Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”.
In order to be able to solve this set of exercises you should have solved the part 0 and’part 1 of this series, in case you haven’t you can find the solutions to run them in your machine part 0 and part 1. This is the third set of exercise of a series of exercises that aims to provide a descriptive analytics solution to the ‘2008’ data set from here. This data set which contains the arrival and departure information for all domestic flights in the US from 2008 has become the “iris” data set for Big Data. In the exercises below we will try to impute the missing values in order to be able to analyse the data later on. Before proceeding, it might be helpful to look over the help pages for the mean, median, transform, impute, lm, predict.
For this set of exercises you will need to install and load the package Hmisc.
install.packages('Hmisc')
library(Hmisc)
Answers to the exercises are available here.
If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.
Exercise 1
Print the summary statistics in order to have an actual idea of the missing values.
Exercise 2
Impute the missing values of flights$ArrTime with the mean using which.
Exercise 3
Impute the missing values of flights$CRSArrTime with the median using which.
Exercise 4
Impute the missing values of flights$AirTime with the median using the transform operator.
Exercise 5
Impute the missing values of flights$DepTime with the median using the transform operator. Note: mind the data formatting .
Exercise 6
Impute the missing values of flights$ArrDelay with the median using the impute operator.
Exercise 7
Impute the missing values of flights$CRSElapsedTime with the median using the impute operator.
Exercise 8
Make a linear regression model named lm_dep_time_delay with target variable flights$DepDelay and independent variables : flights$ArrTime, flights$AirTime, flights$ArrDelay, flights$DepTime.
Exercise 9
Create an object pred_dep_time_delay and assign the predicted values.
Exercise 10
Impute the missing values based using the pred_dep_time_delay and ifelse function.
Print the summary statistics to see the changes that you made.
Related exercise sets:
Descriptive Analytics-Part 6: Interactive dashboard ( 1/2)
Descriptive Analytics-Part 6: Interactive dashboard ( 2/2)
Descriptive Analytics-Part 3 : Outlier treatment
Descriptive Analytics-Part 5: Data Visualisation (Categorical variables)
Descriptive Analytics-Part 5: Data Visualisation (Spatial data)
I think there are some troubles about the right visualization of answers
Hello there , can you be a bit more specific ? Are you referring to the results of the results of the last summary execution ?
Hi Vasileios,
for example solution to exercise 4 which ask to transform variable flights$AirTime, but the solution refers to flights$DepTime and it’s only partially visible. The solution code also seems to be truncated in visualization.
I am sorry for the mistake , I fixed it.Regarding the visibility of the solutions they were just all in a big chunk of code . I fixed that too and each solution corresponds to one chunk of code.
Thank you very much for your feedback.
Many thanks,
and go on 🙂
I’m waiting for next Part
In ex #2, you replace NAs with NA:
flights$ArrTime[which(is.na(flights$ArrTime))] <- mean(flights$ArrTime)
Did you even test this once?
Hello ! Thanks for your comment.
Sorry for that , it is just like in exercise 3 . You should use the ‘na.rm=TRUE’ argument . I fixed it.
Cheers!