Question

我正在尝试使用pivot_wider转换数据帧。输出应具有3列（处理C，处理E和发布年份）。那是我的数据框：

        Fv.Fm treatment release.year
1   0.3131340         E         1995
2   0.2829015         E         1995
3   0.4881591         E         1995
4   0.4319261         E         1995
5   0.3045167         E         1995
6   0.4241259         E         1995
7   0.4086531         E         1995
8   0.5774378         E         1995
9   0.5450547         E         1995
10  0.5587373         E         1986
11  0.4988792         E         1986
...
13378   0.5938378   C   1956
13379   0.4781431   C   1956
13380   0.3494923   C   2004
13381   0.3617828   C   2004
13382   0.3898748   C   2004
13383   0.4318397   C   2004
13384   0.4780491   C   2004
13385   0.4950537   C   2004
13386   0.3579608   C   2004
13387   0.5040526   C   2004
13388   0.4333380   C   2004

我使用了该代码：

A%>%
  group_by(treatment) %>%
  mutate(row =row_number()) %>%
  tidyr::pivot_wider(names_from = treatment,values_from = Fv.Fm)%>%
  select(-row)

这是输出：

release.year E           C
1   1995    0.3131340   NA
2   1995    0.2829015   NA
3   1995    0.4881591   NA
4   1995    0.4319261   NA
5   1995    0.3045167   NA
6   1995    0.4241259   NA
7   1995    0.4086531   NA
8   1995    0.5774378   NA
9   1995    0.5450547   NA
10  1986    0.5587373   NA
11  1986    0.4988792   NA
...
13089   1956    NA  0.4781431
13090   2004    NA  0.3494923
13091   2004    NA  0.3617828
13092   2004    NA  0.3898748
13093   2004    NA  0.4318397
13094   2004    NA  0.4780491
13095   2004    NA  0.4950537
13096   2004    NA  0.3579608
13097   2004    NA  0.5040526
13098   2004    NA  0.4333380

但是我想将处理C和E的值放在一行中，所以没有NA。我该怎么办？

Answer 1

您得到的输出是正确的，因为release.year和E C组之间没有公用的treatment。如果您希望它们彼此相邻，则需要删除release.year列并展开

library(dplyr)

A %>%
  group_by(treatment) %>%
  mutate(row =row_number()) %>%
  select(-release.year) %>%
  tidyr::pivot_wider(names_from = treatment,values_from = Fv.Fm) %>%
  select(-row)

#      E     C
#   <dbl> <dbl>
# 1 0.313 0.594
# 2 0.283 0.478
# 3 0.488 0.349
# 4 0.432 0.362
# 5 0.305 0.390
# 6 0.424 0.432
# 7 0.409 0.478
# 8 0.577 0.495
# 9 0.545 0.358
#10 0.559 0.504
#11 0.499 0.433
#....
#....

不适用，通过使用pivot_wider

1 个答案: