Working in small groups or pairs, complete the following exercises.

Required Packages

The following packages will be required or may come in handy.

library(dplyr)
library(magrittr)
library(readr)
library(tidyr)
library(knitr)
library(here)

Exercises

  1. Use command c() to create vectors as listed below and check their class as you go. For factor class, check it’s levels and label it.

          i) Integers from 1 to 5 and name it vect_int.

          ii) Double numeric variables from 0.5 to 3.5 incrementing it 1, and name it vect_dbl.

          iii) Character variables using name of the colours red, green, blue, yellow, white and name it vect_char.

         iv) Factor variables using very low, low, medium, high, very high and name it vect_fact. Order the levels and name it vect_fact2 then check the levels again.

  1. Use ordered = TRUE argument for vect_fact2 and name it vect_fact3. Type vect_fact3 in the console to see the structure.

  2. Combine vect_int and vect_fact3 using c() command, name it as vect_comb. Guess the type of vect_comb’s class.

  3. Use the vectors you created in the previous exercise and create a list and name it vect_list. Check the structure of vect_list. Add states of Australia as a vector to the list and name it vect_list2 (Hint: Use append() function). Check the structure then name the elements of the list as comp1, comp2, …,comp8.

           i) Select the third element of comp5.

           ii) Select the second, fourth and eighth component of the list all together.

  1. Create a \(5\times 4\) numeric matrix using seq(0, 36, by = 2). Check out the warning message, notice that 5th row, 4th column is 0. Explain in a few words the reason of the warning and what this is called. (Hint: Refresh your memory with swirl package). Save this matrix as mat1, check the structure and attributes of it.

  2. Create a matrix from vect_char and vect_fact3 using row-bind and column-bind and name it m1 and m2 respectively. Pick a suitable bind function to add m2 onto mat1 to create \(5\times6\) matrix, name it mat2, check the attributes and structure. Have you noticed that the columns don’t have names?

  3. Create a matrix with vect_dbl and c(1,2,3,4), name it m3. Then combine m2 and m3 using column-bind. Explain in a few words what went wrong.

  4. Add column names to the matrix mat2 and name it seq1, seq2, seq3, seq4, colours, factor1. Add row names to the matrix mat2 and name it x1, x2, x3, x4, x5. Check attributes.

  5. Create a data frame using vectors vect_int and vect_char and name it df1. Check it’s structure.

  6. Add vect_fact3 onto df1 as a third column and name it df3. Check the structure. Now add vect_dbl to df3. Discuss the reason why we can’t combine vect_dbl and df3.

  7. Add column and row names to df3. Set the column names to numbers, colours, scale and row names to r1, r2, r3, r4, r5.

  8. Subset df3 by row numbers, only select the fourth and fifth rows. Then subset df3 by column numbers, only select first and third columns. For both tasks use subsetting by row/column number and then the row/column name. Subset the third column using $ operator.

  9. Convert df3’s columns using as.

         i) The numbers column into numeric,

         ii) The colours and the scale column into character.

         iii) Check the structure of df3.

  1. Convert mat2 into a dataframe and df3 to a matrix. Use is.matrix() and is.data.frame() functions to check the type after you convert.

Australian Retail Trade

In week 2, we imported some Australian Retail Trade data from Excel, and saved out the two worksheets as RDS files. If you need to re-download the data, it is taken from Australian Bureau of Statistics website located at https://www.abs.gov.au/statistics/industry/retail-and-wholesale-trade/retail-trade-australia/jun-2025 and is located under the data downloads as Table 11 (Retail turnover, state by industry subgroup, original).

  1. Import the two data frames (index and data). Create a list with these two data frames as elements of the list. Export the list to your data folder in RDS format.

German General Social Survey Data

The following exercise is based on German General Society Survey germangss.csv data set. This data set has 400 rows of categorical data which was used to study what affects political attitude in Germany through 1991-1992. This data set is taken from the book Analyzing Categorical Data by Jeffrey S. Simonoff (Simonoff, J. (2003). Analyzing Categorical Data. New York: Springer New York).

Variables in this data set contains:

Political_system: Political attitude

Age: Age categories

Year: Year that survey is recorded

Schooling: Education level

Region: Region name in Germany

binaryClass: Binary class, P=positive, N=Negative

  1. Load germangss.csv data set.

         i) Find out the types of variables and the data structures. Rename the variables as Political Attitude, Age Category, Year, Education Level, Region and Binary Class.

         ii) Check the class of each variable.

         iii) Check the structure of the data set.

         iv) Convert Political Attitude, Age Category and Education Level columns into factors and order the levels.

         v) Convert Year column into numeric.

         vi) Subset first, second and the fourth column and first 30 rows. Create a data frame with this subset and name it as subgss. Then check it with is.data.frame().

Finished?

If you have finished the above tasks, work through the weekly list of tasks posted on the Canvas announcement page.

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