Assignment 1

Assignment 1

Setup

#Importing the necessary packages
library(readr) 

package <U+393C><U+3E31>readr<U+393C><U+3E32> was built under R version 3.5.3

library(knitr) 

package <U+393C><U+3E31>knitr<U+393C><U+3E32> was built under R version 3.5.3

library(readxl)

package <U+393C><U+3E31>readxl<U+393C><U+3E32> was built under R version 3.5.3

library(zoo)

package <U+393C><U+3E31>zoo<U+393C><U+3E32> was built under R version 3.5.3
Attaching package: <U+393C><U+3E31>zoo<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    as.Date, as.Date.numeric

Data Description

The data being analysed in this assignment is the average montly temperatures in Malaysia between the years 1991 and 2016.

Source: https://climateknowledgeportal.worldbank.org/download-data

Read/Import Data

# 1. Importing the data from the working directory.
temp <- read_csv("tas_1991_2016_MYS.csv")

Parsed with column specification:
cols(
  `Temperature - (Celsius)` = col_double(),
  Year = col_double(),
  Month = col_character(),
  Country = col_character(),
  ISO3 = col_character()
)

# 2. Viewing the data and column names.
head(temp)

# 3. Converting months from mmm format to numbers.
temp$Month <- match(temp$Month, month.abb)
# 4. Combining the month and year columns into a new YearMonth column
temp$MonthYear <- as.yearmon(paste(temp$Year, " ", temp$Month), "%Y %m")
# 5. Removing unnecessary columns. 
temp <- temp[-(2:5)]
# 6. Rearranging the columns for presentation. 
temp <- temp[,c(2, 1)]
# 7. Reviewing data.
head(temp)

# 8. Saving data as a data frame. 
temp1 <- data.frame(MonthYear = temp$MonthYear, Temperature = temp$`Temperature - (Celsius)`)
saveRDS(temp1, file = "temp1.RData")

Inspect and Understand

# Checking the structure of the data
str(temp1)

'data.frame':   312 obs. of  2 variables:
 $ MonthYear  : 'yearmon' num  Jan 1991 Feb 1991 Mar 1991 Apr 1991 ...
 $ Temperature: num  25.4 25.3 25.8 26 26.1 ...

#Checking the classes of the variables
class(temp1$MonthYear)

[1] "yearmon"

typeof(temp1$MonthYear)

[1] "double"

class(temp1$Temperature)

[1] "numeric"

typeof(temp1$Temperature)

[1] "double"

Subsetting I

After subsetting the first 10 observations and converting them to a matrix, the data ends up being a matrix with a list of 2. This is due to there being only 2 variables in the original dataset.

#Subsetting first 10 observations
temp2 <- temp1[(1:10),]
head(temp2)

#Converting subsetted data into a matrix
tempmatrix <- matrix(temp2)
#Checking structure of the matrix
str(tempmatrix)

List of 2
 $ : num [1:10] 1991 1991 1991 1991 1991 ...
 $ : num [1:10] 25.4 25.3 25.8 26 26.1 ...
 - attr(*, "dim")= int [1:2] 2 1

Subsetting II

# Subsetting the first and last variable of the dataset
temp3 <- temp2[, 1:2]
head(temp3)

#Saving the dataset as RData
save(temp3, file = "temp3.RData")

Create a new Data Frame

# Creating new variables and observations
G <- factor( c("Very Fast", "Fast", "Normal", "Slow"), levels = c("Very Fast", "Fast", "Normal", "Slow") ,ordered=TRUE)
  
M <- c(120, 105, 80, 30) 
#Converting variables and observations into a data frame
data <- data.frame( Speed = M, Level = G)
head(data)

#View structure of data and levels of ordinal variable
str(data)

'data.frame':   4 obs. of  2 variables:
 $ Speed: num  120 105 80 30
 $ Level: Ord.factor w/ 4 levels "Very Fast"<"Fast"<..: 1 2 3 4

levels(data$Level)

[1] "Very Fast" "Fast"      "Normal"    "Slow"     

#Creating new numeric vector
num <- c(7,15, 4, 9)
#Adding numeric vector to the data frame
data1 <- cbind(data, num)
head(data1)

#Checking structure and attributes of new data frame
str(data1)

'data.frame':   4 obs. of  3 variables:
 $ Speed: num  120 105 80 30
 $ Level: Ord.factor w/ 4 levels "Very Fast"<"Fast"<..: 1 2 3 4
 $ num  : num  7 15 4 9

class(data1$Speed)

[1] "numeric"

class(data1$Level)

[1] "ordered" "factor" 

class(data1$num)

[1] "numeric"