MATH 2405: Data Wrangling - Mathematics Assignment Help

Assignment Task:

Course Learning outcomes 

This assessment is linked to the following course learning outcomes: 

1. Accurately, logically and ethically combine data from multiple sources to make suitable for statistical analysis and draw valid interpretations. 

2. Articulate how data meets the best practice standards (e.g. tidy data principles). 

3. Select, perform and justify data validation processes for raw datasets. 

4. Use leading open source software (e.g. R) for reproducible, automated data processing. 

Get, import, inspect, tidy and manipulate data: 

Step 1: Locate an open source of data from the web. This can be a tabular, spreadsheet data (i.e., .txt, .csv, .xls, .xlsx files), data sets from other statistical software (i.e., SPSS, SAS, Stata etc. data files), or you can scrape HTML table data. 

As a minimum, the data set should include: 

? one numeric variable. 

? one qualitative (categorical) variable. 

There is no limit on the number of observations and number of variables. But keep in mind that when you have a very large data set, it will increase your reading time. 

Step 2: Read/Import the data into R, then save it as a data frame. You can use Base R functions or readr, xlsx, readxl, foreign, rvest packages for this purpose. In this step, you must provide the R codes with outputs (i.e. head of data set) and explain everything that you do in order to import/read/scrape the data set. 

Step 3: Provide a clear description of the data and its source (i.e. URL of the web site). Provide variable descriptions. 

Step 4: Inspect the dataset and variables using R functions. You should: 

? check the dimensions of the data frame. 

? summarise the types of variables by checking the data types (i.e., character, numeric, integer, factor, and logical) of the variables in the data set. If variables are not in the correct data type, apply proper type conversions. 

? check the levels of factor variables, rename/rearrange them if required. 

? check the column names in the data frame, rename them if required. 

? provide the R codes with outputs and explain everything that you do in this step. 

Step 5: Check if the data conforms the tidy data principles. If your data is untidy, reshape your data into a tidy format. If the data is in a tidy format, you will be expected to explain why the data is originally 'tidy'. In addition to the R codes and outputs, explain everything that you do in this step. 

Step 6: Provide summary statistics (mean, median, minimum, maximum, standard deviation) of numeric variables grouped by one of the qualitative (categorical) variable. For example, if your categorical variable is age groups and quantitative variable is income, provide summary statistics of income grouped by the age groups. Provide the R codes with outputs and explain everything that you do in this step. 

Step 7: Create a list that contains a numeric value for each response to the categorical variable. Typically, they are numbered from 1-n (n being the number of categories in the variable). Provide the R codes with outputs and explain everything that you do in this step. 

Step 8: Join this list on using a join of your choice. Remember that this has to keep the numeric variable, as well as matching to your categorical variable. Provide the R codes with outputs and explain everything that you do in this step. 

Step 9: Subset the data frame using the first 10 observations (include all variables). Then convert it to a matrix. Check the structure of that matrix (i.e. check whether the matrix is a character, numeric, integer, factor, or logical) and explain in a few words why you ended up with that structure. Provide the R codes with outputs and explain everything that you do in this step. 

Step 10: Subset the data frame including only first and the last variable in the data set, save it as an R object file (.RData). Provide the R codes with outputs and explain everything that you do in this step. 

Step 11: Create a data frame from scratch with 2 variables (i.e. one integer variable and one ordinal variable) using data.frame() function. Make sure that you factorized and ordered the ordinal variable properly. Then create another numeric vector and use cbind() to add this vector to your data frame. After this step you should have 3 variables in the data frame. Provide the R codes with outputs and explain everything that you do in this step. 

Step 12: Create another data frame with a common variable to the dataset created in step 11. Join the data frame to the dataset above, and ensure that the dataset is joined properly, ensuring the new categorical variable is carried to the larger dataset. Eg. A dataset to join could be State, Abbreviation, Municipality, Prevailing Religion. 

Provide the R codes with outputs and explain everything that you do in this step. 

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