R_BrdgWrkshp_Week3_Assignment
Vinayak Kamath
1/11/2020
*** Data set “Forbes2000.csv” from http://vincentarelbundock.github.io/Rdatasets/ ***
#library(readr)
Forbes2000.data <-read.delim(file="Forbes2000.csv", sep=',')
str(Forbes2000.data)## 'data.frame': 2000 obs. of 9 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ name : Factor w/ 2000 levels "Aareal Bank",..: 438 747 100 659 311 219 870 1827 663 1921 ...
## $ country : Factor w/ 61 levels "Africa","Australia",..: 60 60 60 60 56 60 56 28 60 60 ...
## $ category : Factor w/ 27 levels "Aerospace & defense",..: 2 6 16 19 19 2 2 8 9 20 ...
## $ sales : num 94.7 134.2 76.7 222.9 232.6 ...
## $ profits : num 17.85 15.59 6.46 20.96 10.27 ...
## $ assets : num 1264 627 648 167 178 ...
## $ marketvalue: num 255 329 195 277 174 ...1. Data Exploration: This should include summary statistics, means, medians, quartiles, or any other relevant information about the data set. Please include some conclusions in the R Markdown text.
summary(Forbes2000.data)## X rank name
## Min. : 1.0 Min. : 1.0 Aareal Bank : 1
## 1st Qu.: 500.8 1st Qu.: 500.8 ABB Group : 1
## Median :1000.5 Median :1000.5 Abbey National : 1
## Mean :1000.5 Mean :1000.5 Abbott Laboratories : 1
## 3rd Qu.:1500.2 3rd Qu.:1500.2 Abercrombie & Fitch : 1
## Max. :2000.0 Max. :2000.0 Abertis Infraestructuras: 1
## (Other) :1994
## country category sales
## United States :751 Banking : 313 Min. : 0.010
## Japan :316 Diversified financials: 158 1st Qu.: 2.018
## United Kingdom:137 Insurance : 112 Median : 4.365
## Germany : 65 Utilities : 110 Mean : 9.697
## France : 63 Materials : 97 3rd Qu.: 9.547
## Canada : 56 Oil & gas operations : 90 Max. :256.330
## (Other) :612 (Other) :1120
## profits assets marketvalue
## Min. :-25.8300 Min. : 0.270 Min. : 0.02
## 1st Qu.: 0.0800 1st Qu.: 4.025 1st Qu.: 2.72
## Median : 0.2000 Median : 9.345 Median : 5.15
## Mean : 0.3811 Mean : 34.042 Mean : 11.88
## 3rd Qu.: 0.4400 3rd Qu.: 22.793 3rd Qu.: 10.60
## Max. : 20.9600 Max. :1264.030 Max. :328.54
## NA's :5-> Conclusion: USA as a country tops wth around 751 companies in Forbes 2000 list. Banking , Diversified Financials and Insurance as categories tops with approx 600 companies in Forbes 2000 list.
2. Data wrangling: Please perform some basic transformations. They will need to make sense but could include column renaming, creating a subset of the data, replacing values, or creating new columns with derived data (for example – if it makes sense you could sum two columns together):
# Data Sub set to analyze Compaines in Forbes 2000 based in Country Mexico:
Forbes2000.Mexico.data <- subset(Forbes2000.data, grepl("Mexico", Forbes2000.data$country) , select = c("rank","name","category","marketvalue", "profits"), drop = FALSE )
summary(Forbes2000.Mexico.data)## rank name category
## Min. : 375 ALFA : 1 Food drink & tobacco :4
## 1st Qu.: 829 America Telecom : 1 Banking :2
## Median :1440 Carso Global Telecom: 1 Conglomerates :2
## Mean :1240 Cemex : 1 Telecommunications services:2
## 3rd Qu.:1612 Coca-Cola Femsa : 1 Capital goods :1
## Max. :1953 Femsa : 1 Construction :1
## (Other) :11 (Other) :5
## marketvalue profits
## Min. :0.62 Min. :-0.0300
## 1st Qu.:2.21 1st Qu.: 0.1300
## Median :3.70 Median : 0.2000
## Mean :4.10 Mean : 0.2006
## 3rd Qu.:5.47 3rd Qu.: 0.2500
## Max. :9.93 Max. : 0.6300
## # Renaming Columns:
names(Forbes2000.Mexico.data) <- c("rank" = "Rank","name"="CompanyName","category"="Category","marketvalue"="MarketValue","profits"="ProfitsInTrillions")
# Adding New Column to indicate as factor the year on year (YoY) Trend in profits:
Forbes2000.Mexico.data$ProfitsYoY = factor(ifelse(Forbes2000.Mexico.data$ProfitsInTrillions > 0 , "Up",(ifelse(Forbes2000.Mexico.data$ProfitsInTrillions <0, "Down", "Same" )) ) , levels = c("Up","Down","Same"))
summary(Forbes2000.Mexico.data)## Rank CompanyName Category
## Min. : 375 ALFA : 1 Food drink & tobacco :4
## 1st Qu.: 829 America Telecom : 1 Banking :2
## Median :1440 Carso Global Telecom: 1 Conglomerates :2
## Mean :1240 Cemex : 1 Telecommunications services:2
## 3rd Qu.:1612 Coca-Cola Femsa : 1 Capital goods :1
## Max. :1953 Femsa : 1 Construction :1
## (Other) :11 (Other) :5
## MarketValue ProfitsInTrillions ProfitsYoY
## Min. :0.62 Min. :-0.0300 Up :15
## 1st Qu.:2.21 1st Qu.: 0.1300 Down: 1
## Median :3.70 Median : 0.2000 Same: 1
## Mean :4.10 Mean : 0.2006
## 3rd Qu.:5.47 3rd Qu.: 0.2500
## Max. :9.93 Max. : 0.6300
## # Showing Sub Set of companies with downward YoY profit trend:
subset(Forbes2000.Mexico.data, grepl("Down", Forbes2000.Mexico.data$ProfitsYoY) , select = c("Rank","CompanyName","Category","MarketValue", "ProfitsInTrillions"), drop = FALSE )## Rank CompanyName Category MarketValue ProfitsInTrillions
## 1490 1490 Grupo Televisa Media 6.42 -0.03-> Conclusion: Minimum Rank for Mexico is 375 and Food, Drink and Tibacco Category dominates as the top category in Mexico.
3. Graphics: Please make sure to display at least one scatter plot, box plot and histogram. Don’t be limited to this. Please explore the many other options in R packages such as ggplot2:
# Boxplot and ggplot for Rank for All company in Mexico country
library(ggplot2)
boxplot(Forbes2000.Mexico.data$Rank , main = 'Forbes2000 Mexico Rank Data')
ggplot(Forbes2000.Mexico.data, aes(y=CompanyName, x=Rank) ) + geom_point()
# Pie Chart for MarketValue for Mexico Country:
pie(Forbes2000.Mexico.data$MarketValue, paste(Forbes2000.Mexico.data$CompanyName, " - " ,Forbes2000.Mexico.data$MarketValue, " T." , sep="" ), main = "Market Value Pie Chart for Mexico", radius=1, col = rainbow(length(Forbes2000.Mexico.data$MarketValue)))
# 3D Pie Chart for Profits Year on Year for Mexico Country:
library(plotrix)
data <- summary(Forbes2000.Mexico.data$ProfitsYoY)
owner <- levels(Forbes2000.Mexico.data$ProfitsYoY)
pie3D(data,labels= paste(owner,data, sep=' - '), explode=0.03, start=pi/2,main="ProfitsYoY Pie Chart for Mexico", col=c("brown","#ddaa00","#dd00dd"))
# Bar Graph for marketvalue total at Country grouping:
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, unionForbes2000.data %>% group_by(country) %>% summarise(sum.marketvalue = sum(marketvalue)) %>%
ggplot(aes(x = country, y = sum.marketvalue)) +
geom_bar(stat = "identity") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
labs(
x = "Country",
y = "Market Value",
title = paste(
"Country wise Market holding"
)
)
# Showing the countries in Forbes 2000 in world map:
library(maptools)## Loading required package: sp## Checking rgeos availability: TRUEdata(wrld_simpl)
myCountries = wrld_simpl@data$NAME %in% names(table(Forbes2000.data$country))
plot(wrld_simpl, col = c(gray(.80), "blue")[myCountries+1])
4. Meaningful question for analysis: Please state at the beginning a meaningful question for analysis. Use the first three steps and anything else that would be helpful to answer the question you are posing from the data set you chose. Please write a brief conclusion paragraph in R markdown at the end:
# Question: How does USA tally with rest of the World in terms of the market value and assets and positioing:
Forbes2000.data$country <- factor(Forbes2000.data$country)
Usa.data <- filter(Forbes2000.data, country == "United States" )
Rest.data <- filter(Forbes2000.data, country != "United States" )
x <- matrix( , nrow = 3, ncol = 4 , dimnames = list(c("Count","Asset","Market Value"), c("USA","Rest", "USA %", "Rest %")))
x["Count", c("USA","Rest")] <- c( nrow(Usa.data), nrow(Rest.data))
x["Asset", c("USA","Rest")] <- c( sum(Usa.data$asset) , sum(Rest.data$asset))
x["Market Value", c("USA","Rest")] <- c( sum(Usa.data$marketvalue), sum(Rest.data$marketvalue))
x["Count", c("USA %","Rest %")] <- c( ((x["Count","USA"] / (x["Count","USA"] + x["Count","Rest"] )) *100 ), ((x["Count","Rest"] / (x["Count","USA"] + x["Count","Rest"] )) *100 ) )
x["Asset", c("USA %","Rest %")] <- c( ((x["Asset","USA"] / (x["Asset","USA"] + x["Asset","Rest"] )) *100 ), ((x["Asset","Rest"] / (x["Asset","USA"] + x["Asset","Rest"] )) *100 ) )
x["Market Value", c("USA %","Rest %")] <- c( ((x["Market Value","USA"] / (x["Market Value","USA"] + x["Market Value","Rest"] )) *100 ), ((x["Market Value","Rest"] / (x["Market Value","USA"] + x["Market Value","Rest"] )) *100 ) )
analysis.data <- x
analysis.data## USA Rest USA % Rest %
## Count 751.00 1249.00 37.55000 62.45000
## Asset 22781.89 45301.81 33.46159 66.53841
## Market Value 11575.58 12179.73 48.72839 51.27161-> Conclusion: 33% of the Asset across forbes 2000 countries belongs to USA; 37% of the 2000 companies are based in USA and they hold a total market value of almost 49% of 2000 companies.
5. BONUS – place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career:
# Access csv file via github:
csvFile.URL <- "https://raw.githubusercontent.com/kamathvk1982/CunyBridgeR/master/Forbes2000.csv"
git.Forbes2000.data <-read.delim(file=csvFile.URL, sep=',')
summary(git.Forbes2000.data)## X rank name
## Min. : 1.0 Min. : 1.0 Aareal Bank : 1
## 1st Qu.: 500.8 1st Qu.: 500.8 ABB Group : 1
## Median :1000.5 Median :1000.5 Abbey National : 1
## Mean :1000.5 Mean :1000.5 Abbott Laboratories : 1
## 3rd Qu.:1500.2 3rd Qu.:1500.2 Abercrombie & Fitch : 1
## Max. :2000.0 Max. :2000.0 Abertis Infraestructuras: 1
## (Other) :1994
## country category sales
## United States :751 Banking : 313 Min. : 0.010
## Japan :316 Diversified financials: 158 1st Qu.: 2.018
## United Kingdom:137 Insurance : 112 Median : 4.365
## Germany : 65 Utilities : 110 Mean : 9.697
## France : 63 Materials : 97 3rd Qu.: 9.547
## Canada : 56 Oil & gas operations : 90 Max. :256.330
## (Other) :612 (Other) :1120
## profits assets marketvalue
## Min. :-25.8300 Min. : 0.270 Min. : 0.02
## 1st Qu.: 0.0800 1st Qu.: 4.025 1st Qu.: 2.72
## Median : 0.2000 Median : 9.345 Median : 5.15
## Mean : 0.3811 Mean : 34.042 Mean : 11.88
## 3rd Qu.: 0.4400 3rd Qu.: 22.793 3rd Qu.: 10.60
## Max. : 20.9600 Max. :1264.030 Max. :328.54
## NA's :5