Business Analytics Lab Worksheet 04
CME Group Foundation Business Analytics Lab
Elio Vento
Summer, July 2017
About
R can be used to make basic visual analytics, which can be helpful in understanding the data holistically. Additionally, R can help find correlations between variables and create scatter plots.
In this lab we will delve deeper into the marketing data set with R visual analytics and use Tableau to visualize the credit risk data set.
Setup
Make sure to download the folder titled ‘bsad_lab04’ zip folder and extract the folder to unzip it. Next, we must set this folder as the working directory. The way to do this is to open R Studio, go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Now, follow the directions to complete the lab.
Task 1
First, read in the creditrisk.csv file and make sure all the columns are captured and check the first couple of rows of the dataset “head(mydata)”
mydata = read.csv(file="data/states_purchase_orders.csv")
head(mydata)#Vendor State Data
vendorstate = mydata$VENDOR.STATE
vendorstate [1] LA LA LA LA LA LA LA LA LA LA LA LA LA CA LA
[16] LA LA TX LA NJ LA LA LA LA LA VA VA VA VA PA
[31] PA PA , LA LA LA LA LA DC DC DC DC DC LA LA
[46] LA LA LA LA LA LA TX TX LA LA LA LA LA LA LA
[61] LA LA LA LA LA NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ
[76] NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ
[91] NJ NJ NJ NJ LA GA LA LA LA LA WI CA CA CA CA
[106] CA LA LA LA LA NY NY LA LA LA LA LA LA LA LA
[121] LA LA LA LA LA LA LA IL MI LA LA LA LA TX TX
[136] TX TX TX LA LA LA LA TX LA LA LA LA LA LA LA
[151] IL LA LA NJ NJ NJ NJ NJ NJ NJ NJ NJ NJ LA LA
[166] LA LA LA LA LA LA LA IA IA IA IA IA LA LA LA
[181] LA LA LA LA LA LA LA LA LA LA LA LA LA MS LA
[196] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[211] TX TX LA OH OH OH OH LA LA CA CA CA CA IA IA
[226] IA IA IA IA IA IA IA IA IA IA IA IA IA IA IA
[241] IA IA LA IA IA IA IA IA IA IA GA LA LA LA GA
[256] LA LA LA LA LA LA LA LA LA LA LA LA NJ NJ LA
[271] CA LA LA LA LA LA LA LA LA LA TX LA LA LA LA
[286] IA IA IA IA IA IA IA IA IA IA IA IA LA LA LA
[301] LA TX TX TX CA CA DC TX TX TX TX TX TX LA TX
[316] TX TX TX TX TX TX LA LA LA LA LA LA LA LA LA
[331] LA LA LA LA LA TN TN KS TX TX TX TX FL TX TX
[346] TX TX TX TX CO IL IL TX TX TX TX TX TX TX TX
[361] TX TX LA LA LA LA LA LA LA LA LA OH OH OH OH
[376] LA LA LA LA LA LA NC LA LA LA WI WI WI WI WI
[391] WI WI WI WI WI WI WI WI WI WI WI WI WI WI WI
[406] WI WI WI LA LA LA LA GA GA GA GA GA GA LA LA
[421] LA LA LA LA LA LA RI RI RI RI RI RI RI RI RI
[436] RI RI RI RI LA LA LA LA LA LA LA LA LA LA LA
[451] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[466] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[481] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[496] LA LA LA LA LA LA LA LA LA MO LA LA IA IA LA
[511] LA LA WA LA CA LA LA LA LA LA LA TX TX LA LA
[526] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[541] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[556] LA CA CA CA CA CA CA CA CA CA CA CA CA CA CA
[571] CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA
[586] CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA
[601] CA CA CA CA CA CA CA CA CA CA CA CA CA CA CA
[616] CA CA LA CA CA CA CA LA FL LA NV LA OH LA LA
[631] MA MA MA TX TX LA LA TX LA LA LA LA LA LA LA
[646] LA LA LA LA LA LA LA LA LA LA LA LA LA CA LA
[661] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[676] LA CA LA LA LA LA LA LA LA LA LA LA LA LA LA
[691] TX NJ LA LA LA LA TX TX TX TX TX TX TX TX LA
[706] LA LA FL FL LA LA TX TX TX TX TX TX TX CA CA
[721] CA CA CA CA CA CA CA CA CA CA CA LA LA LA ,
[736] , , NJ LA LA LA LA LA LA LA LA LA LA LA LA
[751] GA GA GA PA PA PA PA PA CA CA CA CA CA TX TX
[766] LA LA LA LA LA LA GA GA LA KY LA LA LA LA LA
[781] LA LA LA LA LA LA MO LA LA LA LA LA LA LA LA
[796] LA LA LA CA LA LA LA LA CT LA LA LA LA LA LA
[811] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[826] NY NY FL FL FL LA LA LA LA LA LA LA LA VA VA
[841] VA VA VA VA VA VA VA VA VA VA VA VA VA VA VA
[856] VA VA LA LA LA LA LA LA LA LA LA LA LA LA LA
[871] LA LA LA LA LA LA LA LA LA LA LA OH LA LA LA
[886] LA LA NJ NJ LA LA LA LA LA LA LA LA LA LA LA
[901] LA LA AZ LA LA LA LA FL LA LA LA LA LA LA LA
[916] LA LA LA LA LA MN LA LA LA LA LA LA LA LA LA
[931] LA LA LA LA LA LA LA LA LA LA LA LA VA VA VA
[946] VA VA CA CA CA CA CA TX TX TX TX LA LA LA LA
[961] LA LA LA LA LA LA LA LA LA LA LA LA LA LA LA
[976] DC LA NJ NJ NJ NJ NJ NJ NJ NJ LA TX LA LA LA
[991] LA LA LA PA PA LA LA CA CA LA
[ reached getOption("max.print") -- omitted 31452 entries ]
49 Levels: , AL AR AZ CA CD CO CT DC FL GA ... WV#Department Name Data
departmentname = mydata$DEPARTMENT.NAME
departmentname [1] DEPARTMENT OF PUBLIC WORKS
[2] DEPARTMENT OF PUBLIC WORKS
[3] ROAD REHABILITATION PROGRAM
[4] COMMUNITY DEVELOPMENT PROGRAMS
[5] ROAD REHABILITATION PROGRAM
[6] CONSTRUCTION PROJECTS
[7] CONSTRUCTION PROJECTS
[8] CONSTRUCTION PROJECTS
[9] CONSTRUCTION PROJECTS
[10] POLICE DEPARTMENT
[11] POLICE DEPARTMENT
[12] POLICE DEPARTMENT
[13] POLICE DEPARTMENT
[14] PARISH ATTORNEY
[15] DEPT OF FLEET MANAGEMENT
[16] ROAD REHABILITATION PROGRAM
[17] DEPARTMENT OF PUBLIC WORKS
[18] LIBRARY BOARD OF CONTROL
[19] CONSTRUCTION PROJECTS
[20] DEPARTMENT OF PUBLIC WORKS
[21] LIBRARY BOARD OF CONTROL
[22] OFC. OF HOMELAND SECURITY & EMERGENCY PREPA
[23] DEPT OF FLEET MANAGEMENT
[24] DEPT OF FLEET MANAGEMENT
[25] DEPT OF FLEET MANAGEMENT
[26] POLICE DEPARTMENT
[27] POLICE DEPARTMENT
[28] POLICE DEPARTMENT
[29] POLICE DEPARTMENT
[30] DEPARTMENT OF PUBLIC WORKS
[31] DEPARTMENT OF PUBLIC WORKS
[32] DEPARTMENT OF PUBLIC WORKS
[33] DEPARTMENT OF PUBLIC WORKS
[34] DEPT OF FLEET MANAGEMENT
[35] DEPT OF FLEET MANAGEMENT
[36] DEPT OF FLEET MANAGEMENT
[37] COMMUNITY DEVELOPMENT PROGRAMS
[38] COMMUNITY DEVELOPMENT PROGRAMS
[39] EMERGENCY MEDICAL SERVICES
[40] EMERGENCY MEDICAL SERVICES
[41] EMERGENCY MEDICAL SERVICES
[42] EMERGENCY MEDICAL SERVICES
[43] EMERGENCY MEDICAL SERVICES
[44] CITY CONSTABLE
[45] CITY CONSTABLE
[46] CITY CONSTABLE
[47] CITY CONSTABLE
[48] POLICE DEPARTMENT
[49] POLICE DEPARTMENT
[50] POLICE DEPARTMENT
[51] POLICE DEPARTMENT
[52] DEPT OF TRANSPORTATION AND DRAINAGE
[53] DEPT OF TRANSPORTATION AND DRAINAGE
[54] POLICE DEPARTMENT
[55] POLICE DEPARTMENT
[56] POLICE DEPARTMENT
[57] POLICE DEPARTMENT
[58] POLICE DEPARTMENT
[59] POLICE DEPARTMENT
[60] POLICE DEPARTMENT
[61] POLICE DEPARTMENT
[62] POLICE DEPARTMENT
[63] POLICE DEPARTMENT
[64] POLICE DEPARTMENT
[65] POLICE DEPARTMENT
[66] HUMAN RESOURCES
[67] HUMAN RESOURCES
[68] HUMAN RESOURCES
[69] HUMAN RESOURCES
[70] HUMAN RESOURCES
[71] HUMAN RESOURCES
[72] HUMAN RESOURCES
[73] HUMAN RESOURCES
[74] HUMAN RESOURCES
[75] HUMAN RESOURCES
[76] HUMAN RESOURCES
[77] HUMAN RESOURCES
[78] HUMAN RESOURCES
[79] HUMAN RESOURCES
[80] HUMAN RESOURCES
[81] HUMAN RESOURCES
[82] HUMAN RESOURCES
[83] HUMAN RESOURCES
[84] HUMAN RESOURCES
[85] HUMAN RESOURCES
[86] HUMAN RESOURCES
[87] HUMAN RESOURCES
[88] HUMAN RESOURCES
[89] HUMAN RESOURCES
[90] HUMAN RESOURCES
[91] HUMAN RESOURCES
[92] HUMAN RESOURCES
[93] HUMAN RESOURCES
[94] HUMAN RESOURCES
[95] ROAD REHABILITATION PROGRAM
[96] GREATER BATON ROUGE AIRPORT DISTRICT
[97] POLICE DEPARTMENT
[98] HUMAN DEVELOPMENT AND SERVICES
[99] HUMAN DEVELOPMENT AND SERVICES
[100] HUMAN DEVELOPMENT AND SERVICES
[101] CONSTRUCTION PROJECTS
[102] DEPARTMENT OF PUBLIC WORKS
[103] DEPARTMENT OF PUBLIC WORKS
[104] DEPARTMENT OF PUBLIC WORKS
[105] DEPARTMENT OF PUBLIC WORKS
[106] DEPARTMENT OF PUBLIC WORKS
[107] POLICE DEPARTMENT
[108] DEPT OF MAINTENANCE
[109] CONSTRUCTION PROJECTS
[110] ROAD REHABILITATION PROGRAM
[111] HUMAN DEVELOPMENT AND SERVICES
[112] HUMAN DEVELOPMENT AND SERVICES
[113] DEPT OF MAINTENANCE
[114] POLICE DEPARTMENT
[115] POLICE DEPARTMENT
[116] POLICE DEPARTMENT
[117] POLICE DEPARTMENT
[118] POLICE DEPARTMENT
[119] POLICE DEPARTMENT
[120] POLICE DEPARTMENT
[121] POLICE DEPARTMENT
[122] POLICE DEPARTMENT
[123] POLICE DEPARTMENT
[124] POLICE DEPARTMENT
[125] POLICE DEPARTMENT
[126] POLICE DEPARTMENT
[127] POLICE DEPARTMENT
[128] POLICE DEPARTMENT
[129] INFORMATION SERVICES
[130] INFORMATION SERVICES
[131] INFORMATION SERVICES
[132] INFORMATION SERVICES
[133] DEPT OF TRANSPORTATION AND DRAINAGE
[134] FINANCE DEPARTMENT
[135] FINANCE DEPARTMENT
[136] FINANCE DEPARTMENT
[137] FINANCE DEPARTMENT
[138] FINANCE DEPARTMENT
[139] COUNCIL ADMINISTRATOR
[140] FINANCE DEPARTMENT
[141] DEPT OF MAINTENANCE
[142] DEPT OF MAINTENANCE
[143] FINANCE DEPARTMENT
[144] DEPT OF MAINTENANCE
[145] DEPT OF MAINTENANCE
[146] DEPARTMENT OF PUBLIC WORKS
[147] DEPT OF FLEET MANAGEMENT
[148] DEPT OF FLEET MANAGEMENT
[149] OFFICE OF THE MAYOR-PRESIDENT
[150] POLICE DEPARTMENT
[151] GREATER BATON ROUGE AIRPORT DISTRICT
[152] HUMAN DEVELOPMENT AND SERVICES
[153] HUMAN DEVELOPMENT AND SERVICES
[154] POLICE DEPARTMENT
[155] POLICE DEPARTMENT
[156] POLICE DEPARTMENT
[157] POLICE DEPARTMENT
[158] POLICE DEPARTMENT
[159] POLICE DEPARTMENT
[160] POLICE DEPARTMENT
[161] POLICE DEPARTMENT
[162] POLICE DEPARTMENT
[163] POLICE DEPARTMENT
[164] LIBRARY BOARD OF CONTROL
[165] LIBRARY BOARD OF CONTROL
[166] LIBRARY BOARD OF CONTROL
[167] DEPARTMENT OF PUBLIC WORKS
[168] DEPARTMENT OF PUBLIC WORKS
[169] DEPARTMENT OF PUBLIC WORKS
[170] FINANCE DEPARTMENT
[171] FINANCE DEPARTMENT
[172] FINANCE DEPARTMENT
[173] DEPT OF MAINTENANCE
[174] DEPT OF MAINTENANCE
[175] DEPT OF MAINTENANCE
[176] DEPT OF MAINTENANCE
[177] DEPT OF MAINTENANCE
[178] DISTRICT COURT
[179] POLICE DEPARTMENT
[180] LIBRARY BOARD OF CONTROL
[181] FIRE DEPARTMENT
[182] LIBRARY BOARD OF CONTROL
[183] LIBRARY BOARD OF CONTROL
[184] DEPT OF BUILDINGS AND GROUNDS
[185] DEPT OF FLEET MANAGEMENT
[186] DEPT OF FLEET MANAGEMENT
[187] DEPT OF FLEET MANAGEMENT
[188] DEPARTMENT OF PUBLIC WORKS
[189] DEPARTMENT OF PUBLIC WORKS
[190] COMMUNITY DEVELOPMENT PROGRAMS
[191] OFFICE OF THE MAYOR-PRESIDENT
[192] CONSTRUCTION PROJECTS
[193] DEPT OF BUILDINGS AND GROUNDS
[194] DEPARTMENT OF PUBLIC WORKS
[195] COMMUNITY DEVELOPMENT PROGRAMS
[196] COMMUNITY DEVELOPMENT PROGRAMS
[197] COMMUNITY DEVELOPMENT PROGRAMS
[198] EMERGENCY MEDICAL SERVICES
[199] EMERGENCY MEDICAL SERVICES
[200] DEPARTMENT OF PUBLIC WORKS
[201] DEPARTMENT OF PUBLIC WORKS
[202] DEPARTMENT OF PUBLIC WORKS
[203] DEPARTMENT OF PUBLIC WORKS
[204] DEPARTMENT OF PUBLIC WORKS
[205] DEPARTMENT OF PUBLIC WORKS
[206] DEPARTMENT OF PUBLIC WORKS
[207] DEPARTMENT OF PUBLIC WORKS
[208] DEPARTMENT OF PUBLIC WORKS
[209] MOSQUITO & RODENT CONTROL
[210] HUMAN DEVELOPMENT AND SERVICES
[211] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[212] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[213] HUMAN DEVELOPMENT AND SERVICES
[214] LIBRARY BOARD OF CONTROL
[215] LIBRARY BOARD OF CONTROL
[216] LIBRARY BOARD OF CONTROL
[217] LIBRARY BOARD OF CONTROL
[218] CONSTRUCTION PROJECTS
[219] CONSTRUCTION PROJECTS
[220] CITY COURT
[221] CITY COURT
[222] CITY COURT
[223] CITY COURT
[224] DEPT OF MAINTENANCE
[225] DEPT OF MAINTENANCE
[226] DEPT OF MAINTENANCE
[227] DEPT OF MAINTENANCE
[228] DEPT OF MAINTENANCE
[229] DEPT OF MAINTENANCE
[230] DEPT OF MAINTENANCE
[231] DEPT OF MAINTENANCE
[232] DEPT OF MAINTENANCE
[233] DEPT OF MAINTENANCE
[234] DEPT OF MAINTENANCE
[235] DEPT OF MAINTENANCE
[236] DEPT OF MAINTENANCE
[237] DEPT OF MAINTENANCE
[238] DEPT OF MAINTENANCE
[239] DEPT OF MAINTENANCE
[240] DEPT OF MAINTENANCE
[241] DEPT OF MAINTENANCE
[242] DEPT OF MAINTENANCE
[243] PARISH ATTORNEY
[244] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[245] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[246] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[247] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[248] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[249] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[250] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[251] LIBRARY BOARD OF CONTROL
[252] DEPT OF FLEET MANAGEMENT
[253] DEPT OF FLEET MANAGEMENT
[254] DEPT OF FLEET MANAGEMENT
[255] LIBRARY BOARD OF CONTROL
[256] OFFICE OF THE MAYOR-PRESIDENT
[257] OFFICE OF THE MAYOR-PRESIDENT
[258] OFFICE OF THE MAYOR-PRESIDENT
[259] OFFICE OF THE MAYOR-PRESIDENT
[260] OFFICE OF THE MAYOR-PRESIDENT
[261] OFFICE OF THE MAYOR-PRESIDENT
[262] OFFICE OF THE MAYOR-PRESIDENT
[263] OFFICE OF THE MAYOR-PRESIDENT
[264] CONSTRUCTION PROJECTS
[265] SHERIFF'S OFFICE
[266] SHERIFF'S OFFICE
[267] SHERIFF'S OFFICE
[268] EMERGENCY MEDICAL SERVICES
[269] EMERGENCY MEDICAL SERVICES
[270] CONSTRUCTION PROJECTS
[271] DEPARTMENT OF PUBLIC WORKS
[272] POLICE DEPARTMENT
[273] POLICE DEPARTMENT
[274] POLICE DEPARTMENT
[275] POLICE DEPARTMENT
[276] POLICE DEPARTMENT
[277] DEPARTMENT OF PUBLIC WORKS
[278] DEPARTMENT OF PUBLIC WORKS
[279] COUNCIL ADMINISTRATOR
[280] LIBRARY BOARD OF CONTROL
[281] EMERGENCY MEDICAL SERVICES
[282] DEPT OF FLEET MANAGEMENT
[283] DEPT OF FLEET MANAGEMENT
[284] COUNCIL ADMINISTRATOR
[285] DEPT OF MAINTENANCE
[286] DEPT OF MAINTENANCE
[287] DEPT OF MAINTENANCE
[288] DEPT OF MAINTENANCE
[289] DEPT OF MAINTENANCE
[290] DEPT OF MAINTENANCE
[291] DEPT OF MAINTENANCE
[292] DEPT OF MAINTENANCE
[293] DEPT OF MAINTENANCE
[294] DEPT OF MAINTENANCE
[295] DEPT OF MAINTENANCE
[296] DEPT OF MAINTENANCE
[297] DEPT OF MAINTENANCE
[298] DEPT OF BUILDINGS AND GROUNDS
[299] DEPARTMENT OF PUBLIC WORKS
[300] CITY COURT
[301] CITY COURT
[302] FINANCE DEPARTMENT
[303] FINANCE DEPARTMENT
[304] FINANCE DEPARTMENT
[305] POLICE DEPARTMENT
[306] POLICE DEPARTMENT
[307] OFFICE OF THE MAYOR-PRESIDENT
[308] METROPOLITAN COUNCIL
[309] METROPOLITAN COUNCIL
[310] METROPOLITAN COUNCIL
[311] METROPOLITAN COUNCIL
[312] METROPOLITAN COUNCIL
[313] METROPOLITAN COUNCIL
[314] OFFICE OF THE MAYOR-PRESIDENT
[315] LIBRARY BOARD OF CONTROL
[316] DEPT OF TRANSPORTATION AND DRAINAGE
[317] DEPT OF TRANSPORTATION AND DRAINAGE
[318] DEPT OF TRANSPORTATION AND DRAINAGE
[319] DEPT OF TRANSPORTATION AND DRAINAGE
[320] DEPARTMENT OF PUBLIC WORKS
[321] DEPARTMENT OF PUBLIC WORKS
[322] HUMAN DEVELOPMENT AND SERVICES
[323] HUMAN DEVELOPMENT AND SERVICES
[324] DEPARTMENT OF PUBLIC WORKS
[325] DEPARTMENT OF PUBLIC WORKS
[326] LIBRARY BOARD OF CONTROL
[327] LIBRARY BOARD OF CONTROL
[328] POLICE DEPARTMENT
[329] POLICE DEPARTMENT
[330] POLICE DEPARTMENT
[331] POLICE DEPARTMENT
[332] POLICE DEPARTMENT
[333] DOWNTOWN DEVELOPMENT DISTRICT
[334] HUMAN DEVELOPMENT AND SERVICES
[335] HUMAN DEVELOPMENT AND SERVICES
[336] EMERGENCY MEDICAL SERVICES
[337] EMERGENCY MEDICAL SERVICES
[338] INFORMATION SERVICES
[339] ANIMAL CONTROL CENTER
[340] ANIMAL CONTROL CENTER
[341] ANIMAL CONTROL CENTER
[342] ANIMAL CONTROL CENTER
[343] DEPARTMENT OF PUBLIC WORKS
[344] HUMAN DEVELOPMENT AND SERVICES
[345] HUMAN DEVELOPMENT AND SERVICES
[346] HUMAN DEVELOPMENT AND SERVICES
[347] HUMAN DEVELOPMENT AND SERVICES
[348] HUMAN DEVELOPMENT AND SERVICES
[349] HUMAN DEVELOPMENT AND SERVICES
[350] CITY COURT
[351] INFORMATION SERVICES
[352] INFORMATION SERVICES
[353] POLICE DEPARTMENT
[354] POLICE DEPARTMENT
[355] POLICE DEPARTMENT
[356] POLICE DEPARTMENT
[357] POLICE DEPARTMENT
[358] POLICE DEPARTMENT
[359] POLICE DEPARTMENT
[360] POLICE DEPARTMENT
[361] POLICE DEPARTMENT
[362] POLICE DEPARTMENT
[363] POLICE DEPARTMENT
[364] POLICE DEPARTMENT
[365] DEPARTMENT OF PUBLIC WORKS
[366] POLICE DEPARTMENT
[367] DEPT OF MAINTENANCE
[368] CONSTRUCTION PROJECTS
[369] DEPARTMENT OF PUBLIC WORKS
[370] HUMAN DEVELOPMENT AND SERVICES
[371] HUMAN DEVELOPMENT AND SERVICES
[372] HUMAN DEVELOPMENT AND SERVICES
[373] HUMAN DEVELOPMENT AND SERVICES
[374] HUMAN DEVELOPMENT AND SERVICES
[375] HUMAN DEVELOPMENT AND SERVICES
[376] HUMAN DEVELOPMENT AND SERVICES
[377] INFORMATION SERVICES
[378] GREATER BATON ROUGE AIRPORT DISTRICT
[379] GREATER BATON ROUGE AIRPORT DISTRICT
[380] EMERGENCY MEDICAL SERVICES
[381] EMERGENCY MEDICAL SERVICES
[382] BATON ROUGE RIVER CENTER
[383] HUMAN DEVELOPMENT AND SERVICES
[384] DEPT OF BUILDINGS AND GROUNDS
[385] HUMAN DEVELOPMENT AND SERVICES
[386] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[387] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[388] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[389] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[390] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[391] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[392] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[393] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[394] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[395] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[396] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[397] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[398] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[399] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[400] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[401] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[402] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[403] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[404] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[405] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[406] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[407] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[408] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[409] POLICE DEPARTMENT
[410] POLICE DEPARTMENT
[411] POLICE DEPARTMENT
[412] GREATER BATON ROUGE AIRPORT DISTRICT
[413] EMERGENCY MEDICAL SERVICES
[414] EMERGENCY MEDICAL SERVICES
[415] EMERGENCY MEDICAL SERVICES
[416] EMERGENCY MEDICAL SERVICES
[417] EMERGENCY MEDICAL SERVICES
[418] EMERGENCY MEDICAL SERVICES
[419] COMMUNITY DEVELOPMENT PROGRAMS
[420] OFC. OF HOMELAND SECURITY & EMERGENCY PREPA
[421] OFC. OF HOMELAND SECURITY & EMERGENCY PREPA
[422] COMMUNITY DEVELOPMENT PROGRAMS
[423] COMMUNITY DEVELOPMENT PROGRAMS
[424] COMMUNITY DEVELOPMENT PROGRAMS
[425] COMMUNITY DEVELOPMENT PROGRAMS
[426] DEPT OF TRANSPORTATION AND DRAINAGE
[427] POLICE DEPARTMENT
[428] POLICE DEPARTMENT
[429] POLICE DEPARTMENT
[430] POLICE DEPARTMENT
[431] POLICE DEPARTMENT
[432] POLICE DEPARTMENT
[433] POLICE DEPARTMENT
[434] POLICE DEPARTMENT
[435] POLICE DEPARTMENT
[436] POLICE DEPARTMENT
[437] POLICE DEPARTMENT
[438] POLICE DEPARTMENT
[439] POLICE DEPARTMENT
[440] HUMAN DEVELOPMENT AND SERVICES
[441] HUMAN DEVELOPMENT AND SERVICES
[442] HUMAN DEVELOPMENT AND SERVICES
[443] HUMAN DEVELOPMENT AND SERVICES
[444] HUMAN DEVELOPMENT AND SERVICES
[445] HUMAN DEVELOPMENT AND SERVICES
[446] HUMAN DEVELOPMENT AND SERVICES
[447] HUMAN DEVELOPMENT AND SERVICES
[448] HUMAN DEVELOPMENT AND SERVICES
[449] HUMAN DEVELOPMENT AND SERVICES
[450] HUMAN DEVELOPMENT AND SERVICES
[451] HUMAN DEVELOPMENT AND SERVICES
[452] HUMAN DEVELOPMENT AND SERVICES
[453] HUMAN DEVELOPMENT AND SERVICES
[454] HUMAN DEVELOPMENT AND SERVICES
[455] HUMAN DEVELOPMENT AND SERVICES
[456] HUMAN DEVELOPMENT AND SERVICES
[457] HUMAN DEVELOPMENT AND SERVICES
[458] HUMAN DEVELOPMENT AND SERVICES
[459] HUMAN DEVELOPMENT AND SERVICES
[460] HUMAN DEVELOPMENT AND SERVICES
[461] HUMAN DEVELOPMENT AND SERVICES
[462] HUMAN DEVELOPMENT AND SERVICES
[463] HUMAN DEVELOPMENT AND SERVICES
[464] HUMAN DEVELOPMENT AND SERVICES
[465] HUMAN DEVELOPMENT AND SERVICES
[466] HUMAN DEVELOPMENT AND SERVICES
[467] HUMAN DEVELOPMENT AND SERVICES
[468] HUMAN DEVELOPMENT AND SERVICES
[469] HUMAN DEVELOPMENT AND SERVICES
[470] HUMAN DEVELOPMENT AND SERVICES
[471] HUMAN DEVELOPMENT AND SERVICES
[472] HUMAN DEVELOPMENT AND SERVICES
[473] HUMAN DEVELOPMENT AND SERVICES
[474] HUMAN DEVELOPMENT AND SERVICES
[475] HUMAN DEVELOPMENT AND SERVICES
[476] HUMAN DEVELOPMENT AND SERVICES
[477] HUMAN DEVELOPMENT AND SERVICES
[478] HUMAN DEVELOPMENT AND SERVICES
[479] HUMAN DEVELOPMENT AND SERVICES
[480] HUMAN DEVELOPMENT AND SERVICES
[481] HUMAN DEVELOPMENT AND SERVICES
[482] HUMAN DEVELOPMENT AND SERVICES
[483] HUMAN DEVELOPMENT AND SERVICES
[484] HUMAN DEVELOPMENT AND SERVICES
[485] HUMAN DEVELOPMENT AND SERVICES
[486] HUMAN DEVELOPMENT AND SERVICES
[487] HUMAN DEVELOPMENT AND SERVICES
[488] HUMAN DEVELOPMENT AND SERVICES
[489] HUMAN DEVELOPMENT AND SERVICES
[490] HUMAN DEVELOPMENT AND SERVICES
[491] HUMAN DEVELOPMENT AND SERVICES
[492] HUMAN DEVELOPMENT AND SERVICES
[493] HUMAN DEVELOPMENT AND SERVICES
[494] HUMAN DEVELOPMENT AND SERVICES
[495] HUMAN DEVELOPMENT AND SERVICES
[496] HUMAN DEVELOPMENT AND SERVICES
[497] HUMAN DEVELOPMENT AND SERVICES
[498] POLICE DEPARTMENT
[499] POLICE DEPARTMENT
[500] POLICE DEPARTMENT
[501] POLICE DEPARTMENT
[502] POLICE DEPARTMENT
[503] POLICE DEPARTMENT
[504] POLICE DEPARTMENT
[505] DEPT OF TRANSPORTATION AND DRAINAGE
[506] DEPT OF FLEET MANAGEMENT
[507] DEPT OF BUILDINGS AND GROUNDS
[508] HUMAN DEVELOPMENT AND SERVICES
[509] HUMAN DEVELOPMENT AND SERVICES
[510] HUMAN DEVELOPMENT AND SERVICES
[511] INFORMATION SERVICES
[512] INFORMATION SERVICES
[513] INFORMATION SERVICES
[514] EMERGENCY MEDICAL SERVICES
[515] INFORMATION SERVICES
[516] INFORMATION SERVICES
[517] GREATER BATON ROUGE AIRPORT DISTRICT
[518] INFORMATION SERVICES
[519] INFORMATION SERVICES
[520] INFORMATION SERVICES
[521] INFORMATION SERVICES
[522] GREATER BATON ROUGE AIRPORT DISTRICT
[523] POLICE DEPARTMENT
[524] POLICE DEPARTMENT
[525] POLICE DEPARTMENT
[526] POLICE DEPARTMENT
[527] POLICE DEPARTMENT
[528] POLICE DEPARTMENT
[529] CONSTRUCTION PROJECTS
[530] ROAD REHABILITATION PROGRAM
[531] FIRE DEPARTMENT
[532] FIRE DEPARTMENT
[533] FIRE DEPARTMENT
[534] FIRE DEPARTMENT
[535] FIRE DEPARTMENT
[536] FIRE DEPARTMENT
[537] FIRE DEPARTMENT
[538] FIRE DEPARTMENT
[539] FIRE DEPARTMENT
[540] FIRE DEPARTMENT
[541] FIRE DEPARTMENT
[542] FIRE DEPARTMENT
[543] FIRE DEPARTMENT
[544] FIRE DEPARTMENT
[545] FIRE DEPARTMENT
[546] FIRE DEPARTMENT
[547] FIRE DEPARTMENT
[548] FIRE DEPARTMENT
[549] FIRE DEPARTMENT
[550] FIRE DEPARTMENT
[551] FIRE DEPARTMENT
[552] FIRE DEPARTMENT
[553] FIRE DEPARTMENT
[554] FIRE DEPARTMENT
[555] FIRE DEPARTMENT
[556] FIRE DEPARTMENT
[557] BATON ROUGE RIVER CENTER
[558] BATON ROUGE RIVER CENTER
[559] BATON ROUGE RIVER CENTER
[560] BATON ROUGE RIVER CENTER
[561] BATON ROUGE RIVER CENTER
[562] BATON ROUGE RIVER CENTER
[563] BATON ROUGE RIVER CENTER
[564] BATON ROUGE RIVER CENTER
[565] BATON ROUGE RIVER CENTER
[566] BATON ROUGE RIVER CENTER
[567] BATON ROUGE RIVER CENTER
[568] BATON ROUGE RIVER CENTER
[569] BATON ROUGE RIVER CENTER
[570] BATON ROUGE RIVER CENTER
[571] BATON ROUGE RIVER CENTER
[572] BATON ROUGE RIVER CENTER
[573] BATON ROUGE RIVER CENTER
[574] BATON ROUGE RIVER CENTER
[575] BATON ROUGE RIVER CENTER
[576] BATON ROUGE RIVER CENTER
[577] BATON ROUGE RIVER CENTER
[578] BATON ROUGE RIVER CENTER
[579] BATON ROUGE RIVER CENTER
[580] BATON ROUGE RIVER CENTER
[581] BATON ROUGE RIVER CENTER
[582] BATON ROUGE RIVER CENTER
[583] BATON ROUGE RIVER CENTER
[584] BATON ROUGE RIVER CENTER
[585] BATON ROUGE RIVER CENTER
[586] BATON ROUGE RIVER CENTER
[587] BATON ROUGE RIVER CENTER
[588] BATON ROUGE RIVER CENTER
[589] BATON ROUGE RIVER CENTER
[590] BATON ROUGE RIVER CENTER
[591] BATON ROUGE RIVER CENTER
[592] BATON ROUGE RIVER CENTER
[593] BATON ROUGE RIVER CENTER
[594] BATON ROUGE RIVER CENTER
[595] BATON ROUGE RIVER CENTER
[596] BATON ROUGE RIVER CENTER
[597] BATON ROUGE RIVER CENTER
[598] BATON ROUGE RIVER CENTER
[599] BATON ROUGE RIVER CENTER
[600] BATON ROUGE RIVER CENTER
[601] BATON ROUGE RIVER CENTER
[602] BATON ROUGE RIVER CENTER
[603] BATON ROUGE RIVER CENTER
[604] BATON ROUGE RIVER CENTER
[605] BATON ROUGE RIVER CENTER
[606] BATON ROUGE RIVER CENTER
[607] BATON ROUGE RIVER CENTER
[608] BATON ROUGE RIVER CENTER
[609] BATON ROUGE RIVER CENTER
[610] BATON ROUGE RIVER CENTER
[611] BATON ROUGE RIVER CENTER
[612] BATON ROUGE RIVER CENTER
[613] BATON ROUGE RIVER CENTER
[614] BATON ROUGE RIVER CENTER
[615] BATON ROUGE RIVER CENTER
[616] BATON ROUGE RIVER CENTER
[617] BATON ROUGE RIVER CENTER
[618] DEPT OF FLEET MANAGEMENT
[619] FINANCE DEPARTMENT
[620] FINANCE DEPARTMENT
[621] FINANCE DEPARTMENT
[622] FINANCE DEPARTMENT
[623] COMMUNITY DEVELOPMENT PROGRAMS
[624] FINANCE DEPARTMENT
[625] CONSTRUCTION PROJECTS
[626] INFORMATION SERVICES
[627] DEPARTMENT OF PUBLIC WORKS
[628] LIBRARY BOARD OF CONTROL
[629] OFFICE OF THE MAYOR-PRESIDENT
[630] CONSTRUCTION PROJECTS
[631] LIBRARY BOARD OF CONTROL
[632] LIBRARY BOARD OF CONTROL
[633] LIBRARY BOARD OF CONTROL
[634] DEPARTMENT OF PUBLIC WORKS
[635] DEPARTMENT OF PUBLIC WORKS
[636] DEPARTMENT OF PUBLIC WORKS
[637] DEPARTMENT OF PUBLIC WORKS
[638] GREATER BATON ROUGE AIRPORT DISTRICT
[639] GREATER BATON ROUGE AIRPORT DISTRICT
[640] COMMUNITY DEVELOPMENT PROGRAMS
[641] COMMUNITY DEVELOPMENT PROGRAMS
[642] COMMUNITY DEVELOPMENT PROGRAMS
[643] COMMUNITY DEVELOPMENT PROGRAMS
[644] COMMUNITY DEVELOPMENT PROGRAMS
[645] COMMUNITY DEVELOPMENT PROGRAMS
[646] COMMUNITY DEVELOPMENT PROGRAMS
[647] COMMUNITY DEVELOPMENT PROGRAMS
[648] COMMUNITY DEVELOPMENT PROGRAMS
[649] COMMUNITY DEVELOPMENT PROGRAMS
[650] COMMUNITY DEVELOPMENT PROGRAMS
[651] COMMUNITY DEVELOPMENT PROGRAMS
[652] FINANCE DEPARTMENT
[653] FINANCE DEPARTMENT
[654] HUMAN DEVELOPMENT AND SERVICES
[655] HUMAN DEVELOPMENT AND SERVICES
[656] HUMAN DEVELOPMENT AND SERVICES
[657] HUMAN DEVELOPMENT AND SERVICES
[658] HUMAN DEVELOPMENT AND SERVICES
[659] EMERGENCY MEDICAL SERVICES
[660] FINANCE DEPARTMENT
[661] FINANCE DEPARTMENT
[662] GREATER BATON ROUGE AIRPORT DISTRICT
[663] GREATER BATON ROUGE AIRPORT DISTRICT
[664] GREATER BATON ROUGE AIRPORT DISTRICT
[665] GREATER BATON ROUGE AIRPORT DISTRICT
[666] GREATER BATON ROUGE AIRPORT DISTRICT
[667] GREATER BATON ROUGE AIRPORT DISTRICT
[668] GREATER BATON ROUGE AIRPORT DISTRICT
[669] GREATER BATON ROUGE AIRPORT DISTRICT
[670] GREATER BATON ROUGE AIRPORT DISTRICT
[671] GREATER BATON ROUGE AIRPORT DISTRICT
[672] GREATER BATON ROUGE AIRPORT DISTRICT
[673] GREATER BATON ROUGE AIRPORT DISTRICT
[674] FINANCE DEPARTMENT
[675] FINANCE DEPARTMENT
[676] GREATER BATON ROUGE AIRPORT DISTRICT
[677] PARISH ATTORNEY
[678] HUMAN DEVELOPMENT AND SERVICES
[679] HUMAN DEVELOPMENT AND SERVICES
[680] HUMAN DEVELOPMENT AND SERVICES
[681] POLICE DEPARTMENT
[682] LIBRARY BOARD OF CONTROL
[683] LIBRARY BOARD OF CONTROL
[684] LIBRARY BOARD OF CONTROL
[685] LIBRARY BOARD OF CONTROL
[686] LIBRARY BOARD OF CONTROL
[687] LIBRARY BOARD OF CONTROL
[688] LIBRARY BOARD OF CONTROL
[689] POLICE DEPARTMENT
[690] POLICE DEPARTMENT
[691] HUMAN DEVELOPMENT AND SERVICES
[692] INFORMATION SERVICES
[693] COMMUNITY DEVELOPMENT PROGRAMS
[694] EMERGENCY MEDICAL SERVICES
[695] COMMUNITY DEVELOPMENT PROGRAMS
[696] COMMUNITY DEVELOPMENT PROGRAMS
[697] HUMAN RESOURCES
[698] HUMAN RESOURCES
[699] HUMAN RESOURCES
[700] HUMAN RESOURCES
[701] HUMAN RESOURCES
[702] HUMAN RESOURCES
[703] HUMAN RESOURCES
[704] HUMAN RESOURCES
[705] POLICE DEPARTMENT
[706] POLICE DEPARTMENT
[707] POLICE DEPARTMENT
[708] LIBRARY BOARD OF CONTROL
[709] LIBRARY BOARD OF CONTROL
[710] OUTSIDE AGENCIES
[711] FINANCE DEPARTMENT
[712] HUMAN DEVELOPMENT AND SERVICES
[713] HUMAN DEVELOPMENT AND SERVICES
[714] HUMAN DEVELOPMENT AND SERVICES
[715] HUMAN DEVELOPMENT AND SERVICES
[716] HUMAN DEVELOPMENT AND SERVICES
[717] HUMAN DEVELOPMENT AND SERVICES
[718] HUMAN DEVELOPMENT AND SERVICES
[719] INFORMATION SERVICES
[720] INFORMATION SERVICES
[721] INFORMATION SERVICES
[722] INFORMATION SERVICES
[723] INFORMATION SERVICES
[724] INFORMATION SERVICES
[725] INFORMATION SERVICES
[726] INFORMATION SERVICES
[727] INFORMATION SERVICES
[728] INFORMATION SERVICES
[729] INFORMATION SERVICES
[730] INFORMATION SERVICES
[731] INFORMATION SERVICES
[732] HUMAN DEVELOPMENT AND SERVICES
[733] HUMAN DEVELOPMENT AND SERVICES
[734] HUMAN RESOURCES
[735] DEPARTMENT OF PUBLIC WORKS
[736] DEPARTMENT OF PUBLIC WORKS
[737] DEPARTMENT OF PUBLIC WORKS
[738] DEPARTMENT OF PUBLIC WORKS
[739] CONSTRUCTION PROJECTS
[740] CONSTRUCTION PROJECTS
[741] DEPARTMENT OF PUBLIC WORKS
[742] JUVENILE SERVICES
[743] CONSTRUCTION PROJECTS
[744] DEPT OF TRANSPORTATION AND DRAINAGE
[745] POLICE DEPARTMENT
[746] POLICE DEPARTMENT
[747] DEPT OF BUILDINGS AND GROUNDS
[748] DEPARTMENT OF PUBLIC WORKS
[749] DEPARTMENT OF PUBLIC WORKS
[750] HUMAN DEVELOPMENT AND SERVICES
[751] INFORMATION SERVICES
[752] INFORMATION SERVICES
[753] INFORMATION SERVICES
[754] DEPARTMENT OF PUBLIC WORKS
[755] DEPARTMENT OF PUBLIC WORKS
[756] DEPARTMENT OF PUBLIC WORKS
[757] DEPARTMENT OF PUBLIC WORKS
[758] DEPT OF FLEET MANAGEMENT
[759] INFORMATION SERVICES
[760] INFORMATION SERVICES
[761] INFORMATION SERVICES
[762] INFORMATION SERVICES
[763] INFORMATION SERVICES
[764] HUMAN DEVELOPMENT AND SERVICES
[765] HUMAN DEVELOPMENT AND SERVICES
[766] COMMUNITY DEVELOPMENT PROGRAMS
[767] COMMUNITY DEVELOPMENT PROGRAMS
[768] MOSQUITO & RODENT CONTROL
[769] LIBRARY BOARD OF CONTROL
[770] OFFICE OF THE MAYOR-PRESIDENT
[771] HUMAN DEVELOPMENT AND SERVICES
[772] INFORMATION SERVICES
[773] INFORMATION SERVICES
[774] HUMAN DEVELOPMENT AND SERVICES
[775] COMMUNITY DEVELOPMENT PROGRAMS
[776] DEPARTMENT OF PUBLIC WORKS
[777] DEPARTMENT OF PUBLIC WORKS
[778] DEPARTMENT OF PUBLIC WORKS
[779] DEPARTMENT OF PUBLIC WORKS
[780] DEPARTMENT OF PUBLIC WORKS
[781] POLICE DEPARTMENT
[782] POLICE DEPARTMENT
[783] POLICE DEPARTMENT
[784] POLICE DEPARTMENT
[785] POLICE DEPARTMENT
[786] POLICE DEPARTMENT
[787] EMERGENCY MEDICAL SERVICES
[788] LIBRARY BOARD OF CONTROL
[789] LIBRARY BOARD OF CONTROL
[790] LIBRARY BOARD OF CONTROL
[791] LIBRARY BOARD OF CONTROL
[792] OFFICE OF THE MAYOR-PRESIDENT
[793] DEPT OF BUILDINGS AND GROUNDS
[794] DEPARTMENT OF PUBLIC WORKS
[795] CONSTRUCTION PROJECTS
[796] CONSTRUCTION PROJECTS
[797] HUMAN DEVELOPMENT AND SERVICES
[798] PARISH ATTORNEY
[799] POLICE DEPARTMENT
[800] GREATER BATON ROUGE AIRPORT DISTRICT
[801] LIBRARY BOARD OF CONTROL
[802] HUMAN RESOURCES
[803] LIBRARY BOARD OF CONTROL
[804] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[805] JUVENILE SERVICES
[806] DEPARTMENT OF PUBLIC WORKS
[807] DEPT OF FLEET MANAGEMENT
[808] DEPT OF FLEET MANAGEMENT
[809] DEPT OF FLEET MANAGEMENT
[810] DEPT OF FLEET MANAGEMENT
[811] DEPT OF FLEET MANAGEMENT
[812] DEPT OF FLEET MANAGEMENT
[813] DEPT OF FLEET MANAGEMENT
[814] DEPT OF FLEET MANAGEMENT
[815] DEPT OF FLEET MANAGEMENT
[816] DEPT OF FLEET MANAGEMENT
[817] DEPT OF FLEET MANAGEMENT
[818] DEPT OF FLEET MANAGEMENT
[819] DEPT OF FLEET MANAGEMENT
[820] DEPT OF FLEET MANAGEMENT
[821] DEPT OF FLEET MANAGEMENT
[822] DEPT OF FLEET MANAGEMENT
[823] DEPT OF FLEET MANAGEMENT
[824] DEPT OF FLEET MANAGEMENT
[825] DEPARTMENT OF PUBLIC WORKS
[826] DEPT OF TRANSPORTATION AND DRAINAGE
[827] DEPT OF TRANSPORTATION AND DRAINAGE
[828] POLICE DEPARTMENT
[829] POLICE DEPARTMENT
[830] POLICE DEPARTMENT
[831] METROPOLITAN COUNCIL
[832] METROPOLITAN COUNCIL
[833] METROPOLITAN COUNCIL
[834] METROPOLITAN COUNCIL
[835] COMMUNITY DEVELOPMENT PROGRAMS
[836] COMMUNITY DEVELOPMENT PROGRAMS
[837] COMMUNITY DEVELOPMENT PROGRAMS
[838] COMMUNITY DEVELOPMENT PROGRAMS
[839] INFORMATION SERVICES
[840] INFORMATION SERVICES
[841] INFORMATION SERVICES
[842] INFORMATION SERVICES
[843] INFORMATION SERVICES
[844] INFORMATION SERVICES
[845] INFORMATION SERVICES
[846] INFORMATION SERVICES
[847] INFORMATION SERVICES
[848] INFORMATION SERVICES
[849] INFORMATION SERVICES
[850] INFORMATION SERVICES
[851] INFORMATION SERVICES
[852] INFORMATION SERVICES
[853] INFORMATION SERVICES
[854] INFORMATION SERVICES
[855] INFORMATION SERVICES
[856] INFORMATION SERVICES
[857] INFORMATION SERVICES
[858] DEPARTMENT OF PUBLIC WORKS
[859] DEPARTMENT OF PUBLIC WORKS
[860] DEPARTMENT OF PUBLIC WORKS
[861] DEPARTMENT OF PUBLIC WORKS
[862] DEPARTMENT OF PUBLIC WORKS
[863] DEPARTMENT OF PUBLIC WORKS
[864] DEPARTMENT OF PUBLIC WORKS
[865] DEPARTMENT OF PUBLIC WORKS
[866] DEPARTMENT OF PUBLIC WORKS
[867] DEPARTMENT OF PUBLIC WORKS
[868] DEPARTMENT OF PUBLIC WORKS
[869] DEPARTMENT OF PUBLIC WORKS
[870] DEPARTMENT OF PUBLIC WORKS
[871] DEPARTMENT OF PUBLIC WORKS
[872] DEPARTMENT OF PUBLIC WORKS
[873] DEPARTMENT OF PUBLIC WORKS
[874] DEPARTMENT OF PUBLIC WORKS
[875] GREATER BATON ROUGE AIRPORT DISTRICT
[876] GREATER BATON ROUGE AIRPORT DISTRICT
[877] GREATER BATON ROUGE AIRPORT DISTRICT
[878] GREATER BATON ROUGE AIRPORT DISTRICT
[879] OFFICE OF THE MAYOR-PRESIDENT
[880] OFFICE OF THE MAYOR-PRESIDENT
[881] OFFICE OF THE MAYOR-PRESIDENT
[882] LIBRARY BOARD OF CONTROL
[883] DEPARTMENT OF PUBLIC WORKS
[884] DEPARTMENT OF PUBLIC WORKS
[885] DEPARTMENT OF PUBLIC WORKS
[886] DEPARTMENT OF PUBLIC WORKS
[887] LIBRARY BOARD OF CONTROL
[888] EMERGENCY MEDICAL SERVICES
[889] EMERGENCY MEDICAL SERVICES
[890] DEPT OF FLEET MANAGEMENT
[891] DEPT OF FLEET MANAGEMENT
[892] DEPT OF FLEET MANAGEMENT
[893] DEPT OF FLEET MANAGEMENT
[894] DEPT OF FLEET MANAGEMENT
[895] DEPT OF FLEET MANAGEMENT
[896] DEPT OF FLEET MANAGEMENT
[897] DEPT OF FLEET MANAGEMENT
[898] DEPT OF FLEET MANAGEMENT
[899] DEPT OF TRANSPORTATION AND DRAINAGE
[900] HUMAN DEVELOPMENT AND SERVICES
[901] HUMAN DEVELOPMENT AND SERVICES
[902] HUMAN DEVELOPMENT AND SERVICES
[903] DEPT OF BUILDINGS AND GROUNDS
[904] DEPARTMENT OF PUBLIC WORKS
[905] DEPARTMENT OF PUBLIC WORKS
[906] HUMAN DEVELOPMENT AND SERVICES
[907] MUNICIPAL FIRE & POLICE CIVIL SERVICE BOARD
[908] FINANCE DEPARTMENT
[909] JUVENILE SERVICES
[910] HUMAN DEVELOPMENT AND SERVICES
[911] HUMAN DEVELOPMENT AND SERVICES
[912] HUMAN DEVELOPMENT AND SERVICES
[913] HUMAN DEVELOPMENT AND SERVICES
[914] HUMAN DEVELOPMENT AND SERVICES
[915] COMMUNITY DEVELOPMENT PROGRAMS
[916] COMMUNITY DEVELOPMENT PROGRAMS
[917] COMMUNITY DEVELOPMENT PROGRAMS
[918] COMMUNITY DEVELOPMENT PROGRAMS
[919] COMMUNITY DEVELOPMENT PROGRAMS
[920] COMMUNITY DEVELOPMENT PROGRAMS
[921] ROAD REHABILITATION PROGRAM
[922] COMMUNITY DEVELOPMENT PROGRAMS
[923] DEPT OF BUILDINGS AND GROUNDS
[924] COMMUNITY DEVELOPMENT PROGRAMS
[925] COMMUNITY DEVELOPMENT PROGRAMS
[926] COMMUNITY DEVELOPMENT PROGRAMS
[927] COMMUNITY DEVELOPMENT PROGRAMS
[928] LIBRARY BOARD OF CONTROL
[929] COMMUNITY DEVELOPMENT PROGRAMS
[930] COMMUNITY DEVELOPMENT PROGRAMS
[931] COMMUNITY DEVELOPMENT PROGRAMS
[932] DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
[933] COMMUNITY DEVELOPMENT PROGRAMS
[934] COMMUNITY DEVELOPMENT PROGRAMS
[935] COMMUNITY DEVELOPMENT PROGRAMS
[936] COMMUNITY DEVELOPMENT PROGRAMS
[937] COMMUNITY DEVELOPMENT PROGRAMS
[938] COMMUNITY DEVELOPMENT PROGRAMS
[939] DEPT OF DEVELOPMENT
[940] DEPT OF DEVELOPMENT
[941] COMMUNITY DEVELOPMENT PROGRAMS
[942] COMMUNITY DEVELOPMENT PROGRAMS
[943] CITY COURT
[944] CITY COURT
[945] CITY COURT
[946] CITY COURT
[947] CITY COURT
[948] COUNCIL ADMINISTRATOR
[949] COUNCIL ADMINISTRATOR
[950] COUNCIL ADMINISTRATOR
[951] COUNCIL ADMINISTRATOR
[952] COUNCIL ADMINISTRATOR
[953] DEPT OF FLEET MANAGEMENT
[954] DEPT OF FLEET MANAGEMENT
[955] DEPT OF FLEET MANAGEMENT
[956] DEPT OF FLEET MANAGEMENT
[957] DEPT OF DEVELOPMENT
[958] DEPT OF DEVELOPMENT
[959] HUMAN DEVELOPMENT AND SERVICES
[960] HUMAN DEVELOPMENT AND SERVICES
[961] HUMAN DEVELOPMENT AND SERVICES
[962] HUMAN DEVELOPMENT AND SERVICES
[963] HUMAN DEVELOPMENT AND SERVICES
[964] HUMAN DEVELOPMENT AND SERVICES
[965] DEPT OF FLEET MANAGEMENT
[966] CITY COURT
[967] POLICE DEPARTMENT
[968] POLICE DEPARTMENT
[969] POLICE DEPARTMENT
[970] OUTSIDE AGENCIES
[971] HUMAN DEVELOPMENT AND SERVICES
[972] HUMAN DEVELOPMENT AND SERVICES
[973] HUMAN DEVELOPMENT AND SERVICES
[974] HUMAN DEVELOPMENT AND SERVICES
[975] LIBRARY BOARD OF CONTROL
[976] LIBRARY BOARD OF CONTROL
[977] JUVENILE SERVICES
[978] OUTSIDE AGENCIES
[979] OUTSIDE AGENCIES
[980] DEPT OF DEVELOPMENT
[981] DEPT OF DEVELOPMENT
[982] DEPT OF DEVELOPMENT
[983] DEPT OF DEVELOPMENT
[984] DEPT OF DEVELOPMENT
[985] DEPT OF DEVELOPMENT
[986] JUVENILE SERVICES
[987] DEPARTMENT OF PUBLIC WORKS
[988] DEPT OF BUILDINGS AND GROUNDS
[989] DEPT OF DEVELOPMENT
[990] HUMAN DEVELOPMENT AND SERVICES
[991] CONSTRUCTION PROJECTS
[992] CONSTRUCTION PROJECTS
[993] CONSTRUCTION PROJECTS
[994] DEPARTMENT OF PUBLIC WORKS
[995] DEPARTMENT OF PUBLIC WORKS
[996] FIRE DEPARTMENT
[997] HUMAN RESOURCES
[998] EMERGENCY MEDICAL SERVICES
[999] EMERGENCY MEDICAL SERVICES
[1000] DEPT OF BUILDINGS AND GROUNDS
[ reached getOption("max.print") -- omitted 31452 entries ]
45 Levels: ANIMAL CONTROL CENTER ...For CATEGORICAL variables it is useful to know the frequency of the different categories/levels of the variable. We can do that by using the table() function.
- Create a frequency table for the Loan Purpose and Job
# Command to create a frequency table: variable_table = table(variable)
# variable_table
vendorstate_table = table(vendorstate)
vendorstate_tablevendorstate
, AL AR AZ CA CD CO
2 62 400 125 189 877 1 114
CT DC FL GA IA ID IL IN
99 69 418 555 1089 14 330 180
KS KY LA MA MD ME MI MN
76 100 20178 149 32 12 55 233
MO MS NC ND NE NH NJ NM
74 361 313 4 56 14 1633 3
NV NY OH OK OR PA RI SC
12 210 180 9 27 401 15 41
SD TN TX UT VA VT WA WI
6 152 2561 77 295 3 60 583
WV
3 departmentname_table = table(departmentname)
departmentname_tabledepartmentname
ANIMAL CONTROL CENTER
79
BATON ROUGE CONVENTION & VISTORS COMMISSION
4
BATON ROUGE RIVER CENTER
134
CITY CONSTABLE
115
CITY COURT
1412
COMMUNITY DEVELOPMENT PROGRAMS
1674
CONSTRUCTION PROJECTS
2224
CORONER
47
COUNCIL ADMINISTRATOR
182
COUNCIL BUDGET OFFICE
84
DEPARTMENT OF PUBLIC WORKS
5523
DEPT OF BUILDINGS AND GROUNDS
326
DEPT OF BUSINESS OPERATIONS AND CAPITAL PRO
107
DEPT OF DEVELOPMENT
69
DEPT OF FLEET MANAGEMENT
325
DEPT OF MAINTENANCE
220
DEPT OF TRANSPORTATION AND DRAINAGE
200
DISTRICT COURT
20
DOWNTOWN DEVELOPMENT DISTRICT
182
EMERGENCY MEDICAL SERVICES
1640
FINANCE DEPARTMENT
758
FIRE DEPARTMENT
498
FIRE PROTECTION DISTRICT
217
GREATER BATON ROUGE AIRPORT DISTRICT
1677
HUMAN DEVELOPMENT AND SERVICES
2733
HUMAN RESOURCES
561
INFORMATION SERVICES
1156
JUVENILE COURT
1
JUVENILE SERVICES
376
LIBRARY BOARD OF CONTROL
2620
METROPOLITAN COUNCIL
186
MOSQUITO & RODENT CONTROL
413
MUNICIPAL FIRE & POLICE CIVIL SERVICE BOARD
11
OFC. OF HOMELAND SECURITY & EMERGENCY PREPA
472
OFFICE OF THE MAYOR-PRESIDENT
386
OUTSIDE AGENCIES
161
PARISH ATTORNEY
259
PLANNING COMMISSION
138
POLICE DEPARTMENT
4066
PUBLIC INFORMATION OFFICE
94
PURCHASING
253
REGISTRAR OF VOTERS
43
RETIREMENT OFFICE
4
ROAD REHABILITATION PROGRAM
607
SHERIFF'S OFFICE
195 Now, we will visualize some of the veriables to observe some patterns and differences.
First create a bar chart of the loan purpose frequency table and job frequency table Remember to extract the sales variable before calling it.
VARIABLES: Job and Loan Purpose
# Plot a bar plot using the " barplot(variable) " command where variable is the extracted variable
barplot(vendorstate_table)
barplot(departmentname_table)
Next, create a histogram of loan purpose frequency table and job frequency table
# Plot a histogram using the " hist(variable) " command where variable is the extracted variable
hist(vendorstate_table)
hist(departmentname_table)
- What are some observable differences between the boxplot and the histogram? Please record your observations here.
A boxplot is best used for frequencies that are moderate in information while a histogram is used in showing the many different varities of frequencies.
Lastly, create a pie chart of Loan purpose frequency table. Plot only values greather than 10.
VARIABLE: Loan Purpose
# Plot a pie chart using:
# pie(variable) (command to make a pie chart)
vendorstatepie = pie(vendorstate_table)
vendorstatepieNULL# To get only values greather than N from a frequency table use:
# new_table = my_table[ my_table > N ] (command to select values greather than N)
new_table = vendorstatepie[vendorstatepie > 10]
new_tableNULLWhy is this visual not a good representation of the data? What makes the barplot a better representation?
This is not a good visual representation because there are no numeric values within the data. This creates problems when trying to analyze the data.
Task 2
Lets explore the Illinois Tax dataset (READ the “state_tax_illinois.csv” file) and create a scatterplot of corporate tax vs. personal income tax.
The dataset start in 1994 but we want to tax revenue since 2000 for the first quarter of the year “Q1”
VARIABLES: personal_income_tax and corporate_income_tax
mydata = read.csv(file="data/state_tax_illinois.csv")
head(mydata)# To change the way that R shows very large or small values we can use the following command:
options(scipen = 9999)
# read the dataset extract the year variable and order the dataset by year in decreasing order using the command:
# mydata = mydata[ order(year, decreasing = TRUE) , ] (! Order the data in decreasing order)
# From the ordered dataset Extract personal_income_tax variable
# From the ordered dataset Extract corporate_income_tax variable# To change the way that R shows very large or small values we can use the following command:
options(scipen = 9999)
# read the dataset extract the year variable and order the dataset by year in decreasing order using the command:
year = mydata$year
year [1] 1994 1994 1994 1994 1995 1995 1995 1995 1996
[10] 1996 1996 1996 1997 1997 1997 1997 1998 1998
[19] 1998 1998 1999 1999 1999 1999 2000 2000 2000
[28] 2000 2001 2001 2001 2001 2002 2002 2002 2002
[37] 2003 2003 2003 2003 2004 2004 2004 2004 2005
[46] 2005 2005 2005 2006 2006 2006 2006 2007 2007
[55] 2007 2007 2008 2008 2008 2008 2009 2009 2009
[64] 2009 2010 2010 2010 2010 2011 2011 2011 2011
[73] 2012 2012 2012 2012 2013 2013 2013 2013 2014
[82] 2014 2014 2014 2015 2015 2015 2015 2016 2016# mydata = mydata[ order(year, decreasing = TRUE) , ] (! Order the data in decreasing order)
mydata = mydata[ order(year, decreasing = TRUE) , ]
# From the ordered dataset Extract personal_income_tax variable
personalincometax = mydata$personal_income_tax
personalincometax [1] 3746068 3950197 3982255 4527933 3059415 3048487
[7] 4505921 4961971 3680480 3723148 4345335 5353786
[13] 3608644 3565618 4197619 4790521 3403778 3435763
[19] 3195378 4096206 3343258 3180912 2515231 2702995
[25] 2134865 2291055 2448000 2899039 2104226 2110793
[31] 2803540 3427232 2149834 1968785 2522527 3101128
[37] 2071646 2017821 2264920 2817633 1963943 1820838
[43] 2130841 2511261 1817907 1734644 1902394 2181615
[49] 1675372 1661305 1862628 2169229 1600339 1571274
[55] 1945837 2195228 1648066 1661059 1867375 2405920
[61] 1679520 1650800 2272950 2713373 1763852 1631196
[67] 1877350 1984974 1644788 1640535 1877350 1984974
[73] 1579539 1545132 1689205 1786043 1579539 1545132
[79] 1553374 1642423 1421240 1390281 1427336 1509160
[85] 1306955 1278486 1360884 1425714 1200911 1174752# From the ordered dataset Extract corporate_income_tax variable
corporateincometax = mydata$corporate_income_tax
corporateincometax [1] 903017 1163900 1055932 1425459 674725 625819
[7] 1105062 1508593 787764 785112 1028486 1764708
[13] 877978 948199 834075 1365912 778008 891425
[19] 632404 1160954 655115 639437 729180 971001
[25] 507167 504933 642923 1403105 459494 527011
[31] 790459 1225043 519552 439074 704820 1158924
[37] 537828 562274 591623 901551 556344 516272
[43] 557161 735924 438193 468903 431059 470507
[49] 324041 354688 362344 421964 222017 395572
[55] 362374 432374 261916 246964 583639 786706
[61] 274660 313983 792893 828315 433323 412970
[67] 473585 814038 476261 457064 473585 814038
[73] 370340 303603 435527 748621 370340 303603
[79] 391428 672820 340579 279205 357650 614760
[85] 306094 250934 281377 532591 279680 229280The previous task focused on visualizing one variable. A good way to visualize two variables would be a scatter plot or a correlation matrix.
# Plot personal_income_tax vs. corporate_income_tax
# personal_income_tax will be on the x-axis
# corporate_income_tax will be on the y-axis
plot(personalincometax,corporateincometax)
- Select only entries grather than year 1999
- After selecting entries greather than year 1999, select entries for quarter “Q1”
Naming Quarter
quarter = mydata$quarter
quarter [1] Q1 Q2 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
[17] Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
[33] Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
[49] Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
[65] Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
[81] Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Levels: Q1 Q2 Q3 Q4# To create a new_data filtering for an N NUMERIC value we can do the following
# new_data = mydata[ mydata$NUMERIC_VARIABLE > N, ]
new_data = mydata[mydata$year > 1999 , ]
# If we need to make another filter on a NON-NUMERIC we can do the following
# new_data = new_data[ new_data$CHAR_VARIABLE == "Q1", ]
new_data = new_data[new_data$quarter == "Q1" ,]
head(new_data)It might be easier to notice a trend if there was a line that fit through the points. So, lets repeat using the function ‘scatter.smooth’. This functions adds a smooth curve computed by loess, a method to locally smooth a curve.
scatter.smooth(personalincometax,corporateincometax)
Now, repeat below for total_tax_US vs. total_state, following the example that was just provided. Observe and note any trends in the scatter plots and potential relationships below. Do the same for all numeric variables.
# total_tax_US vs. total_state
totaltaxUS = mydata$total_tax_US
totaltaxUS [1] 222287206 271263780 218877926 277053403
[5] 212615988 217443823 207783584 258554691
[9] 204440284 212998203 207501165 260880904
[13] 195785584 201352044 188942321 236842680
[17] 185252300 195080471 181829622 228937671
[21] 178686442 184695636 165109821 205868100
[25] 170085231 179201087 159740520 201526713
[29] 160974777 165585529 181894597 240545336
[33] 180767288 170932192 176839082 228361858
[37] 176066516 177849972 168140829 216447550
[41] 171164001 172458009 156957207 196599615
[45] 162245445 165474617 141953121 169692044
[49] 147190204 153350871 131305076 152533357
[53] 138163664 140126145 129267848 149346624
[57] 129451673 131008319 137649419 164910108
[61] 127472720 126663650 133987401 162988584
[65] 126852612 128084166 119587068 145886283
[69] 118794468 122866643 115219000 140371196
[73] 111865625 114456862 108012557 126304908
[77] 106395324 107393718 102677692 119200042
[81] 100543930 102569374 93944974 109609563
[85] 94627837 94984540 91075715 104324554
[89] 90160343 91149689totalstate = mydata$total_state
totalstate [1] 9336875 10045643 9622256 10953294 8685450
[6] 8564364 10046029 11338476 9390536 9316965
[11] 9832505 11622644 9097693 9440340 9082749
[16] 10459078 8555361 8718812 7624965 9234900
[21] 8538565 8177370 7091648 7770695 7128096
[26] 6892476 6728486 8365087 6422330 6497882
[31] 7546742 8756653 6955768 6359947 7132023
[36] 8487980 6858279 6723925 6763926 8019376
[41] 6781697 6337485 6988957 7463948 6434580
[46] 6094696 6047406 6612900 6011795 5786577
[51] 5392903 6123292 5637669 5655878 5670224
[56] 6207903 5396931 5298567 5648355 6633372
[61] 5315230 5280986 6142447 7000901 5581641
[66] 5280512 4999164 5587108 5247786 5268964
[71] 4999164 5587107 4721429 4463584 4680894
[76] 5236808 4721429 4463584 4367395 4862738
[81] 4433669 4193211 4028215 4480566 4117755
[86] 3929430 3684023 4299796 3803301 3627632scatter.smooth(totaltaxUS,totalstate)
To quantify the data, its best to look at the correlation between two variables. What does this correlation explain?
Shhowing the relative information according to the data. .96 is very high which means it is very correlated.
Below, compute the correlation between every pair of variables (total_tax_US,total_state, personal_income_tax, corporate_income_tax, general_sales_tax, motor_fuel_tax ).
cor(totalstate,personalincometax)[1] 0.9652176Make sure to extract the variables first and then compute the correlation.
# To make a correlation table of all the NUMERIC variables you can select some columns or a range of columns, with the following command:
# corr01 = mydata[ c(3,4) ] (The command c(3,4,...) inside the brackets selects a given column )
# cor(corr01) (will give you the correlation table for variables in columns 3 and 4)
corr01 = mydata [c(3,4,5,6,7,8)]
cor(corr01) total_tax_US total_state
total_tax_US 1.0000000 0.9621443
total_state 0.9621443 1.0000000
personal_income_tax 0.8921627 0.9652176
corporate_income_tax 0.8378959 0.8547191
general_sales_tax 0.8811287 0.8632035
motor_fuel_tax 0.2108519 0.1397864
personal_income_tax
total_tax_US 0.89216270
total_state 0.96521760
personal_income_tax 1.00000000
corporate_income_tax 0.87948231
general_sales_tax 0.73636583
motor_fuel_tax -0.02182754
corporate_income_tax
total_tax_US 0.83789591
total_state 0.85471906
personal_income_tax 0.87948231
corporate_income_tax 1.00000000
general_sales_tax 0.55037249
motor_fuel_tax -0.05636253
general_sales_tax
total_tax_US 0.8811287
total_state 0.8632035
personal_income_tax 0.7363658
corporate_income_tax 0.5503725
general_sales_tax 1.0000000
motor_fuel_tax 0.3603301
motor_fuel_tax
total_tax_US 0.21085187
total_state 0.13978644
personal_income_tax -0.02182754
corporate_income_tax -0.05636253
general_sales_tax 0.36033009
motor_fuel_tax 1.00000000# corr02 = mydata[3:5] ( the command [3:5] selects a range of columns from 3 to 5)
# cor(corr02)
corr02 = mydata [3:8]
cor(corr02) total_tax_US total_state
total_tax_US 1.0000000 0.9621443
total_state 0.9621443 1.0000000
personal_income_tax 0.8921627 0.9652176
corporate_income_tax 0.8378959 0.8547191
general_sales_tax 0.8811287 0.8632035
motor_fuel_tax 0.2108519 0.1397864
personal_income_tax
total_tax_US 0.89216270
total_state 0.96521760
personal_income_tax 1.00000000
corporate_income_tax 0.87948231
general_sales_tax 0.73636583
motor_fuel_tax -0.02182754
corporate_income_tax
total_tax_US 0.83789591
total_state 0.85471906
personal_income_tax 0.87948231
corporate_income_tax 1.00000000
general_sales_tax 0.55037249
motor_fuel_tax -0.05636253
general_sales_tax
total_tax_US 0.8811287
total_state 0.8632035
personal_income_tax 0.7363658
corporate_income_tax 0.5503725
general_sales_tax 1.0000000
motor_fuel_tax 0.3603301
motor_fuel_tax
total_tax_US 0.21085187
total_state 0.13978644
personal_income_tax -0.02182754
corporate_income_tax -0.05636253
general_sales_tax 0.36033009
motor_fuel_tax 1.00000000Task 3 - TABLEAU
Follow the directions on the worksheet, download tableau academic on your personal computer or use one of the labs computers.
- Download Tablea academic here: https://www.tableau.com/academic/students
Refer to file ‘states_purchase_orders.csv’ in the data folder
Start Tableau and enter your LUC email if prompted.
Import the file into Tableau. Choose the Text File option when importing
- Under the dimensions tab located on the left side of the screen.
DOUBLE click on the ‘Department.Name’, then DOUBLE click on ‘Number of Records’ variable located on the bottom left of the screen. Note the bar like view and breakdowns.
- What department ‘bin’ has the highest count? What is the count?
- From the upper right corner of the screen select other views (select one of the other views, talk about it and post an screenshot)
# Change the image for your screenshots and MAKE COMMENTS
- Create a new sheet by clicking on the icon next to your current sheet.
Then DOUBLE click on ‘Number of Records’ variable then DOUBLE click on ‘Vendor.Zip’, then on the top left select the recommend view.
- What are the highlights on the map?
- Can you give an explanation of why there is a concentration on a particular region?
# Change the image for your screenshots and MAKE COMMENTS— title: “Business Analytics Lab Worksheet 04” author: “Your Name Here” date: “Summer 2017” output: html_notebook: default html_document: default pdf_document: default subtitle: CME Group Foundation Business Analytics Lab —
About
R can be used to make basic visual analytics, which can be helpful in understanding the data holistically. Additionally, R can help find correlations between variables and create scatter plots.
In this lab we will delve deeper into the marketing data set with R visual analytics and use Tableau to visualize the credit risk data set.
Setup
Make sure to download the folder titled ‘bsad_lab04’ zip folder and extract the folder to unzip it. Next, we must set this folder as the working directory. The way to do this is to open R Studio, go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Now, follow the directions to complete the lab.
Task 1
First, read in the creditrisk.csv file and make sure all the columns are captured and check the first couple of rows of the dataset “head(mydata)”
mydata = read.csv(file="data/creditrisk.csv")
#loan_purpose = mydata$Loan.Purpose !Do the same for all the columns!For CATEGORICAL variables it is useful to know the frequency of the different categories/levels of the variable. We can do that by using the table() function.
- Create a frequency table for the Loan Purpose and Job
# Command to create a frequency table: variable_table = table(variable)
# variable_tableNow, we will visualize some of the veriables to observe some patterns and differences.
First create a bar chart of the loan purpose frequency table and job frequency table Remember to extract the sales variable before calling it.
VARIABLES: Job and Loan Purpose
# Plot a bar plot using the " barplot(variable) " command where variable is the extracted variableNext, create a histogram of loan purpose frequency table and job frequency table
# Plot a histogram using the " hist(variable) " command where variable is the extracted variable- What are some observable differences between the boxplot and the histogram? Please record your observations here.
Lastly, create a pie chart of Loan purpose frequency table. Plot only values greather than 10.
VARIABLE: Loan Purpose
# Plot a pie chart using:
# pie(variable) (command to make a pie chart)
# To get only values greather than N from a frequency table use:
# new_table = my_table[ my_table > N ] (command to select values greather than N)Why is this visual not a good representation of the data? What makes the barplot a better representation?
Task 2
Lets explore the Illinois Tax dataset (READ the “state_tax_illinois.csv” file) and create a scatterplot of corporate tax vs. personal income tax.
The dataset start in 1994 but we want to tax revenue since 2000 for the first quarter of the year “Q1”
VARIABLES: personal_income_tax and corporate_income_tax
# To change the way that R shows very large or small values we can use the following command:
options(scipen = 9999)
# read the dataset extract the year variable and order the dataset by year in decreasing order using the command:
# mydata = mydata[ order(year, decreasing = TRUE) , ] (! Order the data in decreasing order)
# From the ordered dataset Extract personal_income_tax variable
# From the ordered dataset Extract corporate_income_tax variableThe previous task focused on visualizing one variable. A good way to visualize two variables would be a scatter plot or a correlation matrix.
# Plot personal_income_tax vs. corporate_income_tax
# personal_income_tax will be on the x-axis
# corporate_income_tax will be on the y-axis
plot(personalincometax,corporateincometax)
- Select only entries grather than year 1999
- After selecting entries greather than year 1999, select entries for quarter “Q1”
# To create a new_data filtering for an N NUMERIC value we can do the following
# new_data = mydata[ mydata$NUMERIC_VARIABLE > N, ]
# If we need to make another filter on a NON-NUMERIC we can do the following
# new_data = new_data[ new_data$CHAR_VARIABLE == "Q1", ]
head(new_data)It might be easier to notice a trend if there was a line that fit through the points. So, lets repeat using the function ‘scatter.smooth’. This functions adds a smooth curve computed by loess, a method to locally smooth a curve.
scatter.smooth(personalincometax,corporateincometax)
Now, repeat below for total_tax_US vs. total_state, following the example that was just provided. Observe and note any trends in the scatter plots and potential relationships below. Do the same for all numeric variables.
# total_tax_US vs. total_stateTo quantify the data, its best to look at the correlation between two variables. What does this correlation explain?
cor(totalstate,personalincometax)[1] 0.9652176Below, compute the correlation between every pair of variables (total_tax_US,total_state, personal_income_tax, corporate_income_tax, general_sales_tax, motor_fuel_tax ).
Make sure to extract the variables first and then compute the correlation.
# To make a correlation table of all the NUMERIC variables you can select some columns or a range of columns, with the following command:
# corr01 = mydata[ c(3,4) ] (The command c(3,4,...) inside the brackets selects a given column )
# cor(corr01) (will give you the correlation table for variables in columns 3 and 4)
# corr02 = mydata[3:5] ( the command [3:5] selects a range of columns from 3 to 5)
# cor(corr02)Task 3 - TABLEAU
Follow the directions on the worksheet, download tableau academic on your personal computer or use one of the labs computers.
- Download Tablea academic here: https://www.tableau.com/academic/students
Refer to file ‘states_purchase_orders.csv’ in the data folder
Start Tableau and enter your LUC email if prompted.
Import the file into Tableau. Choose the Text File option when importing
- Under the dimensions tab located on the left side of the screen.
DOUBLE click on the ‘Department.Name’, then DOUBLE click on ‘Number of Records’ variable located on the bottom left of the screen. Note the bar like view and breakdowns.
- What department ‘bin’ has the highest count? What is the count? Department of public works has the highest count and it is 5523.
- From the upper right corner of the screen select other views (select one of the other views, talk about it and post an screenshot) This view shows how many people are in each section of information. Biggest to smallest.
# Change the image for your screenshots and MAKE COMMENTS
- Create a new sheet by clicking on the icon next to your current sheet.
Then DOUBLE click on ‘Number of Records’ variable then DOUBLE click on ‘Vendor.Zip’, then on the top left select the recommend view.
- What are the highlights on the map? The larfest concentrations of records appears in Louisiana, Iowa, and Texas.
- Can you give an explanation of why there is a concentration on a particular region? There is a concentration on particular regions because of different wages and financial stability.
# Change the image for your screenshots and MAKE COMMENTS
---
title: "Business Analytics Lab Worksheet 04"
author: "Elio Vento"
date: "Summer, July 2017"
output:
  html_notebook: default
  html_document: default
  pdf_document: default
subtitle: CME Group Foundation Business Analytics Lab
---

### About

R can be used to make basic visual analytics, which can be helpful in understanding the data holistically. Additionally, R can help find correlations between variables and create scatter plots. 

In this lab we will delve deeper into the marketing data set with R visual analytics and use Tableau to visualize the credit risk data set. 

### Setup

Make sure to download the folder titled 'bsad_lab04' zip folder and extract the folder to unzip it. Next, we must set this folder as the working directory. The way to do this is to open R Studio, go to 'Session', scroll down to 'Set Working Directory', and click 'To Source File Location'. Now, follow the directions to complete the lab.

----------

### Task 1

First, read in the `creditrisk.csv` file and make sure all the columns are captured and check the first couple of rows of the dataset "head(mydata)"

```{r}
mydata = read.csv(file="data/states_purchase_orders.csv")
head(mydata)
```

```{r}
#Vendor State Data
vendorstate = mydata$VENDOR.STATE
vendorstate
```

```{r}
#Department Name Data
departmentname = mydata$DEPARTMENT.NAME
departmentname
```


For CATEGORICAL variables it is useful to know the frequency of the different categories/levels of the variable. We can do that by using the table() function. 

* Create a frequency table for the Loan Purpose and Job
```{r}
# Command to create a frequency table: variable_table = table(variable) 
# variable_table
vendorstate_table = table(vendorstate)
vendorstate_table
```

```{r}
departmentname_table = table(departmentname)
departmentname_table
```


Now, we will visualize some of the veriables to observe some patterns and differences. 

First create a bar chart of the loan purpose frequency table and job frequency table Remember to extract the sales variable before calling it.

VARIABLES: Job and Loan Purpose

```{r}
# Plot a bar plot using the " barplot(variable) " command where variable is the extracted variable
barplot(vendorstate_table)
```

```{r}
barplot(departmentname_table)
```


Next, create a histogram of loan purpose frequency table and job frequency table

```{r}
# Plot a histogram using the " hist(variable) " command where variable is the extracted variable
hist(vendorstate_table)
```

```{r}
hist(departmentname_table)
```


* What are some observable differences between the boxplot and the histogram? Please record your observations here. 

A boxplot is best used for frequencies that are moderate in information while a histogram is used in showing the many different varities of frequencies.

Lastly, create a pie chart of Loan purpose frequency table. Plot only values greather than 10.

VARIABLE: Loan Purpose
```{r}
# Plot a pie chart using:
# pie(variable)  (command to make a pie chart)
vendorstatepie = pie(vendorstate_table)
```

```{r}
vendorstatepie
```


```{r}
# To get only values greather than N from a frequency table use:
# new_table = my_table[ my_table > N ]  (command to select values greather than N)
new_table = vendorstatepie[vendorstatepie > 10]
new_table
```


Why is this visual not a good representation of the data? What makes the barplot a better representation?

This is not a good visual representation because there are no numeric values within the data. This creates problems when trying to analyze the data. 


----------

### Task 2

Lets explore the Illinois Tax dataset (READ the "state_tax_illinois.csv" file) and create a scatterplot of corporate tax vs. personal income tax.  

The dataset start in 1994 but we want to tax revenue since 2000 for the first quarter of the year "Q1"

VARIABLES: personal_income_tax and corporate_income_tax

```{r}
mydata = read.csv(file="data/state_tax_illinois.csv")
head(mydata)
```


```{r}
# To change the way that R shows very large or small values we can use the following command:
options(scipen = 9999)

# read the dataset extract the year variable and order the dataset by year in decreasing order using the command:

# mydata = mydata[ order(year, decreasing = TRUE) , ] (! Order the data in decreasing order)

# From the ordered dataset Extract personal_income_tax variable

# From the ordered dataset Extract corporate_income_tax variable

```


```{r}
# To change the way that R shows very large or small values we can use the following command:
options(scipen = 9999)
# read the dataset extract the year variable and order the dataset by year in decreasing order using the command:
year = mydata$year
year
```

```{r}
# mydata = mydata[ order(year, decreasing = TRUE) , ] (! Order the data in decreasing order)
mydata = mydata[ order(year, decreasing = TRUE) , ]
# From the ordered dataset Extract personal_income_tax variable
personalincometax = mydata$personal_income_tax
personalincometax
```

```{r}
# From the ordered dataset Extract corporate_income_tax variable
corporateincometax = mydata$corporate_income_tax
corporateincometax
```


The previous task focused on visualizing one variable. A good way to visualize two variables would be a scatter plot or a correlation matrix.

```{r}
# Plot personal_income_tax vs. corporate_income_tax 
# personal_income_tax will be on the x-axis
# corporate_income_tax will be on the y-axis
plot(personalincometax,corporateincometax)

```

* Select only entries grather than year 1999
* After selecting entries greather than year 1999, select entries for quarter "Q1"

Naming Quarter

```{r}
quarter = mydata$quarter
quarter
```



```{r}
# To create a new_data filtering for an N NUMERIC value we can do the following
# new_data = mydata[ mydata$NUMERIC_VARIABLE > N, ]
new_data = mydata[mydata$year > 1999 , ]
# If we need to make another filter on a NON-NUMERIC we can do the following
# new_data = new_data[ new_data$CHAR_VARIABLE == "Q1", ]
new_data = new_data[new_data$quarter == "Q1" ,]
head(new_data)
```

It might be easier to notice a trend if there was a line that fit through the points. So, lets repeat using the function 'scatter.smooth'. This functions adds a smooth curve computed by loess, a method to locally smooth a curve. 

```{r}
scatter.smooth(personalincometax,corporateincometax)
```

Now, repeat below for total_tax_US vs. total_state, following the example that was just provided. Observe and note any trends in the scatter plots and potential relationships below. Do the same for all numeric variables.

```{r}
# total_tax_US vs. total_state
totaltaxUS = mydata$total_tax_US
totaltaxUS
```



```{r}
totalstate = mydata$total_state
totalstate
```

```{r}
scatter.smooth(totaltaxUS,totalstate)
```

To quantify the data, its best to look at the correlation between two variables.
What does this correlation explain?

Shhowing the relative information according to the data. .96 is very high which means it is very correlated. 

Below, compute the correlation between every pair of variables (total_tax_US,total_state, personal_income_tax, corporate_income_tax, general_sales_tax, motor_fuel_tax ). 


```{r}
cor(totalstate,personalincometax)
```



Make sure to extract the variables first and then compute the correlation. 

```{r}
# To make a correlation table of all the NUMERIC variables you can select some columns or a range of columns, with the following command:
# corr01 = mydata[ c(3,4) ] (The command c(3,4,...) inside the brackets selects a given column )
# cor(corr01) (will give you the correlation table for variables in columns 3 and 4)
corr01 = mydata [c(3,4,5,6,7,8)]
cor(corr01)
```

```{r}
# corr02 = mydata[3:5] ( the command [3:5] selects a range of columns from 3 to 5)
# cor(corr02)
corr02 = mydata [3:8]
cor(corr02)
```



----------

### Task 3 - TABLEAU

Follow the directions on the worksheet, download tableau academic on your personal computer or use one of the labs computers.

* Download Tablea academic here: https://www.tableau.com/academic/students

1. Refer to file 'states_purchase_orders.csv' in the data folder

2. Start Tableau and enter your LUC email if prompted.

3. Import the file into Tableau. Choose the Text File option when importing

![](imgs/tableau_importfile.png)


4. Under the dimensions tab located on the left side of the screen.

![](imgs/tableau_variables.png)

DOUBLE click  on the 'Department.Name', then DOUBLE click on 'Number of Records' variable located on the bottom left of the screen. Note the bar like view and breakdowns.

* What department 'bin' has the highest count? What is the count? 
* From the upper right corner of the screen select other views (select one of the other views, talk about it and post an screenshot)

![](imgs/tableau_visualizations.png)

`# Change the image for your screenshots and MAKE COMMENTS`

5. Create a new sheet by clicking on the icon next to your current sheet.

![](imgs/tableau_newsheet.png)

Then DOUBLE click on 'Number of Records' variable then DOUBLE click on 'Vendor.Zip', then on the top left select the recommend view. 

* What are the highlights on the map?
* Can you give an explanation of why there is a concentration on a particular region?

![](imgs/tableau_showme.png)

`# Change the image for your screenshots and MAKE COMMENTS`---
title: "Business Analytics Lab Worksheet 04"
author: "Your Name Here"
date: "Summer 2017"
output:
  html_notebook: default
  html_document: default
  pdf_document: default
subtitle: CME Group Foundation Business Analytics Lab
---

### About

R can be used to make basic visual analytics, which can be helpful in understanding the data holistically. Additionally, R can help find correlations between variables and create scatter plots. 

In this lab we will delve deeper into the marketing data set with R visual analytics and use Tableau to visualize the credit risk data set. 

### Setup

Make sure to download the folder titled 'bsad_lab04' zip folder and extract the folder to unzip it. Next, we must set this folder as the working directory. The way to do this is to open R Studio, go to 'Session', scroll down to 'Set Working Directory', and click 'To Source File Location'. Now, follow the directions to complete the lab.

----------

### Task 1

First, read in the `creditrisk.csv` file and make sure all the columns are captured and check the first couple of rows of the dataset "head(mydata)"

```{r}
mydata = read.csv(file="data/creditrisk.csv")
#loan_purpose = mydata$Loan.Purpose !Do the same for all the columns!

```


For CATEGORICAL variables it is useful to know the frequency of the different categories/levels of the variable. We can do that by using the table() function. 

* Create a frequency table for the Loan Purpose and Job
```{r}
# Command to create a frequency table: variable_table = table(variable) 
# variable_table

```

Now, we will visualize some of the veriables to observe some patterns and differences. 

First create a bar chart of the loan purpose frequency table and job frequency table Remember to extract the sales variable before calling it.

VARIABLES: Job and Loan Purpose

```{r}
# Plot a bar plot using the " barplot(variable) " command where variable is the extracted variable
```

Next, create a histogram of loan purpose frequency table and job frequency table

```{r}
# Plot a histogram using the " hist(variable) " command where variable is the extracted variable
```

* What are some observable differences between the boxplot and the histogram? Please record your observations here. 



Lastly, create a pie chart of Loan purpose frequency table. Plot only values greather than 10.

VARIABLE: Loan Purpose
```{r}
# Plot a pie chart using:
# pie(variable)  (command to make a pie chart)
# To get only values greather than N from a frequency table use:
# new_table = my_table[ my_table > N ]  (command to select values greather than N)

```

Why is this visual not a good representation of the data? What makes the barplot a better representation?

----------

### Task 2

Lets explore the Illinois Tax dataset (READ the "state_tax_illinois.csv" file) and create a scatterplot of corporate tax vs. personal income tax.  

The dataset start in 1994 but we want to tax revenue since 2000 for the first quarter of the year "Q1"

VARIABLES: personal_income_tax and corporate_income_tax

```{r}
# To change the way that R shows very large or small values we can use the following command:
options(scipen = 9999)

# read the dataset extract the year variable and order the dataset by year in decreasing order using the command:

# mydata = mydata[ order(year, decreasing = TRUE) , ] (! Order the data in decreasing order)

# From the ordered dataset Extract personal_income_tax variable

# From the ordered dataset Extract corporate_income_tax variable

```

The previous task focused on visualizing one variable. A good way to visualize two variables would be a scatter plot or a correlation matrix.

```{r}
# Plot personal_income_tax vs. corporate_income_tax 
# personal_income_tax will be on the x-axis
# corporate_income_tax will be on the y-axis

plot(personalincometax,corporateincometax)
```

* Select only entries grather than year 1999
* After selecting entries greather than year 1999, select entries for quarter "Q1"

```{r}
# To create a new_data filtering for an N NUMERIC value we can do the following
# new_data = mydata[ mydata$NUMERIC_VARIABLE > N, ]

# If we need to make another filter on a NON-NUMERIC we can do the following
# new_data = new_data[ new_data$CHAR_VARIABLE == "Q1", ]

head(new_data)
```

It might be easier to notice a trend if there was a line that fit through the points. So, lets repeat using the function 'scatter.smooth'. This functions adds a smooth curve computed by loess, a method to locally smooth a curve. 

```{r}
scatter.smooth(personalincometax,corporateincometax)
```

Now, repeat below for total_tax_US vs. total_state, following the example that was just provided. Observe and note any trends in the scatter plots and potential relationships below. Do the same for all numeric variables.

```{r}
# total_tax_US vs. total_state
```

To quantify the data, its best to look at the correlation between two variables.
What does this correlation explain?

```{r}
cor(totalstate,personalincometax)
```

Below, compute the correlation between every pair of variables (total_tax_US,total_state, personal_income_tax, corporate_income_tax, general_sales_tax, motor_fuel_tax ). 

Make sure to extract the variables first and then compute the correlation. 

```{r}
# To make a correlation table of all the NUMERIC variables you can select some columns or a range of columns, with the following command:

# corr01 = mydata[ c(3,4) ] (The command c(3,4,...) inside the brackets selects a given column )
# cor(corr01) (will give you the correlation table for variables in columns 3 and 4)

# corr02 = mydata[3:5] ( the command [3:5] selects a range of columns from 3 to 5)
# cor(corr02)
```

----------

### Task 3 - TABLEAU

Follow the directions on the worksheet, download tableau academic on your personal computer or use one of the labs computers.

* Download Tablea academic here: https://www.tableau.com/academic/students

1. Refer to file 'states_purchase_orders.csv' in the data folder

2. Start Tableau and enter your LUC email if prompted.

3. Import the file into Tableau. Choose the Text File option when importing

![](imgs/tableau_importfile.png)


4. Under the dimensions tab located on the left side of the screen.


DOUBLE click  on the 'Department.Name', then DOUBLE click on 'Number of Records' variable located on the bottom left of the screen. Note the bar like view and breakdowns.

* What department 'bin' has the highest count? What is the count? Department of public works has the highest count and it is 5523.
* From the upper right corner of the screen select other views (select one of the other views, talk about it and post an screenshot) This view shows how many people are in each section of information. Biggest to smallest. 

![](imgs/image.png)

`# Change the image for your screenshots and MAKE COMMENTS`

5. Create a new sheet by clicking on the icon next to your current sheet.

![](imgs/tableau_newsheet.png)

Then DOUBLE click on 'Number of Records' variable then DOUBLE click on 'Vendor.Zip', then on the top left select the recommend view. 

* What are the highlights on the map? The larfest concentrations of records appears in Louisiana, Iowa, and Texas. 
* Can you give an explanation of why there is a concentration on a particular region? There is a concentration on particular regions because of different wages and financial stability. 



![](imgs/image1.png)

`# Change the image for your screenshots and MAKE COMMENTS`