Data Export and Basic Analysis

reVISit allows you to export in Tidy format for analysis in Python notebooks or R.

The exported tidy data includes all the responses from the participants, including the participant ID, trial ID, trial order, and response ID. Additionally, we provide the parameters and correct answers that were set in the study configuration file (or by dynamic block). This data can be used to analyze the performance of participants, the accuracy of responses, and the time taken to complete the study.

The tidy data is missing some data that is available in the JSON download, such as the provenance graphs. If you need this data, you can download the JSON data and parse it in your analysis platform, instead of using this Tidy data.

Steps to Download Data

1. Navigate to the Analysis platform for the current study.

2. Open the Participant View, then click on the Download all participants' data as a tidy CSV button. Here's a direct link for the example below.

3. The CSV Explorer will open, where you can select the columns to export. A preview of the first five rows of the output CSV will be displayed.

4. Once ready, click the Download button at the bottom right.

After downloading the Tidy data, you can import it into your favorite analysis platform for further analysis. Below is an example of how to work with exported data from the Interactive Selections in Scatterplots study in R.

What is Tidy data format?

Tidy data format is a structured approach to organizing tabular data where each variable is a column, each observation is a row, and each type of observational unit is a separate table. You may check more details Here.

Example Workflow in R

1. Install Necessary Packages

list.of.packages <- c("ggplot2", "Hmisc")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

library(ggplot2)

2. Read and Preview the Data

df <- read.csv("data/example-brush-interactions_all_tidy.csv")
head(df)

	participantId	trialId	trialOrder	responseId
1	017ab8bd-33d1-4f95-ac07-5fedef30928a	introduction	0	prolificId
2	017ab8bd-33d1-4f95-ac07-5fedef30928a	consent	1	signature
3	017ab8bd-33d1-4f95-ac07-5fedef30928a	consent	1	accept
4	017ab8bd-33d1-4f95-ac07-5fedef30928a	paintBrush_q1	5	response
5	017ab8bd-33d1-4f95-ac07-5fedef30928a	paintBrush_q2	6	max-response

3. Filter Data for Task q2

q2 <- subset(df, grepl("_q2", trialId) & status == "completed")
q2$isCorrect <- ifelse(q2$answer == q2$correctAnswer, 1, 0)
q2 <- subset(df, grepl("_q2", trialId) & status == "completed")
q2$isCorrect <- ifelse(q2$answer == q2$correctAnswer, 1, 0)

4. Create a Violin Plot

The plot displays correct answers on the right and incorrect answers on the left.

ggplot(q2, aes(x = isCorrect, y = trialId)) +
  geom_violin(aes(fill = trialId), color = "#888", alpha = 0.7) +
  stat_summary(fun.data = "mean_cl_boot", colour = "#333", size = 0.5, alpha=0.5) +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(
    title = "Violin Plot for q2 (Finding most/least value)",
  )

We find accuracy using paint brush technique is much less than that of the others.

5. Export the generated plot.

ggsave("plot.pdf", width = 5, height = 2, units = "in")

Download Audio

There are two ways to download audio files if you have enabled recordAudio: true in your study. For applying Think Aloud in your study, please visit Think Aloud.

Download Participants' Audio

1. Navigate to the Analysis platform for the current study.

2. Open the Participant View, then click on the Download participants audio button.

note

To download audio from specific participants, select the participants in the table.

Download Single Participant's Task Audio

1. Navigate to the Analysis platform for the current study.

2. Open the Participant View, then expand the participant’s timeline by clicking the expand button.

3. Click on the task.

4. Click on the Download Audio button.

note

If you are using Firebase, it will download the transcript of the audio file as well.