# Install and load package
install.packages("titanic")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)library(titanic)
# Load dataset
titanic <- titanic_train
# Display first rows
head(titanic)## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## 6 6 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## 6 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 S
## 6 330877 8.4583 Q# Dataset dimensions
dim(titanic)## [1] 891 12# Column names
colnames(titanic)## [1] "PassengerId" "Survived" "Pclass" "Name" "Sex"
## [6] "Age" "SibSp" "Parch" "Ticket" "Fare"
## [11] "Cabin" "Embarked"# Split into two datasets
titanic_personal <- titanic[, c("PassengerId", "Sex", "Age", "Name")]
titanic_travel <- titanic[, c("PassengerId", "Sex",
"Pclass", "Fare",
"Survived")]
# Merge by 1 variable
merged_1var <- merge(
titanic_personal,
titanic_travel,
by = "PassengerId"
)
head(merged_1var)## PassengerId Sex.x Age Name
## 1 1 male 22 Braund, Mr. Owen Harris
## 2 2 female 38 Cumings, Mrs. John Bradley (Florence Briggs Thayer)
## 3 3 female 26 Heikkinen, Miss. Laina
## 4 4 female 35 Futrelle, Mrs. Jacques Heath (Lily May Peel)
## 5 5 male 35 Allen, Mr. William Henry
## 6 6 male NA Moran, Mr. James
## Sex.y Pclass Fare Survived
## 1 male 3 7.2500 0
## 2 female 1 71.2833 1
## 3 female 3 7.9250 1
## 4 female 1 53.1000 1
## 5 male 3 8.0500 0
## 6 male 3 8.4583 0# Merge by 2 variables
merged_2var <- merge(
titanic_personal,
titanic_travel,
by = c("PassengerId", "Sex")
)
head(merged_2var)## PassengerId Sex Age Name Pclass Fare
## 1 1 male 22 Braund, Mr. Owen Harris 3 7.2500
## 2 10 female 14 Nasser, Mrs. Nicholas (Adele Achem) 2 30.0708
## 3 100 male 34 Kantor, Mr. Sinai 2 26.0000
## 4 101 female 28 Petranec, Miss. Matilda 3 7.8958
## 5 102 male NA Petroff, Mr. Pastcho ("Pentcho") 3 7.8958
## 6 103 male 21 White, Mr. Richard Frasar 1 77.2875
## Survived
## 1 0
## 2 1
## 3 0
## 4 0
## 5 0
## 6 0# Add Pclass to personal dataset
titanic_personal <- titanic[, c("PassengerId", "Sex",
"Pclass", "Age", "Name")]
# Merge by 3 variables
merged_3var <- merge(
titanic_personal,
titanic_travel,
by = c("PassengerId", "Sex", "Pclass")
)
head(merged_3var)## PassengerId Sex Pclass Age Name Fare
## 1 1 male 3 22 Braund, Mr. Owen Harris 7.2500
## 2 10 female 2 14 Nasser, Mrs. Nicholas (Adele Achem) 30.0708
## 3 100 male 2 34 Kantor, Mr. Sinai 26.0000
## 4 101 female 3 28 Petranec, Miss. Matilda 7.8958
## 5 102 male 3 NA Petroff, Mr. Pastcho ("Pentcho") 7.8958
## 6 103 male 1 21 White, Mr. Richard Frasar 77.2875
## Survived
## 1 0
## 2 1
## 3 0
## 4 0
## 5 0
## 6 0$ and %>%and
additional dplyr Functions# Load dplyr
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Using $
mean(titanic$Age, na.rm = TRUE)## [1] 29.69912table(titanic$Sex)##
## female male
## 314 577# Using %>%
titanic %>%
group_by(Pclass, Sex) %>%
summarise(
avg_age = mean(Age, na.rm = TRUE),
avg_fare = mean(Fare, na.rm = TRUE),
survival_rate = mean(Survived, na.rm = TRUE),
total = n()
)## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by Pclass and Sex.
## ℹ Output is grouped by Pclass.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(Pclass, Sex))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.## # A tibble: 6 × 6
## # Groups: Pclass [3]
## Pclass Sex avg_age avg_fare survival_rate total
## <int> <chr> <dbl> <dbl> <dbl> <int>
## 1 1 female 34.6 106. 0.968 94
## 2 1 male 41.3 67.2 0.369 122
## 3 2 female 28.7 22.0 0.921 76
## 4 2 male 30.7 19.7 0.157 108
## 5 3 female 21.8 16.1 0.5 144
## 6 3 male 26.5 12.7 0.135 347# filter() — keep only rows that meet a condition
titanic %>%
filter(Sex == "female" & Age > 18) %>%
head(10)## PassengerId Survived Pclass
## 1 2 1 1
## 2 3 1 3
## 3 4 1 1
## 4 9 1 3
## 5 12 1 1
## 6 16 1 2
## 7 19 0 3
## 8 26 1 3
## 9 41 0 3
## 10 42 0 2
## Name Sex Age SibSp
## 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1
## 2 Heikkinen, Miss. Laina female 26 0
## 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1
## 4 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0
## 5 Bonnell, Miss. Elizabeth female 58 0
## 6 Hewlett, Mrs. (Mary D Kingcome) female 55 0
## 7 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31 1
## 8 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38 1
## 9 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40 1
## 10 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27 1
## Parch Ticket Fare Cabin Embarked
## 1 0 PC 17599 71.2833 C85 C
## 2 0 STON/O2. 3101282 7.9250 S
## 3 0 113803 53.1000 C123 S
## 4 2 347742 11.1333 S
## 5 0 113783 26.5500 C103 S
## 6 0 248706 16.0000 S
## 7 0 345763 18.0000 S
## 8 5 347077 31.3875 S
## 9 0 7546 9.4750 S
## 10 0 11668 21.0000 S# arrange() — sort rows
titanic %>%
arrange(desc(Fare)) %>%
head(5)## PassengerId Survived Pclass Name Sex Age
## 1 259 1 1 Ward, Miss. Anna female 35
## 2 680 1 1 Cardeza, Mr. Thomas Drake Martinez male 36
## 3 738 1 1 Lesurer, Mr. Gustave J male 35
## 4 28 0 1 Fortune, Mr. Charles Alexander male 19
## 5 89 1 1 Fortune, Miss. Mabel Helen female 23
## SibSp Parch Ticket Fare Cabin Embarked
## 1 0 0 PC 17755 512.3292 C
## 2 0 1 PC 17755 512.3292 B51 B53 B55 C
## 3 0 0 PC 17755 512.3292 B101 C
## 4 3 2 19950 263.0000 C23 C25 C27 S
## 5 3 2 19950 263.0000 C23 C25 C27 S# rename() — rename columns
titanic %>%
rename(
Gender = Sex,
TicketClass = Pclass
) %>%
head(5)## PassengerId Survived TicketClass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## Name Gender Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 S# mutate() — create a new column
titanic %>%
mutate(FarePerAge = Fare / Age) %>%
head(5)## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## Ticket Fare Cabin Embarked FarePerAge
## 1 A/5 21171 7.2500 S 0.3295455
## 2 PC 17599 71.2833 C85 C 1.8758763
## 3 STON/O2. 3101282 7.9250 S 0.3048077
## 4 113803 53.1000 C123 S 1.5171429
## 5 373450 8.0500 S 0.2300000# Define a function that takes a dataset and column name as parameters
# It automatically detects if the column is numeric or categorical
column_summary <- function(data, col_name) {
x <- data[[col_name]]
# If the column is numeric, calculate statistical summaries
if (is.numeric(x)) {
cat("\nSummary for", col_name, "\n")
cat("Mean:", mean(x, na.rm = TRUE), "\n")
cat("Median:", median(x, na.rm = TRUE), "\n")
cat("Minimum:", min(x, na.rm = TRUE), "\n")
cat("Maximum:", max(x, na.rm = TRUE), "\n")
cat("Standard Deviation:", sd(x, na.rm = TRUE), "\n")
} else {
# If the column is categorical, show a frequency table
cat("\nFrequency table for", col_name, "\n")
print(table(x))
}
}
# Numeric columns — returns statistical summary
column_summary(titanic, "Age")##
## Summary for Age
## Mean: 29.69912
## Median: 28
## Minimum: 0.42
## Maximum: 80
## Standard Deviation: 14.5265column_summary(titanic, "Fare")##
## Summary for Fare
## Mean: 32.20421
## Median: 14.4542
## Minimum: 0
## Maximum: 512.3292
## Standard Deviation: 49.69343column_summary(titanic, "Survived")##
## Summary for Survived
## Mean: 0.3838384
## Median: 0
## Minimum: 0
## Maximum: 1
## Standard Deviation: 0.4865925# Categorical columns — returns frequency table
column_summary(titanic, "Sex")##
## Frequency table for Sex
## x
## female male
## 314 577column_summary(titanic, "Pclass")##
## Summary for Pclass
## Mean: 2.308642
## Median: 3
## Minimum: 1
## Maximum: 3
## Standard Deviation: 0.8360712column_summary(titanic, "Embarked")##
## Frequency table for Embarked
## x
## C Q S
## 2 168 77 644# Extract numeric columns
x_numeric <- titanic[, sapply(titanic, is.numeric)]
# lapply() — returns a list
lapply(
x_numeric,
mean,
na.rm = TRUE
)## $PassengerId
## [1] 446
##
## $Survived
## [1] 0.3838384
##
## $Pclass
## [1] 2.308642
##
## $Age
## [1] 29.69912
##
## $SibSp
## [1] 0.5230079
##
## $Parch
## [1] 0.3815937
##
## $Fare
## [1] 32.20421# sapply() — returns a named vector
sapply(
x_numeric,
mean,
na.rm = TRUE
)## PassengerId Survived Pclass Age SibSp Parch
## 446.0000000 0.3838384 2.3086420 29.6991176 0.5230079 0.3815937
## Fare
## 32.2042080# vapply() — safer and strict return type
vapply(
x_numeric,
mean,
numeric(1),
na.rm = TRUE
)## PassengerId Survived Pclass Age SibSp Parch
## 446.0000000 0.3838384 2.3086420 29.6991176 0.5230079 0.3815937
## Fare
## 32.2042080# tapply() — average fare grouped by passenger class
tapply(
titanic$Fare,
titanic$Pclass,
mean,
na.rm = TRUE
)## 1 2 3
## 84.15469 20.66218 13.67555# mapply() — apply function over two columns
mapply(
function(x, y) x + y,
titanic$Age,
titanic$Fare
)## [1] 29.2500 109.2833 33.9250 88.1000 43.0500 NA 105.8625 23.0750
## [9] 38.1333 44.0708 20.7000 84.5500 28.0500 70.2750 21.8542 71.0000
## [17] 31.1250 NA 49.0000 NA 61.0000 47.0000 23.0292 63.5000
## [25] 29.0750 69.3875 NA 282.0000 NA NA 67.7208 NA
## [33] NA 76.5000 110.1708 94.0000 NA 29.0500 36.0000 25.2417
## [41] 49.4750 48.0000 NA 44.5792 26.8792 NA NA NA
## [49] NA 35.8000 46.6875 28.8000 125.7292 55.0000 126.9792 NA
## [57] 31.5000 35.7292 32.7500 57.9000 29.2292 118.0000 128.4750 31.9000
## [65] NA NA 39.5000 27.1583 24.9250 34.6625 42.5000 62.9000
## [73] 94.5000 40.4542 88.4958 32.6500 NA NA 29.8300 42.4750
## [81] 31.0000 38.5000 NA 75.1000 27.5000 48.8500 50.3750 NA
## [89] 286.0000 32.0500 37.0500 27.8542 107.1750 46.5750 66.2500 NA
## [97] 105.6542 86.3583 57.0000 60.0000 35.8958 NA 98.2875 41.6542
## [105] 44.9250 35.8958 28.6500 NA 45.8958 NA 99.0000 28.9542
## [113] 30.0500 29.8250 31.4583 28.9250 78.2500 50.0000 271.5208 33.2750
## [121] 94.5000 NA 62.5708 45.5000 131.2875 23.2417 NA 31.1417
## [129] NA 51.9750 40.8958 27.0500 61.5000 55.0000 38.0000 38.0458
## [137] 45.2833 90.1000 25.2167 103.2000 NA 29.7500 39.8500 25.7500
## [145] 29.5000 55.7500 34.7958 43.3750 62.5000 55.0000 63.5250 88.6000
## [153] 63.5500 55.0000 NA 112.3792 23.7333 38.0500 NA NA
## [161] 60.1000 55.7500 33.7750 25.6625 40.6875 29.5250 NA 72.9000
## [169] NA 84.4958 94.5000 33.1250 12.1333 28.9250 86.6958 25.8542
## [177] NA 78.7125 43.0000 36.0000 NA NA 40.3875 40.0000
## [185] 26.0250 NA NA 71.5500 55.5000 43.8958 45.0000 32.0000
## [193] 26.8542 29.0000 71.7208 204.5208 NA 50.4042 NA 37.0000
## [201] 37.5000 NA 40.4958 52.7250 26.0500 12.4625 47.8500 44.7875
## [209] 23.7500 71.0000 31.0500 56.0000 29.2500 43.0000 NA 144.2750
## [217] 34.9250 69.0000 108.2917 40.5000 24.0500 40.0000 59.0500 NA
## [225] 128.0000 31.3500 29.5000 27.7500 31.0000 NA 118.4750 36.7750
## [233] 72.5000 36.3875 34.5000 NA 70.0000 34.2500 29.5000 45.2750
## [241] NA NA 39.5000 29.1250 37.2250 134.0000 32.7750 38.5000
## [249] 89.5542 80.0000 NA 39.4625 88.5500 46.1000 61.2125 44.2458
## [257] NA 116.5000 547.3292 76.0000 NA 34.3875 131.6500 40.0000
## [265] NA 46.5000 55.6875 32.7750 211.4625 170.6333 NA 25.0000
## [273] 60.5000 66.7000 NA 140.9583 52.7500 NA 36.1250 55.2500
## [281] 72.7500 35.8542 25.5000 27.0500 NA 41.6625 39.5000 29.8958
## [289] 55.0000 29.7500 104.8500 110.0792 48.8750 32.8500 31.8958 NA
## [297] 30.7292 153.5500 NA 297.5208 NA NA 19.0000 NA
## [305] NA 152.4700 NA 125.9000 54.0000 86.9292 107.1583 280.3750
## [313] 52.0000 35.8958 69.2500 33.8542 50.0000 68.0000 195.8667 174.5000
## [321] 29.2500 34.8958 42.3500 51.0000 NA 171.6333 67.2375 49.0000
## [329] 51.5250 73.9792 NA 74.0000 191.4625 34.0000 NA NA
## [337] 95.6000 175.5000 53.0500 80.5000 28.0000 287.0000 41.0000 38.0000
## [345] 49.0000 37.0000 53.0000 NA 18.9000 50.6625 32.2250 NA
## [353] 22.2292 42.8000 NA 37.5000 77.0000 51.0000 NA NA
## [361] 67.9000 56.7208 59.4542 42.0500 NA 37.2500 135.2500 NA
## [369] NA 93.3000 80.4417 24.4958 27.0500 157.6333 24.0750 NA
## [377] 29.2500 238.5000 24.0125 26.7750 269.5250 16.7417 39.9250 87.0000
## [385] NA 91.5000 47.9000 49.0000 NA 29.0000 156.0000 28.7958
## [393] 35.9250 136.2750 40.7000 29.7958 38.8542 72.0000 33.5000 40.6500
## [401] 46.9250 34.0500 30.8250 43.8500 28.6625 55.0000 58.7500 21.7500
## [409] 28.7750 NA NA NA 123.0000 NA 51.9250 NA
## [417] 66.5000 31.0000 43.0000 34.1500 NA 28.7333 36.8750 42.4000
## [425] 38.2125 NA 54.0000 45.0000 NA 40.0500 54.5500 NA
## [433] 68.0000 24.1250 105.9000 134.0000 55.3750 42.7500 327.0000 41.5000
## [441] 71.2500 29.5000 32.7750 41.0000 NA 85.8583 32.5000 60.5500
## [449] 24.2583 82.5000 63.7500 NA 57.7500 138.1042 NA 36.8958
## [457] 91.5500 NA 60.5000 NA 74.5500 42.0500 85.5000 61.0000
## [465] NA 45.0500 NA 82.5500 NA 20.0083 NA 46.6625
## [473] 60.7500 36.7917 31.8375 NA 55.0000 36.0458 29.5208 14.2875
## [481] 55.9000 NA 58.0500 72.5875 116.0792 NA 125.0000 87.7000
## [489] 38.0500 24.9000 NA 28.2500 85.5000 120.5042 29.0500 NA
## [497] 132.2667 NA 176.5500 31.7958 25.6625 28.7500 NA 46.5875
## [505] 102.5000 126.9000 59.0000 NA 50.5250 82.4958 36.7500 NA
## [513] 62.2875 113.4000 31.4958 81.0208 44.5000 NA 62.0000 39.8958
## [521] 123.5000 29.8958 NA 101.9792 NA 48.2500 60.5000 NA
## [529] 46.9250 34.5000 28.0000 NA 24.2292 NA 38.6625 33.2500
## [537] 71.5500 136.4250 NA 71.5000 107.0000 40.2750 42.2750 58.0000
## [545] 156.4250 90.0000 45.0000 NA 53.5250 44.7500 127.8833 53.0000
## [553] NA 29.2250 29.7750 88.5500 87.6000 NA 118.6500 53.4000
## [561] NA 47.8958 41.5000 NA NA 48.1500 26.8958 50.0750
## [569] NA 39.8542 72.5000 104.4792 62.3875 NA 24.0500 33.5000
## [577] 47.0000 94.9000 NA 39.9250 55.0000 149.8833 80.0000 76.1250
## [585] NA 97.6500 62.0000 139.2000 30.0500 NA 42.1250 130.2667
## [593] 54.2500 NA 63.0000 60.1500 NA 49.0000 NA 105.9292
## [601] 51.0000 NA NA 52.0500 61.5500 51.5500 37.8958 57.5000
## [609] 63.5792 193.4625 70.2750 NA NA NA 43.0500 89.0000
## [617] 48.4000 42.1000 43.0000 36.5000 41.4542 94.5542 35.7417 28.8542
## [625] 37.1000 93.3208 69.3500 98.9583 33.8958 NA 110.0000 58.0542
## [633] 62.5000 NA 36.9000 41.0000 39.9250 57.2500 80.6875 NA
## [641] 27.8542 93.3000 29.9000 NA 20.0083 124.7292 26.8958 91.5000
## [649] NA 30.5500 NA 41.0000 29.4333 NA 24.7500 97.5000
## [657] NA 47.5000 36.0000 171.2750 183.6500 47.2250 72.5875 43.4958
## [665] 27.9250 105.5000 38.0000 NA 51.0500 NA 79.0000 83.0000
## [673] 80.5000 44.0000 NA 25.7750 32.5500 27.8417 89.9000 548.3292
## [681] NA 103.7292 29.2250 60.9000 99.0000 66.5792 53.6875 29.1708
## [689] 25.7958 226.3375 88.0000 17.4167 NA 32.2250 86.5500 65.5000
## [697] 52.0500 NA 159.8833 49.6500 245.5250 61.2875 32.4542 32.7417
## [705] 33.8542 65.0000 58.5000 68.2875 173.5500 NA 73.5042 NA
## [713] 100.0000 38.4833 65.0000 26.6500 265.5250 37.5000 NA 40.7750
## [721] 39.0000 24.0542 47.0000 63.0000 80.1000 28.6625 51.0000 NA
## [729] 51.0000 32.9250 240.3375 29.7875 NA 36.0000 36.0000 44.6000
## [737] 82.3750 547.3292 NA NA NA 114.8500 283.3750 40.1000
## [745] 38.9250 141.0000 36.2500 43.0000 72.1000 38.7500 27.0000 18.4750
## [753] 42.5000 30.8958 113.0000 15.1700 35.7958 29.5000 42.0500 119.5000
## [761] NA 48.1250 27.2292 156.0000 23.7750 128.9583 NA 38.2500
## [769] NA 40.3625 33.5000 55.8542 67.5000 NA 77.0000 25.7500
## [777] NA 17.4750 NA 254.3375 20.2292 74.0000 59.0000 NA
## [785] 32.0500 32.2500 25.4958 37.1250 21.5750 125.2000 NA 42.0000
## [793] NA NA 32.8958 52.0000 74.9292 39.6833 37.2292 54.1500
## [801] 47.0000 57.2500 131.0000 8.9367 33.9750 38.7750 39.0000 25.7750
## [809] 52.0000 86.1000 33.8875 63.1500 45.5000 37.2750 38.5500 NA
## [817] 30.9250 68.0042 49.4500 37.9000 145.5000 35.6625 38.0000 39.4750
## [825] 41.6875 NA NA 38.0042 NA 142.0000 29.4542 19.5800
## [833] NA 30.8542 26.3000 122.1583 29.6625 NA 88.4958 NA
## [841] 27.9250 26.5000 61.0000 40.9375 25.6625 49.5500 NA 42.8958
## [849] 61.0000 NA 35.2750 81.7750 24.2458 55.4000 70.0000 27.3500
## [857] 209.8667 77.5500 43.2583 NA 55.1083 32.5000 73.9292 NA
## [865] 37.0000 55.0000 40.8583 81.4958 NA 15.1333 33.8958 99.5542
## [873] 38.0000 56.0000 52.0000 22.2250 29.8458 26.8958 NA 139.1583
## [881] 51.0000 40.8958 32.5167 38.5000 32.0500 68.1250 40.0000 49.0000
## [889] NA 56.0000 39.7500# Define a function that calculates fare per age
fare_per_age <- function(fare, age) {
if (age == 0)
stop("Age cannot be zero!")
result <- fare / age
return(result)
}
# trace() monitors every time the function is called
trace(
fare_per_age,
quote(
cat(
"fare_per_age() called with fare =",
fare,
"and age =",
age,
"\n"
)
)
)## [1] "fare_per_age"# Call function normally
fare_per_age(100, 25)## Tracing fare_per_age(100, 25) on entry
## fare_per_age() called with fare = 100 and age = 25## [1] 4# Set recover() as the error handler
options(error = recover)
# Trigger an error because age = 0
try(fare_per_age(50, 0))## Tracing fare_per_age(50, 0) on entry
## fare_per_age() called with fare = 50 and age = 0
## Error in fare_per_age(50, 0) : Age cannot be zero!# Remove trace and reset error handling
untrace(fare_per_age)
options(error = NULL)This homework demonstrated how to: