---
title: "MLS colors"
---

Here I'm trying out different API products to find colors to use in these charts. I end up creating my own color listings based on several sources because all the existing R sources are out of date or I don't like their choices.

The results of this work ends up in a file that is used in the analysis notebook.

```{r}
#| label: setup
#| echo: true
#| results: hide
#| message: false
#| warning: false

library(tidyverse)
library(janitor)
library(usfootballR)
library(teamcolors)
library(scales)
```


## Import

### Getting our data

```{r}
salaries <- read_rds("data-processed/mls-salaries.rds")

salaries |> glimpse()
```

I need to make some data to use

```{r}
sal_team <- salaries |> 
  group_by(year, club_long) |> 
  summarise(total_compensation = sum(compensation)) |> 
  arrange(total_compensation |> desc())

sal_team_rank <- salaries |> 
  filter(club_short != "MLS" | club_short |> is.na()) |> 
  group_by(year, club_short, club_long) |> 
  summarise(
    total_comp = sum(compensation, na.rm = TRUE)
  ) |> 
  arrange(year, total_comp |> desc()) |> 
  ungroup() |>
  mutate(rank = rank(-total_comp), .by = year)

sal_team_rank_top <- sal_team_rank |> 
  filter(rank <= 5,
         year >= "2020")

sal_team_rank_top
```


## Manual colors

My attempt to make a record of MLS colors. This is based on [TeamColorCodes](https://teamcolorcodes.com/) but with additional column `favored_color` that is my best choice for charts. I'm building this in the same [spreadsheet](https://docs.google.com/spreadsheets/d/1rH1pbOZYAv_ep4iCihzqrzOYBTreV2oJ014Un4TgEjw/edit?gid=1348329208#gid=1348329208) as my teams/seasons list but on a different sheet.

I started with the list above fro usfootballr's espn colors.

### Download the data

```{r}
# download.file("https://docs.google.com/spreadsheets/d/e/2PACX-1vQqXJxbbrBsikirZrGyXYV_G6cFZp_dYmcf52UfSYM7Kw3akGlkO5jKP8ZL8WtRA5qUJgFMNPG8JYov/pub?output=csv&&gid=1348329208", "data-processed/clubs-colors.csv")
```

### Import that data

```{r}
mls_colors_data <- read_csv("data-processed/clubs-colors.csv")

mls_colors_data
```

### Preview the colors

This chart is totally a Claude.ai chart. I startd with this prompt: "I want to use ggplot, or at least R, to print a color swatch based on a data frame like this" and listed some data. Claude said it can't display R so it did it in React! So I asked "Can you guess at the ggplot code?" and it gave me what was below (as well as another alternative I didn't like.) All I had to do is change the data frame name.

That said, I've made several modifications since.

```{r}
# Convert the data from wide to long format for plotting
teams_long <- mls_colors_data |> 
  pivot_longer(
    cols = !c(starts_with("club")),
    names_to = "color_type",
    values_to = "color_hex"
  ) |> 
  # Clean up color_type names for display
  mutate(
    color_type = str_to_title(gsub("_color", "", color_type)),
    color_type = fct_relevel(color_type, "Favored", "Primary", "Secondary", "Tertiary"),
    # Ensure all color codes have # prefix
    color_hex = ifelse(startsWith(color_hex, "#"), 
                      color_hex, 
                      paste0("#", color_hex))
  )

teams_long |> slice_sample(n = 8) # I added this line
```

Then plot a visual of theses so I can check them.

```{r}
# Create the plot
mls_team_colors_plot <- ggplot(teams_long, 
       aes(x = color_type, 
           y = club_long)) +
  geom_tile(aes(fill = color_hex), 
            color = "gray80",  # Add border to tiles
            width = 0.9, 
            height = 0.9) +
  scale_fill_identity() +  # Use actual hex colors
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid = element_blank(),
    plot.title = element_text(hjust = 0.5)
  ) +
  labs(
    title = "MLS Team Colors",
    x = "Color Type",
    y = "Team"
  )

ggsave("figures/mls_team_colors_plot.png")

```

![](figures/mls_team_colors_plot.png)

> I can use this to adjust the data on which is the `favored_color`. I'll update the data and then charts that use that data will update when they are next downloaded and run.

### Do the salary chart

I need to prepare the data so I can test it within a real chart.

```{r}
sal_team_2024_data <- sal_team_rank |> 
  filter(year == "2024") |> 
  drop_na(club_short) |> 
  left_join(mls_colors_data, join_by(club_short, club_long)) |> 
  select(!c(primary_color, secondary_color, tertiary_color))

sal_team_2024_data  
```

Then plot it.

For the life of me I can't figure out how to reverse the team names to start with Atlanta.

```{r}
sal_top_2024_col_plot <- sal_team_2024_data |> 
  ggplot(aes(x = reorder(club_short, total_comp), y = total_comp)) +
  # geom_col(color = sal_top_2023_col$color, fill = sal_top_2023_col$alternate_color) +
  geom_col(fill = sal_team_2024_data$favored_color) +
  scale_y_continuous(labels = label_dollar(scale = .000001, accuracy = 2),
                     limits = c(0, 45000000)) +
  # geom_text(aes(
  #   label = dollar(total_comp, scale = .000001, accuracy = 3, digits = 2), hjust = -.25)
  # ) +
  coord_flip() +
  labs(
    title = "Top five spending teams 2024",
    y = "Total spending in $ millions",
    x = ""
  ) 

ggsave("figures/team-salary-2024-color-manual.png")
```

![](figures/team-salary-2024-color-manual.png)

::: callout

## Before manual

Before I settled on the manual process above, I tried some other R packages that included color values for MLS teams. Those results are below.

:::

## usfootballr package

Here I try to use the colors from the usfootballr package and apply them to a chart.

Here is what comes from the package for teams:

```{r}
espn_teams <- espn_mls_teams()

espn_teams |> head()
```

Now I want to use my ranking data to try and color bars based on data from the package.


```{r}
sal_top_2023 <- sal_team_rank |> left_join(espn_teams, join_by(club_short == abbreviation)) |> 
  select(1:4, 11:12) |> 
  filter(year == "2023")

sal_top_2023_col <- sal_top_2023 |> 
  mutate(color = paste("#", color, sep = ""),
         alternate_color = paste("#", alternate_color, sep = "")
        ) |> 
  drop_na()

sal_top_2023_col
```

Now to plot with the color?

The geom_text label I was trying below needed a decimal point and not as much rounding, but I'm not going to figure that out right now.

```{r}

sal_top_2023_col_plot <- sal_top_2023_col |> 
  ggplot(aes(x = reorder(club_short, total_comp), y = total_comp)) +
  # geom_col(color = sal_top_2023_col$color, fill = sal_top_2023_col$alternate_color) +
  geom_col(fill = sal_top_2023_col$color) +
  scale_y_continuous(labels = label_dollar(scale = .000001, accuracy = 2),
                     limits = c(0, 40000000)) +
  # geom_text(aes(
  #   label = dollar(total_comp, scale = .000001, accuracy = 3, digits = 2), hjust = -.25)
  # ) +
  coord_flip() +
  labs(
    title = "Top five spending teams 2023",
    y = "Total spending in $ millions",
    x = ""
  ) 

ggsave("figures/team-salary-2023-color-usfootballr.png")
```


![](figures/team-salary-2023-color-usfootballr.png)

While this works and they have all the current teams, in some cases we would want the alternative color for a team if the main color is black or white.

## teamcolors package

We'll try this, but from older data because they won't have some teams.

```{r}
mls_colors <- teamcolors |> filter(league == "mls") |> 
  select(1, 3:4)

mls_colors_udpated <- mls_colors |> 
  mutate(club_long = recode(
    name,
    "Chicago Fire" = "Chicago Fire FC"
  )) |> select(-name)

sal_team_2019 <- sal_team |> filter(year == 2019) |>
  left_join(mls_colors_udpated, join_by(club_long)) |> 
  drop_na(primary, secondary) # <1>


sal_team_2019
```

1. I had to drop rows that didn't have their color or this would break.

```{r}
#| label: fig-colors-teamcolors
#| fig-height: 7
#| fig-cap: Incomplete team spending, 2019

sal_team_2019  |> 
  # drop_na(primary, secondary) |> 
  ggplot(aes(y = club_long |> reorder(total_compensation), x = total_compensation)) +
  geom_col(fill = sal_team_2019$primary) +
  scale_x_continuous(labels = label_dollar(scale = .00001, accuracy = 2)) +
  geom_text(aes(
    label = dollar(total_compensation, scale = .00001, accuracy = 2, digits = 3)),
    color = "white", hjust = 1.25
  ) +
  labs(
    title = "Totally incomplete list of 2019 salaries",
    subtitle = "Only includes clubs with color values in \"teamcolors\" package.",
    y = "",
    x = "Team spending in $ millions"
  )

```

The colors are much nicer here, but not all the teams are represented. It is at least four years out of date.