MATH2349 Semester 1, 2020

Required packages

library(readxl)
library(knitr) 
library(stringr)
library(dplyr)
library(tidyr)
library(magrittr)
library(kableExtra)
library(ggplot2)

Executive Summary

This study involves 3 datasets merged together for analysis purpose.

UN_MigrantStockByOriginAndDestination_2019 (Table 2-Male migrants). [1]
UN_MigrantStockByOriginAndDestination_2019 (Table 3-Female migrants). [1]
continents-according-to-our-world-in-data. [2]

Steps performed before importing the datasets in R.

From the UN_MigrantStockByOriginAndDestination_2019 dataset, data of Male and Female migrants were transferred into two excel sheets.
Rows 1-14 were deleted.
Rows 15 and 16 were merged for the first 6 columns, hence unmerged the cells and pasted the content in 16th row.
Deleted 15th row.

The final Male and Female datasets contains 1982 rows and 241 columns each.

For the Continent dataset, there are 286 rows and 4 columns. Entire file is imported in R and then the columns containing Countries and Continents information are retained.

Data

The “UN_MigrantStockByOriginAndDestination_2019.xlsx” dataset present in the UN- Department of Economic and social affairs website, shows an approximated number of international migrants by origin, sex and age. Data are presented for years 1990, 1995, 2000, 2005, 2010, 2015 and 2019 and includes all countries/areas of the world. The data is divided into 3 tables, out of which we will use Table 2 (Stats of Male migrants) and Table 3 (Stats of Female migrants) for this study.

The data contains the following attributes:

Year: Year of migration
Major area,region,country or area of destinations: The place of origination
Code: Codes of respective countries.
Type of data(a): Indicates the type of data used in deriving the estimates presented. More details present in the Notes tab in the dataset.
Country or area of origin: Contains the estimates of migrants from different parts of the world.

Another dataset “continents-according-to-our-world-in-data.csv” present in the Our world in data website, is joined to create buckets of the countries with respect to the Continents they are located in.

Male_migrant<- read_excel("Male Migrants.xlsx")
Male_migrant$Gender <- "Male"

#[3]
head(Male_migrant) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down"))%>% scroll_box(width="100%",height="350px")

Year	Sort order	Major area,region,country or area of destinations	Notes	Code	Type of data(a)	Total	Other South	Other North	Afghanistan	Albania	Algeria	American Samoa	Andorra	Angola	Anguilla	Antigua and Barbuda	Argentina	Armenia	Aruba	Australia	Austria	Azerbaijan	Bahamas	Bahrain	Bangladesh	Barbados	Belarus	Belgium	Belize	Benin	Bermuda	Bhutan	Bolivia (Plurinational State of)	Bonaire, Sint Eustatius and Saba	Bosnia and Herzegovina	Botswana	Brazil	British Virgin Islands	Brunei Darussalam	Bulgaria	Burkina Faso	Burundi	Cabo Verde	Cambodia	Cameroon	Canada	Cayman Islands	Central African Republic	Chad	Channel Islands	Chile	China	China, Hong Kong SAR	China, Macao SAR	Colombia	Comoros	Congo	Cook Islands	Costa Rica	Côte d'Ivoire	Croatia	Cuba	Curaçao	Cyprus	Czechia	Dem. People's Republic of Korea	Democratic Republic of the Congo	Denmark	Djibouti	Dominica	Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea	Eritrea	Estonia	Eswatini	Ethiopia	Falkland Islands (Malvinas)	Faroe Islands	Fiji	Finland	France	French Guiana	French Polynesia	Gabon	Gambia	Georgia	Germany	Ghana	Gibraltar	Greece	Greenland	Grenada	Guadeloupe	Guam	Guatemala	Guinea	Guinea-Bissau	Guyana	Haiti	Holy See	Honduras	Hungary	Iceland	India	Indonesia	Iran (Islamic Republic of)	Iraq	Ireland	Isle of Man	Israel	Italy	Jamaica	Japan	Jordan	Kazakhstan	Kenya	Kiribati	Kuwait	Kyrgyzstan	Lao People's Democratic Republic	Latvia	Lebanon	Lesotho	Liberia	Libya	Liechtenstein	Lithuania	Luxembourg	Madagascar	Malawi	Malaysia	Maldives	Mali	Malta	Marshall Islands	Martinique	Mauritania	Mauritius	Mayotte	Mexico	Micronesia (Fed. States of)	Monaco	Mongolia	Montenegro	Montserrat	Morocco	Mozambique	Myanmar	Namibia	Nauru	Nepal	Netherlands	New Caledonia	New Zealand	Nicaragua	Niger	Nigeria	Niue	North Macedonia	Northern Mariana Islands	Norway	Oman	Pakistan	Palau	Panama	Papua New Guinea	Paraguay	Peru	Philippines	Poland	Portugal	Puerto Rico	Qatar	Republic of Korea	Republic of Moldova	Réunion	Romania	Russian Federation	Rwanda	Saint Helena	Saint Kitts and Nevis	Saint Lucia	Saint Pierre and Miquelon	Saint Vincent and the Grenadines	Samoa	San Marino	Sao Tome and Principe	Saudi Arabia	Senegal	Serbia	Seychelles	Sierra Leone	Singapore	Sint Maarten (Dutch part)	Slovakia	Slovenia	Solomon Islands	Somalia	South Africa	South Sudan	Spain	Sri Lanka	State of Palestine	Sudan	Suriname	Sweden	Switzerland	Syrian Arab Republic	Tajikistan	Thailand	Timor-Leste	Togo	Tokelau	Tonga	Trinidad and Tobago	Tunisia	Turkey	Turkmenistan	Turks and Caicos Islands	Tuvalu	Uganda	Ukraine	United Arab Emirates	United Kingdom	United Republic of Tanzania	United States of America	United States Virgin Islands	Uruguay	Uzbekistan	Vanuatu	Venezuela (Bolivarian Republic of)	Viet Nam	Wallis and Futuna Islands	Western Sahara	Yemen	Zambia	Zimbabwe	Gender
1990	1990001	WORLD	NA	900	NA	77661689	3412163	1159981	3711922	109462	521333	1049	1769	405495	1069	9817	203585	436870	4742	140739	234736	767943	10628	6120	3162922	40155	815549	157201	15094	127252	34703	13925	115446	1627	427370	19235	226370	1340	15605	300437	573295	173900	42683	174750	60068	367069	192	24207	168103	8755	248431	2197578	272012	48482	458889	22062	51516	8377	29259	185637	214262	399247	21911	90736	130307	19237	217217	91640	2890	19308	198693	105646	822942	608584	17710	89618	54319	25590	856303	141	2989	43627	101915	572512	1251	1595	7736	20808	446509	1341083	187378	5671	533599	3774	19604	2590	694	175902	201530	27432	110794	295179	13	66907	192180	8627	3808569	952658	362238	854950	413220	5098	139359	1739405	268189	301529	187416	1427482	123049	1937	45235	252638	237318	99944	292677	145916	260645	40088	1447	152604	18562	25594	84442	276687	1698	360545	56497	707	5609	75174	51465	986	2358113	3935	2256	11762	38819	3366	984687	1202056	376109	9361	627	389294	357079	1928	196197	219810	94787	255399	2832	217104	1204	59894	6450	1971601	1369	66563	1573	131629	150342	833735	706964	965010	574860	6028	808147	290611	1571	377349	5516075	276832	386	9534	10053	240	17907	36602	572	6742	57765	230827	393867	17221	31968	74563	7556	65930	39984	1134	448855	150244	266794	683813	459025	888310	336129	87554	87854	145574	355555	264426	122822	5921	95240	836	16197	87603	281462	1362030	127446	1006	1203	157870	2634767	44699	1876268	107187	869586	1175	116499	685800	2646	83012	636418	3664	92655	290313	42599	105757	Male
1990	1990002	UN development groups	NA	NA	NA	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	Male
1990	1990003	More developed regions	b	901	NA	40426798	1809849	507312	69707	108188	494762	505	1735	80403	280	6595	101201	321103	2020	109322	218340	516211	9741	1005	93241	37394	724917	143609	13289	8519	34623	353	21162	1465	423323	1516	114297	84	3341	63086	3635	2398	36722	114773	24650	336941	141	4634	2131	8720	89567	722302	254737	6926	157368	9295	33711	8358	19481	24371	212337	368086	19805	85825	126337	7690	50315	87959	1832	11381	166342	81237	159680	257214	4069	12907	52689	267	58633	130	2989	41697	100465	394850	29	137	4262	8577	392298	1123196	73769	5633	470860	3587	10793	81	22	123986	8558	9544	97936	145320	5	48351	179630	8584	635626	150834	302178	85827	409302	5098	88873	1448019	262462	204669	48955	1368478	78800	444	8502	234508	126654	93400	208539	179	10070	14153	1335	138545	18435	18026	6004	92098	163	33381	56397	547	158	9331	46175	0	2337466	1352	2135	11675	38702	2413	896077	37792	21839	633	217	4503	326551	536	187589	85610	2080	89477	2812	191059	136	55383	416	260857	6	44012	934	5946	101746	507133	631908	751116	565279	477	678290	263476	64	308201	3278092	4492	283	4522	4593	211	9419	28658	536	2760	12206	94339	392273	13807	11074	42019	7053	65626	39016	530	35133	107091	0	425753	148767	18878	9471	77368	82846	125358	76064	229232	70955	5436	11271	755	14745	80657	257235	1311909	122635	107	550	36354	2241388	3215	1713505	31252	436579	39	26639	521303	461	48599	557518	743	265	7284	12964	19649	Male
1990	1990004	Less developed regions	c	902	NA	37234891	1602314	652669	3642215	1274	26571	544	34	325092	789	3222	102384	115767	2722	31417	16396	251732	887	5115	3069681	2761	90632	13592	1805	118733	80	13572	94284	162	4047	17719	112073	1256	12264	237351	569660	171502	5961	59977	35418	30128	51	19573	165972	35	158864	1475276	17275	41556	301521	12767	17805	19	9778	161266	1925	31161	2106	4911	3970	11547	166902	3681	1058	7927	32351	24409	663262	351370	13641	76711	1630	25323	797670	11	0	1930	1450	177662	1222	1458	3474	12231	54211	217887	113609	38	62739	187	8811	2509	672	51916	192972	17888	12858	149859	8	18556	12550	43	3172943	801824	60060	769123	3918	0	50486	291386	5727	96860	138461	59004	44249	1493	36733	18130	110664	6544	84138	145737	250575	25935	112	14059	127	7568	78438	184589	1535	327164	100	160	5451	65843	5290	986	20647	2583	121	87	117	953	88610	1164264	354270	8728	410	384791	30528	1392	8608	134200	92707	165922	20	26045	1068	4511	6034	1710744	1363	22551	639	125683	48596	326602	75056	213894	9581	5551	129857	27135	1507	69148	2237983	272340	103	5012	5460	29	8488	7944	36	3982	45559	136488	1594	3414	20894	32544	503	304	968	604	413722	43153	266794	258060	310258	869432	326658	10186	5008	20216	279491	35194	51867	485	83969	81	1452	6946	24227	50121	4811	899	653	121516	393379	41484	162763	75935	433007	1136	89860	164497	2185	34413	78900	2921	92390	283029	29635	86108	Male
1990	1990005	Least developed countries	d	941	NA	5550233	244501	135262	0	0	2501	0	0	296132	0	0	296	0	0	809	155	0	0	49	1287	0	0	503	0	21560	0	8065	0	0	0	278	843	0	0	0	23068	169652	5780	7676	17913	976	0	13807	104323	0	214	129813	31	0	421	4777	12856	0	106	156487	0	858	0	3	0	60	127832	106	475	0	1345	0	16095	0	152	69934	0	58	714494	0	0	376	0	20115	0	0	2815	6676	0	2564	25683	0	21	0	0	0	0	0	118144	16933	0	0	0	0	1	0	158283	40346	0	2776	0	0	0	1696	0	5324	0	0	37703	483	9	0	31777	0	4398	1473	239503	1954	0	0	0	5007	27145	50228	0	66751	0	0	0	55824	70	0	131	0	0	0	0	0	1778	621480	106389	1683	400	5453	296	128	271	0	31791	57513	0	0	0	2299	0	10535	0	0	592	0	208	476	0	5151	703	0	171	0	507	0	772	268902	0	0	0	0	0	18	0	1659	1038	104819	0	3300	19179	5773	0	0	8	39	400790	10672	257061	1069	68	1482	124612	0	628	400	1313	22879	16699	0	12256	0	14	0	92	1	0	0	203	75525	39	24	8556	38482	20539	0	112	1144	3	1325	36996	0	0	175	10694	37287	Male
1990	1990006	Less developed regions, excluding least developed countries	NA	934	NA	31684658	1357813	517407	3642215	1274	24070	544	34	28960	789	3222	102088	115767	2722	30608	16241	251732	887	5066	3068394	2761	90632	13089	1805	97173	80	5507	94284	162	4047	17441	111230	1256	12264	237351	546592	1850	181	52301	17505	29152	51	5766	61649	35	158650	1345463	17244	41556	301100	7990	4949	19	9672	4779	1925	30303	2106	4908	3970	11487	39070	3575	583	7927	31006	24409	647167	351370	13489	6777	1630	25265	83176	11	0	1554	1450	157547	1222	1458	659	5555	54211	215323	87926	38	62718	187	8811	2509	672	51916	74828	955	12858	149859	8	18556	12549	43	3014660	761478	60060	766347	3918	0	50486	289690	5727	91536	138461	59004	6546	1010	36724	18130	78887	6544	79740	144264	11072	23981	112	14059	127	2561	51293	134361	1535	260413	100	160	5451	10019	5220	986	20516	2583	121	87	117	953	86832	542784	247881	7045	10	379338	30232	1264	8337	134200	60916	108409	20	26045	1068	2212	6034	1700209	1363	22551	47	125683	48388	326126	75056	208743	8878	5551	129686	27135	1000	69148	2237211	3438	103	5012	5460	29	8488	7926	36	2323	44521	31669	1594	114	1715	26771	503	304	960	565	12932	32481	9733	256991	310190	867950	202046	10186	4380	19816	278178	12315	35168	485	71713	81	1438	6946	24135	50120	4811	899	450	45991	393340	41460	154207	37453	412468	1136	89748	163353	2182	33088	41904	2921	92390	282854	18941	48821	Male



Female_migrant<- read_excel("Female Migrants.xlsx")
Female_migrant$Gender <- "Female"
head(Female_migrant) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down"))%>% scroll_box(width="100%",height="350px")

Year	Sort order	Major area,region,country or area of destinations	Notes	Code	Type of data(a)	Total	Other South	Other North	Afghanistan	Albania	Algeria	American Samoa	Andorra	Angola	Anguilla	Antigua and Barbuda	Argentina	Armenia	Aruba	Australia	Austria	Azerbaijan	Bahamas	Bahrain	Bangladesh	Barbados	Belarus	Belgium	Belize	Benin	Bermuda	Bhutan	Bolivia (Plurinational State of)	Bonaire, Sint Eustatius and Saba	Bosnia and Herzegovina	Botswana	Brazil	British Virgin Islands	Brunei Darussalam	Bulgaria	Burkina Faso	Burundi	Cabo Verde	Cambodia	Cameroon	Canada	Cayman Islands	Central African Republic	Chad	Channel Islands	Chile	China	China, Hong Kong SAR	China, Macao SAR	Colombia	Comoros	Congo	Cook Islands	Costa Rica	Côte d'Ivoire	Croatia	Cuba	Curaçao	Cyprus	Czechia	Dem. People's Republic of Korea	Democratic Republic of the Congo	Denmark	Djibouti	Dominica	Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea	Eritrea	Estonia	Eswatini	Ethiopia	Falkland Islands (Malvinas)	Faroe Islands	Fiji	Finland	France	French Guiana	French Polynesia	Gabon	Gambia	Georgia	Germany	Ghana	Gibraltar	Greece	Greenland	Grenada	Guadeloupe	Guam	Guatemala	Guinea	Guinea-Bissau	Guyana	Haiti	Holy See	Honduras	Hungary	Iceland	India	Indonesia	Iran (Islamic Republic of)	Iraq	Ireland	Isle of Man	Israel	Italy	Jamaica	Japan	Jordan	Kazakhstan	Kenya	Kiribati	Kuwait	Kyrgyzstan	Lao People's Democratic Republic	Latvia	Lebanon	Lesotho	Liberia	Libya	Liechtenstein	Lithuania	Luxembourg	Madagascar	Malawi	Malaysia	Maldives	Mali	Malta	Marshall Islands	Martinique	Mauritania	Mauritius	Mayotte	Mexico	Micronesia (Fed. States of)	Monaco	Mongolia	Montenegro	Montserrat	Morocco	Mozambique	Myanmar	Namibia	Nauru	Nepal	Netherlands	New Caledonia	New Zealand	Nicaragua	Niger	Nigeria	Niue	North Macedonia	Northern Mariana Islands	Norway	Oman	Pakistan	Palau	Panama	Papua New Guinea	Paraguay	Peru	Philippines	Poland	Portugal	Puerto Rico	Qatar	Republic of Korea	Republic of Moldova	Réunion	Romania	Russian Federation	Rwanda	Saint Helena	Saint Kitts and Nevis	Saint Lucia	Saint Pierre and Miquelon	Saint Vincent and the Grenadines	Samoa	San Marino	Sao Tome and Principe	Saudi Arabia	Senegal	Serbia	Seychelles	Sierra Leone	Singapore	Sint Maarten (Dutch part)	Slovakia	Slovenia	Solomon Islands	Somalia	South Africa	South Sudan	Spain	Sri Lanka	State of Palestine	Sudan	Suriname	Sweden	Switzerland	Syrian Arab Republic	Tajikistan	Thailand	Timor-Leste	Togo	Tokelau	Tonga	Trinidad and Tobago	Tunisia	Turkey	Turkmenistan	Turks and Caicos Islands	Tuvalu	Uganda	Ukraine	United Arab Emirates	United Kingdom	United Republic of Tanzania	United States of America	United States Virgin Islands	Uruguay	Uzbekistan	Vanuatu	Venezuela (Bolivarian Republic of)	Viet Nam	Wallis and Futuna Islands	Western Sahara	Yemen	Zambia	Zimbabwe	Gender
1990	1990001	WORLD	NA	900	NA	75349784	3136363	1206819	3111428	70822	400394	992	2023	419447	978	11936	226584	462779	5854	162957	271352	866138	14554	6700	2288909	44776	952057	208159	21023	107062	36999	14540	109247	1579	434396	6818	274022	1754	10718	312656	448037	163218	48685	180680	55785	631094	181	22155	168699	9971	244595	2034070	279068	47166	551046	18021	44856	9111	40452	180711	211545	436549	21279	83642	146953	20547	219309	110121	2418	23129	267523	108362	499236	633491	18468	80985	59586	9591	833652	119	4531	46539	148850	643383	1593	1554	7616	15472	472945	1588365	183784	6323	488860	5736	23645	3238	682	172430	151233	27977	122937	233694	18	89687	195334	8994	2814608	685707	269101	651752	504066	5637	142238	1611601	320821	307392	126581	1544157	127291	2116	36376	269940	245703	115190	216646	45423	256241	36168	1981	188446	17579	33830	58995	286075	495	286891	54249	719	5432	59314	59243	849	2037252	3779	2223	12704	38565	3822	763564	1020313	309201	6718	792	358766	366559	2223	191976	222316	54992	191407	3028	215192	1321	78642	6085	1371727	1589	68180	1538	166350	164512	1199949	803451	908447	625961	6176	816582	335072	1516	435717	7146818	270886	498	11180	11952	245	19142	38259	847	7235	49401	139436	348680	18412	29886	81905	7267	67076	51512	1078	399212	158059	248149	755206	426926	924753	248811	92316	118994	180702	266326	273275	188486	5340	98590	848	16469	109918	184114	1278003	132541	1305	1147	153732	2910993	34846	1918065	95883	869647	1187	120987	742220	2414	102934	601455	2820	75584	165179	42604	98608	Female
1990	1990002	UN development groups	NA	NA	NA	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	Female
1990	1990003	More developed regions	b	901	NA	42340418	1575254	569867	49679	69798	372253	522	2002	86978	260	7966	122107	333008	2619	135934	253610	540332	13635	778	67850	42169	845375	194460	18894	6319	36927	201	22110	1435	431026	1327	168672	74	3282	59672	2751	2222	43505	122501	25516	601645	128	4480	1477	9905	102127	738043	262012	7456	207772	9651	27884	9083	28559	24866	209773	406448	19262	78160	142135	7953	45213	106404	1634	12433	221224	84364	110141	250438	4374	12658	57479	279	58267	107	4529	44300	146623	486283	32	187	4455	4079	409412	1365739	53851	6287	419709	5410	13033	81	21	121311	6154	6562	108929	149446	5	65984	180530	8949	597328	158325	216259	59350	499650	5637	80499	1341396	312670	225889	21576	1465350	75825	545	7923	248535	125123	107254	162029	186	8284	11169	1915	171753	17344	25474	5580	96119	141	17973	54105	568	158	3924	55442	0	2013120	1412	2065	12625	38441	2761	671665	41165	22859	671	248	2674	334258	732	183899	93393	1353	58725	3009	189708	138	74829	298	186487	6	46132	911	8625	126327	842509	715062	724340	615648	427	704102	303689	61	352881	4288108	3935	256	5364	5515	222	9005	30900	840	3121	12699	36288	346703	15183	8644	48535	6858	66821	50448	585	32269	117213	1	491248	111353	16722	5612	83012	114275	162845	50289	242001	135064	5078	8087	768	15229	102247	159249	1236547	126578	114	621	34775	2426968	2385	1749026	29781	452835	31	30199	557260	556	66392	527792	141	68	4173	13098	21308	Female
1990	1990004	Less developed regions	c	902	NA	33009366	1561109	636952	3061749	1024	28141	470	21	332469	718	3970	104477	129771	3235	27023	17742	325806	919	5922	2221059	2607	106682	13699	2129	100743	72	14339	87137	144	3370	5491	105350	1680	7436	252984	445286	160996	5180	58179	30269	29449	53	17675	167222	66	142468	1296027	17056	39710	343274	8370	16972	28	11893	155845	1772	30101	2017	5482	4818	12594	174096	3717	784	10696	46299	23998	389095	383053	14094	68327	2107	9312	775385	12	2	2239	2227	157100	1561	1367	3161	11393	63533	222626	129933	36	69151	326	10612	3157	661	51119	145079	21415	14008	84248	13	23703	14804	45	2217280	527382	52842	592402	4416	0	61739	270205	8151	81503	105005	78807	51466	1571	28453	21405	120580	7936	54617	45237	247957	24999	66	16693	235	8356	53415	189956	354	268918	144	151	5274	55390	3801	849	24132	2367	158	79	124	1061	91899	979148	286342	6047	544	356092	32301	1491	8077	128923	53639	132682	19	25484	1183	3813	5787	1185240	1583	22048	627	157725	38185	357440	88389	184107	10313	5749	112480	31383	1455	82836	2858710	266951	242	5816	6437	23	10137	7359	7	4114	36702	103148	1977	3229	21242	33370	409	255	1064	493	366943	40846	248148	263958	315573	908031	243199	9304	4719	17857	216037	31274	53422	262	90503	80	1240	7671	24865	41456	5963	1191	526	118957	484025	32461	169039	66102	416812	1156	90788	184960	1858	36542	73663	2679	75516	161006	29506	77300	Female
1990	1990005	Least developed countries	d	941	NA	5509988	238252	104494	0	0	3121	0	0	311976	0	0	230	0	0	656	35	0	0	40	759	0	0	470	0	20173	0	8740	0	0	0	374	788	0	0	0	20094	159952	5016	6646	15467	900	0	12633	103838	0	119	116110	23	0	407	3772	12312	0	168	151994	0	635	0	2	0	18	129378	83	429	0	1061	0	14487	0	203	64537	0	44	718456	0	0	345	0	18426	0	0	2458	6388	0	2212	33307	0	19	0	0	0	0	0	82808	20697	0	0	0	0	9	0	304187	34900	0	2244	1	0	0	705	0	4405	0	0	45228	571	20	0	27066	0	2939	1481	238230	1803	0	0	0	5803	20470	42652	0	65506	0	0	0	51977	43	0	175	0	0	0	0	0	1933	643873	119906	1397	528	4572	252	164	216	0	24143	56763	0	0	0	1950	0	6780	0	0	568	0	143	342	0	3043	843	0	183	0	450	0	693	263493	0	0	0	0	0	3	0	1560	1094	91335	0	3126	19860	4905	0	0	7	44	356631	10369	241547	709	47	1215	119995	0	542	286	1080	17658	15377	0	20612	0	10	0	129	5	0	0	174	73783	45	38	7204	34211	17777	0	174	883	6	1185	34583	0	0	182	15560	37835	Female
1990	1990006	Less developed regions, excluding least developed countries	NA	934	NA	27499378	1322857	532458	3061749	1024	25020	470	21	20493	718	3970	104247	129771	3235	26367	17707	325806	919	5882	2220300	2607	106682	13229	2129	80570	72	5599	87137	144	3370	5117	104562	1680	7436	252984	425192	1044	164	51533	14802	28549	53	5042	63384	66	142349	1179917	17033	39710	342867	4598	4660	28	11725	3851	1772	29466	2017	5480	4818	12576	44718	3634	355	10696	45238	23998	374608	383053	13891	3790	2107	9268	56929	12	2	1894	2227	138674	1561	1367	703	5005	63533	220414	96626	36	69132	326	10612	3157	661	51119	62271	718	14008	84248	13	23703	14795	45	1913093	492482	52842	590158	4415	0	61739	269500	8151	77098	105005	78807	6238	1000	28433	21405	93514	7936	51678	43756	9727	23196	66	16693	235	2553	32945	147304	354	203412	144	151	5274	3413	3758	849	23957	2367	158	79	124	1061	89966	335275	166436	4650	16	351520	32049	1327	7861	128923	29496	75919	19	25484	1183	1863	5787	1178460	1583	22048	59	157725	38042	357098	88389	181064	9470	5749	112297	31383	1005	82836	2858017	3458	242	5816	6437	23	10137	7356	7	2554	35608	11813	1977	103	1382	28465	409	255	1057	449	10312	30477	6601	263249	315526	906816	123204	9304	4177	17571	214957	13616	38045	262	69891	80	1230	7671	24736	41451	5963	1191	352	45174	483980	32423	161835	31891	399035	1156	90614	184077	1852	35357	39080	2679	75516	160824	13946	39465	Female



Countrymap<- read.csv("continents-according-to-our-world-in-data.csv", header = TRUE, stringsAsFactors = FALSE)
Countrymap<- Countrymap %>%  select (Entity, X)
head(Countrymap)

The Male and Female migrants data are then merged together into one dataset “All_migrants” by row. Also, renamed the 3rd column for convenience.

All_migrants<- rbind(Male_migrant,Female_migrant)
names(All_migrants)[3] <- "Destination"
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

Year	Sort order	Destination	Notes	Code	Type of data(a)	Total	Other South	Other North	Afghanistan	Albania	Algeria	American Samoa	Andorra	Angola	Anguilla	Antigua and Barbuda	Argentina	Armenia	Aruba	Australia	Austria	Azerbaijan	Bahamas	Bahrain	Bangladesh	Barbados	Belarus	Belgium	Belize	Benin	Bermuda	Bhutan	Bolivia (Plurinational State of)	Bonaire, Sint Eustatius and Saba	Bosnia and Herzegovina	Botswana	Brazil	British Virgin Islands	Brunei Darussalam	Bulgaria	Burkina Faso	Burundi	Cabo Verde	Cambodia	Cameroon	Canada	Cayman Islands	Central African Republic	Chad	Channel Islands	Chile	China	China, Hong Kong SAR	China, Macao SAR	Colombia	Comoros	Congo	Cook Islands	Costa Rica	Côte d'Ivoire	Croatia	Cuba	Curaçao	Cyprus	Czechia	Dem. People's Republic of Korea	Democratic Republic of the Congo	Denmark	Djibouti	Dominica	Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea	Eritrea	Estonia	Eswatini	Ethiopia	Falkland Islands (Malvinas)	Faroe Islands	Fiji	Finland	France	French Guiana	French Polynesia	Gabon	Gambia	Georgia	Germany	Ghana	Gibraltar	Greece	Greenland	Grenada	Guadeloupe	Guam	Guatemala	Guinea	Guinea-Bissau	Guyana	Haiti	Holy See	Honduras	Hungary	Iceland	India	Indonesia	Iran (Islamic Republic of)	Iraq	Ireland	Isle of Man	Israel	Italy	Jamaica	Japan	Jordan	Kazakhstan	Kenya	Kiribati	Kuwait	Kyrgyzstan	Lao People's Democratic Republic	Latvia	Lebanon	Lesotho	Liberia	Libya	Liechtenstein	Lithuania	Luxembourg	Madagascar	Malawi	Malaysia	Maldives	Mali	Malta	Marshall Islands	Martinique	Mauritania	Mauritius	Mayotte	Mexico	Micronesia (Fed. States of)	Monaco	Mongolia	Montenegro	Montserrat	Morocco	Mozambique	Myanmar	Namibia	Nauru	Nepal	Netherlands	New Caledonia	New Zealand	Nicaragua	Niger	Nigeria	Niue	North Macedonia	Northern Mariana Islands	Norway	Oman	Pakistan	Palau	Panama	Papua New Guinea	Paraguay	Peru	Philippines	Poland	Portugal	Puerto Rico	Qatar	Republic of Korea	Republic of Moldova	Réunion	Romania	Russian Federation	Rwanda	Saint Helena	Saint Kitts and Nevis	Saint Lucia	Saint Pierre and Miquelon	Saint Vincent and the Grenadines	Samoa	San Marino	Sao Tome and Principe	Saudi Arabia	Senegal	Serbia	Seychelles	Sierra Leone	Singapore	Sint Maarten (Dutch part)	Slovakia	Slovenia	Solomon Islands	Somalia	South Africa	South Sudan	Spain	Sri Lanka	State of Palestine	Sudan	Suriname	Sweden	Switzerland	Syrian Arab Republic	Tajikistan	Thailand	Timor-Leste	Togo	Tokelau	Tonga	Trinidad and Tobago	Tunisia	Turkey	Turkmenistan	Turks and Caicos Islands	Tuvalu	Uganda	Ukraine	United Arab Emirates	United Kingdom	United Republic of Tanzania	United States of America	United States Virgin Islands	Uruguay	Uzbekistan	Vanuatu	Venezuela (Bolivarian Republic of)	Viet Nam	Wallis and Futuna Islands	Western Sahara	Yemen	Zambia	Zimbabwe	Gender
1990	1990001	WORLD	NA	900	NA	77661689	3412163	1159981	3711922	109462	521333	1049	1769	405495	1069	9817	203585	436870	4742	140739	234736	767943	10628	6120	3162922	40155	815549	157201	15094	127252	34703	13925	115446	1627	427370	19235	226370	1340	15605	300437	573295	173900	42683	174750	60068	367069	192	24207	168103	8755	248431	2197578	272012	48482	458889	22062	51516	8377	29259	185637	214262	399247	21911	90736	130307	19237	217217	91640	2890	19308	198693	105646	822942	608584	17710	89618	54319	25590	856303	141	2989	43627	101915	572512	1251	1595	7736	20808	446509	1341083	187378	5671	533599	3774	19604	2590	694	175902	201530	27432	110794	295179	13	66907	192180	8627	3808569	952658	362238	854950	413220	5098	139359	1739405	268189	301529	187416	1427482	123049	1937	45235	252638	237318	99944	292677	145916	260645	40088	1447	152604	18562	25594	84442	276687	1698	360545	56497	707	5609	75174	51465	986	2358113	3935	2256	11762	38819	3366	984687	1202056	376109	9361	627	389294	357079	1928	196197	219810	94787	255399	2832	217104	1204	59894	6450	1971601	1369	66563	1573	131629	150342	833735	706964	965010	574860	6028	808147	290611	1571	377349	5516075	276832	386	9534	10053	240	17907	36602	572	6742	57765	230827	393867	17221	31968	74563	7556	65930	39984	1134	448855	150244	266794	683813	459025	888310	336129	87554	87854	145574	355555	264426	122822	5921	95240	836	16197	87603	281462	1362030	127446	1006	1203	157870	2634767	44699	1876268	107187	869586	1175	116499	685800	2646	83012	636418	3664	92655	290313	42599	105757	Male
1990	1990002	UN development groups	NA	NA	NA	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	Male
1990	1990003	More developed regions	b	901	NA	40426798	1809849	507312	69707	108188	494762	505	1735	80403	280	6595	101201	321103	2020	109322	218340	516211	9741	1005	93241	37394	724917	143609	13289	8519	34623	353	21162	1465	423323	1516	114297	84	3341	63086	3635	2398	36722	114773	24650	336941	141	4634	2131	8720	89567	722302	254737	6926	157368	9295	33711	8358	19481	24371	212337	368086	19805	85825	126337	7690	50315	87959	1832	11381	166342	81237	159680	257214	4069	12907	52689	267	58633	130	2989	41697	100465	394850	29	137	4262	8577	392298	1123196	73769	5633	470860	3587	10793	81	22	123986	8558	9544	97936	145320	5	48351	179630	8584	635626	150834	302178	85827	409302	5098	88873	1448019	262462	204669	48955	1368478	78800	444	8502	234508	126654	93400	208539	179	10070	14153	1335	138545	18435	18026	6004	92098	163	33381	56397	547	158	9331	46175	0	2337466	1352	2135	11675	38702	2413	896077	37792	21839	633	217	4503	326551	536	187589	85610	2080	89477	2812	191059	136	55383	416	260857	6	44012	934	5946	101746	507133	631908	751116	565279	477	678290	263476	64	308201	3278092	4492	283	4522	4593	211	9419	28658	536	2760	12206	94339	392273	13807	11074	42019	7053	65626	39016	530	35133	107091	0	425753	148767	18878	9471	77368	82846	125358	76064	229232	70955	5436	11271	755	14745	80657	257235	1311909	122635	107	550	36354	2241388	3215	1713505	31252	436579	39	26639	521303	461	48599	557518	743	265	7284	12964	19649	Male
1990	1990004	Less developed regions	c	902	NA	37234891	1602314	652669	3642215	1274	26571	544	34	325092	789	3222	102384	115767	2722	31417	16396	251732	887	5115	3069681	2761	90632	13592	1805	118733	80	13572	94284	162	4047	17719	112073	1256	12264	237351	569660	171502	5961	59977	35418	30128	51	19573	165972	35	158864	1475276	17275	41556	301521	12767	17805	19	9778	161266	1925	31161	2106	4911	3970	11547	166902	3681	1058	7927	32351	24409	663262	351370	13641	76711	1630	25323	797670	11	0	1930	1450	177662	1222	1458	3474	12231	54211	217887	113609	38	62739	187	8811	2509	672	51916	192972	17888	12858	149859	8	18556	12550	43	3172943	801824	60060	769123	3918	0	50486	291386	5727	96860	138461	59004	44249	1493	36733	18130	110664	6544	84138	145737	250575	25935	112	14059	127	7568	78438	184589	1535	327164	100	160	5451	65843	5290	986	20647	2583	121	87	117	953	88610	1164264	354270	8728	410	384791	30528	1392	8608	134200	92707	165922	20	26045	1068	4511	6034	1710744	1363	22551	639	125683	48596	326602	75056	213894	9581	5551	129857	27135	1507	69148	2237983	272340	103	5012	5460	29	8488	7944	36	3982	45559	136488	1594	3414	20894	32544	503	304	968	604	413722	43153	266794	258060	310258	869432	326658	10186	5008	20216	279491	35194	51867	485	83969	81	1452	6946	24227	50121	4811	899	653	121516	393379	41484	162763	75935	433007	1136	89860	164497	2185	34413	78900	2921	92390	283029	29635	86108	Male
1990	1990005	Least developed countries	d	941	NA	5550233	244501	135262	0	0	2501	0	0	296132	0	0	296	0	0	809	155	0	0	49	1287	0	0	503	0	21560	0	8065	0	0	0	278	843	0	0	0	23068	169652	5780	7676	17913	976	0	13807	104323	0	214	129813	31	0	421	4777	12856	0	106	156487	0	858	0	3	0	60	127832	106	475	0	1345	0	16095	0	152	69934	0	58	714494	0	0	376	0	20115	0	0	2815	6676	0	2564	25683	0	21	0	0	0	0	0	118144	16933	0	0	0	0	1	0	158283	40346	0	2776	0	0	0	1696	0	5324	0	0	37703	483	9	0	31777	0	4398	1473	239503	1954	0	0	0	5007	27145	50228	0	66751	0	0	0	55824	70	0	131	0	0	0	0	0	1778	621480	106389	1683	400	5453	296	128	271	0	31791	57513	0	0	0	2299	0	10535	0	0	592	0	208	476	0	5151	703	0	171	0	507	0	772	268902	0	0	0	0	0	18	0	1659	1038	104819	0	3300	19179	5773	0	0	8	39	400790	10672	257061	1069	68	1482	124612	0	628	400	1313	22879	16699	0	12256	0	14	0	92	1	0	0	203	75525	39	24	8556	38482	20539	0	112	1144	3	1325	36996	0	0	175	10694	37287	Male
1990	1990006	Less developed regions, excluding least developed countries	NA	934	NA	31684658	1357813	517407	3642215	1274	24070	544	34	28960	789	3222	102088	115767	2722	30608	16241	251732	887	5066	3068394	2761	90632	13089	1805	97173	80	5507	94284	162	4047	17441	111230	1256	12264	237351	546592	1850	181	52301	17505	29152	51	5766	61649	35	158650	1345463	17244	41556	301100	7990	4949	19	9672	4779	1925	30303	2106	4908	3970	11487	39070	3575	583	7927	31006	24409	647167	351370	13489	6777	1630	25265	83176	11	0	1554	1450	157547	1222	1458	659	5555	54211	215323	87926	38	62718	187	8811	2509	672	51916	74828	955	12858	149859	8	18556	12549	43	3014660	761478	60060	766347	3918	0	50486	289690	5727	91536	138461	59004	6546	1010	36724	18130	78887	6544	79740	144264	11072	23981	112	14059	127	2561	51293	134361	1535	260413	100	160	5451	10019	5220	986	20516	2583	121	87	117	953	86832	542784	247881	7045	10	379338	30232	1264	8337	134200	60916	108409	20	26045	1068	2212	6034	1700209	1363	22551	47	125683	48388	326126	75056	208743	8878	5551	129686	27135	1000	69148	2237211	3438	103	5012	5460	29	8488	7926	36	2323	44521	31669	1594	114	1715	26771	503	304	960	565	12932	32481	9733	256991	310190	867950	202046	10186	4380	19816	278178	12315	35168	485	71713	81	1438	6946	24135	50120	4811	899	450	45991	393340	41460	154207	37453	412468	1136	89748	163353	2182	33088	41904	2921	92390	282854	18941	48821	Male

Tidy & Manipulate Data I

‘All_migrants’ dataset contains data that sums up region, country or area wise data. Exluding these as these will result in data duplicity.

All_migrants <- All_migrants %>% filter(`Type of data (a)`!= "")

Eleminating the columns which are not necessary for analysis.

All_migrants <- All_migrants %>% select(-c(`Sort\r\norder`, Notes, Code, `Type of data (a)`, Total, `Other South`, `Other North`))

head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

Year	Destination	Afghanistan	Albania	Algeria	American Samoa	Andorra	Angola	Anguilla	Antigua and Barbuda	Argentina	Armenia	Aruba	Australia	Austria	Azerbaijan	Bahamas	Bahrain	Bangladesh	Barbados	Belarus	Belgium	Belize	Benin	Bermuda	Bhutan	Bolivia (Plurinational State of)	Bonaire, Sint Eustatius and Saba	Bosnia and Herzegovina	Botswana	Brazil	British Virgin Islands	Brunei Darussalam	Bulgaria	Burkina Faso	Burundi	Cabo Verde	Cambodia	Cameroon	Canada	Cayman Islands	Central African Republic	Chad	Channel Islands	Chile	China	China, Hong Kong SAR	China, Macao SAR	Colombia	Comoros	Congo	Cook Islands	Costa Rica	Côte d'Ivoire	Croatia	Cuba	Curaçao	Cyprus	Czechia	Dem. People's Republic of Korea	Democratic Republic of the Congo	Denmark	Djibouti	Dominica	Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea	Eritrea	Estonia	Eswatini	Ethiopia	Falkland Islands (Malvinas)	Faroe Islands	Fiji	Finland	France	French Guiana	French Polynesia	Gabon	Gambia	Georgia	Germany	Ghana	Gibraltar	Greece	Greenland	Grenada	Guadeloupe	Guam	Guatemala	Guinea	Guinea-Bissau	Guyana	Haiti	Holy See	Honduras	Hungary	Iceland	India	Indonesia	Iran (Islamic Republic of)	Iraq	Ireland	Isle of Man	Israel	Italy	Jamaica	Japan	Jordan	Kazakhstan	Kenya	Kiribati	Kuwait	Kyrgyzstan	Lao People's Democratic Republic	Latvia	Lebanon	Lesotho	Liberia	Libya	Liechtenstein	Lithuania	Luxembourg	Madagascar	Malawi	Malaysia	Maldives	Mali	Malta	Marshall Islands	Martinique	Mauritania	Mauritius	Mayotte	Mexico	Micronesia (Fed. States of)	Monaco	Mongolia	Montenegro	Montserrat	Morocco	Mozambique	Myanmar	Namibia	Nauru	Nepal	Netherlands	New Caledonia	New Zealand	Nicaragua	Niger	Nigeria	Niue	North Macedonia	Northern Mariana Islands	Norway	Oman	Pakistan	Palau	Panama	Papua New Guinea	Paraguay	Peru	Philippines	Poland	Portugal	Puerto Rico	Qatar	Republic of Korea	Republic of Moldova	Réunion	Romania	Russian Federation	Rwanda	Saint Helena	Saint Kitts and Nevis	Saint Lucia	Saint Pierre and Miquelon	Saint Vincent and the Grenadines	Samoa	San Marino	Sao Tome and Principe	Saudi Arabia	Senegal	Serbia	Seychelles	Sierra Leone	Singapore	Sint Maarten (Dutch part)	Slovakia	Slovenia	Solomon Islands	Somalia	South Africa	South Sudan	Spain	Sri Lanka	State of Palestine	Sudan	Suriname	Sweden	Switzerland	Syrian Arab Republic	Tajikistan	Thailand	Timor-Leste	Togo	Tokelau	Tonga	Trinidad and Tobago	Tunisia	Turkey	Turkmenistan	Turks and Caicos Islands	Tuvalu	Uganda	Ukraine	United Arab Emirates	United Kingdom	United Republic of Tanzania	United States of America	United States Virgin Islands	Uruguay	Uzbekistan	Vanuatu	Venezuela (Bolivarian Republic of)	Viet Nam	Wallis and Futuna Islands	Western Sahara	Yemen	Zambia	Zimbabwe	Gender
1990	WORLD	3711922	109462	521333	1049	1769	405495	1069	9817	203585	436870	4742	140739	234736	767943	10628	6120	3162922	40155	815549	157201	15094	127252	34703	13925	115446	1627	427370	19235	226370	1340	15605	300437	573295	173900	42683	174750	60068	367069	192	24207	168103	8755	248431	2197578	272012	48482	458889	22062	51516	8377	29259	185637	214262	399247	21911	90736	130307	19237	217217	91640	2890	19308	198693	105646	822942	608584	17710	89618	54319	25590	856303	141	2989	43627	101915	572512	1251	1595	7736	20808	446509	1341083	187378	5671	533599	3774	19604	2590	694	175902	201530	27432	110794	295179	13	66907	192180	8627	3808569	952658	362238	854950	413220	5098	139359	1739405	268189	301529	187416	1427482	123049	1937	45235	252638	237318	99944	292677	145916	260645	40088	1447	152604	18562	25594	84442	276687	1698	360545	56497	707	5609	75174	51465	986	2358113	3935	2256	11762	38819	3366	984687	1202056	376109	9361	627	389294	357079	1928	196197	219810	94787	255399	2832	217104	1204	59894	6450	1971601	1369	66563	1573	131629	150342	833735	706964	965010	574860	6028	808147	290611	1571	377349	5516075	276832	386	9534	10053	240	17907	36602	572	6742	57765	230827	393867	17221	31968	74563	7556	65930	39984	1134	448855	150244	266794	683813	459025	888310	336129	87554	87854	145574	355555	264426	122822	5921	95240	836	16197	87603	281462	1362030	127446	1006	1203	157870	2634767	44699	1876268	107187	869586	1175	116499	685800	2646	83012	636418	3664	92655	290313	42599	105757	Male
1990	UN development groups	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	..	Male
1990	More developed regions	69707	108188	494762	505	1735	80403	280	6595	101201	321103	2020	109322	218340	516211	9741	1005	93241	37394	724917	143609	13289	8519	34623	353	21162	1465	423323	1516	114297	84	3341	63086	3635	2398	36722	114773	24650	336941	141	4634	2131	8720	89567	722302	254737	6926	157368	9295	33711	8358	19481	24371	212337	368086	19805	85825	126337	7690	50315	87959	1832	11381	166342	81237	159680	257214	4069	12907	52689	267	58633	130	2989	41697	100465	394850	29	137	4262	8577	392298	1123196	73769	5633	470860	3587	10793	81	22	123986	8558	9544	97936	145320	5	48351	179630	8584	635626	150834	302178	85827	409302	5098	88873	1448019	262462	204669	48955	1368478	78800	444	8502	234508	126654	93400	208539	179	10070	14153	1335	138545	18435	18026	6004	92098	163	33381	56397	547	158	9331	46175	0	2337466	1352	2135	11675	38702	2413	896077	37792	21839	633	217	4503	326551	536	187589	85610	2080	89477	2812	191059	136	55383	416	260857	6	44012	934	5946	101746	507133	631908	751116	565279	477	678290	263476	64	308201	3278092	4492	283	4522	4593	211	9419	28658	536	2760	12206	94339	392273	13807	11074	42019	7053	65626	39016	530	35133	107091	0	425753	148767	18878	9471	77368	82846	125358	76064	229232	70955	5436	11271	755	14745	80657	257235	1311909	122635	107	550	36354	2241388	3215	1713505	31252	436579	39	26639	521303	461	48599	557518	743	265	7284	12964	19649	Male
1990	Less developed regions	3642215	1274	26571	544	34	325092	789	3222	102384	115767	2722	31417	16396	251732	887	5115	3069681	2761	90632	13592	1805	118733	80	13572	94284	162	4047	17719	112073	1256	12264	237351	569660	171502	5961	59977	35418	30128	51	19573	165972	35	158864	1475276	17275	41556	301521	12767	17805	19	9778	161266	1925	31161	2106	4911	3970	11547	166902	3681	1058	7927	32351	24409	663262	351370	13641	76711	1630	25323	797670	11	0	1930	1450	177662	1222	1458	3474	12231	54211	217887	113609	38	62739	187	8811	2509	672	51916	192972	17888	12858	149859	8	18556	12550	43	3172943	801824	60060	769123	3918	0	50486	291386	5727	96860	138461	59004	44249	1493	36733	18130	110664	6544	84138	145737	250575	25935	112	14059	127	7568	78438	184589	1535	327164	100	160	5451	65843	5290	986	20647	2583	121	87	117	953	88610	1164264	354270	8728	410	384791	30528	1392	8608	134200	92707	165922	20	26045	1068	4511	6034	1710744	1363	22551	639	125683	48596	326602	75056	213894	9581	5551	129857	27135	1507	69148	2237983	272340	103	5012	5460	29	8488	7944	36	3982	45559	136488	1594	3414	20894	32544	503	304	968	604	413722	43153	266794	258060	310258	869432	326658	10186	5008	20216	279491	35194	51867	485	83969	81	1452	6946	24227	50121	4811	899	653	121516	393379	41484	162763	75935	433007	1136	89860	164497	2185	34413	78900	2921	92390	283029	29635	86108	Male
1990	Least developed countries	0	0	2501	0	0	296132	0	0	296	0	0	809	155	0	0	49	1287	0	0	503	0	21560	0	8065	0	0	0	278	843	0	0	0	23068	169652	5780	7676	17913	976	0	13807	104323	0	214	129813	31	0	421	4777	12856	0	106	156487	0	858	0	3	0	60	127832	106	475	0	1345	0	16095	0	152	69934	0	58	714494	0	0	376	0	20115	0	0	2815	6676	0	2564	25683	0	21	0	0	0	0	0	118144	16933	0	0	0	0	1	0	158283	40346	0	2776	0	0	0	1696	0	5324	0	0	37703	483	9	0	31777	0	4398	1473	239503	1954	0	0	0	5007	27145	50228	0	66751	0	0	0	55824	70	0	131	0	0	0	0	0	1778	621480	106389	1683	400	5453	296	128	271	0	31791	57513	0	0	0	2299	0	10535	0	0	592	0	208	476	0	5151	703	0	171	0	507	0	772	268902	0	0	0	0	0	18	0	1659	1038	104819	0	3300	19179	5773	0	0	8	39	400790	10672	257061	1069	68	1482	124612	0	628	400	1313	22879	16699	0	12256	0	14	0	92	1	0	0	203	75525	39	24	8556	38482	20539	0	112	1144	3	1325	36996	0	0	175	10694	37287	Male
1990	Less developed regions, excluding least developed countries	3642215	1274	24070	544	34	28960	789	3222	102088	115767	2722	30608	16241	251732	887	5066	3068394	2761	90632	13089	1805	97173	80	5507	94284	162	4047	17441	111230	1256	12264	237351	546592	1850	181	52301	17505	29152	51	5766	61649	35	158650	1345463	17244	41556	301100	7990	4949	19	9672	4779	1925	30303	2106	4908	3970	11487	39070	3575	583	7927	31006	24409	647167	351370	13489	6777	1630	25265	83176	11	0	1554	1450	157547	1222	1458	659	5555	54211	215323	87926	38	62718	187	8811	2509	672	51916	74828	955	12858	149859	8	18556	12549	43	3014660	761478	60060	766347	3918	0	50486	289690	5727	91536	138461	59004	6546	1010	36724	18130	78887	6544	79740	144264	11072	23981	112	14059	127	2561	51293	134361	1535	260413	100	160	5451	10019	5220	986	20516	2583	121	87	117	953	86832	542784	247881	7045	10	379338	30232	1264	8337	134200	60916	108409	20	26045	1068	2212	6034	1700209	1363	22551	47	125683	48388	326126	75056	208743	8878	5551	129686	27135	1000	69148	2237211	3438	103	5012	5460	29	8488	7926	36	2323	44521	31669	1594	114	1715	26771	503	304	960	565	12932	32481	9733	256991	310190	867950	202046	10186	4380	19816	278178	12315	35168	485	71713	81	1438	6946	24135	50120	4811	899	450	45991	393340	41460	154207	37453	412468	1136	89748	163353	2182	33088	41904	2921	92390	282854	18941	48821	Male

For this analysis we need total number of migrants from each country. In order to achieve this, we will tidy up the data and convert the Country’s names to a single column and the number of migrants in another. We will use gather() function to reorganize the data.

All_migrants <- All_migrants %>% gather(key = Origination, value = "no_of_migrants", -c(Year, Destination, Gender)) 
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

Year	Destination	Gender	Origination	no_of_migrants
1990	WORLD	Male	Afghanistan	3711922
1990	UN development groups	Male	Afghanistan	..
1990	More developed regions	Male	Afghanistan	69707
1990	Less developed regions	Male	Afghanistan	3642215
1990	Least developed countries	Male	Afghanistan	0
1990	Less developed regions, excluding least developed countries	Male	Afghanistan	3642215

Understanding:

The data contains different data types which needs to be altered according to the analysis need. In this study, we had to transform the data twice.

The variable no_of_migrants is in character format, this needs to be changed to numeric in order to perform mathematical operations on it.

All_migrants$no_of_migrants <- as.numeric(All_migrants$no_of_migrants)
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

Year	Destination	Gender	Origination	no_of_migrants	X
1990	Namibia	Male	Afghanistan	26	Africa
1990	South Africa	Male	Afghanistan	37	Africa
1990	Egypt	Male	Afghanistan	194	Africa
1990	Libya	Male	Afghanistan	556	Africa
1990	Azerbaijan	Male	Afghanistan	175	Asia
1990	Bahrain	Male	Afghanistan	154	Asia

NA

Scan I

Removing observations having “..” values in no_of_migrants.

missingValues<- sapply(All_migrants, is.special)
All_migrants <- All_migrants %>% filter(no_of_migrants != "..")

Removing the “,” from the no_of_migrants in order to translate the data to all digit value. Followed by removal of the NA values.

All_migrants$no_of_migrants <- str_replace_all(All_migrants$no_of_migrants, "\\,","")
All_migrants <- All_migrants %>% filter(!is.na(no_of_migrants))
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

Year	Destination	Gender	Origination	no_of_migrants
1990	WORLD	Male	Afghanistan	3711922
1990	More developed regions	Male	Afghanistan	69707
1990	Less developed regions	Male	Afghanistan	3642215
1990	Least developed countries	Male	Afghanistan	0
1990	Less developed regions, excluding least developed countries	Male	Afghanistan	3642215
1990	High-income countries	Male	Afghanistan	172416

Merging dataset for grouping:

Merging the Countrymap dataset with the All_migrants in order to map the countries to the respective continents.

All_migrants <- All_migrants %>% inner_join(Countrymap, by = c("Destination" = "Entity"))

head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

Year	Destination	Gender	Origination	no_of_migrants	X
1990	Namibia	Male	Afghanistan	26	Africa
1990	South Africa	Male	Afghanistan	37	Africa
1990	Egypt	Male	Afghanistan	194	Africa
1990	Libya	Male	Afghanistan	556	Africa
1990	Azerbaijan	Male	Afghanistan	175	Asia
1990	Bahrain	Male	Afghanistan	154	Asia

Tidy & Manipulate Data II

In this step, we are summing up the total number of migrants from each Continent for every year. A new variable is introduced which contains the total no. of migrant counts in million.

MigrantDetails <- All_migrants %>% group_by(Year, X) %>% summarise(Migrants_in_mil = 
                                            sum(no_of_migrants)) %>% mutate(Migrants_in_mil = Migrants_in_mil/1000000)
names(MigrantDetails)[2]<- "Continent"

head(MigrantDetails) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

Year	Continent	Migrants_in_mil
1990	Africa	11.476494
1990	Asia	38.001374
1990	Europe	36.095745
1990	North America	6.932044
1990	Oceania	4.683402
1990	South America	3.036789

NA

Understanding (2):

The second conversion of the data is to be done for the Year variable. Year is changed to an ordered factor for further analysis.

MigrantDetails$Year<- factor(MigrantDetails$Year, levels= c(1990,1995,2000,2005,2010,2015,2019), ordered = TRUE)
class(MigrantDetails$Year)

[1] "ordered" "factor"

MigrantDetails$Year

 [1] 1990 1990 1990 1990 1990 1990 1995 1995 1995 1995 1995 1995 2000 2000 2000 2000 2000 2000 2005 2005 2005 2005
[23] 2005 2005 2010 2010 2010 2010 2010 2010 2015 2015 2015 2015 2015 2015 2019 2019 2019 2019 2019 2019
Levels: 1990 < 1995 < 2000 < 2005 < 2010 < 2015 < 2019

Transform

We will now see if the data is normally distributed or not. Normalisation is necessary in order to avoid any incorrect prediction or keep the error at minimal.

Data distribution of MigrantDetails is shown by plotting a histogram of the data followed by a line graph representing the normality of the data.


hist(MigrantDetails$Migrants_in_mil, probability = TRUE)

curve(dnorm(x, mean = mean(MigrantDetails$Migrants_in_mil),
            sd = sd(MigrantDetails$Migrants_in_mil)) , add = TRUE, col= "red")

NA
NA

As we can see the data is not normally distributed but right skewed instead, we will apply transformation to it in order to achieve normal distribution. We take the logarithmic value of Migrants_in_mil to attain normality.

MigrantDetails$Migrants_in_mil <- log(MigrantDetails$Migrants_in_mil)

hist(MigrantDetails$Migrants_in_mil, probability = TRUE)
curve(dnorm(x, mean = mean(MigrantDetails$Migrants_in_mil),
            sd = sd(MigrantDetails$Migrants_in_mil)) , add = TRUE, col= "red")

Scan II

As we can see the data is normally distributed we will check for the univariate outliers by plotting the data in a boxplot. Note: We are not performing a “Tukey’s method of outlier detection” as the data is normally distributed.

boxplot(MigrantDetails$Migrants_in_mil)

From the bpoxplot we can see that the data doesn’t contain any outliers hence no data modification were done.

Visualisation of the data:

We can view the stats using ggplot. The graph shows the number of migrants migrated from each Continents in a span of 19 years.

ggplot(data=MigrantDetails, aes(x = Continent, 
y = Migrants_in_mil, fill= Year))+
  geom_bar(stat="identity", position=position_dodge() ) + ggtitle("Migration in various Continents from 1990 to 2019")

Conclusion:

From the above analysis we can conclude that migration have increased in all over the world over the time. This data can also be used for analysis of the significance raise in migration continent wise.

Reference:

[1] Country_continent data: https://ourworldindata.org/grapher/continents-according-to-our-world-in-data?overlay=data

[2] UN_MigrantStockByOriginAndDestination_2019 data: https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates17.asp

[3] Kabel styling: https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html

[4] R Markdown formatting: https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf

---
title: "MATH2349 Semester 1, 2020"
author: "Sudeshna Nayek- S3821041"
subtitle: Assignment 2
output:
  html_notebook: default
---

### Required packages 


```{r}
library(readxl)
library(knitr) 
library(stringr)
library(dplyr)
library(tidyr)
library(magrittr)
library(kableExtra)
library(ggplot2)
```


### Executive Summary 

This study involves 3 datasets merged together for analysis purpose.

1. UN_MigrantStockByOriginAndDestination_2019 (Table 2-Male migrants). [1]
2. UN_MigrantStockByOriginAndDestination_2019 (Table 3-Female migrants). [1]
3. continents-according-to-our-world-in-data. [2]

**Steps performed before importing the datasets in R.**

* From the UN_MigrantStockByOriginAndDestination_2019 dataset, data of Male and Female migrants were transferred into two excel sheets.
* Rows 1-14 were deleted.
* Rows 15 and 16 were merged for the first 6 columns, hence unmerged the cells and pasted the content in 16th row.
* Deleted 15th row.

The final Male and Female datasets contains 1982 rows and 241 columns each.

For the Continent dataset, there are 286 rows and 4 columns. Entire file is imported in R and then the columns containing Countries and Continents information are retained.

### Data 

The "UN_MigrantStockByOriginAndDestination_2019.xlsx" dataset present in the UN- Department of Economic and social affairs website, shows an approximated number of international migrants by origin, sex and age. Data are presented for years 1990, 1995, 2000, 2005, 2010, 2015 and 2019 and includes all countries/areas of the world. 
The data is divided into 3 tables, out of which we will use Table 2 (Stats of Male migrants) and Table 3 (Stats of Female migrants) for this study. 

The data contains the following attributes:

 * *Year:* Year of migration
 * *Major area,region,country or area of destinations:* The place of origination 
 * *Code:* Codes of respective countries. 
 * *Type of data(a):* Indicates the type of data used in deriving the estimates presented. More details present in the Notes tab in the dataset.
 * *Country or area of origin:* Contains the estimates of migrants from different parts of the world.

Another dataset "continents-according-to-our-world-in-data.csv" present in the Our world in data website, is joined to create buckets of the countries with respect to the Continents they are located in.


```{r}
Male_migrant<- read_excel("Male Migrants.xlsx")
Male_migrant$Gender <- "Male"

#[3]
head(Male_migrant) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down"))%>% scroll_box(width="100%",height="350px")


Female_migrant<- read_excel("Female Migrants.xlsx")
Female_migrant$Gender <- "Female"
head(Female_migrant) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down"))%>% scroll_box(width="100%",height="350px")


Countrymap<- read.csv("continents-according-to-our-world-in-data.csv", header = TRUE, stringsAsFactors = FALSE)
Countrymap<- Countrymap %>%  select (Entity, X)
head(Countrymap)
```

The Male and Female migrants data are then merged together into one dataset "All_migrants" by row. Also, renamed the 3rd column for convenience.

```{r}
All_migrants<- rbind(Male_migrant,Female_migrant)
names(All_migrants)[3] <- "Destination"
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")
```


###	Tidy & Manipulate Data I 

'All_migrants' dataset contains data that sums up region, country or area wise data. Exluding these as these will result in data duplicity.

```{r}
All_migrants <- All_migrants %>% filter(`Type of data (a)`!= "")
```

Eleminating the columns which are not necessary for analysis.

```{r}
All_migrants <- All_migrants %>% select(-c(`Sort\r\norder`, Notes, Code, `Type of data(a)`, Total, `Other South`, `Other North`))

head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")
```

For this analysis we need total number of migrants from each country. In order to achieve this, we will tidy up the data and convert the Country's names to a single column and the number of migrants in another. We will use gather() function to reorganize the data.

```{r}
All_migrants <- All_migrants %>% gather(key = Origination, value = "no_of_migrants", -c(Year, Destination, Gender)) 
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")
```

### Understanding:

The data contains different data types which needs to be altered according to the analysis need. In this study, we had to transform the data twice.

1. The variable no_of_migrants is in character format, this needs to be changed to numeric in order to perform mathematical operations on it. 

```{r}
All_migrants$no_of_migrants <- as.numeric(All_migrants$no_of_migrants)
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

```


###	Scan I 

Removing observations having ".." values in no_of_migrants.

```{r}
missingValues<- sapply(All_migrants, is.special)
All_migrants <- All_migrants %>% filter(no_of_migrants != "..")
```

Removing the "," from the no_of_migrants in order to translate the data to all digit value. Followed by removal of the NA values. 

```{r}
All_migrants$no_of_migrants <- str_replace_all(All_migrants$no_of_migrants, "\\,","")
All_migrants <- All_migrants %>% filter(!is.na(no_of_migrants))
head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")
```

**Merging dataset for grouping:**

Merging the Countrymap dataset with the All_migrants in order to map the countries to the respective continents.

```{r}
All_migrants <- All_migrants %>% inner_join(Countrymap, by = c("Destination" = "Entity"))

head(All_migrants) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")
```

###	Tidy & Manipulate Data II 

In this step, we are summing up the total number of migrants from each Continent for every year. A new variable is introduced which contains the total no. of migrant counts in million.

```{r}
MigrantDetails <- All_migrants %>% group_by(Year, X) %>% summarise(Migrants_in_mil = 
                                            sum(no_of_migrants)) %>% mutate(Migrants_in_mil = Migrants_in_mil/1000000)
names(MigrantDetails)[2]<- "Continent"

head(MigrantDetails) %>% kable(booktabs= T) %>% kable_styling(latex_options = c("striped", "scale_down")) %>% scroll_box(width="100%",height="350px")

```


### Understanding (2):

2. The second conversion of the data is to be done for the Year variable. Year is changed to an ordered factor for further analysis. 

```{r}
MigrantDetails$Year<- factor(MigrantDetails$Year, levels= c(1990,1995,2000,2005,2010,2015,2019), ordered = TRUE)
class(MigrantDetails$Year)
MigrantDetails$Year

```


###	Transform 
We will now see if the data is normally distributed or not. Normalisation is necessary in order to avoid any incorrect prediction or keep the error at minimal.

Data distribution of MigrantDetails is shown by plotting a histogram of the data followed by a line graph representing the normality of the data.

```{r}

hist(MigrantDetails$Migrants_in_mil, probability = TRUE)

curve(dnorm(x, mean = mean(MigrantDetails$Migrants_in_mil),
            sd = sd(MigrantDetails$Migrants_in_mil)) , add = TRUE, col= "red")


```

As we can see the data is not normally distributed but right skewed instead, we will apply transformation to it in order to achieve normal distribution. We take the logarithmic value of Migrants_in_mil to attain normality.

```{r}
MigrantDetails$Migrants_in_mil <- log(MigrantDetails$Migrants_in_mil)

hist(MigrantDetails$Migrants_in_mil, probability = TRUE)
curve(dnorm(x, mean = mean(MigrantDetails$Migrants_in_mil),
            sd = sd(MigrantDetails$Migrants_in_mil)) , add = TRUE, col= "red")

```


###	Scan II

As we can see the data is normally distributed we will check for the univariate outliers by plotting the data in a boxplot.
*Note:* We are not performing a "Tukey's method of outlier detection" as the data is normally distributed.

```{r}
boxplot(MigrantDetails$Migrants_in_mil)
```

From the bpoxplot we can see that the data doesn't contain any outliers hence no data modification were done.


### Visualisation of the data:

We can view the stats using ggplot. The graph shows the number of migrants migrated from each Continents in a span of 19 years.

```{r}
ggplot(data=MigrantDetails, aes(x = Continent, 
y = Migrants_in_mil, fill= Year))+
  geom_bar(stat="identity", position=position_dodge() ) + ggtitle("Migration in various Continents from 1990 to 2019")

```

### Conclusion:

From the above analysis we can conclude that migration have increased in all over the world over the time. 
This data can also be used for analysis of the significance raise in migration continent wise.


### Reference: 


[1] Country_continent data: https://ourworldindata.org/grapher/continents-according-to-our-world-in-data?overlay=data

[2] UN_MigrantStockByOriginAndDestination_2019 data: https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates17.asp

[3] Kabel styling: https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html

[4] R Markdown formatting: https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf

