Crabs tutorial
Caleb Collins
2023-11-30
crabstutorial.RmdStep 1 download R package from Github
devtools::install_github("calebcollins/crabs.pkg")## Using github PAT from envvar GITHUB_PAT## Downloading GitHub repo calebcollins/crabs.pkg@HEAD## bslib (0.6.0 -> 0.6.1) [CRAN]
## scales (1.2.1 -> 1.3.0) [CRAN]## Installing 2 packages: bslib, scales## Installing packages into '/tmp/RtmpYqARke/temp_libpath131226108e4'
## (as 'lib' is unspecified)## ── R CMD build ─────────────────────────────────────────────────────────────────
## * checking for file ‘/tmp/Rtmps2caxJ/remotes750217427b3/calebcollins-crabs.pkg-34a5e8c/DESCRIPTION’ ... OK
## * preparing ‘collins.pkg’:
## * checking DESCRIPTION meta-information ... OK
## * checking for LF line-endings in source and make files and shell scripts
## * checking for empty or unneeded directories
## * building ‘collins.pkg_0.1.0.tar.gz’
## Warning: invalid uid value replaced by that for user 'nobody'## Installing package into '/tmp/RtmpYqARke/temp_libpath131226108e4'
## (as 'lib' is unspecified)
library(collins.pkg)Step 2, Download the crabs data set from googlesheets
library(googlesheets4)
googlesheets4::gs4_deauth()
crabs <- read_sheet("https://docs.google.com/spreadsheets/d/14LA9eB3CEgEosqUwT7zkhjfiNsGt6zKu9BU9xAx3BUo/edit?usp=sharing")## ✔ Reading from crabs.## ✔ Range crabs.Step 3 install any packages not all ready installed
install.packages("tidyverse")## Installing package into '/tmp/RtmpYqARke/temp_libpath131226108e4'
## (as 'lib' is unspecified)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
install.packages("dplyr")## Installing package into '/tmp/RtmpYqARke/temp_libpath131226108e4'
## (as 'lib' is unspecified)
library(dplyr)
install.packages("ggplot2")## Installing package into '/tmp/RtmpYqARke/temp_libpath131226108e4'
## (as 'lib' is unspecified)Now we will check and clean the data set if need by removing N/A’s
clean_data(crabs)Now we will sort it by a chosen column, this allows us to prioritisze one column of data
mutate_data(crabs, "hindfoot_lenght")
mutate_data(crabs,"rear_width")## # A tibble: 200 × 8
## color sex frontal_lobe rear_width carapace_length carapace_width body_depth
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 blue F 7.2 6.5 14.7 17.1 6.1
## 2 blue M 8.1 6.7 16.1 19 7
## 3 oran… M 9.1 6.9 16.7 18.6 7.4
## 4 blue M 8.8 7.7 18.1 20.8 7.4
## 5 blue M 9.2 7.8 19 22.4 7.7
## 6 blue M 9.6 7.9 20.1 23.1 8.2
## 7 blue M 9.8 8 20.3 23 8.2
## 8 blue F 9.1 8.1 18.5 21.6 7.7
## 9 blue F 9.1 8.2 19.2 22.2 7.7
## 10 blue F 9.5 8.2 19.6 22.4 7.8
## # ℹ 190 more rows
## # ℹ 1 more variable: Latitude <dbl>Now we will make a scatter plot letting us compare two columns
library(ggplot2)
function_graph(crabs, rear_width, body_depth)
An example of a useless graph
function_graph(crabs, sex, color)
Next we will perform an linear model analysis
lm_function(crabs, "rear_width", "body_depth")##
## Call:
## lm(formula = a ~ ., data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.09599 -1.04998 -0.01039 0.97169 3.11330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.36416 0.35279 9.536 <2e-16 ***
## body_depth 0.66814 0.02443 27.349 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.18 on 198 degrees of freedom
## Multiple R-squared: 0.7907, Adjusted R-squared: 0.7896
## F-statistic: 747.9 on 1 and 198 DF, p-value: < 2.2e-16
lm_function(crabs, "rear_width", "hindfoot_length")Last we will kill the data because i did it on accident
fun_function(crabs)## color sex frontal_lobe rear_width carapace_length carapace_width
## 1 blue M 8.1 6.7 16.1 19.0
## 2 blue M 8.8 7.7 18.1 20.8
## 3 blue M 9.2 7.8 19.0 22.4
## 4 blue M 9.6 7.9 20.1 23.1
## 5 blue M 9.8 8.0 20.3 23.0
## 6 blue M 10.8 9.0 23.0 26.5
## 7 blue M 11.1 9.9 23.8 27.1
## 8 blue M 11.6 9.1 24.5 28.4
## 9 blue M 11.8 9.6 24.2 27.8
## 10 blue M 11.8 10.5 25.2 29.3
## 11 blue M 12.2 10.8 27.3 31.6
## 12 blue M 12.3 11.0 26.8 31.5
## 13 blue M 12.6 10.0 27.7 31.7
## 14 blue M 12.8 10.2 27.2 31.8
## 15 blue M 12.8 10.9 27.4 31.5
## 16 blue M 12.9 11.0 26.8 30.9
## 17 blue M 13.1 10.6 28.2 32.3
## 18 blue M 13.1 10.9 28.3 32.4
## 19 blue M 13.3 11.1 27.8 32.3
## 20 blue M 13.9 11.1 29.2 33.3
## 21 blue M 14.3 11.6 31.3 35.5
## 22 blue M 14.6 11.3 31.9 36.4
## 23 blue M 15.0 10.9 31.4 36.4
## 24 blue M 15.0 11.5 32.4 37.0
## 25 blue M 15.0 11.9 32.5 37.2
## 26 blue M 15.2 12.1 32.3 36.7
## 27 blue M 15.4 11.8 33.0 37.5
## 28 blue M 15.7 12.6 35.8 40.3
## 29 blue M 15.9 12.7 34.0 38.9
## 30 blue M 16.1 11.6 33.8 39.0
## 31 blue M 16.1 12.8 34.9 40.7
## 32 blue M 16.2 13.3 36.0 41.7
## 33 blue M 16.3 12.7 35.6 40.9
## 34 blue M 16.4 13.0 35.7 41.8
## 35 blue M 16.6 13.5 38.1 43.4
## 36 blue M 16.8 12.8 36.2 41.8
## 37 blue M 16.9 13.2 37.3 42.7
## 38 blue M 17.1 12.6 36.4 42.0
## 39 blue M 17.1 12.7 36.7 41.9
## 40 blue M 17.2 13.5 37.6 43.9
## 41 blue M 17.7 13.6 38.7 44.5
## 42 blue M 17.9 14.1 39.7 44.6
## 43 blue M 18.0 13.7 39.2 44.4
## 44 blue M 18.8 15.8 42.1 49.0
## 45 blue M 19.3 13.5 41.6 47.4
## 46 blue M 19.3 13.8 40.9 46.5
## 47 blue M 19.7 15.3 41.9 48.5
## 48 blue M 19.8 14.2 43.2 49.7
## 49 blue M 19.8 14.3 42.4 48.9
## 50 blue M 21.3 15.7 47.1 54.6
## 51 blue F 7.2 6.5 14.7 17.1
## 52 blue F 9.0 8.5 19.3 22.7
## 53 blue F 9.1 8.1 18.5 21.6
## 54 blue F 9.1 8.2 19.2 22.2
## 55 blue F 9.5 8.2 19.6 22.4
## 56 blue F 9.8 8.9 20.4 23.9
## 57 blue F 10.1 9.3 20.9 24.4
## 58 blue F 10.3 9.5 21.3 24.7
## 59 blue F 10.4 9.7 21.7 25.4
## 60 blue F 10.8 9.5 22.5 26.3
## 61 blue F 11.0 9.8 22.5 25.7
## 62 blue F 11.2 10.0 22.8 26.9
## 63 blue F 11.5 11.0 24.7 29.2
## 64 blue F 11.6 11.0 24.6 28.5
## 65 blue F 11.6 11.4 23.7 27.7
## 66 blue F 11.7 10.6 24.9 28.5
## 67 blue F 11.9 11.4 26.0 30.1
## 68 blue F 12.0 10.7 24.6 28.9
## 69 blue F 12.0 11.1 25.4 29.2
## 70 blue F 12.6 12.2 26.1 31.6
## 71 blue F 12.8 11.7 27.1 31.2
## 72 blue F 12.8 12.2 26.7 31.1
## 73 blue F 12.8 12.2 27.9 31.9
## 74 blue F 13.0 11.4 27.3 31.8
## 75 blue F 13.1 11.5 27.6 32.6
## 76 blue F 13.2 12.2 27.9 32.1
## 77 blue F 13.4 11.8 28.4 32.7
## 78 blue F 13.7 12.5 28.6 33.8
## 79 blue F 13.9 13.0 30.0 34.9
## 80 blue F 14.7 12.5 30.1 34.7
## 81 blue F 14.9 13.2 30.1 35.6
## 82 blue F 15.0 13.8 31.7 36.9
## 83 blue F 15.0 14.2 32.8 37.4
## 84 blue F 15.1 13.3 31.8 36.3
## 85 blue F 15.1 13.5 31.9 37.0
## 86 blue F 15.1 13.8 31.7 36.6
## 87 blue F 15.2 14.3 33.9 38.5
## 88 blue F 15.3 14.2 32.6 38.3
## 89 blue F 15.4 13.3 32.4 37.6
## 90 blue F 15.5 13.8 33.4 38.7
## 91 blue F 15.6 13.9 32.8 37.9
## 92 blue F 15.6 14.7 33.9 39.5
## 93 blue F 15.7 13.9 33.6 38.5
## 94 blue F 15.8 15.0 34.5 40.3
## 95 blue F 16.2 15.2 34.5 40.1
## 96 blue F 16.4 14.0 34.2 39.8
## 97 blue F 16.7 16.1 36.6 41.9
## 98 blue F 17.4 16.9 38.2 44.1
## 99 blue F 17.5 16.7 38.6 44.5
## 100 blue F 19.2 16.5 40.9 47.9
## 101 orange M 9.1 6.9 16.7 18.6
## 102 orange M 10.2 8.2 20.2 22.2
## 103 orange M 10.7 8.6 20.7 22.7
## 104 orange M 11.4 9.0 22.7 24.8
## 105 orange M 12.5 9.4 23.2 26.0
## 106 orange M 12.5 9.4 24.2 27.0
## 107 orange M 12.7 10.4 26.0 28.8
## 108 orange M 13.2 11.0 27.1 30.4
## 109 orange M 13.4 10.1 26.6 29.6
## 110 orange M 13.7 11.0 27.5 30.5
## 111 orange M 14.0 11.5 29.2 32.2
## 112 orange M 14.1 10.4 28.9 31.8
## 113 orange M 14.1 10.5 29.1 31.6
## 114 orange M 14.1 10.7 28.7 31.9
## 115 orange M 14.2 10.6 28.7 31.7
## 116 orange M 14.2 10.7 27.8 30.9
## 117 orange M 14.2 11.3 29.2 32.2
## 118 orange M 14.6 11.3 29.9 33.5
## 119 orange M 14.7 11.1 29.0 32.1
## 120 orange M 15.1 11.4 30.2 33.3
## 121 orange M 15.1 11.5 30.9 34.0
## 122 orange M 15.4 11.1 30.2 33.6
## 123 orange M 15.7 12.2 31.7 34.2
## 124 orange M 16.2 11.8 32.3 35.3
## 125 orange M 16.3 11.6 31.6 34.2
## 126 orange M 17.1 12.6 35.0 38.9
## 127 orange M 17.4 12.8 36.1 39.5
## 128 orange M 17.5 12.0 34.4 37.3
## 129 orange M 17.5 12.7 34.6 38.4
## 130 orange M 17.8 12.5 36.0 39.8
## 131 orange M 17.9 12.9 36.9 40.9
## 132 orange M 18.0 13.4 36.7 41.3
## 133 orange M 18.2 13.7 38.8 42.7
## 134 orange M 18.4 13.4 37.9 42.2
## 135 orange M 18.6 13.4 37.8 41.9
## 136 orange M 18.6 13.5 36.9 40.2
## 137 orange M 18.8 13.4 37.2 41.1
## 138 orange M 18.8 13.8 39.2 43.3
## 139 orange M 19.4 14.1 39.1 43.2
## 140 orange M 19.4 14.4 39.8 44.3
## 141 orange M 20.1 13.7 40.6 44.5
## 142 orange M 20.6 14.4 42.8 46.5
## 143 orange M 21.0 15.0 42.9 47.2
## 144 orange M 21.5 15.5 45.5 49.7
## 145 orange M 21.6 15.4 45.7 49.7
## 146 orange M 21.6 14.8 43.4 48.2
## 147 orange M 21.9 15.7 45.4 51.0
## 148 orange M 22.1 15.8 44.6 49.6
## 149 orange M 23.0 16.8 47.2 52.1
## 150 orange M 23.1 15.7 47.6 52.8
## 151 orange F 10.7 9.7 21.4 24.0
## 152 orange F 11.4 9.2 21.7 24.1
## 153 orange F 12.5 10.0 24.1 27.0
## 154 orange F 12.6 11.5 25.0 28.1
## 155 orange F 12.9 11.2 25.8 29.1
## 156 orange F 14.0 11.9 27.0 31.4
## 157 orange F 14.0 12.8 28.8 32.4
## 158 orange F 14.3 12.2 28.1 31.8
## 159 orange F 14.7 13.2 29.6 33.4
## 160 orange F 14.9 13.0 30.0 33.7
## 161 orange F 15.0 12.3 30.1 33.3
## 162 orange F 15.6 13.5 31.2 35.1
## 163 orange F 15.6 14.0 31.6 35.3
## 164 orange F 15.6 14.1 31.0 34.5
## 165 orange F 15.7 13.6 31.0 34.8
## 166 orange F 16.1 13.6 31.6 36.0
## 167 orange F 16.1 13.7 31.4 36.1
## 168 orange F 16.2 14.0 31.6 35.6
## 169 orange F 16.7 14.3 32.3 37.0
## 170 orange F 17.1 14.5 33.1 37.2
## 171 orange F 17.5 14.3 34.5 39.6
## 172 orange F 17.5 14.4 34.5 39.0
## 173 orange F 17.5 14.7 33.3 37.6
## 174 orange F 17.6 14.0 34.0 38.6
## 175 orange F 18.0 14.9 34.7 39.5
## 176 orange F 18.0 16.3 37.9 43.0
## 177 orange F 18.3 15.7 35.1 40.5
## 178 orange F 18.4 15.5 35.6 40.0
## 179 orange F 18.4 15.7 36.5 41.6
## 180 orange F 18.5 14.6 37.0 42.0
## 181 orange F 18.6 14.5 34.7 39.4
## 182 orange F 18.8 15.2 35.8 40.5
## 183 orange F 18.9 16.7 36.3 41.7
## 184 orange F 19.1 16.0 37.8 42.3
## 185 orange F 19.1 16.3 37.9 42.6
## 186 orange F 19.7 16.7 39.9 43.6
## 187 orange F 19.9 16.6 39.4 43.9
## 188 orange F 19.9 17.9 40.1 46.4
## 189 orange F 20.0 16.7 40.4 45.1
## 190 orange F 20.1 17.2 39.8 44.1
## 191 orange F 20.3 16.0 39.4 44.1
## 192 orange F 20.5 17.5 40.0 45.5
## 193 orange F 20.6 17.5 41.5 46.2
## 194 orange F 20.9 16.5 39.9 44.7
## 195 orange F 21.3 18.4 43.8 48.4
## 196 orange F 21.4 18.0 41.2 46.2
## 197 orange F 21.7 17.1 41.7 47.2
## 198 orange F 21.9 17.2 42.6 47.4
## 199 orange F 22.5 17.2 43.0 48.7
## 200 orange F 23.1 20.2 46.2 52.5
## body_depth Latitude
## 1 7.0 22.0
## 2 7.4 22.1
## 3 7.7 22.4
## 4 8.2 22.6
## 5 8.2 22.0
## 6 9.8 22.1
## 7 9.8 22.4
## 8 10.4 22.6
## 9 9.7 22.0
## 10 10.3 22.1
## 11 10.9 22.4
## 12 11.4 22.6
## 13 11.4 22.0
## 14 10.9 30.0
## 15 11.0 22.4
## 16 11.4 31.2
## 17 11.0 22.0
## 18 11.2 32.2
## 19 11.3 22.4
## 20 12.1 22.6
## 21 12.7 22.0
## 22 13.7 22.0
## 23 13.2 22.1
## 24 13.4 22.4
## 25 13.6 22.6
## 26 13.6 30.4
## 27 13.6 32.4
## 28 14.5 33.4
## 29 14.2 33.1
## 30 14.4 33.6
## 31 15.7 30.4
## 32 15.4 32.4
## 33 14.9 33.4
## 34 15.2 33.1
## 35 14.9 33.6
## 36 14.9 30.4
## 37 15.6 32.4
## 38 15.1 33.4
## 39 15.6 33.1
## 40 16.1 30.4
## 41 16.0 32.4
## 42 16.8 33.4
## 43 16.2 33.1
## 44 17.8 33.6
## 45 17.8 30.4
## 46 16.8 32.4
## 47 17.8 33.4
## 48 18.6 33.1
## 49 18.3 36.4
## 50 20.0 36.2
## 51 6.1 36.8
## 52 7.7 36.4
## 53 7.7 36.2
## 54 7.7 36.8
## 55 7.8 36.4
## 56 8.8 36.2
## 57 8.4 36.8
## 58 8.9 36.4
## 59 8.3 36.2
## 60 9.1 36.8
## 61 8.2 36.4
## 62 9.4 36.2
## 63 10.1 36.8
## 64 10.4 36.4
## 65 10.0 36.2
## 66 10.4 36.8
## 67 10.9 36.4
## 68 10.5 38.7
## 69 11.0 36.7
## 70 11.2 34.2
## 71 11.9 36.8
## 72 11.1 36.4
## 73 11.5 36.2
## 74 11.3 36.8
## 75 11.1 36.4
## 76 11.5 36.2
## 77 11.7 36.8
## 78 11.9 36.4
## 79 13.1 38.7
## 80 12.5 36.7
## 81 12.0 34.2
## 82 14.0 36.8
## 83 14.0 36.4
## 84 13.5 36.2
## 85 13.8 36.8
## 86 13.0 36.4
## 87 14.7 36.2
## 88 13.8 36.8
## 89 13.8 36.4
## 90 14.7 38.7
## 91 13.4 36.7
## 92 14.3 34.2
## 93 14.1 36.8
## 94 15.3 36.4
## 95 13.9 36.2
## 96 15.2 36.8
## 97 15.4 36.4
## 98 16.6 36.2
## 99 17.0 36.8
## 100 18.1 36.4
## 101 7.4 38.7
## 102 9.0 36.7
## 103 9.2 34.2
## 104 10.1 36.8
## 105 10.8 36.4
## 106 11.2 36.2
## 107 12.1 36.8
## 108 12.2 36.4
## 109 12.0 36.2
## 110 12.2 36.8
## 111 13.1 36.4
## 112 13.5 38.7
## 113 13.1 36.7
## 114 13.3 34.2
## 115 12.9 36.8
## 116 12.7 36.4
## 117 13.5 36.2
## 118 12.8 36.8
## 119 13.1 36.4
## 120 14.0 36.2
## 121 13.9 36.8
## 122 13.5 36.4
## 123 14.2 38.7
## 124 14.7 36.7
## 125 14.5 34.2
## 126 15.7 36.8
## 127 16.2 36.4
## 128 15.3 36.2
## 129 16.1 36.8
## 130 16.7 36.4
## 131 16.5 36.2
## 132 17.1 36.8
## 133 17.2 36.4
## 134 17.7 38.7
## 135 17.3 36.7
## 136 17.0 34.2
## 137 17.5 36.8
## 138 17.9 36.4
## 139 17.8 36.2
## 140 17.9 36.8
## 141 18.0 36.4
## 142 19.6 36.2
## 143 19.4 36.8
## 144 20.9 36.4
## 145 20.6 38.7
## 146 20.1 36.7
## 147 21.1 34.2
## 148 20.5 36.8
## 149 21.5 36.4
## 150 21.6 36.2
## 151 9.8 36.8
## 152 9.7 36.4
## 153 10.9 36.2
## 154 11.5 36.8
## 155 11.9 36.4
## 156 12.6 38.7
## 157 12.7 36.7
## 158 12.5 34.2
## 159 12.9 36.8
## 160 13.3 36.4
## 161 14.0 36.2
## 162 14.1 36.8
## 163 13.8 36.4
## 164 13.8 36.2
## 165 13.8 36.8
## 166 14.0 36.4
## 167 13.9 38.7
## 168 13.7 36.7
## 169 14.7 34.2
## 170 14.6 36.8
## 171 15.6 36.4
## 172 16.0 36.2
## 173 14.6 36.8
## 174 15.5 36.4
## 175 15.7 36.2
## 176 17.2 36.8
## 177 16.1 36.4
## 178 15.9 38.7
## 179 16.4 36.7
## 180 16.6 34.2
## 181 15.0 36.8
## 182 16.6 36.4
## 183 15.3 36.2
## 184 16.8 36.8
## 185 17.2 36.4
## 186 18.2 36.2
## 187 17.9 36.8
## 188 17.9 36.4
## 189 17.7 38.7
## 190 18.6 36.7
## 191 18.0 34.2
## 192 19.2 36.8
## 193 19.2 36.4
## 194 17.5 36.2
## 195 20.0 36.8
## 196 18.7 36.4
## 197 19.6 36.2
## 198 19.5 36.8
## 199 19.8 36.4
## 200 21.1 38.7