Take Home Ex 4

Author

Daisy

Published

February 26, 2023

pacman::p_load(stringr,scales, viridis, lubridate, ggthemes, gridExtra, readxl, knitr, data.table, ggHoriPlot, plotly,tidyverse,ggiraph,ggstatsplot, performance,ggHoriPlot,ggplot2,braidR)

# library(CGPfunctions)
# Error: package or namespace load failed for ‘CGPfunctions’:'namespace:parameters'没有出口‘standard_error_robust’这个对象

CGPfunctions

im <- read_excel("trade.xlsx",sheet = "T1",range = "A10:UT129")
ex <- read_excel("trade.xlsx",sheet = "T2",range = "A10:UT101")

kable(head(im))

Data Series	2023 Jan	2022 Dec	2022 Nov	2022 Oct	2022 Sep	2022 Aug	2022 Jul	2022 Jun	2022 May	2022 Apr	2022 Mar	2022 Feb	2022 Jan	2021 Dec	2021 Nov	2021 Oct	2021 Sep	2021 Aug	2021 Jul	2021 Jun	2021 May	2021 Apr	2021 Mar	2021 Feb	2021 Jan	2020 Dec	2020 Nov	2020 Oct	2020 Sep	2020 Aug	2020 Jul	2020 Jun	2020 May	2020 Apr	2020 Mar	2020 Feb	2020 Jan	2019 Dec	2019 Nov	2019 Oct	2019 Sep	2019 Aug	2019 Jul	2019 Jun	2019 May	2019 Apr	2019 Mar	2019 Feb	2019 Jan	2018 Dec	2018 Nov	2018 Oct	2018 Sep	2018 Aug	2018 Jul	2018 Jun	2018 May	2018 Apr	2018 Mar	2018 Feb	2018 Jan	2017 Dec	2017 Nov	2017 Oct	2017 Sep	2017 Aug	2017 Jul	2017 Jun	2017 May	2017 Apr	2017 Mar	2017 Feb	2017 Jan	2016 Dec	2016 Nov	2016 Oct	2016 Sep	2016 Aug	2016 Jul	2016 Jun	2016 May	2016 Apr	2016 Mar	2016 Feb	2016 Jan	2015 Dec	2015 Nov	2015 Oct	2015 Sep	2015 Aug	2015 Jul	2015 Jun	2015 May	2015 Apr	2015 Mar	2015 Feb	2015 Jan	2014 Dec	2014 Nov	2014 Oct	2014 Sep	2014 Aug	2014 Jul	2014 Jun	2014 May	2014 Apr	2014 Mar	2014 Feb	2014 Jan	2013 Dec	2013 Nov	2013 Oct	2013 Sep	2013 Aug	2013 Jul	2013 Jun	2013 May	2013 Apr	2013 Mar	2013 Feb	2013 Jan	2012 Dec	2012 Nov	2012 Oct	2012 Sep	2012 Aug	2012 Jul	2012 Jun	2012 May	2012 Apr	2012 Mar	2012 Feb	2012 Jan	2011 Dec	2011 Nov	2011 Oct	2011 Sep	2011 Aug	2011 Jul	2011 Jun	2011 May	2011 Apr	2011 Mar	2011 Feb	2011 Jan	2010 Dec	2010 Nov	2010 Oct	2010 Sep	2010 Aug	2010 Jul	2010 Jun	2010 May	2010 Apr	2010 Mar	2010 Feb	2010 Jan	2009 Dec	2009 Nov	2009 Oct	2009 Sep	2009 Aug	2009 Jul	2009 Jun	2009 May	2009 Apr	2009 Mar	2009 Feb	2009 Jan	2008 Dec	2008 Nov	2008 Oct	2008 Sep	2008 Aug	2008 Jul	2008 Jun	2008 May	2008 Apr	2008 Mar	2008 Feb	2008 Jan	2007 Dec	2007 Nov	2007 Oct	2007 Sep	2007 Aug	2007 Jul	2007 Jun	2007 May	2007 Apr	2007 Mar	2007 Feb	2007 Jan	2006 Dec	2006 Nov	2006 Oct	2006 Sep	2006 Aug	2006 Jul	2006 Jun	2006 May	2006 Apr	2006 Mar	2006 Feb	2006 Jan	2005 Dec	2005 Nov	2005 Oct	2005 Sep	2005 Aug	2005 Jul	2005 Jun	2005 May	2005 Apr	2005 Mar	2005 Feb	2005 Jan	2004 Dec	2004 Nov	2004 Oct	2004 Sep	2004 Aug	2004 Jul	2004 Jun	2004 May	2004 Apr	2004 Mar	2004 Feb	2004 Jan	2003 Dec	2003 Nov	2003 Oct	2003 Sep	2003 Aug	2003 Jul	2003 Jun	2003 May	2003 Apr	2003 Mar	2003 Feb	2003 Jan	2002 Dec	2002 Nov	2002 Oct	2002 Sep	2002 Aug	2002 Jul	2002 Jun	2002 May	2002 Apr	2002 Mar	2002 Feb	2002 Jan	2001 Dec	2001 Nov	2001 Oct	2001 Sep	2001 Aug	2001 Jul	2001 Jun	2001 May	2001 Apr	2001 Mar	2001 Feb	2001 Jan	2000 Dec	2000 Nov	2000 Oct	2000 Sep	2000 Aug	2000 Jul	2000 Jun	2000 May	2000 Apr	2000 Mar	2000 Feb	2000 Jan	1999 Dec	1999 Nov	1999 Oct	1999 Sep	1999 Aug	1999 Jul	1999 Jun	1999 May	1999 Apr	1999 Mar	1999 Feb	1999 Jan	1998 Dec	1998 Nov	1998 Oct	1998 Sep	1998 Aug	1998 Jul	1998 Jun	1998 May	1998 Apr	1998 Mar	1998 Feb	1998 Jan	1997 Dec	1997 Nov	1997 Oct	1997 Sep	1997 Aug	1997 Jul	1997 Jun	1997 May	1997 Apr	1997 Mar	1997 Feb	1997 Jan	1996 Dec	1996 Nov	1996 Oct	1996 Sep	1996 Aug	1996 Jul	1996 Jun	1996 May	1996 Apr	1996 Mar	1996 Feb	1996 Jan	1995 Dec	1995 Nov	1995 Oct	1995 Sep	1995 Aug	1995 Jul	1995 Jun	1995 May	1995 Apr	1995 Mar	1995 Feb	1995 Jan	1994 Dec	1994 Nov	1994 Oct	1994 Sep	1994 Aug	1994 Jul	1994 Jun	1994 May	1994 Apr	1994 Mar	1994 Feb	1994 Jan	1993 Dec	1993 Nov	1993 Oct	1993 Sep	1993 Aug	1993 Jul	1993 Jun	1993 May	1993 Apr	1993 Mar	1993 Feb	1993 Jan	1992 Dec	1992 Nov	1992 Oct	1992 Sep	1992 Aug	1992 Jul	1992 Jun	1992 May	1992 Apr	1992 Mar	1992 Feb	1992 Jan	1991 Dec	1991 Nov	1991 Oct	1991 Sep	1991 Aug	1991 Jul	1991 Jun	1991 May	1991 Apr	1991 Mar	1991 Feb	1991 Jan	1990 Dec	1990 Nov	1990 Oct	1990 Sep	1990 Aug	1990 Jul	1990 Jun	1990 May	1990 Apr	1990 Mar	1990 Feb	1990 Jan	1989 Dec	1989 Nov	1989 Oct	1989 Sep	1989 Aug	1989 Jul	1989 Jun	1989 May	1989 Apr	1989 Mar	1989 Feb	1989 Jan	1988 Dec	1988 Nov	1988 Oct	1988 Sep	1988 Aug	1988 Jul	1988 Jun	1988 May	1988 Apr	1988 Mar	1988 Feb	1988 Jan	1987 Dec	1987 Nov	1987 Oct	1987 Sep	1987 Aug	1987 Jul	1987 Jun	1987 May	1987 Apr	1987 Mar	1987 Feb	1987 Jan	1986 Dec	1986 Nov	1986 Oct	1986 Sep	1986 Aug	1986 Jul	1986 Jun	1986 May	1986 Apr	1986 Mar	1986 Feb	1986 Jan	1985 Dec	1985 Nov	1985 Oct	1985 Sep	1985 Aug	1985 Jul	1985 Jun	1985 May	1985 Apr	1985 Mar	1985 Feb	1985 Jan	1984 Dec	1984 Nov	1984 Oct	1984 Sep	1984 Aug	1984 Jul	1984 Jun	1984 May	1984 Apr	1984 Mar	1984 Feb	1984 Jan	1983 Dec	1983 Nov	1983 Oct	1983 Sep	1983 Aug	1983 Jul	1983 Jun	1983 May	1983 Apr	1983 Mar	1983 Feb	1983 Jan	1982 Dec	1982 Nov	1982 Oct	1982 Sep	1982 Aug	1982 Jul	1982 Jun	1982 May	1982 Apr	1982 Mar	1982 Feb	1982 Jan	1981 Dec	1981 Nov	1981 Oct	1981 Sep	1981 Aug	1981 Jul	1981 Jun	1981 May	1981 Apr	1981 Mar	1981 Feb	1981 Jan	1980 Dec	1980 Nov	1980 Oct	1980 Sep	1980 Aug	1980 Jul	1980 Jun	1980 May	1980 Apr	1980 Mar	1980 Feb	1980 Jan	1979 Dec	1979 Nov	1979 Oct	1979 Sep	1979 Aug	1979 Jul	1979 Jun	1979 May	1979 Apr	1979 Mar	1979 Feb	1979 Jan	1978 Dec	1978 Nov	1978 Oct	1978 Sep	1978 Aug	1978 Jul	1978 Jun	1978 May	1978 Apr	1978 Mar	1978 Feb	1978 Jan	1977 Dec	1977 Nov	1977 Oct	1977 Sep	1977 Aug	1977 Jul	1977 Jun	1977 May	1977 Apr	1977 Mar	1977 Feb	1977 Jan	1976 Dec	1976 Nov	1976 Oct	1976 Sep	1976 Aug	1976 Jul	1976 Jun	1976 May	1976 Apr	1976 Mar	1976 Feb	1976 Jan
Total Merchandise Imports (Thousand Dollars)	44393664.0	49869770.0	50653907.0	53182943.0	55799312.0	58466009.0	61029374.0	59649162.0	57604263.0	56116002.0	58079982.0	44958373.0	50026788.0	54349357.0	50674908.0	47945213.0	45980374.0	44714491.0	46107788.0	45039845.0	41559697.0	45169547.0	47668437.0	37643664.0	39028616.0	40154550.0	38477878.0	38173829.0	38801413.0	36472279.0	37843646.0	35120892.0	31458238.0	35878828.0	40433029.0	39472637.0	41180224.0	41580603.0	42489886.0	42282537.0	39450625.0	40998179.0	42030046.0	38979868.0	42637579.0	41294888.0	40340098.0	36074448.0	41553606.0	42566514.0	45114564.0	47127052.0	41439605.0	43802120.0	44972517.0	41054575.0	42887787.0	38382736.0	39745452.0	34609596.0	38491510.0	40104280.0	41476983.0	39347126.0	35732238.0	38702897.0	36874744.0	36416610.0	39149517.0	34542072.0	39987555.0	33032643.0	36734901.0	39473257.0	37482999.0	33929792.0	32761041.0	33527949.0	31906437.0	34067889.0	32922385.0	32237957.0	33661999.0	30949946.0	30382944.0	34868755.0	33630636.0	35998057.0	35278954.0	35260544.0	37430942.0	36614847.0	33572417.0	36970386.0	38824707.0	29731205.0	35221197.0	38374997.0	35762528.0	41160397.0	40573602.0	36885234.0	40809150.0	38216943.0	41829362.0	42799285.0	43813748.0	37214009.0	41143329.0	38686538	38981788	44871411	41616787	40477278	43645756	39241029	41984109	42058336	38088858	35355063	40563794	37259840	40081332	41031806	37875867	38629287	38990798	41641022	42810254	39686751	43698690	40218167	40266013	38210081	41868766	39592049	38911564	42209188	37264457	39185518	39756662	38518652	43060778	32126643	37583825	36115015	35795622	35674370	35562647	36208637	38506428	37674529	33511638	36719932	36362935	30904685	33290120	33885826	31415491	32568130	33242874	30161686	31670300	29799099	27818221	27868130	27730194	26488010	25726467	29158912	32855642	38957660	41584525	38926024	43365604	40693260	38619579	40555557	38648064	33413957	38681524	34935734	36053976	37110162	32811183	33723677	34151496	34076139	32340922	31945246	32640492	27851480	30763489	33117053	31957329	31068778	32818225	34163426	33155215	33778399	32627841	29327041	31515804	29937624	27375920	30768661	29184752	32075302	29312727	29639219	27943026	28003355	26203333	26658197	28178684	21400853	24945420	25299368	24894387	26438682	25520660	25375603	26647804	24845482	24467390	24103838	25214316	20335381	21035150	22369973	20077600	22247169	20630297	19534347	19832506	19773576	18135977	19325007	20441818	17162800	18539098	17021067	17690794	18915666	17047877	18224600	18668721	18290300	17487220	18179783	17547760	14816724	15909729	15524076	16568462	18073404	16029478	17270265	17417877	17509415	17125687	17491623	19116724	17952511	17612616	20984719	21041672	21400038	21325111	20917474	19865437	20281078	18669571	17564650	18704237	15557241	15863851	17928291	17374626	16958676	16444304	16507617	16699646	15738641	15009917	15254783	15235020	11997660	12992380	14334730	13093541	13587124	14058810	14005064	14735024	14468062	12963883	13958753	16061183	14766759	13830549	17502726	16775834	17623903	17412112	16546053	17547538	15923212	16289060	15832565	16729284	12634879	15788034	15962353	15880423	16335830	14761790	14383157	15785983	15175692	15245468	15494902	16418429	14200033	15539334	15830770	15691830	16058778	14787061	15730943	15532592	14718979	14922431	13734522	14664246	12396937	12244388	14012963	13438282	13689237	13661781	13698273	13591796	13100849	13021032	13560716	12544778	10306501	11769574	12551076	11534142	11935375	12051975	11337445	11852930	11723084	11419871	11578361	11561628	10482915	9574146	11790640	10663094	10522213	9726572	9312418	10213734	10314947	8786922	9337442	9725652	8133215	9002815	9078154	9313806	9060661	8905213	9505211	10418246	9776340	9785944	9161131	9668039	9350929	10171241	9962846	10242614	10218287	8704397	9114427	8836001	8957917	8818823	8569262	9262216	8406030	8712990	8308040	8598657	8828501	8451166	8647696	7657119	8097023	8015335	8303029	8490927	6822113	6644115	8259539	7799082	8102209	7166715	8429892	7503029	8158578	6838830	6790755	6268633	6354236	6555218	6652230	6629129	6190999	6215154	5540682	6219723	5557727	5235972	5532124	4826591	4986178	4828720	4960162	4864192	4952515	4601977	4816309	4491147	4467418	4335254	4613095	4787314	4183673	4472337	4658699	4567826	4830563	4661139	4634948	4505947	4683737	4843832	5118133	5012976	4744192	5555553	4485253	5888685	5224181	4607949	4959779	5587835	5158092	5449376	4704522	5179362	4356968	5531613	4854182	5231031	4837197	4772244	5116032	5045172	5251715	4799308	5316575	4921836	4272017	5086907	4911003	4828021	5271758	4917847	4878895	5136811	4678344	5274497	5027263	5425186	4773926	5121029	4992982	4389186	5069083	4759595	4897741	5416758	4601529	4856759	5247435	4904126	3659930	5452863	4191641	4599084	4612468	4531820	4364383	4610652	4098498	4237842	4315873	4202221	3502180	4078139	3642324	3738350	3372278	3577580	3721574	3218006	3142256	2913750	3045825	2752707	2626946	2582839	2547477	2745427	2798938	2554868	2481365	2615636	2501445	2215845	2382856	2322582	1981827	2452992	2294084	2198239	2310009	2281908	2284011	2157971	2110730	1885173	2147905	2006166	1744496	2101238	2015057	1987479	2036514	1941389	2056149	2041451	1649794	1462483	1942839	1781332	1658989	1830978
America (Million Dollars)	6267.4	6901.5	7529.4	7666.4	7995.9	8633.8	7879.7	8024.0	8521.1	7822.1	7176.1	5385.2	5850.9	6261.1	6127.4	6027.6	5631.6	5750.1	5728.6	5457.4	5191.8	6195.9	5303.5	4164.2	4580.0	4676.4	4588.2	4869.7	4886.4	4132.0	4667.3	4686.2	4259.0	5183.5	5910.8	5314.1	5844.1	5686.4	6274.7	5754.5	5633.1	6128.8	5986.2	5876.4	5797.3	6128.0	5528.7	5381.1	5543.6	6184.6	6013.9	6409.9	5466.5	6031.7	5590.7	5961.2	5661.3	5122.6	5316.7	4113.3	4706.6	5014.4	4895.6	4983.1	4435.1	4888.7	4684.4	5147.3	5372.0	4481.4	5262.9	4097.8	4743.8	4645.3	4908.6	4145.9	4121.3	4754.1	4107.4	4396.5	4284.6	4251.0	4437.5	3813.2	3818.0	4515.7	4773.8	4711.8	5147.4	4484.8	4468.0	4886.9	4418.3	4725.5	5555.1	3842.0	4751.9	5546.4	5087.7	5927.0	4999.0	5276.0	4755.4	4635.5	5276.7	6046.6	6117.0	5579.0	5570.0	4793.3	5269.7	5847.2	6441.8	5843.8	6344.8	5004.4	5550.9	5697.4	4717.4	5173.2	5553.4	4705.3	4806.6	5670.2	5248.5	5521	5556.7	5098.6	5876.5	5073.8	5539.4	5312.3	5349.6	5489.2	6298.3	4816.1	5093.9	5678.4	4871	5308.5	5658.1	5702.4	5382.1	4542.4	4964.5	4842.5	5033	4822.5	4760.2	5220.1	5044.1	5236.8	4845.9	5512.7	5475.1	4255.2	4687.7	4756.3	4653.1	4434	4687.6	4587.5	4335.4	4062	3907.3	4123.3	4114.8	4181.7	4362.4	4585	4657.5	5995.1	5589.1	5483.1	5484.8	5674.7	5361.7	5725.3	5562.2	4929.1	4800.5	4664.4	4818	5116.9	4379.3	4340	4627.6	4883.5	4602.7	4411.5	5232.8	4163.6	4170.7	6260.1	5104.3	4533.3	4380.4	4259.6	4401.5	4313	4209.1	3980.9	4154	4364.3	3499.5	3797.8	3875.5	4519.8	3816.1	4219.3	3622.7	3460	3393.4	3676.4	3624.7	2991.3	2744.1	2896.6	2789.2	3218.7	3145.2	3369.4	4304.7	3418.6	3350.4	3439.7	3753.7	2606.4	2554.4	2649.3	2624.1	3430	3061.2	3087.6	2670.2	2852.8	2460.4	2825.5	3455.3	2755.3	2385.2	2353	2615.5	3059.6	2327	2829.1	2744.2	3265	2628.2	2978.2	2714.8	2672.8	2481.5	2455.7	2829.2	3602.7	2817.7	2777.9	3077.8	3278.4	2919	2974.2	3434.9	3437.6	3263.5	3306.2	3129.6	3568.8	3537.4	3270.4	3440.7	3153.3	2874.4	2774.1	3293.9	2547	2596.4	2981.1	3170.6	2896.5	3014.3	3573.6	2704.7	2870.7	2775.4	2664.8	2952.3	2234.5	2958	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Asia (Million Dollars)	30174.0	33611.7	34733.7	36120.9	37696.3	40911.9	43214.2	42507.2	40534.7	38735.7	42199.9	31611.3	35014.0	39140.3	35949.6	33552.7	32533.4	31492.5	31645.0	31021.0	28497.2	30623.1	31367.8	26122.6	27413.7	28200.4	25844.9	26127.9	27823.2	26052.3	26767.4	24779.3	21718.9	24534.5	26783.6	26588.1	27128.1	27688.7	27905.0	28378.3	26350.3	26671.9	28473.3	26052.4	27541.0	26807.2	26773.8	22762.9	27643.4	27352.6	29765.8	31710.5	27426.4	28371.5	29381.2	26804.0	28718.4	26249.7	26481.1	23434.2	26477.0	27349.4	28430.0	27283.6	25101.0	26491.1	24750.6	24011.2	26420.2	23510.6	27215.5	22103.1	25008.4	26302.5	25122.1	23492.4	23237.1	23121.5	22039.0	23479.2	22261.9	21145.8	21940.0	20726.8	20169.1	23757.8	22217.7	23871.5	24404.3	24174.4	25221.3	24850.7	22616.4	24671.7	23668.0	19628.9	23268.9	24966.7	23279.3	26010.9	27147.5	24350.1	28201.5	26630.8	28177.4	28145.1	28647.9	24865.1	27473.3	26194.8	26960.8	29434	28099.3	26865.3	28328.7	26388.3	27479.2	27552.5	26017.4	23175.7	27814.1	26030.8	28706	27659	26139.6	25790.8	26712.7	28764.9	28848.5	27714	29417.2	28344.3	27629	25534.4	28768.5	27327.1	25482.6	29680.5	26645.3	27103.5	27180.8	25992.4	30249.7	21631.9	25562.3	24935.7	23996.3	24418.9	24785.3	24992.2	26413.2	26406.9	22765.9	25090.5	24259.5	21219.2	23073.5	22487.1	20776.7	22080.2	22322.2	19972.8	21579.4	19884.7	18334	18269.8	18236.3	17144	16335.8	19514.2	21649.8	26393.9	29379.1	26991.5	31290.3	27963.8	26682.5	27528.7	26276	23240.2	27400.2	24856	25264.5	25697.5	23693.4	23975.4	24127.7	23352.4	22424	22114.8	22153.7	19277	22255.5	21897.9	22074.7	21440.3	23585.1	24903.8	23415.6	24201.2	23544.4	20782.2	22493	21273.8	19858.7	21922.2	20785.4	22398.4	21032.8	20528.6	19551.7	20004.7	18552.4	18830.9	20300	15041.3	18349.3	18286.4	17904.6	19014.6	18226.7	17940	18018.1	17472.4	17060.7	15897.4	17154.8	13995.8	14282.4	15920.4	14106.6	15274.3	14344.3	13199.8	13598.3	13564.9	12461.7	12841.7	13511.2	11197.3	12979.4	11348	11960.5	12574.3	11608.9	11990.5	12441.8	12118.3	11479.9	12145.2	11687.5	9607.6	10625.2	10344.1	10938.6	11310.6	10507.3	11285	11146.8	11237.7	10935.5	11443.2	12363.4	11353.3	11516.7	14222.4	14381	14362.3	14492.5	14115.7	13055.8	13690.6	12685.3	11993.5	12326.9	10354.7	10569	11843.6	11309.5	11154.8	10772.4	10074.6	10923.5	10216.6	9653.2	9991.1	9487.9	7499.7	7813.7	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Europe (Million Dollars)	6427.8	7541.8	7242.8	7475.9	8167.6	7433.2	8300.5	7300.2	7030.8	7407.2	7203.2	6479.0	7821.6	7586.3	6872.0	6714.8	6882.1	5919.4	6919.2	7011.2	6563.5	6740.5	8964.0	5403.7	5749.6	6087.4	6133.5	6285.4	5316.9	5225.0	5475.3	4960.7	4629.0	5150.6	6333.3	6209.6	6859.7	6314.8	6827.1	6624.7	6310.1	6533.8	6325.3	6054.8	7987.4	7157.0	7128.7	6730.3	7489.3	7742.8	8032.7	7805.9	7542.6	8218.9	8694.4	7126.1	7181.7	6237.3	6963.0	6061.9	6318.0	6809.7	6876.0	6292.9	5484.9	6491.4	6169.1	6428.7	6434.0	5985.2	6570.9	6067.0	6105.0	7658.1	6535.5	5667.2	4809.6	4920.3	5153.8	5302.1	5624.9	6051.1	6250.8	5365.3	5450.5	5918.4	6080.4	6404.1	4818.9	5765.3	6870.3	6040.6	5765.4	6603.1	8133.0	5199.5	6054.5	6627.2	6444.4	7811.7	6998.1	5962.7	6782.3	5703.1	7038.5	7411.0	7700.1	5447.2	6463.7	6886.7	6065.7	8486.1	6238.1	6664.6	7708.3	7068.7	7676.5	7679.2	6399	6302.6	6531.6	5582.2	5702.9	6536.2	5552.8	6349.8	5869.5	6618.4	7496.7	6261.5	7704.4	6058.4	6675	6551.8	5991.1	6767.4	7664.2	6070.3	5184.6	6047.2	6271.8	6249.8	6845.6	5348.6	6309.2	5790.3	6205.1	6011.9	5466.6	5192.6	6252	5556.7	5374.8	5295.7	5764.4	4877.6	4815.7	5785.2	5179.1	5232.2	5583.9	4841.2	5041.3	5226.4	4834	4893.6	4626.8	4614.2	4384.9	4506.4	5779.6	5746.8	5732.3	5634.4	5627.3	6048.3	5651.5	6380	6028.6	4599.9	5601.7	4821.2	5249.4	5597	4255.6	4730.5	4761.7	5082.3	4663.9	4834.8	4666	3869.9	3820.9	4133.7	4152.9	4475.3	4122.9	4251	4530.7	4137.4	4245.7	3918.8	4276.9	3591.6	3559.3	4305.6	3774.7	4389.5	3807.4	4131.5	4055.9	3978.5	3545.2	3709.8	3575.2	3000.7	3254.6	3544.8	3658.5	3625	3444.2	3531.4	3661.4	3445.1	3477.3	4235.3	3794.5	3205	3803.3	3254.4	2932.6	3109.5	2778.8	2840.2	3074.3	2898.5	2727.9	3112.4	2860.6	2596.1	2657.6	2831.8	2579.4	2818.9	2439.5	2806.3	2926.2	2439.5	2706	2556.9	2591.2	2087.8	2219.7	2325.6	2359.2	2533.7	2309.5	2639.7	2803.8	2424.4	2698.9	2562.2	2803.4	2566.8	2473.3	2939.2	2964.8	2853.7	2780.9	2980.7	2875.4	2852.4	2724.1	2374.3	2719.1	2344	2314.3	2700.2	2477.1	2585.9	2306.3	2440.3	2798.7	2362.7	2301.9	2218.9	2525.5	2030.4	1998.6	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Oceania (Million Dollars)	983.3	1399.9	664.4	1329.8	1544.6	935.9	1060.6	1141.8	1164.7	1559.1	863.9	814.4	810.4	744.8	994.1	1021.2	599.8	744.0	1201.2	890.1	1001.7	1030.5	1131.0	1134.7	705.5	540.9	1412.8	577.3	477.7	586.5	493.1	456.4	441.8	637.6	845.9	694.7	819.7	1369.3	841.1	941.8	866.8	1015.1	815.3	503.7	889.4	820.3	518.9	787.2	614.4	895.3	875.7	801.7	686.2	827.4	773.2	828.5	793.2	490.3	651.5	600.3	591.8	576.9	992.2	570.3	462.7	436.4	1014.6	486.0	674.8	409.0	708.7	402.9	456.0	524.8	576.5	471.9	434.6	433.7	394.6	588.5	498.8	545.2	642.5	665.9	658.6	513.8	377.5	869.1	614.7	595.3	707.2	554.5	545.9	794.3	1145.2	871.8	778.3	1027.5	694.5	1065.0	1173.7	865.4	754.7	844.3	952.1	755.7	802.2	907.8	1280.1	602.8	496.1	845	704.2	770.3	763.5	484.7	810.2	753.7	709.2	550.6	386.7	777.3	713	943.7	767.3	731.7	620.8	766.6	443.9	483.5	876.3	395.7	415	509.8	705.5	537	591.9	601.1	469.8	617.1	523.3	391.5	441.2	485.4	620.3	449.3	450.7	330.1	439.4	714.1	628.5	386.3	430.9	655.5	629.4	438.6	590.1	722.7	643.8	665.7	495.9	586.8	614.6	541.1	668.1	514.9	649.6	437	552.2	453.1	601.7	671.3	751.9	639.5	763.3	810.9	793.6	770.3	575.1	479.9	751.6	461.9	530.8	569.6	374.5	539	500.9	489.9	509	424.4	446	350.4	430.7	618	459.9	490	535.3	609	627.1	916.9	484.9	422.6	457.8	612.9	373	585.2	604.9	589.3	499.9	450.5	488.6	426.7	549	336.9	511.7	283.9	358.8	426.9	432.2	302.2	451.8	440.2	450.9	373.7	383.1	409.4	324.2	336.8	291.4	355.5	335.4	310.4	370.8	296.1	307.1	315	357.5	457.3	471.3	525.6	427.8	321.1	463.6	383.1	506.7	496.9	398.5	367.1	475.5	405.2	460.7	364.7	517.1	323.1	284.6	504.7	322.3	487.3	291.4	460.9	441.2	370.6	404.3	523.1	268.6	365.5	460.1	450.7	408.5	445.8	359.2	409.5	314.6	356.8	276.1	261.3	275.9	336.2	268.6	244.1	259.3	255.3	200.5	216.6	235.5	235.9	215.1	187.4	190.8	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Africa (Million Dollars)	541.1	414.9	483.6	589.9	395.0	551.2	574.4	675.9	352.9	591.9	636.9	668.5	529.9	616.8	731.8	628.8	333.4	808.4	613.8	660.2	305.4	579.6	902.2	818.5	579.9	649.4	498.6	313.5	297.2	476.5	440.6	238.2	409.6	372.6	559.4	666.1	528.6	521.4	642.0	583.2	290.4	648.6	430.0	492.6	422.5	382.4	390.0	413.0	262.8	391.1	426.5	399.1	317.9	352.6	533.0	334.9	533.2	282.7	333.2	399.8	398.1	353.8	283.2	217.2	248.5	395.2	256.1	343.4	248.6	155.9	229.6	361.8	421.7	342.6	340.2	152.3	158.4	298.4	211.7	301.6	252.1	244.8	391.2	378.8	286.8	163.0	181.3	141.6	293.7	240.7	164.1	282.2	226.4	175.9	323.4	189.0	367.6	207.1	256.6	345.7	255.3	431.1	315.2	403.1	384.6	440.9	546.6	415.0	356.3	208.9	189.5	259.1	133.5	333.3	500.4	294.8	467.3	375.5	245.8	152.9	277.9	164.2	152.9	222.8	167.6	235.9	231.1	392.5	144.6	153.9	161.4	107.4	197.5	124.8	105.3	144.4	79	178.9	93.8	109.2	122.7	182.4	142.1	118.3	127.5	97.2	110.5	91	111.2	89.7	168.6	87.8	94.2	165.5	234.6	114.1	123.1	134.6	162.9	156.1	153.3	173.4	99.7	84.9	74.8	66.5	102.6	111.2	91.1	100.2	167.1	150.5	132.2	177.5	200	195.6	130.3	151.2	206.2	165	127.5	132.2	191.3	129.1	108.4	138.7	133.6	268	141.2	159.8	142	190.7	85.8	207.4	165.6	129.9	194.5	140	180.3	209.8	143.8	222.5	134.2	95	85.4	157.8	144.2	178.3	156.6	309.3	224.1	133.5	163.3	104.1	167.1	83.7	238.6	144.6	109.8	278.2	252.8	94.6	212.7	135.7	195.8	122	187.2	191.4	103.6	190.4	78.9	122.9	75.2	110.6	182.7	142.3	128.4	88.1	143.3	88.4	89.1	167.2	71.7	79.8	165.8	101.8	158	100.3	197.7	94.2	93.5	83.8	66.2	75.5	156.8	121.7	72.6	80.3	98.2	107.9	131.1	141.4	110.8	71.8	90.4	151.3	106.2	164.5	105.8	104.9	134.3	175.3	71.1	65.9	88.3	50.3	108.2	67.2	148.9	77.3	92	163.8	72.3	72.1	43.9	144	54.2	45.6	31.3	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na

kable(head(ex))

Data Series	2023 Jan	2022 Dec	2022 Nov	2022 Oct	2022 Sep	2022 Aug	2022 Jul	2022 Jun	2022 May	2022 Apr	2022 Mar	2022 Feb	2022 Jan	2021 Dec	2021 Nov	2021 Oct	2021 Sep	2021 Aug	2021 Jul	2021 Jun	2021 May	2021 Apr	2021 Mar	2021 Feb	2021 Jan	2020 Dec	2020 Nov	2020 Oct	2020 Sep	2020 Aug	2020 Jul	2020 Jun	2020 May	2020 Apr	2020 Mar	2020 Feb	2020 Jan	2019 Dec	2019 Nov	2019 Oct	2019 Sep	2019 Aug	2019 Jul	2019 Jun	2019 May	2019 Apr	2019 Mar	2019 Feb	2019 Jan	2018 Dec	2018 Nov	2018 Oct	2018 Sep	2018 Aug	2018 Jul	2018 Jun	2018 May	2018 Apr	2018 Mar	2018 Feb	2018 Jan	2017 Dec	2017 Nov	2017 Oct	2017 Sep	2017 Aug	2017 Jul	2017 Jun	2017 May	2017 Apr	2017 Mar	2017 Feb	2017 Jan	2016 Dec	2016 Nov	2016 Oct	2016 Sep	2016 Aug	2016 Jul	2016 Jun	2016 May	2016 Apr	2016 Mar	2016 Feb	2016 Jan	2015 Dec	2015 Nov	2015 Oct	2015 Sep	2015 Aug	2015 Jul	2015 Jun	2015 May	2015 Apr	2015 Mar	2015 Feb	2015 Jan	2014 Dec	2014 Nov	2014 Oct	2014 Sep	2014 Aug	2014 Jul	2014 Jun	2014 May	2014 Apr	2014 Mar	2014 Feb	2014 Jan	2013 Dec	2013 Nov	2013 Oct	2013 Sep	2013 Aug	2013 Jul	2013 Jun	2013 May	2013 Apr	2013 Mar	2013 Feb	2013 Jan	2012 Dec	2012 Nov	2012 Oct	2012 Sep	2012 Aug	2012 Jul	2012 Jun	2012 May	2012 Apr	2012 Mar	2012 Feb	2012 Jan	2011 Dec	2011 Nov	2011 Oct	2011 Sep	2011 Aug	2011 Jul	2011 Jun	2011 May	2011 Apr	2011 Mar	2011 Feb	2011 Jan	2010 Dec	2010 Nov	2010 Oct	2010 Sep	2010 Aug	2010 Jul	2010 Jun	2010 May	2010 Apr	2010 Mar	2010 Feb	2010 Jan	2009 Dec	2009 Nov	2009 Oct	2009 Sep	2009 Aug	2009 Jul	2009 Jun	2009 May	2009 Apr	2009 Mar	2009 Feb	2009 Jan	2008 Dec	2008 Nov	2008 Oct	2008 Sep	2008 Aug	2008 Jul	2008 Jun	2008 May	2008 Apr	2008 Mar	2008 Feb	2008 Jan	2007 Dec	2007 Nov	2007 Oct	2007 Sep	2007 Aug	2007 Jul	2007 Jun	2007 May	2007 Apr	2007 Mar	2007 Feb	2007 Jan	2006 Dec	2006 Nov	2006 Oct	2006 Sep	2006 Aug	2006 Jul	2006 Jun	2006 May	2006 Apr	2006 Mar	2006 Feb	2006 Jan	2005 Dec	2005 Nov	2005 Oct	2005 Sep	2005 Aug	2005 Jul	2005 Jun	2005 May	2005 Apr	2005 Mar	2005 Feb	2005 Jan	2004 Dec	2004 Nov	2004 Oct	2004 Sep	2004 Aug	2004 Jul	2004 Jun	2004 May	2004 Apr	2004 Mar	2004 Feb	2004 Jan	2003 Dec	2003 Nov	2003 Oct	2003 Sep	2003 Aug	2003 Jul	2003 Jun	2003 May	2003 Apr	2003 Mar	2003 Feb	2003 Jan	2002 Dec	2002 Nov	2002 Oct	2002 Sep	2002 Aug	2002 Jul	2002 Jun	2002 May	2002 Apr	2002 Mar	2002 Feb	2002 Jan	2001 Dec	2001 Nov	2001 Oct	2001 Sep	2001 Aug	2001 Jul	2001 Jun	2001 May	2001 Apr	2001 Mar	2001 Feb	2001 Jan	2000 Dec	2000 Nov	2000 Oct	2000 Sep	2000 Aug	2000 Jul	2000 Jun	2000 May	2000 Apr	2000 Mar	2000 Feb	2000 Jan	1999 Dec	1999 Nov	1999 Oct	1999 Sep	1999 Aug	1999 Jul	1999 Jun	1999 May	1999 Apr	1999 Mar	1999 Feb	1999 Jan	1998 Dec	1998 Nov	1998 Oct	1998 Sep	1998 Aug	1998 Jul	1998 Jun	1998 May	1998 Apr	1998 Mar	1998 Feb	1998 Jan	1997 Dec	1997 Nov	1997 Oct	1997 Sep	1997 Aug	1997 Jul	1997 Jun	1997 May	1997 Apr	1997 Mar	1997 Feb	1997 Jan	1996 Dec	1996 Nov	1996 Oct	1996 Sep	1996 Aug	1996 Jul	1996 Jun	1996 May	1996 Apr	1996 Mar	1996 Feb	1996 Jan	1995 Dec	1995 Nov	1995 Oct	1995 Sep	1995 Aug	1995 Jul	1995 Jun	1995 May	1995 Apr	1995 Mar	1995 Feb	1995 Jan	1994 Dec	1994 Nov	1994 Oct	1994 Sep	1994 Aug	1994 Jul	1994 Jun	1994 May	1994 Apr	1994 Mar	1994 Feb	1994 Jan	1993 Dec	1993 Nov	1993 Oct	1993 Sep	1993 Aug	1993 Jul	1993 Jun	1993 May	1993 Apr	1993 Mar	1993 Feb	1993 Jan	1992 Dec	1992 Nov	1992 Oct	1992 Sep	1992 Aug	1992 Jul	1992 Jun	1992 May	1992 Apr	1992 Mar	1992 Feb	1992 Jan	1991 Dec	1991 Nov	1991 Oct	1991 Sep	1991 Aug	1991 Jul	1991 Jun	1991 May	1991 Apr	1991 Mar	1991 Feb	1991 Jan	1990 Dec	1990 Nov	1990 Oct	1990 Sep	1990 Aug	1990 Jul	1990 Jun	1990 May	1990 Apr	1990 Mar	1990 Feb	1990 Jan	1989 Dec	1989 Nov	1989 Oct	1989 Sep	1989 Aug	1989 Jul	1989 Jun	1989 May	1989 Apr	1989 Mar	1989 Feb	1989 Jan	1988 Dec	1988 Nov	1988 Oct	1988 Sep	1988 Aug	1988 Jul	1988 Jun	1988 May	1988 Apr	1988 Mar	1988 Feb	1988 Jan	1987 Dec	1987 Nov	1987 Oct	1987 Sep	1987 Aug	1987 Jul	1987 Jun	1987 May	1987 Apr	1987 Mar	1987 Feb	1987 Jan	1986 Dec	1986 Nov	1986 Oct	1986 Sep	1986 Aug	1986 Jul	1986 Jun	1986 May	1986 Apr	1986 Mar	1986 Feb	1986 Jan	1985 Dec	1985 Nov	1985 Oct	1985 Sep	1985 Aug	1985 Jul	1985 Jun	1985 May	1985 Apr	1985 Mar	1985 Feb	1985 Jan	1984 Dec	1984 Nov	1984 Oct	1984 Sep	1984 Aug	1984 Jul	1984 Jun	1984 May	1984 Apr	1984 Mar	1984 Feb	1984 Jan	1983 Dec	1983 Nov	1983 Oct	1983 Sep	1983 Aug	1983 Jul	1983 Jun	1983 May	1983 Apr	1983 Mar	1983 Feb	1983 Jan	1982 Dec	1982 Nov	1982 Oct	1982 Sep	1982 Aug	1982 Jul	1982 Jun	1982 May	1982 Apr	1982 Mar	1982 Feb	1982 Jan	1981 Dec	1981 Nov	1981 Oct	1981 Sep	1981 Aug	1981 Jul	1981 Jun	1981 May	1981 Apr	1981 Mar	1981 Feb	1981 Jan	1980 Dec	1980 Nov	1980 Oct	1980 Sep	1980 Aug	1980 Jul	1980 Jun	1980 May	1980 Apr	1980 Mar	1980 Feb	1980 Jan	1979 Dec	1979 Nov	1979 Oct	1979 Sep	1979 Aug	1979 Jul	1979 Jun	1979 May	1979 Apr	1979 Mar	1979 Feb	1979 Jan	1978 Dec	1978 Nov	1978 Oct	1978 Sep	1978 Aug	1978 Jul	1978 Jun	1978 May	1978 Apr	1978 Mar	1978 Feb	1978 Jan	1977 Dec	1977 Nov	1977 Oct	1977 Sep	1977 Aug	1977 Jul	1977 Jun	1977 May	1977 Apr	1977 Mar	1977 Feb	1977 Jan	1976 Dec	1976 Nov	1976 Oct	1976 Sep	1976 Aug	1976 Jul	1976 Jun	1976 May	1976 Apr	1976 Mar	1976 Feb	1976 Jan
Total Merchandise Exports (Thousand Dollars)	49575205.0	55000084.0	54162780.0	56576348.0	62507132.0	63363749.0	64124991.0	64177050.0	59847424.0	60237343.0	63132579.0	51991457.0	54845697.0	59213794.0	56720130.0	53639342.0	52034038.0	52038522.0	49834010.0	49618994.0	47355225.0	50591895.0	55444082.0	42602227.0	44988833.0	46246876.0	43291938.0	44088350.0	43907435.0	44333640.0	42892684.0	40640766.0	36482259.0	39946596.0	45800019.0	43512320.0	44501656.0	45063716.0	45758743.0	46828474.0	43494319.0	45157925.0	45390949.0	41424050.0	46631547.0	44138798.0	44232102.0	39862596.0	44530898.0	43492397.0	48699986.0	51656381.0	45850324.0	50448644.0	48306776.0	45693748.0	48359712.0	44492111.0	45602187.0	38936317.0	44126494.0	44593215.0	45886157.0	43739574.0	41178777.0	44464801.0	42629367.0	42324249.0	43945230.0	40431020.0	46072685.0	39340293.0	40395481.0	44321513.0	41889050.0	39645848.0	39689453.0	38628013.0	38163143.0	39303025.0	38907678.0	38777004.0	38819978.0	32392691.0	36374220.0	40604216.0	39152052.0	43473113.0	40566767.0	38499547.0	42883682.0	41324718.0	39419524.0	42126061.0	46341479.0	34745340.0	42679649.0	42154405.0	41651672.0	45076081.0	45258026.0	43395650.0	43601754.0	43604893.0	43741676.0	46201066.0	45280758.0	41135162.0	44966281.0	42513470.0	43029311.0	47916281.0	45161084.0	43843516.0	46363855.0	43436371.0	45728321.0	45691253.0	42285166.0	37560458.0	41978071.0	39765478.0	42485012.0	44485232.0	41848907.0	41848307.0	42514918.0	43409919.0	44546230.0	44829816.0	46121631.0	45664111.0	41834582.0	45643446.0	43199909.0	44554630.0	45395626.0	45126888.0	42644188.0	43871813.0	42640217.0	42763382.0	46700562.0	36963226.0	43766735.0	41851138.0	39772855.0	42651466.0	41615386.0	42941604.0	41170201.0	41020375.0	38693721.0	40644667.0	41005505.0	32951382.0	36391172.0	37143715	35692666	35846049	35213854	34146163	35293204	32152964	30119931	31378096	31798409	27677912	26463376	30052576	34292711	39502257	43655958	42473576	45379085	42500904	40940336	42515891	40431403	36428265	42682417	37852753	39009550	41079240	39118022	39110149	39517632	38400858	36405981	36442327	38614025	30938211	37598082	35837917	37337369	37008885	38632796	38055717	36213556	37974531	35996421	33659297	37718897	33003078	32617981	37079003	34576557	35802282	34073829	34304221	31706705	31155372	30570564	29900198	31463425	25507262	27543861	30269392	28384129	30325197	31152615	29176182	29140380	28647333	27292036	26802507	27854957	24128127	23603218	26185024	23724843	25982255	25186305	22765100	23527960	22304369	22026559	22747877	23418350	19831071	23447601	18663757	20429746	20410619	18857072	19562983	19960987	18600298	18757283	19538466	18447696	14710314	17908294	16729548	18057067	18935541	16797935	17673383	17462519	18461988	18700517	18056811	20431794	18092493	18626667	20797582	21961829	22863664	21590916	21962554	20321223	19458200	19738164	17461126	19842497	16200493	15628098	19007924	17957284	17783665	17625416	15957530	16153014	16366907	15465904	15909114	16250359	12496922	13315576	15044377	14509326	15348390	16344414	15290254	15633170	15919596	14305934	15156585	16952639	14763661	14494973	16781544	16041994	16899732	16936893	14933191	15912565	15075534	15389820	15521270	15475125	11582485	15062352	15081351	14812547	15689795	14663818	14252549	15040339	13977050	14816175	14613388	15658331	12362258	15304338	14359141	15503060	14980902	14877990	14921935	14092604	14373998	13777819	12650418	14104206	11560926	12311698	13294931	13289481	13204683	13659397	13324595	12461425	13220737	12321943	12259655	11439361	8521399	10329591	10742880	10437879	9942710	11307053	9712732	10423206	9972816	9656549	10291679	10457906	8504712	8023323	10137415	9101823	9008731	9105708	8370183	9089735	8746449	8047092	7852989	8746372	6954198	8190350	7892995	8255293	8813517	8469943	8491990	9102128	8894216	8309277	8352230	8855071	7201714	9241136	8841587	8733492	9265922	7978423	7999123	7628568	7615410	7750740	7009944	8573594	6573310	7235714	8098894	8072301	7581214	7771485	7818370	6975522	7335741	7016390	7259816	7422577	5762413	6001724	7606901	6814662	7623999	7082711	6565749	6882356	6817987	6335007	6245803	5495095	5899650	5681340	5900718	5811270	5472747	5882200	5050975	5158567	4890618	4639482	5084631	4185709	3983800	4205018	4582282	4254654	4160951	4099391	4216640	3990786	4150132	3772098	4375574	3641581	4020547	3720863	3701760	4189247	4240507	3920246	4306642	3843946	4181432	4058852	4694408	4084492	4237539	4719777	3994671	4416916	4484980	4133037	4317381	4691737	4130340	4687092	3864347	4274929	3718666	4625925	4175031	3761799	3996464	4070734	4139927	3622241	4079805	3690551	4023232	3853320	3261901	3479886	3711973	3428724	3904021	3512393	3463197	3941166	3740146	3900358	4096249	3952424	3520827	3301310	3749817	3565264	3826484	3775500	3689837	3901835	3609284	3703412	3696834	3738954	3165449	3868119	3502958	3419322	3748290	3467725	3326672	3705497	3470084	3506037	3365594	3444920	3126000	3369215	3176254	3052365	2767576	2669904	2890462	2744672	2383410	2533619	2293413	2363937	1969117	2095359	2114417	2158880	1995092	2026507	2046411	1875875	1880926	1761878	1929828	2004845	1430064	1760728	1726198	1817398	1684055	1730959	1791175	1768697	1675542	1588420	1675618	1723749	1322808	1585691	1544967	1508412	1479737	1432917	1291818	1505808	1292978	1245481	1302876	1261592	1156733	1242543
America (Million Dollars)	5823.5	6217.5	6394.2	6653.9	7091.9	7931.0	6731.2	7508.6	6341.1	7075.6	7933.0	5396.8	6576.2	6093.6	6097.6	5684.9	5683.5	5551.9	5279.8	5389.4	5711.4	5558.4	6053.3	4815.3	4809.6	5654.2	5401.9	5098.7	4795.5	5164.4	6883.8	4895.5	5922.2	6569.1	6021.1	5870.7	5557.8	4613.7	4822.3	4938.3	4754.5	4837.7	5209.4	4990.1	5809.4	5023.6	5281.2	4841.7	5055.8	4449.3	4848.5	5238.2	5048.3	5264.7	5238.4	4815.0	5307.9	4917.3	4909.7	4054.1	4321.3	3775.8	3793.3	4004.7	3721.9	4136.4	3792.4	3965.2	4483.3	4069.7	4562.6	3371.5	3754.4	3588.8	3704.8	3524.2	3754.2	3807.0	3963.3	4110.8	3748.8	3761.4	3629.7	3102.4	3244.8	3352.7	3394.5	4289.2	4036.7	4028.4	4047.9	4045.8	3803.6	4216.4	4424.2	3192.2	3750.0	3844.3	3756.2	4161.6	4261.3	4341.4	4230.2	4288.7	3827.4	4576.6	3942.2	3943.2	4026.0	3895.1	4317.5	4468.1	4649.1	4593.2	4433.9	4615.7	5750.5	4534.7	4415.9	3486.3	4070.2	3325.5	3960.1	4724.6	4036.2	4640.9	4125.5	4094.0	4243.0	4379.3	4354.7	4955.8	4773.6	4685.3	3960.8	4345.1	4228.9	4410.2	4511.9	4667.4	4177.0	4848.6	5650.0	3783.5	4181.6	3823.7	3796.1	4962.2	4925.1	4648.0	3987.5	4154.7	3745.0	5016.6	3753.2	3479.3	3652.5	3737.6	4230.7	3898.1	3930.7	3915.3	3858.6	4004.6	3127.4	3786.1	3393.7	3093.8	2934	3376.8	4757.6	4312	4917.8	4634.8	4433.9	4465.7	4147.7	4595.7	5187.7	4249	4950.7	4210.4	4528.3	4783.9	4564.9	4741.9	4717.3	4719.3	4395.6	4523.4	4903.5	4033.8	4618.1	3961.3	4284.1	4911.8	4868	4753.4	4865.4	4994.5	4373	4553	5056.8	4224.2	3975.3	4674.7	4571.7	4469.6	4167.9	4207.1	4055.6	3912.1	3723.8	3703.3	3858.7	3506.3	3529.3	3772.3	3692.3	3928.5	4160.1	4252.8	4263.1	3980.8	3973.1	3635.7	3920	3009.7	3224	3610	3296.2	3734.5	3461.1	3263.4	3327.5	3594.9	3412.3	3374.5	3329.3	3046.3	3599	2880.2	4006.9	3340	3261.1	3777.9	3559	3291.4	3388.4	3234.6	3076.2	2632.4	2926.4	2939.7	3035.6	3215.6	2818.4	3076.7	3015.5	3521.3	3185.5	3237.9	3817.6	3485.2	3548.1	4258.6	4593	4548.6	4381.9	4444.1	3961.8	3707.2	3525.5	3278.8	3714.6	3278.8	3047.7	3907.8	3586.6	3633.4	3675.1	3573.7	3891.2	3575.9	3411.5	3401.6	3291.3	2640.1	2842.7	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Asia (Million Dollars)	34646.8	39734.8	37973.2	40500.8	45605.1	44106.2	46129.5	46328.8	43779.3	42328.6	45030.9	37113.1	40021.0	44688.2	42167.8	39493.6	38941.0	38677.8	36349.8	36341.1	34344.3	37686.2	40954.4	31562.0	33377.7	34687.8	32142.6	32140.9	31782.3	31657.5	29808.7	28994.5	24437.8	26245.7	32534.9	29713.9	31668.0	34177.0	34252.7	33529.6	32055.9	33370.5	32193.3	29551.6	32511.7	32069.7	31577.5	28614.5	32195.7	32236.4	35349.1	37466.0	33281.8	37323.4	35889.6	33376.8	34376.3	31955.3	33727.8	28186.1	32613.2	33913.1	35045.5	33213.5	30973.0	33893.3	32939.8	31910.8	33198.7	30643.3	34878.2	29332.7	30504.5	34352.4	31838.0	30474.4	29574.6	29162.0	27689.8	29126.5	27807.5	29165.9	29150.2	24434.1	27229.3	31331.8	30026.4	32986.8	30146.3	28566.7	32368.1	30823.2	29570.2	31611.5	34627.8	26321.8	32446.3	31853.1	31710.4	33783.5	33865.6	31933.3	31726.4	31990.8	32339.8	34479.5	33747.6	29920.3	33618.7	32285.3	31735.9	35719.3	32951.7	32450.0	35068.6	31395.5	32580.4	33910.0	30710.2	28173.2	31276.3	29441.2	30918.5	31151.0	30038.6	29946.2	31030.7	31220.7	32672.2	33057.5	33526.5	31208.5	29926.3	32826.3	31471.2	32505.5	33099.5	31932.0	30371.5	31749.3	30678.8	30124.3	33118.7	25786.5	32069.3	30755.3	28633.0	29180.5	29293.2	30342.8	30078.5	29407.7	28062.4	28762.3	30343.9	23522.5	27357.2	27281.8	25000.6	25550.9	25052.1	24627	25364.5	22944.6	22103.2	22721.5	22793	19221.4	17634.7	21512.8	23198.9	27616.4	30270.9	29623.8	32405.3	30202.8	29695.8	30312.9	27924.7	24915.8	30016	27180.9	27458.3	28613.6	27121	27622.2	27576.1	27100.6	25539.8	25878.1	26528	21043.9	26121.9	25160.3	25479.7	25492.6	27288.7	27036.6	25528.4	26324.1	24757.2	22589.9	25653.3	22232.9	21676.9	24060.3	23388.3	24509.8	23794.4	23840.6	22169.8	21442.1	21232.4	20478.1	21783.6	16835.4	18870.1	20838.8	18884.8	20469.2	21168.8	19441.4	19636.4	18839.8	18074.5	18142.5	18700.7	16137.6	15522.8	17880.7	15798.4	17351.6	16774.5	15322	16112.9	14721.2	14753.1	15359.1	15814	13135.3	15581.2	12197.2	12555.2	12976.8	11990.9	12246.2	12801.3	11757.6	12236.6	12861.9	11750.6	9184.7	11608.7	10372.5	11291.6	11861.2	10628.6	11264.4	11311.1	11517	11534.7	11321.9	12762.1	11230.8	11472.3	12474.1	13092.5	13665.9	13232.6	13394.9	12805.8	12293.7	12207.5	10953.9	12342.8	9805.2	9549.3	11555.9	10532.7	10161.4	10038.3	9090.9	9156.8	9466.7	8875	9194.7	9461.1	7022.7	7541.4	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Europe (Million Dollars)	5384.0	4924.4	5025.2	5121.5	5155.1	6109.6	6250.6	5455.3	5141.7	5726.7	6166.0	6128.8	4878.2	4965.3	4848.6	5269.6	4350.9	4698.0	5260.6	4758.3	4555.8	4562.7	5516.7	3865.8	4604.8	3785.5	3594.1	4757.3	5116.7	5178.3	4013.8	4880.4	4398.0	5051.3	4468.2	4996.4	4432.5	3851.0	4173.3	5162.7	4007.2	4278.1	5231.8	3890.6	5479.5	4107.2	4421.2	3994.3	4585.6	3968.8	4961.4	5444.4	4689.5	4662.3	4171.9	4599.0	5576.3	4366.9	4152.3	3881.7	4453.4	4397.5	4426.2	4046.9	4107.9	4040.9	3689.6	4072.0	3934.0	3449.9	4061.5	4147.7	3923.0	3993.1	4067.5	3426.8	3957.3	3683.3	4645.0	3996.6	5474.2	3971.4	3995.8	3231.3	3997.6	3542.5	3526.3	3676.1	3397.3	3401.8	3587.4	3738.2	3281.0	3580.5	4222.5	2898.7	3732.0	3701.3	3195.0	3832.2	3558.9	3723.2	4205.9	3917.4	3745.2	3634.0	3574.7	3856.5	3976.3	3619.4	3441.2	4112.0	3753.5	3489.7	3508.5	4521.5	3949.6	3657.0	3761.1	2754.9	3248.6	3828.5	3848.4	4413.0	4370.7	4011.6	4124.9	4761.6	4112.7	3909.3	4556.9	4650.3	3909.5	4317.3	3975.0	3861.8	4582.0	5182.3	4365.7	4255.1	4085.9	4112.6	4508.0	4790.1	4249.8	4224.6	4433.7	4975.7	4258.6	5188.1	4508.0	4324.9	3780.4	3660.6	4191.7	3579.8	3148.6	3547.4	3775.7	3526.8	3538.8	3176.4	3500.3	2952.2	3095.6	2884.2	3322.3	3224.6	3626	3014.5	3936.8	4534.3	4889.2	4488.7	4489.4	4512	4093.3	4423	4429.1	4222.4	4435.5	4099.1	4331.8	4378.3	4600.6	4430.5	4162.3	4102.8	4208.7	3539.9	4705.2	3917.5	4564.4	4611	5107.4	4391.5	4101.6	3830.7	3624.6	3884.1	3862	4034.3	4488.7	4187.3	4848.8	6060.1	4227.4	4589.7	3967.6	4004.8	3627.6	3695.1	3413	3698.7	3712.2	3760.2	3553	3801.4	4088.9	4181.9	4090.3	3822.7	3609.4	4097.2	3802.5	3432.8	3743.6	3689.4	3443.3	3178.3	3444.6	3545.7	3663.1	3077.9	2861.9	2946.6	2790.8	2868.4	3141.8	2644.8	3039.8	2604	2828.7	3068.6	2652.3	2620.1	2571.3	2740.2	2273.5	2531.7	2815.3	2113.9	2507.7	2667.9	2827.7	2932.4	2579.3	2456.5	2300.1	2602.5	2849.8	2547.5	3045.4	2652.8	2899.9	3144.3	3405.9	3606.6	3149.9	3267.5	2664.8	2557.2	2774.2	2498	2904.1	2390.1	2354.8	2788.8	2972.5	3014	3038.3	2518.1	2361.2	2547.1	2443.1	2534.9	2705.1	2236.2	2366.2	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Oceania (Million Dollars)	2844.5	3034.8	3243.1	3114.5	3453.3	3608.4	3916.1	3669.6	3671.1	3899.9	3054.0	2572.3	2681.5	2743.9	2746.5	2434.8	2462.1	2539.3	2340.8	2499.3	2133.4	2018.0	2285.1	1834.1	1728.3	1655.8	1648.8	1581.0	1640.4	1811.1	1725.8	1418.3	1311.4	1511.8	1859.3	2090.4	2121.6	1918.4	2078.1	2591.2	2113.4	2046.0	2021.7	2117.9	2069.6	2235.3	2167.6	1754.0	2046.4	2115.7	2698.2	2849.0	2074.6	2343.5	2314.2	2190.0	2301.3	2574.4	2197.9	2059.3	2147.9	1917.3	1960.1	1861.2	1770.1	1814.3	1709.4	1791.6	1780.2	1738.5	2092.2	1930.6	1653.7	1841.6	1733.7	1692.3	1951.5	1487.3	1450.8	1603.4	1481.4	1390.3	1664.7	1274.8	1472.9	1938.9	1721.5	1932.4	2304.1	1734.8	1892.1	1960.5	1983.0	1972.0	1993.9	1521.1	1962.3	1933.8	2047.4	2463.9	2510.3	2379.9	2433.6	2545.3	2985.9	2572.8	2844.7	2307.0	2437.1	1973.4	2510.3	2610.4	2606.2	2361.7	2292.9	1879.7	2301.5	2528.2	2598.3	2333.6	2616.1	2456.8	2837.6	3125.3	2430.7	2281.3	2329.4	2538.4	2723.6	2707.1	2719.5	2869.4	2289.4	2543.5	2586.8	2524.0	2600.5	2704.6	2535.9	2320.5	2646.3	2612.6	2471.0	1834.4	2110.6	2073.1	2070.2	2637.8	2077.9	1779.5	1871.7	2213.1	2179.1	2193.0	1927.6	1592.6	1606.2	1982.1	1968.5	2027.3	1997.2	1524.2	1911.2	1640.8	1348	1408.5	1756.4	1575.6	1753.3	1610.1	1727.4	2265.8	2247.8	2198	2927.9	2477.3	2344.2	2399.1	2346.3	2460.1	2564.6	1875.1	2013.6	2653.5	2234.8	1727.1	2006.7	1854.7	1767.5	1997.2	1970.4	1551	1830.3	1635.8	1989	1740.4	1799.6	1865.1	1643.1	2201.1	2458.2	1946.6	1965.7	1899.8	1673.4	1851.5	1978.3	1749.4	1720.2	1817.6	1481.6	1666.6	1784.6	1633.6	1673.9	1092.6	1298.6	1485.7	1420.7	1398	1375.3	1303.6	1265	1397.2	1097.4	1303.4	1191	1032.3	1100.7	1151.1	915.6	1052.6	985.6	864.9	953.3	780.5	815.3	879	826.4	784.8	952.1	739.9	793.3	765.2	697	641.4	785	614.2	646.9	702	604	599.7	659.9	551.7	666.8	693.3	556.2	643.6	619.9	631.8	889.6	738.5	587.7	537.1	527.9	624	611	674.5	587.8	639	671	671.1	689.7	538.9	674	560.8	507.9	561.5	637.9	770.2	642.7	556.6	551.5	576.4	560.5	587.5	579.4	435.1	413.6	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na
Africa (Million Dollars)	876.3	1088.6	1527.0	1185.6	1201.8	1608.5	1097.6	1214.7	914.2	1206.6	948.8	780.5	688.8	722.8	859.6	756.4	596.6	571.4	603.1	630.8	610.3	766.6	634.5	525.0	468.3	463.5	504.5	510.4	572.5	522.3	460.6	452.1	412.8	568.8	916.5	841.0	721.8	503.6	432.3	606.7	563.4	625.7	734.7	873.9	761.3	703.1	784.6	658.0	647.4	722.2	842.7	658.9	756.1	854.7	692.8	712.9	797.9	678.2	614.6	755.2	590.7	589.5	661.0	613.3	605.9	580.0	498.2	584.7	549.0	529.7	478.2	557.7	559.9	545.6	545.1	528.1	451.8	488.4	414.2	465.7	395.7	488.0	379.6	350.2	429.5	438.4	483.4	588.6	682.3	767.8	988.1	757.0	781.7	745.6	1073.1	811.4	789.0	821.9	942.6	835.0	1061.9	1017.9	1005.6	862.7	843.4	938.1	1171.6	1108.2	908.1	740.2	1024.5	1006.5	1200.5	948.9	1060.0	1024.0	1146.4	1061.4	799.7	812.5	766.8	713.4	920.5	1071.4	972.8	968.2	904.5	795.2	794.7	776.6	964.1	1980.2	935.9	1271.0	1206.0	1318.3	884.8	897.8	859.2	879.5	1052.2	1065.2	952.9	768.7	1155.4	974.5	839.8	895.4	1060.6	983.2	724.5	920.0	926.8	1012.2	789.1	777.3	626.6	594.9	717.1	843	695.1	903.3	658.6	610.7	445.8	577.7	533	562.5	515.4	538.4	672	773.8	1330.4	1528.2	1122.6	843	659.4	785.1	543.5	580.9	715.6	487.2	677.5	650	596.8	588.5	1055.2	623.3	494.4	503.7	507	392.1	463.3	469.5	477.2	472.6	574.8	569.9	552.2	570.7	546	535.4	554.4	458.9	443.7	432.4	410.9	483.8	423.7	434.1	372.1	439.4	416.7	386.5	435.1	312.8	292.8	371.2	297.5	347.5	358.1	355.6	366.5	332.4	344.5	288.1	299.7	259.1	312.5	365	270	297.9	302.1	236.9	272.4	261.2	254.9	266.9	306.9	219.8	275.4	242.5	245.7	260	255.8	277.4	244.4	196.7	211.9	208.2	201.6	179.7	205.6	197.7	235.5	233.2	215.4	232.2	216	189.4	240.8	211	219	186.7	178.5	296.5	259.4	368.1	238.8	217.1	217.9	229	541.3	191.5	207	165.6	168.4	193.9	227.6	204.6	231.1	218.2	192.3	200.8	175.9	190.4	213.4	162.8	151.5	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na	na

df<- im[, 0:98]

dfe<- ex[,0:98]

kable(head(df))

Data Series	2023 Jan	2022 Dec	2022 Nov	2022 Oct	2022 Sep	2022 Aug	2022 Jul	2022 Jun	2022 May	2022 Apr	2022 Mar	2022 Feb	2022 Jan	2021 Dec	2021 Nov	2021 Oct	2021 Sep	2021 Aug	2021 Jul	2021 Jun	2021 May	2021 Apr	2021 Mar	2021 Feb	2021 Jan	2020 Dec	2020 Nov	2020 Oct	2020 Sep	2020 Aug	2020 Jul	2020 Jun	2020 May	2020 Apr	2020 Mar	2020 Feb	2020 Jan	2019 Dec	2019 Nov	2019 Oct	2019 Sep	2019 Aug	2019 Jul	2019 Jun	2019 May	2019 Apr	2019 Mar	2019 Feb	2019 Jan	2018 Dec	2018 Nov	2018 Oct	2018 Sep	2018 Aug	2018 Jul	2018 Jun	2018 May	2018 Apr	2018 Mar	2018 Feb	2018 Jan	2017 Dec	2017 Nov	2017 Oct	2017 Sep	2017 Aug	2017 Jul	2017 Jun	2017 May	2017 Apr	2017 Mar	2017 Feb	2017 Jan	2016 Dec	2016 Nov	2016 Oct	2016 Sep	2016 Aug	2016 Jul	2016 Jun	2016 May	2016 Apr	2016 Mar	2016 Feb	2016 Jan	2015 Dec	2015 Nov	2015 Oct	2015 Sep	2015 Aug	2015 Jul	2015 Jun	2015 May	2015 Apr	2015 Mar	2015 Feb	2015 Jan
Total Merchandise Imports (Thousand Dollars)	44393664.0	49869770.0	50653907.0	53182943.0	55799312.0	58466009.0	61029374.0	59649162.0	57604263.0	56116002.0	58079982.0	44958373.0	50026788.0	54349357.0	50674908.0	47945213.0	45980374.0	44714491.0	46107788.0	45039845.0	41559697.0	45169547.0	47668437.0	37643664.0	39028616.0	40154550.0	38477878.0	38173829.0	38801413.0	36472279.0	37843646.0	35120892.0	31458238.0	35878828.0	40433029.0	39472637.0	41180224.0	41580603.0	42489886.0	42282537.0	39450625.0	40998179.0	42030046.0	38979868.0	42637579.0	41294888.0	40340098.0	36074448.0	41553606.0	42566514.0	45114564.0	47127052.0	41439605.0	43802120.0	44972517.0	41054575.0	42887787.0	38382736.0	39745452.0	34609596.0	38491510.0	40104280.0	41476983.0	39347126.0	35732238.0	38702897.0	36874744.0	36416610.0	39149517.0	34542072.0	39987555.0	33032643.0	36734901.0	39473257.0	37482999.0	33929792.0	32761041.0	33527949.0	31906437.0	34067889.0	32922385.0	32237957.0	33661999.0	30949946.0	30382944.0	34868755.0	33630636.0	35998057.0	35278954.0	35260544.0	37430942.0	36614847.0	33572417.0	36970386.0	38824707.0	29731205.0	35221197.0
America (Million Dollars)	6267.4	6901.5	7529.4	7666.4	7995.9	8633.8	7879.7	8024.0	8521.1	7822.1	7176.1	5385.2	5850.9	6261.1	6127.4	6027.6	5631.6	5750.1	5728.6	5457.4	5191.8	6195.9	5303.5	4164.2	4580.0	4676.4	4588.2	4869.7	4886.4	4132.0	4667.3	4686.2	4259.0	5183.5	5910.8	5314.1	5844.1	5686.4	6274.7	5754.5	5633.1	6128.8	5986.2	5876.4	5797.3	6128.0	5528.7	5381.1	5543.6	6184.6	6013.9	6409.9	5466.5	6031.7	5590.7	5961.2	5661.3	5122.6	5316.7	4113.3	4706.6	5014.4	4895.6	4983.1	4435.1	4888.7	4684.4	5147.3	5372.0	4481.4	5262.9	4097.8	4743.8	4645.3	4908.6	4145.9	4121.3	4754.1	4107.4	4396.5	4284.6	4251.0	4437.5	3813.2	3818.0	4515.7	4773.8	4711.8	5147.4	4484.8	4468.0	4886.9	4418.3	4725.5	5555.1	3842.0	4751.9
Asia (Million Dollars)	30174.0	33611.7	34733.7	36120.9	37696.3	40911.9	43214.2	42507.2	40534.7	38735.7	42199.9	31611.3	35014.0	39140.3	35949.6	33552.7	32533.4	31492.5	31645.0	31021.0	28497.2	30623.1	31367.8	26122.6	27413.7	28200.4	25844.9	26127.9	27823.2	26052.3	26767.4	24779.3	21718.9	24534.5	26783.6	26588.1	27128.1	27688.7	27905.0	28378.3	26350.3	26671.9	28473.3	26052.4	27541.0	26807.2	26773.8	22762.9	27643.4	27352.6	29765.8	31710.5	27426.4	28371.5	29381.2	26804.0	28718.4	26249.7	26481.1	23434.2	26477.0	27349.4	28430.0	27283.6	25101.0	26491.1	24750.6	24011.2	26420.2	23510.6	27215.5	22103.1	25008.4	26302.5	25122.1	23492.4	23237.1	23121.5	22039.0	23479.2	22261.9	21145.8	21940.0	20726.8	20169.1	23757.8	22217.7	23871.5	24404.3	24174.4	25221.3	24850.7	22616.4	24671.7	23668.0	19628.9	23268.9
Europe (Million Dollars)	6427.8	7541.8	7242.8	7475.9	8167.6	7433.2	8300.5	7300.2	7030.8	7407.2	7203.2	6479.0	7821.6	7586.3	6872.0	6714.8	6882.1	5919.4	6919.2	7011.2	6563.5	6740.5	8964.0	5403.7	5749.6	6087.4	6133.5	6285.4	5316.9	5225.0	5475.3	4960.7	4629.0	5150.6	6333.3	6209.6	6859.7	6314.8	6827.1	6624.7	6310.1	6533.8	6325.3	6054.8	7987.4	7157.0	7128.7	6730.3	7489.3	7742.8	8032.7	7805.9	7542.6	8218.9	8694.4	7126.1	7181.7	6237.3	6963.0	6061.9	6318.0	6809.7	6876.0	6292.9	5484.9	6491.4	6169.1	6428.7	6434.0	5985.2	6570.9	6067.0	6105.0	7658.1	6535.5	5667.2	4809.6	4920.3	5153.8	5302.1	5624.9	6051.1	6250.8	5365.3	5450.5	5918.4	6080.4	6404.1	4818.9	5765.3	6870.3	6040.6	5765.4	6603.1	8133.0	5199.5	6054.5
Oceania (Million Dollars)	983.3	1399.9	664.4	1329.8	1544.6	935.9	1060.6	1141.8	1164.7	1559.1	863.9	814.4	810.4	744.8	994.1	1021.2	599.8	744.0	1201.2	890.1	1001.7	1030.5	1131.0	1134.7	705.5	540.9	1412.8	577.3	477.7	586.5	493.1	456.4	441.8	637.6	845.9	694.7	819.7	1369.3	841.1	941.8	866.8	1015.1	815.3	503.7	889.4	820.3	518.9	787.2	614.4	895.3	875.7	801.7	686.2	827.4	773.2	828.5	793.2	490.3	651.5	600.3	591.8	576.9	992.2	570.3	462.7	436.4	1014.6	486.0	674.8	409.0	708.7	402.9	456.0	524.8	576.5	471.9	434.6	433.7	394.6	588.5	498.8	545.2	642.5	665.9	658.6	513.8	377.5	869.1	614.7	595.3	707.2	554.5	545.9	794.3	1145.2	871.8	778.3
Africa (Million Dollars)	541.1	414.9	483.6	589.9	395.0	551.2	574.4	675.9	352.9	591.9	636.9	668.5	529.9	616.8	731.8	628.8	333.4	808.4	613.8	660.2	305.4	579.6	902.2	818.5	579.9	649.4	498.6	313.5	297.2	476.5	440.6	238.2	409.6	372.6	559.4	666.1	528.6	521.4	642.0	583.2	290.4	648.6	430.0	492.6	422.5	382.4	390.0	413.0	262.8	391.1	426.5	399.1	317.9	352.6	533.0	334.9	533.2	282.7	333.2	399.8	398.1	353.8	283.2	217.2	248.5	395.2	256.1	343.4	248.6	155.9	229.6	361.8	421.7	342.6	340.2	152.3	158.4	298.4	211.7	301.6	252.1	244.8	391.2	378.8	286.8	163.0	181.3	141.6	293.7	240.7	164.1	282.2	226.4	175.9	323.4	189.0	367.6

df <- df[-1,]

dfe<-dfe[-1,]

colnames <- colnames(df)
colnames

 [1] "Data Series" "2023 Jan"    "2022 Dec"    "2022 Nov"    "2022 Oct"   
 [6] "2022 Sep"    "2022 Aug"    "2022 Jul"    "2022 Jun"    "2022 May"   
[11] "2022 Apr"    "2022 Mar"    "2022 Feb"    "2022 Jan"    "2021 Dec"   
[16] "2021 Nov"    "2021 Oct"    "2021 Sep"    "2021 Aug"    "2021 Jul"   
[21] "2021 Jun"    "2021 May"    "2021 Apr"    "2021 Mar"    "2021 Feb"   
[26] "2021 Jan"    "2020 Dec"    "2020 Nov"    "2020 Oct"    "2020 Sep"   
[31] "2020 Aug"    "2020 Jul"    "2020 Jun"    "2020 May"    "2020 Apr"   
[36] "2020 Mar"    "2020 Feb"    "2020 Jan"    "2019 Dec"    "2019 Nov"   
[41] "2019 Oct"    "2019 Sep"    "2019 Aug"    "2019 Jul"    "2019 Jun"   
[46] "2019 May"    "2019 Apr"    "2019 Mar"    "2019 Feb"    "2019 Jan"   
[51] "2018 Dec"    "2018 Nov"    "2018 Oct"    "2018 Sep"    "2018 Aug"   
[56] "2018 Jul"    "2018 Jun"    "2018 May"    "2018 Apr"    "2018 Mar"   
[61] "2018 Feb"    "2018 Jan"    "2017 Dec"    "2017 Nov"    "2017 Oct"   
[66] "2017 Sep"    "2017 Aug"    "2017 Jul"    "2017 Jun"    "2017 May"   
[71] "2017 Apr"    "2017 Mar"    "2017 Feb"    "2017 Jan"    "2016 Dec"   
[76] "2016 Nov"    "2016 Oct"    "2016 Sep"    "2016 Aug"    "2016 Jul"   
[81] "2016 Jun"    "2016 May"    "2016 Apr"    "2016 Mar"    "2016 Feb"   
[86] "2016 Jan"    "2015 Dec"    "2015 Nov"    "2015 Oct"    "2015 Sep"   
[91] "2015 Aug"    "2015 Jul"    "2015 Jun"    "2015 May"    "2015 Apr"   
[96] "2015 Mar"    "2015 Feb"    "2015 Jan"

dfim<-pivot_longer(df, cols =c("2023 Jan" ,  "2022 Dec"   , "2022 Nov"  ,  "2022 Oct"  ,  "2022 Sep"  ,  "2022 Aug","2022 Jul" ,   "2022 Jun",    "2022 May",    "2022 Apr" ,   "2022 Mar",    "2022 Feb",    "2022 Jan","2021 Dec" ,   "2021 Nov"   , "2021 Oct",    "2021 Sep",    "2021 Aug" ,   "2021 Jul" ,   "2021 Jun","2021 May"  ,  "2021 Apr",    "2021 Mar" ,   "2021 Feb" ,   "2021 Jan"  ,  "2020 Dec",    "2020 Nov","2020 Oct" ,   "2020 Sep"   , "2020 Aug"  ,  "2020 Jul" ,   "2020 Jun"  ,  "2020 May" ,   "2020 Apr","2020 Mar" ,   "2020 Feb" ,   "2020 Jan"  ,  "2019 Dec"  ,  "2019 Nov"  ,  "2019 Oct"  ,  "2019 Sep" ,  "2019 Aug"   , "2019 Jul"  , "2019 Jun"  ,  "2019 May" ,   "2019 Apr" ,   "2019 Mar"  ,  "2019 Feb" ,  "2019 Jan"  ,  "2018 Dec"  ,  "2018 Nov"  ,  "2018 Oct"  ,  "2018 Sep"  ,  "2018 Aug"  ,  "2018 Jul" ,  "2018 Jun"   ,"2018 May"   , "2018 Apr"  ,  "2018 Mar"  ,  "2018 Feb" ,   "2018 Jan"  ,  "2017 Dec"   ,"2017 Nov"  ,  "2017 Oct"  ,  "2017 Sep" ,   "2017 Aug"  ,  "2017 Jul"  ,  "2017 Jun"   , "2017 May"  , "2017 Apr" ,   "2017 Mar" ,   "2017 Feb" ,   "2017 Jan"  ,  "2016 Dec"   , "2016 Nov" ,   "2016 Oct" , "2016 Sep" ,  "2016 Aug",   "2016 Jul",  "2016 Jun",    "2016 May",    "2016 Apr" ,   "2016 Mar" ,"2016 Feb" ,   "2016 Jan" ,   "2015 Dec" ,   "2015 Nov"  ,  "2015 Oct" ,   "2015 Sep"  ,  "2015 Aug" ,"2015 Jul",    "2015 Jun" ,   "2015 May"   , "2015 Apr" ,   "2015 Mar" ,   "2015 Feb",    "2015 Jan"   ),names_to = "Date", values_to = "import")

dfex<-pivot_longer(dfe, cols =c("2023 Jan" ,  "2022 Dec"   , "2022 Nov"  ,  "2022 Oct"  ,  "2022 Sep"  ,  "2022 Aug","2022 Jul" ,   "2022 Jun",    "2022 May",    "2022 Apr" ,   "2022 Mar",    "2022 Feb",    "2022 Jan","2021 Dec" ,   "2021 Nov"   , "2021 Oct",    "2021 Sep",    "2021 Aug" ,   "2021 Jul" ,   "2021 Jun","2021 May"  ,  "2021 Apr",    "2021 Mar" ,   "2021 Feb" ,   "2021 Jan"  ,  "2020 Dec",    "2020 Nov","2020 Oct" ,   "2020 Sep"   , "2020 Aug"  ,  "2020 Jul" ,   "2020 Jun"  ,  "2020 May" ,   "2020 Apr","2020 Mar" ,   "2020 Feb" ,   "2020 Jan"  ,  "2019 Dec"  ,  "2019 Nov"  ,  "2019 Oct"  ,  "2019 Sep" ,  "2019 Aug"   , "2019 Jul"  , "2019 Jun"  ,  "2019 May" ,   "2019 Apr" ,   "2019 Mar"  ,  "2019 Feb" ,  "2019 Jan"  ,  "2018 Dec"  ,  "2018 Nov"  ,  "2018 Oct"  ,  "2018 Sep"  ,  "2018 Aug"  ,  "2018 Jul" ,  "2018 Jun"   ,"2018 May"   , "2018 Apr"  ,  "2018 Mar"  ,  "2018 Feb" ,   "2018 Jan"  ,  "2017 Dec"   ,"2017 Nov"  ,  "2017 Oct"  ,  "2017 Sep" ,   "2017 Aug"  ,  "2017 Jul"  ,  "2017 Jun"   , "2017 May"  , "2017 Apr" ,   "2017 Mar" ,   "2017 Feb" ,   "2017 Jan"  ,  "2016 Dec"   , "2016 Nov" ,   "2016 Oct" , "2016 Sep" ,  "2016 Aug",   "2016 Jul",  "2016 Jun",    "2016 May",    "2016 Apr" ,   "2016 Mar" ,"2016 Feb" ,   "2016 Jan" ,   "2015 Dec" ,   "2015 Nov"  ,  "2015 Oct" ,   "2015 Sep"  ,  "2015 Aug" ,"2015 Jul",    "2015 Jun" ,   "2015 May"   , "2015 Apr" ,   "2015 Mar" ,   "2015 Feb",    "2015 Jan"   ),names_to = "Date", values_to = "export")

data<- merge(dfim, dfex, by = c("Date", "Data Series"), all.x = TRUE)

colnames(data)[2] <- "area"

data <- data %>%
  mutate(month = substr(data$Date, start = 6, stop = 9))

data <- data %>%
  mutate(year = substr(data$Date, start = 1, stop = 4))

data$month[data$month=='Jan']<-'01'
data$month[data$month=='Feb']<-'02'
data$month[data$month=='Mar']<-'03'
data$month[data$month=='Apr']<-'04'
data$month[data$month=='May']<-'05'
data$month[data$month=='Jun']<-'06'
data$month[data$month=='Jul']<-'07'
data$month[data$month=='Aug']<-'08'
data$month[data$month=='Sep']<-'09'
data$month[data$month=='Oct']<-'10'
data$month[data$month=='Nov']<-'11'
data$month[data$month=='Dec']<-'12'

data <- data %>%
  mutate(import = import/(10^2))

data <- data %>%
  mutate(export = export/(10^2))

data <- data %>%
  mutate(tb = export-import)

data <- data %>%
  mutate(area1=str_remove(data$area, "(Thousand Dollars)"))

data <- data %>%
  mutate(area2 = str_remove_all(data$area1, "\\(|\\)"))

Import from China
Export to China

mChina <- data %>% 
  filter(area2=='Mainland China ')

ggplot(data = mChina, 
       aes(month, 
           year, 
           fill =import)) + 
geom_tile(color = "white", 
          size = 0.3) + 
theme_tufte(base_family = NULL) + 
coord_equal() +
scale_fill_gradient(name = " Import Volumn (million dollar) ",
                    limits = c(30000, 95000),
                    low = 'white', 
                    high = "Dark green") +
labs(x = 'Month', 
     y = 'Year', 
     title = "Import Volumn from Mainland China before & after Covid") +
theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

ggplot(data = mChina, 
       aes(month, 
           year, 
           fill =export)) + 
geom_tile(color = "white", 
          size = 0.3) + 
theme_tufte(base_family = NULL) + 
coord_equal() +
scale_fill_gradient(name = " Export Volumn (million dollar) ",
                    limits = c(30000, 95000),
                    low = 'white', 
                    high = "Dark green") +
labs(x = 'Month', 
     y = 'Year', 
     title = "Emport Volumn to Mainland China before & after Covid") +
theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

Import from China in 2022
Export to China in 2022

mChina2022 <- data %>% 
  filter(area2=='Mainland China ') %>%
  filter(year=='2022')


ggplot(data = mChina2022, 
       aes(month, 
           year, 
           fill =import)) + 
geom_tile(color = "white", 
          size = 0.3) + 
theme_tufte(base_family = NULL) + 
coord_equal() +
scale_fill_gradient(name = " Import Volumn (million dollar) ",
                    limits = c(30000, 95000),
                    low = 'white', 
                    high = "Dark green") +
labs(x = 'Month', 
     y = 'Year', 
     title = "Import Volumn from Mainland China in 2022") +
theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

ggplot(data = mChina2022, 
       aes(month, 
           year, 
           fill =export)) + 
geom_tile(color = "white", 
          size = 0.3) + 
theme_tufte(base_family = NULL) + 
coord_equal() +
scale_fill_gradient(name = " Export Volumn (million dollar) ",
                    limits = c(30000, 95000),
                    low = 'white', 
                    high = "Dark green") +
labs(x = 'Month', 
     y = 'Year', 
     title = "Export Volumn from Mainland China in 2022") +
theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

china <- mChina %>% 
  group_by(month) %>%
  summarise(avgvalue = mean(import))


p1<-ggplot() + 
  geom_line(data=mChina,
            aes(x=year, 
                y=import, 
                group=month),
            colour= "black") +
  geom_hline(aes(yintercept=avgvalue), 
             data=china, 
             linetype=6, 
             colour="blue", 
             size=0.5) + 
  facet_grid(~month) +
  labs(axis.text.x = element_blank(),
       title = "Trade Balance with Mainland China") +
  xlab("") +
  ylab("Trade Balance")+
  theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )
ggplotly(p1)

echina <- mChina %>% 
  group_by(month) %>%
  summarise(avg = mean(export))


p2<-ggplot() + 
  geom_line(data=mChina,
            aes(x=year, 
                y=export, 
                group=month), 
            colour= "black") +
  geom_hline(aes(yintercept=avg), 
             data=echina, 
             linetype=6, 
             colour="blue", 
             size=0.5) + 
  facet_grid(~month) +
  labs(axis.text.x = element_blank(),
       title = "Trade Balance with Mainland China") +
  xlab("") +
  ylab("Trade Balance")+
  theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )
ggplotly(p2)

Import About 2022
Export About 2022

china2022 <- mChina2022 %>% 
  group_by(year) %>%
  summarise(avgvalue = mean(import))


p1<-ggplot(data=mChina2022,aes(x=Date, 
                y=import,group=1)) + 
  geom_line() +
  geom_hline(aes(yintercept=avgvalue), 
             data=china2022, 
             linetype=6, 
             colour="blue", 
             size=0.5) + 
  labs(axis.text.x = element_blank(),
       title = "Import with Mainland China") +
  xlab("") +
  ylab("Import")+
  theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )
ggplotly(p1)

china2022e <- mChina2022 %>% 
  group_by(year) %>%
  summarise(avgvalue = mean(export))

p2<-ggplot(data=mChina2022,aes(x=Date, 
                y=export,group=1)) + 
  geom_line() +
  geom_hline(aes(yintercept=avgvalue), 
             data=china2022e, 
             linetype=6, 
             colour="blue", 
             size=0.5) + 
  labs(axis.text.x = element_blank(),
       title = "Export with Mainland China") +
  xlab("") +
  ylab("Export")+
  theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )
ggplotly(p2)

p3<- ggplot()+
  geom_line(data=mChina2022,aes(x=Date, 
                y=export,group=1),color='red')+
  geom_line(data=mChina2022,aes(x=Date, 
                y=import,group=1),color='blue')
ggplotly(p3)

ggplot(data=mChina2022,aes(x=Date, 
                y=tb,group=1)) + geom_line()+
  labs(axis.text.x = element_blank(),
       title = "Trade Balance with Mainland China") +
  xlab("") +
  ylab("")

rank<- data %>%
  filter(year==2015) %>%
  group_by(area2,year) %>%
  summarize(import=sum(import),export=sum(export),area2=area2) %>%
  distinct(area2,year,import) %>%
  arrange(desc(import))

top_n(rank,n=10,wt=import)

# A tibble: 118 × 3
# Groups:   area2, year [118]
   area2                           year   import
   <chr>                           <chr>   <dbl>
 1 "Mainland China "               2015  579153.
 2 "United States "                2015  458866.
 3 "Malaysia "                     2015  454397.
 4 "Taiwan "                       2015  340659.
 5 "Japan "                        2015  263778.
 6 "Republic Of Korea "            2015  251659.
 7 "Indonesia "                    2015  208731.
 8 "Germany, Federal Republic Of " 2015  123072.
 9 "Switzerland "                  2015  121072.
10 "United Arab Emirates "         2015  118980.
# … with 108 more rows

newggslopegraph(year, import, area2,

Title = "import fropm Top 10 Counties",

SubTitle = "2015-2022")