Project_2_Covid19_Cases_Data

Objective

Transform the wide-format global COVID-19 confirmed cases dataset into a tidy (long) format using the tidyr and dplyr packages in R. Once the data has been cleaned up, examine how confirmed COVID-19 cases have evolved over time across nations to find broad global patterns.

Approach

Dataset description

For this project, I will use a global COVID-19 confirmed cases dataset provided by the Johns Hopkins University Center for Systems Science and Engineering (CSSE). The dataset is publicly available and can be accessed here:

https://raw.githubusercontent.com/NafeesKhandker/Project-2-Tidy-Format-Transformation/refs/heads/main/Covid19_confirmed_global.csv

Beginning in January 2020, the dataset includes daily confirmed COVID-19 case counts for various nations and regions worldwide.

At the moment, the dataset is kept in a wide format in which a particular date is represented by each column and a country or region by each row. For instance, the number of confirmed cases reported on those dates is shown in columns like 1/22/20, 1/23/20, and 1/24/20.

The dataset does not adhere to tidy data principles because the date variable is embedded within column headers rather than being stored as a separate variable. Every variable in tidy data should have its own column, and every observation should have its own row.

The following significant variables are included in the dataset:

• Province/State: Sub-national region
• Country/Region: Country name
• Lat: Latitude coordinate
• Long: Longitude coordinate
• Date columns: Daily confirmed case counts

Anticipated Challenges

One anticipated challenge is handling the extremely large number of date columns in the dataset. These columns span multiple years and must be transformed into a single date variable during the tidying process.

Another potential challenge is the presence of missing values in the Province/State column, since not all countries report regional data. Care must be taken to preserve country information while handling these missing values appropriately.

Additionally, date values stored as column names will need to be converted into proper date formats after the reshaping process.

Implementation of Data Import

library(tidyverse) 

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.1     ✔ tibble    3.3.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lubridate)
library(gt)

url <- "https://raw.githubusercontent.com/NafeesKhandker/Project-2-Tidy-Format-Transformation/refs/heads/main/Covid19_confirmed_global.csv"

df <- read_csv(file = url, show_col_types = FALSE)

df |> 
  head(n = 5) |> 
  gt()

Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	1/31/20	2/1/20	2/2/20	2/3/20	2/4/20	2/5/20	2/6/20	2/7/20	2/8/20	2/9/20	2/10/20	2/11/20	2/12/20	2/13/20	2/14/20	2/15/20	2/16/20	2/17/20	2/18/20	2/19/20	2/20/20	2/21/20	2/22/20	2/23/20	2/24/20	2/25/20	2/26/20	2/27/20	2/28/20	2/29/20	3/1/20	3/2/20	3/3/20	3/4/20	3/5/20	3/6/20	3/7/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20	3/23/20	3/24/20	3/25/20	3/26/20	3/27/20	3/28/20	3/29/20	3/30/20	3/31/20	4/1/20	4/2/20	4/3/20	4/4/20	4/5/20	4/6/20	4/7/20	4/8/20	4/9/20	4/10/20	4/11/20	4/12/20	4/13/20	4/14/20	4/15/20	4/16/20	4/17/20	4/18/20	4/19/20	4/20/20	4/21/20	4/22/20	4/23/20	4/24/20	4/25/20	4/26/20	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20	5/7/20	5/8/20	5/9/20	5/10/20	5/11/20	5/12/20	5/13/20	5/14/20	5/15/20	5/16/20	5/17/20	5/18/20	5/19/20	5/20/20	5/21/20	5/22/20	5/23/20	5/24/20	5/25/20	5/26/20	5/27/20	5/28/20	5/29/20	5/30/20	5/31/20	6/1/20	6/2/20	6/3/20	6/4/20	6/5/20	6/6/20	6/7/20	6/8/20	6/9/20	6/10/20	6/11/20	6/12/20	6/13/20	6/14/20	6/15/20	6/16/20	6/17/20	6/18/20	6/19/20	6/20/20	6/21/20	6/22/20	6/23/20	6/24/20	6/25/20	6/26/20	6/27/20	6/28/20	6/29/20	6/30/20	7/1/20	7/2/20	7/3/20	7/4/20	7/5/20	7/6/20	7/7/20	7/8/20	7/9/20	7/10/20	7/11/20	7/12/20	7/13/20	7/14/20	7/15/20	7/16/20	7/17/20	7/18/20	7/19/20	7/20/20	7/21/20	7/22/20	7/23/20	7/24/20	7/25/20	7/26/20	7/27/20	7/28/20	7/29/20	7/30/20	7/31/20	8/1/20	8/2/20	8/3/20	8/4/20	8/5/20	8/6/20	8/7/20	8/8/20	8/9/20	8/10/20	8/11/20	8/12/20	8/13/20	8/14/20	8/15/20	8/16/20	8/17/20	8/18/20	8/19/20	8/20/20	8/21/20	8/22/20	8/23/20	8/24/20	8/25/20	8/26/20	8/27/20	8/28/20	8/29/20	8/30/20	8/31/20	9/1/20	9/2/20	9/3/20	9/4/20	9/5/20	9/6/20	9/7/20	9/8/20	9/9/20	9/10/20	9/11/20	9/12/20	9/13/20	9/14/20	9/15/20	9/16/20	9/17/20	9/18/20	9/19/20	9/20/20	9/21/20	9/22/20	9/23/20	9/24/20	9/25/20	9/26/20	9/27/20	9/28/20	9/29/20	9/30/20	10/1/20	10/2/20	10/3/20	10/4/20	10/5/20	10/6/20	10/7/20	10/8/20	10/9/20	10/10/20	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20	10/21/20	10/22/20	10/23/20	10/24/20	10/25/20	10/26/20	10/27/20	10/28/20	10/29/20	10/30/20	10/31/20	11/1/20	11/2/20	11/3/20	11/4/20	11/5/20	11/6/20	11/7/20	11/8/20	11/9/20	11/10/20	11/11/20	11/12/20	11/13/20	11/14/20	11/15/20	11/16/20	11/17/20	11/18/20	11/19/20	11/20/20	11/21/20	11/22/20	11/23/20	11/24/20	11/25/20	11/26/20	11/27/20	11/28/20	11/29/20	11/30/20	12/1/20	12/2/20	12/3/20	12/4/20	12/5/20	12/6/20	12/7/20	12/8/20	12/9/20	12/10/20	12/11/20	12/12/20	12/13/20	12/14/20	12/15/20	12/16/20	12/17/20	12/18/20	12/19/20	12/20/20	12/21/20	12/22/20	12/23/20	12/24/20	12/25/20	12/26/20	12/27/20	12/28/20	12/29/20	12/30/20	12/31/20	1/1/21	1/2/21	1/3/21	1/4/21	1/5/21	1/6/21	1/7/21	1/8/21	1/9/21	1/10/21	1/11/21	1/12/21	1/13/21	1/14/21	1/15/21	1/16/21	1/17/21	1/18/21	1/19/21	1/20/21	1/21/21	1/22/21	1/23/21	1/24/21	1/25/21	1/26/21	1/27/21	1/28/21	1/29/21	1/30/21	1/31/21	2/1/21	2/2/21	2/3/21	2/4/21	2/5/21	2/6/21	2/7/21	2/8/21	2/9/21	2/10/21	2/11/21	2/12/21	2/13/21	2/14/21	2/15/21	2/16/21	2/17/21	2/18/21	2/19/21	2/20/21	2/21/21	2/22/21	2/23/21	2/24/21	2/25/21	2/26/21	2/27/21	2/28/21	3/1/21	3/2/21	3/3/21	3/4/21	3/5/21	3/6/21	3/7/21	3/8/21	3/9/21	3/10/21	3/11/21	3/12/21	3/13/21	3/14/21	3/15/21	3/16/21	3/17/21	3/18/21	3/19/21	3/20/21	3/21/21	3/22/21	3/23/21	3/24/21	3/25/21	3/26/21	3/27/21	3/28/21	3/29/21	3/30/21	3/31/21	4/1/21	4/2/21	4/3/21	4/4/21	4/5/21	4/6/21	4/7/21	4/8/21	4/9/21	4/10/21	4/11/21	4/12/21	4/13/21	4/14/21	4/15/21	4/16/21	4/17/21	4/18/21	4/19/21	4/20/21	4/21/21	4/22/21	4/23/21	4/24/21	4/25/21	4/26/21	4/27/21	4/28/21	4/29/21	4/30/21	5/1/21	5/2/21	5/3/21	5/4/21	5/5/21	5/6/21	5/7/21	5/8/21	5/9/21	5/10/21	5/11/21	5/12/21	5/13/21	5/14/21	5/15/21	5/16/21	5/17/21	5/18/21	5/19/21	5/20/21	5/21/21	5/22/21	5/23/21	5/24/21	5/25/21	5/26/21	5/27/21	5/28/21	5/29/21	5/30/21	5/31/21	6/1/21	6/2/21	6/3/21	6/4/21	6/5/21	6/6/21	6/7/21	6/8/21	6/9/21	6/10/21	6/11/21	6/12/21	6/13/21	6/14/21	6/15/21	6/16/21	6/17/21	6/18/21	6/19/21	6/20/21	6/21/21	6/22/21	6/23/21	6/24/21	6/25/21	6/26/21	6/27/21	6/28/21	6/29/21	6/30/21	7/1/21	7/2/21	7/3/21	7/4/21	7/5/21	7/6/21	7/7/21	7/8/21	7/9/21	7/10/21	7/11/21	7/12/21	7/13/21	7/14/21	7/15/21	7/16/21	7/17/21	7/18/21	7/19/21	7/20/21	7/21/21	7/22/21	7/23/21	7/24/21	7/25/21	7/26/21	7/27/21	7/28/21	7/29/21	7/30/21	7/31/21	8/1/21	8/2/21	8/3/21	8/4/21	8/5/21	8/6/21	8/7/21	8/8/21	8/9/21	8/10/21	8/11/21	8/12/21	8/13/21	8/14/21	8/15/21	8/16/21	8/17/21	8/18/21	8/19/21	8/20/21	8/21/21	8/22/21	8/23/21	8/24/21	8/25/21	8/26/21	8/27/21	8/28/21	8/29/21	8/30/21	8/31/21	9/1/21	9/2/21	9/3/21	9/4/21	9/5/21	9/6/21	9/7/21	9/8/21	9/9/21	9/10/21	9/11/21	9/12/21	9/13/21	9/14/21	9/15/21	9/16/21	9/17/21	9/18/21	9/19/21	9/20/21	9/21/21	9/22/21	9/23/21	9/24/21	9/25/21	9/26/21	9/27/21	9/28/21	9/29/21	9/30/21	10/1/21	10/2/21	10/3/21	10/4/21	10/5/21	10/6/21	10/7/21	10/8/21	10/9/21	10/10/21	10/11/21	10/12/21	10/13/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NA	Algeria	28.03390	1.65960	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	3	5	12	12	17	17	19	20	20	20	24	26	37	48	54	60	74	87	90	139	201	230	264	302	367	409	454	511	584	716	847	986	1171	1251	1320	1423	1468	1572	1666	1761	1825	1914	1983	2070	2160	2268	2418	2534	2629	2718	2811	2910	3007	3127	3256	3382	3517	3649	3848	4006	4154	4295	4474	4648	4838	4997	5182	5369	5558	5723	5891	6067	6253	6442	6629	6821	7019	7201	7377	7542	7728	7918	8113	8306	8503	8697	8857	8997	9134	9267	9394	9513	9626	9733	9831	9935	10050	10154	10265	10382	10484	10589	10698	10810	10919	11031	11147	11268	11385	11504	11631	11771	11920	12076	12248	12445	12685	12968	13273	13571	13907	14272	14657	15070	15500	15941	16404	16879	17348	17808	18242	18712	19195	19689	20216	20770	21355	21948	22549	23084	23691	24278	24872	25484	26159	26764	27357	27973	28615	29229	29831	30394	30950	31465	31972	32504	33055	33626	34155	34693	35160	35712	36204	36699	37187	37664	38133	38583	39025	39444	39847	40258	40667	41068	41460	41858	42228	42619	43016	43403	43781	44146	44494	44833	45158	45469	45773	46071	46364	46653	46938	47216	47488	47752	48007	48254	48496	48734	48966	49194	49413	49623	49826	50023	50214	50400	50579	50754	50914	51067	51213	51368	51530	51690	51847	51995	52136	52270	52399	52520	52658	52804	52940	53072	53325	53399	53584	53777	53998	54203	54402	54616	54829	55081	55357	55630	55880	56143	56419	56706	57026	57332	57651	57942	58272	58574	58979	59527	60169	60800	61381	62051	62693	63446	64257	65108	65975	66819	67679	68589	69591	70629	71652	72755	73774	74862	75867	77000	78025	79110	80168	81212	82221	83199	84152	85084	85927	86730	87502	88252	88825	89416	90014	90579	91121	91638	92102	92597	93065	93507	93933	94371	94781	95203	95659	96069	96549	97007	97441	97857	98249	98631	98988	99311	99610	99897	100159	100408	100645	100873	101120	101382	101657	101913	102144	102369	102641	102860	103127	103381	103611	103833	104092	104341	104606	104852	105124	105369	105596	105854	106097	106359	106610	106887	107122	107339	107578	107841	108116	108381	108629	108629	109088	109313	109559	109782	110049	110303	110513	110711	110894	111069	111247	111418	111600	111764	111917	112094	112279	112461	112622	112805	112960	113092	113255	113430	113593	113761	113948	114104	114234	114382	114543	114681	114851	115008	115143	115265	115410	115540	115688	115842	115970	116066	116157	116255	116349	116438	116543	116657	116750	116836	116946	117061	117192	117304	117429	117524	117622	117739	117879	118004	118116	118251	118378	118516	118645	118799	118975	119142	119323	119486	119642	119805	119992	120174	120363	120562	120736	120922	121112	121344	121580	121866	122108	122311	122522	122717	122999	123272	123473	123692	123900	124104	124288	124483	124682	124889	125059	125194	125311	125485	125693	125896	126156	126434	126651	126860	127107	127361	127646	127926	128198	128456	128725	128913	129218	129640	129976	130361	130681	130958	131283	131647	132034	132355	132727	133070	133388	133742	134115	134458	134840	135219	135586	135821	136294	136679	137049	137403	137772	138113	138465	138840	139229	139626	140075	140550	141007	141471	141966	142447	143032	143652	144483	145296	146064	146942	147883	148797	149906	151103	152210	153309	154486	155784	157005	158213	159563	160868	162155	163660	165204	167131	168668	170189	171392	172564	173922	175229	176724	178013	179216	180356	181376	182368	183347	184191	185042	185902	186655	187258	187968	188663	189384	190078	190656	191171	191583	192089	192626	193171	193674	194186	194671	195162	195574	196080	196527	196915	197308	197659	198004	198313	198645	198962	199275	199560	199822	200068	200301	200528	200770	200989	201224	201425	201600	201766	201948	202122	202283	202449	202574	202722	202877	203045	203198	203359	203517	203657	203789	203915	204046	204171	204276	204388	204490	204597	204695	204790	204900	205005	205106	205199	205286	205364	205453	205529	205599	205683	205750	205822	205903	205990	206069	206160	206270	206358	206452	206566	206649	206754	206878	206995	207079	207156	207254	207385	207509	207624	207764	207873	207970	208104	208245	208380	208532	208695	208839	208952	209111	209283	209463	209624	209817	209980	210152	210344	210531	210723	210921	211112	211297	211469	211662	211859	212047	212224	212434	212652	212848	213058	213288	213533	213745	214044	214330	214592	214835	215145	215430	215723	216098	216376	216637	216930	217265	217647	218037	218432	218818	219159	219532	219953	220415	220825	221316	221742	222157	222639	223196	223806	224383	224979	225484	226057	226749	227559	228918	230470	232325	234536	236670	238885	241406	243568	245698	247568	249310	250774	252117	253520	254885	255836	256806	257598	257976	258478	259088	259673	260191	260723	261226	261752	262165	262570	262994	263369	263685	263936	264054	264201	264365	264488	264603	264706	264778	264855	264936	265010	265079	265130	265186	265227	265265	265297	265323	265346	265366	265391	265410	265432	265457	265478	265496	265511	265524	265539	265550	265562	265573	265585	265599	265612	265621	265629	265641	265651	265662	265671	265679	265684	265691	265694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NA	Andorra	42.50630	1.52180	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	39	39	53	75	88	113	133	164	188	224	267	308	334	370	376	390	428	439	466	501	525	545	564	583	601	601	638	646	659	673	673	696	704	713	717	717	723	723	731	738	738	743	743	743	745	745	747	748	750	751	751	752	752	754	755	755	758	760	761	761	761	761	761	761	762	762	762	762	762	763	763	763	763	764	764	764	765	844	851	852	852	852	852	852	852	852	852	853	853	853	853	854	854	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	855	858	861	862	877	880	880	880	884	884	889	889	897	897	897	907	907	918	922	925	925	925	937	939	939	944	955	955	955	963	963	977	981	989	989	989	1005	1005	1024	1024	1045	1045	1045	1060	1060	1098	1098	1124	1124	1124	1176	1184	1199	1199	1215	1215	1215	1261	1261	1301	1301	1344	1344	1344	1438	1438	1483	1483	1564	1564	1564	1681	1681	1753	1753	1836	1836	1836	1966	1966	2050	2050	2110	2110	2110	2370	2370	2568	2568	2696	2696	2696	2995	2995	3190	3190	3377	3377	3377	3623	3623	3811	3811	4038	4038	4038	4325	4410	4517	4567	4665	4756	4825	4888	4910	5045	5135	5135	5319	5383	5437	5477	5567	5616	5725	5725	5872	5914	5951	6018	6066	6142	6207	6256	6304	6351	6428	6534	6610	6610	6712	6745	6790	6842	6904	6955	7005	7050	7084	7127	7162	7190	7236	7288	7338	7382	7382	7446	7466	7519	7560	7577	7602	7633	7669	7699	7756	7806	7821	7875	7919	7983	8049	8117	8166	8192	8249	8308	8348	8348	8489	8586	8586	8586	8682	8818	8868	8946	9038	9083	9083	9194	9308	9379	9416	9499	9549	9596	9638	9716	9779	9837	9885	9937	9972	10017	10070	10137	10172	10206	10251	10275	10312	10352	10391	10427	10463	10503	10538	10555	10583	10610	10645	10672	10699	10712	10739	10775	10799	10822	10849	10866	10889	10908	10948	10976	10998	11019	11042	11069	11089	11130	11130	11199	11228	11266	11289	11319	11360	11393	11431	11481	11517	11545	11591	11638	11687	11732	11809	11850	11888	11944	12010	12053	12115	12174	12231	12286	12328	12363	12409	12456	12497	12545	12581	12614	12641	12641	12712	12771	12805	12805	12874	12917	12942	13007	13024	13060	13083	13121	13148	13198	13232	13232	13282	13295	13316	13340	13363	13390	13406	13423	13429	13447	13470	13470	13510	13510	13510	13555	13569	13569	13569	13569	13569	13569	13569	13664	13671	13682	13693	13693	13693	13727	13729	13744	13752	13758	13758	13758	13777	13781	13791	13805	13813	13813	13813	13826	13828	13836	13839	13842	13842	13842	13864	13864	13873	13877	13882	13882	13882	13882	13900	13911	13918	13918	13918	13918	13918	13991	14021	14050	14075	14075	14075	14155	14167	14167	14239	14273	14273	14273	14359	14379	14379	14464	14498	14498	14498	14577	14586	14586	14655	14678	14678	14678	14747	14766	14797	14809	14836	14836	14836	14836	14873	14891	14908	14924	14924	14924	14954	14960	14976	14981	14988	14988	14988	15002	15003	15014	15016	15025	15025	15025	15032	15033	15046	15052	15055	15055	15055	15069	15070	15070	15078	15083	15083	15083	15096	15099	15108	15113	15124	15124	15124	15140	15140	15153	15156	15167	15167	15167	15189	15192	15209	15222	15222	15222	15222	15267	15271	15284	15288	15291	15291	15291	15307	15307	15314	15326	15338	15338	15338	15367	15369	15382	15382	15404	15404	15404	15425	15425	15462	15505	15516	15516	15516	15516	15516	15572	15618	15618	15618	15618	15705	15717	15744	15744	15819	15819	15819	15907	15929	15972	16035	16086	16086	16086	16299	16342	16426	16566	16712	16712	16712	16712	17115	17426	17658	18010	18010	18010	18631	18815	18815	19272	19440	19440	19440	19440	20136	20136	20549	20549	20549	20549	21062	21062	21372	21571	21730	21730	21730	22332	22540	22823	23122	23740	23740	23740	24502	24802	25289	25289	26408	26408	26408	27983	28542	28899	28899	29888	29888	29888	29888	29888	29888	32201	33025	33025	33025	33025	34701	35028	35028	35556	35556	35556	35958	35958	36315	36470	36599	36599	36599	36808	36808	36989	37074	37140	37140	37140	37277	37361	37452	37522	37589	37589	37589	37589	37820	37901	37958	37999	37999	37999	37999	38165	38249	38342	38434	38434	38434	38620	38710	38794	38794	38794	38794	38794	38794	38794	38794	39234	39234	39234	39234	39234	39234	39713	39713	39713	39713	39713	39713	39713	40024	40024	40024	40024	40024	40024	40024	40024	40328	40328	40328	40328	40328	40328	40709	40709	40709	40709	40709	40709	40709	41013	41013	41013	41013	41013	41013	41013	41013	41349	41349	41349	41349	41349	41349	41717	41717	41717	41717	41717	41717	41717	41717	42156	42156	42156	42156	42156	42156	42572	42572	42572	42572	42572	42572	42572	42894	42894	42894	42894	42894	42894	42894	42894	42894	43067	43067	43067	43067	43067	43224	43224	43224	43224	43224	43224	43224	43449	43449	43449	43449	43449	43449	43449	43774	43774	43774	43774	43774	43774	43774	43774	43774	44177	44177	44177	44177	44177	44671	44671	44671	44671	44671	44671	44671	44671	44671	44671	44671	44671	45061	45061	45061	45326	45326	45326	45326	45326	45326	45326	45508	45508	45508	45508	45508	45508	45793	45793	45793	45793	45793	45793	45793	45899	45899	45899	45899	45899	45899	45899	45975	45975	45975	45975	45975	45975	45975	46027	46027	46027	46027	46027	46027	46027	46027	46027	46027	46027	46027	46027	46027	46113	46113	46113	46113	46113	46113	46113	46147	46147	46147	46147	46147	46147	46147	46147	46147	46147	46147	46147	46147	46147	46227	46227	46227	46227	46227	46227	46227	46227	46275	46275	46275	46275	46275	46275	46366	46366	46366	46366	46366	46366	46366	46449	46449	46449	46449	46449	46449	46449	46535	46535	46535	46535	46535	46535	46535	46588	46588	46588	46588	46588	46588	46588	46664	46664	46664	46664	46664	46664	46664	46824	46824	46824	46824	46824	46824	46824	46824	46824	46824	46824	46824	46824	46824	47219	47219	47219	47219	47219	47219	47219	47446	47446	47446	47446	47446	47446	47446	47606	47606	47606	47606	47606	47606	47686	47686	47686	47686	47686	47686	47686	47686	47751	47751	47751	47751	47751	47751	47751	47751	47781	47781	47781	47781	47781	47781	47781	47781	47781	47781	47781	47781	47781	47781	47820	47820	47820	47820	47820	47820	47820	47820	47820	47839	47839	47839	47839	47839	47839	47850	47850	47850	47850	47850	47850	47860	47860	47860	47860	47860	47860	47860	47866	47866	47866	47866	47866	47866	47866	47866	47866	47866	47866	47866	47866	47875	47875	47875	47875	47875	47875	47875	47890	47890
NA	Angola	-11.20270	17.87390	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2	2	3	3	3	4	4	5	7	7	7	8	8	8	10	14	16	17	19	19	19	19	19	19	19	19	19	19	24	24	24	24	25	25	25	25	26	27	27	27	27	30	35	35	35	36	36	36	43	43	45	45	45	45	48	48	48	48	50	52	52	58	60	61	69	70	70	71	74	81	84	86	86	86	86	86	86	88	91	92	96	113	118	130	138	140	142	148	155	166	172	176	183	186	189	197	212	212	259	267	276	284	291	315	328	346	346	346	386	386	396	458	462	506	525	541	576	607	638	687	705	749	779	812	851	880	916	932	950	1000	1078	1109	1148	1164	1199	1280	1344	1395	1483	1538	1572	1672	1679	1735	1762	1815	1852	1879	1906	1935	1966	2015	2044	2068	2134	2171	2222	2283	2332	2415	2471	2551	2624	2654	2729	2777	2805	2876	2935	2965	2981	3033	3092	3217	3279	3335	3388	3439	3569	3675	3789	3848	3901	3991	4117	4236	4363	4475	4590	4672	4718	4797	4905	4972	5114	5211	5370	5402	5530	5725	5725	5958	6031	6246	6366	6488	6680	6846	7096	7222	7462	7622	7829	8049	8338	8582	8829	9026	9381	9644	9871	10074	10269	10558	10805	11035	11228	11577	11813	12102	12223	12335	12433	12680	12816	12953	13053	13228	13374	13451	13615	13818	13922	14134	14267	14413	14493	14634	14742	14821	14920	15008	15087	15103	15139	15251	15319	15361	15493	15536	15591	15648	15729	15804	15925	16061	16161	16188	16277	16362	16407	16484	16562	16626	16644	16686	16802	16931	17029	17099	17149	17240	17296	17371	17433	17553	17568	17608	17642	17684	17756	17864	17974	18066	18156	18193	18254	18343	18425	18613	18679	18765	18875	18926	19011	19093	19177	19269	19367	19399	19476	19553	19580	19672	19723	19782	19796	19829	19900	19937	19996	20030	20062	20086	20112	20163	20210	20261	20294	20329	20366	20381	20389	20400	20452	20478	20499	20519	20548	20584	20640	20695	20759	20782	20807	20854	20882	20923	20981	21026	21055	21086	21108	21114	21161	21205	21265	21323	21380	21407	21446	21489	21558	21642	21696	21733	21757	21774	21836	21914	21961	22031	22063	22132	22182	22311	22399	22467	22579	22631	22717	22885	23010	23108	23242	23331	23457	23549	23697	23841	23951	24122	24300	24389	24518	24661	24883	25051	25279	25492	25609	25710	25942	26168	26431	26652	26815	26993	27133	27284	27529	27921	28201	28477	28740	28875	29146	29405	29695	30030	30354	30637	30787	31045	31438	31661	31909	32149	32441	32623	32933	33338	33607	33944	34180	34366	34551	34752	34960	35140	35307	35594	35772	35854	36004	36115	36325	36455	36600	36705	36790	36921	37094	37289	37467	37604	37678	37748	37874	38002	38091	38371	38528	38556	38613	38682	38849	38965	39089	39172	39230	39300	39375	39491	39593	39791	39881	39958	40055	40138	40327	40530	40631	40707	40805	40906	41061	41227	41405	41629	41736	41780	41879	42110	42288	42486	42646	42777	42815	42970	43070	43158	43269	43487	43592	43662	43747	43890	43998	44174	44328	44534	44617	44739	44972	45175	45325	45583	45817	45945	46076	46340	46539	46726	46929	47079	47168	47331	47544	47781	48004	48261	48475	48656	48790	49114	49349	49628	49943	50348	50446	50738	51047	51407	51827	52208	52307	52307	52644	52968	53387	53840	54280	54795	55121	55583	56040	56583	56583	58076	58603	58943	58943	59895	60448	60803	61023	61245	61378	61580	61794	62143	62385	62606	62789	62842	63012	63197	63340	63567	63691	63775	63861	63930	64033	64126	64226	64301	64374	64433	64458	64487	64533	64583	64612	64654	64674	64724	64762	64815	64857	64875	64899	64913	64913	64940	64968	64985	64997	65011	65024	65033	65061	65080	65105	65130	65139	65144	65155	65168	65183	65208	65223	65244	65259	65259	65301	65332	65346	65371	65397	65404	65404	65431	65565	65648	65760	65868	65938	66086	66566	67199	68362	70221	71142	71752	71752	76787	78475	79871	81593	82398	82920	83764	84666	86636	87625	88775	89251	89718	90316	91148	91907	92581	93302	93524	93694	93974	94275	94779	95220	95676	95902	96582	97263	97594	97812	97901	98029	98057	98076	98116	98226	98267	98319	98340	98351	98364	98409	98424	98453	98474	98501	98514	98514	98514	98555	98568	98585	98605	98617	98638	98658	98671	98698	98701	98701	98701	98701	98741	98746	98746	98746	98796	98796	98806	98806	98829	98855	98855	98855	98909	98927	98931	98956	98985	99003	99003	99003	99003	99010	99058	99058	99081	99102	99106	99115	99115	99138	99138	99169	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99194	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99287	99433	99527	99527	99527	99527	99527	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	99761	101320	101320	101320	101320	101320	101320	101320	101320	101320	101320	101320	101320	101320	101320	101320	101320	101600	101901	101901	101901	102209	102209	102209	102209	102301	102301	102301	102301	102301	102301	102301	102301	102301	102301	102301	102301	102301	102301	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	102636	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	103131	104491	104491	104491	104491	104491	104491	104491	104676	104676	104676	104676	104676	104750	104750	104808	104808	104808	104808	104808	104808	104808	104946	104946	104946	104946	104946	104946	104946	104973	104973	104973	104973	104973	104973	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105095	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105184	105205	105205	105205	105205	105205	105255	105255	105277	105277	105277	105277	105277	105277	105277	105288	105288

Converting Wide Data to Tidy (Long) Format

This step reshapes the dataset from wide format to tidy format using pivot_longer(). The date columns are converted into a single date variable and the corresponding values are stored in confirmed_cases. Additional cleaning steps convert dates to proper date format, ensure numeric case counts, trim extra spaces in region names, and rename columns for consistency. The resulting dataset follows tidy data principles where each variable has its own column and each observation has its own row.

# Tidying pipeline and computing daily cases
df_tidy <- df |> 
  pivot_longer(
    cols = 5:last_col(),
    names_to = "date",
    values_to = "cumulative_cases"
  ) |>
  mutate(
    date = mdy(date),
    cumulative_cases = as.integer(cumulative_cases),
    province = str_trim(`Province/State`),
    country = str_trim(`Country/Region`)
  ) |> 
  select(province, country, lat = Lat, long = Long, date, cumulative_cases) |>
  arrange(country, province, date) |> 
  group_by(country, province) |> 
  mutate(
    confirmed_cases = cumulative_cases - lag(cumulative_cases, default = 0)
  ) |>
  ungroup() |>
  select(province, country, lat, long, date, confirmed_cases)

df_tidy |> 
  head(n = 5) |> 
  gt()

province	country	lat	long	date	confirmed_cases
NA	Afghanistan	33.93911	67.70995	2020-01-22	0
NA	Afghanistan	33.93911	67.70995	2020-01-23	0
NA	Afghanistan	33.93911	67.70995	2020-01-24	0
NA	Afghanistan	33.93911	67.70995	2020-01-25	0
NA	Afghanistan	33.93911	67.70995	2020-01-26	0

Handling Missing Values and Data Consistency

This step addresses missing or inconsistent values in the dataset. Missing values in the province column are replaced with "None" to maintain consistent categorical values. Any missing case counts are replaced with 0 to avoid errors in later calculations. The dataset is then sorted by country, province, and date to maintain a logical chronological structure.

# Handle missing or inconsistent values
df_tidy <- df_tidy %>%
  arrange(country, province, date) %>%
  group_by(country, province) %>%
  mutate(
    daily_cases = confirmed_cases - lag(confirmed_cases, default = 0),
    daily_cases = if_else(daily_cases < 0, 0L, daily_cases)  # replace negatives with 0
  ) %>%
  ungroup()

df_tidy |> 
  head(n = 5) |> 
  gt()

province	country	lat	long	date	confirmed_cases	daily_cases
NA	Afghanistan	33.93911	67.70995	2020-01-22	0	0
NA	Afghanistan	33.93911	67.70995	2020-01-23	0	0
NA	Afghanistan	33.93911	67.70995	2020-01-24	0	0
NA	Afghanistan	33.93911	67.70995	2020-01-25	0	0
NA	Afghanistan	33.93911	67.70995	2020-01-26	0	0

Country-Level Summary of COVID-19 Cases

This analysis aggregates the tidy dataset to produce summary statistics for each country. The total confirmed cases, maximum daily cases, and average daily cases are calculated using dplyr functions. The resulting summary table is sorted by total cases and displayed using the gt package for clearer presentation and formatted numeric values.

# Country-level summary using daily cases
country_summary <- df_tidy %>%
  group_by(country) %>%
  summarise(
    total_cases = sum(confirmed_cases, na.rm = TRUE),
    max_daily_cases = max(confirmed_cases, na.rm = TRUE),
    avg_daily_cases = round(mean(confirmed_cases, na.rm = TRUE))
  ) %>%
  arrange(desc(total_cases))

country_summary |> 
  gt() |> 
  tab_header(
    title = "Total Confirmed Cases per Country"
  ) |>
  cols_label(
    country = "Country",
    total_cases = "Total Cases",
    max_daily_cases = "Max Daily Cases",
    avg_daily_cases = "Average Daily Cases"
  ) |>
  fmt_number(
    columns = c(total_cases, max_daily_cases, avg_daily_cases),
    decimals = 0,
    use_seps = TRUE
  )

Total Confirmed Cases per Country
Country	Total Cases	Max Daily Cases	Average Daily Cases
US	103,802,702	1,354,505	90,816
India	44,690,738	414,188	39,100
France	39,866,718	501,635	2,907
Germany	38,249,060	484,345	33,464
Brazil	37,076,053	287,149	32,437
Japan	33,320,438	260,999	29,152
Korea, South	30,615,522	621,317	26,785
Italy	25,603,510	229,122	22,400
United Kingdom	24,658,705	847,371	1,438
Russia	22,075,858	202,211	19,314
Turkey	17,042,722	823,225	14,911
Spain	13,770,429	372,766	12,048
Vietnam	11,526,994	454,212	10,085
Australia	11,399,460	92,264	1,247
Argentina	10,044,957	139,853	8,788
Taiwan*	9,970,937	128,954	8,723
Netherlands	8,712,835	380,388	1,525
Iran	7,572,311	50,228	6,625
Mexico	7,483,444	109,895	6,547
Indonesia	6,738,225	64,718	5,895
Poland	6,444,960	57,659	5,639
Colombia	6,359,093	35,575	5,564
Austria	5,961,143	64,038	5,215
Ukraine	5,711,929	79,325	4,997
Portugal	5,570,473	77,281	4,874
Greece	5,548,487	367,629	4,854
Chile	5,192,286	41,651	4,543
Malaysia	5,044,718	33,406	4,414
China	4,903,524	76,991	126
Israel	4,803,824	243,295	4,203
Belgium	4,739,365	133,480	4,146
Thailand	4,728,182	47,149	4,137
Czechia	4,618,256	67,107	4,040
Canada	4,617,095	39,625	252
Peru	4,487,553	67,101	3,926
Switzerland	4,413,911	89,462	3,862
Philippines	4,077,452	38,867	3,567
South Africa	4,067,067	37,875	3,558
Denmark	3,451,036	55,120	1,006
Romania	3,346,046	40,018	2,927
Sweden	2,699,339	138,985	2,362
Slovakia	2,667,551	33,633	2,334
Serbia	2,500,142	36,737	2,187
Iraq	2,465,545	13,515	2,157
New Zealand	2,236,114	42,699	652
Singapore	2,235,294	39,252	1,956
Hungary	2,196,804	45,047	1,922
Bangladesh	2,037,871	16,230	1,783
Georgia	1,827,537	26,524	1,599
Jordan	1,746,997	25,502	1,528
Ireland	1,704,502	43,199	1,491
Pakistan	1,577,411	12,073	1,380
Kazakhstan	1,498,668	66,121	1,311
Norway	1,479,506	26,109	1,294
Finland	1,463,644	50,889	1,281
Slovenia	1,331,707	23,332	1,165
Lithuania	1,307,448	15,412	1,144
Bulgaria	1,297,523	12,399	1,135
Morocco	1,272,490	12,039	1,113
Croatia	1,269,326	11,812	1,111
Guatemala	1,238,247	9,946	1,083
Lebanon	1,232,828	10,760	1,079
Costa Rica	1,209,725	55,406	1,058
Bolivia	1,194,277	23,611	1,045
Tunisia	1,151,126	27,340	1,007
Cuba	1,112,643	9,907	973
Ecuador	1,057,121	17,670	925
United Arab Emirates	1,053,213	4,471	921
Uruguay	1,034,303	13,612	905
Panama	1,031,731	19,722	903
Mongolia	1,007,900	24,556	882
Nepal	1,001,154	10,052	876
Belarus	994,037	11,170	870
Latvia	976,255	11,992	854
Saudi Arabia	830,127	5,928	726
Azerbaijan	828,825	7,779	725
Paraguay	808,401	59,361	707
Bahrain	710,693	8,173	622
West Bank and Gaza	703,228	30,356	615
Sri Lanka	672,039	11,366	588
Kuwait	663,860	6,913	581
Dominican Republic	660,790	7,439	578
Cyprus	650,685	15,386	569
Burma	633,950	7,083	555
Estonia	615,433	8,438	538
Moldova	611,140	10,147	535
Venezuela	552,162	4,418	483
Egypt	515,759	5,516	451
Libya	507,187	5,694	444
Ethiopia	500,116	5,185	438
Qatar	495,090	4,206	433
Honduras	472,250	12,890	413
Armenia	447,308	4,388	391
Bosnia and Herzegovina	401,729	5,254	351
Oman	399,449	6,146	349
North Macedonia	346,852	2,332	303
Zambia	343,135	5,555	300
Kenya	342,937	3,749	300
Albania	334,457	4,789	293
Botswana	329,758	41,576	289
Luxembourg	317,367	5,497	278
Mauritius	296,042	30,006	259
Montenegro	288,808	2,960	253
Brunei	279,661	7,380	245
Kosovo	273,312	4,397	239
Algeria	271,496	2,521	238
Nigeria	266,598	6,158	233
Zimbabwe	264,276	9,185	231
Uzbekistan	251,247	1,478	220
Mozambique	233,214	4,947	204
Laos	218,023	3,915	191
Afghanistan	209,451	3,243	183
Iceland	209,137	7,408	183
Kyrgyzstan	206,708	11,505	181
El Salvador	201,785	12,677	177
Trinidad and Tobago	189,918	1,259	166
Maldives	185,738	2,838	163
Ghana	171,229	2,521	150
Namibia	171,156	4,210	150
Uganda	170,544	21,324	149
Jamaica	154,416	1,968	135
Cambodia	138,719	1,130	121
Rwanda	133,194	3,072	117
Cameroon	124,392	9,668	109
Malta	117,610	1,677	103
Barbados	106,798	1,329	93
Angola	105,288	5,035	92
Congo (Kinshasa)	95,749	4,481	84
Senegal	88,926	1,722	78
Malawi	88,707	1,316	78
Cote d'Ivoire	88,263	2,858	77
Suriname	82,467	1,404	72
Eswatini	74,267	1,642	65
Guyana	73,075	1,186	64
Belize	70,757	1,517	62
Fiji	68,898	1,854	60
Madagascar	67,889	2,295	59
Sudan	63,829	1,284	56
Mauritania	63,668	1,211	56
Cabo Verde	63,244	1,469	55
Bhutan	62,627	2,291	55
Syria	57,467	905	50
Burundi	53,631	4,710	47
Seychelles	50,665	2,068	44
Gabon	48,981	1,871	43
Andorra	47,890	2,313	42
Papua New Guinea	46,825	1,543	41
Tanzania	42,906	24,307	38
Togo	39,396	1,002	34
Guinea	38,267	534	33
Bahamas	37,491	1,497	33
Lesotho	34,790	6,925	30
Haiti	34,202	284	30
Mali	33,062	1,217	29
Saint Lucia	30,004	722	26
Benin	27,999	2,566	24
Somalia	27,324	1,066	24
Congo (Brazzaville)	25,087	1,188	22
Solomon Islands	24,575	3,063	22
Micronesia	23,948	3,652	21
San Marino	23,616	491	21
Timor-Leste	23,419	532	20
Burkina Faso	22,056	1,005	19
Liechtenstein	21,432	349	19
Grenada	19,680	902	17
South Sudan	18,368	503	16
Tajikistan	17,786	407	16
Equatorial Guinea	17,229	1,750	15
Tonga	16,810	832	15
Samoa	16,607	1,166	15
Monaco	16,121	520	14
Dominica	15,760	895	14
Djibouti	15,690	415	14
Nicaragua	15,655	516	14
Marshall Islands	15,649	3,130	14
Central African Republic	15,368	4,044	13
Gambia	12,598	587	11
Vanuatu	12,014	616	11
Yemen	11,945	287	10
Eritrea	10,189	282	9
Saint Vincent and the Grenadines	9,589	2,723	8
Niger	9,508	301	8
Antigua and Barbuda	9,106	468	8
Comoros	9,008	275	8
Guinea-Bissau	8,960	305	8
Liberia	8,090	447	7
Sierra Leone	7,760	192	7
Chad	7,679	596	7
Saint Kitts and Nevis	6,597	238	6
Sao Tome and Principe	6,281	319	5
Palau	5,991	371	5
Nauru	5,247	1,241	5
Kiribati	5,014	1,491	4
Tuvalu	2,805	793	2
Summer Olympics 2020	865	112	1
Diamond Princess	712	99	1
Winter Olympics 2022	535	72	0
Holy See	29	7	0
Antarctica	11	11	0
MS Zaandam	9	7	0
Korea, North	1	1	0

Interactive Global Map of Average Daily COVID-19 Cases

This step creates an interactive choropleth map that visualizes the global distribution of COVID-19 average daily cases by country. Country names are converted to ISO-3 country codes to enable geographic mapping. The map uses a reversed yellow-to-red color gradient where darker red indicates higher average daily case counts. Hovering over a country displays its name along with the average and maximum daily confirmed cases.

library(dplyr)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

library(countrycode)

country_map_data <- df_tidy %>%
  group_by(country) %>%
  summarise(
    avg_daily_cases = round(mean(confirmed_cases, na.rm = TRUE)),
    max_daily_cases = max(confirmed_cases, na.rm = TRUE)
  ) %>%
  ungroup() %>%
  mutate(
    iso3 = countrycode(country, "country.name", "iso3c")
  ) %>%
  filter(!is.na(iso3))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `iso3 = countrycode(country, "country.name", "iso3c")`.
Caused by warning:
! Some values were not matched unambiguously: Diamond Princess, Kosovo, Micronesia, MS Zaandam, Summer Olympics 2020, Winter Olympics 2022
To fix unmatched values, please use the `custom_match` argument. If you think the default matching rules should be improved, please file an issue at https://github.com/vincentarelbundock/countrycode/issues

plot_ly(
  data = country_map_data,
  type = "choropleth",
  locations = ~iso3,
  z = ~avg_daily_cases,
  text = ~paste(
    "Country:", country,
    "<br>Average Daily Cases:", format(avg_daily_cases, big.mark = ",", scientific = FALSE),
    "<br>Max Daily Cases:", format(max_daily_cases, big.mark = ",", scientific = FALSE)
  ),
  hoverinfo = "text",
  colorscale = "YlOrRd_r",
  marker = list(line = list(color = "white", width = 0.3)),
  colorbar = list(
    title = "Avg Daily Cases",
    tickformat = ","
  )
) %>%
  layout(
    title = "Global COVID-19 Average Daily Cases by Country",
    geo = list(
      showframe = FALSE,
      showcoastlines = TRUE,
      coastlinecolor = "gray",
      projection = list(type = "natural earth")
    )
  )

Time Series for Daily COVID-19 Confirmed Cases in the USA

I have filtered for the USA and created a time series plot of daily confirmed cases below.

library(scales)

Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

# Filter for USA daily cases
usa_data <- df_tidy %>%
  filter(country == "US") %>%
  group_by(date) %>%
  summarise(
    daily_cases = sum(confirmed_cases, na.rm = TRUE)
  ) %>%
  arrange(date)

ggplot(usa_data, aes(x = date, y = daily_cases)) +
  geom_line(color = "skyblue", size = 1) +
  scale_y_continuous(labels = comma) +
  labs(
    title = "Daily COVID-19 Confirmed Cases in the USA",
    x = "Date",
    y = "Number of Cases",
    caption = "Data Source: Johns Hopkins University CSSE"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Conclusion

This project transformed a wide-format global COVID-19 dataset into a tidy structure using tidyr and dplyr, making the data easier to analyze and visualize. After cleaning and standardizing the variables, summary statistics were generated to examine country-level trends in confirmed cases. An interactive choropleth map was then created to visualize average daily cases across countries, highlighting regions with higher case levels. Overall, the analysis demonstrates how tidy data principles support clearer insights and more effective visualization of global health data.

Reference

1. OpenAI. (2026, March 8). ChatGPT conversation with K. M. Qaiduzzaman on tidying and visualizing global COVID-19 data in R [Large language model]. OpenAI. https://chat.openai.com/