Challenge 1

challenge_1

Author

Akhilesh Kumar Meghwal

Published

August 21, 2022

Code

library(tidyverse)
library(Hmisc)
library(psych)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to

read in a dataset, and
describe the dataset using both words and any supporting information (e.g., tables, etc)

Read in the Data

Read in one (or more) of the following data sets, using the correct R package and command.

railroad_2012_clean_county.csv ⭐
birds.csv ⭐⭐
FAOstat*.csv ⭐⭐
wild_bird_data.xlsx ⭐⭐⭐
StateCounty2012.xlsx ⭐⭐⭐⭐

Find the _data folder, located inside the posts folder. Then you can read in the data, using either one of the readr standard tidy read commands, or a specialized package such as readxl.

Code

# 
railroad <- read.csv('_data/railroad_2012_clean_county.csv', stringsAsFactors = TRUE, header = TRUE)

Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.

Describe the data

Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).

Code

# check first 6 rows in the dataset, to get primary understading of the dataset structure
# check first 6 rows in the dataset, to get primary understanding of the dataframe structure

head(railroad)

  state               county total_employees
1    AE                  APO               2
2    AK            ANCHORAGE               7
3    AK FAIRBANKS NORTH STAR               2
4    AK               JUNEAU               3
5    AK    MATANUSKA-SUSITNA               2
6    AK                SITKA               1

Code

View(railroad)
dim(railroad) # numbers of rows. columns

[1] 2930    3

Code

#Dataset Description
  # "state" column contains name of different states from United States of America in upper case, abbreviated format
  # "county" column contains names of different counties, for the states in 'state' column, in upper case format
  # "total_employees" column contains state-county wise number of railroad employees.


# struture of railroad dataset

str(railroad)

'data.frame':   2930 obs. of  3 variables:
 $ state          : Factor w/ 53 levels "AE","AK","AL",..: 1 2 2 2 2 2 2 3 3 3 ...
 $ county         : Factor w/ 1709 levels "ABBEVILLE","ACADIA",..: 44 33 524 794 963 1415 1417 78 85 95 ...
 $ total_employees: int  2 7 2 3 2 1 88 102 143 1 ...

Code

# Output

  # 'data.frame':   2930 obs. of  3 variables:
  #  $ state          : Factor w/ 53 levels "AE","AK","AL",..: 1 2 2 2 2 2 2 3 3 3 ...
  #  $ county         : Factor w/ 1709 levels "ABBEVILLE","ACADIA",..: 44 33 524 794 963 1415 1417 78 85 95 ...
  #  $ total_employees: int  2 7 2 3 2 1 88 102 143 1 ...

#summary

  # railroad dataset has 2930 rows and 3 columns
  # State column is a factor class; as converted from character class during file read command read.csv() and it has 53 levels.
  # county column has 1709 levels

# Missing Values, check NA value

sum(is.na(railroad))

[1] 0

Code

# check column wise NA values

sum(is.na(railroad$state))

[1] 0

Code

sum(is.na(railroad$county))

[1] 0

Code

sum(is.na(railroad$total_employees))

[1] 0

Code

# Missing Value, check NULL value

sum(is.null(railroad))

[1] 0

Code

# check column wise NULL values

sum(is.null(railroad$state))

[1] 0

Code

sum(is.null(railroad$county))

[1] 0

Code

sum(is.null(railroad$total_employees))

[1] 0

Code

# column, row names of the dataset

colnames(railroad)

[1] "state"           "county"          "total_employees"

Code

row.names(railroad)

   [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"
[1301] "1301" "1302" "1303" "1304" "1305" "1306" "1307" "1308" "1309" "1310"
[1311] "1311" "1312" "1313" "1314" "1315" "1316" "1317" "1318" "1319" "1320"
[1321] "1321" "1322" "1323" "1324" "1325" "1326" "1327" "1328" "1329" "1330"
[1331] "1331" "1332" "1333" "1334" "1335" "1336" "1337" "1338" "1339" "1340"
[1341] "1341" "1342" "1343" "1344" "1345" "1346" "1347" "1348" "1349" "1350"
[1351] "1351" "1352" "1353" "1354" "1355" "1356" "1357" "1358" "1359" "1360"
[1361] "1361" "1362" "1363" "1364" "1365" "1366" "1367" "1368" "1369" "1370"
[1371] "1371" "1372" "1373" "1374" "1375" "1376" "1377" "1378" "1379" "1380"
[1381] "1381" "1382" "1383" "1384" "1385" "1386" "1387" "1388" "1389" "1390"
[1391] "1391" "1392" "1393" "1394" "1395" "1396" "1397" "1398" "1399" "1400"
[1401] "1401" "1402" "1403" "1404" "1405" "1406" "1407" "1408" "1409" "1410"
[1411] "1411" "1412" "1413" "1414" "1415" "1416" "1417" "1418" "1419" "1420"
[1421] "1421" "1422" "1423" "1424" "1425" "1426" "1427" "1428" "1429" "1430"
[1431] "1431" "1432" "1433" "1434" "1435" "1436" "1437" "1438" "1439" "1440"
[1441] "1441" "1442" "1443" "1444" "1445" "1446" "1447" "1448" "1449" "1450"
[1451] "1451" "1452" "1453" "1454" "1455" "1456" "1457" "1458" "1459" "1460"
[1461] "1461" "1462" "1463" "1464" "1465" "1466" "1467" "1468" "1469" "1470"
[1471] "1471" "1472" "1473" "1474" "1475" "1476" "1477" "1478" "1479" "1480"
[1481] "1481" "1482" "1483" "1484" "1485" "1486" "1487" "1488" "1489" "1490"
[1491] "1491" "1492" "1493" "1494" "1495" "1496" "1497" "1498" "1499" "1500"
[1501] "1501" "1502" "1503" "1504" "1505" "1506" "1507" "1508" "1509" "1510"
[1511] "1511" "1512" "1513" "1514" "1515" "1516" "1517" "1518" "1519" "1520"
[1521] "1521" "1522" "1523" "1524" "1525" "1526" "1527" "1528" "1529" "1530"
[1531] "1531" "1532" "1533" "1534" "1535" "1536" "1537" "1538" "1539" "1540"
[1541] "1541" "1542" "1543" "1544" "1545" "1546" "1547" "1548" "1549" "1550"
[1551] "1551" "1552" "1553" "1554" "1555" "1556" "1557" "1558" "1559" "1560"
[1561] "1561" "1562" "1563" "1564" "1565" "1566" "1567" "1568" "1569" "1570"
[1571] "1571" "1572" "1573" "1574" "1575" "1576" "1577" "1578" "1579" "1580"
[1581] "1581" "1582" "1583" "1584" "1585" "1586" "1587" "1588" "1589" "1590"
[1591] "1591" "1592" "1593" "1594" "1595" "1596" "1597" "1598" "1599" "1600"
[1601] "1601" "1602" "1603" "1604" "1605" "1606" "1607" "1608" "1609" "1610"
[1611] "1611" "1612" "1613" "1614" "1615" "1616" "1617" "1618" "1619" "1620"
[1621] "1621" "1622" "1623" "1624" "1625" "1626" "1627" "1628" "1629" "1630"
[1631] "1631" "1632" "1633" "1634" "1635" "1636" "1637" "1638" "1639" "1640"
[1641] "1641" "1642" "1643" "1644" "1645" "1646" "1647" "1648" "1649" "1650"
[1651] "1651" "1652" "1653" "1654" "1655" "1656" "1657" "1658" "1659" "1660"
[1661] "1661" "1662" "1663" "1664" "1665" "1666" "1667" "1668" "1669" "1670"
[1671] "1671" "1672" "1673" "1674" "1675" "1676" "1677" "1678" "1679" "1680"
[1681] "1681" "1682" "1683" "1684" "1685" "1686" "1687" "1688" "1689" "1690"
[1691] "1691" "1692" "1693" "1694" "1695" "1696" "1697" "1698" "1699" "1700"
[1701] "1701" "1702" "1703" "1704" "1705" "1706" "1707" "1708" "1709" "1710"
[1711] "1711" "1712" "1713" "1714" "1715" "1716" "1717" "1718" "1719" "1720"
[1721] "1721" "1722" "1723" "1724" "1725" "1726" "1727" "1728" "1729" "1730"
[1731] "1731" "1732" "1733" "1734" "1735" "1736" "1737" "1738" "1739" "1740"
[1741] "1741" "1742" "1743" "1744" "1745" "1746" "1747" "1748" "1749" "1750"
[1751] "1751" "1752" "1753" "1754" "1755" "1756" "1757" "1758" "1759" "1760"
[1761] "1761" "1762" "1763" "1764" "1765" "1766" "1767" "1768" "1769" "1770"
[1771] "1771" "1772" "1773" "1774" "1775" "1776" "1777" "1778" "1779" "1780"
[1781] "1781" "1782" "1783" "1784" "1785" "1786" "1787" "1788" "1789" "1790"
[1791] "1791" "1792" "1793" "1794" "1795" "1796" "1797" "1798" "1799" "1800"
[1801] "1801" "1802" "1803" "1804" "1805" "1806" "1807" "1808" "1809" "1810"
[1811] "1811" "1812" "1813" "1814" "1815" "1816" "1817" "1818" "1819" "1820"
[1821] "1821" "1822" "1823" "1824" "1825" "1826" "1827" "1828" "1829" "1830"
[1831] "1831" "1832" "1833" "1834" "1835" "1836" "1837" "1838" "1839" "1840"
[1841] "1841" "1842" "1843" "1844" "1845" "1846" "1847" "1848" "1849" "1850"
[1851] "1851" "1852" "1853" "1854" "1855" "1856" "1857" "1858" "1859" "1860"
[1861] "1861" "1862" "1863" "1864" "1865" "1866" "1867" "1868" "1869" "1870"
[1871] "1871" "1872" "1873" "1874" "1875" "1876" "1877" "1878" "1879" "1880"
[1881] "1881" "1882" "1883" "1884" "1885" "1886" "1887" "1888" "1889" "1890"
[1891] "1891" "1892" "1893" "1894" "1895" "1896" "1897" "1898" "1899" "1900"
[1901] "1901" "1902" "1903" "1904" "1905" "1906" "1907" "1908" "1909" "1910"
[1911] "1911" "1912" "1913" "1914" "1915" "1916" "1917" "1918" "1919" "1920"
[1921] "1921" "1922" "1923" "1924" "1925" "1926" "1927" "1928" "1929" "1930"
[1931] "1931" "1932" "1933" "1934" "1935" "1936" "1937" "1938" "1939" "1940"
[1941] "1941" "1942" "1943" "1944" "1945" "1946" "1947" "1948" "1949" "1950"
[1951] "1951" "1952" "1953" "1954" "1955" "1956" "1957" "1958" "1959" "1960"
[1961] "1961" "1962" "1963" "1964" "1965" "1966" "1967" "1968" "1969" "1970"
[1971] "1971" "1972" "1973" "1974" "1975" "1976" "1977" "1978" "1979" "1980"
[1981] "1981" "1982" "1983" "1984" "1985" "1986" "1987" "1988" "1989" "1990"
[1991] "1991" "1992" "1993" "1994" "1995" "1996" "1997" "1998" "1999" "2000"
[2001] "2001" "2002" "2003" "2004" "2005" "2006" "2007" "2008" "2009" "2010"
[2011] "2011" "2012" "2013" "2014" "2015" "2016" "2017" "2018" "2019" "2020"
[2021] "2021" "2022" "2023" "2024" "2025" "2026" "2027" "2028" "2029" "2030"
[2031] "2031" "2032" "2033" "2034" "2035" "2036" "2037" "2038" "2039" "2040"
[2041] "2041" "2042" "2043" "2044" "2045" "2046" "2047" "2048" "2049" "2050"
[2051] "2051" "2052" "2053" "2054" "2055" "2056" "2057" "2058" "2059" "2060"
[2061] "2061" "2062" "2063" "2064" "2065" "2066" "2067" "2068" "2069" "2070"
[2071] "2071" "2072" "2073" "2074" "2075" "2076" "2077" "2078" "2079" "2080"
[2081] "2081" "2082" "2083" "2084" "2085" "2086" "2087" "2088" "2089" "2090"
[2091] "2091" "2092" "2093" "2094" "2095" "2096" "2097" "2098" "2099" "2100"
[2101] "2101" "2102" "2103" "2104" "2105" "2106" "2107" "2108" "2109" "2110"
[2111] "2111" "2112" "2113" "2114" "2115" "2116" "2117" "2118" "2119" "2120"
[2121] "2121" "2122" "2123" "2124" "2125" "2126" "2127" "2128" "2129" "2130"
[2131] "2131" "2132" "2133" "2134" "2135" "2136" "2137" "2138" "2139" "2140"
[2141] "2141" "2142" "2143" "2144" "2145" "2146" "2147" "2148" "2149" "2150"
[2151] "2151" "2152" "2153" "2154" "2155" "2156" "2157" "2158" "2159" "2160"
[2161] "2161" "2162" "2163" "2164" "2165" "2166" "2167" "2168" "2169" "2170"
[2171] "2171" "2172" "2173" "2174" "2175" "2176" "2177" "2178" "2179" "2180"
[2181] "2181" "2182" "2183" "2184" "2185" "2186" "2187" "2188" "2189" "2190"
[2191] "2191" "2192" "2193" "2194" "2195" "2196" "2197" "2198" "2199" "2200"
[2201] "2201" "2202" "2203" "2204" "2205" "2206" "2207" "2208" "2209" "2210"
[2211] "2211" "2212" "2213" "2214" "2215" "2216" "2217" "2218" "2219" "2220"
[2221] "2221" "2222" "2223" "2224" "2225" "2226" "2227" "2228" "2229" "2230"
[2231] "2231" "2232" "2233" "2234" "2235" "2236" "2237" "2238" "2239" "2240"
[2241] "2241" "2242" "2243" "2244" "2245" "2246" "2247" "2248" "2249" "2250"
[2251] "2251" "2252" "2253" "2254" "2255" "2256" "2257" "2258" "2259" "2260"
[2261] "2261" "2262" "2263" "2264" "2265" "2266" "2267" "2268" "2269" "2270"
[2271] "2271" "2272" "2273" "2274" "2275" "2276" "2277" "2278" "2279" "2280"
[2281] "2281" "2282" "2283" "2284" "2285" "2286" "2287" "2288" "2289" "2290"
[2291] "2291" "2292" "2293" "2294" "2295" "2296" "2297" "2298" "2299" "2300"
[2301] "2301" "2302" "2303" "2304" "2305" "2306" "2307" "2308" "2309" "2310"
[2311] "2311" "2312" "2313" "2314" "2315" "2316" "2317" "2318" "2319" "2320"
[2321] "2321" "2322" "2323" "2324" "2325" "2326" "2327" "2328" "2329" "2330"
[2331] "2331" "2332" "2333" "2334" "2335" "2336" "2337" "2338" "2339" "2340"
[2341] "2341" "2342" "2343" "2344" "2345" "2346" "2347" "2348" "2349" "2350"
[2351] "2351" "2352" "2353" "2354" "2355" "2356" "2357" "2358" "2359" "2360"
[2361] "2361" "2362" "2363" "2364" "2365" "2366" "2367" "2368" "2369" "2370"
[2371] "2371" "2372" "2373" "2374" "2375" "2376" "2377" "2378" "2379" "2380"
[2381] "2381" "2382" "2383" "2384" "2385" "2386" "2387" "2388" "2389" "2390"
[2391] "2391" "2392" "2393" "2394" "2395" "2396" "2397" "2398" "2399" "2400"
[2401] "2401" "2402" "2403" "2404" "2405" "2406" "2407" "2408" "2409" "2410"
[2411] "2411" "2412" "2413" "2414" "2415" "2416" "2417" "2418" "2419" "2420"
[2421] "2421" "2422" "2423" "2424" "2425" "2426" "2427" "2428" "2429" "2430"
[2431] "2431" "2432" "2433" "2434" "2435" "2436" "2437" "2438" "2439" "2440"
[2441] "2441" "2442" "2443" "2444" "2445" "2446" "2447" "2448" "2449" "2450"
[2451] "2451" "2452" "2453" "2454" "2455" "2456" "2457" "2458" "2459" "2460"
[2461] "2461" "2462" "2463" "2464" "2465" "2466" "2467" "2468" "2469" "2470"
[2471] "2471" "2472" "2473" "2474" "2475" "2476" "2477" "2478" "2479" "2480"
[2481] "2481" "2482" "2483" "2484" "2485" "2486" "2487" "2488" "2489" "2490"
[2491] "2491" "2492" "2493" "2494" "2495" "2496" "2497" "2498" "2499" "2500"
[2501] "2501" "2502" "2503" "2504" "2505" "2506" "2507" "2508" "2509" "2510"
[2511] "2511" "2512" "2513" "2514" "2515" "2516" "2517" "2518" "2519" "2520"
[2521] "2521" "2522" "2523" "2524" "2525" "2526" "2527" "2528" "2529" "2530"
[2531] "2531" "2532" "2533" "2534" "2535" "2536" "2537" "2538" "2539" "2540"
[2541] "2541" "2542" "2543" "2544" "2545" "2546" "2547" "2548" "2549" "2550"
[2551] "2551" "2552" "2553" "2554" "2555" "2556" "2557" "2558" "2559" "2560"
[2561] "2561" "2562" "2563" "2564" "2565" "2566" "2567" "2568" "2569" "2570"
[2571] "2571" "2572" "2573" "2574" "2575" "2576" "2577" "2578" "2579" "2580"
[2581] "2581" "2582" "2583" "2584" "2585" "2586" "2587" "2588" "2589" "2590"
[2591] "2591" "2592" "2593" "2594" "2595" "2596" "2597" "2598" "2599" "2600"
[2601] "2601" "2602" "2603" "2604" "2605" "2606" "2607" "2608" "2609" "2610"
[2611] "2611" "2612" "2613" "2614" "2615" "2616" "2617" "2618" "2619" "2620"
[2621] "2621" "2622" "2623" "2624" "2625" "2626" "2627" "2628" "2629" "2630"
[2631] "2631" "2632" "2633" "2634" "2635" "2636" "2637" "2638" "2639" "2640"
[2641] "2641" "2642" "2643" "2644" "2645" "2646" "2647" "2648" "2649" "2650"
[2651] "2651" "2652" "2653" "2654" "2655" "2656" "2657" "2658" "2659" "2660"
[2661] "2661" "2662" "2663" "2664" "2665" "2666" "2667" "2668" "2669" "2670"
[2671] "2671" "2672" "2673" "2674" "2675" "2676" "2677" "2678" "2679" "2680"
[2681] "2681" "2682" "2683" "2684" "2685" "2686" "2687" "2688" "2689" "2690"
[2691] "2691" "2692" "2693" "2694" "2695" "2696" "2697" "2698" "2699" "2700"
[2701] "2701" "2702" "2703" "2704" "2705" "2706" "2707" "2708" "2709" "2710"
[2711] "2711" "2712" "2713" "2714" "2715" "2716" "2717" "2718" "2719" "2720"
[2721] "2721" "2722" "2723" "2724" "2725" "2726" "2727" "2728" "2729" "2730"
[2731] "2731" "2732" "2733" "2734" "2735" "2736" "2737" "2738" "2739" "2740"
[2741] "2741" "2742" "2743" "2744" "2745" "2746" "2747" "2748" "2749" "2750"
[2751] "2751" "2752" "2753" "2754" "2755" "2756" "2757" "2758" "2759" "2760"
[2761] "2761" "2762" "2763" "2764" "2765" "2766" "2767" "2768" "2769" "2770"
[2771] "2771" "2772" "2773" "2774" "2775" "2776" "2777" "2778" "2779" "2780"
[2781] "2781" "2782" "2783" "2784" "2785" "2786" "2787" "2788" "2789" "2790"
[2791] "2791" "2792" "2793" "2794" "2795" "2796" "2797" "2798" "2799" "2800"
[2801] "2801" "2802" "2803" "2804" "2805" "2806" "2807" "2808" "2809" "2810"
[2811] "2811" "2812" "2813" "2814" "2815" "2816" "2817" "2818" "2819" "2820"
[2821] "2821" "2822" "2823" "2824" "2825" "2826" "2827" "2828" "2829" "2830"
[2831] "2831" "2832" "2833" "2834" "2835" "2836" "2837" "2838" "2839" "2840"
[2841] "2841" "2842" "2843" "2844" "2845" "2846" "2847" "2848" "2849" "2850"
[2851] "2851" "2852" "2853" "2854" "2855" "2856" "2857" "2858" "2859" "2860"
[2861] "2861" "2862" "2863" "2864" "2865" "2866" "2867" "2868" "2869" "2870"
[2871] "2871" "2872" "2873" "2874" "2875" "2876" "2877" "2878" "2879" "2880"
[2881] "2881" "2882" "2883" "2884" "2885" "2886" "2887" "2888" "2889" "2890"
[2891] "2891" "2892" "2893" "2894" "2895" "2896" "2897" "2898" "2899" "2900"
[2901] "2901" "2902" "2903" "2904" "2905" "2906" "2907" "2908" "2909" "2910"
[2911] "2911" "2912" "2913" "2914" "2915" "2916" "2917" "2918" "2919" "2920"
[2921] "2921" "2922" "2923" "2924" "2925" "2926" "2927" "2928" "2929" "2930"

Code

# dimension of dataset

dim(railroad) # 2930 observations & 3 columns

[1] 2930    3

Code

# number of rows, number of columns


nrow(railroad)

[1] 2930

Code

ncol(railroad)

[1] 3

Code

# Describe & Summary for descriptive analysis
 
 #describe from Hmisc Package

  Hmisc::describe(railroad)

railroad 

 3  Variables      2930  Observations
--------------------------------------------------------------------------------
state 
       n  missing distinct 
    2930        0       53 

lowest : AE AK AL AP AR, highest: VT WA WI WV WY
--------------------------------------------------------------------------------
county 
       n  missing distinct 
    2930        0     1709 

lowest : ABBEVILLE ACADIA    ACCOMACK  ADA       ADAIR    
highest: YOUNG     YUBA      YUMA      ZAPATA    ZAVALA   
--------------------------------------------------------------------------------
total_employees 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    2930        0      404    0.999    87.18    133.1      2.0      3.0 
     .25      .50      .75      .90      .95 
     7.0     21.0     65.0    193.0    357.5 

lowest :    1    2    3    4    5, highest: 3249 3685 3797 4235 8207
--------------------------------------------------------------------------------

Code

  #describe from psych Pakcage

  psych::describe(railroad)

                vars    n   mean     sd median trimmed    mad min  max range
state*             1 2930  28.72  14.03   28.0   28.91  17.79   1   53    52
county*            2 2930 859.58 478.85  858.5  857.74 592.30   1 1709  1708
total_employees    3 2930  87.18 283.64   21.0   37.41  25.20   1 8207  8206
                 skew kurtosis   se
state*           0.00    -1.15 0.26
county*          0.03    -1.11 8.85
total_employees 13.42   283.73 5.24

Code

  # summary

  summary(railroad)

     state             county     total_employees  
 TX     : 221   WASHINGTON:  31   Min.   :   1.00  
 GA     : 152   JEFFERSON :  26   1st Qu.:   7.00  
 KY     : 119   FRANKLIN  :  24   Median :  21.00  
 MO     : 115   LINCOLN   :  24   Mean   :  87.18  
 IL     : 103   JACKSON   :  22   3rd Qu.:  65.00  
 IA     :  99   MADISON   :  19   Max.   :8207.00  
 (Other):2121   (Other)   :2784