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pacman::p_load(ggstatsplot, tidyverse)Hands-On Exercise 4
1 Loading Packages
2 Importing the Data
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exam_data <- read_csv("data/Exam_data.csv")
head(exam_data, 10)# A tibble: 10 × 7
ID CLASS GENDER RACE ENGLISH MATHS SCIENCE
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Student321 3I Male Malay 21 9 15
2 Student305 3I Female Malay 24 22 16
3 Student289 3H Male Chinese 26 16 16
4 Student227 3F Male Chinese 27 77 31
5 Student318 3I Male Malay 27 11 25
6 Student306 3I Female Malay 31 16 16
7 Student313 3I Male Chinese 31 21 25
8 Student316 3I Male Malay 31 18 27
9 Student312 3I Male Malay 33 19 15
10 Student297 3H Male Indian 34 49 373 Use gghistostats() - One-Sample Test
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set.seed(1234)
gghistostats(
data = exam_data,
x = ENGLISH,
type = "bayes",
test.value = 60,
xlab = "English scores") +
theme_classic()
4 Use ggbetweenstats()
4.1 Two-Sample Test
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ggbetweenstats(
data = exam_data,
x = GENDER,
y = MATHS,
type = "np",
messages = FALSE) +
theme_classic()
4.2 One-Way ANOVA Test (difference of means)
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ggbetweenstats(
data = exam_data,
x = RACE,
y = ENGLISH,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE) +
theme_classic()
5 ggscatterstats - Correlation Test
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ggscatterstats(
data = exam_data,
x = MATHS,
y = ENGLISH,
marginal = FALSE) +
theme_classic() +
theme(plot.background = element_rect(fill = "#F8F3E6", color = "#F8F3E6"))
6 ggbarstats() - Association Test of Dependence
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exam1 <- exam_data %>%
mutate(MATHS_bins =
cut(MATHS,
breaks = c(0,60,75,85,100))
)
ggbarstats(exam1,
x = MATHS_bins,
y = GENDER) +
theme_classic() +
theme(plot.background = element_rect(fill = "#F8F3E6", color = "#F8F3E6"))