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How to Use filter() functions with Dates

Selecting specific rows based on Dates and Date Ranges

Peter Higgins

2021-01-10

1 / 3

How to Use the filter() function for Rows by Date

Format:

dataset %>% filter(between(variable, start, end))

Note that between is nested within filter.

Start and end dates usually use the {lubridate} package, and the format
ymd("2015-12-31")

Remember that filteR is for selecting Rows because it ends with an R.

Let's step through some filter Examples!

2 / 3

Example 1/4: Filter Rows Between Dates

# how many rows when you start
nrow(covid_dates)
[1] 15524
2 / 3

Example 1/4: Filter Rows Between Dates

# how many rows when you start
nrow(covid_dates)
covid_dates
[1] 15524
# A tibble: 15,524 x 18
   subject_id fake_first_name fake_last_name gender pan_day test_id clinic_name
        <dbl> <chr>           <chr>          <chr>    <dbl> <chr>   <chr>      
 1       1412 jhezane         westerling     female       4 covid   inpatient …
 2        533 penny           targaryen      female       7 covid   clinical l…
 3       9134 grunt           rivers         male         7 covid   clinical l…
 4       8518 melisandre      swyft          female       8 covid   clinical l…
 5       8967 rolley          karstark       male         8 covid   emergency …
 6      11048 megga           karstark       female       8 covid   oncology d…
 7        663 ithoke          targaryen      male         9 covid   clinical l…
 8       2158 ravella         frey           female       9 covid   emergency …
 9       3794 styr            tyrell         male         9 covid   clinical l…
10       4706 wynafryd        seaworth       male         9 covid   clinical l…
# … with 15,514 more rows, and 11 more variables: result <chr>,
#   demo_group <chr>, age <dbl>, drive_thru_ind <dbl>, ct_result <dbl>,
#   orderset <dbl>, payor_group <chr>, patient_class <chr>, col_rec_tat <dbl>,
#   rec_ver_tat <dbl>, fake_date <date>
2 / 3

Example 1/4: Filter Rows Between Dates

# how many rows when you start
nrow(covid_dates)
covid_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_date, result, gender)
[1] 15524
# A tibble: 15,524 x 3
   fake_date  result   gender
   <date>     <chr>    <chr> 
 1 2020-03-05 negative female
 2 2020-03-08 negative female
 3 2020-03-08 negative male  
 4 2020-03-09 negative female
 5 2020-03-09 negative male  
 6 2020-03-09 negative female
 7 2020-03-10 negative male  
 8 2020-03-10 negative female
 9 2020-03-10 negative male  
10 2020-03-10 negative male  
# … with 15,514 more rows
2 / 3

Example 1/4: Filter Rows Between Dates

# how many rows when you start
nrow(covid_dates)
covid_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_date, result, gender) %>%
  filter(between(fake_date,
      ymd("2020-03-01"), ymd("2020-03-31")))
[1] 15524
# A tibble: 2,421 x 3
   fake_date  result   gender
   <date>     <chr>    <chr> 
 1 2020-03-05 negative female
 2 2020-03-08 negative female
 3 2020-03-08 negative male  
 4 2020-03-09 negative female
 5 2020-03-09 negative male  
 6 2020-03-09 negative female
 7 2020-03-10 negative male  
 8 2020-03-10 negative female
 9 2020-03-10 negative male  
10 2020-03-10 negative male  
# … with 2,411 more rows
2 / 3

Example 1/4: Filter Rows Between Dates

# how many rows when you start
nrow(covid_dates)
covid_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_date, result, gender) %>%
  filter(between(fake_date,
      ymd("2020-03-01"), ymd("2020-03-31")))
# see how many rows now
# check dates - between is inclusive
# of end dates - tests on 3/1
# and on 3/31 are included.
# Format:
# filter(between(variable, date, date))       <br>  # filter(between(variable, date, date))       <br>
[1] 15524
# A tibble: 2,421 x 3
   fake_date  result   gender
   <date>     <chr>    <chr> 
 1 2020-03-05 negative female
 2 2020-03-08 negative female
 3 2020-03-08 negative male  
 4 2020-03-09 negative female
 5 2020-03-09 negative male  
 6 2020-03-09 negative female
 7 2020-03-10 negative male  
 8 2020-03-10 negative female
 9 2020-03-10 negative male  
10 2020-03-10 negative male  
# … with 2,411 more rows
2 / 3

Example 1/4: Filter Rows Between Dates

# how many rows when you start
nrow(covid_dates)
covid_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_date, result, gender) %>%
  filter(between(fake_date,
      ymd("2020-03-01"), ymd("2020-03-31")))
# see how many rows now
# check dates - between is inclusive
# of end dates - tests on 3/1
# and on 3/31 are included.
# Format:
# filter(between(variable, date, date))       <br>  # filter(between(variable, date, date))       <br>
[1] 15524
# A tibble: 2,421 x 3
   fake_date  result   gender
   <date>     <chr>    <chr> 
 1 2020-03-05 negative female
 2 2020-03-08 negative female
 3 2020-03-08 negative male  
 4 2020-03-09 negative female
 5 2020-03-09 negative male  
 6 2020-03-09 negative female
 7 2020-03-10 negative male  
 8 2020-03-10 negative female
 9 2020-03-10 negative male  
10 2020-03-10 negative male  
# … with 2,411 more rows
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
[1] 64
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
bmt_dates
[1] 64
# A tibble: 64 x 29
      id   age   sex  race diagnosis diagnosis_type time_to_transpl…
   <dbl> <dbl> <dbl> <dbl> <chr>              <dbl>            <dbl>
 1     1    61     1     0 acute my…              1             5.16
 2     2    62     1     1 non-Hodg…              0            79.0 
 3     3    63     0     1 non-Hodg…              0            35.6 
 4     4    33     0     1 Hodgkin …              0            33.0 
 5     5    54     0     1 acute ly…              0            11.4 
 6     6    55     1     1 myelofib…              1             2.43
 7     7    67     1     1 acute my…              1             9.59
 8     8    51     1     1 acute my…              1            NA   
 9     9    44     0     0 multiple…              0            43.4 
10    10    59     1     1 chronic …              0            92.7 
# … with 54 more rows, and 22 more variables: prior_radiation <dbl>,
#   prior_chemo <dbl>, prior_transplant <dbl>, recipient_cmv <dbl>,
#   donor_cmv <dbl>, donor_sex <dbl>, tnc_dose <dbl>, cd34_dose <dbl>,
#   cd3_dose <dbl>, cd8_dose <dbl>, tbi_dose <dbl>, c1_c2 <dbl>, a_ki_rs <dbl>,
#   cmv <dbl>, time_to_cmv <dbl>, agvhd <dbl>, time_to_agvhd <dbl>,
#   cgvhd <dbl>, time_to_cgvhd <dbl>, fake_dx_date <date>,
#   fake_bmt_date <date>, fake_agvhd_date <date>
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age)
[1] 64
# A tibble: 64 x 3
   fake_bmt_date   sex   age
   <date>        <dbl> <dbl>
 1 2015-04-09        1    61
 2 2021-05-04        1    62
 3 2017-10-07        0    63
 4 2017-07-23        0    33
 5 2015-10-13        0    54
 6 2015-01-17        1    55
 7 2015-08-20        1    67
 8 NA                1    51
 9 2018-05-31        0    44
10 2022-06-17        1    59
# … with 54 more rows
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
  filter(between(fake_bmt_date,
      ymd("2008-01-01"), ymd("2018-12-31")))
[1] 64
# A tibble: 53 x 3
   fake_bmt_date   sex   age
   <date>        <dbl> <dbl>
 1 2015-04-09        1    61
 2 2017-10-07        0    63
 3 2017-07-23        0    33
 4 2015-10-13        0    54
 5 2015-01-17        1    55
 6 2015-08-20        1    67
 7 2018-05-31        0    44
 8 2018-01-18        1    45
 9 2016-04-23        1    57
10 2015-03-21        0    52
# … with 43 more rows
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
  filter(between(fake_bmt_date,
      ymd("2008-01-01"), ymd("2018-12-31"))) %>%
  filter(between(age, 40, 70))
[1] 64
# A tibble: 44 x 3
   fake_bmt_date   sex   age
   <date>        <dbl> <dbl>
 1 2015-04-09        1    61
 2 2017-10-07        0    63
 3 2015-10-13        0    54
 4 2015-01-17        1    55
 5 2015-08-20        1    67
 6 2018-05-31        0    44
 7 2018-01-18        1    45
 8 2016-04-23        1    57
 9 2015-03-21        0    52
10 2015-12-21        0    62
# … with 34 more rows
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
  filter(between(fake_bmt_date,
      ymd("2008-01-01"), ymd("2018-12-31"))) %>%
  filter(between(age, 40, 70)) %>%
  arrange(fake_bmt_date)  # sort
[1] 64
# A tibble: 44 x 3
   fake_bmt_date   sex   age
   <date>        <dbl> <dbl>
 1 2015-01-17        1    55
 2 2015-01-20        1    61
 3 2015-02-09        1    57
 4 2015-02-23        1    48
 5 2015-03-05        0    61
 6 2015-03-07        1    49
 7 2015-03-20        1    57
 8 2015-03-21        0    52
 9 2015-03-21        1    62
10 2015-04-09        1    61
# … with 34 more rows
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
  filter(between(fake_bmt_date,
      ymd("2008-01-01"), ymd("2018-12-31"))) %>%
  filter(between(age, 40, 70)) %>%
  arrange(fake_bmt_date)  # sort
# see how many rows now
# sequential filters combine with AND
# check dates
# Format:
# filter(between(variable, start, end))       <br>  # filter(between(variable, start, end))       <br>
[1] 64
# A tibble: 44 x 3
   fake_bmt_date   sex   age
   <date>        <dbl> <dbl>
 1 2015-01-17        1    55
 2 2015-01-20        1    61
 3 2015-02-09        1    57
 4 2015-02-23        1    48
 5 2015-03-05        0    61
 6 2015-03-07        1    49
 7 2015-03-20        1    57
 8 2015-03-21        0    52
 9 2015-03-21        1    62
10 2015-04-09        1    61
# … with 34 more rows
2 / 3

Example 2/4: Find Bone Marrow Transplants in 2008-2018 in 40-70 yo

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
  filter(between(fake_bmt_date,
      ymd("2008-01-01"), ymd("2018-12-31"))) %>%
  filter(between(age, 40, 70)) %>%
  arrange(fake_bmt_date)  # sort
# see how many rows now
# sequential filters combine with AND
# check dates
# Format:
# filter(between(variable, start, end))       <br>  # filter(between(variable, start, end))       <br>
[1] 64
# A tibble: 44 x 3
   fake_bmt_date   sex   age
   <date>        <dbl> <dbl>
 1 2015-01-17        1    55
 2 2015-01-20        1    61
 3 2015-02-09        1    57
 4 2015-02-23        1    48
 5 2015-03-05        0    61
 6 2015-03-07        1    49
 7 2015-03-20        1    57
 8 2015-03-21        0    52
 9 2015-03-21        1    62
10 2015-04-09        1    61
# … with 34 more rows
2 / 3

Example 3/4: Find Acute GVHD Cases in last 24 Months

# how many rows when you start
nrow(bmt_dates)
[1] 64
2 / 3

Example 3/4: Find Acute GVHD Cases in last 24 Months

# how many rows when you start
nrow(bmt_dates)
bmt_dates
[1] 64
# A tibble: 64 x 29
      id   age   sex  race diagnosis diagnosis_type time_to_transpl…
   <dbl> <dbl> <dbl> <dbl> <chr>              <dbl>            <dbl>
 1     1    61     1     0 acute my…              1             5.16
 2     2    62     1     1 non-Hodg…              0            79.0 
 3     3    63     0     1 non-Hodg…              0            35.6 
 4     4    33     0     1 Hodgkin …              0            33.0 
 5     5    54     0     1 acute ly…              0            11.4 
 6     6    55     1     1 myelofib…              1             2.43
 7     7    67     1     1 acute my…              1             9.59
 8     8    51     1     1 acute my…              1            NA   
 9     9    44     0     0 multiple…              0            43.4 
10    10    59     1     1 chronic …              0            92.7 
# … with 54 more rows, and 22 more variables: prior_radiation <dbl>,
#   prior_chemo <dbl>, prior_transplant <dbl>, recipient_cmv <dbl>,
#   donor_cmv <dbl>, donor_sex <dbl>, tnc_dose <dbl>, cd34_dose <dbl>,
#   cd3_dose <dbl>, cd8_dose <dbl>, tbi_dose <dbl>, c1_c2 <dbl>, a_ki_rs <dbl>,
#   cmv <dbl>, time_to_cmv <dbl>, agvhd <dbl>, time_to_agvhd <dbl>,
#   cgvhd <dbl>, time_to_cgvhd <dbl>, fake_dx_date <date>,
#   fake_bmt_date <date>, fake_agvhd_date <date>
2 / 3

Example 3/4: Find Acute GVHD Cases in last 24 Months

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_agvhd_date, sex, age)
[1] 64
# A tibble: 64 x 3
   fake_agvhd_date   sex   age
   <date>          <dbl> <dbl>
 1 2015-07-24          1    61
 2 2026-09-09          1    62
 3 2018-01-27          0    63
 4 2019-11-26          0    33
 5 2016-01-05          0    54
 6 2015-05-13          1    55
 7 2015-11-18          1    67
 8 NA                  1    51
 9 2018-06-21          0    44
10 2024-04-09          1    59
# … with 54 more rows
2 / 3

Example 3/4: Find Acute GVHD Cases in last 24 Months

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_agvhd_date, sex, age) %>%
  filter(fake_agvhd_date >
           today() - months(24))
[1] 64
# A tibble: 16 x 3
   fake_agvhd_date   sex   age
   <date>          <dbl> <dbl>
 1 2026-09-09          1    62
 2 2019-11-26          0    33
 3 2024-04-09          1    59
 4 2034-07-21          0    61
 5 2021-02-26          1    62
 6 2024-05-21          0    51
 7 2022-08-19          0    62
 8 2021-09-22          0    52
 9 2027-08-11          0    48
10 2020-04-26          1    46
11 2023-02-08          1    51
12 2022-03-19          1    56
13 2019-03-31          1    62
14 2020-09-26          1    58
15 2029-01-28          0    41
16 2029-05-25          1    50
2 / 3

Example 3/4: Find Acute GVHD Cases in last 24 Months

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_agvhd_date, sex, age) %>%
  filter(fake_agvhd_date >
           today() - months(24)) %>%
  arrange(fake_agvhd_date)  # sort
[1] 64
# A tibble: 16 x 3
   fake_agvhd_date   sex   age
   <date>          <dbl> <dbl>
 1 2019-03-31          1    62
 2 2019-11-26          0    33
 3 2020-04-26          1    46
 4 2020-09-26          1    58
 5 2021-02-26          1    62
 6 2021-09-22          0    52
 7 2022-03-19          1    56
 8 2022-08-19          0    62
 9 2023-02-08          1    51
10 2024-04-09          1    59
11 2024-05-21          0    51
12 2026-09-09          1    62
13 2027-08-11          0    48
14 2029-01-28          0    41
15 2029-05-25          1    50
16 2034-07-21          0    61
2 / 3

Example 3/4: Find Acute GVHD Cases in last 24 Months

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_agvhd_date, sex, age) %>%
  filter(fake_agvhd_date >
           today() - months(24)) %>%
  arrange(fake_agvhd_date)  # sort
# see how many rows now
# note we have some "future" fake dates
# Format:
# filter(between(variable, start, end))       <br>  # filter(between(variable, start, end))       <br>
[1] 64
# A tibble: 16 x 3
   fake_agvhd_date   sex   age
   <date>          <dbl> <dbl>
 1 2019-03-31          1    62
 2 2019-11-26          0    33
 3 2020-04-26          1    46
 4 2020-09-26          1    58
 5 2021-02-26          1    62
 6 2021-09-22          0    52
 7 2022-03-19          1    56
 8 2022-08-19          0    62
 9 2023-02-08          1    51
10 2024-04-09          1    59
11 2024-05-21          0    51
12 2026-09-09          1    62
13 2027-08-11          0    48
14 2029-01-28          0    41
15 2029-05-25          1    50
16 2034-07-21          0    61
2 / 3

Example 3/4: Find Acute GVHD Cases in last 24 Months

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_agvhd_date, sex, age) %>%
  filter(fake_agvhd_date >
           today() - months(24)) %>%
  arrange(fake_agvhd_date)  # sort
# see how many rows now
# note we have some "future" fake dates
# Format:
# filter(between(variable, start, end))       <br>  # filter(between(variable, start, end))       <br>
[1] 64
# A tibble: 16 x 3
   fake_agvhd_date   sex   age
   <date>          <dbl> <dbl>
 1 2019-03-31          1    62
 2 2019-11-26          0    33
 3 2020-04-26          1    46
 4 2020-09-26          1    58
 5 2021-02-26          1    62
 6 2021-09-22          0    52
 7 2022-03-19          1    56
 8 2022-08-19          0    62
 9 2023-02-08          1    51
10 2024-04-09          1    59
11 2024-05-21          0    51
12 2026-09-09          1    62
13 2027-08-11          0    48
14 2029-01-28          0    41
15 2029-05-25          1    50
16 2034-07-21          0    61
2 / 3

Example 4/4: Find BMTs on (Fake) Weekends

# how many rows when you start
nrow(bmt_dates)
[1] 64
2 / 3

Example 4/4: Find BMTs on (Fake) Weekends

# how many rows when you start
nrow(bmt_dates)
bmt_dates
[1] 64
# A tibble: 64 x 29
      id   age   sex  race diagnosis diagnosis_type time_to_transpl…
   <dbl> <dbl> <dbl> <dbl> <chr>              <dbl>            <dbl>
 1     1    61     1     0 acute my…              1             5.16
 2     2    62     1     1 non-Hodg…              0            79.0 
 3     3    63     0     1 non-Hodg…              0            35.6 
 4     4    33     0     1 Hodgkin …              0            33.0 
 5     5    54     0     1 acute ly…              0            11.4 
 6     6    55     1     1 myelofib…              1             2.43
 7     7    67     1     1 acute my…              1             9.59
 8     8    51     1     1 acute my…              1            NA   
 9     9    44     0     0 multiple…              0            43.4 
10    10    59     1     1 chronic …              0            92.7 
# … with 54 more rows, and 22 more variables: prior_radiation <dbl>,
#   prior_chemo <dbl>, prior_transplant <dbl>, recipient_cmv <dbl>,
#   donor_cmv <dbl>, donor_sex <dbl>, tnc_dose <dbl>, cd34_dose <dbl>,
#   cd3_dose <dbl>, cd8_dose <dbl>, tbi_dose <dbl>, c1_c2 <dbl>, a_ki_rs <dbl>,
#   cmv <dbl>, time_to_cmv <dbl>, agvhd <dbl>, time_to_agvhd <dbl>,
#   cgvhd <dbl>, time_to_cgvhd <dbl>, fake_dx_date <date>,
#   fake_bmt_date <date>, fake_agvhd_date <date>
2 / 3

Example 4/4: Find BMTs on (Fake) Weekends

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age)
[1] 64
# A tibble: 64 x 3
   fake_bmt_date   sex   age
   <date>        <dbl> <dbl>
 1 2015-04-09        1    61
 2 2021-05-04        1    62
 3 2017-10-07        0    63
 4 2017-07-23        0    33
 5 2015-10-13        0    54
 6 2015-01-17        1    55
 7 2015-08-20        1    67
 8 NA                1    51
 9 2018-05-31        0    44
10 2022-06-17        1    59
# … with 54 more rows
2 / 3

Example 4/4: Find BMTs on (Fake) Weekends

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
# wday gives weekday 1-7, labeled
# note one NA date
  mutate(weekday = wday(fake_bmt_date,
              label=TRUE))
[1] 64
# A tibble: 64 x 4
   fake_bmt_date   sex   age weekday
   <date>        <dbl> <dbl> <ord>  
 1 2015-04-09        1    61 Thu    
 2 2021-05-04        1    62 Tue    
 3 2017-10-07        0    63 Sat    
 4 2017-07-23        0    33 Sun    
 5 2015-10-13        0    54 Tue    
 6 2015-01-17        1    55 Sat    
 7 2015-08-20        1    67 Thu    
 8 NA                1    51 <NA>   
 9 2018-05-31        0    44 Thu    
10 2022-06-17        1    59 Fri    
# … with 54 more rows
2 / 3

Example 4/4: Find BMTs on (Fake) Weekends

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
# wday gives weekday 1-7, labeled
# note one NA date
  mutate(weekday = wday(fake_bmt_date,
              label=TRUE)) %>%
# %in% must be in a vector of values
  filter(wday(fake_bmt_date) %in% c(1,7))
[1] 64
# A tibble: 17 x 4
   fake_bmt_date   sex   age weekday
   <date>        <dbl> <dbl> <ord>  
 1 2017-10-07        0    63 Sat    
 2 2017-07-23        0    33 Sun    
 3 2015-01-17        1    55 Sat    
 4 2016-04-23        1    57 Sat    
 5 2015-03-21        0    52 Sat    
 6 2016-08-06        0    38 Sat    
 7 2015-03-21        1    62 Sat    
 8 2015-04-18        0    52 Sat    
 9 2015-08-22        0    45 Sat    
10 2015-05-31        1    48 Sun    
11 2026-02-01        0    48 Sun    
12 2015-03-07        1    49 Sat    
13 2016-05-22        1    58 Sun    
14 2015-08-30        0    39 Sun    
15 2022-02-13        1    56 Sun    
16 2015-06-28        1    62 Sun    
17 2016-05-08        1    54 Sun
2 / 3

Example 4/4: Find BMTs on (Fake) Weekends

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
# wday gives weekday 1-7, labeled
# note one NA date
  mutate(weekday = wday(fake_bmt_date,
              label=TRUE)) %>%
# %in% must be in a vector of values
  filter(wday(fake_bmt_date) %in% c(1,7)) %>%
  arrange(fake_bmt_date)  # sort
[1] 64
# A tibble: 17 x 4
   fake_bmt_date   sex   age weekday
   <date>        <dbl> <dbl> <ord>  
 1 2015-01-17        1    55 Sat    
 2 2015-03-07        1    49 Sat    
 3 2015-03-21        0    52 Sat    
 4 2015-03-21        1    62 Sat    
 5 2015-04-18        0    52 Sat    
 6 2015-05-31        1    48 Sun    
 7 2015-06-28        1    62 Sun    
 8 2015-08-22        0    45 Sat    
 9 2015-08-30        0    39 Sun    
10 2016-04-23        1    57 Sat    
11 2016-05-08        1    54 Sun    
12 2016-05-22        1    58 Sun    
13 2016-08-06        0    38 Sat    
14 2017-07-23        0    33 Sun    
15 2017-10-07        0    63 Sat    
16 2022-02-13        1    56 Sun    
17 2026-02-01        0    48 Sun
2 / 3

Example 4/4: Find BMTs on (Fake) Weekends

# how many rows when you start
nrow(bmt_dates)
bmt_dates %>%
# selected 3 columns
# see how date changes w/filter
  select(fake_bmt_date, sex, age) %>%
# wday gives weekday 1-7, labeled
# note one NA date
  mutate(weekday = wday(fake_bmt_date,
              label=TRUE)) %>%
# %in% must be in a vector of values
  filter(wday(fake_bmt_date) %in% c(1,7)) %>%
  arrange(fake_bmt_date)  # sort
# see how many rows now
# lots of handy functions in lubridate
[1] 64
# A tibble: 17 x 4
   fake_bmt_date   sex   age weekday
   <date>        <dbl> <dbl> <ord>  
 1 2015-01-17        1    55 Sat    
 2 2015-03-07        1    49 Sat    
 3 2015-03-21        0    52 Sat    
 4 2015-03-21        1    62 Sat    
 5 2015-04-18        0    52 Sat    
 6 2015-05-31        1    48 Sun    
 7 2015-06-28        1    62 Sun    
 8 2015-08-22        0    45 Sat    
 9 2015-08-30        0    39 Sun    
10 2016-04-23        1    57 Sat    
11 2016-05-08        1    54 Sun    
12 2016-05-22        1    58 Sun    
13 2016-08-06        0    38 Sat    
14 2017-07-23        0    33 Sun    
15 2017-10-07        0    63 Sat    
16 2022-02-13        1    56 Sun    
17 2026-02-01        0    48 Sun
2 / 3

End of This Flipbook

On to The Coding Exercises!

3 / 3

How to Use the filter() function for Rows by Date

Format:

dataset %>% filter(between(variable, start, end))

Note that between is nested within filter.

Start and end dates usually use the {lubridate} package, and the format
ymd("2015-12-31")

Remember that filteR is for selecting Rows because it ends with an R.

Let's step through some filter Examples!

2 / 3
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