Handling missing values
We fearlessly remove the voids using na.omit(), rescuing columns from emptiness. With a triumphant flourish, we check for any stragglers using colSums(is.na(data1)). Victory!
R
#removing missing values data1 <- na.omit(data) #see if there is any missing values left colSums(is.na(data1)) |
Output:
date 0 open 0 high 0 low 0 close 0 volume 0 Name 0
Checking the dimension after removing missing values warriors.
R
#See the dimension of the new dataframe dim(data1) |
Output:
619029 * 7
The summary reflects the essence of our data, focusing on minimum and maximum values, mean and median, and first and third quartile.
R
# giving the summary of data summary(data) |
Output:
summary
S&P 500 Companies Data Analysis Tutorial using R
R is a powerful programming language and environment for statistical computation and data analysis. It is backed by data scientists, accountants, and educators because of its various features and capabilities. This project will use R to search and analyze stock market data for S&P 500 companies.
Tidyverse, ggplot2, and dplyr are just a few of the many libraries provided by R Programming Language that simplify data processing, visualization, and statistical modeling. These libraries allow us to perform many tasks such as data cleaning, filtering, aggregation, and visualization.
In this work, we will analyze the S&P 500 stock market dataset using these packages using R capabilities.
Hey! Hey! Hey! Welcome, adventurous data enthusiasts! Grab your virtual backpacks, put on your data detective hats, Ready to unravel this mysterious project journey with me.
- Dataset introduction – All files contain the following column.
- Date – In format: yy-mm-dd.
- Open – Price of the stock at the market open (this is NYSE data so everything is in USD).
- High – The highest value achieved for the day.
- Low Close – The lowest price achieved on the day.
- Volume – The number of transactions.
- Name – The stock’s ticker name.