Arrange y-tick values on faceted dumbbell plots in a specific order in R-CodePudding

I want to arrange the y-values such that the two facets are arranged similarly. For example, I want the y-tick labels "RRR" to come first followed by the "LLL" groups under the "retention" group. In the transfer group, I want the 3 "RLR" groups to follow the 3 "LLL". Currently, they don't follow any order and it makes it difficult to follow

data10 <- structure(list(Trial_type = structure(c(2L, 1L, 2L, 1L, 2L, 1L, 
1L, 2L, 1L, 2L, 1L, 2L), .Label = c("1", "2"), class = "factor"), 
    Trial_type2 = c(2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2), GROUP = c("LRL", 
    "RRR", "LRL", "RRR", "LRL", "RRR", "LLL", "RLR", "LLL", "RLR", 
    "LLL", "RLR"), conditon2 = structure(c(11L, 9L, 3L, 1L, 7L, 
    5L, 12L, 10L, 4L, 2L, 8L, 6L), .Label = c("IPS_RRR", "IPS_RLR", 
    "IPS_LRL", "IPS_LLL", "CTL_RRR", "CTL_RLR", "CTL_LRL", "CTL_LLL", 
    "CEN_RRR", "CEN_RLR", "CEN_LRL", "CEN_LLL"), class = "factor"), 
    condition20 = c(1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3), Training = c("right", 
    "right", "right", "right", "right", "right", "left", "left", 
    "left", "left", "left", "left"), Pathlength_102 = c(3.187250625, 
    2.922721625, 3.405212, 2.775334063, 2.776401875, 3.149278375, 
    2.847822, 3.03391925, 3.674062375, 2.726527125, 2.78150325, 
    3.6662945), Pathlength_104 = c(2.8958505, 2.540974188, 3.413019688, 
    2.639986, 2.675723688, 2.761517625, 2.631161188, 2.664713, 
    2.889202063, 2.6051525, 2.568229875, 2.911062813), MovementTime_102 = c(1586.5625, 
    1977.5, 2181.25, 1664.6875, 2658.125, 2384.375, 1931.875, 
    1595, 2140.9375, 1652.8125, 1962.1875, 3074.375), MovementTime_104 = c(1445.625, 
    1327.1875, 2008.75, 1312.1875, 2302.8125, 1658.75, 1589.6875, 
    1387.1875, 1627.8125, 1611.25, 1519.0625, 2316.5625), AveResultantVel_102 = c(2.40919925, 
    2.069098625, 2.010902313, 1.943953438, 1.174490563, 1.548894813, 
    1.958885813, 2.343926125, 1.904319375, 2.122309625, 1.7739885, 
    1.391745563), AveResultantVel_104 = c(2.771204063, 2.5431425, 
    2.053414375, 2.505344125, 1.298380563, 1.983456875, 2.314399688, 
    2.350340875, 2.187628625, 2.051979125, 2.110770188, 1.434088938
    ), NormalizedJerk_102 = c(1270.168699, 2099.703957, 3259.268053, 
    1152.257445, 3810.890123, 4601.918336, 1792.371775, 1288.768888, 
    2699.08162, 1650.968794, 2018.457394, 6159.567785), NormalizedJerk_104 = c(853.7034116, 
    924.8554548, 2268.966702, 675.7160839, 2442.874632, 1603.954653, 
    1010.111276, 794.1752256, 1313.813984, 1197.638788, 1039.577947, 
    3125.131019), EndpointError_102 = c(1.441449813, 1.899916938, 
    1.666946063, 1.493975375, 1.945765938, 2.001344875, 1.530830188, 
    1.197009313, 2.1758275, 1.990092563, 2.106561813, 2.593554438
    ), EndpointError_104 = c(1.196976438, 1.203483813, 1.32090125, 
    1.176485125, 1.648146125, 1.253227125, 0.86855425, 0.803545313, 
    1.502868938, 1.338789625, 1.499023188, 1.504206125), RV_diff = c(0.36, 
    0.47, 0.04, 0.56, 0.12, 0.43, 0.36, 0.01, 0.28, -0.07, 0.34, 
    0.04), EE_diff = c(-0.24, -0.7, -0.35, -0.32, -0.3, -0.75, 
    -0.66, -0.39, -0.67, -0.65, -0.61, -1.09), PL_diff = c(-0.29, 
    -0.38, 0.01, -0.14, -0.1, -0.39, -0.22, -0.37, -0.78, -0.12, 
    -0.21, -0.76), NJ_diff = c(-416.47, -1174.85, -990.3, -476.54, 
    -1368.02, -2997.96, -782.26, -494.59, -1385.27, -453.33, 
    -978.88, -3034.44), MT_diff = c(-140.94, -650.31, -172.5, 
    -352.5, -355.31, -725.63, -342.19, -207.81, -513.13, -41.56, 
    -443.13, -757.81)), row.names = c(NA, -12L), spec = structure(list(
    cols = list(Trial_type = structure(list(), class = c("collector_character", 
    "collector")), Trial_type2 = structure(list(), class = c("collector_double", 
    "collector")), GROUP = structure(list(), class = c("collector_character", 
    "collector")), conditon2 = structure(list(), class = c("collector_character", 
    "collector")), condition20 = structure(list(), class = c("collector_double", 
    "collector")), Training = structure(list(), class = c("collector_character", 
    "collector")), Pathlength_102 = structure(list(), class = c("collector_double", 
    "collector")), Pathlength_104 = structure(list(), class = c("collector_double", 
    "collector")), MovementTime_102 = structure(list(), class = c("collector_double", 
    "collector")), MovementTime_104 = structure(list(), class = c("collector_double", 
    "collector")), AveResultantVel_102 = structure(list(), class = c("collector_double", 
    "collector")), AveResultantVel_104 = structure(list(), class = c("collector_double", 
    "collector")), NormalizedJerk_102 = structure(list(), class = c("collector_double", 
    "collector")), NormalizedJerk_104 = structure(list(), class = c("collector_double", 
    "collector")), EndpointError_102 = structure(list(), class = c("collector_double", 
    "collector")), EndpointError_104 = structure(list(), class = c("collector_double", 
    "collector")), RV_diff = structure(list(), class = c("collector_double", 
    "collector")), EE_diff = structure(list(), class = c("collector_double", 
    "collector")), PL_diff = structure(list(), class = c("collector_double", 
    "collector")), NJ_diff = structure(list(), class = c("collector_double", 
    "collector")), MT_diff = structure(list(), class = c("collector_double", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1), class = "col_spec"), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"))

library(tidyverse)
  library(ggplot2) 
  library(rlang)
  library(utils)
  library(data.table)
  library(dumbbell)
  
  
  data10$conditon2 <- forcats::fct_reorder(data10$conditon2, data10$conditon2, .desc = TRUE)
  
  data10A<-data10 %>% select(conditon2,Trial_type,EndpointError_102,EndpointError_104) %>% mutate("key"="Change In Endpoint Error (cm)")
  data10B<-data10A %>% mutate("diff"=EndpointError_104-EndpointError_102) %>% arrange(desc(diff))
  data10A$conditon2<-factor(data10A$conditon2, data10B$conditon2)
  
  data10A$Trial_type <- factor(data10A$Trial_type, levels = 1:2, labels = c("Retention", "Transfer"))
  
  dumbbell::dumbbell(data10A, id="conditon2", key="Trial_type", leg = "Test time", column1 = "EndpointError_102", column2="EndpointError_104", delt=1, lab1="Pretest", lab2="Posttest", p_col1 = "black", p_col2 = "grey40", textsize = 4, segsize = 1.5, pointsize = 2.5, title = "Change in Endpoint error (cm)")    
    xlim(0.7,3.0)   facet_wrap( Trial_type ~., scales="free", ncol=1)  
    theme(axis.text.x = element_text(size = 12,face="bold"),
          axis.text.y = element_text(size = 11, face = "bold"))   theme(legend.position="right")   theme(legend.text=element_text(size=12), legend.title=element_text(size=14))   theme(strip.text = element_text(face="bold", size=14, color = "black"))

CodePudding user response：

If the order you want is

RRR then LLL and
LRL then RLR

change the factor condition2 levels then plot as in the question.

library(tidyverse)
library(dumbbell)

data10A <- data10 %>% select(conditon2,Trial_type,EndpointError_102,EndpointError_104) %>% mutate("key"="Change In Endpoint Error (cm)")
data10A$Trial_type <- factor(data10A$Trial_type, levels = 1:2, labels = c("Retention", "Transfer"))
i1 <- grepl("_RR", levels(data10A$conditon2))
i2 <- grepl("_RL", levels(data10$conditon2))
i3 <- grepl("_LL", levels(data10A$conditon2))
RRR_levels <- levels(data10A$conditon2)[i1]
RLR_levels <- levels(data10A$conditon2)[i2]
LLL_levels <- levels(data10A$conditon2)[i3]
LRL_levels <- levels(data10A$conditon2)[!i1 & !i2 & !i3]
ord_levels <- c(LLL_levels, RRR_levels, RLR_levels, LRL_levels)
data10A$conditon2 <- factor(data10A$conditon2, levels = ord_levels)

dumbbell(data10A, id = "conditon2", key = "Trial_type", 
         leg = "Test time", 
         column1 = "EndpointError_102", 
         column2 = "EndpointError_104", 
         delt = 1, lab1 = "Pretest", lab2 = "Posttest", 
         p_col1 = "black", p_col2 = "grey40", 
         textsize = 4, segsize = 1.5, 
         pointsize = 2.5, 
         title = "Change in Endpoint error (cm)")    
  xlim(0.7,3.0)   
  facet_wrap( Trial_type ~., scales="free", ncol=1)  
  theme(axis.text.x = element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 11, face = "bold"),
        legend.position = "right",
        legend.text = element_text(size = 12), 
        legend.title = element_text(size = 14),
        strip.text = element_text(face = "bold", size = 14, color = "black"))