codes/stat_distance_robot.Rmd

+## packages loading
+```{r}
+#General utility packages
+library(tidyverse)
+library(glue)
+library(knitr)
+library(data.table)
+#Stats packages
+library(lme4)
+library(multcomp)
+library(parameters)
+library(effectsize)
+library(performance)
+library(emmeans)
+#Plot packages
+library(ggpubr)
+```
+## loading table
+```{r}
+df <- read_csv('C:/Users/xiamen/Desktop/data_for_R/modified_robogator_paper/robotmod_distance.csv') %>% 
+          mutate(trials = as.factor(trials)) %>% 
+          mutate(group = as.factor(group)) %>% 
+          mutate(sex = as.factor(sex)) %>% 
+          mutate(rat_ID = as.factor(rat_ID)) %>% 
+          mutate(restriction = as.factor(restriction))%>%
+          mutate(age = as.factor(age))%>%
+          mutate(food.distance.cm = as.factor(food.distance.cm))%>%
+          mutate(robot = as.factor(robot))%>%
+         
+        # Create score based on time. Following log attribution of score as a function of time. 
+          mutate(leaving.score =  case_when(is.na(leaving.sec) ~ 7,
+                                                  leaving.sec >= 200 ~ 6,
+                                                  leaving.sec >= 100 ~ 5,
+                                                  leaving.sec >= 50 ~ 4,
+                                                  leaving.sec >= 25  ~ 3,
+                                                  leaving.sec >= 12.5 ~ 2,
+                                                  leaving.sec >= 6.25 ~ 1,
+                                                  leaving.sec <6.25 ~ 0)) %>% 
+            mutate(approaching.score =  case_when(is.na(approaching.sec) ~ 7,
+                                                  approaching.sec >= 200 ~ 6,
+                                                  approaching.sec >= 100 ~ 5,
+                                                  approaching.sec >= 50 ~ 4,
+                                                  approaching.sec >= 25  ~ 3,
+                                                  approaching.sec >= 12.5 ~ 2,
+                                                  approaching.sec >= 6.25 ~ 1,
+                                                  approaching.sec <6.25 ~ 0)) %>% 
+            mutate(backing.score =  case_when(is.na(backing.sec) ~ 7,
+                                                  backing.sec >= 200 ~ 6,
+                                                  backing.sec >= 100 ~ 5,
+                                                  backing.sec >= 50 ~ 4,
+                                                  backing.sec >= 25  ~ 3,
+                                                  backing.sec >= 12.5 ~ 2,
+                                                  backing.sec >= 6.25 ~ 1,
+                                                  backing.sec <6.25 ~ 0))  %>%
+            mutate(foraging.score =  case_when(is.na(foraging.sec) ~ 7,
+                                                  foraging.sec >= 200 ~ 6,
+                                                  foraging.sec >= 100 ~ 5,
+                                                  foraging.sec >= 50 ~ 4,
+                                                  foraging.sec >= 25  ~ 3,
+                                                  foraging.sec >= 12.5 ~ 2,
+                                                  foraging.sec >= 6.25 ~ 1,
+                                                  foraging.sec <6.25 ~ 0))
+
+#head(df) %>% kable("pipe")
+```
+## distance effect
+```{r}
+df.Bline <- df %>% filter(df$baseline.day == 6)
+df.Rday <- df %>% filter(df$robot.day == 1)
+```
+## liner mixed effect model
+```{r}
+robot <- lmer(foraging.score~ sex + trials + robot + sex:trials + sex:robot + trials:robot + (1|rat_ID), df)
+robot_sex_trials <- update(robot, . ~ . - sex:trials)
+
+robot_sex_robot <- update(robot, . ~ . - sex:robot)
+
+robot_trials_robot <- update(robot, . ~ . - trials:robot)
+
+robot_robot <- lmer(foraging.score~ sex + trials + sex:trials + (1|rat_ID), df)
+
+anova(robot, robot_robot)
+
+eta_squared(robot)
+```
+