Return population data stored by package
Examples
package_cohort(country = "Romania", year = 2022)
#> sex age count
#> 1 female 0 90186.5
#> 2 male 0 95036.5
#> 3 female 1 91387.0
#> 4 male 1 96506.0
#> 5 female 2 94625.5
#> 6 male 2 99351.5
#> 7 female 3 98734.0
#> 8 male 3 102915.0
#> 9 female 4 101340.5
#> 10 male 4 105859.0
#> 11 female 5 102128.0
#> 12 male 5 106854.0
#> 13 female 6 100515.0
#> 14 male 6 105217.5
#> 15 female 7 98829.0
#> 16 male 7 103423.5
#> 17 female 8 98006.5
#> 18 male 8 102429.0
#> 19 female 9 98619.5
#> 20 male 9 103069.5
#> 21 female 10 99315.0
#> 22 male 10 103926.5
#> 23 female 11 102352.5
#> 24 male 11 106775.0
#> 25 female 12 107345.5
#> 26 male 12 111924.0
#> 27 female 13 108951.0
#> 28 male 13 114051.0
#> 29 female 14 107547.5
#> 30 male 14 112506.0
#> 31 female 15 106339.5
#> 32 male 15 110777.5
#> 33 female 16 107032.5
#> 34 male 16 111166.5
#> 35 female 17 105926.0
#> 36 male 17 110200.5
#> 37 female 18 102740.5
#> 38 male 18 107178.5
#> 39 female 19 97705.5
#> 40 male 19 101891.5
#> 41 female 20 94462.5
#> 42 male 20 98565.0
#> 43 female 21 96822.0
#> 44 male 21 101126.0
#> 45 female 22 98910.0
#> 46 male 22 103320.5
#> 47 female 23 99273.0
#> 48 male 23 103487.5
#> 49 female 24 98606.0
#> 50 male 24 102833.0
#> 51 female 25 96798.5
#> 52 male 25 101277.0
#> 53 female 26 96418.5
#> 54 male 26 101032.5
#> 55 female 27 98279.5
#> 56 male 27 103167.5
#> 57 female 28 99758.5
#> 58 male 28 104816.0
#> 59 female 29 101338.0
#> 60 male 29 107028.5
#> 61 female 30 103659.0
#> 62 male 30 109916.5
#> 63 female 31 111865.0
#> 64 male 31 118353.0
#> 65 female 32 125097.5
#> 66 male 32 132084.0
#> 67 female 33 133381.0
#> 68 male 33 141071.0
#> 69 female 34 135656.0
#> 70 male 34 144036.5
#> 71 female 35 132874.5
#> 72 male 35 141258.5
#> 73 female 36 128279.5
#> 74 male 36 136887.0
#> 75 female 37 123241.0
#> 76 male 37 132244.0
#> 77 female 38 109083.5
#> 78 male 38 118533.0
#> 79 female 39 107049.0
#> 80 male 39 117012.5
#> 81 female 40 124205.0
#> 82 male 40 133864.5
#> 83 female 41 135004.0
#> 84 male 41 144231.5
#> 85 female 42 140662.0
#> 86 male 42 149923.5
#> 87 female 43 145024.5
#> 88 male 43 153644.5
#> 89 female 44 148473.5
#> 90 male 44 156071.5
#> 91 female 45 150220.5
#> 92 male 45 156633.5
#> 93 female 46 149224.0
#> 94 male 46 154741.5
#> 95 female 47 150125.0
#> 96 male 47 155110.0
#> 97 female 48 145646.0
#> 98 male 48 149541.0
#> 99 female 49 141185.0
#> 100 male 49 144235.5
#> 101 female 50 144367.5
#> 102 male 50 148379.0
#> 103 female 51 149455.5
#> 104 male 51 152896.0
#> 105 female 52 160997.0
#> 106 male 52 161850.0
#> 107 female 53 180062.0
#> 108 male 53 178428.0
#> 109 female 54 185535.5
#> 110 male 54 181918.0
#> 111 female 55 142979.5
#> 112 male 55 139875.0
#> 113 female 56 104152.0
#> 114 male 56 100763.0
#> 115 female 57 102801.5
#> 116 male 57 98051.5
#> 117 female 58 104424.0
#> 118 male 58 98356.0
#> 119 female 59 107083.5
#> 120 male 59 98111.5
#> 121 female 60 109203.5
#> 122 male 60 97947.5
#> 123 female 61 113189.5
#> 124 male 61 99712.0
#> 125 female 62 120645.5
#> 126 male 62 101935.0
#> 127 female 63 128567.5
#> 128 male 63 104200.0
#> 129 female 64 135612.5
#> 130 male 64 107476.5
#> 131 female 65 141080.5
#> 132 male 65 112095.5
#> 133 female 66 147097.0
#> 134 male 66 116159.5
#> 135 female 67 144805.0
#> 136 male 67 110681.5
#> 137 female 68 135852.5
#> 138 male 68 101355.0
#> 139 female 69 131853.0
#> 140 male 69 97231.5
#> 141 female 70 128745.5
#> 142 male 70 93676.0
#> 143 female 71 127295.5
#> 144 male 71 91127.5
#> 145 female 72 127889.5
#> 146 male 72 89455.5
#> 147 female 73 115556.5
#> 148 male 73 78975.0
#> 149 female 74 97483.0
#> 150 male 74 66341.5
#> 151 female 75 90781.0
#> 152 male 75 60868.0
#> 153 female 76 81054.5
#> 154 male 76 52424.0
#> 155 female 77 75456.5
#> 156 male 77 47119.5
#> 157 female 78 72661.0
#> 158 male 78 43979.0
#> 159 female 79 66930.0
#> 160 male 79 39199.0
#> 161 female 80 66204.0
#> 162 male 80 37224.0
#> 163 female 81 65604.5
#> 164 male 81 35528.0
#> 165 female 82 65403.5
#> 166 male 82 34227.5
#> 167 female 83 62590.0
#> 168 male 83 31621.5
#> 169 female 84 56464.0
#> 170 male 84 27901.5
#> 171 female 85 50166.0
#> 172 male 85 24362.5
#> 173 female 86 42735.0
#> 174 male 86 20474.0
#> 175 female 87 35439.5
#> 176 male 87 16730.0
#> 177 female 88 29516.5
#> 178 male 88 13740.5
#> 179 female 89 24913.5
#> 180 male 89 11601.5
#> 181 female 90 20067.5
#> 182 male 90 9207.0
#> 183 female 91 15040.0
#> 184 male 91 6753.5
#> 185 female 92 11187.5
#> 186 male 92 4983.0
#> 187 female 93 8125.0
#> 188 male 93 3607.5
#> 189 female 94 5744.0
#> 190 male 94 2566.0
#> 191 female 95 3980.0
#> 192 male 95 1806.0
#> 193 female 96 2700.0
#> 194 male 96 1266.0
#> 195 female 97 1734.5
#> 196 male 97 857.0
#> 197 female 98 1023.5
#> 198 male 98 541.5
#> 199 female 99 521.5
#> 200 male 99 309.5