selecting age focused sample about ialsa-2018-amsterdam HOT 1 OPEN

> ds <- ds_valid %>% 
+   dplyr::select(id, age, wave, firstobs, gait) %>% 
+   dplyr::filter( id %in% c("617643","709354","228190"))
> 
> ds %>% glimpse()
Observations: 22
Variables: 5
$ id       <int> 228190, 228190, 617643, 617643, 617643, 617643, 617643, 617643...
$ age      <dbl> 74, 74, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 78, 69, 70...
$ wave     <int> 0, 1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 0, 1, 2, 3, 4, 5, ...
$ firstobs <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0
$ gait     <dbl> 0.57, NA, 0.87, 0.76, 0.94, 0.83, 0.80, 0.70, 0.92, 0.75, NA, ...
> ids_gait_70 <- ds %>% 
+   dplyr::filter( age > 67 & age <  73) %>%
+   dplyr::group_by(id) %>% 
+   dplyr::arrange(age) %>% 
+   dplyr::mutate(gait70 = dplyr::first(gait)) %>% 
+   dplyr::distinct(id, gait70)
> ids_gait_70 %>% glimpse()
Observations: 2
Variables: 2
$ id     <int> 617643, 709354
$ gait70 <dbl> 0.94, 1.08
> 
> d2 <- ds %>% 
+   dplyr::left_join(ids_gait_70, by = "id") %>% 
+   # keep only people who have a non-missing gait70
+   dplyr::filter(!is.na(gait70)) %>% 
+   dplyr::filter(age > 68 )
> 
> d2 %>% head(40)
       id age wave firstobs gait gait70
1  617643  68    3        0 0.83   0.94
2  617643  69    4        0 0.80   0.94
3  617643  70    5        0 0.70   0.94
4  617643  71    6        0 0.92   0.94
5  617643  72    7        0 0.75   0.94
6  617643  73    8        0   NA   0.94
7  617643  74    9        0 0.81   0.94
8  617643  75   10        0 0.70   0.94
9  617643  78   13        0   NA   0.94
10 709354  69    0        1 1.08   1.08
11 709354  70    1        0 0.96   1.08
12 709354  71    2        0 0.61   1.08
13 709354  72    3        0 0.44   1.08
14 709354  73    4        0 0.54   1.08
15 709354  75    5        0 0.69   1.08
16 709354  75    6        0 0.59   1.08
17 709354  76    7        0   NA   1.08
>