Hi there, I'm really stuck on how to make the graphs for my data set work, especially how to make my axis work.
ggplot(data, aes(x = x, y = y)) +
geom_point(color = "blue") +
labs(title = "Effect of GNI on Mean Years of Schooling", x = "Gross National Income (GNI)", y = "Mean Years of Schooling")
It looks like your values are stored as characters or as a factor rather than as numbers. Please post the output of
dput(head(data,10))
Where data is the data frame that you are using to make the plot.
Try converting data$x to numeric. You can do that is the aes, with as.numeric(x). Then you can format the numbers with the label functions from the {scales} package. I like to use cut_short_scale() because it's very compact and intuitive for most people. 100K, 1.0M in stead of 1e5, 1e6 etc.
ggplot(data, aes(x = as.numeric(x), y = y)) +
scale_x_continuous(labels = scales::label_number(scale_cut = scales::cut_short_scale())) +
geom_point(color = "blue") +
labs(title = "Effect of GNI on Mean Years of Schooling", x = "Gross National Income (GNI)", y = "Mean Years of Schooling")
structure(list(Rank = structure(1:10, levels = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70",
"71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81",
"82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92",
"93", "94", "95", "96", "97", "98", "99", "100", "101", "102",
"103", "104", "105", "106", "107", "108", "109", "110", "111",
"112", "113", "114", "115", "116", "117", "118", "119", "120",
"121", "122", "123", "124", "125", "126", "127", "128", "129",
"130", "131", "132", "133", "134", "135", "136", "137", "138",
"139", "140", "141", "142", "143", "144", "145", "146", "147",
"148", "149", "150", "151", "152", "153", "154", "155", "156",
"157", "158", "159", "160", "161", "162", "163", "164", "165",
"166", "167", "168", "169", "170", "171", "172", "173", "174",
"175", "176", "177", "178", "179", "180", "181", "182", "183",
"184", "185", "186", "187", "188", "189", "190", "191", "192",
"193"), class = "factor"), Country = structure(c(88L, 171L, 156L,
77L, 38L, 162L, 16L, 63L, 100L, 68L), levels = c("Afghanistan",
"Albania", "Algeria", "Andorra", "Angola", "Antigua and Barbuda",
"Argentina", "Armenia", "Australia", "Austria", "Azerbaijan",
"Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium",
"Belize", "Benin", "Bermuda", "Bhutan", "Bolivia", "Bosnia and Herzegovina",
"Botswana", "Brazil", "British Virgin Islands", "Brunei", "Bulgaria",
"Burkina Faso", "Burundi", "Cambodia", "Cameroon", "Canada",
"Cayman Islands", "Central African Republic", "Chad", "Chile",
"China", "Colombia", "Comoros", "Congo", "Congo Republic", "Costa do Marfim",
"Costa Rica", "Croatia", "Cuba", "Cyprus", "Czech republic",
"Denmark", "Djibouti", "Dominica", "Dominican Republic", "East Timor",
"Ecuador", "Egypt", "El Salvador", "Eritrea", "Estonia", "Eswatini",
"Ethiopia", "Federated States of Micronesia", "Fiji", "Finland",
"France", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Greece",
"Grenade", "Guatemala", "Guinea", "Guyana", "Haiti", "Honduras",
"Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran",
"Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan",
"Kazakhstan", "Kenya", "Kuwait", "Kyrgyzstan", "Laos", "Latvia",
"Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", "Lithuania",
"Luxembourg", "Macao", "Madagascar", "Malawi", "Malaysia", "Maldives",
"Mali", "Malta", "Marshall Islands", "Mauritania", "Mauritius",
"Mexico", "Moldavia", "Mongolia", "Montenegro", "Morocco", "Mozambique",
"Myanmar", "Namibia", "Nepal", "Netherlands", "New Caledonia",
"New Zealand", "Nicaragua", "Niger", "Nigeria", "North Korea",
"North Macedonia", "Northern Mariana Islands", "Norway", "Oman",
"Pakistan", "Palestine", "Panama", "Papua New Guinea", "Paraguay",
"Peru", "Philippines", "Poland", "Portugal", "Puerto Rico", "Qatar",
"Romania", "Russia", "Rwanda", "Saint Helena", "Saint Lucia",
"Saint Vincent and the Grenadines", "Sao Tome and Principe",
"Saudi Arabia", "Senegal", "Serbia", "Seychelles", "Sierra Leone",
"Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia",
"South Africa", "South Korea", "South Sudan", "Spain", "Sri Lanka",
"Sudan", "Suriname", "Sweden", "Switzerland", "Syria", "Taiwan",
"Tajikistan", "Tanzania", "Thailand", "Togo", "Trinidad and Tobago",
"Tunisia", "Türkiye", "Turkmenistan", "Turks and Caicos Islands",
"Uganda", "Ukraine", "United Arab Emirates", "United Kingdom",
"United States", "Uruguay", "Uzbekistan", "Vanuatu", "Venezuela",
"Vietnam", "Yemen", "Zambia", "Zimbabwe"), class = "factor"),
Average.IQ = c(106.48, 106.47, 105.89, 105.37, 104.1, 102.35,
101.6, 101.2, 101.07, 100.74), Continent = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L), levels = c("Africa",
"Asia", "Central America", "Europe", "Europe/Asia", "North America",
"Oceania", "South America"), class = "factor"), Literacy.Rate = structure(c(51L,
48L, 49L, 46L, 48L, 50L, 52L, 52L, 52L, 51L), levels = c("0.19",
"0.3", "0.32", "0.33", "0.37", "0.38", "0.4", "0.43", "0.48",
"0.49", "0.52", "0.56", "0.58", "0.59", "0.6", "0.61", "0.63",
"0.64", "0.65", "0.66", "0.67", "0.68", "0.7", "0.71", "0.72",
"0.74", "0.75", "0.76", "0.77", "0.78", "0.79", "0.8", "0.81",
"0.82", "0.83", "0.84", "0.85", "0.86", "0.87", "0.88", "0.89",
"0.9", "0.91", "0.92", "0.93", "0.94", "0.95", "0.96", "0.97",
"0.98", "0.99", "1"), class = "factor"), Nobel.Prizes = structure(c(17L,
5L, 1L, 2L, 8L, 1L, 3L, 6L, 1L, 20L), levels = c("0", "1",
"2", "3", "4", "5", "6", "8", "11", "12", "13", "19", "21",
"22", "27", "28", "29", "32", "71", "111", "137", "400"), class = "factor"),
HDI..2021. = structure(c(136L, NA, 142L, 150L, 88L, 136L,
103L, 143L, 139L, 145L), levels = c("0.385", "0.394", "0.4",
"0.404", "0.426", "0.428", "0.446", "0.449", "0.455", "0.465",
"0.477", "0.478", "0.479", "0.481", "0.492", "0.498", "0.5",
"0.501", "0.508", "0.509", "0.511", "0.512", "0.514", "0.525",
"0.534", "0.535", "0.539", "0.544", "0.549", "0.556", "0.558",
"0.564", "0.565", "0.571", "0.575", "0.576", "0.577", "0.585",
"0.586", "0.593", "0.597", "0.602", "0.607", "0.615", "0.618",
"0.621", "0.627", "0.628", "0.632", "0.633", "0.639", "0.661",
"0.666", "0.667", "0.675", "0.683", "0.685", "0.686", "0.691",
"0.692", "0.693", "0.699", "0.703", "0.705", "0.706", "0.709",
"0.713", "0.714", "0.715", "0.717", "0.718", "0.72", "0.727",
"0.73", "0.731", "0.739", "0.74", "0.745", "0.747", "0.751",
"0.752", "0.754", "0.758", "0.759", "0.762", "0.764", "0.767",
"0.768", "0.77", "0.773", "0.774", "0.78", "0.782", "0.785",
"0.788", "0.79", "0.795", "0.796", "0.8", "0.802", "0.803",
"0.805", "0.808", "0.809", "0.81", "0.811", "0.812", "0.816",
"0.821", "0.822", "0.829", "0.831", "0.832", "0.838", "0.842",
"0.846", "0.848", "0.855", "0.858", "0.863", "0.866", "0.875",
"0.876", "0.887", "0.889", "0.89", "0.895", "0.896", "0.903",
"0.905", "0.911", "0.916", "0.918", "0.919", "0.921", "0.925",
"0.929", "0.93", "0.935", "0.936", "0.937", "0.939", "0.94",
"0.941", "0.942", "0.945", "0.947", "0.948", "0.951", "0.952",
"0.959", "0.961", "0.962"), class = "factor"), Mean.years.of.schooling...2021 = structure(c(80L,
NA, 67L, 69L, 33L, 72L, 68L, 76L, 72L, 85L), levels = c("2.1",
"2.2", "2.3", "2.6", "2.9", "3", "3.1", "3.2", "3.8", "4.1",
"4.3", "4.4", "4.5", "4.6", "4.7", "4.9", "5", "5.1", "5.2",
"5.4", "5.6", "5.7", "5.9", "6", "6.2", "6.4", "6.7", "7",
"7.1", "7.2", "7.3", "7.4", "7.6", "7.8", "7.9", "8.1", "8.3",
"8.4", "8.5", "8.6", "8.7", "8.8", "8.9", "9", "9.2", "9.3",
"9.4", "9.6", "9.8", "9.9", "10", "10.2", "10.3", "10.4",
"10.5", "10.6", "10.7", "10.8", "10.9", "11", "11.1", "11.3",
"11.4", "11.6", "11.7", "11.8", "11.9", "12.1", "12.2", "12.3",
"12.4", "12.5", "12.6", "12.7", "12.8", "12.9", "13", "13.2",
"13.3", "13.4", "13.5", "13.7", "13.8", "13.9", "14.1"), class = "factor"),
GNI...2021 = structure(c(150L, NA, 178L, 170L, 111L, 153L,
114L, 159L, 179L, 165L), levels = c("732", "768", "966",
"1076", "1198", "1240", "1289", "1314", "1364", "1466", "1484",
"1622", "1729", "1824", "2118", "2133", "2167", "2172", "2181",
"2210", "2361", "2481", "2482", "2664", "2700", "2848", "2889",
"3085", "3142", "3218", "3344", "3409", "3575", "3621", "3696",
"3810", "3851", "3877", "4009", "4021", "4079", "4192", "4474",
"4548", "4566", "4620", "4624", "4790", "4811", "5025", "5075",
"5298", "5466", "5472", "5625", "5745", "6309", "6583", "6590",
"7303", "7679", "7700", "7867", "7879", "7917", "8111", "8296",
"8634", "8723", "8834", "8920", "9438", "9526", "9924", "9977",
"9980", "10258", "10312", "10588", "10800", "11466", "11488",
"11732", "11961", "12048", "12246", "12306", "12349", "12578",
"12672", "12948", "13001", "13021", "13158", "13256", "13367",
"13484", "14131", "14257", "14370", "14384", "14664", "14875",
"15242", "15336", "15448", "15918", "16198", "16792", "17030",
"17504", "17896", "17990", "18849", "19123", "19974", "20839",
"20925", "21269", "22025", "22465", "23079", "23392", "23943",
"24563", "25831", "26658", "26957", "27054", "27166", "29002",
"30027", "30132", "30486", "30690", "31033", "32789", "32803",
"33034", "33155", "37931", "38048", "38188", "38354", "38745",
"38884", "39497", "39746", "41524", "42274", "42840", "44057",
"44501", "45225", "45937", "46112", "46808", "49238", "49452",
"51167", "52293", "52920", "53619", "54489", "54534", "55782",
"55979", "60365", "62574", "62607", "64490", "64660", "64765",
"66933", "76169", "84649", "87134", "90919", "146830"), class = "factor"),
Population...2023 = structure(c(26L, 3L, 151L, 174L, 40L,
134L, 190L, 145L, 1L, 179L), levels = c("\t39315", " 32291",
"10143543", "10156239", "102262809", "10247605", "103.699",
"10329931", "10341277", "10412652", "10495295", "10593798",
"10612086", "107.66", "11088796", "11194449", "112716599",
"11332973", "11337053", "1136455", "115.224", "11686140",
"11724764", "117337368", "1210822", "123294513", "12388571",
"12458223", "126.184", "1260138", "126527060", "128455567",
"1300557", "1322766", "13238559", "1360596", "13712828",
"14094683", "14190612", "1425671352", "1428627663", "144444359",
"1485510", "1534937", "16665409", "16944826", "172954319",
"17618299", "17763163", "180.251", "18092026", "18143379",
"18190484", "18278568", "1830212", "19606634", "19629590",
"19892812", "20569738", "2085679", "20931751", "2119675",
"216422446", "21893579", "223804632", "231.856", "23227014",
"23251485", "23293699", "2330318", "240485658", "2436567",
"2604172", "26160822", "26439112", "2675353", "2716391",
"2718352", "27202843", "2773168", "277534123", "2777971",
"281.996", "2825544", "2832439", "28647293", "28838499",
"28873034", "292.991", "30325732", "30896590", "3210848",
"3260314", "334.506", "33897354", "339996564", "34121985",
"3423109", "34308525", "34352719", "3435931", "34449825",
"3447157", "35163944", "36684203", "36744634", "36947025",
"3728282", "3748902", "375.319", "37840044", "38781292",
"4008617", "41.996", "410.825", "41026068", "412.624", "42239854",
"4310108", "44.104", "4468087", "452.524", "45504560", "45606481",
"45773884", "4644384", "47519628", "48109006", "48582334",
"4862989", "49.796", "5040000", "5056935", "51784059", "52085168",
"521.022", "5212173", "5228100", "535.065", "5353930", "5418377",
"54577997", "5474360", "55100587", "5545475", "5742316",
"5795199", "58870763", "5910913", "6.115", "6014723", "60414495",
"6106869", "623.237", "626.485", "63.837", "6364943", "64756584",
"6516100", "654.768", "6687717", "6735348", "67438106", "67736802",
"6861524", "6888388", "69310", "704.15", "7046311", "7149077",
"71801279", "73.161", "740.425", "7491609", "76.965", "7633779",
"787.425", "813.834", "83294633", "852.075", "85816199",
"8791092", "8796669", "89172767", "8958961", "9053799", "9174520",
"936.375", "94.298", "9498238", "9516871", "98858950"), class = "factor")), row.names = c(NA,
10L), class = "data.frame")
The data frame you posted does not have any columns named x or y, but those are the column names you used in your ggplot code. Working from the axis labels of your plot, I guessed at the correct columns to plot. They were indeed factors, converting them first to characters, to recover their original text appearance, and then with as.numeric. You need to check carefully that the numbers shown are correct.
library(tidyverse)
#> Warning: package 'ggplot2' was built under R version 4.3.3
data <- structure(list(Rank = structure(1:10, levels = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70",
"71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81",
"82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92",
"93", "94", "95", "96", "97", "98", "99", "100", "101", "102",
"103", "104", "105", "106", "107", "108", "109", "110", "111",
"112", "113", "114", "115", "116", "117", "118", "119", "120",
"121", "122", "123", "124", "125", "126", "127", "128", "129",
"130", "131", "132", "133", "134", "135", "136", "137", "138",
"139", "140", "141", "142", "143", "144", "145", "146", "147",
"148", "149", "150", "151", "152", "153", "154", "155", "156",
"157", "158", "159", "160", "161", "162", "163", "164", "165",
"166", "167", "168", "169", "170", "171", "172", "173", "174",
"175", "176", "177", "178", "179", "180", "181", "182", "183",
"184", "185", "186", "187", "188", "189", "190", "191", "192",
"193"), class = "factor"),
Country = structure(c(88L, 171L, 156L,
77L, 38L, 162L, 16L, 63L, 100L, 68L),
levels = c("Afghanistan","Albania", "Algeria", "Andorra", "Angola", "Antigua and Barbuda",
"Argentina", "Armenia", "Australia", "Austria", "Azerbaijan",
"Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium",
"Belize", "Benin", "Bermuda", "Bhutan", "Bolivia", "Bosnia and Herzegovina",
"Botswana", "Brazil", "British Virgin Islands", "Brunei", "Bulgaria",
"Burkina Faso", "Burundi", "Cambodia", "Cameroon", "Canada",
"Cayman Islands", "Central African Republic", "Chad", "Chile",
"China", "Colombia", "Comoros", "Congo", "Congo Republic", "Costa do Marfim",
"Costa Rica", "Croatia", "Cuba", "Cyprus", "Czech republic",
"Denmark", "Djibouti", "Dominica", "Dominican Republic", "East Timor",
"Ecuador", "Egypt", "El Salvador", "Eritrea", "Estonia", "Eswatini",
"Ethiopia", "Federated States of Micronesia", "Fiji", "Finland",
"France", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Greece",
"Grenade", "Guatemala", "Guinea", "Guyana", "Haiti", "Honduras",
"Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran",
"Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan",
"Kazakhstan", "Kenya", "Kuwait", "Kyrgyzstan", "Laos", "Latvia",
"Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", "Lithuania",
"Luxembourg", "Macao", "Madagascar", "Malawi", "Malaysia", "Maldives",
"Mali", "Malta", "Marshall Islands", "Mauritania", "Mauritius",
"Mexico", "Moldavia", "Mongolia", "Montenegro", "Morocco", "Mozambique",
"Myanmar", "Namibia", "Nepal", "Netherlands", "New Caledonia",
"New Zealand", "Nicaragua", "Niger", "Nigeria", "North Korea",
"North Macedonia", "Northern Mariana Islands", "Norway", "Oman",
"Pakistan", "Palestine", "Panama", "Papua New Guinea", "Paraguay",
"Peru", "Philippines", "Poland", "Portugal", "Puerto Rico", "Qatar",
"Romania", "Russia", "Rwanda", "Saint Helena", "Saint Lucia",
"Saint Vincent and the Grenadines", "Sao Tome and Principe",
"Saudi Arabia", "Senegal", "Serbia", "Seychelles", "Sierra Leone",
"Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia",
"South Africa", "South Korea", "South Sudan", "Spain", "Sri Lanka",
"Sudan", "Suriname", "Sweden", "Switzerland", "Syria", "Taiwan",
"Tajikistan", "Tanzania", "Thailand", "Togo", "Trinidad and Tobago",
"Tunisia", "Türkiye", "Turkmenistan", "Turks and Caicos Islands",
"Uganda", "Ukraine", "United Arab Emirates", "United Kingdom",
"United States", "Uruguay", "Uzbekistan", "Vanuatu", "Venezuela",
"Vietnam", "Yemen", "Zambia", "Zimbabwe"), class = "factor"),
Average.IQ = c(106.48, 106.47, 105.89, 105.37, 104.1, 102.35,
101.6, 101.2, 101.07, 100.74),
Continent = structure(c(2L,2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L), levels = c("Africa","Asia", "Central America", "Europe", "Europe/Asia", "North America",
"Oceania", "South America"), class = "factor"),
Literacy.Rate = structure(c(51L,48L, 49L, 46L, 48L, 50L, 52L, 52L, 52L, 51L),
levels = c("0.19","0.3", "0.32", "0.33", "0.37", "0.38", "0.4", "0.43", "0.48",
"0.49", "0.52", "0.56", "0.58", "0.59", "0.6", "0.61", "0.63",
"0.64", "0.65", "0.66", "0.67", "0.68", "0.7", "0.71", "0.72",
"0.74", "0.75", "0.76", "0.77", "0.78", "0.79", "0.8", "0.81",
"0.82", "0.83", "0.84", "0.85", "0.86", "0.87", "0.88", "0.89",
"0.9", "0.91", "0.92", "0.93", "0.94", "0.95", "0.96", "0.97",
"0.98", "0.99", "1"), class = "factor"),
Nobel.Prizes = structure(c(17L,5L, 1L, 2L, 8L, 1L, 3L, 6L, 1L, 20L),
levels = c("0", "1","2", "3", "4", "5", "6", "8", "11", "12", "13", "19", "21","22", "27", "28", "29", "32", "71", "111", "137", "400"), class = "factor"),
HDI..2021. = structure(c(136L, NA, 142L, 150L, 88L, 136L,
103L, 143L, 139L, 145L), levels = c("0.385", "0.394", "0.4",
"0.404", "0.426", "0.428", "0.446", "0.449", "0.455", "0.465",
"0.477", "0.478", "0.479", "0.481", "0.492", "0.498", "0.5",
"0.501", "0.508", "0.509", "0.511", "0.512", "0.514", "0.525",
"0.534", "0.535", "0.539", "0.544", "0.549", "0.556", "0.558",
"0.564", "0.565", "0.571", "0.575", "0.576", "0.577", "0.585",
"0.586", "0.593", "0.597", "0.602", "0.607", "0.615", "0.618",
"0.621", "0.627", "0.628", "0.632", "0.633", "0.639", "0.661",
"0.666", "0.667", "0.675", "0.683", "0.685", "0.686", "0.691",
"0.692", "0.693", "0.699", "0.703", "0.705", "0.706", "0.709",
"0.713", "0.714", "0.715", "0.717", "0.718", "0.72", "0.727",
"0.73", "0.731", "0.739", "0.74", "0.745", "0.747", "0.751",
"0.752", "0.754", "0.758", "0.759", "0.762", "0.764", "0.767",
"0.768", "0.77", "0.773", "0.774", "0.78", "0.782", "0.785",
"0.788", "0.79", "0.795", "0.796", "0.8", "0.802", "0.803",
"0.805", "0.808", "0.809", "0.81", "0.811", "0.812", "0.816",
"0.821", "0.822", "0.829", "0.831", "0.832", "0.838", "0.842",
"0.846", "0.848", "0.855", "0.858", "0.863", "0.866", "0.875",
"0.876", "0.887", "0.889", "0.89", "0.895", "0.896", "0.903",
"0.905", "0.911", "0.916", "0.918", "0.919", "0.921", "0.925",
"0.929", "0.93", "0.935", "0.936", "0.937", "0.939", "0.94",
"0.941", "0.942", "0.945", "0.947", "0.948", "0.951", "0.952",
"0.959", "0.961", "0.962"), class = "factor"),
Mean.years.of.schooling...2021 = structure(c(80L,NA, 67L, 69L, 33L, 72L, 68L, 76L, 72L, 85L),
levels = c("2.1","2.2", "2.3", "2.6", "2.9", "3", "3.1", "3.2", "3.8", "4.1",
"4.3", "4.4", "4.5", "4.6", "4.7", "4.9", "5", "5.1", "5.2",
"5.4", "5.6", "5.7", "5.9", "6", "6.2", "6.4", "6.7", "7",
"7.1", "7.2", "7.3", "7.4", "7.6", "7.8", "7.9", "8.1", "8.3",
"8.4", "8.5", "8.6", "8.7", "8.8", "8.9", "9", "9.2", "9.3",
"9.4", "9.6", "9.8", "9.9", "10", "10.2", "10.3", "10.4",
"10.5", "10.6", "10.7", "10.8", "10.9", "11", "11.1", "11.3",
"11.4", "11.6", "11.7", "11.8", "11.9", "12.1", "12.2", "12.3",
"12.4", "12.5", "12.6", "12.7", "12.8", "12.9", "13", "13.2",
"13.3", "13.4", "13.5", "13.7", "13.8", "13.9", "14.1"), class = "factor"),
GNI...2021 = structure(c(150L, NA, 178L, 170L, 111L, 153L,
114L, 159L, 179L, 165L), levels = c("732", "768", "966",
"1076", "1198", "1240", "1289", "1314", "1364", "1466", "1484",
"1622", "1729", "1824", "2118", "2133", "2167", "2172", "2181",
"2210", "2361", "2481", "2482", "2664", "2700", "2848", "2889",
"3085", "3142", "3218", "3344", "3409", "3575", "3621", "3696",
"3810", "3851", "3877", "4009", "4021", "4079", "4192", "4474",
"4548", "4566", "4620", "4624", "4790", "4811", "5025", "5075",
"5298", "5466", "5472", "5625", "5745", "6309", "6583", "6590",
"7303", "7679", "7700", "7867", "7879", "7917", "8111", "8296",
"8634", "8723", "8834", "8920", "9438", "9526", "9924", "9977",
"9980", "10258", "10312", "10588", "10800", "11466", "11488",
"11732", "11961", "12048", "12246", "12306", "12349", "12578",
"12672", "12948", "13001", "13021", "13158", "13256", "13367",
"13484", "14131", "14257", "14370", "14384", "14664", "14875",
"15242", "15336", "15448", "15918", "16198", "16792", "17030",
"17504", "17896", "17990", "18849", "19123", "19974", "20839",
"20925", "21269", "22025", "22465", "23079", "23392", "23943",
"24563", "25831", "26658", "26957", "27054", "27166", "29002",
"30027", "30132", "30486", "30690", "31033", "32789", "32803",
"33034", "33155", "37931", "38048", "38188", "38354", "38745",
"38884", "39497", "39746", "41524", "42274", "42840", "44057",
"44501", "45225", "45937", "46112", "46808", "49238", "49452",
"51167", "52293", "52920", "53619", "54489", "54534", "55782",
"55979", "60365", "62574", "62607", "64490", "64660", "64765",
"66933", "76169", "84649", "87134", "90919", "146830"), class = "factor"),
Population...2023 = structure(c(26L, 3L, 151L, 174L, 40L,
134L, 190L, 145L, 1L, 179L),
levels = c("\t39315", " 32291","10143543", "10156239", "102262809", "10247605", "103.699",
"10329931", "10341277", "10412652", "10495295", "10593798",
"10612086", "107.66", "11088796", "11194449", "112716599",
"11332973", "11337053", "1136455", "115.224", "11686140",
"11724764", "117337368", "1210822", "123294513", "12388571",
"12458223", "126.184", "1260138", "126527060", "128455567",
"1300557", "1322766", "13238559", "1360596", "13712828",
"14094683", "14190612", "1425671352", "1428627663", "144444359",
"1485510", "1534937", "16665409", "16944826", "172954319",
"17618299", "17763163", "180.251", "18092026", "18143379",
"18190484", "18278568", "1830212", "19606634", "19629590",
"19892812", "20569738", "2085679", "20931751", "2119675",
"216422446", "21893579", "223804632", "231.856", "23227014",
"23251485", "23293699", "2330318", "240485658", "2436567",
"2604172", "26160822", "26439112", "2675353", "2716391",
"2718352", "27202843", "2773168", "277534123", "2777971",
"281.996", "2825544", "2832439", "28647293", "28838499",
"28873034", "292.991", "30325732", "30896590", "3210848",
"3260314", "334.506", "33897354", "339996564", "34121985",
"3423109", "34308525", "34352719", "3435931", "34449825",
"3447157", "35163944", "36684203", "36744634", "36947025",
"3728282", "3748902", "375.319", "37840044", "38781292",
"4008617", "41.996", "410.825", "41026068", "412.624", "42239854",
"4310108", "44.104", "4468087", "452.524", "45504560", "45606481",
"45773884", "4644384", "47519628", "48109006", "48582334",
"4862989", "49.796", "5040000", "5056935", "51784059", "52085168",
"521.022", "5212173", "5228100", "535.065", "5353930", "5418377",
"54577997", "5474360", "55100587", "5545475", "5742316",
"5795199", "58870763", "5910913", "6.115", "6014723", "60414495",
"6106869", "623.237", "626.485", "63.837", "6364943", "64756584",
"6516100", "654.768", "6687717", "6735348", "67438106", "67736802",
"6861524", "6888388", "69310", "704.15", "7046311", "7149077",
"71801279", "73.161", "740.425", "7491609", "76.965", "7633779",
"787.425", "813.834", "83294633", "852.075", "85816199",
"8791092", "8796669", "89172767", "8958961", "9053799", "9174520",
"936.375", "94.298", "9498238", "9516871", "98858950"), class = "factor")), row.names = c(NA,10L), class = "data.frame")
data$GNI...2021 <- as.numeric(as.character(data$GNI...2021))
data$Mean.years.of.schooling...2021 <- as.numeric(as.character(data$Mean.years.of.schooling...2021))
ggplot(data, aes(x = GNI...2021, y = Mean.years.of.schooling...2021)) +
geom_point(color = "blue") +
labs(title = "Effect of GNI on Mean Years of Schooling", x = "Gross National Income (GNI)", y = "Mean Years of Schooling")
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).
Created on 2024-05-19 with reprex v2.0.2
Thank you so so much for your help. That makes a lot more sense!
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