Assignment 1

For Assignment 1, I created a dataframe on flight schedules we usually see at the airport.

1. Creating a dataframe

Flight <- c(102,255,329,180,321,216,114,260)
Destination <- c("Paris", "Tokyo", "Sydney", "London", "Beijing", "Singapore", "Delhi","New York")
Departure <- c("09:00","11:45","10:30", "14:30", "16:00", "21:15", "22:45", "16:30")
capacity <- c(165,248,248,248,440,165,440,248)
Status <- c("Delayed","Cancelled","Boarding", "Boarding", "On Time", "On Time", "Cancelled", "Delayed")

df <- data.frame(Flight,Destination,Departure,capacity,Status)
df

##   Flight Destination Departure capacity    Status
## 1    102       Paris     09:00      165   Delayed
## 2    255       Tokyo     11:45      248 Cancelled
## 3    329      Sydney     10:30      248  Boarding
## 4    180      London     14:30      248  Boarding
## 5    321     Beijing     16:00      440   On Time
## 6    216   Singapore     21:15      165   On Time
## 7    114       Delhi     22:45      440 Cancelled
## 8    260    New York     16:30      248   Delayed

2. Exploring dataframe

a. Components of the dataframe

str(df)

## 'data.frame':    8 obs. of  5 variables:
##  $ Flight     : num  102 255 329 180 321 216 114 260
##  $ Destination: Factor w/ 8 levels "Beijing","Delhi",..: 5 8 7 3 1 6 2 4
##  $ Departure  : Factor w/ 8 levels "09:00","10:30",..: 1 3 2 4 5 7 8 6
##  $ capacity   : num  165 248 248 248 440 165 440 248
##  $ Status     : Factor w/ 4 levels "Boarding","Cancelled",..: 3 2 1 1 4 4 2 3

class(df)

## [1] "data.frame"

typeof(df)

## [1] "list"

b. Columns names, number of columns and number of rows.

names(df)

## [1] "Flight"      "Destination" "Departure"   "capacity"    "Status"

nrow(df)

## [1] 8

ncol(df)

## [1] 5

length(df)

## [1] 5

c. Assign rows name.

row.names(df)<-c(1,2,3,4,5,6,7,8)
df

##   Flight Destination Departure capacity    Status
## 1    102       Paris     09:00      165   Delayed
## 2    255       Tokyo     11:45      248 Cancelled
## 3    329      Sydney     10:30      248  Boarding
## 4    180      London     14:30      248  Boarding
## 5    321     Beijing     16:00      440   On Time
## 6    216   Singapore     21:15      165   On Time
## 7    114       Delhi     22:45      440 Cancelled
## 8    260    New York     16:30      248   Delayed

d. Return the first 6 rows of the dataframe

head(df)

##   Flight Destination Departure capacity    Status
## 1    102       Paris     09:00      165   Delayed
## 2    255       Tokyo     11:45      248 Cancelled
## 3    329      Sydney     10:30      248  Boarding
## 4    180      London     14:30      248  Boarding
## 5    321     Beijing     16:00      440   On Time
## 6    216   Singapore     21:15      165   On Time

e. Return the last 6 rows of the dataframe

tail(df)

##   Flight Destination Departure capacity    Status
## 3    329      Sydney     10:30      248  Boarding
## 4    180      London     14:30      248  Boarding
## 5    321     Beijing     16:00      440   On Time
## 6    216   Singapore     21:15      165   On Time
## 7    114       Delhi     22:45      440 Cancelled
## 8    260    New York     16:30      248   Delayed

f. Accessing the column by its name

df["Destination"]

##   Destination
## 1       Paris
## 2       Tokyo
## 3      Sydney
## 4      London
## 5     Beijing
## 6   Singapore
## 7       Delhi
## 8    New York

df$Destination

## [1] Paris     Tokyo     Sydney    London    Beijing   Singapore Delhi    
## [8] New York 
## Levels: Beijing Delhi London New York Paris Singapore Sydney Tokyo

df[[2]]

## [1] Paris     Tokyo     Sydney    London    Beijing   Singapore Delhi    
## [8] New York 
## Levels: Beijing Delhi London New York Paris Singapore Sydney Tokyo

g. Accessing an element in the column

df[["Status"]][3]

## [1] Boarding
## Levels: Boarding Cancelled Delayed On Time

df$Status[3]

## [1] Boarding
## Levels: Boarding Cancelled Delayed On Time

df[4,2]

## [1] London
## Levels: Beijing Delhi London New York Paris Singapore Sydney Tokyo

h. Getting the 5th row

df[5,]

##   Flight Destination Departure capacity  Status
## 5    321     Beijing     16:00      440 On Time

i. Getting the 4th column

df1<-df[,4]
df1

## [1] 165 248 248 248 440 165 440 248

j. Type of the data

class(df1)

## [1] "numeric"

k. As the result return as vector, to change it back to dataframe use (drop=FALSE)

df2<-df[,4, drop=FALSE]
df2

##   capacity
## 1      165
## 2      248
## 3      248
## 4      248
## 5      440
## 6      165
## 7      440
## 8      248

class(df2)

## [1] "data.frame"

3. Modifying dataframe

a. Change the 2nd row capacity (248 -> 440)

df[2,"capacity"]<- 440; df

##   Flight Destination Departure capacity    Status
## 1    102       Paris     09:00      165   Delayed
## 2    255       Tokyo     11:45      440 Cancelled
## 3    329      Sydney     10:30      248  Boarding
## 4    180      London     14:30      248  Boarding
## 5    321     Beijing     16:00      440   On Time
## 6    216   Singapore     21:15      165   On Time
## 7    114       Delhi     22:45      440 Cancelled
## 8    260    New York     16:30      248   Delayed

b. Chnage the column name (capacity -> Capacity)

names(df)[names(df)=='capacity']<-'Capacity'
df

##   Flight Destination Departure Capacity    Status
## 1    102       Paris     09:00      165   Delayed
## 2    255       Tokyo     11:45      440 Cancelled
## 3    329      Sydney     10:30      248  Boarding
## 4    180      London     14:30      248  Boarding
## 5    321     Beijing     16:00      440   On Time
## 6    216   Singapore     21:15      165   On Time
## 7    114       Delhi     22:45      440 Cancelled
## 8    260    New York     16:30      248   Delayed

c. Adding new variable (column)

df4<-cbind(df,Gate=c(3,12,4,15,7,9,27,30))
df4

##   Flight Destination Departure Capacity    Status Gate
## 1    102       Paris     09:00      165   Delayed    3
## 2    255       Tokyo     11:45      440 Cancelled   12
## 3    329      Sydney     10:30      248  Boarding    4
## 4    180      London     14:30      248  Boarding   15
## 5    321     Beijing     16:00      440   On Time    7
## 6    216   Singapore     21:15      165   On Time    9
## 7    114       Delhi     22:45      440 Cancelled   27
## 8    260    New York     16:30      248   Delayed   30

d. Adding new observation (row)

bangkok<-data.frame(Flight=349, Destination="Bangkok", Departure="22.00", Capacity=165, Status="On Time", Gate=11)

rbind(df4, bangkok)

##    Flight Destination Departure Capacity    Status Gate
## 1     102       Paris     09:00      165   Delayed    3
## 2     255       Tokyo     11:45      440 Cancelled   12
## 3     329      Sydney     10:30      248  Boarding    4
## 4     180      London     14:30      248  Boarding   15
## 5     321     Beijing     16:00      440   On Time    7
## 6     216   Singapore     21:15      165   On Time    9
## 7     114       Delhi     22:45      440 Cancelled   27
## 8     260    New York     16:30      248   Delayed   30
## 11    349     Bangkok     22.00      165   On Time   11

e. Retrieve the components information (mean, median, quatiles)

summary(df4)

##      Flight         Destination   Departure    Capacity           Status 
##  Min.   :102.0   Beijing  :1    09:00  :1   Min.   :165.0   Boarding :2  
##  1st Qu.:163.5   Delhi    :1    10:30  :1   1st Qu.:227.2   Cancelled:2  
##  Median :235.5   London   :1    11:45  :1   Median :248.0   Delayed  :2  
##  Mean   :222.1   New York :1    14:30  :1   Mean   :299.2   On Time  :2  
##  3rd Qu.:275.2   Paris    :1    16:00  :1   3rd Qu.:440.0                
##  Max.   :329.0   Singapore:1    16:30  :1   Max.   :440.0                
##                  (Other)  :2    (Other):2                                
##       Gate      
##  Min.   : 3.00  
##  1st Qu.: 6.25  
##  Median :10.50  
##  Mean   :13.38  
##  3rd Qu.:18.00  
##  Max.   :30.00  
##

f. Sorting data in ascending order (Column:Gate)

df4[order(df4$Gate, decreasing=FALSE),]

##   Flight Destination Departure Capacity    Status Gate
## 1    102       Paris     09:00      165   Delayed    3
## 3    329      Sydney     10:30      248  Boarding    4
## 5    321     Beijing     16:00      440   On Time    7
## 6    216   Singapore     21:15      165   On Time    9
## 2    255       Tokyo     11:45      440 Cancelled   12
## 4    180      London     14:30      248  Boarding   15
## 7    114       Delhi     22:45      440 Cancelled   27
## 8    260    New York     16:30      248   Delayed   30

g. Removing component in dataframe (Remove: Capacity)

df4$Capacity <- NULL
df4

##   Flight Destination Departure    Status Gate
## 1    102       Paris     09:00   Delayed    3
## 2    255       Tokyo     11:45 Cancelled   12
## 3    329      Sydney     10:30  Boarding    4
## 4    180      London     14:30  Boarding   15
## 5    321     Beijing     16:00   On Time    7
## 6    216   Singapore     21:15   On Time    9
## 7    114       Delhi     22:45 Cancelled   27
## 8    260    New York     16:30   Delayed   30

h. Removing a row (3rd row)

df4 <-df4[-3,]
df4

##   Flight Destination Departure    Status Gate
## 1    102       Paris     09:00   Delayed    3
## 2    255       Tokyo     11:45 Cancelled   12
## 4    180      London     14:30  Boarding   15
## 5    321     Beijing     16:00   On Time    7
## 6    216   Singapore     21:15   On Time    9
## 7    114       Delhi     22:45 Cancelled   27
## 8    260    New York     16:30   Delayed   30

Assignment 1

Sarah Nur Syaza Binti Mohd Yunos (17199299)

11/11/2020

1. Creating a dataframe

2. Exploring dataframe

a. Components of the dataframe

b. Columns names, number of columns and number of rows.

c. Assign rows name.

d. Return the first 6 rows of the dataframe

e. Return the last 6 rows of the dataframe

f. Accessing the column by its name

g. Accessing an element in the column

h. Getting the 5th row

i. Getting the 4th column

j. Type of the data

k. As the result return as vector, to change it back to dataframe use (drop=FALSE)

3. Modifying dataframe

a. Change the 2nd row capacity (248 -> 440)

b. Chnage the column name (capacity -> Capacity)

c. Adding new variable (column)

d. Adding new observation (row)

e. Retrieve the components information (mean, median, quatiles)

f. Sorting data in ascending order (Column:Gate)

g. Removing component in dataframe (Remove: Capacity)

h. Removing a row (3rd row)

Thank you