# 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(...
用于根据给定的变量名对数据进行分组并计算平均值calculate_average<-function(data,variable){data%>%group_by({{variable}})%>%summarise(average_salary=mean(salary))}# 使用函数计算不同年龄段的平均工资result<-calculate_average(data,age)print(result)# 使用函数计算不同姓名的平均工资result<-calculate_...
# 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...
DT[i,j,by] take DT,subset rows using i,then calculate j grouped by by 我们在横轴上过滤数据,用by进行分组,然后在列上面进行计算。 使用i DT[3:5] #选取3到5行的数据 class(DT) [1] "data.table" "data.frame" DT[v1=="A"] #基于条件的选择 ...
(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 ...
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(relationship) %>% summarise(total = n()) df %>% group_by(relationship) %>% summarise(total = n(), mean_age = mean(age)) The mutate() verb is used to create new variables from existing local variables or global objects. New variables, such as sequences, can be also speci...
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 ...
Convert sequences of expressions (such as in the summarize()) to explicit vectors by adding the “c()” notation. Replace “=” in expressions with “:=”. This is the result: library(seplyr) starwars_mean <- function(my_var) { starwars %>% group_by_se(my_var) %>% summarize_se...