WHO_Stat_Analysis
Aung Thura Htoo
2024-08-15
options(repos = c(CRAN = "https://cran.rstudio.com/"))
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsLoad and check the WHO health statistics data
Dataset link - https://www.who.int/data/gho/publications/world-health-statistics
data <- read.csv("D:/me/R-Language/Practice/Dataset/health_stat_WHO_May2024.csv")
head(data)## IND_NAME
## 1 Adolescent birth rate (per 1000 women)
## 2 Adolescent birth rate (per 1000 women)
## 3 Age-standardized mortality rate attributed to household and ambient air pollution (per 100 000 population)
## 4 Age-standardized prevalence of hypertension among adults aged 30?79 years (%)
## 5 Age-standardized prevalence of obesity among adults (18+ years) (%)
## 6 Age-standardized prevalence of tobacco use among persons 15 years and older (%)
## DIM_GEO_NAME IND_CODE DIM_GEO_CODE DIM_TIME_YEAR
## 1 Afghanistan MDG_0000000003 AFG 2021
## 2 Afghanistan MDG_0000000003 AFG 2021
## 3 Afghanistan SDGAIRBODA AFG 2019
## 4 Afghanistan NCD_HYP_PREVALENCE_A AFG 2019
## 5 Afghanistan NCD_BMI_30A AFG 2022
## 6 Afghanistan M_Est_tob_curr_std AFG 2022
## DIM_1_CODE VALUE_NUMERIC VALUE_STRING
## 1 AGEGROUP_YEARS15-19 62.00000 62.0
## 2 AGEGROUP_YEARS10-14 18.00000 18.0
## 3 SEX_BTSX 265.66452 265.7
## 4 SEX_BTSX 40.20000 40.2
## 5 SEX_BTSX 19.22259 19.2
## 6 SEX_BTSX 22.70000 22.7
## VALUE_COMMENTS
## 1 Afghanistan 2022-2023 Multiple Indicator Cluster Survey
## 2 Afghanistan 2022-2023 Multiple Indicator Cluster Survey
## 3
## 4
## 5
## 6 The most recent survey was conducted in 2019. This is a projection.str(data)## 'data.frame': 10503 obs. of 9 variables:
## $ IND_NAME : chr "Adolescent birth rate (per 1000 women)" "Adolescent birth rate (per 1000 women)" "Age-standardized mortality rate attributed to household and ambient air pollution (per 100 000 population) " "Age-standardized prevalence of hypertension among adults aged 30?79 years (%)" ...
## $ DIM_GEO_NAME : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ IND_CODE : chr "MDG_0000000003" "MDG_0000000003" "SDGAIRBODA" "NCD_HYP_PREVALENCE_A" ...
## $ DIM_GEO_CODE : chr "AFG" "AFG" "AFG" "AFG" ...
## $ DIM_TIME_YEAR : int 2021 2021 2019 2019 2022 2022 2022 2019 2023 2019 ...
## $ DIM_1_CODE : chr "AGEGROUP_YEARS15-19" "AGEGROUP_YEARS10-14" "SEX_BTSX" "SEX_BTSX" ...
## $ VALUE_NUMERIC : num 62 18 265.7 40.2 19.2 ...
## $ VALUE_STRING : chr "62.0" "18.0" "265.7" "40.2" ...
## $ VALUE_COMMENTS: chr "Afghanistan 2022-2023 Multiple Indicator Cluster Survey" "Afghanistan 2022-2023 Multiple Indicator Cluster Survey" "" "" ...#10503 rows and 9 columnsWHO data for ASEAN countries
data_ASEAN <- data %>% filter(DIM_GEO_CODE %in% c("MMR", "BRN","KHM", "IDN", "LAO", "MYS", "PHL", "SGP", "THA", "VNM"))To understand the indicator values
unique(data_ASEAN$IND_NAME)## [1] "Adolescent birth rate (per 1000 women)"
## [2] "Age-standardized mortality rate attributed to household and ambient air pollution (per 100 000 population) "
## [3] "Age-standardized prevalence of hypertension among adults aged 30?79 years (%)"
## [4] "Age-standardized prevalence of obesity among adults (18+ years) (%)"
## [5] "Age-standardized prevalence of tobacco use among persons 15 years and older (%) "
## [6] "Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (\xe6g/m3)"
## [7] "Average of 15 International Health Regulations core capacity scores"
## [8] "Density of dentists (per 10 000 population) "
## [9] "Density of medical doctors (per 10 000 population) "
## [10] "Density of nursing and midwifery personnel (per 10 000 population) "
## [11] "Density of pharmacists (per 10 000 population) "
## [12] "Diphtheria-tetanus-pertussis (DTP3) immunization coverage among 1-year-olds (%)"
## [13] "Domestic general government health expenditure (GGHE-D) as percentage of general government expenditure (GGE) (%)"
## [14] "Healthy life expectancy at birth (years)"
## [15] "Hepatitis B surface antigen (HBsAg) prevalence among children under 5 years (%)"
## [16] "Human papillomavirus (HPV) immunization coverage estimates among 15 year-old girls (%)"
## [17] "Life expectancy at birth (years)"
## [18] "Maternal mortality ratio (per 100 000 live births)"
## [19] "Measles-containing-vaccine second-dose (MCV2) immunization coverage by the locally recommended age (%)"
## [20] "Mortality rate attributed to exposure to unsafe WASH services (per 100 000 population)"
## [21] "Mortality rate due to homicide (per 100 000 population)"
## [22] "Mortality rate from unintentional poisoning (per 100 000 population)"
## [23] "Neonatal mortality rate (per 1000 live births)"
## [24] "Number of cases of poliomyelitis caused by wild poliovirus (WPV)"
## [25] "Percentage of bloodstream infection due to Escherichia coli resistant to 3rd-generation cephalosporin (%)"
## [26] "Percentage of bloodstream infections due methicillin-resistant Staphylococcus aureus (%)"
## [27] "Percentage of total antibiotic consumption being from the AWaRe \"Access\"\x9d antibiotics category (%)"
## [28] "Prevalence of anaemia in women of reproductive age (15?49 years) (%)"
## [29] "Prevalence of obesity among children and adolescents (5?19 years) (%)"
## [30] "Prevalence of overweight in children under 5 (%)"
## [31] "Prevalence of stunting in children under 5 (%)"
## [32] "Probability of dying from any of CVD, cancer, diabetes, CRD between age 30 and exact age 70 (%)"
## [33] "Proportion of births attended by skilled health personnel (%)"
## [34] "Proportion of population with primary reliance on clean fuels and technology (%)"
## [35] "Reported number of people requiring interventions against NTDs"
## [36] "Road traffic mortality rate (per 100 000 population)"
## [37] "Suicide mortality rate (per 100 000 population)"
## [38] "Total alcohol per capita (\xf2\xff15 years of age) consumption (litres of pure alcohol)"
## [39] "Tuberculosis incidence (per 100 000 population)"
## [40] "UHC: Service coverage index"
## [41] "Under-five mortality rate (per 1000 live births)"
## [42] "Amount of water- and sanitation-related official development assistance that is part of a government-coordinated spending plan (constant 2020 US$ millions)"
## [43] "Malaria incidence (per 1000 population at risk)"
## [44] "New HIV infections (per 1000 uninfected population)"
## [45] "Pneumococcal conjugate 3rd dose (PCV3) immunization coverage among 1-year olds (%)"
## [46] "Population with household expenditures on health > 10% of total household expenditure or income (%)"
## [47] "Population with household expenditures on health > 25% of total household expenditure or income (%)"
## [48] "Prevalence of wasting in children under 5 (%)"
## [49] "Proportion of ever-partnered women and girls aged 15?49 years subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months (%) "
## [50] "Proportion of ever-partnered women and girls aged 15?49 years subjected to physical and/or sexual violence by a current or former intimate partner in their lifetime (%) "
## [51] "Proportion of population using a hand-washing facility with soap and water (%)"
## [52] "Proportion of population using safely-managed drinking-water services (%)"
## [53] "Proportion of population using safely-managed sanitation services (%)"
## [54] "Proportion of safely treated domestic wastewater flows (%)"
## [55] "Proportion of women of reproductive age who have their need for family planning satisfied with modern methods (%)"
## [56] "Total net official development assistance to medical research and basic health sectors per capita (US$), by recipient country "unique(data_ASEAN$IND_CODE)## [1] "MDG_0000000003"
## [2] "SDGAIRBODA"
## [3] "NCD_HYP_PREVALENCE_A"
## [4] "NCD_BMI_30A"
## [5] "M_Est_tob_curr_std"
## [6] "SDGPM25"
## [7] "SDGIHR2021"
## [8] "HWF_0010"
## [9] "HWF_0001"
## [10] "HWF_0006"
## [11] "HWF_0014"
## [12] "WHS4_100"
## [13] "GHED_GGHE-DGGE_SHA2011"
## [14] "WHOSIS_0002"
## [15] "SDGHEPHBSAGPRV"
## [16] "SDGHPVRECEIVED"
## [17] "WHOSIS_0001"
## [18] "MDG_0000000026"
## [19] "MCV2"
## [20] "SDGWSHBOD"
## [21] "VIOLENCE_HOMICIDERATE"
## [22] "SDGPOISON"
## [23] "WHOSIS_000003"
## [24] "VACCINEPREVENTABLE_WILDPOLIO"
## [25] "AMR_INFECT_ECOLI"
## [26] "AMR_INFECT_MRSA"
## [27] "GLASSAMC_AWARE"
## [28] "NUTRITION_ANAEMIA_REPRODUCTIVEAGE_PREV"
## [29] "NCD_BMI_PLUS2C"
## [30] "NUTOVERWEIGHTPREV"
## [31] "NUTSTUNTINGPREV"
## [32] "NCDMORT3070"
## [33] "MDG_0000000025"
## [34] "PHE_HHAIR_PROP_POP_CLEAN_FUELS"
## [35] "SDGNTDTREATMENT"
## [36] "RS_198"
## [37] "SDGSUICIDE"
## [38] "SA_0000001688"
## [39] "MDG_0000000020"
## [40] "UHC_INDEX_REPORTED"
## [41] "MDG_0000000007"
## [42] "SDGODAWS"
## [43] "MALARIA_EST_INCIDENCE"
## [44] "SDGHIV"
## [45] "PCV3"
## [46] "FINPROTECTION_CATA_TOT_10_POP"
## [47] "FINPROTECTION_CATA_TOT_25_POP"
## [48] "NUTRITION_WH_2"
## [49] "SDGIPV12M"
## [50] "SDGIPVLT"
## [51] "WSH_HYGIENE_BASIC"
## [52] "WSH_WATER_SAFELY_MANAGED"
## [53] "WSH_SANITATION_SAFELY_MANAGED"
## [54] "WSH_DOMESTIC_WASTE_SAFELY_TREATED"
## [55] "SDGFPALL"
## [56] "SDGODA01"There are 56 indicators in this dataset for ASEAN.
- Age-standardized prevalence of obesity among adults (18+ years) (%)
data_ASEAN_NCD_BMI_30A <- data_ASEAN %>%
group_by(DIM_GEO_CODE, DIM_TIME_YEAR) %>%
filter(IND_CODE == "NCD_BMI_30A") %>%
select(DIM_GEO_CODE, DIM_TIME_YEAR,DIM_1_CODE, IND_CODE, VALUE_NUMERIC) %>%
arrange(desc(VALUE_NUMERIC))
data_ASEAN_NCD_BMI_30A## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR DIM_1_CODE IND_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 BRN 2022 SEX_BTSX NCD_BMI_30A 31.7
## 2 MYS 2022 SEX_BTSX NCD_BMI_30A 22.1
## 3 THA 2022 SEX_BTSX NCD_BMI_30A 15.4
## 4 SGP 2022 SEX_BTSX NCD_BMI_30A 13.9
## 5 IDN 2022 SEX_BTSX NCD_BMI_30A 11.2
## 6 PHL 2022 SEX_BTSX NCD_BMI_30A 8.74
## 7 LAO 2022 SEX_BTSX NCD_BMI_30A 8.01
## 8 MMR 2022 SEX_BTSX NCD_BMI_30A 7.43
## 9 KHM 2022 SEX_BTSX NCD_BMI_30A 4.36
## 10 VNM 2022 SEX_BTSX NCD_BMI_30A 2.02data_ASEAN_NCD_BMI_30A %>%
ggplot(aes(x = VALUE_NUMERIC, y = reorder(DIM_GEO_CODE, VALUE_NUMERIC), color = VALUE_NUMERIC)) +
geom_point(size = 4) +
geom_segment(aes(xend = 0, yend = reorder(DIM_GEO_CODE, VALUE_NUMERIC)), linewidth = 2) +
geom_text(aes(label = round(VALUE_NUMERIC, 2)), color = "white", size = 1.5) +
geom_vline(xintercept = mean(data_ASEAN_NCD_BMI_30A$VALUE_NUMERIC), color = "black", linetype = "dashed") + # Add vertical line at the average VALUE_NUMERIC
scale_x_continuous(
"",
expand = c(0, 0),
limits = c(0, 50),
breaks = seq(0, 50, by = 5), # Set breaks by 5
position = "top"
) +
scale_y_discrete("", expand = expansion(mult = c(0.05, 0.1))) + # Add space on y-axis and "", = removes the y-axis title
scale_color_gradientn(colors = c("lightblue", "darkblue"), name = NULL) + # name = NULL - Remove legend title
labs(
title = "NCD_BMI_30A % in ASEAN Countries - 2022 ",
caption = "Data Source: WHO_Health_Statistics_2024"
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(hjust = 0, size = 8), # Align "Source" to the left
plot.caption.position = "panel" # Adjust position of caption inside the panel
) +
annotate("text",x = Inf,y = -Inf,label = "\u00A9 Thura",hjust = 1.1, vjust = -1,size = 3)
Brunei has the highest percentage of population that are over 30 BMI and Vietnam has the lowest.
Creating Functions for table and plot
table <- function(data, ind_code) {
data <- data %>%
group_by(DIM_GEO_CODE, DIM_TIME_YEAR) %>%
filter(IND_CODE == ind_code) %>%
select(DIM_GEO_CODE, DIM_TIME_YEAR, IND_CODE,DIM_1_CODE, VALUE_NUMERIC) %>%
arrange(desc(VALUE_NUMERIC)) }
plot <- function(data, ind_code, title_suffix, unit, x,y) {
data %>%
ggplot(aes(x = VALUE_NUMERIC, y = reorder(DIM_GEO_CODE, VALUE_NUMERIC), color = VALUE_NUMERIC)) +
geom_point(size = 4) +
geom_segment(aes(xend = 0, yend = reorder(DIM_GEO_CODE, VALUE_NUMERIC)), linewidth = 2) +
geom_text(aes(label = round(VALUE_NUMERIC, 2)), color = "white", size = 1.5) +
geom_vline(xintercept = mean(data$VALUE_NUMERIC), color = "black", linetype = "dashed") +
scale_x_continuous(
"",
expand = c(0,0),
limits = c(0, x),
breaks = seq(0, x, by = y),
position = "top"
) +
scale_y_discrete("", expand = expansion(mult = c(0.05, 0.1))) +
scale_color_gradientn(colors = c("lightblue", "darkblue"), name = NULL) +
labs(
title = paste(ind_code,unit, " in ASEAN Countries -", title_suffix),
caption = "Data Source: WHO_Health_Statistics_2024"
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(hjust = 0, size = 8), # Align "Source" to the left
plot.caption.position = "panel" # Adjust position of caption inside the panel
) +
annotate("text",x = Inf,y = -Inf,label = "\u00A9 Thura",hjust = 1.1, vjust = -1,size = 3)
}- Age-standardized prevalence of tobacco use among persons 15 years and older (%)
t_M_Est_tob_curr_std <- table(data_ASEAN,"M_Est_tob_curr_std")
t_M_Est_tob_curr_std## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 MMR 2022 M_Est_tob_curr_std SEX_BTSX 44.4
## 2 IDN 2022 M_Est_tob_curr_std SEX_BTSX 38.2
## 3 LAO 2022 M_Est_tob_curr_std SEX_BTSX 27.2
## 4 VNM 2022 M_Est_tob_curr_std SEX_BTSX 22.5
## 5 MYS 2022 M_Est_tob_curr_std SEX_BTSX 22
## 6 PHL 2022 M_Est_tob_curr_std SEX_BTSX 20.4
## 7 THA 2022 M_Est_tob_curr_std SEX_BTSX 19.2
## 8 KHM 2022 M_Est_tob_curr_std SEX_BTSX 17.2
## 9 BRN 2022 M_Est_tob_curr_std SEX_BTSX 16.4
## 10 SGP 2022 M_Est_tob_curr_std SEX_BTSX 16.4#Plotting
plot(t_M_Est_tob_curr_std, "M_Est_tob_curr_std", "2022", "%", 50, 5)
Myanmar has the highest percentage of population in tobacco use (45%) and Brunei and Singapore have the lowest (16.4%).
3. Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (**6g/m3)**
#Table
t_SDGPM25 <- table(data_ASEAN,"SDGPM25")
t_SDGPM25## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 MMR 2019 SDGPM25 <NA> 27.8
## 2 THA 2019 SDGPM25 <NA> 25.5
## 3 PHL 2019 SDGPM25 <NA> 24.2
## 4 LAO 2019 SDGPM25 <NA> 24.2
## 5 MYS 2019 SDGPM25 <NA> 23.7
## 6 VNM 2019 SDGPM25 <NA> 22.1
## 7 IDN 2019 SDGPM25 <NA> 19.9
## 8 KHM 2019 SDGPM25 <NA> 18.3
## 9 SGP 2019 SDGPM25 <NA> 13.3
## 10 BRN 2019 SDGPM25 <NA> 6.76#Plotting
plot(t_SDGPM25, "SDGPM25", "2019", "%", 50,5)
Myanmar has the highest mean concentration of PM2.5 in urban areas (27.75%) and Brunei has the lowest (6.76%).
- Diphtheria-tetanus-pertussis (DTP3) immunization coverage among 1-year-olds (%)
#Table
t_WHS4_100 <- table(data_ASEAN,"WHS4_100")
t_WHS4_100## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 BRN 2022 WHS4_100 <NA> 99
## 2 MYS 2022 WHS4_100 <NA> 97
## 3 SGP 2022 WHS4_100 <NA> 97
## 4 THA 2022 WHS4_100 <NA> 97
## 5 VNM 2022 WHS4_100 <NA> 91
## 6 KHM 2022 WHS4_100 <NA> 85
## 7 IDN 2022 WHS4_100 <NA> 85
## 8 LAO 2022 WHS4_100 <NA> 80
## 9 PHL 2022 WHS4_100 <NA> 72
## 10 MMR 2022 WHS4_100 <NA> 71#Plotting
plot(t_WHS4_100, "WHS4_100", "2022", "%", 100,10)
Brunei has the highest DTP3 immunization coverage (99%) and Myanmar has the lowest (71%).
- Healthy life expectancy at birth (years)
t_WHOSIS_0002 <- table(data_ASEAN,"WHOSIS_0002")
t_WHOSIS_0002## # A tibble: 30 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 SGP 2021 WHOSIS_0002 SEX_FMLE 75.0
## 2 SGP 2021 WHOSIS_0002 SEX_BTSX 73.6
## 3 SGP 2021 WHOSIS_0002 SEX_MLE 72.4
## 4 THA 2021 WHOSIS_0002 SEX_FMLE 68.1
## 5 VNM 2021 WHOSIS_0002 SEX_FMLE 68.0
## 6 BRN 2021 WHOSIS_0002 SEX_FMLE 67.9
## 7 BRN 2021 WHOSIS_0002 SEX_BTSX 67.1
## 8 BRN 2021 WHOSIS_0002 SEX_MLE 66.3
## 9 THA 2021 WHOSIS_0002 SEX_BTSX 65.8
## 10 VNM 2021 WHOSIS_0002 SEX_BTSX 65.4
## # ℹ 20 more rows#Plotting
plot(t_WHOSIS_0002, "WHOSIS_0002", "2021", "%", 100,20)+facet_wrap(~DIM_1_CODE)
Singapore has the highest healthy life expectancy at birth while Philippines and Myanmar have the lowest.
- Life expectancy at birth (years)
t_WHOSIS_0001 <- table(data_ASEAN,"WHOSIS_0001")
t_WHOSIS_0001## # A tibble: 30 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 SGP 2021 WHOSIS_0001 SEX_FMLE 86.3
## 2 SGP 2021 WHOSIS_0001 SEX_BTSX 83.9
## 3 SGP 2021 WHOSIS_0001 SEX_MLE 81.6
## 4 THA 2021 WHOSIS_0001 SEX_FMLE 79.0
## 5 BRN 2021 WHOSIS_0001 SEX_FMLE 78.6
## 6 VNM 2021 WHOSIS_0001 SEX_FMLE 78.0
## 7 BRN 2021 WHOSIS_0001 SEX_BTSX 76.9
## 8 BRN 2021 WHOSIS_0001 SEX_MLE 75.3
## 9 MYS 2021 WHOSIS_0001 SEX_FMLE 75.3
## 10 THA 2021 WHOSIS_0001 SEX_BTSX 75.3
## # ℹ 20 more rows#Plotting
plot(t_WHOSIS_0001, "WHOSIS_0001", "2021", "%", 100,20)+facet_wrap(~DIM_1_CODE)
Singapore has the highest healthy life expectancy at birth while Philippines and Myanmar have the lowest.
- Domestic general government health expenditure (GGHE-D) as percentage of general government expenditure (GGE) (%)
t_GHED_GGHE_DGGE_SHA2011 <- table(data_ASEAN,"GHED_GGHE-DGGE_SHA2011")
t_GHED_GGHE_DGGE_SHA2011## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 SGP 2021 GHED_GGHE-DGGE_SHA2011 <NA> 20.8
## 2 THA 2021 GHED_GGHE-DGGE_SHA2011 <NA> 13.5
## 3 IDN 2021 GHED_GGHE-DGGE_SHA2011 <NA> 12.1
## 4 MYS 2021 GHED_GGHE-DGGE_SHA2011 <NA> 10.1
## 5 VNM 2021 GHED_GGHE-DGGE_SHA2011 <NA> 8.97
## 6 PHL 2021 GHED_GGHE-DGGE_SHA2011 <NA> 8.46
## 7 BRN 2021 GHED_GGHE-DGGE_SHA2011 <NA> 7.06
## 8 KHM 2021 GHED_GGHE-DGGE_SHA2011 <NA> 6.99
## 9 LAO 2021 GHED_GGHE-DGGE_SHA2011 <NA> 4.41
## 10 MMR 2021 GHED_GGHE-DGGE_SHA2011 <NA> 4.39#Plotting
plot(t_GHED_GGHE_DGGE_SHA2011, "GHED_GGHE-DGGE_SHA2011", "2021", "%", 25,5)
Singapore has the highest government health expenditure (20.81%) while Myanmar has the lowest (4.39%).
- Probability of dying from any of CVD, cancer, diabetes, CRD between age 30 and exact age 70 (%)
t_NCDMORT3070 <- table(data_ASEAN,"NCDMORT3070")
t_NCDMORT3070## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 LAO 2019 NCDMORT3070 <NA> 27.1
## 2 MMR 2019 NCDMORT3070 <NA> 25.6
## 3 PHL 2019 NCDMORT3070 <NA> 25.5
## 4 IDN 2019 NCDMORT3070 <NA> 24.2
## 5 KHM 2019 NCDMORT3070 <NA> 23.4
## 6 VNM 2019 NCDMORT3070 <NA> 20.9
## 7 MYS 2019 NCDMORT3070 <NA> 19.7
## 8 BRN 2019 NCDMORT3070 <NA> 15.2
## 9 THA 2019 NCDMORT3070 <NA> 14.3
## 10 SGP 2019 NCDMORT3070 <NA> 8.74#Plotting
plot(t_NCDMORT3070, "NCDMORT3070", "2019", "%", 30,5)
Laos has the highest probability (27.07%) of people between 30 and 70 dying from any of CVD, cancer, diabetes, CRD while Singapore has the lowest (8.74%).
- Suicide mortality rate (per 100 000 population)
t_SDGSUICIDE <- table(data_ASEAN,"SDGSUICIDE")
t_SDGSUICIDE## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 THA 2021 SDGSUICIDE <NA> 16.4
## 2 SGP 2021 SDGSUICIDE <NA> 8.44
## 3 VNM 2021 SDGSUICIDE <NA> 7.56
## 4 MYS 2021 SDGSUICIDE <NA> 5.64
## 5 KHM 2021 SDGSUICIDE <NA> 4.67
## 6 LAO 2021 SDGSUICIDE <NA> 4.61
## 7 PHL 2021 SDGSUICIDE <NA> 3.55
## 8 MMR 2021 SDGSUICIDE <NA> 2.89
## 9 BRN 2021 SDGSUICIDE <NA> 2.74
## 10 IDN 2021 SDGSUICIDE <NA> 1.21#Plotting
plot(t_SDGSUICIDE, "SDGSUICIDE", "2021", "per 100,000", 20,5)
Thailand has the highest suicide mortality rate (16.39) per 100000 while Indonesia has the lowest (1.21).
- Road traffic mortality rate (per 100 000 population)
t_SA_0000001688 <- table(data_ASEAN,"SA_0000001688")
t_SA_0000001688## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 LAO 2019 SA_0000001688 SEX_BTSX 11.5
## 2 VNM 2019 SA_0000001688 SEX_BTSX 9.34
## 3 KHM 2019 SA_0000001688 SEX_BTSX 8.48
## 4 THA 2019 SA_0000001688 SEX_BTSX 7.85
## 5 PHL 2019 SA_0000001688 SEX_BTSX 6.18
## 6 MMR 2019 SA_0000001688 SEX_BTSX 2.12
## 7 SGP 2019 SA_0000001688 SEX_BTSX 1.87
## 8 MYS 2019 SA_0000001688 SEX_BTSX 0.759
## 9 BRN 2019 SA_0000001688 SEX_BTSX 0.412
## 10 IDN 2019 SA_0000001688 SEX_BTSX 0.110#Plotting
plot(t_SA_0000001688, "SA_0000001688", "2019", "per 100,000", 15,5)
Laos has the highest Road Traffic mortality rate (11.52) per 100000 while Indonesia has the lowest (0.11).
- Tuberculosis incidence (per 100 000 population)
t_MDG_0000000020 <- table(data_ASEAN,"MDG_0000000020")
t_MDG_0000000020## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 PHL 2022 MDG_0000000020 <NA> 638
## 2 MMR 2022 MDG_0000000020 <NA> 475
## 3 IDN 2022 MDG_0000000020 <NA> 385
## 4 KHM 2022 MDG_0000000020 <NA> 320
## 5 VNM 2022 MDG_0000000020 <NA> 176
## 6 THA 2022 MDG_0000000020 <NA> 155
## 7 LAO 2022 MDG_0000000020 <NA> 138
## 8 MYS 2022 MDG_0000000020 <NA> 113
## 9 BRN 2022 MDG_0000000020 <NA> 57
## 10 SGP 2022 MDG_0000000020 <NA> 51#Plotting
plot(t_MDG_0000000020, "MDG_0000000020", "2022", "per 100,000", 700,100)
Philippines has the highest tuberculosis incidence (638) per 100000 while Singapore has the lowest (51).
- Malaria incidence (per 1000 population at risk)
t_MDG_MALARIA_EST_INCIDENCE <- table(data_ASEAN,"MALARIA_EST_INCIDENCE")
t_MDG_MALARIA_EST_INCIDENCE## # A tibble: 8 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [8]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 MMR 2022 MALARIA_EST_INCIDENCE <NA> 12.4
## 2 IDN 2022 MALARIA_EST_INCIDENCE <NA> 4.19
## 3 KHM 2022 MALARIA_EST_INCIDENCE <NA> 1.48
## 4 LAO 2022 MALARIA_EST_INCIDENCE <NA> 0.948
## 5 THA 2022 MALARIA_EST_INCIDENCE <NA> 0.460
## 6 PHL 2022 MALARIA_EST_INCIDENCE <NA> 0.122
## 7 VNM 2022 MALARIA_EST_INCIDENCE <NA> 0.00569
## 8 MYS 2022 MALARIA_EST_INCIDENCE <NA> 0#Plotting
plot(t_MDG_MALARIA_EST_INCIDENCE, "MDG_MALARIA_EST_INCIDENCE", "2022", "per 100,000", 20,2)
Myanmar has the highest malaria estimate incidence per 100,000 at risk while Malaysia has the lowest.
- Proportion of population using safely-managed drinking-water services (%)
t_WSH_WATER_SAFELY_MANAGED <- table(data_ASEAN,"WSH_WATER_SAFELY_MANAGED")
t_WSH_WATER_SAFELY_MANAGED## # A tibble: 8 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [8]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 SGP 2022 WSH_WATER_SAFELY_MANAGED <NA> 100
## 2 MYS 2022 WSH_WATER_SAFELY_MANAGED <NA> 93.9
## 3 VNM 2022 WSH_WATER_SAFELY_MANAGED <NA> 57.8
## 4 MMR 2022 WSH_WATER_SAFELY_MANAGED <NA> 57.4
## 5 PHL 2022 WSH_WATER_SAFELY_MANAGED <NA> 47.9
## 6 IDN 2022 WSH_WATER_SAFELY_MANAGED <NA> 30.3
## 7 KHM 2022 WSH_WATER_SAFELY_MANAGED <NA> 29.1
## 8 LAO 2022 WSH_WATER_SAFELY_MANAGED <NA> 17.9#Plotting
plot(t_WSH_WATER_SAFELY_MANAGED, "WSH_WATER_SAFELY_MANAGED", "2022", "%", 100,20)
Singapore has the all its population using safely-managed drinking-water services while Laos has the lowest percentage, only 17%.
- Measles-containing-vaccine second-dose (MCV2) immunization coverage by the locally recommended age (%)
t_MCV2 <- table(data_ASEAN,"MCV2")
t_MCV2## # A tibble: 10 × 5
## # Groups: DIM_GEO_CODE, DIM_TIME_YEAR [10]
## DIM_GEO_CODE DIM_TIME_YEAR IND_CODE DIM_1_CODE VALUE_NUMERIC
## <chr> <int> <chr> <chr> <dbl>
## 1 BRN 2022 MCV2 <NA> 99
## 2 MYS 2022 MCV2 <NA> 96
## 3 SGP 2022 MCV2 <NA> 92
## 4 THA 2022 MCV2 <NA> 87
## 5 VNM 2022 MCV2 <NA> 81
## 6 KHM 2022 MCV2 <NA> 69
## 7 IDN 2022 MCV2 <NA> 67
## 8 MMR 2022 MCV2 <NA> 64
## 9 PHL 2022 MCV2 <NA> 64
## 10 LAO 2022 MCV2 <NA> 55#Plotting
plot(t_MCV2, "MCV2", "2022", "%", 100,20)
Brunei has highest vaccine coverage for MCV2 (99%) while Laos has the lowest, 55%.
Estimate of current tobacco use prevalence (%) (age-standardized rate) - MMR - SDG 3.a
data2 <- read.csv("D:/me/R-Language/Practice/Dataset/M_Est_tob_curr_std_2024.csv")
head(data2)## IndicatorCode
## 1 M_Est_tob_curr_std
## 2 M_Est_tob_curr_std
## 3 M_Est_tob_curr_std
## 4 M_Est_tob_curr_std
## 5 M_Est_tob_curr_std
## 6 M_Est_tob_curr_std
## Indicator
## 1 Estimate of current tobacco use prevalence (%) (age-standardized rate)
## 2 Estimate of current tobacco use prevalence (%) (age-standardized rate)
## 3 Estimate of current tobacco use prevalence (%) (age-standardized rate)
## 4 Estimate of current tobacco use prevalence (%) (age-standardized rate)
## 5 Estimate of current tobacco use prevalence (%) (age-standardized rate)
## 6 Estimate of current tobacco use prevalence (%) (age-standardized rate)
## ValueType ParentLocationCode ParentLocation Location.type
## 1 numeric SEAR South-East Asia Country
## 2 numeric EUR Europe Country
## 3 numeric AFR Africa Country
## 4 numeric EMR Eastern Mediterranean Country
## 5 numeric AFR Africa Country
## 6 numeric AFR Africa Country
## SpatialDimValueCode Location Period.type Period
## 1 PRK Democratic People's Republic of Korea Year 2030
## 2 AZE Azerbaijan Year 2030
## 3 GHA Ghana Year 2030
## 4 EGY Egypt Year 2030
## 5 NGA Nigeria Year 2030
## 6 GMB Gambia Year 2030
## IsLatestYear Dim1.type Dim1 Dim1ValueCode Dim2.type Dim2 Dim2ValueCode
## 1 true Sex Female SEX_FMLE NA NA NA
## 2 true Sex Female SEX_FMLE NA NA NA
## 3 true Sex Female SEX_FMLE NA NA NA
## 4 true Sex Female SEX_FMLE NA NA NA
## 5 true Sex Female SEX_FMLE NA NA NA
## 6 true Sex Female SEX_FMLE NA NA NA
## Dim3.type Dim3 Dim3ValueCode DataSourceDimValueCode DataSource
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## FactValueNumericPrefix FactValueNumeric FactValueUoM
## 1 NA 0.0 NA
## 2 NA 0.1 NA
## 3 NA 0.2 NA
## 4 NA 0.3 NA
## 5 NA 0.3 NA
## 6 NA 0.4 NA
## FactValueNumericLowPrefix FactValueNumericLow FactValueNumericHighPrefix
## 1 NA 0.0 NA
## 2 NA 0.0 NA
## 3 NA 0.1 NA
## 4 NA 0.1 NA
## 5 NA 0.1 NA
## 6 NA 0.1 NA
## FactValueNumericHigh Value FactValueTranslationID
## 1 0.0 0 [0-0] NA
## 2 0.2 0.1 [0-0.2] NA
## 3 0.3 0.2 [0.1-0.3] NA
## 4 0.4 0.3 [0.1-0.4] NA
## 5 0.5 0.3 [0.1-0.5] NA
## 6 0.6 0.4 [0.1-0.6] NA
## FactComments
## 1 Tobacco use estimates are not available. Tobacco smoking estimates are substituted for missing tobacco use estimates. The most recent survey was conducted in 2017. This is a projection.
## 2 The most recent survey was conducted in 2020. This is a projection.
## 3 The most recent survey was conducted in 2017-18. This is a projection.
## 4 Tobacco use estimates are not available. Tobacco smoking estimates are substituted for missing tobacco use estimates. The most recent survey was conducted in 2016-17. This is a projection.
## 5 The most recent survey was conducted in 2018. This is a projection.
## 6 The most recent survey was conducted in 2018. This is a projection.
## Language DateModified
## 1 EN 2024-01-15T17:00:00.000Z
## 2 EN 2024-01-15T17:00:00.000Z
## 3 EN 2024-01-15T17:00:00.000Z
## 4 EN 2024-01-15T17:00:00.000Z
## 5 EN 2024-01-15T17:00:00.000Z
## 6 EN 2024-01-15T17:00:00.000Zstr(data2)## 'data.frame': 14850 obs. of 34 variables:
## $ IndicatorCode : chr "M_Est_tob_curr_std" "M_Est_tob_curr_std" "M_Est_tob_curr_std" "M_Est_tob_curr_std" ...
## $ Indicator : chr "Estimate of current tobacco use prevalence (%) (age-standardized rate)" "Estimate of current tobacco use prevalence (%) (age-standardized rate)" "Estimate of current tobacco use prevalence (%) (age-standardized rate)" "Estimate of current tobacco use prevalence (%) (age-standardized rate)" ...
## $ ValueType : chr "numeric" "numeric" "numeric" "numeric" ...
## $ ParentLocationCode : chr "SEAR" "EUR" "AFR" "EMR" ...
## $ ParentLocation : chr "South-East Asia" "Europe" "Africa" "Eastern Mediterranean" ...
## $ Location.type : chr "Country" "Country" "Country" "Country" ...
## $ SpatialDimValueCode : chr "PRK" "AZE" "GHA" "EGY" ...
## $ Location : chr "Democratic People's Republic of Korea" "Azerbaijan" "Ghana" "Egypt" ...
## $ Period.type : chr "Year" "Year" "Year" "Year" ...
## $ Period : int 2030 2030 2030 2030 2030 2030 2030 2030 2030 2030 ...
## $ IsLatestYear : chr "true" "true" "true" "true" ...
## $ Dim1.type : chr "Sex" "Sex" "Sex" "Sex" ...
## $ Dim1 : chr "Female" "Female" "Female" "Female" ...
## $ Dim1ValueCode : chr "SEX_FMLE" "SEX_FMLE" "SEX_FMLE" "SEX_FMLE" ...
## $ Dim2.type : logi NA NA NA NA NA NA ...
## $ Dim2 : logi NA NA NA NA NA NA ...
## $ Dim2ValueCode : logi NA NA NA NA NA NA ...
## $ Dim3.type : logi NA NA NA NA NA NA ...
## $ Dim3 : logi NA NA NA NA NA NA ...
## $ Dim3ValueCode : logi NA NA NA NA NA NA ...
## $ DataSourceDimValueCode : logi NA NA NA NA NA NA ...
## $ DataSource : logi NA NA NA NA NA NA ...
## $ FactValueNumericPrefix : logi NA NA NA NA NA NA ...
## $ FactValueNumeric : num 0 0.1 0.2 0.3 0.3 0.4 0.4 0.4 0.4 0.4 ...
## $ FactValueUoM : logi NA NA NA NA NA NA ...
## $ FactValueNumericLowPrefix : logi NA NA NA NA NA NA ...
## $ FactValueNumericLow : num 0 0 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 ...
## $ FactValueNumericHighPrefix: logi NA NA NA NA NA NA ...
## $ FactValueNumericHigh : num 0 0.2 0.3 0.4 0.5 0.6 0.6 0.6 0.6 0.6 ...
## $ Value : chr "0 [0-0]" "0.1 [0-0.2]" "0.2 [0.1-0.3]" "0.3 [0.1-0.4]" ...
## $ FactValueTranslationID : logi NA NA NA NA NA NA ...
## $ FactComments : chr "Tobacco use estimates are not available. Tobacco smoking estimates are substituted for missing tobacco use esti"| __truncated__ "The most recent survey was conducted in 2020. This is a projection." "The most recent survey was conducted in 2017-18. This is a projection." "Tobacco use estimates are not available. Tobacco smoking estimates are substituted for missing tobacco use esti"| __truncated__ ...
## $ Language : chr "EN" "EN" "EN" "EN" ...
## $ DateModified : chr "2024-01-15T17:00:00.000Z" "2024-01-15T17:00:00.000Z" "2024-01-15T17:00:00.000Z" "2024-01-15T17:00:00.000Z" ...data2_MMR <- data2 %>% filter(SpatialDimValueCode =="MMR", IndicatorCode == "M_Est_tob_curr_std") %>% select(IndicatorCode,Period, Dim1ValueCode, FactValueNumeric)
str(data2_MMR)## 'data.frame': 30 obs. of 4 variables:
## $ IndicatorCode : chr "M_Est_tob_curr_std" "M_Est_tob_curr_std" "M_Est_tob_curr_std" "M_Est_tob_curr_std" ...
## $ Period : int 2030 2030 2030 2025 2025 2025 2022 2022 2022 2021 ...
## $ Dim1ValueCode : chr "SEX_FMLE" "SEX_BTSX" "SEX_MLE" "SEX_FMLE" ...
## $ FactValueNumeric: num 13.1 39.3 65.4 16.6 42.3 68 19.3 44.4 69.5 20.2 ...ggplot(data2_MMR, aes(x = Period, y = FactValueNumeric)) +
geom_line() +
labs(title = "Trend of Estimate of current tobacco use % over Time",
caption = "Data Source: WHO_Health_Statistics_2024") +
facet_wrap(~Dim1ValueCode) +
annotate("text", x = Inf, y = -Inf, label = "\u00A9 Thura", hjust = 1.1, vjust = -1, size = 3) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
DTP3 immunization - MMR from 2000 to 2022
WHO_Data_link - https://data.who.int/countries/104 or https://www.who.int/data/gho
data3 <- read.csv("D:/me/R-Language/Practice/Dataset/RELAY_WHS_DTP3_2024.csv")
head(data3)## IND_ID IND_CODE IND_UUID IND_PER_CODE DIM_TIME DIM_TIME_TYPE
## 1 F8E084CWHS4_100 WHS4_100 F8E084C WHS4_100 2003 YEAR
## 2 F8E084CWHS4_100 WHS4_100 F8E084C WHS4_100 2019 YEAR
## 3 F8E084CWHS4_100 WHS4_100 F8E084C WHS4_100 2021 YEAR
## 4 F8E084CWHS4_100 WHS4_100 F8E084C WHS4_100 2000 YEAR
## 5 F8E084CWHS4_100 WHS4_100 F8E084C WHS4_100 2013 YEAR
## 6 F8E084CWHS4_100 WHS4_100 F8E084C WHS4_100 2019 YEAR
## DIM_GEO_CODE_M49 DIM_GEO_CODE_TYPE DIM_PUBLISH_STATE_CODE
## 1 583 COUNTRY PUBLISHED
## 2 583 COUNTRY PUBLISHED
## 3 586 COUNTRY PUBLISHED
## 4 28 COUNTRY PUBLISHED
## 5 28 COUNTRY PUBLISHED
## 6 52 COUNTRY PUBLISHED
## IND_NAME GEO_NAME_SHORT
## 1 DTP3 immunization coverage (age 1) Micronesia (Federated States of)
## 2 DTP3 immunization coverage (age 1) Micronesia (Federated States of)
## 3 DTP3 immunization coverage (age 1) Pakistan
## 4 DTP3 immunization coverage (age 1) Antigua and Barbuda
## 5 DTP3 immunization coverage (age 1) Antigua and Barbuda
## 6 DTP3 immunization coverage (age 1) Barbados
## RATE_PER_100_N
## 1 92
## 2 78
## 3 83
## 4 95
## 5 99
## 6 90str(data3)## 'data.frame': 4719 obs. of 12 variables:
## $ IND_ID : chr "F8E084CWHS4_100" "F8E084CWHS4_100" "F8E084CWHS4_100" "F8E084CWHS4_100" ...
## $ IND_CODE : chr "WHS4_100" "WHS4_100" "WHS4_100" "WHS4_100" ...
## $ IND_UUID : chr "F8E084C" "F8E084C" "F8E084C" "F8E084C" ...
## $ IND_PER_CODE : chr "WHS4_100" "WHS4_100" "WHS4_100" "WHS4_100" ...
## $ DIM_TIME : int 2003 2019 2021 2000 2013 2019 2020 2015 2010 2009 ...
## $ DIM_TIME_TYPE : chr "YEAR" "YEAR" "YEAR" "YEAR" ...
## $ DIM_GEO_CODE_M49 : int 583 583 586 28 28 52 84 100 120 148 ...
## $ DIM_GEO_CODE_TYPE : chr "COUNTRY" "COUNTRY" "COUNTRY" "COUNTRY" ...
## $ DIM_PUBLISH_STATE_CODE: chr "PUBLISHED" "PUBLISHED" "PUBLISHED" "PUBLISHED" ...
## $ IND_NAME : chr "DTP3 immunization coverage (age 1)" "DTP3 immunization coverage (age 1)" "DTP3 immunization coverage (age 1)" "DTP3 immunization coverage (age 1)" ...
## $ GEO_NAME_SHORT : chr "Micronesia (Federated States of)" "Micronesia (Federated States of)" "Pakistan" "Antigua and Barbuda" ...
## $ RATE_PER_100_N : num 92 78 83 95 99 90 79 91 84 25 ...data3_MMR <- data3 %>% filter(GEO_NAME_SHORT =="Myanmar") %>% select(DIM_TIME,RATE_PER_100_N)pDTP3<- ggplot(data3_MMR, aes(x = DIM_TIME, y = RATE_PER_100_N)) +
geom_line(color="blue") +
geom_hline(yintercept = mean(data3_MMR$RATE_PER_100_N), color = "darkgray", linetype = "dashed") +
coord_fixed(0.1) +
labs(title = "Trend of DTP3 immunization - MMR from 2000 to 2022",
caption = "Data Source: WHO_Health_Statistics_2024") +
annotate("text", x = Inf, y = -Inf, label = "\u00A9 Thura", hjust = 1.1, vjust = -1, size = 3) +
scale_x_continuous(breaks = seq(min(data3_MMR$DIM_TIME), max(data3_MMR$DIM_TIME), by = 2)) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
pDTP3
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':
##
## layoutplotDTP3 <- ggplotly(pDTP3) %>%
layout(
hoverlabel = list(bgcolor = "gray")
) %>%
add_annotations(
x = 1,
y = 0,
text = "\u00A9 Thura",
showarrow = FALSE,
xref = "paper",
yref = "paper",
xanchor = "right",
yanchor = "bottom",
font = list(size = 12)
)
plotDTP3MCV2 immunization - MMR from 2008 to 2022
data4 <- read.csv("D:/me/R-Language/Practice/Dataset/RELAY_WHS_MCV2_2024.csv")
head(data4)## IND_ID IND_CODE IND_UUID IND_PER_CODE DIM_TIME DIM_TIME_TYPE
## 1 BB4567BMCV2 MCV2 BB4567B MCV2 2014 YEAR
## 2 BB4567BMCV2 MCV2 BB4567B MCV2 2008 YEAR
## 3 BB4567BMCV2 MCV2 BB4567B MCV2 2019 YEAR
## 4 BB4567BMCV2 MCV2 BB4567B MCV2 2020 YEAR
## 5 BB4567BMCV2 MCV2 BB4567B MCV2 2018 YEAR
## 6 BB4567BMCV2 MCV2 BB4567B MCV2 2022 YEAR
## DIM_GEO_CODE_M49 DIM_GEO_CODE_TYPE DIM_PUBLISH_STATE_CODE
## 1 191 COUNTRY PUBLISHED
## 2 208 COUNTRY PUBLISHED
## 3 250 COUNTRY PUBLISHED
## 4 262 COUNTRY PUBLISHED
## 5 834 COUNTRY PUBLISHED
## 6 496 COUNTRY PUBLISHED
## IND_NAME GEO_NAME_SHORT
## 1 Measles (MCV2) immunization coverage Croatia
## 2 Measles (MCV2) immunization coverage Denmark
## 3 Measles (MCV2) immunization coverage France
## 4 Measles (MCV2) immunization coverage Djibouti
## 5 Measles (MCV2) immunization coverage United Republic of Tanzania
## 6 Measles (MCV2) immunization coverage Mongolia
## RATE_PER_100_N
## 1 97
## 2 87
## 3 86
## 4 60
## 5 68
## 6 93str(data4)## 'data.frame': 3304 obs. of 12 variables:
## $ IND_ID : chr "BB4567BMCV2" "BB4567BMCV2" "BB4567BMCV2" "BB4567BMCV2" ...
## $ IND_CODE : chr "MCV2" "MCV2" "MCV2" "MCV2" ...
## $ IND_UUID : chr "BB4567B" "BB4567B" "BB4567B" "BB4567B" ...
## $ IND_PER_CODE : chr "MCV2" "MCV2" "MCV2" "MCV2" ...
## $ DIM_TIME : int 2014 2008 2019 2020 2018 2022 2007 2007 2016 2017 ...
## $ DIM_TIME_TYPE : chr "YEAR" "YEAR" "YEAR" "YEAR" ...
## $ DIM_GEO_CODE_M49 : int 191 208 250 262 834 496 504 36 192 232 ...
## $ DIM_GEO_CODE_TYPE : chr "COUNTRY" "COUNTRY" "COUNTRY" "COUNTRY" ...
## $ DIM_PUBLISH_STATE_CODE: chr "PUBLISHED" "PUBLISHED" "PUBLISHED" "PUBLISHED" ...
## $ IND_NAME : chr "Measles (MCV2) immunization coverage" "Measles (MCV2) immunization coverage" "Measles (MCV2) immunization coverage" "Measles (MCV2) immunization coverage" ...
## $ GEO_NAME_SHORT : chr "Croatia" "Denmark" "France" "Djibouti" ...
## $ RATE_PER_100_N : num 97 87 86 60 68 93 92 89 99 88 ...data4_MMR <- data4 %>% filter(GEO_NAME_SHORT =="Myanmar") %>% select(DIM_TIME,RATE_PER_100_N)pMCV2 <- ggplot(data4_MMR, aes(x = DIM_TIME, y = RATE_PER_100_N)) +
geom_line(color = "brown") +
coord_fixed(0.08) +
geom_hline(yintercept = mean(data4_MMR$RATE_PER_100_N), color = "darkgray", linetype = "dashed") +
labs(title = "Trend of MCV2 immunization - MMR from 2008 to 2022",
caption = "Data Source: WHO_Health_Statistics_2024") +
annotate("text", x = Inf, y = -Inf, label = "\u00A9 Thura", hjust = 1.1, vjust = -1, size = 3) +
scale_x_continuous(breaks = seq(min(data3_MMR$DIM_TIME), max(data3_MMR$DIM_TIME), by = 2)) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
pMCV2
plotMCV2<- ggplotly(pMCV2) %>%
layout(
hoverlabel = list(bgcolor = "gray")
) %>%
add_annotations(
x = 1,
y = 0,
text = "\u00A9 Thura",
showarrow = FALSE,
xref = "paper",
yref = "paper",
xanchor = "right",
yanchor = "bottom",
font = list(size = 12)
)
plotMCV2