PSYC121_2021-22/Week_6_script_answers.R at ff15dfed06b8433583dee0cf6a6d6d4dc47cdad4

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	library(tidyverse) # load the tidyverse functions

	# Week_6_Task_2
	# this is a command to read in the data -
	# note that this is (pretty much) the same as using the "Import Dataset" button
	data_w6 <- read_csv("data_phone.csv") # read in the data

	# Note here that a line of code can be split across multiple lines
	# It's good practice to break the lines in logical places (e.g., after a pipe)

	# draw a graph Q1-7
	data_w6 %>%
	ggplot() +
	geom_point(aes(y = screen_time_actual, x = screen_time_estimate), colour = "red") # this is the line of code you eventually need to edit


	# draw a graph Q8
	data_w6 %>%
	ggplot() +
	geom_point(aes(y = screen_time_actual,
	x = screen_time_estimate,
	colour = phone_type)) # answer to Q8

	# draw a graph Q9
	data_w6 %>%
	ggplot() +
	geom_jitter(aes(y = screen_time_actual,
	x = screen_time_estimate,
	colour = phone_type)) # answer to Q8

	# Week_6_Task_3 - Do people underestimate or overestimate their phone time use?

	# create a new column (mutate) to reflect the difference
	data_phone_diff <- # note: assigning the output to a new object (data_phone_diff)
	data_w6 %>%
	mutate(est_diff = screen_time_actual - screen_time_estimate)

	# remove the 0s and count how many positives and negatives
	data_phone_diff %>%
	filter(est_diff != 0) %>% # remove the 0s
	count(est_diff > 0) # count how many positive and negatives

	# conduct a binomial test to see whether people underestimated or overestimated phone usage

	binom.test(61,99) # they can also use (38, 99) to get the same result (probabilities are symmetrical)

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