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+---
+title: "Winners and losers of tax legislation changes"
+description: |
+ Recreating the ABC news article Graph for MCSS Data Viz 24/25
+categories: "2024"
+author: Brad McKenzie
+date: "`r Sys.Date()`"
+output:
+ distill::distill_article:
+ self_contained: false
+ toc: true
+---
+
+## About the graph
+
+I have recreated the following graph from [this February 2024 article by ABC news Australia](https://www.abc.net.au/news/2024-02-02/biggest-winners-and-losers-of-revised-stage-3-tax-cuts/103415830).
+
+```{r original-plot, echo=FALSE, fig.cap="Original graph from ABC article", out.extra="class=external"}
+knitr::include_graphics("ABC_tax_income_plot.png")
+```
+
+The graph shows the median income of Australians by occupation compared to who would benefit from proposed tax changes. The graph highlights those who will benefit under the changes and those who will be worse off. To distinguish the two groups, the authors use a horizontal line from the y-axis at the benefit cutoff point, \$146,486, and background shading.
+
+The purpose of the graph is to highlight that most people are expected to be better off under the changes. Instead of estimating the number of people in raw terms that would benefit, the plot uses disagregated tax data by occupation and gender to display which groups are better and worse off.
+
+The graph is effective because you can easily conclude that the majority of people will be better off under the changes, regardless of sex or occupation. The use of a linear y-axis to show income is effective in isolating the very high income occupations which are outliers to the data. It creates a white space which contrasts against the concentrated grouping of occupations below the cutoff point.
+
+## Setup
+
+Load packages, set global and local parameters, and read in our dataset.
+
+### Load required packages
+
+The packages are for data cleaning, graph generation, adding themes and organising subplots into the final graph.
+
+```{r packages, message=FALSE, warning=FALSE}
+
+library(tidyverse)
+library(readxl)
+library(patchwork)
+library(grid)
+library(magick)
+library(ggbeeswarm)
+library(cowplot)
+library(showtext) # most similar font found but couldn't match exact
+sysfonts::font_add_google("Hind", family="hind")
+showtext_auto()
+library(geomtextpath)
+
+```
+
+```{r parameters, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE,
+ fig.align="center",
+ fig.showtext=TRUE)
+```
+
+### Load data
+
+Now we load the data from the `data.gov.au` page. The data comes from Table 15B of the 2020-21 Australian Tax Office individual statistics.
+
+This is some of the most granular tax data available. This choice of data allows the authors to create a graph with these dimensions:
+
+- Occupation
+
+- Number of employees in the occupation (shown as % of total employees on the x-axis)
+
+- Median income of each occupation
+
+- Splits by gender
+
+```{r raw_data}
+## if reading from web
+# library(httr)
+# url <- "https://data.gov.au/data/dataset/07b51b39-254a-4177-8b4c-497f17eddb80/resource/1535c9eb-8385-41af-ae65-f71b2be77d6d/download/ts21individual15occupationsex.xlsx"
+# response <- GET(url)
+# file_path <- tempfile(fileext = ".xlsx")
+# writeBin(content(response, "raw"), file_path)
+#
+# data <- read_excel(file_path,
+# sheet="Table 15B",
+# skip=1) # Initial clean step to remove unused row
+
+
+data <- read_excel("ts21individual15occupationsex.xlsx",
+ sheet = "Table 15B",
+ skip = 1)
+head(data, 5)
+
+```
+
+*Important data consideration:* The original graph was published in February 2024, the ATO then revised the data in March 2024 (as seen on the data updated row of the metadata online). This has led to some variation between figures in our graph and in the actual graph I will recreate. So I am not able to create a 100% like for like replication. Spot checks have been completed and the occupation and number of employee figures appear the same. There appears to be more revisions to some median income figures however the changes are minor overall.
+
+### Set parameters
+
+Define parameters that are used multiple times through the recreation. This saves us writing them out multiple times, including the income cutoff point and colours.
+
+```{r define_parameters}
+
+benefit_cut <- 146486 # Those below $146,486 benefit from the redesigned plan
+options(scipen = 999)
+
+
+# colour for female purple; HEX = #a175b4 or RGB rgba(161,117,180,255)
+colour_fem <- "#a175b4"
+# colour for male blue; HEX = #ca555d or RGB rgba(202,85,93,255)
+colour_male <- "#ca555d"
+
+```
+
+## Data cleaning
+
+First, remove unused columns, clean column names and also filter rows to only have the groups by gender we are interested in (remove totals).
+
+```{r basic-data-cleaning}
+
+clean_data <- data |>
+ rename("occupation" = "Occupation - unit group1",
+ "sex" = "Sex4",
+ "count" = "Individuals\r\nno.",
+ "income" = "Median taxable income2,5\r\n$") |>
+ select(occupation, sex, count, income) |>
+ filter(sex %in% c("Female", "Male"))
+
+```
+
+Second, create the additional variable required, which is % of total workers in that occupation. I also create a negative income % variable for females, this is used to create the mirrored graphs by gender in later stages.
+
+```{r add-proportion-workers-var}
+
+
+clean_data <- clean_data |>
+ group_by(sex) |>
+ mutate(prop_workers_sex = count/sum(count), # create proportion of workers variable
+ income_display = ifelse(sex=="Female", -prop_workers_sex, prop_workers_sex))|> # create negative female proportion variable
+ ungroup() |>
+ filter(occupation != "0000 Occupation blank") # remove unused occupation
+
+```
+
+The authors make their calculations of total % of workers including the "0000 Occupation blank" variable, but then remove it from all of the visualisations.
+
+### Basic plotting to check variable functionality
+
+Now, to check all the required variables are available, I create a very basic version of the graph to check our data completeness. Some parameters for axis are included for comparison purposes.
+
+```{r test-plot}
+
+basic_test_plot <- clean_data |>
+ filter(occupation != "0000 Occupation blank") |>
+ ggplot(aes(x=income_display, y=income, size=count ,color=sex))+
+ geom_point(alpha=0.8)+
+
+ # now add split around the y axis and change the x axis to be equal, plus change the axix units
+ scale_x_continuous(name = "Percentage of workers",
+ breaks= c(-0.04, -0.035, -0.03, -0.025, -0.02, -0.015, -0.01, -0.005,
+ 0,
+ 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04),
+ minor_breaks = NULL,
+ limits = c(-0.04, 0.04),
+ labels = function(x) abs(x) %>% scales::percent())+ # apply absolute vals
+
+ scale_y_continuous(name = "Median taxable income ($)",
+ breaks = c(0, 50000, 100000, 150000, 200000, 250000,
+ 300000, 350000, 400000, 450000, 500000),
+ minor_breaks = NULL,
+ limits = c(0,500000),
+ labels = scales::label_number(suffix="k", scale = 1e-3)) +
+ theme_minimal()
+
+basic_test_plot
+
+```
+
+The initial plotting shows that I have all required variables for our graph.
+
+*Challenge detected:*
+
+There is one one major problem in the basic replication. There are two X=0 values on the original graph with a gap without data to divide the male and female data. I need to replicate this gap and double y-axis aesthetic for the replication too.
+
+There is no option to do this manually within one ggplot. Instead, I will create two separate graphs (for male and female) that are manually positioned together with `patchwork` to create the mirrored effect. This mirrored presentation also means we have to create two positive X-axis graphs, however the X-axis are going in opposite directions.
+
+This is a source of repetition in the next few steps to create almost identical but symmetrical plots.
+
+## Replication
+
+Primarily because I cannot impute the gap around x=0, individual graphs by gender are created. To start, I split the data by gender to work with on each graph.
+
+```{r data-bysex}
+data_male <- clean_data |> filter(sex == "Male")
+data_female <- clean_data |> filter(sex == "Female")
+
+```
+
+### Create male plot
+
+First, create the basic male plot, these are all positive values and easier to work with.
+
+```{r basic_maleplot}
+plot_male <- data_male |>
+ filter(occupation != "0000 Occupation blank") |>
+ arrange(occupation) |> # this matches the layering of plot points
+ ggplot(aes(x=income_display, y=income, size = prop_workers_sex))+
+
+ annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = benefit_cut,
+ fill = "grey", alpha = 0.5) + # add a background box for the benefit area
+ geom_point(color = "white",
+ fill = colour_male,
+ shape = 21, # circle with different fill and border
+ alpha = 0.85,
+ position = "identity")+
+
+ geom_hline(yintercept = benefit_cut, linetype = 'dashed')+ # add cutoff line
+
+ annotate("text", x=0.04, y=135000, # add label for cutoff line
+ label = "Bigger tax cut",
+ size = 3.3,
+ hjust = 0.85,
+ family = "hind")+
+
+ labs(y="Median taxable income $",
+ x="Percentage of workers") # include both of these so the labels combine with female site in patchwork join.
+
+plot_male
+
+```
+
+#### Specify male plot axis and theme settings
+
+Now, I update some of the presentation and layout of the male graph. This includes things such as updating the x and y axis scales, manipulating the size of the plot points, setting limits and updating different theme settings.
+
+```{r specify-maleplot2}
+
+plot_male2 <- plot_male +
+
+ # set x-axis, percentages will only show decimil point if non-zero.
+ scale_x_continuous(n.breaks= 9,
+ minor_breaks = NULL,
+ limits = c(0, 0.04),
+ # labels = function(x) abs(x) %>% scales::percent()+
+ labels = function(x) {paste0((x) * 100, "%")})+
+
+ # specify y-axis conditions
+ scale_y_continuous(n.breaks = 11,
+ minor_breaks = NULL,
+ limits = c(0,500000),
+ labels = scales::label_number(suffix="k", scale = 1e-3))+
+
+ # set scale size for plot points (manual for consistency with female plot)
+ scale_size(range = c(1,10),
+ breaks=c(1000, 290000),
+ labels = c("<1000 individuals", "290k individuals"))+
+
+ labs(title = "Male") + # add title, then add details below for title colour and position
+
+ theme(
+ # update title, position and color
+ plot.title = element_text(hjust = 0.5,
+ color = colour_male,
+ size = 14,
+ family = "sans", # closest font but not perfect
+ face = "bold"),
+
+ # update x axis positions and conditions
+ axis.ticks.x = element_blank(),
+ axis.title.x = element_text(size = 12, family = "hind",
+ margin = margin(t = 10)), # no X-axis label
+ axis.text.x = element_text(size = 10, family = "hind",
+ margin = margin(t = 10)), # X-axis values, move values off axis a little
+
+ # y axis conditions
+ axis.ticks.y = element_blank(), #remove the x and y axis labels and tick marks
+ axis.title.y = element_blank(),
+ axis.text.y = element_blank(), # remove y axis labels to join upto female graph
+
+ # general conditions
+ plot.margin = unit(c(0.5, 0.5, 0.5, -0.1), "cm"), #move the graph -0.1cm to the left so it is touching the female graph when they combine in grid.arrange
+ legend.position = "none", # remove legend
+
+
+ # Other visualisation conditions
+ panel.background = element_rect(fill = "white"),
+ panel.border = element_rect(color = "grey",
+ fill = NA,
+ size = 0.5),
+ panel.grid = element_line(color = "grey",
+ size = 0.2,
+ linetype = 1),
+ axis.line.y.left = element_blank()) # Ensure left border removed to match up with Female graph
+
+
+plot_male2
+```
+
+### Create basic female plot
+
+This is similar to the initial male plot development. All of the conditions around borders, shading and axis have to be identical, so the two plots can be combined and give the impression of being a single plot.
+
+```{r basic_femaleplot}
+
+### now replicate with female, minor changes
+plot_female <- data_female |>
+ filter(occupation != "0000 Occupation blank") |>
+ arrange(occupation) |> #same as male, order appearance of plots
+ ggplot(aes(x=income_display, y=income, size=prop_workers_sex))+
+ annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = benefit_cut,
+ fill = "grey", alpha = 0.5) + # add a background box in for colouring the line under cutoff
+ geom_point(color = "white", fill = colour_fem, shape = 21, alpha = 0.85)+
+ geom_hline(yintercept = benefit_cut, linetype = 'dashed') +
+
+ annotate("text", x=-0.04, y=135000,
+ label = "Bigger tax cut",
+ size = 3.3,
+ hjust = 0.15,
+ family = "hind")+
+
+ labs(y="Median taxable income $",
+ x="Percentage of workers")
+
+plot_female
+
+
+```
+
+#### Specify female plot axis and theme settings
+
+Now I refine the left hand side plot. It is very similar to the male updates, where we add in the scale and conditions. This plot is a positive axis left of the y-axis. To create this effect, negative values are used, but their absolute values are displayed on the x-axis.
+
+```{r specify-female-plot2}
+
+plot_female2 <- plot_female +
+
+ # update labels to percentage, but this will only show the decimal point if it is non-zero
+ # need to apply absolute vals first for label, otherwise it gives an error as the scales function returns a vector of characters for the label
+ scale_x_continuous(n.breaks= 9,
+ minor_breaks = NULL,
+ limits = c(-0.04, 0),
+ labels = function(x) {paste0(abs(x) * 100, "%")})+
+
+ scale_y_continuous(n.breaks = 11,
+ minor_breaks = NULL,
+ limits = c(0,500000),
+ labels = scales::label_number(suffix="k", scale = 1e-3)) +
+
+ labs(title = "Female") + # add title, then add details below for title colour and posiiton
+
+ scale_size(breaks=c(1000, 290000),
+ labels = c("<1000 individuals", "290k individuals"),
+ range = c(1, 14))+
+
+ theme(
+ # title conditions, colour
+ plot.title = element_text(hjust = 0.5,
+ color = colour_fem,
+ size = 14,
+ family = "sans",
+ face = "bold"),
+
+ # update x axis positions and conditions
+ axis.ticks.x = element_blank(),
+ axis.title.x = element_text(size = 12, family = "hind",
+ margin = margin(t = 10)), # move X-axis label
+ axis.text.x = element_text(size = 10, family = "hind",
+ margin = margin(t = 10)), # X-axis values, move values off axis a little
+
+
+ # y-axis conditions and position, incl move further away from axis with margin
+ axis.title.y = element_text(size = 12, family = "hind",
+ margin = margin(r=10)), # Y-axis label
+ axis.text.y = element_text(size = 10, family = "hind",
+ margin = margin(r=10)), # Y-axis values
+ axis.ticks.y = element_blank(), #remove the x and y axis labels and tick marks
+
+
+ # general conditions
+ plot.margin = unit(c(0.5, 0, 0.5, 0.5), "cm"),
+ legend.position = "none", # Adjust left margin to overlap slightly
+
+
+ # create border and update function for female graph - remov the right border
+ panel.background = element_rect(fill = "white"),
+ panel.border = element_rect(color = "grey",
+ fill = NA,
+ size = 0.5),
+ panel.grid = element_line(color = "grey",
+ size = 0.2,
+ linetype = 1),
+
+ axis.line.y.right = element_blank()) # Ensure right border is removed
+
+
+plot_female2
+
+
+```
+
+### Initial combining of plots
+
+Here, I test the join of the plots using patchwork. As our plot axis and design are built to be very similar, they do join together well. The key settings that create the impression of a single plot are:
+
+- Use plot margin to remove gaps where the graphs will be joined (i.e. right y-axis for female graph and left hand y-axis for male plot)
+
+- For the female plot, we adjust the plot margin to be zero on the right hand side `plot_margin = unit(c(0.5, 0, 0.5, 0.5), "cm")`
+
+- For males, we remove the left margin and actually make it negative to allow for the overlapping, `plot.margin = unit(c(0.5, 0.5, 0.5, -0.1), "cm")`
+
+- The final gap removal is through setting the right hand axis tick marks to zero. Even though there is no data on the male y axis, R still leaves space for the tick marks. We remove this gap too with `axis.ticks.length.y = unit(0, "pt"))` in our combination.
+
+```{r first-combined-plot, layout="l-body-outset"}
+
+# First test -Combine the plots without space between them and with one legend
+combined_plot <- plot_female2 + plot_male2 +
+ plot_layout(ncol = 2, widths = c(6,6),
+ guides = "collect",
+ axis_titles = "collect") + # to join the two x-axis labels into one
+ theme(panel.background = element_rect(fill = "white"),
+ panel.border = element_rect(color = "grey",
+ fill = NA,
+ size = 0.5),
+ panel.grid = element_line(color = "grey",
+ size = 0.2,
+ linetype = 1),
+ #axis.title.x = element_text(size = 16, family = "roboto", margin = margin(t = 10)),
+ axis.ticks.length.y = unit(0, "pt")) # to remove the axis gaps on the Male y-axis
+
+
+combined_plot
+
+```
+
+This is getting closer to a nice replication of the original. There is one major piece missing however, the legend.
+
+### Create the legend for the plot
+
+One of the biggest challenges for the replication has been the legend. The legend uses concentric circles that are outside of the plot area, have different colours to the actual plot and use specific call out labels.
+
+Because of these issues, I have built the legend manually then used `inset_element` to position it with the other plots. The legend is created as a grob, saved as an image and then called back and sized and positioned. Sizing and positioning within the two other plots has been a big challenge.
+
+```{r develop-legend, size='30%'}
+
+# Create data for concentric circles
+circle_data <- function(radius, npoints=100) {
+ angles <- seq(0, 2*pi, length.out = npoints)
+ data.frame(
+ x = radius * cos(angles),
+ y = radius * sin(angles)
+ )
+}
+
+# Create concentric circles data
+circlesmall <- circle_data(0.7)
+circlebig <- circle_data(3)
+
+# Plot concentric circles with grey fill
+concentric_plot <- ggplot() +
+ geom_polygon(data = circlebig, aes(x = x, y = y), fill = "grey90", color = "black") +
+ geom_polygon(data = circlesmall, aes(x = x, y = y - 2.3), fill = "grey90", color = "black")+
+ lims(x=c(-3,20))+
+
+ annotate("segment", # line for <1k label
+ x=0, xend = 6,
+ y=-2.3, yend = -2.3,
+ size=1.5)+
+ annotate("segment",
+ x = 0, xend = 6,
+ y = 0, yend = 0,
+ size=1.5)+
+ annotate("text", x = 6.5, y = -2.3, label = "<1k individuals", size = 14, hjust = 0)+
+ annotate("text", x = 6.5, y = 0, label = "290k individuals", size = 14, hjust = 0)+
+ coord_fixed()+
+ theme_void()
+
+concentric_plot
+```
+
+We now save the plot created above as a .png to preserve our sizing ratios. If we overlay the raw grob, it distorts when joined to the plots. Commented allow to allow direct reading from the git folder
+
+```{r save-legendpng, message = FALSE}
+png(file="legend_test1.png",
+width=600, height=250)
+concentric_plot
+dev.off()
+
+```
+
+Now we read in the image and adjust and paste on our graph with inset_element.
+
+```{r read-image}
+img_path <- "legend_test1.png"
+img <- magick::image_read(img_path)
+# Trim the image to remove unnecessary whitespace
+img_trimmed <- magick::image_trim(img)
+# Make the white background transparent
+img_transparent <- magick::image_transparent(img_trimmed, "white")
+
+img_grob <- rasterGrob(as.raster(img_transparent), interpolate = TRUE)
+
+
+```
+
+## Final replication
+
+To include the legend, I re-build our combined plot with the order of 1) set female plot, 2) join on legend and position, and 3) join onto the male plot as above. The plot is layered in this way because of issues when trying to combined the to initial plots then add on the legend. When creating the combined plot first, the legend would obscure parts of the female plot. With this layering, the inset_element does not disturb the female plot.
+
+This is the final version of the replication!
+
+```{r final-legend-joinplot,layout="l-body-outset", fig.width=12, fig.height=8, preview=TRUE}
+
+final_plot <-
+ # first layer of female plot
+ plot_female2 +
+
+ # use inset_element to include our legend and position appropriately
+ inset_element(img_grob,
+ left=0.4,
+ bottom=1.01,
+ right=0,
+ top=0.955,
+ align_to="full",
+ on_top = FALSE,
+ clip=FALSE) +
+
+ # add in our male plot with patchwork too
+ plot_male2 +
+
+ # set the join conditions for patchwork again. These are the same as our first combined plot attempt
+ plot_layout(ncol = 2, widths = c(6,6),
+ guides = "collect",
+ axis_titles = "collect") + # to join the two x-axis labels into one
+ theme(panel.background = element_rect(fill = "white"),
+ panel.border = element_rect(color = "lightgrey",
+ fill = NA,
+ size = 0.5),
+ panel.grid = element_line(color = "lightgrey",
+ size = 0.2,
+ linetype = 1),
+ axis.ticks.length.y = unit(0, "pt")) # to remove the axis gaps on the Male y-axis
+
+final_plot
+```
+
+### Compare to the original
+
+```{r plot_compare, echo=FALSE, layout="l-body-outset"}
+knitr::include_graphics("ABC_tax_income_plot.png")
+```
+
+## Alternative plots
+
+Here I have created alternatives of the original plot. The focus of the alternatives is conveying the message that *most people will be better off under the tax changes*. To do this, I attempted to make more simple plots with fewer dimensions. Three alternative plots have been created:
+
+- A violin plot, to better represent the volume of occupations that will benefit at their medium income level
+
+- A beeswarm plot, to show a plot that still uses bubbles for occupation data, but is more clean with less overlap and a categorical x-axis (sex) instead of the initial continuous X-axis (% of workers).
+
+- A line plot using information on taxable income percentiles to estimate the proportion of people that will benefit rather than occupation numbers.
+
+With each, I have tried to include different presentation characteristics such as relabeling the text to help the reader take away key messages, however I tried to include elements from the initital plot I liked, such as the shading and colouring.
+
+One of the changes I use in the alternatives is having income on a log scale. Even though the original plot uses white space effectively to show the disparity of incomes by occupation, we will use the log scale to highlight the majority of occupations are in the benefit area.
+
+```{r create_improvdata}
+# create a new data to work with for improvements
+improv <- clean_data
+
+improv <- improv |>
+ mutate(benefit = ifelse(income <= benefit_cut, TRUE, FALSE))
+
+```
+
+### Violin plot
+
+In this plot, we will only drop one dimension; number of workers by occupation. In the original plot, this % of workers shown twice, as the size of circles and the x-axis. However, neither add much value because the overall message is that most occupations are better off.
+
+In this violin plot, the volume of occupations is still represented by the width of the violin. It could be considered a limitation that each occupation is weighted equally here, but individual occupations are hard to distinguish in the original, so we don't lose any meaning here.
+
+```{r violin}
+# Create the violin plot
+improv |>
+ filter(occupation != "0000 Occupation blank") |>
+ ggplot(aes(x=sex, y=income, fill = sex))+
+ annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0, ymax = benefit_cut,
+ fill = "green", alpha = 0.1)+ # add a background box in for colouring the pos area
+ annotate("rect", xmin = -Inf, xmax = Inf, ymin = benefit_cut, ymax = Inf,
+ fill = "red", alpha = 0.1)+ # add a background box in for colouring the neg area
+ geom_violin(trim=TRUE)+
+ scale_fill_manual(values = c("Female" = colour_fem, "Male" = colour_male), name = "sex") +
+ labs(title = "Will you be better off under the new tax changes?",
+ caption = "Each occupation weighted equally, number of employees is not factored in.\n Log scale for income used.",
+ y = "Median income ($)",
+ x = "") +
+ theme_minimal()+
+ geom_hline(yintercept = benefit_cut, color = "black", linetype = "dashed") + # Cutoff line
+ annotate("text",
+ x = c(0.65,0.65),
+ y = c(125000,170000),
+ label = c("Better off", "Worse off") ,
+ color="black",
+ size=4,
+ face = "sans",
+ fontface="bold")+
+
+ ## adding label with annotate and geom_label to point to plots
+ annotate("segment", y = 20000, yend = 50000,
+ x = 1.5, xend = 1,
+ colour = "black", size=0.75, alpha=0.5,
+ linetype = "dotted")+
+ annotate("segment", y = 20000, yend = 50000,
+ x = 1.5, xend = 2,
+ colour = "black", size=0.75, alpha=0.5,
+ linetype = "dotted")+
+
+ geom_label(aes(x = 1.5, y = 20000,
+ label = "Nearly every occupation will benefit\n at the median income level "), fill = "lightyellow",
+ color = "black", size = 3, fontface = "bold")+
+
+
+# scale_y_continuous(labels = scales::label_number(suffix="k", scale = 1e-3))+
+ scale_y_log10(labels = scales::label_number(suffix="k", scale = 1e-3),
+ n.breaks=6)+
+ theme(legend.position = "none")
+
+```
+
+### Beeswarm graph
+
+To try and update the individual to a plot that also uses one individual plot point for each occupation, I have created a beeswarm graph. I have also re-included the number of workers as the size of the plot points here. This is to try and reduce data loss from the original plot. However only those with over 1000 workers are included to reduce clutter and overlapping, which I don't like in the original plot around the two y-axes.
+
+Further, I have updated the labels with arrows to draw the readers attention and added in other potentially relevant information.
+
+Finally, I have included the overall median income by gender (`male = $59,415 female = $44,547`). Overlaying aggregate statistics allows the reader to compare between groups. While it is clear that both male and female median incomes are well under the cutoff, trends by sex are hard to identify in the original plot. This provides an easy overall comparion.
+
+```{r besswarm}
+
+improv_small <- improv |>
+ filter(occupation != "0000 Occupation blank",
+ count > 1000)
+male_med <- 59415
+fem_med <- 44547
+####### BEESWARM GRAPH
+
+# Basic beeswarm plot in ggplot2
+ggplot(improv_small, aes(x = sex,
+ y = income,
+ colour = sex,
+ size=prop_workers_sex,
+ )) +
+ annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0, ymax = benefit_cut,
+ fill = "grey", alpha = 0.2)+ # add a background box in for colouring the pos area
+
+ geom_quasirandom()+
+ labs(title = "Most people will be better off under the tax bracket changes",
+ caption = "Only includes occupations with over 1000 workers by gender",
+ x = element_blank(),
+ y = "Median income $")+
+ scale_color_manual(values = c("Female" = colour_fem, "Male" = colour_male), name = "sex") +
+ theme_minimal()+
+ theme(axis.text.x = element_text(size = 14))+
+
+ # add cutoff line and label
+ geom_hline(yintercept = benefit_cut, color = "black", linetype = "dashed") + # Cutoff line
+ annotate("text", x=0.6, y=135000, # add label for cutoff line
+ label = paste0("Cutoff: $",benefit_cut),
+ size = 3.3,
+ hjust = 0.35,
+ family = "sans")+
+
+ # add labels for the median income by gender
+ geom_labelsegment(aes(x=0.6, xend=1.4, y=fem_med, yend=fem_med,
+ label = "Median = $44,547"), fill = "lightyellow", size=3,
+ inherit.aes = FALSE)+
+
+ geom_labelsegment(aes(x=1.6, xend=2.4, y=male_med, yend=male_med,
+ label = "Median = $59,415"), fill = "lightyellow",size = 3,
+ inherit.aes = FALSE)+
+
+ # add directional arrows and labels for axis
+ annotate(
+ "textsegment",
+ x = 0.5, xend = 0.5, yend = benefit_cut-25000, y = 25000,
+ label = "Better off", size=3,
+ angle = 180,
+ arrow = arrow(length = unit(0.05, "inches"), type="closed",
+ ends = "first"))+
+
+ annotate(
+ "textsegment",
+ x = 0.5, xend = 0.5, y = benefit_cut + 20000, yend = 480000,
+ label = "Worse off", size=3,
+ arrow = arrow(length = unit(0.05, "inches"), type="closed"))+
+
+ scale_y_log10(labels = scales::label_number(suffix="k", scale = 1e-3))+
+ theme(legend.position = "none")
+```
+
+### Additional option - using percentile data
+
+The primary purpose of this data is to show that most people are better off under the changes rather than worse off. The ideal plot to convey the message would be showing a ridge or density plot of each individual's income level and have that compared to the benefit cutoff line. We cannot access data this granular. But using slightly different data, we can try to convey this message more clearly.
+
+By incorporating slightly different tax data, we can view individual tax data by median taxable income, gender and percentile of income. This data is available in table 16B of the individual tax statistics, where our original data is from 15B.
+
+First, we read in the new data table and do basic cleaning:
+
+```{r read_percentiledata}
+### read in table 16B first
+ # url <- "https://data.gov.au/data/dataset/07b51b39-254a-4177-8b4c-497f17eddb80/resource/d902104e-a9c1-4d14-8d21-f4538bda037a/download/ts21individual16percentiledistributionontaxableincomebysex.xlsx"
+ # response <- GET(url)
+ # file_path <- tempfile(fileext = ".xlsx")
+ # writeBin(content(response, "raw"), file_path)
+ #
+ # data <- read_excel(file_path,
+ # sheet="Table 16B",
+ # skip=1)
+ #
+ # percentile_counts <- read_excel(file_path,
+ # sheet="Table 16A",
+ # skip=1)
+
+#read from file:
+data <- read_excel("ts21individual16percentiledistributionontaxableincomebysex.xlsx",
+ sheet="Table 16B",
+ skip=1)
+
+percentile_counts <- read_excel("ts21individual16percentiledistributionontaxableincomebysex.xlsx",
+ sheet="Table 16A",
+ skip=1)
+
+data <- data |>
+ filter(Statistic3 == "Median") |>
+ rename("Range" = "Ranged Taxable Income",
+ "Income" = "Taxable income or loss \r\n$") |>
+ select(Percentile, Sex, Range, Income)
+
+percentile_counts <- percentile_counts |>
+ rename("Count" = "Individuals \r\nno.",
+ "Range" = "Ranged Taxable Income") |>
+ select(Percentile, Range, Sex, Count)
+
+# join on the number of workers
+data <- data |>
+ left_join(percentile_counts, by = c("Percentile", "Range", "Sex"))
+
+data$benefit <- ifelse(data$Income
+ group_by(Sex) |>
+ arrange(Percentile) |>
+ mutate(prop_Sex = Count/sum(Count)*100,
+ cumul_count = cumsum(Count),
+ total = sum(Count),
+ new_ntile = cumul_count / total * 100)
+head(data)
+
+```
+
+We then create a graph of log income compared to the median income at each percentile
+
+```{r log-plot-ntile, warning=FALSE}
+
+data |>
+ ggplot(aes(y=Income, x=new_ntile, colour = Sex))+
+
+ annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0, ymax = benefit_cut,
+ fill = "grey", alpha = 0.5) + # add a background box in for colouring the
+
+ geom_line(size=2, show.legend = FALSE) +
+
+ labs(title = "Most people benefit from the income tax changes",
+ caption = "Note: Log scale used for income",
+ y = "Median income ($)",
+ x = "Proportion of workers (%)")+
+
+ theme_minimal_grid()+
+ theme(axis.ticks.x=element_blank(),
+ axis.ticks.y=element_blank(),
+ axis.title.x = element_text(size = 12), # reduce y and x axis label size
+ axis.title.y = element_text(size = 12)) + # Adjust y-axis label
+
+ # create a log scale on the y-axis
+ scale_y_log10(n.breaks =5,
+ labels = scales::label_number(suffix="k", scale = 1e-3))+
+
+ scale_x_continuous(breaks = c(50,100),
+ expand = c(0,0.5))+ #to avoid x-axis label cutoff
+
+ scale_colour_manual(values = c("Female" = colour_fem, "Male" = colour_male), name = "Sex") +
+
+ geom_hline(yintercept = benefit_cut, colour="black", linetype="dashed")+
+
+ ## adding female labels with geom_curve and geom_label
+ annotate("segment", y = 40000, yend = benefit_cut,
+ x = 85, xend = 95,
+ colour = colour_fem, size=0.75, alpha=1,
+ linetype = "dotted")+
+
+ geom_label(aes(x = 80, y = 42000,
+ label = "Better for 95% \n of females"), fill = "lightyellow",
+ color = colour_fem, size = 3, fontface = "bold")+
+
+
+ ## adding male labels with annotate and geom_label
+ annotate("segment", y = 300000, yend = benefit_cut,
+ x = 75, xend = 88,
+ colour = colour_male, size=0.75, alpha=1,
+ linetype = "dotted")+
+
+ geom_label(aes(x = 70, y = 320000,
+ label = "Better for 88% \n of males"), fill = "lightyellow",
+ color = colour_male, size = 3, fontface = "bold")+
+
+ # create manual coloured text labels for clea
+ annotate("text",
+ y=75000, x= 40,
+ label = "Male",
+ color=colour_male,
+ face = "sans", fontface = "bold",
+ size = 4)+
+ annotate("text",
+ y=40000, x= 41,
+ label = "Female",
+ color=colour_fem,
+ face = "sans", fontface = "bold",
+ size = 4)+
+
+ # create label for cutoff point and arrow
+ annotate("text",
+ y=300000, x= 30,
+ label = "Everyone earning under $146,486\n per year will be better off",
+ color="black",
+ size = 4)+
+ geom_curve(aes(x = 15, y = 270000, xend = 13, yend = benefit_cut),
+ colour = "black",
+ arrow = arrow(length = unit(0.03, "npc")))
+
+```
+
+From this graph, a better estimate of the impacted population can be made. This proportion of people measure is more tangible than the count of occupations by size. I have included this as the final option as it is the one that allows the reader most quickly realise they are likely to benefit.
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Winners and losers of tax legislation changes
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+
2024
+
+
+
Recreating the ABC news article Graph for MCSS Data Viz 24/25
The graph shows the median income of Australians by occupation compared to who would benefit from proposed tax changes. The graph highlights those who will benefit under the changes and those who will be worse off. To distinguish the two groups, the authors use a horizontal line from the y-axis at the benefit cutoff point, $146,486, and background shading.
+
The purpose of the graph is to highlight that most people are expected to be better off under the changes. Instead of estimating the number of people in raw terms that would benefit, the plot uses disagregated tax data by occupation and gender to display which groups are better and worse off.
+
The graph is effective because you can easily conclude that the majority of people will be better off under the changes, regardless of sex or occupation. The use of a linear y-axis to show income is effective in isolating the very high income occupations which are outliers to the data. It creates a white space which contrasts against the concentrated grouping of occupations below the cutoff point.
+
Setup
+
Load packages, set global and local parameters, and read in our dataset.
+
Load required packages
+
The packages are for data cleaning, graph generation, adding themes and organising subplots into the final graph.
# A tibble: 5 × 10
+ `Occupation - sub-major group1` Occupation - unit gr…¹ Sex4
+ <chr> <chr> <chr>
+1 00 Occupation blank 0000 Occupation blank Fema…
+2 00 Occupation blank 0000 Occupation blank Male
+3 00 Occupation blank 0000 Occupation blank Total
+4 11 Chief executives, general managers … 1100 Manager or direc… Male
+5 11 Chief executives, general managers … 1100 Manager or direc… Total
+# ℹ abbreviated name: ¹`Occupation - unit group1`
+# ℹ 7 more variables: `Individuals\r\nno.` <dbl>,
+# `Average taxable income2,5\r\n$` <dbl>,
+# `Median taxable income2,5\r\n$` <dbl>,
+# `Average salary or wage income5 \r\n$` <dbl>,
+# `Median salary or wage income5\r\n$` <dbl>,
+# `Average total income2,5\r\n$` <dbl>, …
+
+
Important data consideration: The original graph was published in February 2024, the ATO then revised the data in March 2024 (as seen on the data updated row of the metadata online). This has led to some variation between figures in our graph and in the actual graph I will recreate. So I am not able to create a 100% like for like replication. Spot checks have been completed and the occupation and number of employee figures appear the same. There appears to be more revisions to some median income figures however the changes are minor overall.
+
Set parameters
+
Define parameters that are used multiple times through the recreation. This saves us writing them out multiple times, including the income cutoff point and colours.
+
+
+
benefit_cut<-146486# Those below $146,486 benefit from the redesigned plan
+options(scipen =999)
+
+
+# colour for female purple; HEX = #a175b4 or RGB rgba(161,117,180,255)
+colour_fem<-"#a175b4"
+# colour for male blue; HEX = #ca555d or RGB rgba(202,85,93,255)
+colour_male<-"#ca555d"
+
+
+
Data cleaning
+
First, remove unused columns, clean column names and also filter rows to only have the groups by gender we are interested in (remove totals).
Second, create the additional variable required, which is % of total workers in that occupation. I also create a negative income % variable for females, this is used to create the mirrored graphs by gender in later stages.
The authors make their calculations of total % of workers including the “0000 Occupation blank” variable, but then remove it from all of the visualisations.
+
Basic plotting to check variable functionality
+
Now, to check all the required variables are available, I create a very basic version of the graph to check our data completeness. Some parameters for axis are included for comparison purposes.
+
+
+
basic_test_plot<-clean_data|>
+filter(occupation!="0000 Occupation blank")|>
+ggplot(aes(x=income_display, y=income, size=count ,color=sex))+
+geom_point(alpha=0.8)+
+
+# now add split around the y axis and change the x axis to be equal, plus change the axix units
+scale_x_continuous(name ="Percentage of workers",
+ breaks=c(-0.04, -0.035, -0.03, -0.025, -0.02, -0.015, -0.01, -0.005,
+0,
+0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04),
+ minor_breaks =NULL,
+ limits =c(-0.04, 0.04),
+ labels =function(x)abs(x)%>%scales::percent())+# apply absolute vals
+
+scale_y_continuous(name ="Median taxable income ($)",
+ breaks =c(0, 50000, 100000, 150000, 200000, 250000,
+300000, 350000, 400000, 450000, 500000),
+ minor_breaks =NULL,
+ limits =c(0,500000),
+ labels =scales::label_number(suffix="k", scale =1e-3))+
+theme_minimal()
+
+basic_test_plot
+
+
+
+
The initial plotting shows that I have all required variables for our graph.
+
Challenge detected:
+
There is one one major problem in the basic replication. There are two X=0 values on the original graph with a gap without data to divide the male and female data. I need to replicate this gap and double y-axis aesthetic for the replication too.
+
There is no option to do this manually within one ggplot. Instead, I will create two separate graphs (for male and female) that are manually positioned together with patchwork to create the mirrored effect. This mirrored presentation also means we have to create two positive X-axis graphs, however the X-axis are going in opposite directions.
+
This is a source of repetition in the next few steps to create almost identical but symmetrical plots.
+
Replication
+
Primarily because I cannot impute the gap around x=0, individual graphs by gender are created. To start, I split the data by gender to work with on each graph.
First, create the basic male plot, these are all positive values and easier to work with.
+
+
+
plot_male<-data_male|>
+filter(occupation!="0000 Occupation blank")|>
+arrange(occupation)|># this matches the layering of plot points
+ggplot(aes(x=income_display, y=income, size =prop_workers_sex))+
+
+annotate("rect", xmin =-Inf, xmax =Inf, ymin =-Inf, ymax =benefit_cut,
+ fill ="grey", alpha =0.5)+# add a background box for the benefit area
+geom_point(color ="white",
+ fill =colour_male,
+ shape =21, # circle with different fill and border
+ alpha =0.85,
+ position ="identity")+
+
+geom_hline(yintercept =benefit_cut, linetype ='dashed')+# add cutoff line
+
+annotate("text", x=0.04, y=135000, # add label for cutoff line
+ label ="Bigger tax cut",
+ size =3.3,
+ hjust =0.85,
+ family ="hind")+
+
+labs(y="Median taxable income $",
+ x="Percentage of workers")# include both of these so the labels combine with female site in patchwork join.
+
+plot_male
+
+
+
+
Specify male plot axis and theme settings
+
Now, I update some of the presentation and layout of the male graph. This includes things such as updating the x and y axis scales, manipulating the size of the plot points, setting limits and updating different theme settings.
+
+
+
plot_male2<-plot_male+
+
+# set x-axis, percentages will only show decimil point if non-zero.
+scale_x_continuous(n.breaks=9,
+ minor_breaks =NULL,
+ limits =c(0, 0.04),
+# labels = function(x) abs(x) %>% scales::percent()+
+ labels =function(x){paste0((x)*100, "%")})+
+
+# specify y-axis conditions
+scale_y_continuous(n.breaks =11,
+ minor_breaks =NULL,
+ limits =c(0,500000),
+ labels =scales::label_number(suffix="k", scale =1e-3))+
+
+# set scale size for plot points (manual for consistency with female plot)
+scale_size(range =c(1,10),
+ breaks=c(1000, 290000),
+ labels =c("<1000 individuals", "290k individuals"))+
+
+labs(title ="Male")+# add title, then add details below for title colour and position
+
+theme(
+# update title, position and color
+ plot.title =element_text(hjust =0.5,
+ color =colour_male,
+ size =14,
+ family ="sans", # closest font but not perfect
+ face ="bold"),
+
+# update x axis positions and conditions
+ axis.ticks.x =element_blank(),
+ axis.title.x =element_text(size =12, family ="hind",
+ margin =margin(t =10)), # no X-axis label
+ axis.text.x =element_text(size =10, family ="hind",
+ margin =margin(t =10)), # X-axis values, move values off axis a little
+
+# y axis conditions
+ axis.ticks.y =element_blank(), #remove the x and y axis labels and tick marks
+ axis.title.y =element_blank(),
+ axis.text.y =element_blank(), # remove y axis labels to join upto female graph
+
+# general conditions
+ plot.margin =unit(c(0.5, 0.5, 0.5, -0.1), "cm"), #move the graph -0.1cm to the left so it is touching the female graph when they combine in grid.arrange
+ legend.position ="none", # remove legend
+
+
+# Other visualisation conditions
+ panel.background =element_rect(fill ="white"),
+ panel.border =element_rect(color ="grey",
+ fill =NA,
+ size =0.5),
+ panel.grid =element_line(color ="grey",
+ size =0.2,
+ linetype =1),
+ axis.line.y.left =element_blank())# Ensure left border removed to match up with Female graph
+
+
+plot_male2
+
+
+
+
Create basic female plot
+
This is similar to the initial male plot development. All of the conditions around borders, shading and axis have to be identical, so the two plots can be combined and give the impression of being a single plot.
+
+
+
### now replicate with female, minor changes
+plot_female<-data_female|>
+filter(occupation!="0000 Occupation blank")|>
+arrange(occupation)|>#same as male, order appearance of plots
+ggplot(aes(x=income_display, y=income, size=prop_workers_sex))+
+annotate("rect", xmin =-Inf, xmax =Inf, ymin =-Inf, ymax =benefit_cut,
+ fill ="grey", alpha =0.5)+# add a background box in for colouring the line under cutoff
+geom_point(color ="white", fill =colour_fem, shape =21, alpha =0.85)+
+geom_hline(yintercept =benefit_cut, linetype ='dashed')+
+
+annotate("text", x=-0.04, y=135000,
+ label ="Bigger tax cut",
+ size =3.3,
+ hjust =0.15,
+ family ="hind")+
+
+labs(y="Median taxable income $",
+ x="Percentage of workers")
+
+plot_female
+
+
+
+
Specify female plot axis and theme settings
+
Now I refine the left hand side plot. It is very similar to the male updates, where we add in the scale and conditions. This plot is a positive axis left of the y-axis. To create this effect, negative values are used, but their absolute values are displayed on the x-axis.
+
+
+
plot_female2<-plot_female+
+
+# update labels to percentage, but this will only show the decimal point if it is non-zero
+# need to apply absolute vals first for label, otherwise it gives an error as the scales function returns a vector of characters for the label
+scale_x_continuous(n.breaks=9,
+ minor_breaks =NULL,
+ limits =c(-0.04, 0),
+ labels =function(x){paste0(abs(x)*100, "%")})+
+
+scale_y_continuous(n.breaks =11,
+ minor_breaks =NULL,
+ limits =c(0,500000),
+ labels =scales::label_number(suffix="k", scale =1e-3))+
+
+labs(title ="Female")+# add title, then add details below for title colour and posiiton
+
+scale_size(breaks=c(1000, 290000),
+ labels =c("<1000 individuals", "290k individuals"),
+ range =c(1, 14))+
+
+theme(
+# title conditions, colour
+ plot.title =element_text(hjust =0.5,
+ color =colour_fem,
+ size =14,
+ family ="sans",
+ face ="bold"),
+
+# update x axis positions and conditions
+ axis.ticks.x =element_blank(),
+ axis.title.x =element_text(size =12, family ="hind",
+ margin =margin(t =10)), # move X-axis label
+ axis.text.x =element_text(size =10, family ="hind",
+ margin =margin(t =10)), # X-axis values, move values off axis a little
+
+
+# y-axis conditions and position, incl move further away from axis with margin
+ axis.title.y =element_text(size =12, family ="hind",
+ margin =margin(r=10)), # Y-axis label
+ axis.text.y =element_text(size =10, family ="hind",
+ margin =margin(r=10)), # Y-axis values
+ axis.ticks.y =element_blank(), #remove the x and y axis labels and tick marks
+
+
+# general conditions
+ plot.margin =unit(c(0.5, 0, 0.5, 0.5), "cm"),
+ legend.position ="none", # Adjust left margin to overlap slightly
+
+
+# create border and update function for female graph - remov the right border
+ panel.background =element_rect(fill ="white"),
+ panel.border =element_rect(color ="grey",
+ fill =NA,
+ size =0.5),
+ panel.grid =element_line(color ="grey",
+ size =0.2,
+ linetype =1),
+
+ axis.line.y.right =element_blank())# Ensure right border is removed
+
+
+plot_female2
+
+
+
+
Initial combining of plots
+
Here, I test the join of the plots using patchwork. As our plot axis and design are built to be very similar, they do join together well. The key settings that create the impression of a single plot are:
+
+
Use plot margin to remove gaps where the graphs will be joined (i.e. right y-axis for female graph and left hand y-axis for male plot)
+
For the female plot, we adjust the plot margin to be zero on the right hand side plot_margin = unit(c(0.5, 0, 0.5, 0.5), "cm")
+
For males, we remove the left margin and actually make it negative to allow for the overlapping, plot.margin = unit(c(0.5, 0.5, 0.5, -0.1), "cm")
+
The final gap removal is through setting the right hand axis tick marks to zero. Even though there is no data on the male y axis, R still leaves space for the tick marks. We remove this gap too with axis.ticks.length.y = unit(0, "pt")) in our combination.
+
+
+
+
# First test -Combine the plots without space between them and with one legend
+combined_plot<-plot_female2+plot_male2+
+plot_layout(ncol =2, widths =c(6,6),
+ guides ="collect",
+ axis_titles ="collect")+# to join the two x-axis labels into one
+theme(panel.background =element_rect(fill ="white"),
+ panel.border =element_rect(color ="grey",
+ fill =NA,
+ size =0.5),
+ panel.grid =element_line(color ="grey",
+ size =0.2,
+ linetype =1),
+#axis.title.x = element_text(size = 16, family = "roboto", margin = margin(t = 10)),
+ axis.ticks.length.y =unit(0, "pt"))# to remove the axis gaps on the Male y-axis
+
+
+combined_plot
+
+
+
+
This is getting closer to a nice replication of the original. There is one major piece missing however, the legend.
+
Create the legend for the plot
+
One of the biggest challenges for the replication has been the legend. The legend uses concentric circles that are outside of the plot area, have different colours to the actual plot and use specific call out labels.
+
Because of these issues, I have built the legend manually then used inset_element to position it with the other plots. The legend is created as a grob, saved as an image and then called back and sized and positioned. Sizing and positioning within the two other plots has been a big challenge.
+
+
+
# Create data for concentric circles
+circle_data<-function(radius, npoints=100){
+angles<-seq(0, 2*pi, length.out =npoints)
+data.frame(
+ x =radius*cos(angles),
+ y =radius*sin(angles)
+)
+}
+
+# Create concentric circles data
+circlesmall<-circle_data(0.7)
+circlebig<-circle_data(3)
+
+# Plot concentric circles with grey fill
+concentric_plot<-ggplot()+
+geom_polygon(data =circlebig, aes(x =x, y =y), fill ="grey90", color ="black")+
+geom_polygon(data =circlesmall, aes(x =x, y =y-2.3), fill ="grey90", color ="black")+
+lims(x=c(-3,20))+
+
+annotate("segment", # line for <1k label
+ x=0, xend =6,
+ y=-2.3, yend =-2.3,
+ size=1.5)+
+annotate("segment",
+ x =0, xend =6,
+ y =0, yend =0,
+ size=1.5)+
+annotate("text", x =6.5, y =-2.3, label ="<1k individuals", size =14, hjust =0)+
+annotate("text", x =6.5, y =0, label ="290k individuals", size =14, hjust =0)+
+coord_fixed()+
+theme_void()
+
+concentric_plot
+
+
+
+
We now save the plot created above as a .png to preserve our sizing ratios. If we overlay the raw grob, it distorts when joined to the plots. Commented allow to allow direct reading from the git folder
Now we read in the image and adjust and paste on our graph with inset_element.
+
+
+
img_path<-"legend_test1.png"
+img<-magick::image_read(img_path)
+# Trim the image to remove unnecessary whitespace
+img_trimmed<-magick::image_trim(img)
+# Make the white background transparent
+img_transparent<-magick::image_transparent(img_trimmed, "white")
+
+img_grob<-rasterGrob(as.raster(img_transparent), interpolate =TRUE)
+
+
+
Final replication
+
To include the legend, I re-build our combined plot with the order of 1) set female plot, 2) join on legend and position, and 3) join onto the male plot as above. The plot is layered in this way because of issues when trying to combined the to initial plots then add on the legend. When creating the combined plot first, the legend would obscure parts of the female plot. With this layering, the inset_element does not disturb the female plot.
+
This is the final version of the replication!
+
+
+
final_plot<-
+# first layer of female plot
+plot_female2+
+
+# use inset_element to include our legend and position appropriately
+inset_element(img_grob,
+ left=0.4,
+ bottom=1.01,
+ right=0,
+ top=0.955,
+ align_to="full",
+ on_top =FALSE,
+ clip=FALSE)+
+
+# add in our male plot with patchwork too
+plot_male2+
+
+# set the join conditions for patchwork again. These are the same as our first combined plot attempt
+plot_layout(ncol =2, widths =c(6,6),
+ guides ="collect",
+ axis_titles ="collect")+# to join the two x-axis labels into one
+theme(panel.background =element_rect(fill ="white"),
+ panel.border =element_rect(color ="lightgrey",
+ fill =NA,
+ size =0.5),
+ panel.grid =element_line(color ="lightgrey",
+ size =0.2,
+ linetype =1),
+ axis.ticks.length.y =unit(0, "pt"))# to remove the axis gaps on the Male y-axis
+
+final_plot
+
+
+
+
Compare to the original
+
+
+
+
Alternative plots
+
Here I have created alternatives of the original plot. The focus of the alternatives is conveying the message that most people will be better off under the tax changes. To do this, I attempted to make more simple plots with fewer dimensions. Three alternative plots have been created:
+
+
A violin plot, to better represent the volume of occupations that will benefit at their medium income level
+
A beeswarm plot, to show a plot that still uses bubbles for occupation data, but is more clean with less overlap and a categorical x-axis (sex) instead of the initial continuous X-axis (% of workers).
+
A line plot using information on taxable income percentiles to estimate the proportion of people that will benefit rather than occupation numbers.
+
+
With each, I have tried to include different presentation characteristics such as relabeling the text to help the reader take away key messages, however I tried to include elements from the initital plot I liked, such as the shading and colouring.
+
One of the changes I use in the alternatives is having income on a log scale. Even though the original plot uses white space effectively to show the disparity of incomes by occupation, we will use the log scale to highlight the majority of occupations are in the benefit area.
+
+
+
# create a new data to work with for improvements
+improv<-clean_data
+
+improv<-improv|>
+mutate(benefit =ifelse(income<=benefit_cut, TRUE, FALSE))
+
+
+
Violin plot
+
In this plot, we will only drop one dimension; number of workers by occupation. In the original plot, this % of workers shown twice, as the size of circles and the x-axis. However, neither add much value because the overall message is that most occupations are better off.
+
In this violin plot, the volume of occupations is still represented by the width of the violin. It could be considered a limitation that each occupation is weighted equally here, but individual occupations are hard to distinguish in the original, so we don’t lose any meaning here.
+
+
+
# Create the violin plot
+improv|>
+filter(occupation!="0000 Occupation blank")|>
+ggplot(aes(x=sex, y=income, fill =sex))+
+annotate("rect", xmin =-Inf, xmax =Inf, ymin =0, ymax =benefit_cut,
+ fill ="green", alpha =0.1)+# add a background box in for colouring the pos area
+annotate("rect", xmin =-Inf, xmax =Inf, ymin =benefit_cut, ymax =Inf,
+ fill ="red", alpha =0.1)+# add a background box in for colouring the neg area
+geom_violin(trim=TRUE)+
+scale_fill_manual(values =c("Female"=colour_fem, "Male"=colour_male), name ="sex")+
+labs(title ="Will you be better off under the new tax changes?",
+ caption ="Each occupation weighted equally, number of employees is not factored in.\n Log scale for income used.",
+ y ="Median income ($)",
+ x ="")+
+theme_minimal()+
+geom_hline(yintercept =benefit_cut, color ="black", linetype ="dashed")+# Cutoff line
+annotate("text",
+ x =c(0.65,0.65),
+ y =c(125000,170000),
+ label =c("Better off", "Worse off") ,
+ color="black",
+ size=4,
+ face ="sans",
+ fontface="bold")+
+
+## adding label with annotate and geom_label to point to plots
+annotate("segment", y =20000, yend =50000,
+ x =1.5, xend =1,
+ colour ="black", size=0.75, alpha=0.5,
+ linetype ="dotted")+
+annotate("segment", y =20000, yend =50000,
+ x =1.5, xend =2,
+ colour ="black", size=0.75, alpha=0.5,
+ linetype ="dotted")+
+
+geom_label(aes(x =1.5, y =20000,
+ label ="Nearly every occupation will benefit\n at the median income level "), fill ="lightyellow",
+ color ="black", size =3, fontface ="bold")+
+
+
+# scale_y_continuous(labels = scales::label_number(suffix="k", scale = 1e-3))+
+scale_y_log10(labels =scales::label_number(suffix="k", scale =1e-3),
+ n.breaks=6)+
+theme(legend.position ="none")
+
+
+
+
Beeswarm graph
+
To try and update the individual to a plot that also uses one individual plot point for each occupation, I have created a beeswarm graph. I have also re-included the number of workers as the size of the plot points here. This is to try and reduce data loss from the original plot. However only those with over 1000 workers are included to reduce clutter and overlapping, which I don’t like in the original plot around the two y-axes.
+
Further, I have updated the labels with arrows to draw the readers attention and added in other potentially relevant information.
+
Finally, I have included the overall median income by gender (male = $59,415 female = $44,547). Overlaying aggregate statistics allows the reader to compare between groups. While it is clear that both male and female median incomes are well under the cutoff, trends by sex are hard to identify in the original plot. This provides an easy overall comparion.
+
+
+
improv_small<-improv|>
+filter(occupation!="0000 Occupation blank",
+count>1000)
+male_med<-59415
+fem_med<-44547
+####### BEESWARM GRAPH
+
+# Basic beeswarm plot in ggplot2
+ggplot(improv_small, aes(x =sex,
+ y =income,
+ colour =sex,
+ size=prop_workers_sex,
+))+
+annotate("rect", xmin =-Inf, xmax =Inf, ymin =0, ymax =benefit_cut,
+ fill ="grey", alpha =0.2)+# add a background box in for colouring the pos area
+
+geom_quasirandom()+
+labs(title ="Most people will be better off under the tax bracket changes",
+ caption ="Only includes occupations with over 1000 workers by gender",
+ x =element_blank(),
+ y ="Median income $")+
+scale_color_manual(values =c("Female"=colour_fem, "Male"=colour_male), name ="sex")+
+theme_minimal()+
+theme(axis.text.x =element_text(size =14))+
+
+# add cutoff line and label
+geom_hline(yintercept =benefit_cut, color ="black", linetype ="dashed")+# Cutoff line
+annotate("text", x=0.6, y=135000, # add label for cutoff line
+ label =paste0("Cutoff: $",benefit_cut),
+ size =3.3,
+ hjust =0.35,
+ family ="sans")+
+
+# add labels for the median income by gender
+geom_labelsegment(aes(x=0.6, xend=1.4, y=fem_med, yend=fem_med,
+ label ="Median = $44,547"), fill ="lightyellow", size=3,
+ inherit.aes =FALSE)+
+
+geom_labelsegment(aes(x=1.6, xend=2.4, y=male_med, yend=male_med,
+ label ="Median = $59,415"), fill ="lightyellow",size =3,
+ inherit.aes =FALSE)+
+
+# add directional arrows and labels for axis
+annotate(
+"textsegment",
+ x =0.5, xend =0.5, yend =benefit_cut-25000, y =25000,
+ label ="Better off", size=3,
+ angle =180,
+ arrow =arrow(length =unit(0.05, "inches"), type="closed",
+ ends ="first"))+
+
+annotate(
+"textsegment",
+ x =0.5, xend =0.5, y =benefit_cut+20000, yend =480000,
+ label ="Worse off", size=3,
+ arrow =arrow(length =unit(0.05, "inches"), type="closed"))+
+
+scale_y_log10(labels =scales::label_number(suffix="k", scale =1e-3))+
+theme(legend.position ="none")
+
+
+
+
Additional option - using percentile data
+
The primary purpose of this data is to show that most people are better off under the changes rather than worse off. The ideal plot to convey the message would be showing a ridge or density plot of each individual’s income level and have that compared to the benefit cutoff line. We cannot access data this granular. But using slightly different data, we can try to convey this message more clearly.
+
By incorporating slightly different tax data, we can view individual tax data by median taxable income, gender and percentile of income. This data is available in table 16B of the individual tax statistics, where our original data is from 15B.
+
First, we read in the new data table and do basic cleaning:
+
+
+
### read in table 16B first
+# url <- "https://data.gov.au/data/dataset/07b51b39-254a-4177-8b4c-497f17eddb80/resource/d902104e-a9c1-4d14-8d21-f4538bda037a/download/ts21individual16percentiledistributionontaxableincomebysex.xlsx"
+# response <- GET(url)
+# file_path <- tempfile(fileext = ".xlsx")
+# writeBin(content(response, "raw"), file_path)
+#
+# data <- read_excel(file_path,
+# sheet="Table 16B",
+# skip=1)
+#
+# percentile_counts <- read_excel(file_path,
+# sheet="Table 16A",
+# skip=1)
+
+#read from file:
+data<-read_excel("ts21individual16percentiledistributionontaxableincomebysex.xlsx",
+ sheet="Table 16B",
+ skip=1)
+
+percentile_counts<-read_excel("ts21individual16percentiledistributionontaxableincomebysex.xlsx",
+ sheet="Table 16A",
+ skip=1)
+
+data<-data|>
+filter(Statistic3=="Median")|>
+rename("Range"="Ranged Taxable Income",
+"Income"="Taxable income or loss \r\n$")|>
+select(Percentile, Sex, Range, Income)
+
+percentile_counts<-percentile_counts|>
+rename("Count"="Individuals \r\nno.",
+"Range"="Ranged Taxable Income")|>
+select(Percentile, Range, Sex, Count)
+
+# join on the number of workers
+data<-data|>
+left_join(percentile_counts, by =c("Percentile", "Range", "Sex"))
+
+data$benefit<-ifelse(data$Income<benefit_cut, TRUE, FALSE)
+
+# create actual % value to show the distribution of male and female
+data<-data|>
+group_by(Sex)|>
+arrange(Percentile)|>
+mutate(prop_Sex =Count/sum(Count)*100,
+ cumul_count =cumsum(Count),
+ total =sum(Count),
+ new_ntile =cumul_count/total*100)
+head(data)
+
+
# A tibble: 6 × 10
+# Groups: Sex [2]
+ Percentile Sex Range Income Count benefit prop_Sex cumul_count
+ <dbl> <chr> <chr> <dbl> <dbl> <lgl> <dbl> <dbl>
+1 1 Female $23,629… 22887 60650 TRUE 1.10 60650
+2 1 Male $23,629… 18164 56406 TRUE 0.911 56406
+3 2 Female $23,630… 24089 65518 TRUE 1.19 126168
+4 2 Male $23,630… 24090 51508 TRUE 0.832 107914
+5 3 Female $24,557… 25011 65241 TRUE 1.18 191409
+6 3 Male $24,557… 25012 51748 TRUE 0.836 159662
+# ℹ 2 more variables: total <dbl>, new_ntile <dbl>
+
+
We then create a graph of log income compared to the median income at each percentile
+
+
+
data|>
+ggplot(aes(y=Income, x=new_ntile, colour =Sex))+
+
+annotate("rect", xmin =-Inf, xmax =Inf, ymin =0, ymax =benefit_cut,
+ fill ="grey", alpha =0.5)+# add a background box in for colouring the
+
+geom_line(size=2, show.legend =FALSE)+
+
+labs(title ="Most people benefit from the income tax changes",
+ caption ="Note: Log scale used for income",
+ y ="Median income ($)",
+ x ="Proportion of workers (%)")+
+
+theme_minimal_grid()+
+theme(axis.ticks.x=element_blank(),
+ axis.ticks.y=element_blank(),
+ axis.title.x =element_text(size =12), # reduce y and x axis label size
+ axis.title.y =element_text(size =12))+# Adjust y-axis label
+
+# create a log scale on the y-axis
+scale_y_log10(n.breaks =5,
+ labels =scales::label_number(suffix="k", scale =1e-3))+
+
+scale_x_continuous(breaks =c(50,100),
+ expand =c(0,0.5))+#to avoid x-axis label cutoff
+
+scale_colour_manual(values =c("Female"=colour_fem, "Male"=colour_male), name ="Sex")+
+
+geom_hline(yintercept =benefit_cut, colour="black", linetype="dashed")+
+
+## adding female labels with geom_curve and geom_label
+annotate("segment", y =40000, yend =benefit_cut,
+ x =85, xend =95,
+ colour =colour_fem, size=0.75, alpha=1,
+ linetype ="dotted")+
+
+geom_label(aes(x =80, y =42000,
+ label ="Better for 95% \n of females"), fill ="lightyellow",
+ color =colour_fem, size =3, fontface ="bold")+
+
+
+## adding male labels with annotate and geom_label
+annotate("segment", y =300000, yend =benefit_cut,
+ x =75, xend =88,
+ colour =colour_male, size=0.75, alpha=1,
+ linetype ="dotted")+
+
+geom_label(aes(x =70, y =320000,
+ label ="Better for 88% \n of males"), fill ="lightyellow",
+ color =colour_male, size =3, fontface ="bold")+
+
+# create manual coloured text labels for clea
+annotate("text",
+ y=75000, x=40,
+ label ="Male",
+ color=colour_male,
+ face ="sans", fontface ="bold",
+ size =4)+
+annotate("text",
+ y=40000, x=41,
+ label ="Female",
+ color=colour_fem,
+ face ="sans", fontface ="bold",
+ size =4)+
+
+# create label for cutoff point and arrow
+annotate("text",
+ y=300000, x=30,
+ label ="Everyone earning under $146,486\n per year will be better off",
+ color="black",
+ size =4)+
+geom_curve(aes(x =15, y =270000, xend =13, yend =benefit_cut),
+ colour ="black",
+ arrow =arrow(length =unit(0.03, "npc")))
+
+
+
+
From this graph, a better estimate of the impacted population can be made. This proportion of people measure is more tangible than the count of occupations by size. I have included this as the final option as it is the one that allows the reader most quickly realise they are likely to benefit.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_projects/2024/100535241/100535241_files/anchor-4.2.2/anchor.min.js b/_projects/2024/100535241/100535241_files/anchor-4.2.2/anchor.min.js
new file mode 100644
index 00000000..1342f5f6
--- /dev/null
+++ b/_projects/2024/100535241/100535241_files/anchor-4.2.2/anchor.min.js
@@ -0,0 +1,9 @@
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+//
+// AnchorJS - v4.2.2 - 2019-11-14
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2019 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function f(A){A.icon=A.hasOwnProperty("icon")?A.icon:"",A.visible=A.hasOwnProperty("visible")?A.visible:"hover",A.placement=A.hasOwnProperty("placement")?A.placement:"right",A.ariaLabel=A.hasOwnProperty("ariaLabel")?A.ariaLabel:"Anchor",A.class=A.hasOwnProperty("class")?A.class:"",A.base=A.hasOwnProperty("base")?A.base:"",A.truncate=A.hasOwnProperty("truncate")?Math.floor(A.truncate):64,A.titleText=A.hasOwnProperty("titleText")?A.titleText:""}function p(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new Error("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],f(this.options),this.isTouchDevice=function(){return!!("ontouchstart"in window||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,i,n,o,s,a,r,c,h,l,u,d=[];if(f(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=p(A=A||"h2, h3, h4, h5, h6")).length)return this;for(!function(){if(null!==document.head.querySelector("style.anchorjs"))return;var A,e=document.createElement("style");e.className="anchorjs",e.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"], style'))?document.head.appendChild(e):document.head.insertBefore(e,A);e.sheet.insertRule(" .anchorjs-link { opacity: 0; text-decoration: none; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; }",e.sheet.cssRules.length),e.sheet.insertRule(" *:hover > .anchorjs-link, .anchorjs-link:focus { opacity: 1; }",e.sheet.cssRules.length),e.sheet.insertRule(" [data-anchorjs-icon]::after { content: attr(data-anchorjs-icon); }",e.sheet.cssRules.length),e.sheet.insertRule(' @font-face { font-family: "anchorjs-icons"; src: url(data:n/a;base64,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) format("truetype"); }',e.sheet.cssRules.length)}(),t=document.querySelectorAll("[id]"),i=[].map.call(t,function(A){return A.id}),o=0;o\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),t=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||t||!1}}});
+// @license-end
\ No newline at end of file
diff --git a/_projects/2024/100535241/100535241_files/bowser-1.9.3/bowser.min.js b/_projects/2024/100535241/100535241_files/bowser-1.9.3/bowser.min.js
new file mode 100644
index 00000000..3da61049
--- /dev/null
+++ b/_projects/2024/100535241/100535241_files/bowser-1.9.3/bowser.min.js
@@ -0,0 +1,6 @@
+/*!
+ * Bowser - a browser detector
+ * https://github.com/ded/bowser
+ * MIT License | (c) Dustin Diaz 2015
+ */
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diff --git a/_projects/2024/100535241/100535241_files/figure-html5/test-plot-1.png b/_projects/2024/100535241/100535241_files/figure-html5/test-plot-1.png
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diff --git a/_projects/2024/100535241/100535241_files/figure-html5/violin-1.png b/_projects/2024/100535241/100535241_files/figure-html5/violin-1.png
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diff --git a/_projects/2024/100535241/100535241_files/header-attrs-2.29/header-attrs.js b/_projects/2024/100535241/100535241_files/header-attrs-2.29/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/_projects/2024/100535241/100535241_files/header-attrs-2.29/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/_projects/2024/100535241/100535241_files/jquery-3.6.0/jquery-3.6.0.js b/_projects/2024/100535241/100535241_files/jquery-3.6.0/jquery-3.6.0.js
new file mode 100644
index 00000000..fc6c299b
--- /dev/null
+++ b/_projects/2024/100535241/100535241_files/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+ "use strict";
+
+ if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+ // For CommonJS and CommonJS-like environments where a proper `window`
+ // is present, execute the factory and get jQuery.
+ // For environments that do not have a `window` with a `document`
+ // (such as Node.js), expose a factory as module.exports.
+ // This accentuates the need for the creation of a real `window`.
+ // e.g. var jQuery = require("jquery")(window);
+ // See ticket #14549 for more info.
+ module.exports = global.document ?
+ factory( global, true ) :
+ function( w ) {
+ if ( !w.document ) {
+ throw new Error( "jQuery requires a window with a document" );
+ }
+ return factory( w );
+ };
+ } else {
+ factory( global );
+ }
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+ // Support: Chrome <=57, Firefox <=52
+ // In some browsers, typeof returns "function" for HTML
+
