[英]How to include missing data points in r
This problem is a spin-off from my last post ( How to calculate moving average for two years in r ).这个问题是我上一篇文章( 如何计算 r 中两年的移动平均值)的衍生问题。
I have a big data frame (900k rows) about mergers and acquisitions (M&As).我有一个关于并购 (M&A) 的大数据框(90 万行)。
The df has four columns: date (when the M&A was completed), target_nation (a company of which country was merged/acquired), acquiror_nation (corporation of which country was the acquiror), and big_corp_TF (whether the acquiror was a big corporation or not, where TRUE means that corporation is big). df 有四列: date (并购完成时间)、 target_nation (被兼并/收购的国家/地区的公司)、 acquiror_nation (收购方是哪个国家/地区的公司)和big_corp_TF (收购方是大公司还是不是,TRUE 表示公司很大)。 Here is a sample of my data:
这是我的数据示例:
> df <- structure(list(date = c(2000L, 2000L, 2001L, 2001L, 2001L, 2002L,
2002L, 2002L, 2003L, 2003L, 2004L, 2004L, 2004L, 2006L, 2006L
), target_nation = c("Uganda", "Uganda", "Uganda", "Uganda",
"Uganda", "Uganda", "Uganda", "Uganda", "Uganda", "Uganda", "Uganda",
"Uganda", "Uganda", "Uganda", "Uganda"), acquiror_nation = c("France",
"Germany", "France", "France", "Germany", "France", "France",
"Germany", "Germany", "Germany", "France", "France", "Germany",
"France", "France"), big_corp_TF = c(TRUE, FALSE, TRUE, FALSE, FALSE,
TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE)), row.names = c(NA,
-15L))
> df
date target_nation acquiror_nation big_corp_TF
1: 2000 Uganda France TRUE
2: 2000 Uganda Germany FALSE
3: 2001 Uganda France TRUE
4: 2001 Uganda France FALSE
5: 2001 Uganda Germany FALSE
6: 2002 Uganda France TRUE
7: 2002 Uganda France TRUE
8: 2002 Uganda Germany TRUE
9: 2003 Uganda Germany TRUE
10: 2003 Uganda Germany FALSE
11: 2004 Uganda France TRUE
12: 2004 Uganda France FALSE
13: 2004 Uganda Germany TRUE
14: 2006 Uganda France TRUE
15: 2006 Uganda France TRUE
NB: There are no rows for France in 2003;注意: 2003 年法国没有行; and there is no year 2005.
并且没有 2005 年。
From these data, I want to create a new variable that denotes the share of M&As done by big corporations of specific acquiror nations, counting the average for 2 years.根据这些数据,我想创建一个新变量,表示特定收购国的大公司进行的并购份额,计算 2 年的平均值。 (For my actual exercise, I will count the averages for 5 years, but let's keep things simpler here).
(对于我的实际练习,我将计算 5 年的平均值,但让我们在这里保持简单)。 So there would be a new variable for France's big corporations, and a new variable for Germany's big corporations.
所以法国的大公司会有一个新的变量,德国的大公司会有一个新的变量。
I was suggested to use the following code:有人建议我使用以下代码:
library(runner)
library(tidyverse)
df <- df %>% as.data.frame()
param <- 'France'
df %>%
group_by(date, target_nation) %>%
mutate(n1 = n()) %>%
group_by(date, target_nation, acquiror_nation) %>%
summarise(n1 = mean(n1),
n2 = sum(big_corp_TF), .groups = 'drop') %>%
filter(acquiror_nation == param) %>%
mutate(share = sum_run(n2, k=2)/sum_run(n1, k=2))
Which outputs this tibble:输出这个小标题:
date target_nation acquiror_nation n1 n2 share
<int> <chr> <chr> <dbl> <int> <dbl>
1 2000 Uganda France 2 1 0.5
2 2001 Uganda France 3 1 0.4
3 2002 Uganda France 3 2 0.5
4 2004 Uganda France 3 1 0.5
5 2006 Uganda France 2 2 0.6
NB: there is no result for France for 2003 and 2005;注意: 2003 年和 2005 年法国没有结果; I would like there to be results for 2003 and 2005 (because we are calculating 2-year averages and thus we should be able to have results for 2003 and 2005).
我希望有 2003 年和 2005 年的结果(因为我们正在计算 2 年的平均值,因此我们应该能够获得 2003 年和 2005 年的结果)。 Also, the share for 2006 is incorrect in reality, because it should be 1 (it should take the values of 2005 (which are 0s) rather than the values of 2004 for the calculation of average).
另外,2006 年的份额实际上是不正确的,因为它应该是 1(它应该取 2005 年的值(即 0)而不是 2004 年的值来计算平均值)。
I would like to be able to receive the following tibble:我希望能够收到以下 tibble:
date target_nation acquiror_nation n1 n2 share
<int> <chr> <chr> <dbl> <int> <dbl>
1 2000 Uganda France 2 1 0.5
2 2001 Uganda France 3 1 0.4
3 2002 Uganda France 3 2 0.5
4 2003 Uganda France 2 0 0.4
5 2004 Uganda France 3 1 0.2
6 2005 Uganda France 0 0 0.33
7 2006 Uganda France 2 2 1.0
NB: notice that the result for 2006 is also different (because we now take 2005 instead of 2004 for a two-year average).注意:请注意,2006 年的结果也有所不同(因为我们现在将 2005 年而不是 2004 年作为两年平均值)。
I understand that this is a problem with the original data: it simply lacks certain data points.我知道这是原始数据的问题:它只是缺少某些数据点。 However, including them to the original data set seems to be highly inconvenient;
但是,将它们包含在原始数据集中似乎非常不方便; it is probably better to include them mid-way, eg after counting the n1 and n2.
中途包含它们可能会更好,例如在计算 n1 和 n2 之后。 But what is the most convenient way to do this?
但是最方便的方法是什么?
Any suggestions are much appreciated.任何建议都非常感谢。
use tidyr::complete
along with its arguments nesting
and fill
.使用
tidyr::complete
及其 arguments nesting
和fill
。 Full code that may be used.可以使用的完整代码。
param <- 'France'
df %>%
mutate(d = 1) %>%
complete(date = seq(min(date), max(date), 1), nesting(target_nation, acquiror_nation),
fill = list(d =0, big_corp_TF = FALSE)) %>%
group_by(date, target_nation) %>%
mutate(n1 = sum(d)) %>%
group_by(date, target_nation, acquiror_nation) %>%
summarise(n1 = mean(n1),
n2 = sum(big_corp_TF), .groups = 'drop') %>%
filter(acquiror_nation == param) %>%
mutate(share = sum_run(n2, k=2)/sum_run(n1, k=2))
# A tibble: 7 x 6
date target_nation acquiror_nation n1 n2 share
<dbl> <chr> <chr> <dbl> <int> <dbl>
1 2000 Uganda France 2 1 0.5
2 2001 Uganda France 3 1 0.4
3 2002 Uganda France 3 2 0.5
4 2003 Uganda France 2 0 0.4
5 2004 Uganda France 3 1 0.2
6 2005 Uganda France 0 0 0.333
7 2006 Uganda France 2 2 1
df2 = df %>%
group_by(date, target_nation) %>%
mutate(n1 = n()) %>%
group_by(date, target_nation, acquiror_nation) %>%
summarise(n1 = mean(n1),
n2 = sum(big_corp_TF), .groups = 'drop') %>%
filter(acquiror_nation == param)
dates = seq(min(df2$date), max(df2$date), by = 1)
dates = setdiff(dates, df2$date)
df3 = df2[rep(nrow(df2), each = length(dates)), ]
df3$n1 = 0; df3$n2 = 0; df3$date = dates
df2 = arrange(rbind(df2,df3), date)
df2 = df2 %>% mutate(share = sum_run(n2, k=2)/sum_run(n1, k=2))
df2
# A tibble: 7 x 6
date target_nation acquiror_nation n1 n2 share
<dbl> <fct> <fct> <dbl> <dbl> <dbl>
1 2000 Uganda France 2 1 0.5
2 2001 Uganda France 3 1 0.4
3 2002 Uganda France 3 2 0.5
4 2003 Uganda France 0 0 0.667
5 2004 Uganda France 3 1 0.333
6 2005 Uganda France 0 0 0.333
7 2006 Uganda France 2 2 1
First, create df2
based on your df
but without calculating share
.首先,根据您的
df
创建df2
但不计算share
。 Create a sequence of dates from the minimum to the maximum:创建从最小值到最大值的日期序列:
dates = seq(min(df2$date), max(df2$date), by = 1)
Leave just the ones that are missing in df2
:只留下
df2
中缺少的那些:
dates = setdiff(dates, df2$date)
Create a row for each missing date and set n1
and n2
to 0:为每个缺失的日期创建一行并将
n1
和n2
设置为 0:
df3 = df2[rep(nrow(df2), each = length(dates)), ]
df3$n1 = 0; df3$n2 = 0; df3$date = dates
Combine the rows and sort by date:合并行并按日期排序:
df2 = arrange(rbind(df2,df3), date)
Finally, calculate share
:最后,计算
share
:
df2 = df2 %>% mutate(share = sum_run(n2, k=2)/sum_run(n1, k=2))
I apologise that this doesn't adhere to the tidyverse syntax我很抱歉这不符合 tidyverse 语法
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.