diff --git a/.DS_Store b/.DS_Store
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index 0000000000000000000000000000000000000000..3f5991bb2ac324dd8e23b185d0fbc96299e0db44
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diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5b6a0652566d10360493952aec6d4a4febc77083
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
diff --git a/README.md b/README.md
index f832dcab8f91e261a0ee8eb2daf400d1528eb143..84d86517f062d2c6db999c33b24a37bc7af3fdfc 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,9 @@
# PHILIPPE-iihm
+## Auteur : PHILIPPE Lucas
+## Structure
-## Getting started
+Le dossier `TP1` comprend le TP1 et le TP2 (expérience contrôlée).
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.univ-lille.fr/lucas.philippe.etu/philippe-iihm.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.univ-lille.fr/lucas.philippe.etu/philippe-iihm/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+Le dossier `TP3` comprend l'analyse des résultats.
\ No newline at end of file
diff --git a/TP1/.DS_Store b/TP1/.DS_Store
index 3021d6e961b7ddf8b91823c11496546e2b0ba4fd..c221fa5cb0a0fe926a5b28f7b9d5ecb5d59d7d47 100644
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diff --git a/TP1/__pycache__/ExpSetup.cpython-39.pyc b/TP1/__pycache__/ExpSetup.cpython-39.pyc
index e9a51bd6c6a2c58cc96513723bb414ab55cc8943..c1009dba01c74ffdcdcca748b44bd8e79a064208 100644
Binary files a/TP1/__pycache__/ExpSetup.cpython-39.pyc and b/TP1/__pycache__/ExpSetup.cpython-39.pyc differ
diff --git a/TP1/data/response.csv b/TP1/data/response.csv
index 1e7a1a198e20ad7e0c8f708c1e05854a734b245e..065e80e3d63d9309eec73372241739e404a6b210 100644
--- a/TP1/data/response.csv
+++ b/TP1/data/response.csv
@@ -3238,3 +3238,8 @@
1603, 1, 13, 90, 18, NORMAL, 1612.5524044036865, 0
1603, 1, 14, 90, 18, NORMAL, 1196.0630416870117, 1
1603, 1, 15, 90, 18, NORMAL, 909.4836711883545, 0
+9, 0, 1, 30, 9, BUBBLE, 24034.497022628784, 0
+9, 0, 2, 30, 9, BUBBLE, 1729.8812866210938, 0
+9, 0, 3, 30, 9, BUBBLE, 897.9692459106445, 0
+9, 0, 4, 30, 9, BUBBLE, 2222.6080894470215, 0
+9, 0, 5, 30, 9, BUBBLE, 1909.3399047851562, 0
diff --git a/TP2/README.md b/TP2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..76348970306e5b81a13f1099f7c11a61f1a3e12f
--- /dev/null
+++ b/TP2/README.md
@@ -0,0 +1,115 @@
+# Expérience contrôlée et analyse des résultats :
+
+En premier lieu, il faut ajouter en en-tête du fichier response.csv :
+
+`numero utilisateur, repetition, numero sequence, densite, taille, technique, temps, erreur`
+
+Ensuite, dans R, il faut faire ceci pour importer le csv :
+
+```
+library(readr)
+data <- read_csv("response.csv")
+```
+
+Si `readr` n'est pas installé, il faut faire :
+
+```
+install.packages("tidyverse")
+```
+
+Pour pouvoir voir la data il faut faire :
+
+```
+View(data)
+```
+
+Avant d'utiliser la data, il faut marquer les colonnes adéquates comme des facteurs et supprimer les lignes ou l'utilisateur a fait des erreurs.
+
+La commande pour marquer les colonnes adéquates comme des facteurs :
+
+```
+data_clean$numero_utilisateur <- as.factor(data_clean$numero_utilisateur)
+data_clean$densite <- as.factor(data_clean$densite)
+data_clean$taille <- as.factor(data_clean$taille)
+data_clean$technique <- as.factor(data_clean$technique)
+data_clean$repetition <- as.factor(data_clean$repetition)
+```
+
+La commande pour retirer les erreurs des utilisateurs, et les temps d'oubli des utilisateurs > 10s:
+
+```
+data_clean <- data[data$erreur == 0 & data$temps <= 10000, ]
+```
+
+Pour les graphiques, je ne vais pas parler de temps de réponse mais de temps de réponse moyen.
+
+Pour pouvoir faire cela, voici un exemple :
+
+```
+participant_means <- data_clean %>%
+ group_by(numero_utilisateur) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+```
+
+Voici le temps de réponse moyen entre participants.
+
+
+
+Sur ce graphique, chaque barre représente un participant avec son temps moyen.
+On constate qu'il y a une différence du temps moyen entre chaque utilisateur.
+Cela dépend à la réactivité de chacun ainsi à la précision pour éviter les fautes.
+
+Voici le temps de réponse moyen entre blocs/répétitions.
+
+
+
+Sur ce graphique, la barre de gauche représente la répétition 0 et la barre de droite la répétition 1.
+On peut constater que le temps moyen est plus faible pour la répétition 1, on peut donc dire qu'il y a un effet d'apprentissage étant donné que la séquence des 15 bulles à sélectionner reste le même.
+
+Voici le temps de réponse moyen entre techniques.
+
+
+
+Sur ce graphique, chaque barre correspond aux trois techniques.
+On peut constater ici que les techniques Bubble et Rope sont plus ou moins similaires en terme de temps moyen, et la technique Normal a un temps moyen bien plus elevé que les deux autres.
+Cela peut s'expliquer car Bubble et Rope n'ont pas besoin d'être dans la bulle pour être sélectionné, il suffit juste que le curseur soit le plus proche de la bulle.
+Tandis que la technique Normal doit forcément être sélectionné à l'intérieur de la bulle.
+
+Voici le temps moyen de réponse entre densités.
+
+
+
+Sur ce graphique, chaque barre correspond aux trois densités différentes (30, 60, 90).
+On peut constater ici que il n'y a pas de différence notable sur le temps moyen lorsque l'on passe de 30 à 60 de densités. Par contre, avec 90 on peut constater que le temps moyen augmente comparer aux densités de 30 et 60.
+On peut expliquer cela parce que plus il y a de bulles dans l'application, plus il faut être précis.
+
+Voici le temps moyen de réponse entre chaque tailles
+
+
+
+Sur ce graphique, chaque barre correspond aux trois tailles différentes de bulles (9, 12 et 18).
+On peut constater ici que plus la taille des bulles augmentent, plus le temps moyen des réponses est réduit.
+On peut expliquer cela par le fait que plus la taille des bulles est grosses, moins il faut être précis pour cliquer.
+
+
+Voici le temps moyen de réponse entre techniques et densités
+
+
+
+Sur ce graphique, on peut voir qu'on a trois groupements de barres (correspondant aux trois densités : 30, 60, 90) qui contient trois barres (correspondant aux techniques : Bubble, Normal, Rope).
+On peut constater que le changement de densité affecte certaines techniques lorsqu'il y a plus de bulles/cibles.
+On peut en effet constater une augmentation du temps moyen de réponse pour les techniques Bubble et Rope, alors que Normal stagne sur les trois densités.
+On peut expliquer cela car Bubble et Rope sont deux techniques n'ayant pas besoin d'être dans la bulle/cible pour être sélectionner, mais il faut être le plus proche possible. Le fait d'avoir plus de bulles/cibles entraînent donc de la précision supplémentaire pour ces deux techniques.
+
+Voici le temps moyen de réponse entre techniques et tailles de cibles.
+
+
+
+Sur ce graphique, on peut voir qu'on a trois groupements de barres (correspondant aux tailles des bulles/cibles) qui contient trois barres (correspondant aux techniques : Bubble, Normal, Rope).
+On peut constater que le changement de taille a un impact majeur de la diminution de temps moyen de réponse pour les trois techniques.
+On peut donc en déduire que plus la cible est grande, moins l'utilisateur doit être précis, ce qui raccourcit donc le temps moyen des réponses.
+
+Conclusion :
+
+Pour conclure, pour avoir le temps de réponse le plus bas possible, il faudrait optimiser les facteurs importants comme la densité, la taille et la technique choisit.
+Pour pouvoir être le plus efficace possible, il faudrait une taille des bulles/cibles plus grandes, une densité plutôt faible (avec une densité elevé, cela pourrait augmenter la précision et donc le temps de réponse), et plutôt choisir un style de technique comme Bubble ou Rope (de sorte à choisir la cible/bulle la plus proche et non devoir sélectionner la cible).
\ No newline at end of file
diff --git a/TP2/TP2.R b/TP2/TP2.R
new file mode 100644
index 0000000000000000000000000000000000000000..53e8fbf703adcdf65e351869eac60acd221ef271
--- /dev/null
+++ b/TP2/TP2.R
@@ -0,0 +1,150 @@
+library(readr)
+library(ggplot2)
+library(RColorBrewer)
+library(dplyr)
+data <- read_csv("response.csv")
+
+#View(data)
+
+data$numero_sequence <- NULL
+
+summary(data)
+
+data_clean <- data[data$erreur == 0 & data$temps <= 10000, ]
+
+View(data_clean)
+
+summary(data_clean)
+
+data_clean$numero_utilisateur <- as.factor(data_clean$numero_utilisateur)
+data_clean$densite <- as.factor(data_clean$densite)
+data_clean$taille <- as.factor(data_clean$taille)
+data_clean$technique <- as.factor(data_clean$technique)
+data_clean$repetition <- as.factor(data_clean$repetition)
+
+levels(data_clean$numero_utilisateur)
+summary(data_clean$numero_utilisateur)
+
+levels(data_clean$technique)
+summary(data_clean$technique)
+
+levels(data_clean$densite)
+summary(data_clean$densite)
+
+levels(data_clean$taille)
+summary(data_clean$taille)
+
+levels(data_clean$repetition)
+summary(data_clean$repetition)
+
+participant_means <- data_clean %>%
+ group_by(numero_utilisateur) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+
+rep_between_participants <- ggplot(participant_means, aes(x=numero_utilisateur, y=mean_time, fill=numero_utilisateur)) +
+ geom_bar(stat="identity", position="dodge") +
+ scale_fill_brewer(palette = "Set1") +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse entre participants", x="Participant ID", y="Temps de réponse moyen (ms)")
+
+ggsave("mean_rep_between_participants.jpg", plot = rep_between_participants, width = 11, height = 8, units = "in")
+
+repetition_means <- data_clean %>%
+ group_by(repetition) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+
+rep_between_repetitions <- ggplot(repetition_means, aes(x=repetition, y=mean_time, fill=repetition)) +
+ geom_bar(stat="identity", position="dodge") +
+ scale_fill_brewer(palette = "Set1") +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse entre répétitions", x="Répétition", y="Temps de réponse moyen (ms)")
+
+ggsave("mean_rep_between_repetitions.jpg", plot = rep_between_repetitions, width = 11, height = 8, units = "in")
+
+
+data_summary <- data_clean %>%
+ group_by(technique) %>%
+ summarise(temps_moyen = mean(temps, na.rm = TRUE))
+
+rep_between_techniques <- ggplot(data_summary, aes(x=technique, y=temps_moyen, fill=technique)) +
+ geom_bar(stat="identity", position="dodge") +
+ scale_fill_brewer(palette = "Set1") +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse par technique",
+ x="Technique",
+ y="Temps de réponse moyen (ms)")
+
+ggsave("mean_rep_between_techniques.jpg", plot = rep_between_techniques, width = 11, height = 8, units = "in")
+
+densite_means <- data_clean %>%
+ group_by(densite) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+
+rep_between_densities <- ggplot(densite_means, aes(x=densite, y=mean_time, fill=densite)) +
+ geom_col() +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse par densité",
+ x="Densité",
+ y="Temps de réponse moyen (ms)") +
+ theme(legend.position="none")
+
+ggsave("mean_rep_between_densities.jpg", plot = rep_between_densities, width = 11, height = 8, units = "in")
+
+taille_means <- data_clean %>%
+ group_by(taille) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+
+rep_between_tailles <- ggplot(taille_means, aes(x=taille, y=mean_time, fill=taille)) +
+ geom_col() +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse par taille de cible",
+ x="Taille de cible",
+ y="Temps de réponse moyen (ms)") +
+ theme(legend.position="none")
+
+ggsave("mean_rep_between_tailles.jpg", plot = rep_between_tailles, width = 11, height = 8, units = "in")
+
+technique_densite_means <- data_clean %>%
+ group_by(technique, densite) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+
+rep_between_techniques_densities <- ggplot(technique_densite_means, aes(x=densite, y=mean_time, fill=technique)) +
+ geom_col(position = position_dodge()) +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse par technique et densité",
+ x="Densité",
+ y="Temps de réponse moyen (ms)") +
+ scale_fill_brewer(palette = "Set1") +
+ theme(legend.title = element_blank(), legend.position = "right")
+
+ggsave("mean_rep_between_techniques_densities.jpg", plot = rep_between_techniques_densities, width = 11, height = 8, units = "in")
+
+technique_taille_means <- data_clean %>%
+ group_by(technique, taille) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+
+rep_between_techniques_tailles <- ggplot(technique_taille_means, aes(x=taille, y=mean_time, fill=technique)) +
+ geom_col(position = position_dodge()) +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse par technique et taille de cible",
+ x="Taille de cible",
+ y="Temps de réponse moyen (ms)") +
+ scale_fill_brewer(palette = "Set1") +
+ theme(legend.title = element_blank(), legend.position = "right")
+
+ggsave("mean_rep_between_techniques_tailles.jpg", plot = rep_between_techniques_tailles, width = 11, height = 8, units = "in")
+
+technique_taille_means <- data_clean %>%
+ group_by(technique, taille) %>%
+ summarise(mean_time = mean(temps, na.rm = TRUE))
+
+rep_between_techniques_tailles <- ggplot(technique_taille_means, aes(x=taille, y=mean_time, fill=technique)) +
+ geom_col(position = position_dodge()) +
+ theme_minimal() +
+ labs(title="Moyenne du temps de réponse par technique et taille de cible",
+ x="Taille de cible",
+ y="Temps de réponse moyen (ms)") +
+ scale_fill_brewer(palette = "Set1") +
+ theme(legend.title = element_blank(), legend.position = "right")
+
+ggsave("mean_rep_between_techniques_tailles.jpg", plot = rep_between_techniques_tailles, width = 11, height = 8, units = "in")
diff --git a/TP2/TP2.Rproj b/TP2/TP2.Rproj
new file mode 100644
index 0000000000000000000000000000000000000000..8e3c2ebc99e2e337f7d69948b93529a437590b27
--- /dev/null
+++ b/TP2/TP2.Rproj
@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
diff --git a/TP2/mean_rep_between_densities.jpg b/TP2/mean_rep_between_densities.jpg
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index 0000000000000000000000000000000000000000..01f1ccec031c62cb5d0a11948d9405b8206a1406
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diff --git a/TP2/mean_rep_between_techniques_tailles.jpg b/TP2/mean_rep_between_techniques_tailles.jpg
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diff --git a/TP2/response.csv b/TP2/response.csv
new file mode 100644
index 0000000000000000000000000000000000000000..12cb2a9381823872c16d509ff3eb3dfe8793c6df
--- /dev/null
+++ b/TP2/response.csv
@@ -0,0 +1,3241 @@
+numero_utilisateur, repetition, numero_sequence, densite, taille, technique, temps, erreur
+28, 0, 1, 30, 9, BUBBLE, 2484.447956085205, 0
+28, 0, 2, 30, 9, BUBBLE, 862.4460697174072, 0
+28, 0, 3, 30, 9, BUBBLE, 753.0219554901123, 0
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