library(dplyr)
library(purrr)
n_samples <- 500
n_features <- 100000
df <- bind_cols(
  tibble(sample_id = paste0("sample_", 1:n_samples)),
  1:n_features %>%
    map(rnorm, n = n_samples) %>%
      map(as_tibble) %>%
      bind_cols()
)
df

# A tibble: 500 x 100,001
   sample_id  value value1 value2 value3 value4 value5 value6 value7 value8
   <chr>      <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
 1 sample_1   1.56   2.33   0.571   5.20   3.15   4.15   6.82   6.17  10.8
 2 sample_2   0.891  1.64   4.83    3.32   4.30   6.82   7.07   9.94   9.02
 3 sample_3  -0.125  1.39   2.89    1.66   4.53   5.15   4.75   8.59   9.98
 4 sample_4   0.239  0.789  1.92    5.52   6.93   4.82   7.22   9.37   8.75
 5 sample_5   0.337  3.61   1.33    2.84   4.55   6.84   5.67   8.19   8.35
 6 sample_6   1.50   2.96   4.08    2.63   6.12   6.41   6.86   7.73   9.21
 7 sample_7   0.887  1.37   4.02    5.53   3.29   7.68   7.63   8.72   8.24
 8 sample_8   1.27   2.06   2.56    4.87   4.32   5.73   9.13   7.75   7.74
 9 sample_9  -0.565  2.19   3.23    3.01   3.45   6.18   7.63   8.35  10.2
10 sample_10  1.67   1.10   1.20    4.94   4.33   7.46   7.08   7.71   8.61
# ... with 490 more rows, and 99,991 more variables: value9 <dbl>,
#   value10 <dbl>, value11 <dbl>, value12 <dbl>, value13 <dbl>, value14 <dbl>,
#   value15 <dbl>, value16 <dbl>, value17 <dbl>, value18 <dbl>, value19 <dbl>,
#   value20 <dbl>, value21 <dbl>, value22 <dbl>, value23 <dbl>, value24 <dbl>,
#   value25 <dbl>, value26 <dbl>, value27 <dbl>, value28 <dbl>, value29 <dbl>,
#   value30 <dbl>, value31 <dbl>, value32 <dbl>, value33 <dbl>, value34 <dbl>,
#   value35 <dbl>, value36 <dbl>, value37 <dbl>, value38 <dbl>, value39 <dbl>,
#   value40 <dbl>, value41 <dbl>, value42 <dbl>, value43 <dbl>, value44 <dbl>,
#   value45 <dbl>, value46 <dbl>, value47 <dbl>, value48 <dbl>, value49 <dbl>,
#   value50 <dbl>, value51 <dbl>, value52 <dbl>, value53 <dbl>, value54 <dbl>,
#   value55 <dbl>, value56 <dbl>, value57 <dbl>, value58 <dbl>, value59 <dbl>,
#   value60 <dbl>, value61 <dbl>, value62 <dbl>, value63 <dbl>, value64 <dbl>,
#   value65 <dbl>, value66 <dbl>, value67 <dbl>, value68 <dbl>, value69 <dbl>,
#   value70 <dbl>, value71 <dbl>, value72 <dbl>, value73 <dbl>, value74 <dbl>,
#   value75 <dbl>, value76 <dbl>, value77 <dbl>, value78 <dbl>, value79 <dbl>,
#   value80 <dbl>, value81 <dbl>, value82 <dbl>, value83 <dbl>, value84 <dbl>,
#   value85 <dbl>, value86 <dbl>, value87 <dbl>, value88 <dbl>, value89 <dbl>,
#   value90 <dbl>, value91 <dbl>, value92 <dbl>, value93 <dbl>, value94 <dbl>,
#   value95 <dbl>, value96 <dbl>, value97 <dbl>, value98 <dbl>, value99 <dbl>,
#   value100 <dbl>, value101 <dbl>, value102 <dbl>, value103 <dbl>,
#   value104 <dbl>, value105 <dbl>, value106 <dbl>, value107 <dbl>,
#   value108 <dbl>, …

system.time(fetched_sample <- filter(df, sample_id == "sample_1"))

user  system elapsed
165.060   0.110 165.446

system.time(fetched_sample <- df[which(df$sample_id == "sample_1"),])

> system.time(fetched_sample <- df[which(df$sample_id == "sample_1"),])
   user  system elapsed
  0.050   0.000   0.054