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| library(tidyverse) | |
| library(pwr) | |
| library(rstatix) | |
| data_w9 <- read_csv("music_smedia.csv") # read in the data | |
| # TASK 1 - data visualisation and cleaning | |
| data_w9 %>% | |
| ggplot() + | |
| geom_jitter(aes(x = rock_score, y = pop_score, colour = music_pref)) | |
| # draw a histogram of the instagram followers data | |
| data_w9 %>% | |
| ggplot() + | |
| geom_histogram(aes(x = instagram_followers)) | |
| # draw a histogram of the facebook friends data | |
| data_w9 %>% | |
| ggplot() + | |
| geom_histogram(aes(x = facebook_friends)) | |
| data_w9 <- | |
| data_w9 %>% | |
| mutate(z_FF = scale(facebook_friends), # z-transform of facebook friends | |
| z_IF = scale(instagram_followers)) # z-transform of instagram followers | |
| # descriptive stats | |
| mean(data_w9$z_FF) # for example - also sd(), min(), max() | |
| # removing outliers | |
| data_w9_f <- | |
| data_w9 %>% | |
| filter(z_FF < 2.5 & z_IF < 2.5) # | |
| # TASK 2 - Testing differences with a t-test | |
| #optional plot - probably remove | |
| data_w9_f %>% | |
| ggplot() + | |
| geom_point(aes(x = instagram_followers, y = facebook_friends, colour = music_pref)) | |
| # summarise the data | |
| data_w9_f %>% | |
| group_by(music_pref) %>% | |
| summarise(meanFF = mean(facebook_friends), | |
| meanIF = mean(instagram_followers), | |
| N = n()) # how many in each group - no need to edit this | |
| # collect the relevant data for t-test | |
| # test for equality of variances | |
| var.test(data = data_w9_f, facebook_friends ~ music_pref) | |
| # perform the t-test | |
| t.test(data = data_w9_f, | |
| facebook_friends ~ music_pref, | |
| paired = FALSE, | |
| var.equal = TRUE) # change this based on the results of your levene test | |
| # TASK 3 - Power calculations | |
| data_stroop <- read_csv("data_stroop.csv") | |
| # this code removes the control condition from the Stroop data | |
| data_stroop <- | |
| data_stroop %>% | |
| filter(condition == "compatible" | condition == "incompatible") | |
| # find the effect size (d) for the Stroop effect | |
| cohens_d(data = data_stroop, | |
| time ~ condition) | |
| pwr.t.test(n = 20, d = .92, type = "paired") # what power would we achieve? | |
| pwr.t.test(power = .8, d = .92, type = "paired") # what N would we need? | |
| pwr.t.test(power = .9, n = 40, type = "paired") # what effect size could we detect? | |