Shelly Trigg 9/24/2019
Load libraries
library(readxl)
library(ggplot2)Read in data
female_data <- read_xlsx("HistologyScores.xlsx", sheet = 1)Plot percent follicle area for each treatment group
ggplot(data = female_data, aes(x = pH,y = perc_follicle_area, group = as.factor(pH), fill = pH)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.2) + geom_jitter(shape =16, position= position_jitter(0.1)) + ylab("follicle area (%)") + theme_bw()
Run wilcox test to see if follicle area is significantly different
pH6.8 <- subset(female_data, pH == "6.8", perc_follicle_area, drop = TRUE)
pHamb <- subset(female_data, pH == "amb", perc_follicle_area, drop = TRUE)
wt <- wilcox.test(pH6.8, pHamb)
print(wt$p.value)## [1] 0.3884116
Plot percent follicle area for each tank
ggplot(data = female_data, aes(x = tank,y = perc_follicle_area, group = as.factor(tank), fill = pH)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.2) + geom_jitter(shape =16, position= position_jitter(0.1)) + ylab("follicle area (%)") + theme_bw()
anova to see if there is a tank or a pH effect
model <- aov(perc_follicle_area ~ pH * tank, data = female_data)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## pH 1 323.9 323.9 1.983 0.187
## tank 1 11.9 11.9 0.073 0.792
## pH:tank 1 19.2 19.2 0.118 0.738
## Residuals 11 1796.4 163.3
Plot percent egg area for each treatment group
ggplot(data = female_data, aes(x = pH,y = perc_egg_area, group = as.factor(pH), fill = pH)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.2) + geom_jitter(shape =16, position= position_jitter(0.1)) + ylab("egg area (%)") + theme_bw()
Run wilcox test to see if egg area is significantly different
pH6.8 <- subset(female_data, pH == "6.8", perc_egg_area, drop = TRUE)
pHamb <- subset(female_data, pH == "amb", perc_egg_area, drop = TRUE)
wt <- wilcox.test(pH6.8, pHamb)
print(wt$p.value)## [1] 0.1446553
Plot percent egg area for each tank
ggplot(data = female_data, aes(x = tank,y = perc_egg_area, group = as.factor(tank), fill = pH)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.2) + geom_jitter(shape =16, position= position_jitter(0.1)) + ylab("egg area (%)") + theme_bw()
anova to see if there is a tank or a pH effect
model <- aov(perc_egg_area ~ pH * tank, data = female_data)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## pH 1 19.75 19.746 2.942 0.114
## tank 1 3.85 3.851 0.574 0.465
## pH:tank 1 0.36 0.357 0.053 0.822
## Residuals 11 73.83 6.712
Plot egg:follicle ratio for each treatment group
ggplot(data = female_data, aes(x = pH,y = follicle_egg_ratio, group = as.factor(pH), fill = pH)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.2) + geom_jitter(shape =16, position= position_jitter(0.1)) + ylab("egg:follicle ratio") + theme_bw()
Run wilcox test to see if egg:follicle ratio is significantly different
pH6.8 <- subset(female_data, pH == "6.8", follicle_egg_ratio, drop = TRUE)
pHamb <- subset(female_data, pH == "amb", follicle_egg_ratio, drop = TRUE)
wt <- wilcox.test(pH6.8, pHamb)
print(wt$p.value)## [1] 0.9546454
Plot egg:follicle ratio for each tank
ggplot(data = female_data, aes(x = tank,y = follicle_egg_ratio, group = as.factor(tank), fill = pH)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.2) + geom_jitter(shape =16, position= position_jitter(0.1)) + ylab("egg:follicle ratio") + theme_bw()
anova to see if there is a tank or a pH effect
model <- aov(follicle_egg_ratio ~ pH * tank, data = female_data)
summary(model)## Df Sum Sq Mean Sq F value Pr(>F)
## pH 1 0.00005 0.000050 0.012 0.915
## tank 1 0.00478 0.004784 1.139 0.309
## pH:tank 1 0.00010 0.000102 0.024 0.879
## Residuals 11 0.04619 0.004199