exercise-04
Chigbogwu G Nnamani
2024-05-22
check each column has expected data
spec(deaths)## cols(
## Country = col_character(),
## CancerSiteICD10Code = col_character(),
## CancerSite = col_character(),
## Sex = col_character(),
## SexQF = col_character(),
## Year = col_double(),
## DeathsAgeUnder5 = col_double(),
## DeathsAge5To9 = col_double(),
## DeathsAge10To14 = col_double(),
## DeathsAge15To19 = col_double(),
## DeathsAge20To24 = col_double(),
## DeathsAge25To29 = col_double(),
## DeathsAge30To34 = col_double(),
## DeathsAge35To39 = col_double(),
## DeathsAge40To44 = col_double(),
## DeathsAge45To49 = col_double(),
## DeathsAge50To54 = col_double(),
## DeathsAge55To59 = col_double(),
## DeathsAge60To64 = col_double(),
## DeathsAge65To69 = col_double(),
## DeathsAge70To74 = col_double(),
## DeathsAge75To79 = col_double(),
## DeathsAge80To84 = col_double(),
## DeathsAge85To89 = col_double(),
## DeathsAge90AndOver = col_double(),
## DeathsAllAges = col_double(),
## MortalityRateAgeUnder5 = col_double(),
## MortalityRateAge5To9 = col_double(),
## MortalityRateAge10To14 = col_double(),
## MortalityRateAge15To19 = col_double(),
## MortalityRateAge20To24 = col_double(),
## MortalityRateAge25To29 = col_double(),
## MortalityRateAge30To34 = col_double(),
## MortalityRateAge35To39 = col_double(),
## MortalityRateAge40To44 = col_double(),
## MortalityRateAge45To49 = col_double(),
## MortalityRateAge50To54 = col_double(),
## MortalityRateAge55To59 = col_double(),
## MortalityRateAge60To64 = col_double(),
## MortalityRateAge65To69 = col_double(),
## MortalityRateAge70To74 = col_double(),
## MortalityRateAge75To79 = col_double(),
## MortalityRateAge80To84 = col_double(),
## MortalityRateAge85To89 = col_double(),
## MortalityRateAge90AndOver = col_double(),
## CrudeRate = col_double(),
## CrudeRateLower95pcConfidenceInterval = col_double(),
## CrudeRateUpper95pcConfidenceInterval = col_double(),
## EASR = col_double(),
## EASRLower95pcConfidenceInterval = col_double(),
## EASRLower95pcConfidenceIntervalQF = col_character(),
## EASRUpper95pcConfidenceInterval = col_double(),
## EASRUpper95pcConfidenceIntervalQF = col_character(),
## WASR = col_double(),
## WASRLower95pcConfidenceInterval = col_double(),
## WASRLower95pcConfidenceIntervalQF = col_character(),
## WASRUpper95pcConfidenceInterval = col_double(),
## WASRUpper95pcConfidenceIntervalQF = col_character(),
## StandardisedMortalityRatio = col_double(),
## StandardisedMortalityRatioQF = col_logical()
## )glimpse(deaths)## Rows: 3,123
## Columns: 60
## $ Country <chr> "S92000003", "S92000003", "S92000…
## $ CancerSiteICD10Code <chr> "C00-C97, excluding C44", "C00-C9…
## $ CancerSite <chr> "All cancer types", "All cancer t…
## $ Sex <chr> "All", "All", "All", "All", "All"…
## $ SexQF <chr> "d", "d", "d", "d", "d", "d", "d"…
## $ Year <dbl> 1995, 1996, 1997, 1998, 1999, 200…
## $ DeathsAgeUnder5 <dbl> 7, 10, 4, 10, 6, 7, 7, 14, 11, 16…
## $ DeathsAge5To9 <dbl> 10, 14, 12, 11, 12, 7, 11, 3, 9, …
## $ DeathsAge10To14 <dbl> 7, 10, 6, 8, 8, 8, 9, 12, 10, 15,…
## $ DeathsAge15To19 <dbl> 9, 15, 11, 14, 16, 13, 10, 13, 12…
## $ DeathsAge20To24 <dbl> 18, 19, 14, 14, 19, 14, 17, 24, 1…
## $ DeathsAge25To29 <dbl> 31, 26, 25, 24, 27, 19, 20, 21, 2…
## $ DeathsAge30To34 <dbl> 57, 66, 56, 53, 54, 58, 42, 56, 5…
## $ DeathsAge35To39 <dbl> 87, 108, 115, 99, 99, 103, 81, 99…
## $ DeathsAge40To44 <dbl> 218, 177, 196, 209, 175, 196, 202…
## $ DeathsAge45To49 <dbl> 399, 397, 339, 364, 330, 318, 342…
## $ DeathsAge50To54 <dbl> 644, 679, 645, 642, 701, 600, 663…
## $ DeathsAge55To59 <dbl> 998, 1019, 1009, 932, 989, 897, 1…
## $ DeathsAge60To64 <dbl> 1559, 1475, 1425, 1453, 1376, 144…
## $ DeathsAge65To69 <dbl> 2256, 2179, 2075, 2049, 1958, 198…
## $ DeathsAge70To74 <dbl> 2731, 2653, 2575, 2555, 2550, 253…
## $ DeathsAge75To79 <dbl> 2328, 2441, 2548, 2579, 2739, 268…
## $ DeathsAge80To84 <dbl> 2026, 2046, 1994, 1846, 1819, 200…
## $ DeathsAge85To89 <dbl> 1253, 1240, 1224, 1250, 1249, 134…
## $ DeathsAge90AndOver <dbl> 554, 546, 565, 596, 620, 683, 705…
## $ DeathsAllAges <dbl> 15192, 15120, 14838, 14708, 14747…
## $ MortalityRateAgeUnder5 <dbl> 2.1990035, 3.2249430, 1.3214185, …
## $ MortalityRateAge5To9 <dbl> 3.0827946, 4.3271311, 3.7062775, …
## $ MortalityRateAge10To14 <dbl> 2.1830180, 3.1331560, 1.8740103, …
## $ MortalityRateAge15To19 <dbl> 2.923606, 4.831743, 3.489471, 4.3…
## $ MortalityRateAge20To24 <dbl> 5.038065, 5.580898, 4.347124, 4.5…
## $ MortalityRateAge25To29 <dbl> 7.784563, 6.642413, 6.596480, 6.6…
## $ MortalityRateAge30To34 <dbl> 13.957794, 16.231091, 13.800582, …
## $ MortalityRateAge35To39 <dbl> 23.20650, 28.26249, 29.59631, 25.…
## $ MortalityRateAge40To44 <dbl> 64.93429, 52.08717, 56.41716, 58.…
## $ MortalityRateAge45To49 <dbl> 113.76207, 111.77651, 99.66279, 1…
## $ MortalityRateAge50To54 <dbl> 221.9939, 228.9224, 202.6951, 194…
## $ MortalityRateAge55To59 <dbl> 362.2018, 373.2614, 369.3103, 337…
## $ MortalityRateAge60To64 <dbl> 600.5578, 570.1298, 552.1886, 557…
## $ MortalityRateAge65To69 <dbl> 938.6585, 910.9379, 864.9113, 854…
## $ MortalityRateAge70To74 <dbl> 1294.7976, 1272.2512, 1249.7209, …
## $ MortalityRateAge75To79 <dbl> 1605.362, 1614.066, 1608.525, 156…
## $ MortalityRateAge80To84 <dbl> 1906.734, 1949.035, 1955.036, 188…
## $ MortalityRateAge85To89 <dbl> 2240.421, 2186.178, 2122.605, 212…
## $ MortalityRateAge90AndOver <dbl> 2350.346, 2241.747, 2259.367, 228…
## $ CrudeRate <dbl> 297.6670, 296.9253, 291.8947, 289…
## $ CrudeRateLower95pcConfidenceInterval <dbl> 292.9522, 292.2111, 287.2167, 285…
## $ CrudeRateUpper95pcConfidenceInterval <dbl> 302.4386, 301.6965, 296.6298, 294…
## $ EASR <dbl> 401.5007, 396.4153, 387.1550, 378…
## $ EASRLower95pcConfidenceInterval <dbl> 394.5945, 389.6004, 380.4548, 372…
## $ EASRLower95pcConfidenceIntervalQF <chr> NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ EASRUpper95pcConfidenceInterval <dbl> 408.4659, 403.2884, 393.9128, 385…
## $ EASRUpper95pcConfidenceIntervalQF <chr> NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ WASR <dbl> 161.9578, 159.7564, 154.1558, 151…
## $ WASRLower95pcConfidenceInterval <dbl> 159.1775, 156.9981, 151.4711, 149…
## $ WASRLower95pcConfidenceIntervalQF <chr> NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ WASRUpper95pcConfidenceInterval <dbl> 164.7618, 162.5383, 156.8638, 154…
## $ WASRUpper95pcConfidenceIntervalQF <chr> NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ StandardisedMortalityRatio <dbl> 100, 100, 100, 100, 100, 100, 100…
## $ StandardisedMortalityRatioQF <lgl> NA, NA, NA, NA, NA, NA, NA, NA, N…count(deaths, EASRUpper95pcConfidenceIntervalQF)## # A tibble: 2 × 2
## EASRUpper95pcConfidenceIntervalQF n
## <chr> <int>
## 1 z 431
## 2 <NA> 2692StandardisedMortalityRatioQF = col_skip()
WASRLower95pcConfidenceIntervalQF = col_skip()
WASRUpper95pcConfidenceIntervalQF = col_skip()
EASRLower95pcConfidenceIntervalQF = col_skip()deaths_cols <- cols(
EASRLower95pcConfidenceIntervalQF = col_skip(),
WASRLower95pcConfidenceIntervalQF = col_skip(),
WASRUpper95pcConfidenceIntervalQF = col_skip(),
EASRLower95pcConfidenceIntervalQF = col_skip()
)dput(deaths$Country)## c("S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
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## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
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## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003", "S92000003", "S92000003",
## "S92000003", "S92000003", "S92000003")dput(deaths$Year)## c(1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
## )dput(deaths$DeathsAllAges)## c(15192, 15120, 14838, 14708, 14747, 14913, 15137, 14994, 15063,
## 14997, 15087, 15025, 15202, 15211, 15119, 15270, 15375, 15787,
## 15764, 15746, 16011, 15813, 16105, 16153, 16366, 16184, 7753,
## 7684, 7521, 7420, 7451, 7453, 7738, 7737, 7602, 7640, 7637, 7692,
## 7739, 7691, 7694, 7775, 7952, 7931, 8047, 8081, 8321, 8243, 8327,
## 8292, 8375, 8335, 7439, 7436, 7317, 7288, 7296, 7460, 7399, 7257,
## 7461, 7357, 7450, 7333, 7463, 7520, 7425, 7495, 7423, 7856, 7717,
## 7665, 7690, 7570, 7778, 7861, 7991, 7849, 15239, 15178, 14901,
## 14763, 14796, 14958, 15196, 15051, 15116, 15047, 15135, 15084,
## 15274, 15269, 15187, 15323, 15457, 15864, 15858, 15840, 16093,
## 15900, 16207, 16256, 16478, 16311, 7780, 7711, 7549, 7455, 7478,
## 7480, 7772, 7770, 7638, 7664, 7664, 7728, 7783, 7729, 7731, 7807,
## 8005, 7978, 8110, 8146, 8378, 8302, 8403, 8360, 8456, 8426, 7459,
## 7467, 7352, 7308, 7318, 7478, 7424, 7281, 7478, 7383, 7471, 7356,
## 7491, 7540, 7456, 7516, 7452, 7886, 7748, 7694, 7715, 7598, 7804,
## 7896, 8022, 7885, 508, 505, 486, 469, 449, 425, 463, 418, 457,
## 443, 457, 425, 477, 457, 429, 440, 472, 482, 531, 480, 522, 519,
## 477, 491, 513, 466, 328, 314, 297, 284, 291, 256, 287, 266, 277,
## 275, 281, 281, 299, 276, 258, 269, 294, 303, 318, 300, 329, 335,
## 311, 308, 349, 269, 180, 191, 189, 185, 158, 169, 176, 152, 180,
## 168, 176, 144, 178, 181, 171, 171, 178, 179, 213, 180, 193, 184,
## 166, 183, 164, 197, 30, 22, 11, 14, 28, 38, 22, 27, 24, 29, 19,
## 23, 20, 42, 35, 24, 37, 28, 37, 32, 32, 37, 39, 36, 52, 42, 17,
## 10, 5, 8, 17, 20, 13, 16, 15, 13, 11, 13, 14, 26, 18, 13, 24,
## 15, 21, 15, 20, 26, 23, 19, 27, 20, 13, 12, 6, 6, 11, 18, 9,
## 11, 9, 16, 8, 10, 6, 16, 17, 11, 13, 13, 16, 17, 12, 11, 16,
## 17, 25, 22, 85, 82, 74, 63, 97, 99, 88, 91, 72, 97, 93, 76, 100,
## 107, 104, 101, 112, 94, 115, 99, 107, 110, 122, 118, 118, 128,
## 47, 32, 42, 26, 49, 63, 46, 45, 33, 41, 51, 42, 53, 57, 58, 47,
## 60, 53, 59, 59, 62, 66, 66, 63, 61, 66, 38, 50, 32, 37, 48, 36,
## 42, 46, 39, 56, 42, 34, 47, 50, 46, 54, 52, 41, 56, 40, 45, 44,
## 56, 55, 57, 62, 55, 60, 63, 49, 69, 61, 66, 64, 48, 68, 74, 53,
## 80, 65, 69, 77, 75, 66, 78, 67, 75, 73, 83, 82, 66, 86, 30, 22,
## 37, 18, 32, 43, 33, 29, 18, 28, 40, 29, 39, 31, 40, 34, 36, 38,
## 38, 44, 42, 40, 43, 44, 34, 46, 25, 38, 26, 31, 37, 18, 33, 35,
## 30, 40, 34, 24, 41, 34, 29, 43, 39, 28, 40, 23, 33, 33, 40, 38,
## 32, 40, 296, 262, 306, 274, 332, 327, 329, 335, 327, 337, 353,
## 337, 339, 344, 345, 359, 340, 376, 388, 388, 402, 379, 382, 419,
## 389, 355, 162, 161, 157, 151, 182, 201, 181, 176, 185, 194, 207,
## 213, 196, 191, 197, 208, 190, 224, 232, 209, 237, 229, 229, 227,
## 210, 201, 134, 101, 149, 123, 150, 126, 148, 159, 142, 143, 146,
## 124, 143, 153, 148, 151, 150, 152, 156, 179, 165, 150, 153, 192,
## 179, 154, 298, 270, 310, 279, 333, 335, 341, 340, 332, 347, 361,
## 347, 347, 350, 350, 367, 347, 388, 397, 393, 410, 391, 394, 421,
## 395, 359, 164, 165, 159, 153, 183, 206, 187, 179, 187, 198, 209,
## 220, 201, 193, 200, 211, 192, 230, 238, 211, 241, 234, 236, 227,
## 211, 203, 134, 105, 151, 126, 150, 129, 154, 161, 145, 149, 152,
## 127, 146, 157, 150, 156, 155, 158, 159, 182, 169, 157, 158, 194,
## 184, 156, 1249, 1200, 1161, 1147, 1136, 1122, 1150, 1110, 1149,
## 1093, 1151, 1112, 1067, 1050, 1010, 1032, 1041, 1071, 1020, 976,
## 992, 1029, 954, 1001, 995, 974, 5, 10, 7, 5, 7, 6, 7, 5, 11,
## 11, 7, 4, 5, 7, 8, 10, 5, 8, 7, 10, 3, 9, 8, 8, 10, 5, 1244,
## 1190, 1154, 1142, 1129, 1116, 1143, 1105, 1138, 1082, 1144, 1108,
## 1062, 1043, 1002, 1022, 1036, 1063, 1013, 966, 989, 1020, 946,
## 993, 985, 969, 1708, 1729, 1670, 1639, 1662, 1596, 1615, 1555,
## 1582, 1550, 1550, 1550, 1539, 1565, 1555, 1501, 1526, 1621, 1578,
## 1525, 1565, 1617, 1687, 1743, 1742, 1696, 807, 865, 889, 848,
## 870, 839, 835, 842, 830, 844, 855, 835, 812, 829, 825, 782, 824,
## 837, 871, 786, 844, 857, 884, 941, 926, 908, 901, 864, 781, 791,
## 792, 757, 780, 713, 752, 706, 695, 715, 727, 736, 730, 719, 702,
## 784, 707, 739, 721, 760, 803, 802, 816, 788, 1144, 1189, 1065,
## 996, 1018, 1052, 1062, 975, 966, 917, 966, 922, 899, 940, 846,
## 857, 893, 917, 923, 900, 928, 956, 971, 1047, 989, 982, 490,
## 551, 526, 487, 490, 515, 513, 507, 488, 479, 485, 464, 443, 500,
## 416, 412, 454, 464, 471, 432, 462, 464, 473, 514, 485, 470, 654,
## 638, 539, 509, 528, 537, 549, 468, 478, 438, 481, 458, 456, 440,
## 430, 445, 439, 453, 452, 468, 466, 492, 498, 533, 504, 512, 564,
## 540, 605, 643, 644, 544, 553, 580, 616, 633, 584, 628, 640, 625,
## 709, 644, 633, 704, 655, 625, 637, 661, 716, 696, 753, 714, 317,
## 314, 363, 361, 380, 324, 322, 335, 342, 365, 370, 371, 369, 329,
## 409, 370, 370, 373, 400, 354, 382, 393, 411, 427, 441, 438, 247,
## 226, 242, 282, 264, 220, 231, 245, 274, 268, 214, 257, 271, 296,
## 300, 274, 263, 331, 255, 271, 255, 268, 305, 269, 312, 276, 147,
## 138, 144, 145, 122, 117, 113, 100, 120, 102, 127, 92, 105, 102,
## 107, 99, 108, 112, 91, 88, 116, 106, 105, 80, 100, 109, 78, 82,
## 84, 92, 122, 102, 123, 133, 96, 113, 105, 104, 82, 120, 124,
## 152, 117, 148, 165, 162, 184, 183, 170, 192, 201, 187, 368, 408,
## 413, 405, 403, 409, 378, 413, 419, 429, 366, 400, 381, 411, 400,
## 387, 363, 383, 396, 375, 362, 349, 404, 387, 379, 367, 245, 243,
## 250, 255, 260, 245, 263, 252, 267, 245, 263, 233, 231, 265, 260,
## 302, 262, 306, 293, 300, 346, 336, 317, 321, 351, 344, 7, 9,
## 11, 14, 7, 9, 8, 9, 5, 6, 12, 8, 7, 7, 5, 6, 9, 12, 9, 10, 13,
## 13, 12, 16, 16, 12, 38, 36, 27, 24, 40, 23, 31, 30, 30, 26, 39,
## 26, 42, 47, 45, 41, 35, 41, 36, 49, 51, 35, 36, 47, 47, 49, 400,
## 346, 358, 394, 366, 358, 353, 375, 365, 364, 360, 351, 356, 378,
## 396, 380, 426, 422, 451, 429, 452, 489, 521, 474, 552, 495, 282,
## 237, 231, 275, 259, 252, 264, 266, 252, 259, 260, 244, 240, 274,
## 283, 266, 293, 291, 312, 299, 314, 342, 377, 332, 396, 333, 118,
## 109, 127, 119, 107, 106, 89, 109, 113, 105, 100, 107, 116, 104,
## 113, 114, 133, 131, 139, 130, 138, 147, 144, 142, 156, 162, 115,
## 99, 91, 128, 109, 112, 118, 122, 103, 100, 106, 97, 72, 114,
## 102, 94, 103, 93, 110, 108, 127, 111, 124, 106, 107, 113, 94,
## 74, 72, 102, 81, 90, 97, 103, 76, 76, 81, 72, 59, 95, 85, 70,
## 72, 72, 82, 86, 100, 91, 97, 79, 90, 74, 21, 25, 19, 26, 28,
## 22, 21, 19, 27, 24, 25, 25, 13, 19, 17, 24, 31, 21, 28, 22, 27,
## 20, 27, 27, 17, 39, 270, 231, 251, 255, 248, 232, 226, 236, 255,
## 251, 242, 247, 277, 259, 281, 280, 310, 315, 324, 308, 311, 358,
## 380, 349, 427, 369, 184, 155, 146, 167, 174, 155, 160, 154, 172,
## 176, 171, 166, 178, 177, 190, 192, 212, 209, 219, 206, 204, 239,
## 268, 241, 296, 254, 86, 76, 105, 88, 74, 77, 66, 82, 83, 75,
## 71, 81, 99, 82, 91, 88, 98, 106, 105, 102, 107, 119, 112, 108,
## 131, 115, 70, 62, 58, 67, 73, 70, 59, 52, 71, 64, 65, 63, 76,
## 66, 72, 59, 83, 50, 63, 67, 60, 69, 71, 58, 86, 71, 46, 40, 32,
## 50, 53, 43, 43, 32, 40, 43, 43, 39, 44, 41, 45, 37, 47, 28, 38,
## 41, 36, 35, 51, 34, 54, 48, 24, 22, 26, 17, 20, 27, 16, 20, 31,
## 21, 22, 24, 32, 25, 27, 22, 36, 22, 25, 26, 24, 34, 20, 24, 32,
## 23, 128, 121, 108, 117, 129, 124, 116, 117, 113, 117, 122, 113,
## 146, 132, 134, 111, 139, 121, 151, 135, 132, 162, 158, 144, 180,
## 141, 83, 79, 60, 75, 86, 78, 81, 76, 70, 79, 86, 75, 90, 87,
## 80, 74, 83, 73, 93, 78, 82, 94, 106, 95, 115, 92, 45, 42, 48,
## 42, 43, 46, 35, 41, 43, 38, 36, 38, 56, 45, 54, 37, 56, 48, 58,
## 57, 50, 68, 52, 49, 65, 49, 18, 9, 21, 12, 22, 14, 13, 14, 12,
## 17, 14, 15, 12, 16, 15, 13, 20, 33, 25, 23, 19, 24, 29, 24, 29,
## 26, 9, 7, 9, 9, 14, 6, 6, 9, 7, 9, 11, 8, 8, 11, 11, 8, 14, 24,
## 17, 13, 11, 15, 18, 13, 20, 17, 9, 2, 12, 3, 8, 8, 7, 5, 5, 8,
## 3, 7, 4, 5, 4, 5, 6, 9, 8, 10, 8, 9, 11, 11, 9, 9, 28, 31, 26,
## 27, 34, 41, 32, 21, 32, 27, 27, 30, 16, 32, 26, 37, 26, 29, 30,
## 32, 30, 40, 32, 31, 35, 39, 13, 7, 7, 5, 6, 13, 10, 9, 9, 11,
## 5, 15, 6, 14, 13, 13, 11, 14, 13, 12, 11, 18, 10, 8, 13, 19,
## 15, 24, 19, 22, 28, 28, 22, 12, 23, 16, 22, 15, 10, 18, 13, 24,
## 15, 15, 17, 20, 19, 22, 22, 23, 22, 20, 58, 59, 50, 50, 56, 54,
## 57, 65, 42, 53, 57, 50, 70, 66, 62, 52, 56, 71, 88, 68, 72, 93,
## 87, 86, 94, 70, 37, 39, 28, 25, 33, 35, 38, 44, 30, 36, 43, 36,
## 46, 46, 35, 37, 36, 45, 55, 37, 46, 59, 55, 61, 61, 44, 21, 20,
## 22, 25, 23, 19, 19, 21, 12, 17, 14, 14, 24, 20, 27, 15, 20, 26,
## 33, 31, 26, 34, 32, 25, 33, 26, 42, 46, 54, 49, 34, 43, 52, 50,
## 61, 63, 55, 60, 57, 61, 68, 78, 87, 91, 94, 86, 103, 106, 125,
## 111, 142, 126, 31, 32, 33, 35, 26, 33, 42, 31, 43, 47, 40, 41,
## 40, 45, 50, 54, 64, 60, 69, 65, 66, 73, 101, 79, 103, 92, 11,
## 14, 21, 14, 8, 10, 10, 19, 18, 16, 15, 19, 17, 16, 18, 24, 23,
## 31, 25, 21, 37, 33, 24, 32, 39, 34, 23, 32, 33, 42, 32, 21, 31,
## 19, 22, 12, 30, 42, 24, 21, 23, 34, 29, 39, 30, 34, 36, 22, 41,
## 26, 31, 26, 11, 21, 16, 21, 20, 13, 23, 10, 12, 9, 19, 22, 12,
## 7, 12, 14, 7, 25, 16, 22, 18, 12, 15, 17, 22, 14, 12, 11, 17,
## 21, 12, 8, 8, 9, 10, 3, 11, 20, 12, 14, 11, 20, 22, 14, 14, 12,
## 18, 10, 26, 9, 9, 12, 318, 311, 269, 300, 308, 309, 315, 310,
## 294, 351, 343, 346, 366, 374, 349, 316, 345, 352, 322, 409, 361,
## 410, 387, 395, 412, 412, 177, 195, 156, 182, 190, 175, 198, 178,
## 166, 197, 181, 209, 222, 223, 210, 174, 209, 208, 187, 254, 214,
## 255, 232, 241, 237, 256, 141, 116, 113, 118, 118, 134, 117, 132,
## 128, 154, 162, 137, 144, 151, 139, 142, 136, 144, 135, 155, 147,
## 155, 155, 154, 175, 156, 288, 323, 293, 330, 314, 325, 350, 330,
## 367, 352, 351, 362, 348, 366, 388, 401, 399, 373, 378, 364, 392,
## 404, 388, 392, 377, 394, 160, 154, 159, 176, 172, 162, 189, 206,
## 190, 173, 191, 208, 209, 211, 235, 231, 235, 200, 232, 210, 227,
## 233, 236, 229, 212, 241, 128, 169, 134, 154, 142, 163, 161, 124,
## 177, 179, 160, 154, 139, 155, 153, 170, 164, 173, 146, 154, 165,
## 171, 152, 163, 165, 153, 20, 26, 29, 20, 35, 20, 30, 26, 23,
## 23, 25, 23, 17, 13, 22, 16, 24, 25, 22, 13, 17, 12, 11, 20, 8,
## 17, 12, 18, 16, 15, 16, 16, 12, 11, 9, 10, 8, 15, 18, 15, 6,
## 10, 8, 15, 9, 12, 18, 8, 12, 10, 8, 7, 9, 11, 6, 4, 12, 8, 9,
## 7, 7, 7, 7, 150, 169, 158, 187, 164, 175, 170, 163, 177, 190,
## 208, 188, 178, 176, 187, 207, 218, 209, 217, 210, 206, 73, 84,
## 99, 96, 85, 87, 101, 104, 93, 111, 120, 102, 97, 103, 106, 117,
## 119, 119, 121, 119, 123, 77, 85, 59, 91, 79, 88, 69, 59, 84,
## 79, 88, 86, 81, 73, 81, 90, 99, 90, 96, 91, 83, 81, 87, 81, 98,
## 81, 85, 95, 94, 92, 97, 99, 117, 94, 109, 87, 103, 71, 83, 74,
## 65, 81, 42, 48, 49, 56, 39, 52, 54, 52, 61, 66, 54, 84, 45, 67,
## 57, 60, 44, 57, 46, 37, 55, 39, 39, 32, 42, 42, 33, 41, 42, 31,
## 31, 45, 33, 49, 42, 30, 43, 27, 26, 28, 28, 26, 26, 37, 27, 23,
## 24, 29, 24, 15, 20, 18, 20, 17, 19, 18, 19, 16, 19, 14, 17, 20,
## 14, 9, 25, 16, 8, 12, 16, 13, 8, 13, 7, 9, 9, 7, 9, 10, 10, 12,
## 6, 10, 13, 8, 17, 12, 11, 15, 12, 13, 11, 7, 7, 11, 11, 8, 12,
## 9, 9, 6, 7, 8, 7, 7, 6, 212, 236, 228, 244, 243, 254, 254, 270,
## 284, 324, 320, 345, 378, 343, 347, 362, 420, 431, 533, 535, 572,
## 584, 631, 578, 585, 670, 127, 134, 136, 139, 142, 140, 133, 167,
## 165, 186, 189, 217, 256, 213, 227, 230, 263, 249, 343, 338, 351,
## 373, 375, 384, 377, 416, 85, 102, 92, 105, 101, 114, 121, 103,
## 119, 138, 131, 128, 122, 130, 120, 132, 157, 182, 190, 197, 221,
## 211, 256, 194, 208, 254, 4221, 4125, 4106, 3984, 3961, 3948,
## 3915, 4039, 3893, 3923, 4009, 4062, 4115, 4080, 4147, 4055, 4178,
## 4189, 4120, 4117, 4047, 4035, 4069, 3980, 4095, 3874, 2581, 2446,
## 2448, 2332, 2305, 2225, 2277, 2317, 2186, 2151, 2195, 2162, 2239,
## 2114, 2196, 2107, 2200, 2094, 2127, 2119, 2075, 2081, 2036, 1981,
## 2043, 1938, 1640, 1679, 1658, 1652, 1656, 1723, 1638, 1722, 1707,
## 1772, 1814, 1900, 1876, 1966, 1951, 1948, 1978, 2095, 1993, 1998,
## 1972, 1954, 2033, 1999, 2052, 1936, 116, 136, 147, 148, 161,
## 155, 149, 140, 162, 164, 177, 162, 169, 169, 177, 153, 144, 168,
## 156, 139, 153, 100, 119, 123, 130, 144, 136, 129, 122, 144, 130,
## 144, 141, 145, 146, 146, 128, 120, 146, 132, 111, 133, 16, 17,
## 24, 18, 17, 19, 20, 18, 18, 34, 33, 21, 24, 23, 31, 25, 24, 22,
## 24, 28, 20, 10, 14, 13, 13, 7, 4, 12, 11, 11, 10, 13, 10, 11,
## 12, 16, 14, 14, 14, 14, 13, 23, 14, 16, 19, 19, 18, 776, 743,
## 708, 677, 769, 773, 777, 775, 786, 802, 765, 779, 793, 792, 790,
## 849, 900, 881, 876, 906, 985, 894, 986, 923, 1033, 1038, 9, 11,
## 6, 11, 8, 10, 7, 11, 13, 8, 8, 14, 8, 8, 3, 9, 1, 9, 6, 9, 11,
## 7, 4, 9, 8, 5, 226, 201, 176, 187, 199, 212, 227, 225, 232, 200,
## 221, 239, 224, 247, 200, 231, 238, 245, 223, 237, 273, 231, 266,
## 230, 279, 231, 107, 99, 96, 83, 94, 85, 112, 116, 105, 110, 102,
## 125, 102, 122, 109, 114, 140, 125, 118, 124, 164, 122, 157, 130,
## 153, 123, 119, 102, 80, 104, 105, 127, 115, 109, 127, 90, 119,
## 114, 122, 125, 91, 117, 98, 120, 105, 113, 109, 109, 109, 100,
## 126, 108, 391, 438, 418, 403, 420, 419, 419, 417, 417, 368, 403,
## 397, 437, 352, 361, 397, 371, 418, 410, 375, 432, 416, 420, 425,
## 414, 415, 195, 227, 202, 182, 201, 190, 204, 200, 206, 176, 194,
## 202, 224, 191, 185, 203, 165, 207, 219, 179, 229, 228, 214, 225,
## 223, 237, 196, 211, 216, 221, 219, 229, 215, 217, 211, 192, 209,
## 195, 213, 161, 176, 194, 206, 211, 191, 196, 203, 188, 206, 200,
## 191, 178, 762, 723, 734, 730, 776, 708, 752, 763, 776, 801, 798,
## 765, 786, 831, 746, 811, 824, 813, 828, 850, 816, 820, 833, 873,
## 833, 840, 443, 421, 426, 471, 443, 427, 472, 462, 501, 511, 495,
## 490, 485, 520, 507, 533, 541, 533, 538, 535, 552, 553, 555, 584,
## 526, 550, 319, 302, 308, 259, 333, 281, 280, 301, 275, 290, 303,
## 275, 301, 311, 239, 278, 283, 280, 290, 315, 264, 267, 278, 289,
## 307, 290, 538, 560, 556, 607, 574, 633, 595, 562, 641, 615, 603,
## 567, 713, 642, 691, 669, 702, 742, 742, 713, 749, 719, 781, 811,
## 822, 877, 248, 280, 269, 289, 257, 294, 280, 281, 297, 300, 284,
## 269, 338, 319, 309, 346, 358, 369, 354, 364, 365, 353, 403, 419,
## 423, 424, 290, 280, 287, 318, 317, 339, 315, 281, 344, 315, 319,
## 298, 375, 323, 382, 323, 344, 373, 388, 349, 384, 366, 378, 392,
## 399, 453, 111, 119, 118, 144, 131, 115, 145, 132, 146, 151, 158,
## 158, 164, 171, 185, 195, 176, 190, 180, 176, 174, 162, 185, 173,
## 183, 173, 60, 63, 69, 68, 71, 56, 79, 73, 78, 93, 87, 90, 94,
## 93, 101, 114, 105, 115, 88, 100, 99, 102, 101, 95, 97, 91, 51,
## 56, 49, 76, 60, 59, 66, 59, 68, 58, 71, 68, 70, 78, 84, 81, 71,
## 75, 92, 76, 75, 60, 84, 78, 86, 82, 47, 58, 63, 55, 49, 45, 59,
## 57, 53, 50, 48, 59, 72, 58, 68, 53, 82, 77, 94, 94, 82, 87, 102,
## 103, 112, 127, 27, 27, 28, 35, 27, 27, 34, 33, 36, 24, 27, 36,
## 44, 38, 37, 32, 53, 47, 63, 65, 57, 59, 76, 68, 81, 91, 20, 31,
## 35, 20, 22, 18, 25, 24, 17, 26, 21, 23, 28, 20, 31, 21, 29, 30,
## 31, 29, 25, 28, 26, 35, 31, 36, 758, 699, 710, 680, 650, 649,
## 678, 621, 579, 615, 590, 552, 506, 511, 535, 505, 472, 494, 491,
## 477, 442, 408, 429, 454, 426, 441, 422, 386, 392, 387, 368, 364,
## 365, 388, 370, 356, 342, 338, 304, 300, 323, 334, 279, 306, 265,
## 285, 262, 272, 273, 273, 266, 293, 336, 313, 318, 293, 282, 285,
## 313, 233, 209, 259, 248, 214, 202, 211, 212, 171, 193, 188, 226,
## 192, 180, 136, 156, 181, 160, 148)dput(deaths$MortalityRateAge5To9)## c(3.08279461497437, 4.3271311120727, 3.70627750752838, 3.42997549126604,
## 3.76825247291569, 2.23398076223424, 3.59696938979049, 0.998864623877526,
## 3.04309015661771, 3.08206512061148, 2.4299143281634, 2.4719609006413,
## 1.44387651968004, 2.5714684554291, 2.96753516529171, 2.22553579774331,
## 1.4765596160945, 1.81424326099341, 1.06120687520561, 2.77084105416648,
## 1.36819494041511, 2.00761555500532, 1.65589781123427, 3.65340480721647,
## 2.67276056074517, 2.34975814275116, 3.61269501026608, 3.62157007134493,
## 2.41098928918008, 5.48767103241384, 3.68652268747504, 4.37000181042932,
## 4.47699146173771, 0.649599521894752, 3.95556581072618, 2.66915788068864,
## 3.37698651231587, 2.75628260165515, 0.705412631118573, 2.15625673830231,
## 2.1754106087524, 2.90239955883527, 2.88614865108627, 1.41707289422968,
## 1.38282940725017, 4.07257325541143, 2.01485620642873, 3.28316654847267,
## 1.29765643248294, 3.25000325000325, 2.60931394613071, 1.96274706078628,
## 2.52684775742262, 5.06758896785882, 5.0675247675273, 1.27634047658553,
## 3.8536883008446, 0, 2.67633716495604, 1.36612021857923, 2.08236445542699,
## 3.51736169733806, 1.4284183837446, 2.17307845537583, 2.21777025378684,
## 3.00553017552296, 3.79711267552153, 1.51766946676683, 0, 2.23113021619652,
## 0.72429128098156, 1.41448717767373, 0.69704869582189, 0.682267858361193,
## 2.02939923018123, 4.07489659949879, 2.73936953410173, 2.75755570262519,
## 3.08279461497437, 4.3271311120727, 3.70627750752838, 3.42997549126604,
## 3.76825247291569, 2.23398076223424, 3.59696938979049, 0.998864623877526,
## 3.04309015661771, 3.08206512061148, 2.4299143281634, 2.4719609006413,
## 1.44387651968004, 2.5714684554291, 2.96753516529171, 2.22553579774331,
## 1.4765596160945, 1.81424326099341, 1.06120687520561, 2.77084105416648,
## 1.36819494041511, 2.34221814750621, 1.65589781123427, 3.65340480721647,
## 2.67276056074517, 2.34975814275116, 3.61269501026608, 3.62157007134493,
## 2.41098928918008, 5.48767103241384, 3.68652268747504, 4.37000181042932,
## 4.47699146173771, 0.649599521894752, 3.95556581072618, 2.66915788068864,
## 3.37698651231587, 2.75628260165515, 0.705412631118573, 2.15625673830231,
## 2.1754106087524, 2.90239955883527, 2.88614865108627, 1.41707289422968,
## 1.38282940725017, 4.07257325541143, 2.01485620642873, 3.93979985816721,
## 1.29765643248294, 3.25000325000325, 2.60931394613071, 1.96274706078628,
## 2.52684775742262, 5.06758896785882, 5.0675247675273, 1.27634047658553,
## 3.8536883008446, 0, 2.67633716495604, 1.36612021857923, 2.08236445542699,
## 3.51736169733806, 1.4284183837446, 2.17307845537583, 2.21777025378684,
## 3.00553017552296, 3.79711267552153, 1.51766946676683, 0, 2.23113021619652,
## 0.72429128098156, 1.41448717767373, 0.69704869582189, 0.682267858361193,
## 2.02939923018123, 4.07489659949879, 2.73936953410173, 2.75755570262519,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.342451680067942, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.667289470172161,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0.309080793719478, 0, 0, 0, 0, 0.326997217253681, 0, 0, 0,
## 0.347130618309057, 0, 0, 0, 0, 0, 0.369139904023625, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.675397302463174,
## 0, 0, 0, 0, 0, 0.721537162771569, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0.633448620982352, 0, 0, 0, 0, 0.66908429123901, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.308279461497437,
## 0.927242381158435, 0.308856458960698, 0, 0.31402103940964, 0.319140108890605,
## 0.326997217253681, 0, 0, 0, 1.04139185492717, 0, 0.360969129920009,
## 0, 0.370941895661464, 0, 0.369139904023625, 0, 0, 0, 0, 0, 0.331179562246855,
## 0, 0, 0, 0.602115835044346, 0, 0.602747322295021, 0, 0, 0.624285972918475,
## 0, 0, 0, 0, 0.675397302463174, 0, 0, 0, 0, 0, 0.721537162771569,
## 0, 0, 0, 0, 0, 0.648828216241468, 0, 0, 0, 0, 1.90034586294706,
## 0, 0, 0.6422813834741, 0, 0.66908429123901, 0, 0, 0, 1.4284183837446,
## 0, 0.739256751262281, 0, 0.759422535104307, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0.308279461497437, 0.618161587438957, 0.308856458960698,
## 0, 0.31402103940964, 0.319140108890605, 0, 0, 0, 0, 0.694261236618115,
## 0, 0.360969129920009, 0, 0.370941895661464, 0, 0, 0, 0, 0, 0,
## 0, 0.331179562246855, 0, 0, 0, 0.602115835044346, 0, 0.602747322295021,
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)dput(deaths$Year)## c(1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
## 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
## 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
## 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
## 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
## 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997,
## 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
## 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
## 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,
## 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
## 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000,
## 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
## 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996,
## 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
## 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
## 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,
## 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
## 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999,
## 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
## 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995,
## 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
## 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
## 2018, 2019, 2020, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
## 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
## 2014, 2015, 2016, 2017, 2018, 2019, 2020, 1995, 1996, 1997, 1998,
## 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
## 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
## )count(deaths, DeathsAllAges, sort = TRUE)## # A tibble: 956 × 2
## DeathsAllAges n
## <dbl> <int>
## 1 9 40
## 2 8 37
## 3 11 37
## 4 12 36
## 5 7 33
## 6 13 33
## 7 20 30
## 8 24 29
## 9 14 28
## 10 22 28
## # ℹ 946 more rowscount(deaths, MortalityRateAge5To9, sort = TRUE)## # A tibble: 200 × 2
## MortalityRateAge5To9 n
## <dbl> <int>
## 1 0 2667
## 2 0.675 7
## 3 0.331 6
## 4 0.347 6
## 5 0.319 5
## 6 0.333 5
## 7 0.603 5
## 8 0.624 5
## 9 0.309 4
## 10 0.327 4
## # ℹ 190 more rowscount(deaths, Year, sort = TRUE)## # A tibble: 26 × 2
## Year n
## <dbl> <int>
## 1 2000 123
## 2 2001 123
## 3 2002 123
## 4 2003 123
## 5 2004 123
## 6 2005 123
## 7 2006 123
## 8 2007 123
## 9 2008 123
## 10 2009 123
## # ℹ 16 more rowscount(deaths, Country, sort = TRUE)## # A tibble: 1 × 2
## Country n
## <chr> <int>
## 1 S92000003 3123Exploring Data set
library(readr)
library(dplyr)
deaths_cols <- cols(
CancerSiteICD10Code = col_skip(),
CancerSite = col_skip(), Sex = col_skip(), SexQF = col_skip(),
DeathsAgeUnder5 = col_skip(), DeathsAge10To14 = col_skip(), DeathsAge15To19 = col_skip(), DeathsAge20To24 = col_skip(), DeathsAge25To29 = col_skip(), DeathsAge30To34 = col_skip(), DeathsAge35To39 = col_skip(), DeathsAge40To44 = col_skip(), DeathsAge45To49 = col_skip(), DeathsAge55To59 = col_skip(), DeathsAge60To64 = col_skip(), DeathsAge65To69 = col_skip(), DeathsAge70To74 = col_skip(), DeathsAge75To79 = col_skip(), DeathsAge80To84 = col_skip(), DeathsAge85To89 = col_skip(), DeathsAge90AndOver = col_skip(),
Year = col_integer(), DeathsAllAges = col_integer(), Country = col_character()
)
# Correctly read the CSV file with column specifications
deaths_cols <- read_csv("opendata_mort9520_scotland.csv", col_types = deaths_cols)
#cols(
#Year = col_integer(),
# specify other columns as needed
#DeathsAllAges = col_integer(),
#Country = col_character()
#))
# Check the class of the object
class(deaths_cols) # Should return "tbl_df" "tbl" "data.frame"## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"# Now you can use mutate on the resulting dataframe
deaths_cols <- deaths_cols %>%
mutate(Year = factor(Year, levels = as.character(1995:2020)))
deaths_cols <- deaths_cols %>%
mutate(DeathsAllAges = factor(DeathsAllAges, levels = as.character(1:3123)))
# Verify the changes
str(deaths_cols)## tibble [3,123 × 39] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:3123] "S92000003" "S92000003" "S92000003" "S92000003" ...
## $ Year : Factor w/ 26 levels "1995","1996",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ DeathsAge5To9 : num [1:3123] 10 14 12 11 12 7 11 3 9 9 ...
## $ DeathsAge50To54 : num [1:3123] 644 679 645 642 701 600 663 640 541 535 ...
## $ DeathsAllAges : Factor w/ 3123 levels "1","2","3","4",..: NA NA NA NA NA NA NA NA NA NA ...
## $ MortalityRateAgeUnder5 : num [1:3123] 2.2 3.22 1.32 3.37 2.07 ...
## $ MortalityRateAge5To9 : num [1:3123] 3.08 4.33 3.71 3.43 3.77 ...
## $ MortalityRateAge10To14 : num [1:3123] 2.18 3.13 1.87 2.47 2.46 ...
## $ MortalityRateAge15To19 : num [1:3123] 2.92 4.83 3.49 4.4 5.04 ...
## $ MortalityRateAge20To24 : num [1:3123] 5.04 5.58 4.35 4.52 6.18 ...
## $ MortalityRateAge25To29 : num [1:3123] 7.78 6.64 6.6 6.61 7.8 ...
## $ MortalityRateAge30To34 : num [1:3123] 14 16.2 13.8 13.2 13.6 ...
## $ MortalityRateAge35To39 : num [1:3123] 23.2 28.3 29.6 25.1 24.8 ...
## $ MortalityRateAge40To44 : num [1:3123] 64.9 52.1 56.4 58.9 48.2 ...
## $ MortalityRateAge45To49 : num [1:3123] 113.8 111.8 99.7 108.9 99.2 ...
## $ MortalityRateAge50To54 : num [1:3123] 222 229 203 194 207 ...
## $ MortalityRateAge55To59 : num [1:3123] 362 373 369 337 352 ...
## $ MortalityRateAge60To64 : num [1:3123] 601 570 552 558 524 ...
## $ MortalityRateAge65To69 : num [1:3123] 939 911 865 855 820 ...
## $ MortalityRateAge70To74 : num [1:3123] 1295 1272 1250 1242 1240 ...
## $ MortalityRateAge75To79 : num [1:3123] 1605 1614 1609 1568 1610 ...
## $ MortalityRateAge80To84 : num [1:3123] 1907 1949 1955 1887 1928 ...
## $ MortalityRateAge85To89 : num [1:3123] 2240 2186 2123 2130 2115 ...
## $ MortalityRateAge90AndOver : num [1:3123] 2350 2242 2259 2285 2288 ...
## $ CrudeRate : num [1:3123] 298 297 292 290 291 ...
## $ CrudeRateLower95pcConfidenceInterval: num [1:3123] 293 292 287 285 286 ...
## $ CrudeRateUpper95pcConfidenceInterval: num [1:3123] 302 302 297 294 295 ...
## $ EASR : num [1:3123] 402 396 387 379 379 ...
## $ EASRLower95pcConfidenceInterval : num [1:3123] 395 390 380 372 372 ...
## $ EASRLower95pcConfidenceIntervalQF : chr [1:3123] NA NA NA NA ...
## $ EASRUpper95pcConfidenceInterval : num [1:3123] 408 403 394 385 385 ...
## $ EASRUpper95pcConfidenceIntervalQF : chr [1:3123] NA NA NA NA ...
## $ WASR : num [1:3123] 162 160 154 152 150 ...
## $ WASRLower95pcConfidenceInterval : num [1:3123] 159 157 151 149 147 ...
## $ WASRLower95pcConfidenceIntervalQF : chr [1:3123] NA NA NA NA ...
## $ WASRUpper95pcConfidenceInterval : num [1:3123] 165 163 157 154 153 ...
## $ WASRUpper95pcConfidenceIntervalQF : chr [1:3123] NA NA NA NA ...
## $ StandardisedMortalityRatio : num [1:3123] 100 100 100 100 100 100 100 100 100 100 ...
## $ StandardisedMortalityRatioQF : logi [1:3123] NA NA NA NA NA NA ...Counting
count(deaths, Country)## # A tibble: 1 × 2
## Country n
## <chr> <int>
## 1 S92000003 3123count(deaths, Year, sort = TRUE)## # A tibble: 26 × 2
## Year n
## <dbl> <int>
## 1 2000 123
## 2 2001 123
## 3 2002 123
## 4 2003 123
## 5 2004 123
## 6 2005 123
## 7 2006 123
## 8 2007 123
## 9 2008 123
## 10 2009 123
## # ℹ 16 more rowscount(deaths, DeathsAllAges, sort = TRUE)## # A tibble: 956 × 2
## DeathsAllAges n
## <dbl> <int>
## 1 9 40
## 2 8 37
## 3 11 37
## 4 12 36
## 5 7 33
## 6 13 33
## 7 20 30
## 8 24 29
## 9 14 28
## 10 22 28
## # ℹ 946 more rowslibrary(dplyr)
years <- deaths$year## Warning: Unknown or uninitialised column: `year`.deaths$Year## [1] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [15] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [29] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [43] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [57] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [71] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [85] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [99] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [113] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [127] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [141] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [155] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [169] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [183] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [197] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [211] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [225] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [239] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [253] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [267] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [281] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [295] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [309] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [323] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [337] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [351] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [365] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [379] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [393] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [407] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [421] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [435] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [449] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [463] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [477] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [491] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [505] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [519] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [533] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [547] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [561] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [575] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [589] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [603] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [617] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [631] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [645] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [659] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [673] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [687] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [701] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [715] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [729] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [743] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [757] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [771] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [785] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [799] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [813] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [827] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [841] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [855] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [869] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [883] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [897] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [911] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [925] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [939] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [953] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [967] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [981] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [995] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [1009] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [1023] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [1037] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [1051] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [1065] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [1079] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [1093] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [1107] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [1121] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [1135] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [1149] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [1163] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [1177] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [1191] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [1205] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [1219] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [1233] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [1247] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [1261] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [1275] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [1289] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [1303] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [1317] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [1331] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [1345] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [1359] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [1373] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [1387] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [1401] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [1415] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [1429] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [1443] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [1457] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [1471] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [1485] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [1499] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [1513] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [1527] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [1541] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [1555] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [1569] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [1583] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [1597] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [1611] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [1625] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [1639] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [1653] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [1667] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [1681] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [1695] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [1709] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [1723] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [1737] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [1751] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [1765] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [1779] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [1793] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [1807] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [1821] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [1835] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [1849] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [1863] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [1877] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [1891] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [1905] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [1919] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [1933] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [1947] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [1961] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [1975] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [1989] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [2003] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [2017] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2000 2001
## [2031] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2045] 2016 2017 2018 2019 2020 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [2059] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2000 2001
## [2073] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2087] 2016 2017 2018 2019 2020 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [2101] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2000 2001
## [2115] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2129] 2016 2017 2018 2019 2020 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [2143] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2000 2001
## [2157] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2171] 2016 2017 2018 2019 2020 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [2185] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2000 2001
## [2199] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2213] 2016 2017 2018 2019 2020 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [2227] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2000 2001
## [2241] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2255] 2016 2017 2018 2019 2020 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [2269] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996
## [2283] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
## [2297] 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998
## [2311] 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## [2325] 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000
## [2339] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [2353] 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002
## [2367] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [2381] 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [2395] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
## [2409] 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## [2423] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [2437] 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
## [2451] 2014 2015 2016 2017 2018 2019 2020 2000 2001 2002 2003 2004 2005 2006
## [2465] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## [2479] 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
## [2493] 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001
## [2507] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2521] 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003
## [2535] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
## [2549] 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [2563] 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
## [2577] 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
## [2591] 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995
## [2605] 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
## [2619] 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997
## [2633] 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
## [2647] 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999
## [2661] 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
## [2675] 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001
## [2689] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2703] 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003
## [2717] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
## [2731] 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [2745] 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
## [2759] 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
## [2773] 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995
## [2787] 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
## [2801] 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997
## [2815] 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
## [2829] 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999
## [2843] 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
## [2857] 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001
## [2871] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [2885] 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003
## [2899] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
## [2913] 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [2927] 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
## [2941] 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
## [2955] 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995
## [2969] 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
## [2983] 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997
## [2997] 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
## [3011] 2012 2013 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999
## [3025] 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
## [3039] 2014 2015 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001
## [3053] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## [3067] 2016 2017 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003
## [3081] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
## [3095] 2018 2019 2020 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [3109] 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
## [3123] 2020country <- deaths$country## Warning: Unknown or uninitialised column: `country`.deaths$Country## [1] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [7] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [13] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [19] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [25] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [31] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [37] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [43] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [49] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [55] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [61] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [67] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [73] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [79] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [85] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [91] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [97] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [103] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [109] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [115] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [121] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [127] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [133] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [139] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [145] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [151] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [157] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [163] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [169] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [175] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [181] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [187] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [193] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [199] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [205] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [211] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [217] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [223] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [229] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [235] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [241] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [247] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [253] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [259] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [265] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [271] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [277] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [283] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [289] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [295] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [301] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [307] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [313] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [319] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [325] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [331] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [337] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [343] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [349] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [355] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [361] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [367] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [373] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [379] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [385] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [391] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [397] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [403] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [409] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [415] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [421] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [427] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [433] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [439] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [445] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [451] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [457] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [463] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [469] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [475] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [481] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [487] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [493] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [499] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [505] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [511] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [517] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [523] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [529] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [535] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [541] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [547] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [553] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [559] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [565] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [571] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [577] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [583] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [589] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [595] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [601] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [607] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [613] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [619] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [625] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [631] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [637] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [643] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [649] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [655] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [661] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [667] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [673] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [679] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [685] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [691] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [697] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [703] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [709] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [715] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [721] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [727] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [733] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [739] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [745] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [751] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [757] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [763] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [769] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [775] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [781] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [787] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [793] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [799] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [805] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [811] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [817] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [823] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [829] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [835] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [841] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [847] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [853] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [859] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [865] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [871] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [877] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [883] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [889] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [895] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [901] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [907] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [913] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [919] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [925] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [931] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [937] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [943] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [949] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [955] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [961] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [967] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [973] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [979] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [985] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [991] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [997] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1003] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1009] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1015] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1021] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1027] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1033] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1039] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1045] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1051] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1057] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1063] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1069] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1075] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1081] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1087] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1093] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1099] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1105] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1111] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1117] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1123] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1129] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1135] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1141] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1147] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1153] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1159] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1165] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1171] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1177] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1183] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1189] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1195] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1201] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1207] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1213] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1219] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1225] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1231] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1237] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1243] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1249] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1255] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1261] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1267] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1273] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1279] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1285] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1291] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1297] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1303] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1309] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1315] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1321] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1327] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1333] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1339] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1345] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1351] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1357] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1363] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1369] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1375] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1381] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1387] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1393] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1399] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1405] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1411] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1417] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1423] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1429] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1435] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1441] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1447] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1453] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1459] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1465] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1471] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1477] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1483] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1489] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1495] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1501] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1507] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1513] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1519] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1525] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1531] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1537] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1543] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1549] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1555] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1561] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1567] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1573] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1579] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1585] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1591] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1597] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1603] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1609] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1615] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1621] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1627] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1633] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1639] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1645] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1651] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1657] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1663] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1669] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1675] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1681] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1687] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1693] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1699] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1705] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1711] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1717] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1723] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1729] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1735] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1741] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1747] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1753] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1759] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1765] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1771] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1777] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1783] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1789] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1795] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1801] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1807] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1813] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1819] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1825] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1831] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1837] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1843] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1849] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1855] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1861] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1867] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1873] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1879] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1885] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1891] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1897] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1903] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1909] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1915] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1921] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1927] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1933] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1939] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1945] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1951] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1957] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1963] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1969] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1975] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1981] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1987] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1993] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [1999] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2005] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2011] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2017] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2023] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2029] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2035] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2041] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2047] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2053] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2059] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2065] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2071] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2077] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2083] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2089] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2095] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2101] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2107] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2113] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2119] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2125] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2131] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2137] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2143] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2149] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2155] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2161] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2167] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2173] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2179] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2185] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2191] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2197] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2203] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2209] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2215] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2221] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2227] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2233] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2239] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2245] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2251] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2257] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2263] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2269] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2275] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2281] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2287] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2293] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2299] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2305] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2311] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2317] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2323] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2329] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2335] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2341] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2347] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2353] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2359] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2365] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2371] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2377] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2383] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2389] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2395] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2401] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2407] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2413] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2419] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2425] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2431] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2437] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2443] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2449] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2455] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2461] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2467] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2473] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2479] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2485] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2491] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2497] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2503] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2509] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2515] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2521] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2527] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2533] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2539] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2545] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2551] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2557] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2563] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2569] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2575] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2581] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2587] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2593] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2599] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2605] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2611] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2617] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2623] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2629] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2635] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2641] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2647] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2653] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2659] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2665] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2671] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2677] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2683] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2689] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2695] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2701] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2707] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2713] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2719] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2725] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2731] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2737] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2743] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2749] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2755] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2761] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2767] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2773] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2779] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2785] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2791] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2797] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2803] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2809] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2815] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2821] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2827] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2833] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2839] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2845] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2851] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2857] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2863] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2869] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2875] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2881] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2887] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2893] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2899] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2905] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2911] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2917] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2923] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2929] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2935] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2941] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2947] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2953] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2959] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2965] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2971] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2977] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2983] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2989] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [2995] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3001] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3007] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3013] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3019] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3025] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3031] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3037] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3043] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3049] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3055] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3061] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3067] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3073] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3079] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3085] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3091] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3097] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3103] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3109] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3115] "S92000003" "S92000003" "S92000003" "S92000003" "S92000003" "S92000003"
## [3121] "S92000003" "S92000003" "S92000003"deathsallages <- deaths$deaths_all_ages## Warning: Unknown or uninitialised column: `deaths_all_ages`.deaths$DeathsAllAges## [1] 15192 15120 14838 14708 14747 14913 15137 14994 15063 14997 15087 15025
## [13] 15202 15211 15119 15270 15375 15787 15764 15746 16011 15813 16105 16153
## [25] 16366 16184 7753 7684 7521 7420 7451 7453 7738 7737 7602 7640
## [37] 7637 7692 7739 7691 7694 7775 7952 7931 8047 8081 8321 8243
## [49] 8327 8292 8375 8335 7439 7436 7317 7288 7296 7460 7399 7257
## [61] 7461 7357 7450 7333 7463 7520 7425 7495 7423 7856 7717 7665
## [73] 7690 7570 7778 7861 7991 7849 15239 15178 14901 14763 14796 14958
## [85] 15196 15051 15116 15047 15135 15084 15274 15269 15187 15323 15457 15864
## [97] 15858 15840 16093 15900 16207 16256 16478 16311 7780 7711 7549 7455
## [109] 7478 7480 7772 7770 7638 7664 7664 7728 7783 7729 7731 7807
## [121] 8005 7978 8110 8146 8378 8302 8403 8360 8456 8426 7459 7467
## [133] 7352 7308 7318 7478 7424 7281 7478 7383 7471 7356 7491 7540
## [145] 7456 7516 7452 7886 7748 7694 7715 7598 7804 7896 8022 7885
## [157] 508 505 486 469 449 425 463 418 457 443 457 425
## [169] 477 457 429 440 472 482 531 480 522 519 477 491
## [181] 513 466 328 314 297 284 291 256 287 266 277 275
## [193] 281 281 299 276 258 269 294 303 318 300 329 335
## [205] 311 308 349 269 180 191 189 185 158 169 176 152
## [217] 180 168 176 144 178 181 171 171 178 179 213 180
## [229] 193 184 166 183 164 197 30 22 11 14 28 38
## [241] 22 27 24 29 19 23 20 42 35 24 37 28
## [253] 37 32 32 37 39 36 52 42 17 10 5 8
## [265] 17 20 13 16 15 13 11 13 14 26 18 13
## [277] 24 15 21 15 20 26 23 19 27 20 13 12
## [289] 6 6 11 18 9 11 9 16 8 10 6 16
## [301] 17 11 13 13 16 17 12 11 16 17 25 22
## [313] 85 82 74 63 97 99 88 91 72 97 93 76
## [325] 100 107 104 101 112 94 115 99 107 110 122 118
## [337] 118 128 47 32 42 26 49 63 46 45 33 41
## [349] 51 42 53 57 58 47 60 53 59 59 62 66
## [361] 66 63 61 66 38 50 32 37 48 36 42 46
## [373] 39 56 42 34 47 50 46 54 52 41 56 40
## [385] 45 44 56 55 57 62 55 60 63 49 69 61
## [397] 66 64 48 68 74 53 80 65 69 77 75 66
## [409] 78 67 75 73 83 82 66 86 30 22 37 18
## [421] 32 43 33 29 18 28 40 29 39 31 40 34
## [433] 36 38 38 44 42 40 43 44 34 46 25 38
## [445] 26 31 37 18 33 35 30 40 34 24 41 34
## [457] 29 43 39 28 40 23 33 33 40 38 32 40
## [469] 296 262 306 274 332 327 329 335 327 337 353 337
## [481] 339 344 345 359 340 376 388 388 402 379 382 419
## [493] 389 355 162 161 157 151 182 201 181 176 185 194
## [505] 207 213 196 191 197 208 190 224 232 209 237 229
## [517] 229 227 210 201 134 101 149 123 150 126 148 159
## [529] 142 143 146 124 143 153 148 151 150 152 156 179
## [541] 165 150 153 192 179 154 298 270 310 279 333 335
## [553] 341 340 332 347 361 347 347 350 350 367 347 388
## [565] 397 393 410 391 394 421 395 359 164 165 159 153
## [577] 183 206 187 179 187 198 209 220 201 193 200 211
## [589] 192 230 238 211 241 234 236 227 211 203 134 105
## [601] 151 126 150 129 154 161 145 149 152 127 146 157
## [613] 150 156 155 158 159 182 169 157 158 194 184 156
## [625] 1249 1200 1161 1147 1136 1122 1150 1110 1149 1093 1151 1112
## [637] 1067 1050 1010 1032 1041 1071 1020 976 992 1029 954 1001
## [649] 995 974 5 10 7 5 7 6 7 5 11 11
## [661] 7 4 5 7 8 10 5 8 7 10 3 9
## [673] 8 8 10 5 1244 1190 1154 1142 1129 1116 1143 1105
## [685] 1138 1082 1144 1108 1062 1043 1002 1022 1036 1063 1013 966
## [697] 989 1020 946 993 985 969 1708 1729 1670 1639 1662 1596
## [709] 1615 1555 1582 1550 1550 1550 1539 1565 1555 1501 1526 1621
## [721] 1578 1525 1565 1617 1687 1743 1742 1696 807 865 889 848
## [733] 870 839 835 842 830 844 855 835 812 829 825 782
## [745] 824 837 871 786 844 857 884 941 926 908 901 864
## [757] 781 791 792 757 780 713 752 706 695 715 727 736
## [769] 730 719 702 784 707 739 721 760 803 802 816 788
## [781] 1144 1189 1065 996 1018 1052 1062 975 966 917 966 922
## [793] 899 940 846 857 893 917 923 900 928 956 971 1047
## [805] 989 982 490 551 526 487 490 515 513 507 488 479
## [817] 485 464 443 500 416 412 454 464 471 432 462 464
## [829] 473 514 485 470 654 638 539 509 528 537 549 468
## [841] 478 438 481 458 456 440 430 445 439 453 452 468
## [853] 466 492 498 533 504 512 564 540 605 643 644 544
## [865] 553 580 616 633 584 628 640 625 709 644 633 704
## [877] 655 625 637 661 716 696 753 714 317 314 363 361
## [889] 380 324 322 335 342 365 370 371 369 329 409 370
## [901] 370 373 400 354 382 393 411 427 441 438 247 226
## [913] 242 282 264 220 231 245 274 268 214 257 271 296
## [925] 300 274 263 331 255 271 255 268 305 269 312 276
## [937] 147 138 144 145 122 117 113 100 120 102 127 92
## [949] 105 102 107 99 108 112 91 88 116 106 105 80
## [961] 100 109 78 82 84 92 122 102 123 133 96 113
## [973] 105 104 82 120 124 152 117 148 165 162 184 183
## [985] 170 192 201 187 368 408 413 405 403 409 378 413
## [997] 419 429 366 400 381 411 400 387 363 383 396 375
## [1009] 362 349 404 387 379 367 245 243 250 255 260 245
## [1021] 263 252 267 245 263 233 231 265 260 302 262 306
## [1033] 293 300 346 336 317 321 351 344 7 9 11 14
## [1045] 7 9 8 9 5 6 12 8 7 7 5 6
## [1057] 9 12 9 10 13 13 12 16 16 12 38 36
## [1069] 27 24 40 23 31 30 30 26 39 26 42 47
## [1081] 45 41 35 41 36 49 51 35 36 47 47 49
## [1093] 400 346 358 394 366 358 353 375 365 364 360 351
## [1105] 356 378 396 380 426 422 451 429 452 489 521 474
## [1117] 552 495 282 237 231 275 259 252 264 266 252 259
## [1129] 260 244 240 274 283 266 293 291 312 299 314 342
## [1141] 377 332 396 333 118 109 127 119 107 106 89 109
## [1153] 113 105 100 107 116 104 113 114 133 131 139 130
## [1165] 138 147 144 142 156 162 115 99 91 128 109 112
## [1177] 118 122 103 100 106 97 72 114 102 94 103 93
## [1189] 110 108 127 111 124 106 107 113 94 74 72 102
## [1201] 81 90 97 103 76 76 81 72 59 95 85 70
## [1213] 72 72 82 86 100 91 97 79 90 74 21 25
## [1225] 19 26 28 22 21 19 27 24 25 25 13 19
## [1237] 17 24 31 21 28 22 27 20 27 27 17 39
## [1249] 270 231 251 255 248 232 226 236 255 251 242 247
## [1261] 277 259 281 280 310 315 324 308 311 358 380 349
## [1273] 427 369 184 155 146 167 174 155 160 154 172 176
## [1285] 171 166 178 177 190 192 212 209 219 206 204 239
## [1297] 268 241 296 254 86 76 105 88 74 77 66 82
## [1309] 83 75 71 81 99 82 91 88 98 106 105 102
## [1321] 107 119 112 108 131 115 70 62 58 67 73 70
## [1333] 59 52 71 64 65 63 76 66 72 59 83 50
## [1345] 63 67 60 69 71 58 86 71 46 40 32 50
## [1357] 53 43 43 32 40 43 43 39 44 41 45 37
## [1369] 47 28 38 41 36 35 51 34 54 48 24 22
## [1381] 26 17 20 27 16 20 31 21 22 24 32 25
## [1393] 27 22 36 22 25 26 24 34 20 24 32 23
## [1405] 128 121 108 117 129 124 116 117 113 117 122 113
## [1417] 146 132 134 111 139 121 151 135 132 162 158 144
## [1429] 180 141 83 79 60 75 86 78 81 76 70 79
## [1441] 86 75 90 87 80 74 83 73 93 78 82 94
## [1453] 106 95 115 92 45 42 48 42 43 46 35 41
## [1465] 43 38 36 38 56 45 54 37 56 48 58 57
## [1477] 50 68 52 49 65 49 18 9 21 12 22 14
## [1489] 13 14 12 17 14 15 12 16 15 13 20 33
## [1501] 25 23 19 24 29 24 29 26 9 7 9 9
## [1513] 14 6 6 9 7 9 11 8 8 11 11 8
## [1525] 14 24 17 13 11 15 18 13 20 17 9 2
## [1537] 12 3 8 8 7 5 5 8 3 7 4 5
## [1549] 4 5 6 9 8 10 8 9 11 11 9 9
## [1561] 28 31 26 27 34 41 32 21 32 27 27 30
## [1573] 16 32 26 37 26 29 30 32 30 40 32 31
## [1585] 35 39 13 7 7 5 6 13 10 9 9 11
## [1597] 5 15 6 14 13 13 11 14 13 12 11 18
## [1609] 10 8 13 19 15 24 19 22 28 28 22 12
## [1621] 23 16 22 15 10 18 13 24 15 15 17 20
## [1633] 19 22 22 23 22 20 58 59 50 50 56 54
## [1645] 57 65 42 53 57 50 70 66 62 52 56 71
## [1657] 88 68 72 93 87 86 94 70 37 39 28 25
## [1669] 33 35 38 44 30 36 43 36 46 46 35 37
## [1681] 36 45 55 37 46 59 55 61 61 44 21 20
## [1693] 22 25 23 19 19 21 12 17 14 14 24 20
## [1705] 27 15 20 26 33 31 26 34 32 25 33 26
## [1717] 42 46 54 49 34 43 52 50 61 63 55 60
## [1729] 57 61 68 78 87 91 94 86 103 106 125 111
## [1741] 142 126 31 32 33 35 26 33 42 31 43 47
## [1753] 40 41 40 45 50 54 64 60 69 65 66 73
## [1765] 101 79 103 92 11 14 21 14 8 10 10 19
## [1777] 18 16 15 19 17 16 18 24 23 31 25 21
## [1789] 37 33 24 32 39 34 23 32 33 42 32 21
## [1801] 31 19 22 12 30 42 24 21 23 34 29 39
## [1813] 30 34 36 22 41 26 31 26 11 21 16 21
## [1825] 20 13 23 10 12 9 19 22 12 7 12 14
## [1837] 7 25 16 22 18 12 15 17 22 14 12 11
## [1849] 17 21 12 8 8 9 10 3 11 20 12 14
## [1861] 11 20 22 14 14 12 18 10 26 9 9 12
## [1873] 318 311 269 300 308 309 315 310 294 351 343 346
## [1885] 366 374 349 316 345 352 322 409 361 410 387 395
## [1897] 412 412 177 195 156 182 190 175 198 178 166 197
## [1909] 181 209 222 223 210 174 209 208 187 254 214 255
## [1921] 232 241 237 256 141 116 113 118 118 134 117 132
## [1933] 128 154 162 137 144 151 139 142 136 144 135 155
## [1945] 147 155 155 154 175 156 288 323 293 330 314 325
## [1957] 350 330 367 352 351 362 348 366 388 401 399 373
## [1969] 378 364 392 404 388 392 377 394 160 154 159 176
## [1981] 172 162 189 206 190 173 191 208 209 211 235 231
## [1993] 235 200 232 210 227 233 236 229 212 241 128 169
## [2005] 134 154 142 163 161 124 177 179 160 154 139 155
## [2017] 153 170 164 173 146 154 165 171 152 163 165 153
## [2029] 20 26 29 20 35 20 30 26 23 23 25 23
## [2041] 17 13 22 16 24 25 22 13 17 12 11 20
## [2053] 8 17 12 18 16 15 16 16 12 11 9 10
## [2065] 8 15 18 15 6 10 8 15 9 12 18 8
## [2077] 12 10 8 7 9 11 6 4 12 8 9 7
## [2089] 7 7 7 150 169 158 187 164 175 170 163 177
## [2101] 190 208 188 178 176 187 207 218 209 217 210 206
## [2113] 73 84 99 96 85 87 101 104 93 111 120 102
## [2125] 97 103 106 117 119 119 121 119 123 77 85 59
## [2137] 91 79 88 69 59 84 79 88 86 81 73 81
## [2149] 90 99 90 96 91 83 81 87 81 98 81 85
## [2161] 95 94 92 97 99 117 94 109 87 103 71 83
## [2173] 74 65 81 42 48 49 56 39 52 54 52 61
## [2185] 66 54 84 45 67 57 60 44 57 46 37 55
## [2197] 39 39 32 42 42 33 41 42 31 31 45 33
## [2209] 49 42 30 43 27 26 28 28 26 26 37 27
## [2221] 23 24 29 24 15 20 18 20 17 19 18 19
## [2233] 16 19 14 17 20 14 9 25 16 8 12 16
## [2245] 13 8 13 7 9 9 7 9 10 10 12 6
## [2257] 10 13 8 17 12 11 15 12 13 11 7 7
## [2269] 11 11 8 12 9 9 6 7 8 7 7 6
## [2281] 212 236 228 244 243 254 254 270 284 324 320 345
## [2293] 378 343 347 362 420 431 533 535 572 584 631 578
## [2305] 585 670 127 134 136 139 142 140 133 167 165 186
## [2317] 189 217 256 213 227 230 263 249 343 338 351 373
## [2329] 375 384 377 416 85 102 92 105 101 114 121 103
## [2341] 119 138 131 128 122 130 120 132 157 182 190 197
## [2353] 221 211 256 194 208 254 4221 4125 4106 3984 3961 3948
## [2365] 3915 4039 3893 3923 4009 4062 4115 4080 4147 4055 4178 4189
## [2377] 4120 4117 4047 4035 4069 3980 4095 3874 2581 2446 2448 2332
## [2389] 2305 2225 2277 2317 2186 2151 2195 2162 2239 2114 2196 2107
## [2401] 2200 2094 2127 2119 2075 2081 2036 1981 2043 1938 1640 1679
## [2413] 1658 1652 1656 1723 1638 1722 1707 1772 1814 1900 1876 1966
## [2425] 1951 1948 1978 2095 1993 1998 1972 1954 2033 1999 2052 1936
## [2437] 116 136 147 148 161 155 149 140 162 164 177 162
## [2449] 169 169 177 153 144 168 156 139 153 100 119 123
## [2461] 130 144 136 129 122 144 130 144 141 145 146 146
## [2473] 128 120 146 132 111 133 16 17 24 18 17 19
## [2485] 20 18 18 34 33 21 24 23 31 25 24 22
## [2497] 24 28 20 10 14 13 13 7 4 12 11 11
## [2509] 10 13 10 11 12 16 14 14 14 14 13 23
## [2521] 14 16 19 19 18 776 743 708 677 769 773 777
## [2533] 775 786 802 765 779 793 792 790 849 900 881 876
## [2545] 906 985 894 986 923 1033 1038 9 11 6 11 8
## [2557] 10 7 11 13 8 8 14 8 8 3 9 1
## [2569] 9 6 9 11 7 4 9 8 5 226 201 176
## [2581] 187 199 212 227 225 232 200 221 239 224 247 200
## [2593] 231 238 245 223 237 273 231 266 230 279 231 107
## [2605] 99 96 83 94 85 112 116 105 110 102 125 102
## [2617] 122 109 114 140 125 118 124 164 122 157 130 153
## [2629] 123 119 102 80 104 105 127 115 109 127 90 119
## [2641] 114 122 125 91 117 98 120 105 113 109 109 109
## [2653] 100 126 108 391 438 418 403 420 419 419 417 417
## [2665] 368 403 397 437 352 361 397 371 418 410 375 432
## [2677] 416 420 425 414 415 195 227 202 182 201 190 204
## [2689] 200 206 176 194 202 224 191 185 203 165 207 219
## [2701] 179 229 228 214 225 223 237 196 211 216 221 219
## [2713] 229 215 217 211 192 209 195 213 161 176 194 206
## [2725] 211 191 196 203 188 206 200 191 178 762 723 734
## [2737] 730 776 708 752 763 776 801 798 765 786 831 746
## [2749] 811 824 813 828 850 816 820 833 873 833 840 443
## [2761] 421 426 471 443 427 472 462 501 511 495 490 485
## [2773] 520 507 533 541 533 538 535 552 553 555 584 526
## [2785] 550 319 302 308 259 333 281 280 301 275 290 303
## [2797] 275 301 311 239 278 283 280 290 315 264 267 278
## [2809] 289 307 290 538 560 556 607 574 633 595 562 641
## [2821] 615 603 567 713 642 691 669 702 742 742 713 749
## [2833] 719 781 811 822 877 248 280 269 289 257 294 280
## [2845] 281 297 300 284 269 338 319 309 346 358 369 354
## [2857] 364 365 353 403 419 423 424 290 280 287 318 317
## [2869] 339 315 281 344 315 319 298 375 323 382 323 344
## [2881] 373 388 349 384 366 378 392 399 453 111 119 118
## [2893] 144 131 115 145 132 146 151 158 158 164 171 185
## [2905] 195 176 190 180 176 174 162 185 173 183 173 60
## [2917] 63 69 68 71 56 79 73 78 93 87 90 94
## [2929] 93 101 114 105 115 88 100 99 102 101 95 97
## [2941] 91 51 56 49 76 60 59 66 59 68 58 71
## [2953] 68 70 78 84 81 71 75 92 76 75 60 84
## [2965] 78 86 82 47 58 63 55 49 45 59 57 53
## [2977] 50 48 59 72 58 68 53 82 77 94 94 82
## [2989] 87 102 103 112 127 27 27 28 35 27 27 34
## [3001] 33 36 24 27 36 44 38 37 32 53 47 63
## [3013] 65 57 59 76 68 81 91 20 31 35 20 22
## [3025] 18 25 24 17 26 21 23 28 20 31 21 29
## [3037] 30 31 29 25 28 26 35 31 36 758 699 710
## [3049] 680 650 649 678 621 579 615 590 552 506 511 535
## [3061] 505 472 494 491 477 442 408 429 454 426 441 422
## [3073] 386 392 387 368 364 365 388 370 356 342 338 304
## [3085] 300 323 334 279 306 265 285 262 272 273 273 266
## [3097] 293 336 313 318 293 282 285 313 233 209 259 248
## [3109] 214 202 211 212 171 193 188 226 192 180 136 156
## [3121] 181 160 148Counting continued
count(deaths, Country) %>%
mutate(percent = n / sum(n),
percent = round(100 * percent))## # A tibble: 1 × 3
## Country n percent
## <chr> <int> <dbl>
## 1 S92000003 3123 100count(deaths, Year) %>%
mutate(percent = n / sum(n),
percent = round(100 * percent))## # A tibble: 26 × 3
## Year n percent
## <dbl> <int> <dbl>
## 1 1995 108 3
## 2 1996 108 3
## 3 1997 108 3
## 4 1998 108 3
## 5 1999 108 3
## 6 2000 123 4
## 7 2001 123 4
## 8 2002 123 4
## 9 2003 123 4
## 10 2004 123 4
## # ℹ 16 more rowscount(deaths, DeathsAllAges) %>%
mutate(percent = n / sum(n),
percent = round(100 * percent))## # A tibble: 956 × 3
## DeathsAllAges n percent
## <dbl> <int> <dbl>
## 1 1 1 0
## 2 2 1 0
## 3 3 5 0
## 4 4 6 0
## 5 5 16 1
## 6 6 18 1
## 7 7 33 1
## 8 8 37 1
## 9 9 40 1
## 10 10 26 1
## # ℹ 946 more rowsPIpping
# filter the table to just deathsAllAges
d_allages <- filter(deaths,
DeathsAllAges == "Deaths_All_Ages")
# calculate the total number of deaths per year
# sort by n, descending##Summerising
summarise(deaths,
total_deaths = sum(DeathsAllAges))## # A tibble: 1 × 1
## total_deaths
## <dbl>
## 1 2445785summarise(deaths,
total_deaths = sum(Year))## # A tibble: 1 × 1
## total_deaths
## <dbl>
## 1 6270210## Grouping
deaths %>%
group_by(DeathsAllAges) %>%
summarise(deaths = sum(DeathsAllAges)) %>%
ungroup()## # A tibble: 956 × 2
## DeathsAllAges deaths
## <dbl> <dbl>
## 1 1 1
## 2 2 2
## 3 3 15
## 4 4 24
## 5 5 80
## 6 6 108
## 7 7 231
## 8 8 296
## 9 9 360
## 10 10 260
## # ℹ 946 more rows## mulitiple grouping
deaths %>%
group_by(DeathsAllAges, Year, Country) %>%
summarise(n = sum(DeathsAllAges)) %>%
ungroup()## `summarise()` has grouped output by 'DeathsAllAges', 'Year'. You can override
## using the `.groups` argument.## # A tibble: 2,698 × 4
## DeathsAllAges Year Country n
## <dbl> <dbl> <chr> <dbl>
## 1 1 2011 S92000003 1
## 2 2 1996 S92000003 2
## 3 3 1998 S92000003 3
## 4 3 2004 S92000003 3
## 5 3 2005 S92000003 3
## 6 3 2009 S92000003 3
## 7 3 2015 S92000003 3
## 8 4 2000 S92000003 4
## 9 4 2006 S92000003 4
## 10 4 2007 S92000003 4
## # ℹ 2,688 more rowsPercent By grouping
count(deaths, DeathsAllAges) %>%
mutate(percent = n/sum(n)*100)## # A tibble: 956 × 3
## DeathsAllAges n percent
## <dbl> <int> <dbl>
## 1 1 1 0.0320
## 2 2 1 0.0320
## 3 3 5 0.160
## 4 4 6 0.192
## 5 5 16 0.512
## 6 6 18 0.576
## 7 7 33 1.06
## 8 8 37 1.18
## 9 9 40 1.28
## 10 10 26 0.833
## # ℹ 946 more rowsMultiple percent grouping with multiple variable
count(deaths, DeathsAllAges, Year, Country = TRUE)%>%
mutate(percent = n/sum(n)*100)## # A tibble: 2,698 × 5
## DeathsAllAges Year Country n percent
## <dbl> <dbl> <lgl> <int> <dbl>
## 1 1 2011 TRUE 1 0.0320
## 2 2 1996 TRUE 1 0.0320
## 3 3 1998 TRUE 1 0.0320
## 4 3 2004 TRUE 1 0.0320
## 5 3 2005 TRUE 1 0.0320
## 6 3 2009 TRUE 1 0.0320
## 7 3 2015 TRUE 1 0.0320
## 8 4 2000 TRUE 1 0.0320
## 9 4 2006 TRUE 1 0.0320
## 10 4 2007 TRUE 1 0.0320
## # ℹ 2,688 more rows# group by year, percentages within each age group will sum to 100
count(deaths, DeathsAllAges, Year, Country)%>%
group_by(Year) %>%
mutate(percent = n/sum(n)*100)## # A tibble: 2,698 × 5
## # Groups: Year [26]
## DeathsAllAges Year Country n percent
## <dbl> <dbl> <chr> <int> <dbl>
## 1 1 2011 S92000003 1 0.813
## 2 2 1996 S92000003 1 0.926
## 3 3 1998 S92000003 1 0.926
## 4 3 2004 S92000003 1 0.813
## 5 3 2005 S92000003 1 0.813
## 6 3 2009 S92000003 1 0.813
## 7 3 2015 S92000003 1 0.813
## 8 4 2000 S92000003 1 0.813
## 9 4 2006 S92000003 1 0.813
## 10 4 2007 S92000003 1 0.813
## # ℹ 2,688 more rows# group by deaths_all_ages, percentages within each induction group will sum to 100
count(deaths, DeathsAllAges, Year, Country)%>%
group_by(DeathsAllAges) %>%
mutate(percent = n/sum(n)*100)## # A tibble: 2,698 × 5
## # Groups: DeathsAllAges [956]
## DeathsAllAges Year Country n percent
## <dbl> <dbl> <chr> <int> <dbl>
## 1 1 2011 S92000003 1 100
## 2 2 1996 S92000003 1 100
## 3 3 1998 S92000003 1 20
## 4 3 2004 S92000003 1 20
## 5 3 2005 S92000003 1 20
## 6 3 2009 S92000003 1 20
## 7 3 2015 S92000003 1 20
## 8 4 2000 S92000003 1 16.7
## 9 4 2006 S92000003 1 16.7
## 10 4 2007 S92000003 1 16.7
## # ℹ 2,688 more rowscount(deaths, DeathsAllAges, Year, Country)%>%
group_by(Country)%>%
mutate(percent = n/sum(n)*100)## # A tibble: 2,698 × 5
## # Groups: Country [1]
## DeathsAllAges Year Country n percent
## <dbl> <dbl> <chr> <int> <dbl>
## 1 1 2011 S92000003 1 0.0320
## 2 2 1996 S92000003 1 0.0320
## 3 3 1998 S92000003 1 0.0320
## 4 3 2004 S92000003 1 0.0320
## 5 3 2005 S92000003 1 0.0320
## 6 3 2009 S92000003 1 0.0320
## 7 3 2015 S92000003 1 0.0320
## 8 4 2000 S92000003 1 0.0320
## 9 4 2006 S92000003 1 0.0320
## 10 4 2007 S92000003 1 0.0320
## # ℹ 2,688 more rows# group by deaths_all_ages, percentages within each age group will sum to 100
count(deaths, DeathsAllAges, Year, Country)%>% #age then induced
mutate(percent = n/sum(n)*100,
.by = DeathsAllAges)## # A tibble: 2,698 × 5
## DeathsAllAges Year Country n percent
## <dbl> <dbl> <chr> <int> <dbl>
## 1 1 2011 S92000003 1 100
## 2 2 1996 S92000003 1 100
## 3 3 1998 S92000003 1 20
## 4 3 2004 S92000003 1 20
## 5 3 2005 S92000003 1 20
## 6 3 2009 S92000003 1 20
## 7 3 2015 S92000003 1 20
## 8 4 2000 S92000003 1 16.7
## 9 4 2006 S92000003 1 16.7
## 10 4 2007 S92000003 1 16.7
## # ℹ 2,688 more rows# group by year, percentages within each induction group will sum to 100
count(deaths, DeathsAllAges, Year, Country)%>% #induced then age
mutate(percent = n/sum(n)*100,
.by = Year)## # A tibble: 2,698 × 5
## DeathsAllAges Year Country n percent
## <dbl> <dbl> <chr> <int> <dbl>
## 1 1 2011 S92000003 1 0.813
## 2 2 1996 S92000003 1 0.926
## 3 3 1998 S92000003 1 0.926
## 4 3 2004 S92000003 1 0.813
## 5 3 2005 S92000003 1 0.813
## 6 3 2009 S92000003 1 0.813
## 7 3 2015 S92000003 1 0.813
## 8 4 2000 S92000003 1 0.813
## 9 4 2006 S92000003 1 0.813
## 10 4 2007 S92000003 1 0.813
## # ℹ 2,688 more rowsdeaths_per_year_type_age <- deaths %>%
mutate(Year = str_extract(Year, ".{4}"), # takes first four digits
Year = as.integer(Year)) %>% # transform to numeric
filter(DeathsAllAges != "Unknown", # remove unknown
Country != "Not Known") %>% # remove not known
summarise(n = sum(DeathsAllAges),
.by = c(Year, Country, DeathsAllAges)) %>%
mutate(pcnt = n / sum(n) * 100,
.by = c(Year, DeathsAllAges))
# show just the first 6 rows
head(deaths_per_year_type_age)## # A tibble: 6 × 5
## Year Country DeathsAllAges n pcnt
## <int> <chr> <dbl> <dbl> <dbl>
## 1 1995 S92000003 15192 15192 100
## 2 1996 S92000003 15120 15120 100
## 3 1997 S92000003 14838 14838 100
## 4 1998 S92000003 14708 14708 100
## 5 1999 S92000003 14747 14747 100
## 6 2000 S92000003 14913 14913 100install.packages("patchwork")## Installing package into 'C:/Users/gboch/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'patchwork' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\gboch\AppData\Local\Temp\RtmpGAfBQ0\downloaded_packageslibrary(patchwork)
install.packages("ggthemes")## Installing package into 'C:/Users/gboch/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'ggthemes' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\gboch\AppData\Local\Temp\RtmpGAfBQ0\downloaded_packageslibrary(ggthemes)
##deaths_per_year_type_age %>%
point_first <-
ggplot(deaths, aes(
x = DeathsAllAges, y = Year)) +
geom_point() + # scatterplot
geom_smooth(method = lm) # line of best fit
line_first <-
ggplot(deaths, aes(
x = Year, y = DeathsAllAges)) +
geom_smooth(method = lm) + # line of best fit
geom_point()
point_first # view first plot## `geom_smooth()` using formula = 'y ~ x'
line_first # view second plot## `geom_smooth()` using formula = 'y ~ x'
# add plots together in 1 row
point_first + line_first + plot_layout(nrow = 1)## `geom_smooth()` using formula = 'y ~ x'## `geom_smooth()` using formula = 'y ~ x'
ggplot(deaths_per_year_type_age, aes(
x = Year, y = DeathsAllAges)) +
geom_point(colour = "dodgerblue",
alpha = 0.2, # 20% transparency
shape = 18, # solid diamond
size = 2) + # scatterplot
geom_smooth(method = lm, formula = y~x, # formula used to draw line,
# setting method & formula avoids an annoying message
colour = rgb(0, .5, .8),
linetype = 3) + # line of best fit
# customise axis labels and breaks
scale_x_continuous(name = "year (5 years)",
breaks = seq(from = 1995, to = 2020, by = 5)) +
scale_y_continuous(name = "DeathsAllAges(hundreds)")
#breaks = seq(from = 0, to = 1500, by = 300)) +
#coord_cartesian(xlim = c(1995, 2020),
#ylim = c(0, 1600)) +
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## ..$ colour : chr "#757575"
## ..$ size : 'rel' num 1
## ..$ hjust : NULL
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi FALSE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ legend.text.position : NULL
## $ legend.title :List of 11
## ..$ family : NULL
## ..$ face : chr "plain"
## ..$ colour : chr "#666666"
## ..$ size : 'rel' num 1
## ..$ hjust : num 0
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi FALSE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ legend.title.position : NULL
## $ legend.position : chr "right"
## $ legend.position.inside : NULL
## $ legend.direction : chr "vertical"
## $ legend.byrow : NULL
## $ legend.justification : chr "center"
## $ legend.justification.top : NULL
## $ legend.justification.bottom : NULL
## $ legend.justification.left : NULL
## $ legend.justification.right : NULL
## $ legend.justification.inside : NULL
## $ legend.location : NULL
## $ legend.box : NULL
## $ legend.box.just : NULL
## $ legend.box.margin : 'margin' num [1:4] 0cm 0cm 0cm 0cm
## ..- attr(*, "unit")= int 1
## $ legend.box.background : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ legend.box.spacing : 'simpleUnit' num 11points
## ..- attr(*, "unit")= int 8
## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUEtheme(axis.line.x = element_blank(),
plot.background = 1)## List of 2
## $ axis.line.x : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ plot.background: num 1
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUElabs(title = "The relationship between Year and Deaths_all_ages",
subtitle = "2020 Mortality rate for cancer patients",
caption = "There was no significat increases in Deaths as
the years go by.") ## $title
## [1] "The relationship between Year and Deaths_all_ages"
##
## $subtitle
## [1] "2020 Mortality rate for cancer patients"
##
## $caption
## [1] "There was no significat increases in Deaths as\nthe years go by."
##
## attr(,"class")
## [1] "labels"ggplot(deaths_per_year_type_age, aes(x = Year)) +
geom_bar()
count_deaths <- count(deaths_per_year_type_age, DeathsAllAges)
count(deaths, DeathsAllAges)## # A tibble: 956 × 2
## DeathsAllAges n
## <dbl> <int>
## 1 1 1
## 2 2 1
## 3 3 5
## 4 4 6
## 5 5 16
## 6 6 18
## 7 7 33
## 8 8 37
## 9 9 40
## 10 10 26
## # ℹ 946 more rowsggplot(deaths_per_year_type_age, aes(x = DeathsAllAges, y = n)) +
geom_col()
ggplot(deaths_per_year_type_age, aes(x = DeathsAllAges)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# adjust width of each bar
ggplot(deaths_per_year_type_age, aes(x = DeathsAllAges)) +
geom_histogram(binwidth = 1000, boundary = 0, fill = "white",
color = "black")
# adjust number of bars
ggplot(deaths_per_year_type_age, aes(x = DeathsAllAges)) +
geom_histogram(bins = 5)
Violin Plot
violin_area <-
ggplot(deaths_per_year_type_age, aes(DeathsAllAges, y = n)) +
geom_violin() +
ggtitle('scale = "area"')
violin_count <-
ggplot(deaths_per_year_type_age, aes(DeathsAllAges, y = n)) +
geom_violin(scale = "count") +
ggtitle('scale = "count"')
violin_area + violin_count
Box Plot
boxplot <- ggplot(deaths_per_year_type_age, aes(x = n, y = DeathsAllAges)) +
geom_boxplot() +
ggtitle("Default vertical boxplot")
custom <- ggplot(deaths_per_year_type_age, aes(x = n, y = DeathsAllAges)) +
geom_boxplot(fill = "grey80",
outlier.colour = "red",
outlier.shape = 10,
coef = 5, # length of whiskers relative to box
varwidth = TRUE, # set width proportional to sample size
notch = TRUE) +
ggtitle("Customised horizontal boxplot")
boxplot + custom## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
## Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
add fill and colour to the mapping
ggplot(deaths_per_year_type_age, aes(x = DeathsAllAges,
y = n,
fill = DeathsAllAges,
colour = DeathsAllAges)) +
scale_x_discrete(name = "year in 5 years") +
scale_y_continuous(name = "DeathsAllAges in hunderds",
breaks = seq(from = 1995, to = 2020, by = 5)) +
coord_cartesian(xlim = c(1995, 2020), ylim = c(0, 1600)) +
guides(fill = "none", colour = "none") +
# add a violin plot
geom_violin(draw_quantiles = 0.5, # adds a line at median (50%) score
alpha = 0.4) +
# add a boxplot
geom_boxplot(width = 0.25,
fill = "white",
alpha = 0.75,
fatten = 1, # removes the median line
outlier.alpha = 10) +
# add a point that represents the mean
stat_summary(fun = mean,
geom = "point",
size = 5) +
ggtitle("ViolinBox")## Warning: The following aesthetics were dropped during statistical transformation: fill
## and colour.
## 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.
## 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?