srearl / cslqualtrics Goto Github PK
View Code? Open in Web Editor NEWR-based tools to format Qualtrics observation data
License: MIT License
R-based tools to format Qualtrics observation data
License: MIT License
Unfortunately, the new program delivers an output data file that is not identical to the old format and as such it will not add the downloaded data to the large existing Query Data file.
For example: the entire ‘Other’ column has NA listed in it and NA is used for missing data cells… they should just be left blank. Also – there are numbers entered in cells instead of names for the behavior categories…
And, I think this matters as well, but the ‘Date’ column is not in the same place…
The two files will not merge. I think it’s because the cells don’t contain exactly the same formatted data.
I am attaching a copy of the final cleaned output file using your new instructions. And the latest cleaned data file so you can see the differences (Go to Merge view). You can also compare the new generated cleaned data file to the Query data file you have on your thumb drive, you will see the differences.
In addition to the behaviour graph, Anne would like also ready access to:
obs <- DBI::dbGetQuery(
conn = csl_obs_db,
statement = "
SELECT
response_id,
question,
response
FROM
observations
WHERE
survey_id = 'SV_6W59cgkw9TdB6BM'
;
"
)
jack_ids <- obs[
obs$response == "Jack B." &
grepl("^q2_", obs$question, ignore.case = TRUE),
]$response_id
jack_data <- obs[obs$response_id %in% c(jack_ids), ]
jack_behaviours_sum <- jack_data |>
dplyr::filter(grepl("^q4$", question, ignore.case = TRUE)) |>
nrow()
student_behaviours_sum <- jack_behaviours_sum
devtools::load_all("~/localRepos/cslqualtrics/")
jack_data |>
dplyr::filter(grepl("^q4$", question, ignore.case = TRUE)) |>
dplyr::group_by(response) |>
dplyr::summarise(
n = dplyr::n(),
percent = round((n / student_behaviours_sum) * 100)
) |>
dplyr::ungroup() |>
dplyr::full_join(
y = cslqualtrics::behaviour_types,
by = c("response" = "behaviour")
) |>
dplyr::arrange(
type,
response
) |>
echarts4r::e_charts(x = response) |>
echarts4r::e_bar(
serie = percent,
name = "student behaviour",
legend = FALSE,
colorBy = data
) |>
echarts4r::e_tooltip(trigger = "axis") |>
echarts4r::e_x_axis(axisLabel = list(interval = 0, rotate = 315)) |>
echarts4r::e_title("Jack") |>
echarts4r::e_grid(bottom = "40%") |>
echarts4r::e_color(color = c(cslqualtrics::behaviour_types$color)) |>
echarts4r::e_y_axis(max = 60)
...because there are not any
Hi Anne,
Trying, though not terribly successfully, to work it in currently.
A couple of questions for you that will help inform the design:
We use these data for parents, but also for research purposes. For example: we are tracking the development of Theory of Mind in children and how this behavior correlates to both problem behavior and to social skills. We need to follow a specific child over their enrollment at the CSL correlating their observed behavior to assessment outcomes.
I was waiting to be able to use those other data as well!
We routinely need to know how many data entries were entered by each TA – this is part of their performance evaluations each semester. Also – it would be wonderful to know in which of the setting categories the child’s behavior occurs – e.g., are most inappropriate behaviors more likely to occur during transitions or large group time, etc. The setting is very valuable in determining the behaviors antecedent stimulus – i.e. under what condition is the behavior more likely to occur.
Also, who is the child targeting – what other child – both socially (e.g., who does the child share a toy with) and antisocially (e.g., who does the child aggress towards) to see if there are any patterns there.
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