Forest Areas (WDI)

Forest area (sq.km)

For China, Mexico, Turkey and USA.

From the year 2000 to 2022

Installing WDI

library(WDI)

## Warning: package 'WDI' was built under R version 4.1.3

df = WDI(indicator='AG.LND.FRST.K2', country=c('MX','TR','CHN','US'), start=2000, end=2022)
head(df)

##   country iso2c iso3c year AG.LND.FRST.K2
## 1   China    CN   CHN 2021             NA
## 2   China    CN   CHN 2020        2199782
## 3   China    CN   CHN 2019        2180986
## 4   China    CN   CHN 2018        2162190
## 5   China    CN   CHN 2017        2143395
## 6   China    CN   CHN 2016        2124599

tail(df)

##          country iso2c iso3c year AG.LND.FRST.K2
## 83 United States    US   USA 2005        3061280
## 84 United States    US   USA 2004        3056096
## 85 United States    US   USA 2003        3050912
## 86 United States    US   USA 2002        3045728
## 87 United States    US   USA 2001        3040544
## 88 United States    US   USA 2000        3035360

Renaming Variables into Columns

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.1.3

## 
## 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

df<-df %>%
  rename(country = 1,
         countrycode = 2,
         countrycodeiso3 = 3,
         year = 4,
         AG.LND.FRST.K2 =5)

To monitor data broadly by Country. We need to install reshape2

library(reshape2)

## Warning: package 'reshape2' was built under R version 4.1.3

data_genis <- dcast(df, year ~ country, value.var="AG.LND.FRST.K2")
head(data_genis)

##   year   China   Mexico  Turkiye United States
## 1 2000 1770006 683813.5 201483.5       3035360
## 2 2001 1793615 682375.5 202418.2       3040544
## 3 2002 1817225 680937.4 203353.0       3045728
## 4 2003 1840835 679499.4 204287.7       3050912
## 5 2004 1864445 678061.3 205222.4       3056096
## 6 2005 1888055 676623.3 206157.1       3061280

tail(data_genis)

##    year   China   Mexico  Turkiye United States
## 17 2016 2124599 662031.5 216303.0       3100950
## 18 2017 2143395 660753.8 217524.6       3097950
## 19 2018 2162190 659476.1 219083.6       3097950
## 20 2019 2180986 658198.5 220643.6       3097950
## 21 2020 2199782 656920.8 222203.6       3097950
## 22 2021      NA       NA       NA            NA

Forest Areas of all Countries data set

df2 <- WDI(
  country = "all",
  indicator = "AG.LND.FRST.K2",
  start = 2010,
  end = 2022,
  extra = TRUE,
  cache = NULL,
  latest = NULL,)

Attach ggplot2

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.1.3

library(ggfortify)

## Warning: package 'ggfortify' was built under R version 4.1.3

To Convert our “data_genis” to Time Series Use “tsveris”

tsveri <- ts(data_genis, start=2000, frequency=1)
head(tsveri)

##      year   China   Mexico  Turkiye United States
## [1,] 2000 1770006 683813.5 201483.5       3035360
## [2,] 2001 1793615 682375.5 202418.2       3040544
## [3,] 2002 1817225 680937.4 203353.0       3045728
## [4,] 2003 1840835 679499.4 204287.7       3050912
## [5,] 2004 1864445 678061.3 205222.4       3056096
## [6,] 2005 1888055 676623.3 206157.1       3061280

tail(tsveri)

##       year   China   Mexico  Turkiye United States
## [17,] 2016 2124599 662031.5 216303.0       3100950
## [18,] 2017 2143395 660753.8 217524.6       3097950
## [19,] 2018 2162190 659476.1 219083.6       3097950
## [20,] 2019 2180986 658198.5 220643.6       3097950
## [21,] 2020 2199782 656920.8 222203.6       3097950
## [22,] 2021      NA       NA       NA            NA

Plot Time series

plot(tsveri[,2:5])

On a Single graph

plot(tsveri[,2:5], plot.type = "single")

Graph The Time series

plot(tsveri[,2],
     type = "l",
     col = 2,
     ylim = c(0, 25),
     xlab = "year",
     ylab = "AG.LND.FRST")
lines(tsveri[,3],
      type = "l",
      col = 3)
lines(tsveri[,4],
      type = "l",
      col = 4)
lines(tsveri[,5],
      type = "l",
      col = 5)
legend("topright",                           
       c("China","Mexico", "Türkey", "US"),
       lty = 2,
       col = 2:5)

data_long <-melt(data_genis, id.vars = "year")
ggplot(data_long,                           
       aes(x = year,
           y = value,
           col = variable)) +
  geom_line()

## Warning: Removed 4 rows containing missing values (`geom_line()`).