library(tidyverse)
(thewhat <- tibble(sample = 1:10L, y= 1.0, z =2.0))

# A tibble: 10 x 3
   sample     y     z
    <int> <dbl> <dbl>
 1      1    1.    2.
 2      2    1.    2.
 3      3    1.    2.
 4      4    1.    2.
 5      5    1.    2.
 6      6    1.    2.
 7      7    1.    2.
 8      8    1.    2.
 9      9    1.    2.
10     10    1.    2.

(thewhere <- tibble(cond = c("a","a","b","c","a"),
     init_sample= c(1,3,4,5,7),
     duration = c(1,2,2,1,3),
     where = c(NA,"y","z","y","z")))

# A tibble: 5 x 4
  cond  init_sample duration where
  <chr>       <dbl>    <dbl> <chr>
1 a              1.       1. <NA>
2 a              3.       2. y
3 b              4.       2. z
4 c              5.       1. y
5 a              7.       3. z

# table with the elements that should be replaced by NA
NAs <- filter(thewhere, cond=="a") %>%
      mutate( sample = map2(init_sample, init_sample + duration - 1,seq)) %>%
      unnest %>%
      select(where, sample)

# Takes into account the different columns but I need to manually add each relevant column
# and another case for mutate_all when the where is NA:
mutate(thewhat, y = if_else(sample %in% NAs$sample[NAs$where =="y"],
        NA_real_, y  ))

# A tibble: 10 x 3
   sample     y     z
    <int> <dbl> <dbl>
 1      1   NA    NA
 2      2    1.    2.
 3      3   NA     2.
 4      4   NA     2.
 5      5    1.    2.
 6      6    1.    2.
 7      7    1.   NA
 8      8    1.   NA
 9      9    1.   NA
10     10    1.    2.

library(tidyverse)
lst <- NAs %>%
         split(.$where)
set_names(names(lst), names(lst)) %>%
     map_df(., ~ thewhat[[.x]] %>%
                 replace(., thewhat$sample %in% lst[[.x]]$sample, NA_real_) ) %>%
     bind_cols(thewhat %>%
                 select(sample), .)
# A tibble: 10 x 3
#   sample     y     z
#    <int> <dbl> <dbl>
# 1      1     1     2
# 2      2     1     2
# 3      3    NA     2
# 4      4    NA     2
# 5      5     1     2
# 6      6     1     2
# 7      7     1    NA
# 8      8     1    NA
# 9      9     1    NA
#10     10     1     2