Filtering for Two Observations per Condition

In this article, we’ll explore how to filter a dataset so that each student has at least one observation for both English and Spanish tests. We’ll dive into the details of data manipulation using R and the dplyr package.

Problem Statement

Suppose you have a dataset with information about students’ test scores and types. You want to filter the observations so that each student_id has at least one Spanish test and one English test. The question arises when trying to achieve this goal, especially in scenarios where data contains multiple tests for some students.

Example Data

Let’s create an example dataframe to illustrate our problem:

student_id <- c(1, 1, 2, 2, 3, 3)
test_score <- c(100, 90, 80, 100, 70, 90)
test_type <- c("English", "English", "English", "Spanish", "Spanish", "Spanish")
time_period <- c(1, 0, 1, 0, 1, 0)

df <- data.frame(student_id, test_score, test_type, time_period)

As shown in the example, there is no student who has done both English and Spanish tests. We will later use this scenario to explain our approach.

Current Code

The current code provided tries to filter observations based on row_number:

df <- df %>%
  group_by(student_id, test_type) %>%
  dplyr::filter(row_number() == 1)

However, this approach is not effective for our purpose and only returns values from the English test.

Alternative Approach

One alternative approach to achieve our goal is to count the number of unique exam types for each student. If that count is greater than 1, then we have found the relevant rows.

df %>% 
  group_by(student_id) %>%
  mutate(n_dist = n_distinct(test_type)) %>%
  filter(n_dist > 1) %>%
  select(-n_dist)

This code first counts the number of unique exam types for each student (n_dist). It then filters those rows where n_dist is greater than 1, effectively giving us all observations from students who have done both English and Spanish tests.

Using this approach with our example data:

student_id <- c(1, 1, 2, 2, 3, 3)
test_score <- c(100, 90, 80, 100, 70, 90)
test_type <- c("English", "English", "English", "Spanish", "Spanish", "Spanish")
time_period <- c(1, 0, 1, 0, 1, 0)

df <- data.frame(student_id, test_score, test_type, time_period)

df %>% 
  group_by(student_id) %>%
  mutate(n_dist = n_distinct(test_type)) %>%
  filter(n_dist > 1) %>%
  select(-n_dist)

Results:

# A tibble: 2 x 4
# Groups:   student_id [1]
  student_id test_score test_type time_period
       <dbl>      <dbl> &lt;fct&gt;         &lt;dbl&gt;
1          2         80 English             1
2          2        100 Spanish             0

This code gives us the observations for students who have done both English and Spanish tests.

Another Option: Using slice

As an alternative, we can use slice to get the first observation per student x test type combination:

df %>% 
  group_by(student_id, test_type) %>%
  slice(1)

Using this approach with our example data:

student_id <- c(1, 1, 2, 2, 3, 3)
test_score <- c(100, 90, 80, 100, 70, 90)
test_type <- c("English", "English", "English", "Spanish", "Spanish", "Spanish")
time_period <- c(1, 0, 1, 0, 1, 0)

df <- data.frame(student_id, test_score, test_type, time_period)

df %>% 
  group_by(student_id, test_type) %>%
  slice(1)

Results:

# A tibble: 4 x 4
# Groups:   student_id [2], test_type [2]
  student_id test_score test_type time_period
       <dbl>      <dbl> &lt;fct&gt;         &lt;dbl&gt;
1          1        100 English             1
2          2         80 English             1
3          2        100 Spanish             0
4          3         70 Spanish             1

This code gives us the observations for students who have done both English and Spanish tests.

Conclusion

We’ve explored different approaches to filter a dataset so that each student has at least one observation for both English and Spanish tests. We used data manipulation techniques in R with dplyr to achieve this goal. Whether you use counting unique exam types or slice, the key is to identify how many students have done both tests, and then retrieve those observations from your dataset.

Last modified on 2024-09-16