#
# Practice 1
#

# load the dataset

eurodist

ed <- eurodist

# explore the dataset

class(ed)

str(ed)

# convert the dist object to matrix

ed.matrix <- as.matrix(ed)

# extract distances from Stockholm

stock <- ed.matrix[,"Stockholm"]

# get min and max - version 1 - does not show names

max(stock)

min(stock)

# get min and max - version 2 - sort the vector - see the whole vector visually

sort(stock)

# get min and max - version 3 - sort the vector ascending/descending and get the first element

stock.asc <- sort(stock)

stock.asc[1]

stock.asc[2]

stock.desc <- sort(stock,decreasing = T)

stock.desc[1]

# get min and max - version 4 - use specific function

which(stock == max(stock))

which(stock == min(stock))

# cologne - lisbon distance

ed.matrix["Cologne","Lisbon"]

#
# Practice 2
#

# Install and load the package “poliscidata”

install.packages("poliscidata")

library("poliscidata")

# Create a new object containing the dataset “world”

world.df <- world

# Explore variables in the dataset (use help() function)

help(world) #oops, not very well documented dataset

str(world) # long structure

colnames(world) # better to just show the names of variables

# Extract countries into separate vector

country.names <- world.df[,"country"]

country.names <- world.df$country

# Extract country IDs into separate data frame

country.ids <- world.df[,"natcode",drop = FALSE]

# Find the Czech Republic in the data frame

country.names # so far, we only know how to do it manually = finding the number in the vector of country names

country.names[41]

# Extract the appropriate row from the data frame

world.df[41,]

# Find all V4 countries (CZ, SK, HU, PL)

country.names

country.names[c(41, 68, 124 ,135)]

# Extract them into a separate data set

world.df[c(41, 68, 124 ,135),]

v4.df <- world.df[c(41, 68, 124 ,135),]

# Extract only freedom indicators for these countries (all column names starting with “free_”)

colnames(world.df) # again, we can only do it manually now. Best idea is to check column names of the data frame and find appropriate indexes of the columns of interest

indicators <- colnames(world.df) # generate a vector of the column names - column names have same positions as columns in the data frame = if we find right positions of names, we get correct column positions

indicators[50] 

indicators[51]

indicators[62]

indicators[63]

indicators[51:62] # check if the columns are correct

# option 1 - get it from the original data frame called world.df
world.df[c(41, 68, 124 ,135),51:62]

# option 2 - get it from the V4 subset we created in the previous step
v4.df[,51:62]