World Health Data Visualization
2024-07-11
The purpose of this project is to visualize the usefulness of R within Data Analysis!
We will be using data that describes the life expectancy of the world based on variables including the continents, and life expectancy.
Lets Install R, Load our Data from gapminder into the sesssion for use, Load the data into the environment, and look at a summary of it.
install.packages("gapminder")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(gapminder)
data("gapminder")
summary(gapminder)## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
## Lets observe the top and bottom ends of our data
head(gapminder)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.tail(gapminder)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1982 60.4 7636524 789.
## 2 Zimbabwe Africa 1987 62.4 9216418 706.
## 3 Zimbabwe Africa 1992 60.4 10704340 693.
## 4 Zimbabwe Africa 1997 46.8 11404948 792.
## 5 Zimbabwe Africa 2002 40.0 11926563 672.
## 6 Zimbabwe Africa 2007 43.5 12311143 470.Now I’ll find the mean for GDP per capita (gdpPercap) and assign it to a variable
mean(gapminder$gdpPercap)## [1] 7215.327x<-(gapminder$gdpPercap)
x## [1] 779.4453 820.8530 853.1007 836.1971 739.9811 786.1134
## [7] 978.0114 852.3959 649.3414 635.3414 726.7341 974.5803
## [13] 1601.0561 1942.2842 2312.8890 2760.1969 3313.4222 3533.0039
## [19] 3630.8807 3738.9327 2497.4379 3193.0546 4604.2117 5937.0295
## [25] 2449.0082 3013.9760 2550.8169 3246.9918 4182.6638 4910.4168
## [31] 5745.1602 5681.3585 5023.2166 4797.2951 5288.0404 6223.3675
## [37] 3520.6103 3827.9405 4269.2767 5522.7764 5473.2880 3008.6474
## [43] 2756.9537 2430.2083 2627.8457 2277.1409 2773.2873 4797.2313
## [49] 5911.3151 6856.8562 7133.1660 8052.9530 9443.0385 10079.0267
## [55] 8997.8974 9139.6714 9308.4187 10967.2820 8797.6407 12779.3796
## [61] 10039.5956 10949.6496 12217.2269 14526.1246 16788.6295 18334.1975
## [67] 19477.0093 21888.8890 23424.7668 26997.9366 30687.7547 34435.3674
## [73] 6137.0765 8842.5980 10750.7211 12834.6024 16661.6256 19749.4223
## [79] 21597.0836 23687.8261 27042.0187 29095.9207 32417.6077 36126.4927
## [85] 9867.0848 11635.7995 12753.2751 14804.6727 18268.6584 19340.1020
## [91] 19211.1473 18524.0241 19035.5792 20292.0168 23403.5593 29796.0483
## [97] 684.2442 661.6375 686.3416 721.1861 630.2336 659.8772
## [103] 676.9819 751.9794 837.8102 972.7700 1136.3904 1391.2538
## [109] 8343.1051 9714.9606 10991.2068 13149.0412 16672.1436 19117.9745
## [115] 20979.8459 22525.5631 25575.5707 27561.1966 30485.8838 33692.6051
## [121] 1062.7522 959.6011 949.4991 1035.8314 1085.7969 1029.1613
## [127] 1277.8976 1225.8560 1191.2077 1232.9753 1372.8779 1441.2849
## [133] 2677.3263 2127.6863 2180.9725 2586.8861 2980.3313 3548.0978
## [139] 3156.5105 2753.6915 2961.6997 3326.1432 3413.2627 3822.1371
## [145] 973.5332 1353.9892 1709.6837 2172.3524 2860.1698 3528.4813
## [151] 4126.6132 4314.1148 2546.7814 4766.3559 6018.9752 7446.2988
## [157] 851.2411 918.2325 983.6540 1214.7093 2263.6111 3214.8578
## [163] 4551.1421 6205.8839 7954.1116 8647.1423 11003.6051 12569.8518
## [169] 2108.9444 2487.3660 3336.5858 3429.8644 4985.7115 6660.1187
## [175] 7030.8359 7807.0958 6950.2830 7957.9808 8131.2128 9065.8008
## [181] 2444.2866 3008.6707 4254.3378 5577.0028 6597.4944 7612.2404
## [187] 8224.1916 8239.8548 6302.6234 5970.3888 7696.7777 10680.7928
## [193] 543.2552 617.1835 722.5120 794.8266 854.7360 743.3870
## [199] 807.1986 912.0631 931.7528 946.2950 1037.6452 1217.0330
## [205] 339.2965 379.5646 355.2032 412.9775 464.0995 556.1033
## [211] 559.6032 621.8188 631.6999 463.1151 446.4035 430.0707
## [217] 368.4693 434.0383 496.9136 523.4323 421.6240 524.9722
## [223] 624.4755 683.8956 682.3032 734.2852 896.2260 1713.7787
## [229] 1172.6677 1313.0481 1399.6074 1508.4531 1684.1465 1783.4329
## [235] 2367.9833 2602.6642 1793.1633 1694.3375 1934.0114 2042.0952
## [241] 11367.1611 12489.9501 13462.4855 16076.5880 18970.5709 22090.8831
## [247] 22898.7921 26626.5150 26342.8843 28954.9259 33328.9651 36319.2350
## [253] 1071.3107 1190.8443 1193.0688 1136.0566 1070.0133 1109.3743
## [259] 956.7530 844.8764 747.9055 740.5063 738.6906 706.0165
## [265] 1178.6659 1308.4956 1389.8176 1196.8106 1104.1040 1133.9850
## [271] 797.9081 952.3861 1058.0643 1004.9614 1156.1819 1704.0637
## [277] 3939.9788 4315.6227 4519.0943 5106.6543 5494.0244 4756.7638
## [283] 5095.6657 5547.0638 7596.1260 10118.0532 10778.7838 13171.6388
## [289] 400.4486 575.9870 487.6740 612.7057 676.9001 741.2375
## [295] 962.4214 1378.9040 1655.7842 2289.2341 3119.2809 4959.1149
## [301] 2144.1151 2323.8056 2492.3511 2678.7298 3264.6600 3815.8079
## [307] 4397.5757 4903.2191 5444.6486 6117.3617 5755.2600 7006.5804
## [313] 1102.9909 1211.1485 1406.6483 1876.0296 1937.5777 1172.6030
## [319] 1267.1001 1315.9808 1246.9074 1173.6182 1075.8116 986.1479
## [325] 780.5423 905.8602 896.3146 861.5932 904.8961 795.7573
## [331] 673.7478 672.7748 457.7192 312.1884 241.1659 277.5519
## [337] 2125.6214 2315.0566 2464.7832 2677.9396 3213.1527 3259.1790
## [343] 4879.5075 4201.1949 4016.2395 3484.1644 3484.0620 3632.5578
## [349] 2627.0095 2990.0108 3460.9370 4161.7278 5118.1469 5926.8770
## [355] 5262.7348 5629.9153 6160.4163 6677.0453 7723.4472 9645.0614
## [361] 1388.5947 1500.8959 1728.8694 2052.0505 2378.2011 2517.7365
## [367] 2602.7102 2156.9561 1648.0738 1786.2654 1648.8008 1544.7501
## [373] 3119.2365 4338.2316 5477.8900 6960.2979 9164.0901 11305.3852
## [379] 13221.8218 13822.5839 8447.7949 9875.6045 11628.3890 14619.2227
## [385] 5586.5388 6092.1744 5180.7559 5690.2680 5305.4453 6380.4950
## [391] 7316.9181 7532.9248 5592.8440 5431.9904 6340.6467 8948.1029
## [397] 6876.1403 8256.3439 10136.8671 11399.4449 13108.4536 14800.1606
## [403] 15377.2285 16310.4434 14297.0212 16048.5142 17596.2102 22833.3085
## [409] 9692.3852 11099.6593 13583.3135 15937.2112 18866.2072 20422.9015
## [415] 21688.0405 25116.1758 26406.7399 29804.3457 32166.5001 35278.4187
## [421] 2669.5295 2864.9691 3020.9893 3020.0505 3694.2124 3081.7610
## [427] 2879.4681 2880.1026 2377.1562 1895.0170 1908.2609 2082.4816
## [433] 1397.7171 1544.4030 1662.1374 1653.7230 2189.8745 2681.9889
## [439] 2861.0924 2899.8422 3044.2142 3614.1013 4563.8082 6025.3748
## [445] 3522.1107 3780.5467 4086.1141 4579.0742 5280.9947 6679.6233
## [451] 7213.7913 6481.7770 7103.7026 7429.4559 5773.0445 6873.2623
## [457] 1418.8224 1458.9153 1693.3359 1814.8807 2024.0081 2785.4936
## [463] 3503.7296 3885.4607 3794.7552 4173.1818 4754.6044 5581.1810
## [469] 3048.3029 3421.5232 3776.8036 4358.5954 4520.2460 5138.9224
## [475] 4098.3442 4140.4421 4444.2317 5154.8255 5351.5687 5728.3535
## [481] 375.6431 426.0964 582.8420 915.5960 672.4123 958.5668
## [487] 927.8253 966.8968 1132.0550 2814.4808 7703.4959 12154.0897
## [493] 328.9406 344.1619 380.9958 468.7950 514.3242 505.7538
## [499] 524.8758 521.1341 582.8585 913.4708 765.3500 641.3695
## [505] 362.1463 378.9042 419.4564 516.1186 566.2439 556.8084
## [511] 577.8607 573.7413 421.3535 515.8894 530.0535 690.8056
## [517] 6424.5191 7545.4154 9371.8426 10921.6363 14358.8759 15605.4228
## [523] 18533.1576 21141.0122 20647.1650 23723.9502 28204.5906 33207.0844
## [529] 7029.8093 8662.8349 10560.4855 12999.9177 16107.1917 18292.6351
## [535] 20293.8975 22066.4421 24703.7961 25889.7849 28926.0323 30470.0167
## [541] 4293.4765 4976.1981 6631.4592 8358.7620 11401.9484 21745.5733
## [547] 15113.3619 11864.4084 13522.1575 14722.8419 12521.7139 13206.4845
## [553] 485.2307 520.9267 599.6503 734.7829 756.0868 884.7553
## [559] 835.8096 611.6589 665.6244 653.7302 660.5856 752.7497
## [565] 7144.1144 10187.8267 12902.4629 14745.6256 18016.1803 20512.9212
## [571] 22031.5327 24639.1857 26505.3032 27788.8842 30035.8020 32170.3744
## [577] 911.2989 1043.5615 1190.0411 1125.6972 1178.2237 993.2240
## [583] 876.0326 847.0061 925.0602 1005.2458 1111.9846 1327.6089
## [589] 3530.6901 4916.2999 6017.1907 8513.0970 12724.8296 14195.5243
## [595] 15268.4209 16120.5284 17541.4963 18747.6981 22514.2548 27538.4119
## [601] 2428.2378 2617.1560 2750.3644 3242.5311 4031.4083 4879.9927
## [607] 4820.4948 4246.4860 4439.4508 4684.3138 4858.3475 5186.0500
## [613] 510.1965 576.2670 686.3737 708.7595 741.6662 874.6859
## [619] 857.2504 805.5725 794.3484 869.4498 945.5836 942.6542
## [625] 299.8503 431.7905 522.0344 715.5806 820.2246 764.7260
## [631] 838.1240 736.4154 745.5399 796.6645 575.7047 579.2317
## [637] 1840.3669 1726.8879 1796.5890 1452.0577 1654.4569 1874.2989
## [643] 2011.1595 1823.0160 1456.3095 1341.7269 1270.3649 1201.6372
## [649] 2194.9262 2220.4877 2291.1568 2538.2694 2529.8423 3203.2081
## [655] 3121.7608 3023.0967 3081.6946 3160.4549 3099.7287 3548.3308
## [661] 3054.4212 3629.0765 4692.6483 6197.9628 8315.9281 11186.1413
## [667] 14560.5305 20038.4727 24757.6030 28377.6322 30209.0152 39724.9787
## [673] 5263.6738 6040.1800 7550.3599 9326.6447 10168.6561 11674.8374
## [679] 12545.9907 12986.4800 10535.6285 11712.7768 14843.9356 18008.9444
## [685] 7267.6884 9244.0014 10350.1591 13319.8957 15798.0636 19654.9625
## [691] 23269.6075 26923.2063 25144.3920 28061.0997 31163.2020 36180.7892
## [697] 546.5657 590.0620 658.3472 700.7706 724.0325 813.3373
## [703] 855.7235 976.5127 1164.4068 1458.8174 1746.7695 2452.2104
## [709] 749.6817 858.9003 849.2898 762.4318 1111.1079 1382.7021
## [715] 1516.8730 1748.3570 2383.1409 3119.3356 2873.9129 3540.6516
## [721] 3035.3260 3290.2576 4187.3298 5906.7318 9613.8186 11888.5951
## [727] 7608.3346 6642.8814 7235.6532 8263.5903 9240.7620 11605.7145
## [733] 4129.7661 6229.3336 8341.7378 8931.4598 9576.0376 14688.2351
## [739] 14517.9071 11643.5727 3745.6407 3076.2398 4390.7173 4471.0619
## [745] 5210.2803 5599.0779 6631.5973 7655.5690 9530.7729 11150.9811
## [751] 12618.3214 13872.8665 17558.8155 24521.9471 34077.0494 40675.9964
## [757] 4086.5221 5385.2785 7105.6307 8393.7414 12786.9322 13306.6192
## [763] 15367.0292 17122.4799 18051.5225 20896.6092 21905.5951 25523.2771
## [769] 4931.4042 6248.6562 8243.5823 10022.4013 12269.2738 14255.9847
## [775] 16537.4835 19207.2348 22013.6449 24675.0245 27968.0982 28569.7197
## [781] 2898.5309 4756.5258 5246.1075 6124.7035 7433.8893 6650.1956
## [787] 6068.0513 6351.2375 7404.9237 7121.9247 6994.7749 7320.8803
## [793] 3216.9563 4317.6944 6576.6495 9847.7886 14778.7864 16610.3770
## [799] 19384.1057 22375.9419 26824.8951 28816.5850 28604.5919 31656.0681
## [805] 1546.9078 1886.0806 2348.0092 2741.7963 2110.8563 2852.3516
## [811] 4161.4160 4448.6799 3431.5936 3645.3796 3844.9172 4519.4612
## [817] 853.5409 944.4383 896.9664 1056.7365 1222.3600 1267.6132
## [823] 1348.2258 1361.9369 1341.9217 1360.4850 1287.5147 1463.2493
## [829] 1088.2778 1571.1347 1621.6936 2143.5406 3701.6215 4106.3012
## [835] 4106.5253 4106.4923 3726.0635 1690.7568 1646.7582 1593.0655
## [841] 1030.5922 1487.5935 1536.3444 2029.2281 3030.8767 4657.2210
## [847] 5622.9425 8533.0888 12104.2787 15993.5280 19233.9882 23348.1397
## [853] 108382.3529 113523.1329 95458.1118 80894.8833 109347.8670 59265.4771
## [859] 31354.0357 28118.4300 34932.9196 40300.6200 35110.1057 47306.9898
## [865] 4834.8041 6089.7869 5714.5606 6006.9830 7486.3843 8659.6968
## [871] 7640.5195 5377.0913 6890.8069 8754.9639 9313.9388 10461.0587
## [877] 298.8462 335.9971 411.8006 498.6390 496.5816 745.3695
## [883] 797.2631 773.9932 977.4863 1186.1480 1275.1846 1569.3314
## [889] 575.5730 620.9700 634.1952 713.6036 803.0055 640.3224
## [895] 572.1996 506.1139 636.6229 609.1740 531.4824 414.5073
## [901] 2387.5481 3448.2844 6757.0308 18772.7517 21011.4972 21951.2118
## [907] 17364.2754 11770.5898 9640.1385 9467.4461 9534.6775 12057.4993
## [913] 1443.0117 1589.2027 1643.3871 1634.0473 1748.5630 1544.2286
## [919] 1302.8787 1155.4419 1040.6762 986.2959 894.6371 1044.7701
## [925] 369.1651 416.3698 427.9011 495.5148 584.6220 663.2237
## [931] 632.8039 635.5174 563.2000 692.2758 665.4231 759.3499
## [937] 1831.1329 1810.0670 2036.8849 2277.7424 2849.0948 3827.9216
## [943] 4920.3560 5249.8027 7277.9128 10132.9096 10206.9779 12451.6558
## [949] 452.3370 490.3822 496.1743 545.0099 581.3689 686.3953
## [955] 618.0141 684.1716 739.0144 790.2580 951.4098 1042.5816
## [961] 743.1159 846.1203 1055.8960 1421.1452 1586.8518 1497.4922
## [967] 1481.1502 1421.6036 1361.3698 1483.1361 1579.0195 1803.1515
## [973] 1967.9557 2034.0380 2529.0675 2475.3876 2575.4842 3710.9830
## [979] 3688.0377 4783.5869 6058.2538 7425.7053 9021.8159 10956.9911
## [985] 3478.1255 4131.5466 4581.6094 5754.7339 6809.4067 7674.9291
## [991] 9611.1475 8688.1560 9472.3843 9767.2975 10742.4405 11977.5750
## [997] 786.5669 912.6626 1056.3540 1226.0411 1421.7420 1647.5117
## [1003] 2000.6031 2338.0083 1785.4020 1902.2521 2140.7393 3095.7723
## [1009] 2647.5856 3682.2599 4649.5938 5907.8509 7778.4140 9595.9299
## [1015] 11222.5876 11732.5102 7003.3390 6465.6133 6557.1943 9253.8961
## [1021] 1688.2036 1642.0023 1566.3535 1711.0448 1930.1950 2370.6200
## [1027] 2702.6204 2755.0470 2948.0473 2982.1019 3258.4956 3820.1752
## [1033] 468.5260 495.5868 556.6864 566.6692 724.9178 502.3197
## [1039] 462.2114 389.8762 410.8968 472.3461 633.6179 823.6856
## [1045] 331.0000 350.0000 388.0000 349.0000 357.0000 371.0000
## [1051] 424.0000 385.0000 347.0000 415.0000 611.0000 944.0000
## [1057] 2423.7804 2621.4481 3173.2156 3793.6948 3746.0809 3876.4860
## [1063] 4191.1005 3693.7313 3804.5380 3899.5243 4072.3248 4811.0604
## [1069] 545.8657 597.9364 652.3969 676.4422 674.7881 694.1124
## [1075] 718.3731 775.6325 897.7404 1010.8921 1057.2063 1091.3598
## [1081] 8941.5719 11276.1934 12790.8496 15363.2514 18794.7457 21209.0592
## [1087] 21399.4605 23651.3236 26790.9496 30246.1306 33724.7578 36797.9333
## [1093] 10556.5757 12247.3953 13175.6780 14463.9189 16046.0373 16233.7177
## [1099] 17632.4104 19007.1913 18363.3249 21050.4138 23189.8014 25185.0091
## [1105] 3112.3639 3457.4159 3634.3644 4643.3935 4688.5933 5486.3711
## [1111] 3470.3382 2955.9844 2170.1517 2253.0230 2474.5488 2749.3210
## [1117] 761.8794 835.5234 997.7661 1054.3849 954.2092 808.8971
## [1123] 909.7221 668.3000 581.1827 580.3052 601.0745 619.6769
## [1129] 1077.2819 1100.5926 1150.9275 1014.5141 1698.3888 1981.9518
## [1135] 1576.9738 1385.0296 1619.8482 1624.9413 1615.2864 2013.9773
## [1141] 10095.4217 11653.9730 13450.4015 16361.8765 18965.0555 23311.3494
## [1147] 26298.6353 31540.9748 33965.6611 41283.1643 44683.9753 49357.1902
## [1153] 1828.2303 2242.7466 2924.6381 4720.9427 10618.0385 11848.3439
## [1159] 12954.7910 18115.2231 18616.7069 19702.0558 19774.8369 22316.1929
## [1165] 684.5971 747.0835 803.3427 942.4083 1049.9390 1175.9212
## [1171] 1443.4298 1704.6866 1971.8295 2049.3505 2092.7124 2605.9476
## [1177] 2480.3803 2961.8009 3536.5403 4421.0091 5364.2497 5351.9121
## [1183] 7009.6016 7034.7792 6618.7431 7113.6923 7356.0319 9809.1856
## [1189] 1952.3087 2046.1547 2148.0271 2299.3763 2523.3380 3248.3733
## [1195] 4258.5036 3998.8757 4196.4111 4247.4003 3783.6742 4172.8385
## [1201] 3758.5234 4245.2567 4957.0380 5788.0933 5937.8273 6281.2909
## [1207] 6434.5018 6360.9434 4446.3809 5838.3477 5909.0201 7408.9056
## [1213] 1272.8810 1547.9448 1649.5522 1814.1274 1989.3741 2373.2043
## [1219] 2603.2738 2189.6350 2279.3240 2536.5349 2650.9211 3190.4810
## [1225] 4029.3297 4734.2530 5338.7521 6557.1528 8006.5070 9508.1415
## [1231] 8451.5310 9082.3512 7738.8812 10159.5837 12002.2391 15389.9247
## [1237] 3068.3199 3774.5717 4727.9549 6361.5180 9022.2474 10172.4857
## [1243] 11753.8429 13039.3088 16207.2666 17641.0316 19970.9079 20509.6478
## [1249] 3081.9598 3907.1562 5108.3446 6929.2777 9123.0417 9770.5249
## [1255] 10330.9891 12281.3419 14641.5871 16999.4333 18855.6062 19328.7090
## [1261] 2718.8853 2769.4518 3173.7233 4021.1757 5047.6586 4319.8041
## [1267] 5267.2194 5303.3775 6101.2558 6071.9414 6316.1652 7670.1226
## [1273] 3144.6132 3943.3702 4734.9976 6470.8665 8011.4144 9356.3972
## [1279] 9605.3141 9696.2733 6598.4099 7346.5476 7885.3601 10808.4756
## [1285] 493.3239 540.2894 597.4731 510.9637 590.5807 670.0806
## [1291] 881.5706 847.9912 737.0686 589.9445 785.6538 863.0885
## [1297] 879.5836 860.7369 1071.5511 1384.8406 1532.9853 1737.5617
## [1303] 1890.2181 1516.5255 1428.7778 1339.0760 1353.0924 1598.4351
## [1309] 6459.5548 8157.5912 11626.4197 16903.0489 24837.4287 34167.7626
## [1315] 33693.1753 21198.2614 24841.6178 20586.6902 19014.5412 21654.8319
## [1321] 1450.3570 1567.6530 1654.9887 1612.4046 1597.7121 1561.7691
## [1327] 1518.4800 1441.7207 1367.8994 1392.3683 1519.6353 1712.4721
## [1333] 3581.4594 4981.0909 6289.6292 7991.7071 10522.0675 12980.6696
## [1339] 15181.0927 15870.8785 9325.0682 7914.3203 7236.0753 9786.5347
## [1345] 879.7877 1004.4844 1116.6399 1206.0435 1353.7598 1348.2852
## [1351] 1465.0108 1294.4478 1068.6963 574.6482 699.4897 862.5408
## [1357] 2315.1382 2843.1044 3674.7356 4977.4185 8597.7562 11210.0895
## [1363] 15169.1611 18861.5308 24769.8912 33519.4766 36023.1054 47143.1796
## [1369] 5074.6591 6093.2630 7481.1076 8412.9024 9674.1676 10922.6640
## [1375] 11348.5459 12037.2676 9498.4677 12126.2306 13638.7784 18678.3144
## [1381] 4215.0417 5862.2766 7402.3034 9405.4894 12383.4862 15277.0302
## [1387] 17866.7218 18678.5349 14214.7168 17161.1073 20660.0194 25768.2576
## [1393] 1135.7498 1258.1474 1369.4883 1284.7332 1254.5761 1450.9925
## [1399] 1176.8070 1093.2450 926.9603 930.5964 882.0818 926.1411
## [1405] 4725.2955 5487.1042 5768.7297 7114.4780 7765.9626 8028.6514
## [1411] 8568.2662 7825.8234 7225.0693 7479.1882 7710.9464 9269.6578
## [1417] 3834.0347 4564.8024 5693.8439 7993.5123 10638.7513 13236.9212
## [1423] 13926.1700 15764.9831 18603.0645 20445.2990 24835.4717 28821.0637
## [1429] 1083.5320 1072.5466 1074.4720 1135.5143 1213.3955 1348.7757
## [1435] 1648.0798 1876.7668 2153.7392 2664.4773 3015.3788 3970.0954
## [1441] 1615.9911 1770.3371 1959.5938 1687.9976 1659.6528 2202.9884
## [1447] 1895.5441 1507.8192 1492.1970 1632.2108 1993.3983 2602.3950
## [1453] 1148.3766 1244.7084 1856.1821 2613.1017 3364.8366 3781.4106
## [1459] 3895.3840 3984.8398 3553.0224 3876.7685 4128.1169 4513.4806
## [1465] 8527.8447 9911.8782 12329.4419 15258.2970 17832.0246 18855.7252
## [1471] 20667.3812 23586.9293 23880.0168 25266.5950 29341.6309 33859.7484
## [1477] 14734.2327 17909.4897 20431.0927 22966.1443 27195.1130 26982.2905
## [1483] 28397.7151 30281.7046 31871.5303 32135.3230 34480.9577 37506.4191
## [1489] 1643.4854 2117.2349 2193.0371 1881.9236 2571.4230 3195.4846
## [1495] 3761.8377 3116.7743 3340.5428 4014.2390 4090.9253 4184.5481
## [1501] 1206.9479 1507.8613 1822.8790 2643.8587 4062.5239 5596.5198
## [1507] 7426.3548 11054.5618 15215.6579 20206.8210 23235.4233 28718.2768
## [1513] 716.6501 698.5356 722.0038 848.2187 915.9851 962.4923
## [1519] 874.2426 831.8221 825.6825 789.1862 899.0742 1107.4822
## [1525] 757.7974 793.5774 1002.1992 1295.4607 1524.3589 1961.2246
## [1531] 2393.2198 2982.6538 4616.8965 5852.6255 5913.1875 7458.3963
## [1537] 859.8087 925.9083 1067.5348 1477.5968 1649.6602 1532.7770
## [1543] 1344.5780 1202.2014 1034.2989 982.2869 886.2206 882.9699
## [1549] 3023.2719 4100.3934 4997.5240 5621.3685 6619.5514 7899.5542
## [1555] 9119.5286 7388.5978 7370.9909 8792.5731 11460.6002 18008.5092
## [1561] 1468.4756 1395.2325 1660.3032 1932.3602 2753.2860 3120.8768
## [1567] 3560.2332 3810.4193 4332.7202 4876.7986 5722.8957 7092.9230
## [1573] 1969.1010 2218.7543 2322.8699 2826.3564 3450.6964 4269.1223
## [1579] 4241.3563 5089.0437 5678.3483 6601.4299 6508.0857 8458.2764
## [1585] 734.7535 774.3711 767.2717 908.9185 950.7359 843.7331
## [1591] 682.2662 617.7244 644.1708 816.5591 927.7210 1056.3801
## [1597] 9979.5085 11283.1779 12477.1771 14142.8509 15895.1164 17428.7485
## [1603] 18232.4245 21664.7877 22705.0925 26074.5314 29478.9992 33203.2613
## [1609] 13990.4821 14847.1271 16173.1459 19530.3656 21806.0359 24072.6321
## [1615] 25009.5591 29884.3504 32003.9322 35767.4330 39097.0995 42951.6531
## [1621] 5716.7667 6150.7730 5603.3577 5444.6196 5703.4089 6504.3397
## [1627] 6920.2231 7452.3990 8137.0048 9230.2407 7727.0020 10611.4630
## [1633] 7689.7998 9802.4665 8422.9742 9541.4742 10505.2597 13143.9510
## [1639] 11152.4101 9883.5846 10733.9263 10165.4952 8605.0478 11415.8057
## [1645] 605.0665 676.2854 772.0492 637.1233 699.5016 713.5371
## [1651] 707.2358 820.7994 989.0231 1385.8968 1764.4567 2441.5764
## [1657] 1515.5923 1827.0677 2198.9563 2649.7150 3133.4093 3682.8315
## [1663] 4336.0321 5107.1974 6017.6548 7110.6676 4515.4876 3025.3498
## [1669] 781.7176 804.8305 825.6232 862.4421 1265.0470 1829.7652
## [1675] 1977.5570 1971.7415 1879.4967 2117.4845 2234.8208 2280.7699
## [1681] 1147.3888 1311.9568 1452.7258 1777.0773 1773.4983 1588.6883
## [1687] 1408.6786 1213.3151 1210.8846 1071.3538 1071.6139 1271.2116
## [1693] 406.8841 518.7643 527.2722 569.7951 799.3622 685.5877
## [1699] 788.8550 706.1573 693.4208 792.4500 672.0386 469.7093Very good! There are a lot of numbers here.
I have some Ideas for visuals, but lets make our variables more accessible first…
attach(gapminder)Lets find the median of the Population variable: “pop”
median(pop)## [1] 7023596Now lets see some visuals with histograms with the “hist” command.
hist(lifeExp)
hist(gdpPercap)
hist(pop)
Lets use the natural logarithm to clean up the histogram of variable “pop.”
hist(log(pop))
Nice!
Now to compare the variables “Life Expectancy” vs. “Continent” within a box plot
boxplot(lifeExp ~ continent)
That was good. What would a scatter plot look like though?
plot(lifeExp ~ log(gdpPercap))
It’s time to get a more detailed breakdown of our data, but first we need to install the “dplyr” package within R, and confirm its within our library.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioninstall.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)Now lets select our data, group it, and “pipe” it using our newly installed data manipulation package “dplyr.”
gapminder %>%
select(country, lifeExp) %>%
filter(country == "Nigeria" |country == "Canada" | country == "Mexico" | country == "Ghana" | country == "Ireland" | country == "Italy") %>%
group_by(country) %>%
summarise(Average_life = mean(lifeExp))## # A tibble: 6 × 2
## country Average_life
## <fct> <dbl>
## 1 Canada 74.9
## 2 Ghana 52.3
## 3 Ireland 73.0
## 4 Italy 74.0
## 5 Mexico 65.4
## 6 Nigeria 43.6That’s our “Tibble” of data comparing various countries.
Lets define a variable and use “pipe” again to compare two randomly picked countries from our list.
df1 <- gapminder %>%
select(country, lifeExp) %>%
filter(country == "Ghana" | country == "Italy")Now lets run a “T-test” to discover a numerical analysis
t.test(data = df1, lifeExp ~ country)##
## Welch Two Sample t-test
##
## data: lifeExp by country
## t = -9.8594, df = 21.316, p-value = 2.139e-09
## alternative hypothesis: true difference in means between group Ghana and group Italy is not equal to 0
## 95 percent confidence interval:
## -26.24052 -17.10582
## sample estimates:
## mean in group Ghana mean in group Italy
## 52.34067 74.01383We were able to discover the significant difference in variables betweem the two countries.
Now lets load our visualiztion package, “ggplot2” to see what this data looks like.
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(ggplot2)Lets see what they look like in their own scatter plots using the “pipe” function and filtering by country, and summarizing with a comparison of average life cycle and mean of life expectancy. We will set the ‘y’ axis as the life expectancy, and ‘x’ axis as a natural logarithm of GDP per capita. Size of our populations will reflect the size of our countries within each continent, and color will be year.
Here’s the code we need to generate our visual
gapminder %>%
filter(gdpPercap < 50000) %>%
ggplot(aes(x=log(gdpPercap), y=lifeExp, col=year, size = pop))+
geom_point(alpha=0.3)+
geom_smooth(method = lm)+
(facet_wrap(~continent))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: colour
## and size.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: The following aesthetics were dropped during statistical transformation: colour
## and size.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: colour
## and size.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: colour
## and size.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: colour
## and size.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
It looks like R studio didn’t like some of our code and did away with a few items including the size aspect of each country, But that’s fine! Our visuals look great.