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DATA423 Assignment
创建日期：2024-07-08 16:17:44
标签： DATA423
Assignment
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DATA423 Assignment 

Summary

This assignment has two parts, coding a shiny app and producing a report.

Disclaimer: The data used in this assignment is not genuine; its has been artificially constructed to have interesting characteristics and challenges embedded in it.

Part 1 - Coding

Create a Shiny app using RStudio. Load the supplied comma separated variables (csv) file and use Shiny to:

1.  Summarise and visualise the data and perform Exploratory Data Analysis. Make good use of controls. This is a minor part of the assignment so borrow from assignment 1 if you can.

2.  From this evidence and the supplied background (see below) develop a strategy to deal with missing values. The strategy can change for different variables.

3.  From this evidence and the supplied background develop a strategy to deal with outliers. The strategy can change for different variables.

4.  Develop a pre-processing strategy for things like centring and scaling.

5.  Implement these strategies using a “recipes” based data processing pipeline.

6.  Develop a tuned glmnet model and visualise its test performance.  Document the model’s optimal hyper-parameters. Note that you DO NOT need to explore other methods for this assignment - just glmnet.

7. Identify any residual outliers. Think about how to show the train and test residuals.

The submission should be a set of files: ui.R, server.R and global.R that we should be able to run and grade (without needing to make any changes). Submit these files as a compressed ZIP file.

Part 2 - Report

Write a report on your modelling. Include appropriate images from your shiny app.

1.  Discuss the data and any curious features that you noticed. Record the issues you would have followed up with a domain expert, were one available.

2.  Document and justify your various strategies using words (rather than code).

3.  Research the glmnet method and briefly explain this method in your report.

4.  Document your glmnet model’s theoretical performance on unseen data.

Submit your report as a PDF, this should be submitted separately from the ZIP file.

The Background

Covid-19 data, all measurements are as at 2019. The supplied CSV contains the following variables:

CODE

Anonymised state or country

GOVERN_TYPE

Type of government:  "STABLE DEM", "UNSTABLE DEM", "DICTATORSHIP", "OTHER"

POPULATION

Total population

AGE25_PROPTN

The proportion of the population that is at or below 25

AGE_MEDIAN

The median age of the population

AGE50_PROPTN

The proportion of the population that is at or above 50

POP_DENSITY

The population density

GDP

The Gross National Product

INFANT_MORT

The infant mortality rate

DOCS

The number of doctors per 10,000

VAX_RATE

The mean vaccination rate for Covid-19

HEALTHCARE_BASIS

Type of healthcare system "INSURANCE", "PRIVATE", "FREE"

HEALTHCARE_COST

Healthcare costs per person where applicable

DEATH_RATE

The projected death rate (across ten years)

OBS_TYPE

The allocation to test or train

The outcome variable is the DEATH_RATE.

The Details

Steps

1.  Create a shell of a Shiny app.  Plan whether you want a sidepanel/main layout or a fluidpage layout or something more ambitious.  Design how the user would progressively get more information by interacting  with the page. Try to avoid long pages - instead use a tabset to control your navigation through the charts. You can continue developing your app from assignment 1 if you feel this is a good starting point.

Add your name to the title part of the UI so it is clear to see whose app is running.

2. Identify all possible missing value placeholders eg ’ ’, ’na’,’N/A’, -1, -99 etc.

3.  Place the CSV file in the same location as the ui.R, server.R files.  Load the CSV file using something like:

dat ← read.csv(、data.csv、, header = TRUE, na.strings = c(、NA、,、N/A、), stringsAsFactors = TRUE)

4.  Additionally replace numeric missing values with NA using something like:

dat[dat == −1] ← NA

5.  Identify any categorical missing values that are “Not Applicable” and create new levels for these values using something like:

# convert away from factor

dat$cat ← as.character(dat$cat)

data$cat[is.na(data$cat)] ← “none、、

# convert back to factor

dat$cat ← as.factor(dat$cat)

6. Identify any numeric missing values that are “Not Applicable” and create new levels for these values using something like:

# create a shadow variable

dat$num_shadow ← as.numeric(is.na(data$num))

# Assign missing to zero

dat$num[is.na(dat$num)] ← 0

7.  Provide a set of EDA visualisations of the data set.  Make a note of any curious things and add these to your report. Use lots of controls to vary the behaviour and scope of the visualisations. Remember to align the visualisation style to the variable type.

8.  Identify any excessively missing variables for some threshold. Remove these.

9.  Identify any excessively missing observations for some threshold. Remove these.

10.  Determine if the missingness has pattern. Try something like:

library(rpart)

library(rpart.plot)

dat$MISSINGNESS ← apply(X = is.na(data), MARGIN = 1, FUN = sum)

tree ← train(MISSINGNESS ∼ . − CODE, −OBS_TYPE,

data = covid,

method = “rpart、、,

na.action = na.rpart)

rpart.plot(tree$finalModel,

main = “TUNED : Predicting_the_number_of__missing_variables_in_an_observation、、,

roundint = TRUE,

clip.facs = TRUE)

11.  Create a test - train split. Something like:

train ← dat[dat$OBS_TYPE == “Train、、, ]

test ← dat[dat$OBS_TYPE == “Test、、]

12.  Develop a recipe-based processing pipeline. Something like:

# id is not a predictor

# obs__type is not a predictor

rec ← recipes :: recipe(Target ∼ ., data = dat)% > %

update_role(“ID、、, new_role = “id、、)% > %

update_role(“OBS_TYPE、、, new_role = “split、、)% > %

step_knnimpute(all_predictors(), neighbors = 5)% > %

step_center(all_numeric_predictors())% > %

step_scale(all_numeric_predictors())% > %

step_dummy(all_nominal_predictors())

13.  Feed your recipe into a training operation that will optimise the hyperparameters of glmnet by resampling the train data. Do some research on the caret package. Choose a suitable metric to use in evaluating the model. Something like:

library(caret)

library(glmnet)

model ← caret :: train(rec, data = train, method = “glmnet、、)

14.  Generate the predictions for the test cases.  Generate an appropriate visualisation for these predictions.

15.  Display the test-RMSE statistic.

16.  Generate a residual box-plot for the test data, the train data and both the test & train data and label the outliers based upon a slider for the IQR-multiplier.

Considerations

Here are some things to consider:

Interactive

The app should allow you to choose your strategy. By trying different strategies it should be possible to quantify whether they improve the model or not. It is not sufficient to just hard-code your optimal decisions.

How?

How can you make an app that allows your variable-missingness-threshold (for example) to affect your choice of whether to centre and scale? The key to this is to use reactive expressions.

Reactive expressions

The code below is a reactive expression that puts the data through a variable-missingness cleaning process:

getClean ← reactive({

#this is another reactive expression

d ← getData()

#process columns

vRatio ← apply(d, 2, pMiss)

d ← d[, vRatio < input$VarThresh]

#process rows

oRatio ← apply(d, 1, pMiss)

d ← d[oRatio < input$ObsThresh, ]

d

})

Whenever the function getClean() is called in Shiny code, the latest values of the VarThresh and ObsThresh sliders are used to generate the data.

Strategies

It may be necessary to have a cascade of reactive expressions that perform, in an appropriate sequence, the various strategies needed to optimally clean the data for a glmnet model.

Marking

This assignment is worth 20% of your final grade.

We mark the assignment out of 100 with 65% of the mark for the Shiny app and 35% for the report.

The Shiny App should run without errors. Test that it does.

Your code, and the use of your app, should allow, exploration of different strategies.

The PDF document should show consistent and correct thinking about outliers, missing data, pre-processing steps and the assessment of a model.

The order of steps in the processing pipeline is important.


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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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