How to handle data in the R language?

When processing data in the R language, common operations include data cleaning, data transformation, data filtering, data aggregation, data visualization, etc. Here are some commonly used data processing operations:

1. Data cleaning involves deleting missing values, handling outliers, and removing duplicates.

# 删除缺失值
na.omit(data)

# 处理异常值
data <- data[data$column_name < 100, ]

# 处理重复值
data <- unique(data)

1. Data transformation: variable recoding, variable grouping, variable conversion, etc.

# 变量重编码
data$column_name <- ifelse(data$column_name == "A", 1, 0)

# 变量分组
data$group <- cut(data$column_name, breaks = c(0, 50, 100), labels = c("low", "high"))

# 变量转换
data$column_name <- as.numeric(data$column_name)

1. Data filtering: Selecting data based on conditions.

# 根据条件筛选数据
data_subset <- subset(data, column_name > 50)

1. Data aggregation involves performing statistical analysis on data.

# 按照某一列进行分组并计算平均值
aggregate(data$column_name, by = list(data$group), FUN = mean)

1. Data visualization: using packages such as ggplot2 for data visualization.

# 使用ggplot2进行散点图可视化
library(ggplot2)
ggplot(data, aes(x = column1, y = column2)) + geom_point()

These are common operations used in data processing in R language, and can be combined based on specific needs and data characteristics for data processing.