Who is 'Peepal baba ?
Widely known as 'peepal baba', Swami Prem parivartan is a revolutionary who from the last 40 years is planting banyan trees and taking care of them, He is also the founder of 'Give me trees' club.
How he get the name 'Peepal baba' ?
This may shock you, till now he has planted more than 20 million banyan trees and also have planted more than 1 crore neem trees.
Let's get deep into how he came with the idea of planting peepal or banyan trees and why only banyan tree ? why not some other plant ? who was his inspiration ?
So, prem parivartan, started planting tree from the age of 11, when he saw that how highways are slowly destroying the jungles and his nearby vegetational area. This very thought made him feel the need to grow more and more trees, So that the earth can be saved from the evil deeds of the mankind(which is more of a danger to this planet than any other creature).
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The ficus or the banyan tree grows when the sparrow eats the seed of the banyan tree and after if poop that is where the peepal tree grows. Therefore you may have seen that peepal plant growing on the rooftops, on pipes and anywhere wherever the sparrow decides to poop.
What helped him grow so much trees ?
In the year 2011, he started the club or the NGO named 'Give me trees', with the motive to grow more trees, which is still in the game and the NGO is growing bigger and bigger.
As the NGO got more and more popularity, the temples or the ashram of uttarakhand started donating and helping it to get the expenditure in it's realm. The priests and other workers of temples also started helping the club. That's the reason the NGO, is able to achieve little-little milestones in a much more faster rate.
To be precise(data given by 'Give more trees', the priests and other temple workers have planted more than 30,000 trees till now). One more thing, the 'Give more trees' NGO have planted trees in 18 different states.
Inspired by prem parivartan ji in 2008, there started a NGO named EcoSikh, which works on the principles of guru nanak.
As Guru Nanak said -
" Air is the Guru, water is the father and soil our mother. To preserve soil; plantation is important, to preserve soil; plantation is important, to retain water, plantation is important, for good air, plantation is important".
That was it about the one of the biggest revolutionary of 21st century, trees are the gods(the very basic principle of hinduism, that all lifes have god).
If you want to check and want to know about swami prem parivartan, you can simply type peepal baba on youtube and there is abundance of valuable knowledge about plant life on his channel.
11 Comments
nice
ReplyDeleteVery nice information 🙂
ReplyDeleteGood ☺️
ReplyDeleteI got inspire to grow Banyan trees 😁
ReplyDeleteVery nice😍
ReplyDeleteBohot khushi hoti h ji Koi pedo k prti Itna deta h 😍😍😍
ReplyDeleteinstall.packages("gapminder")
ReplyDeleteinstall.packages("tidyverse")
# Load packages
library(tidyverse)
library(dplyr)
library(gapminder)
data()
#using dplyr verbs
gapminder%>%select(pop, country, year)
gapminder %>% select(-pop,-year)
#filtering gapminder for 2007
gapminder %>% filter (year == 2007)
#gapminder2007
gapminder2007 <-gapminder %>% filter(year == 2007)
gapminder_asia<-gapminder %>% filter(continent== 'Asia')
#gapminder_US2007
gapminderUS2007 <-gapminder %>%filter(year ==2007)
gapminder_US2007<-gapminder%>% filter(country== 2007,year=='US')
#higher life expectantly in 1927
gapminder_lifeexp <-gapminder%>%filter(year==1977)
gapminder_irelandlifexp <-gapminder%>%filter(year==1977, country=='Ireland')
gapminder_brazillifexp <-gapminder%>%filter(year==1977, country=='Brazil')
gapminder %>% arrange('gdppercap')
gapminder %>% arrange(desc(gdpPercap))
gapminder %>% arrange(desc('lifeexp'))
gapminder %>% filter(year==1992, continent=='Europe')%>% arrange(desc(pop))
gapminder %>% summarize (mean_lifeExp=mean(lifeExp))
gapminder %>% filter(year ==2007)
gapminder %>%group_by(year)%>% summarize(meanPop =(pop)/1000000, meanlifeExp =mean(lifeExp))
gapminder%>% filter(year==2007) %>% group_by(continent) %>% summarize(meanPop =(pop)/1000000, meanlifeExp =mean(lifeExp))
gapminder %>% group_by(year)%>% summarize(meanPop =(pop)/1000000, meanlifeExp =mean(lifeExp))
gapminder %>% group_by(1977)%>% summarize(meanPop =(pop)/1000000, meanlifeExp =mean(lifeExp))
#Practical 3
ReplyDeleterm(list=ls())
#installing packages
install.packages("gapminder")
install.packages("tidyverse")
#Load Packages
library("tidyverse")
library("dplyr")
library("gapminder")
#checking all the datasets under gapminder
data()
#Using the dplyr verb
gapminder%>%select(pop, country, year)
#select everything except population and year
gapminder %>% select(-pop, -year)
str(gapminder)
#Filtering gapminder for 2007
gapminder %>% filter(year==2007)
#assigning the above command to a variable gapminder2007
gapminder2007 <- gapminder %>% filter(year==2007)
#Storing the data for Asian countries in a tibble called gapminder_asia
gapminder_asia <- gapminder %>% filter(continent=='Asia')
#Filtering the data f0r Us 2007
gapminder2007 %>% filter(country == 'Unites States')
gapminder %>% filter(year == 2007, country == 'Unites States')
#Life expectancy of Ireland
gapminder %>% filter(year == 1977, country == 'Ireland')
#Life expectancy of Brazil
gapminder %>% filter(year == 1977, country == 'Brazil')
#GDP per capita of Ireland
gapminder %>% filter(year == 1977, country == 'Ireland')
#GDP per capita of Brazil
gapminder %>% filter(year == 1977, country == 'Brazil')
#arrranging the dataset gapminder acc. to gdpPercap
gapminder %>% arrange(gdpPercap)
gapminder %>% arrange(-gdpPercap)
gapminder %>% arrange(desc(gdpPercap))
#lowest life expectancuy in gapminder datset
gapminder %>% arrange(lifeExp)
#Highest life expectancy in gapminder dataset
gapminder %>% arrange(desc(lifeExp))
gapminder %>% filter(year==1952) %>% arrange(desc(lifeExp))
#Most populous European country in 1992
gapminder %>% filter(year==1992, continent == 'Europe') %>% arrange(desc(pop))
#Calculating sample mean of the column lifeExp in gapminder
gapminder %>% summarize(mean_lifeExp= mean(lifeExp))
#mean life expectancy in year 2007
gapminder %>% filter(year == 2007) %>% summarize(meanPop = mean(pop)/1000000, meanlifeExp = mean(lifeExp))
#Using verb group_by
gapminder %>% group_by(year) %>% summarize(meanPop = mean(pop)/1000000, meanlifeExp= mean(lifeExp))
#calculating mean life expectancy and total population in each continent during the year 2007
gapminder %>% filter(year==2007) %>% group_by(continent) %>% summarize(meanPop = mean(pop), meanlifeExp= mean(lifeExp))
#calculating mean life expectancy and total population in each continent seperately each year
gapminder %>% group_by(year,continent) %>% summarize(meanPop = mean(pop), meanlifeExp= mean(lifeExp))
#GDP per capita in each continent in 1977
gapminder %>% group_by(continent) %>% summarize(mediangdpPercap = median(gdpPercap))
# sorting the above result in ascending order by assigning it to a variable
gapminderGDP1977 <- gapminder %>% group_by(continent) %>% summarize(mediangdpPercap = median(gdpPercap))
gapminderGDP1977 %>% arrange(desc(mediangdpPercap))
#Calculating mean and standard deviation of LifeExp separately for each continent in every year after 1977
gapminder %>% group_by(continent,year) %>% filter(year>1977) %>% summarize (mean=mean(lifeExp),sd=sd(lifeExp))
gapminderdesc<-gapminder %>% group_by(continent,year) %>% filter(year>1977) %>% summarize (mean=mean(lifeExp),sd=sd(lifeExp))
gapminderdesc %>% arrange(desc(sd))
install.packages(dplyr)
ReplyDeleteinstall.packages(ggplot2)
installed.packages()
library(ggplot2)
library(dplyr)
ggplot(gapminder, aes(x=gdpPercap, y=lifeExp))+geom_point()
ggplot(gapminder, aes(x=gdpPercap, y=lifeExp))+geom_point()+scale_x_log10()
really appreciative. keep going
ReplyDeleteinstall.packages("dplyr")
ReplyDeleteinstall.packages("ggplot2")
install.packages("maps")
install.packages("rnaturalearth")
library(dplyr)
library(ggplot2)
library(maps)
library(rnaturalearth)
storms75 <- filter(storms,year ==1975)
storms75
ggplot(data = storms75, aes(x = long, y = lat) + geom_point ()
ggplot(data = storms75, aes (x = long, y = lat, color = name)) + geom_point()
ggplot() + geom_point (data = storms75, aes(x = long, y = lat, color = name))
map("world")
world_map <- map_data("world")
head(world_map)
ggplot() + geom_polygon(data = world_map, aes (x= long, y = lat, group = group)) + theme_bw()
gg_world <- ggplot() + geom_polygon (data = world_map, aes(x = long, y = lat, group = group), fill = "blue"" , colour = "grey" , linewidth = 0.2
gg_world
gg_world + geom_point(data = storms75, aes ( x = long, y = lat, color = name)) + xlim(c(-150,0)) + ylim(c(0,90))
gg_world + geom_point(data = storms75, aes ( x = long, y = lat, color = name, size = wind) alpha = 0.5) + xlim(c(-150,0)) + ylimc(c(0,90))
storms_75_80 <- filter(storms, year %in% 1975:1980)
storms_75_80
gg_world + geom_path (data = storms_75_80, aes ( x = long, y = lat, group = name), lineend = "round") + xlim(c(-150,0)) + ylim(c(0,90)) + facet_wrap(~year)