Face Lab Verification Table 4

# I had to name age groups, Middle Age (50-59)", Older Middle Age (60-69), Seniors (70-76) which was not in the original table but without the names I was unable to use just the numbers in brackets as a column value. 
# Create Age Group variable
ageDataTable <- ageDataTable %>%
  mutate(Age_Group = factor(case_when(
    Age >= 10 & Age <= 17 ~ "Adolescence (10-17)",
    Age >= 18 & Age <= 35 ~ "Early Adult (18-35)",
    Age >= 36 & Age <= 49 ~ "Early Middle Age (36-49)",
    Age >= 50 & Age <= 59 ~ "Middle Age (50-59)",
    Age >= 60 & Age <= 69 ~ "Older Middle Age (60-69)",
    Age >= 70 & Age <= 76 ~ "Seniors (70-76)"
  ), levels = c("Adolescence (10-17)", "Early Adult (18-35)", "Early Middle Age (36-49)", 
               "Middle Age (50-59)", "Older Middle Age (60-69)", "Seniors (70-76)"))) %>%
  filter(Age >= 10 & Age <= 76) %>%
 mutate(Task = recode(Task,
                       "Overall" = "Overall",
                       "Memory Task" = "Memory",
                       "Sorting Task" = "Sorting")) %>%
  mutate(Task = factor(Task, levels = c("Overall", "Memory", "Sorting"), ordered = TRUE))

# Calculate mean, standard deviation, and count of participants for each Age_Group and Task rounded to 1 dp
age_group_summary <- ageDataTable %>%
  group_by(Age_Group, Task) %>%
  summarise(Mean = round(mean(Score, na.rm = TRUE), 1),
           SD = round(sd(Score, na.rm = TRUE), 1),
           N = n())

## `summarise()` has grouped output by 'Age_Group'. You can override using the
## `.groups` argument.

# Pivot the table wider so that the age groups are the column variables 
age_group_summary_wider <- age_group_summary %>%
  pivot_wider(names_from = Age_Group,
              values_from = c(Mean, SD, N),
              names_sep = "_")

# Because the`variable names`must be unique we have to rename manually the column labels to get Mean, SD and N using gt()
age_group_summary_wider %>%
  gt() %>%
  cols_label(
    `Mean_Adolescence (10-17)` = 'Mean',
        `SD_Adolescence (10-17)` = 'SD',
        `N_Adolescence (10-17)` = 'N',
        `Mean_Early Adult (18-35)` = 'Mean',
        `SD_Early Adult (18-35)` = 'SD',
        `N_Early Adult (18-35)` = 'N', 
        `Mean_Early Middle Age (36-49)` = 'Mean',
        `SD_Early Middle Age (36-49)` = 'SD',
        `N_Early Middle Age (36-49)` = 'N', 
        `Mean_Middle Age (50-59)` = 'Mean',
        `SD_Middle Age (50-59)` = 'SD',
        `N_Middle Age (50-59)` = 'N', 
        `Mean_Older Middle Age (60-69)` = 'Mean',
        `SD_Older Middle Age (60-69)` = 'SD',
        `N_Older Middle Age (60-69)` = 'N', 
        `Mean_Seniors (70-76)` = 'Mean',
        `SD_Seniors (70-76)` = 'SD',
        `N_Seniors (70-76)` = 'N', 
  ) %>% 
  tab_header(
    title = md('Table 4. Mean accuracy (%) and standard deviation by participant age from online samples'),
    subtitle = md('Age group (years)')
  ) %>% 
  tab_spanner(
    label = md('**Adolescence (10-17)**'),
    columns = c(2,8,14)
  ) %>% 
  tab_spanner(
    label = md('**Early Adult <br> (18-35)**'), 
    columns = c(3,9,15)
   ) %>% 
  tab_spanner(
    label = md('**Early Middle Age (36-49)**'),
    columns = c(4,10,16)
  ) %>% 
  tab_spanner(
    label = md('**Middle Age <br>(50-59)**'),
    columns = c(5,11,17)
  ) %>% 
  tab_spanner(
    label = md('**Older Middle Age (60-69)**'),
    columns = c(6,12,18)
  ) %>% 
  tab_spanner(
    label = md('**Seniors <br> (70-76)**'),
    columns = c(7,13,19)
  )

Table 4. Mean accuracy (%) and standard deviation by participant age from online samples
Age group (years)
Task	Adolescence (10-17)			Early Adult (18-35)			Early Middle Age (36-49)			Middle Age (50-59)			Older Middle Age (60-69)			Seniors (70-76)
	Mean	SD	N	Mean	SD	N	Mean	SD	N	Mean	SD	N	Mean	SD	N	Mean	SD	N
Overall	59.1	5.8	1470	62.8	6.5	10163	62.5	6.4	6905	61.1	5.9	3149	59.8	5.8	1139	58.8	5.1	169
Memory	62.2	8.4	1470	65.6	8.7	10163	65.3	8.7	6905	62.6	8.3	3149	60.5	8.0	1139	59.5	7.3	169
Sorting	57.5	6.8	1470	61.4	7.6	10163	61.1	7.7	6905	60.4	7.3	3149	59.5	7.2	1139	58.4	6.5	169