filter

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This lesson is called filter, part of the Fundamentals of R (v1) course. This lesson is called filter, part of the Fundamentals of R (v1) course.

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Your Turn

Solution

# 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)
```

Complete the filter sections of the data-wrangling-and-analysis-exercises.Rmd file.

Learn More

General Data Wrangling and Analysis Resources

Because most material that discusses data wrangling and analysis with the dplyr packges does so in a way that covers all of the verbs discussed in this course, I have chosen not to separate them by lesson. Instead, here are some helpful resources for learning more about all of the tidyverse verbs discussed in this course:

Chapter 5 of R for Data Science

RStudio Cloud primer on working with data

Tidyverse for Beginners by Danielle Navarro

Learning Statistics with R by Danielle Navarro

Introduction to the Tidyverse by Alison Hill

A gRadual intRoduction to data wRangling by Chester Ismay and Ted Laderas

Working in the Tidyverse by Desi Quintans and Jeff Powell

Christine Monnier video tutorials on dplyr

Have any questions? Put them below and we will help you out!

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Catherine Roller White

March 27, 2021

There is a line in the data-wrangling-and-analysis-exercises.Rmd file that says: "We can use Use , for numeric data." I think the placement of the equals sign may need to change for "greater or equal than" (i.e., '=>' should be '>=').

Vuk Sekicki

March 27, 2021

nhanes %&gt;%
    filter(drop_na(days_phys_hlth_bad)) %&gt;%
    select(phys_active_days, days_phys_hlth_bad)

returns: Error: Problem with filter() input ..1. x no applicable method for 'drop_na' applied to an object of class "c('double', 'numeric')" ℹ Input ..1 is drop_na(days_phys_hlth_bad). Run rlang::last_error() to see where the error occurred.

Hana Hanfi

April 7, 2021

I'm getting an error that "drop_na" wasn't found even though I previously loaded "dplyr" into the library. Should I have loaded another function?

Zach Tilton

August 19, 2021

Received an error code on the last exercise:

Error in drop_na(., days_phys_hlth_bad) : could not find function "drop_na"

dplyr is loaded. Not sure what is happening here. Moving forward with !is.na for the time being. Thanks.

Ekerette Udoh

September 30, 2021

nhanes %>% filter(Education == "College Grad") %>% select(Education, MaritalStatus)

When running the code above Education and MaritalStatus displays just fine. But when I add the second filter function as below I get an error message.

nhanes %>% filter(Education == "College Grad") %>% filter(MaritalStatus %n% c("Divorced", "Separated", "Widowed")) %>% select(Education, MaritalStatus)

Nathan Welsch

January 17, 2022

Is there a "rule of thumb" or useful way of remembering what needs quotes?

Josh Gutwill

October 4, 2022

I'm wondering if you know why one needs to concatenate multiple observations in the filter function. For example, here's a correct use of filter: filter(marital_status %in% c("Divorced", "Separated", "Widowed")) From what I understand of the concatenate function (from Excel), wouldn't that basically mean R is looking at each observation in marital_status and seeing if that observation can be found anywhere inside "DivorcedSeparatedWidowed"? But when I try it without concatenating by doing this: filter(marital_status %in% ("DivorcedSeparatedWidowed")) I get an error message. Maybe the answer is "That's just how R works," but I'm trying to build a solid mental model for these functions, so if there's anything more to it, I'd love to better understand. Thanks!

Ellen Wilson

October 6, 2022

I'm puzzled because in your video, it looks like there are code chunks that say "Your code here" (or something like that), but when I go to the file, the code is already complete for all the code chunks. This has actually been the case for the past few sections, and looks like it is for the next one, too.

Julieth Silao

November 6, 2022

nhanes %&gt;%
  filter(education == "Collage Grad") %&gt;%
  filter(marital_status %in% c("Divorced","Separated", "Widowed")) %&gt;%
  select(education, marital_status)

A tibble:0 × 2

hello David

when i filter education and marital status , i didnt get any table instead A tibble.0x 2 can i help

Chelsea Ruder

March 29, 2023

I can't seem to get the drop an or !is.na functions to work. oregon_respondents % select_all() %>% filter(!is.na(ip_address))

When I run this code, there are still blanks for ip_address.

Fundamentals of R (v1)

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Data Wrangling and Analysis

Data Visualization

Wrapping Up