tidyr之gather、separate、spread…

使用tidyr的时候也有可能需要dplyr包。

gather的用法：

   gather(data, key, value, ..., na.rm = FALSE, convert = FALSE,
factor_key = FALSE)

data为要处理的数据，key为新创建的变量，value为创建变量下的数值。...为列的设定，比如下面的例子创建变量stock，而不需要把time变成变量下的数值，则用X:Z或-time。

stocks <- data.frame(     time = as.Date("2009-01-01") + 0:9,     X = rnorm(10, 0, 1),     Y = rnorm(10, 0, 2),     Z = rnorm(10, 0, 4) )> stocks         time           X          Y          Z1  2009-01-01 -1.06024371 -1.3799111  1.20763492  2009-01-02 -0.49010731 -2.4870899  3.78885503  2009-01-03  1.15709245 -1.5708653  2.21644644  2009-01-04 -0.52049101  1.9813593  2.00944765  2009-01-05  0.60130291 -2.6300865 -3.96817986  2009-01-06 -0.09306031 -0.8420800  0.67044267  2009-01-07 -0.83323255 -1.4147638  2.33985048  2009-01-08 -0.15517432  1.8773903  1.64376309  2009-01-09  0.36519166  0.3696238  1.574658810 2009-01-10  0.09442596 -0.5840472 -1.0298875

 stocks %>% gather(stock, price, -time)         time stock       price1  2009-01-01     X -1.060243712  2009-01-02     X -0.490107313  2009-01-03     X  1.157092454  2009-01-04     X -0.520491015  2009-01-05     X  0.601302916  2009-01-06     X -0.093060317  2009-01-07     X -0.833232558  2009-01-08     X -0.155174329  2009-01-09     X  0.3651916610 2009-01-10     X  0.0944259611 2009-01-01     Y -1.3799111012 2009-01-02     Y -2.4870899313 2009-01-03     Y -1.5708653314 2009-01-04     Y  1.9813593515 2009-01-05     Y -2.6300864816 2009-01-06     Y -0.8420800317 2009-01-07     Y -1.4147638518 2009-01-08     Y  1.8773902919 2009-01-09     Y  0.3696238220 2009-01-10     Y -0.5840472021 2009-01-01     Z  1.2076349222 2009-01-02     Z  3.7888550023 2009-01-03     Z  2.2164463824 2009-01-04     Z  2.0094476025 2009-01-05     Z -3.9681797826 2009-01-06     Z  0.6704426227 2009-01-07     Z  2.3398503928 2009-01-08     Z  1.6437630029 2009-01-09     Z  1.5746587530 2009-01-10     Z -1.02988745

gather_的用法如上，只是更适合于编程用。

 

separate的用法：

separate(data, col, into, sep = "[^[:alnum:]]+", remove =TRUE,
convert = FALSE, extra = "warn", fill = "warn", ...)

data为要处理的数据；col为要选择的变量名；into为要分割为几个变量的名；sep为正则表达式，用于分割；extra为在字符串过多的情况下处理；fill为在字符串过少的情况下处理。

> df <- data.frame(x = c(NA, "a.b", "a.d","b.c"))> df %>% separate(x, c("A", "B"))     A    B1  2    a    b3    a    d4    b    c> df <- data.frame(x = c("a", "a b", "a b c", NA))> df      x1     a2   a b3 a b c4  

df %>% separate(x, c("a", "b"), extra = "merge", fill = "left")     a    b1     a2    a    b3    a  b c4  

> df <- data.frame(x = c("x: 123", "y: error: 7"))> df            x1      x: 1232 y: error: 7> df %>% separate(x, c("key", "value"), sep = ":", extra = "merge")  key     value1   x       1232   y  error: 7

separate_的用法如上，但更适用于编程。

separate_rows的用法：

separate_rows(data, ..., sep = "[^[:alnum:].]+", convert =FALSE)

> df <- data.frame(+     x = 1:3,+     y = c("a","d,e,f","g,h"),+     z = c("1","2,3,4","5,6"),+     stringAsFactors =F+ )> separate_rows(df, y, z, convert = T)  x stringAsFactors y z1 1           FALSE a 12 2           FALSE d 23 2           FALSE e 34 2           FALSE f 45 3           FALSE g 56 3           FALSE h 6

而使用separate_rows(df, c("y","z"), convert =T)是不对的，必须使用separate_rows_ 才可。

spread的用法：

spread(data, key, value, fill = NA, convert = FALSE, drop =TRUE,
sep = NULL)

data为要扩展的数据，key为列的名字，其值将用于列表的表头， value将填充列表的值；fill为填充数据的缺失值。

使用上面的stocks例子

> stocksm <- stocks %>% gather(stock, price, -time)> stocksm %>% spread(stock, price)         time           X           Y          Z1  2009-01-01 -1.04456577 -0.97179381  2.43301052  2009-01-02 -0.09717072 -0.77313257 -2.34437363  2009-01-03  1.36933739  1.54137383  0.65513254  2009-01-04  2.13655070 -0.05889974  1.99883065  2009-01-05  0.39546822  3.80644394  3.71665466  2009-01-06  0.09720381 -0.44658971  0.51364717  2009-01-07 -0.50775134 -2.53712365  0.20048358  2009-01-08 -1.65134456  1.01235639 10.43771419  2009-01-09 -1.62622446 -0.80074087  3.044451510 2009-01-10 -0.37272122  3.86510320  4.7424977> stocksm %>% spread(time, price)  stock 2009-01-01  2009-01-02 2009-01-03  2009-01-04 2009-01-051     X -1.0445658 -0.09717072  1.3693374  2.13655070  0.39546822     Y -0.9717938 -0.77313257  1.5413738 -0.05889974  3.80644393     Z  2.4330105 -2.34437356  0.6551325  1.99883061  3.7166546   2009-01-06 2009-01-07 2009-01-08 2009-01-09 2009-01-101  0.09720381 -0.5077513  -1.651345 -1.6262245 -0.37272122 -0.44658971 -2.5371237   1.012356 -0.8007409  3.86510323  0.51364707  0.2004835  10.437714  3.0444515  4.7424977

spread_用于编程。

unite的用法：

unite(data, col, ..., sep = "_", remove = TRUE)

data为数据；col为创建的变量；...为要合并的列名；sep为要为合并的变量添加的符号。

> mtcars %>%     unite(vs_am, vs, am)                     mpg cyl  disp  hp drat    wt  qsec vs_am gearMazda RX4           21.0   6 160.0 110 3.90 2.620 16.46   0_1    4Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02   0_1    4Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61   1_1    4Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44   1_0    3Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02   0_0    3Valiant             18.1   6 225.0 105 2.76 3.460 20.22   1_0    3

也可用unite_：

> mtcars %>%     unite_("vs_am", c("vs","am"))                     mpg cyl  disp  hp drat    wt  qsec vs_am gearMazda RX4           21.0   6 160.0 110 3.90 2.620 16.46   0_1    4Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02   0_1    4Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61   1_1    4Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44   1_0    3Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02   0_0    3Valiant             18.1   6 225.0 105 2.76 3.460 20.22   1_0    3