利用subset()筛选数据集

作者: 冬之心 | 来源:发表于2019-12-07 15:52 被阅读0次

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subset()可以方便地提取变量和值。比如：

subset(airquality, Temp > 80, select = c(Ozone, Temp))
subset(airquality, Day == 1, select = -Temp)
subset(airquality, select = Ozone:Wind)

问题

我想从数据集中，根据某一个变量的多个取值筛选子集。

以gapminder数据集为例，我想筛选出中日美三个国家的数据。

出错

只选中国的结果

 china <- subset(gapminder, country=="China")

country	continent	year	lifeExp	pop	gdpPercap
China	Asia	1952	44.00000	556263527	400.4486
China	Asia	1957	50.54896	637408000	575.9870
China	Asia	1962	44.50136	665770000	487.6740
China	Asia	1967	58.38112	754550000	612.7057
China	Asia	1972	63.11888	862030000	676.9001
China	Asia	1977	63.96736	943455000	741.2375
China	Asia	1982	65.52500	1000281000	962.4214
China	Asia	1987	67.27400	1084035000	1378.9040
China	Asia	1992	68.69000	1164970000	1655.7842
China	Asia	1997	70.42600	1230075000	2289.2341
China	Asia	2002	72.02800	1280400000	3119.2809
China	Asia	2007	72.96100	1318683096	4959.1149

同时选三国的错误做法

three <-subset(gapminder, country==c("China","Japan", "United States"))

country	continent	year	lifeExp	pop	gdpPercap
China	Asia	1952	44.00000	556263527	400.4486
China	Asia	1967	58.38112	754550000	612.7057
China	Asia	1982	65.52500	1000281000	962.4214
China	Asia	1997	70.42600	1230075000	2289.2341
Japan	Asia	1957	65.50000	91563009	4317.6944
Japan	Asia	1972	73.42000	107188273	14778.7864
Japan	Asia	1987	78.67000	122091325	22375.9419
Japan	Asia	2002	82.00000	127065841	28604.5919
United States	Americas	1962	70.21000	186538000	16173.1459
United States	Americas	1977	73.38000	220239000	24072.6321
United States	Americas	1992	76.09000	256894189	32003.9322
United States	Americas	2007	78.24200	301139947	42951.6531

缺失了很多数据。实际上，这种做法把12个年份的数据依次轮回分配给三个国家了；而不是我们想要的每个国家都有12个年份的数据。

正确做法

用“|”或判断

three <-subset(gapminder, country=="China"|country=="Japan"|country=="United States")

用“%in%”多项配对

three <-subset(gapminder, country %in% c("China","Japan","United States"))

country	continent	year	lifeExp	pop	gdpPercap
China	Asia	1952	44.00000	556263527	400.4486
China	Asia	1957	50.54896	637408000	575.9870
China	Asia	1962	44.50136	665770000	487.6740
China	Asia	1967	58.38112	754550000	612.7057
China	Asia	1972	63.11888	862030000	676.9001
China	Asia	1977	63.96736	943455000	741.2375
China	Asia	1982	65.52500	1000281000	962.4214
China	Asia	1987	67.27400	1084035000	1378.9040
China	Asia	1992	68.69000	1164970000	1655.7842
China	Asia	1997	70.42600	1230075000	2289.2341
China	Asia	2002	72.02800	1280400000	3119.2809
China	Asia	2007	72.96100	1318683096	4959.1149
Japan	Asia	1952	63.03000	86459025	3216.9563
Japan	Asia	1957	65.50000	91563009	4317.6944
Japan	Asia	1962	68.73000	95831757	6576.6495
Japan	Asia	1967	71.43000	100825279	9847.7886
Japan	Asia	1972	73.42000	107188273	14778.7864
Japan	Asia	1977	75.38000	113872473	16610.3770
Japan	Asia	1982	77.11000	118454974	19384.1057
Japan	Asia	1987	78.67000	122091325	22375.9419
Japan	Asia	1992	79.36000	124329269	26824.8951
Japan	Asia	1997	80.69000	125956499	28816.5850
Japan	Asia	2002	82.00000	127065841	28604.5919
Japan	Asia	2007	82.60300	127467972	31656.0681
United States	Americas	1952	68.44000	157553000	13990.4821
United States	Americas	1957	69.49000	171984000	14847.1271
United States	Americas	1962	70.21000	186538000	16173.1459
United States	Americas	1967	70.76000	198712000	19530.3656
United States	Americas	1972	71.34000	209896000	21806.0359
United States	Americas	1977	73.38000	220239000	24072.6321
United States	Americas	1982	74.65000	232187835	25009.5591
United States	Americas	1987	75.02000	242803533	29884.3504
United States	Americas	1992	76.09000	256894189	32003.9322
United States	Americas	1997	76.81000	272911760	35767.4330
United States	Americas	2002	77.31000	287675526	39097.0995
United States	Americas	2007	78.24200	301139947	42951.6531

附：蝌蚪图

library(ggplot2）
library(gganimate)
ggplot(three, aes(x=gdpPercap, y=lifeExp, size=pop, color=country)) + 
  geom_point(alpha=0.8) + 
  scale_size(range=c(2,12)) + 
  scale_x_log10() + 
  labs(title = "年份: {frame_time}") + 
  transition_time(year) +
  shadow_wake(wake_length = 0.1, alpha = FALSE)