Week 2- r studio notes

Question 1

what packages do you need to load in and in what order

Answer 1

library(broom)
library(car)
library(Hmisc)
library(lsr)
library(tidyverse)

big cats hate losing tennis

Question 2

how to measure the assumption: is there a data point for each participant on both variables

Answer 2

mh_check <- mh %>%
filter(!is.na(Abil)) %>%
filter(!is.na(IQ))

o Mh_check is reading in the file
o Pipe operator
o Filtering out any NAs in ability and IQ (remember to put pipe operator between)
o Looking in the environment there will be the same number of observations if there is no NAs

Question 3

how to check if the data is normally distributed

Answer 3

ggplot(mh_check, aes(x=Abil))+
geom_histogram() +
theme_bw()

also…

qqPlot(x = mh$Abil)

Question 4

how to check if the relationship between variables appear linear?

Answer 4

calculate a ggplot from last week

Question 5

how do you conduct a correlation analysis using spearman’s r

Answer 5

corSp_results <- cor.test(mh$Abil,
mh$IQ,
method = ‘spearman’,
alternative= ‘two.sided’ %>%
tidy()

(very similar to pearson’s R)

Question 6

how do you conduct a correlation matrix

Answer 6

mh <- mh %>%
select (-Participant)

pairs (mh)

Question 7

how do you create an intercorrelation

Answer 7

mh <- as.data.frame(mh)
intercor_results <- correlate (x=mh,
test= TRUE,
corr.method = pearson
p.adjust.method = bonferroni

intercor_results()