Data Visualization
DOEUN
20/01/2021
#install.packages("ggplot2")
#install.packages("dplR")
library(ggplot2)
library(dplR)
#install.packages("installr")
#library(installr)
#check.for.updates.R()
#install.R()
#install.packages("mosaicData")
library(mosaicData)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionScatter Plot
Geom_point Geom_smooth : linear 표시
data(CPS85, package = "mosaicData")
# exper 와 wage 의 산점도
ggplot(CPS85, mapping=aes(exper, wage))+
geom_point()
#wage 가 40 이하인것들을 선택
filter(CPS85, wage < 40)-> plotdata
ggplot(plotdata, aes(exper,wage))+geom_point()
## Warning: Duplicated aesthetics after name standardisation: size## `geom_smooth()` using formula 'y ~ x'
Grouping - ggplot (x, y : numeric , x= color )
ggplot(plotdata, mapping=aes(exper, wage, color=sex))+
geom_point()+
geom_smooth(method="lm", se=FALSE, size=1.5) #se: the confidence interval 표시 ## `geom_smooth()` using formula 'y ~ x'
Scales (sales_x_continues, scale_y_continue : x, y, 축의 범위 표시 )
ggplot(plotdata, mapping = aes(exper, wage, color=sex))+
geom_point()+
geom_smooth(method="lm", se=FALSE)+
scale_x_continuous(breaks = seq(0,60,10))+
scale_y_continuous(breaks = seq(0,30,5),
label= scales:: dollar)+
scale_color_manual(values=c("indianred3", "cornflowerblue"))## `geom_smooth()` using formula 'y ~ x'
facetwrap : 변수 1 개 facet grid: 변수 2개 ~x
ggplot(plotdata, mapping = aes(exper, wage, color=sex))+
geom_point()+
geom_smooth(method="lm", se=FALSE)+
scale_x_continuous(breaks = seq(0,60,10))+
scale_y_continuous(breaks = seq(0,30,5),
label= scales:: dollar)+
scale_color_manual(values=c("indianred3", "cornflowerblue"))+
facet_wrap(~sector)## `geom_smooth()` using formula 'y ~ x'
Labels + Theme
ggplot(plotdata, aes(exper, wage, color=sex))+
geom_point()+
geom_smooth(method="lm", se=FALSE)+
scale_x_continuous(breaks = seq(0,60,10))+
scale_y_continuous(breaks= seq(0,30,5))+
facet_wrap(~sector)+
scale_color_manual(values= c("indianred3", "cornflowerblue"))+
labs(title="Relationship between wage and experience",
subtitle = "Current Populatin Survey",
caption = "source: mosaic data",
x="Year of Experenice ",
y="Hourly Wage",
color="Gender")+
theme_minimal()## `geom_smooth()` using formula 'y ~ x'
linear transformation
geom_smooth 에서 formula 옵션으로 y=ax+b 의 식을 지정해 준다.
ggplot(plotdata, aes(exper, wage, color=sex))+
geom_point(alpha=0.7, size=2)+
geom_smooth(method="lm",
formula = y ~ poly(x,2),
se=FALSE,
size=3)
ggplot(plotdata, aes(exper,wage))+
geom_point(aes(color=sex))+
geom_smooth(method="lm",
formula = y ~poly(x,2),
se=FALSE)
Color 변수를 geom_point로 쓸 경우에는 전체 linear 식을 보여준다.
Univariate Graphs : Categorical and Quantitative (age, weight)
3.1 Categorical (Bar Chart ) - RACE (white, hispanic, black 명목형 변수 )
Categorical 변수는 빈도수 표시 : BAR 차트 몇 개가 있는지 (비율%) 가능
data(Marriage, package="mosaicData")
ggplot(Marriage, aes(race))+
geom_bar(fill="indianred3", color="black")+ #fill 색상, color = 테두리
labs(x="Race", y="Frequency")
Percent - RACE : 각 인종은 몇 %를 차지하는가? 1) ..count../sum(..count..) 2) scale_y_continuous(label= scale:: percent )
ggplot(Marriage, aes(x=race,
y= ..count../sum(..count..)))+
geom_bar(fill="indianred3", color="black")+
scale_y_continuous(labels = scales::percent)
#3) Percent - dplr 활용해서 DB 뽑고 그 안에서 그래프 만들기
Marriage %>%
count(race) %>%
mutate(pct=paste0(round(n/sum(n)*100), "%")) %>%
arrange(desc(n)) -> df1
df1## race n pct
## 1 White 74 76%
## 2 Black 22 22%
## 3 American Indian 1 1%
## 4 Hispanic 1 1%## Categorical 변수의 Count 갯수,% 구하기 : aes(x, count), y=pct
ggplot(df1, aes(reorder(race, n), pct))+ ## bar chart (x, y 필요 )
geom_bar(stat = "identity")+ # stat=identity : plotting function not to calculate counts
geom_text(aes(label=pct), vjust=-0.25)
Marriage %>%
count(officialTitle) %>%
mutate(pct=paste0(round(n/sum(n)*100),"%")) %>%
arrange(desc(pct)) -> df2
ggplot(df2, aes(x=reorder(officialTitle, n), y=pct))+
geom_bar(stat = "identity", fill="darkgreen")+
geom_text(aes(label=pct), vjust=0.15)+
coord_flip()+
theme_update()
Tree MAP - Categorical variable - COUNT/PCT
library(treemapify)
plotdata <- Marriage %>%
count(officialTitle)
ggplot(plotdata, aes(fill=officialTitle,
area=n,
label=officialTitle,n))+
geom_treemap()+
geom_treemap_text(color="white",
place="center")
Categorical + Categrocial
1. Stacked Bar
두 변수가 Categorical 이면, x에 카테고리,fill에 다른 카데로기 변수를 넣어준다
ggplot(mpg, aes(class, fill=drv))+
geom_bar(position = "stack") # position="dodge" : 따로 만들어서 보여준다 / position= "fill" 전체 넣어서 보여주기 
ggplot(mpg, aes(class, fill=drv))+
geom_bar(position= "fill")
class / drv 이 Categorical 범주이기 때문에, 두 변수를 factor 로 변환 시키고, level=c()로 묶어서 이름을 변경
두 범주형 데이터는 교차 빈도, 비율이 나올 수 있기 때문에, x1 *x2 의 비율에 대한 검정을 생각해서 시각화 시킨다
library(scales)
#install.packages('DT')
library(DT)
## % 표시를 위한 ddplr 가공
plotdata <- mpg %>%
group_by(class, drv) %>%
summarise(n=n()) %>%
mutate(pct= n/sum(n),
lbl=scales::percent(pct))## `summarise()` has grouped output by 'class'. You can override using the `.groups` argument.datatable(plotdata)## ddplr 로 데이터 가공 group_by 2 개의 카테고리는 x=class,y=pct,fill=drv 로 지정한다
## geom_bar 에서 ddplr 로 데이터를 가져왔기 때문에, stat="identity" 입력해주기
## geom_text : aes(label=lbl) 로 어떤 컬럼이 들어갈지 지정해준다.
ggplot(plotdata, aes(x=factor(class),
y=pct,
fill=factor(drv)))+
geom_bar(stat="identity",
position = "fill")+
scale_y_continuous(breaks = seq(0,1,2), label =percent)+
scale_fill_brewer(palette = "Set2")+
geom_text(aes(label=lbl), size=3)
theme_minimal()+
labs(y= "Percent",
fill="Drive Train",
x="Class",
title= "Automobile by Class")## List of 96
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## - attr(*, "validate")= logi TRUEQuantitiative + Quantitative
Scatter Plot
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, lastdata(Salaries, package="carData")
as.data.table(Salaries) -> sal
datatable(sal)## phd 가 salary 랑 관련이 있을까?
ggplot(sal, aes(yrs.since.phd, salary))+
geom_point(color="steelblue")+
geom_smooth(method = "lm", color="indianred3")+
theme_minimal()+
scale_x_continuous(breaks = seq(0,50,10))+
scale_y_continuous(limits = c(50000, 250000), labels = scales::dollar)+
labs(x= "Year Since Phd",
y="Salaries",
title = "Relation between Year since phd and Salaries ",
subtitle = "9 month salary for 2008-2009")## `geom_smooth()` using formula 'y ~ x'
Statistical Test - 2 연속형 변수의 관계성을 평가해보기 cor.test (상관분석)
#install.packages("pillar", type="binary")
#install.packages("ggpubr")
library("ggpubr")
# 공식
#cor(x, y, method = c("pearson", "kendall", "spearman"))
#cor.test(x, y, method=c("pearson", "kendall", "spearman"))
cor.test(sal$yrs.since.phd, sal$salary, method="pearson")##
## Pearson's product-moment correlation
##
## data: sal$yrs.since.phd and sal$salary
## t = 9.1775, df = 395, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3346160 0.4971402
## sample estimates:
## cor
## 0.41923110.491211 로 상관이 있어보인다.
ggscatter(sal, x='yrs.since.phd', y='salary',
add = "reg.line", # Add regression line
conf.int = TRUE, # Add confidence interval
add.params = list(color = "blue",
fill = "lightgray"))+
stat_cor(method = "pearson", label.x = 3, label.y = 30) ## `geom_smooth()` using formula 'y ~ x'
남/여 그룹별 phd 와 연봉의 관계를 시각화
ggscatter(sal, x='yrs.since.phd', y='salary',
add='reg.line',
color= 'sex', palette = 'jco',
shape='sex',
conf.int = TRUE
)+
stat_cor(aes(color='sex'), label.x=10)## `geom_smooth()` using formula 'y ~ x'
ggscatter(sal, x='yrs.since.phd', y='salary'
, color = 'sex', shape='sex',
ellipse = TRUE)
4.2.2 LINE PLOT
년도에 따른 그래프 그리기 Column 은 Country, continet, year, lifeExp, pop, gdpPrecap
#install.packages("gapminder")
library(gapminder)
data(gapminder, package = "gapminder")
as.data.frame(gapminder) -> mider
DT::datatable(mider)미국은 년도 별로 lifeExp 은 어떠한가?
mider %>%
filter(country == "United States") %>%
ggplot( aes(x=year, y=lifeExp))+
geom_line(color="lightgrey", size=2)+
geom_point(color="steelblue", size=3)
4.3 Categorical VS Quantitative
4.3.1 Barchart (On sumarry statistics)
예를 들어, 각 그룹별 평균 과 같은 내용을 시각화 할때 쓰임
blue <-"cornflowerblue"
sal %>%
group_by(rank) %>%
summarise(avg_sal=mean(salary)) %>%
ggplot(aes(x=rank, y=avg_sal))+
geom_bar(stat = "identity", fill=blue)+
geom_text(aes(label = dollar(avg_sal), vjust= -0.25))+
scale_y_continuous(labels = dollar)+
theme_minimal()+
labs(x= "" , y=" ")
4.3.2 Grouped Kernel Density Plots
그룹 별 분포도(Density) 확인이 가능
phd 그룹을 명목형 변수로 나누어서 그룹 별 salary 분포를 보여주기
#install.packages("reshape")
library(reshape)##
## Attaching package: 'reshape'## The following object is masked from 'package:data.table':
##
## melt## The following object is masked from 'package:dplyr':
##
## renamesal<-rename( sal, c(yrs.since.phd = "phd"))
summary(sal$phd) ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 12.00 21.00 22.31 32.00 56.00#20년 단위로 쪼개기
ifelse(sal$phd <= 20 , '20',
ifelse( sal$phd >=21 & sal$phd <= 40,'20~40',
ifelse( sal$phd >= 41 ,' Above','기타타'))) -> sal$phd_group
sal %>%
ggplot(aes(x=salary, fill=phd_group))+
geom_density(alpha= 0.4)
4.3.3 Box_Plot
사분위수 및 이상치 검증에 사용
ggplot(sal, aes(x=phd_group, y=salary))+
geom_boxplot(fill=blue, notch = TRUE)
Grammer : stat_compare_mean (mehtod = “t.text”, “anova” )
Ho: 집단간 임금의 차이가 없다 H1: 집단간 임금이 차이가 있다.
p < 2.2e -16 기각 : 차이가 있다
ggboxplot(sal, x= 'phd_group', y= 'salary',
color = 'phd_group',
line.color ='gray',
line.size= 0.4,
palette = 'npg')+
stat_compare_means(method ="anova")
4.3.4 Category 별 분포도
#install.packages("ggridges")
library(ggridges)
ggplot(sal, aes(x=salary, y=phd_group, fill=phd_group))+
geom_density_ridges()+
theme_ridges()## Picking joint bandwidth of 11300
4.3.5 MEAN/SEM Plot
Error Bar 와 함께 각 그룹별 평균의 sd, se 를 나타내 주는데 사용된다.
sal %>%
group_by(phd_group) %>%
summarise( n= n(),
mean = mean(salary),
sd = sd(salary),
se = sd/sqrt(n),
ci =qt (0.975, df= n-1)* sd/sqrt(n)) -> df_2
datatable(df_2)ANOVA 검정으로 집단간 차이가 있다는 것을 검증 평균과 평균오차를 그림으로 나타내기
ggplot(df_2, aes(x=phd_group, y= mean, group= 1))+
geom_point()+
geom_line()+
geom_errorbar(aes(ymin = mean - se ,
ymax = mean + se,
width = .1))
남 여 변수 추가
sal %>%
group_by(phd_group, sex) %>% # group_by 에서 sex 추가 지정
summarise(n = n(),
mean = mean(salary),
sd =sd (salary),
se= sd/sqrt(salary)) -> df_3 ## `summarise()` has grouped output by 'phd_group', 'sex'. You can override using the `.groups` argument.ggplot(df_3,aes(x= phd_group,
y= mean,
group =sex,
color= sex))+
geom_point()+
geom_line()+
geom_errorbar( aes(ymin = mean - se,
ymax= mean + se) ,
)
Rank + Sex 별 Salary 평균 차이
#df_4 table 산출
sal %>%
group_by(rank, sex) %>%
summarise( n= n(),
mean = mean(salary),
sd = sd(salary),
se = sd/sqrt(salary)) -> df_4## `summarise()` has grouped output by 'rank', 'sex'. You can override using the `.groups` argument.ggplot(df_4, aes(x = rank,
y = mean,
group= sex,
color= sex))+
geom_point(size= 3)+
geom_line(size =1)+
geom_errorbar(aes (ymin = mean - se ,
ymax = mean + se))+
scale_y_continuous(label = scales:: dollar)+
scale_color_brewer(palette = "Set1")+
theme_minimal()+
labs(x = "RANK" , y =" Average Salaries by Rank and Sex",
title = "Mean Salary by rank and sex",
subtitle = "(mean +/- standard error)")
## ggpubr 는 좀 더 쉽게 나타낼 수 있음 + 통계값
ggline(df_4 , x = "rank",
y= "mean",
color ="sex",
add = "mean se") +
stat_compare_means(aes(group= sex), label = "p.signif")
Geom_jitter 사용해보기
ggplot(sal, aes(x= rank,
y= salary,
color =rank))+
geom_jitter()+
geom_boxplot(size = 1,
outlier.shape = 1,
outlier.color = "black")+
facet_wrap(~sex) +
theme_minimal()
Cleveland Dot Charts
각 그룹별 수치를 정리할 때 좋음 (그룹이 너무 많을때 )
예를 들어, 각 나라별 예상 수명
head(gapminder, 4)## # A tibble: 4 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.# 2007 년의 Asia 지역의 나라만 골라서 평균 수명이 각각 어떤지 나타내보기
gapminder %>%
filter(continent == "Asia" &
year == 2007) %>%
ggplot(aes(x = lifeExp,
y= reorder(country,lifeExp))) +
geom_point(color = blue)+
geom_segment(aes(x = 40 ,
xend = lifeExp,
y= reorder(country, lifeExp),
yend = reorder(country, lifeExp)),
color = "lightgrey")+
labs(x = "Life Expectancy in 2007",
y = "Countires",
title = "Life Expectancy by Country",
subtitle = "Data for Asia - 2007")+
theme_light()
ggboxplot(gapminder,
x= "continent" ,
y = "lifeExp",
color ="continent")+
stat_compare_means(method ="anova")
cor.test(gapminder$lifeExp, gapminder$pop, method ="pearson")##
## Pearson's product-moment correlation
##
## data: gapminder$lifeExp and gapminder$pop
## t = 2.6854, df = 1702, p-value = 0.007314
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01752303 0.11209600
## sample estimates:
## cor
## 0.06495537ggscatter(gapminder,
x ="lifeExp",
y= "pop",
color ="continent",
add= "reg.line",
conf.int=TRUE)## `geom_smooth()` using formula 'y ~ x'
gapminder %>%
filter(continent == "Asia" ) -> Asia_only
cor.test(Asia_only$lifeExp, Asia_only$pop, method ="pearson")##
## Pearson's product-moment correlation
##
## data: Asia_only$lifeExp and Asia_only$pop
## t = 0.65844, df = 394, p-value = 0.5106
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06560731 0.13127062
## sample estimates:
## cor
## 0.03315327ggscatter(Asia_only,
x ="lifeExp",
y= "pop",
add= "reg.line",
conf.int=TRUE)+
stat_cor(method = "pearson", label.y = 1e+09)## `geom_smooth()` using formula 'y ~ x'
gapminder## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rowsggplot(gapminder, aes (x = lifeExp, y = continent, fill = continent)) +
geom_density_ridges2() +
theme_ridges()## Picking joint bandwidth of 2.23
ggplot(sal, aes( x = phd,
y= salary,
color = rank,
shape = sex,
size =phd))+
geom_point()
ggplot(sal, aes(x =phd,
y= salary,
color = sex))+
geom_point(size= 2, alpha= 0.4)+
geom_smooth(method = "lm",
se = FALSE)+
scale_y_continuous(label = scales :: dollar)+
scale_color_brewer(palette = "Set1")+
theme_minimal()## `geom_smooth()` using formula 'y ~ x'
ggscatter(sal,
x = "phd",
y = "salary",
color ="rank",
palette = "jco",
add ="reg.line" ,
conf.int = TRUE,
fullrange = TRUE, # Extending the regression line
rug = TRUE )+
stat_cor(aes(color='rank'), method ="spearman")## `geom_smooth()` using formula 'y ~ x'
sal %>%
group_by(sex, rank, discipline) %>%
summarise( n = n(),
mean = mean(salary),
sd = sd(salary),
se = sd/ sqrt(n)) -> df5## `summarise()` has grouped output by 'sex', 'rank'. You can override using the `.groups` argument.ggplot(df5, aes(x= sex,
y=mean,
color = sex))+
geom_point()+
geom_errorbar(aes(ymin = mean - se,
ymax = mean + se))+
facet_grid(.~rank + discipline)+
scale_y_continuous(label = scales :: dollar)+
theme(legend.position = "none ",
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank())+
scale_color_brewer(palette = "Set1")+
theme_bw()
Cate 3- Con 1
## CPS85 데이터 활용 연습
CPS85 %>%
group_by(race, married, hispanic) %>%
summarise( n= n(),
mean = mean(wage),
sd = sd(wage),
se = sd/sqrt(n)) -> df6## `summarise()` has grouped output by 'race', 'married'. You can override using the `.groups` argument.ggplot(df6, aes(x= married,
y= mean,
color = married))+
geom_point()+
geom_errorbar(aes(ymin = mean- se,
ymax= mean + se))+
facet_grid(.~ race + hispanic)+
theme_bw()+
scale_color_brewer(palette = "Set1")
gapminder %>%
filter(continent == "Americas") -> pd_1
ggplot(pd_1, aes(x= year, y= lifeExp))+
geom_line(color = blue)+
geom_point(color ="indianred3")+
facet_wrap(~country)+
theme_minimal()
Time Series
ggplot(economics, aes(x=date, y=psavert))+
geom_line(color = "indianred3", size = 1)+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#install.packages("quantmod")
library(quantmod)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:data.table':
##
## first, last## The following objects are masked from 'package:dplyr':
##
## first, last## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoogetSymbols("AAPL",
return.class = "data.frame",
from = "2018-01-01") -> apple## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
##
## This message is shown once per session and may be disabled by setting
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.AAPL %>%
mutate(Date = as.Date(row.names(.))) %>%
select(Date, AAPL.Close) %>%
mutate(Company = "Apple") -> apple
getSymbols("FB",
return.class ="data.frame",
from = "2018-01-01") -> fb
apple <- rename(apple, c(AAPL.Close = "Close"))
#sal<-rename( sal, c(yrs.since.phd = "phd"))#
FB %>%
mutate(Date = as.Date(row.names(.))) %>%
mutate(Company = "Facebook") %>%
select(Date, FB.Close) %>%
mutate(Company = "Face Book")-> fb
fb <- rename(fb, c(FB.Close = "Close"))
rbind(apple, fb) -> mseries
mseries## Date Close Company
## 2018-01-02 2018-01-02 43.0650 Apple
## 2018-01-03 2018-01-03 43.0575 Apple
## 2018-01-04 2018-01-04 43.2575 Apple
## 2018-01-05 2018-01-05 43.7500 Apple
## 2018-01-08 2018-01-08 43.5875 Apple
## 2018-01-09 2018-01-09 43.5825 Apple
## 2018-01-10 2018-01-10 43.5725 Apple
## 2018-01-11 2018-01-11 43.8200 Apple
## 2018-01-12 2018-01-12 44.2725 Apple
## 2018-01-16 2018-01-16 44.0475 Apple
## 2018-01-17 2018-01-17 44.7750 Apple
## 2018-01-18 2018-01-18 44.8150 Apple
## 2018-01-19 2018-01-19 44.6150 Apple
## 2018-01-22 2018-01-22 44.2500 Apple
## 2018-01-23 2018-01-23 44.2600 Apple
## 2018-01-24 2018-01-24 43.5550 Apple
## 2018-01-25 2018-01-25 42.7775 Apple
## 2018-01-26 2018-01-26 42.8775 Apple
## 2018-01-29 2018-01-29 41.9900 Apple
## 2018-01-30 2018-01-30 41.7425 Apple
## 2018-01-31 2018-01-31 41.8575 Apple
## 2018-02-01 2018-02-01 41.9450 Apple
## 2018-02-02 2018-02-02 40.1250 Apple
## 2018-02-05 2018-02-05 39.1225 Apple
## 2018-02-06 2018-02-06 40.7575 Apple
## 2018-02-07 2018-02-07 39.8850 Apple
## 2018-02-08 2018-02-08 38.7875 Apple
## 2018-02-09 2018-02-09 39.1025 Apple
## 2018-02-12 2018-02-12 40.6775 Apple
## 2018-02-13 2018-02-13 41.0850 Apple
## 2018-02-14 2018-02-14 41.8425 Apple
## 2018-02-15 2018-02-15 43.2475 Apple
## 2018-02-16 2018-02-16 43.1075 Apple
## 2018-02-20 2018-02-20 42.9625 Apple
## 2018-02-21 2018-02-21 42.7675 Apple
## 2018-02-22 2018-02-22 43.1250 Apple
## 2018-02-23 2018-02-23 43.8750 Apple
## 2018-02-26 2018-02-26 44.7425 Apple
## 2018-02-27 2018-02-27 44.5975 Apple
## 2018-02-28 2018-02-28 44.5300 Apple
## 2018-03-01 2018-03-01 43.7500 Apple
## 2018-03-02 2018-03-02 44.0525 Apple
## 2018-03-05 2018-03-05 44.2050 Apple
## 2018-03-06 2018-03-06 44.1675 Apple
## 2018-03-07 2018-03-07 43.7575 Apple
## 2018-03-08 2018-03-08 44.2350 Apple
## 2018-03-09 2018-03-09 44.9950 Apple
## 2018-03-12 2018-03-12 45.4300 Apple
## 2018-03-13 2018-03-13 44.9925 Apple
## 2018-03-14 2018-03-14 44.6100 Apple
## 2018-03-15 2018-03-15 44.6625 Apple
## 2018-03-16 2018-03-16 44.5050 Apple
## 2018-03-19 2018-03-19 43.8250 Apple
## 2018-03-20 2018-03-20 43.8100 Apple
## 2018-03-21 2018-03-21 42.8175 Apple
## 2018-03-22 2018-03-22 42.2125 Apple
## 2018-03-23 2018-03-23 41.2350 Apple
## 2018-03-26 2018-03-26 43.1925 Apple
## 2018-03-27 2018-03-27 42.0850 Apple
## 2018-03-28 2018-03-28 41.6200 Apple
## 2018-03-29 2018-03-29 41.9450 Apple
## 2018-04-02 2018-04-02 41.6700 Apple
## 2018-04-03 2018-04-03 42.0975 Apple
## 2018-04-04 2018-04-04 42.9025 Apple
## 2018-04-05 2018-04-05 43.2000 Apple
## 2018-04-06 2018-04-06 42.0950 Apple
## 2018-04-09 2018-04-09 42.5125 Apple
## 2018-04-10 2018-04-10 43.3125 Apple
## 2018-04-11 2018-04-11 43.1100 Apple
## 2018-04-12 2018-04-12 43.5350 Apple
## 2018-04-13 2018-04-13 43.6825 Apple
## 2018-04-16 2018-04-16 43.9550 Apple
## 2018-04-17 2018-04-17 44.5600 Apple
## 2018-04-18 2018-04-18 44.4600 Apple
## 2018-04-19 2018-04-19 43.2000 Apple
## 2018-04-20 2018-04-20 41.4300 Apple
## 2018-04-23 2018-04-23 41.3100 Apple
## 2018-04-24 2018-04-24 40.7350 Apple
## 2018-04-25 2018-04-25 40.9125 Apple
## 2018-04-26 2018-04-26 41.0550 Apple
## 2018-04-27 2018-04-27 40.5800 Apple
## 2018-04-30 2018-04-30 41.3150 Apple
## 2018-05-01 2018-05-01 42.2750 Apple
## 2018-05-02 2018-05-02 44.1425 Apple
## 2018-05-03 2018-05-03 44.2225 Apple
## 2018-05-04 2018-05-04 45.9575 Apple
## 2018-05-07 2018-05-07 46.2900 Apple
## 2018-05-08 2018-05-08 46.5125 Apple
## 2018-05-09 2018-05-09 46.8400 Apple
## 2018-05-10 2018-05-10 47.5100 Apple
## 2018-05-11 2018-05-11 47.1475 Apple
## 2018-05-14 2018-05-14 47.0375 Apple
## 2018-05-15 2018-05-15 46.6100 Apple
## 2018-05-16 2018-05-16 47.0450 Apple
## 2018-05-17 2018-05-17 46.7475 Apple
## 2018-05-18 2018-05-18 46.5775 Apple
## 2018-05-21 2018-05-21 46.9075 Apple
## 2018-05-22 2018-05-22 46.7900 Apple
## 2018-05-23 2018-05-23 47.0900 Apple
## 2018-05-24 2018-05-24 47.0375 Apple
## 2018-05-25 2018-05-25 47.1450 Apple
## 2018-05-29 2018-05-29 46.9750 Apple
## 2018-05-30 2018-05-30 46.8750 Apple
## 2018-05-31 2018-05-31 46.7175 Apple
## 2018-06-01 2018-06-01 47.5600 Apple
## 2018-06-04 2018-06-04 47.9575 Apple
## 2018-06-05 2018-06-05 48.3275 Apple
## 2018-06-06 2018-06-06 48.4950 Apple
## 2018-06-07 2018-06-07 48.3650 Apple
## 2018-06-08 2018-06-08 47.9250 Apple
## 2018-06-11 2018-06-11 47.8075 Apple
## 2018-06-12 2018-06-12 48.0700 Apple
## 2018-06-13 2018-06-13 47.6750 Apple
## 2018-06-14 2018-06-14 47.7000 Apple
## 2018-06-15 2018-06-15 47.2100 Apple
## 2018-06-18 2018-06-18 47.1850 Apple
## 2018-06-19 2018-06-19 46.4225 Apple
## 2018-06-20 2018-06-20 46.6250 Apple
## 2018-06-21 2018-06-21 46.3650 Apple
## 2018-06-22 2018-06-22 46.2300 Apple
## 2018-06-25 2018-06-25 45.5425 Apple
## 2018-06-26 2018-06-26 46.1075 Apple
## 2018-06-27 2018-06-27 46.0400 Apple
## 2018-06-28 2018-06-28 46.3750 Apple
## 2018-06-29 2018-06-29 46.2775 Apple
## 2018-07-02 2018-07-02 46.7950 Apple
## 2018-07-03 2018-07-03 45.9800 Apple
## 2018-07-05 2018-07-05 46.3500 Apple
## 2018-07-06 2018-07-06 46.9925 Apple
## 2018-07-09 2018-07-09 47.6450 Apple
## 2018-07-10 2018-07-10 47.5875 Apple
## 2018-07-11 2018-07-11 46.9700 Apple
## 2018-07-12 2018-07-12 47.7575 Apple
## 2018-07-13 2018-07-13 47.8325 Apple
## 2018-07-16 2018-07-16 47.7275 Apple
## 2018-07-17 2018-07-17 47.8625 Apple
## 2018-07-18 2018-07-18 47.6000 Apple
## 2018-07-19 2018-07-19 47.9700 Apple
## 2018-07-20 2018-07-20 47.8600 Apple
## 2018-07-23 2018-07-23 47.9025 Apple
## 2018-07-24 2018-07-24 48.2500 Apple
## 2018-07-25 2018-07-25 48.7050 Apple
## 2018-07-26 2018-07-26 48.5525 Apple
## 2018-07-27 2018-07-27 47.7450 Apple
## 2018-07-30 2018-07-30 47.4775 Apple
## 2018-07-31 2018-07-31 47.5725 Apple
## 2018-08-01 2018-08-01 50.3750 Apple
## 2018-08-02 2018-08-02 51.8475 Apple
## 2018-08-03 2018-08-03 51.9975 Apple
## 2018-08-06 2018-08-06 52.2675 Apple
## 2018-08-07 2018-08-07 51.7775 Apple
## 2018-08-08 2018-08-08 51.8125 Apple
## 2018-08-09 2018-08-09 52.2200 Apple
## 2018-08-10 2018-08-10 51.8825 Apple
## 2018-08-13 2018-08-13 52.2175 Apple
## 2018-08-14 2018-08-14 52.4375 Apple
## 2018-08-15 2018-08-15 52.5600 Apple
## 2018-08-16 2018-08-16 53.3300 Apple
## 2018-08-17 2018-08-17 54.3950 Apple
## 2018-08-20 2018-08-20 53.8650 Apple
## 2018-08-21 2018-08-21 53.7600 Apple
## 2018-08-22 2018-08-22 53.7625 Apple
## 2018-08-23 2018-08-23 53.8725 Apple
## 2018-08-24 2018-08-24 54.0400 Apple
## 2018-08-27 2018-08-27 54.4850 Apple
## 2018-08-28 2018-08-28 54.9250 Apple
## 2018-08-29 2018-08-29 55.7450 Apple
## 2018-08-30 2018-08-30 56.2575 Apple
## 2018-08-31 2018-08-31 56.9075 Apple
## 2018-09-04 2018-09-04 57.0900 Apple
## 2018-09-05 2018-09-05 56.7175 Apple
## 2018-09-06 2018-09-06 55.7750 Apple
## 2018-09-07 2018-09-07 55.3250 Apple
## 2018-09-10 2018-09-10 54.5825 Apple
## 2018-09-11 2018-09-11 55.9625 Apple
## 2018-09-12 2018-09-12 55.2675 Apple
## 2018-09-13 2018-09-13 56.6025 Apple
## 2018-09-14 2018-09-14 55.9600 Apple
## 2018-09-17 2018-09-17 54.4700 Apple
## 2018-09-18 2018-09-18 54.5600 Apple
## 2018-09-19 2018-09-19 54.5925 Apple
## 2018-09-20 2018-09-20 55.0075 Apple
## 2018-09-21 2018-09-21 54.4150 Apple
## 2018-09-24 2018-09-24 55.1975 Apple
## 2018-09-25 2018-09-25 55.5475 Apple
## 2018-09-26 2018-09-26 55.1050 Apple
## 2018-09-27 2018-09-27 56.2375 Apple
## 2018-09-28 2018-09-28 56.4350 Apple
## 2018-10-01 2018-10-01 56.8150 Apple
## 2018-10-02 2018-10-02 57.3200 Apple
## 2018-10-03 2018-10-03 58.0175 Apple
## 2018-10-04 2018-10-04 56.9975 Apple
## 2018-10-05 2018-10-05 56.0725 Apple
## 2018-10-08 2018-10-08 55.9425 Apple
## 2018-10-09 2018-10-09 56.7175 Apple
## 2018-10-10 2018-10-10 54.0900 Apple
## 2018-10-11 2018-10-11 53.6125 Apple
## 2018-10-12 2018-10-12 55.5275 Apple
## 2018-10-15 2018-10-15 54.3400 Apple
## 2018-10-16 2018-10-16 55.5375 Apple
## 2018-10-17 2018-10-17 55.2975 Apple
## 2018-10-18 2018-10-18 54.0050 Apple
## 2018-10-19 2018-10-19 54.8275 Apple
## 2018-10-22 2018-10-22 55.1625 Apple
## 2018-10-23 2018-10-23 55.6825 Apple
## 2018-10-24 2018-10-24 53.7725 Apple
## 2018-10-25 2018-10-25 54.9500 Apple
## 2018-10-26 2018-10-26 54.0750 Apple
## 2018-10-29 2018-10-29 53.0600 Apple
## 2018-10-30 2018-10-30 53.3250 Apple
## 2018-10-31 2018-10-31 54.7150 Apple
## 2018-11-01 2018-11-01 55.5550 Apple
## 2018-11-02 2018-11-02 51.8700 Apple
## 2018-11-05 2018-11-05 50.3975 Apple
## 2018-11-06 2018-11-06 50.9425 Apple
## 2018-11-07 2018-11-07 52.4875 Apple
## 2018-11-08 2018-11-08 52.1225 Apple
## 2018-11-09 2018-11-09 51.1175 Apple
## 2018-11-12 2018-11-12 48.5425 Apple
## 2018-11-13 2018-11-13 48.0575 Apple
## 2018-11-14 2018-11-14 46.7000 Apple
## 2018-11-15 2018-11-15 47.8525 Apple
## 2018-11-16 2018-11-16 48.3825 Apple
## 2018-11-19 2018-11-19 46.4650 Apple
## 2018-11-20 2018-11-20 44.2450 Apple
## 2018-11-21 2018-11-21 44.1950 Apple
## 2018-11-23 2018-11-23 43.0725 Apple
## 2018-11-26 2018-11-26 43.6550 Apple
## 2018-11-27 2018-11-27 43.5600 Apple
## 2018-11-28 2018-11-28 45.2350 Apple
## 2018-11-29 2018-11-29 44.8875 Apple
## 2018-11-30 2018-11-30 44.6450 Apple
## 2018-12-03 2018-12-03 46.2050 Apple
## 2018-12-04 2018-12-04 44.1725 Apple
## 2018-12-06 2018-12-06 43.6800 Apple
## 2018-12-07 2018-12-07 42.1225 Apple
## 2018-12-10 2018-12-10 42.4000 Apple
## 2018-12-11 2018-12-11 42.1575 Apple
## 2018-12-12 2018-12-12 42.2750 Apple
## 2018-12-13 2018-12-13 42.7375 Apple
## 2018-12-14 2018-12-14 41.3700 Apple
## 2018-12-17 2018-12-17 40.9850 Apple
## 2018-12-18 2018-12-18 41.5175 Apple
## 2018-12-19 2018-12-19 40.2225 Apple
## 2018-12-20 2018-12-20 39.2075 Apple
## 2018-12-21 2018-12-21 37.6825 Apple
## 2018-12-24 2018-12-24 36.7075 Apple
## 2018-12-26 2018-12-26 39.2925 Apple
## 2018-12-27 2018-12-27 39.0375 Apple
## 2018-12-28 2018-12-28 39.0575 Apple
## 2018-12-31 2018-12-31 39.4350 Apple
## 2019-01-02 2019-01-02 39.4800 Apple
## 2019-01-03 2019-01-03 35.5475 Apple
## 2019-01-04 2019-01-04 37.0650 Apple
## 2019-01-07 2019-01-07 36.9825 Apple
## 2019-01-08 2019-01-08 37.6875 Apple
## 2019-01-09 2019-01-09 38.3275 Apple
## 2019-01-10 2019-01-10 38.4500 Apple
## 2019-01-11 2019-01-11 38.0725 Apple
## 2019-01-14 2019-01-14 37.5000 Apple
## 2019-01-15 2019-01-15 38.2675 Apple
## 2019-01-16 2019-01-16 38.7350 Apple
## 2019-01-17 2019-01-17 38.9650 Apple
## 2019-01-18 2019-01-18 39.2050 Apple
## 2019-01-22 2019-01-22 38.3250 Apple
## 2019-01-23 2019-01-23 38.4800 Apple
## 2019-01-24 2019-01-24 38.1750 Apple
## 2019-01-25 2019-01-25 39.4400 Apple
## 2019-01-28 2019-01-28 39.0750 Apple
## 2019-01-29 2019-01-29 38.6700 Apple
## 2019-01-30 2019-01-30 41.3125 Apple
## 2019-01-31 2019-01-31 41.6100 Apple
## 2019-02-01 2019-02-01 41.6300 Apple
## 2019-02-04 2019-02-04 42.8125 Apple
## 2019-02-05 2019-02-05 43.5450 Apple
## 2019-02-06 2019-02-06 43.5600 Apple
## 2019-02-07 2019-02-07 42.7350 Apple
## 2019-02-08 2019-02-08 42.6025 Apple
## 2019-02-11 2019-02-11 42.3575 Apple
## 2019-02-12 2019-02-12 42.7225 Apple
## 2019-02-13 2019-02-13 42.5450 Apple
## 2019-02-14 2019-02-14 42.7000 Apple
## 2019-02-15 2019-02-15 42.6050 Apple
## 2019-02-19 2019-02-19 42.7325 Apple
## 2019-02-20 2019-02-20 43.0075 Apple
## 2019-02-21 2019-02-21 42.7650 Apple
## 2019-02-22 2019-02-22 43.2425 Apple
## 2019-02-25 2019-02-25 43.5575 Apple
## 2019-02-26 2019-02-26 43.5825 Apple
## 2019-02-27 2019-02-27 43.7175 Apple
## 2019-02-28 2019-02-28 43.2875 Apple
## 2019-03-01 2019-03-01 43.7425 Apple
## 2019-03-04 2019-03-04 43.9625 Apple
## 2019-03-05 2019-03-05 43.8825 Apple
## 2019-03-06 2019-03-06 43.6300 Apple
## 2019-03-07 2019-03-07 43.1250 Apple
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## 2019-03-11 2019-03-11 44.7250 Apple
## 2019-03-12 2019-03-12 45.2275 Apple
## 2019-03-13 2019-03-13 45.4275 Apple
## 2019-03-14 2019-03-14 45.9325 Apple
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## 2019-03-18 2019-03-18 47.0050 Apple
## 2019-03-19 2019-03-19 46.6325 Apple
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## 2019-03-21 2019-03-21 48.7725 Apple
## 2019-03-22 2019-03-22 47.7625 Apple
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## 2019-03-28 2019-03-28 47.1800 Apple
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## 2019-04-01 2019-04-01 47.8100 Apple
## 2019-04-02 2019-04-02 48.5050 Apple
## 2019-04-03 2019-04-03 48.8375 Apple
## 2019-04-04 2019-04-04 48.9225 Apple
## 2019-04-05 2019-04-05 49.2500 Apple
## 2019-04-08 2019-04-08 50.0250 Apple
## 2019-04-09 2019-04-09 49.8750 Apple
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## 2019-04-11 2019-04-11 49.7375 Apple
## 2019-04-12 2019-04-12 49.7175 Apple
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## 2019-04-16 2019-04-16 49.8125 Apple
## 2019-04-17 2019-04-17 50.7825 Apple
## 2019-04-18 2019-04-18 50.9650 Apple
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## 2019-05-02 2019-05-02 52.2875 Apple
## 2019-05-03 2019-05-03 52.9375 Apple
## 2019-05-06 2019-05-06 52.1200 Apple
## 2019-05-07 2019-05-07 50.7150 Apple
## 2019-05-08 2019-05-08 50.7250 Apple
## 2019-05-09 2019-05-09 50.1800 Apple
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## 2019-05-13 2019-05-13 46.4300 Apple
## 2019-05-14 2019-05-14 47.1650 Apple
## 2019-05-15 2019-05-15 47.7300 Apple
## 2019-05-16 2019-05-16 47.5200 Apple
## 2019-05-17 2019-05-17 47.2500 Apple
## 2019-05-20 2019-05-20 45.7725 Apple
## 2019-05-21 2019-05-21 46.6500 Apple
## 2019-05-22 2019-05-22 45.6950 Apple
## 2019-05-23 2019-05-23 44.9150 Apple
## 2019-05-24 2019-05-24 44.7425 Apple
## 2019-05-28 2019-05-28 44.5575 Apple
## 2019-05-29 2019-05-29 44.3450 Apple
## 2019-05-30 2019-05-30 44.5750 Apple
## 2019-05-31 2019-05-31 43.7675 Apple
## 2019-06-03 2019-06-03 43.3250 Apple
## 2019-06-04 2019-06-04 44.9100 Apple
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## 2019-06-06 2019-06-06 46.3050 Apple
## 2019-06-07 2019-06-07 47.5375 Apple
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## 2019-06-19 2019-06-19 49.4675 Apple
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## 2019-06-26 2019-06-26 49.9500 Apple
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## 2019-07-01 2019-07-01 50.3875 Apple
## 2019-07-02 2019-07-02 50.6825 Apple
## 2019-07-03 2019-07-03 51.1025 Apple
## 2019-07-05 2019-07-05 51.0575 Apple
## 2019-07-08 2019-07-08 50.0050 Apple
## 2019-07-09 2019-07-09 50.3100 Apple
## 2019-07-10 2019-07-10 50.8075 Apple
## 2019-07-11 2019-07-11 50.4375 Apple
## 2019-07-12 2019-07-12 50.8250 Apple
## 2019-07-15 2019-07-15 51.3025 Apple
## 2019-07-16 2019-07-16 51.1250 Apple
## 2019-07-17 2019-07-17 50.8375 Apple
## 2019-07-18 2019-07-18 51.4150 Apple
## 2019-07-19 2019-07-19 50.6475 Apple
## 2019-07-22 2019-07-22 51.8050 Apple
## 2019-07-23 2019-07-23 52.2100 Apple
## 2019-07-24 2019-07-24 52.1675 Apple
## 2019-07-25 2019-07-25 51.7550 Apple
## 2019-07-26 2019-07-26 51.9350 Apple
## 2019-07-29 2019-07-29 52.4200 Apple
## 2019-07-30 2019-07-30 52.1950 Apple
## 2019-07-31 2019-07-31 53.2600 Apple
## 2019-08-01 2019-08-01 52.1075 Apple
## 2019-08-02 2019-08-02 51.0050 Apple
## 2019-08-05 2019-08-05 48.3350 Apple
## 2019-08-06 2019-08-06 49.2500 Apple
## 2019-08-07 2019-08-07 49.7600 Apple
## 2019-08-08 2019-08-08 50.8575 Apple
## 2019-08-09 2019-08-09 50.2475 Apple
## 2019-08-12 2019-08-12 50.1200 Apple
## 2019-08-13 2019-08-13 52.2425 Apple
## 2019-08-14 2019-08-14 50.6875 Apple
## 2019-08-15 2019-08-15 50.4350 Apple
## 2019-08-16 2019-08-16 51.6250 Apple
## 2019-08-19 2019-08-19 52.5875 Apple
## 2019-08-20 2019-08-20 52.5900 Apple
## 2019-08-21 2019-08-21 53.1600 Apple
## 2019-08-22 2019-08-22 53.1150 Apple
## 2019-08-23 2019-08-23 50.6600 Apple
## 2019-08-26 2019-08-26 51.6225 Apple
## 2019-08-27 2019-08-27 51.0400 Apple
## 2019-08-28 2019-08-28 51.3825 Apple
## 2019-08-29 2019-08-29 52.2525 Apple
## 2019-08-30 2019-08-30 52.1850 Apple
## 2019-09-03 2019-09-03 51.4250 Apple
## 2019-09-04 2019-09-04 52.2975 Apple
## 2019-09-05 2019-09-05 53.3200 Apple
## 2019-09-06 2019-09-06 53.3150 Apple
## 2019-09-09 2019-09-09 53.5425 Apple
## 2019-09-10 2019-09-10 54.1750 Apple
## 2019-09-11 2019-09-11 55.8975 Apple
## 2019-09-12 2019-09-12 55.7725 Apple
## 2019-09-13 2019-09-13 54.6875 Apple
## 2019-09-16 2019-09-16 54.9750 Apple
## 2019-09-17 2019-09-17 55.1750 Apple
## 2019-09-18 2019-09-18 55.6925 Apple
## 2019-09-19 2019-09-19 55.2400 Apple
## 2019-09-20 2019-09-20 54.4325 Apple
## 2019-09-23 2019-09-23 54.6800 Apple
## 2019-09-24 2019-09-24 54.4200 Apple
## 2019-09-25 2019-09-25 55.2575 Apple
## 2019-09-26 2019-09-26 54.9725 Apple
## 2019-09-27 2019-09-27 54.7050 Apple
## 2019-09-30 2019-09-30 55.9925 Apple
## 2019-10-01 2019-10-01 56.1475 Apple
## 2019-10-02 2019-10-02 54.7400 Apple
## 2019-10-03 2019-10-03 55.2050 Apple
## 2019-10-04 2019-10-04 56.7525 Apple
## 2019-10-07 2019-10-07 56.7650 Apple
## 2019-10-08 2019-10-08 56.1000 Apple
## 2019-10-09 2019-10-09 56.7575 Apple
## 2019-10-10 2019-10-10 57.5225 Apple
## 2019-10-11 2019-10-11 59.0525 Apple
## 2019-10-14 2019-10-14 58.9675 Apple
## 2019-10-15 2019-10-15 58.8300 Apple
## 2019-10-16 2019-10-16 58.5925 Apple
## 2019-10-17 2019-10-17 58.8200 Apple
## 2019-10-18 2019-10-18 59.1025 Apple
## 2019-10-21 2019-10-21 60.1275 Apple
## 2019-10-22 2019-10-22 59.9900 Apple
## 2019-10-23 2019-10-23 60.7950 Apple
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## 2019-10-28 2019-10-28 62.2625 Apple
## 2019-10-29 2019-10-29 60.8225 Apple
## 2019-10-30 2019-10-30 60.8150 Apple
## 2019-10-31 2019-10-31 62.1900 Apple
## 2019-11-01 2019-11-01 63.9550 Apple
## 2019-11-04 2019-11-04 64.3750 Apple
## 2019-11-05 2019-11-05 64.2825 Apple
## 2019-11-06 2019-11-06 64.3100 Apple
## 2019-11-07 2019-11-07 64.8575 Apple
## 2019-11-08 2019-11-08 65.0350 Apple
## 2019-11-11 2019-11-11 65.5500 Apple
## 2019-11-12 2019-11-12 65.4900 Apple
## 2019-11-13 2019-11-13 66.1175 Apple
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## 2019-11-15 2019-11-15 66.4400 Apple
## 2019-11-18 2019-11-18 66.7750 Apple
## 2019-11-19 2019-11-19 66.5725 Apple
## 2019-11-20 2019-11-20 65.7975 Apple
## 2019-11-21 2019-11-21 65.5025 Apple
## 2019-11-22 2019-11-22 65.4450 Apple
## 2019-11-25 2019-11-25 66.5925 Apple
## 2019-11-26 2019-11-26 66.0725 Apple
## 2019-11-27 2019-11-27 66.9600 Apple
## 2019-11-29 2019-11-29 66.8125 Apple
## 2019-12-02 2019-12-02 66.0400 Apple
## 2019-12-03 2019-12-03 64.8625 Apple
## 2019-12-04 2019-12-04 65.4350 Apple
## 2019-12-05 2019-12-05 66.3950 Apple
## 2019-12-06 2019-12-06 67.6775 Apple
## 2019-12-09 2019-12-09 66.7300 Apple
## 2019-12-10 2019-12-10 67.1200 Apple
## 2019-12-11 2019-12-11 67.6925 Apple
## 2019-12-12 2019-12-12 67.8650 Apple
## 2019-12-13 2019-12-13 68.7875 Apple
## 2019-12-16 2019-12-16 69.9650 Apple
## 2019-12-17 2019-12-17 70.1025 Apple
## 2019-12-18 2019-12-18 69.9350 Apple
## 2019-12-19 2019-12-19 70.0050 Apple
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## 2019-12-26 2019-12-26 72.4775 Apple
## 2019-12-27 2019-12-27 72.4500 Apple
## 2019-12-30 2019-12-30 72.8800 Apple
## 2019-12-31 2019-12-31 73.4125 Apple
## 2020-01-02 2020-01-02 75.0875 Apple
## 2020-01-03 2020-01-03 74.3575 Apple
## 2020-01-06 2020-01-06 74.9500 Apple
## 2020-01-07 2020-01-07 74.5975 Apple
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## 2018-06-041 2018-06-04 193.2800 Face Book
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## 2018-06-111 2018-06-11 191.5400 Face Book
## 2018-06-121 2018-06-12 192.4000 Face Book
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## 2018-06-141 2018-06-14 196.8100 Face Book
## 2018-06-151 2018-06-15 195.8500 Face Book
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## 2018-06-191 2018-06-19 197.4900 Face Book
## 2018-06-201 2018-06-20 202.0000 Face Book
## 2018-06-211 2018-06-21 201.5000 Face Book
## 2018-06-221 2018-06-22 201.7400 Face Book
## 2018-06-251 2018-06-25 196.3500 Face Book
## 2018-06-261 2018-06-26 199.0000 Face Book
## 2018-06-271 2018-06-27 195.8400 Face Book
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## 2018-07-031 2018-07-03 192.7300 Face Book
## 2018-07-051 2018-07-05 198.4500 Face Book
## 2018-07-061 2018-07-06 203.2300 Face Book
## 2018-07-091 2018-07-09 204.7400 Face Book
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## 2018-07-111 2018-07-11 202.5400 Face Book
## 2018-07-121 2018-07-12 206.9200 Face Book
## 2018-07-131 2018-07-13 207.3200 Face Book
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## 2018-07-171 2018-07-17 209.9900 Face Book
## 2018-07-181 2018-07-18 209.3600 Face Book
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## 2018-07-231 2018-07-23 210.9100 Face Book
## 2018-07-241 2018-07-24 214.6700 Face Book
## 2018-07-251 2018-07-25 217.5000 Face Book
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## 2018-07-271 2018-07-27 174.8900 Face Book
## 2018-07-301 2018-07-30 171.0600 Face Book
## 2018-07-311 2018-07-31 172.5800 Face Book
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## 2018-08-021 2018-08-02 176.3700 Face Book
## 2018-08-031 2018-08-03 177.7800 Face Book
## 2018-08-061 2018-08-06 185.6900 Face Book
## 2018-08-071 2018-08-07 183.8100 Face Book
## 2018-08-081 2018-08-08 185.1800 Face Book
## 2018-08-091 2018-08-09 183.0900 Face Book
## 2018-08-101 2018-08-10 180.2600 Face Book
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## 2018-08-171 2018-08-17 173.8000 Face Book
## 2018-08-201 2018-08-20 172.5000 Face Book
## 2018-08-211 2018-08-21 172.6200 Face Book
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## 2018-08-231 2018-08-23 172.9000 Face Book
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## 2018-09-041 2018-09-04 171.1600 Face Book
## 2018-09-051 2018-09-05 167.1800 Face Book
## 2018-09-061 2018-09-06 162.5300 Face Book
## 2018-09-071 2018-09-07 163.0400 Face Book
## 2018-09-101 2018-09-10 164.1800 Face Book
## 2018-09-111 2018-09-11 165.9400 Face Book
## 2018-09-121 2018-09-12 162.0000 Face Book
## 2018-09-131 2018-09-13 161.3600 Face Book
## 2018-09-141 2018-09-14 162.3200 Face Book
## 2018-09-171 2018-09-17 160.5800 Face Book
## 2018-09-181 2018-09-18 160.3000 Face Book
## 2018-09-191 2018-09-19 163.0600 Face Book
## 2018-09-201 2018-09-20 166.0200 Face Book
## 2018-09-211 2018-09-21 162.9300 Face Book
## 2018-09-241 2018-09-24 165.4100 Face Book
## 2018-09-251 2018-09-25 164.9100 Face Book
## 2018-09-261 2018-09-26 166.9500 Face Book
## 2018-09-271 2018-09-27 168.8400 Face Book
## 2018-09-281 2018-09-28 164.4600 Face Book
## 2018-10-011 2018-10-01 162.4400 Face Book
## 2018-10-021 2018-10-02 159.3300 Face Book
## 2018-10-031 2018-10-03 162.4300 Face Book
## 2018-10-041 2018-10-04 158.8500 Face Book
## 2018-10-051 2018-10-05 157.3300 Face Book
## 2018-10-081 2018-10-08 157.2500 Face Book
## 2018-10-091 2018-10-09 157.9000 Face Book
## 2018-10-101 2018-10-10 151.3800 Face Book
## 2018-10-111 2018-10-11 153.3500 Face Book
## 2018-10-121 2018-10-12 153.7400 Face Book
## 2018-10-151 2018-10-15 153.5200 Face Book
## 2018-10-161 2018-10-16 158.7800 Face Book
## 2018-10-171 2018-10-17 159.4200 Face Book
## 2018-10-181 2018-10-18 154.9200 Face Book
## 2018-10-191 2018-10-19 154.0500 Face Book
## 2018-10-221 2018-10-22 154.7800 Face Book
## 2018-10-231 2018-10-23 154.3900 Face Book
## 2018-10-241 2018-10-24 146.0400 Face Book
## 2018-10-251 2018-10-25 150.9500 Face Book
## 2018-10-261 2018-10-26 145.3700 Face Book
## 2018-10-291 2018-10-29 142.0900 Face Book
## 2018-10-301 2018-10-30 146.2200 Face Book
## 2018-10-311 2018-10-31 151.7900 Face Book
## 2018-11-011 2018-11-01 151.7500 Face Book
## 2018-11-021 2018-11-02 150.3500 Face Book
## 2018-11-051 2018-11-05 148.6800 Face Book
## 2018-11-061 2018-11-06 149.9400 Face Book
## 2018-11-071 2018-11-07 151.5300 Face Book
## 2018-11-081 2018-11-08 147.8700 Face Book
## 2018-11-091 2018-11-09 144.9600 Face Book
## 2018-11-121 2018-11-12 141.5500 Face Book
## 2018-11-131 2018-11-13 142.1600 Face Book
## 2018-11-141 2018-11-14 144.2200 Face Book
## 2018-11-151 2018-11-15 143.8500 Face Book
## 2018-11-161 2018-11-16 139.5300 Face Book
## 2018-11-191 2018-11-19 131.5500 Face Book
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## 2018-11-211 2018-11-21 134.8200 Face Book
## 2018-11-231 2018-11-23 131.7300 Face Book
## 2018-11-261 2018-11-26 136.3800 Face Book
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## 2018-11-301 2018-11-30 140.6100 Face Book
## 2018-12-031 2018-12-03 141.0900 Face Book
## 2018-12-041 2018-12-04 137.9300 Face Book
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## 2018-12-071 2018-12-07 137.4200 Face Book
## 2018-12-101 2018-12-10 141.8500 Face Book
## 2018-12-111 2018-12-11 142.0800 Face Book
## 2018-12-121 2018-12-12 144.5000 Face Book
## 2018-12-131 2018-12-13 145.0100 Face Book
## 2018-12-141 2018-12-14 144.0600 Face Book
## 2018-12-171 2018-12-17 140.1900 Face Book
## 2018-12-181 2018-12-18 143.6600 Face Book
## 2018-12-191 2018-12-19 133.2400 Face Book
## 2018-12-201 2018-12-20 133.4000 Face Book
## 2018-12-211 2018-12-21 124.9500 Face Book
## 2018-12-241 2018-12-24 124.0600 Face Book
## 2018-12-261 2018-12-26 134.1800 Face Book
## 2018-12-271 2018-12-27 134.5200 Face Book
## 2018-12-281 2018-12-28 133.2000 Face Book
## 2018-12-311 2018-12-31 131.0900 Face Book
## 2019-01-021 2019-01-02 135.6800 Face Book
## 2019-01-031 2019-01-03 131.7400 Face Book
## 2019-01-041 2019-01-04 137.9500 Face Book
## 2019-01-071 2019-01-07 138.0500 Face Book
## 2019-01-081 2019-01-08 142.5300 Face Book
## 2019-01-091 2019-01-09 144.2300 Face Book
## 2019-01-101 2019-01-10 144.2000 Face Book
## 2019-01-111 2019-01-11 143.8000 Face Book
## 2019-01-141 2019-01-14 145.3900 Face Book
## 2019-01-151 2019-01-15 148.9500 Face Book
## 2019-01-161 2019-01-16 147.5400 Face Book
## 2019-01-171 2019-01-17 148.3000 Face Book
## 2019-01-181 2019-01-18 150.0400 Face Book
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## 2019-01-231 2019-01-23 144.3000 Face Book
## 2019-01-241 2019-01-24 145.8300 Face Book
## 2019-01-251 2019-01-25 149.0100 Face Book
## 2019-01-281 2019-01-28 147.4700 Face Book
## 2019-01-291 2019-01-29 144.1900 Face Book
## 2019-01-301 2019-01-30 150.4200 Face Book
## 2019-01-311 2019-01-31 166.6900 Face Book
## 2019-02-011 2019-02-01 165.7100 Face Book
## 2019-02-041 2019-02-04 169.2500 Face Book
## 2019-02-051 2019-02-05 171.1600 Face Book
## 2019-02-061 2019-02-06 170.4900 Face Book
## 2019-02-071 2019-02-07 166.3800 Face Book
## 2019-02-081 2019-02-08 167.3300 Face Book
## 2019-02-111 2019-02-11 165.7900 Face Book
## 2019-02-121 2019-02-12 165.0400 Face Book
## 2019-02-131 2019-02-13 164.0700 Face Book
## 2019-02-141 2019-02-14 163.9500 Face Book
## 2019-02-151 2019-02-15 162.5000 Face Book
## 2019-02-191 2019-02-19 162.2900 Face Book
## 2019-02-201 2019-02-20 162.5600 Face Book
## 2019-02-211 2019-02-21 160.0400 Face Book
## 2019-02-221 2019-02-22 161.8900 Face Book
## 2019-02-251 2019-02-25 164.6200 Face Book
## 2019-02-261 2019-02-26 164.1300 Face Book
## 2019-02-271 2019-02-27 162.8100 Face Book
## 2019-02-281 2019-02-28 161.4500 Face Book
## 2019-03-011 2019-03-01 162.2800 Face Book
## 2019-03-041 2019-03-04 167.3700 Face Book
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## 2019-04-181 2019-04-18 178.2800 Face Book
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## 2019-07-051 2019-07-05 196.4000 Face Book
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## 2019-07-101 2019-07-10 202.7300 Face Book
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## 2019-08-021 2019-08-02 189.0200 Face Book
## 2019-08-051 2019-08-05 181.7300 Face Book
## 2019-08-061 2019-08-06 184.5100 Face Book
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## 2019-08-081 2019-08-08 190.1600 Face Book
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## 2019-09-131 2019-09-13 187.1900 Face Book
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## 2019-09-241 2019-09-24 181.2800 Face Book
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## 2019-09-271 2019-09-27 177.1000 Face Book
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## 2019-11-151 2019-11-15 195.1000 Face Book
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## 2019-12-031 2019-12-03 198.8200 Face Book
## 2019-12-041 2019-12-04 198.7100 Face Book
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## 2019-12-181 2019-12-18 202.5000 Face Book
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## 2019-12-261 2019-12-26 207.7900 Face Book
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## 2020-01-101 2020-01-10 218.0600 Face Book
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## 2020-01-151 2020-01-15 221.1500 Face Book
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## 2020-02-101 2020-02-10 213.0600 Face Book
## 2020-02-111 2020-02-11 207.1900 Face Book
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## 2020-02-141 2020-02-14 214.1800 Face Book
## 2020-02-181 2020-02-18 217.8000 Face Book
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## 2020-02-211 2020-02-21 210.1800 Face Book
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## 2020-02-251 2020-02-25 196.7700 Face Book
## 2020-02-261 2020-02-26 197.2000 Face Book
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## 2020-03-091 2020-03-09 169.5000 Face Book
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## 2020-03-131 2020-03-13 170.2800 Face Book
## 2020-03-161 2020-03-16 146.0100 Face Book
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## 2020-03-231 2020-03-23 148.1000 Face Book
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## 2020-03-251 2020-03-25 156.2100 Face Book
## 2020-03-261 2020-03-26 163.3400 Face Book
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## 2020-03-301 2020-03-30 165.9500 Face Book
## 2020-03-311 2020-03-31 166.8000 Face Book
## 2020-04-011 2020-04-01 159.6000 Face Book
## 2020-04-021 2020-04-02 158.1900 Face Book
## 2020-04-031 2020-04-03 154.1800 Face Book
## 2020-04-061 2020-04-06 165.5500 Face Book
## 2020-04-071 2020-04-07 168.8300 Face Book
## 2020-04-081 2020-04-08 174.2800 Face Book
## 2020-04-091 2020-04-09 175.1900 Face Book
## 2020-04-131 2020-04-13 174.7900 Face Book
## 2020-04-141 2020-04-14 178.1700 Face Book
## 2020-04-151 2020-04-15 176.9700 Face Book
## 2020-04-161 2020-04-16 176.2500 Face Book
## 2020-04-171 2020-04-17 179.2400 Face Book
## 2020-04-201 2020-04-20 178.2400 Face Book
## 2020-04-211 2020-04-21 170.8000 Face Book
## 2020-04-221 2020-04-22 182.2800 Face Book
## 2020-04-231 2020-04-23 185.1300 Face Book
## 2020-04-241 2020-04-24 190.0700 Face Book
## 2020-04-271 2020-04-27 187.5000 Face Book
## 2020-04-281 2020-04-28 182.9100 Face Book
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## 2020-04-301 2020-04-30 204.7100 Face Book
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## 2020-05-051 2020-05-05 207.0700 Face Book
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## 2020-05-121 2020-05-12 210.1000 Face Book
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## 2020-05-261 2020-05-26 232.2000 Face Book
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## 2020-06-011 2020-06-01 231.9100 Face Book
## 2020-06-021 2020-06-02 232.7200 Face Book
## 2020-06-031 2020-06-03 230.1600 Face Book
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## 2020-06-051 2020-06-05 230.7700 Face Book
## 2020-06-081 2020-06-08 231.4000 Face Book
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## 2020-06-121 2020-06-12 228.5800 Face Book
## 2020-06-151 2020-06-15 232.5000 Face Book
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## 2020-07-061 2020-07-06 240.2800 Face Book
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## 2020-07-091 2020-07-09 244.5000 Face Book
## 2020-07-101 2020-07-10 245.0700 Face Book
## 2020-07-131 2020-07-13 239.0000 Face Book
## 2020-07-141 2020-07-14 239.7300 Face Book
## 2020-07-151 2020-07-15 240.2800 Face Book
## 2020-07-161 2020-07-16 240.9300 Face Book
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## 2020-07-201 2020-07-20 245.4200 Face Book
## 2020-07-211 2020-07-21 241.7500 Face Book
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## 2020-07-231 2020-07-23 232.6000 Face Book
## 2020-07-241 2020-07-24 230.7100 Face Book
## 2020-07-271 2020-07-27 233.5000 Face Book
## 2020-07-281 2020-07-28 230.1200 Face Book
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## 2020-07-301 2020-07-30 234.5000 Face Book
## 2020-07-311 2020-07-31 253.6700 Face Book
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## 2020-08-051 2020-08-05 249.1200 Face Book
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## 2020-08-171 2020-08-17 261.1600 Face Book
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## 2020-08-201 2020-08-20 269.0100 Face Book
## 2020-08-211 2020-08-21 267.0100 Face Book
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## 2020-09-011 2020-09-01 295.4400 Face Book
## 2020-09-021 2020-09-02 302.5000 Face Book
## 2020-09-031 2020-09-03 291.1200 Face Book
## 2020-09-041 2020-09-04 282.7300 Face Book
## 2020-09-081 2020-09-08 271.1600 Face Book
## 2020-09-091 2020-09-09 273.7200 Face Book
## 2020-09-101 2020-09-10 268.0900 Face Book
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## 2020-09-141 2020-09-14 266.1500 Face Book
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## 2020-09-211 2020-09-21 248.1500 Face Book
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## 2020-09-231 2020-09-23 249.0200 Face Book
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## 2020-09-301 2020-09-30 261.9000 Face Book
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## 2020-10-021 2020-10-02 259.9400 Face Book
## 2020-10-051 2020-10-05 264.6500 Face Book
## 2020-10-061 2020-10-06 258.6600 Face Book
## 2020-10-071 2020-10-07 258.1200 Face Book
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## 2020-10-131 2020-10-13 276.1400 Face Book
## 2020-10-141 2020-10-14 271.8200 Face Book
## 2020-10-151 2020-10-15 266.7200 Face Book
## 2020-10-161 2020-10-16 265.9300 Face Book
## 2020-10-191 2020-10-19 261.4000 Face Book
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## 2020-10-211 2020-10-21 278.7300 Face Book
## 2020-10-221 2020-10-22 278.1200 Face Book
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## 2020-10-261 2020-10-26 277.1100 Face Book
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## 2020-10-281 2020-10-28 267.6700 Face Book
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## 2020-10-301 2020-10-30 263.1100 Face Book
## 2020-11-021 2020-11-02 261.3600 Face Book
## 2020-11-031 2020-11-03 265.3000 Face Book
## 2020-11-041 2020-11-04 287.3800 Face Book
## 2020-11-051 2020-11-05 294.6800 Face Book
## 2020-11-061 2020-11-06 293.4100 Face Book
## 2020-11-091 2020-11-09 278.7700 Face Book
## 2020-11-101 2020-11-10 272.4300 Face Book
## 2020-11-111 2020-11-11 276.4800 Face Book
## 2020-11-121 2020-11-12 275.0800 Face Book
## 2020-11-131 2020-11-13 276.9500 Face Book
## 2020-11-161 2020-11-16 278.9600 Face Book
## 2020-11-171 2020-11-17 275.0000 Face Book
## 2020-11-181 2020-11-18 271.9700 Face Book
## 2020-11-191 2020-11-19 272.9400 Face Book
## 2020-11-201 2020-11-20 269.7000 Face Book
## 2020-11-231 2020-11-23 268.4300 Face Book
## 2020-11-241 2020-11-24 276.9200 Face Book
## 2020-11-251 2020-11-25 275.5900 Face Book
## 2020-11-271 2020-11-27 277.8100 Face Book
## 2020-11-301 2020-11-30 276.9700 Face Book
## 2020-12-011 2020-12-01 286.5500 Face Book
## 2020-12-021 2020-12-02 287.5200 Face Book
## 2020-12-031 2020-12-03 281.8500 Face Book
## 2020-12-041 2020-12-04 279.7000 Face Book
## 2020-12-071 2020-12-07 285.5800 Face Book
## 2020-12-081 2020-12-08 283.4000 Face Book
## 2020-12-091 2020-12-09 277.9200 Face Book
## 2020-12-101 2020-12-10 277.1200 Face Book
## 2020-12-111 2020-12-11 273.5500 Face Book
## 2020-12-141 2020-12-14 274.1900 Face Book
## 2020-12-151 2020-12-15 275.5500 Face Book
## 2020-12-161 2020-12-16 275.6700 Face Book
## 2020-12-171 2020-12-17 274.4800 Face Book
## 2020-12-181 2020-12-18 276.4000 Face Book
## 2020-12-211 2020-12-21 272.7900 Face Book
## 2020-12-221 2020-12-22 267.0900 Face Book
## 2020-12-231 2020-12-23 268.1100 Face Book
## 2020-12-241 2020-12-24 267.4000 Face Book
## 2020-12-281 2020-12-28 277.0000 Face Book
## 2020-12-291 2020-12-29 276.7800 Face Book
## 2020-12-301 2020-12-30 271.8700 Face Book
## 2020-12-311 2020-12-31 273.1600 Face Book
## 2021-01-041 2021-01-04 268.9400 Face Book
## 2021-01-051 2021-01-05 270.9700 Face Book
## 2021-01-061 2021-01-06 263.3100 Face Book
## 2021-01-071 2021-01-07 268.7400 Face Book
## 2021-01-081 2021-01-08 267.5700 Face Book
## 2021-01-111 2021-01-11 256.8400 Face Book
## 2021-01-121 2021-01-12 251.0900 Face Book
## 2021-01-131 2021-01-13 251.6400 Face Book
## 2021-01-141 2021-01-14 245.6400 Face Book
## 2021-01-151 2021-01-15 251.3600 Face Book
## 2021-01-191 2021-01-19 261.1000 Face Book
## 2021-01-201 2021-01-20 267.4800 Face Book
## 2021-01-211 2021-01-21 272.8700 Face Book
## 2021-01-221 2021-01-22 274.5000 Face Book
## 2021-01-251 2021-01-25 278.0100 Face Book
## 2021-01-261 2021-01-26 282.0500 Face Book
## 2021-01-271 2021-01-27 272.1400 Face Book
## 2021-01-281 2021-01-28 265.0000 Face Book
## 2021-01-291 2021-01-29 258.3300 Face Book
## 2021-02-011 2021-02-01 259.0450 Face Bookmerge(apple, fb, by = 'Date') -> merge_table
head(merge_table)## Date Close.x Company.x Close.y Company.y
## 1 2018-01-02 43.0650 Apple 181.42 Face Book
## 2 2018-01-03 43.0575 Apple 184.67 Face Book
## 3 2018-01-04 43.2575 Apple 184.33 Face Book
## 4 2018-01-05 43.7500 Apple 186.85 Face Book
## 5 2018-01-08 43.5875 Apple 188.28 Face Book
## 6 2018-01-09 43.5825 Apple 187.87 Face Bookggplot(mseries, aes(x= Date, y =Close, color=Company ))+
geom_line()+
scale_x_date(date_breaks = '1 month',
labels = scales :: date_format("%b"))+
theme_minimal()+
scale_color_brewer(palette = "Dark2")+
labs(title = "NSDAQ Closting Price",
subtitle = "Jan-Aug 2018",
y= "Closing Price")
Dummmbell Charts
x= year y= country value = lifeExp
x 축 : 구간 y 축 : Categorical value : dummbell
#install.packages("ggalt")
library(ggalt)## Registered S3 methods overwritten by 'ggalt':
## method from
## grid.draw.absoluteGrob ggplot2
## grobHeight.absoluteGrob ggplot2
## grobWidth.absoluteGrob ggplot2
## grobX.absoluteGrob ggplot2
## grobY.absoluteGrob ggplot2library(tidyr)##
## Attaching package: 'tidyr'## The following objects are masked from 'package:reshape':
##
## expand, smithsgapminder %>%
filter(continent == "Americas" &
year %in% c(1952, 2007)) %>%
select(country, year, lifeExp) -> plot_long
head(plot_long) ## # A tibble: 6 x 3
## country year lifeExp
## <fct> <int> <dbl>
## 1 Argentina 1952 62.5
## 2 Argentina 2007 75.3
## 3 Bolivia 1952 40.4
## 4 Bolivia 2007 65.6
## 5 Brazil 1952 50.9
## 6 Brazil 2007 72.4plot_wide <- spread(plot_long, year, lifeExp) #spread 1952 와 2007 부분이 따로 구분해되서 나옴 year 변수가 2 컬럼으로 spread 됨
datatable(plot_wide)## 숫자형 이름을 문자형으로 만들어줘야함
names(plot_wide) <- c("country", "y1952", "y2007")
head(plot_wide)## # A tibble: 6 x 3
## country y1952 y2007
## <fct> <dbl> <dbl>
## 1 Argentina 62.5 75.3
## 2 Bolivia 40.4 65.6
## 3 Brazil 50.9 72.4
## 4 Canada 68.8 80.7
## 5 Chile 54.7 78.6
## 6 Colombia 50.6 72.9ggplot(plot_wide, aes(x= y1952,
xend =y2007,
y = reorder(country, y1952))) +
geom_dumbbell(size = 1.2,
size_x = 3,
size_xend = 3,
colour= "grey",
colour_x = "blue",
colour_xend = "red")+
theme_minimal()+
labs(x = "Life Expectancy (Year)",
y= "")
Slope Charts
구간을 끊어서 각 나라별 lifeExp 를 나타낸다 x= year, y= country value= lifeExp
#install.packages("CGPfunctions")
library(CGPfunctions)## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod cargapminder %>%
filter(year %in% c (1992, 1997, 2002, 2007) &
country %in% c("Panama", "Costa Rica", "Nicaragua", "Honduras",
"El Salvador", "Guatemala", "Belize")) %>%
mutate(year = factor(year),
lifeExp = round(lifeExp)) -> slope_df
newggslopegraph(slope_df, year, lifeExp, country)+
labs(title = "Life Expency by Country",
subtitle = "Central America",
caption = "Data Source : Gapminder")##
## Converting 'year' to an ordered factor
Area Charts - Density Chart
datatable(economics)ggplot(economics, aes(x= date, y= psavert))+
geom_area(fill= "lightblue", color ="black")
Statistical Model
1.1 Correlation plots
#install.packages("ggcorrplot")
library(ggcorrplot)
df_economic <- economics[, c(2:5)]
round(cor(df_economic, use = "complete.obs"),2) -> cor_da
ggcorrplot(cor_da,
type = "lower",
lab = TRUE)
## selec_if (data, is_numeric) 숫자형 변수만 뽑아라
select_if(SaratogaHouses, is.numeric) -> cor_da_1
cor(cor_da_1, use = "complete.obs")-> cor_matrix
ggcorrplot(cor_matrix,
type = "lower",
lab= TRUE)
1.2 Linear Regression
- Set the regression calculation
- visreg package
#install.packages("visreg")
library(visreg)
lm(price ~ lotSize + age + landValue + livingArea + bedrooms +bathrooms + waterfront,
data = SaratogaHouses) -> house_lmvisreg 함수는 안에 각 변수를 바꿔주면서 x 에 대한 price 정보만을 시각화 전체 회귀식의 그래프는 아님
#gg= TRUE: ggplot 함수를 사용할 수 있게 해줌
visreg::visreg(house_lm, "livingArea", gg=TRUE)+
scale_y_continuous(label = scales ::dollar)
1.2 Linear Regression
CPS85 에서 결혼을 한다/ 안한다 logistic 식 세우기 sex + race + sector 전부 categorical 변수 / age 아님
glm(married ~ sex + age + race + sector, family = "binomial", data=CPS85) -> glm_cps85
glm_cps85##
## Call: glm(formula = married ~ sex + age + race + sector, family = "binomial",
## data = CPS85)
##
## Coefficients:
## (Intercept) sexM age raceW sectorconst
## 1.77034 0.01404 -0.05722 -0.21608 -0.32020
## sectormanag sectormanuf sectorother sectorprof sectorsales
## -0.25537 -0.45568 -0.08359 -0.39546 -0.73076
## sectorservice
## 0.18357
##
## Degrees of Freedom: 533 Total (i.e. Null); 523 Residual
## Null Deviance: 687.8
## Residual Deviance: 634.9 AIC: 656.9visreg(glm_cps85, "age", gg=TRUE, scale = "response")
1.3 Biplots
각 변수 간의 거리를 보여주는 그래프 2 차원에서 보여줌 factoextra 사용
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBadata(mtcars)
# PCA 구성 모델 생성
prcomp(x=mtcars,
center = TRUE,
scale = TRUE) -> fit
fviz_pca(fit,
repel = TRUE,
labelsize= 3)+
theme_bw()## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Forrest Yong 의 설명을 찾아야함
그래프 해석 ) dim1 - 전체 변동량의 60%를 설명한다. dim2 - 전체 변동량의 24%를 설명한다. dim1 + dim2 = 84% 를 설명한다.
점의 위치 : 중앙으로 부터 비슷한 거리에 있는 점들 Honda Civic & Toyota Corolla 비슷하다
각도 : 각 뻗어나가는 두 선의 각도가 90도면 양의 상관 (gear, carb) 각도가 더 벌어지면 음의 상관 (gear, wt)
뻗어나아가는 선과 점의 위치 : Duster 360 은 cyl 값이 높다. Toyota Corona 는 gsec 값이 높다.
1.4 Bubble Chart
언제 쓰면 좋은가? x, y가 연속형 축으로 놓고, 연속형을 볼 때 (size 로 지정 ), 보통 fill= 그룹 지정을 해주는데 그러면 각 그룹별 x,y 의 산점도가 나온다. bubble 은 x, y, 알고자 하는 값이 전부 연속형이면 굿굿
ggplot(mtcars,
aes(x= wt,
y= mpg,
size= hp ))+
geom_point(alpha= .5,
fill= blue,
shape=21)+
scale_y_continuous(breaks = seq(0,50,10))
labs(x= "Weight (1000 lbs)",
y="Miles/(US) gallon",
size = "Gross Horsepower")## $x
## [1] "Weight (1000 lbs)"
##
## $y
## [1] "Miles/(US) gallon"
##
## $size
## [1] "Gross Horsepower"
##
## attr(,"class")
## [1] "labels"1.5 Sankey diagrams
변수의 관계를 흐름으로 보여준다, 노드들의 네트워크로 보여주는 것들 UK 에너지 예측 데이터 활용 - 에너지 생산과 소비 예상 2050년도
#load("Energy.RData")
#head(node)
#library(networkD3)
#sankeyNetwork(Links = links,
# Source = "Source",
# Target = "target",
# Value = "value",
# NodeId= "name",
# units="TWh",
# fontSize=12,
# nodeWidth = 30)1.5 Alluvial diagra
각 범주형 변수들의 관계성을 보여주는 것.
Marriage 데이터 사용 : prevconc 이혼하거나 혹은 죽거나 (NA 값이 있음으로 일단 제외)
각 여러 범주형 변수들이 : 이혼과 죽음에 어떻게 영향을 가지는가에 대해서 살펴보기
#install.packages("ggalluvial")
library(ggalluvial)
# 생략된 변수 제거
na.omit(Marriage)-> na_x_marriage
# 범주형만 선택하기
select_if(na_x_marriage, is.factor) %>%
select(-1) -> all_data
# 중요한 점 : count 기준 연결된다 COUNT!!!! group_by 는 끝에 보고자 하는 걸로 놓는다
all_data %>%
group_by(officialTitle, person, race, sign, prevconc) %>%
count() -> all_table
ggplot(all_table, aes(axis1 = officialTitle,
axis2 = person,
axis4= sign,
axis5=prevconc,
y=n))+
geom_alluvium(aes(fill=race)) +
geom_stratum()+
geom_text(stat = "stratum",
aes(label = after_stat(stratum)))
table(all_table$prevconc)##
## Death Divorce
## 6 36재미있는 사실 : 백인과 흑인 중 death 가 뜬 특정 별자리가 있다. (순서를 바꿔서 보면 좋을것 같지만 생략 )