---
title: "loops and stuff"
author: "Emily Davenport"
date: "June 14, 2016"
output: html_document
---

# Load the data:

Load the pruned survey data:
```{r}
surveys <- read.csv('svy_complete.csv')
```


Load libraries:

```{r messages=FALSE}
library("dplyr")
library("ggplot2")
```

# If/else statements

Is the first year 1984?

```{r}
if (surveys$year[1] == 1984) {
  print("Great Scott, it's 1984!")
} else {
  print("it's not 1984 :(")
}
```

**CHallenge!** Write an if/else statement that evaluates whether the first animal in our data is larger than an ounce.
Ounce = 28.3g

```{r}
if (surveys$weight[1] > 28.3){
  print("the first animal weighs more than one ounce")
} else {
  print("nope, too tiny.")
}
```

# Loop

```{r}
for (i in 1:dim(surveys)[1]) {
  if (surveys$year[i] == 1984) {
    print("Great Scott, it's 1984!")
  } else {
    print("it's not 1984 :(")
  }
}
```

```{r}
surveys_adjusted <- surveys
```

Just print for 1984:

```{r}
for (i in 1:dim(surveys_adjusted)[1]) {
  if (surveys_adjusted$year[i] == 1984) {
    surveys_adjusted$weight[i] <- surveys$weight[i]*1.1
  }
}
```

```{r}
surveys %>%
  group_by(year) %>%
  summarize(mean_weight = mean(weight))
```

```{r}
surveys_adjusted %>%
  group_by(year) %>%
  summarize(mean_weight = mean(weight))
```

Challenge!
1. Using a for loop and an if/else statement but without using dplyr, tally the number of animals that weight over an oucne in our dataset.
2. For the animals that are not over an ounce in weight, how many of them are female and how many of them are male?

var <- 0
var <- var + 1

```{r}
skinnyM <- 0
skinnyF <- 0
largeA <- 0
for (j in 1:dim(surveys)[1]) {
  if (surveys$weight[j] > 28.3) {
    largeA <- largeA + 1
  } else {
    if (surveys$sex[j] == "M") {
      skinnyM <- skinnyM + 1
    } else {
      skinnyF <- skinnyF + 1
    }
  }
}
```

# Functions

```{r}
convert_1984 <- function(myval) {
  myval*1.1245697375083747 + 10
}
```


```{r}
convert_1984(1)
```

```{r}
for (i in 1:dim(surveys_adjusted)[1]) {
  if (surveys_adjusted$year[i] == 1984) {
    surveys_adjusted$weight[i] <- convert_1984(surveys$weight[i])
    surveys_adjusted$hindfoot_length[i] <- convert_1984(surveys$hindfoot_length[i])
  }
}
```

Challenges!

1. Write a function that will calculate the volume of a sphere based off of radius (4/3 * pi * r^3)
```{r}
lengthToVolume <- function(len) {
  return(4/3 * pi * len^3)
}
```


2. Estimate the skull volume for each animal in the dataset by applying the volume function to the hindfoot_length, where hindfoot_length is a proxy for radius of the skull. 

```{r}
A <- for (i in 1:dim(surveys_adjusted)[1]) {
  print(lengthToVolume(surveys_adjusted$hindfoot_length[i]))
}
```