Linear Regression Model doesn't pull up a statistic or plot?

[saltbox]

library(tidyverse)
library(ggplot2)
library(dplyr)
library(statsr)
yankees <- read_csv("YankeesDataPackage.csv")
yankees
yankees$type <- ifelse(yankees$type == "S", 1, 0)
yankees$type

# Release Speed Plot
mp <-ggplot(yankees, aes(release_speed, type)) + 
  labs (title = "New York Yankees Pitcher Stats from Release Speed",
        x = "Release Speed (mph)",
        y = "Probability of a Strike") +
  geom_point(size = 4, pch = 20, stroke = 0.50, fill = "pink", alpha = 0.8)   + 
  theme(axis.title  = element_text(family="Times New Roman",
                                   size = 10))

mp + geom_smooth(method = "glm", method.args = list(family = "binomial"),
                 color = "red", size = 2.5, se = FALSE)

# Release Spin Rate Plot
mp2 <-ggplot(yankees, aes(release_spin_rate, type)) + 
  labs (title = "New York Yankees Pitcher Stats from Release Spin Rate",
        x = "Release Spin Rate (RPS)",
        y = "Probability of a Strike") +
  geom_point(size = 4, pch = 20, stroke = 0.50, fill = "pink", alpha = 0.8)   + 
  theme(axis.title  = element_text(family="Times New Roman",
                                   size = 10))

mp2 + geom_smooth(method = "glm", method.args = list(family = "binomial"),
                  color = "red", size = 2.5, se = FALSE)

# Linear Regression Plot

freq_strike <- 0 

mutate(bin = ifelse(freq_strike == "Strike", 1, 0))


# this bit !!!!
it <- glm(strikes ~ release_speed + release_spin_rate, data = yankees, family = "binomial", data = df)
 


it + geom_smooth(method = "glm", method.args = list(family = "binomial"),
                  color = "red", size = 2.5, se = FALSE)

[williaml]

Hi, we don't have your dataset, but you could provide a reproducible example. Also, what error do you get?

It might possible because you have saved the results in to the object it, but then don't run or print the results. What happens if you run it?

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…