Assignment 1
Basim Nihal
2025-11-02
library(rvest)
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, unionlibrary(viridis)## Loading required package: viridisLitelibrary(ggrepel)## Loading required package: ggplot2library(readr)##
## Attaching package: 'readr'## The following object is masked from 'package:rvest':
##
## guess_encodinglibrary(ggthemes)
library(ggplot2)Rainfall Analysis
The historical Kerala rainfall data is read, and the focus is narrowed down to the period from 2000 to 2017.
#import the data
df_rain <- read.csv("Kerala-Rainfall-Historical.csv")#Filter the data
df_filtered <- df_rain %>%
filter(YEAR >= 2000 & YEAR <= 2017)#View the new data frame to verify the filter
head(df_filtered)## SUBDIVISION YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
## 1 Kerala 2000 11.7 57.8 21.5 96.3 124.5 633.8 343.2 566.5 195.8 214.2
## 2 Kerala 2001 16.5 28.3 7.0 238.0 238.6 715.3 598.5 361.3 216.8 319.6
## 3 Kerala 2002 4.7 8.7 35.7 117.3 330.8 503.1 318.7 438.2 99.0 511.7
## 4 Kerala 2003 0.7 50.9 82.1 134.4 91.0 566.7 532.0 350.3 93.6 407.0
## 5 Kerala 2004 2.4 8.1 37.9 113.2 610.9 673.4 385.4 417.9 192.8 320.6
## 6 Kerala 2005 19.8 7.0 25.3 205.9 134.8 619.2 832.7 291.0 414.7 240.1
## NOV DEC ANNUAL JF MAM JJAS OND
## 1 78.1 69.1 2412.6 69.5 242.3 1739.4 361.5
## 2 181.0 10.1 2931.1 44.7 483.7 1892.0 510.7
## 3 137.5 2.1 2507.4 13.3 483.7 1359.0 651.3
## 4 76.4 9.7 2394.9 51.6 307.5 1542.6 493.1
## 5 120.7 2.7 2886.1 10.5 762.0 1669.5 444.0
## 6 184.3 56.4 3031.1 26.8 366.0 2157.6 480.7df_filtered## SUBDIVISION YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
## 1 Kerala 2000 11.7 57.8 21.5 96.3 124.5 633.8 343.2 566.5 195.8 214.2
## 2 Kerala 2001 16.5 28.3 7.0 238.0 238.6 715.3 598.5 361.3 216.8 319.6
## 3 Kerala 2002 4.7 8.7 35.7 117.3 330.8 503.1 318.7 438.2 99.0 511.7
## 4 Kerala 2003 0.7 50.9 82.1 134.4 91.0 566.7 532.0 350.3 93.6 407.0
## 5 Kerala 2004 2.4 8.1 37.9 113.2 610.9 673.4 385.4 417.9 192.8 320.6
## 6 Kerala 2005 19.8 7.0 25.3 205.9 134.8 619.2 832.7 291.0 414.7 240.1
## 7 Kerala 2006 8.1 0.5 90.7 65.3 521.2 482.4 804.0 432.6 474.8 376.4
## 8 Kerala 2007 0.5 5.6 7.3 138.5 192.7 705.9 966.3 489.6 526.7 357.2
## 9 Kerala 2008 0.8 30.3 217.2 108.4 81.2 469.9 505.1 349.0 347.0 343.4
## 10 Kerala 2009 3.3 1.5 62.6 69.0 191.6 438.2 924.9 269.3 326.5 205.2
## 11 Kerala 2010 18.6 1.0 31.4 138.9 190.6 667.5 629.0 356.0 275.6 441.4
## 12 Kerala 2011 20.5 45.7 24.1 165.2 124.2 788.5 536.8 492.7 391.2 227.2
## 13 Kerala 2012 7.4 11.0 21.0 171.1 95.3 430.3 362.6 501.6 241.1 187.5
## 14 Kerala 2013 3.9 40.1 49.9 49.3 119.3 1042.7 830.2 369.7 318.6 259.9
## 15 Kerala 2014 4.6 10.3 17.9 95.7 251.0 454.4 677.8 733.9 298.8 355.5
## 16 Kerala 2015 3.1 5.8 50.1 214.1 201.8 563.6 406.0 252.2 292.9 308.1
## 17 Kerala 2016 3.0 16.4 22.4 33.3 258.4 595.7 441.5 231.0 84.1 105.1
## 18 Kerala 2017 12.7 0.3 87.8 52.8 213.3 579.8 378.5 462.6 435.5 228.0
## NOV DEC ANNUAL JF MAM JJAS OND
## 1 78.1 69.1 2412.6 69.5 242.3 1739.4 361.5
## 2 181.0 10.1 2931.1 44.7 483.7 1892.0 510.7
## 3 137.5 2.1 2507.4 13.3 483.7 1359.0 651.3
## 4 76.4 9.7 2394.9 51.6 307.5 1542.6 493.1
## 5 120.7 2.7 2886.1 10.5 762.0 1669.5 444.0
## 6 184.3 56.4 3031.1 26.8 366.0 2157.6 480.7
## 7 162.8 1.8 3420.6 8.6 677.2 2193.8 541.0
## 8 87.4 11.9 3489.6 6.1 338.4 2688.5 456.5
## 9 55.4 17.0 2524.5 31.1 406.7 1670.9 415.7
## 10 274.4 44.2 2810.6 4.8 323.1 1958.9 523.8
## 11 335.1 46.8 3131.8 19.6 360.9 1928.0 823.3
## 12 169.7 49.5 3035.1 66.2 313.5 2209.1 446.3
## 13 112.9 9.4 2151.1 18.3 287.4 1535.6 309.8
## 14 154.9 17.0 3255.4 43.9 218.5 2561.2 431.8
## 15 99.5 47.2 3046.4 14.9 364.5 2164.8 502.1
## 16 223.6 79.4 2600.6 8.9 465.9 1514.7 611.1
## 17 57.9 22.0 1870.9 19.3 314.2 1352.3 185.0
## 18 152.1 61.4 2664.9 13.0 353.9 1856.5 441.5# Display a statistical summary of the data
summary(df_filtered)## SUBDIVISION YEAR JAN FEB
## Length:18 Min. :2000 Min. : 0.500 Min. : 0.30
## Class :character 1st Qu.:2004 1st Qu.: 3.025 1st Qu.: 5.65
## Mode :character Median :2008 Median : 4.650 Median : 9.50
## Mean :2008 Mean : 7.906 Mean :18.29
## 3rd Qu.:2013 3rd Qu.:12.450 3rd Qu.:29.80
## Max. :2017 Max. :20.500 Max. :57.80
## MAR APR MAY JUN
## Min. : 7.00 Min. : 33.30 Min. : 81.2 Min. : 430.3
## 1st Qu.: 21.73 1st Qu.: 75.67 1st Qu.:124.3 1st Qu.: 487.6
## Median : 33.55 Median :115.25 Median :192.2 Median : 587.8
## Mean : 49.55 Mean :122.59 Mean :220.6 Mean : 607.2
## 3rd Qu.: 59.48 3rd Qu.:158.62 3rd Qu.:247.9 3rd Qu.: 671.9
## Max. :217.20 Max. :238.00 Max. :610.9 Max. :1042.7
## JUL AUG SEP OCT
## Min. :318.7 Min. :231.0 Min. : 84.1 Min. :105.1
## 1st Qu.:390.6 1st Qu.:349.3 1st Qu.:201.1 1st Qu.:227.4
## Median :534.4 Median :393.8 Median :295.9 Median :313.9
## Mean :581.8 Mean :409.2 Mean :290.3 Mean :300.4
## 3rd Qu.:772.5 3rd Qu.:482.9 3rd Qu.:380.1 3rd Qu.:356.8
## Max. :966.3 Max. :733.9 Max. :526.7 Max. :511.7
## NOV DEC ANNUAL JF
## Min. : 55.40 Min. : 1.80 Min. :1871 Min. : 4.80
## 1st Qu.: 90.42 1st Qu.: 9.80 1st Qu.:2512 1st Qu.:11.12
## Median :144.80 Median :19.50 Median :2848 Median :18.80
## Mean :147.98 Mean :30.98 Mean :2787 Mean :26.17
## 3rd Qu.:178.18 3rd Qu.:48.92 3rd Qu.:3044 3rd Qu.:40.70
## Max. :335.10 Max. :79.40 Max. :3490 Max. :69.50
## MAM JJAS OND
## Min. :218.5 Min. :1352 Min. :185.0
## 1st Qu.:313.7 1st Qu.:1574 1st Qu.:434.2
## Median :357.4 Median :1874 Median :468.6
## Mean :392.7 Mean :1889 Mean :479.4
## 3rd Qu.:451.1 3rd Qu.:2163 3rd Qu.:520.5
## Max. :762.0 Max. :2688 Max. :823.3# Visualization: Annual Rainfall Bar Plot
barplot(df_filtered$ANNUAL,
main='Annual Rainfall in Kerala (2000-2017)',
xlab='Year',ylab = 'Rainfall(cm)',xlim = c(0,20),ylim =c(0,3500))
- The rainfall amounts fluctuate significantly from year to year, with no clear, consistent upward or downward trend over this 18-year period.
- The lowest annual rainfall appears to be around 2016 (just below 2000 cm), while the highest rainfall years are around 2007 and 2013 (approaching3500 cm).
- Rainfall in recent years (2016, 2017) appears to be on the lower end of the observed range.
Pollution Analysis
The Delhi pollution data is imported, and a summary is checked, along with a count of missing values.
#import data
Delhi_Pollution <- read.csv("delhi pollution.csv")# Display the first few rows of the data
head(Delhi_Pollution)## Date Month Year Holidays_Count Days PM2.5 PM10 NO2 SO2 CO Ozone AQI
## 1 1 1 2021 0 5 408.80 442.42 160.61 12.95 2.77 43.19 462
## 2 2 1 2021 0 6 404.04 561.95 52.85 5.18 2.60 16.43 482
## 3 3 1 2021 1 7 225.07 239.04 170.95 10.93 1.40 44.29 263
## 4 4 1 2021 0 1 89.55 132.08 153.98 10.42 1.01 49.19 207
## 5 5 1 2021 0 2 54.06 55.54 122.66 9.70 0.64 48.88 149
## 6 6 1 2021 0 3 155.59 180.14 142.71 10.29 1.18 44.47 252# Check the class/data type of each column
cls <- as.data.frame(sapply(Delhi_Pollution,FUN = class))
cls## sapply(Delhi_Pollution, FUN = class)
## Date integer
## Month integer
## Year integer
## Holidays_Count integer
## Days integer
## PM2.5 numeric
## PM10 numeric
## NO2 numeric
## SO2 numeric
## CO numeric
## Ozone numeric
## AQI integer# Display a statistical summary of the data
summary(Delhi_Pollution)## Date Month Year Holidays_Count
## Min. : 1.00 Min. : 1.000 Min. :2021 Min. :0.0000
## 1st Qu.: 8.00 1st Qu.: 4.000 1st Qu.:2022 1st Qu.:0.0000
## Median :16.00 Median : 7.000 Median :2023 Median :0.0000
## Mean :15.73 Mean : 6.523 Mean :2023 Mean :0.1896
## 3rd Qu.:23.00 3rd Qu.:10.000 3rd Qu.:2024 3rd Qu.:0.0000
## Max. :31.00 Max. :12.000 Max. :2024 Max. :1.0000
## Days PM2.5 PM10 NO2
## Min. :1.000 Min. : 0.05 Min. : 9.69 Min. : 2.16
## 1st Qu.:2.000 1st Qu.: 41.28 1st Qu.: 115.11 1st Qu.: 17.28
## Median :4.000 Median : 72.06 Median : 199.80 Median : 30.49
## Mean :4.001 Mean : 90.77 Mean : 218.22 Mean : 37.18
## 3rd Qu.:6.000 3rd Qu.: 118.50 3rd Qu.: 297.75 3rd Qu.: 45.01
## Max. :7.000 Max. :1000.00 Max. :1000.00 Max. :433.98
## SO2 CO Ozone AQI
## Min. : 1.21 Min. :0.270 Min. : 2.70 Min. : 19.0
## 1st Qu.: 7.71 1st Qu.:0.610 1st Qu.: 24.10 1st Qu.:108.0
## Median : 15.43 Median :0.850 Median : 32.47 Median :189.0
## Mean : 20.10 Mean :1.026 Mean : 36.34 Mean :202.2
## 3rd Qu.: 26.62 3rd Qu.:1.240 3rd Qu.: 45.73 3rd Qu.:284.0
## Max. :113.40 Max. :4.700 Max. :115.87 Max. :500.0#Data Check: Missing Values
sum(is.na(Delhi_Pollution))## [1] 0#Data Manipulation: Filter Pollution Data for 2023
df_2023 <- Delhi_Pollution[Delhi_Pollution$Year == 2023, ]#Visualization: PM 2.5 vs PM 10 Box Plot
boxplot(df_2023$PM2.5,df_2023$PM10, main= 'Comparison of PM-2.5 and PM-10 in 2023',names = c("PM 2.5","PM 10"),
xlab="Pollutents", ylab=" Concentration (µg/m³)",xlim=c(0,3),ylim=c(0,550))
- PM10 Concentrations are Higher: The entire box plot for PM10 (including the median, interquartile range, and whiskers) is significantly higher than the box plot for PM2.5.
- PM10 Variability: The spread of the PM10 data (the length of the box and whiskers) appears to be wider than PM2.5, indicating greater fluctuation in PM10 concentrations throughout the year.
Population Analysis
The population data is imported from “populaion.csv”, and its structure is inspected.
#import data
populaion <- read.csv("populaion.csv")
View(populaion)# Display the first few rows of the data
head(populaion)## Year Population X..Increase.in.Population Population.Density
## 1 1950 357021100 0.00% 108.61
## 2 1951 36,49,22,360 2.21% 111.01
## 3 1952 37,29,97,188 2.21% 113.47
## 4 1953 38,12,27,705 2.21% 115.97
## 5 1954 38,97,31,406 2.23% 118.56
## 6 1955 39,85,77,992 2.27% 121.25
## X..Increase.in.Population.Density Urban.Population
## 1 0.00% Null
## 2 2.21% Null
## 3 2.21% Null
## 4 2.21% Null
## 5 2.23% Null
## 6 2.27% Null
## Urban.Population...of.Total.Population X..Increase.in.Urban.Population
## 1 Null Null
## 2 Null Null
## 3 Null Null
## 4 Null Null
## 5 Null Null
## 6 Null Null
## Rural.Population Rural.Population...of.Total.Population
## 1 Null Null
## 2 Null Null
## 3 Null Null
## 4 Null Null
## 5 Null Null
## 6 Null Null
## X..Increase.in.Rural.Population Life.Expectancy
## 1 Null 35.21
## 2 Null 35.80
## 3 Null 36.39
## 4 Null 36.98
## 5 Null 37.57
## 6 Null 38.16
## X..Increase.in.Life.Expectancy Birth.Rate X..Change.in.Birth.Rate Death.Rate
## 1 0.00% 44.175 0.00% 28.161
## 2 1.68% 43.970 -0.46% 27.584
## 3 1.65% 43.764 -0.47% 27.008
## 4 1.62% 43.558 -0.47% 26.432
## 5 1.60% 43.352 -0.47% 25.856
## 6 1.57% 43.146 -0.48% 25.280
## X..Change.in.Death.Rate Infant.Mortality.Rate
## 1 0.00% 189.629
## 2 -2.05% 186.737
## 3 -2.09% 183.846
## 4 -2.13% 180.954
## 5 -2.18% 178.062
## 6 -2.23% 175.171
## X..Change.in.Infant.Mortality.Rate Fertility.Rate X..Change.in.Fertility.Rate
## 1 0.00% 5.907 0.00%
## 2 -1.53% 5.906 -0.02%
## 3 -1.55% 5.904 -0.03%
## 4 -1.57% 5.903 -0.02%
## 5 -1.60% 5.902 -0.02%
## 6 -1.62% 5.900 -0.03%
## Net.Migration.Rate X..Change.in.Net.Migration.Rate
## 1 -0.043 0.00%
## 2 -0.047 9.30%
## 3 -0.050 6.38%
## 4 -0.054 8.00%
## 5 -0.058 7.41%
## 6 -0.061 5.17%# Check the class/data type of each column
cls <- as.data.frame(sapply(populaion,FUN = class))
cls## sapply(populaion, FUN = class)
## Year integer
## Population character
## X..Increase.in.Population character
## Population.Density numeric
## X..Increase.in.Population.Density character
## Urban.Population character
## Urban.Population...of.Total.Population character
## X..Increase.in.Urban.Population character
## Rural.Population character
## Rural.Population...of.Total.Population character
## X..Increase.in.Rural.Population character
## Life.Expectancy numeric
## X..Increase.in.Life.Expectancy character
## Birth.Rate numeric
## X..Change.in.Birth.Rate character
## Death.Rate numeric
## X..Change.in.Death.Rate character
## Infant.Mortality.Rate numeric
## X..Change.in.Infant.Mortality.Rate character
## Fertility.Rate numeric
## X..Change.in.Fertility.Rate character
## Net.Migration.Rate numeric
## X..Change.in.Net.Migration.Rate character# Display a statistical summary of the data
summary(populaion)## Year Population X..Increase.in.Population Population.Density
## Min. :1950 Length:73 Length:73 Min. :108.6
## 1st Qu.:1968 Class :character Class :character 1st Qu.:162.3
## Median :1986 Mode :character Mode :character Median :242.7
## Mean :1986 Mean :254.8
## 3rd Qu.:2004 3rd Qu.:345.7
## Max. :2022 Max. :431.1
## X..Increase.in.Population.Density Urban.Population
## Length:73 Length:73
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## Urban.Population...of.Total.Population X..Increase.in.Urban.Population
## Length:73 Length:73
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## Rural.Population Rural.Population...of.Total.Population
## Length:73 Length:73
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## X..Increase.in.Rural.Population Life.Expectancy X..Increase.in.Life.Expectancy
## Length:73 Min. :35.21 Length:73
## Class :character 1st Qu.:46.10 Class :character
## Mode :character Median :55.98 Mode :character
## Mean :54.86
## 3rd Qu.:63.91
## Max. :70.19
## Birth.Rate X..Change.in.Birth.Rate Death.Rate
## Min. :17.16 Length:73 Min. : 7.237
## 1st Qu.:24.75 Class :character 1st Qu.: 8.261
## Median :34.02 Mode :character Median :12.008
## Mean :32.18 Mean :13.869
## 3rd Qu.:39.77 3rd Qu.:18.368
## Max. :44.17 Max. :28.161
## X..Change.in.Death.Rate Infant.Mortality.Rate
## Length:73 Min. : 27.70
## Class :character 1st Qu.: 57.85
## Mode :character Median : 98.21
## Mean :101.89
## 3rd Qu.:145.81
## Max. :189.63
## X..Change.in.Infant.Mortality.Rate Fertility.Rate X..Change.in.Fertility.Rate
## Length:73 Min. :2.159 Length:73
## Class :character 1st Qu.:3.071 Class :character
## Mode :character Median :4.432 Mode :character
## Mean :4.292
## 3rd Qu.:5.723
## Max. :5.907
## Net.Migration.Rate X..Change.in.Net.Migration.Rate
## Min. :-0.44600 Length:73
## 1st Qu.:-0.34300 Class :character
## Median :-0.06800 Mode :character
## Mean :-0.06993
## 3rd Qu.: 0.03900
## Max. : 0.71500#Filter the data and create a new data frame called 'df_filtered'
df_filtered_pop <- populaion %>%
filter(Year >= 2010 & Year <= 2022)%>%
select(Year, Population)
df_filtered_pop$Population <- as.numeric(df_filtered_pop$Population)
df_filtered_pop## Year Population
## 1 2010 1240613620
## 2 2011 1257621191
## 3 2012 1274487215
## 4 2013 1291132063
## 5 2014 1307246509
## 6 2015 1322866505
## 7 2016 1338636340
## 8 2017 1354195680
## 9 2018 1369003306
## 10 2019 1383112050
## 11 2020 1396387127
## 12 2021 1407563842
## 13 2022 1417173173#Visualization: Population Trend
ggplot(data = df_filtered_pop, aes(x = Year, y = Population)) +
geom_line(color = "darkblue", linewidth = 1.2) +
labs(
title = "Population Trend (2000-2022)",
x = "Year",
y = "Total Population")
- Steep Upward Trend: The line plot shows a clear and consistent upward slope, indicating steady population growth every year between 2010 and 2022.
- Magnitude: The population count reaches over 1.4 billion by 2022.