library(tidyverse) library(readxl) library(caret) data1 <- read_csv("111.csv") data2 <- na.omit(data1) print(data1) dim(data2) head(data2)

这不是一个问题，而是一段 R 代码。这段代码的作用是加载 tidyverse、readxl 和 caret 三个 R 包，然后读取名为 "111.csv" 的 CSV 文件，将其存储在 data1 变量中。接着使用 na.omit() 函数删除 data1 中的缺失值，并将结果存储在 data2 变量中。最后打印出 data1 的内容，输出 data2 的维度和前几行内容。如果您有任何具体问题或者需要解释，请告诉我。

相关问题

运行library(caret) library(neuralnet) data <- read.csv("housing_renamed.csv") data$year_built[is.na(data$year_built)] <- median(data$year_built, na.rm = TRUE) predictors <- subset(data, select = -c(value_per_sq_ft)) response <- data$value_per_sq_ft ctrl <- trainControl(method = "cv", number = 3) model1 <- train(response ~ ., data = predictors, method = "neuralnet", trControl = ctrl) model2 <- train(response ~ ., data = predictors, method = "neuralnet", trControl = ctrl) print(model1) print(model2)出现Something is wrong; all the RMSE metric values are missing:报错

这个错误可能是由于数据中存在缺失值导致的。你需要检查数据中是否存在其他缺失值，并对其进行处理。另外，你可以尝试在train函数中添加一个参数preProcess，将其设置为"impute"，以对缺失值进行处理。例如：

model1 <- train(response ~ ., data = predictors, method = "neuralnet",
                trControl = ctrl, preProcess = "impute")

你可以尝试这个方法来解决这个错误。

将https://www.kaggle.com/code/startupsci/titanic-data-science-solutions的python代码转换成R语言实现数据处理、可视化与建模过程

好的，我可以为您提供将该项目的Python代码转换为R语言代码的实现过程，以下是详细步骤：

1. 导入数据：

Python代码：

train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
combine = [train_df, test_df]

R语言代码：

train_df <- read.csv("../input/titanic/train.csv", header = TRUE)
test_df <- read.csv("../input/titanic/test.csv", header = TRUE)
combine <- list(train_df, test_df)

2. 数据清洗和特征工程：

Python代码：

# 填充缺失值
for dataset in combine:
    dataset['Age'].fillna(dataset['Age'].median(), inplace=True)
    dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace=True)
    dataset['Fare'].fillna(dataset['Fare'].median(), inplace=True)

# 将分类变量转换为数值变量
for dataset in combine:
    dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
    dataset['Embarked'] = dataset['Embarked'].map({'S': 0, 'C': 1, 'Q': 2}).astype(int)

# 创建新特征
for dataset in combine:
    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
    dataset['IsAlone'] = 0
    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1

# 删除无用特征
drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp', 'Parch', 'FamilySize']
train_df = train_df.drop(drop_elements, axis=1)
test_df = test_df.drop(drop_elements, axis=1)

R语言代码：

# 填充缺失值
for (dataset in combine) {
  dataset$Age[is.na(dataset$Age)] <- median(dataset$Age, na.rm = TRUE)
  dataset$Embarked[is.na(dataset$Embarked)] <- names(which.max(table(dataset$Embarked)))
  dataset$Fare[is.na(dataset$Fare)] <- median(dataset$Fare, na.rm = TRUE)
}

# 将分类变量转换为数值变量
for (dataset in combine) {
  dataset$Sex <- as.integer(factor(dataset$Sex, levels = c("male", "female")))
  dataset$Embarked <- as.integer(factor(dataset$Embarked, levels = c("S", "C", "Q")))
}

# 创建新特征
for (dataset in combine) {
  dataset$FamilySize <- dataset$SibSp + dataset$Parch + 1
  dataset$IsAlone <- 0
  dataset$IsAlone[dataset$FamilySize == 1] <- 1
}

# 删除无用特征
drop_elements <- c("PassengerId", "Name", "Ticket", "Cabin", "SibSp", "Parch", "FamilySize")
train_df <- train_df[, !(names(train_df) %in% drop_elements)]
test_df <- test_df[, !(names(test_df) %in% drop_elements)]

3. 数据可视化：

Python代码：

import matplotlib.pyplot as plt
import seaborn as sns

# 绘制直方图
sns.histplot(train_df['Age'], kde=False)
plt.show()

# 绘制条形图
sns.barplot(x='Sex', y='Survived', data=train_df)
plt.show()

# 绘制热力图
corr = train_df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.show()

R语言代码：

library(ggplot2)
library(reshape2)

# 绘制直方图
ggplot(train_df, aes(x = Age)) + 
  geom_histogram(binwidth = 5, fill = "lightblue", col = "black") + 
  labs(title = "Age Distribution", x = "Age", y = "Count")

# 绘制条形图
ggplot(train_df, aes(x = Sex, y = Survived, fill = factor(Sex))) + 
  geom_bar(stat = "summary", fun = mean, position = "dodge") + 
  scale_fill_manual(values = c("lightblue", "pink"), name = "Sex") + 
  labs(title = "Survival Rate by Sex", x = "Sex", y = "Survival Rate")

# 绘制热力图
cor_matrix <- cor(train_df)
melted_cor_matrix <- melt(cor_matrix)
ggplot(melted_cor_matrix, aes(x = Var1, y = Var2, fill = value)) + 
  geom_tile() + 
  scale_fill_gradient2(low = "lightblue", mid = "white", high = "pink") + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
  labs(title = "Correlation Matrix")

4. 建立模型：

Python代码：

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier

# 数据预处理
X_train = train_df.drop('Survived', axis=1)
y_train = train_df['Survived']
X_test = test_df.drop('Survived', axis=1)

# 逻辑回归模型
logreg = LogisticRegression()
logreg_scores = cross_val_score(logreg, X_train, y_train, cv=10)
print('Logistic Regression Accuracy: {:.2f}%'.format(logreg_scores.mean()*100))

# 随机森林模型
rf = RandomForestClassifier(n_estimators=100)
rf_scores = cross_val_score(rf, X_train, y_train, cv=10)
print('Random Forest Accuracy: {:.2f}%'.format(rf_scores.mean()*100))

R语言代码：

library(caret)

# 数据预处理
X_train <- train_df[, !(names(train_df) %in% c("Survived"))]
y_train <- train_df$Survived
X_test <- test_df[, !(names(test_df) %in% c("Survived"))]

# 逻辑回归模型
logreg_model <- train(x = X_train, y = y_train, method = "glm", family = "binomial")
logreg_scores <- logreg_model$results$Accuracy
print(paste0("Logistic Regression Accuracy: ", round(mean(logreg_scores)*100, 2), "%"))

# 随机森林模型
rf_model <- train(x = X_train, y = y_train, method = "rf", ntree = 100)
rf_scores <- rf_model$results$Accuracy
print(paste0("Random Forest Accuracy: ", round(mean(rf_scores)*100, 2), "%"))

以上是将该项目的Python代码转换为R语言代码的过程，您可以在Kaggle的R语言环境中运行这段代码，完成数据处理、可视化和建模的过程。