Capstone Project Submission

Author

foma Ovolevor

Published

December 10, 2025

INTRODUCTION

  1. The faac dataset contains data about allocation to state and local council for the period of January 2017 to May 2024

  2. The capstone Project objective is to wrangle the dataset to achieve the following :

  • Rename the columns with the appropriate month and year from 2007 to 2018

  • Compute the Total and Average allocation to states

Import/Load libraries

library(tidyverse)
library(skimr)
library(readxl)
library(ggthemes)
library(gtExtras)
library(kableExtra)
library(tidymodels)
library(timetk)
library(lubridate)
library(here)

import data/read data

faac_lga <- read_excel(here("data/raw/FAAC DATA - Data Community.xlsx"))

Glimpse and Skim to Explore Data

#The skim gives an overview statistics of the dataset
faac_lga |> 
  glimpse() |> 
  skim()
Rows: 776
Columns: 211
$ STATE   <chr> "ABIA", "ABIA", "ABIA", "ABIA", "ABIA", "ABIA", "ABIA", "ABIA"…
$ LGC     <chr> "ABA NORTH", "ABA SOUTH", "AROCHUKWU", "BENDE", "IKWUANO", "IS…
$ `39083` <dbl> 53322336, 108671797, 58711837, 62200145, 48361687, 56949311, 5…
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$ `45017` <dbl> 168605107, 286851007, 196176744, 201958713, 182422108, 1885439…
$ `45047` <dbl> 215330417, 362678547, 249336420, 256746129, 232342458, 2399506…
$ `45078` <dbl> 218300828, 371554526, 254263064, 261624183, 236341684, 2442890…
$ `45108` <dbl> 213219215, 363720222, 248190805, 255620475, 230734430, 2385191…
$ `45139` <dbl> 163415914, 280765757, 189949771, 196170674, 176643469, 1826710…
$ `45170` <dbl> 163961194, 288611876, 191659874, 198550849, 177727481, 1841020…
$ `45200` <dbl> 208118833, 353532453, 241634750, 248972587, 224882010, 2323878…
$ `45231` <dbl> 217989381, 373070646, 253354214, 261400845, 235645179, 2436281…
$ `45261` <dbl> 252759514, 431306438, 294102628, 303001531, 273453633, 2826806…
$ `45292` <dbl> 278003873, 476637692, 322679309, 333347512, 300254798, 3104437…
$ `45323` <dbl> 269102657, 460649005, 312944619, 322789171, 291004472, 3008738…
$ `45352` <dbl> 257449362, 441085983, 298700871, 308593225, 277990562, 2874073…
$ `45383` <dbl> 273282171, 471356324, 317075058, 328153917, 295027870, 3051444…
$ `45413` <dbl> 284382966, 486378492, 329815370, 340663614, 307010981, 3173724…
Data summary
Name glimpse(faac_lga)
Number of rows 776
Number of columns 211
_______________________
Column type frequency:
character 2
logical 9
numeric 200
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
STATE 0 1 3 11 0 39 0
LGC 2 1 3 17 0 770 0

Variable type: logical

skim_variable n_missing complete_rate mean count
44256 776 0 NaN :
44287 776 0 NaN :
44317 776 0 NaN :
44348 776 0 NaN :
44378 776 0 NaN :
44409 776 0 NaN :
44440 776 0 NaN :
44470 776 0 NaN :
44501 776 0 NaN :

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
39083 0 1 123385195 1715300378 38322584 52128366 57995878 66369929 47842549659 ▇▁▁▁▁
39114 0 1 136596241 1898961664 19860722 57970067 64556153 73778430 52965126370 ▇▁▁▁▁
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39173 1 1 154254702 2143056952 46409223 65207767 72929021 83677218 59735058685 ▇▁▁▁▁
39203 0 1 158659907 2205691023 47763376 66833199 74753571 85746966 61520302204 ▇▁▁▁▁
39234 0 1 159479966 2217092243 48015362 67146771 75091370 86137030 61838279447 ▇▁▁▁▁
39264 0 1 200607343 2788831385 60651442 84994544 95117490 108076478 77785400294 ▇▁▁▁▁
39295 0 1 198996471 2766447299 61975161 84418174 93828548 106789357 77160785139 ▇▁▁▁▁
39326 0 1 166963450 2321117620 52053406 71048686 78968226 89889568 64739996784 ▇▁▁▁▁
39356 0 1 176047712 2447420660 54841193 74373541 82917025 94255464 68262414929 ▇▁▁▁▁
39387 0 1 171114760 2378834876 53351167 72511100 80772770 92004620 66349665333 ▇▁▁▁▁
39417 0 1 180467423 2508852943 56283599 76528224 85229237 96992723 69976155930 ▇▁▁▁▁
39448 0 1 164454077 2286252769 51190512 69265543 77200875 87768933 63766988733 ▇▁▁▁▁
39479 0 1 151084993 2100397561 47014686 63574059 70818835 80542549 58583132692 ▇▁▁▁▁
39508 0 1 261524380 3635692238 81651110 111349496 124087099 141105087 101405951547 ▇▁▁▁▁
39539 0 1 222036130 3086730414 74795949 96648927 105059023 117864280 86094401799 ▇▁▁▁▁
39569 0 1 358403094 4982461067 118866262 156166498 171032441 193522415 138970626117 ▇▁▁▁▁
39600 0 1 219126169 3046281677 72729877 94834001 103464557 116647327 84966066117 ▇▁▁▁▁
39630 0 1 221017747 3072580334 73312563 95594366 104326148 117609147 85699524304 ▇▁▁▁▁
39661 0 1 221017747 3072580334 73312563 95594366 104326148 117609147 85699524304 ▇▁▁▁▁
39692 0 1 224962304 3127413543 74932336 97409931 106200963 120638559 87229024489 ▇▁▁▁▁
39722 0 1 216040210 3003366790 72213833 93886527 102223963 116494866 83769486870 ▇▁▁▁▁
39753 0 1 222845270 3097990991 73823781 96268938 105220564 118378061 86408145579 ▇▁▁▁▁
39783 0 1 222845270 3097990991 73823781 96268938 105220564 118378061 86408145579 ▇▁▁▁▁
39814 0 1 217649259 3025748247 72274325 94180443 102865672 115708958 84393394625 ▇▁▁▁▁
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39934 0 1 163503880 2273039898 54333525 70185597 76773680 86365576 63398550199 ▇▁▁▁▁
39965 0 1 174394976 2424478194 57066535 74475955 81328497 92393373 67621567634 ▇▁▁▁▁
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40057 0 1 182829844 2541723655 60178325 78209704 85493015 97033172 70892183425 ▇▁▁▁▁
40087 0 1 319558637 4442470717 106651588 138555319 151388224 170477156 123908706874 ▇▁▁▁▁
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40148 0 1 194702437 2706766241 64223157 83537527 91184626 102991091 75495775847 ▇▁▁▁▁
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40238 0 1 218048770 3031333715 71724040 93389164 102103073 115691440 84548304956 ▇▁▁▁▁
40269 0 1 157700062 2192414000 51202912 66559043 72860620 81988115 61148122721 ▇▁▁▁▁
40299 0 1 369242379 5133181523 122883826 159739916 174433486 196429939 143173553563 ▇▁▁▁▁
40330 0 1 284225554 3951290112 94666276 122762749 134178873 150964834 110208321135 ▇▁▁▁▁
40360 0 1 220097346 3059836260 72012281 93901810 102645152 115901870 85342639376 ▇▁▁▁▁
40391 0 1 419536768 5832346396 140219674 182309517 199256706 223817583 162675178622 ▇▁▁▁▁
40422 0 1 227903828 3168342286 74918575 97761942 106713335 120381094 88369599155 ▇▁▁▁▁
40452 0 1 216637640 3011722076 71398070 92867392 101412624 115023689 84001139901 ▇▁▁▁▁
40483 0 1 220273994 3062254404 72130520 94473508 103556587 116852196 85411134579 ▇▁▁▁▁
40513 0 1 284225554 3951290112 94666276 122762749 134178873 150964834 110208321135 ▇▁▁▁▁
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40634 0 1 213304277 2965443417 65761404 89482404 98682519 112283400 82708630212 ▇▁▁▁▁
40664 0 1 222975379 3099827715 69351905 95358035 104597768 118171257 86458595228 ▇▁▁▁▁
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40817 0 1 365425192 5080135178 121293413 157679716 171964033 194635330 141693441333 ▇▁▁▁▁
40848 0 1 497659658 6918403311 165654707 216234653 236069939 266735655 192967291478 ▇▁▁▁▁
40878 0 1 315136916 4381045642 104561250 135568845 148355667 166551714 122194186757 ▇▁▁▁▁
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40969 0 1 392928714 5462477431 130584830 169557797 185538217 208878475 152357918523 ▇▁▁▁▁
41000 0 1 319634946 4443578360 105698663 137073895 150106982 169814678 123938295407 ▇▁▁▁▁
41030 0 1 294789910 4098187825 97374968 126740513 138149186 156213567 114304644695 ▇▁▁▁▁
41061 0 1 296002614 4115043843 97539437 127148902 138910084 156665144 114774870222 ▇▁▁▁▁
41091 0 1 282284032 3924332050 92926464 121110961 132294277 149214483 109455496620 ▇▁▁▁▁
41122 0 1 361166460 5020916127 120009471 156293704 170514685 192910418 140042120083 ▇▁▁▁▁
41153 0 1 283771211 3945026936 93407479 121360146 132579941 149594037 110032149596 ▇▁▁▁▁
41183 0 1 279177668 3881158878 92107368 119428807 130673133 147438381 108251005456 ▇▁▁▁▁
41214 0 1 366628631 5096880743 121159264 157577058 172375139 193864307 142160074298 ▇▁▁▁▁
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41306 0 1 300435979 4176707843 98950520 128586651 140316771 157880309 116493905582 ▇▁▁▁▁
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41365 0 1 370997018 5157595280 123358736 159889836 174353663 196767068 143853914177 ▇▁▁▁▁
41395 0 1 391468632 5442167965 130619194 169215530 184909113 209424683 151791772327 ▇▁▁▁▁
41426 0 1 382555311 5318308968 126279005 164193038 179428578 203444730 148335636660 ▇▁▁▁▁
41456 0 1 407469857 5664604810 135665622 176432340 192859372 219077032 157996239882 ▇▁▁▁▁
41487 0 1 317612464 4415516334 104899394 135611268 147923691 166764671 123154079084 ▇▁▁▁▁
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41548 0 1 315056132 4379944857 103738556 134766418 147436129 166387143 122162862678 ▇▁▁▁▁
41579 0 1 366419553 5093979833 113339891 156796808 172645044 195289420 142079004212 ▇▁▁▁▁
41609 0 1 355194927 4938010622 109380590 150480786 165328045 187062103 137726661136 ▇▁▁▁▁
41640 0 1 300765003 4181286541 93056966 127730336 140747318 159472774 116621484204 ▇▁▁▁▁
41671 1 1 329772419 4581623651 101832701 139513332 153342764 173949751 127704209321 ▇▁▁▁▁
41699 0 1 326744988 4542453837 101130218 138968407 153220918 173461212 126695210731 ▇▁▁▁▁
41730 0 1 328130450 4561704356 101620175 139726386 154202090 174873923 127232423249 ▇▁▁▁▁
41760 0 1 324298463 4508429076 100136687 138513925 152160142 172581042 125746571924 ▇▁▁▁▁
41791 0 1 421401333 5858346245 116630062 179212466 199953318 228583572 163398162952 ▇▁▁▁▁
41821 0 1 398954071 5546379466 118745183 166030430 184772805 209761069 154694247697 ▇▁▁▁▁
41852 0 1 332624865 4624183752 102691577 141705564 156186426 177375210 128975130220 ▇▁▁▁▁
41883 0 1 326624689 4540761415 100885942 139360303 153592603 174429554 126648565183 ▇▁▁▁▁
41913 0 1 322079245 4477578563 98989214 137434157 151335669 171263989 124886071367 ▇▁▁▁▁
41944 0 1 300737748 4180918475 92180290 127562753 140555646 159106356 116610916264 ▇▁▁▁▁
41974 0 1 311411462 4329279862 96021155 132800166 146480257 166269156 120749643582 ▇▁▁▁▁
42005 0 1 296883338 4127358648 91280331 125977750 137975882 156778790 115116370514 ▇▁▁▁▁
42036 0 1 256589866 3567185267 78896259 108464703 119383022 135536061 99492596364 ▇▁▁▁▁
42064 0 1 284795659 3959262587 87890363 121458897 133923124 151651345 110429378943 ▇▁▁▁▁
42095 0 1 229133471 3185520148 70012159 96229307 105992666 119712473 88846392478 ▇▁▁▁▁
42125 0 1 209653139 2914755016 63893647 87198340 96117432 108348565 81292903018 ▇▁▁▁▁
42156 0 1 216745460 3013262951 66365719 91528669 101007570 113711785 84042947156 ▇▁▁▁▁
42186 0 1 463343291 6441389447 143054228 199538519 219701772 248321989 179661136623 ▇▁▁▁▁
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44136 1 1 313928924 4364717056 91478720 127259027 140773998 159021902 121647457974 ▇▁▁▁▁
44166 1 1 326723691 4542494737 95910446 134379789 147565988 167763387 126605430326 ▇▁▁▁▁
44197 1 1 338948158 4712308771 102138476 141154899 154566455 173920862 131340954852 ▇▁▁▁▁
44228 2 1 169694920 68552659 102138476 141146678 154499609 173856286 792173652 ▇▁▁▁▁
44531 2 1 196487914 78985810 115359665 161792760 178166992 201066888 838130197 ▇▁▁▁▁
44562 2 1 209221048 78828637 124601682 173769689 190143801 216332933 863743127 ▇▁▁▁▁
44593 2 1 167334917 72396815 99017792 137102018 150432422 171060399 751903616 ▇▁▁▁▁
44621 2 1 179614919 70836016 106081660 148940577 162740260 184980412 767357420 ▇▁▁▁▁
44652 2 1 214450329 85743077 127553578 176821260 193381689 218989835 910430405 ▇▁▁▁▁
44682 2 1 190377976 68522670 113258720 158499005 173485080 197664291 777583030 ▇▁▁▁▁
44713 2 1 225001508 110623784 131161986 180138119 197947872 223881165 1116429368 ▇▁▁▁▁
44743 2 1 232899056 83050809 139220487 195186813 212525540 241692720 977305463 ▇▁▁▁▁
44774 2 1 269186963 84081596 162341273 227476045 248948270 284224887 1049897677 ▇▁▁▁▁
44805 2 1 209729551 83062238 124455818 172047830 188302633 213974277 916967087 ▇▁▁▁▁
44835 2 1 219429857 80429103 132564671 180654550 199433792 227834795 920978497 ▇▁▁▁▁
44866 2 1 226220390 87724934 134522936 186851857 205388947 233872458 993327793 ▇▁▁▁▁
44896 2 1 258840236 85706327 155772124 217225168 237882197 270529216 1034434527 ▇▁▁▁▁
44927 2 1 283773848 100143430 172820362 236370341 258860394 293376781 1166493339 ▇▁▁▁▁
44958 2 1 227602547 100196315 134863740 184770090 203465143 230522104 1082564036 ▇▁▁▁▁
44986 2 1 221460616 86469363 132133815 181359201 199759514 225816146 958956998 ▇▁▁▁▁
45017 2 1 218767558 80310445 132630726 181152412 198661828 225035012 920521275 ▇▁▁▁▁
45047 2 1 273431404 91830658 170819362 227516081 249439187 285863656 1045728352 ▇▁▁▁▁
45078 2 1 283773212 105662462 170736094 235185078 257842658 291608992 1203452783 ▇▁▁▁▁
45108 2 1 280190499 113483531 170658451 229749788 252275533 286891790 1248307542 ▇▁▁▁▁
45139 2 1 301784986 115590153 144385458 248542762 274128309 312543353 1258151384 ▇▁▁▁▁
45170 2 1 332288932 134717109 143894593 272441673 301749404 342278779 1439986831 ▇▁▁▁▁
45200 2 1 271007240 114541318 163221014 221495708 243001635 275531089 1196709976 ▇▁▁▁▁
45231 2 1 290262591 130587290 173294386 236379260 258574936 293654405 1359322749 ▇▁▁▁▁
45261 2 1 332694735 132726614 202335304 274741998 300649231 340500750 1476458076 ▇▁▁▁▁
45292 2 1 371716714 185944786 221854905 298404613 329305975 371906910 1920066645 ▇▁▁▁▁
45323 2 1 357550861 157476952 215164870 292000180 319835392 362385845 1703593552 ▇▁▁▁▁
45352 2 1 343672972 171607475 205078814 278041081 303582908 340805338 1780967855 ▇▁▁▁▁
45383 2 1 371179825 189508822 220171361 295943432 324661189 369187442 1914102610 ▇▁▁▁▁
45413 2 1 378255634 186359150 225464032 306895290 335136179 378755701 1949941436 ▇▁▁▁▁

METHOD

Faac data-set is a publicly available data-set published for competition purpose by the Revenue Mobilization Allocation and Fiscal Commission(RMAFC) of Nigeria in 2024.

The data-set is unlikely to be easily accessed/available since the competition was closed in 2024.

RESULT

Rename columns with the correct month and year from January 2007 to May 2024

#Rename columns with the correct month and year from January 2007 to May 2024
correct_year <-  faac_lga %>%
  rename(
    Jan_2007 = `39083`,
    Feb_2007 = `39114`,
    Mar_2007 = `39142`,
    Apr_2007 = `39173`,
    May_2007 = `39203`,
    Jun_2007 = `39234`,
    Jul_2007 = `39264`,
    Aug_2007 = `39295`,
    Sep_2007 = `39326`,
    Oct_2007 = `39356`,
    Nov_2007 = `39387`,
    Dec_2007 = `39417`,
    Jan_2008 = `39448`,
    Feb_2008 = `39479`,
    Mar_2008 = `39508`,
    Apr_2008 = `39539`,
    May_2008 = `39569`,
    Jun_2008 = `39600`,
    Jul_2008 = `39630`,
    Aug_2008 = `39661`,
    Sep_2008 = `39692`,
    Oct_2008 = `39722`,
    Nov_2008 = `39753`,
    Dec_2008 = `39783`,
    Jan_2009 = `39814`,
    Feb_2009 = `39845`,
    Mar_2009 = `39873`,
    Apr_2009 = `39904`,
    May_2009 = `39934`,
    Jun_2009 = `39965`,
    Jul_2009 = `39995`,
    Aug_2009 = `40026`,
    Sep_2009 = `40057`,
    Oct_2009 = `40087`,
    Nov_2009 = `40118`,
    Dec_2009 = `40148`,
    Jan_2010 = `40179`,
    Feb_2010 = `40210`,
    Mar_2010 = `40238`,
    Apr_2010 = `40269`,
    May_2010 = `40299`,
    Jun_2010 = `40330`,
    Jul_2010 = `40360`,
    Aug_2010 = `40391`,
    Sep_2010 = `40422`,
    Oct_2010 = `40452`,
    Nov_2010 = `40483`,
    Dec_2010 = `40513`,
    Jan_2011 = `40544`,
    Feb_2011 = `40575`,
    Mar_2011 = `40603`,
    Apr_2011 = `40634`,
    May_2011 = `40664`,
    Jun_2011 = `40695`,
    Jul_2011 = `40725`,
    Aug_2011 = `40756`,
    Sep_2011 = `40787`,
    Oct_2011 = `40817`,
    Nov_2011 = `40848`,
    Dec_2011 = `40878`,
    Jan_2012 = `40909`,
    Feb_2012 = `40940`,
    Mar_2012 = `40969`,
    Apr_2012 = `41000`,
    May_2012 = `41030`,
    Jun_2012 = `41061`,
    Jul_2012 = `41091`,
    Aug_2012 = `41122` ,
    Sep_2012 = `41153`,
    Oct_2012 = `41183`,
    Nov_2012 = `41214`,
    Dec_2012= `41244`,
    Jan_2013 = `41275`,
    Feb_2013 = `41306`,
    Mar_2013 = `41334`,
    Apr_2013 = `41365`,
    May_2013 = `41395`,
    Jun_2013 = `41426`,
    Jul_2013 = `41456`,
    Aug_2013 = `41487` ,
    Sep_2013 = `41518`,
    Oct_2013 = `41548`,
    Nov_2013 = `41579` ,
    Dec_2013 = `41609`,
    Jan_2014 = `41640` ,
    Feb_2014 = `41671`,
    Mar_2014 = `41699`,
    Apr_2014 = `41730`,
    May_2014 = `41760`,
    Jun_2014 = `41791`,
    Jul_2014 = `41821`,
    Aug_2014 = `41852` ,
    Sep_2014 = `41883`,
    Oct_2014 = `41913`,
    Nov_2014 = `41944` ,
    Dec_2014 = `41974`,
    Jan_2015 = `42005` ,
    Feb_2015 = `42036`,
    Mar_2015 = `42064` ,
    Apr_2015 = `42095`,
    May_2015 = `42125`,
    Jun_2015 = `42156`,
    Jul_2015 = `42186`,
    Aug_2015 = `42217` ,
    Sep_2015 = `42248`,
    Oct_2015 = `42278`,
    Nov_2015 = `42309` ,
    Dec_2015 = `42339`,
    Jan_2016 = `42370` ,
    Feb_2016 = `42401`,
    Mar_2016 = `42430` ,
    Apr_2016 = `42461`,
    May_2016 = `42491`,
    Jun_2016 = `42522`,
    Jul_2016 = `42552`,
    Aug_2016 = `42583`,
    Sep_2016 = `42614` ,
    Oct_2016 = `42644` ,
    Nov_2016 = `42675`,
    Dec_2016  = `42705` ,
    Jan_2017= `42736`,
    Feb_2017= `42767` ,
    Mar_2017 = `42795`,
    Apr_2017  = `42826` ,
    May_2017 = `42856`,
    Jun_2017 = `42887`,
    Jul_2017 = `42917`,
    Aug_2017 = `42948` ,
    Sep_2017 = `42979` ,
    Oct_2017 = `43009` ,
    Nov_2017 =  `43040`,
    Dec_2017 = `43070`  ,
    Jan_2018 = `43101`,
    Feb_2018 = `43132` ,
    Mar_2018 = `43160`,
    Apr_2018  = `43191`  ,
    May_2018 = `43221`,
    Jun_2018 = `43252`,
    Jul_2018 = `43282`,
    Aug_2018 = `43313` ,
    Sep_2018 = `43344`,
    Oct_2018 = `43374` ,
    Nov_2018 =  `43405`,
    Dec_2018 = `43435`,
    Jan_2019 = `43466`,
    Feb_2019 = `43497` ,
    Mar_2019 = `43525`,
    Apr_2019  = `43556`,
    May_2019 = `43586`,
    Jun_2019 = `43617`,
    Jul_2019 = `43647`,
    Aug_2019 = `43678` ,
    Sep_2019 = `43709`,
    Oct_2019 = `43739` ,
    Nov_2019 =  `43770`,
    Dec_2019 = `43800`,
    Jan_2020 = `43831`,
    Feb_2020 = `43862` ,
    Mar_2020 = `43891`,
    Apr_2020  = `43922`,
    May_2020 = `43952`,
    Jun_2020 = `43983`,
    Jul_2020 = `44013`,
    Aug_2020 = `44044` ,
    Sep_2020 = `44075`,
    Oct_2020 = `44105` ,
    Nov_2020 =  `44136`,
    Dec_2020 = `44166`,
    Jan_2021 = `44197`,
    Feb_2021 = `44228` ,
    Mar_2021 = `44256`,
    Apr_2021  = `44287`,
    May_2021 = `44317`,
    Jun_2021 = `44348`,
    Jul_2021 = `44378`,
    Aug_2021 = `44409` ,
    Sep_2021 = `44440`,
    Oct_2021 = `44470` ,
    Nov_2021 =  `44501`,
    Dec_2021 = `44531`,
    Jan_2022 = `44562`,
    Feb_2022 = `44593` ,
    Mar_2022 = `44621`,
    Apr_2022  = `44652`,
    May_2022 = `44682`,
    Jun_2022 = `44713`,
    Jul_2022 = `44743`,
    Aug_2022 = `44774` ,
    Sep_2022 = `44805`,
    Oct_2022 = `44835` ,
    Nov_2022 =  `44866`,
    Dec_2022 = `44896`,
    Jan_2023 = `44927`,
    Feb_2023 = `44958` ,
    Mar_2023 = `44986`,
    Apr_2023  = `45017`,
    May_2023 = `45047`,
    Jun_2023 = `45078`,
    Jul_2023 = `45108`,
    Aug_2023 = `45139` ,
    Sep_2023 = `45170`,
    Oct_2023 = `45200` ,
    Nov_2023 =  `45231`,
    Dec_2023 = `45261`,
    Jan_2024 = `45292`,
    Feb_2024 = `45323` ,
    Mar_2024 = `45352`,
    Apr_2024  = `45383`,
    May_2024 = `45413`
  )

Exclude the rows with Total and Average

#Extract the rows of interest
exclude_totalaverage <- correct_year[1:774,]

Create a gt object to use the grand summary function to get the total and average value row

exclude_totalaverage_gtobject <-  correct_year[1:774,] %>% 
  gt()  
 

total_average_resolved <- exclude_totalaverage_gtobject %>%
  tab_header(title = "FAAC Allocation to State and Local Government Councils in Nigeria") %>%
  tab_spanner(columns = everything(), label = "Month & Year") %>%
  grand_summary_rows(
    columns = where(is.numeric),  # Only apply to numeric columns
    fns = list(
      TOTAL ~ sum(., na.rm = TRUE),
      AVERAGE ~ mean(., na.rm = TRUE)
    ),
    fmt = ~ fmt_number(., use_seps = FALSE)
  )

Make a pivot longer data-set to enhance data analysis

View Table 1

Table 1: Pivot long faac data 
faac_data_long <- exclude_totalaverage |>
  select(STATE, LGC, !starts_with("4")) |> 
  pivot_longer(
    cols = -c(STATE, LGC),
    names_to = "date",
    values_to = "amount"
  ) |> 
  separate(date, into = c("Month", "Year"), sep = "_")

Yearly summary

View Table 2

summary_year <- faac_data_long |> 
  group_by(Year) |>
  summarise(
    total = sum(amount, na.rm = TRUE),
    avgerage = mean(amount, na.rm = TRUE)
  ) %>% 
  arrange(desc(total)) %>% 
  kable(col.names = c("Year", "total", "avgerage"))

summary_year
Table 2: Yearly summary
Year total avgerage
2023 2.567388e+12 276419847
2022 2.014185e+12 216858896
2013 1.706123e+12 183691094
2018 1.657922e+12 178501492
2019 1.599813e+12 172245169
2012 1.562572e+12 168235519
2014 1.559963e+12 167972718
2020 1.505631e+12 162104946
2011 1.432177e+12 154196506
2024 1.410519e+12 364475201
2010 1.278298e+12 137628937
2017 1.253812e+12 134992673
2015 1.201493e+12 129359683
2008 1.049001e+12 112941539
2016 1.001320e+12 107807879
2009 9.922825e+11 106834899
2007 7.966507e+11 85781279
2021 4.147694e+11 178625918

Monthly summary

View Table 3

summary_month <- faac_data_long |> 
  group_by(Month) |>
  summarise(
    total = sum(amount, na.rm = TRUE),
    avgerage = mean(amount, na.rm = TRUE)
  )%>% 
  arrange(desc(total)) %>% 
  kable(col.names = c("Month", "total", "avgerage"))

summary_month
Table 3: Monthly summary
Month total avgerage
Jan 2.235241e+12 160439368
Dec 2.222670e+12 168921580
Jul 2.199116e+12 177577175
Feb 2.166862e+12 155542487
May 2.157775e+12 163989551
Aug 2.139995e+12 172803243
Mar 2.133279e+12 162127889
Apr 2.019943e+12 153526143
Nov 1.944923e+12 157051293
Sep 1.935670e+12 156304119
Jun 1.930513e+12 155887683
Oct 1.917929e+12 154871549

Top States Allocation

look Figure 1

state_summary <- faac_data_long %>%
  group_by(STATE) |>
  summarise(
    total = sum(amount, na.rm = TRUE),
    avg = mean(amount, na.rm = TRUE)
  ) %>%
  arrange(desc(total))%>%
  head(n = 10)



state_summary %>%
  ggplot(aes(STATE, total, fill = STATE)) +
  geom_col() +
  coord_flip() +
  labs(
    x = "STATE",
    y = "total",
    fill = "STATE",
    caption = "allocation to state"
  )
Figure 1: Top States Allocation

Top LGC Allocation

See Figure 2

lgc_summary <- faac_data_long %>%
  group_by(LGC) |>
  summarise(
    total = sum(amount, na.rm = TRUE),
    avg = mean(amount, na.rm = TRUE)
  ) %>%
  arrange(desc(total))%>%
  head(n = 10)



lgc_summary %>%
  ggplot(aes(LGC, total, fill = LGC)) +
  geom_col() +
  coord_flip() +
  labs(
    x = "LGC",
    y = "total",
    fill = "LGC",
    caption = "allocation to lgc"
  )
Figure 2: Top 4 lgc

CONVERT DATE CHARACTER TYPE TO DATETIME TYPE TO GENERATE TIME SERIES

faac_long <- exclude_totalaverage |>
  select(STATE, LGC, !starts_with("4")) |>
  pivot_longer(
    cols = -c(STATE, LGC),
    names_to = "date",
    values_to = "amount"
  )


the_date_format <- faac_long %>%
  mutate(
    date_type = my(faac_long$date)
  )

Preprocess for time series

state_summary_ts <- the_date_format %>%
  group_by( date_type) |>
  summarise(
    total = sum(amount, na.rm = TRUE),
    average = mean(amount, na.rm = TRUE)
  ) %>%
  arrange(desc(total))

Select date_type(datetime) and total

data_ts <- state_summary_ts %>%
  select(date_type, total)

Plot time series

See Figure 3

data_ts %>% 
  plot_time_series(date_type, total)
Figure 3: The time series plot

CONCLUSION

  • The objective to get the total and average row without missing value was achieve in the code chuck: Create a gt object to use the grand summary function to get the total and average value row.

  • Base on recommendation , the the time series plot was include to represent the data

  • In addition, findings shows some of the top states and local government councils getting the most allocation from the Revenue Mobilization Allocation and Fiscal Commission(RMAFC) of Nigeria.

The findings shows the some of the top states and local government councils getting the most allocation from the Revenue Mobilization Allocation and Fiscal Commission(RMAFC) of Nigeria.

Top 4 states :

  1. LAGOS

  2. KANO

  3. KATSINA

  4. OYO

Top 4 Local Government Councils :

  1. ALIMOSHO

  2. ABUJA MUNICIPAL

  3. AJEROMI/IFELODUN

  4. KOSOFE

I will like to appreciate Clavijo Daza Adriana Marcela for assistance in wrangling the data .

REFERENCE

The reference bib file contain all project reference (Mock 2025) (Wickham et al. 2019) (Waring et al. 2025) (Dancho and Vaughan 2025)

References

Dancho, Matt, and Davis Vaughan. 2025. “Timetk: A Tool Kit for Working with Time Series.” https://github.com/business-science/timetk.
Mock, Thomas. 2025. “gtExtras: Extending ’Gt’ for Beautiful HTML Tables.” https://github.com/jthomasmock/gtExtras.
Waring, Elin, Michael Quinn, Amelia McNamara, Eduardo Arino de la Rubia, Hao Zhu, and Shannon Ellis. 2025. “Skimr: Compact and Flexible Summaries of Data.” https://docs.ropensci.org/skimr/.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse” 4: 1686. https://doi.org/10.21105/joss.01686.