install.packages("ggformula")
library(ggformula)

tetrismemories <- read.csv("https://raw.githubusercontent.com/smburns47/Psyc158/main/tetrismemories.csv")

gf_histogram(gformula = ~ intrusive_memories, data = tetrismemories)

#Change the plotted variable to STAI_T
gf_histogram(gformula = ~ intrusive_memories, data = tetrismemories)

outcome <- c(1,2,3,4,5)
tiny_data <- data.frame(outcome) #gf_histogram only works on dataframe objects, not vectors

#Write some code below here to plot a histogram of the variable "outcome" in the dataframe "tiny_data"


gf_histogram(gformula = ~ outcome, data = tiny_data, bins = 5)

gf_histogram(gformula = ~ outcome, data = tiny_data, bins = 5, color = "gray", fill = "blue")

gf_dhistogram(gformula = ~ outcome, data = tiny_data, bins = 5, color = "gray", fill = "blue")

#Finish the code below to make a density plot of Age in mindsetmatters
gf_dhistogram(#set formula here, #set data frame here, #set bin and color options here)


normal_data <- rnorm(n = 1000, mean = 0, sd = 1) 
#rnorm is a function that generates data in a normal distribution;
# we'll talk about simulating data like this in a later chapter

gf_dhistogram(gformula = ~ data_pts, data = data.frame(data_pts = normal_data))

test_scores <- c(76,91,86,80,93)
#This computes a mean
sum(test_scores) / length(test_scores)

#Use mean() to calculate the mean of test_scores:

#Evaluate the code below. Do you think it will be the same as your mean calculation?
sum(test_scores) / length(test_scores)

dist1 <- c(10, 10, 11, 9, 11, 12, 8, 9, 10)
dist2 <- c(10, 10, 11, 9, 11, 12, 8, 9, 10, 100)

#Write code to plot the density plot of each of these distributions, using df_dhistogram()


#Calculate mean of dist1

#Calculate mean of dist2


median(test_scores)

#Visualize a weird distribution. Where is the mean versus the median versus the mode?
weird_dist <- data.frame(data_pts = c(10,20,20,30,30,30,40,40,40,40,50,50,50,50,50))
gf_dhistogram(gformula = ~ data_pts, data = weird_dist)

table(weird_dist$data_pts)

#Max of test_scores
max(test_scores)

#Min of test_scores
min(test_scores)

#Calculate the range of test_scores by subtracting the min from the max


range(test_scores)

quantile(tetrismemories$STAI_T, probs = 0.5, na.rm = TRUE)
median(tetrismemories$STAI_T, na.rm = TRUE)

quantile(tetrismemories$STAI_T, probs = c(0.25,0.5,0.75), na.rm = TRUE)

IQR(tetrismemories$STAI_T, na.rm = TRUE)

mean_score <- mean(test_scores)   # mean of the data
devs <- test_scores - mean_score  # all deviations from the mean
sum(devs)                         # sum of the deviations

var(tetrismemories$intrusive_memories, na.rm = TRUE)

# Find the standard deviation of tetrismemories$intrusive_memories using sd(), and verify 
# that it produces the same result as the square root of var()
memories_var <- var(tetrismemories$intrusive_memories, na.rm = TRUE)
memories_sd <- sd( #type your code here )
sqrt(memores_var) == memores_sd