Using Tableau to create visualisations with time filters
Adding Chart as a tooltip
Exam marks as Scatter and box plot
- Change size on dashboard to automatic
- Add blank boxes to manually adjust the 3 graphs to align
- Add the coordinated link view through Dashboard –> actions –> add actions –> select highlight function
- you can also set the data to filter instead of highlight
Bubbleplots plots on population with auto moving year
# USING R FOR GRAPHS
To load packages required today:
Show code
packages = c('tidyverse', 'readxl', 'ggthemes', 'ggiraph', 'plotly',
'gganimate', 'patchwork', 'DT', 'gifski', 'gapminder')
for(p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}
exam_data <- read_csv("data/Exam_data.csv")
head(exam_data,5)
# A tibble: 5 x 7
ID CLASS GENDER RACE ENGLISH MATHS SCIENCE
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Student321 3I Male Malay 21 9 15
2 Student305 3I Female Malay 24 22 16
3 Student289 3H Male Chinese 26 16 16
4 Student227 3F Male Chinese 27 77 31
5 Student318 3I Male Malay 27 11 25Show code
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth =1,
method = "histodot") +
scale_y_continuous(NULL, breaks = NULL)
girafe(ggobj = p,
width_svg = 6,
height_svg = 6*0.618)
changing the mouse-over to show associated elements
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colnames (exam_data)
[1] "ID" "CLASS" "GENDER" "RACE" "ENGLISH" "MATHS"
[7] "SCIENCE"Show code
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(aes(data_id = CLASS),
stackgroups = TRUE, binwidth =1, method = "histodot") +
scale_y_continuous(NULL, breaks = NULL)
#hover_css = "fill: blue"
girafe(ggobj = p,
width_svg = 6,
height_svg = 6*0.618)
varying more of the mouse-over colours
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head(p,2)
$data
# A tibble: 322 x 7
ID CLASS GENDER RACE ENGLISH MATHS SCIENCE
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Student321 3I Male Malay 21 9 15
2 Student305 3I Female Malay 24 22 16
3 Student289 3H Male Chinese 26 16 16
4 Student227 3F Male Chinese 27 77 31
5 Student318 3I Male Malay 27 11 25
6 Student306 3I Female Malay 31 16 16
7 Student313 3I Male Chinese 31 21 25
8 Student316 3I Male Malay 31 18 27
9 Student312 3I Male Malay 33 19 15
10 Student297 3H Male Indian 34 49 37
# ... with 312 more rows
$layers
$layers[[1]]
mapping: data_id = ~CLASS
geom_interactive_dotplot: binaxis = x, stackdir = up, stackratio = 1, dotsize = 1, stackgroups = TRUE, na.rm = FALSE, .ipar = c("data_id", "tooltip", "onclick", "hover_css", "selected_css", "tooltip_fill")
stat_bindot: binaxis = x, binwidth = 1, binpositions = bygroup, method = histodot, origin = NULL, right = TRUE, width = 0.9, drop = FALSE, na.rm = FALSE
position_identity Show code
colnames (exam_data)
[1] "ID" "CLASS" "GENDER" "RACE" "ENGLISH" "MATHS"
[7] "SCIENCE"Show code
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(aes(data_id = CLASS),
stackgroups = TRUE, binwidth =1, method = "histodot") +
scale_y_continuous(NULL, breaks = NULL)
#hover_css = "fill: blue"
girafe(ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(opts_hover(css = "fill: blue"),
opts_hover_inv(css = "opacity:0.2;")))
use patchwork to arrange multiple graphs together
Show code
p1 <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(aes(data_id = ID),
stackgroups = TRUE, binwidth =1, method = "histodot") +
coord_cartesian (xlim=c(0,100)) +
scale_y_continuous(NULL, breaks = NULL)
p2 <- ggplot(data=exam_data,
aes(x = ENGLISH)) +
geom_dotplot_interactive(aes(data_id = ID),
stackgroups = TRUE, binwidth =1, method = "histodot") +
coord_cartesian (xlim=c(0,100)) +
scale_y_continuous(NULL, breaks = NULL)
# girafe(ggobj = print (p1 / p2),
# width_svg = 6,
# height_svg = 6,
# options = list(opts_hover(css = "fill: blue"),
# opts_hover_inv(css = "opacity:0.2;")))