Data analysis
Telling the length or we can say the total number of unique companies in the dataset.
R
#unique companies in the dataset companies <- length(unique(data1$Name)) companies |
Output:
505
Telling the lowest closing price in the dataset.
R
# Lowest closing price price <- min(data1$close) price |
Output:
1.59
Telling the highest closing price in the dataset.
R
# Highest closing price price <- max(data1$close) price |
Output:
2049
Giving the result of the company name with the highest shared volume.
R
# Company with maximum volume maximum_volume <- data1$Name[which.max(data1$volume)] maximum_volume |
Output:
'VZ'
Giving the result of the company name with the minimum shared volume.
R
# Company with minimum volume minimum_volume <- data1$Name[which.min(data1$volume)] minimum_volume |
Output:
'BHF'
We calculate the average closing price by taking the mean of the close column in the dataset data1
R
# Average price of closing mean_price <- mean(data1$close) mean_price |
Output:
83.0433.49787651
We determine the total trading volume by summing up the values in the volume column of the dataset data1.
R
# Total volume total_volume <- sum(data1$volume) total_volume |
Output:
2675376439690
We find the company with the longest consecutive days of price increases by identifying the corresponding “Name” value where the condition for consecutive price increases is met.
R
# We will see the Company which have the # longest consecutive days of price increases price_increase <- data1$Name[rle(data1$close > lag(data1$close))$values & rle(data1$close > lag(data1$close))$lengths > 1][1] price_increase |
Output:
'AAL'
We find the company with the longest consecutive days of price decreases by identifying the corresponding “Name” value where the condition for consecutive price decreases is met.
R
# We will see the Company which have the longest consecutive days of price decreases price_decrease <- data1$Name[rle(data1$close < lag(data1$close))$values & rle(data1$close < lag(data1$close))$lengths > 1][1] price_decrease |
Output:
'AAL'
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.