7 Visualizar datos

WHY Data visualization matters: https://www.autodeskresearch.com/publications/samestats

7.1 Other resources

• http://chartmaker.visualisingdata.com/
  
• https://www.r-graph-gallery.com/
  
• http://pythonplot.com/ (see ggplot tab in plots)
• https://xeno.graphics/

R es un lenguaje especialmente potente para la visualización de datos. Librerias como ggplot2 permiten una cantidad abrumadora de opciones. Aqui presentamos ejemplos de algunas de las posibilidades de R.

7.2 Que gráfica puedo usar (WIP)

• Image FROM Modern Dive

---

DV/IV	IV continua	IV vategórica	IV dicotómica
DV continua	SCATTER PIRATE CORREL	PIRATE CAJA	BEAN DENSITY PIRATE
DV categórica	…	PIRATE	…
DV dicotómica	…	…	…

---

Vamos a usar la siguiente base de datos.

---

• Cargamos librerias y leemos datos

# Cargamos librerias
if (!require('readr')) install.packages('readr'); library('readr')
if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('yarrr')) install.packages('yarrr'); library('yarrr')
if (!require('DT')) install.packages('DT'); library('DT')
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')

# Leemos datos y echamos un vistazo usando el paquete DT::datatable()
datos = read_csv("Data/06_Visualize_data/Visualize_data.csv"); DT::datatable(datos)  

# A algunas de las funciones no les gustan las tibbles! 
datos_no_tibble = read.csv("Data/06_Visualize_data/Visualize_data.csv") 

• Nota sobre sintaxis

En muchos de los gráficos, análisis estadísticos, etc, usaremos la sintaxis: Var_Dep ~ Var_Indep_1 + Var_Indep_2

7.3 Tipos de gráficas

7.3.1 Histogramas

La función hist() nos permite crear de manera muy sencilla histogramas para ver

# Histograma sencillo
hist(datos$Edad)

# Dotplot histogram
qplot(datos$Edad, 
      geom="dotplot", 
      fill = I("forestgreen"),
      xlab = "Age", 
      main = "Age dotplot histogram") +
    theme_minimal()

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

# Y axis shows the proper number
max_bins = datos %>% group_by(Edad) %>% dplyr::summarise(N = n()) %>% arrange(desc(N))#distinct(N) %>% filter(N == max(N)) 
ggplot(datos, aes(Edad)) +
  geom_dotplot(binwidth = 1, fill = "forestgreen") +
  coord_fixed(ratio=1) +
  ylim(0, max_bins$N[1] * 1.5) +
  theme_minimal() 

Si queremos ver los histogramas de todas las variables numéricas de nuestro dataset:

# Metodo 1
if (!require('psych')) install.packages('psych'); library('psych')

## Loading required package: psych

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

#plyr
# multi.hist(datos) #error, not numeric
multi.hist(datos[,sapply(datos, is.numeric)])

# Metodo 2
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')
if (!require('reshape2')) install.packages('reshape2'); library('reshape2')

## Loading required package: reshape2

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

d <- melt(datos)

## Using condition as id variables

ggplot(d,aes(x = value)) + 
    facet_wrap(~variable,scales = "free_x") + 
    geom_histogram(bins = 15) #+ coord_cartesian(ylim = c(0, 100))

7.3.2 Pirate plot

Ver la web del creador: Pirate plot y su página de Github

# Cargamos librerias
if (!require('yarrr')) install.packages('yarrr'); library('yarrr')

# Mostramos gráfico con opciones por defecto
pirateplot(formula = PPV_DECLARED ~ condition, datos)

# Personalizamos el gráfico
pirateplot(formula = PPV_DECLARED ~ condition,
           data = datos,
           main = "PPV by condition",
           avg.line.fun = median,
           #theme.o = 2,
           jitter.val = .2,
           inf.method = "ci", #Show confidence interval (95%)
           inf.f.o = 0.2, #Opacity of ci
           pal = "appletv")

7.3.3 Barplots with individual responses

# https://mvuorre.github.io/post/2017/within-subject-scatter/
  

barplot <- ggplot(datos, aes(x = condition, y = PPV_DECLARED, color = condition)) +
  stat_summary( geom = "bar",
                fun.y = "mean",
                # col = "black",
                fill = "gray70",
                alpha = .6 ) +
  geom_point(position = position_jitter(h = 0, w = 0.5)) +
  scale_y_continuous(limits = c(0, 100),
                     expand = c(0, 0))

## Warning: `fun.y` is deprecated. Use `fun` instead.

barplot

# Adding more geoms
barplot2 <- ggplot(datos, aes(x = condition, y = PPV_DECLARED, color = condition)) +
  stat_summary( geom = "bar",
                fun.y = "mean",
                # col = "black",
                fill = "gray70",
                alpha = .6 ) +
  # stat_summary(fun.data = mean_se, geom = "errorbar") +
  geom_point(position = position_jitter(h = 0, w = 0.5)) +
  geom_boxplot(alpha = .2, outlier.alpha = .1) +
  geom_dotplot(binaxis ="y", stackdir = "center", binwidth = 2) +
  geom_violin(alpha = .2) +
  scale_y_continuous(limits = c(0, 100),
                     expand = c(0, 0))

## Warning: `fun.y` is deprecated. Use `fun` instead.

barplot2

7.3.4 Diagrama de caja y bigotes

boxplot(PPV_DECLARED ~ condition, datos)

7.3.5 Correlation plot

Ver la web del paquete: Correlation plot y su página de Github

# Cargamos libreria
if (!require('corrplot')) install.packages('corrplot'); library('corrplot')

## Loading required package: corrplot

## corrplot 0.84 loaded

corrplot(cor(datos[,c(2:5,7)]), mar = c(1,0, 0, 0),tl.cex = 0.9, 
         method = "square", type = "lower", tl.col = "black", diag = T)

# Correlation with ggplot
# From http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')
if (!require('ggcorrplot')) install.packages('ggcorrplot'); library('ggcorrplot')

## Loading required package: ggcorrplot

corr = round(cor(datos[,c(2:5,7)]), 1)

# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram", 
           ggtheme=theme_bw)

7.3.6 Beanplot

Ver la web del paquete Beanplot

# Cargamos libreria
if (!require('beanplot')) install.packages('beanplot'); library('beanplot')

## Loading required package: beanplot

beanplot(FollowUP ~ Genero, data = datos,  side = "both", log = "", names = c("Hombre","Mujer"),
what = c(1,1,1,0), border = NA, col = list("black", c("grey", "white")))

# legend("bottomleft", fill = c("black", "grey"), legend = c("H", "M"), title = "Genero")

7.3.7 Scatterplot

Ver la web del creador: ggpubr y su página de Github

# Cargamos libreria
if (!require('ggpubr')) install.packages('ggpubr'); library('ggpubr')

## Loading required package: ggpubr

ggscatter(datos_no_tibble, x = "Educacion", y = "Edad",
   color = "black", shape = 21, size = 4, # Points color, shape and size
   add = "reg.line",  # Add regressin line
   add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
   conf.int = TRUE, # Add confidence interval
   cor.coef = TRUE # Add correlation coefficient
   )

## `geom_smooth()` using formula 'y ~ x'

7.3.8 Scatterplot con 3 variables

Ver la web de documentación de ggplot2 y su página de Github

# Cargamos libreria
if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')

ggplot(datos, aes(PPV_DECLARED, FollowUP, color=factor(Genero))) +
    geom_point(shape=1, size = 2) +
    scale_colour_hue(l=50) + # Palette hue
    geom_smooth(method=lm,   # Linear regression lines
                se=T,        # Confidence interval
                fullrange=F) # Extend regression lines

## `geom_smooth()` using formula 'y ~ x'

7.3.9 Density plots

Ver la web del creador: ggpubr y su página de Github

• The variable we use to create two different sub-plots has to be a factor!

if (!require('readr')) install.packages('readr'); library('readr')
if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('ggpubr')) install.packages('ggpubr'); library('ggpubr')
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')


datos = read_csv("Data/06_Visualize_data/Visualize_data.csv")

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   ID = col_double(),
##   Genero = col_double(),
##   Edad = col_double(),
##   Educacion = col_double(),
##   FollowUP = col_double(),
##   condition = col_character(),
##   PPV_DECLARED = col_double()
## )

datos = datos %>% 
  mutate(Genero = as.factor(unlist(Genero))) %>% #Make Genero a factor
  mutate(PPV_DECLARED = as.numeric(unlist(PPV_DECLARED)))

ggdensity(datos, x = "PPV_DECLARED",
   add = "mean", rug = TRUE,
   color = "Genero", palette = c("#00AFBB", "#E7B800"), fill = "Genero")

7.3.10 Density Ridges Plots (ggjoy)

if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('ggridges')) install.packages('ggridges'); library('ggridges')

## Loading required package: ggridges

if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')

datos_density_ridges = datos %>% drop_na() %>% 
  filter(Educacion < 8) %>% 
  mutate(Educacion = as.factor(Educacion)) %>% 
  mutate(PPV_DECLARED = as.double(PPV_DECLARED))

# datos_density_ridges %>% group_by(Genero) %>% dplyr::summarise(N = n())

# Simple version
ggplot(datos_density_ridges, aes(x = FollowUP, y = Genero)) + 
  geom_density_ridges(scale = 2)

## Picking joint bandwidth of 12.4

# Tweak some aesthetics 
ggplot(datos_density_ridges, aes(x = FollowUP, y = Genero, fill = Genero)) + 
  geom_density_ridges(scale = 2, alpha = .7, color = "white") + 
  scale_fill_hue(l=30)

## Picking joint bandwidth of 12.4

# Histogram stat
ggplot(datos_density_ridges, aes(x = FollowUP, y = Genero, fill = Genero)) + 
  geom_density_ridges(scale = 2, alpha = .7, color = "white", stat = "binline", bins = 20) + 
  scale_fill_hue(l=30)

# Combined
ggplot(datos_density_ridges, aes(x = FollowUP, y = Genero, fill = Genero)) + 
  geom_density_ridges(scale = 2, alpha = .7, color = "white", stat = "binline", bins = 20) + 
  geom_density_ridges(scale = 2, alpha = .4, color = "white") + 
  scale_fill_hue(l=30)

## Picking joint bandwidth of 12.4

7.3.11 PP plots

Plots of distributional differences… See http://www.dandersondata.com/post/esvis-part-1/

if (!require('esvis')) devtools::install_github("DJAnderson07/esvis"); library('esvis')

pp_plot(benchmarks, reading ~ frl)

pp_plot(benchmarks, reading ~ ell, ref_group = "Non-ELL")

“Notice in this plot there is actually a reversal of the effect for monitor students. On the lower end of the scale, Monitor students are actually out-performing non-ELL students, but this effect reverses at the top of the scale. A summary measure would not provide this type of information, but it may be incredibly valuable for theory development. For example, for this finding we may theorize that students with very low achievement receive a benefit from essentially any additional attention, even if that attention is not directly related to academics.”(http://www.dandersondata.com/post/esvis-part-1/)

For the sake of comparison, here are the distributions plotted with geom_density_ridges:

ggplot(benchmarks, aes(x = reading, y = ell, fill = ell)) + 
  geom_density_ridges(scale = 2, alpha = .7, color = "white", stat = "binline", bins = 20) + 
  geom_density_ridges(scale = 2, alpha = .4, color = "white") + 
  scale_fill_hue(l=30)

7.3.12 Shaded geom area

FROM @kara_woo: https://gist.github.com/karawoo/14f4f46da900b09997d26171d092fb92

if (!require('readr')) install.packages('readr'); library('readr')
if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')

data_for_plot = read_csv("Data/06_Visualize_data/data_shaded_geom_area.csv")

# Deaths by cause
p_area <- ggplot(data_for_plot, aes(x=year, y=count, group=cat, order=cat)) +
  geom_area(aes(fill=cat), position='stack') + 
  theme_minimal()

p_area

## Calculate total deaths by year
dat <- data_for_plot %>%
  group_by(year) %>%
  mutate(total = sum(count))

ggplot(dat, aes(x = year, y = count, group = cat, order = cat, fill = cat)) +
  geom_area(data = dat, aes(y = total), fill = "grey", alpha = .5, position = 'stack') +
  geom_area(position = 'stack') + #colour = "black",
  facet_wrap(~ cat) +
  guides(fill = FALSE) +  # to remove the legend
  theme_minimal()              # for clean look overall

# 
# 
# 
# ## Colors I chose
# mycolors <- c("#00bf7b", "#59004d", "#ffcb89", "#a76e61", "#ac270d", "#7890ff",
#               "#6ca013", "#c2e05e", "#00300d", "#ff7b90")
# 
# 
# ## Plot as small multiples
# #** BUG: If we run the code for this plot in bookdown after loading packages plyr, Rmisc, the y axis gets mangled.**
# p_small_mult <- ggplot(dat, aes(x = year)) +
#   ## Gray background showing total
#   geom_area(aes(y = total), fill = "grey80", alpha = 0.7) +
#   ## Individual areas for each category
#   geom_area(aes(y = count, fill = cat)) +
#   facet_wrap(~ cat, nrow = 2) +
#   ## Further customization
#   scale_fill_manual(values = mycolors) +
#   scale_y_continuous(limits = c(0, max(dat$total) + 5), expand = c(0, 0)) +
#   theme_minimal() +
#   theme(
#     legend.position = "none",
#     axis.text = element_text(size = 7)
#   ) +
#   labs(
#     y = "Total deaths",
#     x = "Year",
#     title = "On-duty police officer deaths",
#     subtitle = "Data: https://github.com/fivethirtyeight/data/police-deaths"
#   )

# p_small_mult

7.3.13 Multiple Scatterplots

if (!require('readr')) install.packages('readr'); library('readr')
if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')
if (!require('ggthemes')) install.packages('ggthemes'); library('ggthemes')

## Loading required package: ggthemes

if (!require('Rmisc')) install.packages('Rmisc'); library('Rmisc')

## Loading required package: Rmisc

## Loading required package: lattice

## Loading required package: plyr

## ------------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## ------------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following object is masked from 'package:ggpubr':
## 
##     mutate

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following object is masked from 'package:purrr':
## 
##     compact

a = read_csv("Data/06_Visualize_data/data_multiple_scatterplot.csv")

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   ID = col_double(),
##   `Executive functions` = col_double(),
##   `Emotional recognition` = col_double(),
##   ToM = col_double(),
##   `Inhibitory verbal control` = col_double(),
##   `Cryst.Intellig.1 (vocab.)` = col_double(),
##   `Cryst. Intellig.2 (years of studies)` = col_double(),
##   `Fluid intelligence` = col_double(),
##   `Social adaptation` = col_double(),
##   Age = col_double()
## )

# Si se cambia alguno de estos nombres, cambiar tb abajo (o mejor, find and replace all!)
colnames(a) = c("a", "Executive functions", "Emotional recognition", "ToM", "Inhibitory verbal control", "Cryst. Intellig. (vocab)", "Cryst. Intellig. (years of education)", "Fluid intelligence", "w", "gg")


p1 = ggplot(a, aes(x = `Cryst. Intellig. (vocab)`, y = `Executive functions`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank())

p2 = ggplot(a, aes(x = `Cryst. Intellig. (vocab)`, y = `Emotional recognition`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank())

p3 = ggplot(a, aes(x = `Cryst. Intellig. (vocab)`, y = ToM)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte()  + theme(axis.title.x=element_blank())

p4 = ggplot(a, aes(x = `Cryst. Intellig. (vocab)`, y = `Inhibitory verbal control`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() 

p5 = ggplot(a, aes(x = `Cryst. Intellig. (years of education)`, y = `Executive functions`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank(), axis.title.y=element_blank())

p6 = ggplot(a, aes(x = `Cryst. Intellig. (years of education)`, y = `Emotional recognition`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank(), axis.title.y=element_blank())

p7 = ggplot(a, aes(x = `Cryst. Intellig. (years of education)`, y = ToM)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank(), axis.title.y=element_blank())

p8 = ggplot(a, aes(x = `Cryst. Intellig. (years of education)`, y = `Inhibitory verbal control`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.y=element_blank())

p9 = ggplot(a, aes(x = `Fluid intelligence`, y = `Executive functions`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank(), axis.title.y=element_blank())

p10 = ggplot(a, aes(x = `Fluid intelligence`, y = `Emotional recognition`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank(), axis.title.y=element_blank())

p11 = ggplot(a, aes(x = `Fluid intelligence`, y = ToM)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.x=element_blank(), axis.title.y=element_blank())

p12 = ggplot(a, aes(x = `Fluid intelligence`, y = `Inhibitory verbal control`)) + geom_point(shape=16, fill="darkgrey", color="black", size=2) +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + theme_tufte() + theme(axis.title.y=element_blank())


multiplot(p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, cols=3)

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

Using ggforce:

if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')
if (!require('ggforce')) install.packages('ggforce'); library('ggforce')

## Loading required package: ggforce

ggplot(a, aes(x = .panel_x, y = .panel_y)) + 
  geom_point(shape=16, fill="darkgrey", color="black", size=2, position = 'auto') +
  geom_smooth(method=lm, fill="grey", color="black", se = F, size = .5) + 
  theme_tufte() + 
  theme(axis.title.x=element_blank(), axis.title.y=element_blank()) +
  facet_matrix(vars(`Executive functions`, `Emotional recognition`, ToM, `Inhibitory verbal control`), vars(`Cryst. Intellig. (vocab)`, `Cryst. Intellig. (years of education)`, `Fluid intelligence`))

## Warning in rows == cols: longer object length is not a multiple of shorter
## object length

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

7.3.14 Background data

FROM: https://drsimonj.svbtle.com/plotting-background-data-for-groups-with-ggplot2

if (!require('readr')) install.packages('readr'); library('readr')
if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')

d <- iris        # Full data set
d_bg <- d[, -5]  # Background Data - full without the 5th column (Species)

ggplot(d, aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(data = d_bg, fill = "grey", alpha = .5) +
  geom_histogram(colour = "black") +
  facet_wrap(~ Species) +
  guides(fill = FALSE) +  # to remove the legend
  theme_bw()              # for clean look overall

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(d, aes(x = Sepal.Width, y = Sepal.Length, colour = Species)) +
  geom_point(data = d_bg, colour = "grey", alpha = .2) +
  geom_point() + 
  facet_wrap(~ Species) +
  guides(colour = FALSE) +
  theme_bw()

7.3.15 Flip plots

FROM: https://www.displayr.com/how-to-create-sankey-diagrams-from-tables-using-r/?utm_medium=Feed&utm_source=Syndication

#cache=FALSE
if (!require('flipPlots')) remotes::install_github("Displayr/flipPlots"); library('flipPlots')


my.data = data.frame(Married = c("Yes","Yes", "Yes", "No", "No"),
                     Pet = c("Yes", "Yes", "No", "Yes", "No"),
                     Happy = c("Yes", "Yes", "Yes", "Yes", "No"),
                     freq = 5:1)

SankeyDiagram(my.data[, -4],
              link.color = "Source", 
              weights = my.data$freq) 

SankeyDiagram(my.data[, -4],
              link.color = "Source", 
              label.show.varname = FALSE,
              weights = my.data$freq) 

7.3.16 Waffle plots

From: ‘beeboileau’

# remotes::install_github("hrbrmstr/waffle")
if (!require('tidytuesdayR')) install.packages('tidytuesdayR'); library('tidytuesdayR')
if (!require('plyr')) install.packages('plyr'); library('plyr') # Need to load this before dplyr. The count() function used comes from plyr
if (!require('dplyr')) install.packages('dplyr'); library('dplyr')
if (!require('forcats')) install.packages('forcats'); library('forcats')
if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')
if (!require('waffle')) install.packages("waffle", repos = "https://cinc.rud.is"); library('waffle')
if (!require('patchwork')) install.packages('patchwork'); library('patchwork')
if (!require('scales')) install.packages('scales'); library('scales')
if (!require('ggthemes')) install.packages('ggthemes'); library('ggthemes')
if (!require('viridis')) install.packages('viridis'); library('viridis')
if (!require('ggtext')) install.packages('ggtext'); library('ggtext')


#load data
tuesdata <- tidytuesdayR::tt_load(2020, week = 32)

## 
##  Downloading file 1 of 2: `energy_types.csv`
##  Downloading file 2 of 2: `country_totals.csv`

energy_types <- tuesdata$energy_types

#clean up energy types
energy_types_clean <-
  energy_types %>%
  mutate(
    country_name = case_when(
      country == "CZ" ~ "Czech Republic",
      country == "CY" ~ "Cyprus",
      country == "MT" ~ "Malta",
      country == "UK" ~ "UK",
      country == "MK" ~ "Macedonia",
      country == "TR" ~ "Turkey",
      country == "BA" ~ "Bosnia and Herzegovina",
      country == "GE" ~ "Georgia",
      TRUE ~ country_name)
  ) %>%
  mutate(
    country_name = as.factor(country_name),
    type = as.factor(type)
  )



#-----waffle plot-----

#draw waffle plot
energy_composition <- 
  energy_types_clean %>%
  janitor::clean_names() %>% 
  filter(level == "Level 1") %>%
  mutate(
    energy_type = fct_collapse(type, 
                               renewable = c("Wind", "Hydro", "Solar", "Geothermal"),
                               nuclear = "Nuclear",
                               conventional_thermal = "Conventional thermal",
                               other = "Other")) %>%
  dplyr::count(country_name, energy_type, wt = x2018) %>%
  dplyr::group_by(country_name) %>%
  dplyr::summarise(
    total = sum(n),
    energy_type,
    country_name,
    n
  ) %>%
  dplyr::ungroup() %>%
  mutate(
    n = n/1000
  ) %>%
  arrange(
    desc(total),
    desc(country_name)
  ) %>%
  slice(1:40)

energy_composition %>% 
  ggplot(aes(fill = energy_type,
             values = n)) +
  geom_waffle(n_rows = 10, size = 0.33, color = "white", flip = TRUE, show.legend = FALSE)+
  labs(
    title = "the 10 European countries using most energy in 2018",
    subtitle = "each box represents one kilo-watt hour; <br>
    colours demarcate <b style = 'color:#31688EFF'>conventional thermal</b>,
    <b style = 'color:#35B779FF'>renewable</b>, and
    <b style = 'color:#FDE725FF'>nuclear</b> energy.",
    caption = "data: Eurostat\nviz: @beeboileau"
  )+
  coord_equal()+
  scale_fill_manual(values = c("#31688EFF", "#35B779FF", "#FDE725FF"))+
  theme_enhance_waffle()+
  facet_wrap(~fct_reorder(country_name, desc(total)), nrow = 1, strip.position = "bottom")+
  theme_minimal(base_family = "NYTFranklin Light")+
  theme(
    plot.title = element_text(size = rel(2), margin = margin(20,0,0,0)),
    plot.subtitle = element_markdown(size = rel(1), margin = margin(20,0,20,0)),
    plot.margin = margin(10,10,10,10),
    panel.grid = element_blank(),
    axis.text.x = element_blank(),
    legend.position = c(0.8, 0.8),
    legend.title = element_blank()
  )