RPS2026

Abstract

Predictions

Data Processing

Analyses – Skip for now

library(lm.beta)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ lubridate 1.9.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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✖ purrr::flatten()         masks jsonlite::flatten()
✖ kableExtra::group_rows() masks dplyr::group_rows()
✖ dplyr::lag()             masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(emmeans)

Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'

library(gtsummary)

Warning: package 'gtsummary' was built under R version 4.5.2

library(dplyr)

# Update your dataframe before running lm()
df <- df %>%
  mutate(
    actual_participant_outcome = factor(actual_participant_outcome, levels = c("win", "tie", "lose")), #win is the reference group
    info_condition = factor(info_condition, levels = c("none", "skill", "luck")) #none is the reference group 
  )

#table of averages 
df %>%
    select(
        actual_participant_outcome, 
        bonus_belong_deviation, bonus_deserve_deviation, 
        attribution_self_scaled, attribution_other_scaled, 
        state_envy_composite, neg_affect_composite, 
        pos_affect_composite, tar_neg_composite, tar_pos_composite
    ) %>%
    tbl_summary(
        by = actual_participant_outcome,
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        digits = all_continuous() ~ 2,
        label = list(
            bonus_belong_deviation   ~ "Belonging Deviation",
            bonus_deserve_deviation  ~ "Deservingness Deviation",
            attribution_self_scaled  ~ "Attribution (Self)",
            attribution_other_scaled ~ "Attribution (Other)",
            state_envy_composite     ~ "State Envy",
            neg_affect_composite     ~ "Negative Affect",
            pos_affect_composite     ~ "Positive Affect",
            tar_neg_composite        ~ "Negative TAR",
            tar_pos_composite        ~ "Positive TAR"
        )
    ) %>%
    bold_labels() %>%
    # Footnote 1: Deviation variables
        modify_table_styling(
          columns = label,
          rows = variable %in% c("bonus_belong_deviation", "bonus_deserve_deviation"),
          footnote = "Calculated as response minus earned amount. Positive values indicate believing one
          should have/deserves more than received; negative values indicate the opposite."
        ) %>%
        # Footnote 2: Scaled attribution variables
        modify_table_styling(
          columns = label,
          rows = variable %in% c("attribution_self_scaled", "attribution_other_scaled"),
          footnote = "Scaled as (variable - 50) / 50: -1 = skill, 0 = both, 1 = luck. More positive values
          indicate greater attribution to luck."
        )%>%
    modify_caption("**Summary Statistics by Outcome**")

Summary Statistics by Outcome

Characteristic	win N = 581	tie N = 291	lose N = 591
Belonging Deviation2	-0.05 (0.78)	0.16 (0.76)	0.81 (1.06)
Deservingness Deviation2	-0.08 (0.73)	0.14 (0.94)	0.87 (1.08)
Attribution (Self)3	0.34 (0.57)	0.25 (0.45)	0.26 (0.51)
Attribution (Other)3	0.25 (0.52)	0.26 (0.42)	0.41 (0.47)
State Envy	1.53 (1.03)	1.73 (1.21)	2.80 (1.48)
Negative Affect	1.64 (0.93)	1.85 (1.21)	2.63 (1.34)
Positive Affect	4.02 (1.13)	4.24 (1.52)	4.32 (1.33)
Negative TAR	1.97 (1.13)	2.28 (1.35)	2.44 (1.30)
Positive TAR	5.06 (0.91)	4.63 (1.71)	4.75 (1.33)
1 Mean (SD)
2 Calculated as response minus earned amount. Positive values indicate believing one should have/deserves more than received; negative values indicate the opposite.
3 Scaled as (variable - 50) / 50: -1 = skill, 0 = both, 1 = luck. More positive values indicate greater attribution to luck.

df %>%
    select(
        info_condition, 
        bonus_belong_deviation, bonus_deserve_deviation, 
        attribution_self_scaled, attribution_other_scaled, 
        state_envy_composite, neg_affect_composite, 
        pos_affect_composite, tar_neg_composite, tar_pos_composite
    ) %>%
    tbl_summary(
        by = info_condition,
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        digits = all_continuous() ~ 2,
        label = list(
            bonus_belong_deviation   ~ "Belonging Deviation",
            bonus_deserve_deviation  ~ "Deservingness Deviation",
            attribution_self_scaled  ~ "Attribution (Self)",
            attribution_other_scaled ~ "Attribution (Other)",
            state_envy_composite     ~ "State Envy",
            neg_affect_composite     ~ "Negative Affect",
            pos_affect_composite     ~ "Positive Affect",
            tar_neg_composite        ~ "Negative TAR",
            tar_pos_composite        ~ "Positive TAR"
        )
    ) %>%
    bold_labels() %>%
    # Footnote 1: Deviation variables
        modify_table_styling(
          columns = label,
          rows = variable %in% c("bonus_belong_deviation", "bonus_deserve_deviation"),
          footnote = "Calculated as response minus earned amount. Positive values indicate believing one
          should have/deserves more than received; negative values indicate the opposite."
        ) %>%
        # Footnote 2: Scaled attribution variables
        modify_table_styling(
          columns = label,
          rows = variable %in% c("attribution_self_scaled", "attribution_other_scaled"),
          footnote = "Scaled as (variable - 50) / 50: -1 = skill, 0 = both, 1 = luck. More positive values
          indicate greater attribution to luck."
        ) %>%
    modify_caption("**Summary Statistics by Outcome**")

Summary Statistics by Outcome

Characteristic	none N = 531	skill N = 371	luck N = 561
Belonging Deviation2	0.31 (0.90)	0.32 (0.89)	0.38 (1.11)
Deservingness Deviation2	0.35 (1.07)	0.17 (1.00)	0.46 (0.98)
Attribution (Self)3	0.32 (0.52)	0.30 (0.53)	0.25 (0.52)
Attribution (Other)3	0.40 (0.47)	0.12 (0.47)	0.37 (0.48)
State Envy	1.87 (1.20)	2.40 (1.56)	2.08 (1.42)
Negative Affect	1.92 (1.12)	2.36 (1.37)	2.05 (1.26)
Positive Affect	4.04 (1.45)	4.23 (1.25)	4.30 (1.17)
Negative TAR	2.14 (1.25)	2.15 (1.02)	2.33 (1.40)
Positive TAR	4.89 (1.32)	4.75 (1.23)	4.88 (1.29)
1 Mean (SD)
2 Calculated as response minus earned amount. Positive values indicate believing one should have/deserves more than received; negative values indicate the opposite.
3 Scaled as (variable - 50) / 50: -1 = skill, 0 = both, 1 = luck. More positive values indicate greater attribution to luck.

summary_table <- df %>%
  select(
    info_condition, actual_participant_outcome,
    bonus_belong_deviation, bonus_deserve_deviation, 
    attribution_self_scaled, attribution_other_scaled, 
    state_envy_composite, neg_affect_composite, 
    pos_affect_composite, tar_neg_composite, tar_pos_composite
  ) %>%
  tbl_strata(
    strata = info_condition,
    .header = "**Info Condition: {strata}**",  # Custom header for each stratum
    .tbl_fun = ~ .x %>%
      tbl_summary(
        by = actual_participant_outcome,
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        digits = all_continuous() ~ 2,
        label = list(
          bonus_belong_deviation   ~ "Belonging Deviation",
          bonus_deserve_deviation  ~ "Deservingness Deviation",
          attribution_self_scaled  ~ "Attribution (Self)",
          attribution_other_scaled ~ "Attribution (Other)",
          state_envy_composite     ~ "State Envy",
          neg_affect_composite     ~ "Negative Affect",
          pos_affect_composite     ~ "Positive Affect",
          tar_neg_composite        ~ "Negative TAR",
          tar_pos_composite        ~ "Positive TAR"
        )
      ) %>%
      bold_labels() %>%
      # Footnote 1: Deviation variables
      modify_table_styling(
        columns = label,
        rows = variable %in% c("bonus_belong_deviation", "bonus_deserve_deviation"),
        footnote = "Calculated as response minus earned amount. Positive values indicate believing one
        should have/deserves more than received; negative values indicate the opposite."
      ) %>%
      # Footnote 2: Scaled attribution variables
      modify_table_styling(
        columns = label,
        rows = variable %in% c("attribution_self_scaled", "attribution_other_scaled"),
        footnote = "Scaled as (variable - 50) / 50: -1 = skill, 0 = both, 1 = luck. More positive values
        indicate greater attribution to luck."
      )
  ) %>%
  # Overall Table Caption / Title
  modify_caption("**Table 1. Summary Statistics by Info Condition and Participant Outcome**")

summary_table

Table 1. Summary Statistics by Info Condition and Participant Outcome

Characteristic	Info Condition: none			Info Condition: skill			Info Condition: luck
	win N = 201	tie N = 131	lose N = 201	win N = 151	tie N = 61	lose N = 161	win N = 231	tie N = 101	lose N = 231
Belonging Deviation2	-0.05 (0.89)	0.31 (0.75)	0.66 (0.90)	0.00 (0.53)	-0.38 (0.79)	0.89 (0.89)	-0.09 (0.85)	0.29 (0.67)	0.89 (1.30)
Deservingness Deviation2	-0.06 (0.93)	0.31 (1.11)	0.80 (1.04)	-0.33 (0.49)	-0.46 (0.81)	0.88 (1.00)	0.07 (0.63)	0.28 (0.65)	0.92 (1.19)
Attribution (Self)3	0.46 (0.57)	0.13 (0.49)	0.31 (0.46)	0.39 (0.61)	0.42 (0.33)	0.18 (0.50)	0.21 (0.53)	0.30 (0.44)	0.26 (0.57)
Attribution (Other)3	0.44 (0.56)	0.27 (0.42)	0.45 (0.41)	-0.01 (0.49)	0.17 (0.43)	0.23 (0.46)	0.26 (0.44)	0.31 (0.45)	0.51 (0.52)
State Envy	1.51 (1.03)	1.46 (0.68)	2.50 (1.38)	1.47 (0.92)	2.42 (1.55)	3.27 (1.62)	1.60 (1.14)	1.68 (1.48)	2.74 (1.44)
Negative Affect	1.67 (0.98)	1.58 (0.70)	2.41 (1.32)	1.63 (0.77)	2.54 (1.70)	2.99 (1.42)	1.64 (1.02)	1.80 (1.37)	2.57 (1.30)
Positive Affect	3.78 (1.29)	4.26 (1.65)	4.15 (1.50)	4.49 (0.95)	3.33 (1.73)	4.33 (1.24)	3.93 (1.04)	4.77 (1.02)	4.46 (1.27)
Negative TAR	1.88 (1.02)	2.05 (1.25)	2.45 (1.44)	1.80 (0.66)	2.39 (1.45)	2.40 (1.08)	2.14 (1.43)	2.50 (1.53)	2.45 (1.36)
Positive TAR	5.15 (1.00)	4.89 (1.90)	4.64 (1.15)	5.09 (0.96)	4.23 (1.34)	4.61 (1.39)	4.96 (0.83)	4.52 (1.76)	4.95 (1.47)
1 Mean (SD)
2 Calculated as response minus earned amount. Positive values indicate believing one should have/deserves more than received; negative values indicate the opposite.
3 Scaled as (variable - 50) / 50: -1 = skill, 0 = both, 1 = luck. More positive values indicate greater attribution to luck.

# outcome on attribution
summary(lm.beta(lm(attribution_self ~ actual_participant_outcome * info_condition, data = df)))

Call:
lm(formula = attribution_self ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
   Min     1Q Median     3Q    Max 
-72.90 -17.30   0.70  20.07  43.54 

Coefficients:
                                                    Estimate Standardized
(Intercept)                                         72.90000           NA
actual_participant_outcometie                      -16.43846     -0.25309
actual_participant_outcomelose                      -7.20000     -0.13635
info_conditionskill                                 -3.30000     -0.05539
info_conditionluck                                 -12.16087     -0.22819
actual_participant_outcometie:info_conditionskill   17.67179      0.13538
actual_participant_outcomelose:info_conditionskill  -3.40000     -0.04099
actual_participant_outcometie:info_conditionluck    20.79933      0.20274
actual_participant_outcomelose:info_conditionluck    9.46087      0.13301
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                           5.86537  12.429   <2e-16
actual_participant_outcometie                         9.34503  -1.759   0.0808
actual_participant_outcomelose                        8.29488  -0.868   0.3869
info_conditionskill                                   8.95950  -0.368   0.7132
info_conditionluck                                    8.01984  -1.516   0.1317
actual_participant_outcometie:info_conditionskill    15.74404   1.122   0.2636
actual_participant_outcomelose:info_conditionskill   12.55700  -0.271   0.7870
actual_participant_outcometie:info_conditionluck     13.64001   1.525   0.1296
actual_participant_outcomelose:info_conditionluck    11.34177   0.834   0.4056
                                                      
(Intercept)                                        ***
actual_participant_outcometie                      .  
actual_participant_outcomelose                        
info_conditionskill                                   
info_conditionluck                                    
actual_participant_outcometie:info_conditionskill     
actual_participant_outcomelose:info_conditionskill    
actual_participant_outcometie:info_conditionluck      
actual_participant_outcomelose:info_conditionluck     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 26.23 on 137 degrees of freedom
Multiple R-squared:  0.03851,   Adjusted R-squared:  -0.01764 
F-statistic: 0.6859 on 8 and 137 DF,  p-value: 0.7034

attribution_self_mod <- lm(attribution_self ~ actual_participant_outcome * info_condition, data = df) #main + int
emmeans(attribution_self_mod, pairwise ~ actual_participant_outcome, adjust = "tukey")

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome emmean   SE  df lower.CL upper.CL
 win                          67.7 3.50 137     60.8     74.7
 tie                          64.1 5.13 137     54.0     74.3
 lose                         62.6 3.45 137     55.7     69.4

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate   SE  df t.ratio p.value
 win - tie      3.61 6.21 137   0.582  0.8297
 win - lose     5.18 4.92 137   1.054  0.5445
 tie - lose     1.56 6.18 137   0.253  0.9653

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(attribution_self_mod, pairwise ~ actual_participant_outcome * info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition emmean    SE  df lower.CL upper.CL
 win                        none             72.9  5.87 137     61.3     84.5
 tie                        none             56.5  7.28 137     42.1     70.8
 lose                       none             65.7  5.87 137     54.1     77.3
 win                        skill            69.6  6.77 137     56.2     83.0
 tie                        skill            70.8 10.70 137     49.7     92.0
 lose                       skill            59.0  6.56 137     46.0     72.0
 win                        luck             60.7  5.47 137     49.9     71.6
 tie                        luck             65.1  8.29 137     48.7     81.5
 lose                       luck             63.0  5.47 137     52.2     73.8

Confidence level used: 0.95 

summary(lm.beta(lm(attribution_other ~ actual_participant_outcome * info_condition, data = df)))

Call:
lm(formula = attribution_other ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-67.478 -15.400  -2.478  20.302  50.733 

Coefficients:
                                                   Estimate Standardized
(Intercept)                                         71.7500           NA
actual_participant_outcometie                       -8.1346      -0.1343
actual_participant_outcomelose                       0.6500       0.0132
info_conditionskill                                -22.4833      -0.4047
info_conditionluck                                  -8.7065      -0.1752
actual_participant_outcometie:info_conditionskill   17.3680       0.1427
actual_participant_outcomelose:info_conditionskill  11.5208       0.1489
actual_participant_outcometie:info_conditionluck    10.4911       0.1096
actual_participant_outcomelose:info_conditionluck   11.7848       0.1777
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                            5.2866  13.572  < 2e-16
actual_participant_outcometie                          8.4229  -0.966  0.33586
actual_participant_outcomelose                         7.4763   0.087  0.93085
info_conditionskill                                    8.0754  -2.784  0.00613
info_conditionluck                                     7.2284  -1.204  0.23048
actual_participant_outcometie:info_conditionskill     14.1904   1.224  0.22308
actual_participant_outcomelose:info_conditionskill    11.3179   1.018  0.31050
actual_participant_outcometie:info_conditionluck      12.2940   0.853  0.39495
actual_participant_outcomelose:info_conditionluck     10.2226   1.153  0.25099
                                                      
(Intercept)                                        ***
actual_participant_outcometie                         
actual_participant_outcomelose                        
info_conditionskill                                ** 
info_conditionluck                                    
actual_participant_outcometie:info_conditionskill     
actual_participant_outcomelose:info_conditionskill    
actual_participant_outcometie:info_conditionluck      
actual_participant_outcomelose:info_conditionluck     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 23.64 on 137 degrees of freedom
Multiple R-squared:  0.102, Adjusted R-squared:  0.04953 
F-statistic: 1.944 on 8 and 137 DF,  p-value: 0.05815

attribution_other_mod <- lm(attribution_other ~ actual_participant_outcome * info_condition, data = df) #main + int
emmeans(attribution_other_mod, pairwise ~ actual_participant_outcome, adjust = "tukey")

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome emmean   SE  df lower.CL upper.CL
 win                          61.4 3.15 137     55.1     67.6
 tie                          62.5 4.62 137     53.4     71.6
 lose                         69.8 3.11 137     63.6     75.9

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate   SE  df t.ratio p.value
 win - tie     -1.15 5.59 137  -0.206  0.9769
 win - lose    -8.42 4.43 137  -1.900  0.1426
 tie - lose    -7.27 5.57 137  -1.305  0.3951

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(attribution_other_mod, pairwise ~ actual_participant_outcome * info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition emmean   SE  df lower.CL upper.CL
 win                        none             71.8 5.29 137     61.3     82.2
 tie                        none             63.6 6.56 137     50.6     76.6
 lose                       none             72.4 5.29 137     61.9     82.9
 win                        skill            49.3 6.10 137     37.2     61.3
 tie                        skill            58.5 9.65 137     39.4     77.6
 lose                       skill            61.4 5.91 137     49.7     73.1
 win                        luck             63.0 4.93 137     53.3     72.8
 tie                        luck             65.4 7.48 137     50.6     80.2
 lose                       luck             75.5 4.93 137     65.7     85.2

Confidence level used: 0.95 

#==========================

# bonus appraisals 

##How much of the bonus should belong to you? 
summary(lm.beta(lm(bonus_belong_deviation ~ actual_participant_outcome * info_condition, data = df)))

Call:
lm(formula = bonus_belong_deviation ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9500 -0.6615  0.0500  0.3135  2.3360 

Coefficients:
                                                   Estimate Standardized
(Intercept)                                        -0.05000           NA
actual_participant_outcometie                       0.35692      0.14590
actual_participant_outcomelose                      0.71400      0.35898
info_conditionskill                                 0.05333      0.02377
info_conditionluck                                 -0.04391     -0.02188
actual_participant_outcometie:info_conditionskill  -0.74359     -0.15124
actual_participant_outcomelose:info_conditionskill  0.17204      0.05506
actual_participant_outcometie:info_conditionluck    0.03099      0.00802
actual_participant_outcomelose:info_conditionluck   0.27078      0.10107
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                           0.20301  -0.246   0.8058
actual_participant_outcometie                         0.32345   1.103   0.2718
actual_participant_outcomelose                        0.28710   2.487   0.0141
info_conditionskill                                   0.31011   0.172   0.8637
info_conditionluck                                    0.27758  -0.158   0.8745
actual_participant_outcometie:info_conditionskill     0.54493  -1.365   0.1746
actual_participant_outcomelose:info_conditionskill    0.43462   0.396   0.6928
actual_participant_outcometie:info_conditionluck      0.47211   0.066   0.9478
actual_participant_outcomelose:info_conditionluck     0.39256   0.690   0.4915
                                                    
(Intercept)                                         
actual_participant_outcometie                       
actual_participant_outcomelose                     *
info_conditionskill                                 
info_conditionluck                                  
actual_participant_outcometie:info_conditionskill   
actual_participant_outcomelose:info_conditionskill  
actual_participant_outcometie:info_conditionluck    
actual_participant_outcomelose:info_conditionluck   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9079 on 137 degrees of freedom
Multiple R-squared:  0.1881,    Adjusted R-squared:  0.1406 
F-statistic: 3.966 on 8 and 137 DF,  p-value: 0.0002984

bonus_belong_mod <- lm(bonus_belong_deviation ~ actual_participant_outcome * info_condition, data = df)
emmeans(bonus_belong_mod, pairwise ~ actual_participant_outcome, adjust = "tukey")

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome  emmean    SE  df lower.CL upper.CL
 win                        -0.0469 0.121 137   -0.286    0.193
 tie                         0.0725 0.177 137   -0.278    0.423
 lose                        0.8147 0.120 137    0.578    1.051

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate    SE  df t.ratio p.value
 win - tie    -0.119 0.215 137  -0.556  0.8437
 win - lose   -0.862 0.170 137  -5.064  <.0001
 tie - lose   -0.742 0.214 137  -3.470  0.0020

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(bonus_belong_mod, pairwise ~ actual_participant_outcome * info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition   emmean    SE  df lower.CL upper.CL
 win                        none           -0.05000 0.203 137   -0.451    0.351
 tie                        none            0.30692 0.252 137   -0.191    0.805
 lose                       none            0.66400 0.203 137    0.263    1.065
 win                        skill           0.00333 0.234 137   -0.460    0.467
 tie                        skill          -0.38333 0.371 137   -1.116    0.350
 lose                       skill           0.88938 0.227 137    0.441    1.338
 win                        luck           -0.09391 0.189 137   -0.468    0.280
 tie                        luck            0.29400 0.287 137   -0.274    0.862
 lose                       luck            0.89087 0.189 137    0.517    1.265

Confidence level used: 0.95 

#How much of the bonus do you deserve? 
summary(lm.beta(lm(bonus_deserve_deviation ~ actual_participant_outcome * info_condition, data = df)))

Call:
lm(formula = bonus_deserve_deviation ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9415 -0.6817 -0.0285  0.3783  2.2030 

Coefficients:
                                                    Estimate Standardized
(Intercept)                                        -0.058500           NA
actual_participant_outcometie                       0.366192     0.144262
actual_participant_outcomelose                      0.855500     0.414531
info_conditionskill                                -0.274833    -0.118042
info_conditionluck                                  0.128500     0.061698
actual_participant_outcometie:info_conditionskill  -0.487859    -0.095628
actual_participant_outcomelose:info_conditionskill  0.357208     0.110180
actual_participant_outcometie:info_conditionluck   -0.158192    -0.039455
actual_participant_outcomelose:info_conditionluck  -0.004630    -0.001666
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                          0.207223  -0.282   0.7781
actual_participant_outcometie                        0.330160   1.109   0.2693
actual_participant_outcomelose                       0.293058   2.919   0.0041
info_conditionskill                                  0.316539  -0.868   0.3868
info_conditionluck                                   0.283341   0.454   0.6509
actual_participant_outcometie:info_conditionskill    0.556237  -0.877   0.3820
actual_participant_outcomelose:info_conditionskill   0.443639   0.805   0.4221
actual_participant_outcometie:info_conditionluck     0.481901  -0.328   0.7432
actual_participant_outcomelose:info_conditionluck    0.400704  -0.012   0.9908
                                                     
(Intercept)                                          
actual_participant_outcometie                        
actual_participant_outcomelose                     **
info_conditionskill                                  
info_conditionluck                                   
actual_participant_outcometie:info_conditionskill    
actual_participant_outcomelose:info_conditionskill   
actual_participant_outcometie:info_conditionluck     
actual_participant_outcomelose:info_conditionluck    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9267 on 137 degrees of freedom
Multiple R-squared:  0.2143,    Adjusted R-squared:  0.1684 
F-statistic: 4.669 on 8 and 137 DF,  p-value: 4.532e-05

bonus_deserve_mod <- lm(bonus_deserve_deviation ~ actual_participant_outcome * info_condition, data = df)
emmeans(bonus_deserve_mod, pairwise ~ actual_participant_outcome, adjust = "tukey")

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome  emmean    SE  df lower.CL upper.CL
 win                        -0.1073 0.124 137   -0.352    0.137
 tie                         0.0436 0.181 137   -0.314    0.402
 lose                        0.8657 0.122 137    0.624    1.107

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate    SE  df t.ratio p.value
 win - tie    -0.151 0.219 137  -0.688  0.7709
 win - lose   -0.973 0.174 137  -5.602  <.0001
 tie - lose   -0.822 0.218 137  -3.766  0.0007

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(bonus_deserve_mod, pairwise ~ actual_participant_outcome * info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition  emmean    SE  df lower.CL upper.CL
 win                        none           -0.0585 0.207 137   -0.468    0.351
 tie                        none            0.3077 0.257 137   -0.201    0.816
 lose                       none            0.7970 0.207 137    0.387    1.207
 win                        skill          -0.3333 0.239 137   -0.806    0.140
 tie                        skill          -0.4550 0.378 137   -1.203    0.293
 lose                       skill           0.8794 0.232 137    0.421    1.338
 win                        luck            0.0700 0.193 137   -0.312    0.452
 tie                        luck            0.2780 0.293 137   -0.302    0.858
 lose                       luck            0.9209 0.193 137    0.539    1.303

Confidence level used: 0.95 

#============

# emotions

## envy
summary(lm.beta(lm(state_envy_composite ~ actual_participant_outcome * info_condition, data = df))) #main + int

Call:
lm(formula = state_envy_composite ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2656 -0.5978 -0.4615  0.6958  4.0750 

Coefficients:
                                                   Estimate Standardized
(Intercept)                                         1.51250           NA
actual_participant_outcometie                      -0.05096     -0.01468
actual_participant_outcomelose                      0.98750      0.34996
info_conditionskill                                -0.04583     -0.01440
info_conditionluck                                  0.08533      0.02996
actual_participant_outcometie:info_conditionskill   1.00096      0.14350
actual_participant_outcomelose:info_conditionskill  0.81146      0.18306
actual_participant_outcometie:info_conditionluck    0.12814      0.02337
actual_participant_outcomelose:info_conditionluck   0.15380      0.04047
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                           0.28284   5.348 3.64e-07
actual_participant_outcometie                         0.45064  -0.113   0.9101
actual_participant_outcomelose                        0.40000   2.469   0.0148
info_conditionskill                                   0.43205  -0.106   0.9157
info_conditionluck                                    0.38674   0.221   0.8257
actual_participant_outcometie:info_conditionskill     0.75921   1.318   0.1896
actual_participant_outcomelose:info_conditionskill    0.60553   1.340   0.1824
actual_participant_outcometie:info_conditionluck      0.65775   0.195   0.8458
actual_participant_outcomelose:info_conditionluck     0.54693   0.281   0.7790
                                                      
(Intercept)                                        ***
actual_participant_outcometie                         
actual_participant_outcomelose                     *  
info_conditionskill                                   
info_conditionluck                                    
actual_participant_outcometie:info_conditionskill     
actual_participant_outcomelose:info_conditionskill    
actual_participant_outcometie:info_conditionluck      
actual_participant_outcomelose:info_conditionluck     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.265 on 137 degrees of freedom
Multiple R-squared:  0.217, Adjusted R-squared:  0.1712 
F-statistic: 4.745 on 8 and 137 DF,  p-value: 3.708e-05

envy_mod <- lm(state_envy_composite ~ actual_participant_outcome * info_condition, data = df)
emmeans(envy_mod, pairwise ~ actual_participant_outcome, adjust = "tukey") 

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome emmean    SE  df lower.CL upper.CL
 win                          1.53 0.169 137     1.19     1.86
 tie                          1.85 0.247 137     1.36     2.34
 lose                         2.83 0.167 137     2.51     3.16

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate    SE  df t.ratio p.value
 win - tie    -0.325 0.299 137  -1.087  0.5235
 win - lose   -1.309 0.237 137  -5.523  <.0001
 tie - lose   -0.984 0.298 137  -3.301  0.0035

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(envy_mod, pairwise ~ actual_participant_outcome*info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition emmean    SE  df lower.CL upper.CL
 win                        none             1.51 0.283 137    0.953     2.07
 tie                        none             1.46 0.351 137    0.768     2.16
 lose                       none             2.50 0.283 137    1.941     3.06
 win                        skill            1.47 0.327 137    0.821     2.11
 tie                        skill            2.42 0.516 137    1.396     3.44
 lose                       skill            3.27 0.316 137    2.640     3.89
 win                        luck             1.60 0.264 137    1.076     2.12
 tie                        luck             1.68 0.400 137    0.884     2.47
 lose                       luck             2.74 0.264 137    2.218     3.26

Confidence level used: 0.95 

## negative affect 
summary(lm.beta(lm(neg_affect_composite ~ actual_participant_outcome * info_condition, data = df))) #main + int

Call:
lm(formula = neg_affect_composite ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9922 -0.6359 -0.2221  0.6611  3.7000 

Coefficients:
                                                   Estimate Standardized
(Intercept)                                         1.66875           NA
actual_participant_outcometie                      -0.09183     -0.02953
actual_participant_outcomelose                      0.73750      0.29168
info_conditionskill                                -0.04375     -0.01534
info_conditionluck                                 -0.03288     -0.01289
actual_participant_outcometie:info_conditionskill   1.00849      0.16135
actual_participant_outcomelose:info_conditionskill  0.62969      0.15853
actual_participant_outcometie:info_conditionluck    0.25596      0.05211
actual_participant_outcomelose:info_conditionluck   0.19185      0.05633
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                           0.26161   6.379 2.55e-09
actual_participant_outcometie                         0.41681  -0.220   0.8260
actual_participant_outcomelose                        0.36997   1.993   0.0482
info_conditionskill                                   0.39962  -0.109   0.9130
info_conditionluck                                    0.35770  -0.092   0.9269
actual_participant_outcometie:info_conditionskill     0.70222   1.436   0.1532
actual_participant_outcomelose:info_conditionskill    0.56007   1.124   0.2629
actual_participant_outcometie:info_conditionluck      0.60838   0.421   0.6746
actual_participant_outcomelose:info_conditionluck     0.50587   0.379   0.7051
                                                      
(Intercept)                                        ***
actual_participant_outcometie                         
actual_participant_outcomelose                     *  
info_conditionskill                                   
info_conditionluck                                    
actual_participant_outcometie:info_conditionskill     
actual_participant_outcomelose:info_conditionskill    
actual_participant_outcometie:info_conditionluck      
actual_participant_outcomelose:info_conditionluck     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.17 on 137 degrees of freedom
Multiple R-squared:  0.1657,    Adjusted R-squared:  0.1169 
F-statistic:   3.4 on 8 and 137 DF,  p-value: 0.001359

NA_mod <- lm(neg_affect_composite ~ actual_participant_outcome * info_condition, data = df)
emmeans(NA_mod, pairwise ~ actual_participant_outcome, adjust = "tukey") 

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome emmean    SE  df lower.CL upper.CL
 win                          1.64 0.156 137     1.33     1.95
 tie                          1.97 0.229 137     1.52     2.42
 lose                         2.65 0.154 137     2.35     2.96

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate    SE  df t.ratio p.value
 win - tie    -0.330 0.277 137  -1.191  0.4605
 win - lose   -1.011 0.219 137  -4.612  <.0001
 tie - lose   -0.682 0.276 137  -2.473  0.0386

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(NA_mod, pairwise ~ actual_participant_outcome*info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition emmean    SE  df lower.CL upper.CL
 win                        none             1.67 0.262 137    1.151     2.19
 tie                        none             1.58 0.324 137    0.935     2.22
 lose                       none             2.41 0.262 137    1.889     2.92
 win                        skill            1.62 0.302 137    1.028     2.22
 tie                        skill            2.54 0.478 137    1.597     3.49
 lose                       skill            2.99 0.292 137    2.414     3.57
 win                        luck             1.64 0.244 137    1.153     2.12
 tie                        luck             1.80 0.370 137    1.068     2.53
 lose                       luck             2.57 0.244 137    2.083     3.05

Confidence level used: 0.95 

## positive affect 
summary(lm.beta(lm(pos_affect_composite ~ actual_participant_outcome * info_condition, data = df))) #main + int

Call:
lm(formula = pos_affect_composite ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4638 -0.7417  0.2167  0.8647  2.6667 

Coefficients:
                                                   Estimate Standardized
(Intercept)                                         3.78333           NA
actual_participant_outcometie                       0.47308      0.14634
actual_participant_outcomelose                      0.36667      0.13950
info_conditionskill                                 0.70556      0.23794
info_conditionluck                                  0.14420      0.05436
actual_participant_outcometie:info_conditionskill  -1.62863     -0.25066
actual_participant_outcomelose:info_conditionskill -0.52222     -0.12648
actual_participant_outcometie:info_conditionluck    0.36605      0.07169
actual_participant_outcomelose:info_conditionluck   0.16957      0.04789
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                           0.28769  13.151   <2e-16
actual_participant_outcometie                         0.45837   1.032   0.3038
actual_participant_outcomelose                        0.40686   0.901   0.3691
info_conditionskill                                   0.43946   1.606   0.1107
info_conditionluck                                    0.39337   0.367   0.7145
actual_participant_outcometie:info_conditionskill     0.77223  -2.109   0.0368
actual_participant_outcomelose:info_conditionskill    0.61591  -0.848   0.3980
actual_participant_outcometie:info_conditionluck      0.66903   0.547   0.5852
actual_participant_outcomelose:info_conditionluck     0.55631   0.305   0.7610
                                                      
(Intercept)                                        ***
actual_participant_outcometie                         
actual_participant_outcomelose                        
info_conditionskill                                   
info_conditionluck                                    
actual_participant_outcometie:info_conditionskill  *  
actual_participant_outcomelose:info_conditionskill    
actual_participant_outcometie:info_conditionluck      
actual_participant_outcomelose:info_conditionluck     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.287 on 137 degrees of freedom
Multiple R-squared:  0.06629,   Adjusted R-squared:  0.01177 
F-statistic: 1.216 on 8 and 137 DF,  p-value: 0.2942

PA_mod <- lm(pos_affect_composite ~ actual_participant_outcome * info_condition, data = df)
emmeans(PA_mod, pairwise ~ actual_participant_outcome, adjust = "tukey") 

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome emmean    SE  df lower.CL upper.CL
 win                          4.07 0.172 137     3.73     4.41
 tie                          4.12 0.251 137     3.62     4.62
 lose                         4.32 0.169 137     3.98     4.65

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate    SE  df t.ratio p.value
 win - tie   -0.0522 0.304 137  -0.172  0.9839
 win - lose  -0.2491 0.241 137  -1.033  0.5573
 tie - lose  -0.1969 0.303 137  -0.650  0.7929

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(PA_mod, pairwise ~ actual_participant_outcome*info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition emmean    SE  df lower.CL upper.CL
 win                        none             3.78 0.288 137     3.21     4.35
 tie                        none             4.26 0.357 137     3.55     4.96
 lose                       none             4.15 0.288 137     3.58     4.72
 win                        skill            4.49 0.332 137     3.83     5.15
 tie                        skill            3.33 0.525 137     2.29     4.37
 lose                       skill            4.33 0.322 137     3.70     4.97
 win                        luck             3.93 0.268 137     3.40     4.46
 tie                        luck             4.77 0.407 137     3.96     5.57
 lose                       luck             4.46 0.268 137     3.93     4.99

Confidence level used: 0.95 

#==================

# TAR
## negative 
summary(lm.beta(lm(tar_neg_composite ~ actual_participant_outcome * info_condition, data = df))) #main + int

Call:
lm(formula = tar_neg_composite ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5000 -0.8833 -0.3958  0.6154  3.8551 

Coefficients:
                                                    Estimate Standardized
(Intercept)                                         1.883333           NA
actual_participant_outcometie                       0.167949     0.053657
actual_participant_outcomelose                      0.566667     0.222673
info_conditionskill                                -0.083333    -0.029026
info_conditionluck                                  0.261594     0.101859
actual_participant_outcometie:info_conditionskill   0.420940     0.066914
actual_participant_outcomelose:info_conditionskill  0.029167     0.007296
actual_participant_outcometie:info_conditionluck    0.187124     0.037849
actual_participant_outcomelose:info_conditionluck  -0.262319    -0.076524
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                          0.282429   6.668 5.84e-10
actual_participant_outcometie                        0.449981   0.373    0.710
actual_participant_outcomelose                       0.399415   1.419    0.158
info_conditionskill                                  0.431417  -0.193    0.847
info_conditionluck                                   0.386171   0.677    0.499
actual_participant_outcometie:info_conditionskill    0.758106   0.555    0.580
actual_participant_outcomelose:info_conditionskill   0.604644   0.048    0.962
actual_participant_outcometie:info_conditionluck     0.656793   0.285    0.776
actual_participant_outcomelose:info_conditionluck    0.546128  -0.480    0.632
                                                      
(Intercept)                                        ***
actual_participant_outcometie                         
actual_participant_outcomelose                        
info_conditionskill                                   
info_conditionluck                                    
actual_participant_outcometie:info_conditionskill     
actual_participant_outcomelose:info_conditionskill    
actual_participant_outcometie:info_conditionluck      
actual_participant_outcomelose:info_conditionluck     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.263 on 137 degrees of freedom
Multiple R-squared:  0.04009,   Adjusted R-squared:  -0.01596 
F-statistic: 0.7152 on 8 and 137 DF,  p-value: 0.6778

tar_neg_mod <- lm(tar_neg_composite ~ actual_participant_outcome * info_condition, data = df)
emmeans(tar_neg_mod, pairwise ~ actual_participant_outcome, adjustment = "tukey")

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome emmean    SE  df lower.CL upper.CL
 win                          1.94 0.168 137     1.61     2.28
 tie                          2.31 0.247 137     1.83     2.80
 lose                         2.43 0.166 137     2.10     2.76

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate    SE  df t.ratio p.value
 win - tie    -0.371 0.299 137  -1.240  0.4316
 win - lose   -0.489 0.237 137  -2.066  0.1009
 tie - lose   -0.118 0.298 137  -0.398  0.9166

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(tar_neg_mod, pairwise ~ actual_participant_outcome*info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition emmean    SE  df lower.CL upper.CL
 win                        none             1.88 0.282 137     1.32     2.44
 tie                        none             2.05 0.350 137     1.36     2.74
 lose                       none             2.45 0.282 137     1.89     3.01
 win                        skill            1.80 0.326 137     1.16     2.44
 tie                        skill            2.39 0.516 137     1.37     3.41
 lose                       skill            2.40 0.316 137     1.77     3.02
 win                        luck             2.14 0.263 137     1.62     2.67
 tie                        luck             2.50 0.399 137     1.71     3.29
 lose                       luck             2.45 0.263 137     1.93     2.97

Confidence level used: 0.95 

## positive 
summary(lm.beta(lm(tar_pos_composite ~ actual_participant_outcome * info_condition, data = df))) #main + int

Call:
lm(formula = tar_pos_composite ~ actual_participant_outcome * 
    info_condition, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8923 -0.6400  0.0522  0.8500  2.3600 

Coefficients:
                                                    Estimate Standardized
(Intercept)                                         5.150000           NA
actual_participant_outcometie                      -0.257692    -0.080585
actual_participant_outcomelose                     -0.510000    -0.196162
info_conditionskill                                -0.056667    -0.019320
info_conditionluck                                 -0.193478    -0.073741
actual_participant_outcometie:info_conditionskill  -0.602308    -0.093717
actual_participant_outcomelose:info_conditionskill  0.029167     0.007141
actual_participant_outcometie:info_conditionluck   -0.178829    -0.035405
actual_participant_outcomelose:info_conditionluck   0.501304     0.143145
                                                   Std. Error t value Pr(>|t|)
(Intercept)                                          0.289284  17.803   <2e-16
actual_participant_outcometie                        0.460903  -0.559    0.577
actual_participant_outcomelose                       0.409109  -1.247    0.215
info_conditionskill                                  0.441888  -0.128    0.898
info_conditionluck                                   0.395544  -0.489    0.626
actual_participant_outcometie:info_conditionskill    0.776506  -0.776    0.439
actual_participant_outcomelose:info_conditionskill   0.619320   0.047    0.963
actual_participant_outcometie:info_conditionluck     0.672734  -0.266    0.791
actual_participant_outcomelose:info_conditionluck    0.559383   0.896    0.372
                                                      
(Intercept)                                        ***
actual_participant_outcometie                         
actual_participant_outcomelose                        
info_conditionskill                                   
info_conditionluck                                    
actual_participant_outcometie:info_conditionskill     
actual_participant_outcomelose:info_conditionskill    
actual_participant_outcometie:info_conditionluck      
actual_participant_outcomelose:info_conditionluck     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.294 on 137 degrees of freedom
Multiple R-squared:  0.03513,   Adjusted R-squared:  -0.02122 
F-statistic: 0.6234 on 8 and 137 DF,  p-value: 0.757

tar_pos_mod <- lm(tar_pos_composite ~ actual_participant_outcome * info_condition, data = df)
emmeans(tar_pos_mod, pairwise ~ actual_participant_outcome, adjustment = "tukey")

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 actual_participant_outcome emmean    SE  df lower.CL upper.CL
 win                          5.07 0.173 137     4.73     5.41
 tie                          4.55 0.253 137     4.05     5.05
 lose                         4.73 0.170 137     4.40     5.07

Results are averaged over the levels of: info_condition 
Confidence level used: 0.95 

$contrasts
 contrast   estimate    SE  df t.ratio p.value
 win - tie     0.518 0.306 137   1.693  0.2117
 win - lose    0.333 0.242 137   1.374  0.3573
 tie - lose   -0.185 0.305 137  -0.607  0.8168

Results are averaged over the levels of: info_condition 
P value adjustment: tukey method for comparing a family of 3 estimates 

emmeans(tar_pos_mod, pairwise ~ actual_participant_outcome*info_condition, adjust = "tukey")$emmeans

 actual_participant_outcome info_condition emmean    SE  df lower.CL upper.CL
 win                        none             5.15 0.289 137     4.58     5.72
 tie                        none             4.89 0.359 137     4.18     5.60
 lose                       none             4.64 0.289 137     4.07     5.21
 win                        skill            5.09 0.334 137     4.43     5.75
 tie                        skill            4.23 0.528 137     3.19     5.28
 lose                       skill            4.61 0.323 137     3.97     5.25
 win                        luck             4.96 0.270 137     4.42     5.49
 tie                        luck             4.52 0.409 137     3.71     5.33
 lose                       luck             4.95 0.270 137     4.41     5.48

Confidence level used: 0.95 

Visualizations

# Update dataframe before running analyses
df <- df %>%
  mutate(
    actual_participant_outcome = factor(actual_participant_outcome, levels = c("lose", "tie", "win")),
    info_condition = factor(info_condition, levels = c("skill", "none", "luck"))
  )

# outcome attribution (point plot)
ggplot(data = attribution_long, aes(x = factor(info_condition), y = attribution_value, fill = attribution_type)) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.15, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    geom_hline(yintercept = 0) +
    scale_x_discrete(
        labels = c("none" = "None", "skill" = "Skill", "luck" = "Luck")
    ) + 
    scale_fill_manual(values = c("Self" = "#4361EE", "Other" = "#F72585")) + 
    labs(
        title = "Game Attribution Following Rock-Paper-Scissors",
        x = "",
        y = "Game Attribution", 
        fill = "",
        caption = "-1 = Skill; 0 = Equally skillful and luck; 1 = Luck" 
    ) +
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom", 
        plot.caption = element_text(hjust = 0.5)
    )

ggplot(data = attribution_long, aes(x = factor(actual_participant_outcome), y = attribution_value, fill = attribution_type)) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.15, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    geom_hline(yintercept = 0) +
    scale_x_discrete(
        labels = c("lose" = "Loss", "win" = "Won", "tie" = "Tied")
    ) + 
    scale_fill_manual(values = c("Self" = "#4361EE", "Other" = "#F72585")) + 
    labs(
        title = "Game Attribution Following Rock-Paper-Scissors",
        x = "",
        y = "Game Attribution", 
        fill = "",
        caption = "-1 = Skill; 0 = Equally skillful and luck; 1 = Luck" 
    ) +
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom", 
        plot.caption = element_text(hjust = 0.5)
    )

## attribution by the info_condition 
ggplot(data = attribution_long, aes(x = factor(actual_participant_outcome), y = attribution_value, fill = attribution_type)) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.05, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    geom_hline(yintercept = 0) +
    scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied", "win" = "Won")
    ) + 
    scale_y_continuous(
        breaks = c(-1, -0.5, 0, 0.5, 1)
    )+
    coord_cartesian(ylim = c(-1, 1))+
    scale_fill_manual(values = c("Self" = "#4361EE", "Other" = "#F72585")) + 
    facet_wrap(~ info_condition, labeller = labeller(info_condition = c("skill" = "Skill Condition", "luck" = "Luck Condition", "none" = "None Condition"))) +
    labs(
        title = "Game Attribution Following Rock-Paper-Scissors",
        x = "",
        y = "Game Attribution", 
        fill = "Attribution",
        caption = "-1 = Skill; 0 = Equally skillful and luck; 1 = Luck"
    ) +
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 16, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 14),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 14),
        axis.text = element_text(colour = "black", size = 12),
        strip.text = element_text(face = "bold", size = 14), # Styles the facet headers
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom", 
        plot.caption = element_text(hjust = 0.5)
    )

#----------------------
#visualizations for bonuses (amount they should get vs amount they think they deserve)
#df denoting true payout amount 
actual_lines <- data.frame(
  actual_participant_outcome = factor(c("lose", "tie", "win"), levels = c("lose", "tie", "win")),
  actual_amount = c(1, 2, 3)
)
# should get 
ggplot(data = df, aes(x = factor(actual_participant_outcome), y = bonus_belong))+
    geom_jitter (show.legend = FALSE, alpha = .20, position = position_jitterdodge
                 (jitter.height = .15, dodge.width = .8)) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = .7,
        position = position_dodge(width = 0.8),
        color = "black"
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = .7,
        color = "black",
        position = position_dodge(width = 0.8)
    ) + 
    geom_hline(
      yintercept = 2
    )+
    geom_segment(
          data = actual_lines,
          aes(
              x = as.numeric(actual_participant_outcome) - 0.3, 
              xend = as.numeric(actual_participant_outcome) + 0.3, 
              y = actual_amount, 
              yend = actual_amount
          ),
          inherit.aes = FALSE,
          linewidth = 1.2,
          color = "darkred"
      ) +
      scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied","win" = "Won")
    )+ labs(
        title = "How much of the $4 should you receive?",
        x = "",
        y = "Amount of $4"
    )+
    theme_minimal()+
    theme(
        plot.title = element_text(hjust = .5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust =.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust =.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman")
    )

#intereaction 
ggplot(data = df, aes(x = factor(actual_participant_outcome), y = bonus_belong, fill = info_condition))+
    geom_jitter (show.legend = FALSE, alpha = .20, position = position_jitterdodge
                 (jitter.height = .15, dodge.width = .8)) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = .7,
        position = position_dodge(width = 0.8),
        color = "black"
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = .7,
        color = "black",
        position = position_dodge(width = 0.8)
    ) + 
    geom_hline(
      yintercept = 2
    )+
    geom_segment(
          data = actual_lines,
          aes(
              x = as.numeric(actual_participant_outcome) - 0.3, 
              xend = as.numeric(actual_participant_outcome) + 0.3, 
              y = actual_amount, 
              yend = actual_amount
          ),
          inherit.aes = FALSE,
          linewidth = 1.2,
          color = "darkred"
      ) +
      scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied","win" = "Won")
    )+ labs(
        title = "How much of the $4 should you receive?",
        x = "",
        y = "Amount of $4", 
        fill = "Message Condition"
    )+
    theme_minimal()+
    theme(
        plot.title = element_text(hjust = .5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust =.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust =.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman")
    )

# deserve 
ggplot(data = df, aes(x = factor(actual_participant_outcome), y = bonus_deserve))+
    geom_jitter (show.legend = FALSE, alpha = .20, position = position_jitterdodge
                 (jitter.height = .15, dodge.width = .8)) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = .7,
        position = position_dodge(width = 0.8),
        color = "black"
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = .7,
        color = "black",
        position = position_dodge(width = 0.8)
    ) + 
    geom_hline(
      yintercept = 2
    )+
    geom_segment(
          data = actual_lines,
          aes(
              x = as.numeric(actual_participant_outcome) - 0.3, 
              xend = as.numeric(actual_participant_outcome) + 0.3, 
              y = actual_amount, 
              yend = actual_amount
          ),
          inherit.aes = FALSE,
          linewidth = 1.2,
          color = "darkred"
      ) +
      scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied","win" = "Won")
    )+ labs(
        title = "How much of the $4 do you deserve?",
        x = "",
        y = "Amount of $4"
    )+
    theme_minimal()+
    theme(
        plot.title = element_text(hjust = .5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust =.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust =.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman")
    )

#interaction
ggplot(data = df, aes(x = factor(actual_participant_outcome), y = bonus_deserve, fill = factor(info_condition)))+
    geom_jitter (show.legend = FALSE, alpha = .20, position = position_jitterdodge
                 (jitter.height = .15, dodge.width = .8)) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = .7,
        position = position_dodge(width = 0.8),
        color = "black"
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = .7,
        color = "black",
        position = position_dodge(width = 0.8)
    ) + 
    geom_hline(
        yintercept = 2
    )+
    geom_segment(
        data = actual_lines,
        aes(
            x = as.numeric(actual_participant_outcome) - 0.3, 
            xend = as.numeric(actual_participant_outcome) + 0.3, 
            y = actual_amount, 
            yend = actual_amount
        ),
        inherit.aes = FALSE,
        linewidth = 1.2,
        color = "darkred"
    ) +
    scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied","win" = "Won")
    )+ labs(
        title = "How much of the $4 do you deserve?",
        x = "",
        y = "Amount of $4", 
        fill = "Message Condition"
    )+
    theme_minimal()+
    theme(
        plot.title = element_text(hjust = .5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust =.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust =.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman")
    )

#bonus results together 
ggplot(data = bonus_long, aes(x = factor(actual_participant_outcome), y = bonus_value, fill = measure_type)) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.05, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    geom_hline(
        yintercept = 2,
        linetype = "dashed",
        color = "gray40"
    ) +
    geom_segment(
          data = actual_lines,
          aes(
              x = as.numeric(actual_participant_outcome) - 0.3, 
              xend = as.numeric(actual_participant_outcome) + 0.3, 
              y = actual_amount, 
              yend = actual_amount
          ),
          inherit.aes = FALSE,
          linewidth = 1.2,
          color = "darkred"
      ) +
    scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied", "win" = "Won")
    ) + 
    scale_fill_manual(values = c("Should Get" = "#4361EE", "Deserve" = "#F72585")) + 
    labs(
        title = "Bonus Allocation: Should Get vs. Deserve",
        x = "",
        y = "Amount of $4", 
        fill = "Measure"
    ) +
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom"
    )

#correlation for bonus deviations and envy 
ggplot(data = df, aes(x = state_envy_composite, y = bonus_deserve_deviation)) + 
  geom_jitter() + 
  geom_smooth(method = "lm") + 
  labs(
    title = "Relationship Between Deservingness Deviation and Envy", 
    x = "State Envy",
    y = "Deservingness Deviations", 
    caption = "Deviation = What One Believes They Deserve - Actual Bonus"
  )+
  theme_minimal()+
  theme(plot.caption = element_text(hjust = .5))

`geom_smooth()` using formula = 'y ~ x'

ggplot(data = df, aes(x = state_envy_composite, y = bonus_belong_deviation)) + 
  geom_jitter() + 
  geom_smooth(method = "lm") + 
  labs(
    title = "Relationship Between Belong Deviation and Envy", 
    x = "State Envy",
    y = "Belong Deviations", 
    caption = "Deviation = What One Believes Belongs to Them - Actual Bonus"
  )+
  theme_minimal()+
  theme(plot.caption = element_text(hjust = .5))

`geom_smooth()` using formula = 'y ~ x'

# TAR 
ggplot(data = tarComp_longer, aes(x = factor(actual_participant_outcome), y = tar_value, fill = tar_type)) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.05, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied", "win" = "Won")
    ) + 
    scale_fill_manual(values = c("Positive Action Tendencies" = "#4361EE", "Negative Action Tendencies" = "#F72585")) + 
    labs(
        title = "Action Tendencies: Positive and Negative",
        x = "",
        y = "Self-Report (1-7)", 
        fill = "Measure"
    ) +
    scale_y_continuous(
    breaks = seq(1,8,1)
    )+
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom"
    )

# envy 
#point plot for state envy by game condition 
ggplot(data = df, aes(x = factor(actual_participant_outcome), y = state_envy_composite)) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.05, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied", "win" = "Won")
    ) + 
    labs(
        title = "State Envy by Conditon",
        x = "",
        y = "Self-Report (1-7)", 
        fill = "Measure"
    ) +
    scale_y_continuous(
    breaks = seq(1,8,1)
    )+
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom"
    )

Ignoring unknown labels:
• fill : "Measure"

#point plot for state envy by game condition 
ggplot(data = df, aes(x = factor(actual_participant_outcome), y = state_envy_composite, 
                      fill = factor(info_condition))) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.05, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied", "win" = "Won")
    ) + 
    labs(
        title = "State Envy by Conditon",
        x = "",
        y = "Self-Report (1-7)", 
        fill = "Message Condition"
    ) +
    scale_y_continuous(
    breaks = seq(1,8,1)
    )+
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom"
    )

#affect composites 
ggplot(data = emoComp_longer, aes(x = factor(actual_participant_outcome), y = emo_value, fill = emo_type)) +
    geom_jitter(
        show.legend = TRUE, 
        alpha = 0.20,
        shape = 21,
        position = position_jitterdodge(jitter.height = 0.05, dodge.width = 0.8)
    ) +
    stat_summary(
        fun.data = mean_se,
        geom = "errorbar",
        width = 0.225,
        linewidth = 0.7,
        position = position_dodge(width = 0.8),
        color = "black",
        show.legend = FALSE
    ) +
    stat_summary(
        fun = mean,
        geom = "point",
        shape = 21,
        size = 2.7, 
        stroke = 0.7,
        color = "black",
        position = position_dodge(width = 0.8),
        show.legend = FALSE
    ) + 
    scale_x_discrete(
        labels = c("lose" = "Lost", "tie" = "Tied", "win" = "Won")
    ) + 
    scale_fill_manual(values = c("Positive Affect" = "#4361EE", "Negative Affect" = "#F72585")) + 
    labs(
        title = "Affectivity: Positive and Negative",
        x = "",
        y = "Self-Report (1-7)", 
        fill = "Measure"
    ) +
    scale_y_continuous(
    breaks = seq(1,8,1)
    )+
    theme_minimal() +
    theme(
        plot.title = element_text(hjust = 0.5, colour = "black", size = 22, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.title.y = element_text(hjust = 0.5, colour = "black", size = 16),
        axis.text = element_text(colour = "black", size = 16),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom")

# ------------
# large scale visualizations to see trends (aka all emotions or all action tendencies)
ggplot(data = emotion_longer,
  aes(x = emotion_type, y = emotion_score, fill = actual_participant_outcome)
) +
  stat_summary(
    fun = mean,
    geom = "col",
    position = position_dodge(width = 0.8)
  ) +
  stat_summary(
    fun.data = mean_se,
    geom = "errorbar",
    width = 0.2,
    position = position_dodge(width = 0.8)
  ) +
  labs(
    title = "Self Reported Emotions Across Conditions",
    x = "Emotions",
    y = "Self-Report Rating",
    fill = "Outcome"
  ) +
  scale_x_discrete(
    labels = c(
      "dv3_state_envy" = "Envy",
      "dv3_state_jealous" = "Jealous",
      "dv3_state_bitter" = "Bitter",
      "dv3_state_resentful" = "Resentful",
      "dv3_state_happy" = "Happy",
      "dv3_state_compassionate" = "Compassionate",
      "dv3_state_surprised" = "Surprised",
      "dv3_state_grateful" = "Grateful",
      "dv3_state_thankful" = "Thankful",
      "dv3_state_sad"= "Sad",
      "dv3_state_upset" = "Upset",
      "dv3_state_disappointed" = "Disappointed",
      "dv3_state_annoyed" = "Annoyed",
      "dv3_state_angry" = "Angry",
      "dv3_state_shame" = "Shame",
      "dv3_state_contempt" = "Contempt",
      "dv3_state_joy" = "Joy",
      "dv3_state_irritated" = "Irritated")
  )+
  theme_minimal() +
  theme(plot.title = element_text(hjust = .5, face = "bold"),
        axis.text.x = element_text(angle = 45, hjust = 1),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.key.size = unit(.3, "cm"),
        legend.box.margin = margin(t=-5),
        legend.margin = margin(t = -2),
        legend.spacing.x = unit(0.2, "cm"),
        legend.text = element_text(hjust = .5))

ggplot(data = emotion_longer,
  aes(x = emotion_type, y = emotion_score, fill = info_condition)
) +
  stat_summary(
    fun = mean,
    geom = "col",
    position = position_dodge(width = 0.8)
  ) +
  stat_summary(
    fun.data = mean_se,
    geom = "errorbar",
    width = 0.2,
    position = position_dodge(width = 0.8)
  ) +
  labs(
    title = "Self Reported Emotions Across Conditions",
    x = "Emotions",
    y = "Self-Report Rating",
    fill = "Outcome"
  ) +
  scale_x_discrete(
    labels = c(
      "dv3_state_envy" = "Envy",
      "dv3_state_jealous" = "Jealous",
      "dv3_state_bitter" = "Bitter",
      "dv3_state_resentful" = "Resentful",
      "dv3_state_happy" = "Happy",
      "dv3_state_compassionate" = "Compassionate",
      "dv3_state_surprised" = "Surprised",
      "dv3_state_grateful" = "Grateful",
      "dv3_state_thankful" = "Thankful",
      "dv3_state_sad"= "Sad",
      "dv3_state_upset" = "Upset",
      "dv3_state_disappointed" = "Disappointed",
      "dv3_state_annoyed" = "Annoyed",
      "dv3_state_angry" = "Angry",
      "dv3_state_shame" = "Shame",
      "dv3_state_contempt" = "Contempt",
      "dv3_state_joy" = "Joy",
      "dv3_state_irritated" = "Irritated")
  )+
  theme_minimal() +
  theme(plot.title = element_text(hjust = .5, face = "bold"),
        axis.text.x = element_text(angle = 45, hjust = 1),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.key.size = unit(.3, "cm"),
        legend.box.margin = margin(t=-5),
        legend.margin = margin(t = -2),
        legend.spacing.x = unit(0.2, "cm"),
        legend.text = element_text(hjust = .5))

# ------------
# large scale visualizations to see trends (aka all emotions or all action tendencies)
ggplot(data = tarAll_longer,
  aes(x = tar_type, y = tar_value, fill = actual_participant_outcome)
) +
  stat_summary(
    fun = mean,
    geom = "col",
    position = position_dodge(width = 0.8)
  ) +
  stat_summary(
    fun.data = mean_se,
    geom = "errorbar",
    width = 0.2,
    position = position_dodge(width = 0.8)
  ) +
  labs(
    title = "Self Reported Action Tendencies Across Conditions",
    x = "TAR",
    y = "Self-Report Rating",
    fill = "Message Type"
  ) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = .5, face = "bold"),
        axis.text.x = element_text(angle = 45, hjust = 1),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.key.size = unit(.3, "cm"),
        legend.box.margin = margin(t=-5),
        legend.margin = margin(t = -2),
        legend.spacing.x = unit(0.2, "cm"),
        legend.text = element_text(hjust = .5))

ggplot(data = tarAll_longer,
  aes(x = tar_type, y = tar_value, fill = info_condition)
) +
  stat_summary(
    fun = mean,
    geom = "col",
    position = position_dodge(width = 0.8)
  ) +
  stat_summary(
    fun.data = mean_se,
    geom = "errorbar",
    width = 0.2,
    position = position_dodge(width = 0.8)
  ) +
  labs(
    title = "Self Reported Action Tendencies Across Conditions",
    x = "TAR",
    y = "Self-Report Rating",
    fill = "Message Type"
  ) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = .5, face = "bold"),
        axis.text.x = element_text(angle = 45, hjust = 1),
        text = element_text(family = "Times New Roman"),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.key.size = unit(.3, "cm"),
        legend.box.margin = margin(t=-5),
        legend.margin = margin(t = -2),
        legend.spacing.x = unit(0.2, "cm"),
        legend.text = element_text(hjust = .5))