[英]Compare the current row against subsequent rows (order by date and group) in r
我有包含3列(组,日期,值)的数据集。 对于每行(“组”和“日期”的组合),我想评估何时(以天为单位)值增加1%。
df <- read.table(text =
"Group Date value
A 11/1/17 56
A 11/2/17 51
A 11/3/17 58
A 11/4/17 62
A 11/5/17 60
A 11/6/17 55
A 11/7/17 56
A 11/8/17 56
A 11/9/17 53
B 11/1/17 56
B 11/2/17 63
B 11/3/17 50
B 11/4/17 62
B 11/5/17 65
B 11/6/17 61
B 11/7/17 56
B 11/8/17 62
C 11/1/17 50
C 11/2/17 62 ", header = T)
我希望输出看起来像这样……我对creation_by_1 increase_by_1%
列感兴趣。 increase_by_1%
是当前行增加1%时的日期差。 因此,该值增加的日期减去当前值的日期。
例如,第一行的逻辑为
# if group == A & (value on 11/2/17 = 51 – value on 11/1/17 = 56)/ value on 11/1/17 = 56 >= .01 THEN FALSE
# if group == A & (value on 11/3/17 = 58 – value on 11/1/17 = 56)/ value on 11/1/17 = 56 >= .01 = TRUE THEN (**11/3/17 - 11/1/17 = 2**)
第二行的逻辑...
# if group == A & (value on 11/3/17 = 58 – value on 11/2/17 = 51)/ value on 11/2/17 = 51 >= .01 = TRUE THEN (**11/3/17 - 11/2/17 = 1**)
第四行的逻辑...
# if group == A & (value on 11/5/17 = 60 – value on 11/4/17 = 62)/ value on 11/4/17 = 62 >= .01 = FALSE THEN NA
# if group == A & (value on 11/6/17 = 55 – value on 11/4/17 = 62)/ value on 11/4/17 = 62 >= .01 = FALSE THEN NA
# if group == A & (value on 11/7/17 = 56 – value on 11/4/17 = 62)/ value on 11/4/17 = 62 >= .01 = FALSE THEN NA
# if group == A & (value on 11/8/17 = 56 – value on 11/4/17 = 62)/ value on 11/4/17 = 62 >= .01 = FALSE THEN NA
# if group == A & (value on 11/9/17 = 53 – value on 11/4/17 = 62)/ value on 11/4/17 = 62 >= .01 = FALSE THEN NA
NA表示当前行(例如11/4/17)的值对于后续行不会增加。
+=======+=========+=======+================+
| Group | Date | value | increase_by_1% |
+=======+=========+=======+================+
| A | 11/1/17 | 56 | 2 |
+-------+---------+-------+----------------+
| A | 11/2/17 | 51 | 1 |
+-------+---------+-------+----------------+
| A | 11/3/17 | 58 | 1 |
+-------+---------+-------+----------------+
| A | 11/4/17 | 62 | NA |
+-------+---------+-------+----------------+
| A | 11/5/17 | 60 | NA |
+-------+---------+-------+----------------+
| A | 11/6/17 | 55 | 1 |
+-------+---------+-------+----------------+
| A | 11/7/17 | 56 | NA |
+-------+---------+-------+----------------+
| A | 11/8/17 | 56 | NA |
+-------+---------+-------+----------------+
| A | 11/9/17 | 53 | NA |
+-------+---------+-------+----------------+
| B | 11/1/17 | 56 | 1 |
+-------+---------+-------+----------------+
| B | 11/2/17 | 63 | 3 |
+-------+---------+-------+----------------+
| B | 11/3/17 | 50 | 1 |
+-------+---------+-------+----------------+
| B | 11/4/17 | 62 | 1 |
+-------+---------+-------+----------------+
| B | 11/5/17 | 65 | NA |
+-------+---------+-------+----------------+
| B | 11/6/17 | 61 | 2 |
+-------+---------+-------+----------------+
| B | 11/7/17 | 56 | 1 |
+-------+---------+-------+----------------+
| B | 11/8/17 | 62 | NA |
+-------+---------+-------+----------------+
| C | 11/1/17 | 50 | 1 |
+-------+---------+-------+----------------+
| C | 11/2/17 | 62 | NA |
+-------+---------+-------+----------------+
这是我到目前为止所拥有的,但是,这是不可扩展的。 如果还有更多日期,则需要在if else语句中手动添加这些日期。
shift <- function(x, n){
c(x[-(seq(n))], rep(NA, n))
}
df= do.call(rbind,by(df,df$Group, transform,next_1_percent_or_higher_change =
ifelse(((shift(value,1)-value)/value) >= .01,1,
ifelse(((shift(value,2)-value)/value) >= .01,2,
ifelse(((shift(value,3)-value)/value) >= .01,3,
ifelse(((shift(value,4)-value)/value) >= .01,4,
ifelse(((shift(value,5)-value)/value) >= .01,5,
ifelse(((shift(value,6)-value)/value) >= .01,6,
ifelse(((shift(value,7)-value)/value) >= .01,7,
ifelse(((shift(value,8)-value)/value) >= .01,8,
ifelse(((shift(value,9)-value)/value) >= .01,9,NA)))))))))))
如果我的理解正确,您正在寻找的value
变化比上一行高出1%,并且仅限于同一Group
。
library(dplyr)
f_flagged_shorter <- f %>%
group_by(Group) %>%
mutate(flag = value >= lag(value)*1.01) %>%
ungroup()
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