Cómo el consumo de cigarro ha afectado e impactado en la población de 188 paises analizados tanto en hombres como en mujeres desde 1980 hasta 2012, con enfoque y datos especificamente analizados de Guatemala. El análisis se basa en cuanto incrementa el consumo conforme el tiempo y asi tambien a que edad es mayor la prevalencia de su uso, basicamente es la correlacion consumo - tiempo.
Dar a conocer que el consumo de cigarrillo se da a toda edad e independientemente a qué país se pertenece y con el paso del tiempo, el desarrollo y el ambiente en el que se vive el consumo ha aumentado.
Dado a que es la primera experiencia real con programación y más que todo, la primera experiencia real de acercamiento a R y RStudio el proyecto se complicó un poco en cuestión de la programación y realización de las gráficas, así como el análisis y búsqueda de datasets adecuados al tema a tratar.
Los conocimientos que logramos desarrollar y finalmente adquirir después de las clases de Data, lectura de OpenIntro y material proporcionado fueron el del análisis exhaustivo, investigación y generación de conclusiones para extraer datos importantes y significativos para realizar las gráficas correspondientes. Así también el de hacer correlaciones entre variables como la principalmente utilizada en el tema: consumo vs. tiempo
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(nycflights13)
library(tidyverse)## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statsp1980 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,5], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1980’, y = ‘Smoking Prevalence’) png(filename=“PRUEBAAAAAA.png”) plot(p1980) dev.off()
p1981 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,8], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1981’, y = ‘Smoking Prevalence’) png(filename=“p1981.png”) plot(p1981) dev.off()
p1982 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,11], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1982’, y = ‘Smoking Prevalence’) png(filename=“p1982.png”) plot(p1982) dev.off()
p1983 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,14], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1983’, y = ‘Smoking Prevalence’) png(filename=“p1983.png”) plot(p1983) dev.off()
p1984 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,17], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1984’, y = ‘Smoking Prevalence’) png(filename=“p1984.png”) plot(p1984) dev.off()
p1985 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,20], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1985’, y = ‘Smoking Prevalence’) png(filename=“p1985.png”) plot(p1985) dev.off()
p1986 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,23], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1986’, y = ‘Smoking Prevalence’) png(filename=“p1986.png”) plot(p1986) dev.off()
p1987 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,26], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1987’, y = ‘Smoking Prevalence’) png(filename=“p1987.png”) plot(p1987) dev.off()
p1988 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,29], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1988’, y = ‘Smoking Prevalence’) png(filename=“p1988.png”) plot(p1988) dev.off()
p1989 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,32], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1989’, y = ‘Smoking Prevalence’) png(filename=“p1989.png”) plot(p1989) dev.off()
p1990 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,35], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1990’, y = ‘Smoking Prevalence’) png(filename=“p1990.png”) plot(p1990) dev.off()
p1991 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,38], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1991’, y = ‘Smoking Prevalence’) png(filename=“p1991.png”) plot(p1991) dev.off()
p1992 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,41], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1992’, y = ‘Smoking Prevalence’) png(filename=“p1992.png”) plot(p1992) dev.off()
p1993 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,44], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1993’, y = ‘Smoking Prevalence’) png(filename=“p1993.png”) plot(p1993) dev.off()
p1994 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,47], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1994’, y = ‘Smoking Prevalence’) png(filename=“p1994.png”) plot(p1994) dev.off()
p1995 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,50], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1995’, y = ‘Smoking Prevalence’) png(filename=“p1995.png”) plot(p1995) dev.off()
p1996 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,53], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1996’, y = ‘Smoking Prevalence’) png(filename=“p1996.png”) plot(p1996) dev.off()
p1997 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,56], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1997’, y = ‘Smoking Prevalence’) png(filename=“p1997.png”) plot(p1997) dev.off()
p1998 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,59], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1998’, y = ‘Smoking Prevalence’) png(filename=“p1998.png”) plot(p1998) dev.off()
p1999 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,61], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 1999’, y = ‘Smoking Prevalence’) png(filename=“p1999.png”) plot(p1999) dev.off()
p2000 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,64], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2000’, y = ‘Smoking Prevalence’) png(filename=“p2000.png”) plot(p2000) dev.off()
p2001 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,67], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2001’, y = ‘Smoking Prevalence’) png(filename=“p2001.png”) plot(p2001) dev.off()
p2002 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,70], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2002’, y = ‘Smoking Prevalence’) png(filename=“p2002.png”) plot(p2002) dev.off()
p2003 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,73], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2003’, y = ‘Smoking Prevalence’) png(filename=“p2003.png”) plot(p2003) dev.off()
p2004 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,76], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2004’, y = ‘Smoking Prevalence’) png(filename=“p2004.png”) plot(p2004) dev.off()
p2005 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,79], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2005’, y = ‘Smoking Prevalence’) png(filename=“p2005.png”) plot(p2005) dev.off()
p2006 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,82], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2006’, y = ‘Smoking Prevalence’) png(filename=“p2006.png”) plot(p2006) dev.off()
p2007 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,85], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2007’, y = ‘Smoking Prevalence’) png(filename=“p2007.png”) plot(p2007) dev.off()
p2008 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,88], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2008’, y = ‘Smoking Prevalence’) png(filename=“p2008.png”) plot(p2008) dev.off()
p2009 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,91], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2009’, y = ‘Smoking Prevalence’) png(filename=“p2009.png”) plot(p2009) dev.off()
p2010 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,93], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2010’, y = ‘Smoking Prevalence’) png(filename=“p2010.png”) plot(p2010) dev.off()
p2011 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,95], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2011’, y = ‘Smoking Prevalence’) png(filename=“p2011.png”) plot(p2011) dev.off()
p2012 <- ggplot(femaleperage, aes(x = Age, y = femaleperage[,98], col = Age)) + geom_point() + labs(title = ‘female cigarette consumption in 2012’, y = ‘Smoking Prevalence’) png(filename=“p2012.png”) plot(p2012) dev.off()
m1980 <- ggplot(maleperage, aes(x = Age, y = maleperage[,5], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1980’, y = ‘Smoking Prevalence’) png(filename=“m1980.png”) plot(m1980) dev.off()
m1981 <- ggplot(maleperage, aes(x = Age, y = maleperage[,8], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1981’, y = ‘Smoking Prevalence’) png(filename=“m1981.png”) plot(m1981) dev.off()
m1982 <- ggplot(maleperage, aes(x = Age, y = maleperage[,11], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1982’, y = ‘Smoking Prevalence’) png(filename=“m1982.png”) plot(m1982) dev.off()
m1983 <- ggplot(maleperage, aes(x = Age, y = maleperage[,14], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1983’, y = ‘Smoking Prevalence’) png(filename=“m1983.png”) plot(m1983) dev.off()
m1984 <- ggplot(maleperage, aes(x = Age, y = maleperage[,17], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1984’, y = ‘Smoking Prevalence’) png(filename=“m1984.png”) plot(m1984) dev.off()
m1985 <- ggplot(maleperage, aes(x = Age, y = maleperage[,20], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1985’, y = ‘Smoking Prevalence’) png(filename=“m1985.png”) plot(m1985) dev.off()
m1986 <- ggplot(maleperage, aes(x = Age, y = maleperage[,23], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1986’, y = ‘Smoking Prevalence’) png(filename=“m1986.png”) plot(m1986) dev.off()
m1987 <- ggplot(maleperage, aes(x = Age, y = maleperage[,26], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1987’, y = ‘Smoking Prevalence’) png(filename=“m1987.png”) plot(m1987) dev.off()
m1988 <- ggplot(maleperage, aes(x = Age, y = maleperage[,29], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1988’, y = ‘Smoking Prevalence’) png(filename=“m1988.png”) plot(m1988) dev.off()
m1989 <- ggplot(maleperage, aes(x = Age, y = maleperage[,32], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1989’, y = ‘Smoking Prevalence’) png(filename=“m1989.png”) plot(m1989) dev.off()
m1990 <- ggplot(maleperage, aes(x = Age, y = maleperage[,35], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1990’, y = ‘Smoking Prevalence’) png(filename=“m1990.png”) plot(m1990) dev.off()
m1991 <- ggplot(maleperage, aes(x = Age, y = maleperage[,38], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1991’, y = ‘Smoking Prevalence’) png(filename=“m1991.png”) plot(m1991) dev.off()
m1992 <- ggplot(maleperage, aes(x = Age, y = maleperage[,41], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1992’, y = ‘Smoking Prevalence’) png(filename=“m1992.png”) plot(m1992) dev.off()
m1993 <- ggplot(maleperage, aes(x = Age, y = maleperage[,44], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1993’, y = ‘Smoking Prevalence’) png(filename=“m1993.png”) plot(m1993) dev.off()
m1994 <- ggplot(maleperage, aes(x = Age, y = maleperage[,47], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1994’, y = ‘Smoking Prevalence’) png(filename=“m1994.png”) plot(m1994) dev.off()
m1995 <- ggplot(maleperage, aes(x = Age, y = maleperage[,50], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1995’, y = ‘Smoking Prevalence’) png(filename=“m1995.png”) plot(m1995) dev.off()
m1996 <- ggplot(maleperage, aes(x = Age, y = maleperage[,53], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1996’, y = ‘Smoking Prevalence’) png(filename=“m1996.png”) plot(m1996) dev.off()
m1997 <- ggplot(maleperage, aes(x = Age, y = maleperage[,56], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1997’, y = ‘Smoking Prevalence’) png(filename=“m1997.png”) plot(m1997) dev.off()
m1998 <- ggplot(maleperage, aes(x = Age, y = maleperage[,59], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1998’, y = ‘Smoking Prevalence’) png(filename=“m1998.png”) plot(m1998) dev.off()
m1999 <- ggplot(maleperage, aes(x = Age, y = maleperage[,62], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 1999’, y = ‘Smoking Prevalence’) png(filename=“m1999.png”) plot(m1999) dev.off()
m2000 <- ggplot(maleperage, aes(x = Age, y = maleperage[,65], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2000’, y = ‘Smoking Prevalence’) png(filename=“m2000.png”) plot(m2000) dev.off()
m2001 <- ggplot(maleperage, aes(x = Age, y = maleperage[,68], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2001’, y = ‘Smoking Prevalence’) png(filename=“m2001.png”) plot(m2001) dev.off()
m2002 <- ggplot(maleperage, aes(x = Age, y = maleperage[,71], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2002’, y = ‘Smoking Prevalence’) png(filename=“m2002.png”) plot(m2002) dev.off()
m2003 <- ggplot(maleperage, aes(x = Age, y = maleperage[,74], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2003’, y = ‘Smoking Prevalence’) png(filename=“m2003.png”) plot(m2003) dev.off()
m2004 <- ggplot(maleperage, aes(x = Age, y = maleperage[,77], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2004’, y = ‘Smoking Prevalence’) png(filename=“m2004.png”) plot(m2004) dev.off()
m2005 <- ggplot(maleperage, aes(x = Age, y = maleperage[,80], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2005’, y = ‘Smoking Prevalence’) png(filename=“m2005.png”) plot(m2005) dev.off()
m2006 <- ggplot(maleperage, aes(x = Age, y = maleperage[,83], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2006’, y = ‘Smoking Prevalence’) png(filename=“m2006.png”) plot(m2006) dev.off()
m2007 <- ggplot(maleperage, aes(x = Age, y = maleperage[,86], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2007’, y = ‘Smoking Prevalence’) png(filename=“m2007.png”) plot(m2007) dev.off()
m2008 <- ggplot(maleperage, aes(x = Age, y = maleperage[,89], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2008’, y = ‘Smoking Prevalence’) png(filename=“m2008.png”) plot(m2008) dev.off()
m2009 <- ggplot(maleperage, aes(x = Age, y = maleperage[,92], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2009’, y = ‘Smoking Prevalence’) png(filename=“m2009.png”) plot(m2009) dev.off()
m2010 <- ggplot(maleperage, aes(x = Age, y = maleperage[,95], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2010’, y = ‘Smoking Prevalence’) png(filename=“m2010.png”) plot(m2010) dev.off()
m2011 <- ggplot(maleperage, aes(x = Age, y = maleperage[,98], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2011’, y = ‘Smoking Prevalence’) png(filename=“m2011.png”) plot(m2011) dev.off()
m2012 <- ggplot(maleperage, aes(x = Age, y = maleperage[,101], col = Age)) + geom_point(size=5) + labs(title = ‘male cigarette consumption in 2012’, y = ‘Smoking Prevalence’) png(filename=“m2012.png”) plot(m2012) dev.off()
maxcon <- arrange(yearcigarcons, desc(Total.Tobacco.Consumption)) yearmaxcon <- maxcon[1:5,] yearmaxcon1 <- maxcon[1:4,] yearmaxcon2 <- maxcon[1:3,] yearmaxcon3 <- maxcon[1:2,] yearmaxcon4 <- maxcon[1,]
graph <- ggplot(maxcon, aes(x = Year, y = Total.Tobacco.Consumption, fill = Year)) + geom_bar(width = 0.85, stat=“identity”) + labs(title = “Consumo total de tabaco por año”, y = “Consumo total de tabaco”) png(filename=“consumototalporaño.png”) plot(graph) dev.off()
tot1 <- ggplot(yearmaxcon, aes(x = Year, y = Total.Tobacco.Consumption, fill = Year)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“primeros5.png”) plot(tot1) dev.off()
tot2 <- ggplot(yearmaxcon1, aes(x = Year, y = Total.Tobacco.Consumption, fill = Year)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“primeros4.png”) plot(tot2) dev.off()
tot3 <- ggplot(yearmaxcon2, aes(x = Year, y = Total.Tobacco.Consumption, fill = Year)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“primeros3.png”) plot(tot3) dev.off()
tot4 <- ggplot(yearmaxcon3, aes(x = Year, y = Total.Tobacco.Consumption, fill = Year)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“primeros2.png”) plot(tot4) dev.off()
tot5 <- ggplot(yearmaxcon4, aes(x = Year, y = Total.Tobacco.Consumption, fill = Year)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“primeros.png”) plot(tot5) dev.off()
agescon <- read.csv(“PerAgePerCountry.csv”)
china <- filter(globalcigarcon, ISO == ‘CHN’ & Year == 2012) usa <- filter(globalcigarcon, ISO == ‘USA’ & Year == 2012) nigeria <- filter(globalcigarcon, ISO == ‘NGA’ & Year == 2012) rusia <- filter(globalcigarcon, ISO == ‘RUS’ & Year == 2012) australia <- filter(globalcigarcon, ISO == ‘AUS’ & Year == 2012)
countries <- rbind(china, usa, nigeria, rusia, australia)
p <- ggplot(countries, aes(x = Country, y = Total.Tobacco.Consumption , fill=Country)) + geom_bar(stat=“identity”) + ggtitle(“CONSUMPTION PER COUNTRY”) print(p) png(filename=“consumoPAISES.png”) plot(p) dev.off()
paises2 <- countries[1,] p2 <- ggplot(paises2, aes(x = Country, y = Total.Tobacco.Consumption , fill=Country)) + geom_bar(stat=“identity”) + ggtitle(“CONSUMPTION PER COUNTRY”) print(p) png(filename=“consumoPAISES2.png”) plot(p2) dev.off()
paises3 <- countries[1:2,] p3 <- ggplot(paises3, aes(x = Country, y = Total.Tobacco.Consumption , fill=Country)) + geom_bar(stat=“identity”) + ggtitle(“CONSUMPTION PER COUNTRY”) print(p) png(filename=“consumoPAISES3.png”) plot(p3) dev.off()
paises4 <- countries[1:3,] p4 <- ggplot(paises4, aes(x = Country, y = Total.Tobacco.Consumption , fill=Country)) + geom_bar(stat=“identity”) + ggtitle(“CONSUMPTION PER COUNTRY”) print(p4) png(filename=“consumoPAISES4.png”) plot(p4) dev.off()
paises5 <- countries[1:4,] p5 <- ggplot(paises5, aes(x = Country, y = Total.Tobacco.Consumption , fill=Country)) + geom_bar(stat=“identity”) + ggtitle(“CONSUMPTION PER COUNTRY”) print(p5) png(filename=“consumoPAISES5.png”) plot(p5) dev.off()
paises6 <- countries[1:5,] p6 <- ggplot(paises6, aes(x = Country, y = Total.Tobacco.Consumption , fill=Country)) + geom_bar(stat=“identity”) + ggtitle(“CONSUMPTION PER COUNTRY”) print(p) png(filename=“consumoPAISES6.png”) plot(p6) dev.off()
agesgua <- filter(agescon, Country == ‘Guatemala’) agesgtm <- agesgua[1:15,] agesguate <- data.frame(agesgtm\(ISO,agesgtm\)Country,agesgtm\(Age,agesgtm\)Sex,agesgtm$Smoking.Prevalence…..2012) #BAR PLOT
gencongua<- ggplot(agesguate, aes(x = agesguate\(agesgtm.Age, y = agesguate[,5], fill = agesguate\)agesgtm.Age)) + geom_bar(width = 0.85, stat=“identity”) + labs(title = “Consumo por edad en Guatemala”, x = “Edades”, y = “Prevalencia de consumo en porcentaje”) png(filename=“consumoporedadGuatemala.png”) plot(gencongua) dev.off()
congua <- ggplot(agesguate, aes(x = agesguate\(agesgtm.Age, y = agesguate[,5], fill = agesguate\)agesgtm.Age)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“consumoporedadguatemalao.png”) plot(congua) dev.off()
age1 <- agesguate[1:5,] congua1 <- ggplot(age1, aes(x = age1\(agesgtm.Age, y = age1[,5], fill = age1\)agesgtm.Age)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“consumoporedadguatemala1.png”) plot(congua1) dev.off()
age2 <- agesguate[1:10,] congua1 <- ggplot(age2, aes(x = age2\(agesgtm.Age, y = age2[,5], fill = age2\)agesgtm.Age)) + geom_bar(width = 0.85, stat=“identity”) +
# To use a polar plot and not a basic barplot coord_polar(theta = “y”) +
#Remove useless labels of axis xlab(“”) + ylab(“”) png(filename=“consumoporedadguatemala2.png”) plot(congua1) dev.off()
globalpopulation <- read.csv(“WorldPopulation.csv”) poblacion80 <- globalpopulation[11981:12013,] consumo <- filter(globalcigarcon, Country == ‘Global’)
sapply(consumo2, class) gsub(“,”, “”, consumo\(Total.Tobacco.Consumption) consumo2 <- as.numeric(gsub(",", "", consumo\)Total.Tobacco.Consumption))
as.numeric((consumo$Total.Tobacco.Consumption)) pob <- cbind(consumo, consumo2)
poblacion <- cbind(poblacion80, pob) cor(poblacion[,2], poblacion[,11])
ctotal <- ggplot(poblacion, aes(x = poblacion[2], y = poblacion[,11], col = poblacion$year)) + geom_point(size=7) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“ctotal.png”) plot(ctotal) dev.off()
poblacion2 <- poblacion[1:4,] c1981 <- ggplot(poblacion2, aes(x = poblacion2[2], y = poblacion2[,11], col = poblacion2$year)) + geom_point(size=7) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1981.png”) plot(c1981) dev.off()
poblacion3 <- poblacion[1:8,] c1983 <- ggplot(poblacion3, aes(x = poblacion3[2], y = poblacion3[,11], col = poblacion3$year)) + geom_point(size=7) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1982.png”) plot(c1982) dev.off()
poblacion4 <- poblacion[1:12,] c1984 <- ggplot(poblacion4, aes(x = poblacion4[2], y = poblacion4[,11], col = poblacion4$year)) + geom_point(size=7) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1984.png”) plot(c1984) dev.off()
poblacion5 <- poblacion[1:16,] c1985 <- ggplot(poblacion3, aes(x = poblacion5[2], y = poblacion5[,11], col = poblacion5$year)) + geom_point(size=7) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1985.png”) plot(c1985) dev.off()
poblacion6 <- poblacion[1:20,] c1986 <- ggplot(poblacion6, aes(x = poblacion6[2], y = poblacion6[,11], col = poblacion6$year)) + geom_point(size=7) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1986.png”) plot(c1986) dev.off()
poblacion7 <- poblacion[1:24,] c1987 <- ggplot(poblacion7, aes(x = poblacion7[2], y = poblacion7[,11], col = poblacion7$year)) + geom_point(size=7) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1987.png”) plot(c1987) dev.off()
poblacion8<- poblacion[1:28,] c1988 <- ggplot(poblacion8, aes(x = poblacion8[2], y = poblacion8[,11], col = poblacion8$year)) + geom_point(size=4) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1988.png”) plot(c1988) dev.off()
poblacion9 <- poblacion[1:33,] c1989 <- ggplot(poblacion9, aes(x = poblacion9[2], y = poblacion9[,11], col = poblacion9$year)) + geom_point(size=4) + labs(title = ‘Consumo de tabaco VS aumento en la poblacion’, x = ‘crecimiento de la poblacion’, y = ‘Consumo de tabaco’) png(filename=“c1989.png”) plot(c1989) dev.off()
library(dplyr)
country <- filter(globalcigarcon, Year == 2012) countrycon <- country[2:188,] countrycon2 <- arrange(countrycon, desc(consumo2))
gsub(“,”, “”, countrycon\(Total.Tobacco.Consumption) con <- as.numeric(gsub(",", "", countrycon\)Total.Tobacco.Consumption)) pob <- cbind(countrycon, con)
toppaises <- arrange(pob, desc(con)) top10 <- toppaises[1:10,]
gtop10 <- ggplot(top10, aes(x = Country, y = Total.Tobacco.Consumption, fill = Country)) + geom_bar(width = 0.85, stat=“identity”) + labs(title = “10 Paises que mas consumen tabaco”, x = “pais”, y = “Consumo de tabaco”) png(filename=“gtop10.png”) plot(gtop10) dev.off()
top3 <- toppaises[1:3,] gtop11 <- ggplot(top3, aes(x = Country, y = Total.Tobacco.Consumption, fill = Country)) + geom_bar(width = 0.85, stat=“identity”) + labs(title = “10 Paises que mas consumen tabaco”, x = “pais”, y = “Consumo de tabaco”) png(filename=“gtop11.png”) plot(gtop11) dev.off()
top6 <- toppaises[1:6,] gtop12 <- ggplot(top6, aes(x = Country, y = Total.Tobacco.Consumption, fill = Country)) + geom_bar(width = 0.85, stat=“identity”) + labs(title = “10 Paises que mas consumen tabaco”, x = “pais”, y = “Consumo de tabaco”) png(filename=“gtop12.png”) plot(gtop12) dev.off()
GDPglobal <- read.csv(‘GDP.csv’) GDPg <- GDPglobal[5:20,] gsub(“,”, “”, GDPg\(X.3) GDP <- as.numeric(gsub(",", "", GDPg\)X.3)) #top 15 paises mayor gdp – tabla ya ordenada GDPc <- cbind(GDP, GDPg)
countrycon2 <- arrange(countrycon, desc(consumo2))
gdp<- as.numeric(unlist(GDPglobal$Value)) gdp <- cbind(countrycon, con)
toppaisesgdp <- toppaises[1:15,]
cor(GDPc[,1], toppaisesgdp[,9])