library(dplyr) group_by(data, sex) %>% summarize_each(funs(mean), var1, var2, var3...)
当对数据进行一系列的操作而又不想一个一个保存中间值,这无疑是一个最佳选择。 airquality%>%filter(Month==5)%>%group_by(Month)%>%summarize(mean(Temp,na.rm=TRUE)) Month `mean(Temp, na.rm = TRUE)` <int> <dbl> 1 5 65.54839 1. 2. 3. 3.筛选:select/filter 3.1 筛选列select() 根据列...
在dplyr版本1.0.0之后，上面的summarize_all和summarize_at函数被summarize(across(...))取代，在summarize(across(...))中可以选择要操作的列（此处为val1:val2）。我们还可以在across中提供一个函数列表，并使用粘附规范设置列名（{.col}=原始列名，{.fn}=列表中的函数名）。有关across的更多信息，请参阅...
calibration_df <- cv_pred_df %>% mutate(pass = if_else(truth == "pass", 1, 0), pred_rnd = round(prob.pass, 2) ) %>% group_by(pred_rnd) %>% summarize(mean_pred = mean(prob.pass), mean_obs = mean(pass), n = n() ) %>% mutate(group = case_when(n < 100 ~ "<10...
summarize(rating_ave = mean(imdb_rating), sentiment_ave = mean(sentimentc)) %>% ggplot(data = ., aes(x = sentiment_ave, y = rating_ave, color = season)) + geom_point() # Descriptive fig of pos & neg characters glimpse(tidy.token.schrute) ...
each group to fewer rows summarise_all Summarise multiple columns summarise_at Summarise multiple columns summarise_if Summarise multiple columns summarize Summarise each group to fewer rows summarize_all Summarise multiple columns summarize_at Summarise multiple columns summarize_if Summarise multiple columns...
计算平均值、sd等：library(dplyr) B <- A %>% group_by(Treatment) %>% mutate(upper ...
sample_size = data %>% group_by(name) %>% summarize(num=n()) # Plot data %>% left_join(sample_size) %>% mutate(myaxis = paste0(name, "\n", "n=", num)) %>% ggplot( aes(x=myaxis, y=value, fill=name)) + geom_violin(width=1.4) + ...
Summarize data by event type The final data is grouped by event type and the sum is computed for each damage kind. The two datasets are arranged in descending order of the corresponding numerical value, and only the top 10 event types are taken to present the results. Finally, the data is...
dplyr包提供了一系列函数,如filter、select、mutate、summarize等,帮助用户高效地进行数据变换。tidyr包则用于数据整形,如gather和spread函数,可以实现数据的宽表与长表之间的转换。缺失值处理方面,R提供了na.omit函数可以删除含有NA值的行,同时也可以使用mice包进行多重插补来填补缺失值。