DataDiveWeek5

data <- read.csv("D:/2024-2025/FA24/Stats/mergedfile.csv")
colnames(data)

##  [1] "Date"                "Symbol"              "Adj.Close"          
##  [4] "Close"               "High"                "Low"                
##  [7] "Open"                "Volume"              "Exchange"           
## [10] "Shortname"           "Longname"            "Sector"             
## [13] "Industry"            "Currentprice"        "Marketcap"          
## [16] "Ebitda"              "Revenuegrowth"       "City"               
## [19] "State"               "Country"             "Fulltimeemployees"  
## [22] "Longbusinesssummary" "Weight"

Question 1

What are the three unclear columns/values in your data?

• Ebitda

  • EBITDA is a financial metric that represents a company’s earnings before accounting for interest, taxes, depreciation, and amortization. It’s often used as a measure of a company’s operating performance and is a key indicator of its profitability, excluding the effects of capital structure, tax rates, and non-cash accounting charges.

• Weight

  • Weight refers to the percentage a company represents in a market index, such as the S&P 500. Index weighting is used to determine the influence of a company’s stock price movement on the overall index performance. In a market-cap-weighted index like the S&P 500, larger companies (in terms of market capitalization) have more influence on the index movement than smaller companies.

• Symbol

  • The stock symbol (also known as ticker symbol) is a unique series of letters assigned to a publicly traded company to represent it on a stock exchange. For example, “AAPL” is the symbol for Apple Inc.

Question 2

What is still unclear even after reading documentation.

One main issue that I could not resolve after doing a deeper dive into the documentation was the “RevenueGrowth” column. The issue that I see with this column is we have no frame of reference. On what scale is the growth on? It could be twice a year, every quarter, once a year, etc…

• Time Frame Ambiguity: Revenue growth can be reported for different periods like quarterly (QoQ), yearly (YoY), or over multiple years (CAGR). Without knowing which period the values in the “RevenueGrowth” column represent, it’s impossible to compare companies accurately or assess their financial health over time. A company showing 5% growth over a quarter is performing very differently from one growing 5% over a year, yet they may look similar without time frame clarification.

• Comparison Across Companies: If different companies report their growth rates using different time frames (some quarterly, others yearly), it becomes hard to compare their performance directly. This would skew any sector-based analysis or cross-company comparisons unless the data is normalized to a common time frame.

• Impact on Analysis: Any predictive models or trend analysis would suffer from inconsistencies if the time frame isn’t consistent. For instance, calculating the compound annual growth rate (CAGR) or projecting future revenue might lead to incorrect conclusions if the underlying growth rates are not clearly defined in terms of time.

• Scaling and Interpretation: Growth that is reported annually might seem smaller in percentage terms than quarterly growth, leading to potential misinterpretation. For example, 2% quarterly revenue growth may compound to an 8% annual growth rate, which sounds much more impressive when contextualized.

Plots

# Load necessary libraries
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

# Calculate the average revenue growth for each company
sample_data <- data |>
  filter(Symbol %in% c('AAPL', 'CAT', 'CDW', 'BLK', 'GOOGL')) |>
  group_by(Symbol, Sector) |>
  summarize(AverageRevenueGrowth = mean(Revenuegrowth, na.rm = TRUE))

## `summarise()` has grouped output by 'Symbol'. You can override using the
## `.groups` argument.

# Create the bar plot for average revenue growth
ggplot(sample_data, aes(x = Symbol, y = AverageRevenueGrowth, fill = Sector)) +
  geom_bar(stat = 'identity', width = 0.6) +
  
  
  # Add titles and labels
  labs(
    title = "Revenue Growth with Ambiguous Timeframe",
    x = "Company",
    y = "Revenue Growth (%)"
  ) +
  
  # Customize plot theme
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

# These approaches address the issue of summing repeated values by either averaging or selecting a representative value.

To highlight the issue with the unclear time frame of the “RevenueGrowth” column, we can create a visualization that compares the revenue growth of several companies from different sectors. The goal is to show that without knowing whether the growth rate is quarterly, yearly, or over some other period, it’s difficult to draw meaningful conclusions.