Project 2 Unicef

Nick Oliver

Project 2 - Unicef

https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/ https://childmortality.org/data

https://bbhosted.cuny.edu/webapps/discussionboard/do/message?action=list_messages&course_id=_2010109_1&nav=discussion_board_entry&conf_id=_2342994_1&forum_id=_2992508_1&message_id=_53934801_1

For this analysis I used Alec Mcabe’s suggested data set1. Alec linked to a website article by Simon Ejdemyr about wide and long data sets2 using some sample data from the Unicef Child Mortality website3.

I decided to go directly to the Unicef website4 and pull my own data from it for analysis and tidying.

The data I grab was the mortality rates, defined as deaths out of 1000 births, for all countries that had data. The data was offered in different age ranges so I decided to get the mortality rate groupings from 1-59 months, 5-9 years, 10-14 years, 15-19 years, and 20-24 years. This resulted in a rather large, long data set due to the fact that there was data going back to the 1960s, each row in the data set represented one value, for one indicator (e.g. 1-59 months), for one country for one year.

As I tidied the data for analysis I became curious if I could compare how the moratility rate changes over time for the countries with the lowest and highest mortality rates. For example I was curious if countries with high mortality rates at young ages, continue to have high mortality rates and people get older. Similarily for countries with low mortality rates.

Setup

Load Libraries

library(stringr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(kableExtra)
## 
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
## 
##     group_rows
library(readr)
library(forcats)
library(ggplot2)

Load Data

unicefUrl <- 'https://raw.githubusercontent.com/nolivercuny/data607/master/project2/datasets/childmortality/all_countries_dr_u5_5_9_10_14_15_24.csv'
unicefRaw <- read.csv(unicefUrl)

Tidy

Grab only the relevant columns (Country, Sex, Year, Indicator, Value)

Rename them so they are easier to work with

Mutate “Year” so it’s a number and drop extra data from it

Get the last year of data for each observation to make it easier to analyze. I do this with group_by and slice_max on Year. Interestingly this reveals that many indicators do not have data separated by genders. Because of this I also decided to filter out the gender specific observations and just keep the totals.

The last step was to then make the data wider instead of longer so I could see how the mortality rate changed over time by country. I used the pivot_wider function to accomplish this. Unfortunately I discovered that some countries had multiple datasoruces for the Indicator per in a single year. I solved this by using the value_fn parameter and setting that to the mean. There are probably more nuanced ways of handling this but for my purposes this worked well enough in getting me a single value for the observation.

beforeColon <- "^(.*?): "

unicefDf <- as.data.frame(unicefRaw) %>%
  select(c("REF_AREA.Geographic.area","INDICATOR.Indicator","SEX.Sex","TIME_PERIOD.Time.period","OBS_VALUE.Observation.Value" )) %>%
  rename(Country = "REF_AREA.Geographic.area", Indicator = "INDICATOR.Indicator", Sex = "SEX.Sex", Year = "TIME_PERIOD.Time.period", Value = "OBS_VALUE.Observation.Value") %>%
   mutate(Year = as.numeric(gsub("-.*", "", Year)), Sex = gsub(beforeColon, "", Sex),Country = gsub(beforeColon, "", Country),Indicator = gsub(beforeColon, "", Indicator)) %>% 
   filter(Sex == "Total") %>%
   group_by(Country) %>% 
   slice_max(Year) %>% 
   pivot_wider(names_from = Indicator, values_from = Value, values_fn = mean) %>%
      select(-Sex, -Year)

Analysis

kable(unicefDf,caption = "Mortality rates (deaths / 1000 births) by Country for year of 2029", format = "html") %>% kable_styling("striped") %>% scroll_box(width = "100%")
Mortality rates (deaths / 1000 births) by Country for year of 2029
Country Mortality rate 1-59 months Mortality rate age 10-14 Mortality rate age 15-19 Mortality rate age 20-24 Mortality rate age 5-9
Afghanistan 25.3143636 1.9149162 13.5913814 17.580780 2.3892118
Albania 2.1779800 1.0568891 1.7234748 1.818202 0.8559891
Algeria 7.0880726 1.6741230 2.7255660 3.242422 1.6604642
Andorra 1.5437696 0.4425771 1.3494353 2.004404 0.3694054
Angola 48.4491406 6.1926755 12.7935640 19.444002 10.3332650
Antigua and Barbuda 2.9693223 0.9764516 2.4473407 3.166673 0.6936262
Argentina 3.1991424 1.0378119 3.4079363 4.860814 0.8033553
Armenia 5.4191078 0.9437659 2.0172190 2.350256 1.0021272
Australia 1.3289327 0.4198412 1.4727104 1.962517 0.3682458
Austria 1.4131134 0.4368958 1.2175817 1.598094 0.3283050
Azerbaijan 9.6478223 1.4454700 2.5525448 3.203226 1.6055182
Bahamas 5.8954484 1.4150651 4.7523351 12.636353 1.1037302
Bahrain 3.9748161 0.8324837 2.0030620 2.775621 0.9435139
Bangladesh 11.9165102 3.2980984 6.4537930 5.267299 3.5892914
Barbados 4.3212994 1.2005919 2.2912860 3.688842 0.5391762
Belarus 2.0184068 0.6499010 1.4660056 2.322225 0.5932075
Belgium 1.4068809 0.4156989 1.0417298 1.779556 0.3868668
Belize 4.2026186 1.5053202 3.9911346 7.397166 1.1983049
Benin 61.6102576 7.1536294 11.1796378 11.155139 12.5326828
Bhutan 12.1243309 4.7279587 5.4677138 6.756415 2.3924769
Bolivia (Plurinational State of) 11.5823152 1.8500313 4.6007493 5.264309 2.2701647
Bosnia and Herzegovina 1.6427326 0.6757114 1.6736974 2.541683 0.5205431
Botswana 24.1073666 2.2398811 3.8626327 11.370585 3.5843504
Brazil 6.1174099 1.3847007 5.7635455 7.854176 0.9732986
Brunei Darussalam 5.4571122 1.1193954 1.2599821 2.591499 1.2768704
Bulgaria 3.4148140 0.9181536 2.4233529 2.937142 0.7558998
Burkina Faso 63.2389410 6.3311260 6.0691511 9.976072 11.1309518
Burundi 36.2484504 9.4190958 8.7029496 8.924523 12.0167457
Cabo Verde 5.9324643 1.0626895 2.2965355 3.838575 0.8658293
Cambodia 12.2775510 1.7833339 3.5114033 4.677901 3.0311251
Cameroon 50.0271194 8.4192333 12.2545313 17.722721 14.3399342
Canada 1.5326708 0.5358731 1.7126237 2.911541 0.3643013
Central African Republic 73.2287311 5.1535227 11.2881333 23.648029 9.0512330
Chad 83.3010190 10.6415063 15.5254970 19.564061 15.2078485
Chile 2.4296133 0.7884823 2.0939249 2.919560 0.6207646
China 4.0545505 0.9620691 1.3463012 2.486957 0.9335982
Colombia 6.3160589 1.3868552 4.4855653 6.817416 1.0860383
Comoros 34.0883795 2.5501865 3.2990620 5.740678 5.8892405
Congo 29.0518672 3.8369481 5.3719470 11.015678 4.1191732
Cook Islands 3.5100496 1.0648691 5.8415379 3.601053 0.9788919
Costa Rica 2.4770903 0.9839317 2.8031549 4.905682 0.7295631
Côte d’Ivoire 47.8192313 10.5397721 11.0845702 15.484385 14.3072783
Croatia 1.8804079 0.5562643 1.3720212 2.005693 0.5029489
Cuba 2.9447939 1.0754401 2.0309504 2.755630 0.9374979
Cyprus 0.9819611 0.4080798 1.1525641 1.854223 0.4783974
Czechia 1.5495765 0.4628540 1.5485783 2.409537 0.4301735
Democratic People’s Republic of Korea 7.8715983 1.8672526 3.9942315 5.178676 2.0576873
Democratic Republic of the Congo 59.0032022 9.8671066 17.9944983 19.635172 12.3768163
Denmark 0.7640785 0.3034473 1.0216163 1.378119 0.3099458
Djibouti 27.8327352 5.0664345 10.7553736 16.501489 7.7789018
Dominica 6.7944071 1.7247323 1.8843225 4.126755 1.4937026
Dominican Republic 8.7813832 1.4293090 4.0951620 8.440866 1.2026652
Ecuador 6.9773970 1.8714295 3.7949947 6.454059 1.4249211
Egypt 9.2420617 2.0769845 3.4726361 3.883387 2.2634129
El Salvador 6.7690428 2.0921081 11.6781840 12.828955 1.1116222
Equatorial Guinea 54.4198981 6.5015216 10.8731180 14.198672 9.7848282
Eritrea 23.0801888 3.7665558 8.5242966 13.300727 3.7373871
Estonia 1.3348601 0.6198082 1.8390183 2.289101 0.5340208
Eswatini 31.5194837 3.3727443 10.0355724 15.388867 10.0766635
Ethiopia 23.7712743 4.8159788 8.0074376 8.575579 5.8646552
Fiji 15.0113268 2.7375189 3.6868872 6.268576 2.0390287
Finland 0.9935902 0.3967236 1.8510651 3.066747 0.2686926
France 1.8091052 0.3990456 1.1579032 2.031569 0.3421896
Gabon 22.6729760 6.9885222 7.0130929 8.154529 5.3875172
Gambia 25.2792683 4.6044798 8.1154234 10.779024 5.7498422
Georgia 4.7254047 1.2736340 2.8242662 4.207187 1.1445057
Germany 1.5361362 0.4124905 1.1115817 1.526758 0.3855450
Ghana 23.5769134 4.7323058 7.4224899 7.686792 5.9865131
Greece 1.4989437 0.4572024 1.1547295 1.850145 0.4193504
Grenada 5.5944063 2.0209264 2.5006069 3.348643 1.7902246
Guatemala 12.5054929 2.0540538 6.3367142 8.606126 1.6145938
Guinea 70.5790789 6.2295460 13.2900556 18.447630 12.6263053
Guinea-Bissau 44.9657381 3.5379854 10.5852844 13.765697 12.0377002
Guyana 10.8529046 2.8441292 5.7954016 11.434647 2.0237363
Haiti 38.4739993 4.5362396 6.8272688 11.713811 6.3458824
Honduras 7.7199774 2.5194035 3.9988705 6.017370 1.9166922
Hungary 1.6774963 0.5553997 1.4025348 1.985558 0.4032868
Iceland 0.9694852 0.2568733 1.4768768 1.422293 0.2181838
India 12.8937202 2.7006489 3.9622739 6.002941 2.7970701
Indonesia 11.6129351 1.9610076 4.7315381 5.367849 3.1822194
Iran (Islamic Republic of) 5.4040656 1.7682130 4.6922923 5.343087 1.6948478
Iraq 10.7422267 1.8054643 4.3174669 5.209195 1.9864992
Ireland 1.1776630 0.3227365 0.8419783 1.561325 0.2843018
Israel 1.7426474 0.4646563 1.2295725 1.532054 0.3455859
Italy 1.2206782 0.4795020 1.0715790 1.571127 0.3760860
Jamaica 4.0691287 1.4987528 3.6273369 5.501666 1.2337479
Japan 1.6204038 0.3934095 0.9362085 1.627421 0.3558018
Jordan 6.4541904 1.6513408 2.2661547 3.309070 1.7669367
Kazakhstan 5.8131569 1.4213946 2.9799976 4.373079 1.3568077
Kenya 22.5941621 4.5151610 6.3380522 9.746744 5.4082895
Kiribati 29.4204361 3.8576593 7.0970707 8.918926 5.4651220
Kuwait 3.4066875 0.9929709 2.0162887 4.141621 0.8553375
Kyrgyzstan 6.0855344 1.6620877 2.6766445 3.721185 1.2836336
Lao People’s Democratic Republic 24.0930411 4.7397141 4.6453852 7.444066 3.4319196
Latvia 1.7581188 0.6888684 2.4654841 3.608581 0.7009814
Lebanon 3.0698797 0.9633963 2.2949759 2.805123 0.8921717
Lesotho 45.5462695 3.5474475 10.2378400 17.645442 5.0806402
Liberia 53.9547698 7.4987860 18.4959879 10.387892 8.0954783
Libya 5.0981277 2.3138213 4.3386344 5.815742 1.8278579
Lithuania 1.6191482 0.7972886 2.3387571 3.272368 0.6477350
Luxembourg 1.2950718 0.1957404 0.8058929 0.849370 0.1853458
Madagascar 31.1092423 7.4839241 10.7079077 11.980900 10.7912667
Malawi 22.2841641 4.5056365 7.0126155 11.620344 7.5729071
Malaysia 3.9554664 1.4521413 2.9955138 3.517051 1.1658729
Maldives 2.7047743 1.0500712 2.3796137 2.358517 0.8018499
Mali 63.9562447 8.4493229 10.0358153 11.905534 13.7909580
Malta 2.3055480 0.5795598 1.0678000 1.464713 0.2308468
Marshall Islands 16.7529500 2.8232825 5.5255067 7.036392 3.5717020
Mauritania 42.2282123 2.1097068 10.0989625 13.299362 5.1972947
Mauritius 5.8810299 0.9773616 2.9044864 4.942630 1.0184094
Mexico 5.6494251 1.4384773 4.3770364 7.168616 1.0922826
Micronesia (Federated States of) 13.7407078 2.6914813 5.2969374 6.790392 3.3242824
Monaco 1.4081122 0.4581520 1.3902452 2.066367 0.3849248
Mongolia 7.5560737 1.8097753 3.5568817 5.107854 1.6071112
Montenegro 1.0239866 0.4407209 1.2478445 2.181721 0.5148877
Morocco 7.8749357 0.9801402 3.5486712 4.166328 1.6107269
Mozambique 47.0450795 7.3077029 7.5987325 21.265946 6.3520776
Myanmar 22.7210589 2.2416207 3.2981870 5.828985 2.5087942
Namibia 23.6788357 5.1134174 7.4493567 13.942099 6.0596305
Nauru 11.1872544 2.7643415 5.4412470 6.925362 3.4796433
Nepal 11.2344697 2.2503434 4.4566493 5.997941 3.0150929
Netherlands 1.4561172 0.3838528 1.0293658 1.447871 0.2904465
New Zealand 2.1115191 0.5518580 1.8040974 2.384735 0.3992734
Nicaragua 6.4968095 1.9946202 3.9792533 5.975117 1.3433701
Niger 57.5041138 14.7864779 10.2875186 16.980995 15.7382067
Nigeria 84.3763615 7.9336119 8.5316830 10.506583 13.4412207
Niue 10.7873571 2.2764209 4.6684903 5.991759 2.6822217
North Macedonia 2.2236208 0.7666337 1.2388595 1.557852 0.6118348
Norway 1.0435620 0.3742343 1.2676190 2.090803 0.3182938
Oman 6.1964258 1.2651822 2.3937890 3.316748 1.2512152
Pakistan 27.1447612 3.7883779 6.0056966 5.571274 4.5811056
Palau 8.0407989 1.8941470 6.5009327 10.134594 2.0607376
Panama 6.3343440 1.6360535 3.0075142 5.897058 1.2557959
Papua New Guinea 23.3697903 3.5506668 6.6310170 8.369929 4.8693675
Paraguay 8.6793894 1.9646842 4.3023711 6.445637 0.8455794
Peru 6.9008918 1.4626914 2.2796866 4.246283 1.3802739
Philippines 14.1791712 1.9821967 3.3532863 5.646583 2.0488686
Poland 1.6805108 0.5834245 1.7732725 2.814470 0.4469298
Portugal 1.7143724 0.4248140 1.0378156 1.622165 0.3642995
Qatar 3.1289284 0.6997520 1.7008061 1.409168 0.6822453
Republic of Korea 1.6556651 0.3998070 1.0873875 1.633250 0.3782244
Republic of Moldova 3.7351585 1.4944998 2.9989126 4.221472 1.1667434
Romania 3.5414903 0.8967057 2.0851209 2.562165 0.7638357
Russian Federation 3.1466532 1.1090418 2.8478324 4.659971 0.8249106
Rwanda 18.7496800 4.5290536 5.8898185 9.339797 4.9849060
Saint Kitts and Nevis 5.1373159 1.5867078 6.0450558 10.165843 1.3370655
Saint Lucia 9.7116180 1.4003343 4.1658116 5.067985 1.6820363
Saint Vincent and the Grenadines 5.5743651 3.1895462 6.0392897 7.758409 1.5850821
Samoa 6.8486518 1.7059550 3.7033754 4.852195 1.8095991
San Marino 0.8984766 0.2920537 0.9259376 1.394484 0.2195683
Sao Tome and Principe 15.9968189 2.9806805 6.1484500 11.766265 3.0388248
Saudi Arabia 2.9953618 0.9094038 5.3001788 6.825530 0.8377416
Senegal 23.8695364 4.6807802 10.1099725 9.053694 5.7389793
Serbia 2.1032657 0.6985765 1.3896947 1.957962 0.4963845
Seychelles 5.6851333 1.3400156 5.8195123 5.064279 1.2026957
Sierra Leone 80.5697947 8.0085523 24.9021402 27.940298 13.6708138
Singapore 1.6326441 0.3814820 0.8521695 1.196415 0.3423577
Slovakia 2.8509167 0.6649278 1.6872154 2.467630 0.5703238
Slovenia 0.9174696 0.4017523 1.3259491 1.870494 0.3088003
Solomon Islands 11.5315645 2.0479983 4.2768006 5.544760 2.3111283
Somalia 83.1757811 8.6976662 17.2270911 25.858580 16.8142377
South Africa 23.2714252 2.9672468 7.6430579 12.428865 2.5050008
South Sudan 59.9386379 7.5420512 15.1351874 22.889002 13.6043951
Spain 1.3014901 0.3764760 0.9145221 1.381945 0.3305631
Sri Lanka 2.8461624 0.9142782 1.5588859 2.452361 0.7510131
State of Palestine 8.8719052 1.9303579 3.4157602 4.028173 1.1408087
Sudan 32.0678188 2.6879419 10.8695933 16.680105 5.4881928
Suriname 6.8564852 1.6778527 3.2470821 4.579067 1.2576129
Sweden 1.1929759 0.4045116 1.2092377 2.307922 0.2848366
Switzerland 1.2509577 0.3653644 1.1127592 1.400175 0.3014526
Syrian Arab Republic 10.8654771 5.3720498 7.1135794 9.797946 4.6595918
Tajikistan 19.0839727 0.8311735 1.6130785 2.913015 0.6307438
Thailand 3.7218859 2.5525572 6.0812481 6.633417 1.6384618
Timor-Leste 25.1299422 3.5083928 15.0085523 12.048637 4.8126871
Togo 43.1638545 5.5935245 7.5881047 10.153845 7.2848656
Tonga 9.2678615 0.8155921 3.0854914 5.070496 1.5564602
Trinidad and Tobago 6.0539630 1.4080118 3.5521702 7.021582 0.9446353
Tunisia 4.9851914 1.6335485 3.1066112 4.453127 1.6061351
Turkey 4.7932084 1.0492660 1.9712052 2.303867 0.9231292
Turkmenistan 18.8520231 1.8723180 4.4591110 5.726579 1.8300366
Tuvalu 7.9663223 2.3430279 4.7413824 6.125401 2.7537745
Uganda 26.3872999 5.3158730 13.3920262 14.002877 8.5960499
Ukraine 3.3878043 0.9469795 2.3997620 3.754843 0.7748358
United Arab Emirates 3.5237043 0.9867795 2.3302245 2.847775 0.9190207
United Kingdom of Great Britain and Northern Ireland 1.5064718 0.4150586 1.2925340 1.902278 0.3528425
United Republic of Tanzania 30.6242062 3.3594415 7.0383473 10.746122 8.1233196
United States of America 2.7839819 0.7632219 2.6498912 5.130776 0.6099627
Uruguay 2.8561140 0.9380442 3.3818355 5.456138 0.6394778
Uzbekistan 7.5836865 1.9312156 3.6862096 4.338242 1.8370064
Vanuatu 14.6899966 2.4674453 4.9540199 6.363469 2.9669111
Venezuela (Bolivarian Republic of) 9.7503935 1.9885049 10.6795683 15.654086 1.4687534
Viet Nam 9.5705442 1.5068004 2.9543769 3.665779 1.0143663
World 20.5756550 2.8979851 4.8932053 6.339351 3.8260097
Yemen 32.5700832 6.3331305 10.7226614 14.552075 6.2824950
Zambia 39.2980605 4.0278394 9.2943329 12.526980 6.9729929
Zimbabwe 29.5120090 6.6928006 9.6549163 14.475032 5.1928487 
highestMortalityRate <- unicefDf %>% 
  select(Country, `Mortality rate 1-59 months`, `Mortality rate age 5-9`, `Mortality rate age 10-14`, `Mortality rate age 15-19`, `Mortality rate age 20-24`) %>% 
  arrange(desc(`Mortality rate 1-59 months`)) %>%
  ungroup() %>%
  top_n(10, `Mortality rate 1-59 months`)

kable(highestMortalityRate,caption = "Mortality rates (deaths / 1000 births) by Country for year of 2019", format = "html") %>% kable_styling("striped") %>% scroll_box(width = "100%", height="100%")
Mortality rates (deaths / 1000 births) by Country for year of 2019
Country Mortality rate 1-59 months Mortality rate age 5-9 Mortality rate age 10-14 Mortality rate age 15-19 Mortality rate age 20-24
Nigeria 84.37636 13.441221 7.933612 8.531683 10.506583
Chad 83.30102 15.207849 10.641506 15.525497 19.564061
Somalia 83.17578 16.814238 8.697666 17.227091 25.858580
Sierra Leone 80.56979 13.670814 8.008552 24.902140 27.940298
Central African Republic 73.22873 9.051233 5.153523 11.288133 23.648029
Guinea 70.57908 12.626305 6.229546 13.290056 18.447630
Mali 63.95624 13.790958 8.449323 10.035815 11.905534
Burkina Faso 63.23894 11.130952 6.331126 6.069151 9.976072
Benin 61.61026 12.532683 7.153629 11.179638 11.155139
South Sudan 59.93864 13.604395 7.542051 15.135187 22.889002 
highestMortalityRate  %>%
  pivot_longer(-Country)%>% 
  ggplot(aes(x = fct_inorder(name), y = value, col = `Country`)) +
  geom_point() +
  geom_line() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

lowestMortalityRate <- unicefDf %>% 
  select(Country, `Mortality rate 1-59 months`, `Mortality rate age 5-9`, `Mortality rate age 10-14`, `Mortality rate age 15-19`, `Mortality rate age 20-24`) %>% 
  arrange((`Mortality rate 1-59 months`)) %>%
  ungroup() %>%
  head(10, `Mortality rate 1-59 months`)

kable(lowestMortalityRate,caption = "Mortality rates (deaths / 1000 births) by Country for year of 2029", format = "html") %>% kable_styling("striped") %>% scroll_box(width = "100%")
Mortality rates (deaths / 1000 births) by Country for year of 2029
Country Mortality rate 1-59 months Mortality rate age 5-9 Mortality rate age 10-14 Mortality rate age 15-19 Mortality rate age 20-24
Denmark 0.7640785 0.3099458 0.3034473 1.0216163 1.378119
San Marino 0.8984766 0.2195683 0.2920537 0.9259376 1.394484
Slovenia 0.9174696 0.3088003 0.4017523 1.3259491 1.870494
Iceland 0.9694852 0.2181838 0.2568733 1.4768768 1.422293
Cyprus 0.9819611 0.4783974 0.4080798 1.1525641 1.854223
Finland 0.9935902 0.2686926 0.3967236 1.8510651 3.066747
Montenegro 1.0239866 0.5148877 0.4407209 1.2478445 2.181721
Norway 1.0435620 0.3182938 0.3742343 1.2676190 2.090803
Ireland 1.1776630 0.2843018 0.3227365 0.8419783 1.561325
Sweden 1.1929759 0.2848366 0.4045116 1.2092377 2.307922 
lowestMortalityRate %>% 
  pivot_longer(-Country)%>% 
  ggplot(aes(x = fct_inorder(name), y = value, col = `Country`)) +
  geom_point() +
  geom_line() + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

Conclusion

It appears that there is somewhat of a trend when it comes to mortality rates in high vs. low mortality rate countries. Looking at my plots for the high mortality rate countries it appears that there is a dramatic difference in mortality rate after the first 59 months of life. While the mortality rates do seem to creep upwards as people get older they it would appear your chances of surviving if you survive past your first 59 months are much higher in high mortality rate countries.

The trend is similar in the low mortality rate countries but the drop off in mortality rate is not nearly as dramatic after 59 months. The other interesting thing I noticed about the low mortality rate countries is that in the older age groups, 15-19 and 20-24, the mortality rate starts spreading out between countries.

References

Dataset

  1. https://bbhosted.cuny.edu/webapps/discussionboard/do/message?action=list_messages&course_id=_2010109_1&nav=discussion_board_entry&conf_id=_2342994_1&forum_id=_2992508_1&message_id=_53934801_1↩︎

  2. Wide & long data - GitHub Pages. (n.d.). Retrieved October 4, 2021, from https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/.↩︎

  3. CME Info - child mortality estimates. (n.d.). Retrieved October 4, 2021, from https://childmortality.org/data.↩︎

  4. CME Info - child mortality estimates. (n.d.). Retrieved October 4, 2021, from https://childmortality.org/data.↩︎