Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(carData)
library(dplyr)
library(plotly)
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
library(gapminder)

#Scatterplot#

ggplot(data=Salaries,
       mapping=aes(x=yrs.service,
                   y=salary,
                   color=sex))+
       geom_point(size=3,
                  alpha=1.5)+
      labs(x="Length of Service (in years)",
           y="Monthly Salary(in dollar)")+
      geom_smooth(method="lm")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(data=Salaries,
       mapping=aes(x=yrs.service,
                   y=salary))+
       geom_point(size=3,
                  alpha=1.5,
                  color='darkgreen')+
      labs(x="Length of Service (in years)",
           y="Monthly Salary(in dollar)",
  color="Sex")+
      geom_smooth(method="loess",
                  col="maroon")+
  facet_wrap(~sex)+
theme_classic()
## `geom_smooth()` using formula = 'y ~ x'

#Bar plot - Percent

Salaries |>
  select(rank) |>
  count(rank)|>
  mutate(rank_recode=recode(rank,"AsstProf"="Assistant Prof","AssocProf"="Associate Prof"))|>
  mutate(p=round(n/sum(n)*100,1))|>
  ggplot(aes(x=rank_recode,
             y=p,
             label=p))+
  geom_col(fill='darkgreen',
           color='black')+
  geom_text(aes(label=n),
            hjust=-1,
            size=3.5)+
  labs(x="Rank",
       y="% of Faculty")+
  scale_y_continuous(expand=c(0,0),
                     limit=c(0,100))+
  coord_flip()+
theme_classic()

#Bar plot - Average

Salaries |>
  drop_na(salary)|>
  group_by(rank)|>
  summarize(n=length(salary),
            Mean=round(mean(salary),1),
            SD=sd(salary))|>
  mutate(Rank=recode(rank,"AsstProf"="Assistant Prof","AssocProf"="Associate Prof"))|>
  mutate(SE=SD/sqrt(n))|>
  as.data.frame()|>
  ggplot(aes(x=Rank,
             y=Mean))+
  geom_col(fill='darkgreen',
           color='black',
           width=0.7)+
  geom_errorbar(aes(ymin=Mean-SE,
                ymax=Mean+SE),
                size=0.7,
                width=0.15)+
   geom_text(aes(label=Mean),
            size=3,
            vjust=3.5,
            col="white")+
  scale_y_continuous(expand=c(0,0))+
theme_classic()

#Bar plot - Stack

Salaries |>
  select(rank,sex)|>
  group_by(sex)|>
    count(rank)|>
   mutate(Rank=recode(rank,"AsstProf"="Assistant Prof","AssocProf"="Associate Prof"))|>
    as.data.frame()|>
    ggplot(aes(x=Rank,
               y=n,
               fill=reorder(sex,-n)))+
    geom_bar(stat="identity",
             position="stack",
             width=0.5)+
    scale_fill_manual(values=c("Female"="pink","Male"="darkslategray4"))+
    geom_text(aes(label=n),
              hjust=1.3,
              size=3)+
labs(x="Rank",
     y="No. of Faculty",
     fill="Sex")+
 scale_y_continuous(expand=c(0,0))+
coord_flip()

theme_classic()
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#Side-by-side bar plot of 2 categorical variables

Salaries |>
  select(discipline,rank,sex)|>
  group_by(sex) |>
  count(rank)|>
  mutate(Rank=recode(rank,"AsstProf"="Assistant Prof","AssocProf"="Associate Prof"))|>
  as.data.frame()|>
  ggplot(aes(x=Rank,
             y=n,
             fill=sex))+
  geom_bar(stat="identity",
           position="dodge",
           col="black")+
  scale_fill_manual(values=c("Female"="thistle1","Male"="darkslategray"))+
  geom_text(aes(label=n),
            position=position_dodge(0.9),
            vjust=1.0,
            hjust=1.3,
            col="black",
            size=3)+
  labs(x="Rank",
       y="No. of Faculty",
       fill="Sex")+
  scale_y_continuous(expand=c(0,0),
                     limits=c(0,300))+
  coord_flip()

  theme_classic()
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##  - attr(*, "class")= chr [1:2] "theme" "gg"
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#Frequency Histogram

Salaries|>
  ggplot(aes(x=salary))+
  geom_histogram(fill="darkslategray4",
                 color="black",
                 bins=20)+
  labs(x="Salary(in dollars)",
       y="Number of Faculty")+
  scale_y_continuous(expand=c(0,0))+
  facet_wrap(~sex)+
  theme_classic()

#**Density Plot

Salaries |>
  ggplot(aes(x=salary,
             fill=rank,
             color=rank))+
  geom_density()+
  scale_fill_manual(values=c("AsstProf"="darkslategray4","AssocProf"="pink","Prof"="wheat"))

  labs(x="Salary (in Dollars)",
       y="Density",
       fill="Rank")+
  scale_y_continuous(expand=c(0,0))+
  theme_classic()
## NULL

#**Box Plot

Salaries|>
  ggplot(aes(x=salary,
             fill=rank))+
  geom_boxplot()+
   scale_fill_manual(values=c("AsstProf"="darkslategray4","AssocProf"="pink","Prof"="wheat"))

  labs(x="Salary (in Dollars)",
       y="Density",
       fill="Rank")+
  scale_y_continuous(expand=c(0,0))+
  coord_flip()+
  theme_classic()
## NULL

#**Animated Plot

g1<-ggplot(data=Salaries,
           mapping=aes(x=yrs.service,
                       y=salary,
                       color=sex,
                       Label1=rank,
                       Label2=discipline,
                       Label3=sex,
                       Label4=salary,
                       Label5=yrs.service))+
  geom_point(size=2,
             alpha=1.5)+
  labs(x="Years of Service",
       y="Salary")+
  theme_classic()
ggplotly(g1,tooltip=c("Label1","Label2","Label3","Label4","Label5"))

#**Animated Plots

g2<-ggplot(data=gapminder,
           aes(x=gdpPercap,
               y=lifeExp,
               size=pop,
               fill=continent))+
  geom_point(aes(frame=year,
                 id=country))+
  scale_size(range=c(2,12))+
  scale_x_log10()+
  facet_wrap(~continent)
ggplotly(g2)

#Interactive Dashboard