# filter

![](slides/images/filter.png)

We use `filter` to choose a subset of cases.

Use `filter` to keep only respondents who are divorced. Then, use `select` to show only the `marital_status` variable.

```{r}
nhanes %>%
  filter(marital_status == "Divorced") %>%
  select(marital_status)
```

Use `filter` to keep only respondents who are **not** divorced. Then, use `select` to show only the `marital_status` variable.

```{r}
nhanes %>%
  filter(marital_status != "Divorced") %>%
  select(marital_status)
```



Use `filter` to keep only respondents who are divorced or separated. Then, use `select` to show only the `marital_status` variable.

```{r}
nhanes %>%
  filter(marital_status == "Divorced" | marital_status == "Separated") %>%
  select(marital_status)
```


Use `%in%` within the `filter` function to keep only those who are divorced, separated, or widowed. Then, use `select` to show only the `marital_status` variable.

```{r}
nhanes %>%
  filter(marital_status %in% c("Divorced", "Separated", "Widowed")) %>%
  select(marital_status)
```

We can chain together multiple `filter` functions. Doing it this way, we don't have create complex logic in one line.

Create a chain that keeps only those are college grads (line #1). Then, `filter` to keep only those who are divorced, separated, or widowed. Finally, use `select` to show only the `education` and `marital_status` variables.

```{r}
nhanes %>%
  filter(education == "College Grad") %>%
  filter(marital_status %in% c("Divorced", "Separated", "Widowed")) %>%
  select(education, marital_status)
```



We can use Use `<`, `>`, `<=`, and `=>` for numeric data.

Use `filter` to only show those reported at least 5 days of physical activity in the last 30 days (this is the `phys_active_days` variable). Then, use `select` to keep only the `phys_active_days` and the `days_phys_hlth_bad` variables.

```{r}
nhanes %>%
  filter(phys_active_days >= 5) %>%
  select(phys_active_days, days_phys_hlth_bad)
```


We can drop `NAs` with `!is.na`

Do the same thing as above, but drop responses that don't have a response for `days_phys_hlth_bad`. Then, use `select` to keep only the `phys_active_days` and the `days_phys_hlth_bad` variables.

```{r}
nhanes %>%
  filter(phys_active_days >= 5) %>%
  filter(!is.na(days_phys_hlth_bad)) %>%
  select(phys_active_days, days_phys_hlth_bad)
```


You can also drop `NAs` with `drop_na`

Do the same thing as above, but use `drop_na` instead of `!is.na`. Make sure you get the same result!

```{r}
nhanes %>%
  filter(phys_active_days >= 5) %>%
  drop_na(days_phys_hlth_bad) %>%
  select(phys_active_days, days_phys_hlth_bad)
```