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Average storage time

The average solution storage time when the outcomes were measured the first time (Rep 1, 4 days) and the second time (Rep 2, 15 days), and the differences between the two repeated measures (11 days).

rep1.time <- ss.storage %>%
  filter(Measure == "Most.Liked", Rep == "1") %>%
  summarize(mean(Storage_onTestDay))
print(rep1.time)
## # A tibble: 1 × 1
##   `mean(Storage_onTestDay)`
##                       <dbl>
## 1                      4.24
rep2.time <- ss.storage %>%
  filter(Measure == "Most.Liked", Rep == "2") %>%
  summarize(mean(Storage_onTestDay))
print(rep2.time)
## # A tibble: 1 × 1
##   `mean(Storage_onTestDay)`
##                       <dbl>
## 1                      15.3
rep2.time - rep1.time ## the average difference in storage time between Rep 1 and Rep 2 was 11 days
##   mean(Storage_onTestDay)
## 1                11.04762

Storage time on most liked sucrose concentration

The relationship between storage time on most liked sucrose concentration, mM. Measures were taken twice within subject at different time points (Rep 1 = 4 days, Rep 2 = 11 days). The average time elapsed between the Rep 1 and Rep 2 was 11 days. A follow up t test was conducted to determine the difference in most liked concentration between Rep 1 and Rep 2.

The correlation between storage time and most liked concentration was not significant (p = 0.2). There was no statistically significant difference between the average most liked concentration between Rep 1 and Rep 2 (p = 0.7).

## Correlation between storage time and most liked concentration
ss.storage %>%
  filter(Measure == "Most.Liked") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Most Liked Concentration, mM") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 18, label.y = c(250, 150))
## `geom_smooth()` using formula 'y ~ x'

## Correlation between storage time and most liked concentration by Rep
ss.storage %>%
  filter(Measure == "Most.Liked") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result, color = as.factor(Rep)))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Most Liked Concentration, mM") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 18, label.y = c(250, 150))
## `geom_smooth()` using formula 'y ~ x'

## Differences between most liked concentration measured during Rep 1 and Rep 2
mostliked.ttest <- ss.storage %>%
  filter(Measure == "Most.Liked") %>%
  aov(Result ~ Rep, data =.)
summary(mostliked.ttest)
##             Df  Sum Sq Mean Sq F value Pr(>F)
## Rep          1   10299   10299   0.114  0.738
## Residuals   40 3617353   90434

Storage time on preferred sucrose concentration

The relationship between storage time on preferred sucrose concentration, mM. Measures were taken twice within subject at different time points (Rep 1 = 4 days, Rep 2 = 11 days). The average time elapsed between the Rep 1 and Rep 2 was 11 days. A follow up t test was conducted to determine the difference in preferred concentration between Rep 1 and Rep 2.

The correlation between storage time and preferred concentration was not significant (p = 0.14). However, there was a significant positive correlation between storage time and preferred concentration in Rep 2 (r = 0.5, p = 0.02). There was no statistically significant difference between the average most liked concentration between Rep 1 and Rep 2 (p = 0.98).

## Correlation between storage time and preferred concentration
ss.storage %>%
  filter(Measure == "Preferred") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Preferred Concentration, mM") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 10, label.y = c(1500, 1400))
## `geom_smooth()` using formula 'y ~ x'

## Correlation between storage time and preferred concentration by Rep
ss.storage %>%
  filter(Measure == "Preferred") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result, color = as.factor(Rep)))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Preferred Concentration, mM") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 10, label.y = c(1500, 1400))
## `geom_smooth()` using formula 'y ~ x'

## Differences between most preferred measured during Rep 1 and Rep 2
preferred.ttest <- ss.storage %>%
  filter(Measure == "Preferred") %>%
  aov(Result ~ Rep, data =.)
summary(preferred.ttest)
##             Df  Sum Sq Mean Sq F value Pr(>F)
## Rep          1      60      60   0.001  0.978
## Residuals   40 3193505   79838

Storage time on perceived intensity

The relationship between perceived storage time and perceived sweetness intensity. Measures were taken twice within subject at different time points (Rep 1 = 4 days, Rep 2 = 11 days). The average time elapsed between the Rep 1 and Rep 2 was 11 days.

The correlations between storage time and perceived sweetness were not significant with no meaningful patterns.

## for 90 mM sucrose solution
ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.09") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 15)
## `geom_smooth()` using formula 'y ~ x'

ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.09") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result, color = as.factor(Rep)))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 15)
## `geom_smooth()` using formula 'y ~ x'
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## for 0.18 mM sucrose solution
ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.18") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 3)
## `geom_smooth()` using formula 'y ~ x'

ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.18") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result, color = as.factor(Rep)))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 3)
## `geom_smooth()` using formula 'y ~ x'

## for 0.35 mM sucrose solution
ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.35") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 15)
## `geom_smooth()` using formula 'y ~ x'

ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.35") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result, color = as.factor(Rep)))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 15)
## `geom_smooth()` using formula 'y ~ x'

## for 0.70 mM sucrose solution
ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.7") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 15, label.y = c(63, 60))
## `geom_smooth()` using formula 'y ~ x'

ss.storage %>%
  filter(Measure == "Sweet", Concentration == "0.7") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result, color = as.factor(Rep)))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 15, label.y = c(63, 60))
## `geom_smooth()` using formula 'y ~ x'

## for 1.05 mM sucrose solution
ss.storage %>%
  filter(Measure == "Sweet", Concentration == "1.05") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 3, label.y = c(100, 95))
## `geom_smooth()` using formula 'y ~ x'

ss.storage %>%
  filter(Measure == "Sweet", Concentration == "1.05") %>%
  ggplot(aes(x = Storage_onTestDay, y = Result, color = as.factor(Rep)))+
  geom_point(size = 1)+
  geom_smooth(method = lm, fullrange = TRUE)+
  theme_bw() +
  xlab("Storage Time") +ylab("Perceived Sweetness Intensity (gLMS)") +
  geom_label_repel(aes(label = ID), size = 2.5) +
  stat_cor(label.x = 3, label.y = c(100, 95))
## `geom_smooth()` using formula 'y ~ x'