25 0.02 -0.01 0.30 0.01 0.05 0.06 0.04 -0.02 1.00 0.00
# yr 0.05 0.25 0.57 0.01 -0.11 0.00 0.59 0.00 0.05 -0.05 -0.01 -0.01 0.00 1.00
# Sample Size
# [1] 731
# Probability values (Entries above the diagonal are adjusted for multiple tests.)
# atemp casual cnt holiday hum mnth registered season temp weathersit weekday windspeed workingday yr
# atemp 0.00 0.00 0.00 1.00 0.01 0.00 0.00 0.00 0.00 0.05 1.00 0.00 1.00 1.00
# casual 0.00 0.00 0.00 1.00 1.00 0.04 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00
# cnt 0.00 0.00 0.00 1.00 0.28 0.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00 0.00
# holiday 0.38 0.14 0.06 0.00 1.00 1.00 0.15 1.00 1.00 1.00 0.25 1.00 0.00 1.00
# hum 0.00 0.04 0.01 0.67 0.00 0.00 0.58 0.00 0.03 0.00 1.00 0.00 1.00 0.13
# mnth 0.00 0.00 0.00 0.60 0.00 0.00 0.00 0.00 0.00 1.00 1.00 0.00 1.00 1.00
# registered 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00
# season 0.00 0.00 0.00 0.78 0.00 0.00 0.00 0.00 0.00 1.00 1.00 0.00 1.00 1.00
# temp 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.05 1.00 0.00 1.00 1.00
# weathersit 0.00 0.00 0.00 0.35 0.00 0.24 0.00 0.60 0.00 0.00 1.00 1.00 1.00 1.00
# weekday 0.84 0.11 0.07 0.01 0.16 0.80 0.12 0.93 1.00 0.40 0.00 1.00 1.00 1.00
# windspeed 0.00 0.00 0.00 0.87 0.00 0.00 0.00 0.00 0.00 0.29 0.70 0.00 1.00 1.00
# workingday 0.16 0.00 0.10 0.00 0.51 0.87 0.00 0.74 0.15 0.10 0.33 0.61 0.00 1.00
# yr 0.21 0.00 0.00 0.83 0.00 0.96 0.00 0.96 0.20 0.19 0.88 0.75 0.96 0.00
#
# To see confidence intervals of the correlations, print with the short=FALSE option
cheers

Principal component analysis (PCA) in R: why are the scores not orthogonal? (using Psych package)

I ran PCA in R using the principal() function in the "psych" package. I made the argument "rotate="none"", which asks for orthogonal rotation method. From what I understand, the scores of PC1 and PC2 should be orthogonal (i.e. there should be zero correlation between (raw data)(loading of PC1)and (raw data)(loading of PC2). However, I got 90% correlation. Why is that?
> #load the package
> library(psych)
> #calculate the correlation matrix
> corMat <- cor(data)
> #run PCA
> pca.results <- principal(r = corMat,**rotate ="none"**, nfactors = 20,covar=FALSE,scores=TRUE)
> pca.results`enter code here`
Principal Components Analysis
Call: principal(r = corMat, nfactors = 20, rotate = "none", covar = FALSE,
scores = TRUE)
Standardized loadings (pattern matrix) based upon correlation matrix
**PC1 PC2** PC3 PC4 PC5 PC6 PC7 PC8 PC9
payroll.chg -0.30 0.85 0.21 0.35 -0.03 0.02 0.07 -0.11 -0.02
HH.empl.chg -0.26 0.62 0.64 -0.35 0.01 -0.06 0.06 0.00 0.01
pop.empl.ratio -0.92 -0.34 0.13 0.04 0.06 -0.03 -0.04 0.03 -0.04
u.rate 0.99 0.10 0.02 0.04 0.01 0.04 0.04 0.04 0.01
median.duration.unempl 0.88 0.44 -0.02 0.02 -0.04 0.06 0.02 0.13 -0.05
LT.unempl.unempl.ratio 0.86 0.49 -0.04 0.01 -0.07 0.02 0.00 0.08 -0.02
U4 0.99 0.13 0.01 0.03 0.01 0.04 0.04 0.05 0.01
U6 0.98 0.13 -0.05 -0.02 0.00 0.06 0.04 0.03 0.04
vacancy.rate -0.87 0.35 -0.18 -0.11 -0.01 0.22 0.10 0.03 -0.01
hires.rate -0.92 0.08 0.24 0.21 -0.16 0.06 0.00 0.05 0.09
unemployed.to.employed 0.89 0.17 0.21 -0.02 0.05 0.24 -0.25 -0.05 0.00
Layoff.rate..JOLT. 0.23 -0.86 0.19 -0.03 -0.40 0.09 0.03 -0.02 -0.05
Exhaustion.rate 0.95 0.19 0.14 0.14 0.00 -0.07 0.01 0.06 -0.04
Quits.rate..JOLT. -0.98 0.01 0.04 0.04 0.01 0.02 -0.06 0.10 0.13
participation.rate -0.67 -0.61 0.31 0.14 0.16 -0.01 -0.03 0.11 -0.08
insured.u.rate 0.88 -0.40 0.17 0.08 0.12 0.05 0.09 -0.03 0.02
Initial.jobless.claims 0.78 -0.60 0.04 -0.06 0.06 0.05 0.07 0.02 0.07
Continuing.claims 0.86 -0.44 0.15 0.06 0.14 0.08 0.09 -0.05 0.03
Jobs.plentiful.jobs.hardtoget -0.98 0.00 -0.02 0.01 0.08 0.13 0.04 -0.02 -0.04
vacancy.unempl.ratio -0.97 0.04 -0.05 -0.03 0.08 0.18 0.07 0.03 -0.03
PC10 PC11 PC12 PC13 PC14 PC15 PC16 PC17 PC18
payroll.chg -0.06 0.02 -0.02 0.00 0.03 0.00 0.00 0.00 0.00
HH.empl.chg 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
pop.empl.ratio -0.02 0.00 -0.01 0.01 0.00 0.00 0.00 0.01 0.01
u.rate -0.01 0.00 0.03 -0.03 0.02 0.00 0.00 -0.01 -0.01
median.duration.unempl 0.02 0.05 -0.06 -0.01 -0.03 0.01 -0.02 0.00 0.00
LT.unempl.unempl.ratio 0.01 0.02 -0.01 0.02 0.00 0.00 0.05 0.00 0.00
U4 -0.01 0.00 0.04 -0.02 0.02 0.00 -0.01 -0.01 0.01
U6 -0.01 0.01 0.03 -0.03 0.02 -0.02 0.00 0.03 0.00
vacancy.rate -0.08 -0.06 0.01 0.01 -0.01 0.04 0.00 0.00 0.00
hires.rate 0.01 0.00 0.04 0.00 -0.06 -0.01 0.00 0.00 0.00
unemployed.to.employed -0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00
Layoff.rate..JOLT. 0.01 0.00 -0.01 -0.01 0.03 0.00 0.00 0.00 0.00
Exhaustion.rate 0.05 -0.07 0.02 0.06 0.01 -0.01 -0.02 0.00 0.00
Quits.rate..JOLT. 0.04 -0.01 -0.04 0.00 0.05 0.02 0.00 0.00 0.00
participation.rate -0.06 0.00 0.02 -0.02 0.01 0.01 0.01 0.00 0.00
insured.u.rate 0.04 -0.02 -0.02 0.00 -0.02 0.02 0.01 0.00 0.02
Initial.jobless.claims -0.09 0.06 0.00 0.06 0.01 -0.01 -0.01 0.00 0.00
Continuing.claims 0.05 -0.02 -0.02 -0.02 -0.01 0.01 0.01 0.01 -0.02
Jobs.plentiful.jobs.hardtoget 0.11 0.07 0.05 0.02 0.01 0.02 0.00 0.00 0.00
vacancy.unempl.ratio 0.03 -0.01 -0.03 0.00 0.01 -0.06 0.00 0.00 0.00
PC19 PC20 h2 u2
payroll.chg 0.00 0.00 1 5.6e-16
HH.empl.chg 0.00 0.00 1 -2.9e-15
pop.empl.ratio 0.01 0.01 1 -1.6e-15
u.rate -0.01 0.01 1 1.1e-16
median.duration.unempl 0.00 0.00 1 -4.4e-16
LT.unempl.unempl.ratio 0.00 0.00 1 -6.7e-16
U4 0.01 0.00 1 -4.4e-16
U6 0.00 0.00 1 2.2e-16
vacancy.rate 0.00 0.00 1 0.0e+00
hires.rate 0.00 0.00 1 4.4e-16
unemployed.to.employed 0.00 0.00 1 -2.2e-16
Layoff.rate..JOLT. 0.00 0.00 1 -2.2e-15
Exhaustion.rate 0.00 0.00 1 -4.4e-16
Quits.rate..JOLT. 0.00 0.00 1 1.1e-16
participation.rate 0.00 -0.01 1 5.6e-16
insured.u.rate -0.01 0.00 1 -6.7e-16
Initial.jobless.claims 0.00 0.00 1 -2.0e-15
Continuing.claims 0.01 0.00 1 -6.7e-16
Jobs.plentiful.jobs.hardtoget 0.00 0.00 1 2.2e-16
vacancy.unempl.ratio 0.00 0.00 1 -2.2e-16
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
SS loadings 14.23 3.73 0.83 0.37 0.28 0.20 0.12 0.07 0.05 0.05 0.02 0.02
Proportion Var 0.71 0.19 0.04 0.02 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00
Cumulative Var 0.71 0.90 0.94 0.96 0.97 0.98 0.99 0.99 0.99 1.00 1.00 1.00
Proportion Explained 0.71 0.19 0.04 0.02 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00
Cumulative Proportion 0.71 0.90 0.94 0.96 0.97 0.98 0.99 0.99 0.99 1.00 1.00 1.00
PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20
SS loadings 0.01 0.01 0.01 0 0 0 0 0
Proportion Var 0.00 0.00 0.00 0 0 0 0 0
Cumulative Var 1.00 1.00 1.00 1 1 1 1 1
Proportion Explained 0.00 0.00 0.00 0 0 0 0 0
Cumulative Proportion 1.00 1.00 1.00 1 1 1 1 1
Test of the hypothesis that 20 components are sufficient.
The degrees of freedom for the null model are 190 and the objective function was 68.46
The degrees of freedom for the model are -20 and the objective function was 0
Fit based upon off diagonal values = 1

To find the component scores you can skip the step in which you are finding the correlations. principal will do that for you. Then, you can skip the step Hong Ooi suggested andjust find the scores directly. They should be orthogonal.
Using your example:
pca.results <- principal(data,nfactors=20,rotate='none')
#then correlate the scores
cor(pca.results$scores) #these should be orthogonal
Bill

What you've got there are not the PCA scores, but the PCA loadings. To get the latter, use the predict method on your model. You should find that the predicted scores are indeed uncorrelated with each other.

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