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Seminar2_exercise-1.R
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##############################################
## Introductory Data Science for Innovation ##
## Seminar 2 ##
## By Frédérique Bone ##
##############################################
# importing the library for working with tidy data
library(tidyverse) #you may need to install this package if this has not already done before
# Getting the dataset for the seminar
install.packages("titanic") # more info here: https://www.rdocumentation.org/packages/titanic/versions/0.1.0
library(titanic) #call the library
Titanic <- as.data.frame(titanic_train) # this is the main dataset we are going to use throughout the exercise.
#------------------------------
# 1. Explore the given dataset
#------------------------------
## (REPLACE __ by the appropriate value) ##
# 1.1 Look at size of dataset on the right, how many rows and columns?
# 1.2 You can also use the function 'nrow()' and 'ncol()' on the dataset Titanic
nrow(__) # use this function to find the number of rows
ncol(__) # use this function to find the number of columns
# 1.3 Inspect the columns names of the dataset using 'names()'
__(__) # Use this function to find ou the column names of the dataframe
# 1.4 Use 'head()' and 'tail()' on Titanic to check the 5 first /last rows (using n=) of the data
__(__, n=__)
__(__, __=__)
# 1.5 Check the overall structure of the dataset use the function 'str()'
# and summary statistics for each variables using 'summary()'
__(__)
__(__)
# 1.6 Identify which variables may be considered as factors
# 1.7 Change these variables to factor
Titanic$__ <- as.factor(Titanic$__)
class(Titanic$__) # check whether this column has been transformed to a factor
__$__ <- as.factor(__$__)
__(__$__) # check whether this column has been transformed to a factor
# 1.8 Rerun the 'summary()' function and see what has changed for the two ammended variables
__(__)
#------------------------------
# 2. Reshape the given dataset
#------------------------------
# 2.0 Remove variables that not of high relevance and has many blanks
# Example of select
Titanic_clean <- Titanic %>% # %>% this is the pipe symbol it enable you to do a series of operations on the dataframes iteratively.
select(-Cabin) # Here we selected out the vairable Cabin as it is not very informative
# Using select with '-' means remove this specific column, use without minus, with a list separated by commas
# to tell the computer which columns you want to keep.
# 2.1 removing, renaming and creating new variables
Titanic_clean <- Titanic %>%
__(-__, -__) %>% # Using 'select' remove the column 'Ticket' and Cabin
__(Class=Pclass, __=__, __=__) %>% # Using the 'rename' function change the name of Pclass to Class,
# SibSp (siblings or spouse onboard) to something more explicit,
# Parch (parents or children onboard) to something more explicit
separate(Name, into=c("__","__"), # separate the column 'Name' into 'Surname' and 'Firstname', also looking into the column find the right character to use as a separator
sep="__", remove=FALSE) %>%
__(Overall_relatives = __+__) # Using 'mutate' add a new column 'Overall_relatives' which merges siblings and spouses and parents and children
## If you have time - try to separate again firstname to move out the title e.g. Mr Mrs etc. ##
# 2.2 Practice reshaping dataset.
Titanic_reshape __ __ __ # create a new dataset for reshape
__(cols=c(__,__,__), # using pivot longer put all the relatives columns into a unique column, using pivot longer
names_to=__, # name the new column 'family_type' (don't forget the quotation marks)
values_to = __) %>% # name the new value column 'family_count'
__(__, __, __) # keep only the passenger ids and the 'family_type' and 'family_count'
#------------------------------
# 3. Using logic in you code
#------------------------------
# 3.1 Dealing with unexplicit variables in Embarked (town where people embarked)
unique(__$__) # Use unique to check all the possible values of Embarked
value_embarked <- as.vector(unique(__$__)) # Associate all unique values to the vector and then 'print()' it
__(__)
# 3.2 Check which entries have missing values
embarked_missing <- __ %>% # Create a dataframe containing only these missing values using Titanic_clean
filter (__=="") # using filter, select only the obs. where Embarked is of value ""
__(__, n=__) # Using head() inspect the two first rows of this dataframe
# Create a new column in Titanic clean where for the value of the new variable to be entered
Titanic_clean <- Titanic_clean %>%
mutate(Entry_port=NA)
# 3.3 Iterate over the dataset to create the new column with the right
for (i in __:nrow(Titanic_clean)){ # Create a loop iterating over rows 1 to nrow() of the dataframe to
if (__$Embarked[i] == "S"){ #
Titanic_clean$Entry_port[i] <-"Southampton"
} else if(__$__[i] == "__"){ # create an entry for the C = Cherbourg, Q=Queenstown, ""=NA
__[__] <-"__"
} else if(__ == "__"){
__
} else if(__ == ""){
__ <-NA
}
}
# 3.2 Remove the variable Embarked using select, make the column 'Entry_port' a factor
# and reassign the results back to Titanic_clean
__ <- __ __
__(__) __
__(__=__(__))
#--------------------------------
# 4. Dealing with missing values
#--------------------------------
# 4.1 Look again at the summary of Titanic clean to identify the
# column which has NA values
__(__)
# 4.2 Remove the NAs in for all the relevant variables
__ <- __ __ # Use Titanic_clean and re-assign it to itself
__(!is.na(__)) # use the right function to select the rows which have NAs for a specific variable
# repeat the above step for all the variables that you identified which have missing NAs
#--------------------------------
# 5. Creating summary statistics
#--------------------------------
# 5.1 Create a dataframe with sum, and count for the people who died or survived
Tit_summary_surv <- __ __ # Use Titanic_clean as a baseline
group_by(__) __ # Group by Class
__(survived=__(__), # using 'sum()' on survived create a summary on how many survived (using summarise)
Nb_people=__) %>% # using 'n()' count the number of people who were on the boat
__(Perc_died = __*(__-__)/__) # Generate a percentage of those who died
# 5.2 Create a dataframe to check whether there is a difference in percentage of death for sex (copy and alter the code above)
# 5.3 Create a dataframe to check whether there is a difference in percentage of death for both sex and class
# Copy and alter the code above, use group_by both sex and class
# 5.4 Create a dataframe which looks at summary for age groups on top of class and sex
# Create categorical variables for Child and Adult
# e.g. Child < 18 and Adult > 18
# Create a summary table to check whether survival in children is more likely than in adults
# Create a new summary table with summary statstics for all Class, Sex and Age