lundquist-ecology-lab

# Exporting data as a .csv in R

write.csv(iris, "iris_export.csv", row.names = FALSE)

lundquist-ecology-lab

# Subsetting data in R

# Subset Iris data into a dataframe with only one species

virginica <- subset(iris, iris$Species == "virginica")

# Subset a particular vector with only the values from a particular group

virginica_sepals <- iris$Sepal.Length[iris$Species == "virginica"]

lundquist-ecology-lab

## Hardy Weinberg script (2) 

## Simulating changes in genotype frequencies over time 
## under 100% selection against homozygous recessive

## Instruction: Press "-->Source"

##################### CODE: Ignore all of this below ###########################

for (i in 1) {

lundquist-ecology-lab

## Hardy Weinberg script (3) 

## Simulating changes in genotype frequencies over time 
## under 20% selection against homozygous recessive

## Instruction: Press "-->Source"

##################### CODE: Ignore all of this below ###########################

for (i in 1) {

lundquist-ecology-lab

## Hardy Weinberg script (1) 

## Simulating changes in genotype frequencies over time 
## under Hardy-Weinberg Equilibrium

## Instruction: Press "-->Source"

##################### CODE: Ignore all of this below ###########################

  BB.f <- NA

lundquist-ecology-lab

# Survival analysis in R

# Load library (install first if needed)
library(survival)
library(survMisc)

# create a sample data set
data <- lung

# fit a survival model

lundquist-ecology-lab

# Running a principal components analysis (PCA) in Python
#%%

import pandas as pd
# pip install scikit-learn
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns

lundquist-ecology-lab

# Running a principal components analysis (PCA) in R

# Load data
data(iris)

# Remove factors
data <- iris

# Scale data
data_scaled <- scale(data[-5])

lundquist-ecology-lab

#Let's say we have a dataset of images of cats and dogs, and we want to train a 
# model to classify them correctly. We will be using the tf.keras module, which 
# provides a high-level API for building and training neural networks.

import tensorflow as tf
from tensorflow import keras

# load dataset
(x_train, y_train), (x_val, y_val) = keras.datasets.cifar10.load_data()

lundquist-ecology-lab

from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load data
iris = load_iris()
X = iris.data
y = iris.target

# Split data into training and testing sets