86 lines
3.3 KiB
Text
86 lines
3.3 KiB
Text
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---
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title: "Facets and a Lesson in Humility"
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subtitle: |
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A look at Tableau for Healthcare Chapter 8. Table Lens graph.
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date: 01-29-2020
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---
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Todays post is a lesson in Facets, as well as humility. The task this week was to replicate the graph in Chapter 8 of Tableau for Healthcare in R. The graph in question is called a Table Lens (This is the name the book uses, however I did have trouble finding this name in Google searches), it is a collection of charts with a common theme, this time looking at countries in various WHO regions and some statistics associated with mortality as well as health expenditure. I say this is a lesson in humiltiy as I have read through the excellent book [R for Data Science](https://r4ds.had.co.nz/), and yet the idea of faceting a ggplot graph slipped my mind. This ended with hours of trying to find a package in R to line up graphs, and way more time then I care to admit spent on getting things prefect. I did find such a package called cowplots, which can be found [here](https://wilkelab.org/cowplot/index.html). While this is an excellent package, its use was unecessary and I reverted back to using the excellent facet feature of GGplot, which can be seen below! 
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# Load Libraries
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```{r}
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library(magrittr) #pipes
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library(ggplot2) #ploting
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library(dplyr)
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library(tidyr)
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```
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# Import Data
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```{r}
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ds <- readxl::read_xlsx(path = "../2020-01-04_my-start-to-r/Tableau 10 Training Practice Data.xlsx"
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,sheet = "03 - WHO Life Expect & Mort"
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)
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```
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# Clean Names and Transform
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```{r}
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varnames <- c("who_region", "country", "year" , "sex" , "life_expect_birth" , "neo_mort"
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,"under_five_mort" , "health_expenditure")
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names(ds) <- varnames
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# Order Countries based on Life Expectancy at Birth
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ds$country <- factor(ds$country, levels = ds$country[order(ds$life_expect_birth)])
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#To "Long" Form
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ds1 <- ds %>% pivot_longer(5:8)#select columns 5 throuh 8, leave new columns at default names
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# Set up labels for Facet, as well as function for Facet Labeller
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facet_labels <- list(
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"life_expect_birth" = "Life Expectancy at Birth "
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,"neo_mort" = "Neonatal Mortality Rate"
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,"under_five_mort" = "Under-Five Mortality Rate"
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,"health_expenditure" = "Health Expenditure per Capita (US$)" )
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variable_labeller <- function(variable,value){
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return(facet_labels[value])
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}
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```
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# Graphs
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```{r fig.height=7, fig.width=12}
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hightlight_countries <- (c("Mauritania", "South Africa"))
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g1 <- ds1 %>% filter(who_region == "Africa") %>%
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mutate(name = factor(name, levels = c("life_expect_birth" , "neo_mort"
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,"under_five_mort" , "health_expenditure"))
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,highlight = country %in% hightlight_countries) %>%
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ggplot(aes(x = country, y = value, fill = highlight)) +
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geom_col(show.legend = FALSE) +
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coord_flip() +
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labs(
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title = "World Bank Life Expectancy, Neonatal & Under-Five Mortality Rates, and Health Expenditure Analysis"
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,x = NULL
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,y = NULL
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) +
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facet_grid(~name, scales = "free_x",labeller = variable_labeller) +
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theme_bw() +
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geom_text(aes(label = round(value, 0)), hjust = 0) +
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scale_y_continuous(expand = expand_scale(mult = c(0,0.2))) +
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scale_fill_manual(values = c("TRUE" = "#fc8d59", "FALSE" = "#2b83ba"))
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g1
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```
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