[R] 0 rows> (or 0-length row.names)
Shivi Bhatia
shivipmp82 at gmail.com
Tue Aug 30 19:32:51 CEST 2016
Hi William/ Mark,
I am using WOE & IV (weight of evidence) reduce the number of independent
vars.
I have read this data as a csv file.
reproducible example for your reference please:
structure(list(date = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
30L, 30L, 30L, 30L), .Label = c("01-02-2016", "01-03-2016", "01-04-2016",
"01-05-2016", "01-06-2016", "01-11-2015", "01-12-2015", "02-01-2016",
"02-02-2016", "02-03-2016", "02-04-2016", "02-05-2016", "02-06-2016",
"02-11-2015", "02-12-2015", "03-01-2016", "03-02-2016", "03-03-2016",
"03-04-2016", "03-05-2016", "03-06-2016", "03-11-2015", "03-12-2015",
"04-01-2016", "04-02-2016", "04-03-2016", "04-04-2016", "04-05-2016",
"04-06-2016", "04-11-2015", "04-12-2015", "05-01-2016", "05-02-2016",
"05-03-2016", "05-04-2016", "05-05-2016", "05-06-2016", "05-11-2015",
"05-12-2015", "06-01-2016", "06-02-2016", "06-03-2016", "06-04-2016",
"06-05-2016", "06-06-2016", "06-11-2015", "06-12-2015", "07-01-2016",
"07-02-2016", "07-03-2016", "07-04-2016", "07-05-2016", "07-06-2016",
"07-11-2015", "07-12-2015", "08-01-2016", "08-02-2016", "08-03-2016",
"08-04-2016", "08-05-2016", "08-06-2016", "08-11-2015", "08-12-2015",
"09-01-2016", "09-02-2016", "09-03-2016", "09-04-2016", "09-05-2016",
"09-06-2016", "09-11-2015", "09-12-2015", "10-01-2016", "10-02-2016",
"10-03-2016", "10-04-2016", "10-05-2016", "10-06-2016", "10-11-2015",
"10-12-2015", "11-01-2016", "11-02-2016", "11-03-2016", "11-04-2016",
"11-05-2016", "11-11-2015", "11-12-2015", "12-01-2016", "12-02-2016",
"12-04-2016", "12-05-2016", "12-06-2016", "12-11-2015", "12-12-2015",
"13-01-2016", "13-02-2016", "13-03-2016", "13-04-2016", "13-05-2016",
"13-06-2016", "13-11-2015", "13-12-2015", "14-01-2016", "14-02-2016",
"14-03-2016", "14-04-2016", "14-05-2016", "14-06-2016", "14-11-2015",
"14-12-2015", "15-01-2016", "15-02-2016", "15-03-2016", "15-04-2016",
"15-05-2016", "15-06-2016", "15-11-2015", "15-12-2015", "16-01-2016",
"16-02-2016", "16-03-2016", "16-04-2016", "16-05-2016", "16-06-2016",
"16-11-2015", "16-12-2015", "17-01-2016", "17-02-2016", "17-03-2016",
"17-04-2016", "17-05-2016", "17-06-2016", "17-11-2015", "17-12-2015",
"18-01-2016", "18-02-2016", "18-03-2016", "18-04-2016", "18-05-2016",
"18-06-2016", "18-11-2015", "18-12-2015", "19-01-2016", "19-02-2016",
"19-03-2016", "19-04-2016", "19-05-2016", "19-06-2016", "19-11-2015",
"19-12-2015", "20-01-2016", "20-03-2016", "20-04-2016", "20-05-2016",
"20-06-2016", "20-11-2015", "20-12-2015", "21-01-2016", "21-02-2016",
"21-03-2016", "21-04-2016", "21-05-2016", "21-06-2016", "21-11-2015",
"21-12-2015", "22-01-2016", "22-02-2016", "22-03-2016", "22-04-2016",
"22-05-2016", "22-06-2016", "22-11-2015", "22-12-2015", "23-01-2016",
"23-02-2016", "23-03-2016", "23-04-2016", "23-05-2016", "23-06-2016",
"23-11-2015", "23-12-2015", "24-01-2016", "24-02-2016", "24-03-2016",
"24-04-2016", "24-05-2016", "24-06-2016", "24-11-2015", "24-12-2015",
"25-01-2016", "25-02-2016", "25-03-2016", "25-04-2016", "25-05-2016",
"25-06-2016", "25-11-2015", "25-12-2015", "26-01-2016", "26-02-2016",
"26-03-2016", "26-04-2016", "26-05-2016", "26-06-2016", "26-11-2015",
"27-01-2016", "27-02-2016", "27-03-2016", "27-04-2016", "27-05-2016",
"27-06-2016", "27-11-2015", "27-12-2015", "28-01-2016", "28-02-2016",
"28-03-2016", "28-04-2016", "28-05-2016", "28-06-2016", "28-11-2015",
"28-12-2015", "29-01-2016", "29-02-2016", "29-03-2016", "29-04-2016",
"29-05-2016", "29-06-2016", "29-11-2015", "29-12-2015", "30-01-2016",
"30-03-2016", "30-04-2016", "30-05-2016", "30-06-2016", "30-11-2015",
"30-12-2015", "31-01-2016", "31-03-2016", "31-05-2016", "31-12-2015"
), class = "factor"), month = structure(c(8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("Apr",
"Dec", "Feb", "Jan", "Jun", "Mar", "May", "Nov"), class = "factor"),
day = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L), agent = structure(c(30L, 203L,
191L, 127L, 114L, 170L, 41L, 79L, 173L, 247L, 26L, 247L,
23L, 145L, 280L, 101L, 130L, 173L, 62L, 217L, 145L, 140L,
251L, 115L, 62L, 233L, 254L, 85L, 245L, 203L, 174L, 30L,
247L, 238L, 41L, 242L, 267L, 62L, 43L, 127L, 163L, 217L,
275L, 105L, 79L, 191L, 110L, 86L, 247L, 23L), .Label = c("Aakash
Shivach",
"Aanchal Goel", "Abhishek Bisht", "Abhishek Mudireddy", "Abhishek
Singh2",
"Adam Boyle", "Aditi Gogia", "Afsal Backer", "Agam Sud",
"Aishwarya Ramisetti", "Ajay Gangavarapu", "Ajaz Shaikh",
"Akash Chandra", "Akash Jaiswal", "Akhil Jain", "Akhilesh Kumar",
"Akiko Ono", "Akiko Tanaka", "Akilesh Mogulluri", "Akshi Bhutani",
"Alessandro Delgado", "Alex Rozenfeld", "Amarjeet Kumar",
"Ambili Rasu (MCS)", "Ameeduddin Sheikh", "Amit Singh", "Amritansh
Trivedi",
"Anand Vardhan", "Anil Kumar 2", "Anil Pandey", "Anjali Thekkut",
"Ankit Kumar", "Ankit Sharma", "Ankita Sharma", "Anne Margarette
Estipona",
"Anudeep Alapati", "Anuj Malik", "Anurag Sharma", "Anurag Srivastava",
"Anurikt Wadhwa", "Arpit Agarwal", "Arvind Kaushik", "Arvind
Yarlagadda",
"Ashish Kalra", "Ashish Pandey", "Ashish Rana", "Ashish Sharma",
"Ashraf Ahmad", "Ashutosh Tripathi", "Ashwani Mehra", "Atif Ansari",
"Ayush Gupta", "Ayush Rastogi", "Ayush Sodhi", "Bharat Joshi",
"Bharath Kilaparthi", "Bharath Vankayala", "Brandon Parker",
"Carla Franchini", "Celia Oliveira", "Cesar Adames", "Chaten Raghav",
"Chelsea McKay", "Chris Andersen (MCS)", "Clinton Harwood",
"Dan Gustafson", "Darlene Wright", "Daryl Kurtz", "Dave Selisana",
"Dee (Daiichiro) Tanaka (MCS)", "Divyan Solanki", "Efrilyn Tejano",
"Eric Cheong (MCS)", "Ganesh Malla", "Garrett Surgeon", "Garvit
Talwar",
"Gauravsingh Pawar", "Gurjeet Singh", "Gursimran Vohra",
"Guy Halperin", "Harsh Bahadur", "Harshit Mishra", "Harshvardhan
Chauhan",
"Hector Tarrobago", "Himanshu Sharma", "Hiroyoshi Iwakiri",
"Iskander Mukhamedgaliyev", "Jack Ziesing", "Jacobe Bascuguin",
"Janani Rajasekaran", "Jasdeep Singh Talwar", "Jason MacManiman",
"Jason R Lima", "Jayaprakash Narayan (MCS)", "Jen McCarthy",
"Jewin Joy Dsilva", "Jitin Chopra", "John Alvin Garlan",
"John Salvan Khattyan", "Jose Garcia (MCS)", "Joshua Wilhelm",
"Juan Rodriguez", "K N Shashank", "Kalyani Kota", "Kamakshi Nathan
Subbiah",
"Karthik Murari (MCS)", "Kartik Singhal", "Katsuyuki Deguchi (MCS)",
"Kazuya Ouchi", "Kenji Mizutani", "Kenjiro Hosomi", "Kenneth Hadley",
"Kenneth Scholz", "Kiran Vuppuluri (MCS)", "Komal Gupta",
"Kothapalli Yaswanth", "Krishna Neelam", "Kuldeep Negi",
"Lisly Matti", "Luis Fernando Russi", "Luke Walker (MCS)",
"Lynne Ausejo", "Lynne Beckham", "Madhav Prasad", "Maika Dela Cruz",
"Maki Matsumoto", "Manav Vatsyayana", "Mandeep Singh", "Manish Shukla",
"Manmeet Singh", "Mari Delos Santos", "Mari Ganesan Chandran",
"Maria Josel Arce", "Masakazu Furumi", "Masashi Yanagisawa (MCS)",
"Mayur Jain", "Megha Malviya", "Mehdiimam Khan", "Midori Yoshino",
"Mithilesh Singh", "Mohamad Hamdan", "Mohammad Anis", "Moshin Pathan",
"Mudit Mudgal", "Nancy Bhagat", "Nancy McGrew", "Nareshkumarmohan
Thirukkovaluru",
"Nayana Kadiyala(MCS)", "Neena Aggarwal", "Neeraj Kumar",
"Neeraja Nagalla", "Neha Singh", "Nick Martens", "Nikhil Srivastava",
"Nikita Singh", "Nikunj Gupta", "Nilima Madala", "Nishant Chaudhary",
"Norihiko Kodama", "Pablo Alvarez (MCS)", "Padmaja Matlaparti",
"Pallavi Sharma", "Panati Rusia", "Parinitha Vedpathak",
"Patrick Roland Perete", "Paul Bryan Ballesteros", "Piyush Chandani",
"Poornachander Chiliveri", "Pradeep Raju", "Pramod Kumar",
"Praneeth Indraganti", "Pranuthi Vallam", "Prasannta Dubey",
"Prashant Sharma", "Praveen Kandhagatla (MCS)", "Praveen Yadav",
"Priya Adlakha", "Priyank Jain", "Priyanka Kumari", "Priyush Jagadam",
"Pruthvi Lanke", "Pulkit Sharma", "Puneet Gupta2", "Pushkar Diwedi",
"Pushpa Kodwani", "Rachit Joshi", "Raghav Sahore", "Rahul Madhwani",
"Rahul Munot", "Raj Salvi", "Rajat Bansal", "Rakshitha Rakshitha",
"Ramu Adep (MCS)", "Randi Wilson", "Rashmitha Ramaraju (MCS)",
"Reddy Mallareddy", "Renu Adhikari", "Richard Santin", "Ridhima
Bhatia",
"Rie Son", "Rindha Kundur", "Rishika Bisariya", "Rishikant Dubey",
"Ritesh Jaiswal", "Ritesh Srivastava", "Rodrigo Andrade",
"Ross O'Riordan", "Rupal Sachan", "Ryan Klein", "Ryan Ruiz",
"Saihareesh Sapram", "Saikat Banerjee", "Samil Gutierrez",
"Sangam Ravindhar (MCS)", "Sanjeev Soran", "Sanpreet Saini",
"Sarfaraj Siddiqui", "Sarthak Sharma", "Satish Alavarthi",
"Saurav Kumar", "Saurav Sundriyal", "Sean Flynn (MCS)", "Sean Hurst",
"Shalu Gangwar", "Shashank Mehra", "Sheshant Kashyap", "Shikha Raheja",
"Shivani Shukla", "Shivani Singh", "Shreekanth Kyatsandra (MCS)",
"Shubham Rathore", "Shubham Sehgal", "Sibesh Dash", "Simardeep Bindra",
"Sirdikchowdary Marella", "Sivani Mallamapalli", "Somarani Kandar",
"Sonalianil Mahakalkar", "Sowmya Gupta", "Sri Krishna Mantripragada",
"Srikanth Nelluri", "Sudha Kumari", "Sumit Balouria", "Sumit Kumar",
"Sumuga Padman", "Swapnil Deshmukh", "Swapnil Srivastav",
"Swapnil Srivastav2", "Swati Sharma", "Swetha Kiran Nallamothu",
"Tajinder Singh", "Takahiro Mori", "Takeshi Sato", "Tallam Venkatesh",
"Tanu Agarwal", "Tejashree Gosavi", "Thimmaiah Vanganur",
"Tom Graves", "Toyokazu Nakao", "Tracy Stinghen", "Tushar Samar",
"Tushar Uniyal", "Ujjwal Rawat", "Vaibhav Goel", "Vaibhav Jain",
"Vaibhav Kaushik", "Vanathi Vijayakumar", "Venkatesh Reddy Y",
"Vibhor Mundepi", "Vibin Davis", "Vikas Kumar", "Vikash Ujjwal",
"Vikram Kumar Kondapaneni (MCS)", "Vikram Nanduri (MCS)",
"Vineet Goel", "Vinita Mishra (MCS)", "Viswanath Ronda",
"Vivek Nair", "Wayne Cordrey", "Yathish Nimbegondi Shanmukhappa",
"Yatin Mahajan", "Yogesh Lal", "Yoji Taoka", "Yoshiyuki Masuda (MCS)"
), class = "factor"), tenure = structure(c(6L, 1L, 3L, 2L,
1L, 1L, 6L, 2L, 2L, 1L, 1L, 1L, 6L, 3L, 1L, 1L, 2L, 2L, 2L,
3L, 3L, 2L, 2L, 6L, 2L, 2L, 6L, 2L, 3L, 1L, 3L, 6L, 1L, 3L,
6L, 1L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L,
6L), .Label = c("#N/A", "Expert", "Junior", "Newbie A", "Newbie B",
"Senior"), class = "factor"), support_cat = structure(c(10L,
1L, 2L, 1L, 15L, 6L, 6L, 2L, 6L, 1L, 2L, 3L, 6L, 1L, 1L,
6L, 1L, 1L, 1L, 1L, 1L, 1L, 18L, 1L, 18L, 1L, 1L, 1L, 1L,
1L, 1L, 18L, 1L, 6L, 18L, 18L, 1L, 1L, 1L, 15L, 1L, 1L, 18L,
18L, 1L, 1L, 12L, 11L, 6L, 1L), .Label = c("AMER", "APAC",
"BypassTier1", "ByPassTier1", "BYPASSTIER1", "EMEA", "Exception",
"Global", "GOVT", "HIPPA", "JP", "JP MCS", "LACA", "LPL",
"MCS", "None", "Partner Developer", "Special", "US only",
"US Only"), class = "factor"), region = structure(c(1L, 1L,
2L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 1L, 1L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L,
4L, 3L, 1L), .Label = c("AMER", "APAC", "EMEA", "JP", "LACA",
"Unknown"), class = "factor"), support_lvl = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L), .Label = c("Other", "Premier", "Standard"
), class = "factor"), skill_group = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L), .Label = c("Developer", "Integration"), class = "factor"),
application_area = structure(c(1L, 10L, 1L, 7L, 1L, 1L, 1L,
2L, 1L, 3L, 2L, 3L, 1L, 7L, 1L, 1L, 3L, 3L, 1L, 2L, 15L,
3L, 3L, 7L, 1L, 1L, 3L, 1L, 1L, 1L, 7L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 14L, 1L, 2L, 3L, 3L, 1L, 1L), .Label =
c("Apex/API Development",
"API / Apex / Visualforce", "API Integration", "API Tokens",
"Authentication", "CTI", "Deployment", "Environment Hub",
"Flow", "Limit Changes", "Network and Performance", "Other",
"Packaging and Deployment", "Sites & Community", "SOQL / SOSL",
"Workflow Automation"), class = "factor"), functional_area =
structure(c(63L,
36L, 5L, 26L, 5L, 56L, 5L, 4L, 35L, 66L, 4L, 55L, 5L, 26L,
5L, 63L, 8L, 8L, 5L, 63L, 59L, 34L, 1L, 17L, 5L, 5L, 50L,
63L, 5L, 5L, 26L, 5L, 5L, 63L, 5L, 5L, 5L, 5L, 5L, 35L, 5L,
5L, 5L, 35L, 5L, 4L, 57L, 57L, 5L, 5L), .Label = c("-", "Account
Insights",
"Activation", "APEX", "Apex Code Development", "Apex Data Loader",
"API", "API Performance", "API Token Issues", "API Toolkit",
"Application Slowness", "Authentication", "Batch Apex", "Browser
specific issue",
"Bulk API", "Canvas", "Change Sets", "Chatter REST", "Communities &
Chatter",
"Community", "Connected Apps", "Database.com", "Debug Log Size",
"Delagated SSO", "Delegated Authentication", "Deployment -
ANT/IDE/Code",
"Development", "Domain Name Change", "Email", "Feature Activation",
"Federated (SAML) SSO", "Flow Configuration", "Flow Designer",
"Flow Development", "Force.com Sites", "Governor Limits",
"IDE / ANT / Metadata API", "Identity Connect", "Lightning Connect",
"Managed Package Namespaces", "Managed Packages", "Mutual
Authentication",
"Network / ISP Latency", "Oauth", "Open CTI", "Other", "Outbound
Messaging",
"Post Install Script", "Quick Deploy", "REST API", "Salesforce
Maintenance",
"Salesforce1", "SAML", "Setup & Security", "Single Sign On",
"Site.com", "SOAP API", "SOQL Performance", "SOQL Queries",
"SOSL", "SSL Certificates", "Visual Process Manager", "Visualforce",
"WDC1.0", "Workflow", "WSDL2 Apex"), class = "factor"), score = c(9L,
10L, 2L, 10L, 10L, 2L, 8L, 10L, 10L, 10L, 10L, 10L, 10L,
2L, 10L, 4L, 4L, 10L, 9L, 10L, 10L, 10L, 10L, 5L, 9L, 10L,
8L, 10L, 10L, 10L, 10L, 10L, 10L, 1L, 9L, 8L, 10L, 10L, 10L,
10L, 9L, 10L, 10L, 10L, 9L, 10L, 8L, 8L, 10L, 9L), rep_score = c(9.5,
10, 2, 10, 10, 3.5, 7.5, 10, 10, 10, 10, 10, 10, 2, 10, 7.5,
6, 10, 9.5, 10, 9, 10, 10, 5.5, 9, 10, 8, 10, 10, 10, 10,
10, 9.5, 1.5, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 9.5,
10, 10, 8, 10, 10), product_know = structure(c(4L, 4L, 5L,
4L, 1L, 3L, 11L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 10L, 9L,
4L, 4L, 4L, 11L, 4L, 4L, 9L, 12L, 4L, 11L, 4L, 4L, 4L, 4L,
4L, 12L, 3L, 12L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 11L, 4L, 4L), .Label = c("-", "0", "1", "10", "2",
"3", "4", "5", "6", "7", "8", "9"), class = "factor"),
understanding_issue = structure(c(12L,
4L, 5L, 4L, 4L, 9L, 10L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L,
11L, 9L, 4L, 12L, 4L, 4L, 4L, 4L, 8L, 12L, 4L, 11L, 1L, 4L,
4L, 4L, 4L, 4L, 5L, 12L, 11L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
4L, 12L, 4L, 4L, 11L, 4L, 4L), .Label = c("-", "0", "1",
"10", "2", "3", "4", "5", "6", "7", "8", "9"), class = "factor"),
case_age = c(24.84, 0.05, 13.38, 0.15, 11.11, 4.16, 8.13,
0.07, 3.61, 0, 3.11, 20.94, 0.21, 17.49, 1.11, 6.15, 4.32,
4.03, 0.08, 3.06, 4.74, 12.07, 4.79, 5.29, 0.21, 0.06, 3.95,
0.12, 7.27, 4.18, 2.49, 20.95, 0.15, 10.96, 6.99, 47.42,
4.96, 0.06, 4.92, 0.06, 6.84, 0.3, 0.01, 0.07, 15.74, 5.8,
2.85, 0.17, 16.02, 1.33), severity_level = structure(c(3L,
3L, 2L, 4L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 4L, 4L, 4L,
4L, 4L, 2L, 3L, 4L, 4L, 2L, 4L, 3L, 4L, 2L, 3L, 2L, 3L, 2L,
4L, 3L, 3L, 4L, 4L, 4L, 4L, 2L, 4L, 3L, 2L, 2L, 2L, 3L, 3L,
3L, 4L, 4L, 4L), .Label = c("Level 1 - Critical", "Level 2 - Urgent",
"Level 3 - High", "Level 4 - Medium"), class = "factor"),
case_status = structure(c(1L, 6L, 4L, 6L, 6L, 6L, 6L, 6L,
2L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 4L, 6L, 2L, 6L, 6L, 2L,
5L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 2L, 6L, 6L, 2L,
6L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 2L, 2L, 6L, 2L), .Label = c("Closed -
Bug Fix Submitted",
"Closed - Customer Closed", "Closed - Directed to IdeaExchange",
"Closed - No response from customer", "Closed - Request out of Scope",
"Closed - Resolved", "Closed - Routed to Internal Helpdesk",
"Pending Customer Approval", "Working"), class = "factor"),
account_segment = structure(c(3L, 8L, 4L, 3L, 9L, 3L, 4L,
4L, 8L, 9L, 1L, 4L, 3L, 3L, 3L, 9L, 4L, 3L, 9L, 3L, 3L, 3L,
9L, 3L, 9L, 8L, 9L, 8L, 8L, 2L, 8L, 9L, 4L, 8L, 9L, 2L, 3L,
3L, 3L, 9L, 8L, 3L, 9L, 9L, 4L, 4L, 9L, 3L, 9L, 4L), .Label = c("-",
"Flagship", "Large", "Medium", "Mega", "N/A", "Platinum",
"Small", "Top", "Very Small"), class = "factor"), sla_status =
structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = c("Met", "Missed", "N/A", "Pending"
), class = "factor"), delivery_segmentation = structure(c(31L,
31L, 31L, 31L, 10L, 26L, 31L, 31L, 31L, 31L, 25L, 31L, 31L,
31L, 24L, 8L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 28L, 24L,
31L, 31L, 31L, 31L, 31L, 31L, 5L, 31L, 31L, 32L, 33L, 31L,
31L), .Label = c("-", "AMER - IND - T3", "AMER Dev Port",
"AMER Mission Critical Success (MCS)", "AMER Tier 2 Port",
"AMER Tier 3 Port", "AMER TS Tier 2", "AMER TS Tier 2 DEV",
"AMER TS Tier 3", "APAC Mission Critical Success (MCS)",
"APAC TS Tier 2", "COGZ DESM Admin", "COGZ DESM Tier 2",
"COGZ MANL Tier 1", "COGZ MANL Tier 2", "COGZ PUNE Admin",
"COGZ PUNE Tier 1", "EMEA Mission Critical Success (MCS)",
"EMEA Premier T2", "EMEA TS Tier 2 DEV", "EMEA TS Tier 3",
"HCL MANL Tier 1", "HCL MANL Tier 2", "HYDR AMER TS Tier 2 DEV",
"HYDR APAC TS Tier 2 DEV", "HYDR EMEA TS Tier 2 DEV", "HYDR Premier
Internal Admin APAC",
"HYDR Premier Internal Tier 2 AMER", "HYDR Premier Internal Tier 2
APAC",
"HYDR Premier Internal Tier 2 EMEA", "India TS Tier 2 Dev Outsource",
"Japan Premier Internal Tier 2 (PSA)", "Japan TS Tier 1",
"Japan TS Tier 2 Internal", "Japan TS Tier 3", "JP Mission Critical
Success (MCS)",
"Partner - Internal"), class = "factor"), survey = c(1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1), repS = c(1, 1, 0, 1, 1, 0,
0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1), log_caseage = c(3.2519236789144, 0.048790164169432,
2.6658383522929, 0.139761942375159, 2.494031557565, 1.64093657949347,
2.21156569460688, 0.0676586484738148, 1.52822785700856, 0,
1.41342302850814, 3.08831145484708, 0.19062035960865, 2.91723004539903,
0.746687947487975, 1.96711235670592, 1.67147330335355,
1.61541998411165,
0.0769610411361283, 1.40118297361364, 1.74745921033147,
2.57031952763613,
1.7561322915849, 1.83896107071235, 0.19062035960865,
0.0582689081239758,
1.5993875765806, 0.113328685307003, 2.1126345090356, 1.64480505627139,
1.24990173621434, 3.08876713952118, 0.139761942375159,
2.48156774852249,
2.07819075977818, 3.87991295150991, 1.78507048107726,
0.0582689081239758,
1.77833644889591, 0.0582689081239758, 2.05923883436232,
0.262364264467491,
0.00995033085316808, 0.0676586484738148, 2.81780106506133,
1.91692261218206, 1.34807314829969, 0.157003748809665,
2.83438912314523,
0.845868267577609)), .Names = c("date", "month", "day", "agent",
"tenure", "support_cat", "region", "support_lvl", "skill_group",
"application_area", "functional_area", "score", "rep_score",
"product_know", "understanding_issue", "case_age", "severity_level",
"case_status", "account_segment", "sla_status", "delivery_segmentation",
"survey", "repS", "log_caseage"), row.names = c(NA, 50L), class =
"data.frame")
On Tue, Aug 30, 2016 at 10:30 PM, William Dunlap <wdunlap at tibco.com> wrote:
> You did not say what operation gave you the error.
>
> I can get that message (which is not an "error") if I print
> an illegally constructed data.frame, one without the
> row.names attribute.
>
> > illegalDF <- structure(class="data.frame", list(ColumnA = 1:3))
> > illegalDF
> [1] ColumnA
> <0 rows> (or 0-length row.names)
> > str(illegalDF)
> 'data.frame': 0 obs. of 1 variable:
> $ ColumnA: int 1 2 3
>
> Note how str() of the entire data.frame indirectly informs you of the
> problem: the number of observations does not match the length of the
> columns.
>
> How did you make the data.frame?
>
>
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
>
> On Tue, Aug 30, 2016 at 9:24 AM, Shivi Bhatia <shivipmp82 at gmail.com>
> wrote:
>
>> I know this question has been asked zillion times but even after
>> consulting
>> Stack Overflow & other forum cant figure out the reason.
>>
>> I have one var in my data-set names case age. This variable is numeric as:
>>
>> class(SFDC$case_age)
>>
>> *numeric*
>>
>> however it throws this error:
>>
>> <0 rows> (or 0-length row.names)
>> As checked this only happens either there is some space at the end of the
>> variable name, or there are no values whereas this is a numeric variable
>> with no missing values and has a total of 5400 observations.
>>
>> This var has a range from 0 to 240 in number of days for case variable
>> hence i need to do a logarithm transformation & make it use in the model.
>> Total unique obs are around 1500.
>>
>> Please advice.
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posti
>> ng-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
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