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hadley
/ tidyhancements-19.R
Created
July 10, 2019 06:00
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| # ======================================= | |
| # = Enhancements to data tidying = | |
| # = Hadley Wickham = | |
| # = https://rstd.io/tidyhancements-2019 = | |
| # ======================================= | |
| # What is tidy data? ---------------------------------------------------------- | |
| # 1. Each column is a variable. | |
| # 2. Each row is an observation. | |
| # 3. Each cell is a value. |
mschnetzer
/ gerwealth.R
Last active
April 9, 2020 12:21
Wenn Deutschland so verteilt wäre wie Vermögen (https://twitter.com/matschnetzer/status/1178930993186693121)
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| library(tidyverse) | |
| library(msthemes) | |
| library(sf) | |
| # GeoJSON: http://opendatalab.de/projects/geojson-utilities/ | |
| geodat <- st_read("landkreise_simplify200.geojson", quiet=TRUE, stringsAsFactors=FALSE) %>% | |
| mutate( | |
| center = map(geometry, st_centroid), | |
| centercoord = map(center, st_coordinates), |
jlmelville
/ turbo_colormap.R
Last active
March 1, 2020 21:31
Google's Turbo colormap as an R palette based on https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f -- for more details see https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
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| # License: Apache 2.0 | |
| # A straightforward translation of the Python code: | |
| turbo_colormap_data <- matrix( | |
| c( | |
| c(0.18995, 0.07176, 0.23217), | |
| c(0.19483, 0.08339, 0.26149), | |
| c(0.19956, 0.09498, 0.29024), | |
| c(0.20415, 0.10652, 0.31844), |
mertedali
/ JASSSCode.r
Last active
August 21, 2019 08:01
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| ##################################################### | |
| ##################################################### | |
| ### R Script for Metamodeling and Rule Extraction ### | |
| ##################################################### | |
| ##################################################### | |
| ############################# | |
| ### Step I: Load Packages ### | |
| ############################# |
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| library(tidyverse) | |
| library(lubridate) | |
| library(scales) | |
| df <- tibble( | |
| date = seq.Date(from=ymd("2019-01-01"),to=ymd("2019-12-31"),by="day"), | |
| wkdy = wday(date,label=T), | |
| mo = month(date,label=T), | |
| mo.num = month(date,label=F), | |
| day = day(date) |
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| library(ggplot2) | |
| # dput(ls(pattern = "theme",package:ggplot2)) | |
| defaults <- c("theme_bw", "theme_classic", "theme_dark", | |
| "theme_grey", "theme_light", "theme_linedraw", | |
| "theme_minimal", "theme_void") | |
| library(ggthemes) | |
| # dput(ls(pattern = "theme",package:ggthemes)) | |
| addons <- c("theme_base", "theme_calc", "theme_economist", | |
| "theme_economist_white", "theme_excel", "theme_few", "theme_fivethirtyeight", | |
| "theme_foundation", "theme_gdocs", "theme_hc", "theme_igray", |
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