Create don't label everything post

dont-label-everything/dont-label-everything.R

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+## ----------------------------------------------------------------------------------------------------
+library(tidyverse)
+
+
+## ----------------------------------------------------------------------------------------------------
+#| echo: true
+population_projection <- 
+  read_rds("population_projection.rds")
+
+
+## ----------------------------------------------------------------------------------------------------
+#| label: population-projection-data
+#| echo: true
+population_projection |>
+  filter(location == "Hartford")
+
+
+## ----------------------------------------------------------------------------------------------------
+population_projection_plot <- function(town_to_plot, county_to_plot) {
+  population_projection |>
+    # Filter the data to only include town, county, and Connecticut
+    filter(location %in% c(town_to_plot, county_to_plot, "Connecticut")) |>
+    # Make location a factor to ensure the town line shows up on top of the others
+    mutate(location = fct(
+      location,
+      levels = c("Connecticut", county_to_plot, town_to_plot)
+    )) |>
+    ggplot(aes(
+      x = year,
+      y = pct,
+      color = location,
+      group = location
+    )) +
+    # Add dot for each town/county/state in 2020 and 2040
+    geom_point(size = 2) +
+    # Add line connecting each town/county/state
+    geom_line(show.legend = FALSE) +
+    # Remote legend title
+    labs(color = NULL) +
+    # Make the chart faceted by age group
+    facet_wrap(
+      vars(age_group),
+      nrow = 1
+    ) +
+    # Set the y limits to go from 0% to 40%
+    # Use percent formatting from the {scales} package
+    scale_y_continuous(
+      limits = c(0, 0.4),
+      labels = scales::percent_format(1)
+    ) +
+    # Set the colors for town, county, and state
+    scale_color_manual(
+      values = c(
+        "#c4c4c4",
+        "#fbbfb8",
+        "#9f3515"
+      )
+    ) +
+    # Reverse the legend order to put the town first
+    guides(color = guide_legend(reverse = TRUE)) +
+    # Use theme_minimal() and then make additional tweaks
+    theme_minimal() +
+    theme(
+      panel.grid.minor = element_blank(),
+      panel.grid.major.x = element_blank(),
+      legend.position = "bottom",
+      strip.text = element_text(
+        face = "italic",
+        size = 13,
+        color = "grey40"
+      ),
+      legend.text = element_text(
+        size = 13,
+        color = "grey40"
+      ),
+      axis.title = element_blank(),
+      axis.text = element_text(
+        size = 13,
+        color = "grey40"
+      )
+    )
+}
+
+
+## ----------------------------------------------------------------------------------------------------
+#| label: plot-function
+#| echo: true
+#| output: false
+population_projection_plot(
+  town_to_plot = "Hartford",
+  county_to_plot = "Hartford County"
+)
+
+
+## ----------------------------------------------------------------------------------------------------
+#| ref-label: plot-function
+#| echo: false
+
+
+## ----------------------------------------------------------------------------------------------------
+#| label: label-everything
+#| echo: true
+#| eval: false
+## population_projection_plot(
+##   town_to_plot = "Hartford",
+##   county_to_plot = "Hartford County"
+## ) +
+##   geom_text(
+##     aes(
+##       label = pct_formatted
+##     ),
+##     show.legend = FALSE
+##   )
+
+
+## ----------------------------------------------------------------------------------------------------
+#| ref-label: label-everything
+#| echo: false
+
+
+## ----------------------------------------------------------------------------------------------------
+library(ggrepel)
+
+population_projection_plot(
+  town_to_plot = "Hartford",
+  county_to_plot = "Hartford County"
+) +
+  geom_text_repel(
+    aes(
+      label = pct_formatted
+    ),
+    show.legend = FALSE
+  ) 
+
+
+## ----------------------------------------------------------------------------------------------------
+#| label: plot-with-text
+#| echo: true
+#| output: false
+population_projection_plot(
+  town_to_plot = "Hartford",
+  county_to_plot = "Hartford County"
+) +
+  geom_text(
+    data = population_projection |> filter(location == "Hartford"),
+    nudge_y = 0.03,
+    aes(
+      label = pct_formatted
+    ),
+    show.legend = FALSE
+  )
+
+
+## ----------------------------------------------------------------------------------------------------
+#| ref-label: plot-with-text
+#| echo: false
+

dont-label-everything/dont-label-everything.md

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+# Use smart labeling in ggplot to help your audience understand your
+graphs
+David Keyes
+2024-08-01
+
+When making data viz in R, it’s easy to think that adding label to
+everything will make your charts easier to understand. This is rarely
+the case. Instead, when making charts, maps, etc with ggplot, your best
+bet is to label judiciously. Only labeling the most important pieces of
+your data viz ensures that your audience will grasp what you want them
+to grasp. In this blog post, I’ll show you how to use smart labeling in
+ggplot to help your audience understand your graphs.
+
+## An example
+
+The issue of smart labeling came up in our work with the [Partnership
+for Strong Communities](https://www.pschousing.org/) (PSC) and the
+[Connecticut Data Collaborative](https://www.ctdata.org/) (CTData)
+making [housing data profiles for all towns and counties in
+Connecticut](https://rfortherestofus.com/success-stories/pschousing).
+One graph in the reports aims to show population projections for various
+age groups. Here, for example, is the chart we made for the city of
+Hartford.
+
+TODO: Add graph
+
+Note how the only text we add to the graphs is that for the city of
+Hartford. While there are lines that show trends in Hartford County and
+Connectcitu, we don’t try to label these. If we did, the result would be
+completely illegible:
+
+TODO: Add graph
+
+How, then, do you decide what text to add and what to leave off? The
+answer comes down to a simple question that you should ask yourself
+before making any graph: what do you want the reader to take from the
+graph?
+
+When working on the population projection plots for the Connecticut
+Housing Data Profiles, we wanted to highlight the city (the county and
+state were there for comparison). So, when adding text to this plot, we
+didn’t try to label everything; instead, we only labeled the values for
+Hartford.
+
+## Code
+
+How did we do this in ggplot? The answer is actually less about ggplot
+and more about getting our data in the right format using other
+tidyverse packages like {dplyr}. To show you what I mean, let’s look at
+the data. I’ll begin by loading the tidyverse.
+
+``` r
+library(tidyverse)
+```
+
+Now, we can read in the data, saved as an RDS file.
+
+``` r
+population_projection <- 
+  read_rds("population_projection.rds")
+```
+
+Next, we can look at the data for Hartford:
+
+``` r
+population_projection |>
+  filter(location == "Hartford")
+#> # A tibble: 10 × 5
+#>    location year  age_group    pct pct_formatted
+#>    <chr>    <chr> <fct>      <dbl> <chr>        
+#>  1 Hartford 2020  0-4       0.0725 7%           
+#>  2 Hartford 2020  5-19      0.210  21%          
+#>  3 Hartford 2020  20-39     0.336  34%          
+#>  4 Hartford 2020  40-64     0.287  29%          
+#>  5 Hartford 2020  65+       0.0945 9%           
+#>  6 Hartford 2040  0-4       0.0600 6%           
+#>  7 Hartford 2040  5-19      0.200  20%          
+#>  8 Hartford 2040  20-39     0.310  31%          
+#>  9 Hartford 2040  40-64     0.330  33%          
+#> 10 Hartford 2040  65+       0.0993 10%
+```
+
+With this data loaded, we can make a function to create our population
+projection plot. This plot takes two arguments: `town_to_plot` and
+`county_to_plot`. From there, it filters the `population_projection`
+data to only include the town, county, and Connecticut. This data is
+then piped into ggplot, where we make a set of slope graphs (one for
+each age group) to show the population projections from 2020 to 2040.
+
+``` r
+population_projection_plot <- function(town_to_plot, county_to_plot) {
+  population_projection |>
+    # Filter the data to only include town, county, and Connecticut
+    filter(location %in% c(town_to_plot, county_to_plot, "Connecticut")) |>
+    # Make location a factor to ensure the town line shows up on top of the others
+    mutate(location = fct(
+      location,
+      levels = c("Connecticut", county_to_plot, town_to_plot)
+    )) |>
+    ggplot(aes(
+      x = year,
+      y = pct,
+      color = location,
+      group = location
+    )) +
+    # Add dot for each town/county/state in 2020 and 2040
+    geom_point(size = 2) +
+    # Add line connecting each town/county/state
+    geom_line(show.legend = FALSE) +
+    # Remote legend title
+    labs(color = NULL) +
+    # Make the chart facetted by age group
+    facet_wrap(
+      vars(age_group),
+      nrow = 1
+    ) +
+    # Set the y limits to go from 0% to 40% and use percent formatting from the {scales} package
+    scale_y_continuous(
+      limits = c(0, 0.4),
+      labels = scales::percent_format(1)
+    ) +
+    # Set the colors for town, county, and state
+    scale_color_manual(
+      values = c(
+        "#c4c4c4",
+        "#fbbfb8",
+        "#9f3515"
+      )
+    ) +
+    # Reverse the legend order to put the town first
+    guides(color = guide_legend(reverse = TRUE)) +
+    # Use theme_minimal() and then make additional tweaks
+    theme_minimal() +
+    theme(
+      panel.grid.minor = element_blank(),
+      panel.grid.major.x = element_blank(),
+      legend.position = "bottom",
+      strip.text = element_text(
+        face = "italic",
+        size = 13,
+        color = "grey40"
+      ),
+      legend.text = element_text(
+        size = 13,
+        color = "grey40"
+      ),
+      axis.title = element_blank(),
+      axis.text = element_text(
+        size = 13,
+        color = "grey40"
+      )
+    )
+}
+```
+
+We can now use our function as follows:
+
+``` r
+population_projection_plot(
+  town_to_plot = "Hartford",
+  county_to_plot = "Hartford County"
+)
+```
+
+![](dont-label-everything_files/figure-commonmark/unnamed-chunk-6-1.svg)
+
+As you can see, the function only adds points and lines. If we want to
+add labels, we’ll need to add another layer using `geom_text()`. We can
+use the `pct_formatted` variable for the text labels, as follows:
+
+``` r
+population_projection_plot(
+  town_to_plot = "Hartford",
+  county_to_plot = "Hartford County"
+) +
+  geom_text(
+    aes(
+      label = pct_formatted
+    )
+  )
+```
+
+This, however, gives us the less-than-ideal plot we saw before, with all
+of the labels overlapping each other:
+
+![](dont-label-everything_files/figure-commonmark/unnamed-chunk-8-1.svg)
+
+One approach I’ve seen people use in this situation is the [{ggrepel}
+package](https://ggrepel.slowkow.com/). This package, which
+automatically ensures that text labels don’t overlap can be quite
+helpful in certain situations. But in this situation, the result is
+still quite messy.
+
+``` r
+library(ggrepel)
+
+population_projection_plot(
+  town_to_plot = "Hartford",
+  county_to_plot = "Hartford County"
+) +
+  geom_text_repel(
+    aes(
+      label = pct_formatted
+    )
+  )
+```
+
+![](dont-label-everything_files/figure-commonmark/unnamed-chunk-9-1.svg)
+
+The solution, then, is to only add text for Hartford, and not for
+Hartford County and Connecticut. To do this, I use `geom_text()`, but
+within this layer, I filter the data to only include Hartford.
+
+``` r
+population_projection_plot(
+  town_to_plot = "Hartford",
+  county_to_plot = "Hartford County"
+) +
+  geom_text(
+    data = population_projection |> filter(location == "Hartford"),
+    nudge_y = 0.03,
+    aes(
+      label = pct_formatted
+    )
+  )
+```
+
+Doing this gives me labels for Hartford, but not Hartford County and
+Connecticut. The result is much clearer.
+
+![](dont-label-everything_files/figure-commonmark/unnamed-chunk-11-1.svg)
+
+# Conclusion