Machine Learning Made Easy

Saturday, May 23, 2020

Blog 24: Line Plots using ggplot

Line Plots

Introduction

While analysing datasets, it is important to represent summary stats using appropriate graphs.In this series, we will look at how to create most commonly used line plot using ggplot library. Real case scenarios will be taken to understand the nitty-gritties of implementation

Installing the library: dplyr,tidyr and Ecdat package

package.name<-c("dplyr","tidyr","Ecdat","ggplot2")

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:Lets calcualte Average Experience across different regions

interim.df<-df%>%
  select(region,experience)%>%
  group_by(region)%>%
  summarise(AverageExperience=mean(experience))

Line Plot to represent the above information

ggplot(data=interim.df, aes(x=region, y=AverageExperience,group=1)) +
  geom_line(linetype = "dashed")+
  geom_point(color="red")

Final Comments

The above plot helps us to understand how a continuous metric such as experience can be different across various levels of a feature such as region or gender or ethnicity