library(readxl)
library(apaTables)
library(dplyr)
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library(ggplot2)
library(ggside)
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library(rpart)
library(rpart.plot)
library(likert)
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library(tidyverse)
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library(gtsummary)
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library(knitr)
library(pls)
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library(apa)
library (MASS)
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library(glmnet)
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library(caret)
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library(xtable)
library(ggstatsplot)
## You can cite this package as:
## Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
## Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167
library(psych)
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library(nFactors)
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RRSS <- read_excel("RRSS.xlsx", sheet = "RRSS",
col_types = c("text", "text", "text",
"text", "text", "text", "text", "text",
"text", "text"))
RRSS2 <- read_excel("RRSS.xlsx", sheet = "RRSS")
str(RRSS)
## tibble [49 × 10] (S3: tbl_df/tbl/data.frame)
## $ Red : chr [1:49] "TikTok" "TikTok" "TikTok" "TikTok" ...
## $ Purpose of activity : chr [1:49] "3" "5" "2" "3" ...
## $ Acquisition of knowledge : chr [1:49] "3" "2" "4" "4" ...
## $ Development of creativity : chr [1:49] "1" "4" "2" "2" ...
## $ Motivational activity : chr [1:49] "4" "4" "1" "2" ...
## $ Very difficult activity : chr [1:49] "2" "2" "4" "1" ...
## $ Intention to design future activities with this social network: chr [1:49] "1" "3" "1" "2" ...
## $ Intention to design future activities with social networks : chr [1:49] "2" "3" "2" "2" ...
## $ Awareness of the learning process : chr [1:49] "3" "5" "2" "3" ...
## $ Communicate content in a fun, effective way : chr [1:49] "1" "6" "2" "2" ...
str(RRSS2)
## tibble [49 × 10] (S3: tbl_df/tbl/data.frame)
## $ Red : chr [1:49] "TikTok" "TikTok" "TikTok" "TikTok" ...
## $ Purpose of activity : num [1:49] 3 5 2 3 3 4 3 2 3 4 ...
## $ Acquisition of knowledge : num [1:49] 3 2 4 4 4 4 2 1 5 5 ...
## $ Development of creativity : num [1:49] 1 4 2 2 3 4 4 1 4 4 ...
## $ Motivational activity : num [1:49] 4 4 1 2 4 2 4 3 3 6 ...
## $ Very difficult activity : num [1:49] 2 2 4 1 2 3 4 2 1 2 ...
## $ Intention to design future activities with this social network: num [1:49] 1 3 1 2 2 2 3 1 2 3 ...
## $ Intention to design future activities with social networks : num [1:49] 2 3 2 2 3 2 5 2 3 3 ...
## $ Awareness of the learning process : num [1:49] 3 5 2 3 4 4 3 2 4 5 ...
## $ Communicate content in a fun, effective way : num [1:49] 1 6 2 2 3 4 4 2 4 4 ...
TikTok <- filter(RRSS2, Red == "TikTok")
TikTok <- TikTok [ , -1]
Instagram <- filter(RRSS2, Red == "Instagram")
Instagram <- Instagram [ , -1]
DT::datatable (RRSS)
alfa <- alpha(RRSS2 [ , -1], check.keys=TRUE)
## Warning in alpha(RRSS2[, -1], check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
alfa
##
## Reliability analysis
## Call: alpha(x = RRSS2[, -1], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.93 0.45 7.4 0.026 3.9 1 0.4
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.88 0.93
## Duhachek 0.83 0.88 0.93
##
## Reliability if an item is dropped:
## raw_alpha
## Purpose of activity 0.85
## Acquisition of knowledge 0.88
## Development of creativity 0.86
## Motivational activity 0.88
## Very difficult activity- 0.90
## Intention to design future activities with this social network 0.86
## Intention to design future activities with social networks 0.86
## Awareness of the learning process 0.87
## Communicate content in a fun, effective way 0.86
## std.alpha
## Purpose of activity 0.85
## Acquisition of knowledge 0.87
## Development of creativity 0.86
## Motivational activity 0.88
## Very difficult activity- 0.90
## Intention to design future activities with this social network 0.86
## Intention to design future activities with social networks 0.86
## Awareness of the learning process 0.87
## Communicate content in a fun, effective way 0.86
## G6(smc)
## Purpose of activity 0.91
## Acquisition of knowledge 0.91
## Development of creativity 0.92
## Motivational activity 0.93
## Very difficult activity- 0.93
## Intention to design future activities with this social network 0.91
## Intention to design future activities with social networks 0.90
## Awareness of the learning process 0.90
## Communicate content in a fun, effective way 0.91
## average_r S/N
## Purpose of activity 0.42 5.7
## Acquisition of knowledge 0.47 7.0
## Development of creativity 0.44 6.4
## Motivational activity 0.48 7.4
## Very difficult activity- 0.52 8.8
## Intention to design future activities with this social network 0.44 6.2
## Intention to design future activities with social networks 0.43 6.0
## Awareness of the learning process 0.45 6.5
## Communicate content in a fun, effective way 0.43 5.9
## alpha se var.r
## Purpose of activity 0.033 0.054
## Acquisition of knowledge 0.027 0.059
## Development of creativity 0.030 0.047
## Motivational activity 0.025 0.064
## Very difficult activity- 0.022 0.038
## Intention to design future activities with this social network 0.030 0.045
## Intention to design future activities with social networks 0.031 0.047
## Awareness of the learning process 0.028 0.061
## Communicate content in a fun, effective way 0.032 0.057
## med.r
## Purpose of activity 0.38
## Acquisition of knowledge 0.42
## Development of creativity 0.40
## Motivational activity 0.46
## Very difficult activity- 0.46
## Intention to design future activities with this social network 0.40
## Intention to design future activities with social networks 0.40
## Awareness of the learning process 0.40
## Communicate content in a fun, effective way 0.39
##
## Item statistics
## n raw.r std.r
## Purpose of activity 49 0.87 0.87
## Acquisition of knowledge 49 0.63 0.65
## Development of creativity 49 0.77 0.76
## Motivational activity 49 0.60 0.60
## Very difficult activity- 49 0.40 0.40
## Intention to design future activities with this social network 49 0.79 0.78
## Intention to design future activities with social networks 49 0.83 0.82
## Awareness of the learning process 49 0.72 0.74
## Communicate content in a fun, effective way 49 0.83 0.83
## r.cor r.drop
## Purpose of activity 0.86 0.83
## Acquisition of knowledge 0.63 0.54
## Development of creativity 0.74 0.68
## Motivational activity 0.52 0.48
## Very difficult activity- 0.31 0.26
## Intention to design future activities with this social network 0.78 0.72
## Intention to design future activities with social networks 0.83 0.77
## Awareness of the learning process 0.73 0.65
## Communicate content in a fun, effective way 0.81 0.77
## mean sd
## Purpose of activity 4.0 1.4
## Acquisition of knowledge 4.5 1.3
## Development of creativity 3.3 1.5
## Motivational activity 4.1 1.6
## Very difficult activity- 4.6 1.4
## Intention to design future activities with this social network 2.8 1.5
## Intention to design future activities with social networks 3.4 1.5
## Awareness of the learning process 4.5 1.2
## Communicate content in a fun, effective way 3.7 1.6
##
## Non missing response frequency for each item
## 1 2 3
## Purpose of activity 0.02 0.16 0.24
## Acquisition of knowledge 0.02 0.10 0.06
## Development of creativity 0.14 0.20 0.14
## Motivational activity 0.10 0.08 0.08
## Very difficult activity 0.31 0.37 0.10
## Intention to design future activities with this social network 0.22 0.24 0.18
## Intention to design future activities with social networks 0.08 0.24 0.22
## Awareness of the learning process 0.02 0.06 0.12
## Communicate content in a fun, effective way 0.10 0.16 0.14
## 4 5 6
## Purpose of activity 0.16 0.22 0.18
## Acquisition of knowledge 0.24 0.35 0.22
## Development of creativity 0.29 0.14 0.08
## Motivational activity 0.33 0.18 0.22
## Very difficult activity 0.14 0.02 0.06
## Intention to design future activities with this social network 0.20 0.10 0.04
## Intention to design future activities with social networks 0.14 0.22 0.08
## Awareness of the learning process 0.20 0.39 0.20
## Communicate content in a fun, effective way 0.27 0.18 0.14
## miss
## Purpose of activity 0
## Acquisition of knowledge 0
## Development of creativity 0
## Motivational activity 0
## Very difficult activity 0
## Intention to design future activities with this social network 0
## Intention to design future activities with social networks 0
## Awareness of the learning process 0
## Communicate content in a fun, effective way 0
test <- t.test(`Motivational activity` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Motivational activity by Red
## t = 1.4522, df = 44.684, p-value = 0.1534
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## -0.2488987 1.5346130
## sample estimates:
## mean in group Instagram mean in group TikTok
## 4.357143 3.714286
t_apa(t_test(`Motivational activity` ~ Red , data = RRSS2))
## t(44.68) = 1.45, p = .153, d = 0.42
test <- t.test(`Very difficult activity` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Very difficult activity by Red
## t = -0.38726, df = 46.979, p-value = 0.7003
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## -0.9587211 0.6491972
## sample estimates:
## mean in group Instagram mean in group TikTok
## 2.321429 2.476190
t_apa(t_test(`Very difficult activity` ~ Red , data = RRSS2))
## t(46.98) = -0.39, p = .700, d = -0.11
test <- t.test(`Acquisition of knowledge` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Acquisition of knowledge by Red
## t = 1.0665, df = 42.216, p-value = 0.2922
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## -0.3610065 1.1705303
## sample estimates:
## mean in group Instagram mean in group TikTok
## 4.642857 4.238095
t_apa(t_test(`Acquisition of knowledge` ~ Red , data = RRSS2))
## t(42.22) = 1.07, p = .292, d = 0.31
test <- t.test(`Awareness of the learning process` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Awareness of the learning process by Red
## t = 1.5086, df = 46, p-value = 0.1382
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## -0.1751012 1.2227202
## sample estimates:
## mean in group Instagram mean in group TikTok
## 4.714286 4.190476
t_apa(t_test(`Awareness of the learning process` ~ Red , data = RRSS2))
## t(46.00) = 1.51, p = .138, d = 0.43
test <- t.test(`Development of creativity` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Development of creativity by Red
## t = 1.75, df = 45.786, p-value = 0.08682
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## -0.110994 1.587184
## sample estimates:
## mean in group Instagram mean in group TikTok
## 3.642857 2.904762
t_apa(t_test(`Development of creativity` ~ Red , data = RRSS2))
## t(45.79) = 1.75, p = .087, d = 0.50
test <- t.test(`Intention to design future activities with this social network` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Intention to design future activities with this social network by Red
## t = 2.9132, df = 45.168, p-value = 0.005544
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## 0.3491333 1.9127714
## sample estimates:
## mean in group Instagram mean in group TikTok
## 3.321429 2.190476
t_apa(t_test(`Intention to design future activities with this social network` ~ Red , data = RRSS2))
## t(45.17) = 2.91, p = .006, d = 0.83
test <- t.test(`Intention to design future activities with social networks` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Intention to design future activities with social networks by Red
## t = 2.2298, df = 43.725, p-value = 0.03094
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## 0.08800857 1.74532476
## sample estimates:
## mean in group Instagram mean in group TikTok
## 3.821429 2.904762
t_apa(t_test(`Intention to design future activities with social networks` ~ Red , data = RRSS2))
## t(43.72) = 2.23, p = .031, d = 0.64
test <- t.test(`Communicate content in a fun, effective way` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Communicate content in a fun, effective way by Red
## t = 2.2336, df = 43.332, p-value = 0.03072
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## 0.0938256 1.8347458
## sample estimates:
## mean in group Instagram mean in group TikTok
## 4.107143 3.142857
t_apa(t_test(`Communicate content in a fun, effective way` ~ Red , data = RRSS2))
## t(43.33) = 2.23, p = .031, d = 0.64
test <- t.test(`Purpose of activity` ~ Red , data = RRSS2)
print(test)
##
## Welch Two Sample t-test
##
## data: Purpose of activity by Red
## t = 2.8705, df = 45.914, p-value = 0.006178
## alternative hypothesis: true difference in means between group Instagram and group TikTok is not equal to 0
## 95 percent confidence interval:
## 0.3271849 1.8632913
## sample estimates:
## mean in group Instagram mean in group TikTok
## 4.428571 3.333333
t_apa(t_test(`Purpose of activity` ~ Red , data = RRSS2))
## t(45.91) = 2.87, p = .006, d = 0.81
df1 <- data.frame(RRSS)
str(df1)
## 'data.frame': 49 obs. of 10 variables:
## $ Red : chr "TikTok" "TikTok" "TikTok" "TikTok" ...
## $ Purpose.of.activity : chr "3" "5" "2" "3" ...
## $ Acquisition.of.knowledge : chr "3" "2" "4" "4" ...
## $ Development.of.creativity : chr "1" "4" "2" "2" ...
## $ Motivational.activity : chr "4" "4" "1" "2" ...
## $ Very.difficult.activity : chr "2" "2" "4" "1" ...
## $ Intention.to.design.future.activities.with.this.social.network: chr "1" "3" "1" "2" ...
## $ Intention.to.design.future.activities.with.social.networks : chr "2" "3" "2" "2" ...
## $ Awareness.of.the.learning.process : chr "3" "5" "2" "3" ...
## $ Communicate.content.in.a.fun..effective.way : chr "1" "6" "2" "2" ...
df2 <- mutate_if(df1, is.character, as.factor)
str(df2)
## 'data.frame': 49 obs. of 10 variables:
## $ Red : Factor w/ 2 levels "Instagram","TikTok": 2 2 2 2 2 2 2 2 2 2 ...
## $ Purpose.of.activity : Factor w/ 6 levels "1","2","3","4",..: 3 5 2 3 3 4 3 2 3 4 ...
## $ Acquisition.of.knowledge : Factor w/ 6 levels "1","2","3","4",..: 3 2 4 4 4 4 2 1 5 5 ...
## $ Development.of.creativity : Factor w/ 6 levels "1","2","3","4",..: 1 4 2 2 3 4 4 1 4 4 ...
## $ Motivational.activity : Factor w/ 6 levels "1","2","3","4",..: 4 4 1 2 4 2 4 3 3 6 ...
## $ Very.difficult.activity : Factor w/ 6 levels "1","2","3","4",..: 2 2 4 1 2 3 4 2 1 2 ...
## $ Intention.to.design.future.activities.with.this.social.network: Factor w/ 6 levels "1","2","3","4",..: 1 3 1 2 2 2 3 1 2 3 ...
## $ Intention.to.design.future.activities.with.social.networks : Factor w/ 6 levels "1","2","3","4",..: 2 3 2 2 3 2 5 2 3 3 ...
## $ Awareness.of.the.learning.process : Factor w/ 6 levels "1","2","3","4",..: 3 5 2 3 4 4 3 2 4 5 ...
## $ Communicate.content.in.a.fun..effective.way : Factor w/ 6 levels "1","2","3","4",..: 1 6 2 2 3 4 4 2 4 4 ...
xlikgroup <- likert(df2[,2:10], grouping = df2$Red)
plot(xlikgroup, type = "density", centered = T) +
theme ( axis.text.x = element_text( size = 8 ),
axis.text.y = element_text( size = 0, hjust = 0 ),
legend.text = element_text( size = 10),
legend.title = element_text( size = 0 ),
legend.position = "down")
lm.fit <- lm(`Intention to design future activities with this social network` ~
`Purpose of activity`, data = Instagram)
summary(lm.fit)
##
## Call:
## lm(formula = `Intention to design future activities with this social network` ~
## `Purpose of activity`, data = Instagram)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3568 -0.6979 -0.3568 0.4668 1.9609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4036 0.6769 0.596 0.556114
## `Purpose of activity` 0.6589 0.1457 4.521 0.000119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.079 on 26 degrees of freedom
## Multiple R-squared: 0.4401, Adjusted R-squared: 0.4186
## F-statistic: 20.44 on 1 and 26 DF, p-value: 0.0001191
lm.fit <- lm(`Intention to design future activities with social networks` ~
`Purpose of activity`, data = Instagram)
summary(lm.fit)
##
## Call:
## lm(formula = `Intention to design future activities with social networks` ~
## `Purpose of activity`, data = Instagram)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.50781 -0.77604 -0.00781 0.76042 1.76042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5807 0.6358 0.913 0.369
## `Purpose of activity` 0.7318 0.1369 5.345 1.35e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 26 degrees of freedom
## Multiple R-squared: 0.5236, Adjusted R-squared: 0.5052
## F-statistic: 28.57 on 1 and 26 DF, p-value: 1.354e-05
lm.fit <- lm(`Intention to design future activities with this social network` ~
`Purpose of activity`, data = TikTok)
summary(lm.fit)
##
## Call:
## lm(formula = `Intention to design future activities with this social network` ~
## `Purpose of activity`, data = TikTok)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90062 -0.63975 -0.03106 0.22981 1.36025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7081 0.4656 -1.521 0.145
## `Purpose of activity` 0.8696 0.1313 6.621 2.46e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7273 on 19 degrees of freedom
## Multiple R-squared: 0.6976, Adjusted R-squared: 0.6817
## F-statistic: 43.84 on 1 and 19 DF, p-value: 2.462e-06
lm.fit <- lm(`Intention to design future activities with social networks` ~
`Purpose of activity`, data = TikTok)
summary(lm.fit)
##
## Call:
## lm(formula = `Intention to design future activities with social networks` ~
## `Purpose of activity`, data = TikTok)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6258 -0.6258 0.2112 0.5373 2.3742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1149 0.6288 0.183 0.856945
## `Purpose of activity` 0.8370 0.1774 4.719 0.000149 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9821 on 19 degrees of freedom
## Multiple R-squared: 0.5396, Adjusted R-squared: 0.5154
## F-statistic: 22.27 on 1 and 19 DF, p-value: 0.0001493
plot(TikTok$`Intention to design future activities with this social network`,
TikTok$`Purpose of activity`, col = "White",
xlab= "Prospective", ylab = "Purpose of activity",
plot(TikTok$`Intention to design future activities with social networks`,
TikTok$`Purpose of activity`, col = "White",
xlab= "", ylab = "",
plot(Instagram$`Intention to design future activities with this social network`,
Instagram$`Purpose of activity`, col = "White",
xlab= "", ylab = "",
plot(Instagram$`Intention to design future activities with social networks`,
Instagram$`Purpose of activity`, col = "White",
xlab= "", ylab = "",)))) +
(abline(lm(`Intention to design future activities with social networks` ~
`Purpose of activity`, data = TikTok), col = "1", lwd = 3, lty = 1)) +
(abline(lm(`Intention to design future activities with social networks` ~
`Purpose of activity`, data = Instagram), col = "2", lwd = 3, lty = 1))+
(abline(lm(`Intention to design future activities with this social network` ~
`Purpose of activity`, data = TikTok), col = "3", lwd = 3, lty = 2)) +
(abline(lm(`Intention to design future activities with this social network` ~
`Purpose of activity`, data = Instagram), col = "4", lwd = 3, lty = 2))
## integer(0)
legend(x = "topleft", legend = c("Future activities with TikTok",
"Future activities with Instagram",
"TikTok to design future activities with other social media platforms",
"Instagram to design future activities with other social media platforms"),
bty = "n" , col = c(1,2,3,4), lty= c(1,1,2,2), lwd = 4)
