Tugas 6

Algoritma dan Sturktur Data

Email             : albert.prayogo99@gmail.com
RPubs            : https://rpubs.com/albert23899
Jurusan          : Statistika
Address         : ARA Center, Matana University Tower
                         Jl. CBD Barat Kav, RT.1, Curug Sangereng, Kelapa Dua, Tangerang, Banten 15810.

---

1 Visualisasi Data dengan R Univariat

1.1 Diagram Batang

library(ggplot2) #Untuk Visualisasi
Marriage<-read.csv("https://raw.githubusercontent.com/Bakti-Siregar/dataset/master/Bookdown-Data-Science-for-Beginners/Marriage.csv") #Memuat Data
ggplot(Marriage, aes(x=zodiacs))+ #Memplot Distribusi dari Zodiacs
  geom_bar(fill="cornflowerblue",
           color="azure4")+ #Anda dapat mengganti warna
  theme_minimal()+ #Menggunakan tema minimal
  labs(x="Zodiacs",
       y="Frequency",
       title="Marriage Participiants by Zodiacs") #Mengganti label dan juga judul chart

Menambahkan simbol persen untuk label y

library(ggplot2)
ggplot(Marriage,
       aes(
         x= zodiacs,
         y=..count../sum(..count..)))+
  geom_bar(fill=rainbow(12),color="azure4")+
  theme_minimal()+ #Menggunakan tema minimal
  labs(x="Zodiacs",
       y="Percents",
       title="Marriage Participiants in Percents")

scale_y_continuous(labels=scales::percent) #Menggunakan simbol % untuk label

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

library(dplyr) #Untuk memanipulasi data

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2) #Untuk Visualisasi Data
plotdata<-Marriage %>% #Memuat Dataset
  count(zodiacs) #Jumlah peserta di setiap Zodiacs
#Menyusun plot batang secara meningkat
ggplot(plotdata,
       aes(
         x=reorder(zodiacs,n),
         y=n))+
  geom_bar(stat="identity",
           fill=rainbow(12),
           color="azure4")+
  theme_minimal()+ #Menggunakan tema minimal
  labs(x="Zodiacs",
       y="Frequency",
       title="Sorting Categories")

Memberi label setiap batang dengan nilai numeriknya

library(dplyr)
library(ggplot2)
library(scales)
plotdata<-Marriage %>%
  count(zodiacs) %>%
  mutate(pct=n/sum(n),
         pctlabel=paste0(round(pct*100),"%"))
#Plot batang sebagai persentase, dalam urutan menurun dengan diagram batang
ggplot(plotdata,
       aes(x=reorder(zodiacs,-pct),
           y=pct))+
  geom_bar(stat="identity",
           fill=rainbow(12),
           color="azure4")+
  geom_text(aes(label=pctlabel),
            vjust=-0.25)+
  theme_minimal()+
  scale_y_continuous(labels=percent)+
  labs(x="zodiacs",
       y="Percent",
       title="Labeling Bars")

Memutar Label Sumbu

library(ggplot2)
library(scales)
#Plot batang sebagai persentase, dalam urutan menurun dengan diagram batang
ggplot(plotdata,
       aes(x=reorder(zodiacs,-pct),
           y=pct))+
  geom_bar(stat="identity",
           fill=rainbow(12),
           color="azure4")+
  geom_text(aes(label=pctlabel),
            vjust=-0.25)+
  theme_minimal()+
  scale_y_continuous(labels=percent)+
  labs(x="Zodiacs",
       y="Percent",
       title="Overlapping Labels") +
theme(axis.text.x=element_text(angle=45,hjust=1))

Membalik sumbu x dan y

library(ggplot2)
library(scales)
#Plot batang sebagai persentase, dalam urutan menurun dengan diagram batang
ggplot(plotdata,
       aes(x=reorder(zodiacs,-pct),
           y=pct))+
  geom_bar(stat="identity",
           fill=rainbow(12),
           color="azure4")+
  geom_text(aes(label=pctlabel),
            vjust=-0.10)+
  theme_minimal()+
  scale_y_continuous(labels=percent)+
  labs(x="Zodiacs",
       y="Percent",
       title="Overlapping Labels") +
coord_flip()

## Diagram Pie

library(dplyr)
library(ggplot2)
library(scales)
#Persiapan Data
plotdata<-Marriage %>%
  count(race) %>%
  arrange(desc(race))%>%
  mutate(prop=round(n*100/sum(n),1),
         lab.ypos=cumsum(prop)-0.5*prop)
#Membuat Diagram Pie
mycols<-c("#0073C2FF","#EFC000FF","#868686FF","#CD534CFF")
  ggplot(plotdata,aes(x="",y=prop,fill=race))+
    geom_bar(width=1, stat="identity",color="white")+
    coord_polar("y",start=0)+
    geom_text(aes(y=lab.ypos,label=prop),color="white")+
    scale_fill_manual(values = mycols)+
    theme_void()+
    labs(title="Marriage Participany by Race")

Membuat lubang dalam diagram pie

library(ggplot2)
library(scales)
#Membuat diagram donat
ggplot(plotdata,aes(x=2,y=prop,fill=race))+
    geom_bar(stat="identity",color="white")+
    coord_polar(theta="y",start=0)+
    geom_text(aes(y=lab.ypos,label=prop),color="white")+
    scale_fill_manual(values = mycols)+
    theme_void()+
    xlim(0.5,2.5)+
    labs(title="Marriage Participany by Race")

Menambahkan label dan menghapus legenda

library(ggplot2)
library(scales)
#tambahkan label persen
plotdata$percent<-paste0(plotdata$race,"\n",
                         round(plotdata$prop),"%")
#membuat diagram donut dalam persen
ggplot(plotdata,aes(x=2,y=prop,fill=race))+
    geom_bar(stat="identity",color="white")+
    coord_polar(theta="y",start=0)+
    geom_text(aes(y=lab.ypos,label=percent),color="white")+
    scale_fill_manual(values = mycols)+
    theme_void()+
    xlim(0.5,2.5)+
    labs(title="Marriage Participany by Race")

## Peta Pohon

library(ggplot2)
library(treemapify)
library(scales)
plotdata <-Marriage %>%
  count(officialTitle)
ggplot(plotdata,
       aes(fill=officialTitle,
           area=n))+
  geom_treemap()+
  labs(title="Marriage Participants by Officiate")

Menggunakan Label

ggplot(plotdata,
       aes(fill=officialTitle,
           area=n,
           label=officialTitle))+
  geom_treemap()+
  geom_treemap_text(colour="white",
                    place="centre")+
  labs(title="Marriage Participants by Officiate")+
  theme(legend.position="none")

# Variabel Kontinu ## Histogram

library(ggplot2)
ggplot(Marriage,aes(x=age))+
  geom_histogram(fill="cornflowerblue",
                 color="white",bins=20)+
  theme_minimal()+
  labs(title="Marriage Participants by Age (Basic)",
       x="Age")

library(ggplot2)
library(scales)
ggplot(Marriage,
       aes(x=age,
           y=..count../sum(..count..)))+
  geom_histogram(fill="cornflowerblue",
                 color="white",
                 binwidth = 5)+
  theme_minimal()+
  labs(title = "Marriage Participants by Age(Alternative Bins and Binwidth",
       y="Percent",
       x="Age")+
  scale_y_continuous(labels=percent)

Plot Densitas Kernel

library(ggplot2)
ggplot(Marriage,aes(x=age))+
  geom_density(fill="indianred3")+
  theme_minimal()+
  labs(title="Marriage Participants by Age")

Parameter Penghalusan

library(ggplot2)
bw.nrd0(Marriage$age)

## [1] 5.181946

ggplot(Marriage,aes(x=age))+
  geom_density(fill="deepskyblue",
               bw=1)+
  theme_minimal()+
  labs(title="Participants by Age",
       subtitle = "bandwidth=1")

## Diagram Titik

library(ggplot2)
ggplot(Marriage,aes(x=age))+
  geom_dotplot(fill="gold",
               color="azure4",
               binwidth = 2)+
  theme_minimal()+
  labs(title="Participants by age",
       y="Proportion",
       x="Age")

# Data Bivariat ## Kategorikal Vs Kategorikal Diagram Batang Bertumpuk

library(ggplot2)
mpg$drv<-factor(mpg$drv,
                levels=c("f","r","4"),
                labels=c("front-wheel","rear-wheel","4-wheel"))
#membuat diagram batang bertumpuk
ggplot(mpg,
       aes(x=class,
           fill=drv))+
  geom_bar(position="fill")+
  theme_minimal()+
  labs(y="proportion")

Diagram batang yang dikelompokkan

library(ggplot2)
ggplot(mpg,aes(x=class,fill=drv))+
  theme_minimal()+
  geom_bar(position=position_dodge(preserve="single"))

Diagram Batang Tersegmentasi

library(dplyr)
library(ggplot2)
library(scales)
#membuat ringkasan dataset
plotdata<-mpg %>%
  group_by(class,drv)%>%
  dplyr::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.

#membuat diagram batang tersegmentasi
#menambahkan label untuk setiap segmen
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)+
  geom_text(aes(label=lbl),
            size=3,
            position=position_stack(vjust=0.5))+
  scale_fill_brewer(palette = "Set2")+
  theme_minimal()+
  labs(y="Percent",
       fill="Drive Train",
       x="Class",
       title="Automobile Drive by Class")

theme_minimal()

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Plot Mosaik

tbl<-xtabs(Freq~Survived+Class+Sex, Titanic)
ftable(tbl)

##                Sex Male Female
## Survived Class                
## No       1st        118      4
##          2nd        154     13
##          3rd        422    106
##          Crew       670      3
## Yes      1st         62    141
##          2nd         25     93
##          3rd         88     90
##          Crew       192     20

#Membuat plot mosaik dari tabel
library(vcd)

## Loading required package: grid

mosaic(tbl,main="Titanic data")

mosaic(tbl,
       shade=TRUE,
       legend=TRUE,
       labelin_args=list(set_varnames=c(Sex="Gender",
                                        Survived="Survived",
                                        Class="Passenger Class")),
       set_labels=list(Survived=c("No","Yes",
                       Class=c("1st","2nd","3rd","Crew"),
                       Sex=c("F","M")),
       main="Titanic data")
       )

# Kontinu Vs Kontinu Plot Sebaran

library(ggplot2)
library(hrbrthemes)

## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.

##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and

##       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

#Membuat data
d1<-data.frame(x=seq(1,100),
               y=rnorm(100),
               name="No trend")
d2<-d1%>%
mutate(y=x*10 +rnorm(100,sd=60))%>%
mutate(name="Linear Relationship")
d3<-d1%>%
mutate(y=x^2+rnorm(100,sd=140))%>%
mutate(name="Square")
d4<-data.frame(x=seq(1,10,0.1),
               y=sin(seq(1,10,0.1))+
                 rnorm(91,sd=0.6))%>%
mutate(name="Sin")
don<-do.call(rbind,list(d1,d2,d3,d4))
#Plot
don%>%
  ggplot(aes(x=x,y=y))+
  geom_point(color="#69b3a2",alpha=0.8)+
  theme_ipsum()+
  facet_wrap(~name, scale="free")

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

library(ggplot2)
library(scales)
data(Salaries, package="carData")
#plot sebaran yang ditingkatkan
ggplot(Salaries,
       aes(x=yrs.since.phd,
           y=salary))+
  geom_point(color="cornflowerblue",
             size=2,
             alpha=.8)+
  scale_y_continuous(label=scales::dollar,
                     limits=c(50000,250000))+
  scale_x_continuous(breaks=seq(0,60,10),
                   limits = c(0,60))+
  theme_minimal()+
  labs(x="Years Since PhD",
       y="",
       title="Experience Vs. Salary",
       subtitle = "9-months salary for 2008-2009")

Plot Sebaran Menyesuaikan Garis

library(ggplot2)
ggplot(Salaries,
       aes(x=yrs.since.phd,
           y=salary)) +
  geom_point(color="cornflowerblue")+
  geom_smooth(method="lm", color="brown1")+
  theme_minimal()+
  labs(x="Years Since PhD",
       y="",
       title="Experience Vs. Salary",
       subtitle = "9-month salary for 2008-2009")

## `geom_smooth()` using formula 'y ~ x'

library(ggplot2)
ggplot(Salaries,
       aes(x=yrs.since.phd,
           y=salary))+
  geom_point(color="cornflowerblue",
             size=2,
             alpha=1)+
  geom_smooth(size=1,
              color="green")+
  scale_y_continuous(label= scales::dollar,
                     limits=c(50000,250000))+
  scale_x_continuous(breaks=seq(0,60,10),
                     limits=c(0,60))+
  theme_minimal()+
  labs(x="Years Since PhD",
       y="",
       title = "Experience vs. Salary",
       subtitle="9-month salary for 2008-2009")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

theme_minimal()

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1.2 Kategorikal vs .Kontinu

Diagram Batang

library(dplyr)
library(ggplot2)
library(scales)
data(Salaries, package = "carData")
#Menghitung gaji rata-rata untuk setiap jabatan
plotdata <-Salaries %>%
  group_by(rank) %>%
  dplyr::summarize(mean_salary=mean(salary))
#Plot gaji rata-rata dengan cara yang lebih menarik
mycols<-c("#CD534CFF","#EFC000FF","#0073C2FF")
ggplot(plotdata,
       aes(x=factor(rank,
                    labels=c("Assistant\nProfessor",
                             "Associate\nProfessor",
                             "Full\nProfessor")),
           y=mean_salary))+
  geom_bar(stat = "identity",
           fill=mycols)+
  geom_text(aes(label=dollar(mean_salary)),
            vjust=-0.25) +
  scale_y_continuous(breaks=seq(0,130000,20000),
                     label=dollar)+
  theme_minimal()+
  labs(title="Mean Salary by Rank",
       subtitle="9-month academic salary for 2008-2009",
       x="",
       y="")

Plot densitas Kernel yang dikelompokan

ggplot(Salaries,
       aes(x=salary,
           fill=rank))+
  geom_density(alpha=0.4)+
  theme_minimal()+
  labs(title="Salary distribution by rank")

Boxplot

mycols<-c("#CD534CFF","#EFC000FF","#0073C2FF")
ggplot(Salaries,aes(x=rank,
                    y=salary))+
  geom_boxplot(notch=TRUE,
               fill=mycols,
               alpha=.7)+
  theme_minimal()+
  labs(title="Salary Distribution by Rank")

Plot Biola

ggplot(Salaries,
       aes(x=rank,
           y=salary))+
  geom_violin(fill="azure1")+
  geom_boxplot(width=.2,
               fill=mycols,
               outlier.color="red",
               outlier.size = 2)+
  theme_minimal()+
  labs(title="Salary Distribution by Rank")

Plot Garis Punggung

library(dplyr)
library(ggplot2)
library(ggridges)
ggplot(mpg,
       aes(x=cty,
           y=class,
           fill=class))+
  geom_density_ridges(alpha=0.7)+
  theme_ridges()+
  labs("Highway mileage by auto class")+
  theme(legend.position = "none")

## Picking joint bandwidth of 0.929

Plot Garis

library(dplyr)
library(ggplot2)
library(ggridges)
#Menghitung rata-rata, deviasi standar,
#Kesalahan standar, dan 95% interval kepercayaan
#Berdasarkan Jabatan
plotdata<-Salaries %>%
  group_by(rank,sex)%>%
  dplyr::summarize(n=n(),
                   mean=mean(salary),
                   sd=sd(salary),
                   se=sd/sqrt(n),
                   ci=qt(0.975,df =n-1)*sd/sqrt(n))

## `summarise()` has grouped output by 'rank'. You can override using the `.groups` argument.

#Meningkatkan plot rata-rata kesalahan
pd<-position_dodge(0.2)
ggplot(plotdata,
       aes(x=factor(rank,
                    labels=c("Assistant\nProfessor",
                             "Associate\nProfessor",
                             "Full\nProfessor")),
           y=mean,
           group=sex,
           color=sex))+
  geom_point(position = pd,
             size=3)+
  geom_line(position=pd,
            size=1)+
  geom_errorbar(aes(ymin=mean-se,
                    ymax=mean+se),
                    width=.1,
                    position=pd,
                    size=1)+
  scale_y_continuous(label=scales::dollar)+
  scale_color_brewer(palette = "Set1")+
  theme_minimal()+
  labs(title="Mean salary by rank and sex",
       subtitle = "(mean +/- standard error)",
       x="",
       y="",
       color="Gender")

Plot Strip

library(ggplot2)
library(scales)
ggplot(Salaries,
       aes(y=factor(rank,
                    labels=c("Assistant\nProfessor",
                             "Associate\nProfessor",
                             "Full\nProfessor")),
           x=salary,
           color=rank))+
  geom_jitter(alpha=0.7,
              size=1.5)+
  scale_x_continuous(label=dollar)+
  labs(title="Academic Salary by Rank",
       subtitle="9-month salarty for 2008-2009",
       x="",
       y="")+
  theme_minimal()+
  theme(legend.position = "none")

Menggabungkan Jitter dan PlotKotak

library(ggplot2)
library(scales)
ggplot(Salaries,
       aes(y=factor(rank,
                    labels=c("Assistant\nProfessor",
                             "Associate\nProfessor",
                             "Full\nProfessor")),
           x=salary,
           color=rank))+
  geom_boxplot(size=1,
               outlier.shape = 1,
               outlier.color="black",
               outlier.size = 3)+
  geom_jitter(alpha=0.5,
              size=.2)+
  scale_x_continuous(label=dollar)+
  labs(title="Academic Salary by Rank",
       subtitle="9-month salarty for 2008-2009",
       x="",
       y="")+
  theme_minimal()+
  theme(legend.position = "none")+
  coord_flip()

Hibrid Geom Plot Jitter dan Box

library(ggplot2)
library(scales)
library(ggpol)
ggplot(Salaries,
       aes(x=factor(rank,
                    labels=c("Assistant\nProfessor",
                             "Associate\nProfessor",
                             "Full\nProfessor")),
           y=salary,
           fill=rank))+
  geom_boxjitter(color="black",
                 jitter.color="darkgrey",
                 errorbar.draw=TRUE)+
  scale_y_continuous(label=dollar)+
  labs(title="Academic Salary by Rank",
       subtitle="9-month salarty for 2008-2009",
       x="",
       y="")+
  theme_minimal()+
  theme(legend.position = "none")

Plot Kawanan Lebah

library(ggplot2)
library(scales)
library(ggbeeswarm)
ggplot(Salaries,
       aes(x=factor(rank,
                    labels=c("Assistant\nProfessor",
                             "Associate\nProfessor",
                             "Full\nProfessor")),
           y=salary,
           color=rank))+
  geom_quasirandom(alpha=0.7,
                   size=1.5)+
  scale_y_continuous(label=dollar)+
  labs(title="Academic Salary by Rank",
       subtitle="9-month salarty for 2008-2009",
       x="",
       y="")+
  theme_minimal()+
  theme(legend.position = "none")

Diagram Titik Cleveland

library(dplyr)
library(ggplot2)
library(scales)
library(ggbeeswarm)
library(gapminder)
data(gapminder,package="gapminder")
#Subset negara-negara Asian 2007
library(dplyr)
plotdata<-gapminder %>%
  filter(continent=="Asia"&
           year==2007)
#Plot Cleveland
ggplot(plotdata,
       aes(x=lifeExp,
           y=reorder(country,lifeExp)))+
  geom_point(color="blue",
             size=2)+
  geom_segment(aes(x=40,
                   xend=lifeExp,
                   y=reorder(country,lifeExp),
                   yend=reorder(country,lifeExp)),
               color="azure3")+
  labs(x="Life Expectancy (years)",
       y="",
       title="Life Expectancy by Country",
       subtitle="GapMinder data for Asia-2007")+
  theme_minimal()+
  theme(panel.grid.major=element_blank(),
        panel.grid.minor=element_blank())

# Data Multivariat Pengelompokan

library(carData)
library(ggplot2)
data(Salaries,package="carData")
ggplot(Salaries,aes(x=yrs.since.phd,
       y=salary,
       color=rank))+
  geom_point()+
  theme_minimal()+
  labs(title="Academic salary by rank and years since degree")

Menambahkan Jenis Kelamin

library(carData)
library(ggplot2)
ggplot(Salaries,aes(x=yrs.since.phd,
       y=salary,
       color=rank,
       shape=sex))+
  geom_point(size=3, alpha=.6)+
  theme_minimal()+
  labs(title="Academic salary by rank and years since degree")

Membedakan pria dan wanita

library(carData)
library(ggplot2)
ggplot(Salaries,aes(x=yrs.since.phd,
       y=salary,
       color=rank,
       size=yrs.service))+
  geom_point(alpha=.8)+
  theme_minimal()+
  labs(title="Academic salary by rank and years since degree")

Menambahkan garis kuadrat yang cukup

library(carData)
library(ggplot2)
ggplot(Salaries,aes(x=yrs.since.phd,
       y= salary,
       color=sex))+
  geom_point(alpha=.4,
             size=3)+
  geom_smooth(se=FALSE,
              method = "lm",
              formula=y~poly(x,2),
              size=1.5)+
  labs(x="Years Since Ph.D",
       title="Academic salary by rank and years since degree",
       subtitle = "9-month salary for 2008-2009",
       y="",
       color="Sex")+
    scale_y_continuous(label=scales::dollar)+
    scale_color_brewer(palette="Set1")+
    theme_minimal()

Pembagian Faset

library(carData)
library(ggplot2)
ggplot(Salaries,aes(x=salary))+
  geom_histogram(fill="cornflowerblue",
                 color="white")+
  facet_wrap(~rank,ncol=1)+
  theme_minimal()+
  labs(title="Salary Histograms by Rank")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Menetapkan jenis kelamin ke baris dan jabatan ke kolom

library(carData)
library(ggplot2)
ggplot(Salaries,aes(x=salary/10000))+
  geom_histogram(fill="cornflowerblue",
                 color="white")+
  facet_grid(sex~rank)+
  theme_minimal()+
  labs(title="Salary Histograms by Rank")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Menggunakan warna pada pembagian faset

library(carData)
library(ggplot2)
library(dplyr)
#Menghitung rata-rata dan kesalahan standar berdasarkan
#jenis kelamin, jabatan dan disiplin ilmu
plotdata<-Salaries %>%
  group_by(sex,rank,discipline)%>%
  dplyr::summarize(n=n(),
                   mean=mean(salary),
                   sd=sd(salary),
                   se=sd/sqrt(n))

## `summarise()` has grouped output by 'sex', 'rank'. You can override using the `.groups` argument.

#Membuat label yang lebih baik untuk disiplin ilmu
plotdata$discipline<-factor(plotdata$discipline,
                            labels=c("Theoretical",
                                     "Applied"))
#Membuat plot
ggplot(plotdata,
       aes(x=sex,
           y=mean,
           color=sex))+
  geom_point(size=3)+
  geom_errorbar(aes(ymin=mean-se,
                    ymax=mean+se),
                width=.1)+
  scale_y_continuous(breaks=seq(70000,140000,10000),
                     label=scales::dollar)+
  facet_grid(.~rank+discipline)+
  theme_bw()+
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank())+
  labs(x="",
       y="",
       title="Nine month academic salaries by gender, discipline, and rank",
       subtitle = "(Means and standard errors)")+
  scale_color_brewer(palette = "Set1")

Memodifikasi sumbu x memperkecil tulisan dan menyederhanakan warna background

library(gapminder)
library(ggplot2)
library(dplyr)
#Memplot harapan hidup berdasarkan tahun secara terpisah
#Untuk setiap negara di Asia
data(gapminder,package="gapminder")
#Pilih data Asia
plotdata<-dplyr::filter(gapminder,
                        continent=="Asia")
#Memplot harapan hidup berdasarkan tahun, untuk setiap negara
ggplot(plotdata, aes(x=year,y=lifeExp))+
  geom_line(color="grey")+
  geom_point(color="blue")+
  facet_wrap(~country)+
  theme_minimal(base_size=9)+
  theme(axis.text.x = element_text(angle = 45,
                                   hjust=1))+
  labs(title="Change in Life Expectancy",
       x="Year",
       y="Life Expectancy")