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kaggle：R可视化分析金拱门餐厅食物营养性（二）
作者：邬书豪，车联网数据挖掘工程师 ，R语言中文社区专栏作者，R语言中文社区负责人之一。微信ID：wsh137552775
知乎专栏：https://www.zhihu.com/people/wu-shu-hao-67/activities
第一篇请戳：kaggle：R可视化分析金拱门餐厅食物营养性（一）
公众号后台回复“金拱门”下载数据集。
#比较不同的脂肪率（类别=牛肉和猪肉 & 鸡肉和鱼）（图1）
#items=(category= Beef & Park & Chicken & Fish)
#Fats=（Total Fat (% Daily Value)，Saturated Fat (% Daily Value)，Trans Fat）
m1<-menu%>%filter(Category %in% c("Beef & Pork","Chicken & Fish"))%>%arrange(desc(Total.Fat....Daily.Value.,Saturated.Fat....Daily.Value.,Trans.Fat))
p4 <- plot_ly(m1, x = ~factor(Item,levels=Item), y = ~Total.Fat....Daily.Value., name = 'Total Fat DV', type = 'scatter', mode = 'lines+markers', width = 2,color = I('red')) %>%
add_trace(y = ~Saturated.Fat....Daily.Value., name = 'Saturated Fat DV',color=I('blue')) %>%
add_trace(y = ~Trans.Fat, name = 'Trans Fat',color=I("hotpink")) %>%
layout(title = 'Camparing Fat in Items',
xaxis = list(title = "",
showgrid = FALSE),
yaxis = list(title = "value",
showgrid = FALSE),
legend=list(orientation="r",xanchor="center"))图1：Camparing Fat in Items
Chicken Nuggets排在高脂肪的第一名，其次是Double Quarter Pounder with Cheese，Bacon Clubhouse Burger位居第三。
（类别=牛肉和猪肉 & 鸡肉和鱼）的食物Saturated Fat含量很高！saturated fat 会增加血液胆固醇水平！
#不同食物类别中的钠含量绘图分析（图2）
dat<-menu %>% select(Category,Sodium)%>% group_by(Category)%>%summarise(tsodium=sum(Sodium))
dat
dat$fraction = dat$tsodium / sum(dat$tsodium)
dat = dat[order(dat$fraction), ]
dat$ymax = cumsum(dat$fraction)
dat$ymin = c(0, head(dat$ymax, n=-1))
#dat数据 详见图3
p5 = ggplot(dat, aes(fill=Category, ymax=ymax, ymin=ymin, xmax=9, xmin=3)) +
geom_rect() +
coord_polar(theta="y") +
xlim(c(0, 9)) +
theme(panel.grid=element_blank()) +
theme(axis.text=element_blank()) +
theme(axis.ticks=element_blank()) +
annotate("text", x = 0, y = 0, label = "Sodium") +
labs(title="Sodium content in Category")
p5图2：Sodium content in Category
图3：dat数据
Breakfast中占比Sodium（钠）含量占比39%，排名NO.1
Chicken & Fish中Sodium（钠）含量占比26%,排名NO.2
#九种食物类别各自中的Sugars含量是多少？
plot_ly(x = menu$Category, y=menu$Sugars,color = menu$Category,colors =new_col , type = "bar") %>%
layout(title = "Sugars",xaxis = list(title = ""),yaxis = list(title = ""),showlegend=FALSE,autosize = T) 图4：Sugars
Smoothies & Shakes中的Sugars含量最高,排名NO.1
Coffee & Tea中的Sugars含量其次,排名NO.2
#Sugar含量在Smoothies & Shakes这一类食物中的数值可视化绘图（图5）
library(stringr)
ss<-menu%>%select(Category,Item,Sugars,Serving.Size,Sugars)%>%
filter(Category=="Smoothies & Shakes")
ss$size<-NULL
ss$size[str_detect(ss$Item,"Small")]<-"Small"
ss$size[str_detect(ss$Item,"Medium")]<-"Medium"
ss$size[str_detect(ss$Item,"Large")]<-"Large"
ss$size[str_detect(ss$Item,"Snack")]<-"Snack"
ss%>%filter(!size == "Snack")%>%arrange(desc(Sugars))%>% #ss数据 图6
ggplot(aes(x=factor(Item,level=Item),y=Sugars,group=size,fill=size))+
geom_bar(stat="identity",position="dodge",alpha=0.7)+
theme(axis.text.x = element_text(angle=90))+coord_flip()+
labs(x="Item",title="Sugar content in Smoothies & Shakes")#比较胆固醇和胆固醇（每日价值）在各食物名称中含量绘图。（图7）
menu %>% arrange(desc(Cholesterol....Daily.Value.))%>%plot_ly( x = ~factor(Item,levels=Item),
y = ~Cholesterol, type="scatter",color=~Item, size=~Cholesterol,colors='Paired',mode = "markers",
marker=list( opacity=0.7) ) %>% add_trace(x = ~factor(Item,levels=Item),
y = ~Cholesterol....Daily.Value., modee="lines", yaxis = "y2", name = "Cholestrol DV",
color=I('red'),line=list(opacity=0.7)) %>% layout(yaxis2 = list(overlaying = "y",
side = "left",title="Cholesterol DV"),title = "Cholesterol Content",xaxis = list(title = ""),
yaxis = list(title = "Total Cholestrol",side="right"),showlegend=FALSE,autosize = T,
margin = list(pad = 30, b = 90, l = 60, r = 80))图7：Cholesterol Content
#可视化胆固醇含量高的食物类别（Cholestrol Rich Items）与胆固醇含量低一些的食物类别（Cholestrol Low Items）
ch<-menu %>% select(Category,Item,Cholesterol)%>% arrange(desc(Cholesterol))%>%head(25)
p6<-plot_ly(ch, x=factor(ch$Item,level=ch$Item),y=ch$Cholesterol,color=ch$Category,type="bar")%>%
layout(title="Cholestrol Rich Items",height=400)
ch1<-menu %>% select(Category,Item,Cholesterol)%>% arrange(desc(Cholesterol))%>%
filter(Cholesterol >5 & Cholesterol<25)
p7<-plot_ly(ch1, x=factor(ch1$Item,level=ch1$Item),y=ch1$Cholesterol,color=ch1$Category,type="bar")%>%
layout(title="Cholestrol Low Items",height=400)
ggplotly(p6) #图8
ggplotly(p7) #图9 #Dietry Fibre与 Dietary Fiber (% Daily Value)的在各食物中含量绘图（图10）
#Dietry Fibre Content（食物中膳食纤维的数值）
menu %>%
plot_ly( x = menu$Item, y = menu$Dietary.Fiber....Daily.Value., type="scatter", mode = "markers" ,
marker=list( color=colorRampPalette(brewer.pal(30,"Spectral"))(100) , opacity=0.7 ,
size=~Dietary.Fiber....Daily.Value.) ) %>% layout(title = "Dietry Fibre Daily Content ",
xaxis = list(title = ""),
yaxis = list(title = "Daily Dietary fibre"),
showlegend=FALSE,autosize = F, width = 1000, height = 400,margin=m)#Category（食物类别）中Vitamin C的含量占比（图11）
menu%>%group_by(Category)%>%summarise(vitc=sum(Vitamin.C....Daily.Value.))%>%
plot_ly(labels = ~Category, values = ~vitc) %>%
add_pie(hole = 0.3,textinfo = 'label+percent',marker = list(colors = new_col,
line = list(color = '#FFFFFF', width = 1))) %>% layout(title = "Categories & Vitamin C %",
showlegend = F,xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))图11：Categories & Vitamin C %
Beverages（饮料）类食物分类中Vitamin C含量在9类食物中占比28.6%，排名NO.1
Breakfast（早餐）这类食物分类中Vitamin C含量在9类食物中占比16.9% , 排名NO.2
#Vitamin C含量最高滴十种食物！（图12）
vitc<-menu%>% select(Item,Vitamin.C....Daily.Value.,Category)%>%group_by(Item,Category)%>%
summarise(c=mean(Vitamin.C....Daily.Value.))%>%arrange(desc(c))%>%head(10)
p8 <- plot_ly(vitc, labels = ~Item, values = ~c, type = 'pie',textinfo = 'label+percent',
textposition='inside',marker = list(colors = new_col,
line = list(color = '#FFFFFF', width = 1))) %>%
layout(title = 'Top 10 Items which are rich in Vitamin C',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))Minute Maid Orange Juice (Large) 含量最高19.8%
Minute Maid Orange Juice (Medium) 和Apple Slices并列第二名 占比13.2%
#各类食物中的铁元素含量(图13）
p9 <- plot_ly(menu, r = ~Iron....Daily.Value., t = ~Calories)%>% add_area(color = ~Category)
layout(p, radialaxis = list(ticksuffix = "%"), orientation = 270)图13
#金拱门食品当中Daily requirement of Iron的要求。
c<-16.3
m<-20.5
w<-18.9
menu %>% select(Category,Item,Iron....Daily.Value.)%>%arrange(desc(Iron....Daily.Value.))%>%
filter(Iron....Daily.Value.>=15)%>%ggplot(aes(x=substr(Item,1,15),
y=Iron....Daily.Value.,col=Category,size=Iron....Daily.Value.))+geom_point(fill="red")+
theme(axis.text.x = element_text(angle=90),legend.position="bottom")+
geom_hline(yintercept =c,col="red",linetype="dashed")+
geom_text(aes( 0, c, label = "Children",vjust=-1,hjust=0,col="red"), size = 3)+
geom_hline(yintercept =m,col="blue",linetype="dashed")+
geom_text(aes( 0, m, label = "Men",vjust=-1,hjust=0), size = 3,col="blue")+
geom_hline(yintercept =w,col="green",linetype="dashed")+geom_text(aes( 0, w, label = "Women",vjust=-0.5,hjust=0),
size = 3,col="green")+labs(title="Mcdonald's Item -Daily requirement of Iron",x="Item")图14
#金拱门食物各属性营养价值占比（图15）
menu %>% filter(Item %in% c("Egg McMuffin","Big Mac","Chicken McNuggets (10 piece)",
"Large French Fries","Baked Apple Pie","Double Cheeseburger"))%>%
select(Item,Cholesterol....Daily.Value.,
Sodium....Daily.Value.,Carbohydrates....Daily.Value.,Dietary.Fiber....Daily.Value.,Vitamin.A....Daily.Value.,
Calcium....Daily.Value.,Iron....Daily.Value.,Total.Fat....Daily.Value.,Saturated.Fat....Daily.Value.)%>%
gather(nut,value,2:10)%>%ggplot(aes(x="",y=value,fill=nut))+geom_bar(stat="identity",width=1)+
coord_polar(theta = "y", start=0)+facet_wrap(~Item)+theme(legend.position = "bottom",
legend.text=element_text(size=5))+labs(title="Nutritive values in most popular items",fill="Nutrients")图15：金拱门食物各属性营养价值占比
#有助卡路里的营养成分属性综合分析！（图16）
g1<-menu%>%ggplot(aes(x=Cholesterol,y=Calories))+geom_point(col="hotpink")+geom_smooth(method="lm",col="hotpink")
g2<-menu%>%ggplot(aes(x=Carbohydrates,y=Calories))+geom_point(col="navyblue")+geom_smooth(method="lm",col="navyblue")
g3<-menu%>%ggplot(aes(x=Total.Fat,y=Calories))+geom_point(col="magenta")+geom_smooth(method="lm",col="magenta")
g3<-menu%>%ggplot(aes(x=Sugars,y=Calories))+geom_point(col="darkorchid4")+geom_smooth(method="lm",col="darkorchid4")
g4<-menu%>%ggplot(aes(x=Protein,y=Calories))+geom_point(col="firebrick4")+geom_smooth(method="lm",col="firebrick4")
g5<-menu%>%ggplot(aes(x=Sodium,y=Calories))+geom_point(col="olivedrab4")+geom_smooth(method="lm",col="olivedrab4")
g6<-menu%>%ggplot(aes(x=Saturated.Fat,y=Calories))+geom_point(col="orange4")+geom_smooth(method="lm",col="orange4")
g7<-menu%>%ggplot(aes(x=Dietary.Fiber,y=Calories))+geom_point(col="tomato4")+geom_smooth(method="lm",col="tomato4")
g8<-menu%>%ggplot(aes(x=Trans.Fat,y=Calories))+geom_point(col="slateblue4")+geom_smooth(method="lm",col="slateblue4")
grid.arrange(g1,g2,g3,g4,g5,g6,g7,g8,nrow=3,ncol=3)（图16）Sugar,Carbohydrates,Protein,Saturated Fat & cholestrol 对Calories有主要贡献！
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