Formatting geom dumbel plot in R after reordering data in a specific order

Question

I want the retention trials on top and the transfer trials at the bottom. I tried reordering the data but it is doing exactly the opposite of what I want. Thus the grey lines on top and the black at the bottom.

I want the retention trials on top and the transfer trials at the bottom. I tried reordering the data but it is doing exactly the opposite of what I want. Thus the grey lines on top and the black at the bottom.

data10 <- structure(list(GROUP = c("LLL", "LRL", "RLR", "RRR", "LLL", "LRL", 
"RLR", "RRR", "LLL", "LRL", "RLR", "RRR"), conditon2 = c("R-Midline_LLL", 
"T-Midline_LRL", "T-Midline_RLR", "R-Midline_RRR", "R-No Midline crossing_LLL", 
"T-No Midline crossing_LRL", "T-No Midline crossing_RLR", "R-No Midline crossing_RRR", 
"R-Midline crossing_LLL", "T-Midline crossing_LRL", "T-Midline crossing_RLR", 
"R-Midline crossing_RRR"), condition = c(1, 1, 1, 1, 2, 2, 2, 
2, 3, 3, 3, 3), Trial_type = c("Retention", "Transfer", "Transfer", 
"Retention", "Retention", "Transfer", "Transfer", "Retention", 
"Retention", "Transfer", "Transfer", "Retention"), Training = c("left", 
"right", "left", "right", "left", "right", "left", "right", "left", 
"right", "left", "right"), AveResultantVel_102 = c(2.021215719, 
2.389143988, 2.317206313, 2.101220406, 1.994460031, 2.008862717, 
2.162818172, 1.983419094, 1.789839578, 1.178901484, 1.444998757, 
1.595565484), AveResultantVel_104 = c(2.281623618, 2.648132625, 
2.326539432, 2.644351188, 2.156819721, 2.103392923, 2.088311036, 
2.497797074, 2.113643324, 1.301035183, 1.481807047, 1.996934752
), EndpointError_102 = c(1.569026787, 1.372961825, 1.301714076, 
2.008901245, 1.979128047, 1.64400094, 1.891724673, 1.571540547, 
1.878836891, 2.099148779, 2.426338581, 1.872127873), EndpointError_104 = c(0.979220453, 
1.187221906, 0.940806623, 1.483629813, 1.477764016, 1.362604837, 
1.456501911, 1.241231281, 1.409004828, 1.724199558, 1.672676594, 
1.370374047), Pathlength_102 = c(2.822461771, 3.106740763, 3.007654446, 
2.79222754, 3.353013154, 3.411759275, 2.729179389, 2.733244547, 
2.78574039, 2.784003452, 3.290560629, 3.156035284), Pathlength_104 = c(2.615339959, 
2.955880176, 2.691606083, 2.558571102, 2.945664516, 3.266290452, 
2.628399641, 2.629707674, 2.597245428, 2.666422279, 2.968108005, 
2.758916433), NormalizedJerk_102 = c(1585.195804, 1201.912525, 
1203.902331, 1602.550867, 2645.919832, 3053.514635, 1294.166833, 
1151.292095, 1913.713201, 3626.035498, 5851.394788, 3593.940565
), NormalizedJerk_104 = c(999.934117, 890.20336, 847.952528, 
907.134951, 1438.14043, 2335.71867, 1168.53531, 738.136837, 1173.04004, 
2563.16723, 3564.38342, 1705.62942), MovementTime_102 = c(1851.149554, 
1595.078125, 1512.109375, 1857.02753, 2067.526042, 2177.3125, 
1646.96875, 1655.502232, 1934.073661, 2670.412946, 2956.741071, 
2370.65625), MovementTime_104 = c(1531.5625, 1439.955357, 1389.0625, 
1387.946429, 1658.046875, 2013.359375, 1601.316964, 1336.40625, 
1573.839286, 2302.109375, 2399.765625, 1684.53125), RV_diff = c(0.2604079, 
0.258988638, 0.00933312, 0.543130781, 0.16235969, 0.094530207, 
-0.074507136, 0.51437798, 0.323803746, 0.122133699, 0.03680829, 
0.401369268), EE_diff = c(-0.589806333, -0.185739919, -0.360907453, 
-0.525271432, -0.501364031, -0.281396103, -0.435222762, -0.330309266, 
-0.469832063, -0.374949221, -0.753661987, -0.501753826), PL_diff = c(-0.207121812, 
-0.150860586, -0.316048364, -0.233656438, -0.407348638, -0.145468822, 
-0.100779749, -0.103536873, -0.188494962, -0.117581173, -0.322452624, 
-0.397118851), NJ_diff = c(-585.2616874, -311.7091655, -355.9498032, 
-695.4159158, -1207.779402, -717.7959647, -125.6315232, -413.1552577, 
-740.6731613, -1062.868268, -2287.011368, -1888.311145), MT_diff = c(-319.5870536, 
-155.1227679, -123.046875, -469.0811013, -409.4791668, -163.953125, 
-45.65178575, -319.0959821, -360.234375, -368.3035714, -556.9754464, 
-686.125)), class = "data.frame", row.names = c(NA, -12L), spec = structure(list(
    cols = list(GROUP = structure(list(), class = c("collector_character", 
    "collector")), conditon2 = structure(list(), class = c("collector_character", 
    "collector")), condition = structure(list(), class = c("collector_double", 
    "collector")), Trial_type = structure(list(), class = c("collector_character", 
    "collector")), Training = structure(list(), class = c("collector_character", 
    "collector")), AveResultantVel_102 = structure(list(), class = c("collector_double", 
    "collector")), AveResultantVel_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")), Pathlength_102 = structure(list(), class = c("collector_double", 
    "collector")), Pathlength_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")), MovementTime_102 = structure(list(), class = c("collector_double", 
    "collector")), MovementTime_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"))

library(tidyverse)
library(ggalt)


data10$conditon2 <- reorder(data10$conditon2, data10$Trial_type)

data10$conditon2 <- factor(data10$conditon2, levels = levels(data10$conditon2))


ggplot(data = data10,
       aes(y=conditon2, x=EndpointError_102, xend=EndpointError_104)) +
  labs(x=NULL, y=NULL, title="Change in Endpoint error (cm)",fontface="bold") +
  
  geom_dumbbell(aes(colour = factor(Trial_type)), size_x = 2.5, size_xend = 2.5)+
  
  
  scale_color_manual(values = c('Retention' = ' grey40', 'Transfer' = 'black'))+
  
  geom_text(data=filter(data10, conditon2=="R-Midline_RRR"),
            aes(x=EndpointError_104, y=conditon2, label="Post-test"),
            color="black", size=3,  vjust=-3,hjust=1.0) +
  geom_text(data=filter(data10, conditon2=="R-Midline_RRR"),
            aes(x=EndpointError_102, y=conditon2, label="Pre-test"),
            color="grey40", size=3, vjust=-3,hjust=-0.0000) +
  geom_rect(data=data10, aes(xmin=3.1, xmax=3.6, ymin=-Inf, ymax=Inf), fill="grey") +
  geom_text(data=data10, aes(label=paste0(EE_diff), y=conditon2, x=3.35), fontface="bold",size=3) +
  geom_text(data=filter(data10, conditon2=="R-Midline_RRR"), 
            aes(x=3.35, y=conditon2, label="Difference"),
            color="black", size=3.1, vjust=-2, fontface="bold") +
  scale_x_continuous(expand=c(0,0), limits=c(0.7, 3.7)) +
  scale_y_discrete(expand=c(0.1,0))  + 
  theme_bw() +
  theme(axis.text.x = element_text(size = 11,face="bold"),
        axis.text.y = element_text(size = 10, face = "bold")) +
  
  
  guides(col=guide_legend(override.aes = list(size = 4),title="Experimental groups")) 

  
  
 

     

Answer 1

Using the dumbbell R package

##Load packages
library(tidyverse)
library(ggplot2) 
library(rlang)
library(utils)
library(data.table)
library(dumbbell)


##Reformat data
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_102-EndpointError_104)%>% arrange(desc(diff))
 data10A$conditon2<-factor(data10A$conditon2, data10B$conditon2)
 
##Plot the data
 dumbbell::dumbbell(data10A, id="conditon2", key="Trial_type", column1 = "EndpointError_102", column2="EndpointError_104", delt=1, lab1="EndpointError_102", lab2="EndpointError_103", pt_val=1, leg="Change In Endpoint error (cm)")  + 
   xlim(0.7,2.6) + facet_wrap( Trial_type ~., scales="free", ncol=1)

There are several bells and whistle you can add or remove, here is an example of one style that might be useful to you or/and someone else

I dont have enough votes to embed the plot so the link is shown below...

Enjoy!

dumbbell R package

Answer 2

You'll have to play around with the levels of data10$condition2 .

First make it into a factor, R by default arrange them in alphabetical order:

data10$conditon2 <- factor(data10$conditon2)
levels(data10$conditon2)
# [1] "R-Midline crossing_LLL"    "R-Midline crossing_RRR"    "R-Midline_LLL"            
# [4] "R-Midline_RRR"             "R-No Midline crossing_LLL" "R-No Midline crossing_RRR"
# [7] "T-Midline crossing_LRL"    "T-Midline crossing_RLR"    "T-Midline_LRL"            
#[10] "T-Midline_RLR"             "T-No Midline crossing_LRL" "T-No Midline crossing_RLR"

Now you want your levels start with the Ts following by Rs. One way is you painstakingly reset the levels:

# data10$conditon2 <- factor(data10$conditon2,
#                            levels = c("T-Midline crossing_LRL", "T-Midline crossing_RLR", ...)

Another way is I know that R by default creates level based on alphabetical order, and the first 6 are Rs and second 6 are Ls. So you want a vector of second 6 first, and first 6 second:

lvl <- levels(data10$conditon2)
lvl <- c(lvl[7:12], lvl[1:6])

Now you can factor data10$condition2 using lvl as levels:

data10$conditon2 <- factor(data10$conditon2, levels = lvl)
levels(data10$condition2)

# [1] "T-Midline crossing_LRL"    "T-Midline crossing_RLR"    "T-Midline_LRL"            
# [4] "T-Midline_RLR"             "T-No Midline crossing_LRL" "T-No Midline crossing_RLR"
# [7] "R-Midline crossing_LLL"    "R-Midline crossing_RRR"    "R-Midline_LLL"            
# [10] "R-Midline_RRR"             "R-No Midline crossing_LLL" "R-No Midline crossing_RRR"

And you now have black on bottom grey on top once you run your ggplot code. Note there are other ways of reordering your levels this is just one quick and dirty way of doing it.

Answer 3

To get the right order you can make use of forcats::fct_reorder to reorder conditon2 in descending order. I also added the condition conditon2 == levels(conditon2)[length(levels(conditon2))] to put the difference label automatically on top of the last level. Try this:

library(tidyverse)
library(ggalt)

data10$conditon2 <- forcats::fct_reorder(data10$conditon2, data10$conditon2, .desc = TRUE)

ggplot(data = data10,
       aes(y=conditon2, x=EndpointError_102, xend=EndpointError_104)) +
  labs(x=NULL, y=NULL, title="Change in Endpoint error (cm)",fontface="bold") +
  
  geom_dumbbell(aes(colour = factor(Trial_type)), size_x = 2.5, size_xend = 2.5)+
  
  
  scale_color_manual(values = c('Retention' = ' grey40', 'Transfer' = 'black'))+
  
  geom_text(data=filter(data10, conditon2=="R-Midline_RRR"),
            aes(x=EndpointError_104, y=conditon2, label="Post-test"),
            color="black", size=3,  vjust=-3,hjust=1.0) +
  geom_text(data=filter(data10, conditon2=="R-Midline_RRR"),
            aes(x=EndpointError_102, y=conditon2, label="Pre-test"),
            color="grey40", size=3, vjust=-3,hjust=-0.0000) +
  geom_rect(data=data10, aes(xmin=3.1, xmax=3.6, ymin=-Inf, ymax=Inf), fill="grey") +
  geom_text(data=data10, aes(label=paste0(EE_diff), y=conditon2, x=3.35), fontface="bold",size=3) +
  geom_text(data=filter(data10,  conditon2 == levels(conditon2)[length(levels(conditon2))]), 
            aes(x=3.35, y=conditon2, label="Difference"),
            color="black", size=3.1, vjust=-2, fontface="bold") +
  scale_x_continuous(expand=c(0,0), limits=c(0.7, 3.7)) +
  scale_y_discrete(expand=c(0.1,0))  + 
  theme_bw() +
  theme(axis.text.x = element_text(size = 11,face="bold"),
        axis.text.y = element_text(size = 10, face = "bold")) +
  
  
  guides(col=guide_legend(override.aes = list(size = 4),title="Experimental groups"))