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| library(tidyverse) | |
| # TASK 1 - data processing | |
| data_w8 <- read_csv("data_stroop.csv") | |
| view(data_w8) # view the data | |
| data_w8 %>% | |
| ggplot() + | |
| geom_density(aes(x = time, fill = condition), alpha = .5) # you need to EDIT this for Q4 | |
| # create a new column which is the average time | |
| data_w8 <- | |
| data_w8 %>% | |
| group_by(pID) %>% # for each participant... | |
| mutate(avg_time = mean(time)) # create a new average time from the mean of the time column | |
| view(data_w8) # view the data | |
| # distribution of average times | |
| data_w8 %>% | |
| ggplot() + | |
| geom_histogram(aes(avg_time)) # you need to EDIT this for Q6 | |
| # Do we need to filter out outliers? | |
| # hint: you'll probably want to use an OR which is this symbol: | | |
| data_w8_f <- | |
| data_w8 %>% | |
| filter(avg_time > 2 & avg_time < 15) # you need to EDIT this for Q7 - these are suggested values - could vary | |
| # TASK 2 - conducting related samples t-tests | |
| # calculate the means for the 3 levels of the condition IV | |
| data_w8_f %>% | |
| group_by(condition) %>% # you need to EDIT this for Q1 | |
| summarise(stroop_mean = mean(time)) # you need to EDIT this for Q1 | |
| # filter is used to select two levels of the IV - Q3-5 | |
| # COMPARISON 1 | |
| stroop_comparison <- | |
| data_w8_f %>% | |
| filter(condition == "compatible" | condition == "incompatible") | |
| # run the t-test comparing the means of these two levels | |
| t.test(data = stroop_comparison, time ~ condition, paired = TRUE) | |
| # COMPARISON 1 | |
| stroop_comparison <- | |
| data_w8_f %>% | |
| filter(condition == "compatible" | condition == "control") | |
| # run the t-test comparing the means of these two levels | |
| t.test(data = stroop_comparison, time ~ condition, paired = TRUE) | |
| # COMPARISON 1 | |
| stroop_comparison <- | |
| data_w8_f %>% | |
| filter(condition == "incompatible" | condition == "control") | |
| # run the t-test comparing the means of these two levels | |
| t.test(data = stroop_comparison, time ~ condition, paired = TRUE) | |
| # TASK 3 - summarising and visualisation | |
| # calculate the means for the 3 levels of the condition IV | |
| data_w8_summary <-# Notice we are creating/overwriting this object here | |
| data_w8_f %>% | |
| group_by(condition) %>% # you need to EDIT this for Q1 | |
| summarise(stroop_mean = mean(time), | |
| stroop_SE = sd(time)/sqrt(n())) # you need to EDIT the sd() for Q1 | |
| view(data_w8_summary) # if you've done the above step correctly, you should have different means and SEs for each condition | |
| # let's first plot the means | |
| data_w8_summary %>% | |
| ggplot(aes(x = condition, y = stroop_mean)) + # map variables to x and y for Q3 | |
| geom_col(size = 2) | |
| # let's do the same again, but now with error bars | |
| data_w8_summary %>% | |
| ggplot(aes(x = condition, y = stroop_mean)) + # map variables to x and y | |
| geom_col(size = 2) + | |
| geom_errorbar(aes(ymin = stroop_mean - stroop_SE, # edit this for Q5 | |
| ymax = stroop_mean + stroop_SE), # edit this for Q5 | |
| width = .2) | |
| # adding more colour to the figure! | |
| data_w8_summary %>% | |
| ggplot(aes(x = condition, y = stroop_mean, fill = condition)) + # map variables to x and y | |
| geom_col(size = 1, | |
| colour = "black") + | |
| geom_errorbar(aes(ymin = stroop_mean - stroop_SE, # edit this for Q5 | |
| ymax = stroop_mean + stroop_SE), # edit this for Q5 | |
| width = .2, | |
| size = 1) + | |
| scale_fill_manual(values = c("gold", "dark green", "dark red")) + | |
| theme_classic() | |