BUMP CHART FOR RANKING CHANGES OF TOP COMPANIES [LA 1 ASSIGNMENT]
PRACHETAN MS [USN:1NT24IS152 SEC:‘C’] AND JAYANTH Y [USN:1NT24IS094 SEC: ‘B’]
Objective
- To generate a bump chart using ggplot2
- To visualize ranking changes of top companies
- Based on revenue over multiple years
Step 1: Load Required Packages
- We use:
ggplot2 → visualization
dplyr, tidyr → data manipulation
Step 2: Load the Dataset
Dataset: Top US Tech Companies (2022–2023) Inspect structure and preview data
Company.Name Industry Sector HQ.State
1 Apple Inc. Technology Consumer Electronics California
2 Microsoft Corporation Technology Software Infrastructure Washington
3 Alphabet (Google) Technology Software Infrastructure California
4 Amazon Technology Software Application Washington
5 NVIDIA Corporation Technology Semiconductors California
6 Tesla Technology Software Infrastructure Texas
Founding.Year Annual.Revenue.2022.2023..USD.in.Billions.
1 1976 387.53
2 1975 204.09
3 1998 282.83
4 1994 513.98
5 1993 26.97
6 2003 81.46
Market.Cap..USD.in.Trillions. Stock.Name
1 2.520 AAPL
2 2.037 MSFT
3 1.350 GOOG
4 1.030 AMZN
5 0.653 NVDA
6 0.625 TSLA
Annual.Income.Tax.in.2022.2023..USD.in.Billions. Employee.Size
1 18.314 164000
2 15.139 221000
3 11.356 190234
4 -3.217 1541000
5 0.189 22473
6 1.132 127855
'data.frame': 50 obs. of 10 variables:
$ Company.Name : chr "Apple Inc." "Microsoft Corporation" "Alphabet (Google)" "Amazon" ...
$ Industry : chr "Technology" "Technology" "Technology" "Technology" ...
$ Sector : chr "Consumer Electronics" "Software Infrastructure" "Software Infrastructure" "Software Application" ...
$ HQ.State : chr "California" "Washington" "California" "Washington" ...
$ Founding.Year : int 1976 1975 1998 1994 1993 2003 2004 1961 1977 1984 ...
$ Annual.Revenue.2022.2023..USD.in.Billions. : num 388 204 283 514 27 ...
$ Market.Cap..USD.in.Trillions. : num 2.52 2.037 1.35 1.03 0.653 ...
$ Stock.Name : chr "AAPL" "MSFT" "GOOG" "AMZN" ...
$ Annual.Income.Tax.in.2022.2023..USD.in.Billions.: num 18.314 15.139 11.356 -3.217 0.189 ...
$ Employee.Size : int 164000 221000 190234 1541000 22473 127855 86482 20000 143000 83300 ...
Step 3: Select Top 10 Companies
No renaming needed Directly use Revenue
Company.Name Industry Sector HQ.State
1 Amazon Technology Software Application Washington
2 Apple Inc. Technology Consumer Electronics California
3 Alphabet (Google) Technology Software Infrastructure California
4 Microsoft Corporation Technology Software Infrastructure Washington
5 Meta Platforms Technology Software Infrastructure California
6 Dell Technologies Inc. Technology Computer Hardware Texas
7 Tesla Technology Software Infrastructure Texas
8 Intel Corporation Technology Semiconductors California
9 IBM Corporation Technology IT Services New York
10 HP Inc. Technology Computer Hardware California
Founding.Year Revenue Market.Cap..USD.in.Trillions. Stock.Name
1 1994 513.98 1.030 AMZN
2 1976 387.53 2.520 AAPL
3 1998 282.83 1.350 GOOG
4 1975 204.09 2.037 MSFT
5 2004 116.60 0.524 META
6 1984 102.30 0.028 DELL
7 2003 81.46 0.625 TSLA
8 1968 63.05 0.118 INTC
9 1911 60.52 0.113 IBM
10 1939 59.78 0.028 HPQ
Annual.Income.Tax.in.2022.2023..USD.in.Billions. Employee.Size
1 -3.217 1541000
2 18.314 164000
3 11.356 190234
4 15.139 221000
5 5.619 86482
6 0.981 133000
7 1.132 127855
8 -0.249 131900
9 -0.626 345000
10 1.238 51000
Step 4: Create Multi-Year Data
Simulate revenue for 2019–2023 Add variation using random values
Code: set.seed(123)
years <- 2019:2023
bump_data <- top_companies %>% select(Company.Name, Revenue) %>% crossing(Year = years) %>% mutate( Revenue = Revenue * runif(n(), 0.7, 1.2), Company = Company.Name )
Step 5: Ranking Companies
Rank companies year-wise Higher revenue → better rank
Code: bump_data <- bump_data %>% group_by(Year) %>% mutate(Rank = rank(-Revenue, ties.method = “first”))
Step 6: Create Bump Chart
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Understanding the Bump Chart
Bump Chart → shows ranking changes over time Lines → represent companies Points → rank at each year Rank 1 → highest revenue scale_y_reverse() → rank 1 at top Colors differentiate companies
Key Insights
Easy to track rank movement Identify: Consistent performers Sudden changes Helps in trend comparison Conclusion Bump chart clearly shows ranking fluctuations Useful for analyzing multi-year performance Effective and visually intuitive representation
