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library(tidyverse)
smaller <- diamonds |>
filter(carat <=2.5)
smaller |>
ggplot(aes(x=carat))+
geom_bar(binwidth=0.01)library(plotly)
library(ggplot2)
data(economics)
plot_ly(
data=economics,
x= ~date,
y= ~ unemploy,
type="scatter",
mode="lines"
)%>%
layout(
xaxis=list(title="Date"),
yaxis=list(title="Number of Unemployed")
)plot_ly(
data=economics,
x= ~unemploy,
type="histogram"
)library(leaflet)Warning: package 'leaflet' was built under R version 4.5.3
leaflet() %>%
addTiles()%>%
addMarkers(
lng=-84.39,
lat=33.75,
popup="Atlanta"
)leaflet() %>%
addTiles()%>%
addMarkers(
lng=-84.5184,
lat=33.9384,
popup="KSU Marietta"
)subcars <- mtcars[1:5,1:4]
knitr::kable(subcars,
col.names = c("Mile/Gallon","#Cylinders",
"Eng Displacement","Horsepower"),
row.names = F,
caption="Car Data")| Mile/Gallon | #Cylinders | Eng Displacement | Horsepower |
|---|---|---|---|
| 21.0 | 6 | 160 | 110 |
| 21.0 | 6 | 160 | 110 |
| 22.8 | 4 | 108 | 93 |
| 21.4 | 6 | 258 | 110 |
| 18.7 | 8 | 360 | 175 |
library(DT)Warning: package 'DT' was built under R version 4.5.3
datatable(mpg)