在提迪尔中的传播与混乱 [英] Confusion with Spread in tidyr
本文介绍了在提迪尔中的传播与混乱的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个数据框，如下所示:
I have a dataframe as follows:
ddd <- structure(list(sample_date = structure(c(1400612280, 1400612280,
1400612280, 1400612280, 1400612280, 1400612280, 1400616420, 1400616420,
1400616420, 1400616420, 1400616420, 1400616420, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780
), class = c("POSIXct", "POSIXt"), tzone = ""), chemical_name = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L), .Label = c("Conductivity (field)", "Dissolved Oxygen (field)",
"pH (field)", "Salinity (field)", "Temperature (field)", "Turbidity (field)"
), class = "factor"), result_value = c(29188, 6.42, 7.52, 18.08,
15.38, 0, 30780, 5.05, 7.42, 19.17, 14.46, 0.5, 28972, 6.64,
7.47, 17.94, 15.26, -0.1, 29066, 6.42, 7.49, 18, 15.23, 0, 29057,
6.38, 7.49, 18, 15.24, 0, 29058, 6.35, 7.49, 18, 15.22, 0, 29048,
6.31, 7.49, 17.99, 15.24, 0, 29043, 6.31, 7.49, 17.99, 15.25,
0, 29054, 6.3), row = 1:50), .Names = c("sample_date", "chemical_name",
"result_value", "row"), class = c("tbl_df", "data.frame"), row.names = c(NA,
-50L))
数据框的标题如下:
sample_date chemical_name result_value row
1 2014-05-20 11:58:00 Conductivity (field) 29188.00 1
2 2014-05-20 11:58:00 Dissolved Oxygen (field) 6.42 2
3 2014-05-20 11:58:00 pH (field) 7.52 3
4 2014-05-20 11:58:00 Salinity (field) 18.08 4
5 2014-05-20 11:58:00 Temperature (field) 15.38 5
6 2014-05-20 11:58:00 Turbidity (field) 0.00 6
然后我要将该格式转换为宽格式，然后尝试在tidyr中使用spread函数.
What I want to do is then convert this format to the wide format and I tried to use spread function in tidyr.
在使用spread函数之前，我创建了一个名为row的新变量，以删除duplication error.
Before using the spread function I created a new variable called row so as to remove the duplication error.
ddd$row <- 1:nrow(ddd)
现在，我使用了spread函数，如下所示:
Now, I used the spread function as follows:
ddd1 <- ddd %>% spread(key = chemical_name, result_value)
这似乎有效，但与我期望的不完全相同. sample_date是POSIXct对象，转换后hour, minute and second不会显示，因此输出看起来很混乱.
It seems to work but not exactly what I expected. The sample_date is a POSIXct object and when it is transformed the hour, minute and second doesn't show up so the output looks messy.
sample_date row Conductivity (field) Dissolved Oxygen (field) pH (field) Salinity (field) Temperature (field) Turbidity (field)
1 2014-05-19 121 29670 NA NA NA NA NA
2 2014-05-19 122 NA 6 NA NA NA NA
3 2014-05-19 123 NA NA 7.54 NA NA NA
4 2014-05-19 124 NA NA NA 18.37 NA NA
5 2014-05-19 125 NA NA NA NA 14.73 NA
6 2014-05-19 126 NA NA NA NA NA 3
7 2014-05-19 127 30042 NA NA NA NA NA
8 2014-05-19 128 NA 6 NA NA NA NA
9 2014-05-19 129 NA NA 7.54 NA NA NA
10 2014-05-19 130 NA NA NA 18.61 NA NA
列名似乎正确，但值不正确.对于每个日期，我都有conductivity, dissolved oxygen, salinity etc..的值.请帮助我解决此问题需要做什么.
The column names seems to be right but not the values. For each date, I have values for conductivity, dissolved oxygen, salinity etc... Please help me what needs to be done to fix this issue.
更新:
新数据已更新
ddd <- structure(list(sample_date = structure(c(1400612280, 1400612280,
1400612280, 1400612280, 1400612280, 1400612280, 1400616420, 1400616420,
1400616420, 1400616420, 1400616420, 1400616420, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400521500, 1400521500,
1400521500, 1400521500, 1400521500, 1400521500, 1400526900, 1400526900,
1400526900, 1400526900, 1400526900, 1400526900, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400612280, 1400612280,
1400612280, 1400612280, 1400612280, 1400612280, 1400616420, 1400616420,
1400616420, 1400616420, 1400616420, 1400616420, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400604780, 1400604780,
1400604780, 1400604780, 1400604780, 1400604780, 1400521500, 1400521500,
1400521500, 1400521500, 1400521500, 1400521500, 1400526900, 1400526900,
1400526900, 1400526900, 1400526900, 1400526900, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1400482800, 1400482800,
1400482800, 1400482800, 1400482800, 1400482800, 1407430680, 1407430680,
1407430680, 1407430680, 1407430680, 1407430680, 1407433020, 1407433020,
1407433020, 1407433020, 1407433020, 1407433020, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407429900, 1407429900,
1407429900, 1407429900, 1407429900, 1407429900, 1407171180, 1407171180,
1407171180, 1407171180, 1407171180, 1407171180, 1407176400, 1407176400,
1407176400, 1407176400, 1407176400, 1407176400, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040, 1407170040, 1407170040,
1407170040, 1407170040, 1407170040, 1407170040), tzone = "", class = c("POSIXct",
"POSIXt")), chemical_name = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("Conductivity (field)",
"Dissolved Oxygen (field)", "pH (field)", "Salinity (field)",
"Temperature (field)", "Turbidity (field)"), class = "factor"),
result_value = c(29188, 6.42, 7.52, 18.08, 15.38, 0, 30780,
5.05, 7.42, 19.17, 14.46, 0.5, 28972, 6.64, 7.47, 17.94,
15.26, -0.1, 29066, 6.42, 7.49, 18, 15.23, 0, 29057, 6.38,
7.49, 18, 15.24, 0, 29058, 6.35, 7.49, 18, 15.22, 0, 29048,
6.31, 7.49, 17.99, 15.24, 0, 29043, 6.31, 7.49, 17.99, 15.25,
0, 29054, 6.3, 7.49, 18, 15.23, -0.1, 29137, 6.31, 7.49,
18.07, 15.22, 0, 29273, 6.19, 7.48, 18.15, 15.08, 0.1, 29338,
6.2, 7.48, 18.18, 15.01, 0.2, 29465, 6.16, 7.48, 18.3, 14.95,
0.3, 30329, 5.48, 7.42, 18.86, 14.57, 0.4, 30955, 4.88, 7.38,
19.31, 14.34, 0.7, 31179, 4.68, 7.37, 19.43, 14.27, 0.9,
31390, 4.67, 7.37, 19.52, 14.2, 1, 31541, 4.65, 7.37, 19.82,
14.15, 1.9, 29630, 6.74, 7.66, 18.37, 14.29, 10.6, 30175,
6.62, 7.66, 18.46, 14.11, 11.8, 29670, 6, 7.54, 18.37, 14.73,
3, 30042, 6, 7.54, 18.61, 14.56, 4.1, 31403, 6.17, 7.57,
19.6, 14.28, 6.2, 31520, 6.25, 7.59, 19.72, 14.23, 6.6, 31915,
6.35, 7.61, 19.97, 14.14, 7.2, 32080, 6.41, 7.62, 20.05,
14.08, 7.2, 32520, 6.51, 7.64, 20.49, 13.9, 7.6, 32805, 6.58,
7.65, 20.55, 13.78, 8.6, 32876, 6.6, 7.65, 20.6, 13.76, 8.9,
32907, 6.6, 7.65, 20.61, 13.73, 8.9, 32920, 6.62, 7.65, 20.64,
13.72, 10.9, 32980, 6.62, 7.66, 20.65, 13.71, 10.3, 33019,
6.61, 7.66, 20.68, 13.7, 10.7, 33088, 6.61, 7.66, 20.71,
13.69, 10.1, 33125, 6.61, 7.66, 20.73, 13.68, 9.6, 33099,
6.61, 7.66, 20.74, 13.68, 10.3, 29188, 6.42, 7.52, 18.08,
15.38, 0, 30780, 5.05, 7.42, 19.17, 14.46, 0.5, 28972, 6.64,
7.47, 17.94, 15.26, -0.1, 29066, 6.42, 7.49, 18, 15.23, 0,
29057, 6.38, 7.49, 18, 15.24, 0, 29058, 6.35, 7.49, 18, 15.22,
0, 29048, 6.31, 7.49, 17.99, 15.24, 0, 29043, 6.31, 7.49,
17.99, 15.25, 0, 29054, 6.3, 7.49, 18, 15.23, -0.1, 29137,
6.31, 7.49, 18.07, 15.22, 0, 29273, 6.19, 7.48, 18.15, 15.08,
0.1, 29338, 6.2, 7.48, 18.18, 15.01, 0.2, 29465, 6.16, 7.48,
18.3, 14.95, 0.3, 30329, 5.48, 7.42, 18.86, 14.57, 0.4, 30955,
4.88, 7.38, 19.31, 14.34, 0.7, 31179, 4.68, 7.37, 19.43,
14.27, 0.9, 31390, 4.67, 7.37, 19.52, 14.2, 1, 31541, 4.65,
7.37, 19.82, 14.15, 1.9, 29630, 6.74, 7.66, 18.37, 14.29,
10.6, 30175, 6.62, 7.66, 18.46, 14.11, 11.8, 29670, 6, 7.54,
18.37, 14.73, 3, 30042, 6, 7.54, 18.61, 14.56, 4.1, 31403,
6.17, 7.57, 19.6, 14.28, 6.2, 31520, 6.25, 7.59, 19.72, 14.23,
6.6, 31915, 6.35, 7.61, 19.97, 14.14, 7.2, 32080, 6.41, 7.62,
20.05, 14.08, 7.2, 32520, 6.51, 7.64, 20.49, 13.9, 7.6, 32805,
6.58, 7.65, 20.55, 13.78, 8.6, 32876, 6.6, 7.65, 20.6, 13.76,
8.9, 32907, 6.6, 7.65, 20.61, 13.73, 8.9, 32920, 6.62, 7.65,
20.64, 13.72, 10.9, 32980, 6.62, 7.66, 20.65, 13.71, 10.3,
33019, 6.61, 7.66, 20.68, 13.7, 10.7, 33088, 6.61, 7.66,
20.71, 13.69, 10.1, 33125, 6.61, 7.66, 20.73, 13.68, 9.6,
33099, 6.61, 7.66, 20.74, 13.68, 10.3, 32261, 7.84, 7.58,
20.15, 23.32, 2.2, 32894, 1.66, 7.12, 20.58, 23.09, 2.5,
32313, 7.34, 7.52, 20.16, 23.33, 2.1, 32321, 7.33, 7.52,
20.19, 23.35, 2.1, 32318, 7.34, 7.51, 20.19, 23.34, 2.1,
32341, 7.34, 7.47, 20.2, 23.28, 2.1, 32330, 6.92, 7.48, 20.19,
23.31, 2.1, 32338, 6.77, 7.45, 20.2, 23.3, 2.1, 32350, 6.49,
7.42, 20.21, 23.29, 2.2, 32378, 6.24, 7.4, 20.23, 23.23,
2.2, 32386, 5.87, 7.37, 20.24, 23.23, 2.1, 32395, 5.49, 7.34,
20.23, 23.24, 2.2, 32405, 5.28, 7.32, 20.25, 23.23, 2.1,
32509, 4.69, 7.27, 20.32, 23.24, 1.8, 32548, 4.69, 7.25,
20.35, 23.23, 1.8, 32669, 2.97, 7.19, 20.39, 23.21, 2.9,
32712, 2.82, 7.17, 20.46, 23.2, 2.4, 32750, 2.62, 7.16, 20.48,
23.18, 2.2, 35655, 6.54, 7.52, 22.49, 23.48, 4.1, 36934,
4.4, 7.4, 23.42, 22.44, 6.3, 35424, 6.9, 7.46, 22.3, 23.27,
2.2, 35417, 6.89, 7.46, 22.33, 23.27, 2.3, 35459, 6.72, 7.45,
22.36, 23.18, 2.3, 35533, 6.4, 7.43, 22.42, 23.05, 2.6, 35555,
6.13, 7.42, 22.44, 23, 3.1, 35674, 5.59, 7.38, 22.52, 22.91,
3, 35741, 5.19, 7.36, 22.59, 22.87, 2.8, 35959, 4.92, 7.34,
22.72, 22.78, 2.9, 36032, 4.72, 7.33, 22.77, 22.74, 3.3,
36154, 4.69, 7.33, 22.86, 22.7, 3.3, 36175, 4.62, 7.34, 22.87,
22.69, 3.1, 36253, 4.52, 7.34, 22.92, 22.65, 3.5, 36361,
4.43, 7.33, 23, 22.61, 3.4, 36476, 4.33, 7.33, 23.07, 22.56,
3.7, 36590, 4.24, 7.33, 23.17, 22.51, 3.9, 36661, 4.21, 7.33,
23.21, 22.48, 4.3, 36693, 4.14, 7.33, 23.23, 22.46, 4.3),
sys_sample_code = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = c("NC226", "DK011", "NC227", "NC122", "NC208",
"NC172", "MC008", "EB010", "EK022", "WE015", "WE016", "GC015",
"GC016", "SP015", "SP016", "HB015", "HB016"), class = "factor")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -654L), .Names = c("sample_date",
"chemical_name", "result_value", "sys_sample_code"))
然后使用Akrun建议的公式:
Then used the formula Akrun recommended:
res <- ddd %>%
group_by(chemical_name) %>%
mutate(indx=1:n()) %>% #create a sequence column for each group
# select(-row) %>% #remove the row column before spreading
spread(chemical_name, result_value)
res
抱歉，发布大型数据集.
Sorry for posting large dataset.
我得到的结果如下:
sample_date sys_sample_code indx Conductivity (field) Dissolved Oxygen (field) pH (field) Salinity (field) Temperature (field) Turbidity (field)
1 2014-05-19 NC226 21 29670 6.00 7.54 18.37 14.73 3.0
2 2014-05-19 NC226 22 30042 6.00 7.54 18.61 14.56 4.1
3 2014-05-19 NC226 23 31403 6.17 7.57 19.60 14.28 6.2
4 2014-05-19 NC226 24 31520 6.25 7.59 19.72 14.23 6.6
5 2014-05-19 NC226 25 31915 6.35 7.61 19.97 14.14 7.2
6 2014-05-19 NC226 26 32080 6.41 7.62 20.05 14.08 7.2
7 2014-05-19 NC226 27 32520 6.51 7.64 20.49 13.90 7.6
8 2014-05-19 NC226 28 32805 6.58 7.65 20.55 13.78 8.6
9 2014-05-19 NC226 29 32876 6.60 7.65 20.60 13.76 8.9
10 2014-05-19 NC226 30 32907 6.60 7.65 20.61 13.73 8.9
推荐答案
您可以尝试
library(dplyr)
library(tidyr)
res <- ddd %>%
group_by(chemical_name) %>%
mutate(indx=1:n()) %>% #create a sequence column for each group
select(-row) %>% #remove the row column before spreading
spread(chemical_name, result_value)
更新
基于新的数据集
Update
Based on the new dataset
res1 <- ddd %>%
group_by(chemical_name) %>%
mutate(indx=1:n()) %>%
spread(chemical_name, result_value)
head(as.data.frame(res1),2)
# sample_date sys_sample_code indx Conductivity (field)
#1 2014-05-19 03:00:00 NC226 21 29670
#2 2014-05-19 03:00:00 NC226 22 30042
# Dissolved Oxygen (field) pH (field) Salinity (field) Temperature (field)
#1 6 7.54 18.37 14.73
#2 6 7.54 18.61 14.56
# Turbidity (field)
#1 3.0
#2 4.1
或者另一种选择是使用data.table
Or another option would be to use data.table
library(splitstackshape)
library(data.table)
res2 <- dcast.data.table(getanID(as.data.frame(ddd),'chemical_name'),
sample_date+sys_sample_code+.id~chemical_name, value.var='result_value')
head(res2,2)
# sample_date sys_sample_code .id Conductivity (field)
#1: 2014-05-19 03:00:00 NC226 21 29670
#2: 2014-05-19 03:00:00 NC226 22 30042
# Dissolved Oxygen (field) pH (field) Salinity (field) Temperature (field)
#1: 6 7.54 18.37 14.73
#2: 6 7.54 18.61 14.56
# Turbidity (field)
#1: 3.0
#2: 4.1
这篇关于在提迪尔中的传播与混乱的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
