tags: R mtext bold bquote corrplot colorlegend par png diag mtext mar seq(from=,to=,length.out=) forestplot matrix rownames colnames lower.tri upper.tri dplyr::arrange plotrix::ablineclip() ggplot2::geom_linerange() ggplot2::scale_x_continuous() ggplot2::geom_line() gghighlight::gghighlight() ggplot2::geom_tile() ggplot2::scale_fill_continuous() ggplot2::facet_wrap() ggpubr::ggarrange(p1,p2,p3,p4) ggplot2::ylim()

Data visualisation in R

Chang's collection of working R code and plots.

---

Visualising time series data

Fitness data

• Data source: activities.csv from Strava bulk download
• X variable: Activity day of year derived from Activity.Date
• Y variable: Cumulative riding distance derived from Distance
• Legend variable: Activity year derived from Activity.Date
• R script fitness.R
• Core functions ggplot2::ggplot()+ggplot2::geom_line()+gghighlight::gghighlight()

---

• Data source: activities.csv from Strava bulk download
• X variable: Activity day of year derived from Activity.Date
• Y variable: Cumulative riding elevation derived from Elevation.Gain
• Legend variable: Activity year derived from Activity.Date
• R script fitness.R
• Core functions ggplot2::ggplot()+ggplot2::geom_line()+gghighlight::gghighlight()

---

• Data source: activities.csv from Strava bulk export
• X variable: Activity week derived from Activity.Date
• Y variable: Activity day derived from Activity.Date
• Legend variable: Riding distance per day derived from Distance
• R script fitness.R
• Core functions ggplot2::ggplot()+ggplot2::geom_tile()+ggplot2::scale_fill_continuous()+ggplot2::facet_wrap()

---

• Data source: activities.csv from Strava bulk download
• X variable: Activity week derived from Activity.Date
• Y variable: Activity day derived from Activity.Date
• Legend variable: Riding elevation gain per day derived from Elevation.Gain
• R script fitness.R
• Core functions ggplot2::ggplot(x=,y=factor())+ggplot2::geom_tile()+ggplot2::scale_fill_continuous()+ggplot2::facet_wrap()
• References Plot line and bar graph (with secondary axis for line graph) using ggplot ggplot with 2 y axes on each side and different scales Analyzing Strava data using R Error when using gghighlight to highlight one group of data 10 tips for making your R graphics look their best GGplot Inquiry : How can i arrange weekdays to start on Monday to Saturday? Formatting ggplot2 axis labels with commas (and K? MM?) if I already have a y-scale

---

• Data source: activities.csv from Strava bulk download
• Variable: Moving time in hour derived from Moving.Time
• R script fitness.R
• Core functions calendR::calendR() ggplot2::ggsave()
• References graphgallery/calendar-heatmaps

---

• Data source: activities.csv from Strava bulk export
• X variable: Date part of activity start time derived from Activity.Date. This variable captures datetime in UTC. Change time zone to the location where the activity is recorded (e.g., using as.POSIXct(x=Activity.Date, format =, tz="Australia/Brisbane")
• Y variable: Moving time in hour derived from Moving.Time/60/60
• Background color variable:
  • xmin start date of a date range mapped to a time zone
  • xmax end date of a date range mapped to a time zone
  • ymin hard-coded to 0
  • xmax hard-coded to 8
• R script fitness.R
• Core functions ggplot2::ggplot()+ggplot2::geom_bar(position="stack")+ggplot2::scale_x_date()+ggplot2::scale_y_continuous()+ggplot2::scale_fill_manual()+ggnewscale::new_scale_fill()+ ggplot2::geom_rect()+ggplot2::scale_fill_manual()
• References
  • Plotting a bar plot with X-axis defined as a time series in ggplot2
  • Formatting dates with scale_x_date in ggplot2
  • Select first and last row from grouped data
  • ggplot background in three different colours with geom_rect not working - with data and all code
  • How to use geom_rect when having a date variable as x axis [R]?
  • geom_rect as background fill color
  • How can I have two different scale_fill_manual active in a ggplot command
  • Remove space between plotted data and the axes

---

• Data source: Single activity .fit file downloaded from Strava. .fit converted to CSV at Convert a FIT file to a CSV file
• Common X variable: distance.km derived from distance
• Y variables: heart_rate, calories, altitude, grade
• R script fitness.R
• Core functions
  p1<-ggplot2::ggplot()+ggplot2::geom_line()
p2<-ggplot2::ggplot()+ggplot2::geom_line()
p3<-ggplot2::ggplot()+ggplot2::geom_line()
p4<-ggplot2::ggplot()+ggplot2::geom_line()
p<-ggpubr::ggarrange(p1,p2,p3,p4) ggpubr::annotate_figure(p)
• References Convert a FIT file to a CSV file Analysing .fit files in R Shared x and y axis labels ggplot2 with ggarrange ggplot subtitle and title position - overlapping titles [duplicate]

---

• Data source: Single activity .fit file downloaded from Strava. .fit converted to CSV at Convert a FIT file to a CSV file
• Common X variable: distance.km derived from distance
• Y variables: heart_rate, speed, altitude, grade.edited
• R script fitness.R
• Core functions
  p1<-ggplot2::ggplot() + ggplot2::geom_line() + gghighlight::gghighlight + ggplot2::ylim()
p2<-ggplot2::ggplot() + ggplot2::geom_line() + gghighlight::gghighlight + ggplot2::ylim()
p3<-ggplot2::ggplot() + ggplot2::geom_line() + gghighlight::gghighlight + ggplot2::ylim()
p4<-ggplot2::ggplot() + ggplot2::geom_line() + gghighlight::gghighlight + ggplot2::ylim()
p<-ggpubr::ggarrange(p1,p2,p3,p4) ggpubr::annotate_figure(p)
• References How to Set Axis Limits in ggplot2?

---

• R version 4.3.1
• packageVersion("ggplot2") 3.4.3
• Data source: Single activity .fit file downloaded from Strava. .fit converted to CSV at Convert a FIT file to a CSV file
• Common X variable: distance.km derived from distance
• Y variables: heart_rate, speed, altitude, grade.edited
• R script fitness.R
• Core functions
  p1<-ggplot2::ggplot() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::scale_fill_manual + ggplot2::coord_cartesian()
p2<-ggplot2::ggplot() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::scale_fill_manual + ggplot2::coord_cartesian()
p3<-ggplot2::ggplot() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::scale_fill_manual + ggplot2::coord_cartesian()
p4<-ggplot2::ggplot() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::geom_area() + ggplot2::scale_fill_manual + ggplot2::coord_cartesian()
p<-ggpubr::ggarrange(p1,p2,p3,p4)
ggpubr::annotate_figure(p)
• References ptt_r_090923|Wei-Chu Chen Problem with geom_ribbon and alpha combined with coord-cartesian in R 4.1.0 on Windows #4498

---

Viedoc Electronic Data Capture System

• Data source: Visualization report_pending forms.xlsx (exported from Viedoc database)
• X variables on bottom x axis:
  • xmin=PendingSince.date.num
  • xmax=diff.pendingSince.today
• X variables on top x axis:
  • strftime(x=unique(c(plot.data$PendingSince.date, plot.data$today)) ,format = "%d%b\n%y")
• Y variable: SubjectId
• Legends:
  • Items ordered from the most frequent on top to the least frequent at bottom.
  • 20 distinct colors picked from pals::glasbey(n=20)
  • Long legend item text wrapped to multiple lines
• R script Viedoc-dashboard.R
• Core functions ggplot2::ggplot()+ggplot2::geom_linerange()+ggplot2::scale_x_continuous()

---

• Data source: Visualization report_pending forms.xlsx (exported from Viedoc database)
• X variables on bottom x axis:
  • xmin=dummy.xmin (set to 0)
  • xmax=date.pending.until.today
• Y variable: SubjectId
• Legends:
  • Items ordered from the most frequent on top to the least frequent at bottom.
  • 20 distinct colors picked from pals::polychrome(n=20)
  • Long legend item text wrapped to multiple lines
• R script Viedoc-dashboard.R
• Core functions ggplot2::ggplot()+ggplot2::geom_linerange()+ggplot2::scale_x_continuous()

---

Annotating images

Raw image

Annotated image

---

Treemap

Number of protocol deviation coded terms

---

Number of disposition coded terms

---

Medical history coded terms

---

A treemap that visualises hierarchical and categorical data^[1]

Plot relative proportions of categories and their sub-categories with a treemap. The categories and sub-categories are alledged conduct types and subtypes from CCC Allegations Data

---

Wordcloud

---

Donut plot

Number of study participants and sex ratio

Number of study participants and ethicnity ratio

---

Bar charts

Vertical bar plot

This plot displays the distribution of ages in females (F) and males (M) with means (dashed lines). The means are slso shown in a table within the plot

---

Horizontal bar plot. Frequency of ARM

---

Create multiple bar plots on one page^[2].

This plot displays bars in user-defined color (green, blue) for statistically significant result and pale color (pale green, pale blue) for statistically non-sigificant result.

---

Heatmap

Symmetrical heatmap with identical groups in row and column dimension^[3]

The two dimensions are taken from the same group. The diagonal, correlations between variables and themselves, are skipped here. Coefficient data are genetic correlation coefficients calculated using Linux software package Linkage disequilibrium score regression. The input data are three full matrices that are populated from a data frame: rG coefficients, p values and quotients of p values divided by significance threstholds

---

Asymmetrical heatmap with 1 group in row dimension and the other group in column dimension^[4]

Create a heatmap to display proportions of variance (R-squared, R²) of group 1 variables explained by group 2 variables. The row dimension represents 40 variables from group 2. The column dimension represents 30 variables from group 1. Higher the R², larger and more purplish the dots are

---

Forest plot

A table and a forest plot

Create a table and forest plot to display odds ratios of binary outcome variables estimated from two different types of analyses^[5].

---

Venn Diagram

Visualise how participants in three groups overlap^[6]

Visualise the number of participants between three phases of a study (NU1,NU2,NU3). The numbers of overlapped participants are in red and the numbers of total participants are in black.

---

Scatter plot

Multiple scatter plots^[7]

Compare 2 variables (CTT-based scores in orange, IRT-based scores in light blue) between twin 1 and twin 2 in monozygotic twins (MZ) and dyzygotic twins (DZ)

---

Manhattan plot^[8]

A Manhattan plot is a specific type of scatter plot widely used in Genome Wide Association Study (GWAS). Each point represents a genetic variant. The X axis shows its position on a chromosome, the Y axis shows how much it is associated with a trait as –log10(p-value) Manhattan plot in R: a review.

---

Histogram

Multiple histograms on one page^[9]

Compare the distribution of three scores (PSYCH6, SOMA6, and SPHERE12) computed with classical test theory (orange group; panel A, B, C) and item response theory (IRT, blue groups; panel D, E, F)

---

Box plot

Distribution of eye test scores in left and right eyes across visits

A single plain box plot

A box plot^[10] presents information from a five-number summary. It does not show a distribution in as much detail as a stem and leaf plot or histogram does, but is especially useful for indicating whether a distribution is skewed and whether there are potential unusual observations (outliers) in the data set. Box and whisker plots are also very useful when large numbers of observations are involved and when two or more data sets are being compared.

---

Line plot

Visualising different line types

19. R Base Graphs 2

# setting parameters back to default
par(mfrow=c(1,1), col.axis="black")

# Install plotrix package to use the ablineclip()
install.packages("plotrix")

library(plotrix) # add on package for "ablineclip", install if not yet available

plot(1:7, ylab="", main="Line Types lty 0:6", xlab="lty 0:6") # test plot

ablineclip(v=1, lty=1, col="sienna2", lwd=2) # solid (default)
ablineclip(v=2, lty=2, col="sienna2", lwd=2) # dashed
ablineclip(v=3, lty=3, col="sienna2", lwd=2) # dotted
ablineclip(v=4, lty=4, col="sienna2", lwd=2) # dotdash
ablineclip(v=5, lty=5, col="sienna2", lwd=2) # longdash
ablineclip(v=6, lty=6, col="sienna2", lwd=5) # twodash, thicker for comparison
ablineclip(v=7, lty=0, col="sienna2", lwd=2) # blank

---

Multiple lines in one plot

line plots^[11] .

---

Automate the generation of axis tick values from data to plot.

• How do you create vectors with specific intervals in R?

# Calculate the range of data values 
plot.value.ranges <- range(as.vector(plot.data$mean)
                           ,as.vector(plot.data$lower)
                           ,as.vector(plot.data$upper)
                           ,na.rm = TRUE)
# Define lower and upper limit of ticks on x axis                           
x.axis.tick.minimum <- plot.value.ranges[1] - 0.1
x.axis.tick.maximum <- plot.value.ranges[2] + 0.1

# Define ticks as 10 equally spaced numbers from the lower to the upper limit
x.axis.ticks.round <- round(seq( from=x.axis.tick.minimum
                                ,to=x.axis.tick.maximum
                                ,length.out = 10)
                            ,1)
> x.axis.ticks.round
[1] 0.2 0.5 0.8 1.2 1.5 1.8 2.1 2.5 2.8 3.1                            

---

Add text within a plot using legend().

# Add text to top left corner within the subplot
text_topLeft_withinFigure=subplotInfoSource$plotTitle[i]
# Specify the text location. The location may also be specified by setting x to a single keyword from the list "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center" 
legend('topleft', text_topLeft_withinFigure, bty="n", cex=3) # add text to top left corner, remove legend frame

---

Add lower-case bolded letter to a plot

• D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_output_fig12ab_histogram_sumScore_IRTScore_wide.tif

letter=letters[i]
boldedLetter=bquote(bold(.(letter)))
mtext(boldedLetter,side=3, adj=0.9, cex=1.5, line=-1.75)

---

---

1. R script file path:
   D:\googleDrive\Job\Queensland-Crime-Corruption-Commissioin_QLD-332553_data-scientist\Assessment1_create-dashboard\make-treemap_alledged-conduct-types_alledged-conduct-subtypes.R
   plot file path:
   D:\googleDrive\Job\Queensland-Crime-Corruption-Commissioin_QLD-332553_data-scientist\Assessment1_create-dashboard\make-treemap_alledged-conduct-types_alledged-conduct-subtypes.png ↩︎

2. R script file path:
   /mnt/backedup/home/lunC/scripts/PRS_UKB_201711/PRS_UKB_201711_step18-04-06_barPlot_percen-variance-selective-phenotypes_explained-by-GSCAN-PRSs.R
   plot file path:
   /mnt/backedup/home/lunC/plots/zfig44-04_percent-variance-of-cocaine-amphetamine-hallucinogens-ecstasy-cannabis-AUD_explained-by-PRS-GSCAN-SI-DPW.png ↩︎

3. R script file path:
   /mnt/backedup/home/lunC/scripts/MR_ICC_GSCAN_201806/MR_step08-04_heatmap_LDSC-genetic-correlations.R
   plot file path:
   /mnt/backedup/home/lunC/plots/MR_ICC_GSCAN_201806/genetic-correlation-between-use-4-substances.png ↩︎

4. R script file path:
   /mnt/backedup/home/lunC/scripts/PRS_UKB_201711/PRS_UKB_201711_step18-04_heatmap_variance-explained-by-PRS_r-square_p-value.R
   plot file path:
   /mnt/backedup/home/lunC/plots/licit_substance_PRSs_predict_illicit_drug_use/zfig39_heatmap_corrplot_R2-alcoho-toba-drugs-diagSU-explained-by-GSCAN-PRS.pdf ↩︎

5. R script file path:
   /mnt/backedup/home/lunC/scripts/MR_ICC_GSCAN_201806/MR_step10-03_forest-plot_odds-ratio-95percent-CI_observational-associations_MR-IVW.R
   plot file path:
   /mnt/backedup/home/lunC/plots/MR_ICC_GSCAN_201806/manu4_odds-ratio-95CI_observational-association_MR-IVW.png ↩︎

6. R script file path:
   D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_programs_R\NU_014_vennDiagram_ID_with_at_least_1_SPHERE_item.R
   plot file path:
   D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_output\fig06_vennDiagram_ID_with_at_least_1_SPHERE_item.png ↩︎

7. R script file path:
   D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_programs_R\NU_002c_scatterPlot_twinCorr_2Var.R
   plot file path:
   D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_output\fig10c_scatterPlot_twinCorr_2Var.png ↩︎

8. R script file path:
   D:\Now\library_genetics_epidemiology\GWAS\scripts\PRS_UKB_201711\PRS_UKB_201711_step19-02_manhattan-plot_QCed-SNP_clumped-SNPs.R
   plot file path:
   D:\Now\library_genetics_epidemiology\GWAS\plots\zfig43-01-01_manhattan-plot_GSCAN-smoking-initiation_LD-clumped-SNPs_suggestive-line-at-p-smaller-than_5e-08.png ↩︎

9. R script file path:
   D:/Now/library_genetics_epidemiology/slave_NU/NU_analytical_programs_R/NU_007a_histogram_rawScore_IRT_byVar.R
   plot file path:
   D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_output_fig12ab_histogram_sumScore_IRTScore_wide.png ↩︎

10. R script file path
    D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_programs_R\NU_003_boxplot_SPHERE12.R
    plot file path
    D:\Now\library_genetics_epidemiology\slave_NU\NU_analytical_output\fig11_boxPlot_SPHERE12_raw_IRT_twins.png ↩︎

11. R script file path
    D:\z_old_files\national_inpatient_sample\NIS_analytical_programs\NIS_10_lineplot_number_discharge_weighted.R
    plot file path
    D:\z_old_files\national_inpatient_sample\NIS_analytical_output\fig03_weighted_number_discharges_b.png ↩︎