Question

这是我目前的条形图：

我想将所有特朗普数据合并到一个栏中，将所有克林顿数据合并到另一个栏中。

即

我认为基本上我需要计算获胜者是特朗普的所有价值的均值，以及获胜者是克林顿的所有价值的均值，但我不确定如何做到这一点，因为我'是一个菜鸟。

这是我当前的代码，如果它有帮助：

library(ggplot2)

healthd = read.csv("R/states.csv")


states = healthd[[1]]
uninsured2015 = healthd[[3]]
uninsured2015 = abs(as.numeric(as.character(gsub("%","", uninsured2015))))
insuredChange = healthd[[4]]
insuredChange = abs(as.numeric(as.character(gsub("%","", insuredChange))))
winner = healthd[[15]]

ggplot(data = healthd, aes(x = states, y = insuredChange, fill=winner)) +
xlab("State") + ylab("Percent Uninsured (2015)") +
scale_fill_manual(values = c("Trump" = "red4", "Clinton" = "blue4")) +
geom_bar(stat="identity") +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x=element_text(angle = 90, hjust = 1))

此外，这是我的数据主管：

> head(healthd)
       State Uninsured.Rate..2010. Uninsured.Rate..2015. Uninsured.Rate.Change..2010.2015.
1   Alabama                 14.60%                10.10%                            -4.50%
2    Alaska                 19.90%                14.90%                               -5%
3   Arizona                 16.90%                10.80%                            -6.10%
4  Arkansas                 17.50%                 9.50%                               -8%
5 California                18.50%                 8.60%                            -9.90%
6   Colorado                15.90%                 8.10%                            -7.80%
  Health.Insurance.Coverage.Change..2010.2015. Employer.Health.Insurance.Coverage..2015.
1                                       215000                                   2545000
2                                        36000                                    390000
3                                       410000                                   3288000
4                                       234000                                   1365000
5                                      3826000                                  19552000
6                                       419000                                   2949000
  Marketplace.Health.Insurance.Coverage..2016. Marketplace.Tax.Credits..2016.
1                                       165534                         152206
2                                        17995                          16205
3                                       179445                         124346
4                                        63357                          56843
5                                      1415428                        1239893
6                                       108311                          67062
  Average.Monthly.Tax.Credit..2016. State.Medicaid.Expansion..2016. Medicaid.Enrollment..2013.
1                             $310                            FALSE                     799176
2                             $750                             TRUE                     122334
3                             $230                             TRUE                    1201770
4                             $306                             TRUE                     556851
5                             $309                             TRUE                    7755381
6                             $318                             TRUE                     783420
  Medicaid.Enrollment..2016. Medicaid.Enrollment.Change..2013.2016. Medicare.Enrollment..2016.
1                     910775                                 111599                     989855
2                     166625                                  44291                      88966
3                    1716198                                 514428                    1175624
4                     920194                                 363343                     606146
5                   11843081                                4087700                    5829777
6                    1375264                                 591844                     820234
  X2016.Election.Winner
1                 Trump
2                 Trump
3                 Trump
4                 Trump
5               Clinton
6               Clinton

Answer 1

您必须先将数据汇总到新的数据框中，然后重新绘制它。在R中有很多方法可以做到这一点，但可能dplyr具有易学，强大和编程安全的最佳组合 - 所以我将使用它。

我愚弄了一些数据，这是代码：

library(ggplot2)
library(dplyr)

n <- 50
ss <- sprintf("State-%.2d",1:n)
u15 <- 10*(runif(n) + 0.5)
icg = 4*(runif(n) + 0.5)
w = sample(c("Candidate-1","Candidate-2"),n,replace=T)

healthd <- data.frame(states=ss,uninsured2015=u15,insuredChange=icg,winner=w)

ggplot(data = healthd, aes(x = states, y = insuredChange, fill=winner)) +
  xlab("State") + ylab("Percent Uninsured (2015)") +
  scale_fill_manual(values = c("Candidate-1" = "red4", "Candidate-2" = "blue4")) +
  geom_bar(stat="identity") + theme_bw() +
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black"), 
        axis.text.x=element_text(angle = 90, hjust = 1))

# make a new aggregated dataframe with dplyr
aghealthd <- healthd %>% group_by(winner) %>% 
                         summarise(uninsured2015=mean(uninsured2015),
                                   insuredChange=mean(insuredChange))

# plot that with the same code, changing only the x-axis
ggplot(data = aghealthd, aes(x = winner, y = insuredChange, fill=winner)) +
  xlab("State") + ylab("Percent Uninsured (2015)") +
  scale_fill_manual(values = c("Candidate-1" = "red4", "Candidate-2" = "blue4")) +
  geom_bar(stat="identity") + theme_bw() +
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black"), 
        axis.text.x=element_text(angle = 90, hjust = 1))

这是情节1：

这是情节2：

合并条形图数据

1 个答案: