# 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 = factor(Task, levels = c("Overall", "Memory Task", "Sorting Task")),
Task = fct_recode(Task, "UNSW FT" = "Overall"))gt table
# Calculate mean, standard deviation, and count of participants for each Age_Group and Task
age_group_summary <- ageDataTable %>%
group_by(Age_Group, Task) %>%
summarise(Mean = mean(Score, na.rm = TRUE),
SD = sd(Score, na.rm = TRUE),
N = n())## `summarise()` has grouped output by 'Age_Group'. You can override using the
## `.groups` argument.# Pivot the table wider
age_group_summary_wider <- age_group_summary %>%
pivot_wider(names_from = Age_Group,
values_from = c(Mean, SD, N),
names_sep = "_")
age_group_summary_wider %>%
gt() %>%
tab_spanner(label = "Adolescence (10-17)", columns = contains("Adolescence (10-17)")) %>%
tab_spanner(label = "Early Adult (18-35)", columns = contains("Early Adult (18-35)")) %>%
tab_spanner(label = "Early Middle Age (36-49)", columns = contains("Early Middle Age (36-49)")) %>%
tab_spanner(label = "Middle Age (50-59)", columns = contains("Middle Age (50-59)")) %>%
tab_spanner(label = "Older Middle Age (60-69)", columns = contains("Older Middle Age (60-69)")) %>%
tab_spanner(label = "Seniors (70-76)", columns = contains("Seniors (70-76)"))| 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_Adolescence (10-17) | SD_Adolescence (10-17) | N_Adolescence (10-17) | Mean_Early Adult (18-35) | SD_Early Adult (18-35) | N_Early Adult (18-35) | Mean_Early Middle Age (36-49) | SD_Early Middle Age (36-49) | N_Early Middle Age (36-49) | Mean_Middle Age (50-59) | SD_Middle Age (50-59) | N_Middle Age (50-59) | Mean_Older Middle Age (60-69) | SD_Older Middle Age (60-69) | N_Older Middle Age (60-69) | Mean_Seniors (70-76) | SD_Seniors (70-76) | N_Seniors (70-76) | |
| UNSW FT | 59.0873 | 5.776641 | 1470 | 62.84287 | 6.489126 | 10163 | 62.52136 | 6.388784 | 6905 | 61.12893 | 5.891448 | 3149 | 59.81782 | 5.757233 | 1139 | 58.78205 | 5.051815 | 169 |
| Memory Task | 62.2415 | 8.366469 | 1470 | 65.64818 | 8.681314 | 10163 | 65.26973 | 8.675535 | 6905 | 62.56907 | 8.301923 | 3149 | 60.50044 | 8.013458 | 1139 | 59.45266 | 7.331691 | 169 |
| Sorting Task | 57.5102 | 6.828947 | 1470 | 61.44027 | 7.649999 | 10163 | 61.14718 | 7.651214 | 6905 | 60.40886 | 7.273730 | 3149 | 59.47651 | 7.210779 | 1139 | 58.44675 | 6.538756 | 169 |