library(ggplot2)
library(ggpubr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ✔ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(effectsize)
library(tidyr)
library(rstatix)
##
## Attaching package: 'rstatix'
##
## The following objects are masked from 'package:effectsize':
##
## cohens_d, eta_squared
##
## The following object is masked from 'package:stats':
##
## filter
library(naniar)
library(dplyr)
lec<-read.csv("LEC.SEX.AGE.csv") #Loading in data file
Sex <- factor(lec$chsex_d1, levels = 1:2, labels = c("Male", "Female")) #Turning sex into a factor
summary(Sex) #Getting summary of sex
## Male Female
## 111 109
Ethnicity <- factor(lec$cethn_d1, levels = 1:6, labels = c("White", "Black", "Hispanic", "Asian-Oriental", "Mixed", "Other")) #Turning ethnicity into a factor
summary(Ethnicity) #Getting summary of ethnicity
## White Black Hispanic Asian-Oriental Mixed
## 154 35 4 6 21
## Other
## 0
Income <- factor(lec$inc_d1, levels = 1:6, labels = c("Less than $20k", "$20k-40k", "$41k-$60k", "$61k-$80k", "$81k-$100k", "Over $100k")) #Turning income into a factor
summary(Income) #Getting summary of income
## Less than $20k $20k-40k $41k-$60k $61k-$80k $81k-$100k
## 12 25 40 41 35
## Over $100k NA's
## 66 1
JoinClub <- factor(lec$lc21e_c1, levels = 1:2, labels = c("Did not experience life event", "Did experience life event")) #Turning join new club variable into a factor
summary(JoinClub) #Getting summary of join new club variable
## Did not experience life event Did experience life event
## 71 0
## NA's
## 149
mean(lec$cage_d1) #Calculating adolescents' mean age
## [1] 13.67034
sd(lec$cage_d1) #Calculating adolescents' age sd
## [1] 1.520727
lec2 <- subset(lec,select=c(chsex_d1,ltpi_c1,lc21e_c1)) #Designating columns to run analyses on
vis_miss(lec2) #Looking at missing data
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
lec2 <- na.omit(lec2) #Excluding missing data
lec2$chsex_d1 <- as.factor(lec2$chsex_d1)
levels(lec2$chsex_d1) <- list('Male'="1",'Female'="2") #Relabel adolescents' sex to ensure it is treated as a factor, not numerical
lec2$lc21e_c1 <- as.factor(lec2$lc21e_c1)
levels(lec2$lc21e_c1) <- list('Did not experience life event'="0",'Did experience life event'="1") #Relabel adolescents' arguments with parents to ensure it is treated as a factor, not numerical
table(lec2) #Getting table of adolescents' sex and arguments with parents without missing data
## , , lc21e_c1 = Did not experience life event
##
## ltpi_c1
## chsex_d1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## Male 8 11 3 9 3 7 5 2 2 6 4 2 3 0 2 1 0 2 0 1 0 0 0
## Female 4 2 7 6 12 6 5 3 8 2 2 5 3 3 1 0 1 1 2 0 0 0 0
## ltpi_c1
## chsex_d1 24 28
## Male 1 0
## Female 0 0
##
## , , lc21e_c1 = Did experience life event
##
## ltpi_c1
## chsex_d1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## Male 1 3 2 1 2 7 0 3 3 2 2 2 1 1 2 0 1 1 0 0 1 0 1
## Female 1 1 3 3 2 2 5 3 2 0 1 1 0 1 1 2 2 2 1 0 0 1 0
## ltpi_c1
## chsex_d1 24 28
## Male 0 1
## Female 0 0
ggplot(lec2, aes(fill=lc21e_c1, y=ltpi_c1, x=chsex_d1)) +
geom_bar(position="dodge", stat="identity")+
xlab("Joining New Club")+
ylab("Impact of Positive Life Events") #Creating bar graph of variables
ggline(lec2, x = "chsex_d1", y = "ltpi_c1", color = "lc21e_c1",
add = c("mean_se"))+
xlab("oining New Club")+
ylab("Impact of Positive Life Events") #Creating line graph of variables
cellDescript <- with(lec2, aggregate(x=list(Mean=ltpi_c1,SD=ltpi_c1),
by=list(F1=chsex_d1, F2=lc21e_c1),
FUN=mean_sd) )
View(cellDescript) #Finding mean/SD of cells
## Warning in format.data.frame(x0): corrupt data frame: columns will be truncated
## or padded with NAs
## Warning in system2("/usr/bin/otool", c("-L", shQuote(DSO)), stdout = TRUE):
## running command ''/usr/bin/otool' -L '/Library/Frameworks/R.framework/Resources/
## modules/R_de.so'' had status 1
model1 <- aov(ltpi_c1~chsex_d1*lc21e_c1,data=lec2)
summary(model1) #Running factorial ANOVA
## Df Sum Sq Mean Sq F value Pr(>F)
## chsex_d1 1 7 7.00 0.252 0.61622
## lc21e_c1 1 235 234.51 8.442 0.00405 **
## chsex_d1:lc21e_c1 1 8 7.74 0.279 0.59813
## Residuals 212 5889 27.78
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(report)
report(model1)
## Warning: Could not find Sum-of-Squares for the (Intercept) in the ANOVA table.
## The ANOVA (formula: ltpi_c1 ~ chsex_d1 * lc21e_c1) suggests that:
##
## - The main effect of chsex_d1 is statistically not significant and very small
## (F(1, 212) = 0.25, p = 0.616; Eta2 (partial) = 1.19e-03, 95% CI [0.00, 1.00])
## - The main effect of lc21e_c1 is statistically significant and small (F(1, 212)
## = 8.44, p = 0.004; Eta2 (partial) = 0.04, 95% CI [7.21e-03, 1.00])
## - The interaction between chsex_d1 and lc21e_c1 is statistically not
## significant and very small (F(1, 212) = 0.28, p = 0.598; Eta2 (partial) =
## 1.31e-03, 95% CI [0.00, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.
Sex2<-subset(lec2,select=c(chsex_d1)) #Creating sex as an object
JoinClub2<-subset(lec2,select=c(lc21e_c1)) #Creating join new club variable as an object
simpEff1 <- lec2 %>% filter(Sex2=="Male") %>%
aov(ltpi_c1~lc21e_c1,data=.)
summary(simpEff1) #Examining simple effects of adolescent males and joining new club
## Df Sum Sq Mean Sq F value Pr(>F)
## lc21e_c1 1 167 166.68 5.303 0.0232 *
## Residuals 107 3363 31.43
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(simpEff1) #Conducting post-hoc test on simple effect
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ltpi_c1 ~ lc21e_c1, data = .)
##
## $lc21e_c1
## diff lwr
## Did experience life event-Did not experience life event 2.611486 0.3634003
## upr p adj
## Did experience life event-Did not experience life event 4.859573 0.0232225
report(simpEff1)
## Warning: Could not find Sum-of-Squares for the (Intercept) in the ANOVA table.
## The ANOVA (formula: ltpi_c1 ~ lc21e_c1) suggests that:
##
## - The main effect of lc21e_c1 is statistically significant and small (F(1, 107)
## = 5.30, p = 0.023; Eta2 = 0.05, 95% CI [3.49e-03, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.
simpEff2 <- lec2 %>% filter(Sex2=="Female") %>%
aov(ltpi_c1~lc21e_c1,data=.)
summary(simpEff2) #Examining simple effects of adolescent females and joining new club
## Df Sum Sq Mean Sq F value Pr(>F)
## lc21e_c1 1 75.6 75.57 3.142 0.0792 .
## Residuals 105 2525.8 24.06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(simpEff2) #Conducting post-hoc test on simple effect
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ltpi_c1 ~ lc21e_c1, data = .)
##
## $lc21e_c1
## diff lwr
## Did experience life event-Did not experience life event 1.804996 -0.214217
## upr p adj
## Did experience life event-Did not experience life event 3.824209 0.0792185
report(simpEff2)
## Warning: Could not find Sum-of-Squares for the (Intercept) in the ANOVA table.
## The ANOVA (formula: ltpi_c1 ~ lc21e_c1) suggests that:
##
## - The main effect of lc21e_c1 is statistically not significant and small (F(1,
## 105) = 3.14, p = 0.079; Eta2 = 0.03, 95% CI [0.00, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.
lec2 %>% filter(JoinClub2=="Did not experience life event") %>%
t.test(ltpi_c1~chsex_d1,data=.)
##
## Welch Two Sample t-test
##
## data: ltpi_c1 by chsex_d1
## t = -0.83369, df = 138.9, p-value = 0.4059
## alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
## 95 percent confidence interval:
## -2.2689192 0.9230288
## sample estimates:
## mean in group Male mean in group Female
## 5.875000 6.547945
library(lsr)
lec2 %>% filter(JoinClub2=="Did not experience life event") %>%
cohensD(ltpi_c1~chsex_d1,data=.) #Examining simple effects of adolescents' sex and not joining new club
## [1] 0.1386231
lec2 %>% filter(JoinClub2=="Did experience life event") %>%
t.test(ltpi_c1~chsex_d1,data=.)
##
## Welch Two Sample t-test
##
## data: ltpi_c1 by chsex_d1
## t = 0.093383, df = 69, p-value = 0.9259
## alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
## 95 percent confidence interval:
## -2.719379 2.986470
## sample estimates:
## mean in group Male mean in group Female
## 8.486486 8.352941
lec2 %>% filter(JoinClub2=="Did experience life event") %>%
cohensD(ltpi_c1~chsex_d1,data=.) #Examining simple effects of adolescents' sex and joining new club
## [1] 0.02210235
partial_eta_squared(model1) #Computing effect size of model
## chsex_d1 lc21e_c1 chsex_d1:lc21e_c1
## 0.001187061 0.038297326 0.001312725
sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lsr_0.5.2 report_0.5.5 naniar_0.6.1 rstatix_0.7.1
## [5] effectsize_0.8.2 forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
## [9] purrr_0.3.4 readr_2.1.2 tidyr_1.2.1 tibble_3.1.8
## [13] tidyverse_1.3.2 ggpubr_0.4.0 ggplot2_3.3.6
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.4 sass_0.4.2 jsonlite_1.8.0
## [4] carData_3.0-5 modelr_0.1.9 bslib_0.4.0
## [7] datawizard_0.6.3 assertthat_0.2.1 highr_0.9
## [10] googlesheets4_1.0.1 cellranger_1.1.0 yaml_2.3.5
## [13] bayestestR_0.13.0 pillar_1.8.1 backports_1.4.1
## [16] glue_1.6.2 visdat_0.5.3 digest_0.6.29
## [19] ggsignif_0.6.4 rvest_1.0.3 colorspace_2.0-3
## [22] htmltools_0.5.3 pkgconfig_2.0.3 broom_1.0.1
## [25] haven_2.5.1 scales_1.2.1 tzdb_0.3.0
## [28] googledrive_2.0.0 generics_0.1.3 farver_2.1.1
## [31] car_3.1-1 ellipsis_0.3.2 cachem_1.0.6
## [34] withr_2.5.0 cli_3.4.0 magrittr_2.0.3
## [37] crayon_1.5.1 readxl_1.4.1 evaluate_0.16
## [40] fs_1.5.2 fansi_1.0.3 xml2_1.3.3
## [43] tools_4.2.1 hms_1.1.2 gargle_1.2.1
## [46] lifecycle_1.0.2 munsell_0.5.0 reprex_2.0.2
## [49] compiler_4.2.1 jquerylib_0.1.4 rlang_1.0.5
## [52] grid_4.2.1 parameters_0.19.0 rstudioapi_0.14
## [55] labeling_0.4.2 rmarkdown_2.16 gtable_0.3.1
## [58] abind_1.4-5 DBI_1.1.3 R6_2.5.1
## [61] lubridate_1.8.0 knitr_1.40 fastmap_1.1.0
## [64] utf8_1.2.2 insight_0.18.6 stringi_1.7.8
## [67] vctrs_0.4.1 dbplyr_2.2.1 tidyselect_1.1.2
## [70] xfun_0.33
citation("lsr")
##
## To cite the lsr package in publications use:
##
## Navarro, D. J. (2015) Learning statistics with R: A tutorial for
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##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6)},
## author = {Danielle Navarro},
## organization = {University of New South Wales},
## address = {Sydney, Australia},
## year = {2015},
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##
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## <https://CRAN.R-project.org/package=ggpubr>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {ggpubr: 'ggplot2' Based Publication Ready Plots},
## author = {Alboukadel Kassambara},
## year = {2020},
## note = {R package version 0.4.0},
## url = {https://CRAN.R-project.org/package=ggpubr},
## }
citation("forcats")
##
## To cite package 'forcats' in publications use:
##
## Wickham H (2022). _forcats: Tools for Working with Categorical
## Variables (Factors)_. R package version 0.5.2,
## <https://CRAN.R-project.org/package=forcats>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {forcats: Tools for Working with Categorical Variables (Factors)},
## author = {Hadley Wickham},
## year = {2022},
## note = {R package version 0.5.2},
## url = {https://CRAN.R-project.org/package=forcats},
## }
citation("readr")
##
## To cite package 'readr' in publications use:
##
## Wickham H, Hester J, Bryan J (2022). _readr: Read Rectangular Text
## Data_. R package version 2.1.2,
## <https://CRAN.R-project.org/package=readr>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {readr: Read Rectangular Text Data},
## author = {Hadley Wickham and Jim Hester and Jennifer Bryan},
## year = {2022},
## note = {R package version 2.1.2},
## url = {https://CRAN.R-project.org/package=readr},
## }
citation("report")
##
## To cite in publications use:
##
## Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020).
## Automated Results Reporting as a Practical Tool to Improve
## Reproducibility and Methodological Best Practices Adoption. CRAN.
## Available from https://github.com/easystats/report. doi: .
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption},
## author = {Dominique Makowski and Mattan S. Ben-Shachar and Indrajeet Patil and Daniel Lüdecke},
## year = {2021},
## journal = {CRAN},
## url = {https://github.com/easystats/report},
## }