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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

setwd("C:/Users/XJimm/Desktop/Study/Semester 2/Data VisuAL/A3")

library(readr)
## Warning: package 'readr' was built under R version 4.1.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.3
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## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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##     filter, lag
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##     intersect, setdiff, setequal, union
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.3
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
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## v tidyr   1.1.4     v forcats 0.5.1
## Warning: package 'ggplot2' was built under R version 4.1.3
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
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library(magrittr)
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## Attaching package: 'magrittr'
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library(ggplot2)
library(plotly)
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library(knitr)
## Warning: package 'knitr' was built under R version 4.1.3
gender <- read.csv("male-vs-female-suicide.csv")

gender %>% head()
##        Entity     Code Year Male.suicide.rate..age.standardized.
## 1    Abkhazia OWID_ABK 2015                                   NA
## 2 Afghanistan      AFG 1990                                14.75
## 3 Afghanistan      AFG 1991                                14.81
## 4 Afghanistan      AFG 1992                                14.80
## 5 Afghanistan      AFG 1993                                15.02
## 6 Afghanistan      AFG 1994                                15.34
##   Female.suicide.rate..age.standardized. Continent
## 1                                     NA      Asia
## 2                                   5.46          
## 3                                   5.43          
## 4                                   5.38          
## 5                                   5.41          
## 6                                   5.50
cost <- read.csv("rogs-2020-parte-section13-dataset.csv")

cause <- read.csv("annual-number-of-deaths-by-cause.csv")
# Cause of death 


# Suicide in young people 15-19 years old


# Cause of depression 
#Hospital 



Crude_Suicide_Rates <- read.csv("Crude Suicide rates.csv")

SR <- Crude_Suicide_Rates %>% select(c("Indicator", "Location", "Period", "Dim1", "FactValueNumeric","Dim2" ))

SRAus <- SR %>% filter(Location == "Australia")

popGH <- aggregate(formula = FactValueNumeric ~ Dim1 + Dim2, data = SRAus, FUN = sum)

popGH <- with(popGH, popGH[order(Dim1,Dim2),])

popGH <- popGH[,c("FactValueNumeric","Dim1","Dim2")]

popGH1 <- popGH %>% filter(Dim1 != c("Both sexes"))

colnames(popGH1) <- c("Crude_Suicide_Rate", "Gender", "Age")
popGH1$Crude_Suicide_Rate <- ifelse(popGH1$Gender == "Male", -1*popGH1$Crude_Suicide_Rate, popGH1$Crude_Suicide_Rate)

pyramidGH2 <- ggplot(popGH1, aes(x = Age, y = Crude_Suicide_Rate, fill = Gender)) +
geom_bar(data = subset(popGH1, Gender == "Female"), stat = "identity") +
geom_bar(data = subset(popGH1, Gender == "Male"), stat = "identity") +
scale_y_continuous(labels = paste0(as.character(c(seq(2, 0, -1), seq(1, 2, 1))), "")) +
coord_flip()


depression <- read.csv("Estimated Population-based prevalence of depression.csv")


depression1 <- depression %>% select(c("Indicator","Location", "SpatialDimValueCode", "FactValueNumeric"))

colnames(depression1) <- c("Indicator", "Location", "CODE", "Depression Rate")


library(plotly)
fig <- plot_ly(depression1, type='choropleth', locations=depression1$CODE, z=depression1$`Depression Rate`, text=depression1$Location, colorscale="Purples")%>%
  layout(title = "Estimated population-based prevalence of depression  - WHO data 2015")


fig

Population pyramid - Australia Crude Suicide rate in 2019 (per 100 000 population)

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.