Removing some x_scales-CodePudding

          mean_math mean_read mean_science country
    1   -4.1976624 30.971633   -1.9381413     AUS
    2   -4.0193006 30.542376    8.7161472     CAN
    3  -21.8172403  9.638434  -14.6299379     COL
    4   -8.6399967 25.467815   -0.6883553     DEU
    5    5.4746023 53.493446   22.4550453     FIN
    6   -0.0951738 39.918658   12.1398932     GRC
    7   14.1162251 27.930612    8.2923826     IDN
    8    6.4402359 45.649534   17.5006916     ISR
    9   -6.4779173 20.323509   -8.1990175     KOR
    10  -1.0302412 26.280249   11.1133112     MAR
    11   7.5553361 25.145138    6.7470357     MYS
    12   0.1138526 30.203776    8.6622651     NLD
    13  -0.7025857 32.751286   -0.4696149     POL
    14  21.4963288 64.712999   41.3613420     QAT
    15  -5.9149028 22.572479   -2.8969491     SGP

Dataframe diff looks like the result above.

diff %>%
tidyr::pivot_longer(-country) %>% 
  ggplot(aes(value, country, color = value > 0))   
  geom_point()   
  labs(x = "diff", y = "country", colour = "diff > 0")  
  facet_wrap(~name, scales = "free_x")  
  coord_cartesian(xlim = c( -100, 100))

This code above is what I've done. and the result is the graph below.

result1

I'd like like to add some codes to make my graph to like this below

result2

I'd like to put lines on each graph like "result2" graph. ---> (sorted out)

Also for x_scale, how can I remove -100, 100.

Thanks

CodePudding user response：

library(tidyr)
library(ggplot2)


diff %>%
  tidyr::pivot_longer(-country) %>% 
  ggplot(aes(value, country, color = value > 0))   
  geom_point()   
  labs(x = "diff", 
       y = "country", 
       colour = "diff > 0")  
  geom_vline(xintercept= 0)   
  scale_x_continuous(breaks = c(-50, 0, 50)) 
  annotate("text", x = -100, y = 1, label = "Boys", hjust = -0.1) 
  annotate("text", x = 100, y = 1, label = "Girls", hjust = 1.1) 
  facet_wrap(~name, scales = "free_x")  
  coord_cartesian(xlim = c( -100, 100)) 
  theme(legend.position = "none")

data

diff <- data.frame(mean_math = c(-4.1976624, -4.0193006, -21.8172403, 
                                  -8.6399967, 5.4746023, -0.0951738, 14.1162251, 6.4402359, -6.4779173, 
                                  -1.0302412, 7.5553361, 0.1138526, -0.7025857, 21.4963288, -5.9149028),
                   mean_read = c(30.971633, 30.542376, 9.638434, 25.467815, 53.493446, 
                                 39.918658, 27.930612, 45.649534, 20.323509, 26.280249, 25.145138, 
                                 30.203776, 32.751286, 64.712999, 22.572479),
                   mean_science = c(-1.9381413, 8.7161472, -14.6299379, -0.6883553, 22.4550453, 12.1398932, 8.2923826, 
                                    17.5006916, -8.1990175, 11.1133112, 6.7470357, 8.6622651, -0.4696149, 
                                    41.361342, -2.8969491), 
                   country = c("AUS", "CAN", "COL", "DEU","FIN", "GRC", "IDN", 
                               "ISR", "KOR", "MAR", "MYS", "NLD", "POL",  "QAT", "SGP"))

^{Created on 2022-09-28 with reprex v2.0.2}