Demographics and data filtering

data_raw <- subj_data_raw

df <- subj_data %>%
  mutate(
    Participant = as.factor(player_id),
    Group = as.factor(group),
    Score = as.numeric(final_score),
    Age = as.numeric(Age),
    Sex = as.factor(Sex),
    Approvals = as.numeric(Total.approvals)
  )

nrow(data_raw)

[1] 737

Count excluded participants on each step of data cleaning

init <- nrow(data_raw)
print(init)

[1] 737

print("Did not complete the study")

[1] “Did not complete the study”

nans <- nrow(data_raw %>% filter(nan_percentage >= 5))
print(nans)

[1] 21

print("Have lags")

[1] “Have lags”

lags <- nrow(data_raw %>% filter((duplicates_resource_percentage > 5 | time_in_experiment > 15 * 1.05) & (nan_percentage < 5)))
print(lags)

[1] 27

print("Is it equal to is_valid condition?")

[1] “Is it equal to is_valid condition?”

nrow(data_raw %>% filter(is_valid == "True")) == (init - nans - lags)

[1] TRUE

print("Have more than 25% of duplicates")

[1] “Have more than 25% of duplicates”

duplicates <- nrow(data_raw %>% filter(is_valid == "True") %>% filter(duplicates_percentage >= 25))
print(duplicates)

[1] 29

print("Is not in the group of 4 or 5 valid participants")

[1] “Is not in the group of 4 or 5 valid participants”

group_size <- nrow(data_raw %>% filter(is_valid == "True") %>% filter(duplicates_percentage < 25) %>% 
                     filter(condition_signaling != 'A') %>% filter(valid_group_size < 4))
print(group_size)

[1] 39

print("final sample")

[1] “final sample”

print(init - nans - lags - duplicates - group_size)

[1] 621

nrow(data_raw %>% filter(is_valid == "True") %>% filter(duplicates_percentage < 25))

[1] 660

nrow(data_raw %>% filter(is_valid == "True", duplicates_percentage < 25, condition_signaling != 'A'))

[1] 516

nrow(data_raw %>% filter(valid_group_size >= 4, condition_signaling != 'A'))

[1] 512

nrow(df)

[1] 621

(nrow(data_raw) - nrow(df)) / nrow(data_raw) 

[1] 0.1573948

(nrow(data_raw) - nrow(df))

[1] 116

Sex

sum(df$Sex == "Male")

[1] 381

sum(df$Sex == "Female")

[1] 239

sum(df$Sex == "Prefer not to say")

[1] 1

nrow(df) - sum(df$Sex == "Male") - sum(df$Sex == "Female")

[1] 1

Age

ggplot(df, aes(Age)) +
  geom_histogram(bins = 25) +
  theme_nice()

mean(df$Age)

[1] 33.562

median(df$Age)

[1] 31

sd(df$Age)

[1] 10.17182

colors = get_colors("Rainbow", num.colors = 4, reverse = F, gradient = F)

Approvals

ggplot(df, aes(Approvals, Score, fill = SignalingType, shape = SignalingType)) +
  geom_point(alpha = 0.4, size = 2, stroke = 0.1) +
  scale_shape_manual(values = c(21, 22, 24, 23)) +
  scale_fill_manual(values = colors) +
  facet_grid(cols = vars(ResourceSpeed), rows=vars(SignalingType)) +
  theme_nice(legend.pos = "bottom") +
  ylim(0, 26000) +
  geom_smooth(formula = y ~ x, method = 'lm', se=F)

ggplot(df, aes(duplicates_percentage, Score, fill = SignalingType, shape = SignalingType)) +
  geom_point(alpha = 0.4, size = 2, stroke = 0.1) +
  scale_shape_manual(values = c(21, 22, 24, 23)) +
  scale_fill_manual(values = colors) +
  facet_grid(cols = vars(ResourceSpeed), rows=vars(SignalingType)) +
  theme_nice(legend.pos = "bottom") +
  ylim(0, 26000) +
  geom_smooth(formula = y ~ x, method = 'lm', se=F, aes(color = SignalingType)) +
  scale_color_manual(values = colors) 

Duplicates (time steps when the participant was not moving)

ggplot(df, aes(duplicates_percentage, fill = SignalingType, shape = SignalingType)) +
  geom_density() +
  facet_grid(cols = vars(ResourceSpeed), rows=vars(SignalingType)) +
  theme_nice(legend.pos = "bottom")

data_raw %>% 
  filter(duplicates_percentage < 25) %>%
  ggplot(aes(duplicates_percentage, final_score, fill = condition_signaling, shape = condition_signaling)) +
  scale_fill_manual(values = colors) +
  scale_shape_manual(values = c(21, 22, 24, 23)) +
  geom_point(alpha = 0.4, size = 2, stroke = 0.1) +
  facet_wrap(~ condition_resource, scales = "free_y") +
  theme_nice(legend.pos = "bottom") +
  scale_color_manual(values = colors) 

df %>%
  group_by(condition_resource, condition_signaling) %>%
  summarise(correlation = cor(duplicates_percentage, final_score, use = "complete.obs", method = 'pearson'))