1

我有一个面板数据集,其中包含 60 个国家的 10 个变量,跨越 18 年(2000-2017 年),我有很多缺失的数据。

Country Year    Broadband

Albania 2000    NA
Albania 2001    NA
Albania 2002    NA
Albania 2003    NA
Albania 2004    NA
Albania 2005    272
Albania 2006    NA
Albania 2007    10000
Albania 2008    64000
Albania 2009    92000
Albania 2010    105539
Albania 2011    128210
Albania 2012    160088
Albania 2013    182556
Albania 2014    207931
Albania 2015    242870
Albania 2016    263874
Albania 2017    NA
Algeria 2000    NA
Algeria 2001    NA
Algeria 2002    NA
Algeria 2003    18000
Algeria 2004    36000

我想使用 R 中的 na.approx 函数进行插值(并使用 rule = 2 进行推断),但仅限于每个国家/地区。例如,在此示例数据集中,我想插入 Albania 2006 的值,并推断 Albania 2000-2004 和 2017 的值。但我想确保 Albania 2017 的值不使用 Albania 2016 和 Algeria 2003 进行插值。对于阿尔及利亚 2000-2002,我希望使用阿尔及利亚 2003 和 2004 的数据来推断这些值。我尝试了以下代码:

data <- group_by(data, country)
data$broadband <- na.approx(data$broadband, maxgap = Inf, rule = 2)
data <- as.data.frame(data)

并尝试了不同的 maxgap 值,但似乎没有一个能解决我的问题。我假设通过使用 group_by 函数它可以正常工作,但事实并非如此。有谁知道任何解决方案?

编辑:我想到的唯一方法是使用以下代码将数据集拆分为每个唯一国家/地区的单独数据集:

mylist <- split(data, data$country)

alb <- mylist[1]
alb <- as_data_frame(alb)
alg <- mylist[2]
alg <- as_data_frame(alg)
ang <- mylist[3]
ang <- as_data_frame(ang)

然后在单独的数据集上一次使用一个 na.approx 函数。

编辑2:

我已经尝试了下面 Markus 建议的解决方案,但它似乎不起作用。这是使用您建议的安哥拉值编码的结果:

Country Year    Broadband   Broadband_imp

Algeria 2014    1599692 1599692
Algeria 2015    2269348 2269348
Algeria 2016    2858906 2858906
Angola  2000    NA  2451556.286
Angola  2001    NA  2044206.571
Angola  2002    NA  1636856.857
Angola  2003    NA  1229507.143
Angola  2004    NA  822157.429
Angola  2005    NA  414807.714
Angola  2006    7458    7458
Angola  2007    11700   11700

如您所见,安哥拉 2000-2005 年的估算值似乎是使用阿尔及利亚的值计算的,因为估算值远高于安哥拉 2006 年 7458 的值。

编辑 3:这是我使用的完整代码 -

data <- read_excel("~/Documents/data.xlsx")

> dput(head(data))
structure(list(continent = c("Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe"), country = c("Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania"), Year = c(2000, 2001, 2002, 
2003, 2004, 2005), `Individuals Using Internet, %, WB` = c(0.114097347, 
0.325798377, 0.390081273, 0.971900415, 2.420387798, 6.043890864
), `Secure Internet Servers, WB` = c(NA, 1, NA, 1, 2, 1), `Mobile Cellular 
Subscriptions, WB` = c(29791, 
392650, 851000, 1100000, 1259590, 1530244), `Fixed Broadband Subscriptions, 
WB` = c(NA, 
NA, NA, NA, NA, 272), `Trade, % GDP, WB` = c(55.9204287230026, 
57.4303612453301, 63.9342407411882, 65.4406219482911, 66.3578254370479, 
70.2953012017195), `Air transport, freight (million ton-km)` = c(0.003, 
0.003, 0.144, 0.088, 0.099, 0.1), `Air Transport, registered carrier 
departures worldwide, WB` = c(3885, 
3974, 3762, 3800, 4104, 4309), `FDI, net, inflows, % GDP, WB` = 
c(3.93717707227928, 
5.10495722596557, 3.04391445388559, 3.09793068135411, 4.66563777108359, 
3.21722676118428), `Number of Airports, WFB` = c(10, 11, 11, 
11, 11, 11), `Currently under EU Arms Sanctions` = c(0, 0, 0, 
0, 0, 0), `Currently under EU Economic Sanctions` = c(0, 0, 0, 
0, 0, 0), `Currently under UN Arms Sanctions` = c(0, 0, 0, 0, 
0, 0), `Currently under UN Economic Sanctions` = c(0, 0, 0, 0, 
0, 0), `Currently under US Arms Embargo` = c(0, 0, 0, 0, 0, 0
), `Currently under US Economic Sanctions` = c(0, 0, 0, 0, 0, 
0)), .Names = c("continent", "country", "Year", "Individuals Using Internet, 
%, WB", 
"Secure Internet Servers, WB", "Mobile Cellular Subscriptions, WB", 
"Fixed Broadband Subscriptions, WB", "Trade, % GDP, WB", "Air transport, 
freight (million ton-km)", 
"Air Transport, registered carrier departures worldwide, WB", 
"FDI, net, inflows, % GDP, WB", "Number of Airports, WFB", "Currently under EU 
 Arms Sanctions", 
"Currently under EU Economic Sanctions", "Currently under UN Arms Sanctions", 
"Currently under UN Economic Sanctions", "Currently under US Arms Embargo", 
"Currently under US Economic Sanctions"), row.names = c(NA, -6L
), class = c("tbl_df", "tbl", "data.frame"))

 data_imputed <- data %>% 
group_by(country) %>% 
mutate(broadband_imp = na.approx(broadband, maxgap=Inf, rule = 2))
4

1 回答 1

3

您可以使用group_bymutate

library(tidyverse)
library(zoo)

df_imputed <- df %>% 
group_by(Country) %>% 
mutate(Broadband_imputed = na.approx(Broadband, maxgap = Inf, rule = 2))

这使

> head(df_imputed)
# A tibble: 6 x 4
# Groups:   Country [1]
  Country  Year Broadband Broadband_imputed
   <fctr> <int>     <int>             <dbl>
1 Albania  2000        NA               272
2 Albania  2001        NA               272
3 Albania  2002        NA               272
4 Albania  2003        NA               272
5 Albania  2004        NA               272
6 Albania  2005       272               272

> df_imputed %>% filter(Country == 'Algeria')
# A tibble: 5 x 4
# Groups:   Country [1]
  Country  Year Broadband Broadband_imputed
   <fctr> <int>     <int>             <dbl>
1 Algeria  2000        NA             18000
2 Algeria  2001        NA             18000
3 Algeria  2002        NA             18000
4 Algeria  2003     18000             18000
5 Algeria  2004     36000             36000

数据

df <- read.table(text = "Country Year    Broadband
Albania 2000    NA
Albania 2001    NA
Albania 2002    NA
Albania 2003    NA
Albania 2004    NA
Albania 2005    272
Albania 2006    NA
Albania 2007    10000
Albania 2008    64000
Albania 2009    92000
Albania 2010    105539
Albania 2011    128210
Albania 2012    160088
Albania 2013    182556
Albania 2014    207931
Albania 2015    242870
Albania 2016    263874
Albania 2017    NA
Algeria 2000    NA
Algeria 2001    NA
Algeria 2002    NA
Algeria 2003    18000
Algeria 2004    36000", header = TRUE)
于 2017-10-27T13:02:54.163 回答