I have a panel dataset with 10 variables for 60 countries, across 18 years (2000-2017), and I have a lot of missing data.
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
I would like to interpolate using the na.approx function in R (and extrapolate using rule = 2), but only within each country. In this sample dataset for example, I want to interpolate the value for Albania 2006, and extrapolate for Albania 2000-2004 and 2017. But I want to make sure that the value for Albania 2017 isn't interpolated using Albania 2016 and Algeria 2003. For Algeria 2000-2002, I want the values to be extrapolated using the data for Algeria 2003 and 2004. I have tried the following code:
data <- group_by(data, country)
data$broadband <- na.approx(data$broadband, maxgap = Inf, rule = 2)
data <- as.data.frame(data)
and have tried different values for maxgap, but none seem to fix my problem. I assumed by using the group_by function it would work correctly but it doesn't. Does anyone know of any solutions?
EDIT: The only way I have thought of to do what I need is to split the dataset into a separate dataset for each unique country, using the following code:
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)
and then use the na.approx function on the separate datasets one at a time.
EDIT 2:
I have tried the solution suggested by Markus below, and it doesn't seem to work. This is the result using your suggested coded for values for Angola:
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
As you can see, the imputed values for Angola 2000-2005 seem to have been calculated using values from Algeria, as the imputed values are much higher than they should be given the Angola 2006 value of 7458.
EDIT 3: This is the full code I have used -
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))
You can use group_by
and mutate
:
library(tidyverse)
library(zoo)
df_imputed <- df %>%
group_by(Country) %>%
mutate(Broadband_imputed = na.approx(Broadband, maxgap = Inf, rule = 2))
Which gives
> 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
and
> 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
DATA
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)
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