forbes <- read.csv("Forbes2000.csv")
str(forbes)
## '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 : chr "Citigroup" "General Electric" "American Intl Group" "ExxonMobil" ...
## $ country : chr "United States" "United States" "United States" "United States" ...
## $ category : chr "Banking" "Conglomerates" "Insurance" "Oil & gas operations" ...
## $ 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 ...
profit_by_category <- aggregate(profits ~ category, data = forbes, FUN = sum)
profit_by_category_desc <-profit_by_category[order(-profit_by_category$profits),]
profit_by_category_desc
## category profits
## 2 Banking 132.11
## 19 Oil & gas operations 117.50
## 9 Diversified financials 78.93
## 10 Drugs & biotechnology 65.15
## 11 Food drink & tobacco 49.29
## 8 Consumer durables 41.91
## 20 Retailing 41.88
## 16 Insurance 37.73
## 6 Conglomerates 31.45
## 15 Household & personal products 24.19
## 13 Health care equipment & services 23.46
## 27 Utilities 23.26
## 17 Materials 19.01
## 22 Software & services 17.60
## 7 Construction 15.65
## 5 Chemicals 13.03
## 18 Media 12.85
## 23 Technology hardware & equipment 12.13
## 3 Business services & supplies 11.95
## 21 Semiconductors 11.35
## 26 Transportation 10.83
## 14 Hotels restaurants & leisure 9.57
## 12 Food markets 8.22
## 1 Aerospace & defense 5.48
## 4 Capital goods 5.06
## 25 Trading companies 0.70
## 24 Telecommunications services -59.93
profit_by_country <- aggregate(profits ~ country, data = forbes, FUN = sum)
profit_by_country
## country profits
## 1 Africa -0.01
## 2 Australia 18.08
## 3 Australia/ United Kingdom 2.35
## 4 Austria 0.86
## 5 Bahamas 0.20
## 6 Belgium 3.54
## 7 Bermuda 9.11
## 8 Brazil 6.78
## 9 Canada 23.30
## 10 Cayman Islands 0.74
## 11 Chile 0.65
## 12 China 15.54
## 13 Czech Republic 0.42
## 14 Denmark 5.22
## 15 Finland 7.05
## 16 France 7.37
## 17 France/ United Kingdom -2.83
## 18 Germany -2.48
## 19 Greece 2.20
## 20 Hong Kong/China 7.33
## 21 Hungary 0.55
## 22 India 9.37
## 23 Indonesia 2.69
## 24 Ireland 3.88
## 25 Islands 0.74
## 26 Israel 0.79
## 27 Italy 7.52
## 28 Japan 7.07
## 29 Jordan 0.23
## 30 Kong/China 4.76
## 31 Korea 0.12
## 32 Liberia 0.28
## 33 Luxembourg -0.25
## 34 Malaysia 3.41
## 35 Mexico 3.41
## 36 Netherlands -1.09
## 37 Netherlands/ United Kingdom 10.64
## 38 New Zealand 0.42
## 39 Norway 5.05
## 40 Pakistan 0.41
## 41 Panama/ United Kingdom 1.18
## 42 Peru 0.11
## 43 Philippines 0.22
## 44 Poland 0.28
## 45 Portugal 1.55
## 46 Russia 14.87
## 47 Singapore 3.58
## 48 South Africa 6.33
## 49 South Korea 15.60
## 50 Spain 11.74
## 51 Sweden 4.78
## 52 Switzerland 13.75
## 53 Taiwan 5.68
## 54 Thailand 1.64
## 55 Turkey 2.71
## 56 United Kingdom 21.72
## 57 United Kingdom/ Australia 1.64
## 58 United Kingdom/ Netherlands 0.14
## 59 United Kingdom/ South Africa -0.10
## 60 United States 487.40
## 61 Venezuela 0.12
sales_by_country <- aggregate(sales ~ country, data = forbes, FUN = sum)
sales_by_country_desc <- sales_by_country[order(-sales_by_country$sales),]
sales_by_country_desc
## country sales
## 60 United States 7553.75
## 28 Japan 3220.24
## 56 United Kingdom 1430.98
## 18 Germany 1350.79
## 16 France 1266.43
## 36 Netherlands 476.58
## 52 Switzerland 423.53
## 27 Italy 418.77
## 9 Canada 360.06
## 49 South Korea 358.62
## 50 Spain 227.46
## 51 Sweden 199.31
## 2 Australia 194.05
## 37 Netherlands/ United Kingdom 184.20
## 7 Bermuda 136.81
## 12 China 127.49
## 15 Finland 113.21
## 22 India 104.44
## 53 Taiwan 96.30
## 8 Brazil 95.08
## 46 Russia 92.07
## 6 Belgium 91.03
## 39 Norway 86.24
## 35 Mexico 66.94
## 14 Denmark 63.49
## 48 South Africa 61.86
## 31 Korea 60.02
## 47 Singapore 58.96
## 55 Turkey 56.56
## 20 Hong Kong/China 40.88
## 24 Ireland 38.12
## 4 Austria 33.14
## 19 Greece 30.34
## 33 Luxembourg 28.37
## 34 Malaysia 27.46
## 45 Portugal 27.19
## 3 Australia/ United Kingdom 23.19
## 30 Kong/China 22.87
## 54 Thailand 22.62
## 23 Indonesia 17.15
## 26 Israel 16.48
## 1 Africa 13.64
## 57 United Kingdom/ Australia 10.01
## 10 Cayman Islands 8.30
## 58 United Kingdom/ Netherlands 7.54
## 21 Hungary 6.74
## 25 Islands 6.67
## 11 Chile 6.41
## 41 Panama/ United Kingdom 5.93
## 44 Poland 4.41
## 32 Liberia 3.78
## 13 Czech Republic 3.61
## 43 Philippines 3.13
## 38 New Zealand 2.64
## 59 United Kingdom/ South Africa 2.06
## 5 Bahamas 1.35
## 29 Jordan 1.33
## 40 Pakistan 1.23
## 17 France/ United Kingdom 1.01
## 61 Venezuela 0.98
## 42 Peru 0.17
Which company types generate the highest (and lowest) profits?
Banking
Which countries generate the most profits? The United States
What about sales? The United States
``` r
usa_japan <- subset(forbes, country %in% c("USA", "Japan"))
usa_japan_rank <- aggregate(rank ~ country, data = forbes, FUN = min)
usa_japan_rank
## country rank
## 1 Africa 950
## 2 Australia 86
## 3 Australia/ United Kingdom 122
## 4 Austria 363
## 5 Bahamas 1754
## 6 Belgium 99
## 7 Bermuda 76
## 8 Brazil 127
## 9 Canada 79
## 10 Cayman Islands 1101
## 11 Chile 951
## 12 China 55
## 13 Czech Republic 1125
## 14 Denmark 133
## 15 Finland 83
## 16 France 17
## 17 France/ United Kingdom 1497
## 18 Germany 21
## 19 Greece 578
## 20 Hong Kong/China 144
## 21 Hungary 994
## 22 India 243
## 23 Indonesia 688
## 24 Ireland 221
## 25 Islands 668
## 26 Israel 792
## 27 Italy 37
## 28 Japan 8
## 29 Jordan 962
## 30 Kong/China 112
## 31 Korea 998
## 32 Liberia 675
## 33 Luxembourg 595
## 34 Malaysia 515
## 35 Mexico 375
## 36 Netherlands 12
## 37 Netherlands/ United Kingdom 13
## 38 New Zealand 896
## 39 Norway 117
## 40 Pakistan 1410
## 41 Panama/ United Kingdom 259
## 42 Peru 1944
## 43 Philippines 1674
## 44 Poland 1073
## 45 Portugal 442
## 46 Russia 87
## 47 Singapore 318
## 48 South Africa 359
## 49 South Korea 45
## 50 Spain 39
## 51 Sweden 136
## 52 Switzerland 11
## 53 Taiwan 312
## 54 Thailand 461
## 55 Turkey 566
## 56 United Kingdom 5
## 57 United Kingdom/ Australia 188
## 58 United Kingdom/ Netherlands 840
## 59 United Kingdom/ South Africa 1489
## 60 United States 1
## 61 Venezuela 1861
usa_cat <- subset(forbes, country == "United States")
category_type_usa <- table(usa_cat$category)
category_type_usa_desc <- sort(category_type_usa, decreasing = TRUE)
category_type_usa_desc
##
## Banking Diversified financials
## 83 60
## Utilities Health care equipment & services
## 54 53
## Retailing Insurance
## 53 46
## Technology hardware & equipment Oil & gas operations
## 33 32
## Business services & supplies Food drink & tobacco
## 31 28
## Media Materials
## 28 26
## Consumer durables Drugs & biotechnology
## 25 21
## Software & services Household & personal products
## 21 20
## Construction Hotels restaurants & leisure
## 18 17
## Transportation Semiconductors
## 17 16
## Telecommunications services Chemicals
## 16 13
## Conglomerates Aerospace & defense
## 11 10
## Capital goods Food markets
## 10 9
japan_cat <- subset(forbes, country == "Japan")
category_type_japan <- table(japan_cat$category)
cat_type_japan_desc <- sort(category_type_japan, decreasing = TRUE)
cat_type_japan_desc
##
## Banking Diversified financials
## 69 24
## Consumer durables Transportation
## 22 20
## Capital goods Construction
## 19 18
## Business services & supplies Chemicals
## 17 14
## Trading companies Materials
## 13 12
## Retailing Utilities
## 12 11
## Food drink & tobacco Drugs & biotechnology
## 10 9
## Insurance Household & personal products
## 9 7
## Media Technology hardware & equipment
## 7 6
## Health care equipment & services Oil & gas operations
## 4 3
## Semiconductors Software & services
## 3 3
## Food markets Telecommunications services
## 2 2
Which country has the highest rank using Forbes ranking? United
states
Which company types are more common in the USA? Banking
In Japan? Banking
``` r
lr_model <- forbes[, c("profits", "assets", "marketvalue", "sales")]
summary(lr_model)
## profits assets marketvalue sales
## Min. :-25.8300 Min. : 0.270 Min. : 0.02 Min. : 0.010
## 1st Qu.: 0.0800 1st Qu.: 4.025 1st Qu.: 2.72 1st Qu.: 2.018
## Median : 0.2000 Median : 9.345 Median : 5.15 Median : 4.365
## Mean : 0.3811 Mean : 34.042 Mean : 11.88 Mean : 9.697
## 3rd Qu.: 0.4400 3rd Qu.: 22.793 3rd Qu.: 10.60 3rd Qu.: 9.547
## Max. : 20.9600 Max. :1264.030 Max. :328.54 Max. :256.330
## NA's :5
lm_model <- lm(profits ~ assets + marketvalue + sales, data = forbes)
anova(lm_model)
## Analysis of Variance Table
##
## Response: profits
## Df Sum Sq Mean Sq F value Pr(>F)
## assets 1 312.8 312.84 144.40 < 2.2e-16 ***
## marketvalue 1 1552.8 1552.77 716.71 < 2.2e-16 ***
## sales 1 35.8 35.79 16.52 5.001e-05 ***
## Residuals 1991 4313.6 2.17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
usa_lm <- lm(profits ~ assets + marketvalue + sales, data = subset(forbes, country == "United States"))
anova(usa_lm)
## Analysis of Variance Table
##
## Response: profits
## Df Sum Sq Mean Sq F value Pr(>F)
## assets 1 975.23 975.23 1479.783 < 2.2e-16 ***
## marketvalue 1 895.96 895.96 1359.498 < 2.2e-16 ***
## sales 1 30.41 30.41 46.142 2.253e-11 ***
## Residuals 744 490.32 0.66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
japan_lm <- lm(profits ~ assets + marketvalue + sales, data = subset(forbes, country == "Japan"))
anova(japan_lm)
## Analysis of Variance Table
##
## Response: profits
## Df Sum Sq Mean Sq F value Pr(>F)
## assets 1 284.846 284.846 433.311 <2e-16 ***
## marketvalue 1 162.574 162.574 247.310 <2e-16 ***
## sales 1 0.024 0.024 0.036 0.8497
## Residuals 312 205.100 0.657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
which variables are the most important for each region? assets
What differences do you observe? the US has far greater sales