Note that we could apply exactly the same code to replace numeric values (such as the numbers in column x1). Furthermore, we couldreplace a value by NAinstead of a character. However, with factors it gets a bit more complicated… Example 2: Replace Factor Values in Data Frame Again, we...
values: 替换值45 对缺失值NA的处理 1、直接删除 直接采用函数na.omit()来去掉带有NA的行,也可以使用tidyr包的drop_na()函数来指定去除哪一列的NA ,drop_na(df,X1) # 去除X1列的NA 2 填充法 用其他数值填充数据框中的缺失值NA。 2.1 df[is.na(df)] #df[is.na(df)] = 0 2.2 replace_na() ...
data_new2 <- data # Duplicate data frame data_new2[col_repl] <- sapply(data_new2[col_repl], # Replace values in certain columns function(x) replace(x, x %in% val_repl, 99)) data_new2 # Print updated data frameBy running the previous R syntax, we have created Table 3, i.e. ...
dataframe.reindex(index,columns,method,fill_values)#插值方法 method 参数只能应用于行,即轴0state=['Texas','Utha','California']df.reindex(columns=state,method='ffill')#只能行插补 df.T.reindex(index=[1,6,3],fill_value=0).T#列插补技巧 ———- 三、切片与删除、增加操作与选中 dataframe实质是...
To replace zero with previous value in an R data frame column, we can use na.locf function of zoo package but to apply this we first need to replace the zero values with NA. For example, if we have a data frame called df that contains a column say Rate then we can use the below...
在介绍了缺失值处理的方法之后,我们可以得到完整的数据集,但在进行数据分析之前,还需要对数据进行整理,下面我们将介绍数据整理的相关知识。 1.数据合并 我们在R语言基础语法教程中介绍了数据合并的一般方法,即利用函数cbind()和rbind()来进行合并,但这只是对数据进行简单的连接,且要求用于合并的数据集有相同的维数,...
Returns a data frame containing the matrix of the correlation coefficients. The output has an attribute named “pvalue”, which contains the matrix of the correlation test p-values. cor_get_pval(): extract a correlation matrix p-values from an object of class cor_mat(). cor_pmat(): ...
set.seed(123) df_dup <- df %>% slice_sample(n = 60, replace = TRUE) 1. 删除行 (1) 删除重复行 用dplyr 包中的 distinct() 删除重复行(只保留第 1 个,删除其余)。 df_dup %>% distinct()也可以只根据某些列判定重复: df_dup %>% distinct(sex, math, .keep_all = TRUE) # 只根据se...
adjust_pvalue(): add an adjusted p-values column to a data frame containing statistical test p-values add_significance(): add a column containing the p-value significance level p_round(),p_format(),p_mark_significant(): rounding and formatting p-values ...
> d <-data.frame(matrix(sample(c(NA,1:4),12, replace = TRUE),4))>d X1 X2 X31NA41212NA31424NA NA4 1,把变量的缺失值替换为默认值 替换数据框中变量X1的缺失值,使用within()函数,不会修改数据框的数据,需要把函数返回的结果重新赋值给d对象: ...