Assignment 2
Deconstruct, Reconstruct Web Report
This is a template file. The example included is not considered a good example to follow for Assignment 2. Remove this warning prior to submitting.
Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.
Original
Objective
Explain the objective of the original data visualisation and the targetted audience.
The objective of this data visualization is to present the top performing universities in each state of US. The Universities are ranked according to the QS World University Rankings. By doing this it will give its users a quick and easy summary of universities academic performance based on which students can decide which university is the best for them and also they can shortlist it and after that they can compare their fees too, whether or not that particular university fits in their budget or not. The intended audience for this visualization are individuals who have done their schooling and now they are interested in doing higher education. This is also for those students who wants to decide which university to go by seeing their academic performance. Academic researchers, parents of the students,policymakers, educators, faculty members, university administrators and admission officers can also benefit from this data visualization.
The visualisation chosen had the following three main issues:
Briefly explain issue 1 This visualization uses different shades of orange for representation of the Universities Rank throughout different states. However, the shades of the orange are not a very good choice here as it is not easily distinguishable. For instance, Universities from 1-10 are not easily readable in the bar graph and also not in the map, it mixes with the background color which is white in this case. It becomes very hard for the user to see representation of that color.
Briefly explain issue 2 It will misguide the students because the visualization does not tells us that of which year this visualization of Universities Ranking has been done and because of that the ranking of Universities could be wrong and misleading.
Briefly explain issue 3 The visualization highlights the highest ranked universities in each state but it only provides limited data such as it only tells the user about university name and its position in the current year. Adding more information for instance showing Universities Ranking in previous year will help students or parents to decide University more confidently.
Reference
USA 2020 Rankings (no date) Top Universities. Available at: https://www.topuniversities.com/university-rankings/usa-rankings/2020 (Accessed: May 1, 2023). Dive into anything (no date) Reddit. Available at: https://www.reddit.com/r/dataisbeautiful/comments/131s94j/oc_each_us_states_highestranked_university_in_the/ (Accessed: May 1, 2023). *QS World University Rankings 2022 (no date) Top Universities. Available at: https://www.topuniversities.com/university-rankings/world-university-rankings/2022 (Accessed: May 1, 2023).
Code
The following code was used to fix the issues identified in the original.
library(readr)
library(readxl)
library(dplyr)
library(ggplot2)
#library(tidyverse)
#Here we are reading our data to work on it in R
Rankings <- read.csv("Data3.csv")
colnames(Rankings)## [1] "Rank_2022" "Rank_2021" "University" "Location" "Country"
## [6] "State"rownames(Rankings)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
## [11] "11" "12" "13" "14" "15" "16" "17" "18" "19" "20"
## [21] "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
## [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40"
## [41] "41" "42" "43" "44" "45" "46" "47" "48" "49" "50"
## [51] "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70"
## [71] "71" "72" "73" "74" "75" "76" "77" "78" "79" "80"
## [81] "81" "82" "83" "84" "85" "86" "87" "88" "89" "90"
## [91] "91" "92" "93" "94" "95" "96" "97" "98" "99" "100"
## [101] "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130"
## [131] "131" "132" "133" "134" "135" "136" "137" "138" "139" "140"
## [141] "141" "142" "143" "144" "145" "146" "147" "148" "149" "150"
## [151] "151" "152" "153" "154" "155" "156" "157" "158" "159" "160"
## [161] "161" "162" "163" "164" "165" "166" "167" "168" "169" "170"
## [171] "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190"
## [191] "191" "192" "193" "194" "195" "196" "197" "198" "199" "200"
## [201] "201" "202" "203" "204" "205" "206" "207" "208" "209" "210"
## [211] "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
## [221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230"
## [231] "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250"
## [251] "251" "252" "253" "254" "255" "256" "257" "258" "259" "260"
## [261] "261" "262" "263" "264" "265" "266" "267" "268" "269" "270"
## [271] "271" "272" "273" "274" "275" "276" "277" "278" "279" "280"
## [281] "281" "282" "283" "284" "285" "286" "287" "288" "289" "290"
## [291] "291" "292" "293" "294" "295" "296" "297" "298" "299" "300"
## [301] "301" "302" "303" "304" "305" "306" "307" "308" "309" "310"
## [311] "311" "312" "313" "314" "315" "316" "317" "318" "319" "320"
## [321] "321" "322" "323" "324" "325" "326" "327" "328" "329" "330"
## [331] "331" "332" "333" "334" "335" "336" "337" "338" "339" "340"
## [341] "341" "342" "343" "344" "345" "346" "347" "348" "349" "350"
## [351] "351" "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369" "370"
## [371] "371" "372" "373" "374" "375" "376" "377" "378" "379" "380"
## [381] "381" "382" "383" "384" "385" "386" "387" "388" "389" "390"
## [391] "391" "392" "393" "394" "395" "396" "397" "398" "399" "400"
## [401] "401" "402" "403" "404" "405" "406" "407" "408" "409" "410"
## [411] "411" "412" "413" "414" "415" "416" "417" "418" "419" "420"
## [421] "421" "422" "423" "424" "425" "426" "427" "428" "429" "430"
## [431] "431" "432" "433" "434" "435" "436" "437" "438" "439" "440"
## [441] "441" "442" "443" "444" "445" "446" "447" "448" "449" "450"
## [451] "451" "452" "453" "454" "455" "456" "457" "458" "459" "460"
## [461] "461" "462" "463" "464" "465" "466" "467" "468" "469" "470"
## [471] "471" "472" "473" "474" "475" "476" "477" "478" "479" "480"
## [481] "481" "482" "483" "484" "485" "486" "487" "488" "489" "490"
## [491] "491" "492" "493" "494" "495" "496" "497" "498" "499" "500"
## [501] "501" "502" "503" "504" "505" "506" "507" "508" "509" "510"
## [511] "511" "512" "513" "514" "515" "516" "517" "518" "519" "520"
## [521] "521" "522" "523" "524" "525" "526" "527" "528" "529" "530"
## [531] "531" "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549" "550"
## [551] "551" "552" "553" "554" "555" "556" "557" "558" "559" "560"
## [561] "561" "562" "563" "564" "565" "566" "567" "568" "569" "570"
## [571] "571" "572" "573" "574" "575" "576" "577" "578" "579" "580"
## [581] "581" "582" "583" "584" "585" "586" "587" "588" "589" "590"
## [591] "591" "592" "593" "594" "595" "596" "597" "598" "599" "600"
## [601] "601" "602" "603" "604" "605" "606" "607" "608" "609" "610"
## [611] "611" "612" "613" "614" "615" "616" "617" "618" "619" "620"
## [621] "621" "622" "623" "624" "625" "626" "627" "628" "629" "630"
## [631] "631" "632" "633" "634" "635" "636" "637" "638" "639" "640"
## [641] "641" "642" "643" "644" "645" "646" "647" "648" "649" "650"
## [651] "651" "652" "653" "654" "655" "656" "657" "658" "659" "660"
## [661] "661" "662" "663" "664" "665" "666" "667" "668" "669" "670"
## [671] "671" "672" "673" "674" "675" "676" "677" "678" "679" "680"
## [681] "681" "682" "683" "684" "685" "686" "687" "688" "689" "690"
## [691] "691" "692" "693" "694" "695" "696" "697" "698" "699" "700"
## [701] "701" "702" "703" "704" "705" "706" "707" "708" "709" "710"
## [711] "711" "712" "713" "714" "715" "716" "717" "718" "719" "720"
## [721] "721" "722" "723" "724" "725" "726" "727" "728" "729" "730"
## [731] "731" "732" "733" "734" "735" "736" "737" "738" "739" "740"
## [741] "741" "742" "743" "744" "745" "746" "747" "748" "749" "750"
## [751] "751" "752" "753" "754" "755" "756" "757" "758" "759" "760"
## [761] "761" "762" "763" "764" "765" "766" "767" "768" "769" "770"
## [771] "771" "772" "773" "774" "775" "776" "777" "778" "779" "780"
## [781] "781" "782" "783" "784" "785" "786" "787" "788" "789" "790"
## [791] "791" "792" "793" "794" "795" "796" "797" "798" "799" "800"
## [801] "801" "802" "803" "804" "805" "806" "807" "808" "809" "810"
## [811] "811" "812" "813" "814" "815" "816" "817" "818" "819" "820"
## [821] "821" "822" "823" "824" "825" "826" "827" "828" "829" "830"
## [831] "831" "832" "833" "834" "835" "836" "837" "838" "839" "840"
## [841] "841" "842" "843" "844" "845" "846" "847" "848" "849" "850"
## [851] "851" "852" "853" "854" "855" "856" "857" "858" "859" "860"
## [861] "861" "862" "863" "864" "865" "866" "867" "868" "869" "870"
## [871] "871" "872" "873" "874" "875" "876" "877" "878" "879" "880"
## [881] "881" "882" "883" "884" "885" "886" "887" "888" "889" "890"
## [891] "891" "892" "893" "894" "895" "896" "897" "898" "899" "900"
## [901] "901" "902" "903" "904" "905" "906" "907" "908" "909" "910"
## [911] "911" "912" "913" "914" "915" "916" "917" "918" "919" "920"
## [921] "921" "922" "923" "924" "925" "926" "927" "928" "929" "930"
## [931] "931" "932" "933" "934" "935" "936" "937" "938" "939" "940"
## [941] "941" "942" "943" "944" "945" "946" "947" "948" "949" "950"
## [951] "951" "952" "953" "954" "955" "956" "957" "958" "959" "960"
## [961] "961" "962" "963" "964" "965" "966" "967" "968" "969" "970"
## [971] "971" "972" "973" "974" "975" "976" "977" "978" "979" "980"
## [981] "981" "982" "983" "984" "985" "986" "987" "988" "989" "990"
## [991] "991" "992" "993" "994" "995" "996" "997" "998" "999" "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045" "1046" "1047" "1048" "1049" "1050"
## [1051] "1051" "1052" "1053" "1054" "1055" "1056" "1057" "1058" "1059" "1060"
## [1061] "1061" "1062" "1063" "1064" "1065" "1066" "1067" "1068" "1069" "1070"
## [1071] "1071" "1072" "1073" "1074" "1075" "1076" "1077" "1078" "1079" "1080"
## [1081] "1081" "1082" "1083" "1084" "1085" "1086" "1087" "1088" "1089" "1090"
## [1091] "1091" "1092" "1093" "1094" "1095" "1096" "1097" "1098" "1099" "1100"
## [1101] "1101" "1102" "1103" "1104" "1105" "1106" "1107" "1108" "1109" "1110"
## [1111] "1111" "1112" "1113" "1114" "1115" "1116" "1117" "1118" "1119" "1120"
## [1121] "1121" "1122" "1123" "1124" "1125" "1126" "1127" "1128" "1129" "1130"
## [1131] "1131" "1132" "1133" "1134" "1135" "1136" "1137" "1138" "1139" "1140"
## [1141] "1141" "1142" "1143" "1144" "1145" "1146" "1147" "1148" "1149" "1150"
## [1151] "1151" "1152" "1153" "1154" "1155" "1156" "1157" "1158" "1159" "1160"
## [1161] "1161" "1162" "1163" "1164" "1165" "1166" "1167" "1168" "1169" "1170"
## [1171] "1171" "1172" "1173" "1174" "1175" "1176" "1177" "1178" "1179" "1180"
## [1181] "1181" "1182" "1183" "1184" "1185" "1186" "1187" "1188" "1189" "1190"
## [1191] "1191" "1192" "1193" "1194" "1195" "1196" "1197" "1198" "1199" "1200"
## [1201] "1201" "1202" "1203" "1204" "1205" "1206" "1207" "1208" "1209" "1210"
## [1211] "1211" "1212" "1213" "1214" "1215" "1216" "1217" "1218" "1219" "1220"
## [1221] "1221" "1222" "1223" "1224" "1225" "1226" "1227" "1228" "1229" "1230"
## [1231] "1231" "1232" "1233" "1234" "1235" "1236" "1237" "1238" "1239" "1240"
## [1241] "1241" "1242" "1243" "1244" "1245" "1246" "1247" "1248" "1249" "1250"
## [1251] "1251" "1252" "1253" "1254" "1255" "1256" "1257" "1258" "1259" "1260"
## [1261] "1261" "1262" "1263" "1264" "1265" "1266" "1267" "1268" "1269" "1270"
## [1271] "1271" "1272" "1273" "1274" "1275" "1276" "1277" "1278" "1279" "1280"
## [1281] "1281" "1282" "1283" "1284" "1285" "1286" "1287" "1288" "1289" "1290"
## [1291] "1291" "1292" "1293" "1294" "1295" "1296" "1297" "1298" "1299" "1300"sorted_data <- Rankings[order(Rankings$Rank_2022),]
filtered_data <- filter(Rankings, Location == "US" & Country == "United States")
Rankings$University <- iconv(Rankings$University, to = "UTF-8", sub = "")
#gsub() is used as replacement function which means it can replace any character that we wish to remove from our data.
Rankings$Rank_2022 <- gsub("=", "", Rankings$Rank_2022)
Rankings$Rank_2022 <- gsub("-\\d+", "", Rankings$Rank_2022)
Rankings$University <- gsub("-\\d+", "", Rankings$University)
Rankings$Rank_2022 <- as.numeric(Rankings$Rank_2022)
omit_na <- na.omit(Rankings$Rank_2022)
#Now we will define a function colors we want to give to our bar plot
sortedRankings <- Rankings %>% filter(Location == "US") %>% mutate
(categorisedRank = cut(Rankings$Rank_2022, breaks = c(0,10,100,500,Inf),
labels = c ("1-10","10-100","100-500","500+")))## [1] 1-10 1-10 1-10 1-10 1-10 1-10 1-10 1-10 1-10
## [10] 1-10 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [19] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [28] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [37] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [46] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [55] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [64] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [73] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [82] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [91] 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100 10-100
## [100] 10-100 10-100 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [109] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [118] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [127] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [136] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [145] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [154] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [163] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [172] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [181] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [190] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [199] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [208] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [217] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [226] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [235] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [244] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [253] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [262] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [271] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [280] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [289] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [298] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [307] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [316] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [325] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [334] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [343] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [352] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [361] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [370] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [379] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [388] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [397] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [406] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [415] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [424] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [433] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [442] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [451] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [460] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [469] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [478] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [487] 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500 100-500
## [496] 100-500 100-500 100-500 100-500 100-500 100-500 500+ 500+ 500+
## [505] 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+
## [514] 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+
## [523] 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+
## [532] 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+
## [541] 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+ 500+
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## Levels: 1-10 10-100 100-500 500+x11() #this will open the plot in new window
#Create the ranking plot
ranking_plot <- ggplot(data = sortedRankings, aes(x=Rank_2022, y=University)) +
geom_bar(stat="identity")+
ylab("University") +
xlab("Rankings") +
scale_fill_manual(values = c("1-10" = "green", "10-100" = "darkgreen", "100-500" = "orange", "500+" = "red")) +
geom_text(aes(label = paste(University, State, sep = ", ")), position = position_stack(vjust = 0.5)) +
#We will use coord_flip() function to flip our bar
coord_flip() +
theme_minimal() +
theme(legend.position = "bottom")Data Reference
QS World University Rankings 2022 (no date) Top Universities. Available at: https://www.topuniversities.com/university-rankings/world-university-rankings/2022 (Accessed: May 1, 2023).
Reconstruction
The following plot fixes the main issues in the original.
