Intro:

For my project I choose bee colony loss by year from the google drive data on annual loss of bee colonies because I am interested in conservation research. Bee are a very important part of a healthy biodiversity.

Setting working directory.

setwd("/Users/shoshigilg/Desktop/DATA 110")
df <- read.csv("bee_colony_BIP21_v2.csv")

Loading required libraries

library(tidyverse)
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library(dbplyr)
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library(ggplot2)
library(lubridate)
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library(ggthemes)
library(plotly)
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library(scales)
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library(ggrepel)
library(hrbrthemes)
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library(magrittr)
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library("ggthemes")
library("knitr")
str(df)
## 'data.frame':    581 obs. of  10 variables:
##  $ X                            : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Year                         : chr  "2020/21" "2020/21" "2020/21" "2020/21" ...
##  $ Season                       : chr  "Annual" "Annual" "Annual" "Annual" ...
##  $ State                        : chr  "Alaska" "District of Columbia" "Guam" "Hawaii" ...
##  $ Total.Annual.Loss            : num  NA NA NA NA NA NA NA 56.5 64.6 52.5 ...
##  $ Beekeepers                   : int  NA NA NA NA NA NA NA 11 11 110 ...
##  $ Beekeepers.Exclusive.to.State: num  NA NA NA NA NA NA NA 90.9 90.9 93.6 ...
##  $ Colonies                     : int  NA NA NA NA NA NA NA 1229 513 3863 ...
##  $ Colonies.Exclusive.to.State  : num  NA NA NA NA NA NA NA 4.8 33.2 40.6 ...
##  $ State.Abbreviation           : chr  "AK" "DC" "GU" "HI" ...

Data Cleaning:

# Use gsub() to remove some strings. 

df$Total.Annual.Loss <- gsub("%", "", df$Total.Annual.Loss) 

df$Beekeepers.Exclusive.to.State <- gsub("%", "", df$Beekeepers.Exclusive.to.State) 

df$Colonies.Exclusive.to.State <-gsub("%","",df$Colonies.Exclusive.to.State)

df$State <- gsub("\t", "", df$State) 
# convert the character type to numeric type.
df$Total.Annual.Loss <- as.numeric(df$Total.Annual.Loss)

df$Beekeepers.Exclusive.to.State <- as.numeric(df$Beekeepers.Exclusive.to.State)

df$Colonies.Exclusive.to.State <-as.numeric(df$Colonies.Exclusive.to.State)

Using dplyr commands to compute the total number of bee colonies, the total beekeepers, and the average bee loss ratio across all the states 2010 to 2021

library(knitr)
total_bee<- df %>%
  group_by(Year) %>%
  summarise(total_colony  = sum(Colonies, na.rm= T),
            total_beekeeper = sum(Beekeepers, na.rm = T),
            average_loss = round(mean(Total.Annual.Loss, na.rm = T),2)
            )

kable(total_bee)
Year total_colony total_beekeeper average_loss
2010/11 611060 2639 40.82
2011/12 698560 3234 34.19
2012/13 3008783 4522 43.95
2013/14 2750706 6205 40.22
2014/15 2517979 5139 44.67
2015/16 2341670 4976 42.40
2016/17 1640309 4324 41.46
2017/18 706208 4113 47.40
2018/19 1386504 3911 42.25
2019/20 1260420 2861 44.38
2020/21 446024 2895 50.95 
mean(total_bee$average_loss)
## [1] 42.97182

There has been an average 43% loss of honeybees in the U.S during 11 years.

p1<- ggplot(data = total_bee, aes(x=Year, y=average_loss)) +
  labs(title= "Annual Loss Throughout 2010 - 2021",
  x= "Year",
  y= "Annual Loss") +
  geom_col(aes(fill= average_loss)) 
p1

Statistical Anaylsis

ggplot(df, aes(x= Year, y= Total.Annual.Loss, fill = "Year")) +
  geom_boxplot( fill = "7") +
  labs(x = "Year", y = "Total Annual Loss (%)", title ="Summary Statistics of Bee Loss by Year") +
  theme(legend.position = "none") +
  stat_summary(fun = mean, shape = 4, color = "darkgrey")  
## Warning: Removed 58 rows containing non-finite values (stat_boxplot).
## Warning: Removed 58 rows containing non-finite values (stat_summary).
## Warning: Removed 11 rows containing missing values (geom_segment).

From the Boxplot, we can see the average, IQR, median values, and outliers of the annual honey bee loss rate. In 2020/21, both the average and median values go above 50%.

Essay

Bees and other pollinators face increasing risks to their survival, threatening foods such as apples, blueberries and coffee worth hundreds of billions of dollars a year. Pesticides, loss of habitats to farms and cities, disease and climate change were among threats to about 20,000 species of bees as well as creatures such as birds, butterflies, beetles and bats that fertilize flowers by spreading pollen.