Agenda

```{R}
# R objects and value storage

myList <- list("Sunday", "Monday", "Wednesday")
print(myList)
myList <- append(myList, "Tuesday", after = 2)
print(myList)
```

```{python}
# Python objects and value storage
x = ["Sunday", "Monday", "Wednesday"]
print(x)
x.append("Tuesday") # append 4 to the list pointed to by x
print(x)
x.insert(2, "Tuesday")
print(x) # y's list is modified as well!
```

## ggplot2 - Adv data visualisations
library(ggplot2)
df %>%
filter(gdpPercap<50000) %>%
ggplot(aes(x=log(gdpPercap),y=lifeExp, col=continent, size= pop)) +
geom_point(alpha=0.3) +
geom_smooth(method = lm) +
facet_wrap(~continent)

## machine learning - linear models
model <- lm(df$lifeExp~df$gdpPercap) # univariate simple linear model
summary(model)
model2 <- lm(df$lifeExp~df$gdpPercap+df$pop) # multi variate linear models
summary(model2)

##working with a dataset
install.packages("gapminder") # to install package
library(gapminder) # to load package into session
data("gapminder") # load data to environment
head(gapminder) #sneak peek
?gapminder # dataset details
str(gapminder) # dataset structure
summary(gapminder) # statistical summaries
## understand data using basic visualisations
df <- gapminder
hist(df$lifeExp) # life expectency distribution looks - normal
hist(df$pop) # population distribution looks - skewed
hist(log(df$pop)) # applying log transformation
barplot(table(df$continent)) # continents comparison
boxplot(df$lifeExp~df$continent) # numerical vs categorical
plot(df$lifeExp~df$gdpPercap) # 2 numerical relation between life exp and gdp per cap
plot(df$lifeExp~log(df$gdpPercap)) # log transformation

# packages usage

##dplyr - data manipulation
library(dplyr)
df1 <- data.frame(eye_color=rep(c("blue","brown"),c(1,1)),height=round(runif(4, 4.0,
6.5),1),weight=seq(45,60,by= 4)) # creating data frame
print(df1) # looking at data
##applying on data frame
df1 %>%
select(eye_color,height,weight) %>%
filter(eye_color=="blue") %>%
mutate(BMI= weight/(height^2)) %>%
summarise(avg_BMI=mean(BMI))

## applying on gapminder
df %>%
select(country,lifeExp) %>%
filter(country=="India" | country == "Australia") %>%
group_by(country) %>%
summarise(Avg_life=mean(lifeExp))

## applying statistical significance test
df2 <- df %>%
select(country,lifeExp) %>%
filter(country=="India" | country == "Australia")
print(df2)
t.test(data=df2,lifeExp ~ country)

# Applying basic statistics in Python
import pandas as pd
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])
print(df)
# Minimum value of preTestScore
df['preTestScore'].min()

# Maximum value of preTestScore
df['preTestScore'].max()

# The sum of all the ages
df['age'].sum() 

# Mean preTestScore
df['preTestScore'].mean()

# Median value of preTestScore
df['preTestScore'].median()

#Sample variance of preTestScore values
df['preTestScore'].var()

#Sample standard deviation of preTestScore values
df['preTestScore'].std()

# Cumulative sum of preTestScores, moving from the rows from the top
df['preTestScore'].cumsum()

# Summary statistics on preTestScore
df['preTestScore'].describe()

# Basic plotting in Python

import pandas as pd
import matplotlib.pyplot as plt
iris = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data", 
                   header=None)
iris.columns = ['Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width',
       'Species']
iris.head()
iris['Sepal.Length'].plot(kind='hist')
iris.plot(kind='box')
plt.show()
iris.plot(kind='scatter', x='Sepal.Length', y='Sepal.Width')
plt.show()

#console and script
> x = 7
> x + 9
[1] 16

#comments
# COMMENTS ARE SUPER IMPORTANT so we learned about them

#graphics
x = rnorm(1000, mean = 100, sd = 3)
hist(x)

#getting help
# if you know the function name, but not how to use it:
?chisq.test
# if you know what you want to do, but don't know the function name:
??chisquare

#data types
# character vector
> y = c("apple", "apple", "banana", "kiwi", "bear", "strawberry", "strawberry")
> length(y)
[1] 7
# numeric vector
> numbers = rep(3, 99)
> numbers
 [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[39] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[77] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

#matrices
> mymatrix = matrix(c(10, 15, 3, 29), nrow = 2, byrow = TRUE)
> mymatrix
     [,1] [,2]
[1,]   10   15
[2,]    3   29
> t(mymatrix)
     [,1] [,2]
[1,]   10    3
[2,]   15   29
> solve(mymatrix)
           [,1]        [,2]
[1,]  0.1183673 -0.06122449
[2,] -0.0122449  0.04081633
> mymatrix %*% solve(mymatrix)
     [,1] [,2]
[1,]    1    0
[2,]    0    1
> chisq.test(mymatrix)
Pearson's Chi-squared test with Yates' continuity correction
data:  mymatrix
X-squared = 5.8385, df = 1, p-value = 0.01568

#data frames (the mother of all R data types)
# set working directory
setwd("~/Documents/R_intro")
# read in a dataset
wages = read.table("wages.csv", sep = ",", header = TRUE)

#exploratory data analysis
> names(wages)
[1] "edlevel" "south"   "sex"     "workyr"  "union"   "wage"    "age"    
[8] "race"    "marital"
> class(wages$marital)
[1] "integer"
> table(wages$union)
not union member     union member 
             438               96 
> summary(wages$workyr)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    8.00   15.00   17.82   26.00   55.00 
> nrow(wages)
[1] 534
> length(which(is.na(wages$sex)))
[1] 0
> linmod = lm(workyr ~ age, data = wages)
> summary(linmod)

hist(wages$wage, xlab = "hourly wage", main = "wages in our dataset", col = "purple")
plot(wages$age, wages$workyr, xlab = "age", ylab="years worked", main = "age vs. years worked")
abline(lm(wages$workyr ~ wages$age), col="red", lwd = 2)

                                     mtcars[mtcars$mpg>=20,c('gear','mpg')]
                  aggregate(. ~ gear,mtcars[mtcars$mpg>=20,c('gear','mpg')],mean)
           subset(aggregate(. ~ gear,mtcars[mtcars$mpg>=20,c('gear','mpg')],mean),mpg>25)

                                       #output      
               
                                          gear   mpg
                                        2    4 25.74
                                        3    5 28.20

myData <- data.frame(age=c(10,20,30), weight=c(100,200,300))
myPersonsAge <- data.frame(age=c(20,25,30))
myTrainModel <- lm(weight~age,data = myData)
predict(myTrainModel,myPersonsAge)

# install.packages("kernlab")
# install.packages("caret")
library(kernlab);library(caret)
data("spam")
predictors <- names(churnTrain)[names(churnTrain)=='churn']
predictors
set.seed(1)
inTrain <- createDataPartition(y=spam$type, p=0.75, list = FALSE)
training <- spam[inTrain,]
testing <- spam[-inTrain,]
dim(training)
set.seed(32343)
modelFit <- train(type~.,data = training, method='glm')
modelFit
modelFit$finalModel
predictions <- predict(modelFit,newdata=testing)
predictions
confusionMatrix(predictions, testing$type)

# Load required packages
library(ggplot2)
library(datasets)
# Load data
data(iris)
# Set seed to make results reproducible
set.seed(20)
# Implement k-means with 3 clusters
iris_cl <- kmeans(iris[, 3:4], 3, nstart = 20)
iris_cl$cluster <- as.factor(iris_cl$cluster)
# Plot points colored by predicted cluster
ggplot(iris, aes(Petal.Length, Petal.Width, color = iris_cl$cluster)) + geom_point()

# Include required packages
library(party)
library(partykit)
# Have a look at the first ten observations of the dataset
print(head(readingSkills))
input.dat <- readingSkills[c(1:105),]
# Grow the decision tree
output.tree <- ctree(
 nativeSpeaker ~ age + shoeSize + score,
 data = input.dat)
# Plot the results
plot(as.simpleparty(output.tree))

# Load required packages
library(ggplot2)
# Load iris dataset
data(iris)
# Have a look at the first 10 observations of the dataset
head(iris)
# Fit the regression line
fitted_model <- lm(Sepal.Length ~ Petal.Width + Petal.Length, data = iris)
# Get details about the parameters of the selected model
summary(fitted_model)
# Plot the data points along with the regression line
 ggplot(iris, aes(x = Petal.Width, y = Petal.Length, color = Species)) +
    geom_point(alpha = 6/10) +
    stat_smooth(method = "lm", fill="blue", colour="grey50", size=0.5, alpha = 0.1)

#svm
library(e1071)
# quick look at the data
plot(iris)

# feature importance
plot(iris$Sepal.Length, iris$Sepal.Width, col=iris$Species)
plot(iris$Petal.Length, iris$Petal.Width, col=iris$Species)
#split data
s <- sample(150, 100)
col <- c('Petal.Length','Petal.Width','Species')
iris_train <- iris[s,col]
iris_test <- iris[-s,col]
#create model
svmfit <- svm(Species ~ ., data = iris_train, kernel="linear", cost=.1, scale = FALSE)
print(svmfit)
plot(svmfit, iris_train[, col])

tuned <- tune(svm, Species~., data = iris_train, kernel='linear', ranges = list(cost=c(0.001, 0.01,.1, 1.10, 100)))
summary(tuned)

p <- predict(svmfit, iris_test[,col], type='class')
plot(p)
table(p, iris_test[,3])
mean(p==iris_test[,3])

# importing necessary libraries 
from sklearn import datasets 
from sklearn.metrics import confusion_matrix 
from sklearn.model_selection import train_test_split 
  
# loading the iris dataset 
iris = datasets.load_iris() 
  
# X -> features, y -> label 
X = iris.data 
y = iris.target 
  
# dividing X, y into train and test data 
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) 
  
# training a KNN classifier 
from sklearn.neighbors import KNeighborsClassifier 
knn = KNeighborsClassifier(n_neighbors = 7).fit(X_train, y_train) 
  
# accuracy on X_test 
accuracy = knn.score(X_test, y_test) 
print (accuracy)
  
# creating a confusion matrix 
knn_predictions = knn.predict(X_test)  
cm = confusion_matrix(y_test, knn_predictions)