Data Transformation with Dplyr contd

• We can use transmute() to just keep the new variables
  
• Grouped summaries with Summarize():
• Summarize collapses a data frame into a single row
  
• summarize() function is not useful until we pair with group_by()
  
• Using the pipe operator makes it much cleaner
  
• Using pipe operator try to show the count, average distance, average delay by destination
  
• Using pipe operator try to fill the object not_cancelled where the condition is dep_delay is not na and arrival delay is not na && Summarize the mean of arrival delays and departure delays of not cancelled flights by month
  
• Sample plotting
  
  
• Some other useful stastical functions sd(x), IQR(), mad()
  
  
• Lets try to summarize the flights data by number of flights per day

daily <- flights %>%
  group_by(year, month, day)

per_day <- daily %>%
  summarize(flights = n())
print(per_day)

per_month <- per_day %>%
  summarize(flights = sum(flights))

per_year <- per_month %>%
  summarize(flights = sum(flights))

• Refer Here for the changes done in the R file
• Dplyr roots are in an earlier package called plyr, which implements split-apply-combine strategy for data analysis.
• Dplyr has focus on data frames or in the tidyverse tibbles.
• Install and load gapminder Refer Here
• Exercise:
• filter the data where lifeExp is less than 29 gapminder::gapminder %>% filter(lifeExp < 29)
• filter the data where country is Rwnada or Afganishtan
• Refer Here for the and Refer Here cheatset with Dplyr