我想用SKScene替换给定列中各行的重复值。我在这里提供了数据框的一个例子以及列的所需性质。
NA由reprex package（v0.2.0）创建于2018-03-21。
换句话说，我希望# defining the dataframe
df <- structure(
list(
condition = c(4, 4, 6, 6, 8, 8),
main = structure(
c(2L,
1L, 1L, 2L, 1L, 2L),
.Label = c("0", "1"),
class = "factor"
),
counts = c(8L, 3L, 4L, 3L, 12L, 2L),
perc = c(
72.7272727272727,
27.2727272727273,
57.1428571428571,
42.8571428571429,
85.7142857142857,
14.2857142857143
),
`0` = c("27.27%", "27.27%", "57.14%",
"57.14%", "85.71%", "85.71%"),
`1` = c("72.73%", "72.73%",
"42.86%", "42.86%", "14.29%", "14.29%"),
`Chi-squared` = c("2.273",
"2.273", "0.143", "0.143", "7.143", "7.143"),
df = structure(c(1L,
1L, 1L, 1L, 1L, 1L), .Label = "1", class = "factor"),
`p-value` = c("0.132",
"0.132", "0.705", "0.705", "0.008", "0.008"),
significance = c("ns",
"ns", "ns", "ns", "**", "**")
),
class = c("grouped_df", "tbl_df",
"tbl", "data.frame"),
row.names = c(NA, -6L),
vars = "condition",
.Names = c(
"condition",
"main",
"counts",
"perc",
"0",
"1",
"Chi-squared",
"df",
"p-value",
"significance"
),
indices = list(0:1, 2:3, 4:5),
group_sizes = c(2L,
2L, 2L),
biggest_group_size = 2L,
labels = structure(
list(condition = c(4,
6, 8)),
class = "data.frame",
row.names = c(NA, -3L),
vars = "condition",
.Names = "condition"
)
)
# print the dataframe
print(df)
#> condition main counts perc 0 1 Chi-squared df p-value
#> 1 4 1 8 72.72727 27.27% 72.73% 2.273 1 0.132
#> 2 4 0 3 27.27273 27.27% 72.73% 2.273 1 0.132
#> 3 6 0 4 57.14286 57.14% 42.86% 0.143 1 0.705
#> 4 6 1 3 42.85714 57.14% 42.86% 0.143 1 0.705
#> 5 8 0 12 85.71429 85.71% 14.29% 7.143 1 0.008
#> 6 8 1 2 14.28571 85.71% 14.29% 7.143 1 0.008
#> significance
#> 1 ns
#> 2 ns
#> 3 ns
#> 4 ns
#> 5 **
#> 6 **
# I want the significance column to be
# ns
# NA
# ns
# NA
# **
# NA
的每个重复值都替换为significance。我怎样才能做到这一点？如果可能的话，更愿意使用NA回答。
答案 0 :(得分：3)
这似乎是一种替代操作：
moduleName-pages/index.js如果需要dplyr，可以通过always.js包装它：
apos.ui.link('manage', 'placement-guide', function () {
self.manage();
});
self.manage = function() {
return self.api('manager-modal', {}, function(data) { ... }