5.8 LAB: Structuring data using mutate() The hmeq_small dataset contains information on 5960 home equity loans, including 7 features on the characteristics of the loan. • Load the hmeq_small.csv data set as a data frame. • Create a new data frame with the columns LOAN and VALUE standardized and saved as LOAN STAND and VALUE_STAND. • Create a second data frame with the columns LOAN and VALUE normalized and saved as LOAN_NORM and VALUE_NORM. • Print the summaries of the new data frames. Ex: Using the first 100 rows, found in hmeq_sample.csv, the output is:
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- Create a new dataset called "life_exp_2" using subset() with the following variables: life expectancy, polio vaccination, measles vaccination, obesity 9-15 year old's, basic water, alcohol consumption, and current healthcare spending. Use View() and str) to check if you obtained all the relevant variables in the new dataset.Open the link below and select 2 CSV datasets and 2 xlsx datasets from it. Or you can select any data sets from the web. https://data.europe.eu/euodp/en/data/dataset?sort=views total+desc Please use the R programming software to answer the following Questions: 1- Describe the selected table and its columns in words, list down the data types of all columns. 2- Apply (sum, average, count, max, and min) function where applicable, justfy why you applied that function and write the results. 3- Draw pie, bar, boxplot, and histogram where possible in order to draw the specific type of chart the data appropriation is important. Some charts will not be feasible for some data. So you need to find out the appropriate data set and its properties that are suitable for a specific type of chart.Q7: Using the AIRPORT KLX Table (Textbook page 131), describe an example that illustrates the insertion anomaly. Q8: Using the AIRPORT KLX Table (Textbook page 131), describe an example that illustrates the deletion anomaly. Q9: Using the AIRPORT KLX Table (Textbook page 131), describe an example that illustrates the modification anomaly.
- DTSC670: Foundations of Machine Learning Models Module 1 Assignment 2: COVID-19 Data Wrangling Name: The purpose of this assignment is to hone your data wrangling skills. Your task for this assignment is to perform the data preparation as instructed in the DTSC670_Assignment_2 pdf listed in Brightspace. After performing all the data preparation tasks outlined in the document, run the code in the "Prepare DataFrames for Grading" section. You are supplied an Excel file called BrazilcOVIDData.xlsx - be sure to put the data file in the same directory as this Jupyter Notebook. Please note that it may take around 5 minutes to read-in all of the data in this file. In [ ]: N ### ENTER CODE HERE ### # Insert as many cells as you need, but be sure your code is very neat and very well documented. In [ ]: N # Get Final Features DataFrame # features = ### ENTER CODE HERE ### # Get Final Response DataFrame # response = ### ENTER CODE HERE ###Kaggle data set (kukuroo3) (body performance data) Use the above data set Describe the data set Use descriptive statistic Draw a table with attribute of the data setdataset in picture Use the given dataset to perform the following using python: Name of dataframe (your reg_no+your_name) Import the given data set Find out its header and information Calculate the mean, median and mode of covid_deaths, covid_recoveries, and covid_cases Find out the total of covid affected cases(all three cases) in all countries Find out the total of covid related deaths in all countries Countries covid_deaths covid_recoveries covid_cases Pakistan 10000 520000 55000 India 12000 120000 130000 Bangladesh 14000 123000 120000 Srilanka 19000 213000 45000 Bhutan 12000 212 44000 Nepal 1000 9000 12300 Australia 80000 560000 32100 Singapore 2000 130000 12345 England 100000 320000 89765 USA 100000 890000 87898
- Given the following average precipitation data which contains both categorical and numerical data: data = [ ('city': 'Atlanta', 'precipitation in mm': '1263'}, ['city': 'Austin', 'precipitation in mm': '870'} ['city': 'Chicago', 'precipitation in mm': '937"} 3 Using one-hot encoding, what would be your feature matrix? Insert a table with column names to show your answer:One of the columns (named location) in my data frame (df_covid) is the countries of the world. How would I select only the rows that have a country ending in "land"?In R please provide the code and explanation for the following One Way ANOVA with the coagulation data seta. Load the coagulation data set (it is from the faraway library)b. Data Summaries & Assumption Check i. Use the names() function to identify the column names ii. How many rows of data are there? iii. Create a single graph with 4 boxplots on the same scale, one for the coagulation for each of the diets. Each boxplot should be a different color. Use the plot function for this task.colNameData is the name of the column in the data set that you want to create a boxplot forcolNameCategory is the name of the column that you want to use to split the data into groups (so you want one boxplot for each category/value in this column)dataset is the name of the full dataset col=2:4 will give you 3 different colors (color indices are 1-8, then they repeat) iv. Create 4 different data frames, one for the data corresponding to each of the 4factors. How many observations are there for…
- Use SAS code the west dataset has 68 obsercations and 8 variables; Variable names are :date duration_s latitude longitude posted shape state time; The statenames dataset has 50 observations and 2 variables; Variable names are :state_abbrev state_name; U don't need to give the exact result since u don't have datasets, I just what to know how to write this code, Thx!!7]: fig, ax = plt.subplots() data_d.plot.hist(density=False, ax=ax, title='Histogram: Set1 and Setl samples vs. Set2 and Set2 samples', bins=40) data.plot.hist (density=False, ax=ax, bins=40) ax.set_ylabel('Count') ax.grid(axis='y') Count Histogram: Set1 and Set1 samples vs. Set2 and Set2 samples 35 30 25 20 15 10 600 500 400 300 5 200 0 100 0 100 Use boxplots to compare the four sets. Discuss their differences. 0 200 8]: fig = plt.figure(figsize =(10, 7)) plt.boxplot ([set1, set1_s, set2, set2_s],1, '') plt.show() 300 400 500 Setls Set2s Set1 Set2 2 600 3 The first pair and the second pair look similar while the two pairs look differnet, right? The question is how can we KNOW if two sets are truly (significantly) different or not?R7