Tracking_Nitrate_RepeatedMeasures.Rmd

---
title: "Tracking_Nitrate_Repeated Measures"
author: "Kolby Grint"
date: "2022-08-04"
output: pdf_document
---

```{r}
#loading packages
library(tidyverse)
library(ggpubr)
library(rstatix)
library(lme4)
```

```{r}
getwd()
data1= read.csv(file="Season_nitrate.csv")

data1 = data1 %>%
  mutate(Rep= as.factor(Rep),
         EU = as.factor(EU))

str(data1)
```

```{r}
data2 = data1 %>%
  mutate(Timing = fct_recode(Timing,
                    "1" = "PP",
                    "2" = "GU",
                    "3" = "PH"))
str(data2)
```

```{r}
#Filtering dataset
Dane12 = data2 %>%
  filter(County == "Dane", Depth == "0-12")
Dane24 = data2 %>%
  filter(County == "Dane", Depth == "12-24")
Dane36 = data2 %>%
  filter(County == "Dane", Depth == "24-36")
```


#Dane 12
```{r}
#Looking for outliers
Dane12 %>%
  group_by(Timing) %>%
  identify_outliers(N_ppm)#no extreme outliers


#Normality Test
Dane12 %>%
  group_by(Timing) %>%
  shapiro_test(N_ppm) 

ggqqplot(Dane12, "N_ppm", facet.by = "Timing")#timing 3 not normal, outlier is the cause based on qqplot

#Sphericity Assumption
Dane12_aov = anova_test(data = Dane12, dv = N_ppm, wid = EU, within = Timing)
get_anova_table(Dane12_aov)
```

```{r}
Dane12_mod = lmer(N_ppm~ Timing + (Timing|Rep), Dane12)
```


```{r}
Dane12_means = pairwise_t_test(Dane12, N_ppm ~ Timing, paired = TRUE, p.adjust.method = "bonferroni", conf.level = 0.95)
```