filter_if(mtcars, ~ all(floor(.) == .), all_vars(. != 0)) 更多用法: mtcars %>% filter_all(all_vars(.>150)) %>% head() # 筛选所有变量均大于150的行,结果为空 mtcars %>% filter_all(any_vars(.>150)) %>% head() # 筛选存在变量大于150的行, # 针对变量名称为d开头的所有列,...
is.na(type_of_conflict)], default = NA)), max_type_of_conflict = max(type_of_conflict, na.rm = TRUE), avg_issue_count_per_year = mean(issue_count_per_year, na.rm = TRUE), obs_years_per_episode = n(), # nrows per episode across(all_of(issue_columns), ~ sum(.x, na.rm...
使用rowSums对每行求和（rowwise适用于任何聚合，但速度较慢）
使用rowSums对每行求和（rowwise适用于任何聚合，但速度较慢）
This example groups the data by the department and state columns, then aggregates the remaining columns by applying the sum and mean functions to them.# Summarise all columns except grouping columns df2<- df[,c("department","state","age","salary","bonus")] agg_tbl <- df2 %>% group_...
dplyr 定义了数据处理的规范语法,其中主要包含以下10个主要的函数。 mutate select rename filter summarise group_by arrange left_join right_join full_join select选择列 select(df,1)selects the first columnselect(df,last_col())selects the last columnselect(df,c(a,b,c))selects columns a,b,and ...
df_summary <- df %>% group_by(分类cols) %>% summarize(总计数值cols = sum(总计数值cols)) 在上述代码中,group_by函数用于按照分类cols对数据进行分组,summarize函数用于对每个分组计算总计数值cols的和。最终,汇总结果将保存在df_summary数据框中。
Example 1: Sums of Columns Using dplyr Package In this Example, I’ll explain how to use the replace,is.na, summarise_all, andsumfunctions. You can see the colSums in the previous output: The column sum of x1 is 15, the column sum of x2 is 7, the column sum of x3 is 35, and...
# 类似MS_SQL中的sum(s)over(orderbyy) # cumany(), cumall(),则是逻辑判断,并非计算数值 cumall(-5:5) #TRUETRUETRUETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSE# 返回的是逻辑值,0代表FALSE AI代码助手复制代码 最后说一下"判断"函数系列,这类函数应用比较广泛。
# Warning message: Unknown columns: `vss` # Tips: select没找到额变量,系统会返回警告# 匹配关系:筛选Iris数据集,变量名中带有"wid"的变量名>select(iris, matches(".wid."))>select(iris, contains("wid"))# Sepal.Width Petal.Width# 3.5 0.2# 3.0 0.2# num_range能高效匹配变量名称类似x01, x02...