iris %>%select(last_col()) %>% head()# 最后一个变量 iris %>%select(last_col(offset =2)) %>% head()# 倒数第3个变量 iris %>%select(one_of(c("Petal.Length","Petal.Width"))) %>% head() iris %>% group_by(Species) %>%select(group_cols()) %>% distinct() %>% head()#...
select():选择指定的列。 mutate():添加新的列或修改现有列。 arrange():按照指定的列排序数据。 group_by():按照指定的列进行分组。 通过使用上述函数,可以根据参数数据帧中的值生成相应的dplyr参数。例如,如果参数数据帧df_params中有两列"column_name"和"condition",分别表示要操作的列名和筛选条件,...
示例代码:filter(data, condition) select(): 选择指定的列。示例代码:select(data, col1, col2, ...) mutate(): 添加新的列或修改已有列。示例代码:mutate(data, new_col = expression) arrange(): 根据指定列进行排序。示例代码:arrange(data, col1, col2, ...) group_by(): 根据指定列进行分组。
This can happen when the condition of an ifelse is an all NA logical vector (#958). mutate.rowwise_df() handles factors (#886) and correctly handles 0-row inputs (#1300). n_distinct() gains an na_rm argument (#1052). The Progress bar used by do() now respects global option ...
case_when always tests the commands even the condition don't meet #6250 opened 5 days ago by seahearman Consider updating if_else() feature funs 😆 #6243 opened 7 days ago by hadley first(), last(), and nth() should have na.rm argument feature funs 😆 #6242 opened 7 days ...
keep rows matching criteria # base R approach to view all flights on January 1flights[flights$Month==1&flights$DayofMonth==1,]# dplyr approach# note: you can use comma or ampersand to represent AND conditionfilter(flights,Month==1,DayofMonth==1)# use pipe for OR conditionfilter(flights,...
filter(tbl/data.fram, condition) and output a data.frame, filter rows/observation ```{r filter in dplyr} # Load dplyr package in a safer way if(!suppressWarnings(require(dplyr))) { install.packages('dplyr') require(dplyr) } df <- data.frame( ...
用法:select(.data, …) 与之前讲解的filter有所不同,select是筛选变量的,而filter是筛选样本集。 应用场景:假设数据存于宽表中(比如有100个变量的表),而你仅需要其中几个变量。而select的关键在于”…“的判断条件 #mtcars数据集中,筛选mpg、cyl、wt、vs,4个变量数据#mtcars[,c("mpg","cyl","wt","vs"...
So that’s howfilter()works. It’s checking logical conditions. If the logical condition or conditions are not met, then the row is filtered out. Example: city equal Austin OR city equals Houston Let’s try another example. Here, we’ll keep rows wherecityequals Austinorcityequals Houston...
select(ends_with("e")) df %>% select(ends_with("_loss")) The next major verb we look at is filter() which, surprisingly enough, filters a dataframe by row based on one or more conditions. # Filter rows to retain observations where age is greater than 30 df %>% filter(age > 30...