对于dplyr和tidyr包，您可以使用
pivot_longer(cols = everything(), # 变长 names_to = c(".value","group"), names_pattern = "(.)(.)" ) %>% mutate(group = as.factor(group)) # 组别变为因子型 glimpse(df13_11) # 查看数据结构,神奇! ## Rows: 90 ## Columns: 3 ## $ group <fct> 1, 2, 3, 1, 2, ...
Dplyr Join two tables join 函数用于根据指定的键将两个数据框连接起来,可以根据共同的变量将数据框进行合并,支持多种连接操作,如内连接、左连接、右连接和外连接等。...Dplyr Rename columns rename 函数用于重命名数据框中的变量名,能够快速修改变量的名称,使得数据的列名更符合用户的需求和...
names_to=c(".value","group"), names_pattern="(.)(.)" )%>% mutate(group=as.factor(group))# 组别变为因子型 glimpse(df13_11)# 查看数据结构,神奇! ## Rows: 90 ## Columns: 3 ## $ group <fct> 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, ...
# 导入dplyr包 library(dplyr) # 创建一个空的dataframe df <- data.frame() # 定义需要创建的列的值 values <- c(1, 2, 3, 4, 5) # 使用mutate()函数创建新的列,并将其添加到dataframe中 df <- df %>% mutate(new_column = values * 2) # 这里以乘以2为例 # 打印输出dataframe print(df)...
To learn more about the dataset, see help(mtcars). Transpose The transpose (reversing rows and columns) is perhaps the simplest method of reshaping a dataset. Use the t() function to transpose a matrix or a data frame. In the latter case, row names become variable (column) names. An ...
The rows are by default lexicographically sorted on the common columns, but for ‘sort = FALSE’ are in an unspecified order. Or put differently: sort=FALSE doesn’t preserve the order of any of the two entered data.frame objects (x or y); instead it gives us an unspecified (potentially...
# ... with 22 more rows This table is the same as thedata.tableoutput, except that the naming conventions for the created columns are a little different. Because we might be doing this gather-unite-spread step quite often, it’d be useful to have a function to bundle up the steps for...
If you are used to subset dataframes with square brackets, [], instead of the subset() or filter() functions, you may have faced the error “Error in [.data.frame(…) : undefined columns selected”. This occurs when R does not understand the column you want to use while subsetting ...
names_to = c(".value","group"), names_pattern = "(.)(.)" ) %>% mutate(group = as.factor(group)) # 组别变为因子型 glimpse(df13_11) # 查看数据结构,神奇! ## Rows: 90 ## Columns: 3 ## $ group <fct> 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, ...