summarise(across(height:mass, ~ mean(.x, na.rm =TRUE)))#> # A tibble: 1 × 2#> height mass#> <dbl> <dbl>#> 1 174. 97.3# The _if() variants apply a predicate function (a function that# returns TRUE or FALSE) to determine the relevant subset of# columns. Here we apply mean...
Summarise multiple variable columns. R functions: summarise_all(): apply summary functions to every columns in the data frame. summarise_at(): apply summary functions to specific columns selected with a character vector summarise_if(): apply summary functions to columns selected with a predicate fu...
Pct_Recover = scales::percent(N_Recover / N_Known,0.1)) %>%# percent who recovered (to 1 decimal) select(# Re-order columns hospital, N_Known,# Intro columns N_Recover, Pct_Recover, ct_value_Recover,# Recovered columns N_Death, Pct_Death, ct_value_Death) %>%# Death columns arrang...
plotdata <- plotdataall[,c("Country.y","Country.x","connectnumber","airportnumber.x")] plotdata <- plotdata[plotdata$connectnumber > 50,] ## 机场数量 airportnum <- airports%>%group_by(Country)%>% summarise(airportnumber = n()) ## 定义连接的数据 d <- plotdata[,c("Country.y"...
library(dplyr) library(tidyr) data <- iris %>% group_by(Species) %>% summarise_all(mean) %>% pivot_longer(-Species) data %>% ggplot(aes(x = name, y = value, color = Species, group = Species)) + geom_polygon(fill = NA, size = 2, show.legend = FALSE) + coord_radar(start...
select()Selecting columns (variables)SELECT filter()Filter (subset) rows.WHERE group_by()Group the dataGROUP BY summarise()Summarise (or aggregate) data- arrange()Sort the dataORDER BY join()Joining data frames (tables)JOIN mutate()Creating New VariablesCOLUMN ALIAS ...
library(plyr) # 给每一组运行长度、均值、标准差等函数 # 每一组依据性别+条件划分 cdata <- ddply(data, c("sex", "condition"), summarise, N = length(change), mean = mean(change), sd = sd(change), se = sd / sqrt(N) ) cdata #> sex condition N mean sd se #> 1 F aspirin 5...
data %>% group_by(D) %>% summarise(N=n(), Means=mean(RR), SS=sum((RR - Means)^2), SD=sd(RR), SEM=SD/N^.5) 展示数据: boxplot ggboxplot(data_drop, x = "D", y = "RR", color = "D", ylab = "RR", xlab = "D") step5: 单因素方差分析 one-way ANOVAs: 使用aov...
dplyr包的summarise()函数可以对数据框计算统计量。 以肺癌病人化疗数据cancer.csv为例, 有34个肺癌病人的数据: d.cancer <- read_csv( "data/cancer.csv", locale=locale(encoding="GBK")) ## Rows: 34 Columns: 6 ## ── Column specification ────────────────────────...
summarise(sum(v)) }) ## user system elapsed ## 0.60 0.12 0.72 #data.table包 DT = as.data.table(DF) system.time({ DT[,sum(v),by=x] }) ## user system elapsed ## 0.12 0.02 0.14 1. 2. 3. 4. 5. 6. 7. 8. 9.