# two filters equivalent to one

    Code
      lf1 %>% remote_query()
    Output
      <SQL> SELECT `df`.*
      FROM `df`
      WHERE (`x` > 3.0) AND (`y` < 3.0)

---

    Code
      lf1 %>% remote_query()
    Output
      <SQL> SELECT `x`, `y`
      FROM (
        SELECT `df`.*, AVG(`x`) OVER () AS `col01`
        FROM `df`
      ) AS `q01`
      WHERE (`col01` > 3.0) AND (`y` < 3.0)

# errors for named input

    Code
      filter(lf, x = 1)
    Condition
      Error in `filter()`:
      ! Problem with `filter()` input `..1`.
      x Input `..1` is named.
      i This usually means that you've used `=` instead of `==`.
      i Did you mean `x == 1`?

---

    Code
      filter(lf, y > 1, x = 1)
    Condition
      Error in `filter()`:
      ! Problem with `filter()` input `..2`.
      x Input `..2` is named.
      i This usually means that you've used `=` instead of `==`.
      i Did you mean `x == 1`?

# .preserve is not supported

    Code
      lf %>% filter(x == 1, .preserve = TRUE)
    Condition
      Error in `filter()`:
      ! `.preserve = TRUE` isn't supported on database backends.
      i It must be FALSE instead.

# catches `.by` with grouped-df

    Code
      filter(gdf, .by = x)
    Condition
      Error:
      ! Can't supply `.by` when `.data` is a grouped data frame.

# filter() can use window function and external vector - #1048

    Code
      lazy_frame(x = 1L) %>% filter(x == max(x, na.rm = T), x %in% to_filter)
    Output
      <SQL>
      SELECT `x`
      FROM (
        SELECT `df`.*, MAX(`x`) OVER () AS `col01`
        FROM `df`
      ) AS `q01`
      WHERE (`x` = `col01`) AND (`x` IN (1, 2))

# filter() after summarise() uses `HAVING`

    Code
      (out <- lf %>% filter(g == 1))
    Output
      <SQL>
      SELECT `g`, `h`, AVG(`x`) AS `x_mean`
      FROM `df`
      GROUP BY `g`, `h`
      HAVING (`g` = 1.0)

---

    Code
      (out <- lf %>% filter(x_mean > 1))
    Output
      <SQL>
      SELECT `g`, `h`, AVG(`x`) AS `x_mean`
      FROM `df`
      GROUP BY `g`, `h`
      HAVING (AVG(`x`) > 1.0)

---

    Code
      (out <- lf %>% filter(g == 1) %>% filter(g == 2))
    Output
      <SQL>
      SELECT `g`, `h`, AVG(`x`) AS `x_mean`
      FROM `df`
      GROUP BY `g`, `h`
      HAVING (`g` = 1.0) AND (`g` = 2.0)

---

    Code
      (out <- lf %>% filter(g == 1) %>% filter(h == 2))
    Output
      <SQL>
      SELECT `g`, `h`, AVG(`x`) AS `x_mean`
      FROM `df`
      GROUP BY `g`, `h`
      HAVING (`g` = 1.0) AND (`h` = 2.0)

# `HAVING` supports expressions #1128

    Code
      lf %>% summarise(x_sum = sum(x, na.rm = TRUE)) %>% filter(!is.na(x_sum))
    Output
      <SQL>
      SELECT SUM(`x`) AS `x_sum`
      FROM `df`
      HAVING (NOT(((SUM(`x`)) IS NULL)))

# filter() after mutate() does not use `HAVING`

    Code
      (out <- lf %>% filter(x_mean > 1))
    Output
      <SQL>
      SELECT `q01`.*
      FROM (
        SELECT `df`.*, AVG(`x`) OVER (PARTITION BY `g`, `h`) AS `x_mean`
        FROM `df`
      ) AS `q01`
      WHERE (`x_mean` > 1.0)

# filter() using a window function after summarise() does not use `HAVING`

    Code
      (out <- lf %>% filter(cumsum(x_mean) == 1))
    Condition
      Warning:
      Windowed expression `SUM(`x_mean`)` does not have explicit order.
      i Please use `arrange()` or `window_order()` to make deterministic.
    Output
      <SQL>
      SELECT `g`, `h`, `x_mean`
      FROM (
        SELECT
          `q01`.*,
          SUM(`x_mean`) OVER (PARTITION BY `g` ROWS UNBOUNDED PRECEDING) AS `col01`
        FROM (
          SELECT `g`, `h`, AVG(`x`) AS `x_mean`
          FROM `df`
          GROUP BY `g`, `h`
        ) AS `q01`
      ) AS `q01`
      WHERE (`col01` = 1.0)