# Calculate the mean.# The output has 3 significant digits by default.my_t%>%group_by(Col_one, Col_two)%>%summarize(mean(Col_code))# Convert the output to a data frame to see the decimal places.my_t%>%group_by(Col_one, Col_two)%>%summarize(mean(Col_code))%>%as.data.frame(...
# Calculate the average_pop and median_pop columnscounties_selected%>%group_by(region,state)%>%summarize(total_pop=sum(population))%>%summarize(average_pop=mean(total_pop),median_pop=median(total_pop))# A tibble: 4 x 3region average_pop median_pop<chr><dbl><dbl>1North Central5627687.55806...
grouped<-group_by(df,v1,V2)#df被v1,v2进行分组 newdata<-summarise(grouped,mean_age=mean(age),sum_sale=sum(sales)) 以上这段代码我们使用group_by和summarise的结合实现了对数据集分组分析,并进行统计量计算的一个功能。学习了下面的data.table包以后,你再回来看看这个,你绝对不会选择这种写法并且超级鄙...
在介绍下一个包之前,我们先来引入一个dplyr包的综合运用: grouped<-group_by(df,v1,V2) #df被v1,v2进行分组 newdata<-summarise(grouped,mean_age=mean(age),sum_sale=sum(sales)) 以上这段代码我们使用group_by和summarise的结合实现了对数据集分组分析,并进行统计量计算的一个功能。学习了下面的data.tabl...
group_by(year,month,day)%% select(arr_delay,dep_delay)%% summarise( arr=mean(arr_delay,na.rm=TRUE), dep=mean(dep_delay,na.rm=TRUE) )%% filter(arr30|dep30) } computeComputealazytbl. Description computeforcescomputationoflazytbls,leavingdataintheremotesource.collectalsoforces computation,butwil...
group_by(), arrange(), rename(), select(), and distinct() ) and adds some of its own advanced verbs. It is similar to dplyr‘s now-deprecated “SE verbs”, but with a more array and list oriented interface (de-emphasizing use of “ ...” in function arguments). For examp...
(mean),Cancelled,Diverted)# for each carrier, calculate the minimum and maximum arrival and departure delaysflights%>%group_by(UniqueCarrier)%>%summarise_each(funs(min(.,na.rm=TRUE),max(.,na.rm=TRUE)),matches("Delay"))#Helper function n() counts the number of rows in a group#Helper ...
您可以使用tidyr::complete来填充缺失的物种-小区组合，但您需要额外重新聚合到小区-物种级别。在这种情况...
我相信大家经常会使用Excel对数据进行排序。有时候我们会按照两个条件来对数据排序。假设我们手上有下面这...
Part of the tidyverse, it provides practitioners with a host of tools and functions to manipulate data, transform columns and rows, calculate aggregations, and join different datasets together. In this cheat sheet, you'll find a handy list of functions covering dplyr functions —all collected ...