Week3

Data Processing for Plot 1 : Heatmap R Code

library(datasets);library(plotly);library(reshape2)

data("airquality") ## Load the airquality dataset
airquality$Month=as.factor(airquality$Month) ## Convert Month to factor
ozone_daily=airquality[,c(1,5,6)] ## Extract Ozone, Month and Day columns
## Convert Long format to Wide for input to Heatmap
ozone_daily=dcast(ozone_daily,Day~Month,value.var = "Ozone") 
ozone_daily=as.matrix(ozone_daily[,-1]) ## Convert to Matrix
colnames(ozone_daily)=c("May","June","July","August","September")
## Plotly command
plot_ly(z=ozone_daily,colorscale="Hot",x=colnames(ozone_daily),type="heatmap",colorbar = list(title = "Ozone Levels (parts per billion)"))%>%layout(title = "Daily Ozone Levels in New York, May to September 1973", xaxis = list(title = "Month"),yaxis = list(title = "Day"))

Plotly - Interactive Plot 1: Heatmap

Plot 2 : Time-Series Chart R Code

library(datasets)
library(plotly)
data(uspop) ## Load the data set that gives the population of the United States 
## (in millions) as recorded by the decennial census for the period 1790–1970.
## Plotly Command
plot_ly(x=~time(uspop),y=~uspop,type="scatter",mode="lines") %>% layout(title = "U.S. Population in millions for the period 1790-1970", xaxis = list(title = "Year"),yaxis = list(title = "U.S. Population (millions)"))

Plotly - Interactive Plot 2: Time-Series Chart