job
Ruth Muriithi
1/12/2021
final_assessment_dataset <- read.csv("C:/Users/ruth/Downloads/final_assessment_dataset.csv")
View(final_assessment_dataset)
str(final_assessment_dataset)## 'data.frame': 607 obs. of 10 variables:
## $ farm : chr "Benson" "Benson" "Benson" "Benson" ...
## $ latitude : chr "N51:35:53" "N51:35:53" "N51:35:53" "N51:35:53" ...
## $ longitude : chr "W1:05:37" "W1:05:37" "W1:05:37" "W1:05:37" ...
## $ pct_flower : int 10 10 10 10 25 30 30 10 10 50 ...
## $ temp : int 27 27 22 22 25 25 25 25 25 12 ...
## $ variety : chr "Variety_1" "Variety_1" "Variety_1" "Variety_1" ...
## $ type : chr "Hybrid" "Hybrid" "Hybrid" "Hybrid" ...
## $ species : chr "Andrena_carantonica" "Andrena_nigroaenea" "Andrena_pubescens" "Apis_mellifera" ...
## $ group : chr "Solitary bee" "Solitary bee" "Solitary bee" "Honeybee" ...
## $ relative_abundance: num 0.5 0.5 0.5 0.5 0.5 ...dim(final_assessment_dataset)## [1] 607 10class(final_assessment_dataset)## [1] "data.frame"colSums(is.na(final_assessment_dataset))## farm latitude longitude pct_flower
## 0 0 0 1
## temp variety type species
## 0 0 0 0
## group relative_abundance
## 0 0There is one missing value. We’ll impute it with the mean of the data in that column. #Simple Imputation
final_assessment_dataset$pct_flower[which(is.na(final_assessment_dataset$pct_flower))] = mean(final_assessment_dataset$pct_flower, na.rm = TRUE)colSums(is.na(final_assessment_dataset))## farm latitude longitude pct_flower
## 0 0 0 0
## temp variety type species
## 0 0 0 0
## group relative_abundance
## 0 0UNIVARIATE ANALYSIS
summary(final_assessment_dataset)## farm latitude longitude pct_flower
## Length:607 Length:607 Length:607 Min. : 0.00
## Class :character Class :character Class :character 1st Qu.: 5.00
## Mode :character Mode :character Mode :character Median :10.00
## Mean :16.88
## 3rd Qu.:25.00
## Max. :65.00
## temp variety type species
## Min. :12.00 Length:607 Length:607 Length:607
## 1st Qu.:16.00 Class :character Class :character Class :character
## Median :22.00 Mode :character Mode :character Mode :character
## Mean :20.14
## 3rd Qu.:25.00
## Max. :27.00
## group relative_abundance
## Length:607 Min. :0.09091
## Class :character 1st Qu.:0.25000
## Mode :character Median :0.50000
## Mean :0.46674
## 3rd Qu.:0.65152
## Max. :1.00000library(tidyverse)## -- Attaching packages ---------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.2 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()numerical_adv <- final_assessment_dataset %>% select(4, 5, 10)
summary(numerical_adv)## pct_flower temp relative_abundance
## Min. : 0.00 Min. :12.00 Min. :0.09091
## 1st Qu.: 5.00 1st Qu.:16.00 1st Qu.:0.25000
## Median :10.00 Median :22.00 Median :0.50000
## Mean :16.88 Mean :20.14 Mean :0.46674
## 3rd Qu.:25.00 3rd Qu.:25.00 3rd Qu.:0.65152
## Max. :65.00 Max. :27.00 Max. :1.00000str(numerical_adv)## 'data.frame': 607 obs. of 3 variables:
## $ pct_flower : num 10 10 10 10 25 30 30 10 10 50 ...
## $ temp : int 27 27 22 22 25 25 25 25 25 12 ...
## $ relative_abundance: num 0.5 0.5 0.5 0.5 0.5 ...Graphical analysis
for honeey bee only
honeydf <- final_assessment_dataset %>%
select(farm, temp, pct_flower, group, relative_abundance)%>%
filter(group == "Honeybee")
honeydf## farm temp pct_flower group relative_abundance
## 1 Benson 22 10.00000 Honeybee 0.5000000
## 2 Fulborne 25 10.00000 Honeybee 0.5000000
## 3 Fulborne 25 10.00000 Honeybee 0.3333333
## 4 Orford 14 50.00000 Honeybee 0.6666667
## 5 Orford 14 50.00000 Honeybee 0.6666667
## 6 Benson 27 10.00000 Honeybee 0.6666667
## 7 Benson 13 50.00000 Honeybee 0.6666667
## 8 Benson 13 50.00000 Honeybee 0.1250000
## 9 Fulborne 25 10.00000 Honeybee 0.7500000
## 10 Fulborne 25 10.00000 Honeybee 0.7500000
## 11 Fulborne 25 10.00000 Honeybee 0.7500000
## 12 Fulborne 25 10.00000 Honeybee 0.7500000
## 13 Fulborne 25 10.00000 Honeybee 0.7500000
## 14 Fulborne 25 10.00000 Honeybee 0.7500000
## 15 Orford 14 50.00000 Honeybee 0.3333333
## 16 Orford 14 50.00000 Honeybee 0.2500000
## 17 Fulborne 25 10.00000 Honeybee 0.5000000
## 18 Fulborne 25 10.00000 Honeybee 0.5000000
## 19 Fulborne 25 10.00000 Honeybee 0.1666667
## 20 Horncastle 22 0.00000 Honeybee 0.6666667
## 21 Horncastle 12 50.00000 Honeybee 0.6666667
## 22 Horncastle 22 0.00000 Honeybee 0.2500000
## 23 Benson 27 10.00000 Honeybee 0.5000000
## 24 Benson 27 10.00000 Honeybee 0.6666667
## 25 Benson 27 10.00000 Honeybee 0.6666667
## 26 Fulborne 25 10.00000 Honeybee 0.3750000
## 27 Fulborne 25 10.00000 Honeybee 0.3750000
## 28 Fulborne 25 10.00000 Honeybee 0.3750000
## 29 Horncastle 22 0.00000 Honeybee 0.2500000
## 30 Orford 14 50.00000 Honeybee 0.3333333
## 31 Benson 22 10.00000 Honeybee 0.5000000
## 32 Benson 22 10.00000 Honeybee 0.5000000
## 33 Benson 27 10.00000 Honeybee 0.5000000
## 34 Benson 27 10.00000 Honeybee 0.5000000
## 35 Benson 27 10.00000 Honeybee 0.5000000
## 36 Benson 27 10.00000 Honeybee 0.2500000
## 37 Fulborne 16 0.00000 Honeybee 0.2857143
## 38 Fulborne 25 10.00000 Honeybee 0.2857143
## 39 Fulborne 25 10.00000 Honeybee 0.8333333
## 40 Fulborne 25 10.00000 Honeybee 0.8333333
## 41 Fulborne 25 10.00000 Honeybee 0.8333333
## 42 Fulborne 25 10.00000 Honeybee 0.8333333
## 43 Fulborne 25 10.00000 Honeybee 0.8333333
## 44 Benson 13 50.00000 Honeybee 0.3333333
## 45 Benson 27 10.00000 Honeybee 0.5000000
## 46 Fulborne 25 10.00000 Honeybee 0.7500000
## 47 Fulborne 25 10.00000 Honeybee 0.7500000
## 48 Fulborne 25 10.00000 Honeybee 0.7500000
## 49 Fulborne 16 16.88119 Honeybee 0.3333333
## 50 Horncastle 22 0.00000 Honeybee 0.2000000
## 51 Orford 14 50.00000 Honeybee 0.3333333
## 52 Benson 27 10.00000 Honeybee 0.2000000
## 53 Benson 27 10.00000 Honeybee 0.2000000
## 54 Fulborne 25 10.00000 Honeybee 0.8333333
## 55 Fulborne 25 10.00000 Honeybee 0.8333333
## 56 Fulborne 25 10.00000 Honeybee 0.8333333
## 57 Fulborne 25 10.00000 Honeybee 0.8333333
## 58 Fulborne 25 10.00000 Honeybee 0.8333333
## 59 Fulborne 16 0.00000 Honeybee 0.3333333
## 60 Horncastle 22 0.00000 Honeybee 0.5000000
## 61 Fulborne 25 10.00000 Honeybee 0.7500000
## 62 Fulborne 25 10.00000 Honeybee 0.7500000
## 63 Fulborne 25 10.00000 Honeybee 0.7500000
## 64 Fulborne 16 0.00000 Honeybee 0.1250000
## 65 Horncastle 22 0.00000 Honeybee 0.2500000
## 66 Benson 27 10.00000 Honeybee 0.2000000
## 67 Benson 22 10.00000 Honeybee 0.3333333
## 68 Benson 27 10.00000 Honeybee 0.3333333
## 69 Benson 27 10.00000 Honeybee 0.6666667
## 70 Benson 27 10.00000 Honeybee 0.6666667
## 71 Fulborne 25 10.00000 Honeybee 0.2500000
## 72 Fulborne 25 10.00000 Honeybee 0.3333333
## 73 Fulborne 25 10.00000 Honeybee 0.6250000
## 74 Fulborne 25 10.00000 Honeybee 0.6250000
## 75 Fulborne 25 10.00000 Honeybee 0.6250000
## 76 Fulborne 25 10.00000 Honeybee 0.6250000
## 77 Fulborne 25 10.00000 Honeybee 0.6250000
## 78 Fulborne 25 10.00000 Honeybee 0.2500000
## 79 Fulborne 16 0.00000 Honeybee 0.7500000
## 80 Fulborne 16 0.00000 Honeybee 0.7500000
## 81 Fulborne 25 10.00000 Honeybee 0.7500000
## 82 Fulborne 16 0.00000 Honeybee 0.5000000
## 83 Fulborne 25 10.00000 Honeybee 0.5000000
## 84 Fulborne 25 10.00000 Honeybee 0.5000000
## 85 Orford 14 50.00000 Honeybee 0.5000000
## 86 Fulborne 25 10.00000 Honeybee 0.3333333
## 87 Fulborne 16 0.00000 Honeybee 0.5000000
## 88 Benson 27 10.00000 Honeybee 0.1666667
## 89 Fulborne 16 0.00000 Honeybee 0.3636364
## 90 Fulborne 16 0.00000 Honeybee 0.3636364
## 91 Fulborne 16 0.00000 Honeybee 0.3636364
## 92 Fulborne 25 10.00000 Honeybee 0.3636364
## 93 Fulborne 16 0.00000 Honeybee 0.5555556
## 94 Fulborne 25 10.00000 Honeybee 0.5555556
## 95 Fulborne 25 10.00000 Honeybee 0.5555556
## 96 Fulborne 25 10.00000 Honeybee 0.5555556
## 97 Fulborne 25 10.00000 Honeybee 0.5555556
## 98 Horncastle 22 0.00000 Honeybee 0.3333333
## 99 Orford 14 50.00000 Honeybee 0.5000000
## 100 Orford 14 50.00000 Honeybee 0.5000000
## 101 Fulborne 16 0.00000 Honeybee 0.7500000
## 102 Fulborne 16 0.00000 Honeybee 0.7500000
## 103 Fulborne 25 10.00000 Honeybee 0.7500000
## 104 Fulborne 25 10.00000 Honeybee 0.7500000
## 105 Fulborne 25 10.00000 Honeybee 0.7500000
## 106 Fulborne 25 10.00000 Honeybee 0.7500000
## 107 Horncastle 22 0.00000 Honeybee 0.5000000
## 108 Horncastle 12 50.00000 Honeybee 0.5000000
## 109 Orford 14 50.00000 Honeybee 0.5000000
## 110 Orford 14 50.00000 Honeybee 0.5000000
## 111 Benson 27 10.00000 Honeybee 0.2500000
## 112 Fulborne 16 0.00000 Honeybee 0.6250000
## 113 Fulborne 25 10.00000 Honeybee 0.6250000
## 114 Fulborne 25 10.00000 Honeybee 0.6250000
## 115 Fulborne 25 10.00000 Honeybee 0.6250000
## 116 Fulborne 25 10.00000 Honeybee 0.6250000
## 117 Fulborne 16 0.00000 Honeybee 0.2222222
## 118 Fulborne 16 0.00000 Honeybee 0.2222222
## 119 Horncastle 22 0.00000 Honeybee 0.1428571
## 120 Horncastle 22 0.00000 Honeybee 0.7500000
## 121 Horncastle 22 0.00000 Honeybee 0.7500000
## 122 Horncastle 12 50.00000 Honeybee 0.7500000
## 123 Benson 27 10.00000 Honeybee 0.2500000
## 124 Fulborne 25 10.00000 Honeybee 0.5555556
## 125 Fulborne 25 10.00000 Honeybee 0.5555556
## 126 Fulborne 25 10.00000 Honeybee 0.5555556
## 127 Fulborne 25 10.00000 Honeybee 0.5555556
## 128 Fulborne 25 10.00000 Honeybee 0.5555556
## 129 Horncastle 12 50.00000 Honeybee 0.4000000
## 130 Horncastle 12 50.00000 Honeybee 0.4000000
## 131 Benson 27 10.00000 Honeybee 0.3333333
## 132 Benson 22 10.00000 Honeybee 0.3333333
## 133 Fulborne 25 10.00000 Honeybee 0.6666667
## 134 Fulborne 25 10.00000 Honeybee 0.6666667
## 135 Fulborne 25 10.00000 Honeybee 0.6666667
## 136 Fulborne 25 10.00000 Honeybee 0.6666667
## 137 Fulborne 25 10.00000 Honeybee 0.2000000
## 138 Fulborne 16 0.00000 Honeybee 0.3333333
## 139 Fulborne 16 0.00000 Honeybee 0.6666667
## 140 Fulborne 16 0.00000 Honeybee 0.6666667
## 141 Orford 16 5.00000 Honeybee 0.5000000
## 142 Benson 27 10.00000 Honeybee 0.5000000
## 143 Benson 22 10.00000 Honeybee 0.5000000
## 144 Fulborne 25 10.00000 Honeybee 0.6666667
## 145 Fulborne 25 10.00000 Honeybee 0.6666667
## 146 Fulborne 25 10.00000 Honeybee 0.2000000
## 147 Fulborne 25 10.00000 Honeybee 0.5000000
## 148 Fulborne 25 10.00000 Honeybee 0.5000000
## 149 Horncastle 22 0.00000 Honeybee 0.5000000
## 150 Horncastle 12 50.00000 Honeybee 0.5000000
## 151 Orford 14 50.00000 Honeybee 0.5000000
## 152 Orford 14 50.00000 Honeybee 0.5000000
## 153 Benson 27 10.00000 Honeybee 0.3333333
## 154 Benson 27 10.00000 Honeybee 0.2500000
## 155 Benson 22 10.00000 Honeybee 0.2857143
## 156 Benson 27 10.00000 Honeybee 0.2857143
## 157 Fulborne 25 10.00000 Honeybee 0.7500000
## 158 Fulborne 25 10.00000 Honeybee 0.7500000
## 159 Fulborne 25 10.00000 Honeybee 0.7500000
## 160 Fulborne 16 0.00000 Honeybee 1.0000000
## 161 Fulborne 25 10.00000 Honeybee 1.0000000
## 162 Fulborne 25 10.00000 Honeybee 1.0000000
## 163 Fulborne 25 10.00000 Honeybee 1.0000000
## 164 Fulborne 25 10.00000 Honeybee 1.0000000
## 165 Fulborne 25 10.00000 Honeybee 0.8333333
## 166 Fulborne 25 10.00000 Honeybee 0.8333333
## 167 Fulborne 25 10.00000 Honeybee 0.8333333
## 168 Fulborne 25 10.00000 Honeybee 0.8333333
## 169 Fulborne 25 10.00000 Honeybee 0.8333333
## 170 Horncastle 22 0.00000 Honeybee 0.6363636
## 171 Horncastle 22 0.00000 Honeybee 0.6363636
## 172 Horncastle 22 0.00000 Honeybee 0.6363636
## 173 Horncastle 22 0.00000 Honeybee 0.6363636
## 174 Horncastle 22 0.00000 Honeybee 0.6363636
## 175 Horncastle 22 0.00000 Honeybee 0.6363636
## 176 Horncastle 22 0.00000 Honeybee 0.6363636
## 177 Orford 14 50.00000 Honeybee 0.5000000
## 178 Fulborne 25 10.00000 Honeybee 0.5000000
## 179 Fulborne 25 10.00000 Honeybee 0.5000000
## 180 Fulborne 25 10.00000 Honeybee 0.2000000
## 181 Horncastle 22 0.00000 Honeybee 0.5714286
## 182 Horncastle 22 0.00000 Honeybee 0.5714286
## 183 Horncastle 22 0.00000 Honeybee 0.5714286
## 184 Horncastle 22 0.00000 Honeybee 0.5714286
## 185 Horncastle 22 0.00000 Honeybee 0.3333333library(ggplot2)
ggplot(honeydf,
aes(y = relative_abundance,
x = pct_flower,
fill= group)) +
geom_point(color="cornflowerblue",
size = 2,
alpha=.8) +
labs(y = "relative abundance",
x = "percentage flower cover",
title = "Honeybee relative abundance vs percentage flower cover")
ggplot(honeydf,
aes(y = relative_abundance,
x = temp,
fill= group)) +
geom_point(color="cornflowerblue",
size = 2,
alpha=.8) +
labs(y = "relative abundance",
x = "temp",
title = "Honeybee relative abundance vs temperature")
# select numeric variables
df <- dplyr::select_if(final_assessment_dataset, is.numeric)
# calulate the correlations
r <- cor(df, use="complete.obs")
round(r,2)## pct_flower temp relative_abundance
## pct_flower 1.00 -0.20 0.05
## temp -0.20 1.00 0.07
## relative_abundance 0.05 0.07 1.00library(ggplot2)
library(ggcorrplot)
ggcorrplot(r, hc.order = TRUE, type = "full", lab = TRUE,
outline.col = "white",
ggtheme = ggplot2::theme_gray)
None of the numeric values show any correlation