training
library(readxl)
mydata <- read_xlsx("Productivity.xlsx")
mydata <- as.data.frame(mydata)head(mydata)## ID Start AT1 AT2 AT3
## 1 1 85 133 136 134
## 2 2 90 123 138 135
## 3 3 96 120 135 141
## 4 4 93 129 132 134
## 5 5 84 117 138 131
## 6 6 91 128 132 141Description:
- ID: Identifier
- Start: Number of products made per month before training.
- AT1: Number of products made per month after first training.
- AT2: Number of products made per month after second training.
- AT3: Number of products made per month after third training.
library(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterlibrary(tidyverse)## ── Attaching core tidyverse packages ────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2## ── Conflicts ──────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks rstatix::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsmydata_long <- mydata %>%
pivot_longer(cols = Start:AT3,
names_to = "Time",
values_to = "Products") %>%
as.data.frame()
mydata_long$Time <- factor(mydata_long$Time,
levels = c("Start", "AT1", "AT2", "AT3"),
labels = c("Start", "AT1", "AT2", "AT3"))
head(mydata_long, 10)## ID Time Products
## 1 1 Start 85
## 2 1 AT1 133
## 3 1 AT2 136
## 4 1 AT3 134
## 5 2 Start 90
## 6 2 AT1 123
## 7 2 AT2 138
## 8 2 AT3 135
## 9 3 Start 96
## 10 3 AT1 120library(ggpubr)
ggboxplot(mydata_long,
x = "Time",
y = "Products",
xlab = "Time of measurement",
ylab = "Products manufactured") 
library(ggplot2)
ggplot(mydata_long, aes(x = Time, y = Products)) +
geom_violin(fill = "grey90", alpha = 0.6, trim = FALSE) +
geom_boxplot(width = 0.1, fill = "grey60", outlier.shape = 16) +
labs(
title = "Distribution of Products Manufactured Across Measurement Occasions",
x = "Measurement occasion",
y = "Number of products manufactured per month"
) +
theme_bw()
library(rstatix)
mydata_long %>%
group_by(Time) %>%
get_summary_stats(Products, type = "mean_sd")## # A tibble: 4 × 5
## Time variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 Start Products 38 89.9 4.53
## 2 AT1 Products 38 125. 4.97
## 3 AT2 Products 38 135. 5.50
## 4 AT3 Products 38 138. 4.95library(rstatix)
mydata_long %>%
group_by(Time) %>%
shapiro_test(Products)## # A tibble: 4 × 4
## Time variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 Start Products 0.975 0.545
## 2 AT1 Products 0.959 0.179
## 3 AT2 Products 0.957 0.146
## 4 AT3 Products 0.947 0.0685library(rstatix)
#rANOVA
ANOVA_results <- anova_test(dv = Products, #Dependent variable
wid = ID, #Subject identifier
within = Time, #Within-subject factor variable
data = mydata_long)
ANOVA_results #Summary of results.## ANOVA Table (type III tests)
##
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 Time 3 111 760.45 8.09e-74 * 0.938
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 Time 0.981 0.983
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 Time 0.987 2.96, 109.54 7.01e-73 * 1.082 3.25, 120.11
## p[HF] p[HF]<.05
## 1 8.09e-74 *get_anova_table(ANOVA_results, correction = "auto")## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Time 3 111 760.45 8.09e-74 * 0.938library(effectsize)##
## Attaching package: 'effectsize'## The following objects are masked from 'package:rstatix':
##
## cohens_d, eta_squared, omega_squaredinterpret_eta_squared(0.938, rules = "cohen1992")## [1] "large"
## (Rules: cohen1992)library(rstatix)
#Comparing all paires of variables
pwc <- mydata_long %>%
pairwise_t_test(Products ~ Time,
paired = TRUE,
p.adjust.method = "bonferroni")
pwc## # A tibble: 6 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Products Start AT1 38 38 -32.4 37 9.74e-29 5.85e-28
## 2 Products Start AT2 38 38 -39.7 37 6.28e-32 3.77e-31
## 3 Products Start AT3 38 38 -44.7 37 8.16e-34 4.89e-33
## 4 Products AT1 AT2 38 38 -8.35 37 4.88e-10 2.93e- 9
## 5 Products AT1 AT3 38 38 -11.6 37 6.70e-14 4.02e-13
## 6 Products AT2 AT3 38 38 -2.49 37 1.74e- 2 1.05e- 1
## # ℹ 1 more variable: p.adj.signif <chr>library(afex)## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Registered S3 method overwritten by 'lme4':
## method from
## na.action.merMod car## ************
## Welcome to afex. For support visit: http://afex.singmann.science/## - Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
## - Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
## - 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
## - Get and set global package options with: afex_options()
## - Set sum-to-zero contrasts globally: set_sum_contrasts()
## - For example analyses see: browseVignettes("afex")
## ************##
## Attaching package: 'afex'## The following object is masked from 'package:lme4':
##
## lmerrm_model <- aov_ez(
id = "ID",
dv = "Products",
within = "Time",
data = mydata_long
)residuals_rm <- residuals(rm_model$lm)library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## someqqPlot(residuals_rm)
## [1] 8 92