diff --git a/caveat/declutter.Rmd b/caveat/declutter.Rmd
index 259dee7..82e0205 100644
--- a/caveat/declutter.Rmd
+++ b/caveat/declutter.Rmd
@@ -25,7 +25,7 @@ Getting rid of all the unnecessary elements can greatly improve the quality and
Here is a good example that takes a cluttered graphic from [viz.wtf](http://viz.wtf) and gets rid of the unnecessary elements. This example comes from the website [Storytelling with data](http://www.storytellingwithdata.com/blog/2017/3/29/declutter-this-graph) by [Cole Nussbaumer Knaflic](http://www.storytellingwithdata.com/about/).
-#Initial graphic
+# Initial graphic
***
The idea of the chart is to show that women tend to begin Christmas shopping earlier than men:
@@ -36,7 +36,7 @@ The idea of the chart is to show that women tend to begin Christmas shopping ear
-#Final appearance:
+# Final appearance:
***
@@ -48,7 +48,7 @@ The idea of the chart is to show that women tend to begin Christmas shopping ear
-#Step by step
+# Step by step
***
Here are the components you can consider removing when making a chart:
@@ -74,7 +74,7 @@ Here is an animation showing the evolution of the previous chart at each step of
-#Going further
+# Going further
***
- Visit the website [Story Telling with data](http://www.storytellingwithdata.com/)
@@ -82,10 +82,6 @@ Here is an animation showing the evolution of the previous chart at each step of
- [Data looks better naked](https://www.darkhorseanalytics.com/blog/data-looks-better-naked), by Dark Horse Analytics.
-#Comments
-***
-Any thoughts on this? Found any mistake? Disagree? Please drop me a word on [twitter](https://twitter.com/R_Graph_Gallery) or in the comment section below:
-
diff --git a/caveat/declutter.html b/caveat/declutter.html
index 4acc354..6ae740e 100644
--- a/caveat/declutter.html
+++ b/caveat/declutter.html
@@ -1,270 +1,1928 @@
+
Decluttering your chart
+Getting rid of all the unnecessary elements can greatly improve the quality and impact of your chart. First, the chart will be cleaner and thus more likely to be read by people. Second, it will allow people to target directly what is important on the chart, and thus to get your point.
-Here is a good example that takes a cluttered graphic from viz.wtf and gets rid of the unnecessary elements. This example comes from the website Storytelling with data by Cole Nussbaumer Knaflic.
-Getting rid of all the unnecessary elements can greatly improve the +quality and impact of your chart. First, the chart will be cleaner and +thus more likely to be read by people. Second, it will allow people to +target directly what is important on the chart, and thus to get your +point.
+Here is a good example that takes a cluttered graphic from viz.wtf and gets rid of the unnecessary +elements. This example comes from the website Storytelling +with data by Cole Nussbaumer +Knaflic.
+The idea of the chart is to show that women tend to begin Christmas shopping earlier than men:
+The idea of the chart is to show that women tend to begin Christmas +shopping earlier than men:
Here are the components you can consider removing when making a chart:
+Here are the components you can consider removing when making a +chart:
Here is an animation showing the evolution of the previous chart at each step of the improvement:
+Here is an animation showing the evolution of the previous chart at +each step of the improvement:
Any thoughts on this? Found any mistake? Disagree? Please drop me a word on twitter or in the comment section below:
+ Data To Viz is a + comprehensive classification of chart types organized by + data input format. Get a high-resolution version of our decision + tree delivered to your inbox now! +
+A work by Yan Holtz for data-to-viz.com
- +@@ -274,37 +1932,561 @@
Visualizing a unique Numeric variable
+
This document gives a few suggestions to analyse a dataset composed by a unique numeric variable.
It considers the night price of about 10,000 Airbnb appartements on the French Riviera in France.
This example dataset has been downloaded from the Airbnb website and is available on this Github repository. Basically it looks like the table beside.
This document gives a few suggestions to analyse a
+dataset composed by a unique numeric variable.
It considers the
+night price of about 10,000 Airbnb
+appartements on the French Riviera in France.
This example dataset
+has been downloaded from the Airbnb website and
+is available on this Github
+repository. Basically it looks like the table beside.
# Libraries
-library(tidyverse)
-library(hrbrthemes)
-library(kableExtra)
-options(knitr.table.format = "html")
-
-# Load dataset from github
-data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv", header=TRUE)
-
-# show data
-data %>% head(6) %>% kable() %>%
- kable_styling(bootstrap_options = "striped", full_width = F)
# Libraries
+library(tidyverse)
+library(hrbrthemes)
+library(kableExtra)
+options(knitr.table.format = "html")
+
+# Load dataset from github
+data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv", header=TRUE)
+
+# show data
+data %>% head(6) %>% kable() %>%
+ kable_styling(bootstrap_options = "striped", full_width = F)
#Histogram ***
+A work by Yan Holtz for data-to-viz.com
- +@@ -335,37 +2200,562 @@
+ Data To Viz is a + comprehensive classification of chart types organized by + data input format. Get a high-resolution version of our decision + tree delivered to your inbox now! +
+