Study 1

Figure 1

# load packages

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
library(papaja)
## Loading required package: tinylabels
library(glue)
# read in data being used

study_1 <- read_csv(file = "study_1_data.csv")
## Rows: 467 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Gender, Age
## dbl (13): Participant ID, LETHAVERAGE.T1, LETHAVERAGE.T2, LethDiff, SCAVERAG...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
study_2 <- read_csv(file = "study_2_data.csv")
## Rows: 336 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (3): Gender, Ethnicity, Country
## dbl (14): Participant_ID, Age, T1Extraversion, T1SWLS, T2SWLS, SWLS_Diff, T1...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
figure_1_historgram <- hist(study_1$SCdiff,
                 main = "Distribution of Social Connectedness Difference Scores",
                 xlab = "Social Connectedness Difference Score (T2-T1)",
                 xlim = c(-3,3)
)

figure_1_ggplot <- ggplot(data = study_1, mapping = aes(x = SCdiff))+
  geom_histogram(bins = 12, colour = "black", fill = "grey")+
  ylim(0, 150)+
#  xlim(-3,3)+
  labs(title = "Distribution of Social Connectedness Difference Scores",
       x = "Social Connectedness Difference Score (T2-T1)",
       y = "Frequency") +
   theme_apa()

print(figure_1_ggplot)

Table 1

# Solution 1
# Used paste() to stick the output into one cell of the table
table_1 <- study_1 %>% summarise("T1 Lethargy" = paste(round(mean(LETHAVERAGE.T1), 2),"(", round(sd(LETHAVERAGE.T1), 2),")"),
                               "T2 Lethargy" = paste(round(mean(LETHAVERAGE.T2), 2), "(", round(sd(LETHAVERAGE.T2), 2), ")"),
                              "Lethargy diff (T2-T1)" = paste(round(mean(LethDiff), 2), "(", round(sd(LethDiff), 2), ")"),
                              "T1 Social connectedness" = paste(round(mean(SCAVERAGE.T1), 2), "(", round(sd(SCAVERAGE.T1), 2), ")"),
                              "T2 Social connectedness" = paste(round(mean(SCAVERAGE.T2), 2), "(", round(sd(SCAVERAGE.T2), 2), ")"),
                              "Connectedness diff" = paste(round(mean(SCdiff), 2), "(", round(sd(SCdiff), 2), ")"),
                              "Extraversion" = paste(round(mean(EXTRAVERSION), 2), "(", round(sd(EXTRAVERSION), 2), ")")
                                  )

# To add a row name, I converted the tibble into a dataframe (if it was changed back to a tibble, the row name won't appear)
table_1 <- as.data.frame(table_1)
rownames(table_1) <- paste0("Mean (SD)")
print(table_1)
##            T1 Lethargy   T2 Lethargy Lethargy diff (T2-T1)
## Mean (SD) 2.6 ( 1.16 ) 3.16 ( 1.27 )         0.56 ( 1.33 )
##           T1 Social connectedness T2 Social connectedness Connectedness diff
## Mean (SD)           4.11 ( 0.88 )           3.97 ( 0.85 )     -0.14 ( 0.71 )
##            Extraversion
## Mean (SD) 4.17 ( 1.01 )
# Solution 2
# table_1 %>% mutate(new = glue("{t1lethmean} ({t1lethsd})"))

#initally, I tried to just write it out as a string but that didn't seem intuitive
  #           T2_Lethargy = round(mean(LETHAVERAGE.T2), 2),
  #           T2_Lethargy_sd = round(sd(LETHAVERAGE.T2), 2),
  #           Lethargy_diff = round(mean(LethDiff), 2),
  #           Lethargy_diff_sd = round(sd(LethDiff), 2),
  #           T1_SC = round(mean(SCAVERAGE.T1), 2),
  #           T1_SC_sd = round(sd(SCAVERAGE.T1), 2),
  #           T2_SC = round(mean(SCAVERAGE.T2), 2),
  #           T2_SC_sd = round(sd(SCAVERAGE.T2), 2),
  #           SC_diff = round(mean(SCdiff), 2),
  #           SC_diff_sd = round(sd(SCdiff), 2),
  #           Extraversion = round(mean(EXTRAVERSION), 2),
  #           Extraversion_sd = round(sd(EXTRAVERSION), 2)) %>%
  # mutate(T1_Lethargy = "2.60 (1.16)",
  #        T2_Lethargy = "3.16 (1.27)",
  #        Lethargy_diff = "0.56 (1.33)",
  #        T1_SC = "4.11 (0.88)",
  #        T2_SC = "3.97 (0.85)",
  #        SC_diff = "-0.14 (0.71)",
  #        Extraversion = "4.17 (1.01)") %>%
  # select(-ends_with("sd"))
  

# meanT1Lethargy <- study_1 %>% summarise(T1_Lethargy = round(mean(LETHAVERAGE.T1), 2))
# 
# sdT1Lethargy <- study_1 %>% summarise(T1_Lethargy_sd = round(sd(LETHAVERAGE.T1), 2))                                   
# 
# cbind(meanT1Lethargy, sdT1Lethargy)

Study 2

Figure 2 (in progress)

# figure_2_histogram <- hist.default(study_2$BMPN_Diff)
# 
# figure_2_ggplot <- ggplot(data = study_2, mapping = aes(x = BMPN_Diff))+
#   geom_histogram(binwidth = 1,bins = 14, xlim = (-4:4))
# 
# print(figure_2_ggplot)

Table 3

table_3 <- study_2 %>% summarise("T1 Life Satisfaction" = paste(round(mean(T1SWLS), 2),"(", round(sd(T1SWLS), 2),")"),
                                 "T2 Life Satisfaction" = paste(round(mean(T2SWLS), 2),"(", round(sd(T2SWLS), 2),")"),
                                 "Life Satisfaction change (T2-T1)" = paste(round(mean(SWLS_Diff), 2),"(", round(sd(SWLS_Diff), 2),")"),
                                 "T1 Relatedness" = paste(round(mean(T1BMPN), 2),"(", round(sd(T1BMPN), 2),")"),
                                 "T2 Relatedness" = paste(round(mean(T2BMPN), 2),"(", round(sd(T2BMPN), 2),")"),
                                 "Relatedness change (T2-T1)" = paste(round(mean(BMPN_Diff), 2),"(", round(sd(BMPN_Diff), 2),")"),
                                 "T1 Loneliness" = paste(round(mean(T1Lonely), 2),"(", round(sd(T1Lonely), 2),")"),
                                 "T2 Loneliness" = paste(round(mean(T2Lonely), 2),"(", round(sd(T2Lonely), 2),")"),
                                 "Loneliness change (T2-T1)" = paste(round(mean(Lonely_Diff), 2),"(", round(sd(Lonely_Diff), 2),")"),
                                 "T1 Extraversion" = paste(round(mean(T1Extraversion), 2),"(", round(sd(T1Extraversion), 2),")"),
  
)

# To add a row name, I converted the tibble into a dataframe
table_3 <- as.data.frame(table_3)
rownames(table_3) <- paste0("Mean (SD)")
print(table_3)
##           T1 Life Satisfaction T2 Life Satisfaction
## Mean (SD)        3.97 ( 1.53 )        3.99 ( 1.45 )
##           Life Satisfaction change (T2-T1) T1 Relatedness T2 Relatedness
## Mean (SD)                    0.02 ( 0.88 )  4.92 ( 1.09 )  4.91 ( 1.14 )
##           Relatedness change (T2-T1) T1 Loneliness T2 Loneliness
## Mean (SD)             -0.01 ( 1.11 ) 2.12 ( 0.65 ) 2.06 ( 0.62 )
##           Loneliness change (T2-T1) T1 Extraversion
## Mean (SD)             -0.06 ( 0.4 )    3.9 ( 0.79 )