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studentlife: Tidy Handling and Navigation of a Valuable Mobile-Health Dataset

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This package is available on CRAN.

Use this R package to download, navigate and analyse the Student-Life dataset. The Student-Life dataset contains passive and automatic sensing data from the phones of a class of 48 de-identified Dartmouth college students. It was collected over a 10 week term. Additionally, the dataset contains Ecological Momentary Assessment results along with pre- and post-study mental health surveys, such as the PHQ-9. The intended use is to allow researchers and other interested people to assess mental health, academic performance and behavioral trends. The raw dataset and additional information is available at


## Uncomment to install the package from CRAN
# install.packages("studentlife")

## Or, uncomment to install the package from GitHub
# install.packages("devtools")
# devtools::install_github("frycast/studentlife")

This studentlife repository includes a small sample dataset that can be used for practice and testing. You can download the sample data within R:

d <- tempdir()
download_studentlife(location = d, url = "testdata")

This README will use the studentlife data in its original format. Details on the full original dataset are available here. However, an RData version of the dataset has been hosted on Zenodo as of 09/11/2019 here. You can download and use this RData version, which is faster to read and takes up less space. However, load_SL_tibble is not (yet) designed to work with the RData format (but all the transformation and exploration functions, such as regularise_time, add_block_labels, and vis_NAs still work fine, because they work with any SL_tbl object). If you're working with the RData format, just replace instances of load_SL_tibble in this README with equivalent instances of the following:

d <- tempdir()
download_studentlife(location = d, url = "rdata")

# Choose the schema and table from the list SL_tables:

# Example with activity table from sensing schema
schema <- "sensing"
table <- "activity"
act <- readRDS(paste0(d, "/dataset_rds/", schema, "/", table, ".Rds"))

In this README, we will use the full dataset, so the download size is 5GB.

download_studentlife(location = d)

Use the interactive menu to browse the tables and schemas of the downloaded dataset:

tab <- studentlife::load_SL_tibble(location = d)

The object returned by load_SL_tibble, or by loading any table from the RData format, is called a 'StudentLife tibble' (or SL_tbl).

If you're using the load_SL_tibble interactive menu, then restrictions can be placed on the menu options by changing the time_options parameter:

tab_t <- load_SL_tibble(location = d, time_options = "timestamp", csv_nrows = 10)
tab_p <- load_SL_tibble(location = d, time_options = "interval" , csv_nrows = 10)
tab_d <- load_SL_tibble(location = d, time_options = "dateonly" , csv_nrows = 10)
tab_s <- load_SL_tibble(location = d, time_options = "dateless" , csv_nrows = 10)

The regularise_time function can be used to summarise information within blocks of time, producing an object called a 'regularised StudentLife tibble' (or reg_SL_tbl):

tab <- load_SL_tibble(
  loc = d, schema = "sensing", table = "activity", csv_nrows = 10)
  tab, blocks = c("day","weekday"),
  act_inf = max(activity_inference), add_NAs = FALSE)

If you just want to add date, epoch, or other labels, without aggregating, you can use add_block_labels:

tab <- load_SL_tibble(
  loc = d, schema = "sensing", table = "activity", csv_nrows = 10)

b_tab <- add_block_labels(tab)

Produce a histogram showing PAM EMA response frequencies over the course of the study:

tab_PAM <- load_SL_tibble(schema = "EMA", table = "PAM", location = d)
response_hour_hist(tab_PAM, break_hours = 10)

A summary will produce details such as EMA question, UIDs of dropped students, schema name, table name, and summary statistics.

 Refer to: Pollak, J. P., Adams, P., & Gay, G. (2011, May). 
 PAM: a photographic affect meter for frequent, in situ measurement of affect. 
 In Proceedings of the SIGCHI conference on Human factors in computing systems 
 (pp. 725-734). ACM. 


 picture_idx timestamp uid 



Skim summary statistics
 n obs: 9040 
 n variables: 3 

-- Variable type:factor --------------------------------------------------------
 variable missing complete    n n_unique                        top_counts ordered
      uid       0     9040 9040       49 59: 437, 0: 390, 19: 384, 57: 377   FALSE

-- Variable type:numeric -------------------------------------------------------
    variable missing complete    n    mean         sd      p0     p25     p50      p75     p100
 picture_idx       0     9040 9040 8.85          4.17 1       6       8       12       16      
   timestamp       0     9040 9040 1.4e+09 1608614.45 1.4e+09 1.4e+09 1.4e+09  1.4e+09  1.4e+09

After regularising a StudentLife tibble, we can visualise the missing values in each block for each student:

reg_PAM <- regularise_time(tab_PAM, blocks = c("day", "epoch"), m = mean(picture_idx, na.rm = TRUE))
vis_NAs(reg_PAM, response = "m")

We can also visualise and compare the total number of responses received from each student over the course of the study:

vis_response_counts(reg_PAM, response = "m")

Summaries of survey data are formatted with question information and some answer statistics:

tab_PHQ9 <- load_SL_tibble(loc = d, schema = "survey", table = "PHQ-9")

 Q1: Little interest or pleasure in doing things
 Q2: Feeling down, depressed, hopeless.
 Q3: Trouble falling or staying asleep, or sleeping too much.
 Q4: Feeling tired or having little energy
 Q5: Poor appetite or overeating
 Q6: Feeling bad about yourself or that you are a failure or have let yourself or your family down
 Q7: Trouble concentrating on things, such as reading the newspaper or watching television
 Q8: Moving or speaking so slowly that other people could have noticed. Or the opposite being so figety or restless that you have been moving around a lot more than usual
 Q9: Thoughts that you would be better off dead, or of hurting yourself
 Q10: Response

 uid type Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 



Skim summary statistics
 n obs: 84 
 n variables: 12 

-- Variable type:factor --------------------------------------------------------
 variable missing complete  n n_unique                        top_counts ordered
       Q1       0       84 84        4 Not: 42, Sev: 28, Mor: 10, Nea: 4   FALSE
      Q10       5       79 84        4   Som: 37, Not: 36, NA: 5, Ext: 3   FALSE
       Q2       0       84 84        4  Not: 40, Sev: 34, Mor: 6, Nea: 4   FALSE
       Q3       0       84 84        4 Not: 41, Sev: 25, Mor: 10, Nea: 8   FALSE
       Q4       0       84 84        4 Sev: 38, Not: 23, Mor: 17, Nea: 6   FALSE
       Q5       0       84 84        4  Sev: 35, Not: 34, Mor: 9, Nea: 6   FALSE
       Q6       0       84 84        4  Not: 45, Sev: 27, Mor: 6, Nea: 6   FALSE
       Q7       0       84 84        4  Not: 48, Sev: 26, Mor: 7, Nea: 3   FALSE
       Q8       0       84 84        4  Not: 64, Sev: 15, Mor: 4, Nea: 1   FALSE
       Q9       0       84 84        3    Not: 75, Sev: 6, Mor: 3, NA: 0   FALSE
     type       0       84 84        2           pre: 46, pos: 38, NA: 0   FALSE
      uid       0       84 84       46            0: 2, 1: 2, 2: 2, 3: 2   FALSE

Software Testing

This studentlife repository includes many automated software tests implemented via testthat. We use these to check for bugs before releasing new updates. They can be found under the directory tests. Also, we use Travis-CI for continuous integration.

Community Guidelines

  • Please give your feedback and report bugs at the issues page.
  • Contributions are welcome! The best way to contribute is to fork the project on GitHub, make a contribution to your fork, and then submit a pull request. A useful guide can be found here.
  • If you have questions or need support please email Daniel Fryer via the email on his GitHub profile.