zxy1223
2022-12-23
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(ggpubr)
library(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterlibrary(readxl)
dat <- read_excel("/Users/riku/Documents/zxy.xlsx", sheet = "noab")
dat$id <- as.factor(dat$id)
dat$label_1st <- as.factor(dat$label_1st)
#dat$AB <- as.factor(dat$AB)
#dat$label_1st <- as.factor(dat$label_1st)
#dat$score <- as.numeric(dat$score)
dat$naka_2nd <- as.numeric(dat$naka_2nd)
dat$irt_3level <- as.factor(dat$irt_3level)
dat$irt_3level <- ordered(dat$irt_3level, levels = c("L", "M", "H"))
dat_p <- dat %>% filter(dat$label_1st == "p")
dat_t <- dat %>% filter(dat$label_1st == "t")
hist(dat_p$diff_atama)
hist(dat_p$diff_heiban)
hist(dat_p$diff_naka)
hist(dat_p$diff_o)
hist(dat_t$diff_atama)
hist(dat_t$diff_heiban)
hist(dat_t$diff_naka)
hist(dat_t$diff_o)
hist(dat_p$diff_total)
hist(dat_t$diff_total)
#########
library(hrbrthemes)## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow##### PS L------
p_a_l <- dat_p %>% filter(dat_p$irt_3level == "L") %>%
ggplot( aes(x=diff_atama)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#EB455F") + #scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of atama (PS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_h_l <- dat_p %>% filter(dat_p$irt_3level == "L") %>%
ggplot( aes(x=diff_heiban)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#FCFFE7") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of heiban (PS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_n_l <- dat_p %>% filter(dat_p$irt_3level == "L") %>%
ggplot( aes(x=diff_naka)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#BAD7E9") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of naka (PS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_o_l <- dat_p %>% filter(dat_p$irt_3level == "L") %>%
ggplot( aes(x=diff_o)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#2B3467") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of o (PS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
##### PS M ------
p_a_m <- dat_p %>% filter(dat_p$irt_3level == "M") %>%
ggplot( aes(x=diff_atama)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#EB455F") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of atama (PS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_h_m <- dat_p %>% filter(dat_p$irt_3level == "M") %>%
ggplot( aes(x=diff_heiban)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#FCFFE7") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of heiban (PS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_n_m <- dat_p %>% filter(dat_p$irt_3level == "M") %>%
ggplot( aes(x=diff_naka)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#BAD7E9") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of naka (PS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_o_m <- dat_p %>% filter(dat_p$irt_3level == "M") %>%
ggplot( aes(x=diff_o)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#2B3467") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of o (PS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
##### PS H ------
p_a_h <- dat_p %>% filter(dat_p$irt_3level == "H") %>%
ggplot( aes(x=diff_atama)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#EB455F") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of atama (PS, Upper)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_h_h <- dat_p %>% filter(dat_p$irt_3level == "H") %>%
ggplot( aes(x=diff_heiban)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#FCFFE7") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of heiban (PS, Upper)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_n_h <- dat_p %>% filter(dat_p$irt_3level == "H") %>%
ggplot( aes(x=diff_naka)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#BAD7E9") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of naka (PS, Upper)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
p_o_h <- dat_p %>% filter(dat_p$irt_3level == "H") %>%
ggplot( aes(x=diff_o)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#2B3467") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of o (PS, Upper)") +
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinegrid.arrange(p_a_l, p_h_l, p_n_l, p_o_l,
p_a_m, p_h_m, p_n_m, p_o_m,
p_a_h, p_h_h, p_n_h, p_o_h,
nrow = 3)
##### TS L------
t_a_l <- dat_t %>% filter(dat_t$irt_3level == "L") %>%
ggplot( aes(x=diff_atama)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#EB455F") + #scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of atama (TS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_h_l <- dat_t %>% filter(dat_t$irt_3level == "L") %>%
ggplot( aes(x=diff_heiban)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#FCFFE7") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of heiban (TS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_n_l <- dat_t %>% filter(dat_t$irt_3level == "L") %>%
ggplot( aes(x=diff_naka)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#BAD7E9") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of naka (TS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_o_l <- dat_t %>% filter(dat_t$irt_3level == "L") %>%
ggplot( aes(x=diff_o)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#2B3467") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of o (TS, Lower)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
##### TS M ------
t_a_m <- dat_t %>% filter(dat_t$irt_3level == "M") %>%
ggplot( aes(x=diff_atama)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#EB455F") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of atama (TS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_h_m <- dat_t %>% filter(dat_t$irt_3level == "M") %>%
ggplot( aes(x=diff_heiban)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#FCFFE7") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of heiban (TS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_n_m <- dat_t %>% filter(dat_t$irt_3level == "M") %>%
ggplot( aes(x=diff_naka)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#BAD7E9") +
#scale_fill_manual(values=c("#F3EFE0", "#434242", "#22A39F")) +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of naka (TS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_o_m <- dat_t %>% filter(dat_t$irt_3level == "M") %>%
ggplot( aes(x=diff_o)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#2B3467") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of o (TS, Middle)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
##### TS H ------
t_a_h <- dat_t %>% filter(dat_t$irt_3level == "H") %>%
ggplot( aes(x=diff_atama)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#EB455F") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of atama (TS, Upper)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_h_h <- dat_t %>% filter(dat_t$irt_3level == "H") %>%
ggplot( aes(x=diff_heiban)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#FCFFE7") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of heiban (TS, Upper)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_n_h <- dat_t %>% filter(dat_t$irt_3level == "H") %>%
ggplot( aes(x=diff_naka)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#BAD7E9") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of naka (TS, Upper)")+
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
t_o_h <- dat_t %>% filter(dat_t$irt_3level == "H") %>%
ggplot( aes(x=diff_o)) +
geom_histogram( breaks=seq(-6, 6, by=1),
color="black", alpha=0.6, position = 'identity',
binwidth = 1, fill = "#2B3467") +
theme_ipsum() +
labs(fill="") +
ggtitle("Subtraction of o (TS, Upper)") +
theme(plot.title = element_text(size=10)) +
ylim(0, 10)
grid.arrange(t_a_l, t_h_l, t_n_l, t_o_l,
t_a_m, t_h_m, t_n_m, t_o_m,
t_a_h, t_h_h, t_n_h, t_o_h,
nrow = 3)## Warning: Removed 1 rows containing missing values (geom_bar).
Including Plots
You can also embed plots, for example:
##### atama-----
dat %>%
group_by(label_1st, irt_3level) %>%
get_summary_stats(diff_atama, type = "mean_sd")## # A tibble: 6 × 6
## label_1st irt_3level variable n mean sd
## <fct> <ord> <chr> <dbl> <dbl> <dbl>
## 1 p L diff_atama 12 1 1.28
## 2 p M diff_atama 19 0.684 1.16
## 3 p H diff_atama 15 0.467 1.12
## 4 t L diff_atama 12 0.917 1.08
## 5 t M diff_atama 19 0.789 0.855
## 6 t H diff_atama 15 1.07 1.34bxp <- ggboxplot(
dat, x = "label_1st", y = "diff_atama",
color = "irt_3level", palette = "uchicago"
)
bxp
dat %>%
group_by(label_1st, irt_3level) %>%
identify_outliers(diff_atama)## # A tibble: 4 × 23
## label_1st irt_3l…¹ id atama…² heiba…³ naka_…⁴ o_1st label…⁵ atama…⁶ heiba…⁷
## <fct> <ord> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 p L 33 2 6 3 0 ps-b 6 6
## 2 p M 26 3 5 4 0 ps-a 6 6
## 3 p M 42 1 3 3 3 ps-a 4 4
## 4 p H 48 1 5 3 1 ps-a 4 4
## # … with 13 more variables: naka_2nd <dbl>, o_2nd <dbl>, label2nd <chr>,
## # label_2nd <chr>, diff_atama <dbl>, diff_heiban <dbl>, diff_naka <dbl>,
## # diff_o <dbl>, `1st_total` <dbl>, `2nd_total` <dbl>, diff_total <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>, and abbreviated variable names
## # ¹irt_3level, ²atama_1st, ³heiban_1st, ⁴naka_1st, ⁵label1st, ⁶atama_2nd,
## # ⁷heiban_2nd
## # ℹ Use `colnames()` to see all variable namesdat %>%
group_by(label_1st, irt_3level) %>%
shapiro_test(diff_atama)## # A tibble: 6 × 5
## label_1st irt_3level variable statistic p
## <fct> <ord> <chr> <dbl> <dbl>
## 1 p L diff_atama 0.902 0.170
## 2 p M diff_atama 0.884 0.0254
## 3 p H diff_atama 0.905 0.113
## 4 t L diff_atama 0.939 0.487
## 5 t M diff_atama 0.877 0.0189
## 6 t H diff_atama 0.904 0.110ggqqplot(dat, "diff_atama",
ggtheme = theme_bw()) +
facet_grid(label_1st ~ irt_3level)
dat %>%
group_by(label_1st) %>%
levene_test(diff_atama ~ irt_3level)## # A tibble: 2 × 5
## label_1st df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 p 2 43 0.0161 0.984
## 2 t 2 43 2.58 0.0878box_m(dat[, "diff_atama", drop = FALSE], dat$irt_3level)## # A tibble: 1 × 4
## statistic p.value parameter method
## <dbl> <dbl> <dbl> <chr>
## 1 1.59 0.452 2 Box's M-test for Homogeneity of Covariance Matric…res.aov <- anova_test(
data = dat, dv = diff_atama, wid = id,
between = irt_3level, within = label_1st
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 irt_3level 2 43 0.369 0.693 0.007
## 2 label_1st 1 43 0.630 0.432 0.009
## 3 irt_3level:label_1st 2 43 0.581 0.564 0.016##### heiban-----
dat %>%
group_by(label_1st, irt_3level) %>%
get_summary_stats(diff_heiban, type = "mean_sd")## # A tibble: 6 × 6
## label_1st irt_3level variable n mean sd
## <fct> <ord> <chr> <dbl> <dbl> <dbl>
## 1 p L diff_heiban 12 0.083 1.50
## 2 p M diff_heiban 19 0.211 0.713
## 3 p H diff_heiban 15 0.067 1.03
## 4 t L diff_heiban 12 0.5 1
## 5 t M diff_heiban 19 -0.158 1.21
## 6 t H diff_heiban 15 0.267 1.49bxp <- ggboxplot(
dat, x = "label_1st", y = "diff_heiban",
color = "irt_3level", palette = "uchicago"
)
bxp
dat %>%
group_by(label_1st, irt_3level) %>%
identify_outliers(diff_heiban)## # A tibble: 13 × 23
## label_1st irt_3…¹ id atama…² heiba…³ naka_…⁴ o_1st label…⁵ atama…⁶ heiba…⁷
## <fct> <ord> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 p L 30 3 1 2 4 ps-a 2 5
## 2 t M 11 3 4 0 2 ts-a 4 2
## 3 t M 19 3 5 4 3 ts-a 5 3
## 4 t M 22 4 4 1 0 ts-b 5 6
## 5 t M 26 5 5 5 0 ts-b 5 6
## 6 t M 34 4 4 3 2 ts-b 5 6
## 7 t M 42 5 4 6 1 ts-b 5 5
## 8 t M 44 4 5 4 2 ts-b 5 3
## 9 t M 46 5 5 2 0 ts-b 6 3
## 10 t H 7 3 2 5 6 ts-a 3 6
## 11 t H 15 4 6 3 0 ts-a 5 3
## 12 t H 12 4 4 6 1 ts-b 5 6
## 13 t H 48 4 6 4 0 ts-b 4 5
## # … with 13 more variables: naka_2nd <dbl>, o_2nd <dbl>, label2nd <chr>,
## # label_2nd <chr>, diff_atama <dbl>, diff_heiban <dbl>, diff_naka <dbl>,
## # diff_o <dbl>, `1st_total` <dbl>, `2nd_total` <dbl>, diff_total <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>, and abbreviated variable names
## # ¹irt_3level, ²atama_1st, ³heiban_1st, ⁴naka_1st, ⁵label1st, ⁶atama_2nd,
## # ⁷heiban_2nd
## # ℹ Use `colnames()` to see all variable namesdat %>%
group_by(label_1st, irt_3level) %>%
shapiro_test(diff_heiban)## # A tibble: 6 × 5
## label_1st irt_3level variable statistic p
## <fct> <ord> <chr> <dbl> <dbl>
## 1 p L diff_heiban 0.841 0.0285
## 2 p M diff_heiban 0.802 0.00122
## 3 p H diff_heiban 0.932 0.293
## 4 t L diff_heiban 0.783 0.00609
## 5 t M diff_heiban 0.856 0.00844
## 6 t H diff_heiban 0.819 0.00658ggqqplot(dat, "diff_heiban",
ggtheme = theme_bw()) +
facet_grid(label_1st ~ irt_3level)
dat %>%
group_by(label_1st) %>%
levene_test(diff_heiban ~ irt_3level)## # A tibble: 2 × 5
## label_1st df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 p 2 43 0.932 0.402
## 2 t 2 43 0.0674 0.935box_m(dat[, "diff_heiban", drop = FALSE], dat$irt_3level)## # A tibble: 1 × 4
## statistic p.value parameter method
## <dbl> <dbl> <dbl> <chr>
## 1 2.27 0.322 2 Box's M-test for Homogeneity of Covariance Matric…res.aov <- anova_test(
data = dat, dv = diff_heiban, wid = id,
between = irt_3level, within = label_1st
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 irt_3level 2 43 0.366 0.696 0.009
## 2 label_1st 1 43 0.119 0.732 0.001
## 3 irt_3level:label_1st 2 43 1.027 0.367 0.022##### naka-----
dat %>%
group_by(label_1st, irt_3level) %>%
get_summary_stats(diff_naka, type = "mean_sd")## # A tibble: 6 × 6
## label_1st irt_3level variable n mean sd
## <fct> <ord> <chr> <dbl> <dbl> <dbl>
## 1 p L diff_naka 12 1 1.13
## 2 p M diff_naka 19 0.684 0.82
## 3 p H diff_naka 15 0.267 1.34
## 4 t L diff_naka 12 0.333 0.888
## 5 t M diff_naka 19 0.684 1.76
## 6 t H diff_naka 15 0.2 0.941bxp <- ggboxplot(
dat, x = "label_1st", y = "diff_naka",
color = "irt_3level", palette = "uchicago"
)
bxp
dat %>%
group_by(label_1st, irt_3level) %>%
identify_outliers(diff_naka)## # A tibble: 8 × 23
## label_1st irt_3l…¹ id atama…² heiba…³ naka_…⁴ o_1st label…⁵ atama…⁶ heiba…⁷
## <fct> <ord> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 p H 27 4 6 6 0 ps-b 6 6
## 2 p H 49 4 5 5 0 ps-b 5 6
## 3 t M 11 3 4 0 2 ts-a 4 2
## 4 t H 15 4 6 3 0 ts-a 5 3
## 5 t H 17 0 4 5 0 ts-a 2 4
## 6 t H 23 4 3 2 1 ts-a 3 4
## 7 t H 37 2 5 3 2 ts-a 5 5
## 8 t H 45 4 0 4 6 ts-a 4 0
## # … with 13 more variables: naka_2nd <dbl>, o_2nd <dbl>, label2nd <chr>,
## # label_2nd <chr>, diff_atama <dbl>, diff_heiban <dbl>, diff_naka <dbl>,
## # diff_o <dbl>, `1st_total` <dbl>, `2nd_total` <dbl>, diff_total <dbl>,
## # is.outlier <lgl>, is.extreme <lgl>, and abbreviated variable names
## # ¹irt_3level, ²atama_1st, ³heiban_1st, ⁴naka_1st, ⁵label1st, ⁶atama_2nd,
## # ⁷heiban_2nd
## # ℹ Use `colnames()` to see all variable namesdat %>%
group_by(label_1st, irt_3level) %>%
shapiro_test(diff_naka)## # A tibble: 6 × 5
## label_1st irt_3level variable statistic p
## <fct> <ord> <chr> <dbl> <dbl>
## 1 p L diff_naka 0.947 0.598
## 2 p M diff_naka 0.874 0.0167
## 3 p H diff_naka 0.804 0.00413
## 4 t L diff_naka 0.900 0.160
## 5 t M diff_naka 0.940 0.267
## 6 t H diff_naka 0.838 0.0118ggqqplot(dat, "diff_naka",
ggtheme = theme_bw()) +
facet_grid(label_1st ~ irt_3level)
dat %>%
group_by(label_1st) %>%
levene_test(diff_naka ~ irt_3level)## # A tibble: 2 × 5
## label_1st df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 p 2 43 0.349 0.708
## 2 t 2 43 2.56 0.0892box_m(dat[, "diff_naka", drop = FALSE], dat$irt_3level)## # A tibble: 1 × 4
## statistic p.value parameter method
## <dbl> <dbl> <dbl> <chr>
## 1 2.10 0.349 2 Box's M-test for Homogeneity of Covariance Matric…res.aov <- anova_test(
data = dat, dv = diff_naka, wid = id,
between = irt_3level, within = label_1st
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 irt_3level 2 43 1.153 0.325 0.030
## 2 label_1st 1 43 1.061 0.309 0.010
## 3 irt_3level:label_1st 2 43 0.727 0.489 0.014##### o-----
dat %>%
group_by(label_1st, irt_3level) %>%
get_summary_stats(diff_o, type = "mean_sd")## # A tibble: 6 × 6
## label_1st irt_3level variable n mean sd
## <fct> <ord> <chr> <dbl> <dbl> <dbl>
## 1 p L diff_o 12 -0.083 1.44
## 2 p M diff_o 19 -0.368 1.26
## 3 p H diff_o 15 0.267 1.1
## 4 t L diff_o 12 -0.167 1.95
## 5 t M diff_o 19 0 0.816
## 6 t H diff_o 15 -0.4 1.68bxp <- ggboxplot(
dat, x = "label_1st", y = "diff_o",
color = "irt_3level", palette = "uchicago"
)
bxp
dat %>%
group_by(label_1st, irt_3level) %>%
identify_outliers(diff_o)## # A tibble: 22 × 23
## label_1st irt_3…¹ id atama…² heiba…³ naka_…⁴ o_1st label…⁵ atama…⁶ heiba…⁷
## <fct> <ord> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 p L 30 3 1 2 4 ps-a 2 5
## 2 p L 38 3 6 2 1 ps-a 5 5
## 3 p L 9 3 0 5 6 ps-b 4 1
## 4 p L 13 4 5 3 1 ps-b 5 5
## 5 p L 29 1 5 4 0 ps-b 3 6
## 6 p M 34 4 3 2 4 ps-a 4 3
## 7 p M 5 5 6 5 0 ps-b 5 6
## 8 t L 9 2 2 5 4 ts-a 4 4
## 9 t L 13 3 5 2 2 ts-a 3 5
## 10 t L 33 2 5 3 1 ts-a 3 4
## # … with 12 more rows, 13 more variables: naka_2nd <dbl>, o_2nd <dbl>,
## # label2nd <chr>, label_2nd <chr>, diff_atama <dbl>, diff_heiban <dbl>,
## # diff_naka <dbl>, diff_o <dbl>, `1st_total` <dbl>, `2nd_total` <dbl>,
## # diff_total <dbl>, is.outlier <lgl>, is.extreme <lgl>, and abbreviated
## # variable names ¹irt_3level, ²atama_1st, ³heiban_1st, ⁴naka_1st, ⁵label1st,
## # ⁶atama_2nd, ⁷heiban_2nd
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesdat %>%
group_by(label_1st, irt_3level) %>%
shapiro_test(diff_o)## # A tibble: 6 × 5
## label_1st irt_3level variable statistic p
## <fct> <ord> <chr> <dbl> <dbl>
## 1 p L diff_o 0.764 0.00376
## 2 p M diff_o 0.865 0.0117
## 3 p H diff_o 0.881 0.0492
## 4 t L diff_o 0.826 0.0187
## 5 t M diff_o 0.791 0.000851
## 6 t H diff_o 0.885 0.0563ggqqplot(dat, "diff_o",
ggtheme = theme_bw()) +
facet_grid(label_1st ~ irt_3level)
dat %>%
group_by(label_1st) %>%
levene_test(diff_o ~ irt_3level)## # A tibble: 2 × 5
## label_1st df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 p 2 43 0.0428 0.958
## 2 t 2 43 1.95 0.154box_m(dat[, "diff_o", drop = FALSE], dat$irt_3level)## # A tibble: 1 × 4
## statistic p.value parameter method
## <dbl> <dbl> <dbl> <chr>
## 1 6.27 0.0435 2 Box's M-test for Homogeneity of Covariance Matric…res.aov <- anova_test(
data = dat, dv = diff_o, wid = id,
between = irt_3level, within = label_1st
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 irt_3level 2 43 0.047 0.954 0.001
## 2 label_1st 1 43 0.284 0.597 0.002
## 3 irt_3level:label_1st 2 43 1.777 0.181 0.027dat %>%
group_by(label_1st, irt_3level) %>%
get_summary_stats(diff_total, type = "mean_sd")## # A tibble: 6 × 6
## label_1st irt_3level variable n mean sd
## <fct> <ord> <chr> <dbl> <dbl> <dbl>
## 1 p L diff_total 12 2 2.34
## 2 p M diff_total 19 1.21 2.17
## 3 p H diff_total 15 1.07 1.94
## 4 t L diff_total 12 1.58 2.43
## 5 t M diff_total 19 1.32 2.26
## 6 t H diff_total 15 1.13 1.88bxp <- ggboxplot(
dat, x = "label_1st", y = "diff_total",
color = "irt_3level", palette = "uchicago"
)
bxp
dat %>%
group_by(label_1st, irt_3level) %>%
identify_outliers(diff_total)## [1] label_1st irt_3level id atama_1st heiban_1st naka_1st
## [7] o_1st label1st atama_2nd heiban_2nd naka_2nd o_2nd
## [13] label2nd label_2nd diff_atama diff_heiban diff_naka diff_o
## [19] 1st_total 2nd_total diff_total is.outlier is.extreme
## <0 rows> (or 0-length row.names)dat %>%
group_by(label_1st, irt_3level) %>%
shapiro_test(diff_total)## # A tibble: 6 × 5
## label_1st irt_3level variable statistic p
## <fct> <ord> <chr> <dbl> <dbl>
## 1 p L diff_total 0.964 0.840
## 2 p M diff_total 0.948 0.368
## 3 p H diff_total 0.958 0.661
## 4 t L diff_total 0.945 0.565
## 5 t M diff_total 0.920 0.115
## 6 t H diff_total 0.950 0.521ggqqplot(dat, "diff_total",
ggtheme = theme_bw()) +
facet_grid(label_1st ~ irt_3level)
dat %>%
group_by(label_1st) %>%
levene_test(diff_total ~ irt_3level)## # A tibble: 2 × 5
## label_1st df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 p 2 43 0.0596 0.942
## 2 t 2 43 0.352 0.706box_m(dat[, "diff_total", drop = FALSE], dat$irt_3level)## # A tibble: 1 × 4
## statistic p.value parameter method
## <dbl> <dbl> <dbl> <chr>
## 1 1.29 0.525 2 Box's M-test for Homogeneity of Covariance Matric…res.aov <- anova_test(
data = dat, dv = diff_total, wid = id,
between = irt_3level, within = label_1st
)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 irt_3level 2 43 0.728 0.489 0.017000
## 2 label_1st 1 43 0.031 0.860 0.000365
## 3 irt_3level:label_1st 2 43 0.121 0.887 0.003000