5 数据整理、转换（reshape、tidy）
5.1 reshape2
library(reshape2)
subdata <-iris[,4:5]
head(subdata)
#> Petal.Width Species
#> 1 0.2 setosa
#> 2 0.2 setosa
#> 3 0.2 setosa
#> 4 0.2 setosa
#> 5 0.2 setosa
#> 6 0.4 setosa
data_w <- unstack(subdata)
colMeans(data_w)
#> setosa versicolor virginica
#> 0.246 1.326 2.026实践中这种单纯的长宽格式并不多见，更常见的是直接一步得到结果，所以就需要强大的 reshape2 包
加载包和改数据名
library(reshape2)
names(airquality) <- tolower(names(airquality))
head(airquality)
#> ozone solar.r wind temp month day
#> 1 41 190 7.4 67 5 1
#> 2 36 118 8.0 72 5 2
#> 3 12 149 12.6 74 5 3
#> 4 18 313 11.5 62 5 4
#> 5 NA NA 14.3 56 5 5
#> 6 28 NA 14.9 66 5 65.1.1 melt 熔化转长格式
aql <- melt(airquality)
#> No id variables; using all as measure variables
tail(aql)
#> variable value
#> 913 day 25
#> 914 day 26
#> 915 day 27
#> 916 day 28
#> 917 day 29
#> 918 day 30
head(aql)
#> variable value
#> 1 ozone 41
#> 2 ozone 36
#> 3 ozone 12
#> 4 ozone 18
#> 5 ozone NA
#> 6 ozone 28设定不需要变 value 的变量
aql <- melt(airquality,id.vars = c('month', 'day'))
names(airquality)
#> [1] "ozone" "solar.r" "wind" "temp" "month" "day"
head(aql)
#> month day variable value
#> 1 5 1 ozone 41
#> 2 5 2 ozone 36
#> 3 5 3 ozone 12
#> 4 5 4 ozone 18
#> 5 5 5 ozone NA
#> 6 5 6 ozone 28改名
aql <- melt(airquality, id.vars = c("month", "day"),
variable.name = "climate_variable",
value.name = "climate_value")
head(aql)
#> month day climate_variable climate_value
#> 1 5 1 ozone 41
#> 2 5 2 ozone 36
#> 3 5 3 ozone 12
#> 4 5 4 ozone 18
#> 5 5 5 ozone NA
#> 6 5 6 ozone 28
head(airquality)
#> ozone solar.r wind temp month day
#> 1 41 190 7.4 67 5 1
#> 2 36 118 8.0 72 5 2
#> 3 12 149 12.6 74 5 3
#> 4 18 313 11.5 62 5 4
#> 5 NA NA 14.3 56 5 5
#> 6 28 NA 14.9 66 5 65.1.2 dcast 重铸，长转宽
aql <- melt(airquality, id.vars = c("month", "day"))
aqw <- dcast(aql, month + day ~ variable)
head(aql)
#> month day variable value
#> 1 5 1 ozone 41
#> 2 5 2 ozone 36
#> 3 5 3 ozone 12
#> 4 5 4 ozone 18
#> 5 5 5 ozone NA
#> 6 5 6 ozone 28
head(aqw)
#> month day ozone solar.r wind temp
#> 1 5 1 41 190 7.4 67
#> 2 5 2 36 118 8.0 72
#> 3 5 3 12 149 12.6 74
#> 4 5 4 18 313 11.5 62
#> 5 5 5 NA NA 14.3 56
#> 6 5 6 28 NA 14.9 66不足的id.var 会出错，会把值的数量统计出来
dcast(aql, month ~ variable)
#> Aggregation function missing: defaulting to length
#> month ozone solar.r wind temp
#> 1 5 31 31 31 31
#> 2 6 30 30 30 30
#> 3 7 31 31 31 31
#> 4 8 31 31 31 31
#> 5 9 30 30 30 305.1.3 fun,加上集合函数就能按正确方式集合了。
dcast(aql, month ~ variable, fun.aggregate = mean,
na.rm = TRUE)
#> month ozone solar.r wind temp
#> 1 5 23.6 181 11.62 65.5
#> 2 6 29.4 190 10.27 79.1
#> 3 7 59.1 216 8.94 83.9
#> 4 8 60.0 172 8.79 84.0
#> 5 9 31.4 167 10.18 76.95.2 tidyr
新建一列数据
mtcars$car <- rownames(mtcars)
colnames(mtcars)
#> [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
#> [11] "carb" "car"
mtcars <- mtcars[, c(12, 1:11)]
head(mtcars)
#> car mpg cyl disp hp drat wt qsec vs am
#> Mazda RX4 Mazda RX4 21.0 6 160 110 3.90 2.62 16.5 0 1
#> Mazda RX4 Wag Mazda RX4 Wag 21.0 6 160 110 3.90 2.88 17.0 0 1
#> Datsun 710 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1
#> Hornet 4 Drive Hornet 4 Drive 21.4 6 258 110 3.08 3.21 19.4 1 0
#> Hornet Sportabout Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.0 0 0
#> Valiant Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0
#> gear carb
#> Mazda RX4 4 4
#> Mazda RX4 Wag 4 4
#> Datsun 710 4 1
#> Hornet 4 Drive 3 1
#> Hornet Sportabout 3 2
#> Valiant 3 15.2.1 gather 聚合
library(tidyr)
#>
#> Attaching package: 'tidyr'
#> The following object is masked from 'package:reshape2':
#>
#> smiths
mtcarsNew <- mtcars %>%
gather(attribute, value, -car) #不仅聚合，且命名了。
head(mtcarsNew)
#> car attribute value
#> 1 Mazda RX4 mpg 21.0
#> 2 Mazda RX4 Wag mpg 21.0
#> 3 Datsun 710 mpg 22.8
#> 4 Hornet 4 Drive mpg 21.4
#> 5 Hornet Sportabout mpg 18.7
#> 6 Valiant mpg 18.1
tail(mtcarsNew)
#> car attribute value
#> 347 Porsche 914-2 carb 2
#> 348 Lotus Europa carb 2
#> 349 Ford Pantera L carb 4
#> 350 Ferrari Dino carb 6
#> 351 Maserati Bora carb 8
#> 352 Volvo 142E carb 2gather mpg:gear 之间的列
mtcarsNew <- mtcars %>%
gather(attribute, value, mpg:gear)5.2.2 spread 铺开
mtcarsWide <- mtcarsNew %>%
spread(attribute, value)
head(mtcarsWide)
#> car carb am cyl disp drat gear hp mpg qsec vs wt
#> 1 AMC Javelin 2 0 8 304 3.15 3 150 15.2 17.3 0 3.44
#> 2 Cadillac Fleetwood 4 0 8 472 2.93 3 205 10.4 18.0 0 5.25
#> 3 Camaro Z28 4 0 8 350 3.73 3 245 13.3 15.4 0 3.84
#> 4 Chrysler Imperial 4 0 8 440 3.23 3 230 14.7 17.4 0 5.34
#> 5 Datsun 710 1 1 4 108 3.85 4 93 22.8 18.6 1 2.32
#> 6 Dodge Challenger 2 0 8 318 2.76 3 150 15.5 16.9 0 3.525.2.3 unite 合并
set.seed(1)
date <- as.Date('2016-06-01') + 0:14
hour <- sample(1:24, 15)
min <- sample(1:60, 15)
second <- sample(1:60, 15)
event <- sample(letters, 15)
df <- data.frame(date, hour, min, second, event)
head(df)
#> date hour min second event
#> 1 2016-06-01 7 30 29 u
#> 2 2016-06-02 9 43 36 a
#> 3 2016-06-03 13 58 60 l
#> 4 2016-06-04 20 22 11 q
#> 5 2016-06-05 5 44 47 p
#> 6 2016-06-06 18 52 37 k
df1 <- df %>%
unite(datehour, date, hour, sep = ' ') %>%
unite(datetime, datehour, min, second, sep = ':')
df1
#> datetime event
#> 1 2016-06-01 7:30:29 u
#> 2 2016-06-02 9:43:36 a
#> 3 2016-06-03 13:58:60 l
#> 4 2016-06-04 20:22:11 q
#> 5 2016-06-05 5:44:47 p
#> 6 2016-06-06 18:52:37 k
#> 7 2016-06-07 19:12:43 r
#> 8 2016-06-08 12:35:6 i
#> 9 2016-06-09 11:7:38 e
#> 10 2016-06-10 1:14:21 b
#> 11 2016-06-11 3:20:42 w
#> 12 2016-06-12 14:1:32 t
#> 13 2016-06-13 23:19:52 h
#> 14 2016-06-14 21:41:26 s
#> 15 2016-06-15 8:16:25 o5.2.4 separate 拆分
df2 <- df1 %>%
separate(datetime, c('date', 'time'), sep = ' ') %>%
separate(time, c('hour', 'min', 'second'), sep = ':')
df2
#> date hour min second event
#> 1 2016-06-01 7 30 29 u
#> 2 2016-06-02 9 43 36 a
#> 3 2016-06-03 13 58 60 l
#> 4 2016-06-04 20 22 11 q
#> 5 2016-06-05 5 44 47 p
#> 6 2016-06-06 18 52 37 k
#> 7 2016-06-07 19 12 43 r
#> 8 2016-06-08 12 35 6 i
#> 9 2016-06-09 11 7 38 e
#> 10 2016-06-10 1 14 21 b
#> 11 2016-06-11 3 20 42 w
#> 12 2016-06-12 14 1 32 t
#> 13 2016-06-13 23 19 52 h
#> 14 2016-06-14 21 41 26 s
#> 15 2016-06-15 8 16 25 o总的来说，tidyr 包比reshape语法更为简洁易懂， 更加方便。