R 按某些列对单元格求和
我有一张这样的桌子:R 按某些列对单元格求和,r,frequency,R,Frequency,我有一张这样的桌子: df <- read.table(text = " Day location gender hashtags 'Feb 19 2016' 'UK' 'M' '#a' 'Feb 19 2016' 'UK' 'M' '#b' 'Feb 19 2016' 'SP' 'F' '#a
df <- read.table(text =
" Day location gender hashtags
'Feb 19 2016' 'UK' 'M' '#a'
'Feb 19 2016' 'UK' 'M' '#b'
'Feb 19 2016' 'SP' 'F' '#a'
'Feb 19 2016' 'SP' 'F' '#b'
'Feb 19 2016' 'SP' 'M' '#a'
'Feb 19 2016' 'SP' 'M' '#b'
'Feb 20 2016' 'UK' 'F' '#a'",
header = TRUE, stringsAsFactors = FALSE)
Day hashtags Daily_Freq men women Freq_UK Freq_SP
Feb 19 2016 #a 3 2 1 1 2
Feb 19 2016 #b 3 2 1 1 1
Feb 20 2016 #a 1 0 1 1 0
其中每日频率=男性+女性=频率英国+频率SP
我如何才能做到这一点?使用
dplyr
:
library(dplyr)
df %>%
group_by(Day, hashtags) %>%
summarise(Daily_Freq = n(),
men = sum(gender == 'M'),
women = sum(gender == 'F'),
Freq_UK = sum(location == 'UK'),
Freq_SP = sum(location == 'SP'))
给出:
单程
library(data.table)
setDT(df)
df[, gender := as.factor(gender)]
df[, location := as.factor(location)]
df[, c(
N = .N,
dcast(.SD, . ~ gender, fun.agg = length, drop=FALSE)[, !"."],
dcast(.SD, . ~ location, fun.agg = length, drop=FALSE)[, !"."]
), by=.(Day, hashtags)]
# Day hashtags N F M SP UK
# 1: Feb 19 2016 #a 3 1 2 2 1
# 2: Feb 19 2016 #b 3 1 2 2 1
# 3: Feb 20 2016 #a 1 1 0 0 1
以这种方式编码可能更容易维护:不需要手动指定列名;位置和性别将根据其是否出现在原始数据中显示或退出结果;而且列名不需要在多个位置键入(转换为因子后)
如果存在与性别代码匹配的国家代码,这种方式将生成重复列。要绕过这个问题:
df[, c(
N = .N,
gender = dcast(.SD, . ~ gender, fun.agg = length, drop=FALSE)[, !"."],
loc = dcast(.SD, . ~ location, fun.agg = length, drop=FALSE)[, !"."]
), by=.(Day, hashtags)]
# Day hashtags N gender.F gender.M loc.SP loc.UK
# 1: Feb 19 2016 #a 3 1 2 2 1
# 2: Feb 19 2016 #b 3 1 2 2 1
# 3: Feb 20 2016 #a 1 1 0 0 1
使用软件包
重塑2
library(reshape2)
molten <- melt(df, id.vars = c("Day", "hashtags"))
result <- dcast(molten, Day + hashtags ~ variable + value, length)
result$Daily_Freq <- rowSums(result[, c("location_SP", "location_UK")])
result
# Day hashtags location_SP location_UK gender_F gender_M Daily_Freq
#1 Feb 19 2016 #a 2 1 1 2 3
#2 Feb 19 2016 #b 2 1 1 2 3
#3 Feb 20 2016 #a 0 1 1 0 1
library(重塑2)
熔化的
df[, c(
N = .N,
gender = dcast(.SD, . ~ gender, fun.agg = length, drop=FALSE)[, !"."],
loc = dcast(.SD, . ~ location, fun.agg = length, drop=FALSE)[, !"."]
), by=.(Day, hashtags)]
# Day hashtags N gender.F gender.M loc.SP loc.UK
# 1: Feb 19 2016 #a 3 1 2 2 1
# 2: Feb 19 2016 #b 3 1 2 2 1
# 3: Feb 20 2016 #a 1 1 0 0 1
library(reshape2)
molten <- melt(df, id.vars = c("Day", "hashtags"))
result <- dcast(molten, Day + hashtags ~ variable + value, length)
result$Daily_Freq <- rowSums(result[, c("location_SP", "location_UK")])
result
# Day hashtags location_SP location_UK gender_F gender_M Daily_Freq
#1 Feb 19 2016 #a 2 1 1 2 3
#2 Feb 19 2016 #b 2 1 1 2 3
#3 Feb 20 2016 #a 0 1 1 0 1