library(readxl)
world_data_2023 <- read_excel("trabalho bioestatistica_files/world-data-2023.xlsx",
col_types = c("text", "text", "numeric",
"text", "text", "text", "numeric",
"text", "text", "numeric", "numeric",
"text", "text", "text", "text", "text",
"text", "text", "numeric"))
View(world_data_2023)Trabalho Biostatística
##Lendo e vizualizando o arquivo do excel
##Atribuindo o arquivo à dados e fazendo a tabela dos dados
library(gtsummary)
dados<-(world_data_2023)
tbl_summary(dados)| Characteristic | N = 361 |
|---|---|
| País | |
| Afghanistan | 1 (2.8%) |
| Angola | 1 (2.8%) |
| Argentina | 1 (2.8%) |
| Brazil | 1 (2.8%) |
| Cambodia | 1 (2.8%) |
| Canada | 1 (2.8%) |
| China | 1 (2.8%) |
| Djibouti | 1 (2.8%) |
| Egypt | 1 (2.8%) |
| El Salvador | 1 (2.8%) |
| Ethiopia | 1 (2.8%) |
| France | 1 (2.8%) |
| Germany | 1 (2.8%) |
| India | 1 (2.8%) |
| Iran | 1 (2.8%) |
| Italy | 1 (2.8%) |
| Japan | 1 (2.8%) |
| Liechtenstein | 1 (2.8%) |
| Madagascar | 1 (2.8%) |
| Mexico | 1 (2.8%) |
| Mongolia | 1 (2.8%) |
| Nigeria | 1 (2.8%) |
| Republic of the Congo | 1 (2.8%) |
| Russia | 1 (2.8%) |
| Saudi Arabia | 1 (2.8%) |
| Senegal | 1 (2.8%) |
| Slovakia | 1 (2.8%) |
| South Africa | 1 (2.8%) |
| Suriname | 1 (2.8%) |
| Turkey | 1 (2.8%) |
| United Arab Emirates | 1 (2.8%) |
| United Kingdom | 1 (2.8%) |
| United States | 1 (2.8%) |
| Uruguay | 1 (2.8%) |
| Venezuela | 1 (2.8%) |
| Zimbabwe | 1 (2.8%) |
| Taxa Total de Imposto (%) | |
| 106.30 | 1 (2.8%) |
| 15.70 | 1 (2.8%) |
| 15.90 | 1 (2.8%) |
| 21.60 | 1 (2.8%) |
| 23.10 | 1 (2.8%) |
| 24.50 | 1 (2.8%) |
| 25.70 | 1 (2.8%) |
| 27.90 | 1 (2.8%) |
| 29.20 | 1 (2.8%) |
| 30.60 | 1 (2.8%) |
| 31.60 | 1 (2.8%) |
| 34.80 | 1 (2.8%) |
| 36.40 | 1 (2.8%) |
| 36.60 | 1 (2.8%) |
| 37.70 | 1 (2.8%) |
| 37.90 | 1 (2.8%) |
| 38.30 | 1 (2.8%) |
| 41.80 | 1 (2.8%) |
| 42.30 | 1 (2.8%) |
| 44.40 | 1 (2.8%) |
| 44.70 | 1 (2.8%) |
| 44.80 | 1 (2.8%) |
| 46.20 | 1 (2.8%) |
| 46.70 | 1 (2.8%) |
| 48.80 | 1 (2.8%) |
| 49.10 | 1 (2.8%) |
| 49.70 | 2 (5.6%) |
| 54.30 | 1 (2.8%) |
| 55.10 | 1 (2.8%) |
| 59.10 | 1 (2.8%) |
| 59.20 | 1 (2.8%) |
| 60.70 | 1 (2.8%) |
| 65.10 | 1 (2.8%) |
| 71.40 | 1 (2.8%) |
| 73.30 | 1 (2.8%) |
| Densidade (P/Km2)" | 63 (26, 128) |
| Terras Agrícolas(%) | |
| 0.60 | 1 (2.8%) |
| 12.30 | 1 (2.8%) |
| 13.30 | 1 (2.8%) |
| 24.50 | 1 (2.8%) |
| 28.20 | 1 (2.8%) |
| 3.80 | 1 (2.8%) |
| 30.90 | 1 (2.8%) |
| 31.10 | 1 (2.8%) |
| 32.20 | 1 (2.8%) |
| 33.90 | 1 (2.8%) |
| 36.30 | 1 (2.8%) |
| 39.20 | 1 (2.8%) |
| 41.90 | 1 (2.8%) |
| 43.20 | 1 (2.8%) |
| 44.40 | 1 (2.8%) |
| 46.10 | 1 (2.8%) |
| 47.50 | 1 (2.8%) |
| 47.70 | 1 (2.8%) |
| 49.80 | 1 (2.8%) |
| 5.50 | 1 (2.8%) |
| 52.40 | 1 (2.8%) |
| 54.30 | 1 (2.8%) |
| 54.60 | 1 (2.8%) |
| 56.20 | 1 (2.8%) |
| 58.10 | 1 (2.8%) |
| 6.90 | 1 (2.8%) |
| 60.40 | 1 (2.8%) |
| 71.20 | 1 (2.8%) |
| 71.50 | 1 (2.8%) |
| 71.70 | 1 (2.8%) |
| 73.40 | 1 (2.8%) |
| 76.40 | 1 (2.8%) |
| 77.70 | 1 (2.8%) |
| 79.80 | 1 (2.8%) |
| 80.80 | 1 (2.8%) |
| 82.60 | 1 (2.8%) |
| Land Area(Km2) | |
| 1001450 | 1 (2.8%) |
| 1104300 | 1 (2.8%) |
| 1219090 | 1 (2.8%) |
| 1246700 | 1 (2.8%) |
| 1564116 | 1 (2.8%) |
| 160 | 1 (2.8%) |
| 163.82 | 1 (2.8%) |
| 1648195 | 1 (2.8%) |
| 17098240 | 1 (2.8%) |
| 176215 | 1 (2.8%) |
| 181035 | 1 (2.8%) |
| 1964375 | 1 (2.8%) |
| 196722 | 1 (2.8%) |
| 21041 | 1 (2.8%) |
| 2149690 | 1 (2.8%) |
| 23.2 | 1 (2.8%) |
| 243.61 | 1 (2.8%) |
| 2780400 | 1 (2.8%) |
| 301.34 | 1 (2.8%) |
| 3287263 | 1 (2.8%) |
| 342 | 1 (2.8%) |
| 357022 | 1 (2.8%) |
| 377944 | 1 (2.8%) |
| 390757 | 1 (2.8%) |
| 49035 | 1 (2.8%) |
| 587041 | 1 (2.8%) |
| 643801 | 1 (2.8%) |
| 652.23 | 1 (2.8%) |
| 783562 | 1 (2.8%) |
| 83.6 | 1 (2.8%) |
| 8515770 | 1 (2.8%) |
| 912.05 | 1 (2.8%) |
| 923768 | 1 (2.8%) |
| 9596960 | 1 (2.8%) |
| 9833517 | 1 (2.8%) |
| 9984670 | 1 (2.8%) |
| Taxa de Natalidade | |
| 10.1 | 1 (2.8%) |
| 10.33 | 1 (2.8%) |
| 10.6 | 1 (2.8%) |
| 10.9 | 1 (2.8%) |
| 11 | 1 (2.8%) |
| 11.3 | 1 (2.8%) |
| 11.5 | 1 (2.8%) |
| 11.6 | 1 (2.8%) |
| 13.86 | 1 (2.8%) |
| 13.92 | 1 (2.8%) |
| 16.03 | 1 (2.8%) |
| 17.02 | 1 (2.8%) |
| 17.6 | 1 (2.8%) |
| 17.8 | 1 (2.8%) |
| 17.86 | 1 (2.8%) |
| 17.88 | 1 (2.8%) |
| 18.25 | 1 (2.8%) |
| 18.54 | 1 (2.8%) |
| 18.78 | 1 (2.8%) |
| 20.51 | 1 (2.8%) |
| 21.47 | 1 (2.8%) |
| 22.46 | 1 (2.8%) |
| 24.13 | 1 (2.8%) |
| 26.38 | 1 (2.8%) |
| 30.68 | 1 (2.8%) |
| 32.34 | 1 (2.8%) |
| 32.49 | 1 (2.8%) |
| 32.66 | 1 (2.8%) |
| 32.86 | 1 (2.8%) |
| 34.52 | 1 (2.8%) |
| 37.91 | 1 (2.8%) |
| 40.73 | 1 (2.8%) |
| 7.3 | 1 (2.8%) |
| 7.4 | 1 (2.8%) |
| 9.5 | 1 (2.8%) |
| 9.9 | 1 (2.8%) |
| Emissão de Co2 | 142,272 (7,068, 479,085) |
| Taxa de Fertilidade | |
| 1.29 | 1 (2.8%) |
| 1.41 | 1 (2.8%) |
| 1.42 | 1 (2.8%) |
| 1.44 | 1 (2.8%) |
| 1.5 | 1 (2.8%) |
| 1.52 | 1 (2.8%) |
| 1.56 | 1 (2.8%) |
| 1.57 | 1 (2.8%) |
| 1.68 | 1 (2.8%) |
| 1.69 | 1 (2.8%) |
| 1.73 | 2 (5.6%) |
| 1.88 | 1 (2.8%) |
| 1.97 | 1 (2.8%) |
| 2.04 | 1 (2.8%) |
| 2.07 | 1 (2.8%) |
| 2.13 | 1 (2.8%) |
| 2.14 | 1 (2.8%) |
| 2.22 | 1 (2.8%) |
| 2.26 | 1 (2.8%) |
| 2.27 | 1 (2.8%) |
| 2.32 | 1 (2.8%) |
| 2.41 | 1 (2.8%) |
| 2.42 | 1 (2.8%) |
| 2.5 | 1 (2.8%) |
| 2.73 | 1 (2.8%) |
| 2.9 | 1 (2.8%) |
| 3.33 | 1 (2.8%) |
| 3.62 | 1 (2.8%) |
| 4.08 | 1 (2.8%) |
| 4.25 | 1 (2.8%) |
| 4.43 | 1 (2.8%) |
| 4.47 | 1 (2.8%) |
| 4.63 | 1 (2.8%) |
| 5.39 | 1 (2.8%) |
| 5.52 | 1 (2.8%) |
| Area Florestal(%) | |
| 0.10 | 1 (2.8%) |
| 0.20 | 1 (2.8%) |
| 0.50 | 1 (2.8%) |
| 10.70 | 1 (2.8%) |
| 12.50 | 1 (2.8%) |
| 12.60 | 1 (2.8%) |
| 13.10 | 1 (2.8%) |
| 15.40 | 1 (2.8%) |
| 2.10 | 1 (2.8%) |
| 21.40 | 1 (2.8%) |
| 22.40 | 1 (2.8%) |
| 23.80 | 1 (2.8%) |
| 31.20 | 1 (2.8%) |
| 31.80 | 1 (2.8%) |
| 32.70 | 1 (2.8%) |
| 33.90 | 2 (5.6%) |
| 35.50 | 1 (2.8%) |
| 38.20 | 1 (2.8%) |
| 4.60 | 1 (2.8%) |
| 40.40 | 1 (2.8%) |
| 42.80 | 1 (2.8%) |
| 43.10 | 1 (2.8%) |
| 46.30 | 1 (2.8%) |
| 49.80 | 1 (2.8%) |
| 52.70 | 1 (2.8%) |
| 52.90 | 1 (2.8%) |
| 58.90 | 1 (2.8%) |
| 6.60 | 1 (2.8%) |
| 65.40 | 1 (2.8%) |
| 68.50 | 1 (2.8%) |
| 7.20 | 1 (2.8%) |
| 7.60 | 1 (2.8%) |
| 8.00 | 1 (2.8%) |
| 9.80 | 1 (2.8%) |
| 98.30 | 1 (2.8%) |
| PIB ($) | 433,243,775,031 (26,161,501,013, 1,762,258,732,332) |
| Matrícula Bruta no Ensino Primário (%) | 103 (100, 108) |
| Expectativa de Vida | |
| 54.3 | 1 (2.8%) |
| 60.8 | 1 (2.8%) |
| 61.2 | 1 (2.8%) |
| 63.9 | 1 (2.8%) |
| 64.3 | 1 (2.8%) |
| 64.5 | 1 (2.8%) |
| 66.2 | 1 (2.8%) |
| 66.6 | 1 (2.8%) |
| 66.7 | 1 (2.8%) |
| 67.7 | 1 (2.8%) |
| 69.4 | 1 (2.8%) |
| 69.6 | 1 (2.8%) |
| 69.7 | 1 (2.8%) |
| 71.6 | 1 (2.8%) |
| 71.8 | 1 (2.8%) |
| 72.1 | 1 (2.8%) |
| 72.7 | 1 (2.8%) |
| 73.1 | 1 (2.8%) |
| 75 | 2 (5.6%) |
| 75.7 | 1 (2.8%) |
| 76.5 | 2 (5.6%) |
| 77 | 1 (2.8%) |
| 77.2 | 1 (2.8%) |
| 77.4 | 1 (2.8%) |
| 77.8 | 2 (5.6%) |
| 78.5 | 1 (2.8%) |
| 80.9 | 1 (2.8%) |
| 81.3 | 1 (2.8%) |
| 81.9 | 1 (2.8%) |
| 82.5 | 1 (2.8%) |
| 82.9 | 1 (2.8%) |
| 83 | 1 (2.8%) |
| 84.2 | 1 (2.8%) |
| Salário Mínimo | |
| 0.01 | 1 (3.8%) |
| 0.21 | 1 (3.8%) |
| 0.30 | 1 (3.8%) |
| 0.31 | 1 (3.8%) |
| 0.43 | 1 (3.8%) |
| 0.49 | 1 (3.8%) |
| 0.50 | 1 (3.8%) |
| 0.53 | 1 (3.8%) |
| 0.54 | 1 (3.8%) |
| 0.65 | 1 (3.8%) |
| 0.71 | 1 (3.8%) |
| 0.87 | 1 (3.8%) |
| 0.88 | 1 (3.8%) |
| 1.53 | 1 (3.8%) |
| 1.58 | 1 (3.8%) |
| 1.66 | 1 (3.8%) |
| 10.13 | 1 (3.8%) |
| 11.16 | 1 (3.8%) |
| 3.11 | 1 (3.8%) |
| 3.35 | 1 (3.8%) |
| 3.45 | 1 (3.8%) |
| 3.85 | 1 (3.8%) |
| 6.77 | 1 (3.8%) |
| 7.25 | 1 (3.8%) |
| 9.51 | 1 (3.8%) |
| 9.99 | 1 (3.8%) |
| Unknown | 10 |
| Despesas de Saúde do Próprio Bolso | |
| 10.10 | 1 (2.9%) |
| 11.10 | 1 (2.9%) |
| 12.50 | 1 (2.9%) |
| 13.10 | 1 (2.9%) |
| 14.60 | 1 (2.9%) |
| 14.80 | 1 (2.9%) |
| 15.00 | 1 (2.9%) |
| 16.20 | 1 (2.9%) |
| 16.90 | 1 (2.9%) |
| 17.60 | 1 (2.9%) |
| 17.80 | 1 (2.9%) |
| 18.40 | 1 (2.9%) |
| 20.40 | 1 (2.9%) |
| 21.70 | 1 (2.9%) |
| 22.80 | 1 (2.9%) |
| 25.80 | 1 (2.9%) |
| 27.90 | 1 (2.9%) |
| 28.30 | 1 (2.9%) |
| 32.40 | 1 (2.9%) |
| 33.40 | 1 (2.9%) |
| 36.40 | 1 (2.9%) |
| 37.80 | 1 (2.9%) |
| 39.30 | 1 (2.9%) |
| 39.70 | 1 (2.9%) |
| 41.40 | 1 (2.9%) |
| 43.80 | 1 (2.9%) |
| 44.20 | 1 (2.9%) |
| 45.80 | 1 (2.9%) |
| 59.40 | 1 (2.9%) |
| 6.80 | 1 (2.9%) |
| 62.00 | 1 (2.9%) |
| 65.10 | 1 (2.9%) |
| 7.70 | 1 (2.9%) |
| 72.20 | 1 (2.9%) |
| 78.40 | 1 (2.9%) |
| Unknown | 1 |
| População | |
| 100388073 | 1 (2.8%) |
| 112078730 | 1 (2.8%) |
| 126014024 | 1 (2.8%) |
| 126226568 | 1 (2.8%) |
| 1366417754 | 1 (2.8%) |
| 1397715000 | 1 (2.8%) |
| 144373535 | 1 (2.8%) |
| 14645468 | 1 (2.8%) |
| 16296364 | 1 (2.8%) |
| 16486542 | 1 (2.8%) |
| 200963599 | 1 (2.8%) |
| 212559417 | 1 (2.8%) |
| 26969307 | 1 (2.8%) |
| 28515829 | 1 (2.8%) |
| 31825295 | 1 (2.8%) |
| 3225167 | 1 (2.8%) |
| 328239523 | 1 (2.8%) |
| 34268528 | 1 (2.8%) |
| 3461734 | 1 (2.8%) |
| 36991981 | 1 (2.8%) |
| 38019 | 1 (2.8%) |
| 38041754 | 1 (2.8%) |
| 44938712 | 1 (2.8%) |
| 5380508 | 1 (2.8%) |
| 5454073 | 1 (2.8%) |
| 581372 | 1 (2.8%) |
| 58558270 | 1 (2.8%) |
| 60297396 | 1 (2.8%) |
| 6453553 | 1 (2.8%) |
| 66834405 | 1 (2.8%) |
| 67059887 | 1 (2.8%) |
| 82913906 | 1 (2.8%) |
| 83132799 | 1 (2.8%) |
| 83429615 | 1 (2.8%) |
| 973.56 | 1 (2.8%) |
| 9770529 | 1 (2.8%) |
| Taxa de Desemprego | |
| 0.68 | 1 (2.9%) |
| 1.76 | 1 (2.9%) |
| 10.30 | 1 (2.9%) |
| 10.76 | 1 (2.9%) |
| 11.12 | 1 (2.9%) |
| 11.38 | 1 (2.9%) |
| 12.08 | 1 (2.9%) |
| 13.49 | 1 (2.9%) |
| 14.70 | 1 (2.9%) |
| 2.08 | 1 (2.9%) |
| 2.29 | 1 (2.9%) |
| 2.35 | 1 (2.9%) |
| 28.18 | 1 (2.9%) |
| 3.04 | 1 (2.9%) |
| 3.42 | 1 (2.9%) |
| 3.85 | 1 (2.9%) |
| 4.11 | 1 (2.9%) |
| 4.32 | 1 (2.9%) |
| 4.59 | 1 (2.9%) |
| 4.95 | 1 (2.9%) |
| 5.36 | 1 (2.9%) |
| 5.56 | 2 (5.7%) |
| 5.93 | 1 (2.9%) |
| 6.01 | 1 (2.9%) |
| 6.60 | 1 (2.9%) |
| 6.89 | 1 (2.9%) |
| 7.33 | 1 (2.9%) |
| 8.10 | 1 (2.9%) |
| 8.43 | 1 (2.9%) |
| 8.73 | 1 (2.9%) |
| 8.80 | 1 (2.9%) |
| 9.47 | 1 (2.9%) |
| 9.79 | 1 (2.9%) |
| 9.89 | 1 (2.9%) |
| Unknown | 1 |
| População Urbana | |
| 10210849 | 1 (2.8%) |
| 102626859 | 1 (2.8%) |
| 102806948 | 1 (2.8%) |
| 107683889 | 1 (2.8%) |
| 115782416 | 1 (2.8%) |
| 183241641 | 1 (2.8%) |
| 21061025 | 1 (2.8%) |
| 2210626 | 1 (2.8%) |
| 23788710 | 1 (2.8%) |
| 25162368 | 1 (2.8%) |
| 270663028 | 1 (2.8%) |
| 28807838 | 1 (2.8%) |
| 2930419 | 1 (2.8%) |
| 30628482 | 1 (2.8%) |
| 3303394 | 1 (2.8%) |
| 3625010 | 1 (2.8%) |
| 384258 | 1 (2.8%) |
| 39149717 | 1 (2.8%) |
| 3924621 | 1 (2.8%) |
| 41339571 | 1 (2.8%) |
| 42651966 | 1 (2.8%) |
| 42895824 | 1 (2.8%) |
| 4694702 | 1 (2.8%) |
| 471031528 | 1 (2.8%) |
| 4717305 | 1 (2.8%) |
| 54123364 | 1 (2.8%) |
| 5464 | 1 (2.8%) |
| 55908316 | 1 (2.8%) |
| 62509623 | 1 (2.8%) |
| 63097818 | 1 (2.8%) |
| 64324835 | 1 (2.8%) |
| 758549 | 1 (2.8%) |
| 7765706 | 1 (2.8%) |
| 842933962 | 1 (2.8%) |
| 8479744 | 1 (2.8%) |
| 9797273 | 1 (2.8%) |
| Gross primary education enrollment (%) | |
| 100.20 | 1 (2.8%) |
| 100.90 | 2 (5.6%) |
| 101.00 | 1 (2.8%) |
| 101.20 | 1 (2.8%) |
| 101.80 | 1 (2.8%) |
| 101.90 | 1 (2.8%) |
| 102.50 | 1 (2.8%) |
| 102.60 | 1 (2.8%) |
| 104.00 | 3 (8.3%) |
| 104.70 | 1 (2.8%) |
| 105.80 | 1 (2.8%) |
| 106.30 | 1 (2.8%) |
| 106.60 | 1 (2.8%) |
| 107.40 | 1 (2.8%) |
| 108.40 | 1 (2.8%) |
| 108.50 | 1 (2.8%) |
| 108.80 | 1 (2.8%) |
| 109.70 | 1 (2.8%) |
| 109.90 | 1 (2.8%) |
| 110.70 | 1 (2.8%) |
| 113.00 | 1 (2.8%) |
| 113.50 | 1 (2.8%) |
| 115.40 | 1 (2.8%) |
| 142.50 | 1 (2.8%) |
| 75.30 | 1 (2.8%) |
| 81.00 | 1 (2.8%) |
| 84.70 | 1 (2.8%) |
| 93.20 | 1 (2.8%) |
| 94.80 | 1 (2.8%) |
| 97.20 | 1 (2.8%) |
| 98.70 | 1 (2.8%) |
| 98.80 | 1 (2.8%) |
| 99.80 | 1 (2.8%) |
| Infant mortality | 12 (6, 31) |
| Unknown | 1 |
| 1 n (%); Median (IQR) | |
##Apagando variáveis em excesso
library(dados)
dados<- world_data_2023
dados$Latitude<-NULL
dados$Longitude<-NULL
dados$`Tax revenue (%)`<-NULL
dados$`Physicians per thousand`<-NULL
dados$`Armed Forces size`<-NULL
dados$`Capital/Major City`<-NULL
dados$`Currency-Code`<-NULL
dados$`Gasoline Price`<-NULL
dados$`Official language`<-NULL
dados$`Calling Code`<-NULL #não sei o que é isso
dados$`<-`<-NULL #não sei o que é isso
dados$`Gross tertiary education enrollment (%)`<- NULL
dados$`Maternal mortality ratio`<- NULL
dados$CPI<- NULL
dados$`CPI Change (%)`<-NULL
dados$`Largest city`<- NULL
dados$`Terras Agrícolas(%)`<-NULL
dados$`Land Area(Km2)`<-NULL
dados$População<-NULL
dados$`População Urbana`<-NULL
dados$`Taxa de Desemprego`<-NULL
dados$`Densidade (P/Km2)"`<-NULL
dados$`Taxa de Natalidade`<-NULL#Fazendo uma nova tabela, agora sem as variáveis em excesso
library(gtsummary)
dados<-(dados)
tbl_summary(dados)| Characteristic | N = 361 |
|---|---|
| País | |
| Afghanistan | 1 (2.8%) |
| Angola | 1 (2.8%) |
| Argentina | 1 (2.8%) |
| Brazil | 1 (2.8%) |
| Cambodia | 1 (2.8%) |
| Canada | 1 (2.8%) |
| China | 1 (2.8%) |
| Djibouti | 1 (2.8%) |
| Egypt | 1 (2.8%) |
| El Salvador | 1 (2.8%) |
| Ethiopia | 1 (2.8%) |
| France | 1 (2.8%) |
| Germany | 1 (2.8%) |
| India | 1 (2.8%) |
| Iran | 1 (2.8%) |
| Italy | 1 (2.8%) |
| Japan | 1 (2.8%) |
| Liechtenstein | 1 (2.8%) |
| Madagascar | 1 (2.8%) |
| Mexico | 1 (2.8%) |
| Mongolia | 1 (2.8%) |
| Nigeria | 1 (2.8%) |
| Republic of the Congo | 1 (2.8%) |
| Russia | 1 (2.8%) |
| Saudi Arabia | 1 (2.8%) |
| Senegal | 1 (2.8%) |
| Slovakia | 1 (2.8%) |
| South Africa | 1 (2.8%) |
| Suriname | 1 (2.8%) |
| Turkey | 1 (2.8%) |
| United Arab Emirates | 1 (2.8%) |
| United Kingdom | 1 (2.8%) |
| United States | 1 (2.8%) |
| Uruguay | 1 (2.8%) |
| Venezuela | 1 (2.8%) |
| Zimbabwe | 1 (2.8%) |
| Taxa Total de Imposto (%) | |
| 106.30 | 1 (2.8%) |
| 15.70 | 1 (2.8%) |
| 15.90 | 1 (2.8%) |
| 21.60 | 1 (2.8%) |
| 23.10 | 1 (2.8%) |
| 24.50 | 1 (2.8%) |
| 25.70 | 1 (2.8%) |
| 27.90 | 1 (2.8%) |
| 29.20 | 1 (2.8%) |
| 30.60 | 1 (2.8%) |
| 31.60 | 1 (2.8%) |
| 34.80 | 1 (2.8%) |
| 36.40 | 1 (2.8%) |
| 36.60 | 1 (2.8%) |
| 37.70 | 1 (2.8%) |
| 37.90 | 1 (2.8%) |
| 38.30 | 1 (2.8%) |
| 41.80 | 1 (2.8%) |
| 42.30 | 1 (2.8%) |
| 44.40 | 1 (2.8%) |
| 44.70 | 1 (2.8%) |
| 44.80 | 1 (2.8%) |
| 46.20 | 1 (2.8%) |
| 46.70 | 1 (2.8%) |
| 48.80 | 1 (2.8%) |
| 49.10 | 1 (2.8%) |
| 49.70 | 2 (5.6%) |
| 54.30 | 1 (2.8%) |
| 55.10 | 1 (2.8%) |
| 59.10 | 1 (2.8%) |
| 59.20 | 1 (2.8%) |
| 60.70 | 1 (2.8%) |
| 65.10 | 1 (2.8%) |
| 71.40 | 1 (2.8%) |
| 73.30 | 1 (2.8%) |
| Emissão de Co2 | 142,272 (7,068, 479,085) |
| Taxa de Fertilidade | |
| 1.29 | 1 (2.8%) |
| 1.41 | 1 (2.8%) |
| 1.42 | 1 (2.8%) |
| 1.44 | 1 (2.8%) |
| 1.5 | 1 (2.8%) |
| 1.52 | 1 (2.8%) |
| 1.56 | 1 (2.8%) |
| 1.57 | 1 (2.8%) |
| 1.68 | 1 (2.8%) |
| 1.69 | 1 (2.8%) |
| 1.73 | 2 (5.6%) |
| 1.88 | 1 (2.8%) |
| 1.97 | 1 (2.8%) |
| 2.04 | 1 (2.8%) |
| 2.07 | 1 (2.8%) |
| 2.13 | 1 (2.8%) |
| 2.14 | 1 (2.8%) |
| 2.22 | 1 (2.8%) |
| 2.26 | 1 (2.8%) |
| 2.27 | 1 (2.8%) |
| 2.32 | 1 (2.8%) |
| 2.41 | 1 (2.8%) |
| 2.42 | 1 (2.8%) |
| 2.5 | 1 (2.8%) |
| 2.73 | 1 (2.8%) |
| 2.9 | 1 (2.8%) |
| 3.33 | 1 (2.8%) |
| 3.62 | 1 (2.8%) |
| 4.08 | 1 (2.8%) |
| 4.25 | 1 (2.8%) |
| 4.43 | 1 (2.8%) |
| 4.47 | 1 (2.8%) |
| 4.63 | 1 (2.8%) |
| 5.39 | 1 (2.8%) |
| 5.52 | 1 (2.8%) |
| Area Florestal(%) | |
| 0.10 | 1 (2.8%) |
| 0.20 | 1 (2.8%) |
| 0.50 | 1 (2.8%) |
| 10.70 | 1 (2.8%) |
| 12.50 | 1 (2.8%) |
| 12.60 | 1 (2.8%) |
| 13.10 | 1 (2.8%) |
| 15.40 | 1 (2.8%) |
| 2.10 | 1 (2.8%) |
| 21.40 | 1 (2.8%) |
| 22.40 | 1 (2.8%) |
| 23.80 | 1 (2.8%) |
| 31.20 | 1 (2.8%) |
| 31.80 | 1 (2.8%) |
| 32.70 | 1 (2.8%) |
| 33.90 | 2 (5.6%) |
| 35.50 | 1 (2.8%) |
| 38.20 | 1 (2.8%) |
| 4.60 | 1 (2.8%) |
| 40.40 | 1 (2.8%) |
| 42.80 | 1 (2.8%) |
| 43.10 | 1 (2.8%) |
| 46.30 | 1 (2.8%) |
| 49.80 | 1 (2.8%) |
| 52.70 | 1 (2.8%) |
| 52.90 | 1 (2.8%) |
| 58.90 | 1 (2.8%) |
| 6.60 | 1 (2.8%) |
| 65.40 | 1 (2.8%) |
| 68.50 | 1 (2.8%) |
| 7.20 | 1 (2.8%) |
| 7.60 | 1 (2.8%) |
| 8.00 | 1 (2.8%) |
| 9.80 | 1 (2.8%) |
| 98.30 | 1 (2.8%) |
| PIB ($) | 433,243,775,031 (26,161,501,013, 1,762,258,732,332) |
| Matrícula Bruta no Ensino Primário (%) | 103 (100, 108) |
| Expectativa de Vida | |
| 54.3 | 1 (2.8%) |
| 60.8 | 1 (2.8%) |
| 61.2 | 1 (2.8%) |
| 63.9 | 1 (2.8%) |
| 64.3 | 1 (2.8%) |
| 64.5 | 1 (2.8%) |
| 66.2 | 1 (2.8%) |
| 66.6 | 1 (2.8%) |
| 66.7 | 1 (2.8%) |
| 67.7 | 1 (2.8%) |
| 69.4 | 1 (2.8%) |
| 69.6 | 1 (2.8%) |
| 69.7 | 1 (2.8%) |
| 71.6 | 1 (2.8%) |
| 71.8 | 1 (2.8%) |
| 72.1 | 1 (2.8%) |
| 72.7 | 1 (2.8%) |
| 73.1 | 1 (2.8%) |
| 75 | 2 (5.6%) |
| 75.7 | 1 (2.8%) |
| 76.5 | 2 (5.6%) |
| 77 | 1 (2.8%) |
| 77.2 | 1 (2.8%) |
| 77.4 | 1 (2.8%) |
| 77.8 | 2 (5.6%) |
| 78.5 | 1 (2.8%) |
| 80.9 | 1 (2.8%) |
| 81.3 | 1 (2.8%) |
| 81.9 | 1 (2.8%) |
| 82.5 | 1 (2.8%) |
| 82.9 | 1 (2.8%) |
| 83 | 1 (2.8%) |
| 84.2 | 1 (2.8%) |
| Salário Mínimo | |
| 0.01 | 1 (3.8%) |
| 0.21 | 1 (3.8%) |
| 0.30 | 1 (3.8%) |
| 0.31 | 1 (3.8%) |
| 0.43 | 1 (3.8%) |
| 0.49 | 1 (3.8%) |
| 0.50 | 1 (3.8%) |
| 0.53 | 1 (3.8%) |
| 0.54 | 1 (3.8%) |
| 0.65 | 1 (3.8%) |
| 0.71 | 1 (3.8%) |
| 0.87 | 1 (3.8%) |
| 0.88 | 1 (3.8%) |
| 1.53 | 1 (3.8%) |
| 1.58 | 1 (3.8%) |
| 1.66 | 1 (3.8%) |
| 10.13 | 1 (3.8%) |
| 11.16 | 1 (3.8%) |
| 3.11 | 1 (3.8%) |
| 3.35 | 1 (3.8%) |
| 3.45 | 1 (3.8%) |
| 3.85 | 1 (3.8%) |
| 6.77 | 1 (3.8%) |
| 7.25 | 1 (3.8%) |
| 9.51 | 1 (3.8%) |
| 9.99 | 1 (3.8%) |
| Unknown | 10 |
| Despesas de Saúde do Próprio Bolso | |
| 10.10 | 1 (2.9%) |
| 11.10 | 1 (2.9%) |
| 12.50 | 1 (2.9%) |
| 13.10 | 1 (2.9%) |
| 14.60 | 1 (2.9%) |
| 14.80 | 1 (2.9%) |
| 15.00 | 1 (2.9%) |
| 16.20 | 1 (2.9%) |
| 16.90 | 1 (2.9%) |
| 17.60 | 1 (2.9%) |
| 17.80 | 1 (2.9%) |
| 18.40 | 1 (2.9%) |
| 20.40 | 1 (2.9%) |
| 21.70 | 1 (2.9%) |
| 22.80 | 1 (2.9%) |
| 25.80 | 1 (2.9%) |
| 27.90 | 1 (2.9%) |
| 28.30 | 1 (2.9%) |
| 32.40 | 1 (2.9%) |
| 33.40 | 1 (2.9%) |
| 36.40 | 1 (2.9%) |
| 37.80 | 1 (2.9%) |
| 39.30 | 1 (2.9%) |
| 39.70 | 1 (2.9%) |
| 41.40 | 1 (2.9%) |
| 43.80 | 1 (2.9%) |
| 44.20 | 1 (2.9%) |
| 45.80 | 1 (2.9%) |
| 59.40 | 1 (2.9%) |
| 6.80 | 1 (2.9%) |
| 62.00 | 1 (2.9%) |
| 65.10 | 1 (2.9%) |
| 7.70 | 1 (2.9%) |
| 72.20 | 1 (2.9%) |
| 78.40 | 1 (2.9%) |
| Unknown | 1 |
| Gross primary education enrollment (%) | |
| 100.20 | 1 (2.8%) |
| 100.90 | 2 (5.6%) |
| 101.00 | 1 (2.8%) |
| 101.20 | 1 (2.8%) |
| 101.80 | 1 (2.8%) |
| 101.90 | 1 (2.8%) |
| 102.50 | 1 (2.8%) |
| 102.60 | 1 (2.8%) |
| 104.00 | 3 (8.3%) |
| 104.70 | 1 (2.8%) |
| 105.80 | 1 (2.8%) |
| 106.30 | 1 (2.8%) |
| 106.60 | 1 (2.8%) |
| 107.40 | 1 (2.8%) |
| 108.40 | 1 (2.8%) |
| 108.50 | 1 (2.8%) |
| 108.80 | 1 (2.8%) |
| 109.70 | 1 (2.8%) |
| 109.90 | 1 (2.8%) |
| 110.70 | 1 (2.8%) |
| 113.00 | 1 (2.8%) |
| 113.50 | 1 (2.8%) |
| 115.40 | 1 (2.8%) |
| 142.50 | 1 (2.8%) |
| 75.30 | 1 (2.8%) |
| 81.00 | 1 (2.8%) |
| 84.70 | 1 (2.8%) |
| 93.20 | 1 (2.8%) |
| 94.80 | 1 (2.8%) |
| 97.20 | 1 (2.8%) |
| 98.70 | 1 (2.8%) |
| 98.80 | 1 (2.8%) |
| 99.80 | 1 (2.8%) |
| Infant mortality | 12 (6, 31) |
| Unknown | 1 |
| 1 n (%); Median (IQR) | |
##Vendo a estrutura dos dados
str(dados)tibble [36 × 12] (S3: tbl_df/tbl/data.frame)
$ País : chr [1:36] "Saudi Arabia" "Mongolia" "China" "Madagascar" ...
$ Taxa Total de Imposto (%) : chr [1:36] "15.70" "25.70" "59.20" "38.30" ...
$ Emissão de Co2 : num [1:36] 563449 486406 9893038 51 120369 ...
$ Taxa de Fertilidade : chr [1:36] "2.32" "2.13" "1.69" "1.44" ...
$ Area Florestal(%) : chr [1:36] "0.50" "33.90" "22.40" "43.10" ...
$ PIB ($) : num [1:36] 7.93e+11 1.26e+12 1.99e+13 6.55e+09 4.48e+11 ...
$ Matrícula Bruta no Ensino Primário (%): num [1:36] 99.8 105.8 100.2 104.7 84.7 ...
$ Expectativa de Vida : chr [1:36] "75" "75" "77" "83" ...
$ Salário Mínimo : chr [1:36] "1.66" "3.11" "0.88" "3.85" ...
$ Despesas de Saúde do Próprio Bolso : chr [1:36] "44.20" "39.30" "32.40" "21.70" ...
$ Gross primary education enrollment (%): chr [1:36] "99.80" "104.00" "100.20" "142.50" ...
$ Infant mortality : num [1:36] 6 14 7.4 38.2 36.2 51.6 33.9 28.5 49.8 47.9 ...##Traduzindo o nome de variáveis
colnames(dados) [1] "País"
[2] "Taxa Total de Imposto (%)"
[3] "Emissão de Co2"
[4] "Taxa de Fertilidade"
[5] "Area Florestal(%)"
[6] "PIB ($)"
[7] "Matrícula Bruta no Ensino Primário (%)"
[8] "Expectativa de Vida"
[9] "Salário Mínimo"
[10] "Despesas de Saúde do Próprio Bolso"
[11] "Gross primary education enrollment (%)"
[12] "Infant mortality" colnames(dados)[10]<-"PIB"
colnames(dados)[18]<-"Matrícula Bruta no Ensino Primário"
colnames(dados)[19]<-"Mortalidade Infantil"##Precisamos de no mínimo 2 variáveis quali e 2 quanti; vamos transformar uma quanti em quali através da criação de uma faixa de valores:
#Transformar PIB em qualitativa:
dados$PIB<-cut(dados$`PIB ($)`,c(0,3318716360,5354435962732,10712190641821,16069945320911,214277000000000))
levels(dados$`PIB ($)`)<-c("De 0 a 3318716360", "De 3318716360 a 5354435962732", "De 10712190641821 a 16069945320911","De 16069945320911 a 21427700000000 ", "Mais de 21427700000000")#Transformar EMISSÃO DE CARBONO em qualitativa:
# world_data_2023$`Emissão de Co2`<-cut(world_data_2023$`Emissão de Co2`,c(0,1738,2471957,4945651,7419345,9893038))
# world_data_2023$`Emissão de Co2`
world_data_2023$`Emissão de Co2` [1] 563449.00 486406.00 9893038.00 51.00 120369.00 34693.00
[7] 10983.00 476644.00 3282.00 8672.00 14.87 620.00
[13] 3905.00 10902.00 2407672.00 9919.00 25368.00 1738.00
[19] 238.56 164175.00 1732027.00 7169.00 462299.00 201348.00
[25] 661.71 32424.00 372725.00 6766.00 206324.00 5006302.00
[31] 727973.00 379025.00 544894.00 303276.00 320411.00 1135886.00levels(world_data_2023$`Emissão de Co2`)<-c("De 0 a 1738", "De 1738 a 2471957", "De 4945651 a 7419345","De 7419345 a 9893038", "Mais de 9893038")#Transformar MORTALIDADE INFANTIL em qualitativa:
# world_data_2023$`Infant mortality`<-cut(world_data_2023$`Infant mortality`,c(0,40,60,2000))
world_data_2023$`Infant mortality` [1] 6.0 14.0 7.4 38.2 36.2 51.6 33.9 28.5 49.8 47.9 3.4 18.1 11.0 31.8 12.4
[16] 24.0 75.7 16.9 11.8 21.4 6.1 39.1 12.8 8.8 2.6 4.6 9.1 6.4 6.5 5.6
[31] 29.9 3.6 4.3 3.1 1.8 NAlevels(world_data_2023$`Infant mortality`)<-c("Até 40 mortes", "Mais que 40 mortes e até 60 mortes", "Mais que 60 mortes")Perguntas e seus respctivos gráficos:
Criando o Gráfico com o uso de duas qualitativas:
O quanto o PIB aumenta de acordo com a quantidade de emissão de CO2?
str(world_data_2023)tibble [36 × 19] (S3: tbl_df/tbl/data.frame)
$ País : chr [1:36] "Saudi Arabia" "Mongolia" "China" "Madagascar" ...
$ Taxa Total de Imposto (%) : chr [1:36] "15.70" "25.70" "59.20" "38.30" ...
$ Densidade (P/Km2)" : num [1:36] 16 66 153 238 226 26 38 49 16 60 ...
$ Terras Agrícolas(%) : chr [1:36] "80.80" "54.60" "56.20" "32.20" ...
$ Land Area(Km2) : chr [1:36] "2149690" "1964375" "9596960" "160" ...
$ Taxa de Natalidade : chr [1:36] "17.8" "17.6" "10.9" "9.9" ...
$ Emissão de Co2 : num [1:36] 563449 486406 9893038 51 120369 ...
..- attr(*, "levels")= chr [1:5] "De 0 a 1738" "De 1738 a 2471957" "De 4945651 a 7419345" "De 7419345 a 9893038" ...
$ Taxa de Fertilidade : chr [1:36] "2.32" "2.13" "1.69" "1.44" ...
$ Area Florestal(%) : chr [1:36] "0.50" "33.90" "22.40" "43.10" ...
$ PIB ($) : num [1:36] 7.93e+11 1.26e+12 1.99e+13 6.55e+09 4.48e+11 ...
$ Matrícula Bruta no Ensino Primário (%): num [1:36] 99.8 105.8 100.2 104.7 84.7 ...
$ Expectativa de Vida : chr [1:36] "75" "75" "77" "83" ...
$ Salário Mínimo : chr [1:36] "1.66" "3.11" "0.88" "3.85" ...
$ Despesas de Saúde do Próprio Bolso : chr [1:36] "44.20" "39.30" "32.40" "21.70" ...
$ População : chr [1:36] "34268528" "126014024" "1397715000" "38019" ...
$ Taxa de Desemprego : chr [1:36] "6.60" "6.01" "4.32" "1.76" ...
$ População Urbana : chr [1:36] "28807838" "102626859" "842933962" "5464" ...
$ Gross primary education enrollment (%): chr [1:36] "99.80" "104.00" "100.20" "142.50" ...
$ Infant mortality : num [1:36] 6 14 7.4 38.2 36.2 51.6 33.9 28.5 49.8 47.9 ...
..- attr(*, "levels")= chr [1:3] "Até 40 mortes" "Mais que 40 mortes e até 60 mortes" "Mais que 60 mortes"library(ggplot2)
ggplot(world_data_2023) +
aes(x = `Emissão de Co2`, y = `PIB ($)`) +
geom_tile() +
labs(x = " Emissões de dióxido de carbono em toneladas",
y = "PIB, valor total dos bens e serviços produzidos no país", title = "Emissão de C02 em relação ao PIB",
subtitle = "Um panorama global") +
theme_minimal() +
theme(plot.title = element_text(face = "bold",
hjust = 0.5), plot.subtitle = element_text(size = 13L, face = "italic", hjust = 0.5))
Criando o Gráfico com o uso de duas quantitativas:
A presença de maior área florestal aumenta a expectativa de vida da população?
library(readxl)
expect_vida <- read_excel("trabalho bioestatistica_files/expect_vida.xlsx",
col_types = c("text", "numeric", "numeric"))Warning: Coercing text to numeric in B2 / R2C2: '0.50'Warning: Coercing text to numeric in B3 / R3C2: '33.90'Warning: Coercing text to numeric in B4 / R4C2: '22.40'Warning: Coercing text to numeric in B5 / R5C2: '43.10'Warning: Coercing text to numeric in B6 / R6C2: '7.20'Warning: Coercing text to numeric in C6 / R6C3: '54.3'Warning: Coercing text to numeric in B7 / R7C2: '46.30'Warning: Coercing text to numeric in C7 / R7C3: '60.8'Warning: Coercing text to numeric in B8 / R8C2: '35.50'Warning: Coercing text to numeric in C8 / R8C3: '61.2'Warning: Coercing text to numeric in B9 / R9C2: '7.60'Warning: Coercing text to numeric in C9 / R9C3: '63.9'Warning: Coercing text to numeric in B10 / R10C2: '65.40'Warning: Coercing text to numeric in C10 / R10C3: '64.3'Warning: Coercing text to numeric in B11 / R11C2: '2.10'Warning: Coercing text to numeric in C11 / R11C3: '64.5'Warning: Coercing text to numeric in B12 / R12C2: '12.50'Warning: Coercing text to numeric in C12 / R12C3: '66.2'Warning: Coercing text to numeric in B13 / R13C2: '0.20'Warning: Coercing text to numeric in C13 / R13C3: '66.6'Warning: Coercing text to numeric in B14 / R14C2: '21.40'Warning: Coercing text to numeric in C14 / R14C3: '66.7'Warning: Coercing text to numeric in B15 / R15C2: '42.80'Warning: Coercing text to numeric in C15 / R15C3: '67.7'Warning: Coercing text to numeric in B16 / R16C2: '23.80'Warning: Coercing text to numeric in C16 / R16C3: '69.4'Warning: Coercing text to numeric in B17 / R17C2: '52.90'Warning: Coercing text to numeric in C17 / R17C3: '69.6'Warning: Coercing text to numeric in B18 / R18C2: '8.00'Warning: Coercing text to numeric in C18 / R18C3: '69.7'Warning: Coercing text to numeric in B19 / R19C2: '98.30'Warning: Coercing text to numeric in C19 / R19C3: '71.6'Warning: Coercing text to numeric in B20 / R20C2: '0.10'Warning: Coercing text to numeric in C20 / R20C3: '71.8'Warning: Coercing text to numeric in B21 / R21C2: '52.70'Warning: Coercing text to numeric in C21 / R21C3: '72.1'Warning: Coercing text to numeric in B22 / R22C2: '49.80'Warning: Coercing text to numeric in C22 / R22C3: '72.7'Warning: Coercing text to numeric in B23 / R23C2: '12.60'Warning: Coercing text to numeric in C23 / R23C3: '73.1'Warning: Coercing text to numeric in B24 / R24C2: '58.90'Warning: Coercing text to numeric in C24 / R24C3: '75.7'Warning: Coercing text to numeric in B25 / R25C2: '9.80'Warning: Coercing text to numeric in C25 / R25C3: '76.5'Warning: Coercing text to numeric in B26 / R26C2: '6.60'Warning: Coercing text to numeric in C26 / R26C3: '76.5'Warning: Coercing text to numeric in B27 / R27C2: '40.40'Warning: Coercing text to numeric in C27 / R27C3: '77.2'Warning: Coercing text to numeric in B28 / R28C2: '15.40'Warning: Coercing text to numeric in C28 / R28C3: '77.4'Warning: Coercing text to numeric in B29 / R29C2: '10.70'Warning: Coercing text to numeric in C29 / R29C3: '77.8'Warning: Coercing text to numeric in B30 / R30C2: '4.60'Warning: Coercing text to numeric in C30 / R30C3: '77.8'Warning: Coercing text to numeric in B31 / R31C2: '33.90'Warning: Coercing text to numeric in C31 / R31C3: '78.5'Warning: Coercing text to numeric in B32 / R32C2: '32.70'Warning: Coercing text to numeric in C32 / R32C3: '80.9'Warning: Coercing text to numeric in B33 / R33C2: '13.10'Warning: Coercing text to numeric in C33 / R33C3: '81.3'Warning: Coercing text to numeric in B34 / R34C2: '38.20'Warning: Coercing text to numeric in C34 / R34C3: '81.9'Warning: Coercing text to numeric in B35 / R35C2: '31.20'Warning: Coercing text to numeric in C35 / R35C3: '82.5'Warning: Coercing text to numeric in B36 / R36C2: '31.80'Warning: Coercing text to numeric in C36 / R36C3: '82.9'Warning: Coercing text to numeric in B37 / R37C2: '68.50'Warning: Coercing text to numeric in C37 / R37C3: '84.2'View(expect_vida)
library(ggplot2)
ggplot(expect_vida) +
aes(x = `Area Florestal(%)`, y = `Expectativa de Vida`) +
geom_point(shape = "circle",
size = 1.5, colour = "#112446") +
labs(x = "Área Florestal (%): Porcentagem de área coberta por florestas",
y = "Número médio de anos que se espera que um recém-nascido viva", title = "Expectativa de Vida relacionada à Área Florestal",
subtitle = "Um panorama global") +
theme_minimal()
Criando o Gráfico com o uso de uma qualitativa e uma quantitativa:
Países com mais matrículas no ensino primário tem menor mortalidade infantil?
library(readxl)
mort_matri <- read_excel("trabalho bioestatistica_files/mort_matri.xlsx",
col_types = c("text", "numeric", "numeric"))Warning: Coercing text to numeric in B2 / R2C2: '99.80'Warning: Coercing text to numeric in B3 / R3C2: '105.80'Warning: Coercing text to numeric in B4 / R4C2: '100.20'Warning: Coercing text to numeric in C4 / R4C3: '7.4'Warning: Coercing text to numeric in B5 / R5C2: '104.70'Warning: Coercing text to numeric in C5 / R5C3: '38.2'Warning: Coercing text to numeric in B6 / R6C2: '84.70'Warning: Coercing text to numeric in C6 / R6C3: '36.2'Warning: Coercing text to numeric in B7 / R7C2: '113.50'Warning: Coercing text to numeric in C7 / R7C3: '51.6'Warning: Coercing text to numeric in B8 / R8C2: '109.90'Warning: Coercing text to numeric in C8 / R8C3: '33.9'Warning: Coercing text to numeric in B9 / R9C2: '100.90'Warning: Coercing text to numeric in C9 / R9C3: '28.5'Warning: Coercing text to numeric in B10 / R10C2: '106.60'Warning: Coercing text to numeric in C10 / R10C3: '49.8'Warning: Coercing text to numeric in B11 / R11C2: '104.00'Warning: Coercing text to numeric in C11 / R11C3: '47.9'Warning: Coercing text to numeric in B12 / R12C2: '101.00'Warning: Coercing text to numeric in C12 / R12C3: '3.4'Warning: Coercing text to numeric in B13 / R13C2: '75.30'Warning: Coercing text to numeric in C13 / R13C3: '18.1'Warning: Coercing text to numeric in B14 / R14C2: '142.50'Warning: Coercing text to numeric in B15 / R15C2: '81.00'Warning: Coercing text to numeric in C15 / R15C3: '31.8'Warning: Coercing text to numeric in B16 / R16C2: '113.00'Warning: Coercing text to numeric in C16 / R16C3: '12.4'Warning: Coercing text to numeric in B17 / R17C2: '107.40'Warning: Coercing text to numeric in B18 / R18C2: '104.00'Warning: Coercing text to numeric in C18 / R18C3: '75.7'Warning: Coercing text to numeric in B19 / R19C2: '108.80'Warning: Coercing text to numeric in C19 / R19C3: '16.9'Warning: Coercing text to numeric in B20 / R20C2: '106.30'Warning: Coercing text to numeric in C20 / R20C3: '11.8'Warning: Coercing text to numeric in B21 / R21C2: '97.20'Warning: Coercing text to numeric in C21 / R21C3: '21.4'Warning: Coercing text to numeric in B22 / R22C2: '102.60'Warning: Coercing text to numeric in C22 / R22C3: '6.1'Warning: Coercing text to numeric in B23 / R23C2: '94.80'Warning: Coercing text to numeric in C23 / R23C3: '39.1'Warning: Coercing text to numeric in B24 / R24C2: '115.40'Warning: Coercing text to numeric in C24 / R24C3: '12.8'Warning: Coercing text to numeric in B25 / R25C2: '109.70'Warning: Coercing text to numeric in C25 / R25C3: '8.8'Warning: Coercing text to numeric in B26 / R26C2: '110.70'Warning: Coercing text to numeric in C26 / R26C3: '2.6'Warning: Coercing text to numeric in B27 / R27C2: '98.70'Warning: Coercing text to numeric in C27 / R27C3: '4.6'Warning: Coercing text to numeric in B28 / R28C2: '93.20'Warning: Coercing text to numeric in C28 / R28C3: '9.1'Warning: Coercing text to numeric in B29 / R29C2: '108.50'Warning: Coercing text to numeric in C29 / R29C3: '6.4'Warning: Coercing text to numeric in B30 / R30C2: '108.40'Warning: Coercing text to numeric in C30 / R30C3: '6.5'Warning: Coercing text to numeric in B31 / R31C2: '101.80'Warning: Coercing text to numeric in C31 / R31C3: '5.6'Warning: Coercing text to numeric in B32 / R32C2: '104.00'Warning: Coercing text to numeric in C32 / R32C3: '29.9'Warning: Coercing text to numeric in B33 / R33C2: '101.20'Warning: Coercing text to numeric in C33 / R33C3: '3.6'Warning: Coercing text to numeric in B34 / R34C2: '100.90'Warning: Coercing text to numeric in C34 / R34C3: '4.3'Warning: Coercing text to numeric in B35 / R35C2: '102.50'Warning: Coercing text to numeric in C35 / R35C3: '3.1'Warning: Coercing text to numeric in B36 / R36C2: '101.90'Warning: Coercing text to numeric in C36 / R36C3: '1.8'Warning: Coercing text to numeric in B37 / R37C2: '98.80'View(mort_matri)
mort_matri$`Infant mortality`<-cut(mort_matri$`Infant mortality`,c(0,40,60,2000))
mort_matri$`Infant mortality` [1] (0,40] (0,40] (0,40] (0,40] (0,40] (40,60]
[7] (0,40] (0,40] (40,60] (40,60] (0,40] (0,40]
[13] (0,40] (0,40] (0,40] (0,40] (60,2e+03] (0,40]
[19] (0,40] (0,40] (0,40] (0,40] (0,40] (0,40]
[25] (0,40] (0,40] (0,40] (0,40] (0,40] (0,40]
[31] (0,40] (0,40] (0,40] (0,40] (0,40] <NA>
Levels: (0,40] (40,60] (60,2e+03]levels(mort_matri$`Infant mortality`)<-c("Até 40 mortes", "Mais que 40 mortes e até 60 mortes", "Mais que 60 mortes")
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
mort_matri %>%
filter(`Matrícula Bruta no Ensino Primário (%)` >= 75 & `Matrícula Bruta no Ensino Primário (%)` <=
120.5) %>%
filter(!is.na(`Infant mortality`)) %>%
ggplot() +
aes(x = `Matrícula Bruta no Ensino Primário (%)`, y = `Infant mortality`) +
geom_boxplot(fill = "#112446") +
labs(x = "Taxa bruta de matrícula no ensino primário (%)", y = "Mortalidade Infantil: Número de mortes por 1.000 nascidos vivos antes de completar um ano de idade",
title = "Mortalidade Infantil e a percentagem de matriculados no Ensino Primário", subtitle = "Um panorama global") +
coord_flip() +
theme_minimal() +
theme(plot.title = element_text(face = "bold", hjust = 0.5), plot.subtitle = element_text(size = 13L,
face = "italic", hjust = 0.5))
#Gráfico complementar:
library(readxl)
maior_mortalidade_ <- read_excel("trabalho bioestatistica_files/maior_mortalidade,.xlsx",
col_types = c("text", "text", "text"))
View(maior_mortalidade_)
library(ggplot2)
ggplot(maior_mortalidade_) +
aes(x = `Infant mortality`, y = `Gross primary education enrollment (%)`,
fill = País) +
geom_tile() +
scale_fill_hue(direction = 1) +
labs(x = "Número de mortes por 1.000 nascidos vivos antes de completar um ano de idade",
y = "Taxa bruta de matrícula no ensino primário", title = "Mortalidade Infantil e a percentagem de matriculados no Ensino Primário",
subtitle = "Um recorte de quatro países") +
theme_minimal() +
theme(plot.title = element_text(face = "bold",
hjust = 0.5), plot.subtitle = element_text(size = 13L, face = "italic", hjust = 0.5))
Criando o Gráfico com o uso de duas quantitativas:
Como os impostos são direcionados para a saúde pública?
library(readxl)
world_data_2023 <- read_excel("trabalho bioestatistica_files/world-data-2023.xlsx",
col_types = c("text", "numeric", "numeric",
"text", "text", "text", "text", "text",
"text", "numeric", "text", "text",
"text", "numeric", "text", "text",
"numeric", "text", "text"))Warning: Coercing text to numeric in B2 / R2C2: '15.70'Warning: Coercing text to numeric in N2 / R2C14: '44.20'Warning: Coercing text to numeric in B3 / R3C2: '25.70'Warning: Coercing text to numeric in N3 / R3C14: '39.30'Warning: Coercing text to numeric in B4 / R4C2: '59.20'Warning: Coercing text to numeric in N4 / R4C14: '32.40'Warning: Coercing text to numeric in B5 / R5C2: '38.30'Warning: Coercing text to numeric in N5 / R5C14: '21.70'Warning: Coercing text to numeric in B6 / R6C2: '54.30'Warning: Coercing text to numeric in N6 / R6C14: '36.40'Warning: Coercing text to numeric in B7 / R7C2: '49.10'Warning: Coercing text to numeric in N7 / R7C14: '33.40'Warning: Coercing text to numeric in B8 / R8C2: '31.60'Warning: Coercing text to numeric in B9 / R9C2: '29.20'Warning: Coercing text to numeric in N9 / R9C14: '10.10'Warning: Coercing text to numeric in B10 / R10C2: '37.90'Warning: Coercing text to numeric in N10 / R10C14: '43.80'Warning: Coercing text to numeric in B11 / R11C2: '71.40'Warning: Coercing text to numeric in N11 / R11C14: '78.40'Warning: Coercing text to numeric in B12 / R12C2: '60.70'Warning: Coercing text to numeric in N12 / R12C14: '37.80'Warning: Coercing text to numeric in B13 / R13C2: '44.40'Warning: Coercing text to numeric in N13 / R13C14: '20.40'Warning: Coercing text to numeric in B14 / R14C2: '55.10'Warning: Coercing text to numeric in N14 / R14C14: '41.40'Warning: Coercing text to numeric in B15 / R15C2: '44.80'Warning: Coercing text to numeric in N15 / R15C14: '18.40'Warning: Coercing text to numeric in B16 / R16C2: '44.70'Warning: Coercing text to numeric in N16 / R16C14: '65.10'Warning: Coercing text to numeric in B17 / R17C2: '23.10'Warning: Coercing text to numeric in N17 / R17C14: '59.40'Warning: Coercing text to numeric in B18 / R18C2: '34.80'Warning: Coercing text to numeric in N18 / R18C14: '72.20'Warning: Coercing text to numeric in B19 / R19C2: '27.90'Warning: Coercing text to numeric in N19 / R19C14: '16.90'Warning: Coercing text to numeric in B20 / R20C2: '36.40'Warning: Coercing text to numeric in N20 / R20C14: '62.00'Warning: Coercing text to numeric in B21 / R21C2: '73.30'Warning: Coercing text to numeric in N21 / R21C14: '25.80'Warning: Coercing text to numeric in B22 / R22C2: '46.20'Warning: Coercing text to numeric in N22 / R22C14: '15.00'Warning: Coercing text to numeric in B23 / R23C2: '37.70'Warning: Coercing text to numeric in N23 / R23C14: '27.90'Warning: Coercing text to numeric in B24 / R24C2: '65.10'Warning: Coercing text to numeric in N24 / R24C14: '28.30'Warning: Coercing text to numeric in B25 / R25C2: '106.30'Warning: Coercing text to numeric in N25 / R25C14: '17.60'Warning: Coercing text to numeric in B26 / R26C2: '59.10'Warning: Coercing text to numeric in N26 / R26C14: '39.70'Warning: Coercing text to numeric in B27 / R27C2: '49.70'Warning: Coercing text to numeric in N27 / R27C14: '7.70'Warning: Coercing text to numeric in B28 / R28C2: '42.30'Warning: Coercing text to numeric in N28 / R28C14: '17.80'Warning: Coercing text to numeric in B29 / R29C2: '41.80'Warning: Coercing text to numeric in N29 / R29C14: '45.80'Warning: Coercing text to numeric in B30 / R30C2: '15.90'Warning: Coercing text to numeric in N30 / R30C14: '14.80'Warning: Coercing text to numeric in B31 / R31C2: '36.60'Warning: Coercing text to numeric in N31 / R31C14: '16.20'Warning: Coercing text to numeric in B32 / R32C2: '49.70'Warning: Coercing text to numeric in N32 / R32C14: '12.50'Warning: Coercing text to numeric in B33 / R33C2: '30.60'Warning: Coercing text to numeric in N33 / R33C14: '11.10'Warning: Coercing text to numeric in B34 / R34C2: '24.50'Warning: Coercing text to numeric in N34 / R34C14: '14.60'Warning: Coercing text to numeric in B35 / R35C2: '48.80'Warning: Coercing text to numeric in N35 / R35C14: '6.80'Warning: Coercing text to numeric in B36 / R36C2: '46.70'Warning: Coercing text to numeric in N36 / R36C14: '22.80'Warning: Coercing text to numeric in B37 / R37C2: '21.60'Warning: Coercing text to numeric in N37 / R37C14: '13.10'View(world_data_2023)
library(ggplot2)
ggplot(world_data_2023) +
aes(x = `Taxa Total de Imposto (%)`, y = `Despesas de Saúde do Próprio Bolso`) +
geom_point(shape = "circle", size = 2.95, colour = "#0ADF79") +
labs(x = "Taxa total de imposto (%)",
y = "Despesas de Saúde do Próprio Bolso (%)", title = "Como os impostos são direcionados para a saúde pública?") +
theme_minimal() +
theme(plot.title = element_text(size = 18L, face = "bold", hjust = 0.5), axis.title.y = element_text(size = 15L,
face = "bold"), axis.title.x = element_text(size = 15L, face = "bold"))Warning: Removed 1 rows containing missing values (`geom_point()`).
Criando o Gráfico com o uso de duas quantitativas:
Países com um salário mínimo mais elevado influenciam no aumento da taxa de fertilidade?
library(readxl)
fertilidade <- read_excel("trabalho bioestatistica_files/fertilidade.xlsx",
col_types = c("text", "numeric", "numeric"))Warning: Coercing text to numeric in B2 / R2C2: '2.32'Warning: Coercing text to numeric in C2 / R2C3: '1.66'Warning: Coercing text to numeric in B3 / R3C2: '2.13'Warning: Coercing text to numeric in C3 / R3C3: '3.11'Warning: Coercing text to numeric in B4 / R4C2: '1.69'Warning: Coercing text to numeric in C4 / R4C3: '0.88'Warning: Coercing text to numeric in B5 / R5C2: '1.44'Warning: Coercing text to numeric in C5 / R5C3: '3.85'Warning: Coercing text to numeric in B6 / R6C2: '5.39'Warning: Coercing text to numeric in C6 / R6C3: '10.13'Warning: Coercing text to numeric in B7 / R7C2: '5.52'Warning: Coercing text to numeric in C7 / R7C3: '0.71'Warning: Coercing text to numeric in B8 / R8C2: '3.62'Warning: Coercing text to numeric in B9 / R9C2: '2.41'Warning: Coercing text to numeric in B10 / R10C2: '4.43'Warning: Coercing text to numeric in C10 / R10C3: '0.50'Warning: Coercing text to numeric in B11 / R11C2: '4.47'Warning: Coercing text to numeric in C11 / R11C3: '0.43'Warning: Coercing text to numeric in B12 / R12C2: '4.25'Warning: Coercing text to numeric in C12 / R12C3: '1.58'Warning: Coercing text to numeric in B13 / R13C2: '2.73'Warning: Coercing text to numeric in C13 / R13C3: '11.16'Warning: Coercing text to numeric in B14 / R14C2: '4.08'Warning: Coercing text to numeric in C14 / R14C3: '0.31'Warning: Coercing text to numeric in B15 / R15C2: '4.63'Warning: Coercing text to numeric in C15 / R15C3: '0.01'Warning: Coercing text to numeric in B16 / R16C2: '2.22'Warning: Coercing text to numeric in C16 / R16C3: '0.49'Warning: Coercing text to numeric in B17 / R17C2: '2.5'Warning: Coercing text to numeric in C17 / R17C3: '9.51'Warning: Coercing text to numeric in B18 / R18C2: '2.9'Warning: Coercing text to numeric in C18 / R18C3: '3.45'Warning: Coercing text to numeric in B19 / R19C2: '2.42'Warning: Coercing text to numeric in B20 / R20C2: '3.33'Warning: Coercing text to numeric in C20 / R20C3: '9.99'Warning: Coercing text to numeric in B21 / R21C2: '2.27'Warning: Coercing text to numeric in B22 / R22C2: '1.57'Warning: Coercing text to numeric in C22 / R22C3: '7.25'Warning: Coercing text to numeric in B23 / R23C2: '2.04'Warning: Coercing text to numeric in C23 / R23C3: '0.30'Warning: Coercing text to numeric in B24 / R24C2: '1.73'Warning: Coercing text to numeric in C24 / R24C3: '1.53'Warning: Coercing text to numeric in B25 / R25C2: '2.26'Warning: Coercing text to numeric in C25 / R25C3: '3.35'Warning: Coercing text to numeric in B26 / R26C2: '2.14'Warning: Coercing text to numeric in C26 / R26C3: '0.65'Warning: Coercing text to numeric in B27 / R27C2: '1.52'Warning: Coercing text to numeric in B28 / R28C2: '2.07'Warning: Coercing text to numeric in B29 / R29C2: '1.97'Warning: Coercing text to numeric in B30 / R30C2: '1.41'Warning: Coercing text to numeric in B31 / R31C2: '1.73'Warning: Coercing text to numeric in B32 / R32C2: '1.56'Warning: Coercing text to numeric in C32 / R32C3: '0.21'Warning: Coercing text to numeric in B33 / R33C2: '1.68'Warning: Coercing text to numeric in B34 / R34C2: '1.5'Warning: Coercing text to numeric in C34 / R34C3: '0.87'Warning: Coercing text to numeric in B35 / R35C2: '1.88'Warning: Coercing text to numeric in C35 / R35C3: '6.77'Warning: Coercing text to numeric in B36 / R36C2: '1.29'Warning: Coercing text to numeric in C36 / R36C3: '0.54'Warning: Coercing text to numeric in B37 / R37C2: '1.42'Warning: Coercing text to numeric in C37 / R37C3: '0.53'View(fertilidade)
library(dplyr)
library(ggplot2)
fertilidade %>%
filter(!is.na(`Salário Mínimo`)) %>%
ggplot() +
aes(x = `Taxa de Fertilidade`, y = `Salário Mínimo`, fill = País) +
geom_point(shape = "circle",
size = 1.5, colour = "#112446") +
scale_fill_viridis_d(option = "magma", direction = -1) +
labs(x = "Número médio de filhos nascidos de uma mulher durante a sua vida",
y = "Nível do salário mínimo em moeda local", title = "Relação entre Taxa de Fertilidade e Salário Mínimo",
subtitle = "Um panorama global") +
theme_minimal() +
theme(plot.title = element_text(face = "bold",
hjust = 0.5), plot.subtitle = element_text(size = 13L, face = "italic", hjust = 0.5))