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There are \(N\) individuals, each could be observed \(M\) times. In the example below, there are 3 students, each observed over 4 dates. This just uses the uncount function from tidyr.
# Define
it_N <- 3
it_M <- 5
svr_id <- 'student_id'
svr_date <- 'class_day'
# dataframe
df_panel_skeleton <- as_tibble(matrix(it_M, nrow=it_N, ncol=1)) %>%
rowid_to_column(var = svr_id) %>%
uncount(V1) %>%
group_by(!!sym(svr_id)) %>% mutate(!!sym(svr_date) := row_number()) %>%
ungroup()
# Print
kable(df_panel_skeleton) %>%
kable_styling_fc()| student_id | class_day |
|---|---|
| 1 | 1 |
| 1 | 2 |
| 1 | 3 |
| 1 | 4 |
| 1 | 5 |
| 2 | 1 |
| 2 | 2 |
| 2 | 3 |
| 2 | 4 |
| 2 | 5 |
| 3 | 1 |
| 3 | 2 |
| 3 | 3 |
| 3 | 4 |
| 3 | 5 |
Given \(N\) individuals, each with \(G\) observations. There is an initial height variable and height grows every year. There are growth variables, variables for cumulative growth and variables for height at each age for each child.
Individuals are defined by gender (1 = female), race (1=asian), and birth height. Within individual yearly information includes height at each year of age.
# Define
it_N <- 5
it_M <- 3
svr_id <- 'indi_id'
svr_gender <- 'female'
svr_asian <- 'asian'
svr_age <- 'year_of_age'
# Define Height Related Variables
svr_brthgt <- 'birth_height'
svr_hgtgrow <- 'hgt_growth'
svr_hgtgrow_cumu <- 'hgt_growcumu'
svr_height <- 'height'
# panel dataframe following
set.seed(123)
df_panel_indiage <- as_tibble(matrix(it_M, nrow=it_N, ncol=1)) %>%
mutate(!!sym(svr_gender) := rbinom(n(), 1, 0.5),
!!sym(svr_asian) := rbinom(n(), 1, 0.5),
!!sym(svr_brthgt) := rnorm(n(), mean=60,sd=3)) %>%
uncount(V1) %>%
group_by(!!sym(svr_gender), !!sym(svr_asian), !!sym(svr_brthgt)) %>%
mutate(!!sym(svr_age) := row_number(),
!!sym(svr_hgtgrow) := runif(n(), min=5, max=15),
!!sym(svr_hgtgrow_cumu) := cumsum(!!sym(svr_hgtgrow)),
!!sym(svr_height) := !!sym(svr_brthgt) + !!sym(svr_hgtgrow_cumu)) %>%
ungroup()
# Add Height Index
kable(df_panel_indiage) %>% kable_styling_fc()| female | asian | birth_height | year_of_age | hgt_growth | hgt_growcumu | height |
|---|---|---|---|---|---|---|
| 0 | 0 | 65.14520 | 1 | 13.895393 | 13.895393 | 79.04059 |
| 0 | 0 | 65.14520 | 2 | 11.928034 | 25.823427 | 90.96862 |
| 0 | 0 | 65.14520 | 3 | 11.405068 | 37.228495 | 102.37369 |
| 1 | 1 | 61.38275 | 1 | 11.907053 | 11.907053 | 73.28980 |
| 1 | 1 | 61.38275 | 2 | 12.954674 | 24.861727 | 86.24448 |
| 1 | 1 | 61.38275 | 3 | 5.246137 | 30.107864 | 91.49061 |
| 0 | 1 | 56.20482 | 1 | 14.942698 | 14.942698 | 71.14751 |
| 0 | 1 | 56.20482 | 2 | 11.557058 | 26.499756 | 82.70457 |
| 0 | 1 | 56.20482 | 3 | 12.085305 | 38.585060 | 94.78988 |
| 1 | 1 | 57.93944 | 1 | 6.471137 | 6.471137 | 64.41058 |
| 1 | 1 | 57.93944 | 2 | 14.630242 | 21.101379 | 79.04082 |
| 1 | 1 | 57.93944 | 3 | 14.022991 | 35.124369 | 93.06381 |
| 1 | 0 | 58.66301 | 1 | 10.440660 | 10.440660 | 69.10367 |
| 1 | 0 | 58.66301 | 2 | 10.941420 | 21.382081 | 80.04509 |
| 1 | 0 | 58.66301 | 3 | 7.891597 | 29.273678 | 87.93669 |
Given the dataframe just created, generate group IDs for each Gender and Race Groups. Given that both are binary, there can only be 4 unique groups.
# group id
svr_group_id <- 'female_asian_id'
# Define
ls_svr_group_vars <- c('female', 'asian')
# panel dataframe following
df_panel_indiage_id <- df_panel_indiage %>%
arrange(!!!syms(ls_svr_group_vars)) %>%
group_by(!!!syms(ls_svr_group_vars)) %>%
mutate(!!sym(svr_group_id) := (row_number()==1)*1) %>%
ungroup() %>%
mutate(!!sym(svr_group_id) := cumsum(!!sym(svr_group_id))) %>%
select(one_of(svr_group_id, ls_svr_group_vars), everything())
# Add Height Index
kable(df_panel_indiage_id) %>%
kable_styling_fc_wide()| female_asian_id | female | asian | birth_height | year_of_age | hgt_growth | hgt_growcumu | height |
|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 65.14520 | 1 | 13.895393 | 13.895393 | 79.04059 |
| 1 | 0 | 0 | 65.14520 | 2 | 11.928034 | 25.823427 | 90.96862 |
| 1 | 0 | 0 | 65.14520 | 3 | 11.405068 | 37.228495 | 102.37369 |
| 2 | 0 | 1 | 56.20482 | 1 | 14.942698 | 14.942698 | 71.14751 |
| 2 | 0 | 1 | 56.20482 | 2 | 11.557058 | 26.499756 | 82.70457 |
| 2 | 0 | 1 | 56.20482 | 3 | 12.085305 | 38.585060 | 94.78988 |
| 3 | 1 | 0 | 58.66301 | 1 | 10.440660 | 10.440660 | 69.10367 |
| 3 | 1 | 0 | 58.66301 | 2 | 10.941420 | 21.382081 | 80.04509 |
| 3 | 1 | 0 | 58.66301 | 3 | 7.891597 | 29.273678 | 87.93669 |
| 4 | 1 | 1 | 61.38275 | 1 | 11.907053 | 11.907053 | 73.28980 |
| 4 | 1 | 1 | 61.38275 | 2 | 12.954674 | 24.861727 | 86.24448 |
| 4 | 1 | 1 | 61.38275 | 3 | 5.246137 | 30.107864 | 91.49061 |
| 4 | 1 | 1 | 57.93944 | 1 | 6.471137 | 6.471137 | 64.41058 |
| 4 | 1 | 1 | 57.93944 | 2 | 14.630242 | 21.101379 | 79.04082 |
| 4 | 1 | 1 | 57.93944 | 3 | 14.022991 | 35.124369 | 93.06381 |