在使用WDI API之后，如何通过字符串轻松地删除聚集的国家行？
在使用WDI API之后，如何通过字符串轻松地删除聚集的国家行？
提问于 2020-08-20 06:53:08
好吧，我有这个：
library(WDI)
wdi <- WDI(indicator=c("SI.POV.NAHC", "SI.POV.GINI", "SL.UEM.TOTL.ZS",
"SP.POP.TOTL"), start=1991, end=2018)
wdi <- as_tibble(wdi)
wdi <- wdi %>% select(-iso2c)
wdi <- wdi %>% rename(`Poverty Headcount`=SI.POV.NAHC,
`Gini Index`=SI.POV.GINI,
`Total Unemployment`= SL.UEM.TOTL.ZS,
`Total Population`=SP.POP.TOTL)
##Remove rows command goes here
kable(head(wdi))如您所见，第一列是country，行是每年的国家观察。我想从这个数据集中删除所有“组”(阿拉伯世界，世界，南亚，.)我只想要国家。
这是世界银行的数据。
我使用的非基本包是：
library(tidyverse)
library(WDI)
library(psych)
library(pastecs)
library(xlsx)
library(stargazer)
library(xtable)
library(markdown)
library(knitr)
library(haven)
library(readr)
library(ggplot2)
library(dplyr)
library(sf)
library(tmap)
library(spData)
library(RColorBrewer)
library(stringr)回答 1
Stack Overflow用户
回答已采纳
发布于 2020-08-20 07:13:49
您可以检查"iso2c"列的有效性。一个简单的方法是使用countrycode(<values>, <from>, <to>)的countrycode包。
wdi <- WDI::WDI(indicator=c("SI.POV.NAHC", "SI.POV.GINI", "SL.UEM.TOTL.ZS",
"SP.POP.TOTL"), start=1991, end=2018)当您将"iso2c"用于<from>和<to>时，您可以很容易地使用is.na获得NA。然而，在申请之前检查结果，因为否则的话，该方法也将消除有争议的国家，如科索沃。
library(countrycode)
rm.groups <- unique(wdi$country[is.na(countrycode(wdi$iso2c, "iso2c", "iso2c"))])
rm.groups
# [1] "Arab World"
# [2] "World"
# [3] "East Asia & Pacific (excluding high income)"
# [4] "Europe & Central Asia (excluding high income)"
# [5] "South Asia"
# [6] "Central Europe and the Baltics"
# [7] "European Union"
# [8] "Fragile and conflict affected situations"
# [9] "Channel Islands"
# [10] "OECD members"
# [11] "Small states"
# [12] "Pacific island small states"
# [13] "Caribbean small states"
# [14] "Other small states"
# [15] "Latin America & the Caribbean (IDA & IBRD countries)"
# [16] "Middle East & North Africa (IDA & IBRD countries)"
# [17] "East Asia & Pacific (IDA & IBRD countries)"
# [18] "South Asia (IDA & IBRD)"
# [19] "Sub-Saharan Africa (IDA & IBRD countries)"
# [20] "Europe & Central Asia (IDA & IBRD countries)"
# [21] "Pre-demographic dividend"
# [22] "Early-demographic dividend"
# [23] "Late-demographic dividend"
# [24] "Post-demographic dividend"
# [25] "Euro area"
# [26] "High income"
# [27] "Heavily indebted poor countries (HIPC)"
# [28] "IBRD only"
# [29] "IDA total"
# [30] "IDA blend"
# [31] "IDA only"
# [32] "Latin America & Caribbean (excluding high income)"
# [33] "Kosovo"
# [34] "Least developed countries: UN classification"
# [35] "Low income"
# [36] "Lower middle income"
# [37] "Low & middle income"
# [38] "Middle income"
# [39] "Middle East & North Africa (excluding high income)"
# [40] "Upper middle income"
# [41] "North America"
# [42] "Not classified"
# [43] "East Asia & Pacific"
# [44] "Europe & Central Asia"
# [45] "Sub-Saharan Africa (excluding high income)"
# [46] "Sub-Saharan Africa"
# [47] "Latin America & Caribbean"
# [48] "Middle East & North Africa"
# [49] "IDA & IBRD total" 但这很简单。在检查rm.groups向量之后，您可能需要保留以下两个：
wdi$iso2c[wdi$country == "Kosovo"][1]
# [1] "XK"
wdi$iso2c[wdi$country == "Channel Islands"][1]
# [1] "JG"只需像这样使用rm.groups使用%in%将它们从%in%中删除
rm.groups <- rm.groups[-which(rm.groups %in% c("Kosovo", "Channel Islands"))]最后，您可以将这些组从wdi数据框架中删除，方法是保留那些属于!而不是%in% rm.groups的国家行。
wdi.nogroups <- wdi[!wdi$country %in% rm.groups, ]
head(wdi.nogroups)
# iso2c country year SI.POV.NAHC SI.POV.GINI SL.UEM.TOTL.ZS SP.POP.TOTL
# 141 AD Andorra 1991 NA NA NA 56671
# 142 AD Andorra 1992 NA NA NA 58888
# 143 AD Andorra 1993 NA NA NA 60971
# 144 AD Andorra 1994 NA NA NA 62677
# 145 AD Andorra 1995 NA NA NA 63850
# 146 AD Andorra 1996 NA NA NA 64360页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/63499764
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