Machine Learning Made Easy

barplot using plotly library

Introduction

Strong visualization is very important in any data science activity. Conveying the results to the target audience and ensuring that key insights come out in the form of a story is very decisive and can determine the effectiveness of the model building process.In this regard, I introduce you to a very powerful visualization package in R by the name plotly. There are tonnes of features in the library that enhances the look and feel of the visualization and adds value to interpretation. In this blog, we will look at how to create a simple barplot using plotly library

Installing libraries

Lets install plotly and other libraries used to create the plot

package.name<-c("dplyr","tidyr","Ecdat","plotly")

for(i in package.name){

  if(!require(i,character.only = T)){

    install.packages(i)
  }
  library(i,character.only = T)

}


# Ecdat package has the 'Health Insurance and Hours Worked By Wives' data
data(HI)
df<-HI
head(df)

  whrswk hhi whi hhi2  education  race hispanic experience kidslt6 kids618
1      0  no  no   no 13-15years white       no       13.0       2       1
2     50  no yes   no 13-15years white       no       24.0       0       1
3     40 yes  no  yes    12years white       no       43.0       0       0
4     40  no yes  yes 13-15years white       no       17.0       0       1
5      0 yes  no  yes  9-11years white       no       44.5       0       0
6     40 yes yes  yes    12years white       no       32.0       0       0
   husby       region   wght
1 11.960 northcentral 214986
2  1.200 northcentral 210119
3 31.275 northcentral 219955
4  9.000 northcentral 210317
5  0.000 northcentral 219955
6 15.690 northcentral 208148

Step 1:Frequency Profile of the variables

Lets look at the count of records for different levels of categorical variables

interim.df<-df%>%
  select(hhi,whi,hhi2,education,race,hispanic,kidslt6,kids618,region)
  
l1<-lapply(colnames(interim.df),function(x){

  z<-interim.df%>%
    select(x)%>%
    mutate(Feature=x)
  
  colnames(z)<-c("Level","Feature")
  
  z1<-z%>%
    group_by(Feature,Level)%>%
    summarise(Total=n())
  
  z1["Level"]<-sapply(z1["Level"],as.character)
  
  return(z1)
})

df.final<-do.call(rbind.data.frame,l1)%>%
  as.data.frame()
row.names(df.final)<-NULL
head(df.final)

  Feature Level Total
1     hhi    no 11219
2     hhi   yes 11053
3     whi    no 13961
4     whi   yes  8311
5    hhi2    no  8696
6    hhi2   yes 13576

Step 2:Dataset for ‘education’ variable

df.interim<-df.final%>%
  filter(Feature=="education")%>%
  select(-Feature)

df.interim

       Level Total
1    <9years  1122
2  9-11years  1771
3    12years  8677
4 13-15years  5790
5    16years  3472
6   >16years  1440

Step 3:Initialising the plotly object

barplt <- df.interim %>% plot_ly()
barplt <- barplt %>% add_trace(x = df.interim$Level, y = df.interim$Total, type = 'bar',text=paste0(round(df.interim$Total,2)),textposition="Outside",
                         
                         marker = list(color = 'Orange',
                                       line = list(color = 'Orange', width = 1.5)))
barplt <- barplt %>% layout(title = "<b>Frequency Profile of Education",
                      barmode = 'group',
                      xaxis = list(title = "Age Bracket"),
                      yaxis = list(title = "Record Count"),
                      autosize=F,width = 500,
                      margin = list(l = 50, r = 50, b = 50, t = 50, pad = 4))

Warning: Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()

barplt

Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
Please use `arrange()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.