freeLunchNN.Rmd

---
title: "Stor 565 Final Project"
output: html_notebook
---

```{r}
library(readr)
fullData = read.csv("food_data.csv")
```

```{r}
library(neuralnet)
```

```{r}
# Only use if you need to look for some variables you can use
#plot(PCT_FREE_LUNCH14~., data = fullData)
```


```{r}
colNames = c("PCT_FREE_LUNCH14", "PCT_LACCESS_POP15", "PCT_LACCESS_SNAP15", "PCT_LACCESS_CHILD15", "PCT_LACCESS_WHITE15", "PCT_LACCESS_BLACK15", "PCT_LACCESS_HISP15", "PCT_LACCESS_NHASIAN15", "PCT_LACCESS_NHNA15", "PCT_LACCESS_NHPI15", "PCT_LACCESS_MULTIR15", "PCT_SNAP16", "PC_SNAPBEN15", "PC_WIC_REDEMP12", "PCT_OBESE_ADULTS13", "FOODINSEC_13_15", "FOODINSEC_CHILD_03_11", "MILK_PRICE10", "PCT_NHWHITE10", "PCT_NHBLACK10", "PCT_HISP10", "PCT_NHASIAN10", "PCT_NHNA10", "PCT_NHPI10", "MEDHHINC15", "CHILDPOVRATE15", "SNAPS16")
cleanData = fullData[complete.cases(fullData[colNames]),]
nnData = cleanData[names(cleanData) %in% colNames]
```

```{r}
library(caTools)
indexes <- sample.split(seq_len(nrow(nnData)), 0.5)
maxs <-apply(nnData, 2, max)
mins <- apply(nnData, 2, min)
scaled <- as.data.frame(scale(nnData, center = mins, scale = maxs - mins))
train = scaled[indexes,]
test = scaled[!indexes,]
trainUS = nnData[indexes,]
testUS = nnData[!indexes,]
```

```{r}
library(caret)
set.seed(15)
# Partition data into folds, set up storage for calculation
folds <- createFolds(train$PCT_FREE_LUNCH14, k = 5, list = TRUE, returnTrain = FALSE)
errorStore = array(rep(0, 5*15*9), dim = c(5, 15, 9))

for(i in c(1:5)){ # Over which fold is held out
  for(j in c(2:15)) { # Over number of neurons in hidden layer 1
    for(k in c(2:9)) { # Over number of neurons in hidden player 2
      valNet = neuralnet(PCT_FREE_LUNCH14~., data = train[-folds[[i]],], hidden = c(j,k), linear.output = TRUE)
      
      pred <- compute(valNet, train[folds[[i]],])

      pred_ <- pred$net.result*(max(nnData$PCT_FREE_LUNCH14) -  min(nnData$PCT_FREE_LUNCH14)) + min(nnData$PCT_FREE_LUNCH14)

      testReal <- (train[folds[[i]],]$PCT_FREE_LUNCH14) * (max(nnData$PCT_FREE_LUNCH14) -                              min(nnData$PCT_FREE_LUNCH14))+min(nnData$PCT_FREE_LUNCH14)

      MSE.nn <- sum((testReal - pred_)^2)/nrow(train[folds[[i]],])
      errorStore[i,j,k] = MSE.nn
    }
  }
}

errorStore
```

```{r}
cvMSE = array(rep(0, 15*9), dim = c(15,9))
for(i in c(1:15)) {
  for(j in c(1:9)) {
    cvMSE[i,j] = mean(errorStore[1:5,i,j])
  }
}
cvMSE
```

```{r}
finalNN = neuralnet(PCT_FREE_LUNCH14~., data = train, hidden = c(3,2), linear.output = TRUE)
```

```{r}
plot(finalNN)
```

```{r}
predVals <- compute(finalNN, test)

predVals_ <- predVals$net.result*(max(nnData$PCT_FREE_LUNCH14) - min(nnData$PCT_FREE_LUNCH14)) + min(nnData$PCT_FREE_LUNCH14)

testR <- (test$PCT_FREE_LUNCH14) * (max(nnData$PCT_FREE_LUNCH14) -                              min(nnData$PCT_FREE_LUNCH14))+min(nnData$PCT_FREE_LUNCH14)

MSE.nn <- sum((testR - predVals_)^2)/nrow(test)

MSE.nn
(MSE.nn)^.5
```

```{r}
plot(predVals_~testUS$PCT_FREE_LUNCH14)
abline(1,1, col = 'red')
```

```{r}
errors = testR - predVals_
median(abs(errors))
hist(errors)
```