Outline


    01. dplyr, tbl

    02. select, mutate

    03. filter, arrange

    04. summarise, %>%

    05. group_by


    Packages


    library("dplyr")
library("tidyr")
library("stringr")

    dplyr and tibble


    • Data:
    Titanic = read.csv("Train.csv")


    • Object class:
    class(Titanic)
    [1] "data.frame"


    • as tibble:
    Titanic = as_tibble(Titanic)


    • Data structure:
    glimpse(Titanic, width = 50)
    Observations: 891
Variables: 12
$ PassengerId <int> 1, 2, 3, 4, 5, 6, 7, 8, 9...
$ Survived    <int> 0, 1, 1, 1, 0, 0, 0, 0, 1...
$ Pclass      <int> 3, 1, 3, 1, 3, 3, 1, 3, 3...
$ Name        <fct> "Braund, Mr. Owen Harris"...
$ Sex         <fct> male, female, female, fem...
$ Age         <dbl> 22, 38, 26, 35, 35, NA, 5...
$ SibSp       <int> 1, 1, 0, 1, 0, 0, 0, 3, 0...
$ Parch       <int> 0, 0, 0, 0, 0, 0, 0, 1, 2...
$ Ticket      <fct> A/5 21171, PC 17599, STON...
$ Fare        <dbl> 7.2500, 71.2833, 7.9250, ...
$ Cabin       <fct> , C85, , C123, , , E46, ,...
$ Embarked    <fct> S, C, S, S, S, Q, S, S, S...


    • Variable naming:
    # Different variable name
Titanic <- rename(Titanic, # data frame
                  Departure_Point = Embarked) # new name = old name
# Dealing with missing values - dplyr way
Titanic <- mutate(Titanic, Departure_Point = na_if(Departure_Point, ""))
# Different levels
Titanic$Departure_Point <- recode(Titanic2$Departure_Point, 
                                           "C" = "Cherbourg", 
                                           "Q" = "Queenstown", 
                                           "S" = "Southampton")
# Take a look at the data
summary(Titanic$Departure_Point)
                  Cherbourg  Queenstown Southampton        NA's 
          0         168          77         644           2

    select

    Select/rename variables by name


    • Single variable selection:
    head(select(Titanic, Name))


    • Select multiple variables by their order:
    head(select(Titanic, PassengerId:Survived, Name:Age))


    • Select multiple variables based on their wording:
    head(select(Titanic, contains("e")))


    • Select multiple variables - combination of the previous steps
    head(select(Titanic, 1:2, 
                     contains("t")))

    mutate

    Create or transform variables


    • Create a new variable:
    Titanic_2018 = mutate(Titanic, Age_2018 = (Age + (2018-1912)))
Titanic_2018


    • Create more variables at the same time:
    Titanic_02 = mutate(Titanic, Price_Year_Ratio = (Fare / Age), 
                             Family = SibSp + Parch)
Titanic_02


    filter

    Return rows with matching conditions


    • Filter rows based on logical operators:
    filter(Titanic, Age < 18)
    filter(Titanic, Departure_Point %in% c("Southampton", "Queenstown"))


    • Multiple logical filters:
    filter(Titanic, Age < 18, 
                Survived == 1)
    filter(Titanic, Age < 18, 
                Pclass == 1 | Pclass == 2)


    • Filtering a string:
    library(stringr)
filter(Titanic, str_detect(Name, "Rose"))

    arrange

    Arrange rows by variables


    • Arrange (order) survivors by their age. Omit missing values:
    Survived = filter(Titanic, Survived == 1, !is.na(Age))
head(arrange(Survived, Age))


    • Arrange (order) survivors by their age - from the oldest to the youngest. Omit missing values:
    head(arrange(Survived, desc(Age)))


    • Arrange (order) survivors by their age and the port of their embarkement:
    head(arrange(Survived, desc(Age), Departure_Point))

    summarize

    Reduce multiple values down to a single value


    • The lowest ticket price, the oldest passenger:
    summarize(Titanic, Fare_Min = min(Fare), Age_Max = max(Age))


    • The average age of the survivors:
    summarize(filter(Titanic, Survived == 1), Age_Avg = mean(Age, na.rm = TRUE))


    • The average ticket price, its standard deviation, median, min and max:
    Titanic_03 = filter(Titanic, !is.na(Fare))
summarize(Titanic_03, Mean_Fare = mean(Fare), 
                      SD_Fare = sd(Fare), 
                      Median_Fare = median(Fare), 
                      Min_Fare = min(Fare), 
                      Max_Fare = max(Fare))


    %>%

    Control of the code flow


    • The average age of the passengers travelling in the third class and did not survive:
    Titanic %>%
        filter(Pclass == 3, 
               Survived == 0) %>%
        summarize(Age_Died_Avg = mean(Age, na.rm = TRUE))


    • The highest ticket price in the first for passenger by name ‘William’ who survived:
    Titanic %>%
        filter(Pclass == 1,
               str_detect(Name, "William"),
               Survived == 1) %>%
        summarize(Fare_Avg_Will = min(Fare))

    group_by

    Group by one or more variables


    • The average age of the passengers in the third class who did not survive:
    Titanic %>%
          group_by(Sex) %>%
          summarize(Mean_Fare = mean(Fare, na.rm = TRUE), 
                    Count = n(), 
                    Mean_Age = mean(Age, na.rm = TRUE))
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