Plottingworkloop objects
Working with single files
Let’s start by visualizing the raw traces in our data files, specifically position, force, and stimulation over time.
workloop_dat<-system.file("extdata","workloop.ddf",package ='workloopR')%>%read_ddf(phase_from_peak =TRUE)%>%fix_GR(2)# To overlay position and force, we need them to be on comparable scales# We will then use two y-axis to make the units clearscale_position_to_force<-3000workloop_dat%>%# Set the x axis for the whole plotggplot(aes(x = Time))+# Add a line for forcegeom_line(aes(y = Force,color ="Force"),lwd =1)+# Add a line for Position, scaled to approximately the same range as Forcegeom_line(aes(y = Position* scale_position_to_force,color ="Position"))+# For stim, we only want to plot where stimulation happens, so we filter the datageom_point(aes(y =0,color ="Stim"),size =1,data =filter(workloop_dat, Stim==1))+# Next we add the second y-axis with the corrected unitsscale_y_continuous(sec.axis =sec_axis(~ ./ scale_position_to_force,name ="Position (mm)"))+# Finally set colours, labels, and themesscale_color_manual(values =c("#FC4E2A","#4292C6","#373737"))+labs(y ="Force (mN)",x ="Time (secs)",color ="Parameter:")+ggtitle("Time course of\n work loop experiment")+theme_bw()+theme(legend.position ="bottom",legend.direction ="horizontal")Next, we would select cycles from the workloop in preparation for analysis. Before we do this, let’s keep all the cycles and visualize howselect_cycles() splits the data. Note that you can include 0 in thekeep_cycles argument to include data that is categorized as being outside of a complete cycle. This assigns a single cycle label (a) to the data before and after the complete cycles.
# Let's calculate x and y positions to add labels for each cycleworkloop_dat<- workloop_dat%>%select_cycles('lo',0:6)label_dat<- workloop_dat%>%group_by(Cycle)%>%summarize(x =mean(Time) )%>%# And add another row for the incomplete cycles at the beginningbind_rows(data.frame(Cycle ='a',x =0))workloop_dat%>%ggplot(aes(x = Time,y = Position,colour = Cycle))+geom_point(size=1)+geom_text(aes(x,y=2.1,colour = Cycle,label = Cycle),data = label_dat)+labs(y ="Position (mm)",x ="Time (secs)")+ggtitle("Division of position\nby `select_cycles()`")+theme_bw()+theme(legend.position ="none")Visualizing the cycles is a highly recommended in case noise before or after the experimental procedure is interpreted as a cycle.
Let’s go ahead and use cycles 2 to 5 (labeled c-f in the previous plot). Note however that the cycle labels will be reassigned from a-d when we subset the data.
workloop_dat<- workloop_dat%>%select_cycles('p2p',2:5)Now let’s plot some work loops!
# Let's start with a single cycle using colour to indicate timeworkloop_dat%>%filter(Cycle=='a')%>%ggplot(aes(x = Position,y = Force))+geom_path(aes(colour = Time))+labs(y ="Force (mN)",x ="Position (mm)",colour ="Time (sec)")+ggtitle("Single work loop")+theme_bw()+theme(legend.position ="bottom",legend.direction ="horizontal")# Now let's see how the work loop changes across cycles# We can use arrows to indicate direction through timeworkloop_dat%>%ggplot(aes(x = Position,y = Force))+geom_path(aes(colour = Cycle),arrow=arrow())+labs(y ="Force (mN)",x ="Position (mm)",colour ="Cycle index")+ggtitle("Work loops by cycle index")+theme_bw()+theme(legend.position ="bottom",legend.direction ="horizontal")Working with single files
Working with multiple files is a little trickier as multiple the data are stored in separatedata.frames organized into a list. The easiest way to deal with this issue is to add a column specifying the file id and concatenating the data together. Refer to the “Batch processing” vignette for more information on working with multiple files.
multi_workloop_dat<-system.file("extdata/wl_duration_trials",package ='workloopR')%>%read_ddf_dir(phase_from_peak =TRUE)%>%map(fix_GR,2)%>%map(select_cycles,'p2p',4)%>%map(analyze_workloop)# Summarize provides a quick way to pull out most experimental parameters, etcmulti_workloop_dat%>% summarize_wl_trials%>%ggplot(aes(Stimulus_Pulses, Mean_Power))+geom_point()+labs(y ="Mean Power (W)",x ="Stim Duration (pulses)")+ggtitle("Mean power over trial\nby stimulus duration")+theme_bw()+theme(legend.position ="bottom",legend.direction ="horizontal")# Accessing the time course data requires more manipulationmulti_workloop_dat%>%map(~mutate(.x$cycle_a,stim_pulses =attr(.x,"stimulus_pulses")))%>% bind_rows%>%ggplot(aes(Percent_of_Cycle, Inst_Power))+geom_path(aes(colour =as.factor(stim_pulses)))+labs(y ="Power (W)",x ="Percent of Cycle",colour ="Stim Duration")+ggtitle("Time course of instantaneous\npower by stimulus duration")+theme_bw()+theme(legend.position ="bottom",legend.direction ="horizontal")#> Warning: Removed 3 row(s) containing missing values (geom_path).