why date range filre is not working.could any one please guide me through this isssue.
library(shiny)
library(plotly)
library(dplyr)
library(readr)
library(shinydashboard)
library(shinythemes)
library(dashboardthemes)
# total<-read.csv("C:/Users/dell/Downloads/archive (4)/List of Orders.csv")
total<-tibble::tribble(
~Order.ID, ~Order_Date, ~State, ~City, ~CustomerName, ~sales,
"B-25601", "1/4/2018", "Gujarat", "Ahmedabad", "Bharat", 1275L,
"B-25602", "1/4/2018", "Maharashtra", "Pune", "Pearl", 66L,
"B-25603", "3/4/2018", "Madhya Pradesh", "Bhopal", "Jahan", 8L,
"B-25604", "3/4/2018", "Rajasthan", "Jaipur", "Divsha", 80L,
"B-25605", "5/4/2018", "West Bengal", "Kolkata", "Kasheen", 168L,
"B-25606", "6/4/2018", "Karnataka", "Bangalore", "Hazel", 424L,
"B-25607", "6/4/2018", "Jammu and Kashmir", "Kashmir", "Sonakshi", 2617L,
"B-25608", "8/4/2018", "Tamil Nadu", "Chennai", "Aarushi", 561L,
"B-25609", "9/4/2018", "Uttar Pradesh", "Lucknow", "Jitesh", 119L,
"B-25610", "9/4/2018", "Bihar", "Patna", "Yogesh", 1355L,
"B-25611", "11/4/2018", "Kerala", "Thiruvananthapuram", "Anita", 24L,
"B-25612", "12/4/2018", "Punjab", "Chandigarh", "Shrichand", 193L,
"B-25613", "12/4/2018", "Haryana", "Chandigarh", "Mukesh", 180L,
"B-25614", "13-04-2018", "Himachal Pradesh", "Simla", "Vandana", 116L,
"B-25615", "15-04-2018", "Sikkim", "Gangtok", "Bhavna", 107L,
"B-25616", "15-04-2018", "Goa", "Goa", "Kanak", 12L,
"B-25617", "17-04-2018", "Nagaland", "Kohima", "Sagar", 38L,
"B-25618", "18-04-2018", "Andhra Pradesh", "Hyderabad", "Manju", 65L,
"B-25619", "18-04-2018", "Gujarat", "Ahmedabad", "Ramesh", 157L,
"B-25620", "20-04-2018", "Maharashtra", "Pune", "Sarita", 75L,
"B-25621", "20-04-2018", "Madhya Pradesh", "Bhopal", "Deepak", 87L,
"B-25622", "22-04-2018", "Rajasthan", "Jaipur", "Monisha", 50L,
"B-25623", "22-04-2018", "West Bengal", "Kolkata", "Atharv", 1364L,
"B-25624", "22-04-2018", "Karnataka", "Bangalore", "Vini", 476L,
"B-25625", "23-04-2018", "Jammu and Kashmir", "Kashmir", "Pinky", 257L,
"B-25626", "23-04-2018", "Maharashtra", "Mumbai", "Bhishm", 856L,
"B-25627", "23-04-2018", "Madhya Pradesh", "Indore", "Hitika", 485L,
"B-25628", "24-04-2018", "Bihar", "Patna", "Pooja", 25L,
"B-25629", "24-04-2018", "Kerala", "Thiruvananthapuram", "Hemant", 1076L,
"B-25630", "24-04-2018", "Punjab", "Chandigarh", "Sahil", 107L,
"B-25631", "24-04-2018", "Haryana", "Chandigarh", "Ritu", 68L,
"B-25632", "25-04-2018", "Himachal Pradesh", "Simla", "Manish", 781L,
"B-25633", "26-04-2018", "Sikkim", "Gangtok", "Amit", 43L,
"B-25634", "26-04-2018", "Goa", "Goa", "Sanjay", 30L,
"B-25635", "26-04-2018", "Nagaland", "Kohima", "Nidhi", 160L,
"B-25636", "26-04-2018", "Maharashtra", "Mumbai", "Nishi", 259L,
"B-25637", "26-04-2018", "Madhya Pradesh", "Indore", "Ashmi", 1603L,
"B-25638", "26-04-2018", "Maharashtra", "Pune", "Parth", 494L,
"B-25639", "27-04-2018", "Madhya Pradesh", "Bhopal", "Lisha", 98L,
"B-25640", "27-04-2018", "Rajasthan", "Jaipur", "Paridhi", 68L,
"B-25641", "27-04-2018", "West Bengal", "Kolkata", "Parishi", 42L,
"B-25642", "28-04-2018", "Karnataka", "Bangalore", "Ajay", 116L,
"B-25643", "29-04-2018", "Jammu and Kashmir", "Kashmir", "Kirti", 22L,
"B-25644", "30-04-2018", "Maharashtra", "Mumbai", "Mayank", 14L,
"B-25645", "1/5/2018", "Madhya Pradesh", "Indore", "Yaanvi", 305L,
"B-25646", "1/5/2018", "Bihar", "Patna", "Sonal", 362L,
"B-25647", "3/5/2018", "Kerala", "Thiruvananthapuram", "Sharda", 12L,
"B-25648", "4/5/2018", "Punjab", "Chandigarh", "Aditya", 353L,
"B-25649", "5/5/2018", "Haryana", "Chandigarh", "Rachna", 193L,
"B-25650", "6/5/2018", "Maharashtra", "Mumbai", "Chirag", 233L,
"B-25651", "7/5/2018", "Madhya Pradesh", "Indore", "Anurag", 228L,
"B-25652", "8/5/2018", "Goa", "Goa", "Tushina", 333L,
"B-25653", "8/5/2018", "Nagaland", "Kohima", "Farah", 534L,
"B-25654", "10/5/2018", "Maharashtra", "Mumbai", "Sabah", 53L,
"B-25655", "11/5/2018", "Madhya Pradesh", "Indore", "Nida", 158L,
"B-25656", "11/5/2018", "Maharashtra", "Pune", "Priyanka", 149L,
"B-25657", "13-05-2018", "Madhya Pradesh", "Bhopal", "Tulika", 105L,
"B-25658", "14-05-2018", "Rajasthan", "Jaipur", "Shefali", 26L,
"B-25659", "15-05-2018", "West Bengal", "Kolkata", "Sanskriti", 97L,
"B-25660", "16-05-2018", "Karnataka", "Bangalore", "Shruti", 59L,
"B-25661", "17-05-2018", "Jammu and Kashmir", "Kashmir", "Subhashree", 635L,
"B-25662", "17-05-2018", "Maharashtra", "Mumbai", "Sweta", 46L,
"B-25663", "19-05-2018", "Madhya Pradesh", "Indore", "Pournamasi", 1103L,
"B-25664", "20-05-2018", "Bihar", "Patna", "Pratyusmita", 55L,
"B-25665", "21-05-2018", "Kerala", "Thiruvananthapuram", "Chayanika", 45L,
"B-25666", "22-05-2018", "Punjab", "Chandigarh", "Tanvi", 24L,
"B-25667", "23-05-2018", "Haryana", "Chandigarh", "Anjali", 35L,
"B-25668", "24-05-2018", "Himachal Pradesh", "Simla", "Rhea", 1560L,
"B-25669", "25-05-2018", "Sikkim", "Gangtok", "Piyali", 133L,
"B-25670", "25-05-2018", "Goa", "Goa", "Charika", 114L,
"B-25671", "27-05-2018", "Nagaland", "Kohima", "Mitali", 143L,
"B-25672", "28-05-2018", "Andhra Pradesh", "Hyderabad", "Akanksha", 40L,
"B-25673", "28-05-2018", "Gujarat", "Ahmedabad", "Arsheen", 34L,
"B-25674", "28-05-2018", "Maharashtra", "Pune", "Mahima", 42L,
"B-25675", "31-05-2018", "Madhya Pradesh", "Bhopal", "Shreya", 89L,
"B-25676", "1/6/2018", "Rajasthan", "Jaipur", "Chandni", 19L,
"B-25677", "2/6/2018", "West Bengal", "Kolkata", "Ekta", 249L,
"B-25678", "3/6/2018", "Karnataka", "Bangalore", "Bathina", 711L,
"B-25679", "4/6/2018", "Maharashtra", "Mumbai", "Avni", 496L,
"B-25680", "4/6/2018", "Madhya Pradesh", "Indore", "Aayushi", 389L,
"B-25681", "4/6/2018", "Madhya Pradesh", "Indore", "Bhawna", 40L,
"B-25682", "7/6/2018", "Bihar", "Patna", "Krutika", 23L,
"B-25683", "8/6/2018", "Kerala", "Thiruvananthapuram", "Shreya", 382L,
"B-25684", "9/6/2018", "Maharashtra", "Mumbai", "Samiksha", 637L,
"B-25685", "10/6/2018", "Madhya Pradesh", "Indore", "Sheetal", 117L,
"B-25686", "11/6/2018", "Himachal Pradesh", "Simla", "Pooja", 182L,
"B-25687", "11/6/2018", "Maharashtra", "Mumbai", "Sanjna", 880L,
"B-25688", "11/6/2018", "Madhya Pradesh", "Indore", "Swetha", 154L,
"B-25689", "14-06-2018", "Maharashtra", "Mumbai", "Bhaggyasree", 816L,
"B-25690", "15-06-2018", "Madhya Pradesh", "Indore", "Gunjan", 1629L,
"B-25691", "16-06-2018", "Maharashtra", "Mumbai", "Akancha", 68L,
"B-25692", "17-06-2018", "Madhya Pradesh", "Indore", "Rashmi", 314L,
"B-25693", "18-06-2018", "Madhya Pradesh", "Bhopal", "Parna", 122L,
"B-25694", "18-06-2018", "Rajasthan", "Jaipur", "Subhasmita", 22L,
"B-25695", "18-06-2018", "West Bengal", "Kolkata", "Suhani", 434L,
"B-25696", "21-06-2018", "Karnataka", "Bangalore", "Noopur", 1061L,
"B-25697", "22-06-2018", "Jammu and Kashmir", "Kashmir", "Vijay", 50L,
"B-25698", "23-06-2018", "Tamil Nadu", "Chennai", "Amisha", 37L,
"B-25699", "24-06-2018", "Uttar Pradesh", "Lucknow", "Kritika", 263L,
"B-25700", "25-06-2018", "Maharashtra", "Mumbai", "Shubhi", 36L,
"B-25701", "26-06-2018", "Madhya Pradesh", "Indore", "Maithilee", 76L,
"B-25702", "27-06-2018", "Maharashtra", "Mumbai", "Shaily", 273L,
"B-25703", "28-06-2018", "Madhya Pradesh", "Indore", "Ekta", 86L,
"B-25704", "29-06-2018", "Maharashtra", "Mumbai", "Riya", 133L,
"B-25705", "30-06-2018", "Madhya Pradesh", "Indore", "Shweta", 183L,
"B-25706", "1/7/2018", "Goa", "Goa", "Swetlana", 20L,
"B-25707", "1/7/2018", "Maharashtra", "Mumbai", "Shivani", 42L,
"B-25708", "1/7/2018", "Madhya Pradesh", "Indore", "Kishwar", 100L,
"B-25709", "1/7/2018", "Madhya Pradesh", "Indore", "Aakanksha", 30L,
"B-25710", "5/7/2018", "Maharashtra", "Pune", "Megha", 55L,
"B-25711", "6/7/2018", "Madhya Pradesh", "Bhopal", "Sakshi", 130L,
"B-25712", "7/7/2018", "Rajasthan", "Jaipur", "Adhvaita", 27L,
"B-25713", "8/7/2018", "West Bengal", "Kolkata", "Raksha", 245L,
"B-25714", "9/7/2018", "Karnataka", "Bangalore", "Stuti", 211L,
"B-25715", "10/7/2018", "Jammu and Kashmir", "Kashmir", "Srishti", 31L,
"B-25716", "11/7/2018", "Tamil Nadu", "Chennai", "Surabhi", 28L,
"B-25717", "12/7/2018", "Uttar Pradesh", "Lucknow", "Manshul", 512L,
"B-25718", "12/7/2018", "Maharashtra", "Mumbai", "Anjali", 925L,
"B-25719", "12/7/2018", "Madhya Pradesh", "Indore", "Rashmi", 238L,
"B-25720", "15-07-2018", "Punjab", "Chandigarh", "Namrata", 351L,
"B-25721", "16-07-2018", "Haryana", "Chandigarh", "Anchal", 269L,
"B-25722", "17-07-2018", "Himachal Pradesh", "Simla", "Inderpreet", 200L,
"B-25723", "18-07-2018", "Maharashtra", "Mumbai", "Wale", 44L,
"B-25724", "19-07-2018", "Madhya Pradesh", "Indore", "Sheetal", 7L,
"B-25725", "20-07-2018", "Nagaland", "Kohima", "Anisha", 11L,
"B-25726", "21-07-2018", "Maharashtra", "Mumbai", "Kiran", 16L,
"B-25727", "22-07-2018", "Madhya Pradesh", "Indore", "Turumella", 172L,
"B-25728", "22-07-2018", "Maharashtra", "Pune", "Ameesha", 49L,
"B-25729", "22-07-2018", "Madhya Pradesh", "Bhopal", "Madhulika", 823L,
"B-25730", "22-07-2018", "Rajasthan", "Jaipur", "Rishabh", 23L,
"B-25731", "26-07-2018", "West Bengal", "Kolkata", "Akash", 457L,
"B-25732", "27-07-2018", "Karnataka", "Bangalore", "Anubhaw", 24L,
"B-25733", "28-07-2018", "Maharashtra", "Mumbai", "Dhirajendu", 25L,
"B-25734", "29-07-2018", "Madhya Pradesh", "Indore", "Pranav", 174L,
"B-25735", "30-07-2018", "Uttar Pradesh", "Lucknow", "Arindam", 206L,
"B-25736", "31-07-2018", "Maharashtra", "Mumbai", "Akshat", 21L,
"B-25737", "1/8/2018", "Madhya Pradesh", "Indore", "Shubham", 34L,
"B-25738", "2/8/2018", "Punjab", "Chandigarh", "Ayush", 9L,
"B-25739", "3/8/2018", "Haryana", "Chandigarh", "Daksh", 1279L,
"B-25740", "3/8/2018", "Maharashtra", "Mumbai", "Rane", 28L,
"B-25741", "3/8/2018", "Madhya Pradesh", "Indore", "Navdeep", 427L,
"B-25742", "3/8/2018", "Goa", "Goa", "Ashwin", 168L,
"B-25743", "7/8/2018", "Nagaland", "Kohima", "Aman", 1327L,
"B-25744", "8/8/2018", "Andhra Pradesh", "Hyderabad", "Devendra", 195L,
"B-25745", "9/8/2018", "Gujarat", "Ahmedabad", "Kartik", 115L,
"B-25746", "10/8/2018", "Maharashtra", "Pune", "Shivam", 668L,
"B-25747", "11/8/2018", "Madhya Pradesh", "Bhopal", "Harsh", 227L,
"B-25748", "12/8/2018", "Rajasthan", "Jaipur", "Nitant", 34L,
"B-25749", "13-08-2018", "Maharashtra", "Mumbai", "Ayush", 229L,
"B-25750", "14-08-2018", "Madhya Pradesh", "Indore", "Priyanshu", 54L,
"B-25751", "14-08-2018", "Maharashtra", "Mumbai", "Nishant", 269L,
"B-25752", "14-08-2018", "Madhya Pradesh", "Indore", "Vaibhav", 122L,
"B-25753", "17-08-2018", "Uttar Pradesh", "Lucknow", "Shivam", 105L,
"B-25754", "18-08-2018", "Bihar", "Patna", "Akshay", 450L,
"B-25755", "19-08-2018", "Kerala", "Thiruvananthapuram", "Shourya", 121L,
"B-25756", "20-08-2018", "Maharashtra", "Mumbai", "Mohan", 44L,
"B-25757", "21-08-2018", "Madhya Pradesh", "Indore", "Mohit", 7L,
"B-25758", "22-08-2018", "Himachal Pradesh", "Simla", "Shubham", 396L,
"B-25759", "23-08-2018", "Sikkim", "Gangtok", "Soumya", 97L,
"B-25760", "24-08-2018", "Goa", "Goa", "Pooja", 110L,
"B-25761", "25-08-2018", "Maharashtra", "Mumbai", "Surabhi", 312L,
"B-25762", "26-08-2018", "Madhya Pradesh", "Indore", "Anudeep", 9L,
"B-25763", "27-08-2018", "Gujarat", "Ahmedabad", "Noshiba", 6L,
"B-25764", "28-08-2018", "Maharashtra", "Pune", "Sanjova", 74L,
"B-25765", "29-08-2018", "Madhya Pradesh", "Bhopal", "Meghana", 534L,
"B-25766", "30-08-2018", "Rajasthan", "Jaipur", "Surabhi", 30L,
"B-25767", "31-08-2018", "West Bengal", "Kolkata", "Ashmeet", 61L,
"B-25768", "1/9/2018", "Karnataka", "Bangalore", "Shreyoshe", 6L,
"B-25769", "2/9/2018", "Maharashtra", "Mumbai", "Surbhi", 24L,
"B-25770", "2/9/2018", "Madhya Pradesh", "Indore", "Sakshi", 56L,
"B-25771", "2/9/2018", "Uttar Pradesh", "Lucknow", "Vaibhavi", 406L,
"B-25772", "2/9/2018", "Bihar", "Patna", "Sanjana", 624L,
"B-25773", "6/9/2018", "Kerala", "Thiruvananthapuram", "Shreya", 101L,
"B-25774", "7/9/2018", "Punjab", "Chandigarh", "Snehal", 1389L,
"B-25775", "8/9/2018", "Haryana", "Chandigarh", "Duhita", 651L,
"B-25776", "9/9/2018", "Maharashtra", "Mumbai", "Mousam", 13L,
"B-25777", "10/9/2018", "Madhya Pradesh", "Indore", "Aditi", 1021L,
"B-25778", "11/9/2018", "Maharashtra", "Mumbai", "Surabhi", 32L,
"B-25779", "12/9/2018", "Madhya Pradesh", "Indore", "Savi", 332L,
"B-25780", "13-09-2018", "Andhra Pradesh", "Hyderabad", "Teena", 288L,
"B-25781", "14-09-2018", "Gujarat", "Ahmedabad", "Rutuja", 27L,
"B-25782", "15-09-2018", "Maharashtra", "Mumbai", "Aayushi", 148L,
"B-25783", "15-09-2018", "Madhya Pradesh", "Indore", "Shivangi", 245L,
"B-25784", "15-09-2018", "Rajasthan", "Jaipur", "Rohit", 19L,
"B-25785", "15-09-2018", "West Bengal", "Kolkata", "Ayush", 224L,
"B-25786", "19-09-2018", "Karnataka", "Bangalore", "Abhishek", 58L,
"B-25787", "20-09-2018", "Jammu and Kashmir", "Kashmir", "Asish", 145L,
"B-25788", "21-09-2018", "Tamil Nadu", "Chennai", "Dinesh", 55L,
"B-25789", "22-09-2018", "Uttar Pradesh", "Lucknow", "Akshay", 7L,
"B-25790", "23-09-2018", "Bihar", "Patna", "Sajal", 24L,
"B-25791", "24-09-2018", "Kerala", "Thiruvananthapuram", "Avish", 86L,
"B-25792", "24-09-2018", "Maharashtra", "Mumbai", "Abhishek", 385L,
"B-25793", "24-09-2018", "Madhya Pradesh", "Indore", "Siddharth", 294L,
"B-25794", "24-09-2018", "Himachal Pradesh", "Simla", "Aditya", 444L,
"B-25795", "24-09-2018", "Sikkim", "Gangtok", "Sukant", 785L,
"B-25796", "24-09-2018", "Maharashtra", "Mumbai", "Sukrith", 258L,
"B-25797", "30-09-2018", "Madhya Pradesh", "Indore", "Sauptik", 83L,
"B-25798", "1/10/2018", "Andhra Pradesh", "Hyderabad", "Shishu", 166L,
"B-25799", "1/10/2018", "Gujarat", "Ahmedabad", "Divyansh", 934L,
"B-25800", "1/10/2018", "Maharashtra", "Pune", "I■■■■", 11L,
"B-25801", "1/10/2018", "Madhya Pradesh", "Bhopal", "Aryan", 41L,
"B-25802", "5/10/2018", "Maharashtra", "Mumbai", "Yash", 344L,
"B-25803", "5/10/2018", "Madhya Pradesh", "Indore", "Shivanshu", 1030L,
"B-25804", "5/10/2018", "Karnataka", "Bangalore", "Sudheer", 516L,
"B-25805", "5/10/2018", "Maharashtra", "Mumbai", "Ankit", 123L,
"B-25806", "6/10/2018", "Madhya Pradesh", "Indore", "Dhanraj", 610L,
"B-25807", "7/10/2018", "Uttar Pradesh", "Lucknow", "Vipul", 74L,
"B-25808", "8/10/2018", "Bihar", "Patna", "Apsingekar", 24L,
"B-25809", "9/10/2018", "Kerala", "Thiruvananthapuram", "Suman", 14L,
"B-25810", "10/10/2018", "Punjab", "Chandigarh", "Nripraj", 656L,
"B-25811", "10/10/2018", "Maharashtra", "Mumbai", "Utsav", 832L,
"B-25812", "10/10/2018", "Madhya Pradesh", "Indore", "K■■■■ij", 27L,
"B-25813", "10/10/2018", "Maharashtra", "Mumbai", "Hrisheekesh", 143L,
"B-25814", "10/10/2018", "Madhya Pradesh", "Indore", "Swapnil", 44L,
"B-25815", "10/10/2018", "Nagaland", "Kohima", "Harsh", 45L,
"B-25816", "12/10/2018", "Andhra Pradesh", "Hyderabad", "Mane", 16L,
"B-25817", "13-10-2018", "Maharashtra", "Mumbai", "Praneet", 37L,
"B-25818", "14-10-2018", "Madhya Pradesh", "Indore", "Sandeep", 17L,
"B-25819", "15-10-2018", "Madhya Pradesh", "Bhopal", "Ankur", 929L,
"B-25820", "16-10-2018", "Rajasthan", "Jaipur", "Dheeraj", 342L,
"B-25821", "16-10-2018", "West Bengal", "Kolkata", "Ajay", 1263L,
"B-25822", "18-10-2018", "Karnataka", "Bangalore", "Tejas", 674L,
"B-25823", "18-10-2018", "Maharashtra", "Mumbai", "Rohan", 32L,
"B-25824", "20-10-2018", "Madhya Pradesh", "Indore", "Shyam", 79L,
"B-25825", "21-10-2018", "Madhya Pradesh", "Indore", "Kartik", 20L,
"B-25826", "22-10-2018", "Maharashtra", "Mumbai", "Tanushree", 64L,
"B-25827", "23-10-2018", "Madhya Pradesh", "Indore", "Sheetal", 7L,
"B-25828", "24-10-2018", "Punjab", "Chandigarh", "Nikita", 327L,
"B-25829", "25-10-2018", "Haryana", "Chandigarh", "Apoorva", 27L,
"B-25830", "26-10-2018", "Himachal Pradesh", "Simla", "Aastha", 76L,
"B-25831", "27-10-2018", "Sikkim", "Gangtok", "Mahima", 73L,
"B-25832", "28-10-2018", "Maharashtra", "Mumbai", "Har■■■■a", 68L,
"B-25833", "29-10-2018", "Madhya Pradesh", "Indore", "Krishna", 523L,
"B-25834", "29-10-2018", "Andhra Pradesh", "Hyderabad", "Ananya", 44L,
"B-25835", "29-10-2018", "Gujarat", "Ahmedabad", "Moumita", 243L,
"B-25836", "29-10-2018", "Maharashtra", "Pune", "Arti", 1625L,
"B-25837", "29-10-2018", "Maharashtra", "Mumbai", "Palak", 1096L,
"B-25838", "29-10-2018", "Madhya Pradesh", "Indore", "Sanjana", 545L,
"B-25839", "30-10-2018", "West Bengal", "Kolkata", "Pranjali", 433L,
"B-25840", "31-10-2018", "Karnataka", "Bangalore", "Sneha", 245L,
"B-25841", "1/11/2018", "Maharashtra", "Mumbai", "Ashvini", 155L,
"B-25842", "2/11/2018", "Madhya Pradesh", "Indore", "Sheetal", 148L )
ui <- dashboardPage(
dashboardHeader(title="dashboard"),
dashboardSidebar(
sidebarMenu(
dateRangeInput(inputId = "date",
strong("Date Range"),
start = '2012-06-16',
end = '2019-12-31',
min = '2010-01-01',
max = '2019-12-31',
separator = "TO"))),
dashboardBody(
shinyDashboardThemes(
theme = "blue_gradient"
),
fluidPage(
plotlyOutput("state", height = 200),
plotlyOutput("city", height = 200),
plotlyOutput("customer", height = 200),
plotlyOutput("sales", height = 300),
dataTableOutput("datatable"))))
axis_titles <- . %>%
layout(
xaxis = list(title = ""),
yaxis = list(title = "Sales"))
server <- function(input, output, session) {
State <- reactiveVal()
City <- reactiveVal()
CustomerName<- reactiveVal()
Order_Date <- reactiveVal()
observeEvent(event_data("plotly_click", source = "State"), {
State(event_data("plotly_click", source = "State")$x)
City(NULL)
Order_Date(NULL)
})
observeEvent(event_data("plotly_click", source = "City"), {
City(event_data("plotly_click", source = "City")$x)
CustomerName(NULL)
Order_Date(NULL)
})
observeEvent(event_data("plotly_click", source = "CustomerName"), {
CustomerName(event_data("plotly_click", source = "CustomerName")$x)
Order_Date(NULL)
})
observeEvent(event_data("plotly_click", source = "Order_Date"), {
Order_Date(event_data("plotly_click", source = "Order_Date")$x)
})
output$state <- renderPlotly({
total%>%
count(State, wt = sales) %>%
dplyr::mutate(Order_Date >= input$dateRange[1] & Order_Date <= input$dateRange[2]) %>%
plot_ly(x = ~State, y = ~n, source = "State") %>%
axis_titles() %>%
layout(title = "State")
})
output$city <- renderPlotly({
if (is.null(State())) return(NULL)
total%>%
filter( State %in% State()) %>%
dplyr::mutate(Order_Date >= input$dateRange[1] & Order_Date <= input$dateRange[2]) %>%
count(City, wt = sales) %>%
plot_ly(x = ~City, y = ~n, source = "City") %>%
axis_titles() %>%
layout(title = State())
})
output$cutomer <- renderPlotly({
if (is.null(City())) return(NULL)
total%>%
filter(City %in% City()) %>%
count(CustomerName, wt = Sales) %>%
plot_ly(x = ~CustomerName, y = ~n, source = "CustomerName") %>%
axis_titles() %>%
layout(title = City())
})
output$sales <- renderPlotly({
if (is.null(CustomerName())) return(NULL)
total %>%
filter(CustomerName %in% CustomerName()) %>%
count(Order_Date, wt = sales) %>%
plot_ly(x = ~Order_Date, y = ~n, source = "Order_Date") %>%
add_lines() %>%
axis_titles() %>%
layout(title = paste(CustomerName(), "sales over time"))
})
output$datatable <- renderDataTable({
if (is.null(Order_Date())) return(NULL)
total %>%
filter(
CustomerName %in% CustomerName(),
as.Date(Order_Date) %in% as.Date(Order_Date())
)
})
}
runApp(list(ui = ui, server = server), launch.browser = TRUE)
