R Notebook

AIN MARDHIAH BINTI ABDUL HAMID (SD23013) NUR SAFIYYAH BINTI SULAIMAN (SD23049)

1. BARPLOT interpretation:

• MAY; Has the highest number of observations with 32
• the rest have similar number along 30 until 31
• the data is well balanced, as the number of observations across all month is fairly consistent

#data()
dt <- airquality
head(dt)

names(dt)

[1] "Ozone"   "Solar.R" "Wind"    "Temp"    "Month"   "Day"    

str(dt)

'data.frame':   153 obs. of  6 variables:
 $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
 $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
 $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
 $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
 $ Month  : int  5 5 5 5 5 5 5 5 5 5 ...
 $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

library(psych)

Attaching package: ‘psych’

The following object is masked _by_ ‘.GlobalEnv’:

    distance

describe(dt)

t1 <- table(dt$Month)
barplot(t1, ylim=c(0,35), ylab="Number of Observation", xlab="Month",
        main="Frequency of Observations by Month",col=c("lightblue","lightgreen","lightcoral","lightpink","plum"))
box()

(2)BOXPLOT - Temperatures increase steadily from May to July aligning with the seasonal pattern peaking in July and August which is summer season (85 degree Fahrenheit) - May(65 degree Fahrenheit) and September (drop to 78 degree Fahrenheit) show cooler temperatures.

dt <- airquality

t2 <- factor(dt$Month, labels = c("May","June","July","Aug","Sept"))
boxplot(dt$Temp ~ dt$Month, ylab = "Temperature", xlab= "Month", main = "Temperature Distribution by Month", col=c("palevioletred","plum","skyblue","khaki", "peachpuff"))

(3)PIE CHART -May ,August and July each account for 20.3% of the measurements, indicating equal data contributions for these months.
-June and September each contribute 19.6%, which is slightly less than May and August. -The distribution seems fairly uniform, with minor variations.

dt<-airquality
section_labels <- round(prop.table(table(dt$Month))*100, 1)
section_labels <- paste(section_labels,"%", sep="")
par(mar = c(2.5, 2.5, 2.5, 2.5))   
pie(table(dt$Month), col = c("burlywood", "peru","wheat","moccasin","tan"), main="Percentange Days by Month", labels=section_labels,radius=1)
legend(1.5, 0.5, c("May","June","July","Aug","Sept"), cex=0.8,
fill=c("burlywood", "peru","wheat","moccasin","tan"))

(4)HISTOGRAM -The density curve shows a uni modal distribution, with a peak around 7–10 on the X-axis. -The data distribution appears slightly right-skewed, with a longer tail extending toward higher X-axis values (e.g., 15–20). -The most frequent values are concentrated in the middle range

dt <- airquality

hist(
  dt$Temp,
  xlab = "Month", 
  ylab = "Wind", 
  main = "Histogram of Temperature by Month", 
  col = "mistyrose", 
  breaks = 10,  # Adjust number of bins if needed
  freq = FALSE # Set to FALSE to overlay density
)
# Calculate density
dx <- density(dt$Temp, na.rm = TRUE) 
# Add density curve
lines(dx, lwd = 2, col="deeppink")