r-highcharter

Here you go, both graphs operate independently of each other’s drilldowns. I simplified your code as well as you had a lot of observes and reactives that were not needed (in this example at least). cate<-c(“Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”drinks”,”drinks”,”groceries”,”groceries”,”groceries”,”dairy”,”dairy”,”dairy”,”dairy”,”groceries”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”groceries”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”drinks”,”drinks”,”groceries”,”groceries”,”groceries”,”dairy”,”dairy”,”dairy”,”dairy”,”groceries”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”groceries”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”drinks”,”drinks”,”groceries”,”groceries”,”groceries”,”dairy”,”dairy”,”dairy”,”dairy”,”groceries”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”groceries”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”Furniture”,”drinks”,”drinks”,”groceries”,”groceries”,”groceries”,”dairy”,”dairy”,”dairy”,”dairy”,”groceries”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”technology”,”groceries”) Sub_Product<-c(“nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”nov”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”oct”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”sept”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”,”aug”) Main_Product<-c(“outdoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”outdoor”,”n&o”,”n&o”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”n&o”,”outdoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”outdoor”,”n&o”,”n&o”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”n&o”,”outdoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”outdoor”,”n&o”,”n&o”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”n&o”,”outdoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”outdoor”,”n&o”,”n&o”,”indoor”,”indoor”,”indoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”indoor”,”outdoor”,”outdoor”,”indoor”,”outdoor”,”n&o”) Product<-c(“abc”,”def”,”ghh”,”hig”,”lmn”,”opk”,”cba”,”dfw”,”ewr”,”csad”,”wer”,”casd”,”were”,”csad”,”rt”,”hgf”,”qeq”,”hgf”,”qer”,”qer2″,”erqerq”,”qdq”,”dwqer”,”qerqe”,”erqererq”,”e2342″,”ererq”,”qewrw”,”qrerqr”,”qreqw”,”qerqe”,”abc”,”def”,”ghh”,”hig”,”lmn”,”opk”,”cba”,”dfw”,”ewr”,”csad”,”wer”,”casd”,”were”,”csad”,”rt”,”hgf”,”qeq”,”hgf”,”qer”,”qer2″,”erqerq”,”qdq”,”dwqer”,”qerqe”,”erqererq”,”e2342″,”ererq”,”qewrw”,”qrerqr”,”qreqw”,”qerqe”,”abc”,”def”,”ghh”,”hig”,”lmn”,”opk”,”cba”,”dfw”,”ewr”,”csad”,”wer”,”casd”,”were”,”csad”,”rt”,”hgf”,”qeq”,”hgf”,”qer”,”qer2″,”erqerq”,”qdq”,”dwqer”,”qerqe”,”erqererq”,”e2342″,”ererq”,”qewrw”,”qrerqr”,”qreqw”,”qerqe”,”abc”,”def”,”ghh”,”hig”,”lmn”,”opk”,”cba”,”dfw”,”ewr”,”csad”,”wer”,”casd”,”were”,”csad”,”rt”,”hgf”,”qeq”,”hgf”,”qer”,”qer2″,”erqerq”,”qdq”,”dwqer”,”qerqe”,”erqererq”,”e2342″,”ererq”,”qewrw”,”qrerqr”,”qreqw”,”qerqe”) sum1<-c(43,90,135,125,87,4,23,120,4,127,70,68,129,63,131,90,67,110,90,119,81,68,15,29,49,11,76,82,65,83,25,43,90,135,125,87,4,23,120,4,127,70,68,129,63,131,90,67,110,90,119,81,68,15,29,49,11,76,82,65,83,25,43,90,135,125,87,4,23,120,4,127,70,68,129,63,131,90,67,110,90,119,81,68,15,29,49,11,76,82,65,83,25,43,90,135,125,87,4,23,120,4,127,70,68,129,63,131,90,67,110,90,119,81,68,15,29,49,11,76,82,65,83,25) sum2<-c(14567,11111,3287,3563,9633,11162,3044,8437,4382,11250,3932,5587,4175,9708,4970,8388,10673,4301,12475,13494,12519,5632,3898,12472,4381,14085,10041,4276,12953,11143,12905,14567,11111,3287,3563,9633,11162,3044,8437,4382,11250,3932,5587,4175,9708,4970,8388,10673,4301,12475,13494,12519,5632,3898,12472,4381,14085,10041,4276,12953,11143,12905,14567,11111,3287,3563,9633,11162,3044,8437,4382,11250,3932,5587,4175,9708,4970,8388,10673,4301,12475,13494,12519,5632,3898,12472,4381,14085,10041,4276,12953,11143,12905,14567,11111,3287,3563,9633,11162,3044,8437,4382,11250,3932,5587,4175,9708,4970,8388,10673,4301,12475,13494,12519,5632,3898,12472,4381,14085,10041,4276,12953,11143,12905) avg1<-c(48,132,115,83,84,77,111,102,113,96,136,97,89,97,66,18,123,29,37,118,66,87,52,11,97,25,144,21,40,6,36,48,132,115,83,84,77,111,102,113,96,136,97,89,97,66,18,123,29,37,118,66,87,52,11,97,25,144,21,40,6,36,48,132,115,83,84,77,111,102,113,96,136,97,89,97,66,18,123,29,37,118,66,87,52,11,97,25,144,21,40,6,36,48,132,115,83,84,77,111,102,113,96,136,97,89,97,66,18,123,29,37,118,66,87,52,11,97,25,144,21,40,6,36) avg2<-c(6775,3142,3916,12828,9889,4025,11374,10594,4263,8871,11229,4787,7478,5316,5299,14068,3981,12993,12435,13845,4320,7472,14285,10221,11883,7783,13980,11426,13120,8632,14540,6775,3142,3916,12828,9889,4025,11374,10594,4263,8871,11229,4787,7478,5316,5299,14068,3981,12993,12435,13845,4320,7472,14285,10221,11883,7783,13980,11426,13120,8632,14540,6775,3142,3916,12828,9889,4025,11374,10594,4263,8871,11229,4787,7478,5316,5299,14068,3981,12993,12435,13845,4320,7472,14285,10221,11883,7783,13980,11426,13120,8632,14540,6775,3142,3916,12828,9889,4025,11374,10594,4263,8871,11229,4787,7478,5316,5299,14068,3981,12993,12435,13845,4320,7472,14285,10221,11883,7783,13980,11426,13120,8632,14540) dat<-data.frame(cate,Sub_Product,Main_Product,Product,sum1,sum2,avg1,avg2, stringsAsFactors = FALSE) ACClist<-c(“sum1″,”sum2”) AVGlist<-c(“avg1″,”avg2”) library (shinyjs) library (tidyr) library (data.table) … Read more