Interactive Data Visualization

Row

INDIAN AGRICULTURE 1997-2015

Total Production from 1997 to 2015

141

Total Area under Cultivation

2954

Crop Production Per Unit Area of Cultivation

47.79

Agriculture Growth 1997-2015

-26.72

Cultivation Area Growth 1997-2015

-21.77

Row

Production Across 1997-2015

Cultivation Area across 1997-2015

MAP

Column

MAP PRODUCTION STATEWISE

MAP CULTIVATION AREA STATEWISE

Animated Graph

Column

THE PRODUCTION STATEWISE Time Lapse

Cultivation Area Statewise Timelapse (1997-2015)

Column

AGRICULTURE PRODUCTION STATEWISE without AP, Kerala & Tamilnadu 1997-2015

Cultivation Area Statewise without West Bengal 1997-2015

Cropwise Production

Column

CROPWISE PRODUCTION (1997-2015)

CROPWISE CULTIVATION AREA 1997-2015

Crop Type Production & Area

Column

Production Vs Season 1997-2015

Cultivation Area Vs Season 1997-2015

Top Districts Production

Districtwise Production

Error in eval(expr, envir, enclos): object 'DistProTop50' not found

Data Table

Summary Report

Column

  State_Name        District_Name        Crop_Year       Season         
 Length:246091      Length:246091      Min.   :1997   Length:246091     
 Class :character   Class :character   1st Qu.:2002   Class :character  
 Mode  :character   Mode  :character   Median :2006   Mode  :character  
                                       Mean   :2006                     
                                       3rd Qu.:2010                     
                                       Max.   :2015                     
     Crop                Area           Production       
 Length:246091      Min.   :      0   Min.   :0.000e+00  
 Class :character   1st Qu.:     80   1st Qu.:7.700e+01  
 Mode  :character   Median :    582   Median :6.910e+02  
                    Mean   :  12003   Mean   :5.737e+05  
                    3rd Qu.:   4392   3rd Qu.:6.769e+03  
                    Max.   :8580100   Max.   :1.251e+09

Column

 [1] "Andaman and Nicobar Islands" "Andhra Pradesh"             
 [3] "Arunachal Pradesh"           "Assam"                      
 [5] "Bihar"                       "Chandigarh"                 
 [7] "Chhattisgarh"                "Dadra and Nagar Haveli"     
 [9] "Goa"                         "Gujarat"                    
[11] "Haryana"                     "Himachal Pradesh"           
[13] "Jammu and Kashmir "          "Jharkhand"                  
[15] "Karnataka"                   "Kerala"                     
[17] "Madhya Pradesh"              "Maharashtra"                
[19] "Manipur"                     "Meghalaya"                  
[21] "Mizoram"                     "Nagaland"                   
[23] "Odisha"                      "Puducherry"                 
[25] "Punjab"                      "Rajasthan"                  
[27] "Sikkim"                      "Tamil Nadu"                 
[29] "Telangana "                  "Tripura"                    
[31] "Uttar Pradesh"               "Uttarakhand"                
[33] "West Bengal"

  [1] "NICOBARS"                 "NORTH AND MIDDLE ANDAMAN"
  [3] "SOUTH ANDAMANS"           "ANANTAPUR"               
  [5] "CHITTOOR"                 "EAST GODAVARI"           
  [7] "GUNTUR"                   "KADAPA"                  
  [9] "KRISHNA"                  "KURNOOL"                 
 [11] "PRAKASAM"                 "SPSR NELLORE"            
 [13] "SRIKAKULAM"               "VISAKHAPATANAM"          
 [15] "VIZIANAGARAM"             "WEST GODAVARI"           
 [17] "ANJAW"                    "CHANGLANG"               
 [19] "DIBANG VALLEY"            "EAST KAMENG"             
 [21] "EAST SIANG"               "KURUNG KUMEY"            
 [23] "LOHIT"                    "LONGDING"                
 [25] "LOWER DIBANG VALLEY"      "LOWER SUBANSIRI"         
 [27] "NAMSAI"                   "PAPUM PARE"              
 [29] "TAWANG"                   "TIRAP"                   
 [31] "UPPER SIANG"              "UPPER SUBANSIRI"         
 [33] "WEST KAMENG"              "WEST SIANG"              
 [35] "BAKSA"                    "BARPETA"                 
 [37] "BONGAIGAON"               "CACHAR"                  
 [39] "CHIRANG"                  "DARRANG"                 
 [41] "DHEMAJI"                  "DHUBRI"                  
 [43] "DIBRUGARH"                "DIMA HASAO"              
 [45] "GOALPARA"                 "GOLAGHAT"                
 [47] "HAILAKANDI"               "JORHAT"                  
 [49] "KAMRUP"                   "KAMRUP METRO"            
 [51] "KARBI ANGLONG"            "KARIMGANJ"               
 [53] "KOKRAJHAR"                "LAKHIMPUR"               
 [55] "MARIGAON"                 "NAGAON"                  
 [57] "NALBARI"                  "SIVASAGAR"               
 [59] "SONITPUR"                 "TINSUKIA"                
 [61] "UDALGURI"                 "ARARIA"                  
 [63] "ARWAL"                    "AURANGABAD"              
 [65] "BANKA"                    "BEGUSARAI"               
 [67] "BHAGALPUR"                "BHOJPUR"                 
 [69] "BUXAR"                    "DARBHANGA"               
 [71] "GAYA"                     "GOPALGANJ"               
 [73] "JAMUI"                    "JEHANABAD"               
 [75] "KAIMUR (BHABUA)"          "KATIHAR"                 
 [77] "KHAGARIA"                 "KISHANGANJ"              
 [79] "LAKHISARAI"               "MADHEPURA"               
 [81] "MADHUBANI"                "MUNGER"                  
 [83] "MUZAFFARPUR"              "NALANDA"                 
 [85] "NAWADA"                   "PASHCHIM CHAMPARAN"      
 [87] "PATNA"                    "PURBI CHAMPARAN"         
 [89] "PURNIA"                   "ROHTAS"                  
 [91] "SAHARSA"                  "SAMASTIPUR"              
 [93] "SARAN"                    "SHEIKHPURA"              
 [95] "SHEOHAR"                  "SITAMARHI"               
 [97] "SIWAN"                    "SUPAUL"                  
 [99] "VAISHALI"                 "CHANDIGARH"              
[101] "BALOD"                    "BALODA BAZAR"            
[103] "BALRAMPUR"                "BASTAR"                  
[105] "BEMETARA"                 "BIJAPUR"                 
[107] "BILASPUR"                 "DANTEWADA"               
[109] "DHAMTARI"                 "DURG"                    
[111] "GARIYABAND"               "JANJGIR-CHAMPA"          
[113] "JASHPUR"                  "KABIRDHAM"               
[115] "KANKER"                   "KONDAGAON"               
[117] "KORBA"                    "KOREA"                   
[119] "MAHASAMUND"               "MUNGELI"                 
[121] "NARAYANPUR"               "RAIGARH"                 
[123] "RAIPUR"                   "RAJNANDGAON"             
[125] "SUKMA"                    "SURAJPUR"                
[127] "SURGUJA"                  "DADRA AND NAGAR HAVELI"  
[129] "NORTH GOA"                "SOUTH GOA"               
[131] "AHMADABAD"                "AMRELI"                  
[133] "ANAND"                    "BANAS KANTHA"            
[135] "BHARUCH"                  "BHAVNAGAR"               
[137] "DANG"                     "DOHAD"                   
[139] "GANDHINAGAR"              "JAMNAGAR"                
[141] "JUNAGADH"                 "KACHCHH"                 
[143] "KHEDA"                    "MAHESANA"                
[145] "NARMADA"                  "NAVSARI"                 
[147] "PANCH MAHALS"             "PATAN"                   
[149] "PORBANDAR"                "RAJKOT"                  
[151] "SABAR KANTHA"             "SURAT"                   
[153] "SURENDRANAGAR"            "TAPI"                    
[155] "VADODARA"                 "VALSAD"                  
[157] "AMBALA"                   "BHIWANI"                 
[159] "FARIDABAD"                "FATEHABAD"               
[161] "GURGAON"                  "HISAR"                   
[163] "JHAJJAR"                  "JIND"                    
[165] "KAITHAL"                  "KARNAL"                  
[167] "KURUKSHETRA"              "MAHENDRAGARH"            
[169] "MEWAT"                    "PALWAL"                  
[171] "PANCHKULA"                "PANIPAT"                 
[173] "REWARI"                   "ROHTAK"                  
[175] "SIRSA"                    "SONIPAT"                 
[177] "YAMUNANAGAR"              "CHAMBA"                  
[179] "HAMIRPUR"                 "KANGRA"                  
[181] "KINNAUR"                  "KULLU"                   
[183] "LAHUL AND SPITI"          "MANDI"                   
[185] "SHIMLA"                   "SIRMAUR"                 
[187] "SOLAN"                    "UNA"                     
[189] "ANANTNAG"                 "BADGAM"                  
[191] "BANDIPORA"                "BARAMULLA"               
[193] "DODA"                     "GANDERBAL"               
[195] "JAMMU"                    "KARGIL"                  
[197] "KATHUA"                   "KISHTWAR"                
[199] "KULGAM"                   "KUPWARA"                 
[201] "LEH LADAKH"               "POONCH"                  
[203] "PULWAMA"                  "RAJAURI"                 
[205] "RAMBAN"                   "REASI"                   
[207] "SAMBA"                    "SHOPIAN"                 
[209] "SRINAGAR"                 "UDHAMPUR"                
[211] "BOKARO"                   "CHATRA"                  
[213] "DEOGHAR"                  "DHANBAD"                 
[215] "DUMKA"                    "EAST SINGHBUM"           
[217] "GARHWA"                   "GIRIDIH"                 
[219] "GODDA"                    "GUMLA"                   
[221] "HAZARIBAGH"               "JAMTARA"                 
[223] "KHUNTI"                   "KODERMA"                 
[225] "LATEHAR"                  "LOHARDAGA"               
[227] "PAKUR"                    "PALAMU"                  
[229] "RAMGARH"                  "RANCHI"                  
[231] "SAHEBGANJ"                "SARAIKELA KHARSAWAN"     
[233] "SIMDEGA"                  "WEST SINGHBHUM"          
[235] "BAGALKOT"                 "BANGALORE RURAL"         
[237] "BELGAUM"                  "BELLARY"                 
[239] "BENGALURU URBAN"          "BIDAR"                   
[241] "CHAMARAJANAGAR"           "CHIKBALLAPUR"            
[243] "CHIKMAGALUR"              "CHITRADURGA"             
[245] "DAKSHIN KANNAD"           "DAVANGERE"               
[247] "DHARWAD"                  "GADAG"                   
[249] "GULBARGA"                 "HASSAN"                  
[251] "HAVERI"                   "KODAGU"                  
[253] "KOLAR"                    "KOPPAL"                  
[255] "MANDYA"                   "MYSORE"                  
[257] "RAICHUR"                  "RAMANAGARA"              
[259] "SHIMOGA"                  "TUMKUR"                  
[261] "UDUPI"                    "UTTAR KANNAD"            
[263] "YADGIR"                   "ALAPPUZHA"               
[265] "ERNAKULAM"                "IDUKKI"                  
[267] "KANNUR"                   "KASARAGOD"               
[269] "KOLLAM"                   "KOTTAYAM"                
[271] "KOZHIKODE"                "MALAPPURAM"              
[273] "PALAKKAD"                 "PATHANAMTHITTA"          
[275] "THIRUVANANTHAPURAM"       "THRISSUR"                
[277] "WAYANAD"                  "AGAR MALWA"              
[279] "ALIRAJPUR"                "ANUPPUR"                 
[281] "ASHOKNAGAR"               "BALAGHAT"                
[283] "BARWANI"                  "BETUL"                   
[285] "BHIND"                    "BHOPAL"                  
[287] "BURHANPUR"                "CHHATARPUR"              
[289] "CHHINDWARA"               "DAMOH"                   
[291] "DATIA"                    "DEWAS"                   
[293] "DHAR"                     "DINDORI"                 
[295] "GUNA"                     "GWALIOR"                 
[297] "HARDA"                    "HOSHANGABAD"             
[299] "INDORE"                   "JABALPUR"                
[301] "JHABUA"                   "KATNI"                   
[303] "KHANDWA"                  "KHARGONE"                
[305] "MANDLA"                   "MANDSAUR"                
[307] "MORENA"                   "NARSINGHPUR"             
[309] "NEEMUCH"                  "PANNA"                   
[311] "RAISEN"                   "RAJGARH"                 
[313] "RATLAM"                   "REWA"                    
[315] "SAGAR"                    "SATNA"                   
[317] "SEHORE"                   "SEONI"                   
[319] "SHAHDOL"                  "SHAJAPUR"                
[321] "SHEOPUR"                  "SHIVPURI"                
[323] "SIDHI"                    "SINGRAULI"               
[325] "TIKAMGARH"                "UJJAIN"                  
[327] "UMARIA"                   "VIDISHA"                 
[329] "AHMEDNAGAR"               "AKOLA"                   
[331] "AMRAVATI"                 "BEED"                    
[333] "BHANDARA"                 "BULDHANA"                
[335] "CHANDRAPUR"               "DHULE"                   
[337] "GADCHIROLI"               "GONDIA"                  
[339] "HINGOLI"                  "JALGAON"                 
[341] "JALNA"                    "KOLHAPUR"                
[343] "LATUR"                    "MUMBAI"                  
[345] "NAGPUR"                   "NANDED"                  
[347] "NANDURBAR"                "NASHIK"                  
[349] "OSMANABAD"                "PALGHAR"                 
[351] "PARBHANI"                 "PUNE"                    
[353] "RAIGAD"                   "RATNAGIRI"               
[355] "SANGLI"                   "SATARA"                  
[357] "SINDHUDURG"               "SOLAPUR"                 
[359] "THANE"                    "WARDHA"                  
[361] "WASHIM"                   "YAVATMAL"                
[363] "BISHNUPUR"                "CHANDEL"                 
[365] "CHURACHANDPUR"            "IMPHAL EAST"             
[367] "IMPHAL WEST"              "SENAPATI"                
[369] "TAMENGLONG"               "THOUBAL"                 
[371] "UKHRUL"                   "EAST GARO HILLS"         
[373] "EAST JAINTIA HILLS"       "EAST KHASI HILLS"        
[375] "NORTH GARO HILLS"         "RI BHOI"                 
[377] "SOUTH GARO HILLS"         "SOUTH WEST GARO HILLS"   
[379] "SOUTH WEST KHASI HILLS"   "WEST GARO HILLS"         
[381] "WEST JAINTIA HILLS"       "WEST KHASI HILLS"        
[383] "AIZAWL"                   "CHAMPHAI"                
[385] "KOLASIB"                  "LAWNGTLAI"               
[387] "LUNGLEI"                  "MAMIT"                   
[389] "SAIHA"                    "SERCHHIP"                
[391] "DIMAPUR"                  "KIPHIRE"                 
[393] "KOHIMA"                   "LONGLENG"                
[395] "MOKOKCHUNG"               "MON"                     
[397] "PEREN"                    "PHEK"                    
[399] "TUENSANG"                 "WOKHA"                   
[401] "ZUNHEBOTO"                "ANUGUL"                  
[403] "BALANGIR"                 "BALESHWAR"               
[405] "BARGARH"                  "BHADRAK"                 
[407] "BOUDH"                    "CUTTACK"                 
[409] "DEOGARH"                  "DHENKANAL"               
[411] "GAJAPATI"                 "GANJAM"                  
[413] "JAGATSINGHAPUR"           "JAJAPUR"                 
[415] "JHARSUGUDA"               "KALAHANDI"               
[417] "KANDHAMAL"                "KENDRAPARA"              
[419] "KENDUJHAR"                "KHORDHA"                 
[421] "KORAPUT"                  "MALKANGIRI"              
[423] "MAYURBHANJ"               "NABARANGPUR"             
[425] "NAYAGARH"                 "NUAPADA"                 
[427] "PURI"                     "RAYAGADA"                
[429] "SAMBALPUR"                "SONEPUR"                 
[431] "SUNDARGARH"               "KARAIKAL"                
[433] "MAHE"                     "PONDICHERRY"             
[435] "YANAM"                    "AMRITSAR"                
[437] "BARNALA"                  "BATHINDA"                
[439] "FARIDKOT"                 "FATEHGARH SAHIB"         
[441] "FAZILKA"                  "FIROZEPUR"               
[443] "GURDASPUR"                "HOSHIARPUR"              
[445] "JALANDHAR"                "KAPURTHALA"              
[447] "LUDHIANA"                 "MANSA"                   
[449] "MOGA"                     "MUKTSAR"                 
[451] "NAWANSHAHR"               "PATHANKOT"               
[453] "PATIALA"                  "RUPNAGAR"                
[455] "S.A.S NAGAR"              "SANGRUR"                 
[457] "TARN TARAN"               "AJMER"                   
[459] "ALWAR"                    "BANSWARA"                
[461] "BARAN"                    "BARMER"                  
[463] "BHARATPUR"                "BHILWARA"                
[465] "BIKANER"                  "BUNDI"                   
[467] "CHITTORGARH"              "CHURU"                   
[469] "DAUSA"                    "DHOLPUR"                 
[471] "DUNGARPUR"                "GANGANAGAR"              
[473] "HANUMANGARH"              "JAIPUR"                  
[475] "JAISALMER"                "JALORE"                  
[477] "JHALAWAR"                 "JHUNJHUNU"               
[479] "JODHPUR"                  "KARAULI"                 
[481] "KOTA"                     "NAGAUR"                  
[483] "PALI"                     "PRATAPGARH"              
[485] "RAJSAMAND"                "SAWAI MADHOPUR"          
[487] "SIKAR"                    "SIROHI"                  
[489] "TONK"                     "UDAIPUR"                 
[491] "EAST DISTRICT"            "NORTH DISTRICT"          
[493] "SOUTH DISTRICT"           "WEST DISTRICT"           
[495] "ARIYALUR"                 "COIMBATORE"              
[497] "CUDDALORE"                "DHARMAPURI"              
[499] "DINDIGUL"                 "ERODE"                   
[501] "KANCHIPURAM"              "KANNIYAKUMARI"           
[503] "KARUR"                    "KRISHNAGIRI"             
[505] "MADURAI"                  "NAGAPATTINAM"            
[507] "NAMAKKAL"                 "PERAMBALUR"              
[509] "PUDUKKOTTAI"              "RAMANATHAPURAM"          
[511] "SALEM"                    "SIVAGANGA"               
[513] "THANJAVUR"                "THE NILGIRIS"            
[515] "THENI"                    "THIRUVALLUR"             
[517] "THIRUVARUR"               "TIRUCHIRAPPALLI"         
[519] "TIRUNELVELI"              "TIRUPPUR"                
[521] "TIRUVANNAMALAI"           "TUTICORIN"               
[523] "VELLORE"                  "VILLUPURAM"              
[525] "VIRUDHUNAGAR"             "ADILABAD"                
[527] "HYDERABAD"                "KARIMNAGAR"              
[529] "KHAMMAM"                  "MAHBUBNAGAR"             
[531] "MEDAK"                    "NALGONDA"                
[533] "NIZAMABAD"                "RANGAREDDI"              
[535] "WARANGAL"                 "DHALAI"                  
[537] "GOMATI"                   "KHOWAI"                  
[539] "NORTH TRIPURA"            "SEPAHIJALA"              
[541] "SOUTH TRIPURA"            "UNAKOTI"                 
[543] "WEST TRIPURA"             "AGRA"                    
[545] "ALIGARH"                  "ALLAHABAD"               
[547] "AMBEDKAR NAGAR"           "AMETHI"                  
[549] "AMROHA"                   "AURAIYA"                 
[551] "AZAMGARH"                 "BAGHPAT"                 
[553] "BAHRAICH"                 "BALLIA"                  
[555] "BANDA"                    "BARABANKI"               
[557] "BAREILLY"                 "BASTI"                   
[559] "BIJNOR"                   "BUDAUN"                  
[561] "BULANDSHAHR"              "CHANDAULI"               
[563] "CHITRAKOOT"               "DEORIA"                  
[565] "ETAH"                     "ETAWAH"                  
[567] "FAIZABAD"                 "FARRUKHABAD"             
[569] "FATEHPUR"                 "FIROZABAD"               
[571] "GAUTAM BUDDHA NAGAR"      "GHAZIABAD"               
[573] "GHAZIPUR"                 "GONDA"                   
[575] "GORAKHPUR"                "HAPUR"                   
[577] "HARDOI"                   "HATHRAS"                 
[579] "JALAUN"                   "JAUNPUR"                 
[581] "JHANSI"                   "KANNAUJ"                 
[583] "KANPUR DEHAT"             "KANPUR NAGAR"            
[585] "KASGANJ"                  "KAUSHAMBI"               
[587] "KHERI"                    "KUSHI NAGAR"             
[589] "LALITPUR"                 "LUCKNOW"                 
[591] "MAHARAJGANJ"              "MAHOBA"                  
[593] "MAINPURI"                 "MATHURA"                 
[595] "MAU"                      "MEERUT"                  
[597] "MIRZAPUR"                 "MORADABAD"               
[599] "MUZAFFARNAGAR"            "PILIBHIT"                
[601] "RAE BARELI"               "RAMPUR"                  
[603] "SAHARANPUR"               "SAMBHAL"                 
[605] "SANT KABEER NAGAR"        "SANT RAVIDAS NAGAR"      
[607] "SHAHJAHANPUR"             "SHAMLI"                  
[609] "SHRAVASTI"                "SIDDHARTH NAGAR"         
[611] "SITAPUR"                  "SONBHADRA"               
[613] "SULTANPUR"                "UNNAO"                   
[615] "VARANASI"                 "ALMORA"                  
[617] "BAGESHWAR"                "CHAMOLI"                 
[619] "CHAMPAWAT"                "DEHRADUN"                
[621] "HARIDWAR"                 "NAINITAL"                
[623] "PAURI GARHWAL"            "PITHORAGARH"             
[625] "RUDRA PRAYAG"             "TEHRI GARHWAL"           
[627] "UDAM SINGH NAGAR"         "UTTAR KASHI"             
[629] "24 PARAGANAS NORTH"       "24 PARAGANAS SOUTH"      
[631] "BANKURA"                  "BARDHAMAN"               
[633] "BIRBHUM"                  "COOCHBEHAR"              
[635] "DARJEELING"               "DINAJPUR DAKSHIN"        
[637] "DINAJPUR UTTAR"           "HOOGHLY"                 
[639] "HOWRAH"                   "JALPAIGURI"              
[641] "MALDAH"                   "MEDINIPUR EAST"          
[643] "MEDINIPUR WEST"           "MURSHIDABAD"             
[645] "NADIA"                    "PURULIA"

 [1] 2000 2001 2002 2003 2004 2005 2006 2010 1997 1998 1999 2007 2008 2009 2011
[16] 2012 2013 2014 2015

[1] "Kharif     " "Whole Year " "Autumn     " "Rabi       " "Summer     "
[6] "Winter     "

  [1] "Arecanut"                  "Other Kharif pulses"      
  [3] "Rice"                      "Banana"                   
  [5] "Cashewnut"                 "Coconut "                 
  [7] "Dry ginger"                "Sugarcane"                
  [9] "Sweet potato"              "Tapioca"                  
 [11] "Black pepper"              "Dry chillies"             
 [13] "other oilseeds"            "Turmeric"                 
 [15] "Maize"                     "Moong(Green Gram)"        
 [17] "Urad"                      "Arhar/Tur"                
 [19] "Groundnut"                 "Sunflower"                
 [21] "Bajra"                     "Castor seed"              
 [23] "Cotton(lint)"              "Horse-gram"               
 [25] "Jowar"                     "Korra"                    
 [27] "Ragi"                      "Tobacco"                  
 [29] "Gram"                      "Wheat"                    
 [31] "Masoor"                    "Sesamum"                  
 [33] "Linseed"                   "Safflower"                
 [35] "Onion"                     "other misc. pulses"       
 [37] "Samai"                     "Small millets"            
 [39] "Coriander"                 "Potato"                   
 [41] "Other  Rabi pulses"        "Soyabean"                 
 [43] "Beans & Mutter(Vegetable)" "Bhindi"                   
 [45] "Brinjal"                   "Citrus Fruit"             
 [47] "Cucumber"                  "Grapes"                   
 [49] "Mango"                     "Orange"                   
 [51] "other fibres"              "Other Fresh Fruits"       
 [53] "Other Vegetables"          "Papaya"                   
 [55] "Pome Fruit"                "Tomato"                   
 [57] "Rapeseed &Mustard"         "Mesta"                    
 [59] "Cowpea(Lobia)"             "Lemon"                    
 [61] "Pome Granet"               "Sapota"                   
 [63] "Cabbage"                   "Peas  (vegetable)"        
 [65] "Niger seed"                "Bottle Gourd"             
 [67] "Sannhamp"                  "Varagu"                   
 [69] "Garlic"                    "Ginger"                   
 [71] "Oilseeds total"            "Pulses total"             
 [73] "Jute"                      "Peas & beans (Pulses)"    
 [75] "Blackgram"                 "Paddy"                    
 [77] "Pineapple"                 "Barley"                   
 [79] "Khesari"                   "Guar seed"                
 [81] "Moth"                      "Other Cereals & Millets"  
 [83] "Cond-spcs other"           "Turnip"                   
 [85] "Carrot"                    "Redish"                   
 [87] "Arcanut (Processed)"       "Atcanut (Raw)"            
 [89] "Cashewnut Processed"       "Cashewnut Raw"            
 [91] "Cardamom"                  "Rubber"                   
 [93] "Bitter Gourd"              "Drum Stick"               
 [95] "Jack Fruit"                "Snak Guard"               
 [97] "Pump Kin"                  "Tea"                      
 [99] "Coffee"                    "Cauliflower"              
[101] "Other Citrus Fruit"        "Water Melon"              
[103] "Total foodgrain"           "Kapas"                    
[105] "Colocosia"                 "Lentil"                   
[107] "Bean"                      "Jobster"                  
[109] "Perilla"                   "Rajmash Kholar"           
[111] "Ricebean (nagadal)"        "Ash Gourd"                
[113] "Beet Root"                 "Lab-Lab"                  
[115] "Ribed Guard"               "Yam"                      
[117] "Apple"                     "Peach"                    
[119] "Pear"                      "Plums"                    
[121] "Litchi"                    "Ber"                      
[123] "Other Dry Fruit"           "Jute & mesta"

Acknowledgement & Disclaimer

Column

ACKNOWLEDGEMENT & DISCLAIMER

This Flexdashboard is based on the data provided on Indian Agriculture by open data platform–
The data on agriculture contains data from 1997-2015. As the data source doesn’t explain about how variables like Production and Cultivation Area is measured, the interpretation of data analysis should be done with caution.
There are many missing values in data. The missing values are replaced by zeros for smooth mathematical operations on the data.

*Author have not cross checked the validity of data. There may be misreported and not reported data which can affect data analysis presented in the dashboard.

The dashboard should be used only for “how to make a dashboard” purpose, not for agriculture education due to data source limitations.

About Us

DASHBOARD PREPARED BY (CONTACT FOR MACHINE LEARNING TRAINING, COACHING & CONSULTING)

Dr AMITA SHARMA Post Doc from Erasmus University, Rotterdam, the Netherlands Assistant Professor Institute of Agri Business Management, Swami Keshwanand Rajasthan Agricultural University, Bikaner (Raj),India Blog: www.thinkingai.in
ARUN KUMAR SHARMA Machine Learning Enthusiast 13 Years of Financial Services Marketing Exp Blogger, Writer and Machine Learning Consutlant Certified Business Analytics Professional Certified in Predictive Analytics, Indian Institute of Mnamagement,IIMx Bangalore Certified in Macroeconomic Forecasting, International Monetary Fund(IMFx) Certified in Text Analytics, openSAP Email: aks10000@gmail.com Tel:9468567418

---
title: "ML Dashboard"
output: 
  flexdashboard::flex_dashboard:
    theme: cerulean
    orientation: rows
    vertical_layout: fill
    social: ["twitter","facebook", "menu"]
    source_code: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(error = TRUE)
library(plotly)
library(flexdashboard)
library(knitr)
library(DT)
library(Rcpp)
library(rpivotTable)
library(ggplot2)
library(dplyr)
library(openintro)
library(highcharter)
library(plotrix)
library(tis)
library(sp)
library(maptools)
library(rgeos)
library(ggmap)
library(scales)
library(RColorBrewer)
library(gganimate)
library(gapminder)
library(gifski)
library(gridExtra)

set.seed(8888)
```


```{r}

data<-read.csv("agriculture.csv", header=TRUE, stringsAsFactors = FALSE)


```


```{r}

mycolors=c("blue","darkgreen","pink","darkorange","red","grey","yellow","brown")

```

Interactive Data Visualization {data-navmenu="Project Sections"}
================================================================


Row
------------------------------------------------------------------------

### INDIAN AGRICULTURE 1997-2015

```{r}

valueBox(paste("INDIAN AGRICULTURE 1997-2015"),icon="fa-pencil")

```



### Total Production from 1997 to 2015

```{r}

data[is.na(data)]=0

valueBox(round(sum(data$Production/1000000000, na.rm=TRUE),digits = 0),
         icon="fa-random",
         caption = "Total Production (in INR Billon) 1997-2015",
         color="info")


```


### Total Area under Cultivation

```{r}

valueBox(round(sum(data$Area/1000000, na.rm=TRUE),digits = 0),
         icon="fa-random",
         color="success",
         caption="Cultivation Area (in Million Ha) 1997-2015")

```

### Crop Production Per Unit Area of Cultivation

```{r}

valueBox(round(((sum(data$Production, na.rm=TRUE))/(sum(data$Area, na.rm=TRUE))),digits = 2),
           icon="fa-random",
         color="success",
         caption="Agriculuture Production INR Per Unit Area 1997-2015")

```


### Agriculture Growth 1997-2015

```{r}

YearProduction=as.data.frame(aggregate(data$Production, by=list(data$Crop_Year),FUN=sum, na.rm=TRUE))

YearProduction$Year=YearProduction$Group.1
YearProduction$Production=YearProduction$x
YearProduction$Group.1=NULL
YearProduction$x=NULL

GrowthRateProd=round(mean(100*diff(log(YearProduction$Production))),digits = 2)

valueBox(round(GrowthRateProd,digits = 2),
         icon="fa-random",
         color="success",
         caption="Agriculture Production Mean Growth Rate 1997-2015")

```

### Cultivation Area Growth 1997-2015

```{r}

YearArea=as.data.frame(aggregate(data$Area, by=list(data$Crop_Year),FUN=sum, na.rm=TRUE))

YearArea$Year=YearArea$Group.1
YearArea$Area=YearArea$x
YearArea$Group.1=NULL
YearArea$x=NULL

GrowthRateArea=round(mean(100*diff(log(YearArea$Area))),digits = 2)

valueBox(round(GrowthRateArea,digits = 2),
         icon="fa-random",
         color="success",
         caption="Cultivation Area Mean Growth Rate 1997-2015")

```



Row
---------------------------------

### Production Across 1997-2015

```{r}


g1=ggplot(YearProduction,aes(x=Year,y=Production))+geom_line()

ggplotly(g1)

```

### Cultivation Area across 1997-2015

```{r}

g2=ggplot(YearArea,aes(x=Year,y=Area))+geom_line()

fig2=ggplotly(g2)

fig2

```



MAP {data-navmenu="Project Sections"}
======================================

Column {.tabset}
---------------------------------------

### MAP PRODUCTION STATEWISE

```{r}
conts <-data%>%group_by(State_Name)%>%summarise(totalProd=sum(Production,na.rm = TRUE))

conts=as.data.frame(conts)

conts$State_Name=toupper(conts$State_Name)

shp <- rgdal::readOGR('C:\\Users\\arunkumar\\Desktop\\R\\indiaMap\\India Shape\\india_st.shp',verbose = FALSE)

shp.f <- fortify(shp, region = "STATE")

conts[is.na(conts$totalProd)]=0

merge.shp.coef<-merge(shp.f,conts, by.x="id", by.y="State_Name", all.x=TRUE)

final.plot<-merge.shp.coef[order(merge.shp.coef$order), ]
cnames <- aggregate(cbind(long, lat) ~ id, data=final.plot, FUN=function(x) mean(range(x)))

require(ggplot2)
require(plotly)

g1=ggplot(final.plot)+geom_polygon(final.plot,mapping=aes(x = long, y = lat, group = group, fill = totalProd),color = "green", size = 0.25)+coord_map()+scale_fill_gradient(name="TotalProduction",limits=c(0,100000000000), low="white",high="green")+labs(title="Total Agriculture Production Statewise 1997-2015")+xlab('Longitude')+ylab('Latitude')

g1
```


### MAP CULTIVATION AREA STATEWISE

```{r}

conts1 <-data%>%group_by(State_Name)%>%summarise(totalArea=sum(Area,na.rm = TRUE))

conts1=as.data.frame(conts1)

shp <- rgdal::readOGR('C:\\Users\\arunkumar\\Desktop\\R\\indiaMap\\India Shape\\india_st.shp',verbose = FALSE)

shp.f <- fortify(shp, region = "STATE")

conts1[is.na(conts1$totalProd)]=0

conts1$State_Name=toupper(conts1$State_Name)

merge.shp.coef1<-merge(shp.f,conts1, by.x="id", by.y="State_Name", all.x=TRUE)

merge.shp.coef1[is.na(merge.shp.coef1)]=0

final.plot1<-merge.shp.coef1[order(merge.shp.coef1$order), ]

cnames1 <- aggregate(cbind(long, lat) ~ id, data=final.plot1, FUN=function(x) mean(range(x)))

require(ggplot2)
require(plotly)

g2=ggplot()+geom_polygon(final.plot1,mapping=aes(x = long, y = lat, group = group, fill = totalArea),color = "green", size = 0.25)+coord_map()+scale_fill_gradient(name="Total Cultivation Area",limits=c(0,1000000000), low="white",high="brown")+labs(title="Total Cultivation Area Statewise 1997-2015")+xlab('Longitude')+ylab('Latitude')

g2
```



Animated Graph {data-navmenu="Project Sections"}
================================================

Column{.tabset}
----------------------------------
### THE PRODUCTION STATEWISE Time Lapse
```{r }

state1=data[data$State_Name=="Andaman and Nicobar Islands",]

Year_Prod1=aggregate(state1$Production,by=list(state1$Crop_Year), FUN=sum)

UYear1=unique(Year_Prod1$Group.1)

state2=data[data$State_Name=="Andhra Pradesh",]

Year_Prod2=aggregate(state2$Production,by=list(state2$Crop_Year), FUN=sum)

UYear2=unique(Year_Prod2$Group.1)

state3=data[data$State_Name=="Arunachal Pradesh",]

Year_Prod3=aggregate(state3$Production,by=list(state3$Crop_Year), FUN=sum)

UYear3=unique(Year_Prod3$Group.1)


state4=data[data$State_Name=="Assam",]

Year_Prod4=aggregate(state4$Production,by=list(state4$Crop_Year), FUN=sum)

UYear4=unique(Year_Prod4$Group.1)




state5=data[data$State_Name=="Bihar",]

Year_Prod5=aggregate(state5$Production,by=list(state5$Crop_Year), FUN=sum)

UYear5=unique(Year_Prod5$Group.1)




state6=data[data$State_Name=="Chandigarh",]

Year_Prod6=aggregate(state6$Production,by=list(state6$Crop_Year), FUN=sum)

UYear6=unique(Year_Prod6$Group.1)



state7=data[data$State_Name=="Chhattisgarh",]

Year_Prod7=aggregate(state7$Production,by=list(state7$Crop_Year), FUN=sum)

UYear7=unique(Year_Prod7$Group.1)



state8=data[data$State_Name=="Dadra and Nagar Haveli",]

Year_Prod8=aggregate(state8$Production,by=list(state8$Crop_Year), FUN=sum)

UYear8=unique(Year_Prod8$Group.1)


state9=data[data$State_Name=="Goa",]

Year_Prod9=aggregate(state9$Production,by=list(state9$Crop_Year), FUN=sum)

UYear9=unique(Year_Prod9$Group.1)


state10=data[data$State_Name=="Gujarat",]

Year_Prod10=aggregate(state10$Production,by=list(state10$Crop_Year), FUN=sum)

UYear10=unique(Year_Prod10$Group.1)


state11=data[data$State_Name=="Haryana",]

Year_Prod11=aggregate(state11$Production,by=list(state11$Crop_Year), FUN=sum)

UYear11=unique(Year_Prod11$Group.1)


state12=data[data$State_Name=="Himachal Pradesh",]

Year_Prod12=aggregate(state12$Production,by=list(state12$Crop_Year), FUN=sum)

UYear12=unique(Year_Prod12$Group.1)


state13=data[data$State_Name=="Jammu and Kashmir ",]

Year_Prod13=aggregate(state13$Production,by=list(state13$Crop_Year), FUN=sum)

UYear13=unique(Year_Prod13$Group.1)



state14=data[data$State_Name=="Jharkhand",]

Year_Prod14=aggregate(state14$Production,by=list(state14$Crop_Year), FUN=sum)

UYear14=unique(Year_Prod14$Group.1)



state15=data[data$State_Name=="Karnataka",]

Year_Prod15=aggregate(state15$Production,by=list(state15$Crop_Year), FUN=sum)

UYear15=unique(Year_Prod15$Group.1)



state16=data[data$State_Name=="Kerala",]

Year_Prod16=aggregate(state16$Production,by=list(state16$Crop_Year), FUN=sum)

UYear16=unique(Year_Prod16$Group.1)



state17=data[data$State_Name=="Madhya Pradesh",]

Year_Prod17=aggregate(state17$Production,by=list(state17$Crop_Year), FUN=sum)

UYear17=unique(Year_Prod17$Group.1)



state18=data[data$State_Name=="Maharashtra",]

Year_Prod18=aggregate(state18$Production,by=list(state18$Crop_Year), FUN=sum)

UYear18=unique(Year_Prod18$Group.1)



state19=data[data$State_Name=="Manipur",]

Year_Prod19=aggregate(state19$Production,by=list(state19$Crop_Year), FUN=sum)

UYear19=unique(Year_Prod19$Group.1)


state20=data[data$State_Name=="Meghalaya",]

Year_Prod20=aggregate(state20$Production,by=list(state20$Crop_Year), FUN=sum)

UYear20=unique(Year_Prod20$Group.1)



state21=data[data$State_Name=="Mizoram",]

Year_Prod21=aggregate(state21$Production,by=list(state21$Crop_Year), FUN=sum)

UYear21=unique(Year_Prod21$Group.1)


state22=data[data$State_Name=="Nagaland",]

Year_Prod22=aggregate(state22$Production,by=list(state22$Crop_Year), FUN=sum)

UYear22=unique(Year_Prod22$Group.1)


state23=data[data$State_Name=="Odisha",]

Year_Prod23=aggregate(state23$Production,by=list(state23$Crop_Year), FUN=sum)

UYear23=unique(Year_Prod23$Group.1)



state24=data[data$State_Name=="Puducherry",]

Year_Prod24=aggregate(state24$Production,by=list(state24$Crop_Year), FUN=sum)

UYear24=unique(Year_Prod24$Group.1)



state25=data[data$State_Name=="Punjab",]

Year_Prod25=aggregate(state25$Production,by=list(state25$Crop_Year), FUN=sum)

UYear25=unique(Year_Prod25$Group.1)

state26=data[data$State_Name=="Rajasthan",]

Year_Prod26=aggregate(state26$Production,by=list(state26$Crop_Year), FUN=sum)

UYear26=unique(Year_Prod26$Group.1)

state27=data[data$State_Name=="Sikkim",]

Year_Prod27=aggregate(state27$Production,by=list(state27$Crop_Year), FUN=sum)

UYear27=unique(Year_Prod27$Group.1)



state28=data[data$State_Name=="Tamil Nadu",]

Year_Prod28=aggregate(state28$Production,by=list(state28$Crop_Year), FUN=sum)

UYear28=unique(Year_Prod28$Group.1)



state29=data[data$State_Name=="Telangana ",]

Year_Prod29=aggregate(state29$Production,by=list(state29$Crop_Year), FUN=sum)

UYear29=unique(Year_Prod29$Group.1)



state30=data[data$State_Name=="Tripura",]

Year_Prod30=aggregate(state30$Production,by=list(state30$Crop_Year), FUN=sum)

UYear30=unique(Year_Prod30$Group.1)


state31=data[data$State_Name=="Uttar Pradesh",]

Year_Prod31=aggregate(state31$Production,by=list(state31$Crop_Year), FUN=sum)

UYear31=unique(Year_Prod31$Group.1)


state32=data[data$State_Name=="Uttarakhand",]

Year_Prod32=aggregate(state32$Production,by=list(state32$Crop_Year), FUN=sum)

UYear32=unique(Year_Prod32$Group.1)


state33=data[data$State_Name=="West Bengal",]

Year_Prod33=aggregate(state33$Production,by=list(state33$Crop_Year), FUN=sum)

UYear33=unique(Year_Prod33$Group.1)


ListUYear=list(UYear1,UYear2,UYear3,UYear4,UYear5,UYear6,UYear7,UYear8,UYear9,UYear10,UYear11,UYear12,UYear13,UYear14,UYear15,UYear16,UYear17,UYear18,UYear19,UYear20,UYear21,UYear22,UYear23,UYear24,UYear25,UYear26,UYear27,UYear28,UYear29,UYear30,UYear31,UYear32,UYear33)

VectorYear=seq.int(1997,2015,1)
VectorYear=rep(VectorYear,33)
VectorYear=as.data.frame(VectorYear)

StateName=unique(data$State_Name)
StateName=rep(StateName,19)
StateName=as.data.frame(StateName)

newdf=cbind.data.frame(StateName,VectorYear)

Prod=aggregate(data$Production, by=list(data$State_Name,data$Crop_Year), FUN=sum)

Area=aggregate(data$Area, by=list(data$State_Name,data$Crop_Year), FUN=sum)

Prod$Group.1=toupper(Prod$Group.1)

Area$Group.1=toupper(Area$Group.1)

newdf$StateName=toupper(newdf$StateName)

newdf$StateYear=paste(newdf$StateName, newdf$VectorYear)

Prod$StateYear=paste(Prod$Group.1,Prod$Group.2)

Area$StateYear=paste(Area$Group.1,Area$Group.2)

newdf$Prod <- Prod$x[match(newdf$StateYear, Prod$StateYear)]

newdf$Prod[is.na(newdf$Prod)]=0

newdf$Area=Area$x[match(newdf$StateYear,Area$StateYear)]

newdf$Area[is.na(newdf$Area)]=0

newdf$Prod=round(newdf$Prod,digits = 0)

newdf$Area=round(newdf$Area, digits = 0)

newdf$StateName=as.factor(newdf$StateName)

newdf=newdf[order(newdf$StateName),]
newdf=newdf[order(newdf$VectorYear),]

```


```{r, dev="png", interval=1}

theme_set(theme_bw())

newdf$VectorYear=round(newdf$VectorYear,digits = 0)

p2=ggplot(newdf,aes(x=StateName, y=Prod, label=StateName,color=StateName))+
  geom_point(stat = 'identity', size=5, shape=3)+geom_segment(aes(
    y=6.5*10^9,
    x=StateName,
    yend=Prod,
    xend=StateName))+
  geom_text(color="black",size=3)+
  coord_flip()+
  theme(legend.position = "none")+
  labs(title="Year: {frame_time}",x="State Name",y="Prod")+
  transition_time(VectorYear)+
  ease_aes("linear")


animate(p2)

```

### Cultivation Area Statewise Timelapse (1997-2015)

```{r}

p3=ggplot(newdf,aes(x=StateName, y=Area, label=StateName,color=StateName))+
  geom_point(stat = 'identity', size=5, shape=3)+geom_segment(aes(
    y=80000000,
    x=StateName,
    yend=Area,
    xend=StateName))+
  geom_text(color="black",size=3)+
  coord_flip()+
  theme(legend.position = "none")+
  labs(title="Year: {frame_time}",x="State Name",y="Area")+
  transition_time(VectorYear)+
  ease_aes("linear")


animate(p3)
```

Column {.tabset}
-------------------------------------

### AGRICULTURE PRODUCTION STATEWISE without AP, Kerala & Tamilnadu 1997-2015

```{r}

newdfwoKTN=newdf[!(newdf$StateName=="KERALA"),]
newdfwoKTN=newdf[!(newdf$StateName=="TAMIL NADU"),]
newdfwoKTN=newdf[!(newdf$StateName=="ANDHRA PRADESH"),]

p4=ggplot(newdfwoKTN,aes(x=StateName, y=Prod, label=StateName,color=StateName))+
  geom_point(stat = 'identity', size=5, shape=3)+geom_segment(aes(
    y=1000000,
    x=StateName,
    yend=Prod,
    xend=StateName))+
  geom_text(color="black",size=3)+
  coord_flip()+
  theme(legend.position = "none")+
  labs(title="Year: {frame_time}",x="State Name",y="Prod")+
  transition_time(VectorYear)+
  ease_aes("linear")


animate(p4)
```


### Cultivation Area Statewise without West Bengal 1997-2015

```{r}
newdfwoWB=newdf[!(newdf$StateName=="WEST BENGAL"),]

p5=ggplot(newdfwoWB,aes(x=StateName, y=Area, label=StateName,color=StateName))+
  geom_point(stat = 'identity', size=5, shape=3)+geom_segment(aes(
    y=1000000,
    x=StateName,
    yend=Area,
    xend=StateName))+
  geom_text(color="black",size=3)+
  coord_flip()+
  theme(legend.position = "none")+
  labs(title="Year: {frame_time}",x="State Name",y="Area")+
  transition_time(VectorYear)+
  ease_aes("linear")

animate(p5)
```


Cropwise Production {data-navmenu="Project Sections"}
==========================================

Column{.tabset}
---------------------------------------------

### CROPWISE PRODUCTION (1997-2015)

```{r}

CropSum=aggregate(data$Production, by=list(data$Crop), FUN=sum)

CropSum$Crop=CropSum$Group.1
CropSum$Prod=CropSum$x
CropSum$Group.1=NULL
CropSum$x=NULL

CropSum[is.na(CropSum)]=0

CropSum=CropSum[order(-CropSum$Prod),]

CropSum[CropSum$Prod==0,]=NA

CropSum=na.omit(CropSum)

CropSum=CropSum[1:10,]

pie3D(x=CropSum$Prod,labels=CropSum$Crop,explode=0.3,main = "Crop Wise Production 1997-2015", col = rainbow(length(CropSum$Crop)))


```


### CROPWISE CULTIVATION AREA 1997-2015

```{r}
CropSumA=aggregate(data$Area, by=list(data$Crop), FUN=sum)

CropSumA$Crop=CropSumA$Group.1
CropSumA$Area=CropSumA$x
CropSumA$Group.1=NULL
CropSumA$x=NULL

CropSumA[is.na(CropSumA)]=0

CropSumA=CropSumA[order(-CropSumA$Area),]

CropSumA[CropSumA$Area==0,]=NA

CropSumA=na.omit(CropSumA)

CropSumA=CropSumA[1:10,]

pie3D(x=CropSumA$Area, labels=CropSumA$Crop,explode=0.3, main = "Crop Wise Cultivation Area 1997-2015", col = rainbow(length(CropSumA$Crop)),radius = 1)
```

Crop Type Production & Area {data-navmenu="Project Sections"}
==============================================================

Column {.tabset}
----------------------------------------------------------
### Production Vs Season 1997-2015

```{r}
CropType=aggregate(data$Production, by=list(data$Season), FUN=sum, na.rm=TRUE)

CropType$Crop=CropType$Group.1
CropType$Prod=CropType$x
CropType$Group.1=NULL
CropType$x=NULL

CropType=CropType[order(-CropType$Prod),]

pie(x=CropType$Prod, labels=CropType$Crop, main = "Production Vs Season 1997-2015", col = rainbow(length(CropType$Crop)),radius = 1)
```


### Cultivation Area Vs Season 1997-2015

```{r}
CropTypeA=aggregate(data$Area, by=list(data$Season), FUN=sum, na.rm=TRUE)

CropTypeA$Crop=CropTypeA$Group.1
CropTypeA$Area=CropTypeA$x
CropTypeA$Group.1=NULL
CropTypeA$x=NULL

CropTypeA=CropTypeA[order(-CropTypeA$Area),]

pie(x=CropTypeA$Area, labels=CropTypeA$Crop, main = "Cultiavation Area Vs Season 1997-2015", col = rainbow(length(CropTypeA$Crop)),radius = 1)
```


Top Districts Production {data-navmenu="Project Sections"}
===========================================================

### Districtwise Production

```{r}

DistPro=aggregate(data$Production, by=list(data$District_Name), FUN=sum, na.rm=TRUE)
DistPro$District=DistPro$Group.1
DistPro$Prod=DistPro$x
DistPro$Group.1=NULL
DistPro$x=NULL

DistTop=DistProTop50$District

DistPro=DistPro[order(-DistPro$Prod),]

DistPro=DistPro[!(DistPro$Prod==0),]

DistProTop50=DistPro[1:50,]

ggplot(DistProTop50, aes(x=reorder(District,-Prod), y=Prod,fill=as.factor(District)))+geom_bar(stat = "identity")+geom_text(aes(label=round(Prod,digits = 0)), hjust=-0.01, color="black", size=1.5)+coord_flip()+theme(legend.position ="none",text = element_text(size=7),axis.text.x = element_text(angle=45, vjust=1))

```


Data Table {data-navmenu="Project Sections"}
============================================

### Data Table

```{r}

datatable(final.plot,
          caption="Statewise Agriculture Production (Value) 1997-2015",
          rownames = T,
          filter="top",
          options = list(pageLength=25))


```





Summary Report {data-navmenu="Project Sections"}
=========================================================================

Column   {.tabset}
-----------------------------------------

```{r}

summary(data)

```


Column 
------------------------------------

```{r}

unique(data$State_Name)

unique(data$District_Name)

unique(data$Crop_Year)

unique(data$Season)

unique(data$Crop)

```


Acknowledgement & Disclaimer
=========================================

Column
---------------------------------------

### ACKNOWLEDGEMENT & DISCLAIMER

* This Flexdashboard is based on the data provided on Indian Agriculture
by open data platform--
* The data on agriculture contains data from 1997-2015. As the data source doesn't explain about how variables like Production and Cultivation Area is measured, the interpretation of data analysis should be done with caution.

* There are many missing values in data. The missing values are replaced by zeros for smooth mathematical operations on the data.

*Author have not cross checked the validity of data. There may be misreported and not reported data which can affect data analysis presented in the dashboard.

* The dashboard should be used only for "how to make a dashboard" purpose, not for agriculture education due to data source limitations.

About Us {data-navmenu="Project Sections"}
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### DASHBOARD PREPARED BY (CONTACT FOR MACHINE LEARNING TRAINING, COACHING & CONSULTING)


*  Dr AMITA SHARMA
      Post Doc from Erasmus University, Rotterdam, the Netherlands
      Assistant Professor
      Institute of Agri Business Management, 
      Swami Keshwanand Rajasthan Agricultural University, 
      Bikaner (Raj),India
      Blog: www.thinkingai.in

*  ARUN KUMAR SHARMA
      Machine Learning Enthusiast
      13 Years of Financial Services Marketing Exp
      Blogger, Writer and Machine Learning Consutlant
      Certified Business Analytics Professional
      Certified in Predictive Analytics, Indian Institute of     Mnamagement,IIMx Bangalore
      Certified in Macroeconomic Forecasting, International Monetary Fund(IMFx)
      Certified in Text Analytics, openSAP
      Email: aks10000@gmail.com
      Tel:9468567418