| Title: | Get and Modify 'oTree' Data |
|---|---|
| Description: | Efficiently manage and process data from 'oTree' experiments. Import 'oTree' data and clean them by using functions that handle messy data, dropouts, and other problematic cases. Create IDs, calculate the time, transfer variables between app data frames, and delete sensitive information. Review your experimental data prior to running the experiment and automatically generate a detailed summary of the variables used in your 'oTree' code. Information on 'oTree' is found in Chen, D. L., Schonger, M., & Wickens, C. (2016) <doi:10.1016/j.jbef.2015.12.001>. |
| Authors: | Patricia F. Zauchner [aut, trl, cre, cph] (<https://orcid.org/0000-0002-5938-1683>, University of Bremen) |
| Maintainer: | Patricia F. Zauchner <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.3.1 |
| Built: | 2024-12-07 10:45:38 UTC |
| Source: | CRAN |
Calculate the time spent on one app or several apps.
apptime( oTree, apps = NULL, pcode = NULL, plabel = NULL, group_id = NULL, seconds = FALSE, rounded = TRUE, digits = 2, sinfo = "session_code", combine = FALSE )apptime( oTree, apps = NULL, pcode = NULL, plabel = NULL, group_id = NULL, seconds = FALSE, rounded = TRUE, digits = 2, sinfo = "session_code", combine = FALSE )
oTree |
A list of data frames that were created
by |
apps |
Character. Name(s) of the app(s) for which the time should be calculated. |
pcode |
Character. The value of the |
plabel |
Character. The value of the |
group_id |
Integer. The value of the |
seconds |
Logical. |
rounded |
Logical. |
digits |
Integer.
The number of digits to which the output should be rounded.
This parameter has no effect unless |
sinfo |
Character. |
combine |
Logical. |
This function returns a list for each app containing information on the mean, the minimum, and maximum time the participants spent on the app, a data frame with information on the time each participant spent on the app, and eventually, vectors of relevant background information on these numbers.
If the experiment's duration is only calculated for one participant,
the output returns an NA (per app) if the person did not make
it to the app(s).
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show how much time all participants spent on app "survey" apptime(oTree, apps = "survey") # Show how much time the participant "a7dppel1" spent on # the app "survey" apptime(oTree, pcode = "a7dppel1", apps = "survey") # Show how much time the participants in group 4 spent on # the app "survey" oTree <- make_ids(oTree, gmake = TRUE, from_var = "dictator.1.group.id_in_subsession" ) apptime(oTree, group_id = 4, apps = "survey") # Show how much time all participants spent on all apps apptime(oTree)# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show how much time all participants spent on app "survey" apptime(oTree, apps = "survey") # Show how much time the participant "a7dppel1" spent on # the app "survey" apptime(oTree, pcode = "a7dppel1", apps = "survey") # Show how much time the participants in group 4 spent on # the app "survey" oTree <- make_ids(oTree, gmake = TRUE, from_var = "dictator.1.group.id_in_subsession" ) apptime(oTree, group_id = 4, apps = "survey") # Show how much time all participants spent on all apps apptime(oTree)
Assign a variable from $all_apps_wide to the other app data frames.
assignv(oTree, variable, newvar)assignv(oTree, variable, newvar)
oTree |
A list of data frames that were
created by |
variable |
Character. The variable in the |
newvar |
Character. The name of the newly created variable. |
This function returns a duplicate of the
original oTree list of data frames
but with an additional column in all data frames. The additional column
contains data from the specified variable found in $all_apps_wide.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Assign variable "survey.1.player.gender" and name it "gender" oTree <- assignv(oTree = oTree, variable = "survey.1.player.gender", newvar = "gender") # Show the new variable in some of the other app data frames oTree$dictator$gender oTree$chatapp$gender # The variable is now duplicated in app "survey" because it is obtained from # there (This can be avoided by naming the new variable the same as the old # variable) oTree$survey$gender oTree$survey$player.gender # In app "all_apps_wide," the variable is also there twice (This can be # avoided by naming the new variable the same as the old variable) oTree$all_apps_wide$gender oTree$all_apps_wide$survey.1.player.gender# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Assign variable "survey.1.player.gender" and name it "gender" oTree <- assignv(oTree = oTree, variable = "survey.1.player.gender", newvar = "gender") # Show the new variable in some of the other app data frames oTree$dictator$gender oTree$chatapp$gender # The variable is now duplicated in app "survey" because it is obtained from # there (This can be avoided by naming the new variable the same as the old # variable) oTree$survey$gender oTree$survey$player.gender # In app "all_apps_wide," the variable is also there twice (This can be # avoided by naming the new variable the same as the old variable) oTree$all_apps_wide$gender oTree$all_apps_wide$survey.1.player.gender
Assign a variable from one of the app data frames to $all_apps_wide.
assignv_to_aaw(oTree, app, variable, newvar, resafter = NULL)assignv_to_aaw(oTree, app, variable, newvar, resafter = NULL)
oTree |
A list of data frames that were created
by |
app |
Character. The data frame from which the variable is taken. |
variable |
Character.
The name of the variable that should be assigned to |
newvar |
Character.
The name of the newly created variable in the |
resafter |
Character.
The name of the variable that precedes the new variable.
If |
This function returns a duplicate of the original oTree list of
data frames but with an additional column in the $all_apps_wide data
frame that contains the variable in question.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Check out the old variable oTree$survey$player.age # Create a new variable oTree$survey$younger30 <- ifelse(oTree$survey$player.age < 30, 1, 0) # Assign the variable younger30 to all_apps_wide oTree2 <- assignv_to_aaw( oTree = oTree, app = "survey", variable = "younger30", newvar = "younger30") # Show the new variable in the all_apps_wide data frame oTree2$all_apps_wide$younger30 # Check the position of the new variable match("younger30",names(oTree2$all_apps_wide)) # Place the new variable immediately after the "survey.1.player.age" variable oTree2 <- assignv_to_aaw(oTree, app = "survey", variable = "younger30", newvar = "younger30", resafter = "survey.1.player.age") # Show the new variable in the all_apps_wide data frame oTree2$all_apps_wide$younger30 # Show the position of the new variable match("younger30", names(oTree2$all_apps_wide))# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Check out the old variable oTree$survey$player.age # Create a new variable oTree$survey$younger30 <- ifelse(oTree$survey$player.age < 30, 1, 0) # Assign the variable younger30 to all_apps_wide oTree2 <- assignv_to_aaw( oTree = oTree, app = "survey", variable = "younger30", newvar = "younger30") # Show the new variable in the all_apps_wide data frame oTree2$all_apps_wide$younger30 # Check the position of the new variable match("younger30",names(oTree2$all_apps_wide)) # Place the new variable immediately after the "survey.1.player.age" variable oTree2 <- assignv_to_aaw(oTree, app = "survey", variable = "younger30", newvar = "younger30", resafter = "survey.1.player.age") # Show the new variable in the all_apps_wide data frame oTree2$all_apps_wide$younger30 # Show the position of the new variable match("younger30", names(oTree2$all_apps_wide))
Create a codebook of your oTree code by automatically scanning your project folder and retrieving all variables' information.
codebook( path = ".", fsource = "init", output = "both", output_dir = NULL, output_file = "codebook", output_format = "pdf_document_simple", output_open = TRUE, app_doc = TRUE, app = NULL, app_rm = NULL, doc_info = TRUE, sort = NULL, settings_replace = "global", user_settings = NULL, include_cons = TRUE, preamb = FALSE, encoding = "UTF-8", title = "Codebook", subtitle = "created with gmoTree", params = NULL, date = "today", splitvarname = FALSE, sep_list = "newline", initial = TRUE )codebook( path = ".", fsource = "init", output = "both", output_dir = NULL, output_file = "codebook", output_format = "pdf_document_simple", output_open = TRUE, app_doc = TRUE, app = NULL, app_rm = NULL, doc_info = TRUE, sort = NULL, settings_replace = "global", user_settings = NULL, include_cons = TRUE, preamb = FALSE, encoding = "UTF-8", title = "Codebook", subtitle = "created with gmoTree", params = NULL, date = "today", splitvarname = FALSE, sep_list = "newline", initial = TRUE )
path |
Character. Path of the oTree experiment. |
fsource |
Character. |
output |
Character. |
output_dir |
Character. The absolute path where
the function's output will be saved.
Only absolute paths are allowed for this parameter.
Relative paths can be specified in the |
output_file |
Character. The name of the output file generated by the function. The file name can be provided with or without an extension. Relative paths are also allowed in the file name. |
output_format |
Character. Specifies the format of the file output.
This value is passed to the |
output_open |
Logical. |
app_doc |
Logical. |
app |
Character. Name of the included app(s).
Default is to use all apps.
Cannot be used simultaneously with |
app_rm |
Character. Name of the excluded app(s).
Default is to exclude no apps.
Cannot be used simultaneously with |
doc_info |
Logical. |
sort |
Character vector. Vector that specifies the order of the apps in the codebook. |
settings_replace |
Character or |
user_settings |
List. List of variables in the |
include_cons |
Logical.
|
preamb |
Logical.
|
encoding |
Character. Encoding of the created Markdown file.
As in knitr::knit, this argument is
always assumed to be |
title |
Character. Title of output file. |
subtitle |
Character. Subtitle of output file. |
params |
List.
List of variable name and value pairs to be passed to the RmD file.
Only relevant if argument output |
date |
Character or |
splitvarname |
Logical. |
sep_list |
Character. Determines how sub-lists are displayed
in the file output. Use |
initial |
Logical. |
This code works only when there are no dictionaries used (for
example in the session configurations in settings.py).
Caution 1: Multiline comments are ignored, meaning that all variables commented out in this manner will nevertheless be included in the codebook. In contrast, variables commented out with line comments will not appear in the codebook.
Caution 2: If there are commas in the value strings, they might be
used to split the text. Please manually insert a backslash symbol
in front of the commas to avoid that (i.e., escape them).
E.g. "Yes, I will" -> "Yes\, I will".
Caution 3: This code cannot interpret variables that were imported from other
files (for example CSV files) and that have special formatting
included (e.g., special string formatting in Python such
as float(1.4) to represent a float number).
Caution 4: This code was developed and tested with basic oTree codes and has not been verified for compatibility with oTree versions later than 5.4.0. If you experience issues with newer versions or more complex code structures, please open an issue on GitHub.
Caution 5: Custom exports are not part of the codebook.
Further info: None values are presented as "None" (i.e. as a string)
in the list and in the codebook.
The function returns two main types of outputs:
(a) a list of variables along with their information
(b) a file containing the codebook for the experiment
If doc_info is TRUE it also returns a message containing the names of
all variables that have no documentation.
# The examples use a slightly modified version of the official oTree sample codes. # Make a codebook and resort the apps combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), output = "list", fsource = "init", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) str(combined_codebook$bargaining$Player, 1) # Make a codebook with only the "bargaining" app combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), output = "list", fsource = "init", app = "bargaining", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) str(combined_codebook$bargaining$Player, 1) # Make a codebook with all but the "bargaining" app combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), output = "list", fsource = "init", app_rm = "bargaining", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) str(combined_codebook$bargaining$Player, 1) # Use oTree code in 3.x format combined_codebook <- codebook( path = system.file("extdata/ocode_z", package = "gmoTree"), fsource = "model", output = "list", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) # Show information on missing documentation or complex code combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), fsource = "init", output = "list", app_rm = "bargaining", doc_info = TRUE) ## Not run: # Create a codebook PDF with authors' names and todays' date codebook( path = system.file("extdata/ocode_z", package = "gmoTree"), fsource = "init", doc_info = FALSE, output = "file", output_format = "pdf_document", date = "today", title = "My Codebook", subtitle = "codebook created with gmoTree", params = list(author = c("Max Mustermann", "John Doe")) ) # Create a codebook PDF and save it in a subfolder of the # current folder: # "C:/Users/pzauchner/Nextcloud/R_analyses/cb/cb.pdf" getwd() # "C:/Users/pzauchner/Nextcloud/R_analyses" dir.create("cb") combined_codebook <- gmoTree::codebook( path = "C:/Users/pzauchner/Nextcloud/R_analyses/oTree", fsource = "models", output = "both", output_file = "cb/cb.pdf", output_format = "pdf_document") # You can also omit *.pdf after the file name combined_codebook <- gmoTree::codebook( path = "C:/Users/pzauchner/Nextcloud/R_analyses/oTree", fsource = "models", output = "both", output_file = "cb/cb", output_format = "pdf_document") ## End(Not run)# The examples use a slightly modified version of the official oTree sample codes. # Make a codebook and resort the apps combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), output = "list", fsource = "init", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) str(combined_codebook$bargaining$Player, 1) # Make a codebook with only the "bargaining" app combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), output = "list", fsource = "init", app = "bargaining", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) str(combined_codebook$bargaining$Player, 1) # Make a codebook with all but the "bargaining" app combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), output = "list", fsource = "init", app_rm = "bargaining", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) str(combined_codebook$bargaining$Player, 1) # Use oTree code in 3.x format combined_codebook <- codebook( path = system.file("extdata/ocode_z", package = "gmoTree"), fsource = "model", output = "list", doc_info = FALSE) # Show the structure of the codebook str(combined_codebook, 1) # Show information on missing documentation or complex code combined_codebook <- codebook( path = system.file("extdata/ocode_new", package = "gmoTree"), fsource = "init", output = "list", app_rm = "bargaining", doc_info = TRUE) ## Not run: # Create a codebook PDF with authors' names and todays' date codebook( path = system.file("extdata/ocode_z", package = "gmoTree"), fsource = "init", doc_info = FALSE, output = "file", output_format = "pdf_document", date = "today", title = "My Codebook", subtitle = "codebook created with gmoTree", params = list(author = c("Max Mustermann", "John Doe")) ) # Create a codebook PDF and save it in a subfolder of the # current folder: # "C:/Users/pzauchner/Nextcloud/R_analyses/cb/cb.pdf" getwd() # "C:/Users/pzauchner/Nextcloud/R_analyses" dir.create("cb") combined_codebook <- gmoTree::codebook( path = "C:/Users/pzauchner/Nextcloud/R_analyses/oTree", fsource = "models", output = "both", output_file = "cb/cb.pdf", output_format = "pdf_document") # You can also omit *.pdf after the file name combined_codebook <- gmoTree::codebook( path = "C:/Users/pzauchner/Nextcloud/R_analyses/oTree", fsource = "models", output = "both", output_file = "cb/cb", output_format = "pdf_document") ## End(Not run)
Delete specific cases from all data frames in the oTree list.
Caution 1: This function does not delete cases from the original CSV and Excel files!
Caution 2: This function does not delete cases from custom exports and custom data frames if these data frames do not have a variable named participant.code!
Caution 3: This function does not delete any data from
the $Chats data frame!
(As the interpretation of chat data depends on how participants
engage with each other, the data must be deleted
with more care than deleting data in other apps.
Hence, this function does not delete data in this data frame.
Please do this manually if necessary!)
delete_cases( oTree, pcodes = NULL, plabels = NULL, saved_vars = NULL, reason, omit = FALSE, info = FALSE )delete_cases( oTree, pcodes = NULL, plabels = NULL, saved_vars = NULL, reason, omit = FALSE, info = FALSE )
oTree |
A list of data frames that were created
by |
pcodes |
Character. The value(s) of the participant.code variable of the participants whose data should be removed. |
plabels |
Character. The value(s) of the participant.label variable of the participants whose data should be removed. |
saved_vars |
Character. The name(s) of variable(s) that need(s) to be
stored in the list of information on deleted cases
in |
reason |
Character. The reason for deletion that should be stored in
the list of information on deleted cases in |
omit |
Logical. |
info |
Logical. |
This function returns a duplicate of the original oTree list of data frames that do not include the deleted cases.
It adds information on the deleted cases to $info$deleted_cases. (This
list is also filled by other functions.)
In this list, you can find the following information:
- $codes = A vector with the participant codes of all deleted cases.
- $count = The number of participants in $codes.
- $full and $unique = The data frames $full and $unique contain information on each deleted participant and the reason why they were deleted. The entries to the $full and the $unique data frames are the same. Columns "end_app" and "end_page" are left empty intentionally because they are only filled by the delete_dropouts function.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Delete only one case oTree2 <- delete_cases(oTree, pcodes = "xmxl46rm", reason = "requested") # Show changes in row numbers print(paste("Row numbers before deletion: ", nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) print(paste("Row numbers after deletion: ", nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete several cases deletionlist <- c("4zhzdmzo", "xmxl46rm") oTree2 <- delete_cases(oTree, pcodes = deletionlist, reason = "requested") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Show information on all deleted cases (also dropouts): oTree2$info$deleted_cases$full # Save one variable oTree2 <- delete_cases(oTree, pcodes = deletionlist, reason = "requested", saved_vars = "participant._index_in_pages") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Check the "full" deletion information oTree2$info$deleted_cases$full # Save some variables oTree2 <- delete_cases(oTree, pcodes = deletionlist, reason = "requested", saved_vars = c( "participant._index_in_pages", "participant._max_page_index")) # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Check the "full" deletion information oTree2$info$deleted_cases$full # Get a list of all deleted cases # (If there is already a list, the new list is added to it) oTree2$info$deleted_cases$codes # Show number of all deleted cases length(oTree2$info$deleted_cases$codes) oTree2$info$deleted_cases$count # Delete a session and delete a single case from another session oTree2 <- delete_sessions(oTree, scodes = c("vd1h01iv"), reason = "Server Crash", saved_vars = "dictator.1.group.id_in_subsession") oTree2 <- delete_cases(oTree2, pcodes = "4zhzdmzo", reason = "requested") # Check the "full" deletion information oTree2$info$deleted_cases$full # See codes of deleted variables oTree2$info$deleted_cases$codes # See number of deleted variables oTree2$info$deleted_cases$count# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Delete only one case oTree2 <- delete_cases(oTree, pcodes = "xmxl46rm", reason = "requested") # Show changes in row numbers print(paste("Row numbers before deletion: ", nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) print(paste("Row numbers after deletion: ", nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete several cases deletionlist <- c("4zhzdmzo", "xmxl46rm") oTree2 <- delete_cases(oTree, pcodes = deletionlist, reason = "requested") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Show information on all deleted cases (also dropouts): oTree2$info$deleted_cases$full # Save one variable oTree2 <- delete_cases(oTree, pcodes = deletionlist, reason = "requested", saved_vars = "participant._index_in_pages") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Check the "full" deletion information oTree2$info$deleted_cases$full # Save some variables oTree2 <- delete_cases(oTree, pcodes = deletionlist, reason = "requested", saved_vars = c( "participant._index_in_pages", "participant._max_page_index")) # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Check the "full" deletion information oTree2$info$deleted_cases$full # Get a list of all deleted cases # (If there is already a list, the new list is added to it) oTree2$info$deleted_cases$codes # Show number of all deleted cases length(oTree2$info$deleted_cases$codes) oTree2$info$deleted_cases$count # Delete a session and delete a single case from another session oTree2 <- delete_sessions(oTree, scodes = c("vd1h01iv"), reason = "Server Crash", saved_vars = "dictator.1.group.id_in_subsession") oTree2 <- delete_cases(oTree2, pcodes = "4zhzdmzo", reason = "requested") # Check the "full" deletion information oTree2$info$deleted_cases$full # See codes of deleted variables oTree2$info$deleted_cases$codes # See number of deleted variables oTree2$info$deleted_cases$count
Delete the data of all participants who did not end the experiment at (a) certain page(s) and/or app(s).
Caution 1: This function does not delete cases from the original CSV and Excel files!
Caution 2: This function does not delete cases from custom exports if the
custom exports do not have a variable named participant.code and
a variable named session.code!
Caution 3: This function does not delete any data from
the $Chats data frame!
(As the interpretation of chat data depends on how participants engage with
each other, the data must be deleted with more care than deleting data in
other apps. Hence, this function does not delete data in this data frame.
Please do this manually if necessary!)
delete_dropouts( oTree, final_apps = NULL, final_pages = NULL, saved_vars = NULL, inconsistent = NULL, reason = "ENC", info = FALSE )delete_dropouts( oTree, final_apps = NULL, final_pages = NULL, saved_vars = NULL, inconsistent = NULL, reason = "ENC", info = FALSE )
oTree |
A list of data frames that were created
by |
final_apps |
Character. The name(s) of the app(s) at which the participants have to finish the experiment. |
final_pages |
Character. The name(s) of the page(s) at which the participants have to finish the experiment. |
saved_vars |
Character. The name(s) of variable(s) that need(s) to be
stored in the list of information on deleted cases
in |
inconsistent |
Character. Should the function continue or be stopped if
at least one participant has inconsistent end_pages, inconsistent end_apps,
or both? To continue, type |
reason |
Character. The reason for deletion that should be stored in
the list of information on deleted cases in |
info |
Logical. |
This function returns a duplicate of the original oTree list of data frames but without the deleted cases.
It adds information on the deleted cases to $info$deleted_cases. (This
list is also filled by other functions.)
In this list, you can find the following information:
- $full = A data frame that contains information
on all participants who did not finish the study;
it shows their participant codes, the names of the apps in which they
left the experiment,
the names of the pages in which they left the experiment,
the names of the app data frames in which this information was found, and
the dropout reason ("ENC", experiment not completed, combined
with the name of the data frame in which the dropout was observed).
Because participants usually appear in multiple app data frames,
the $info$deleted_cases$full data frame may contain several entries
for each person.
- $unique = A data frame that contains similar information as
the $full data frame but with only one row per participant and
no information on the data frame in which the dropout was observed.
- $all_end = A table that provides information on the app and page
combinations where participants ended the experiment.
This table also includes information for participants who did not drop out
of the experiment.
The $all_end table is only shown if an $all_apps_wide data
frame exists.
- $codes = A vector containing the participant codes of
all deleted participants.
- $count = The number of all deleted participants.
It is important to note that if only the argument final_pages is set,
this function does not distinguish between page names that reoccur in
different apps.
If the columns end_app and end_page in the output are empty,
these variables were not saved by oTree for the specific participants.
This could be because empty rows were not deleted. This can be done
by using the argument del_empty = TRUE when
using import_otree.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # First, show some row numbers print(paste(nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) # Delete all cases that didn't end the experiment on the page "Demographics" # within the app "survey" oTree2 <- delete_dropouts(oTree, final_apps = c("survey"), final_pages = c("Demographics")) # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete all cases that didn't end the experiment on the page "Demographics" # This page can be in any app oTree2 <- delete_dropouts(oTree, final_pages = "Demographics") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete all cases that didn't end the experiment on # any page in the app "survey" oTree <- delete_dropouts(oTree, final_apps = "survey") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Get list of information on all deleted cases # (If there is already a list, the new list is added to it!) oTree2$info$deleted_cases# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # First, show some row numbers print(paste(nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) # Delete all cases that didn't end the experiment on the page "Demographics" # within the app "survey" oTree2 <- delete_dropouts(oTree, final_apps = c("survey"), final_pages = c("Demographics")) # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete all cases that didn't end the experiment on the page "Demographics" # This page can be in any app oTree2 <- delete_dropouts(oTree, final_pages = "Demographics") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete all cases that didn't end the experiment on # any page in the app "survey" oTree <- delete_dropouts(oTree, final_apps = "survey") # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Get list of information on all deleted cases # (If there is already a list, the new list is added to it!) oTree2$info$deleted_cases
Delete duplicate rows from all oTree app data frames
and $all_apps_wide.
delete_duplicate(oTree)delete_duplicate(oTree)
oTree |
A list of data frames that were created
by |
This function returns a duplicate of the original oTree
list of data frames but without duplicate rows in all app data
frames and $all_apps_wide. This function has no effect on the data
frames $Time and $Chats.
This function does NOT add information to $info$deleted_cases,
because it does not delete any important information but only
cleans up a messy data import.
However, the function adjusts $info$initial_n, if
an $all_apps_wide data frame exists.
# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() }) # First, show some row numbers print(paste(nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) # Delete duplicate rows oTree <- delete_duplicate(oTree) # Show row numbers again print(paste(nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats)))# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() }) # First, show some row numbers print(paste(nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) # Delete duplicate rows oTree <- delete_duplicate(oTree) # Show row numbers again print(paste(nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats)))
If you work with MTurk, the MTurk IDs will be stored in the
participant labels variable.
This function deletes this variable in $all_apps_wide
and every app data frame in the list of data frames that was created
by import_otree and/or all
variables referring to MTurk, such as participant.mturk_worker_id.
Caution: This function does not delete the variables from the original CSV and Excel files!
delete_plabels(oTree, del_plabel = TRUE, del_mturk = TRUE)delete_plabels(oTree, del_plabel = TRUE, del_mturk = TRUE)
oTree |
A list of data frames that were created
by |
del_plabel |
Logical. |
del_mturk |
Logical. |
This function returns a duplicate of the original oTree list of data frames that do not include the participant labels and/or the MTurk variables.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show participant labels oTree$all_apps_wide$participant.label oTree$survey$participant.label # Delete all participant labels oTree2 <- delete_plabels(oTree) # Show participant labels again oTree2$all_apps_wide$participant.label oTree2$survey$participant.label# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show participant labels oTree$all_apps_wide$participant.label oTree$survey$participant.label # Delete all participant labels oTree2 <- delete_plabels(oTree) # Show participant labels again oTree2$all_apps_wide$participant.label oTree2$survey$participant.label
Delete cases from specific sessions in all data frames in the oTree list of data frames.
Caution 1: This function does not delete cases from the original CSV and Excel files!
Caution 2: This function does not delete cases from custom exports if the
custom exports do not have a variable named participant.code
and a variable named session.code!
delete_sessions(oTree, scodes, saved_vars = NULL, reason, info = FALSE)delete_sessions(oTree, scodes, saved_vars = NULL, reason, info = FALSE)
oTree |
A list of data frames that were created
by |
scodes |
Character. The session.code(s) of the session(s) whose data should be removed. |
saved_vars |
Character. The name(s) of variable(s) that need(s) to be
stored in the list of information on deleted cases
in |
reason |
Character. The reason for deletion that should be stored in
the list of information on deleted cases in |
info |
Logical. |
This function returns a duplicate of the original oTree list of data frames that do not include the deleted sessions.
It adds information on the deleted cases to $info$deleted_cases.
(This list is also filled by other functions.)
In this list, you can find the following information:
- $full and $unique = The data frames $full
and $unique contain
information on all participants
whose data were deleted. The entries to the $full
and the $unique data
frames in this list are the same. Columns end_app
and end_page are left
empty intentionally because they are only filled by
the delete_dropouts
function. Columns participant.code and reason are filled.
- $codes = A vector containing the participant codes of
all deleted participants.
- $count = The number of all deleted participants.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Delete one session oTree2 <- delete_sessions(oTree, scodes = "7bfqtokx", reason = "Only tests") # Show changes in row numbers print(paste("Row numbers before deletion: ", nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) print(paste("Row numbers after deletion: ", nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete two sessions and show deletion message oTree2 <- delete_sessions(oTree, scodes = c("7bfqtokx", "vd1h01iv"), reason = "Only tests", info = TRUE) # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete session and save variable to the info list oTree2 <- delete_sessions(oTree, scodes = c("7bfqtokx", "vd1h01iv"), reason = "Server Crash", saved_vars = "dictator.1.group.id_in_subsession") # Check the "full" deletion information oTree2$info$deleted_cases$full # See codes of deleted variables oTree2$info$deleted_cases$codes # See number of deleted variables oTree2$info$deleted_cases$count # Delete a single case and then delete a session oTree2 <- delete_cases(oTree, pcodes = "4zhzdmzo", reason = "requested") oTree2 <- delete_sessions(oTree2, scodes = c("vd1h01iv"), reason = "Server Crash", saved_vars = "dictator.1.group.id_in_subsession") # Check the "full" deletion information oTree2$info$deleted_cases$full # See codes of deleted variables oTree2$info$deleted_cases$codes # See number of deleted variables oTree2$info$deleted_cases$count# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Delete one session oTree2 <- delete_sessions(oTree, scodes = "7bfqtokx", reason = "Only tests") # Show changes in row numbers print(paste("Row numbers before deletion: ", nrow(oTree$all_apps_wide), nrow(oTree$survey), nrow(oTree$Time), nrow(oTree$Chats))) print(paste("Row numbers after deletion: ", nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete two sessions and show deletion message oTree2 <- delete_sessions(oTree, scodes = c("7bfqtokx", "vd1h01iv"), reason = "Only tests", info = TRUE) # Show row numbers again print(paste(nrow(oTree2$all_apps_wide), nrow(oTree2$survey), nrow(oTree2$Time), nrow(oTree2$Chats))) # Delete session and save variable to the info list oTree2 <- delete_sessions(oTree, scodes = c("7bfqtokx", "vd1h01iv"), reason = "Server Crash", saved_vars = "dictator.1.group.id_in_subsession") # Check the "full" deletion information oTree2$info$deleted_cases$full # See codes of deleted variables oTree2$info$deleted_cases$codes # See number of deleted variables oTree2$info$deleted_cases$count # Delete a single case and then delete a session oTree2 <- delete_cases(oTree, pcodes = "4zhzdmzo", reason = "requested") oTree2 <- delete_sessions(oTree2, scodes = c("vd1h01iv"), reason = "Server Crash", saved_vars = "dictator.1.group.id_in_subsession") # Check the "full" deletion information oTree2$info$deleted_cases$full # See codes of deleted variables oTree2$info$deleted_cases$codes # See number of deleted variables oTree2$info$deleted_cases$count
Calculate the time spent on the experiment. If not stated otherwise, the calculation only starts at the end of the first page!
extime( oTree, pcode = NULL, plabel = NULL, group_id = NULL, seconds = FALSE, rounded = TRUE, digits = 2L, startat = 1L, tz = "UTC", sinfo = "session_code", combine = TRUE )extime( oTree, pcode = NULL, plabel = NULL, group_id = NULL, seconds = FALSE, rounded = TRUE, digits = 2L, startat = 1L, tz = "UTC", sinfo = "session_code", combine = TRUE )
oTree |
A list of data frames that were created
by |
pcode |
Character. The value of the |
plabel |
Character. The value of the |
group_id |
Integer. The value of the group_id variable if the
time should only be calculated for one specified group. The |
seconds |
Logical. |
rounded |
Logical. |
digits |
Integer. The number of digits to which the output
should be rounded.
This parameter has no effect unless |
startat |
Integer or character string |
tz |
Character. Time zone. |
sinfo |
Character. |
combine |
Logical. |
This functions calculates the time spent on the experiment by using
the variable that refers to the time stamp. If that variable is not
present, the function alternatively uses seconds_on_page2,
which can be created with the pagesec function.
This function returns either a single value if only the data of one person is calculated or a list of information on the time several participants spent on the experiment.
In this list, you can find the following information:
- $mean_duration = The experiment's average duration.
- $min_duration = The experiment's minimum duration.
- $max_duration = The experiment's maximum duration.
- $single_durations = A data frame of all durations that
are used for calculating the min, max, and mean duration.
- $messages = All important notes to the calculations.
- $only_one_page = A vector of all individuals who
only have one time stamp.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show time for one participant extime(oTree, pcode = "wk247s9w") # Make a data frame of durations extime(oTree) # Make a data frame of durations (beginning from the end of the second page) extime(oTree, startat = 2)# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show time for one participant extime(oTree, pcode = "wk247s9w") # Make a data frame of durations extime(oTree) # Make a data frame of durations (beginning from the end of the second page) extime(oTree, startat = 2)
Import data files that were created by oTree.
All files containing the pattern YYYY-MM-DD at the end
of their file names are considered oTree files.
Bot outputs are saved by oTree without the date included. Hence, to
import bot data, you must either rename the original bot files
using the YYYY-MM-DD format or use the argument onlybots = TRUE.
By using the second option, only data of bot files are imported.
Caution! Data can be downloaded from within the
session and globally at the same time. If both files are downloaded,
this can lead to the $all_apps_wide data being there twice! You can
remove duplicate data by using delete_duplicate.
Caution! When importing Excel files, this function does not check
for erroneous data structures
and will combine all data frames with the same file name patterns.
Before using the CSV = FALSE argument,
clean up your data appropriately.
import_otree( path = ".", file_names = NULL, final_apps = NULL, final_pages = NULL, recursive = TRUE, csv = TRUE, onlybots = FALSE, del_empty = TRUE, info = FALSE, encoding = "UTF-8" )import_otree( path = ".", file_names = NULL, final_apps = NULL, final_pages = NULL, recursive = TRUE, csv = TRUE, onlybots = FALSE, del_empty = TRUE, info = FALSE, encoding = "UTF-8" )
path |
Character. The path to the files (default is the working directory). |
file_names |
Character. The name(s) of the file(s) to be imported. If not specified, all files in the path and subfolders are imported. |
final_apps |
Character.
The name(s) of the app(s) at which the participants have to finish the
experiment. If the argument final_apps is left empty, you can still call
for deleting the participants who did not finish the experiment
with |
final_pages |
Character.
The name(s) of the page(s) at which the participants have to finish the
experiment. If the argument final_pages is left empty, you can still
call for deleting the participants who did not finish the experiment
with |
recursive |
Logical. |
csv |
Logical. |
onlybots |
Logical. |
del_empty |
Logical. |
info |
Logical. |
encoding |
Character. Encoding of the CSV files that are imported.
Default is |
Returns a list of data frames (one data frame for each app
and $all_apps_wide) and a list of information on this list
of data frames in $info.
See detailed information on the imported files
in $info$imported_files.
If $all_apps_wide is imported, see the number of imported cases
in $info$initial_n. In this number, empty rows are
already considered. So, if empty rows are deleted
with del_empty=TRUE, initial_n
counts all rows that are not empty.
Cases that are deleted because the participants did not make it to the
last page and/or app are not subtracted from this number.
Information: Empty rows are rows without
the participant._current_app_name
variable set. Empty rows are deleted from all app data frames
and $all_apps_wide when using del_empty = TRUE. Empty rows in
the $Chats and $Time data frames are not deleted.
If old and new oTree versions are combined, the $Time data frame
contains variables called participant_code
and participant__code (the difference is in the underscores).
Caution! If there is an unusual amount of NAs,
check if everything got imported correctly.
Sometimes, the CSV or Excel file may be corrupted, and all information is
only found in one column.
# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() # Show the structure of the import str(oTree, max.level = 1) # Show the names of all imported files oTree$info$imported_files # Delete empty cases and delete every case of a person # who didn't end the experiment in the app "survey" oTree <- import_otree( del_empty = TRUE, final_apps = "survey", info = TRUE) # Show the structure of the import str(oTree, max.level = 1) # Import bot files import_otree( path = "./bot_data", onlybots = TRUE, csv = TRUE, info = TRUE) # Show the structure of the import str(oTree, max.level = 1) # Import with file names (path separately) oTree2 <- import_otree( del_empty = TRUE, path = "./exp_data", file_names = c("all_apps_wide-2023-03-27.csv", "ChatMessages-2023-03-27.csv", "PageTimes-2023-03-27.csv"), onlybots = FALSE, csv = TRUE, info = TRUE) # Show the structure of the import str(oTree, max.level = 1) # Import with file names (without path separately) oTree2 <- import_otree( del_empty = TRUE, file_names = c("exp_data/all_apps_wide-2023-03-27.csv", "exp_data/ChatMessages-2023-03-27.csv", "exp_data/PageTimes-2023-03-27.csv"), onlybots = FALSE, csv = TRUE, info = TRUE) # Show the structure of the import str(oTree, max.level = 1) })# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() # Show the structure of the import str(oTree, max.level = 1) # Show the names of all imported files oTree$info$imported_files # Delete empty cases and delete every case of a person # who didn't end the experiment in the app "survey" oTree <- import_otree( del_empty = TRUE, final_apps = "survey", info = TRUE) # Show the structure of the import str(oTree, max.level = 1) # Import bot files import_otree( path = "./bot_data", onlybots = TRUE, csv = TRUE, info = TRUE) # Show the structure of the import str(oTree, max.level = 1) # Import with file names (path separately) oTree2 <- import_otree( del_empty = TRUE, path = "./exp_data", file_names = c("all_apps_wide-2023-03-27.csv", "ChatMessages-2023-03-27.csv", "PageTimes-2023-03-27.csv"), onlybots = FALSE, csv = TRUE, info = TRUE) # Show the structure of the import str(oTree, max.level = 1) # Import with file names (without path separately) oTree2 <- import_otree( del_empty = TRUE, file_names = c("exp_data/all_apps_wide-2023-03-27.csv", "exp_data/ChatMessages-2023-03-27.csv", "exp_data/PageTimes-2023-03-27.csv"), onlybots = FALSE, csv = TRUE, info = TRUE) # Show the structure of the import str(oTree, max.level = 1) })
Make session IDs and, optionally, group IDs and participant IDs
that span across all data frames created by import_otree.
Information for these IDs is taken from $all_apps_wide
but can be defined otherwise.
Note: Older versions of oTree may already contain a
variable called session_id in their $Time data frames.
This variable is overwritten by this function!
Important: Combine duplicate data before running this function!
make_ids( oTree, gmake = FALSE, pmake = TRUE, from_app = "all_apps_wide", from_var = NULL, sstart = 1L, gstart = 1L, pstart = 1L, emptyrows = NULL, icw = FALSE )make_ids( oTree, gmake = FALSE, pmake = TRUE, from_app = "all_apps_wide", from_var = NULL, sstart = 1L, gstart = 1L, pstart = 1L, emptyrows = NULL, icw = FALSE )
oTree |
A list of data frames that were created
by |
gmake |
Logical. |
pmake |
Logical. |
from_app |
Character. Name of the data frame from which the session,
group, and participant information should be taken.
All normal app data frames and |
from_var |
Character. Name of the variable from which the group
information should be taken. This argument is only relevant
when |
sstart |
Integer. The number that serves as a starting point for session IDs. |
gstart |
Integer. The number that serves as a starting point for group IDs. |
pstart |
Integer. The number that serves as a starting point for participant IDs. |
emptyrows |
Character. |
icw |
Logical. |
ID variables are made in $all_apps_wide, all app data frames,
the $Time data frame, and the $Chats data frame.
See list of the additional ID variables in $info$additional_variables.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Make session IDs only oTree2 <- make_ids(oTree) # Show new variables oTree2$all_apps_wide$session_id # Make session IDs and group IDs # Not working with this data set because group ID is not the same in all apps ## Not run: oTree2 <- make_ids(oTree, gmake = TRUE) # Show new variables oTree2$all_apps_wide$session_id oTree2$all_apps_wide$group_id ## End(Not run) # Get IDs from variable "dictator.1.group.id_in_subsession" # in the data frame "all_apps_wide" oTree2 <- make_ids(oTree, gmake = TRUE, from_var = "dictator.1.group.id_in_subsession") # Show new variables oTree2$all_apps_wide$session_id oTree2$all_apps_wide$group_id # Get IDs from another app than all_apps_wide oTree2 <- make_ids(oTree, gmake = TRUE, from_app = "dictator") # Show new variables oTree2$all_apps_wide$session_id oTree2$all_apps_wide$group_id# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Make session IDs only oTree2 <- make_ids(oTree) # Show new variables oTree2$all_apps_wide$session_id # Make session IDs and group IDs # Not working with this data set because group ID is not the same in all apps ## Not run: oTree2 <- make_ids(oTree, gmake = TRUE) # Show new variables oTree2$all_apps_wide$session_id oTree2$all_apps_wide$group_id ## End(Not run) # Get IDs from variable "dictator.1.group.id_in_subsession" # in the data frame "all_apps_wide" oTree2 <- make_ids(oTree, gmake = TRUE, from_var = "dictator.1.group.id_in_subsession") # Show new variables oTree2$all_apps_wide$session_id oTree2$all_apps_wide$group_id # Get IDs from another app than all_apps_wide oTree2 <- make_ids(oTree, gmake = TRUE, from_app = "dictator") # Show new variables oTree2$all_apps_wide$session_id oTree2$all_apps_wide$group_id
Check if the $Chats data frame includes both
session-related variables and participant-related variables that
appear multiple times. This may occur when data from different
oTree versions, which use different variable names, are combined.
If desired, the function can merge these variables, storing the data using the newer oTree version's variable names and removing the outdated variables.
messy_chat( oTree, combine = FALSE, session = TRUE, participant = TRUE, info = FALSE )messy_chat( oTree, combine = FALSE, session = TRUE, participant = TRUE, info = FALSE )
oTree |
A list of data frames that were created
by |
combine |
Logical. |
session |
Logical. |
participant |
Logical. |
info |
Logical. |
This function searches for multiple variables related to the session code
or the participant code in the $Chats data frame.
which can occur when data from both old and new oTree versions are used.
If combine = FALSE, the function will throw an error
if such variables are found.
If combine = TRUE, the function will not throw an error
if such variables are found.
Instead, it automatically combines the variables into new variables
and adds them to the original $Chats data frame.
This function then returns a duplicate of the original oTree list but
with the $Chats data frame modified.
The new variables are called
participant_code and session_code.
# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() }) # Show all Chats column names print(colnames(oTree$Chats)) # Run function oTree <- messy_chat(oTree, combine = TRUE) # Show all Chats column names again print(colnames(oTree$Chats))# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() }) # Show all Chats column names print(colnames(oTree$Chats)) # Run function oTree <- messy_chat(oTree, combine = TRUE) # Show all Chats column names again print(colnames(oTree$Chats))
Checks if the Time data frame includes both participant-related variables and time stamp variables that appear multiple times. This may occur when data from different oTree versions, which use different variable names, are combined.
If desired, the function can merge these variables, storing the data using the newer oTree version's variable names and removing the outdated variables.
messy_time( oTree, combine = FALSE, epoch_time = TRUE, participant = TRUE, info = FALSE )messy_time( oTree, combine = FALSE, epoch_time = TRUE, participant = TRUE, info = FALSE )
oTree |
A list of data frames that were created
by |
combine |
Logical. |
epoch_time |
Logical. |
participant |
Logical. |
info |
Logical. |
This function searches for multiple variables related to the time stamp
or the participant code in the $Time data frame,
which can occur when data from both old and new oTree versions are used.
If combine = FALSE, the function will throw an error
if such variables are found.
If combine = TRUE, the function will not throw an error
if such variables are found.
Instead, it automatically combines the variables into new variables
and adds them to the original $Time data frame.
This function then returns a duplicate of the original oTree list but
with the $Time data frame modified.
The new variables are
called epoch_time_completed and participant_code.
# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() }) # Show all Time column names print(colnames(oTree$Time)) # Run function oTree <- messy_time(oTree, combine = TRUE) # Show all Time column names again print(colnames(oTree$Time))# Set data folder first withr::with_dir(system.file("extdata", package = "gmoTree"), { # Import all oTree files in this folder and its subfolders oTree <- import_otree() }) # Show all Time column names print(colnames(oTree$Time)) # Run function oTree <- messy_time(oTree, combine = TRUE) # Show all Time column names again print(colnames(oTree$Time))
Sample experimental data
oTreeoTree
A list of data frames created by import_otree().
The data set was created by using modified versions of the official oTree sample experiments that can be downloaded when installing oTree. In detail, the following apps were used: "start," "dictator," "chatapp," "survey."
Create a new variable in the $Time data frame that contains the time
spent on each page.
pagesec(oTree, rounded = TRUE, digits = 2, minutes = FALSE, combine = FALSE)pagesec(oTree, rounded = TRUE, digits = 2, minutes = FALSE, combine = FALSE)
oTree |
A list of data frames that were created
by |
rounded |
Logical. |
digits |
Integer. The number of digits to which the
output should be rounded.
This parameter has no effect unless |
minutes |
Logical. |
combine |
Logical. |
This function returns a duplicate of the original oTree list of
data frames that also contains a column in the $Time data frame
named seconds_on_page2 or minutes_on_page.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Create two new columns: seconds_on_page2 and minutes_on_page oTree <- pagesec(oTree, rounded = TRUE, minutes = TRUE) # Show the Time data frame head(oTree$Time, n = 30)# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Create two new columns: seconds_on_page2 and minutes_on_page oTree <- pagesec(oTree, rounded = TRUE, minutes = TRUE) # Show the Time data frame head(oTree$Time, n = 30)
Show all columns with no variation in their values for each data frame in the oTree list of data frames (except the ones in the info list). This function is helpful before running an experiment to check if there are unnecessary variables. You can check for columns that have any unchanging possible value or for columns containing only a specific value.
show_constant(oTree, value = "any")show_constant(oTree, value = "any")
oTree |
A list of data frames that were created
by |
value |
The value that is controlled to be the same within a column.
If the value is set to |
This function returns a list of vectors, one for each app,
$all_apps_wide, the $Time and/or the $Chats data frame.
Each vector contains the names of all variables with constant values.
If there are no variables with constant values, the vector is empty.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show all columns that contain only NAs show_constant(oTree = oTree) show_constant(oTree = oTree, value = NA) # Show all columns that contain only -99 show_constant(oTree = oTree, value = -99)# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show all columns that contain only NAs show_constant(oTree = oTree) show_constant(oTree = oTree, value = NA) # Show all columns that contain only -99 show_constant(oTree = oTree, value = -99)
Show information on the people who did not finish the experiment at (a) certain page(s) and/or app(s).
show_dropouts(oTree, final_apps = NULL, final_pages = NULL, saved_vars = NULL)show_dropouts(oTree, final_apps = NULL, final_pages = NULL, saved_vars = NULL)
oTree |
A list of data frames that were created
by |
final_apps |
Character. The name(s) of the app(s) at which the participants have to finish the experiment. |
final_pages |
Character. The name(s) of the page(s) at which the participants have to finish the experiment. |
saved_vars |
The name(s) of variable(s) that need(s) to be shown in the list of information on dropout cases. |
This function returns a list of information on participants who did not finish the experiment.
In this list, you can find the following information:
- $full = A data frame that contains information
on all participants who did not finish the study;
it shows their participant codes, the names of the apps in which they
left the experiment,
the names of the pages in which they left the experiment,
the names of the app data frames in which this information was found, and
the dropout reason ("ENC", experiment not completed, combined
with the name of the data frame in which the dropout was observed).
Because participants usually appear in multiple app data frames,
the $full data frame may contain several entries for
each person.
- $unique = A data frame that contains similar information as
the $full data frame but with only one row per participant and
no information on the data frame in which the dropout was observed.
- $all_end = A table that provides information
on the app and page combinations
where participants ended the experiment. This table also includes
information on participants who did not drop out of the experiment.
The $all_end table is only shown if an $all_apps_wide
data frame exists.
- $codes = A vector containing the participant codes of
all participants who did not finish the experiment.
- $count = The number of all participants who did not
finish the experiment.
It is important to note that if only the argument final_pages is set,
this function does not distinguish between page names that reoccur in
different apps.
If the columns end_app and end_page in the output are empty,
these variables were not saved by oTree for the specific participants.
This could be because empty rows were not deleted. This can be done
by using the argument del_empty = TRUE" when
using import_otree.
# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show everyone who did not finish with the app "survey" show_dropouts(oTree, final_apps = "survey") # Show everyone who did not finish with the page "Demographics" show_dropouts(oTree, final_pages = "Demographics") # Show everyone who finished with the following apps: "survey," "dictator" final_apps <- unique(oTree$all_apps_wide$participant._current_app_name) final_apps <- final_apps[final_apps != "survey"] final_apps <- final_apps[final_apps != "dictator"] show_dropouts(oTree, final_apps = final_apps)# Use package-internal list of oTree data frames oTree <- gmoTree::oTree # Show everyone who did not finish with the app "survey" show_dropouts(oTree, final_apps = "survey") # Show everyone who did not finish with the page "Demographics" show_dropouts(oTree, final_pages = "Demographics") # Show everyone who finished with the following apps: "survey," "dictator" final_apps <- unique(oTree$all_apps_wide$participant._current_app_name) final_apps <- final_apps[final_apps != "survey"] final_apps <- final_apps[final_apps != "dictator"] show_dropouts(oTree, final_apps = final_apps)