Publications by Evelyn (Rosie) Lomas
Aquatic Chemistry and Pollution Water Quality against Nerang River Discharge
Step 1: Discharge Graph: library(dplyr) ## ## 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 library(lubridate) ## ## Attaching package: 'lubridate' ## The following objects are maske...
1179 sym R (10288 sym/47 pcs) 33 img
Aquatic Chemistry and Pollution Test for Normal Distribution and Correlations
library(readxl) CHEM_RESULTS <- read_excel("CHEM RESULTS.xlsx", sheet = "Variance tests ") View(CHEM_RESULTS) Test each variable to see if normally distributed: Step 1. Visualise Data it is important to do both visual checks & normality tests in small samples (n<30). 1. Enterocococci Test for Normality QQ plot set.seed(0) library(ggpubr) ##...
2530 sym R (14792 sym/70 pcs) 22 img
Aquatic Chemistry and Pollution Test for Normal Distribution and Correlations
library(readxl) CHEM_RESULTS <- read_excel("CHEM RESULTS.xlsx", sheet = "Variance tests ") View(CHEM_RESULTS) Test each variable to see if normally distributed: Step 1. Visualise Data it is important to do both visual checks & normality tests in small samples (n<30). 1. Enterocococci Test for Normality QQ plot set.seed(0) library(ggpubr) ##...
2530 sym R (14518 sym/70 pcs) 22 img
Aquatic Chemistry and Pollution Water Quality against Nerang River Discharge
Step 1: Discharge Graph: library(dplyr) ## ## 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 library(lubridate) ## ## Attaching package: 'lubridate' ## The following objects are maske...
1019 sym R (9516 sym/46 pcs) 32 img
Aquatic Chemistry and Pollution Test for Normal Distribution and Correlations
library(readxl) CHEM_RESULTS <- read_excel("CHEM RESULTS.xlsx", sheet = "Variance tests ") View(CHEM_RESULTS) Test each variable to see if normally distributed: Step 1. Visualise Data it is important to do both visual checks & normality tests in small samples (n<30). 1. Enterocococci Test for Normality QQ plot set.seed(0) library(ggpubr) ##...
2625 sym R (14518 sym/70 pcs) 22 img
Statistical Tests for Analysis of Mimosa Creek, Toohey Forest, Brisbane/Meanjin
Data was subseted in Excel for convenience, all pond and creek measurements were put together on separate sheets in excel: Pond Measurements: library(readxl) Pond_data_ <- read_excel("Pond data .xlsx") Pond_WQ<- Pond_data_ summary(Pond_data_) ## Week Date Site Reading ## Min. :1.00 Length:6 ...
2963 sym R (45042 sym/63 pcs) 17 img
Statistical Tests for Analysis of Mimosa Creek, Toohey Forest Brisbane Meanjin
Data was subseted in Excel for convenience, all pond and creek measurements were put together on separate sheets in excel: Pond Measurements: library(readxl) Pond_data_ <- read_excel("Pond data .xlsx") Pond_WQ<- Pond_data_ summary(Pond_data_) ## Week Date Site Reading ## Min. :1.00 Length:6 ...
2963 sym R (45042 sym/63 pcs) 17 img