###### 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
:::info
* **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](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **Core functions** `ggplot2::ggplot()+ggplot2::geom_line()+gghighlight::gghighlight()`
:::


---

:::info
* **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](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **Core functions** `ggplot2::ggplot()+ggplot2::geom_line()+gghighlight::gghighlight()`
:::


---

:::info
* **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](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **Core functions** `ggplot2::ggplot()+ggplot2::geom_tile()+ggplot2::scale_fill_continuous()+ggplot2::facet_wrap()`
:::


---

:::info
* **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](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **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](https://stackoverflow.com/questions/53922846/plot-line-and-bar-graph-with-secondary-axis-for-line-graph-using-ggplot) [ggplot with 2 y axes on each side and different scales](https://stackoverflow.com/questions/3099219/ggplot-with-2-y-axes-on-each-side-and-different-scales) [Analyzing Strava data using R](https://www.andrewaage.com/post/analyzing-strava-data-using-r/) [Error when using gghighlight to highlight one group of data](https://stackoverflow.com/questions/75883553/error-when-using-gghighlight-to-highlight-one-group-of-data) [10 tips for making your R graphics look their best](https://blog.revolutionanalytics.com/2009/01/10-tips-for-making-your-r-graphics-look-their-best.html) [GGplot Inquiry : How can i arrange weekdays to start on Monday to Saturday?](https://www.reddit.com/r/rprogramming/comments/ue3aa0/ggplot_inquiry_how_can_i_arrange_weekdays_to/) [Formatting ggplot2 axis labels with commas (and K? MM?) if I already have a y-scale](https://stackoverflow.com/questions/37713351/formatting-ggplot2-axis-labels-with-commas-and-k-mm-if-i-already-have-a-y-sc)
:::


---

:::info
* **Data source**: activities.csv from Strava bulk download
* **Variable**: Moving time in hour derived from Moving.Time
* **R script** [fitness.R](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **Core functions** `calendR::calendR()` `ggplot2::ggsave()`
* **References** [graphgallery/calendar-heatmaps](https://github.com/graphgallery/calendar-heatmaps)
:::


---

:::info
* **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](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **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](https://stackoverflow.com/questions/45485181/plotting-a-bar-plot-with-x-axis-defined-as-a-time-series-in-ggplot2)
* [Formatting dates with scale_x_date in ggplot2](https://stackoverflow.com/questions/10576095/formatting-dates-with-scale-x-date-in-ggplot2)
* [Select first and last row from grouped data](https://stackoverflow.com/questions/31528981/select-first-and-last-row-from-grouped-data)
* [ggplot background in three different colours with geom_rect not working - with data and all code](https://stackoverflow.com/questions/52784250/ggplot-background-in-three-different-colours-with-geom-rect-not-working-with-d)
* [How to use geom_rect when having a date variable as x axis [R]?](https://stackoverflow.com/questions/45174749/how-to-use-geom-rect-when-having-a-date-variable-as-x-axis-r)
* [geom_rect as background fill color](https://community.rstudio.com/t/geom-rect-as-background-fill-color/79917)
* [How can I have two different scale_fill_manual active in a ggplot command](https://stackoverflow.com/questions/10097615/how-can-i-have-two-different-scale-fill-manual-active-in-a-ggplot-command)
* [Remove space between plotted data and the axes](https://stackoverflow.com/questions/22945651/remove-space-between-plotted-data-and-the-axes)
:::


---

:::info
* **Data source**: Single activity .fit file downloaded from Strava. .fit converted to CSV at [Convert a FIT file to a CSV file](https://gotoes.org/strava/convert_fit_files_to_csv.php)
* **Common X variable**: distance.km derived from distance
* **Y variables**: heart_rate, calories, altitude, grade
* **R script** [fitness.R](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **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](https://gotoes.org/strava/convert_fit_files_to_csv.php) [Analysing .fit files in R](https://jonnylaw.rocks/posts/2019-11-04-parsing-strava/) [Shared x and y axis labels ggplot2 with ggarrange](https://stackoverflow.com/questions/64757410/shared-x-and-y-axis-labels-ggplot2-with-ggarrange) [ggplot subtitle and title position - overlapping titles [duplicate]](https://stackoverflow.com/questions/35348781/ggplot-subtitle-and-title-position-overlapping-titles)
:::


---

:::info
* **Data source**: Single activity .fit file downloaded from Strava. .fit converted to CSV at [Convert a FIT file to a CSV file](https://gotoes.org/strava/convert_fit_files_to_csv.php)
* **Common X variable**: distance.km derived from distance
* **Y variables**: heart_rate, speed, altitude, grade.edited
* **R script** [fitness.R](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **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?](https://www.r-bloggers.com/2022/07/how-to-set-axis-limits-in-ggplot2-3/)
:::


---

:::info
* **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](https://gotoes.org/strava/convert_fit_files_to_csv.php)
* **Common X variable**: distance.km derived from distance
* **Y variables**: heart_rate, speed, altitude, grade.edited
* **R script** [fitness.R](https://drive.google.com/file/d/1HaJJiYOFG6Rq7Vv_pusYTTDmAGlwKLwg/view?usp=drive_link)
* **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](https://hackmd.io/@wcchen/rJ7Pz89A2) [Problem with geom_ribbon and alpha combined with coord-cartesian in R 4.1.0 on Windows #4498](https://github.com/tidyverse/ggplot2/issues/4498)
:::


---

### Viedoc Electronic Data Capture System
:::info
* **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](https://drive.google.com/file/d/1HSa-Rp87VY7RXlH6CP1lMAeye-eRAcV9/view?usp=drive_link)
* **Core functions** `ggplot2::ggplot()+ggplot2::geom_linerange()+ggplot2::scale_x_continuous()`
:::


---

:::info
* **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](https://drive.google.com/file/d/1HSa-Rp87VY7RXlH6CP1lMAeye-eRAcV9/view?usp=drive_link)
* **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[^5]
:::info
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](https://www.ccc.qld.gov.au/sites/default/files/Docs/Publications/CCC/CCC-Allegations-Data-30-June-2019.xlsx)
:::

[^5]: **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


---

## Wordcloud


---

## Donut plot

### Number of study participants and sex ratio


### Number of study participants and ethicnity ratio


---

## Bar charts

### Vertical bar plot
:::info
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[^1].
:::info
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.
:::

[^1]: **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


---

## Heatmap

### Symmetrical heatmap with identical groups in row and column dimension[^2]
:::info
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](https://github.com/bulik/ldsc). 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
:::

[^2]: **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


---

### Asymmetrical heatmap with 1 group in row dimension and the other group in column dimension[^3]
:::info
Create a heatmap to display proportions of variance (R-squared, R^2^) 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 R^2^, larger and more purplish the dots are
:::

[^3]: **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


---

## Forest plot

### A table and a forest plot
:::info
Create a table and forest plot to display odds ratios of binary outcome variables estimated from two different types of analyses[^4].
:::

[^4]: **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


---

## Venn Diagram

### Visualise how participants in three groups overlap[^6]
:::info
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.
:::
[^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


---

## Scatter plot

### Multiple scatter plots[^7]
:::info
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)
:::
[^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


---

### Manhattan plot[^8]

:::info
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](https://www.r-graph-gallery.com/101_Manhattan_plot.html).
:::
[^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


---

## Histogram

### Multiple histograms on one page[^9]
:::info
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)
:::
[^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


---

## Box plot

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


### A single plain box plot
:::info
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.

:::
[^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


---

## Line plot

### Visualising different line types
[19. R Base Graphs 2](https://www.udemy.com/course/r-basics/learn/lecture/5516248#overview)

# 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
:::info
line plots[^11] .

:::
[^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


---

#### Automate the generation of axis tick values from data to plot.
* [How do you create vectors with specific intervals in R?](https://stackoverflow.com/questions/15601609/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)

---