MATH2349 Semester 1, 2018
Assignment 3
S3335814- Minh Phan
Required packages
# loadding packages
library(forecast)
library(readr)
library(dplyr)##
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library(knitr)
library(lubridate)##
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## datelibrary(stringr)
library(outliers)
library(MVN)## sROC 0.1-2 loadedlibrary(datasets)
library(MASS)##
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library(validate)##
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## format.pval, unitslibrary (readxl)
library(foreign)
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## guess_encodingExecutive Summary
The final dataset was a productof 7 different data sets. Firstly, all the excels files downloaded from http://www.abs.gov.au/, were converted into a single page excel files, because some of them have 3 sheets in a file. All these data sets were imported to R-studio using various of packages. Variables in some data sets were renamed for easier access. Subsequently, all the data sets relating to consumption of meat were joined using left joint. I did not used merge function because the data sets dont match in numbers of observation and not all the information was interested. The select() function was used to select wanted variables from population and consumption related datasets; this helped to reduced repeated or unwanted variables. All the variables in each data set were checked and make sure that they are appropriate. The popolation dataset requires some tidy up before joining to the meat_cons dataset. After joining the meat_cons and population datasets, the proprocess of checking variables were repeated; some variables were renamed.The dataset is now meat_cons_pop, the next step was scanning for missing values and outliers. The last step was tranforming some of the numerical variables appropriately. In addition, a subset of the dataset was made only including Australia meat consumption. This dataset was joined with the datasets realating to AUstralian meat production and export in order to find out interested information. A statement about Australian meat consumption and wastage was made at the end, using statistical values from the combined dataset.
Data
The data included 7 data sets. Three of them were download from https://data.oecd.org/agroutput/meat-consumption.htm, including: beef and veal.csv, pork.csv,sheep.csv. One was download from https://data.worldbank.org/indicator/SP.POP.TOTL ,australia population.xls. Three was downloaded from http://www.abs.gov.au/, including pork production aus.xls,meat export aus.xls,pork production aus.xls
#import/ scapping data
beef_and_veal_consumption <- read_csv("beef and veal.csv")## Parsed with column specification:
## cols(
## LOCATION = col_character(),
## INDICATOR = col_character(),
## SUBJECT = col_character(),
## MEASURE = col_character(),
## FREQUENCY = col_character(),
## TIME = col_integer(),
## Value = col_double(),
## `Flag Codes` = col_character()
## ) pork_consumption <- read_csv("pork.csv")## Parsed with column specification:
## cols(
## LOCATION = col_character(),
## INDICATOR = col_character(),
## SUBJECT = col_character(),
## MEASURE = col_character(),
## FREQUENCY = col_character(),
## TIME = col_integer(),
## Value = col_double(),
## `Flag Codes` = col_character()
## ) pork_production<-read_excel("pork production aus.xls")
redmeat_production<-read_excel("red meat production aus.xls")
lamb_consumption<-read_csv("sheep.csv")## Parsed with column specification:
## cols(
## LOCATION = col_character(),
## INDICATOR = col_character(),
## SUBJECT = col_character(),
## MEASURE = col_character(),
## FREQUENCY = col_character(),
## TIME = col_integer(),
## Value = col_double(),
## `Flag Codes` = col_character()
## ) meat_export<- read_excel("meat export aus.xls")
population<-read_excel("australia population.xls",skip = 3)
head(beef_and_veal_consumption) head(pork_consumption) head(lamb_consumption) head(redmeat_production) head(pork_production) head(meat_export) head(population) #change value variables to the according consumption
colnames(pork_consumption)## [1] "LOCATION" "INDICATOR" "SUBJECT" "MEASURE" "FREQUENCY"
## [6] "TIME" "Value" "Flag Codes" pork_consumption<-rename(pork_consumption, Pork_cons=Value)
beef_and_veal_consumption<- rename(beef_and_veal_consumption, beef_and_veal_cons=Value)
lamb_consumption<- rename(lamb_consumption, lamb_cons=Value)
#mearging
meat_cons_draft<- lamb_consumption %>% left_join(beef_and_veal_consumption, by =c("TIME","LOCATION")) %>%left_join(pork_consumption,by=c("TIME","LOCATION"))
head(meat_cons_draft)meat_cons<-dplyr::select(meat_cons_draft,TIME,LOCATION, beef_and_veal_cons,lamb_cons,Pork_cons, MEASURE)
head(meat_cons) #meat production dataset, merging pork_production and beef_production
meat_prod_draft<- pork_production %>% left_join(redmeat_production,by=c("X__1"))
head(meat_prod_draft)str(meat_prod_draft)## Classes 'tbl_df', 'tbl' and 'data.frame': 549 obs. of 47 variables:
## $ X__1 : POSIXct, format: "1972-07-01" "1972-08-01" ...
## $ Meat Produced ; PIGS ; Total (State) ; : num 18028 20091 18718 20007 20629 ...
## $ Meat Produced ; PIGS ; New South Wales ; : num 4384 5117 4911 4877 5417 ...
## $ Meat Produced ; PIGS ; Victoria ; : num 4655 5140 4755 5744 5194 ...
## $ Meat Produced ; PIGS ; Queensland ; : num 4018 4623 3927 3756 4499 ...
## $ Meat Produced ; PIGS ; South Australia ; : num 2102 2403 2218 2360 2301 ...
## $ Meat Produced ; PIGS ; Western Australia ; : num 2275 2126 2229 2512 2437 ...
## $ Meat Produced ; PIGS ; Tasmania ; : num 523 614 600 690 693 694 600 627 657 545 ...
## $ Meat Produced ; PIGS ; Northern Territory ; : num 7 10 12 10 11 19 14 19 19 5 ...
## $ Meat Produced ; PIGS ; Australian Capital Territory ; : num 64 58 66 58 77 76 70 79 88 80 ...
## $ Meat Produced ; PIGS ; Total (State) ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; New South Wales ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Victoria ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Queensland ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; South Australia ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Western Australia ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Tasmania ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Total (State) ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; New South Wales ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Victoria ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Queensland ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; South Australia ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Western Australia ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; PIGS ; Tasmania ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Total (State) ; : num 209456 216225 192245 204724 212670 ...
## $ Meat Produced ; Total Red Meat ; New South Wales ; : num 58469 58462 52360 53258 60018 ...
## $ Meat Produced ; Total Red Meat ; Victoria ; : num 63992 67262 59556 70780 75729 ...
## $ Meat Produced ; Total Red Meat ; Queensland ; : num 48697 52003 43267 35750 30851 ...
## $ Meat Produced ; Total Red Meat ; South Australia ; : num 13698 13766 13790 17850 16856 ...
## $ Meat Produced ; Total Red Meat ; Western Australia ; : num 16182 16248 16077 19675 20936 ...
## $ Meat Produced ; Total Red Meat ; Tasmania ; : num 5521 5474 4947 5414 6583 ...
## $ Meat Produced ; Total Red Meat ; Northern Territory ; : num 2265 2377 1679 1388 1005 ...
## $ Meat Produced ; Total Red Meat ; Australian Capital Territory ;: num 632 633 569 609 692 593 753 602 658 622 ...
## $ Meat Produced ; Total Red Meat ; Total (State) ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; New South Wales ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Victoria ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Queensland ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; South Australia ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Western Australia ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Tasmania ;__1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Total (State) ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; New South Wales ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Victoria ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Queensland ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; South Australia ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Western Australia ;__2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Meat Produced ; Total Red Meat ; Tasmania ;__2 : num NA NA NA NA NA NA NA NA NA NA ...meat_prod_draft_2 <- dplyr::select(meat_prod_draft,X__1,"Meat Produced ; PIGS ; Total (State) ;","Meat Produced ; Total Red Meat ; Total (State) ;" )
meat_prod_aus<- rename(meat_prod_draft_2, red_meat_prod="Meat Produced ; PIGS ; Total (State) ;",pork_prod="Meat Produced ; Total Red Meat ; Total (State) ;")
head(meat_prod_aus)population<- population %>% dplyr::select("Country Name","Country Code","1991":"2017")
meat_cons<- rename(meat_cons, 'Country Code'='LOCATION')
meat_cons<- rename(meat_cons, 'year'='TIME')
#the rest of the report will be working on these 2 data sets, they will be joined later for some insights analyisUnderstand
str(meat_cons)## Classes 'tbl_df', 'tbl' and 'data.frame': 1235 obs. of 6 variables:
## $ year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Country Code : chr "AUS" "AUS" "AUS" "AUS" ...
## $ beef_and_veal_cons: num 27.7 26.2 26.2 25.5 25.3 ...
## $ lamb_cons : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork_cons : num 14.4 15.1 14.9 15.6 15.5 ...
## $ MEASURE : chr "KG_CAP" "KG_CAP" "KG_CAP" "KG_CAP" ...meat_cons$beef_and_veal_cons<-as.double(meat_cons$beef_and_veal_cons)
meat_cons$lamb_cons<-as.double(meat_cons$lamb_cons)
meat_cons$Pork_cons<-as.double(meat_cons$Pork_cons)
str(meat_cons)## Classes 'tbl_df', 'tbl' and 'data.frame': 1235 obs. of 6 variables:
## $ year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Country Code : chr "AUS" "AUS" "AUS" "AUS" ...
## $ beef_and_veal_cons: num 27.7 26.2 26.2 25.5 25.3 ...
## $ lamb_cons : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork_cons : num 14.4 15.1 14.9 15.6 15.5 ...
## $ MEASURE : chr "KG_CAP" "KG_CAP" "KG_CAP" "KG_CAP" ...str(population)## Classes 'tbl_df', 'tbl' and 'data.frame': 264 obs. of 29 variables:
## $ Country Name: chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Country Code: chr "ABW" "AFG" "AGO" "ALB" ...
## $ 1991 : num 64622 12993657 12553446 3266790 56671 ...
## $ 1992 : num 68235 13981231 12968345 3247039 58888 ...
## $ 1993 : num 72504 15095099 13403734 3227287 60971 ...
## $ 1994 : num 76700 16172719 13841301 3207536 62677 ...
## $ 1995 : num 80324 17099541 14268994 3187784 63850 ...
## $ 1996 : num 83200 17822884 14682284 3168033 64360 ...
## $ 1997 : num 85451 18381605 15088981 3148281 64327 ...
## $ 1998 : num 87277 18863999 15504318 3128530 64142 ...
## $ 1999 : num 89005 19403676 15949766 3108778 64370 ...
## $ 2000 : num 90853 20093756 16440924 3089027 65390 ...
## $ 2001 : num 92898 20966463 16983266 3060173 67341 ...
## $ 2002 : num 94992 21979923 17572649 3051010 70049 ...
## $ 2003 : num 97017 23064851 18203369 3039616 73182 ...
## $ 2004 : num 98737 24118979 18865716 3026939 76244 ...
## $ 2005 : num 100031 25070798 19552542 3011487 78867 ...
## $ 2006 : num 100832 25893450 20262399 2992547 80991 ...
## $ 2007 : num 101220 26616792 20997687 2970017 82683 ...
## $ 2008 : num 101353 27294031 21759420 2947314 83861 ...
## $ 2009 : num 101453 28004331 22549547 2927519 84462 ...
## $ 2010 : num 101669 28803167 23369131 2913021 84449 ...
## $ 2011 : num 102053 29708599 24218565 2905195 83751 ...
## $ 2012 : num 102577 30696958 25096150 2900401 82431 ...
## $ 2013 : num 103187 31731688 25998340 2895092 80788 ...
## $ 2014 : num 103795 32758020 26920466 2889104 79223 ...
## $ 2015 : num 104341 33736494 27859305 2880703 78014 ...
## $ 2016 : num 104822 34656032 28813463 2876101 77281 ...
## $ 2017 : logi NA NA NA NA NA NA ...population$`2017`<- as.numeric(population$`2017`)
str(population)## Classes 'tbl_df', 'tbl' and 'data.frame': 264 obs. of 29 variables:
## $ Country Name: chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Country Code: chr "ABW" "AFG" "AGO" "ALB" ...
## $ 1991 : num 64622 12993657 12553446 3266790 56671 ...
## $ 1992 : num 68235 13981231 12968345 3247039 58888 ...
## $ 1993 : num 72504 15095099 13403734 3227287 60971 ...
## $ 1994 : num 76700 16172719 13841301 3207536 62677 ...
## $ 1995 : num 80324 17099541 14268994 3187784 63850 ...
## $ 1996 : num 83200 17822884 14682284 3168033 64360 ...
## $ 1997 : num 85451 18381605 15088981 3148281 64327 ...
## $ 1998 : num 87277 18863999 15504318 3128530 64142 ...
## $ 1999 : num 89005 19403676 15949766 3108778 64370 ...
## $ 2000 : num 90853 20093756 16440924 3089027 65390 ...
## $ 2001 : num 92898 20966463 16983266 3060173 67341 ...
## $ 2002 : num 94992 21979923 17572649 3051010 70049 ...
## $ 2003 : num 97017 23064851 18203369 3039616 73182 ...
## $ 2004 : num 98737 24118979 18865716 3026939 76244 ...
## $ 2005 : num 100031 25070798 19552542 3011487 78867 ...
## $ 2006 : num 100832 25893450 20262399 2992547 80991 ...
## $ 2007 : num 101220 26616792 20997687 2970017 82683 ...
## $ 2008 : num 101353 27294031 21759420 2947314 83861 ...
## $ 2009 : num 101453 28004331 22549547 2927519 84462 ...
## $ 2010 : num 101669 28803167 23369131 2913021 84449 ...
## $ 2011 : num 102053 29708599 24218565 2905195 83751 ...
## $ 2012 : num 102577 30696958 25096150 2900401 82431 ...
## $ 2013 : num 103187 31731688 25998340 2895092 80788 ...
## $ 2014 : num 103795 32758020 26920466 2889104 79223 ...
## $ 2015 : num 104341 33736494 27859305 2880703 78014 ...
## $ 2016 : num 104822 34656032 28813463 2876101 77281 ...
## $ 2017 : num NA NA NA NA NA NA NA NA NA NA ...Tidy & Manipulate Data I
pop<-population %>% gather('1991','1992','1993','1994','1995','1996','1997','1998','1999','2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017', key = "year", value = "population")
str(pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 7128 obs. of 4 variables:
## $ Country Name: chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Country Code: chr "ABW" "AFG" "AGO" "ALB" ...
## $ year : chr "1991" "1991" "1991" "1991" ...
## $ population : num 64622 12993657 12553446 3266790 56671 ...pop$year<- as.integer(pop$year)
str(pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 7128 obs. of 4 variables:
## $ Country Name: chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Country Code: chr "ABW" "AFG" "AGO" "ALB" ...
## $ year : int 1991 1991 1991 1991 1991 1991 1991 1991 1991 1991 ...
## $ population : num 64622 12993657 12553446 3266790 56671 ...# joining meat consumtion to countries population by year and Country Code
meat_cons_pop<- meat_cons %>% left_join(pop, by= c("year","Country Code"))
str(meat_cons_pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 1235 obs. of 8 variables:
## $ year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Country Code : chr "AUS" "AUS" "AUS" "AUS" ...
## $ beef_and_veal_cons: num 27.7 26.2 26.2 25.5 25.3 ...
## $ lamb_cons : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork_cons : num 14.4 15.1 14.9 15.6 15.5 ...
## $ MEASURE : chr "KG_CAP" "KG_CAP" "KG_CAP" "KG_CAP" ...
## $ Country Name : chr "Australia" "Australia" "Australia" "Australia" ...
## $ population : num 17284000 17495000 17667000 17855000 18072000 ...str(meat_cons_pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 1235 obs. of 8 variables:
## $ year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Country Code : chr "AUS" "AUS" "AUS" "AUS" ...
## $ beef_and_veal_cons: num 27.7 26.2 26.2 25.5 25.3 ...
## $ lamb_cons : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork_cons : num 14.4 15.1 14.9 15.6 15.5 ...
## $ MEASURE : chr "KG_CAP" "KG_CAP" "KG_CAP" "KG_CAP" ...
## $ Country Name : chr "Australia" "Australia" "Australia" "Australia" ...
## $ population : num 17284000 17495000 17667000 17855000 18072000 ...#change pork consumption to numeric
meat_cons_pop$Pork_cons<- as.numeric(meat_cons_pop$Pork_cons)
#change country code and MEASURE to factors
meat_cons_pop$`Country Code`<-as.factor(meat_cons_pop$`Country Code`)
meat_cons_pop$MEASURE<-as.factor(meat_cons_pop$MEASURE)
str(meat_cons_pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 1235 obs. of 8 variables:
## $ year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Country Code : Factor w/ 46 levels "ARG","AUS","BGD",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ beef_and_veal_cons: num 27.7 26.2 26.2 25.5 25.3 ...
## $ lamb_cons : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork_cons : num 14.4 15.1 14.9 15.6 15.5 ...
## $ MEASURE : Factor w/ 1 level "KG_CAP": 1 1 1 1 1 1 1 1 1 1 ...
## $ Country Name : chr "Australia" "Australia" "Australia" "Australia" ...
## $ population : num 17284000 17495000 17667000 17855000 18072000 ...head(meat_cons_pop)Tidy & Manipulate Data II
meat_cons_pop<-mutate(meat_cons_pop,total_meat_cons_per_capita= beef_and_veal_cons+ lamb_cons+ Pork_cons, volume_of_meat_cons_in_tonne= total_meat_cons_per_capita*population/1000)
meat_cons_pop<- rename(meat_cons_pop, 'Year'='year')
meat_cons_pop<- rename(meat_cons_pop, 'Population (people)'='population')
meat_cons_pop<- rename(meat_cons_pop, 'Beef and veal consumption (kg/capita)'='beef_and_veal_cons')
meat_cons_pop<- rename(meat_cons_pop, 'Lamb consumption (kg/capita)'='lamb_cons')
meat_cons_pop<- rename(meat_cons_pop, 'Pork consumption (kg/capita)'='Pork_cons')
meat_cons_pop<- rename(meat_cons_pop, 'Meat consumption (kg/capita)'='total_meat_cons_per_capita')
meat_cons_pop<- rename(meat_cons_pop, 'Country meat consumption (tonnes)'='volume_of_meat_cons_in_tonne')
head(meat_cons_pop)#drop the variable MEASURE and reaggrage all the variables
meat_cons_pop<-meat_cons_pop[,-6]
head(meat_cons_pop)#rearrange variables for easier reading
meat_cons_pop <- meat_cons_pop[c(6,1,7,3,4,5,8,9,2)]
str(meat_cons_pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 1235 obs. of 9 variables:
## $ Country Name : chr "Australia" "Australia" "Australia" "Australia" ...
## $ Year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Population (people) : num 17284000 17495000 17667000 17855000 18072000 ...
## $ Beef and veal consumption (kg/capita): num 27.7 26.2 26.2 25.5 25.3 ...
## $ Lamb consumption (kg/capita) : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork consumption (kg/capita) : num 14.4 15.1 14.9 15.6 15.5 ...
## $ Meat consumption (kg/capita) : num 61.4 59.2 58.4 59.4 55.9 ...
## $ Country meat consumption (tonnes) : num 1060904 1034837 1031691 1060933 1009328 ...
## $ Country Code : Factor w/ 46 levels "ARG","AUS","BGD",..: 2 2 2 2 2 2 2 2 2 2 ...meat_cons_pop$`Pork consumption (kg/capita)`<- as.numeric(meat_cons_pop$`Pork consumption (kg/capita)`)
head(meat_cons_pop)str(meat_cons_pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 1235 obs. of 9 variables:
## $ Country Name : chr "Australia" "Australia" "Australia" "Australia" ...
## $ Year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Population (people) : num 17284000 17495000 17667000 17855000 18072000 ...
## $ Beef and veal consumption (kg/capita): num 27.7 26.2 26.2 25.5 25.3 ...
## $ Lamb consumption (kg/capita) : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork consumption (kg/capita) : num 14.4 15.1 14.9 15.6 15.5 ...
## $ Meat consumption (kg/capita) : num 61.4 59.2 58.4 59.4 55.9 ...
## $ Country meat consumption (tonnes) : num 1060904 1034837 1031691 1060933 1009328 ...
## $ Country Code : Factor w/ 46 levels "ARG","AUS","BGD",..: 2 2 2 2 2 2 2 2 2 2 ...Scan I
sum(is.na(meat_cons_pop))## [1] 392#missing countries name
which(is.na(meat_cons_pop$`Country Name`))## [1] 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169
## [15] 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183
## [29] 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197
## [43] 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211
## [57] 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225
## [71] 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235#Chose to delete all observation with missing countries names, because all these obervation represents groups of coutries such as : EU, OCEC,etc
meat_cons_pop<-meat_cons_pop[-which(is.na(meat_cons_pop$`Country Name`)),]
sum(is.na(meat_cons_pop$`Country Name`))## [1] 0which(is.na(meat_cons_pop$Year))## integer(0)which(is.na(meat_cons_pop$`Beef and veal consumption (kg/capita)`))## [1] 1103 1104 1105 1106 1107 1108 1109 1110which(is.na(meat_cons_pop$`Lamb consumption (kg/capita)`))## integer(0)which(is.na(meat_cons_pop$`Pork consumption (kg/capita)`))## [1] 835 1103#the missing values in beef_and_veal_cons belong the wolrd, since this will create some confusion for analysis such as calculating total amount of meat consumpted, I choose to delete these observations.
meat_cons_pop<-meat_cons_pop[-(1103:1155), ]
which(is.na(meat_cons_pop$Year))## integer(0)which(is.na(meat_cons_pop$`Beef and veal consumption (kg/capita)`))## integer(0)which(is.na(meat_cons_pop$`Lamb consumption (kg/capita)`))## integer(0)which(is.na(meat_cons_pop$`Pork consumption (kg/capita)`))## [1] 835#in Pork_cons from row 835, relacing with the mean consumprion of that country over the year
russian<- meat_cons_pop %>% filter(`Country Name`=="Russian Federation")
mean_pork<- impute(russian$`Pork consumption (kg/capita)`, fun = mean)
meat_cons_pop$`Pork consumption (kg/capita)`[is.na(meat_cons_pop$`Pork consumption (kg/capita)`)]=14.990506
which(is.na(meat_cons_pop$`Pork consumption (kg/capita)`))## integer(0)which(is.na(meat_cons_pop$`Country Code`))## integer(0)which(is.na(meat_cons_pop$`Meat consumption (kg/capita)`))## [1] 835#Russian Ferdaration is missing because the value of Pork consumption (kg/capita) was recently added, I will use validate package at this step to make sure rules are apply and fill the missing values
which(is.na(meat_cons_pop$`Country meat consumption (tonnes)`))## [1] 27 54 81 108 135 162 189 216 243 270 297 324 351 378
## [15] 405 432 457 484 511 538 565 592 619 645 672 699 726 753
## [29] 780 807 834 835 860 887 914 941 968 995 1021 1048 1075 1102which(is.na(meat_cons_pop$`Population (people)`))## [1] 27 54 81 108 135 162 189 216 243 270 297 324 351 378
## [15] 405 432 457 484 511 538 565 592 619 645 672 699 726 753
## [29] 780 807 834 860 887 914 941 968 995 1021 1048 1075 1102#ignoring Population (people) and Country meat consumption (tonnes) for the year 2017- will attempt to solve this later
#Russian Ferdaration is missing because the value of Pork consumption (kg/capita) was recently added, I will use validate package at this step to make sure rules are apply and fill the missing values
which(is.na(meat_cons_pop$`Meat consumption (kg/capita)`))## [1] 835Rules <- validator(`Beef and veal consumption (kg/capita)` + `Pork consumption (kg/capita)` + `Lamb consumption (kg/capita)` == `Meat consumption (kg/capita)`, `Beef and veal consumption (kg/capita)` >= 0,`Lamb consumption (kg/capita)` >= 0, `Pork consumption (kg/capita)` >= 0, `Population (people)`*`Meat consumption (kg/capita)`/1000==`Country meat consumption (tonnes)`,`Population (people)`>0)
str(meat_cons_pop)## Classes 'tbl_df', 'tbl' and 'data.frame': 1102 obs. of 9 variables:
## $ Country Name : chr "Australia" "Australia" "Australia" "Australia" ...
## $ Year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Population (people) : num 17284000 17495000 17667000 17855000 18072000 ...
## $ Beef and veal consumption (kg/capita): num 27.7 26.2 26.2 25.5 25.3 ...
## $ Lamb consumption (kg/capita) : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork consumption (kg/capita) : num 14.4 15.1 14.9 15.6 15.5 ...
## $ Meat consumption (kg/capita) : num 61.4 59.2 58.4 59.4 55.9 ...
## $ Country meat consumption (tonnes) : num 1060904 1034837 1031691 1060933 1009328 ...
## $ Country Code : Factor w/ 46 levels "ARG","AUS","BGD",..: 2 2 2 2 2 2 2 2 2 2 ...#use the rule to missing data
meat_cons_pop <- impute_lr(meat_cons_pop,Rules)
which(is.na(meat_cons_pop$`Meat consumption (kg/capita)`))## integer(0)#check for infinite values
which(is.infinite(meat_cons_pop$`Pork consumption (kg/capita)`))## integer(0)which(is.infinite(meat_cons_pop$Year))## integer(0)which(is.infinite(meat_cons_pop$`Beef and veal consumption (kg/capita)`))## integer(0)which(is.infinite(meat_cons_pop$`Lamb consumption (kg/capita)`))## integer(0)which(is.infinite(meat_cons_pop$`Meat consumption (kg/capita)`))## integer(0)which(is.infinite(meat_cons_pop$`Country meat consumption (tonnes)`))## integer(0)#removing white space in Country name
meat_cons_pop$`Country Name` <- str_trim(meat_cons_pop$`Country Name`,side = "both")
#clean up Country Code and Country Namemake sure they are both factors with the same amount of levels- 1 country to 1 country code
table(meat_cons_pop$`Country Name`)##
## Algeria Argentina Australia
## 27 27 27
## Bangladesh Brazil Canada
## 27 27 27
## Chile China Colombia
## 27 27 27
## Egypt, Arab Rep. Ethiopia Ghana
## 27 25 27
## Haiti India Indonesia
## 27 27 27
## Iran, Islamic Rep. Israel Japan
## 27 27 27
## Kazakhstan Korea, Rep. Malaysia
## 26 27 27
## Mexico Mozambique New Zealand
## 27 27 27
## Nigeria Pakistan Paraguay
## 27 27 27
## Peru Philippines Russian Federation
## 27 27 26
## Saudi Arabia South Africa Sudan
## 27 27 27
## Tanzania Thailand Turkey
## 27 27 27
## Ukraine United States Uruguay
## 26 27 27
## Vietnam Zambia
## 27 27table(meat_cons_pop$`Country Code`)##
## ARG AUS BGD BRA BRICS CAN CHL CHN COL DZA EGY ETH
## 27 27 27 27 0 27 27 27 27 27 27 25
## EU28 GHA HTI IDN IND IRN ISR JPN KAZ KOR MEX MOZ
## 0 27 27 27 27 27 27 27 26 27 27 27
## MYS NGA NZL OECD PAK PER PHL PRY RUS SAU SDN SSA
## 27 27 27 0 27 27 27 27 26 27 27 0
## THA TUR TZA UKR URY USA VNM WLD ZAF ZMB
## 27 27 27 26 27 27 27 0 27 27meat_cons_pop$`Country Code`<- as.character(meat_cons_pop$`Country Code`)
table(meat_cons_pop$`Country Code`)##
## ARG AUS BGD BRA CAN CHL CHN COL DZA EGY ETH GHA HTI IDN IND IRN ISR JPN
## 27 27 27 27 27 27 27 27 27 27 25 27 27 27 27 27 27 27
## KAZ KOR MEX MOZ MYS NGA NZL PAK PER PHL PRY RUS SAU SDN THA TUR TZA UKR
## 26 27 27 27 27 27 27 27 27 27 27 26 27 27 27 27 27 26
## URY USA VNM ZAF ZMB
## 27 27 27 27 27which(is.na(meat_cons_pop$`Country Code`))## integer(0)meat_cons_pop$`Country Code`<- as.factor(meat_cons_pop$`Country Code`)
str(meat_cons_pop$`Country Code`)## Factor w/ 41 levels "ARG","AUS","BGD",..: 2 2 2 2 2 2 2 2 2 2 ...meat_cons_pop$`Country Name`<- as.factor(meat_cons_pop$`Country Name`)
str(meat_cons_pop$`Country Name`)## Factor w/ 41 levels "Algeria","Argentina",..: 3 3 3 3 3 3 3 3 3 3 ...Scan II
#Investigate numeric variables for outliers - except Country meat consumption, because this value has many missing value in the year 2017 due to the missing in formation of Population in 2017
zscores_beef <- meat_cons_pop$`Beef and veal consumption (kg/capita)` %>% scores(type = "z")
zscores_beef %>% summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.9244 -0.6657 -0.4570 0.0000 0.4054 4.7655which( abs(zscores_beef) >3 )## [1] 244 245 246 247 250 252 253 260 261 262 1022 1023 1024 1025
## [15] 1026 1027 1028 1029 1030 1031 1033 1036 1045 1046zscores_pork <- meat_cons_pop$`Pork consumption (kg/capita)` %>% scores(type = "z")
zscores_pork %>% summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.9360 -0.9004 -0.4437 0.0000 0.6990 3.1010which( abs(zscores_pork) >3 )## [1] 375zscores_lamb <- meat_cons_pop$`Lamb consumption (kg/capita)` %>% scores(type = "z")
zscores_lamb %>% summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.58480 -0.46953 -0.35367 0.00000 0.01835 8.99463which( abs(zscores_lamb) >3 )## [1] 1 2 3 4 5 6 7 8 9 10 11 136 137 138
## [15] 139 140 141 142 143 144 145 146 147 148 150 151 152 153
## [29] 1023 1024zscores_meat <- meat_cons_pop$`Meat consumption (kg/capita)` %>% scores(type = "z")
zscores_meat %>% summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.1565 -0.7704 -0.3530 0.0000 0.6366 3.6761which( abs(zscores_meat) >3 )## [1] 1022 1023 1025#create a subset of the meat_cons_pop data set to analyse multivariate outliers
meat_cons_pop_subset<- meat_cons_pop %>% dplyr::select("Beef and veal consumption (kg/capita)","Pork consumption (kg/capita)","Lamb consumption (kg/capita)","Meat consumption (kg/capita)")
multi_outliers <- mvn(data = meat_cons_pop_subset, multivariateOutlierMethod = "quan", showOutliers = TRUE)## Warning in covMcd(data, alpha = alpha): The covariance matrix of the data is singular.
## There are 1102 observations (in the entire dataset of 1102 obs.)
## lying on the hyperplane with equation a_1*(x_i1 - m_1) + ... +
## a_p*(x_ip - m_p) = 0 with (m_1, ..., m_p) the mean of these
## observations and coefficients a_i from the vector a <- c(-0.5,
## -0.5, -0.5, 0.5)
multi_outliers$multivariateOutliers#12 outliers were found
#These outliers should not be removed or replaced, these are good points of observations for examples: finding out which countries out consumed everyone else. Excluding or impute these outliers can lead to some over generalisations of the data, which possible lead to underfitting in future analysis.
#However I'll perform some handling techniques here just to show the possibilities.
library(outliers)
beef_veal_clean<- meat_cons_pop$`Beef and veal consumption (kg/capita)`[ - which( abs(zscores_beef) >3 )]
lamb_clean<- meat_cons_pop$`Lamb consumption (kg/capita)`[ - which( abs(zscores_lamb) >3 )]
pork_clean<- meat_cons_pop$`Pork consumption (kg/capita)`[ - which( abs(zscores_pork) >3 )]
meat_clean<- meat_cons_pop$`Meat consumption (kg/capita)`[ - which( abs(zscores_meat) >3 )]
lamb_clean## [1] 13.699329624 12.084480410 11.920952053 11.935469129 12.322278981
## [6] 12.550873524 10.972332261 10.324144008 8.969861448 8.044461783
## [11] 8.720105015 8.716901165 8.107499586 9.331542874 8.550365388
## [16] 8.537929538 0.764272223 0.741148210 0.752332884 0.723221435
## [21] 0.713323289 0.664067854 0.651948044 0.730094276 0.753867099
## [26] 0.829785698 0.911196365 0.910415471 0.952507759 0.973777595
## [31] 0.982951162 1.066423447 1.070975244 1.024720129 1.013341834
## [36] 0.954364265 0.932547495 0.848751630 0.879485148 0.924069666
## [41] 0.909873857 0.930115655 0.885564286 0.772784737 0.811072174
## [46] 0.683020401 0.571794327 0.585048734 0.504681216 0.444474214
## [51] 0.425857203 0.357453177 0.324378998 0.310996772 0.297423731
## [56] 0.255047498 0.322503797 0.367721359 0.370241212 0.266869910
## [61] 0.269907059 0.272553472 0.218548879 0.220323466 0.190034662
## [66] 0.125903410 0.141513953 0.132097181 0.146290821 0.147081018
## [71] 0.157340811 0.299883723 0.239479192 0.275852555 0.255908013
## [76] 0.214451912 0.193039231 0.154387424 0.134180509 0.114270204
## [81] 0.094322111 0.112569650 0.111644734 0.111577983 0.110909045
## [86] 0.109924686 0.109530422 0.108842259 0.108179501 0.089629601
## [91] 0.106974771 0.106431882 0.105923057 0.140589216 0.174971546
## [96] 0.191666673 0.192642272 0.595611299 0.636915864 0.644664886
## [101] 0.668995778 0.411689439 0.401294788 0.491592867 0.512224291
## [106] 0.580432640 0.687270364 0.727653473 0.732683948 0.977859783
## [111] 0.839348936 0.689909086 0.661304067 0.671650756 0.656185765
## [116] 0.582026716 0.540572705 0.515065793 0.475062061 0.533069643
## [121] 0.526332280 0.533404590 0.521997460 0.517430527 13.736864230
## [126] 4.920556417 2.663581800 7.773084727 7.559891169 2.439047007
## [131] 4.583594653 3.947113811 0.888752395 3.245971867 5.817571492
## [136] 5.676287149 5.571049307 4.964521895 6.135095123 5.402724137
## [141] 5.478680865 5.336387541 5.184313119 5.190372700 4.798801671
## [146] 4.486088994 4.156214067 4.178365313 4.084843316 4.034657299
## [151] 4.088872854 4.015673751 3.704675089 3.346682262 3.543126080
## [156] 3.785745711 4.063831116 4.097838870 4.105467393 4.078266752
## [161] 4.091713768 0.620546584 0.600503433 0.583741361 0.523016010
## [166] 0.521671269 0.494724777 0.487098617 0.519978664 0.510847964
## [171] 0.499488832 0.513972995 0.527195841 0.503400709 0.485471053
## [176] 0.473107147 0.468799888 0.502829357 0.441804200 0.431340880
## [181] 0.404473802 0.374669357 0.379550900 0.383571245 0.408284037
## [186] 0.417358254 0.415569994 0.378633236 5.070369070 5.341982538
## [191] 5.637375396 5.522880783 5.480153472 5.714705370 5.270391987
## [196] 5.279382718 5.091243575 5.136023161 4.958480952 4.951486993
## [201] 4.889660067 5.255777302 5.449098215 5.397440483 5.368050932
## [206] 5.005305785 5.269790030 5.421221351 6.471094010 6.628306040
## [211] 6.936548182 7.074476432 7.077000957 7.061629510 7.077428347
## [216] 2.338816320 1.886415645 1.860565881 2.362608184 2.222376976
## [221] 1.778622434 1.617024538 1.414468260 1.329264592 1.405007574
## [226] 1.387649645 1.355902715 1.301618494 1.251013872 1.177557860
## [231] 1.140432395 1.170444672 1.092868921 1.038564005 1.027963258
## [236] 1.080659088 1.173898493 1.182358375 1.170208698 1.198974907
## [241] 1.207275443 1.194949059 0.656899919 0.689807659 0.729180945
## [246] 0.759185432 0.795083788 0.865546680 0.897275327 0.913690923
## [251] 0.888569432 0.884821338 0.874894955 0.904363156 0.952379784
## [256] 1.024681315 1.058980068 1.008566463 1.032520789 1.062511808
## [261] 1.097755765 1.131800847 1.164494571 1.156273463 1.164692287
## [266] 1.161697842 1.164255131 1.166773044 1.191488780 0.666600418
## [271] 0.662496452 0.676056046 0.648934241 0.693573444 0.540711169
## [276] 0.562563515 0.390671912 0.390774068 0.406701778 0.374265228
## [281] 0.348373681 0.344449835 0.373847499 0.375936895 0.387750192
## [286] 0.414305262 0.387094780 0.411695955 0.401160456 0.394883592
## [291] 0.400568561 0.407545998 0.407639989 0.388258967 0.389126533
## [296] 0.386311507 0.922566562 0.972953309 0.958001782 0.754932800
## [301] 0.743976852 0.672408068 0.783880028 0.773568364 0.763660387
## [306] 0.696092849 0.630273166 0.622726293 0.559466523 0.553002805
## [311] 0.492030290 0.594604456 0.587995894 0.687254670 0.522877060
## [316] 0.465470275 0.562747837 0.506083352 0.450618833 0.396337258
## [321] 0.441271352 0.436800437 0.434652611 0.884257776 0.924094229
## [326] 1.002906978 1.070269957 1.251366951 1.287768632 1.503520748
## [331] 1.648866186 1.756217649 1.908321673 2.031900081 2.190493351
## [336] 2.118428759 2.262052214 2.334802795 2.412858360 2.538053993
## [341] 2.517240952 2.571766259 2.649248936 2.614854425 2.680799011
## [346] 2.801115637 2.830702128 2.955825099 3.063364339 3.075676965
## [351] 0.302433473 0.296973903 0.316064270 0.310670837 0.329042428
## [356] 0.277536569 0.296024806 0.269153893 0.265198165 0.304920607
## [361] 0.279099883 0.254014133 0.292274534 0.308958678 0.345611154
## [366] 0.240899321 0.237973708 0.215582230 0.232515741 0.210810116
## [371] 0.208591694 0.206480149 0.185880045 0.184133586 0.200710365
## [376] 0.217041052 0.222138987 1.128795145 1.149994518 1.171217919
## [381] 1.249493046 1.268528809 1.245368589 1.277438797 1.281502270
## [386] 1.062091211 0.991587022 1.011494980 0.980415523 0.864803454
## [391] 0.693515292 1.033330789 1.222954681 1.462786126 1.671391302
## [396] 1.586227370 1.405151254 1.344351189 1.356044590 1.325816871
## [401] 1.316370109 1.279012739 1.262748897 1.280147651 1.003778380
## [406] 0.969538992 0.922476507 0.893757762 0.852639520 0.870070723
## [411] 0.831601347 0.807903244 0.849208100 0.987623874 1.032688598
## [416] 1.204719505 1.263568513 1.430609350 1.577412443 1.461730502
## [421] 1.485543732 1.427106178 1.351455118 1.374534514 1.377350308
## [426] 1.343251918 1.301529569 1.226862918 1.224608077 0.643499788
## [431] 0.625665335 0.608527941 0.592435190 0.577531518 0.615054507
## [436] 0.701251894 0.636651657 0.717998872 0.981673828 0.957820647
## [441] 0.978361830 1.083455632 0.928960883 0.946258236 1.042279275
## [446] 1.210935350 1.370484065 1.521518009 1.519878706 1.553242273
## [451] 1.515782617 1.513505167 1.511203363 1.541049599 1.506775137
## [456] 1.525164495 0.485960303 0.476514728 0.467420027 1.146593250
## [461] 1.125347035 0.441902945 0.542409655 0.639342833 0.628242349
## [466] 0.720535255 0.708651997 0.697247457 0.686231697 0.675499332
## [471] 0.664981902 0.654658918 0.736635918 0.725389562 0.625193470
## [476] 0.528020065 0.520459069 0.513177883 0.506171360 0.582669554
## [481] 0.575105942 0.567837709 0.574802612 0.605145309 0.600930193
## [486] 0.593979962 0.584423445 0.598954091 0.595778313 0.592653615
## [491] 0.587877105 0.579877828 0.571362901 0.571402881 0.579569039
## [496] 0.545449831 0.543740674 0.539077014 0.580059192 0.590801466
## [501] 0.568254102 0.518208890 0.543304973 0.531197338 0.517446471
## [506] 0.500008257 0.487797519 0.475221958 0.468917138 0.464825918
## [511] 0.448068231 0.468678184 0.516363608 0.453748793 0.424456299
## [516] 0.440161517 0.468514647 0.350710941 0.328981297 0.328636942
## [521] 0.385733997 0.514148539 0.575195223 0.488738654 0.458951792
## [526] 0.541210181 0.458379049 0.426239258 0.476044363 0.418851409
## [531] 0.409790111 0.393810864 0.399254534 0.401171477 0.406578843
## [536] 0.405248117 0.403752259 6.282637702 6.221905691 5.850520626
## [541] 5.664415007 5.587660065 5.885170984 5.878311456 6.072679435
## [546] 5.420161296 5.826570242 5.847986477 5.849622168 5.355354851
## [551] 5.420505338 5.521795405 4.901078858 4.245347867 3.676236914
## [556] 3.010454542 2.808761407 3.019778592 3.235422413 3.125241000
## [561] 3.175690306 3.248165480 3.254244742 3.199108247 1.137538780
## [566] 1.098402739 1.235546017 1.363310676 1.320424186 1.283215037
## [571] 0.937821377 1.221331668 1.194990545 1.316990676 1.292010741
## [576] 1.128165353 1.247094836 1.088090509 1.199331282 1.302769570
## [581] 1.271536652 1.488621770 1.453952658 1.660294568 1.861613866
## [586] 1.829876106 1.801013582 1.884254384 1.855149528 1.826069337
## [591] 1.819063548 12.477043676 14.200794223 13.098933541 10.609126888
## [596] 9.245657502 8.307442623 6.872139616 5.784864399 5.589442105
## [601] 5.712453966 5.979646817 5.580171368 5.868787869 6.093808145
## [606] 6.485903095 6.925949716 7.243039538 7.379633702 7.715218673
## [611] 7.973754267 8.108689733 8.079744789 8.105173841 8.038476661
## [616] 7.984148195 7.890672859 0.376272793 0.458221165 0.446683275
## [621] 0.391962977 0.551980495 0.620858591 0.524575009 0.551029983
## [626] 0.538024629 0.526029269 0.625392961 0.540939711 0.460002686
## [631] 0.521079440 0.545818518 0.603128730 0.658421188 0.647120205
## [636] 0.636274440 0.751080211 0.615966769 0.667081525 0.806370326
## [641] 0.971172497 1.044475604 1.144655888 1.146792857 0.962109644
## [646] 0.929316340 0.952904328 0.973763707 0.995406300 1.072465782
## [651] 1.095247409 1.219584025 1.188247824 1.204519796 1.217516054
## [656] 1.137026119 1.059788745 1.071959679 1.041337304 0.890612047
## [661] 0.905202409 0.956735133 0.744254965 0.723640768 0.738700048
## [666] 0.786540825 0.764720684 0.776006126 0.786336053 0.765181195
## [671] 0.777123503 1.525200886 1.522181682 1.544232498 1.714181590
## [676] 1.801801469 1.884052169 2.084666776 2.183796793 2.291318019
## [681] 2.413475881 2.555887421 2.532838856 2.502310277 2.497035285
## [686] 2.483466596 2.492917673 2.481779107 2.463278463 2.455041695
## [691] 2.528089430 1.192887591 1.778407564 2.403473927 2.384998669
## [696] 2.352115202 2.296623369 2.306752901 4.128191593 4.306429649
## [701] 4.491351446 4.688331269 4.902456979 3.038402070 2.964165895
## [706] 2.966359244 2.962455903 2.953479586 2.983534784 2.994907036
## [711] 2.994060571 3.004232638 3.024067209 2.209498736 2.208707181
## [716] 2.212180753 2.198401667 2.183906370 2.163648052 2.152968915
## [721] 2.175805696 2.149539443 2.119360993 2.085605454 2.082779310
## [726] 0.610630522 0.595568429 0.581254541 0.567593019 0.554522827
## [731] 0.542017661 0.530083227 0.518734367 0.507991241 0.497859581
## [736] 0.488289378 0.479249756 0.470760260 0.617138930 0.607368833
## [741] 0.448765894 0.442495676 0.436570737 0.430819945 0.566838651
## [746] 0.559284116 0.551796788 0.544413804 0.537197902 0.530190748
## [751] 0.523386609 0.519878837 1.184752770 1.122396651 1.024834996
## [756] 0.968696866 0.951796182 0.972133801 0.992451488 1.012708861
## [761] 1.273800628 1.290378346 1.273353703 1.257073860 1.241381051
## [766] 1.290644769 1.274881467 1.290878413 1.275237455 1.228962312
## [771] 1.213727813 1.198353354 1.212370073 1.254693079 1.180417373
## [776] 1.221703314 1.234037901 1.246293379 1.254116957 0.346402469
## [781] 0.365095292 0.369664969 0.399764772 0.390631097 0.369554427
## [786] 0.373480681 0.365408218 0.380676574 0.383923215 0.386912606
## [791] 0.368045122 0.349998728 0.353679980 0.367767543 0.462137913
## [796] 0.494573684 0.516517027 0.528142810 0.539129332 0.530786742
## [801] 0.504075723 0.505064584 0.505957866 0.498116175 0.490561723
## [806] 0.491407198 2.066867912 2.204858233 1.881499101 1.729601948
## [811] 1.418295496 1.292271876 1.142059080 0.875703300 0.854100272
## [816] 0.818318815 0.824290882 0.758816406 0.798800066 0.949463944
## [821] 1.036540276 1.098663154 1.166805516 1.135655034 1.185623344
## [826] 1.201979683 1.244779029 1.282947680 1.092435341 1.158755185
## [831] 1.170677646 1.171724469 5.678911771 5.931840417 7.621492094
## [836] 6.398629052 7.135870362 6.809464371 6.571200437 6.111655935
## [841] 6.211478366 6.376135559 5.637965207 5.528678784 5.312123655
## [846] 5.267802842 5.582280827 5.088908375 5.330568880 4.573934556
## [851] 4.267402608 4.855708216 5.288233749 5.542091761 5.464209183
## [856] 5.584789498 5.444575036 5.286155463 5.278516523 3.945626131
## [861] 4.075888321 3.664858300 4.252622755 3.313796066 3.439925212
## [866] 3.362416653 3.270922859 3.720237927 4.057299330 3.359430055
## [871] 3.290095007 3.166192086 3.193813769 3.184914261 3.356462383
## [876] 2.886489563 3.443179690 3.347974183 3.136672078 2.914394263
## [881] 3.047848611 3.031266866 3.081766309 3.100729101 3.025160011
## [886] 2.987115063 0.004856906 0.005544624 0.006145457 0.006910559
## [891] 0.007696202 0.007407262 0.007729358 0.007757893 0.007395308
## [896] 0.007333347 0.007463738 0.007118944 0.007209771 0.007135395
## [901] 0.008206554 0.010786647 0.010646953 0.010966434 0.010284843
## [906] 0.010222787 0.010036200 0.009775524 0.009525923 0.009285730
## [911] 0.009072806 0.008811150 0.008699658 1.070417995 1.067495176
## [916] 1.063905443 1.060960810 1.088841945 1.059415096 1.060450095
## [921] 1.034527030 1.035754036 1.035550264 1.033838734 1.006261804
## [926] 1.026413604 0.974289666 0.946100917 0.961728329 0.932517701
## [931] 0.903728206 0.875580111 0.867497814 1.045773826 1.085398897
## [936] 1.104086434 1.086784111 1.069750340 1.053023390 1.066299643
## [941] 0.015377634 0.030470086 0.015110495 0.014985669 0.014848288
## [946] 0.014696314 0.014534658 0.014367119 0.014199512 0.014036583
## [951] 0.013876806 0.013720700 0.013576633 0.026909455 0.040082613
## [956] 0.026596352 0.039786857 0.039727171 0.039670494 0.039584946
## [961] 0.039460137 0.039306695 0.039139280 0.038980610 0.038846746
## [966] 0.051653340 0.051774964 0.600225123 0.515453954 0.741194157
## [971] 0.675435387 0.540309646 0.404020241 0.372166610 0.339858881
## [976] 0.306895276 0.273230717 0.312378303 0.314988682 0.317439368
## [981] 0.300884854 0.283854364 0.285410342 0.325016164 0.345619782
## [986] 0.404841448 0.387002946 0.388349943 0.370196441 0.371534915
## [991] 0.373016454 0.374683161 0.382876477 14.048307019 14.292853573
## [996] 12.006908934 13.546631199 11.837322417 10.424861716 10.370710599
## [1001] 9.008683504 11.108740018 6.611033815 5.556825152 5.029932914
## [1006] 5.821488389 3.434362842 2.898421043 2.888836497 2.093518419
## [1011] 4.433367611 4.938548740 4.663281078 3.356837061 4.374883515
## [1016] 6.411096537 6.643302349 6.590760417 0.025261724 0.024743588
## [1021] 0.036384189 0.035712426 0.046809146 0.057609962 0.056803196
## [1026] 0.056077465 0.055415820 0.054804376 0.054238171 0.053712583
## [1031] 0.063858320 0.084372196 0.094057491 0.113909597 0.133378850
## [1036] 0.111792052 0.140882082 0.169311652 0.128076093 0.116897500
## [1041] 0.134823468 0.123778258 0.113004507 0.111812054 0.113702745
## [1046] 0.210491570 0.205200193 0.200135091 0.292740371 0.285296530
## [1051] 0.277826011 0.270388191 0.350792922 0.341438952 0.415673930
## [1056] 0.405110282 0.473968535 0.539120830 0.600393667 0.584543313
## [1061] 0.568589776 0.552647527 0.536807126 0.586325729 0.569070174
## [1066] 0.613517035 0.595134237 0.577197170 0.559748724 0.542815498
## [1071] 0.526399850 0.521667114#Transform
hist(meat_cons_pop$`Beef and veal consumption (kg/capita)`)
log_beef <- log10(meat_cons_pop$`Beef and veal consumption (kg/capita)`)
hist(log_beef)
ln_beef<-log(meat_cons_pop$`Beef and veal consumption (kg/capita)`)
hist(ln_beef)
sqrt_beef<-sqrt(meat_cons_pop$`Beef and veal consumption (kg/capita)`)
hist(sqrt_beef)
boxcox_beef<- BoxCox(meat_cons_pop$`Beef and veal consumption (kg/capita)`,lambda = "auto")
hist(boxcox_beef)
recip_beef <- 1/meat_cons_pop$`Beef and veal consumption (kg/capita)`
hist(recip_beef)
#square root transformation works best for this variable
hist(meat_cons_pop$`Pork consumption (kg/capita)`)
log_pork <- log10(meat_cons_pop$`Pork consumption (kg/capita)`)
hist(log_pork)
ln_pork<-log(meat_cons_pop$`Pork consumption (kg/capita)`)
hist(ln_pork)
sqrt_pork<-sqrt(meat_cons_pop$`Pork consumption (kg/capita)`)
hist(sqrt_pork)
boxcox_pork<- BoxCox(meat_cons_pop$`Pork consumption (kg/capita)`,lambda = "auto")
hist(boxcox_pork)
recip_pork <- 1/meat_cons_pop$`Pork consumption (kg/capita)`
hist(recip_pork)
#square root seem to perform best
hist(meat_cons_pop$`Lamb consumption (kg/capita)`)
log_lamb <- log10(meat_cons_pop$`Lamb consumption (kg/capita)`)
hist(log_lamb)
ln_lamb<-log(meat_cons_pop$`Lamb consumption (kg/capita)`)
hist(ln_lamb)
sqrt_lamb<-sqrt(meat_cons_pop$`Lamb consumption (kg/capita)`)
hist(sqrt_lamb)
boxcox_lamb<- BoxCox(meat_cons_pop$`Lamb consumption (kg/capita)`,lambda = "auto")
hist(boxcox_lamb)
recip_lamb <- 1/meat_cons_pop$`Lamb consumption (kg/capita)`
hist(recip_lamb)
#log10 and ln seem to perfeorm well
hist(meat_cons_pop$`Meat consumption (kg/capita)`)
log_meat <- log10(meat_cons_pop$`Meat consumption (kg/capita)`)
hist(log_meat)
ln_meat<-log(meat_cons_pop$`Meat consumption (kg/capita)`)
hist(ln_meat)
sqrt_meat<-sqrt(meat_cons_pop$`Meat consumption (kg/capita)`)
hist(sqrt_meat)
boxcox_meat<- BoxCox(meat_cons_pop$`Meat consumption (kg/capita)`,lambda = "auto")
hist(boxcox_meat)
recip_meat <- 1/meat_cons_pop$`Meat consumption (kg/capita)`
hist(recip_meat)
#square root seem to perfeom best in this case
hist(meat_cons_pop$`Country meat consumption (tonnes)`)
log_country <- log10(meat_cons_pop$`Country meat consumption (tonnes)`)
hist(log_country)
ln_country<-log(meat_cons_pop$`Country meat consumption (tonnes)`)
hist(ln_country)
sqrt_country<-sqrt(meat_cons_pop$`Country meat consumption (tonnes)`)
hist(sqrt_country)
boxcox_country<- BoxCox(meat_cons_pop$`Country meat consumption (tonnes)`,lambda = "auto")
hist(boxcox_country)
recip_country <- 1/meat_cons_pop$`Country meat consumption (tonnes)`
hist(recip_country)
#the log10 and ln seem to perform well hereOthers codes
#Use filter() and summarise() to find out wich country eats the most meat in a certain years
cons_by_year <- meat_cons_pop %>% arrange(meat_cons_pop$Year,meat_cons_pop$`Meat consumption (kg/capita)`)
cons_by_yearAnother analysi
#Subseting the meat_cons_pop in order to find some interested information about australia. This process will use some other data sets
meat_prod_aus#step 1 calculating amount of meat producing by Australia each year by seperating the dates
str(meat_prod_aus)## Classes 'tbl_df', 'tbl' and 'data.frame': 549 obs. of 3 variables:
## $ X__1 : POSIXct, format: "1972-07-01" "1972-08-01" ...
## $ red_meat_prod: num 18028 20091 18718 20007 20629 ...
## $ pork_prod : num 209456 216225 192245 204724 212670 ...meat_prod_aus<- rename(meat_prod_aus, 'Date'='X__1')
str(meat_prod_aus)## Classes 'tbl_df', 'tbl' and 'data.frame': 549 obs. of 3 variables:
## $ Date : POSIXct, format: "1972-07-01" "1972-08-01" ...
## $ red_meat_prod: num 18028 20091 18718 20007 20629 ...
## $ pork_prod : num 209456 216225 192245 204724 212670 ...meat_prod_aus$Date<-as.character(meat_prod_aus$Date)
meat_prod_aus$Date<-as.Date(meat_prod_aus$Date)
meat_prod_aus<- meat_prod_aus %>% tidyr::separate(Date, into = c("Year", "Month","Date"))
#calculating meat producing in each year
anual_meat_aus<- meat_prod_aus %>% dplyr::mutate(total_meat = red_meat_prod+pork_prod) %>% dplyr::select("Year","total_meat")
total_meat_anual_aus<-anual_meat_aus %>% group_by(Year)%>% summarise(Meat_produced= sum(total_meat)) #subseting Australian population and meat consumption from the meat_cons_pop data set
aus_cons<- meat_cons_pop %>%dplyr::filter(meat_cons_pop$`Country Name` =="Australia")
str(aus_cons)## Classes 'tbl_df', 'tbl' and 'data.frame': 27 obs. of 9 variables:
## $ Country Name : Factor w/ 41 levels "Algeria","Argentina",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Year : int 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 ...
## $ Population (people) : num 17284000 17495000 17667000 17855000 18072000 ...
## $ Beef and veal consumption (kg/capita): num 27.7 26.2 26.2 25.5 25.3 ...
## $ Lamb consumption (kg/capita) : num 19.3 17.8 17.4 18.4 15 ...
## $ Pork consumption (kg/capita) : num 14.4 15.1 14.9 15.6 15.5 ...
## $ Meat consumption (kg/capita) : num 61.4 59.2 58.4 59.4 55.9 ...
## $ Country meat consumption (tonnes) : num 1060904 1034837 1031691 1060933 1009328 ...
## $ Country Code : Factor w/ 41 levels "ARG","AUS","BGD",..: 2 2 2 2 2 2 2 2 2 2 ...total_meat_anual_aus$Year<- as.integer(total_meat_anual_aus$Year)
a_meat<- aus_cons %>% left_join (total_meat_anual_aus, by =c("Year") ) %>%dplyr:: select("Year","Country meat consumption (tonnes)","Meat consumption (kg/capita)","Population (people)","Meat_produced" )
str(meat_export)## Classes 'tbl_df', 'tbl' and 'data.frame': 121 obs. of 8 variables:
## $ X__1 : POSIXct, format: "1988-03-01" "1988-06-01" ...
## $ Quantity ; Beef Bone In ; : num 3937 4204 4410 12297 20984 ...
## $ Quantity ; Beef Bone Out ; : num 154288 152078 136346 132532 98429 ...
## $ Quantity ; Mutton and Lamb ;: num 52369 30867 20211 40024 38181 ...
## $ Quantity ; Pork ; : num 1446 1278 1297 2225 1356 ...
## $ Quantity ; Bacon and Ham ; : num 9 22 12 15 8 4 25 3 6 3 ...
## $ Quantity ; Edible Offal ; : num 13318 11786 11145 13730 10385 ...
## $ Quantity ; Preserved Meat ; : num 3095 2789 3062 3867 2707 ...meat_export<- rename(meat_export, 'Date'='X__1')
meat_export$Date<-as.character(meat_export$Date)
meat_export$Date<-as.Date(meat_export$Date)
meat_export<- meat_export %>% tidyr::separate(Date, into = c("Year", "Month","Date"))
anual_meat_export_aus<- meat_export %>% dplyr::mutate(total_meat_export = meat_export$`Quantity ; Beef Bone In ;`+meat_export$`Quantity ; Beef Bone Out ;`+meat_export$`Quantity ; Mutton and Lamb ;`+meat_export$`Quantity ; Pork ;`+meat_export$`Quantity ; Bacon and Ham ;`+meat_export$`Quantity ; Edible Offal ;`+meat_export$`Quantity ; Preserved Meat ;`) %>% dplyr::select("Year","total_meat_export")
anual_meat_export_aus$Year<- as.integer(anual_meat_export_aus$Year)
total_meat_export_aus<-anual_meat_export_aus %>% group_by(Year)%>% summarise(Meat_export_total= sum(total_meat_export))
a_meat<- a_meat %>% left_join (total_meat_export_aus, by =c("Year") )
a_meat<- a_meat %>% dplyr::mutate(total_meat_wasted_tones= a_meat$Meat_produced-a_meat$`Country meat consumption (tonnes)`-a_meat$Meat_export_total)
a_meat<- a_meat %>% dplyr::mutate(meat_waste_percapita=total_meat_wasted_tones*1000/ a_meat$`Population (people)`)
mean(a_meat$`Meat consumption (kg/capita)`, na.rm = TRUE)## [1] 55.38919mean(a_meat$total_meat_wasted_tones, na.rm= TRUE)## [1] 897115.1mean(a_meat$meat_waste_percapita,na.rm = TRUE)## [1] 45.11226mean(a_meat$`Country meat consumption (tonnes)`,na.rm = TRUE)## [1] 1122924statement<- cat("From 1991 to 2017, Autralia has produced " , mean(a_meat$Meat_produced,na.rm = TRUE), " tones on non-poultry meat per year on average. During this time, an average Australian has consumed " , mean(a_meat$`Meat consumption (kg/capita)`, na.rm = TRUE) ," kilogram each year. The country has managed to export " , mean(a_meat$Meat_export_total, na.rm = TRUE ), " tonnes each year, on average. This means Australia as a country have wasted or lost " , mean(a_meat$total_meat_wasted_tones, na.rm= TRUE), " tonnes of meat each year, which worked out to be " ,mean(a_meat$meat_waste_percapita,na.rm = TRUE), " kiligram per person, per year. This is the equivelent of $" ,mean(a_meat$meat_waste_percapita,na.rm = TRUE)*10 , " wasted- assuming the meat price is 10$ per kilogram on average" )## From 1991 to 2017, Autralia has produced 3432335 tones on non-poultry meat per year on average. During this time, an average Australian has consumed 55.38919 kilogram each year. The country has managed to export 1414909 tonnes each year, on average. This means Australia as a country have wasted or lost 897115.1 tonnes of meat each year, which worked out to be 45.11226 kiligram per person, per year. This is the equivelent of $ 451.1226 wasted- assuming the meat price is 10$ per kilogram on average