In this exercise we cover the basics on selecting and extracting data using queries. We add a few other materials into it. This should prepare you for the next exercise: Basic Decision Tree. The purpose of this is to give you the 20 percent of the tools to get 80 percent of work done. You will be off and running plotting simple decision trees. It will be helpful to read up on str(),colnames() and data.frame() command.
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.
create vectors. Hint: use c outside brackets to indicate multiple items
a. Create a numeric vector that includes the numbers 4, 5,6,8 and 3 (exact order). Store this in the variable a
b. Create a character vector that includes the characters
"apple","chair","jetplane","salmon","island". Store this in the variable b
c. Create a boolean vector TRUE,TRUE,FALSE,TRUE,FALSE. Note that boolean is sensitive to letter case. Store this in c
create a dataframe using the variables a,b and c. Store this in df
str() command to see the dataframe
print the column names of df
change the column names of df to “id”,”wish”,”real”. View the dataframe again using the
quick selecting exercise
a.select the first column of the df using column name
b.select the row where id==3. Use this logic to select the row.
c.Now use the index method to select the first row in df
d.select the second column of the df using index method
e.select the second item in the third row of df
attach/load the iris dataset and use the
str() command to see the dataset
change the column names of iris to
select exercise using the iris dataset
a. select the rows where ‘sw’ is 3
b. select the first element of the second cloumn
c. select the first 6 rows of the dataset
d. select the last 6 rows of the dataset
e.select every column except the s column
select all the rows where sl is greater than 5 and store all the sample in iris_2 making sure to exclude the s column