README

MLBlogos

Version 0.0.2

The MLBlogos package provides small (150 x 100) logos for the teams in Major League Baseball (from the 2021 season). Right to use these thumbnail images, only for non-commercial purposes, is kindly provided by Chris Creamer of sportslogos.net.

The uses are to create tables and graphs of Teams data from the Lahman package.

Installation

This package is still experimental, and not on CRAN. You can install the current version of MLBlogos from GitHub with:

# install.packages("devtools")
devtools::install_github("friendly/MLBlogos")

Examples

This is a basic example which shows how to access information about the logos and the images themselves.

Information about the logos is contained in the Logos data set. The file name of each logo (in PNG format) is contained in the img variable. TeamID is the matching variable in the Lahman::Teams dataset.

library(MLBlogos)
data(Logos)
library(dplyr)

data(Logos)
knitr::kable(Logos[c(1:5, 26:30), 1:5])

	teamID	lgID	divID	name	img
1	ARI	NL	W	Arizona Diamondbacks	Arizona_Diamondbacks.png
2	ATL	NL	E	Atlanta Braves	Atlanta_Braves.png
3	BAL	AL	E	Baltimore Orioles	Baltimore_Orioles.png
4	BOS	AL	E	Boston Red Sox	Boston_Red_Sox.png
5	CHA	AL	C	Chicago Cubs	Chicago_Cubs.png
26	SLN	NL	C	St. Louis Cardinals	St_Louis_Cardinals.png
27	TBA	AL	E	Tampa Bay Rays	Tampa_Bay_Rays.png
28	TEX	AL	W	Texas Rangers	Texas_Rangers.png
29	TOR	AL	E	Toronto Blue Jays	Toronto_Blue_Jays.png
30	WAS	NL	E	Washington Nationals	Washington_Nationals.png

Retrieving logos

The logoInfo() function allows you to select images from the installed inst/png/ folder. It takes a vector of one or more teamIDs and returns selected variables from the Logos table.

Of these variables, for each teamID,

• name is the team name
• lgID and divID are the league (AL and NL) and division (C, E, W) ID codes.
• img is the filename of the logo in the inst/png/ folder
• image is the filename path to the logo in the package.

The actual image can be retrieved using magick::image_read() or png::readPNG().

(toronto <- logoInfo("TOR"))
#>    teamID              name                   img
#> 29    TOR Toronto Blue Jays Toronto_Blue_Jays.png
#>                                                      image
#> 29 C:/R/R-4.1.2/library/MLBlogos/png/Toronto_Blue_Jays.png

# Get the filename for the Toronto Blue Jays logo. Read it using `magick::image_read()`
# and display it.
imagepath <- toronto[, "image"]
img <- magick::image_read(imagepath)
print(img)
#> # A tibble: 1 x 7
#>   format width height colorspace matte filesize density
#>   <chr>  <int>  <int> <chr>      <lgl>    <int> <chr>  
#> 1 PNG      150    100 sRGB       TRUE      3599 38x38

All logos

Here are all the logos, retrieved from the inst/png folder of the source package. For this document they are displayed using HTML <img > tags. (Except that in a GitHub document they are displayed one-by-one, rather than in an array. I cheated a bit and took a screenshot of how it appears in the R Studio HTML file.)

library(glue)
glue("<img src='", "inst/png/", "{Logos[,'img']}' alt='{Logos[,'teamID']}' height=80 />")

Using logos in graphics

These examples show how to use these logos in ggplot2 graphics. The essential step is to merge (left_join()) the data to be plotted with the names of the logo images from this package.

Images are plotted using ggimage::geom_image().

Load packages and data

library(Lahman)
library(dplyr)
library(ggplot2)
library(ggimage)

data(Salaries)
data(Logos)

Bar charts

Create a simple bar plot of total team salaries for the 2016 season, the last year for which salary data is available.

In this example, I select teams in the American League to avoid too many bars. I also reorder the teamIDs by increasing Salary.

# Total team salaries by league, team for 2016
teamSalaries <- Salaries |>
  filter(yearID == max(yearID),
         lgID == "AL") |>
  group_by(teamID) |>
  summarise(Salary = sum(as.numeric(salary))) |>
  arrange(Salary) |>
  # re-order levels for plotting
  mutate(teamID = factor(teamID, levels = unique(teamID)))

Get the file path of the logo image file for each team using system.file() to find the img filenames in the png directory of the package.

teamSalaries <- teamSalaries |>
  left_join(Logos, by="teamID") |>
  mutate(img = system.file(glue::glue("png/{img}"),
                           package = "MLBlogos")) |>
  select(teamID, name, Salary, divID, img)

Construct the bar plot. It took a bit of fiddling to size and position the logos within the bars.

ggplot(teamSalaries,
       aes(teamID, Salary)) +
  geom_col(aes(fill=divID)) +
  scale_y_continuous(labels = scales::label_number(suffix = " M",
                                                   scale = 1e-6)) +  # millions
  scale_fill_discrete(
    labels = c("Central", "East", "West")
    ) +
  geom_image(aes(image=img, y = .6 *Salary),
             size=0.08) +
  labs(y = "Total Salary",
       fill = "Division") +
  coord_flip() +
  theme_bw(base_size=16) +
  theme(legend.position = c(.9, .2))

Scatterplots

Plot the number of team wins against home runs and attendance.

data(Teams, package="Lahman")

# ------------------------------------------
# get Xs and wins for most recent year, 2021
# ------------------------------------------
teamdata <- Teams |>
  filter(yearID == max(yearID)) |>
  select(teamID, HR, W, attendance)

# ------------------------------------------
# get the logo for each team
# ------------------------------------------
teamdata <- teamdata |>
  left_join(Logos, by="teamID") |>
  mutate(img = system.file(glue::glue("png/{img}"),
                           package = "MLBlogos")) |>
  select(teamID, divID, HR, W, attendance, img)

Home runs and wins. We would expect a positive relationship, and it is largely linear.

# ------------------------------------------
# plot home runs and wins
# ------------------------------------------
ggplot(data=teamdata,
       aes(x = HR, y=W)) +
  geom_point() +
  geom_smooth(method = "loess", formula = y~x, se = FALSE, size = 2) +
  geom_image(aes(image=img, x = HR, y = W),
             size=0.05) +
  labs(x = "Home Runs",
       y = "wins") +
  theme_bw(base_size = 16)

Attendance and wins. The overall relationship is mildly positive, but there are some large outliers.

# ------------------------------------------
# plot attendance and wins
# ------------------------------------------
ggplot(data=teamdata,
       aes(x = attendance, y=W)) +
  geom_point() +
  geom_smooth(method = "loess", formula = y~x, se = FALSE, size = 2) +
  geom_image(aes(image=img, x = attendance, y = W),
             size=0.05) +
  scale_x_continuous(labels = scales::label_number(suffix = " K",
                                                   scale = 1e-3)) +
  labs(x = "Attendance",
       y = "wins") +
  theme_bw(base_size = 16)

Technical note

In spite of some massaging with the magick package, the PNG images do not have a completely transparent background, so appear best in graphs with a white background. You can see this effect in the bar plot above.