R Script Writer for Advanced Data Analytics Coder
Budget: $10 – $30 CAD
Looking for a coder particularly R script for the following Data will be provided:
What percentage of the data set has complete cases?
2. How many patients (# and %) from each site are included?
3. Calculate the age of each person in the dataset and report the mean, median, minimum
and maximum age within each site.
4. Plot a histogram and boxplot to see the distribution of ages. Now provide stratified
histograms and boxplots by site. (You may use density plots instead of histograms if you
wish)
5. How many suspicious values do you see (e.g. are there missing values or unrealistic
values?)? Should we remove these?
6. For now let's remove these values. After removing, what is your mean, min, max age?
How did these values change after removing the outliers?
7. How many observations (and %) in each gender category are there?
8. Do you notice any pattern with how the gender categories are encoded? i.e. what other
piece of data determines how the categories are encoded? Provide code to standardize
the encoding based on your observation above.
9. Plot a scatterplot for the relationship between age and wellbeing. What is the correlation
between age and wellbeing? How do we interpret this?
10. Plot scatterplots for the relationship between age and well being stratified by gender
category. What do you notice?
What percentage of the data set has complete cases?
2. How many patients (# and %) from each site are included?
3. Calculate the age of each person in the dataset and report the mean, median, minimum
and maximum age within each site.
4. Plot a histogram and boxplot to see the distribution of ages. Now provide stratified
histograms and boxplots by site. (You may use density plots instead of histograms if you
wish)
5. How many suspicious values do you see (e.g. are there missing values or unrealistic
values?)? Should we remove these?
6. For now let's remove these values. After removing, what is your mean, min, max age?
How did these values change after removing the outliers?
7. How many observations (and %) in each gender category are there?
8. Do you notice any pattern with how the gender categories are encoded? i.e. what other
piece of data determines how the categories are encoded? Provide code to standardize
the encoding based on your observation above.
9. Plot a scatterplot for the relationship between age and wellbeing. What is the correlation
between age and wellbeing? How do we interpret this?
10. Plot scatterplots for the relationship between age and well being stratified by gender
category. What do you notice?