Project2DATA607DataSet2

Project 2 :DataSet2 Analysis. This dataset has Year data spread in a wide format. Also it has some special characters and formats to be cleaned up.

Your task is to: For each of the three chosen datasets: • Create a .CSV file (or optionally, a MySQL database!) that includes all of the information included in the dataset. You’re encouraged to use a “wide” structure similar to how the information appears in the discussion item, so that you can practice tidying and transformations as described below.

DATASET SOURCE: World Health Organization Data Repository URL link: http://apps.who.int/gho/data/node.main.618?lang=en

myurl <- "https://raw.githubusercontent.com/BanuB/DATA607Project2/master/HIVDatasetSourceWorldHealthOrganizationData.csv"
csvdata3  <- read.csv(file=myurl, header=TRUE,sep=",",stringsAsFactors = FALSE,na.strings=c("NA"))
filename<- "HIVWHODataset2.csv"

After reading in data, rename columns

str(csvdata3)

## 'data.frame':    170 obs. of  7 variables:
##  $ Country                                                                         : chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ Estimated.antiretroviral.therapy.coverage.among.people.living.with.HIV......2018: chr  "13 [7â\200“20]" "No data" "81 [75â\200“86]" "27 [23â\200“31]" ...
##  $ Reported.number.of.people.receiving.antiretroviral.therapy..2018                : chr  "920" "580" "12 800" "88 700" ...
##  $ Estimated.number.of.people..all.ages..living.with.HIV..2018                     : chr  "7200 [4100â\200“11 000]" "No data" "16 000 [15 000â\200“17 000]" "330 000 [290 000â\200“390 000]" ...
##  $ Estimated.number.of.people..all.ages..living.with.HIV..2010                     : chr  "4200 [2500â\200“6200]" "No data" "7100 [6600â\200“7600]" "220 000 [180 000â\200“250 000]" ...
##  $ Estimated.number.of.people..all.ages..living.with.HIV..2005                     : chr  "2900 [1700â\200“5000]" "No data" "3700 [3500â\200“4000]" "150 000 [120 000â\200“170 000]" ...
##  $ Estimated.number.of.people..all.ages..living.with.HIV..2000                     : chr  "1600 [1000â\200“3500]" "No data" "1900 [1700â\200“2000]" "87 000 [72 000â\200“110 000]" ...

view(csvdata3)

names(csvdata3)[2] <- "EstantiretrocoveragepeopleHIV2018"
names(csvdata3)[3] <- "ReportedantiretropeopleHIV2018"
names(csvdata3)[4] <- "EstallHIV2018"
names(csvdata3)[5] <- "EstallHIV2010"
names(csvdata3)[6] <- "EstallHIV2005"
names(csvdata3)[7] <- "EstallHIV2000"
csvdata3 %>% kable() %>% kable_styling()

Country	EstantiretrocoveragepeopleHIV2018	ReportedantiretropeopleHIV2018	EstallHIV2018	EstallHIV2010	EstallHIV2005	EstallHIV2000
Afghanistan	13 [7–20]	920	7200 [4100–11 000]	4200 [2500–6200]	2900 [1700–5000]	1600 [1000–3500]
Albania	No data	580	No data	No data	No data	No data
Algeria	81 [75–86]	12 800	16 000 [15 000–17 000]	7100 [6600–7600]	3700 [3500–4000]	1900 [1700–2000]
Angola	27 [23–31]	88 700	330 000 [290 000–390 000]	220 000 [180 000–250 000]	150 000 [120 000–170 000]	87 000 [72 000–110 000]
Argentina	61 [55–67]	85 500	140 000 [130 000–150 000]	110 000 [96 000–120 000]	85 000 [76 000–94 000]	64 000 [55 000–71 000]
Armenia	53 [44–65]	1900	3500 [3000–4400]	3300 [2800–4100]	2700 [2000–3500]	950 [580–1600]
Australia	83 [70–93]	22 800	28 000 [23 000–31 000]	21 000 [17 000–23 000]	16 000 [14 000–19 000]	13 000 [11 000–15 000]
Austria	No data	No data	No data	No data	No data	No data
Azerbaijan	No data	4400	No data	No data	No data	No data
Bahamas	52 [45–58]	3100	6000 [5300–6700]	5800 [5100–6600]	5100 [4400–6000]	5100 [4400–5900]
Bahrain	No data	No data	No data	No data	No data	No data
Bangladesh	22 [19–25]	3000	14 000 [12 000–16 000]	7700 [6600–8800]	4000 [3500–4600]	940 [800–1100]
Barbados	50 [44–57]	1500	3000 [2700–3400]	2300 [2100–2600]	1700 [1500–1900]	1100 [1000–1300]
Belarus	59 [48–75]	15 500	27 000 [22 000–34 000]	12 000 [10 000–15 000]	5400 [4500–6700]	1400 [1100–1900]
Belgium	No data	No data	No data	No data	No data	No data
Belize	28 [26–31]	1400	4900 [4400–5400]	3700 [3400–4100]	2800 [2600–3100]	1700 [1600–1800]
Benin	61 [40–>95]	44 200	73 000 [48 000–120 000]	61 000 [41 000–98 000]	56 000 [37 000–90 000]	47 000 [31 000–75 000]
Bhutan	37 [20–78]	480	1300 [700–2700]	1300 [590–2700]	1100 [<500–2000]	530 [<200–970]
Bolivia (Plurinational State of)	44 [40–48]	9900	22 000 [20 000–24 000]	23 000 [20 000–25 000]	26 000 [24 000–28 000]	21 000 [20 000–23 000]
Bosnia and Herzegovina	67 [57–78]	220	<500 [<500–<500]	<200 [<200–<200]	<200 [<100–<200]	<100 [<100–<100]
Botswana	83 [75–90]	307 000	370 000 [330 000–400 000]	340 000 [300 000–360 000]	310 000 [280 000–330 000]	280 000 [270 000–300 000]
Brazil	66 [51–82]	593 000	900 000 [690 000–1 100 000]	670 000 [520 000–830 000]	550 000 [420 000–680 000]	410 000 [320 000–510 000]
Brunei Darussalam	No data	150	No data	No data	No data	No data
Bulgaria	41 [35–48]	1500	3500 [3000–4100]	1700 [1600–1900]	980 [910–1100]	<500 [<500–<500]
Burkina Faso	62 [50–75]	59 300	96 000 [78 000–120 000]	110 000 [88 000–130 000]	120 000 [95 000–140 000]	140 000 [110 000–170 000]
Burundi	80 [69–94]	65 500	82 000 [71 000–97 000]	93 000 [79 000–110 000]	110 000 [88 000–120 000]	130 000 [110 000–150 000]
Cabo Verde	89 [75–>95]	2200	2400 [2100–2900]	2100 [1700–2600]	1800 [1400–2700]	1600 [1200–2500]
Cambodia	81 [71–93]	59 500	73 000 [64 000–84 000]	79 000 [68 000–93 000]	82 000 [70 000–97 000]	81 000 [73 000–91 000]
Cameroon	52 [46–57]	281 000	540 000 [470 000–590 000]	520 000 [460 000–560 000]	470 000 [430 000–500 000]	370 000 [350 000–410 000]
Canada	No data	No data	No data	No data	No data	No data
Central African Republic	36 [30–45]	39 600	110 000 [90 000–140 000]	140 000 [110 000–160 000]	150 000 [130 000–170 000]	160 000 [130 000–190 000]
Chad	51 [40–63]	61 400	120 000 [94 000–150 000]	99 000 [80 000–120 000]	88 000 [69 000–110 000]	80 000 [60 000–100 000]
Chile	63 [56–70]	45 100	71 000 [63 000–78 000]	39 000 [34 000–43 000]	25 000 [22 000–27 000]	14 000 [13 000–16 000]
China	No data	718 000	No data	No data	No data	No data
Colombia	73 [60–86]	113 000	160 000 [130 000–180 000]	130 000 [100 000–150 000]	120 000 [98 000–140 000]	110 000 [91 000–130 000]
Comoros	79 [39–>95]	100	<200 [<100–<500]	<200 [<100–<500]	<100 [<100–<200]	<100 [<100–<100]
Congo	35 [27–46]	31 200	89 000 [69 000–120 000]	82 000 [69 000–95 000]	77 000 [63 000–90 000]	80 000 [64 000–96 000]
Costa Rica	49 [44–54]	7200	15 000 [13 000–17 000]	9300 [8400–10 000]	6500 [5800–7200]	4300 [3700–4700]
Côte d’Ivoire	55 [44–70]	252 000	460 000 [360 000–580 000]	480 000 [380 000–610 000]	510 000 [410 000–650 000]	590 000 [470 000–740 000]
Croatia	75 [67–83]	1200	1600 [1400–1700]	1000 [930–1100]	710 [630–800]	<500 [<500–<500]
Cuba	72 [55–85]	21 900	31 000 [24 000–37 000]	17 000 [13 000–21 000]	9000 [6700–11 000]	4100 [2900–5000]
Cyprus	No data	No data	No data	No data	No data	No data
Czechia	60 [51–68]	2600	4400 [3700–5000]	1800 [1500–2000]	970 [820–1100]	510 [<500–580]
Democratic People’s Republic of Korea	No data	No data	No data	No data	No data	No data
Democratic Republic of the Congo	57 [47–67]	256 000	450 000 [370 000–530 000]	480 000 [400 000–560 000]	510 000 [430 000–590 000]	540 000 [470 000–610 000]
Denmark	89 [79–>95]	5500	6200 [5600–7000]	5500 [5000–6200]	4900 [4500–5500]	4000 [3600–4600]
Djibouti	30 [25–38]	2700	8800 [7100–11 000]	9400 [7700–11 000]	11 000 [9000–13 000]	9400 [7200–12 000]
Dominican Republic	56 [43–73]	39 000	70 000 [54 000–92 000]	72 000 [54 000–91 000]	79 000 [61 000–100 000]	85 000 [62 000–120 000]
Ecuador	57 [38–93]	25 100	44 000 [29 000–71 000]	34 000 [22 000–57 000]	29 000 [19 000–49 000]	26 000 [15 000–46 000]
Egypt	31 [28–33]	6700	22 000 [20 000–24 000]	6800 [6100–7400]	3200 [2800–3500]	1500 [1400–1600]
El Salvador	47 [39–55]	11 900	25 000 [21 000–30 000]	26 000 [20 000–31 000]	23 000 [17 000–28 000]	18 000 [14 000–23 000]
Equatorial Guinea	34 [27–44]	21 400	62 000 [50 000–81 000]	35 000 [29 000–41 000]	22 000 [17 000–28 000]	13 000 [9200–18 000]
Eritrea	51 [38–68]	8900	18 000 [13 000–24 000]	17 000 [13 000–22 000]	17 000 [13 000–22 000]	16 000 [12 000–20 000]
Estonia	59 [53–66]	4300	7400 [6600–8200]	6000 [5100–6700]	5400 [4600–6000]	3400 [2900–3900]
Eswatini	86 [80–94]	177 000	210 000 [190 000–220 000]	160 000 [150 000–170 000]	130 000 [120 000–140 000]	110 000 [98 000–120 000]
Ethiopia	65 [50–85]	450 000	690 000 [530 000–900 000]	630 000 [480 000–830 000]	640 000 [490 000–840 000]	750 000 [570 000–980 000]
Fiji	No data	No data	No data	No data	No data	No data
Finland	76 [60–95]	3000	4000 [3100–4900]	2700 [2200–3500]	1900 [1500–2400]	1100 [850–1400]
France	83 [69–>95]	148 000	180 000 [150 000–210 000]	140 000 [120 000–160 000]	110 000 [95 000–130 000]	82 000 [69 000–97 000]
Gabon	67 [54–85]	35 600	53 000 [43 000–67 000]	43 000 [36 000–51 000]	35 000 [27 000–43 000]	28 000 [20 000–38 000]
Gambia	29 [24–38]	7500	26 000 [21 000–33 000]	18 000 [15 000–23 000]	15 000 [11 000–19 000]	9900 [7200–13 000]
Georgia	49 [42–57]	4600	9400 [8100–11 000]	5600 [4500–6700]	2800 [2300–3400]	980 [720–1300]
Germany	80 [65–93]	69 900	87 000 [71 000–100 000]	69 000 [57 000–81 000]	56 000 [46 000–65 000]	45 000 [37 000–54 000]
Ghana	34 [28–39]	113 000	330 000 [280 000–390 000]	300 000 [250 000–340 000]	280 000 [240 000–320 000]	270 000 [240 000–300 000]
Greece	No data	No data	No data	No data	No data	No data
Guatemala	43 [40–47]	20 200	47 000 [43 000–51 000]	49 000 [44 000–53 000]	48 000 [44 000–51 000]	44 000 [41 000–47 000]
Guinea	40 [34–48]	48 600	120 000 [100 000–140 000]	100 000 [90 000–120 000]	93 000 [81 000–110 000]	83 000 [67 000–100 000]
Guinea-Bissau	33 [29–37]	14 600	44 000 [39 000–49 000]	38 000 [34 000–42 000]	31 000 [28 000–35 000]	22 000 [20 000–25 000]
Guyana	68 [60–78]	5600	8200 [7200–9400]	6700 [6000–7400]	5000 [4400–5700]	2300 [1600–3100]
Haiti	58 [52–65]	91 500	160 000 [140 000–180 000]	140 000 [130 000–160 000]	140 000 [120 000–160 000]	150 000 [130 000–180 000]
Honduras	50 [40–61]	11 700	23 000 [18 000–28 000]	26 000 [21 000–32 000]	31 000 [24 000–38 000]	40 000 [34 000–49 000]
Hungary	56 [48–63]	2000	3700 [3200–4200]	2000 [1800–2300]	1200 [1000–1300]	830 [700–950]
Iceland	79 [71–87]	250	<500 [<500–<500]	<500 [<200–<500]	<200 [<200–<200]	<100 [<100–<200]
India	No data	No data	No data	No data	No data	No data
Indonesia	17 [15–20]	108 000	640 000 [550 000–750 000]	510 000 [450 000–590 000]	290 000 [260 000–330 000]	80 000 [72 000–89 000]
Iran (Islamic Republic of)	20 [11–39]	12 400	61 000 [34 000–120 000]	50 000 [37 000–70 000]	37 000 [25 000–56 000]	16 000 [7900–35 000]
Ireland	80 [69–89]	5700	7200 [6200–8000]	4800 [4200–5400]	3200 [2800–3600]	1900 [1700–2200]
Israel	No data	No data	9000 [8000–10 000]	6000 [5400–6800]	4100 [3700–4600]	2700 [2400–3100]
Italy	91 [78–>95]	118 000	130 000 [110 000–140 000]	110 000 [92 000–120 000]	89 000 [76 000–100 000]	68 000 [57 000–78 000]
Jamaica	31 [27–36]	12 600	40 000 [35 000–46 000]	37 000 [32 000–42 000]	38 000 [33 000–42 000]	41 000 [37 000–46 000]
Japan	80 [68–92]	23 700	30 000 [25 000–34 000]	19 000 [16 000–22 000]	12 000 [9700–14 000]	6200 [5100–7200]
Jordan	84 [76–95]	310	<500 [<500–<500]	<200 [<200–<500]	<200 [<200–<200]	<100 [<100–<200]
Kazakhstan	58 [54–62]	15 000	26 000 [24 000–27 000]	11 000 [10 000–11 000]	4000 [3800–4300]	1100 [1100–1200]
Kenya	68 [58–82]	1 068 000	1 600 000 [1 300 000–1 900 000]	1 500 000 [1 200 000–1 800 000]	1 500 000 [1 300 000–1 900 000]	1 700 000 [1 400 000–2 000 000]
Kuwait	62 [55–67]	400	640 [580–700]	<500 [<500–<500]	<500 [<500–<500]	<200 [<200–<200]
Kyrgyzstan	43 [33–59]	3700	8500 [6500–12 000]	4100 [3200–5500]	1500 [1300–1900]	710 [580–840]
Lao People’s Democratic Republic	54 [48–62]	6500	12 000 [11 000–14 000]	9900 [8800–11 000]	6700 [6000–7500]	2200 [2000–2400]
Latvia	45 [41–50]	2400	5300 [4800–5900]	4000 [3500–4500]	3200 [2900–3600]	2300 [2200–2500]
Lebanon	60 [53–67]	1500	2500 [2200–2800]	1600 [1400–1800]	1300 [1100–1400]	910 [790–1000]
Lesotho	61 [57–65]	206 000	340 000 [320 000–360 000]	300 000 [280 000–320 000]	280 000 [260 000–300 000]	260 000 [240 000–290 000]
Liberia	35 [32–39]	13 900	39 000 [36 000–44 000]	41 000 [37 000–46 000]	41 000 [38 000–45 000]	43 000 [41 000–45 000]
Libya	44 [40–49]	4100	9200 [8300–10 000]	6100 [5600–6500]	2900 [2700–3100]	950 [870–1000]
Lithuania	No data	No data	No data	No data	No data	No data
Luxembourg	77 [67–86]	890	1200 [1000–1300]	700 [620–780]	<500 [<500–540]	<500 [<500–<500]
Madagascar	9 [7–13]	3500	39 000 [30 000–55 000]	21 000 [18 000–24 000]	19 000 [15 000–23 000]	13 000 [7900–20 000]
Malawi	78 [70–84]	814 000	1 000 000 [940 000–1 100 000]	870 000 [770 000–960 000]	820 000 [720 000–900 000]	810 000 [740 000–860 000]
Malaysia	48 [42–53]	41 500	87 000 [77 000–98 000]	74 000 [65 000–86 000]	66 000 [57 000–77 000]	55 000 [48 000–65 000]
Maldives	No data	No data	No data	No data	No data	No data
Mali	31 [25–39]	47 100	150 000 [120 000–190 000]	120 000 [94 000–140 000]	110 000 [91 000–130 000]	110 000 [91 000–130 000]
Malta	No data	No data	No data	No data	No data	No data
Mauritania	54 [44–69]	3000	5600 [4500–7200]	7100 [5900–8400]	7500 [6400–8800]	5500 [4500–6500]
Mauritius	22 [18–26]	2800	13 000 [10 000–15 000]	11 000 [9500–12 000]	8000 [6100–11 000]	3200 [1500–6000]
Mexico	70 [60–80]	165 000	230 000 [200 000–270 000]	180 000 [150 000–210 000]	150 000 [120 000–200 000]	130 000 [94 000–190 000]
Mongolia	32 [29–36]	200	600 [530–670]	<500 [<500–<500]	<500 [<200–<500]	<100 [<100–<100]
Montenegro	40 [34–46]	160	<500 [<500–<500]	<200 [<200–<200]	<100 [<100–<100]	<100 [<100–<100]
Morocco	65 [52–86]	13 600	21 000 [17 000–28 000]	17 000 [13 000–22 000]	13 000 [11 000–18 000]	9700 [7800–13 000]
Mozambique	56 [44–68]	1 213 000	2 200 000 [1 700 000–2 700 000]	1 600 000 [1 300 000–1 900 000]	1 200 000 [980 000–1 500 000]	840 000 [670 000–1 000 000]
Myanmar	70 [63–79]	167 000	240 000 [210 000–270 000]	220 000 [190 000–260 000]	210 000 [180 000–240 000]	150 000 [130 000–170 000]
Namibia	92 [84–>95]	184 000	200 000 [190 000–220 000]	170 000 [160 000–180 000]	160 000 [140 000–170 000]	140 000 [130 000–160 000]
Nepal	56 [50–65]	16 900	30 000 [26 000–34 000]	31 000 [27 000–36 000]	29 000 [25 000–33 000]	16 000 [14 000–17 000]
Netherlands	No data	No data	No data	20 000 [19 000–21 000]	16 000 [15 000–17 000]	11 000 [11 000–12 000]
New Zealand	73 [62–84]	2700	3600 [3100–4200]	2500 [2100–2800]	1800 [1600–2100]	1300 [1100–1500]
Nicaragua	53 [43–68]	5000	9400 [7600–12 000]	7900 [6500–10 000]	6100 [4600–8300]	3600 [2100–5300]
Niger	54 [45–65]	19 800	36 000 [30 000–43 000]	37 000 [32 000–42 000]	40 000 [34 000–46 000]	37 000 [31 000–44 000]
Nigeria	53 [40–71]	1 016 000	1 900 000 [1 400 000–2 600 000]	1 500 000 [1 100 000–2 100 000]	1 400 000 [1 000 000–1 900 000]	1 300 000 [940 000–1 700 000]
Norway	82 [74–90]	4700	5800 [5200–6300]	4200 [3800–4600]	3000 [2700–3300]	1900 [1700–2100]
Oman	41 [37–45]	1300	3200 [2900–3600]	2200 [2000–2500]	1700 [1600–1900]	1300 [1100–1400]
Pakistan	10 [9–11]	15 800	160 000 [140 000–190 000]	67 000 [57 000–76 000]	12 000 [10 000–14 000]	<500 [<500–520]
Panama	54 [48–59]	14 200	26 000 [24 000–29 000]	20 000 [18 000–22 000]	16 000 [14 000–17 000]	11 000 [10 000–12 000]
Papua New Guinea	65 [58–71]	29 400	45 000 [41 000–50 000]	38 000 [34 000–42 000]	38 000 [34 000–42 000]	20 000 [17 000–25 000]
Paraguay	40 [31–58]	8500	21 000 [16 000–31 000]	20 000 [14 000–27 000]	19 000 [14 000–25 000]	14 000 [7100–21 000]
Peru	73 [54–>95]	57 800	79 000 [58 000–110 000]	65 000 [49 000–91 000]	65 000 [50 000–91 000]	71 000 [56 000–94 000]
Philippines	44 [37–51]	33 600	77 000 [65 000–90 000]	15 000 [13 000–18 000]	3700 [3100–4300]	1000 [910–1200]
Poland	No data	No data	No data	No data	No data	No data
Portugal	90 [78–>95]	37 200	41 000 [36 000–46 000]	40 000 [35 000–45 000]	37 000 [33 000–42 000]	32 000 [27 000–37 000]
Qatar	No data	150	No data	No data	No data	No data
Republic of Korea	No data	No data	No data	No data	No data	No data
Republic of Moldova	34 [27–45]	6000	17 000 [14 000–23 000]	16 000 [12 000–20 000]	12 000 [9600–16 000]	10 000 [8000–13 000]
Romania	67 [60–73]	12 100	18 000 [16 000–20 000]	14 000 [12 000–15 000]	11 000 [9800–12 000]	7500 [6900–8100]
Russian Federation	No data	No data	No data	No data	No data	No data
Rwanda	87 [76–>95]	194 000	220 000 [200 000–250 000]	220 000 [200 000–250 000]	220 000 [190 000–250 000]	240 000 [220 000–280 000]
Saudi Arabia	No data	6300	No data	No data	No data	No data
Senegal	63 [55–71]	26 600	42 000 [37 000–47 000]	44 000 [39 000–50 000]	42 000 [38 000–48 000]	33 000 [29 000–39 000]
Serbia	65 [47–83]	2000	3000 [2200–3800]	1800 [1300–2200]	1100 [750–1500]	1000 [660–1400]
Sierra Leone	41 [33–50]	28 400	70 000 [56 000–86 000]	58 000 [48 000–70 000]	51 000 [42 000–61 000]	40 000 [31 000–50 000]
Singapore	78 [71–86]	6200	7900 [7200–8700]	6500 [5700–7300]	4100 [3500–4700]	2900 [2600–3300]
Slovakia	54 [40–85]	650	1200 [910–1900]	<500 [<500–730]	<500 [<200–<500]	<200 [<100–<200]
Slovenia	No data	No data	No data	No data	No data	No data
Somalia	30 [23–41]	3300	11 000 [8400–15 000]	17 000 [15 000–20 000]	20 000 [18 000–23 000]	16 000 [14 000–20 000]
South Africa	62 [57–66]	4 788 000	7 700 000 [7 100 000–8 300 000]	6 100 000 [5 500 000–6 600 000]	5 000 000 [4 400 000–5 400 000]	3 300 000 [2 900 000–3 700 000]
South Sudan	16 [12–20]	30 700	190 000 [140 000–240 000]	140 000 [110 000–170 000]	120 000 [89 000–150 000]	90 000 [56 000–120 000]
Spain	84 [73–94]	125 000	150 000 [130 000–170 000]	140 000 [120 000–150 000]	120 000 [100 000–130 000]	92 000 [78 000–110 000]
Sri Lanka	45 [40–52]	1600	3500 [3100–4000]	4000 [3400–4700]	3600 [3100–4100]	2200 [1900–2400]
Sudan	15 [7–28]	9000	59 000 [26 000–110 000]	43 000 [36 000–51 000]	29 000 [20 000–40 000]	15 000 [7500–29 000]
Suriname	52 [35–75]	2900	5600 [3700–8100]	4600 [3300–6200]	4000 [3000–5600]	3100 [2300–4400]
Sweden	No data	No data	No data	No data	No data	No data
Switzerland	No data	14 800	No data	No data	No data	No data
Syrian Arab Republic	20 [18–22]	130	660 [590–720]	570 [510–630]	<500 [<500–<500]	<500 [<500–<500]
Tajikistan	46 [38–56]	6000	13 000 [11 000–16 000]	9200 [7500–11 000]	5200 [3700–6900]	1400 [780–2700]
Thailand	75 [66–86]	359 000	480 000 [420 000–550 000]	580 000 [490 000–690 000]	630 000 [510 000–780 000]	740 000 [610 000–890 000]
Republic of North Macedonia	54 [47–63]	240	<500 [<500–520]	<200 [<200–<200]	<100 [<100–<200]	<100 [<100–<100]
Timor-Leste	No data	No data	No data	No data	No data	No data
Togo	60 [56–65]	64 800	110 000 [100 000–120 000]	100 000 [96 000–110 000]	100 000 [94 000–110 000]	94 000 [87 000–100 000]
Trinidad and Tobago	No data	No data	No data	No data	No data	No data
Tunisia	39 [24–61]	1100	2800 [1700–4400]	1400 [980–2200]	640 [<500–1100]	<500 [<200–710]
Turkey	No data	No data	No data	No data	No data	No data
Turkmenistan	No data	No data	No data	No data	No data	No data
Uganda	72 [68–78]	1 004 000	1 400 000 [1 300 000–1 500 000]	1 200 000 [1 100 000–1 300 000]	1 100 000 [1 000 000–1 100 000]	1 000 000 [930 000–1 100 000]
Ukraine	52 [48–56]	124 000	240 000 [220 000–260 000]	230 000 [220 000–250 000]	230 000 [220 000–240 000]	170 000 [150 000–180 000]
United Arab Emirates	No data	No data	No data	No data	No data	No data
United Kingdom of Great Britain and Northern Ireland	No data	No data	No data	No data	No data	No data
United Republic of Tanzania	71 [64–78]	1 109 000	1 600 000 [1 400 000–1 700 000]	1 300 000 [1 100 000–1 400 000]	1 200 000 [1 000 000–1 300 000]	1 100 000 [1 000 000–1 200 000]
United States of America	No data	No data	No data	990 000 [880 000–1 100 000]	No data	No data
Uruguay	58 [41–76]	8100	14 000 [9900–19 000]	9600 [8000–11 000]	7600 [6200–10 000]	6000 [4200–12 000]
Uzbekistan	51 [47–55]	26 700	52 000 [48 000–56 000]	30 000 [27 000–32 000]	21 000 [19 000–23 000]	14 000 [13 000–16 000]
Venezuela (Bolivarian Republic of)	No data	No data	120 000 [100 000–130 000]	No data	No data	No data
Viet Nam	65 [57–73]	150 000	230 000 [200 000–260 000]	220 000 [180 000–250 000]	180 000 [160 000–210 000]	120 000 [110 000–130 000]
Yemen	21 [12–35]	2200	11 000 [6500–18 000]	5100 [3500–7400]	2400 [1500–4000]	1100 [680–2500]
Zambia	78 [69–88]	965 000	1 200 000 [1 100 000–1 400 000]	1 000 000 [900 000–1 100 000]	920 000 [820 000–1 000 000]	890 000 [800 000–1 000 000]
Zimbabwe	88 [77–>95]	1 151 000	1 300 000 [1 100 000–1 500 000]	1 200 000 [1 100 000–1 400 000]	1 400 000 [1 200 000–1 600 000]	1 600 000 [1 400 000–1 900 000]

apply gsub to cleanup strings

raw.table1 <- csvdata3 %>%
  mutate(EstantiretrocoveragepeopleHIV2018 = type.convert(str_extract(EstantiretrocoveragepeopleHIV2018, "^[0-9]+"),na.strings = "NA", as.is = FALSE, dec= ".")
#         ,ReportedantiretropeopleHIV2018 = type.convert(str_extract(ReportedantiretropeopleHIV2018, "^[0-9]+"),na.strings = "NA", as.is = FALSE, dec= ".")
,ReportedantiretropeopleHIV2018 = type.convert(str_extract(gsub("\\s+", "", ReportedantiretropeopleHIV2018), "^[0-9]+") ,na.strings = "NA", as.is = FALSE, dec= ".")
         ,EstallHIV2018 = type.convert(gsub("\\[","", str_extract(gsub("\\s+", "",EstallHIV2018), "[0-9].*\\[")),na.strings = "NA", as.is = FALSE, dec= ".")
         ,EstallHIV2010 = type.convert(gsub("\\[","", str_extract(gsub("\\s+", "",EstallHIV2010), "[0-9].*\\[")),na.strings = "NA", as.is = FALSE, dec= ".")
         ,EstallHIV2005 = type.convert(gsub("\\[","", str_extract(gsub("\\s+", "",EstallHIV2005), "[0-9].*\\[")),na.strings = "NA", as.is = FALSE, dec= ".")
         ,EstallHIV2000 = type.convert(gsub("\\[","", str_extract(gsub("\\s+", "",EstallHIV2000), "[0-9].*\\[")),na.strings = "NA", as.is = FALSE, dec= ".")
         
  )
#raw.table1[3] <- lapply(raw.table1[3], as.numeric)
str(raw.table1)

## 'data.frame':    170 obs. of  7 variables:
##  $ Country                          : chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ EstantiretrocoveragepeopleHIV2018: int  13 NA 81 27 61 53 83 NA NA 52 ...
##  $ ReportedantiretropeopleHIV2018   : int  920 580 12800 88700 85500 1900 22800 NA 4400 3100 ...
##  $ EstallHIV2018                    : int  7200 NA 16000 330000 140000 3500 28000 NA NA 6000 ...
##  $ EstallHIV2010                    : int  4200 NA 7100 220000 110000 3300 21000 NA NA 5800 ...
##  $ EstallHIV2005                    : int  2900 NA 3700 150000 85000 2700 16000 NA NA 5100 ...
##  $ EstallHIV2000                    : int  1600 NA 1900 87000 64000 950 13000 NA NA 5100 ...

raw.table1 %>% kable() %>% kable_styling()

Country	EstantiretrocoveragepeopleHIV2018	ReportedantiretropeopleHIV2018	EstallHIV2018	EstallHIV2010	EstallHIV2005	EstallHIV2000
Afghanistan	13	920	7200	4200	2900	1600
Albania	NA	580	NA	NA	NA	NA
Algeria	81	12800	16000	7100	3700	1900
Angola	27	88700	330000	220000	150000	87000
Argentina	61	85500	140000	110000	85000	64000
Armenia	53	1900	3500	3300	2700	950
Australia	83	22800	28000	21000	16000	13000
Austria	NA	NA	NA	NA	NA	NA
Azerbaijan	NA	4400	NA	NA	NA	NA
Bahamas	52	3100	6000	5800	5100	5100
Bahrain	NA	NA	NA	NA	NA	NA
Bangladesh	22	3000	14000	7700	4000	940
Barbados	50	1500	3000	2300	1700	1100
Belarus	59	15500	27000	12000	5400	1400
Belgium	NA	NA	NA	NA	NA	NA
Belize	28	1400	4900	3700	2800	1700
Benin	61	44200	73000	61000	56000	47000
Bhutan	37	480	1300	1300	1100	530
Bolivia (Plurinational State of)	44	9900	22000	23000	26000	21000
Bosnia and Herzegovina	67	220	500	200	200	100
Botswana	83	307000	370000	340000	310000	280000
Brazil	66	593000	900000	670000	550000	410000
Brunei Darussalam	NA	150	NA	NA	NA	NA
Bulgaria	41	1500	3500	1700	980	500
Burkina Faso	62	59300	96000	110000	120000	140000
Burundi	80	65500	82000	93000	110000	130000
Cabo Verde	89	2200	2400	2100	1800	1600
Cambodia	81	59500	73000	79000	82000	81000
Cameroon	52	281000	540000	520000	470000	370000
Canada	NA	NA	NA	NA	NA	NA
Central African Republic	36	39600	110000	140000	150000	160000
Chad	51	61400	120000	99000	88000	80000
Chile	63	45100	71000	39000	25000	14000
China	NA	718000	NA	NA	NA	NA
Colombia	73	113000	160000	130000	120000	110000
Comoros	79	100	200	200	100	100
Congo	35	31200	89000	82000	77000	80000
Costa Rica	49	7200	15000	9300	6500	4300
Côte d’Ivoire	55	252000	460000	480000	510000	590000
Croatia	75	1200	1600	1000	710	500
Cuba	72	21900	31000	17000	9000	4100
Cyprus	NA	NA	NA	NA	NA	NA
Czechia	60	2600	4400	1800	970	510
Democratic People’s Republic of Korea	NA	NA	NA	NA	NA	NA
Democratic Republic of the Congo	57	256000	450000	480000	510000	540000
Denmark	89	5500	6200	5500	4900	4000
Djibouti	30	2700	8800	9400	11000	9400
Dominican Republic	56	39000	70000	72000	79000	85000
Ecuador	57	25100	44000	34000	29000	26000
Egypt	31	6700	22000	6800	3200	1500
El Salvador	47	11900	25000	26000	23000	18000
Equatorial Guinea	34	21400	62000	35000	22000	13000
Eritrea	51	8900	18000	17000	17000	16000
Estonia	59	4300	7400	6000	5400	3400
Eswatini	86	177000	210000	160000	130000	110000
Ethiopia	65	450000	690000	630000	640000	750000
Fiji	NA	NA	NA	NA	NA	NA
Finland	76	3000	4000	2700	1900	1100
France	83	148000	180000	140000	110000	82000
Gabon	67	35600	53000	43000	35000	28000
Gambia	29	7500	26000	18000	15000	9900
Georgia	49	4600	9400	5600	2800	980
Germany	80	69900	87000	69000	56000	45000
Ghana	34	113000	330000	300000	280000	270000
Greece	NA	NA	NA	NA	NA	NA
Guatemala	43	20200	47000	49000	48000	44000
Guinea	40	48600	120000	100000	93000	83000
Guinea-Bissau	33	14600	44000	38000	31000	22000
Guyana	68	5600	8200	6700	5000	2300
Haiti	58	91500	160000	140000	140000	150000
Honduras	50	11700	23000	26000	31000	40000
Hungary	56	2000	3700	2000	1200	830
Iceland	79	250	500	500	200	100
India	NA	NA	NA	NA	NA	NA
Indonesia	17	108000	640000	510000	290000	80000
Iran (Islamic Republic of)	20	12400	61000	50000	37000	16000
Ireland	80	5700	7200	4800	3200	1900
Israel	NA	NA	9000	6000	4100	2700
Italy	91	118000	130000	110000	89000	68000
Jamaica	31	12600	40000	37000	38000	41000
Japan	80	23700	30000	19000	12000	6200
Jordan	84	310	500	200	200	100
Kazakhstan	58	15000	26000	11000	4000	1100
Kenya	68	1068000	1600000	1500000	1500000	1700000
Kuwait	62	400	640	500	500	200
Kyrgyzstan	43	3700	8500	4100	1500	710
Lao People’s Democratic Republic	54	6500	12000	9900	6700	2200
Latvia	45	2400	5300	4000	3200	2300
Lebanon	60	1500	2500	1600	1300	910
Lesotho	61	206000	340000	300000	280000	260000
Liberia	35	13900	39000	41000	41000	43000
Libya	44	4100	9200	6100	2900	950
Lithuania	NA	NA	NA	NA	NA	NA
Luxembourg	77	890	1200	700	500	500
Madagascar	9	3500	39000	21000	19000	13000
Malawi	78	814000	1000000	870000	820000	810000
Malaysia	48	41500	87000	74000	66000	55000
Maldives	NA	NA	NA	NA	NA	NA
Mali	31	47100	150000	120000	110000	110000
Malta	NA	NA	NA	NA	NA	NA
Mauritania	54	3000	5600	7100	7500	5500
Mauritius	22	2800	13000	11000	8000	3200
Mexico	70	165000	230000	180000	150000	130000
Mongolia	32	200	600	500	500	100
Montenegro	40	160	500	200	100	100
Morocco	65	13600	21000	17000	13000	9700
Mozambique	56	1213000	2200000	1600000	1200000	840000
Myanmar	70	167000	240000	220000	210000	150000
Namibia	92	184000	200000	170000	160000	140000
Nepal	56	16900	30000	31000	29000	16000
Netherlands	NA	NA	NA	20000	16000	11000
New Zealand	73	2700	3600	2500	1800	1300
Nicaragua	53	5000	9400	7900	6100	3600
Niger	54	19800	36000	37000	40000	37000
Nigeria	53	1016000	1900000	1500000	1400000	1300000
Norway	82	4700	5800	4200	3000	1900
Oman	41	1300	3200	2200	1700	1300
Pakistan	10	15800	160000	67000	12000	500
Panama	54	14200	26000	20000	16000	11000
Papua New Guinea	65	29400	45000	38000	38000	20000
Paraguay	40	8500	21000	20000	19000	14000
Peru	73	57800	79000	65000	65000	71000
Philippines	44	33600	77000	15000	3700	1000
Poland	NA	NA	NA	NA	NA	NA
Portugal	90	37200	41000	40000	37000	32000
Qatar	NA	150	NA	NA	NA	NA
Republic of Korea	NA	NA	NA	NA	NA	NA
Republic of Moldova	34	6000	17000	16000	12000	10000
Romania	67	12100	18000	14000	11000	7500
Russian Federation	NA	NA	NA	NA	NA	NA
Rwanda	87	194000	220000	220000	220000	240000
Saudi Arabia	NA	6300	NA	NA	NA	NA
Senegal	63	26600	42000	44000	42000	33000
Serbia	65	2000	3000	1800	1100	1000
Sierra Leone	41	28400	70000	58000	51000	40000
Singapore	78	6200	7900	6500	4100	2900
Slovakia	54	650	1200	500	500	200
Slovenia	NA	NA	NA	NA	NA	NA
Somalia	30	3300	11000	17000	20000	16000
South Africa	62	4788000	7700000	6100000	5000000	3300000
South Sudan	16	30700	190000	140000	120000	90000
Spain	84	125000	150000	140000	120000	92000
Sri Lanka	45	1600	3500	4000	3600	2200
Sudan	15	9000	59000	43000	29000	15000
Suriname	52	2900	5600	4600	4000	3100
Sweden	NA	NA	NA	NA	NA	NA
Switzerland	NA	14800	NA	NA	NA	NA
Syrian Arab Republic	20	130	660	570	500	500
Tajikistan	46	6000	13000	9200	5200	1400
Thailand	75	359000	480000	580000	630000	740000
Republic of North Macedonia	54	240	500	200	100	100
Timor-Leste	NA	NA	NA	NA	NA	NA
Togo	60	64800	110000	100000	100000	94000
Trinidad and Tobago	NA	NA	NA	NA	NA	NA
Tunisia	39	1100	2800	1400	640	500
Turkey	NA	NA	NA	NA	NA	NA
Turkmenistan	NA	NA	NA	NA	NA	NA
Uganda	72	1004000	1400000	1200000	1100000	1000000
Ukraine	52	124000	240000	230000	230000	170000
United Arab Emirates	NA	NA	NA	NA	NA	NA
United Kingdom of Great Britain and Northern Ireland	NA	NA	NA	NA	NA	NA
United Republic of Tanzania	71	1109000	1600000	1300000	1200000	1100000
United States of America	NA	NA	NA	990000	NA	NA
Uruguay	58	8100	14000	9600	7600	6000
Uzbekistan	51	26700	52000	30000	21000	14000
Venezuela (Bolivarian Republic of)	NA	NA	120000	NA	NA	NA
Viet Nam	65	150000	230000	220000	180000	120000
Yemen	21	2200	11000	5100	2400	1100
Zambia	78	965000	1200000	1000000	920000	890000
Zimbabwe	88	1151000	1300000	1200000	1400000	1600000

arrange data and order

raw.table3 <- raw.table1 %>%
  select(Country,EstantiretrocoveragepeopleHIV2018,ReportedantiretropeopleHIV2018) %>% arrange(desc(ReportedantiretropeopleHIV2018))


data_long3 <-  subset (raw.table3,raw.table3$EstantiretrocoveragepeopleHIV2018 >60) 
data_long4 <-  subset (raw.table3,raw.table3$EstantiretrocoveragepeopleHIV2018 <50) 

str(data_long3)

## 'data.frame':    56 obs. of  3 variables:
##  $ Country                          : chr  "South Africa" "Zimbabwe" "United Republic of Tanzania" "Kenya" ...
##  $ EstantiretrocoveragepeopleHIV2018: int  62 88 71 68 72 78 78 66 65 75 ...
##  $ ReportedantiretropeopleHIV2018   : int  4788000 1151000 1109000 1068000 1004000 965000 814000 593000 450000 359000 ...

data_long3<- data_long3 %>% arrange(desc(ReportedantiretropeopleHIV2018))
data_long4<- data_long4 %>% arrange(desc(ReportedantiretropeopleHIV2018))


data_long3 %>% kable() %>% kable_styling()

Country	EstantiretrocoveragepeopleHIV2018	ReportedantiretropeopleHIV2018
South Africa	62	4788000
Zimbabwe	88	1151000
United Republic of Tanzania	71	1109000
Kenya	68	1068000
Uganda	72	1004000
Zambia	78	965000
Malawi	78	814000
Brazil	66	593000
Ethiopia	65	450000
Thailand	75	359000
Botswana	83	307000
Lesotho	61	206000
Rwanda	87	194000
Namibia	92	184000
Eswatini	86	177000
Myanmar	70	167000
Mexico	70	165000
Viet Nam	65	150000
France	83	148000
Spain	84	125000
Italy	91	118000
Colombia	73	113000
Argentina	61	85500
Germany	80	69900
Burundi	80	65500
Cambodia	81	59500
Burkina Faso	62	59300
Peru	73	57800
Chile	63	45100
Benin	61	44200
Portugal	90	37200
Gabon	67	35600
Papua New Guinea	65	29400
Senegal	63	26600
Japan	80	23700
Australia	83	22800
Cuba	72	21900
Morocco	65	13600
Algeria	81	12800
Romania	67	12100
Singapore	78	6200
Ireland	80	5700
Guyana	68	5600
Denmark	89	5500
Norway	82	4700
Finland	76	3000
New Zealand	73	2700
Cabo Verde	89	2200
Serbia	65	2000
Croatia	75	1200
Luxembourg	77	890
Kuwait	62	400
Jordan	84	310
Iceland	79	250
Bosnia and Herzegovina	67	220
Comoros	79	100

data_long4 %>% kable() %>% kable_styling()

Country	EstantiretrocoveragepeopleHIV2018	ReportedantiretropeopleHIV2018
Ghana	34	113000
Indonesia	17	108000
Angola	27	88700
Guinea	40	48600
Mali	31	47100
Malaysia	48	41500
Central African Republic	36	39600
Philippines	44	33600
Congo	35	31200
South Sudan	16	30700
Sierra Leone	41	28400
Equatorial Guinea	34	21400
Guatemala	43	20200
Pakistan	10	15800
Guinea-Bissau	33	14600
Liberia	35	13900
Jamaica	31	12600
Iran (Islamic Republic of)	20	12400
El Salvador	47	11900
Bolivia (Plurinational State of)	44	9900
Sudan	15	9000
Paraguay	40	8500
Gambia	29	7500
Costa Rica	49	7200
Egypt	31	6700
Republic of Moldova	34	6000
Tajikistan	46	6000
Georgia	49	4600
Libya	44	4100
Kyrgyzstan	43	3700
Madagascar	9	3500
Somalia	30	3300
Bangladesh	22	3000
Mauritius	22	2800
Djibouti	30	2700
Latvia	45	2400
Yemen	21	2200
Sri Lanka	45	1600
Bulgaria	41	1500
Belize	28	1400
Oman	41	1300
Tunisia	39	1100
Afghanistan	13	920
Bhutan	37	480
Mongolia	32	200
Montenegro	40	160
Syrian Arab Republic	20	130

Plot

data_long2 <- gather(data_long3, Year, HIVtotal, ReportedantiretropeopleHIV2018)
data_long2a <- gather(data_long4, Year, HIVtotal, ReportedantiretropeopleHIV2018)
data_long2c <- gather(raw.table1 , Year, HIVtotal, 4:7) 
#data_long2d <- head(data_long2c, n=1000)
#data_long2c  %>% kable() %>% kable_styling()

ggplot(data_long2, aes(x=Country, y=HIVtotal, colour = Year, group = Year, fill = EstantiretrocoveragepeopleHIV2018)) + geom_line(linetype = "dashed") + geom_point(shape = 22, size = 3, fill = "white")+ ggtitle("Countries (% of reported people receiving therapy > 60%), reported number living with HIV") + labs(x="Country", y="HIVTotal")+
  theme(axis.text.x = element_text(angle=90))

ggplot(data_long2a, aes(x=Country, y=HIVtotal, colour = Year, group = Year, fill = EstantiretrocoveragepeopleHIV2018)) + geom_line(linetype = "dashed") + geom_point(shape = 22, size = 3, fill = "white")+ ggtitle("Countries (% of reported people receiving therapy < 50%) , reported number living with HIV") + labs(x="Country", y="HIVTotal")+
  theme(axis.text.x = element_text(angle=90))

data_long2d <-  subset(data_long2c, HIVtotal > 500000 ) 
data_long2d  %>% kable() %>% kable_styling()

	Country	EstantiretrocoveragepeopleHIV2018	ReportedantiretropeopleHIV2018	Year	HIVtotal
22	Brazil	66	593000	EstallHIV2018	900000
29	Cameroon	52	281000	EstallHIV2018	540000
56	Ethiopia	65	450000	EstallHIV2018	690000
75	Indonesia	17	108000	EstallHIV2018	640000
84	Kenya	68	1068000	EstallHIV2018	1600000
96	Malawi	78	814000	EstallHIV2018	1000000
107	Mozambique	56	1213000	EstallHIV2018	2200000
115	Nigeria	53	1016000	EstallHIV2018	1900000
140	South Africa	62	4788000	EstallHIV2018	7700000
158	Uganda	72	1004000	EstallHIV2018	1400000
162	United Republic of Tanzania	71	1109000	EstallHIV2018	1600000
169	Zambia	78	965000	EstallHIV2018	1200000
170	Zimbabwe	88	1151000	EstallHIV2018	1300000
192	Brazil	66	593000	EstallHIV2010	670000
199	Cameroon	52	281000	EstallHIV2010	520000
226	Ethiopia	65	450000	EstallHIV2010	630000
245	Indonesia	17	108000	EstallHIV2010	510000
254	Kenya	68	1068000	EstallHIV2010	1500000
266	Malawi	78	814000	EstallHIV2010	870000
277	Mozambique	56	1213000	EstallHIV2010	1600000
285	Nigeria	53	1016000	EstallHIV2010	1500000
310	South Africa	62	4788000	EstallHIV2010	6100000
320	Thailand	75	359000	EstallHIV2010	580000
328	Uganda	72	1004000	EstallHIV2010	1200000
332	United Republic of Tanzania	71	1109000	EstallHIV2010	1300000
333	United States of America	NA	NA	EstallHIV2010	990000
339	Zambia	78	965000	EstallHIV2010	1000000
340	Zimbabwe	88	1151000	EstallHIV2010	1200000
362	Brazil	66	593000	EstallHIV2005	550000
379	Côte d’Ivoire	55	252000	EstallHIV2005	510000
385	Democratic Republic of the Congo	57	256000	EstallHIV2005	510000
396	Ethiopia	65	450000	EstallHIV2005	640000
424	Kenya	68	1068000	EstallHIV2005	1500000
436	Malawi	78	814000	EstallHIV2005	820000
447	Mozambique	56	1213000	EstallHIV2005	1200000
455	Nigeria	53	1016000	EstallHIV2005	1400000
480	South Africa	62	4788000	EstallHIV2005	5000000
490	Thailand	75	359000	EstallHIV2005	630000
498	Uganda	72	1004000	EstallHIV2005	1100000
502	United Republic of Tanzania	71	1109000	EstallHIV2005	1200000
509	Zambia	78	965000	EstallHIV2005	920000
510	Zimbabwe	88	1151000	EstallHIV2005	1400000
549	Côte d’Ivoire	55	252000	EstallHIV2000	590000
555	Democratic Republic of the Congo	57	256000	EstallHIV2000	540000
566	Ethiopia	65	450000	EstallHIV2000	750000
594	Kenya	68	1068000	EstallHIV2000	1700000
606	Malawi	78	814000	EstallHIV2000	810000
617	Mozambique	56	1213000	EstallHIV2000	840000
625	Nigeria	53	1016000	EstallHIV2000	1300000
650	South Africa	62	4788000	EstallHIV2000	3300000
660	Thailand	75	359000	EstallHIV2000	740000
668	Uganda	72	1004000	EstallHIV2000	1000000
672	United Republic of Tanzania	71	1109000	EstallHIV2000	1100000
679	Zambia	78	965000	EstallHIV2000	890000
680	Zimbabwe	88	1151000	EstallHIV2000	1600000

data_long2e <-  subset(data_long2c, Country %in% c('Estonia','Gambia','Japan')) 
data_long2e  %>% kable() %>% kable_styling()

	Country	EstantiretrocoveragepeopleHIV2018	ReportedantiretropeopleHIV2018	Year	HIVtotal
54	Estonia	59	4300	EstallHIV2018	7400
61	Gambia	29	7500	EstallHIV2018	26000
81	Japan	80	23700	EstallHIV2018	30000
224	Estonia	59	4300	EstallHIV2010	6000
231	Gambia	29	7500	EstallHIV2010	18000
251	Japan	80	23700	EstallHIV2010	19000
394	Estonia	59	4300	EstallHIV2005	5400
401	Gambia	29	7500	EstallHIV2005	15000
421	Japan	80	23700	EstallHIV2005	12000
564	Estonia	59	4300	EstallHIV2000	3400
571	Gambia	29	7500	EstallHIV2000	9900
591	Japan	80	23700	EstallHIV2000	6200

g1 <- ggplot(data_long2e, aes(x=Country, y=HIVtotal, group = Year,fill=Year))
g1 + geom_bar(stat="identity", width = 2) + 
  theme(axis.text.x = element_text(angle=90, vjust=1)) + 
  labs(title="HIV total", 
       subtitle="",
       x="Country",
       y="HIVtotal") +   facet_wrap(~ Year)

## Warning: position_stack requires non-overlapping x intervals

## Warning: position_stack requires non-overlapping x intervals

## Warning: position_stack requires non-overlapping x intervals

## Warning: position_stack requires non-overlapping x intervals

# Plot
g <- ggplot(data_long2d, aes(Country,HIVtotal ))
g + geom_bar(stat="identity", width = 1, fill="Red") + 
      labs(title="Bar Chart", x= "Country", y= "HIVtotal") + 
          theme(axis.text.x = element_text(angle=65, vjust=1.0))