R for bio

Question 1

What is the purpose of a Shiny package?

Answer 1

Shiny is an R package that enables the creation of interactive web applications directly from R.
- web applications for data visualization, analysis, and communication

Question 2

Why should you augment the data?

Answer 2

It will increase:
- dataset size (Q-value, P-value, Residual)
- Robustness to variations
- feature learning

and reduce sensitivity

Question 3

When does it make sense to augment the data?

Answer 3

It makes sense to use the augment() function or similar approaches to augment the data when you want to enrich your dataset with additional information related to the fitted model.

Question 4

what does it mean to perform augment on the data?

Answer 4

Typically refers to a function or operation that adds new columns to a dataset containing additional information related to the model
- P-value, Q-value or residuals

Question 5

What is the 3 tidy data rules?

Answer 5

• each variable must have its own colum
• each observation must have its own row
• each value must have its own cell

Question 6

What does it mean to wrangle the data?

Answer 6

Data wrangling is the process of converting raw data into a usable form and is done before any data analysis is done.
- Ensure reliable and complete data

Question 7

Why should you wrangle your data?

Answer 7

because it cleans the data, transforming and organizing it to a more suitable and structured format for data analysis

Question 8

What is the general pipeline for bio data science?

Answer 8

import -> tidy -> (Transform -> visualize -> model -> ) -> communicate

Question 9

Why is reproducibility of data analysis important?

Answer 9

Ensuring verification and validate the analysis, making it reusable.
- reduce error
- enhance adaptility and learning

Question 10

What is the components of reducebility

Answer 10

• Raw data
• Cleaning data
• code and script
• parameterization and tuning
• explanation
• documented workflow
• results
• accessibility and sharing

Question 11

Which data forms best fit a boxplot?

Answer 11

• 2 < numeric variables
• several groups in the data

Question 12

Which data forms best fit a heatmap?

Answer 12

not ordered numeric variables

Question 13

Which data forms best fit density plot?

Answer 13

Numeric values, not ordered and work with many points

Question 14

Which data forms best fit histogram?

Answer 14

Numeric variables, not ordered and works for a few points

Question 15

What is the purpose of boxplot?

Answer 15

Gives a summary of one or several numeric variables. The line that divides the box into 2 parts represents the median of the data. includes potential outliers in lines

Question 16

What is the purpose of heatmap?

Answer 16

It is really useful to display a general view of numerical data, not to extract specific data point.
- can show potential correlation

Question 17

What is the purpose of density plot?

Answer 17

Shows the distribution of a numeric variable. It takes only numeric variables as input and is very close to a histogram. It can be used in the same exact condition

Question 18

What is the purpose of histogram?

Answer 18

takes as input a numeric variable only. The variable is cut into several bins. It is possible to represent the distribution of several variable on the same axis using this technique

Question 19

Which different types of augment() is there?

Answer 19

• Linear model
• Generalized linear model
• Machine learning models
• Complex models
• Prediction task
• Visualizations

Question 20

How would you unify model output in R?

Answer 20

Use the tidy()-function from the broom-package

Question 21

what does the summary()-function from the base-package do?

Answer 21

used to obtain and print a summary of the results of various model fitting functions.