Refactor code for improved readability and consistency across R Markdown files

- Updated comments and code formatting in `3-td_ggplot2 - enonce.Rmd` for clarity.
- Enhanced code structure in `4-td_graphiques - enonce.Rmd` by organizing options and library calls.
- Replaced pipe operator `%>%` with `|>` in `Code_Lec3.Rmd` for consistency with modern R syntax.
- Cleaned up commented-out code and ensured consistent spacing in ggplot calls.

L3/Analyse Multidimensionnelle/DM ACP/DM_ACP.Rmd

@@ -44,11 +44,10 @@ notes_MAN <- read.table("notes_MAN.csv", sep = ";", dec = ",", row.names = 1, he
 # qui est une variable catégorielle
 notes_MAN_prep <- notes_MAN[, -1]
 
-X <- notes_MAN[1:6,] %>% select(c("Probas", "Analyse", "Anglais", "MAN.Stats", "Stats.Inférentielles"))
+X <- notes_MAN[1:6, ] |> select(c("Probas", "Analyse", "Anglais", "MAN.Stats", "Stats.Inférentielles"))
 # on prépare le jeu de données en retirant la colonne des Mentions
 # qui est une variable catégorielle
 # View(X)
-
 ```
 
 ```{r}
@@ -101,7 +100,7 @@ C[, 1:2]
 deux premières composantes principales (1 point)
 
 ```{r}
-colors <- c('blue', 'red', 'green', 'yellow', 'purple', 'orange')
+colors <- c("blue", "red", "green", "yellow", "purple", "orange")
 plot(
   C[, 1], C[, 2],
   main = "Coordonnées des individus par rapport \n aux deux premières composantes principales",
@@ -111,7 +110,7 @@ plot(
   col = colors,
   pch = 15
 )
-legend(x = 'topleft', legend = rownames(X), col = colors, pch = 15)
+legend(x = "topleft", legend = rownames(X), col = colors, pch = 15)
 ```
 
 ------------------------------------------------------------------------
@@ -130,7 +129,7 @@ ncol(notes_MAN_prep) # Nombre de variables
 ```
 
 ```{r}
-dim(notes_MAN_prep) # On peut également utiliser 'dim' qui renvoit la dimension 
+dim(notes_MAN_prep) # On peut également utiliser 'dim' qui renvoit la dimension
 ```
 
 Il y a donc **42** individus et **14** variables. A noter que la
@@ -146,7 +145,7 @@ library(FactoMineR)
 ```{r}
 # Ne pas oublier de charger la librairie FactoMineR
 
-# Indication : pour afficher les résultats de l'ACP pour tous les individus, utiliser la 
+# Indication : pour afficher les résultats de l'ACP pour tous les individus, utiliser la
 # fonction summary en précisant dedans nbind=Inf et nbelements=Inf
 res.notes <- PCA(notes_MAN_prep, scale.unit = TRUE)
 ```
@@ -190,7 +189,7 @@ avec:
 Depuis notre ACP, on peut donc récupérer les coordonnées:
 
 ```{r}
-coords_man_stats <- res.notes$var$coord["MAN.Stats",]
+coords_man_stats <- res.notes$var$coord["MAN.Stats", ]
 coords_man_stats[1:2]
 ```
 

M1/General Linear Models/TP1-bis/TP1.Rmd

@@ -1,5 +1,5 @@
 ```{r}
-setwd('/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP1-bis')
+setwd("/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP1-bis")
 
 library(tidyverse)
 options(scipen = 999, digits = 5)
@@ -56,8 +56,8 @@ summary(model)
 coef(model)
 ```
 ```{r}
-data <- data %>%
+data <- data |>
-  mutate(yhat = beta0 + beta1 * poids) %>%
+  mutate(yhat = beta0 + beta1 * poids) |>
   mutate(residuals = cholesterol - yhat)
 
 data
@@ -71,8 +71,8 @@ ggplot(data, aes(x = poids, y = cholesterol)) +
 ```{r}
 mean(data[, "cholesterol"])
 mean(data[, "yhat"])
-mean(data[, "residuals"]) %>% round(10)
+mean(data[, "residuals"]) |> round(10)
-cov(data[, "residuals"], data[, "poids"]) %>% round(10)
+cov(data[, "residuals"], data[, "poids"]) |> round(10)
 (RSS <- sum((data[, "residuals"])^2))
 (TSS <- sum((y - mean(y))^2))
 TSS - beta1 * Sxy
@@ -117,10 +117,10 @@ t <- qt(0.975, dof)
 sigma_hat <- sigma(model)
 n <- nrow(data)
 
-data <- data %>%
+data <- data |>
   mutate(error = t *
     sigma_hat *
-    sqrt(1 / n + (poids - mean(poids))^2 / RSS)) %>%
+    sqrt(1 / n + (poids - mean(poids))^2 / RSS)) |>
   mutate(conf.low = yhat - error, conf.high = yhat + error, error = NULL)
 
 ggplot(data, aes(x = poids, y = cholesterol)) +

M1/General Linear Models/TP2-bis/TP2.Rmd

@@ -1,5 +1,5 @@
 ```{r}
-setwd('/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP2-bis')
+setwd("/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP2-bis")
 
 library(tidyverse)
 library(GGally)
@@ -10,9 +10,9 @@ library(qqplotr)
 options(scipen = 999, digits = 5)
 ```
 ```{r}
-data <- read.csv('data02.csv', sep = ',', header = TRUE, dec = ".")
+data <- read.csv("data02.csv", sep = ",", header = TRUE, dec = ".")
-data %>%
+data |>
-  mutate(type = factor(type, levels = c("maths", "english", "final"), labels = c("maths", "english", "final"))) %>%
+  mutate(type = factor(type, levels = c("maths", "english", "final"), labels = c("maths", "english", "final"))) |>
   ggplot(aes(x = note)) +
   facet_wrap(vars(type), scales = "free_x") +
   geom_histogram(binwidth = 4, color = "black", fill = "grey80") +
@@ -21,8 +21,8 @@ data %>%
 ```
 ```{r}
 data_wide <- pivot_wider(data, names_from = type, values_from = note)
-data_wide %>%
+data_wide |>
-  select(-id) %>%
+  select(-id) |>
   ggpairs() + theme_bw(14)
 ```
 ```{r}
@@ -67,12 +67,12 @@ linearHypothesis(model, "maths - english = 0")
 
 # Submodel testing
 ```{r}
-data_predict <- predict(model, newdata = expand.grid(maths = seq(70, 90, 2), english = c(75, 85)), interval = "confidence") %>%
+data_predict <- predict(model, newdata = expand.grid(maths = seq(70, 90, 2), english = c(75, 85)), interval = "confidence") |>
-  as_tibble() %>%
+  as_tibble() |>
   bind_cols(expand.grid(maths = seq(70, 90, 2), english = c(75, 85)))
 
-data_predict %>%
+data_predict |>
-  mutate(english = as.factor(english)) %>%
+  mutate(english = as.factor(english)) |>
   ggplot(aes(x = maths, y = fit, color = english, fill = english, label = round(fit, 1))) +
   geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.2, show.legend = FALSE) +
   geom_point(size = 2) +

M1/General Linear Models/TP2/TP2.rmd

@@ -1,5 +1,5 @@
 ```{r}
-setwd('/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP2')
+setwd("/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP2")
 ```
 
 # Question 1 : Import dataset and check variables
@@ -9,8 +9,8 @@ library(dplyr)
 cepages <- read.csv("Cepages B TP2.csv", header = TRUE, sep = ";", dec = ",")
 cepages$Couleur <- as.factor(cepages$Couleur)
 cepages$Origine <- as.factor(cepages$Origine)
-cepages <- cepages %>% mutate(across(where(is.character), as.numeric))
+cepages <- cepages |> mutate(across(where(is.character), as.numeric))
-cepages <- cepages %>% mutate(across(where(is.integer), as.numeric))
+cepages <- cepages |> mutate(across(where(is.integer), as.numeric))
 paged_table(cepages)
 ```
 
@@ -39,7 +39,7 @@ tapply(cepages$pH, list(cepages$Couleur, cepages$Origine), mean)
 library(ggplot2)
 
 ggplot(cepages, aes(x = AcTot, y = pH, color = Couleur)) +
-  geom_point(col = 'red', size = 0.5) +
+  geom_point(col = "red", size = 0.5) +
   geom_smooth(method = "lm", se = F)
 
 ggplot(cepages, aes(y = pH, x = AcTot, colour = Couleur, fill = Couleur)) +
@@ -50,8 +50,8 @@ ggplot(cepages, aes(y = pH, x = AcTot, colour = Couleur, fill = Couleur)) +
 
 ```{r}
 ggplot(cepages, aes(x = AcTot, y = pH, color = Origine)) +
-  geom_smooth(method = 'lm', se = F) +
+  geom_smooth(method = "lm", se = F) +
-  geom_point(col = 'red', size = 0.5)
+  geom_point(col = "red", size = 0.5)
 
 ggplot(cepages, aes(y = pH, x = AcTot, colour = Origine, fill = Origine)) +
   geom_boxplot(alpha = 0.5, outlier.alpha = 0)

M1/General Linear Models/TP3/TP3.rmd

@@ -1,5 +1,5 @@
 ```{r}
-setwd('/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP3')
+setwd("/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP3")
 ```
 
 # Question 1 : Import dataset and check variables
@@ -9,8 +9,8 @@ library(dplyr)
 ozone <- read.table("ozone.txt", header = TRUE, sep = " ", dec = ".")
 ozone$vent <- as.factor(ozone$vent)
 ozone$temps <- as.factor(ozone$temps)
-ozone <- ozone %>% mutate(across(where(is.character), as.numeric))
+ozone <- ozone |> mutate(across(where(is.character), as.numeric))
-ozone <- ozone %>% mutate(across(where(is.integer), as.numeric))
+ozone <- ozone |> mutate(across(where(is.integer), as.numeric))
 paged_table(ozone)
 ```
 
@@ -25,8 +25,8 @@ summary(model_T12)
 library(ggplot2)
 
 ggplot(ozone, aes(x = T12, y = maxO3)) +
-  geom_smooth(method = 'lm', se = T) +
+  geom_smooth(method = "lm", se = T) +
-  geom_point(col = 'red', size = 0.5) +
+  geom_point(col = "red", size = 0.5) +
   labs(title = "maxO3 ~ T12") +
   theme_minimal()
 ```
@@ -130,5 +130,4 @@ new_obs <- list(
   maxO3v = 85
 )
 predict(model_backward, new_obs, interval = "confidence")
-
 ```

M1/General Linear Models/TP4/TP4.rmd

@@ -1,5 +1,5 @@
 ```{r}
-setwd('/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP4')
+setwd("/Users/arthurdanjou/Workspace/studies/M1/General Linear Models/TP4")
 
 set.seed(0911)
 library(ggplot2)
@@ -22,19 +22,19 @@ library(lmtest) # LRtest
 library(survey) # Wald test
 library(vcdExtra) # deviance test
 
-library(rsample)   # for data splitting
+library(rsample) # for data splitting
 library(glmnet)
 library(nnet) # multinom, glm
 library(caret)
 library(ROCR)
-#library(PRROC) autre package pour courbe roc et courbe pr
+# library(PRROC) autre package pour courbe roc et courbe pr
 library(ISLR) # dataset for statistical learning
 
-ggplot2::theme_set(ggplot2::theme_light())# Set the graphical theme
+ggplot2::theme_set(ggplot2::theme_light()) # Set the graphical theme
 ```
 ```{r}
-car <- read.table('car_income.txt', header = TRUE, sep = ';')
+car <- read.table("car_income.txt", header = TRUE, sep = ";")
-car %>% rmarkdown::paged_table()
+car |> rmarkdown::paged_table()
 summary(car)
 ```
 
@@ -44,7 +44,7 @@ summary(model_purchase)
 ```
 
 ```{r}
-p1 <- car %>%
+p1 <- car |>
   ggplot(aes(y = purchase, x = income + age)) +
   geom_point(alpha = .15) +
   geom_smooth(method = "lm") +
@@ -53,7 +53,7 @@ p1 <- car %>%
   ylab("Probability of Purchase")
 
 
-p2 <- car %>%
+p2 <- car |>
   ggplot(aes(y = purchase, x = income + age)) +
   geom_point(alpha = .15) +
   geom_smooth(method = "glm", method.args = list(family = "binomial")) +
@@ -66,9 +66,9 @@ ggplotly(p2)
 ```
 
 ```{r}
-car <- car %>%
+car <- car |>
   mutate(old = ifelse(car$age > 3, 1, 0))
-car <- car %>%
+car <- car |>
   mutate(rich = ifelse(car$income > 40, 1, 0))
 model_old <- glm(purchase ~ age + income + rich + old, data = car, family = "binomial")
 summary(model_old)
@@ -90,5 +90,5 @@ pima.te$pred <- as.factor(pima.te$pred)
 pima.te$type <- as.factor(pima.te$type)
 
 # Confusion matrix
-confusionMatrix(data = pima.te$type, reference = pima.te$pred, positive = 'Yes')
+confusionMatrix(data = pima.te$type, reference = pima.te$pred, positive = "Yes")
 ```

M2/Data Visualisation/tp1/3-td_ggplot2 - enonce.Rmd

@@ -297,7 +297,7 @@ On présente ci-dessous un aperçu des données.
 fold <- getwd()
 
 # Load data
-# load(paste0(fold, "/M2/Data Visualisation/tp1", "/data/datafreMPTL.RData")) # VSCode # nolint
+# load(paste0(fold, "/M2/Data Visualisation/tp1", "/data/datafreMPTL.RData")) # VSCode
 load(paste0(fold, "/data/datafreMPTL.RData")) # RStudio
 paged_table(dat, options = list(rows.print = 15))
 ```
@@ -505,7 +505,7 @@ df_plot <- dat |>
 p3 <- ggplot(df_plot, aes(x = DrivAge, y = freq)) +
   geom_point() +
   geom_smooth() +
-  labs(x = "Age du conducteur", y = "Frequence")  +
+  labs(x = "Age du conducteur", y = "Frequence") +
   theme_bw()
 p3
 ```
@@ -642,12 +642,16 @@ plot_pairwise_disc <- function(df, var1, var2) {
 
   df |>
     group_by(varx, vary) |>
-    summarize(exp = sum(Exposure),
+    summarize(
-              nb_claims = sum(ClaimNb),
+      exp = sum(Exposure),
-              freq = sum(ClaimNb) / sum(Exposure), .groups = "drop") |>
+      nb_claims = sum(ClaimNb),
+      freq = sum(ClaimNb) / sum(Exposure), .groups = "drop"
+    ) |>
     ggplot(aes(x = varx, y = freq, colour = vary, group = vary), alpha = 0.3) +
-    geom_point() + geom_line() + theme_bw() +
+    geom_point() +
-    labs(x = var1,  y = "Frequence", colour = var2)
+    geom_line() +
+    theme_bw() +
+    labs(x = var1, y = "Frequence", colour = var2)
 }
 ```
 

M2/Data Visualisation/tp2/4-td_graphiques - enonce.Rmd

@@ -23,8 +23,13 @@ editor_options:
 
 ```{r setup, include=FALSE}
 ## Global options
-knitr::opts_chunk$set(cache = FALSE, warning = FALSE, message = FALSE, fig.retina = 2)
+knitr::opts_chunk$set(
-options(encoding = 'UTF-8')
+  cache = FALSE,
+  warning = FALSE,
+  message = FALSE,
+  fig.retina = 2
+)
+options(encoding = "UTF-8")
 ```
 
 
@@ -33,11 +38,11 @@ options(encoding = 'UTF-8')
 library(lattice)
 library(grid)
 library(ggplot2)
-require(gridExtra)
+require(gridExtra) 
 library(locfit)
 library(scales)
 library(formattable)
-library(RColorBrewer)
+library(RColorBrewer) 
 library(plotly)
 library(dplyr)
 library(tidyr)
@@ -88,7 +93,7 @@ de vie par pays sur la période 1952-1990. Les observations ont lieu tous les 5
 Dans un premier temps, il faut installer le package et le charger. 
 
 ```{r}
-# install.packages("gapminder")
+# install.packages("gapminder") #nolint
 library(gapminder)
 ```
 
@@ -140,7 +145,7 @@ pouvez observer entre `gdpPercap` et `lifeExp`.
 :::
 
 ```{r}
-ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
+ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) + 
   geom_point()
 ```
 
@@ -158,7 +163,7 @@ visualisations permettant de comparer des distributions.
 
 ```{r}
 ggplot(data = gapminder, aes(x = lifeExp)) + 
-    geom_density()
+  geom_density()
 ```
 
 
@@ -171,16 +176,16 @@ Il faut au préalable récupérer un fond de carte (ici de l'année 2016). Nous
 les données `gapminder` de 2007.
 
 ```{r}
-library(giscoR)
+library(giscoR) 
 library(sf)
 
 world <- gisco_countries
 world <- subset(world, NAME_ENGL != "Antarctica") # Remove Antartica
 
 # Merge data
-world_df <- gapminder %>%
+world_df <- gapminder |>
   filter(year == "2007")
-world_df <- world %>%
+world_df <- world |>
   left_join(world_df, by = c("NAME_ENGL" = "country"))
 
 ggplot(world_df) +
@@ -231,7 +236,7 @@ accidents <- read_csv("data/accidentsVelo.csv",
                                        date = col_date(format = "%Y-%m-%d")))
 
 # few ajustements 
-accidents <- accidents %>%
+accidents <- accidents |>
   mutate(mois = factor(mois),
          jour = factor(jour),
          dep = factor(dep),
@@ -247,8 +252,8 @@ correct <- paste0("0", str_sub(correct, 1, 1), ":",
 accidents$hrmn[issue] <- correct
 
 # Extract hour
-accidents <- accidents %>%
+accidents <- accidents |>
-  mutate(hour = paste(date, hrmn, sep = " ")) %>%
+  mutate(hour = paste(date, hrmn, sep = " ")) |>
   mutate(hour = strptime(hour, "%Y-%m-%d %H:%M")$hour)
 
 # mapping table for french departments
@@ -327,8 +332,8 @@ library(mapview)
 library(sf)
 
 ## Remove NA
-df_map_dyn <- accidents %>%
+df_map_dyn <- accidents |>
-  filter(???) %>%
+  filter(???) |>
   na.omit()
 
 # Make map and print it
@@ -354,27 +359,27 @@ Voici un premier code à trou pour vous aider.
 
 ```{r, eval = F}
 # get french map - level nuts2
-fr <- gisco_get_nuts(resolution = "20", country = ???, nuts_level = ???) %>%
+fr <- gisco_get_nuts(resolution = "20", country = ???, nuts_level = ???) |>
   mutate(res = "20M")
 
 # Remove white-space to avoid errors.
 library(stringr)
-departements_francais <- departements_francais %>%
+departements_francais <- departements_francais |>
   mutate(dep_name = str_trim(dep_name))
 
-fr <- fr %>%
+fr <- fr |>
   mutate(NUTS_NAME = str_trim(NUTS_NAME))
 
 # Merge and remove departements outside metropolitan France
-fr_map <- fr %>%
+fr_map <- fr |>
-  left_join(???) %>%
+  left_join(???) |>
   filter(! dep %in% c("971", ???) )
 
 # count the number of accidents
 df_acc <-  ???
 
 # merge statistics with the map
-map_acc <- fr_map %>%
+map_acc <- fr_map |>
   left_join(df_acc, by = c("dep" =  "dep"))
 
 # map with all accidents

M2/Linear Models/Biaised Models/Code_Lec3.Rmd

@@ -194,11 +194,11 @@ linear.mod$results
 ```{r}
 Ytrain <- cookie.train$sugars
 dfc_train <- data.frame(ytrain = Ytrain, linear.mod = fitted(linear.mod))
-dfc_train %>% rmarkdown::paged_table()
+dfc_train |> rmarkdown::paged_table()
 ```
 
 ```{r}
-dfc_train %>%
+dfc_train |>
   ggplot(aes(x = ytrain, y = linear.mod)) +
   geom_point(size = 2, color = "#983399") +
   geom_smooth(method = "lm", color = "#389900") +
@@ -211,9 +211,9 @@ dfc_train %>%
 Ytest <- cookie.test$sugars
 dfc_test <- data.frame(ytest = Ytest)
 dfc_test$linear.mod <- predict(linear.mod, newdata = cookie.test)
-# dfc_test%>%rmarkdown::paged_table()
+# dfc_test|>rmarkdown::paged_table()
 
-dfc_test %>%
+dfc_test |>
   ggplot(aes(x = ytest, y = linear.mod)) +
   geom_point(size = 2, color = "#983399") +
   geom_smooth(method = "lm", color = "#389900") +
@@ -244,7 +244,7 @@ ggplotly(ggplot(Lasso))
 ```
 
 ```{r}
-Lasso$results %>% rmarkdown::paged_table()
+Lasso$results |> rmarkdown::paged_table()
 ```
 
 ```{r}
@@ -271,8 +271,8 @@ coef_lasso <- data.frame(
   Variable = rownames(as.matrix(coef(Lasso$finalModel, Lasso$bestTune$lambda))),
   Coefficient = as.matrix(coef(Lasso$finalModel, Lasso$bestTune$lambda))[, 1]
 )
-coef_lasso %>%
+coef_lasso |>
-  subset(Coefficient != 0) %>%
+  subset(Coefficient != 0) |>
   rmarkdown::paged_table()
 ```
 
@@ -298,7 +298,7 @@ ggplotly(ggplot(ridge))
 ```
 
 ```{r}
-ridge$results %>% rmarkdown::paged_table()
+ridge$results |> rmarkdown::paged_table()
 ```
 
 ```{r}
@@ -320,7 +320,7 @@ vip(ridge, num_features = 15)
 ```
 
 ```{r}
-data.frame(as.matrix(coef(ridge$finalModel, ridge$bestTune$lambda))) %>%
+data.frame(as.matrix(coef(ridge$finalModel, ridge$bestTune$lambda))) |>
   rmarkdown::paged_table()
 ```
 
@@ -346,7 +346,7 @@ ggplotly(ggplot(ElNet))
 ```
 
 ```{r}
-ElNet$results %>% rmarkdown::paged_table()
+ElNet$results |> rmarkdown::paged_table()
 ```
 
 ```{r}
@@ -372,8 +372,8 @@ coef_elnet <- data.frame(
   Variable = rownames(as.matrix(coef(ElNet$finalModel, ElNet$bestTune$lambda))),
   Coefficient = as.matrix(coef(ElNet$finalModel, ElNet$bestTune$lambda))[, 1]
 )
-coef_elnet %>%
+coef_elnet |>
-  subset(Coefficient != 0) %>%
+  subset(Coefficient != 0) |>
   rmarkdown::paged_table()
 ```
 
@@ -396,7 +396,7 @@ ggplotly(ggplot(pls_mod))
 ```
 
 ```{r}
-pls_mod$results %>% rmarkdown::paged_table()
+pls_mod$results |> rmarkdown::paged_table()
 ```
 
 ```{r}
@@ -412,7 +412,7 @@ vip(pls_mod, num_features = 20)
 ```
 
 ```{r}
-data.frame(Coefficients = as.matrix(coef(pls_mod$finalModel))) %>%
+data.frame(Coefficients = as.matrix(coef(pls_mod$finalModel))) |>
   rmarkdown::paged_table()
 ```
 
@@ -435,7 +435,7 @@ dTrain$ridge <- fitted(ridge)
 dTrain$ElNet <- fitted(ElNet)
 dTrain$pls <- fitted(pls_mod)
 melt.dTrain <- melt(dTrain, id = "yTrain", variable.name = "model")
-melt.dTrain %>% ggplot() +
+melt.dTrain |> ggplot() +
   aes(x = yTrain, y = value) +
   geom_smooth(method = "lm") +
   geom_point(size = 1, colour = "#983399") +
@@ -446,11 +446,11 @@ melt.dTrain %>% ggplot() +
 ```
 
 ```{r}
-dTrain %>% rmarkdown::paged_table()
+dTrain |> rmarkdown::paged_table()
 ```
 
 ```{r}
-melt.dTrain %>% rmarkdown::paged_table()
+melt.dTrain |> rmarkdown::paged_table()
 ```
 
 ### On the test set
@@ -463,10 +463,10 @@ dTest$Lasso <- predict(Lasso, newdata = cookie.test)
 dTest$ridge <- predict(ridge, newdata = cookie.test)
 dTest$ElNet <- predict(ElNet, newdata = cookie.test)
 dTest$pls <- predict(pls_mod, newdata = cookie.test)
-# dTest%>% rmarkdown::paged_table()
+# dTest|> rmarkdown::paged_table()
 melt.dTest <- melt(dTest, id = "yTest", variable.name = "model")
-# melt.dTest%>% rmarkdown::paged_table()
+# melt.dTest|> rmarkdown::paged_table()
-melt.dTest %>% ggplot() +
+melt.dTest |> ggplot() +
   aes(x = yTest, y = value) +
   geom_smooth(method = "lm") +
   geom_point(size = 1, colour = "#983399") +
@@ -491,8 +491,8 @@ RMSE <- rbind.data.frame(
 )
 names(RMSE) <- c("Train", "Test")
 row.names(RMSE) <- c("Linear", "Lasso", "Ridge", "ElNet", "PLS")
-RMSE %>%
+RMSE |>
-  kableExtra::kbl() %>%
+  kableExtra::kbl() |>
   kableExtra::kable_styling()
 ```