A density plot on the fly:
cols <- c(“#2ECBE9”, “#E998D1”, “#F5B7AB”)cols <- c("#2ECBE9", "#E998D1", "#F5B7AB")
ggplot(df, aes(x = df$Sepal.Length, fill = df$Species)) +
geom_density(alpha = 0.8, color = NA) +
scale_fill_manual(name = "Species",values = cols) + labs(y = "Petal length (cm)", x = "Sepal length (cm)")
The defaults size…
A super simple and graceful way to integrate Google spreadsheet to R life!
library(gsheet)df<-gsheet2tbl('https://docs.google.com/spreadsheets/d/1U7NM4xLMQHlmWV4ZGztiUIjRa9twlXyM3vvAKQwAcmI/edit?usp=sharing')
There are a few other examples that suffer from ‘permission denied’ or format distortion issues, but gsheet never failed me despite being the youngest of the kind.
I’d like to know what other techniques you…
The ‘clipr’ package fascinates those who are unfascinated by R’s poor capacity to transfer outputs to text editors/clipboard.
library(clipr)
& make whatever text related fun you want to have:
text <- "& make whatever text related fun you want to have:"write_clip(text)
To split into several blocks:
text <- c('&…A quick way to export a df to your working directory using a one-line code!
package name: writexl
library('writexl')write_xlsx(df, 'filepath')
or just
write_xlsx(df, 'filename.xls')Enjoy!
Stata has a wonderful arsenal of packages to create eye-catching charts. A great addition to that is the package grc1leg that allows having a common legend when combining more than one graph with the same legends.
net install grc1leg,from( http://www.stata.com/users/vwiggins/)Now let’s say we have 4 graphs on disk: r1, r2, r3, and r3.
To combine them with a single legend, just type:
grc1leg r1.gph r2.gph r3.gph r4.gph, legendfrom(r1)Enjoy!
Thanks to this new package, Stata users can now import data files to R with value labels!
library(readstata13)df <- read.dta13(“mydata.dta”)
enjoy!
I still recall that damp evening I first encountered a classification tree in a conference presentation, and have done my best to farm that in my backyard since then!
I have grown a few so far, but here I will show one that was to become iris one day)))
All I needed to get my hands dirty were a few packages:
library(visTree)library(visNetwork)library(shinyWidgets)
and a one-line romance epic:
net <- visTreeEditor(data=df)and boY o bOy… my hands looked cleaner than ever…
