Analysis of the 100 best hitters in baseball

Author

Brian J

Introduction

I am going to be performing analysis on the top 100 baseball hitters data by year. It includes statistics from the year as well as any awards they had received by their performance. You can learn more about the data at this link: https://myxavier-my.sharepoint.com/:x:/g/personal/janishefskib_xavier_edu/ETjz1TyRf5xNjPd7ooW4qLUBJ6TsJMh43uRJT8ecuqgwvQ?download=1

mydata <- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/janishefskib_xavier_edu/ETjz1TyRf5xNjPd7ooW4qLUBJ6TsJMh43uRJT8ecuqgwvQ?download=1")

Running Code

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The echo: false option disables the printing of code (only output is displayed).

Research Question

Do players that have have more hits have a higher amount of RBI’s?

colnames(mydata) <- as.character(unlist(mydata[1, ])) 
mydata <- mydata[-1, ]
mydata <- mydata %>%
  mutate(RBI = as.numeric(RBI),
         H = as.numeric(H))
mydata %>% 
  ggplot(aes(x= RBI, y=H ))+
  geom_point(color = "darkgreen", alpha= .5, size = .75)+
  labs(x= "Runs Batted In (RBI)", y= "Hits")+
  scale_x_continuous(breaks= seq(0,200, by = 25))+
  geom_smooth(method = "lm")

The analysis performed above shows the relationship between hits and OBP. # Although I expected more drastic results in the relation, there is still a # small correlation between Hits and OBP. In terms of the question I posed, # hits don’t contribute as much to OBP as some people think. This graph shows # that, most likely, walks contribute more to a high OBP (On-base percentage)

--- title: "Analysis of the 100 best hitters in baseball" author: "Brian J" editor: visual toc: true # Generates an automatic table of contents. format: # Options related to formatting. html: # Options related to HTML output. code-tools: TRUE # Allow the code tools option showing in the output. embed-resources: TRUE # Embeds all components into a single HTML file. execute: # Options related to the execution of code chunks. warning: FALSE # FALSE: Code chunk sarnings are hidden by default. message: FALSE # FALSE: Code chunk messages are hidden by default. echo: TRUE # TRUE: Show all code in the output. --- ## Introduction I am going to be performing analysis on the top 100 baseball hitters data by year. It includes statistics from the year as well as any awards they had received by their performance. You can learn more about the data at this link: <https://myxavier-my.sharepoint.com/:x:/g/personal/janishefskib_xavier_edu/ETjz1TyRf5xNjPd7ooW4qLUBJ6TsJMh43uRJT8ecuqgwvQ?download=1> ```{r} #| include: FALSE library(tidyverse) # For All the tidy things library(skimr) # for better summary statistics ``` ```{r} #| label: load data mydata <- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/janishefskib_xavier_edu/ETjz1TyRf5xNjPd7ooW4qLUBJ6TsJMh43uRJT8ecuqgwvQ?download=1") ``` ```{r} ``` ## Running Code When you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this: ```{r} 1 + 1 ``` ```{r} #| title: data import ``` You can add options to executable code like this ```{r} #| echo: false 2 * 2 ``` The `echo: false` option disables the printing of code (only output is displayed). ## Research Question Do players that have have more hits have a higher amount of RBI's? ```{r} colnames(mydata) <- as.character(unlist(mydata[1, ])) mydata <- mydata[-1, ] mydata <- mydata %>% mutate(RBI = as.numeric(RBI), H = as.numeric(H)) mydata %>% ggplot(aes(x= RBI, y=H ))+ geom_point(color = "darkgreen", alpha= .5, size = .75)+ labs(x= "Runs Batted In (RBI)", y= "Hits")+ scale_x_continuous(breaks= seq(0,200, by = 25))+ geom_smooth(method = "lm") ``` The analysis performed above shows the relationship between hits and OBP. \# Although I expected more drastic results in the relation, there is still a \# small correlation between Hits and OBP. In terms of the question I posed, \# hits don't contribute as much to OBP as some people think. This graph shows \# that, most likely, walks contribute more to a high OBP (On-base percentage)