Analysis of ruler RT data

Purpose

This page summarizes our analysis and visualization of the reaction time data collected by Andrea Seisler in May 2023 from students at Our Lady of Victory Middle School.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Description of task

Gathering

The data are in a Google sheet that is accessible to the psubrainlab account.

Here is a link to the data:

https://docs.google.com/spreadsheets/d/1J-S5-kKQpHYW7L1_pqxX57zziS5IPX7ViA2MG3pGFBo/edit#gid=621964648

First, I need to authenticate to Google.

googledrive::drive_auth(email="psubrainlab@gmail.com")

Then, I download the data.

googledrive::drive_download(
  file = "https://docs.google.com/spreadsheets/d/1J-S5-kKQpHYW7L1_pqxX57zziS5IPX7ViA2MG3pGFBo/edit?usp=sharing",
  path = "csv/rt-raw.csv",
  type = 'csv',
  overwrite = TRUE
)

File downloaded:

• 'Ruler Reaction Time Test Data (Responses)'
  <id: 1J-S5-kKQpHYW7L1_pqxX57zziS5IPX7ViA2MG3pGFBo>

Saved locally as:

• 'csv/rt-raw.csv'

Now, I reimport the CSV file as a data frame/tibble.

rt <- readr::read_csv("csv/rt-raw.csv")

Rows: 616 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (7): Timestamp, Class Period, Male or Female, Hand: Left (L) or Right (R...
dbl (2): Participant ID (4 digits e.g. 7000, 7001), Trial Number
lgl (1): Time (s)

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

str(rt)

spc_tbl_ [616 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Timestamp                                                          : chr [1:616] "5/5/2023 9:13:37" "5/5/2023 9:16:18" "5/5/2023 9:16:49" "5/5/2023 9:17:43" ...
 $ Class Period                                                       : chr [1:616] "7-101" "7-101" "7-101" "7-101" ...
 $ Participant ID (4 digits e.g. 7000, 7001)                          : num [1:616] 9010 9011 9010 9011 9010 ...
 $ Trial Number                                                       : num [1:616] 1 1 2 2 3 1 1 2 2 3 ...
 $ Male or Female                                                     : chr [1:616] "m" "m" "m" "m" ...
 $ Hand: Left (L) or Right (R)                                        : chr [1:616] "l" "l" "l" "l" ...
 $ Hand: D for (Dominant - the one you write with) OR N (Non-Dominant): chr [1:616] "n" "n" "n" "n" ...
 $ Distance (cm OR drop)                                              : chr [1:616] "21" "23" "13" "11" ...
 $ age-range                                                          : chr [1:616] "child" "child" "child" "child" ...
 $ Time (s)                                                           : logi [1:616] NA NA NA NA NA NA ...
 - attr(*, "spec")=
  .. cols(
  ..   Timestamp = col_character(),
  ..   `Class Period` = col_character(),
  ..   `Participant ID (4 digits e.g. 7000, 7001)` = col_double(),
  ..   `Trial Number` = col_double(),
  ..   `Male or Female` = col_character(),
  ..   `Hand: Left (L) or Right (R)` = col_character(),
  ..   `Hand: D for (Dominant - the one you write with) OR N (Non-Dominant)` = col_character(),
  ..   `Distance (cm OR drop)` = col_character(),
  ..   `age-range` = col_character(),
  ..   `Time (s)` = col_logical()
  .. )
 - attr(*, "problems")=<externalptr> 

Cleaning

Let’s drop columns we don’t need or won’t use.

rt <- rt |>
  dplyr::select(-Timestamp) |>
  dplyr::rename(
    "class" = "Class Period",
    "part_id" = "Participant ID (4 digits e.g. 7000, 7001)",
    "trial" = "Trial Number",
    "gender" = "Male or Female",
    "hand" = "Hand: Left (L) or Right (R)",
    "dom_nondom" = "Hand: D for (Dominant - the one you write with) OR N (Non-Dominant)",
    "cm" = "Distance (cm OR drop)",
    "age_range" = "age-range"
  ) |>
  dplyr::mutate(cm = as.numeric(cm))

Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `cm = as.numeric(cm)`.
Caused by warning:
! NAs introduced by coercion

Visualization

rt |>
  ggplot() +
  aes(x = cm, fill = hand) +
  geom_histogram() +
  facet_grid(rows = vars(gender), cols = vars(dom_nondom))

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 21 rows containing non-finite values (`stat_bin()`).

rt |>
  ggplot() +
  aes(x = dom_nondom, y = cm, fill = hand) +
  geom_violin() +
  facet_grid(rows = vars(gender))

Warning: Removed 21 rows containing non-finite values (`stat_ydensity()`).

rt |>
  ggplot() +
  aes(x = dom_nondom, y = cm, fill = hand) +
  geom_boxplot() +
  facet_grid(rows = vars(gender))

Warning: Removed 21 rows containing non-finite values (`stat_boxplot()`).

rt |>
  dplyr::mutate(student = paste0(class, "-", part_id)) |>
  ggplot() +
  aes(x = trial, y = cm, group = student, color = as.factor(student)) +
  geom_line() +
  facet_grid(rows = vars(gender)) +
  scale_x_continuous(breaks = c(1:8)) +
  theme(legend.position = "none")

Warning: Removed 6 rows containing missing values (`geom_line()`).