LAB 7
Akshay Palani
17/01/2022
#set path directory
#loading of dataset
globst <- read.csv("Lab 7 Dataset.csv", header = TRUE,sep =',')
globst## Companies Headquarters
## 1 ArcelorMittal Luxembourg
## 2 China Baowu Group (merger of Baosteel Group and Wuhan Group China
## 3 HBIS Group China
## 4 Nippon Steel and Sumitomo Metal Corporation Japan
## 5 POSCO South Korea
## 6 Baosteel Group China
## 7 Shagang Group China
## 8 Ansteel Group China
## 9 JFE Steel Corporation Japan
## 10 Shougang Group China
## 11 Tata Steel Group India
## 12 Wuhan Steel Group China
## 13 Shandong Steel Group China
## 14 Nucor Corporation USA
## 15 Hyundai Steel Company South Korea
## 16 Maanshan Steel China
## 17 thyssenkrupp Germany
## 18 Novolipetsk Steel Russia
## 19 Jianlong Group China
## 20 Gerdau S.A. Brazil
## 21 China Steel Corporation Taiwan, China
## 22 Valin Group China
## 23 JSW Steel Limited India
## 24 Benxi Steel China
## 25 Steel Authority of India Ltd. India
## 26 United States Steel Corporation USA
## 27 IMIDRO Iran
## 28 Rizhao Steel China
## 29 Fangda Steel China
## 30 EVRAZ Russia
## 31 Magnitogorsk Iron & Steel Works Russia
## 32 Baotou Steel China
## 33 Severstal Russia
## 34 Liuzhou Steel China
## 35 Jinxi Steel China
## 36 Jingye Steel China
## 37 Anyang Steel China
## 38 Sanming Steel China
## 39 Metinvest Holding LLC Ukraine
## 40 Taiyuan Steel China
## 41 Zongheng Steel China
## 42 Zenith Steel China
## 43 Erdemir Group Turkey
## 44 Nanjing Steel China
## 45 Xinyu Steel China
## 46 CITIC Pacific Special Steel China
## 47 SSAB Sweden
## 48 Techint Group Argentina
## 49 voestalpine Group Austria
## 50 Essar Steel Group India
## 51 Shaanxi Steel China
## 52 Kobe Steel, Ltd. Japan
## 53 CELSA Steel Group Spain
## 54 Guofeng Steel China
## 55 Salzgitter AG Stahl und Technologie Germany
## 56 Ruifeng Steel China
## 57 Binxin Special Steel China
## 58 Donghai Steel China
## 59 Tianjin Steel China
## 60 Tsingshan Holding Group China
## 61 Hangzhou Steel China
## 62 Yingkou Plate China
## 63 ILVA SpA Italy
## 64 BlueScope Steel Limited Australia
## 65 Jiuquan Steel China
## 66 RIVA Group Luxembourg
## 67 Yuhua Steel China
## 68 Saudi Iron & Steel Co. KSA
## 69 AK Steel Corporation USA
## 70 Ji'nan Steel China
## 71 Puyang Steel China
## 72 Lingyuan Steel China
## 73 Tianjin Metallurgy Group China
## 74 Metalloinvest Management Company Russia
## 75 Altos Hornos de M_xico, S.A.B. de C.V. Mexico
## 76 ISD (Industrial Union of Donbass) Ukraine
## 77 Hangzhou Steel China
## 78 Yuanli Group China
## 79 Mechel Russia
## 80 Usinas Siderìrgicas de Minas Gerais S.A. Brazil
## 81 Companhia Siderìrgica Nacional Brazil
## 82 Rashtriya Ispat Nigam Ltd (VIZAG Steel) India
## 83 Delong Steel China
## 84 Ezz Steel Egypt
## 85 Nisshin Steel Co., Ltd. (acquired by NSSMC in March 2017) Japan
## 86 Jiyuan Steel China
## 87 Shiheng Special Steel China
## 88 Rockcheck Steel China
## 89 Qianjin Steel China
## 90 Tianzhu Steel China
## 91 Jindal Steel and Power Ltd India
## 92 Xinxing Ductile China
## 93 Donghua Steel China
## 94 Dongkuk Steel Mill Co., Ltd. South Korea
## 95 Taishan Steel China
## 96 Outokumpu Oyj Sweden
## 97 Emirates Steel UAE
## 98 Ganglu Steel China
## 99 Tranvic Steel China
## 100 Tosyali Holding Turkey
## X2011.Tonnage..Millions. X2012.Tonnage..Millions. X2013.Tonnage..Millions.
## 1 97.25 93.58 96.10
## 2 81.02 79.12 83.22
## 3 44.36 42.84 45.79
## 4 33.39 47.86 50.13
## 5 39.12 39.88 38.42
## 6 43.34 42.70 43.91
## 7 31.92 32.31 35.08
## 8 29.75 30.23 33.69
## 9 29.90 30.41 31.16
## 10 30.04 31.42 31.52
## 11 23.82 22.97 25.27
## 12 37.68 36.42 39.31
## 13 24.02 23.01 22.79
## 14 19.89 20.13 20.16
## 15 16.29 17.12 17.30
## 16 16.68 17.34 18.79
## 17 17.94 14.46 15.86
## 18 12.11 14.92 15.47
## 19 12.36 13.76 14.30
## 20 20.50 19.81 18.97
## 21 14.01 12.73 14.29
## 22 15.89 14.11 14.99
## 23 7.01 8.48 11.80
## 24 16.49 15.08 16.83
## 25 13.50 13.50 13.52
## 26 21.99 21.44 20.38
## 27 12.58 13.61 14.29
## 28 11.20 13.22 12.68
## 29 2.62 3.28 13.16
## 30 16.77 15.95 16.11
## 31 12.20 13.04 11.94
## 32 10.22 10.19 10.69
## 33 15.29 15.14 15.69
## 34 0.00 0.00 0.00
## 35 9.04 9.10 8.75
## 36 5.83 7.30 9.69
## 37 9.38 7.74 10.32
## 38 5.72 6.95 8.22
## 39 14.38 14.34 14.29
## 40 9.90 10.13 9.99
## 41 8.65 9.11 10.19
## 42 7.01 7.57 8.51
## 43 7.47 7.87 8.27
## 44 7.65 7.18 6.05
## 45 8.72 8.66 8.50
## 46 6.73 6.62 7.66
## 47 5.67 5.25 5.57
## 48 9.53 8.71 9.00
## 49 7.67 7.36 8.02
## 50 6.82 6.70 6.09
## 51 5.20 6.65 8.00
## 52 7.39 7.09 7.53
## 53 7.84 7.62 6.99
## 54 8.21 7.98 8.06
## 55 5.66 6.09 5.58
## 56 0.00 0.00 3.36
## 57 0.00 0.00 3.14
## 58 1.37 2.31 3.88
## 59 0.00 0.00 0.00
## 60 0.00 0.00 0.00
## 61 3.65 3.25 3.43
## 62 2.21 2.25 3.33
## 63 0.00 0.00 5.68
## 64 6.00 4.23 4.20
## 65 10.22 10.10 11.16
## 66 0.00 0.00 7.59
## 67 0.00 0.00 0.00
## 68 5.28 5.20 5.47
## 69 0.00 0.00 0.00
## 70 0.00 0.00 0.00
## 71 4.53 4.35 3.40
## 72 3.59 3.56 5.21
## 73 0.00 0.00 0.00
## 74 5.82 5.61 4.68
## 75 3.81 3.88 4.16
## 76 0.00 8.49 7.94
## 77 3.65 3.25 3.43
## 78 3.11 3.49 3.82
## 79 0.00 6.53 4.65
## 80 6.84 7.16 6.86
## 81 4.87 4.64 4.48
## 82 3.19 3.11 3.11
## 83 3.57 3.75 3.63
## 84 4.32 4.56 4.33
## 85 3.76 3.81 3.70
## 86 2.61 3.25 3.50
## 87 2.81 2.96 3.07
## 88 2.99 3.22 3.30
## 89 3.54 3.43 3.46
## 90 0.00 0.00 0.00
## 91 2.62 2.73 2.87
## 92 3.76 4.36 3.65
## 93 0.00 0.00 2.70
## 94 3.07 3.31 3.33
## 95 2.53 2.81 3.02
## 96 1.68 1.68 2.97
## 97 0.00 2.38 2.88
## 98 3.01 3.06 3.10
## 99 0.00 0.00 0.00
## 100 0.00 0.00 0.00
## X2014.Tonnage..Millions. X2015.Tonnage..Millions. X2016.Tonnage..Millions.
## 1 98.09 97.14 95.45
## 2 76.40 60.71 63.81
## 3 47.09 47.75 46.18
## 4 49.30 46.37 46.16
## 5 41.59 41.97 41.56
## 6 43.35 34.94 0.00
## 7 35.33 34.21 33.25
## 8 34.35 32.50 33.19
## 9 31.41 29.83 30.29
## 10 30.78 28.55 26.80
## 11 26.20 26.31 24.49
## 12 33.05 25.78 0.00
## 13 23.34 21.69 23.02
## 14 21.41 19.62 21.95
## 15 20.58 20.48 20.09
## 16 18.90 18.82 18.63
## 17 17.23 17.34 17.24
## 18 16.11 16.05 16.64
## 19 15.26 15.14 16.45
## 20 19.00 17.03 15.95
## 21 15.40 14.82 15.52
## 22 15.38 14.87 15.48
## 23 12.72 12.42 14.91
## 24 16.26 14.99 14.40
## 25 13.56 14.34 14.38
## 26 19.73 14.52 14.22
## 27 14.42 14.10 14.02
## 28 11.40 14.00 13.86
## 29 13.64 13.21 13.68
## 30 15.54 14.35 13.53
## 31 13.03 12.24 12.54
## 32 10.72 11.86 12.30
## 33 14.23 11.45 11.63
## 34 11.39 10.83 11.05
## 35 9.12 9.77 11.05
## 36 10.54 11.32 11.01
## 37 10.89 10.74 10.48
## 38 9.21 9.58 10.39
## 39 11.18 9.65 10.34
## 40 10.72 10.26 10.28
## 41 10.32 10.38 10.23
## 42 9.01 9.08 9.24
## 43 8.49 8.93 9.18
## 44 8.04 8.59 9.01
## 45 8.82 8.64 8.57
## 46 7.93 7.61 8.40
## 47 8.07 7.59 7.99
## 48 9.38 8.40 7.98
## 49 7.95 7.76 7.47
## 50 5.50 5.66 7.45
## 51 7.91 7.47 7.30
## 52 7.57 7.52 7.26
## 53 7.03 7.08 6.94
## 54 8.40 8.29 6.90
## 55 5.74 6.65 6.80
## 56 5.03 6.29 6.26
## 57 3.12 4.48 6.06
## 58 4.36 3.30 5.90
## 59 0.00 5.94 5.82
## 60 0.00 0.00 5.80
## 61 3.60 7.12 4.50
## 62 4.70 5.68 5.78
## 63 6.22 4.76 5.67
## 64 4.10 4.52 5.63
## 65 10.34 7.69 5.50
## 66 7.76 6.21 5.47
## 67 4.60 4.60 5.42
## 68 6.29 5.23 5.27
## 69 5.95 6.17 5.05
## 70 0.00 4.49 5.01
## 71 3.30 3.55 4.96
## 72 5.14 4.64 4.88
## 73 0.00 5.69 4.88
## 74 4.50 4.50 4.66
## 75 4.42 4.46 4.65
## 76 6.03 4.80 4.61
## 77 3.60 7.12 4.50
## 78 4.29 4.39 4.39
## 79 4.27 4.32 4.19
## 80 6.05 5.01 4.06
## 81 5.41 5.16 4.06
## 82 3.28 3.64 3.82
## 83 3.31 3.31 3.69
## 84 4.01 3.28 3.66
## 85 4.07 3.82 3.65
## 86 3.55 3.41 3.60
## 87 3.16 3.14 3.54
## 88 3.59 3.55 3.51
## 89 3.09 3.65 3.49
## 90 0.00 3.09 3.49
## 91 2.95 3.08 3.48
## 92 3.27 3.83 3.48
## 93 3.38 3.27 3.30
## 94 3.04 3.32 3.29
## 95 3.12 3.26 3.28
## 96 3.41 2.71 3.20
## 97 2.39 3.01 3.15
## 98 3.07 3.07 3.07
## 99 2.63 2.66 3.02
## 100 0.00 0.00 3.00
## X2015.Ranking X2016.Ranking
## 1 1 1
## 2 NA 2
## 3 2 3
## 4 3 4
## 5 4 5
## 6 5 NA
## 7 6 6
## 8 7 7
## 9 8 8
## 10 9 9
## 11 10 10
## 12 11 NA
## 13 12 11
## 14 14 12
## 15 13 13
## 16 15 14
## 17 16 15
## 18 19 16
## 19 20 17
## 20 17 18
## 21 23 19
## 22 22 20
## 23 30 21
## 24 21 22
## 25 26 23
## 26 24 24
## 27 27 25
## 28 28 26
## 29 29 27
## 30 25 28
## 31 31 29
## 32 32 30
## 33 33 31
## 34 35 32
## 35 39 33
## 36 34 34
## 37 36 35
## 38 41 36
## 39 40 37
## 40 38 38
## 41 37 39
## 42 42 40
## 43 43 41
## 44 45 42
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## 46 50 44
## 47 51 45
## 48 46 46
## 49 48 47
## 50 61 48
## 51 53 49
## 52 52 50
## 53 55 51
## 54 47 52
## 55 56 53
## 56 57 54
## 57 71 55
## 58 86 56
## 59 NA 57
## 60 NA 58
## 61 54 59
## 62 60 60
## 63 66 61
## 64 69 62
## 65 49 63
## 66 58 64
## 67 68 65
## 68 62 66
## 69 59 67
## 70 NA 68
## 71 81 69
## 72 67 70
## 73 NA 71
## 74 70 72
## 75 72 73
## 76 65 74
## 77 54 75
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## 80 64 78
## 81 63 79
## 82 79 80
## 83 85 81
## 84 87 82
## 85 77 83
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## 87 90 85
## 88 82 86
## 89 78 87
## 90 NA 88
## 91 92 89
## 92 76 90
## 93 88 91
## 94 84 92
## 95 89 93
## 96 NA 94
## 97 94 95
## 98 93 96
## 99 NA 97
## 100 NA 98#using tibble function to load the dataset
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()as_tibble(globst)## # A tibble: 100 x 10
## Companies Headquarters X2011.Tonnage..M~ X2012.Tonnage..~ X2013.Tonnage..~
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 "ArcelorMit~ Luxembourg 97.2 93.6 96.1
## 2 "China Baow~ China 81.0 79.1 83.2
## 3 "HBIS Group" China 44.4 42.8 45.8
## 4 "Nippon Ste~ Japan 33.4 47.9 50.1
## 5 "POSCO" South Korea 39.1 39.9 38.4
## 6 "Baosteel G~ China 43.3 42.7 43.9
## 7 "Shagang Gr~ China 31.9 32.3 35.1
## 8 "Ansteel Gr~ China 29.8 30.2 33.7
## 9 "JFE Steel ~ Japan 29.9 30.4 31.2
## 10 "Shougang G~ China 30.0 31.4 31.5
## # ... with 90 more rows, and 5 more variables: X2014.Tonnage..Millions. <dbl>,
## # X2015.Tonnage..Millions. <dbl>, X2016.Tonnage..Millions. <dbl>,
## # X2015.Ranking <int>, X2016.Ranking <int>#Finding missing values
is.na(globst)## Companies Headquarters X2011.Tonnage..Millions. X2012.Tonnage..Millions.
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## X2013.Tonnage..Millions. X2014.Tonnage..Millions.
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## X2015.Tonnage..Millions. X2016.Tonnage..Millions. X2015.Ranking
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## [30,] FALSE
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## [47,] FALSE
## [48,] FALSE
## [49,] FALSE
## [50,] FALSE
## [51,] FALSE
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## [57,] FALSE
## [58,] FALSE
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## [61,] FALSE
## [62,] FALSE
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## [64,] FALSE
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## [79,] FALSE
## [80,] FALSE
## [81,] FALSE
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## [94,] FALSE
## [95,] FALSE
## [96,] FALSE
## [97,] FALSE
## [98,] FALSE
## [99,] FALSE
## [100,] FALSEsum(is.na(globst))## [1] 11mean(is.na(globst))## [1] 0.011#Displaying column names in a dataset
names(globst)## [1] "Companies" "Headquarters"
## [3] "X2011.Tonnage..Millions." "X2012.Tonnage..Millions."
## [5] "X2013.Tonnage..Millions." "X2014.Tonnage..Millions."
## [7] "X2015.Tonnage..Millions." "X2016.Tonnage..Millions."
## [9] "X2015.Ranking" "X2016.Ranking"#Finding the sum of 2011 tonnage produced by different headquarters
sum(globst$"X2011.Tonnage..Millions.")## [1] 1104.03#Sorting the headquarters of different companies
globst[order(-globst$X2011.Tonnage..Millions.),]## Companies Headquarters
## 1 ArcelorMittal Luxembourg
## 2 China Baowu Group (merger of Baosteel Group and Wuhan Group China
## 3 HBIS Group China
## 6 Baosteel Group China
## 5 POSCO South Korea
## 12 Wuhan Steel Group China
## 4 Nippon Steel and Sumitomo Metal Corporation Japan
## 7 Shagang Group China
## 10 Shougang Group China
## 9 JFE Steel Corporation Japan
## 8 Ansteel Group China
## 13 Shandong Steel Group China
## 11 Tata Steel Group India
## 26 United States Steel Corporation USA
## 20 Gerdau S.A. Brazil
## 14 Nucor Corporation USA
## 17 thyssenkrupp Germany
## 30 EVRAZ Russia
## 16 Maanshan Steel China
## 24 Benxi Steel China
## 15 Hyundai Steel Company South Korea
## 22 Valin Group China
## 33 Severstal Russia
## 39 Metinvest Holding LLC Ukraine
## 21 China Steel Corporation Taiwan, China
## 25 Steel Authority of India Ltd. India
## 27 IMIDRO Iran
## 19 Jianlong Group China
## 31 Magnitogorsk Iron & Steel Works Russia
## 18 Novolipetsk Steel Russia
## 28 Rizhao Steel China
## 32 Baotou Steel China
## 65 Jiuquan Steel China
## 40 Taiyuan Steel China
## 48 Techint Group Argentina
## 37 Anyang Steel China
## 35 Jinxi Steel China
## 45 Xinyu Steel China
## 41 Zongheng Steel China
## 54 Guofeng Steel China
## 53 CELSA Steel Group Spain
## 49 voestalpine Group Austria
## 44 Nanjing Steel China
## 43 Erdemir Group Turkey
## 52 Kobe Steel, Ltd. Japan
## 23 JSW Steel Limited India
## 42 Zenith Steel China
## 80 Usinas Siderìrgicas de Minas Gerais S.A. Brazil
## 50 Essar Steel Group India
## 46 CITIC Pacific Special Steel China
## 64 BlueScope Steel Limited Australia
## 36 Jingye Steel China
## 74 Metalloinvest Management Company Russia
## 38 Sanming Steel China
## 47 SSAB Sweden
## 55 Salzgitter AG Stahl und Technologie Germany
## 68 Saudi Iron & Steel Co. KSA
## 51 Shaanxi Steel China
## 81 Companhia Siderìrgica Nacional Brazil
## 71 Puyang Steel China
## 84 Ezz Steel Egypt
## 75 Altos Hornos de M_xico, S.A.B. de C.V. Mexico
## 85 Nisshin Steel Co., Ltd. (acquired by NSSMC in March 2017) Japan
## 92 Xinxing Ductile China
## 61 Hangzhou Steel China
## 77 Hangzhou Steel China
## 72 Lingyuan Steel China
## 83 Delong Steel China
## 89 Qianjin Steel China
## 82 Rashtriya Ispat Nigam Ltd (VIZAG Steel) India
## 78 Yuanli Group China
## 94 Dongkuk Steel Mill Co., Ltd. South Korea
## 98 Ganglu Steel China
## 88 Rockcheck Steel China
## 87 Shiheng Special Steel China
## 29 Fangda Steel China
## 91 Jindal Steel and Power Ltd India
## 86 Jiyuan Steel China
## 95 Taishan Steel China
## 62 Yingkou Plate China
## 96 Outokumpu Oyj Sweden
## 58 Donghai Steel China
## 34 Liuzhou Steel China
## 56 Ruifeng Steel China
## 57 Binxin Special Steel China
## 59 Tianjin Steel China
## 60 Tsingshan Holding Group China
## 63 ILVA SpA Italy
## 66 RIVA Group Luxembourg
## 67 Yuhua Steel China
## 69 AK Steel Corporation USA
## 70 Ji'nan Steel China
## 73 Tianjin Metallurgy Group China
## 76 ISD (Industrial Union of Donbass) Ukraine
## 79 Mechel Russia
## 90 Tianzhu Steel China
## 93 Donghua Steel China
## 97 Emirates Steel UAE
## 99 Tranvic Steel China
## 100 Tosyali Holding Turkey
## X2011.Tonnage..Millions. X2012.Tonnage..Millions. X2013.Tonnage..Millions.
## 1 97.25 93.58 96.10
## 2 81.02 79.12 83.22
## 3 44.36 42.84 45.79
## 6 43.34 42.70 43.91
## 5 39.12 39.88 38.42
## 12 37.68 36.42 39.31
## 4 33.39 47.86 50.13
## 7 31.92 32.31 35.08
## 10 30.04 31.42 31.52
## 9 29.90 30.41 31.16
## 8 29.75 30.23 33.69
## 13 24.02 23.01 22.79
## 11 23.82 22.97 25.27
## 26 21.99 21.44 20.38
## 20 20.50 19.81 18.97
## 14 19.89 20.13 20.16
## 17 17.94 14.46 15.86
## 30 16.77 15.95 16.11
## 16 16.68 17.34 18.79
## 24 16.49 15.08 16.83
## 15 16.29 17.12 17.30
## 22 15.89 14.11 14.99
## 33 15.29 15.14 15.69
## 39 14.38 14.34 14.29
## 21 14.01 12.73 14.29
## 25 13.50 13.50 13.52
## 27 12.58 13.61 14.29
## 19 12.36 13.76 14.30
## 31 12.20 13.04 11.94
## 18 12.11 14.92 15.47
## 28 11.20 13.22 12.68
## 32 10.22 10.19 10.69
## 65 10.22 10.10 11.16
## 40 9.90 10.13 9.99
## 48 9.53 8.71 9.00
## 37 9.38 7.74 10.32
## 35 9.04 9.10 8.75
## 45 8.72 8.66 8.50
## 41 8.65 9.11 10.19
## 54 8.21 7.98 8.06
## 53 7.84 7.62 6.99
## 49 7.67 7.36 8.02
## 44 7.65 7.18 6.05
## 43 7.47 7.87 8.27
## 52 7.39 7.09 7.53
## 23 7.01 8.48 11.80
## 42 7.01 7.57 8.51
## 80 6.84 7.16 6.86
## 50 6.82 6.70 6.09
## 46 6.73 6.62 7.66
## 64 6.00 4.23 4.20
## 36 5.83 7.30 9.69
## 74 5.82 5.61 4.68
## 38 5.72 6.95 8.22
## 47 5.67 5.25 5.57
## 55 5.66 6.09 5.58
## 68 5.28 5.20 5.47
## 51 5.20 6.65 8.00
## 81 4.87 4.64 4.48
## 71 4.53 4.35 3.40
## 84 4.32 4.56 4.33
## 75 3.81 3.88 4.16
## 85 3.76 3.81 3.70
## 92 3.76 4.36 3.65
## 61 3.65 3.25 3.43
## 77 3.65 3.25 3.43
## 72 3.59 3.56 5.21
## 83 3.57 3.75 3.63
## 89 3.54 3.43 3.46
## 82 3.19 3.11 3.11
## 78 3.11 3.49 3.82
## 94 3.07 3.31 3.33
## 98 3.01 3.06 3.10
## 88 2.99 3.22 3.30
## 87 2.81 2.96 3.07
## 29 2.62 3.28 13.16
## 91 2.62 2.73 2.87
## 86 2.61 3.25 3.50
## 95 2.53 2.81 3.02
## 62 2.21 2.25 3.33
## 96 1.68 1.68 2.97
## 58 1.37 2.31 3.88
## 34 0.00 0.00 0.00
## 56 0.00 0.00 3.36
## 57 0.00 0.00 3.14
## 59 0.00 0.00 0.00
## 60 0.00 0.00 0.00
## 63 0.00 0.00 5.68
## 66 0.00 0.00 7.59
## 67 0.00 0.00 0.00
## 69 0.00 0.00 0.00
## 70 0.00 0.00 0.00
## 73 0.00 0.00 0.00
## 76 0.00 8.49 7.94
## 79 0.00 6.53 4.65
## 90 0.00 0.00 0.00
## 93 0.00 0.00 2.70
## 97 0.00 2.38 2.88
## 99 0.00 0.00 0.00
## 100 0.00 0.00 0.00
## X2014.Tonnage..Millions. X2015.Tonnage..Millions. X2016.Tonnage..Millions.
## 1 98.09 97.14 95.45
## 2 76.40 60.71 63.81
## 3 47.09 47.75 46.18
## 6 43.35 34.94 0.00
## 5 41.59 41.97 41.56
## 12 33.05 25.78 0.00
## 4 49.30 46.37 46.16
## 7 35.33 34.21 33.25
## 10 30.78 28.55 26.80
## 9 31.41 29.83 30.29
## 8 34.35 32.50 33.19
## 13 23.34 21.69 23.02
## 11 26.20 26.31 24.49
## 26 19.73 14.52 14.22
## 20 19.00 17.03 15.95
## 14 21.41 19.62 21.95
## 17 17.23 17.34 17.24
## 30 15.54 14.35 13.53
## 16 18.90 18.82 18.63
## 24 16.26 14.99 14.40
## 15 20.58 20.48 20.09
## 22 15.38 14.87 15.48
## 33 14.23 11.45 11.63
## 39 11.18 9.65 10.34
## 21 15.40 14.82 15.52
## 25 13.56 14.34 14.38
## 27 14.42 14.10 14.02
## 19 15.26 15.14 16.45
## 31 13.03 12.24 12.54
## 18 16.11 16.05 16.64
## 28 11.40 14.00 13.86
## 32 10.72 11.86 12.30
## 65 10.34 7.69 5.50
## 40 10.72 10.26 10.28
## 48 9.38 8.40 7.98
## 37 10.89 10.74 10.48
## 35 9.12 9.77 11.05
## 45 8.82 8.64 8.57
## 41 10.32 10.38 10.23
## 54 8.40 8.29 6.90
## 53 7.03 7.08 6.94
## 49 7.95 7.76 7.47
## 44 8.04 8.59 9.01
## 43 8.49 8.93 9.18
## 52 7.57 7.52 7.26
## 23 12.72 12.42 14.91
## 42 9.01 9.08 9.24
## 80 6.05 5.01 4.06
## 50 5.50 5.66 7.45
## 46 7.93 7.61 8.40
## 64 4.10 4.52 5.63
## 36 10.54 11.32 11.01
## 74 4.50 4.50 4.66
## 38 9.21 9.58 10.39
## 47 8.07 7.59 7.99
## 55 5.74 6.65 6.80
## 68 6.29 5.23 5.27
## 51 7.91 7.47 7.30
## 81 5.41 5.16 4.06
## 71 3.30 3.55 4.96
## 84 4.01 3.28 3.66
## 75 4.42 4.46 4.65
## 85 4.07 3.82 3.65
## 92 3.27 3.83 3.48
## 61 3.60 7.12 4.50
## 77 3.60 7.12 4.50
## 72 5.14 4.64 4.88
## 83 3.31 3.31 3.69
## 89 3.09 3.65 3.49
## 82 3.28 3.64 3.82
## 78 4.29 4.39 4.39
## 94 3.04 3.32 3.29
## 98 3.07 3.07 3.07
## 88 3.59 3.55 3.51
## 87 3.16 3.14 3.54
## 29 13.64 13.21 13.68
## 91 2.95 3.08 3.48
## 86 3.55 3.41 3.60
## 95 3.12 3.26 3.28
## 62 4.70 5.68 5.78
## 96 3.41 2.71 3.20
## 58 4.36 3.30 5.90
## 34 11.39 10.83 11.05
## 56 5.03 6.29 6.26
## 57 3.12 4.48 6.06
## 59 0.00 5.94 5.82
## 60 0.00 0.00 5.80
## 63 6.22 4.76 5.67
## 66 7.76 6.21 5.47
## 67 4.60 4.60 5.42
## 69 5.95 6.17 5.05
## 70 0.00 4.49 5.01
## 73 0.00 5.69 4.88
## 76 6.03 4.80 4.61
## 79 4.27 4.32 4.19
## 90 0.00 3.09 3.49
## 93 3.38 3.27 3.30
## 97 2.39 3.01 3.15
## 99 2.63 2.66 3.02
## 100 0.00 0.00 3.00
## X2015.Ranking X2016.Ranking
## 1 1 1
## 2 NA 2
## 3 2 3
## 6 5 NA
## 5 4 5
## 12 11 NA
## 4 3 4
## 7 6 6
## 10 9 9
## 9 8 8
## 8 7 7
## 13 12 11
## 11 10 10
## 26 24 24
## 20 17 18
## 14 14 12
## 17 16 15
## 30 25 28
## 16 15 14
## 24 21 22
## 15 13 13
## 22 22 20
## 33 33 31
## 39 40 37
## 21 23 19
## 25 26 23
## 27 27 25
## 19 20 17
## 31 31 29
## 18 19 16
## 28 28 26
## 32 32 30
## 65 49 63
## 40 38 38
## 48 46 46
## 37 36 35
## 35 39 33
## 45 44 43
## 41 37 39
## 54 47 52
## 53 55 51
## 49 48 47
## 44 45 42
## 43 43 41
## 52 52 50
## 23 30 21
## 42 42 40
## 80 64 78
## 50 61 48
## 46 50 44
## 64 69 62
## 36 34 34
## 74 70 72
## 38 41 36
## 47 51 45
## 55 56 53
## 68 62 66
## 51 53 49
## 81 63 79
## 71 81 69
## 84 87 82
## 75 72 73
## 85 77 83
## 92 76 90
## 61 54 59
## 77 54 75
## 72 67 70
## 83 85 81
## 89 78 87
## 82 79 80
## 78 73 76
## 94 84 92
## 98 93 96
## 88 82 86
## 87 90 85
## 29 29 27
## 91 92 89
## 86 83 84
## 95 89 93
## 62 60 60
## 96 NA 94
## 58 86 56
## 34 35 32
## 56 57 54
## 57 71 55
## 59 NA 57
## 60 NA 58
## 63 66 61
## 66 58 64
## 67 68 65
## 69 59 67
## 70 NA 68
## 73 NA 71
## 76 65 74
## 79 74 77
## 90 NA 88
## 93 88 91
## 97 94 95
## 99 NA 97
## 100 NA 98# Finding total tonnage of steel produced between 2011-2016 by headquarters
globst<-aggregate(x = globst$X2011.Tonnage..Millions.,globst$X2012.Tonnage..Millions.,globst$X2013.Tonnage..Millions.,globst$X2014.Tonnage..Millions.,globst$X2015.Tonnage..Millions.,globst$X2016.Tonnage..Millions., # Specify data column
by = list(globst$Headquarters), # Specify group indicator
FUN = sum)
globst## Group.1 x
## 1 Argentina 5975.18
## 2 Australia 5971.65
## 3 Austria 5973.32
## 4 Brazil 5997.86
## 5 China 6522.43
## 6 Egypt 5969.97
## 7 Germany 5989.25
## 8 India 6022.61
## 9 Iran 5978.23
## 10 Italy 5965.65
## 11 Japan 6040.09
## 12 KSA 5970.93
## 13 Luxembourg 6062.90
## 14 Mexico 5969.46
## 15 Russia 6027.84
## 16 South Korea 6024.13
## 17 Spain 5973.49
## 18 Sweden 5973.00
## 19 Taiwan, China 5979.66
## 20 Turkey 5973.12
## 21 UAE 5965.65
## 22 Ukraine 5980.03
## 23 USA 6007.53# plotting the barchart of different headquarters production
ggplot(data=globst, aes(x=Group.1, y=x)) +
geom_bar(stat="identity", width=0.5)
# plotting the piechart of different headquarters production
pie = ggplot(globst, aes(x="", y=x, fill=Group.1)) + geom_bar(stat="identity", width=1)
pie_chart<- pie + coord_polar("y", start=0)
pie_chart