Stata code for a workshop on active citizenship with a partner university from the UK.
Uses data from the European Social Survey (ESS), Round 1 (2002).
Variable selection guided by Hoskins and Mascherini, 'Measuring Active Citizenship through the Development of a Composite Indicator' (Social Indicators Research, 2009).
| // ----------------------------------------------------------------------------- | |
| // DEPENDENCIES | |
| // ----------------------------------------------------------------------------- | |
| // minimum version for `import spss` | |
| version 16 | |
| // version used for estimation | |
| version 17 |
| /* rs-theme-name: Dark UI Base16 Tomorrow Night */ | |
| /* rs-theme-is-dark: TRUE */ | |
| /* Dark UI from Randy3k's Wombat (https://github.com/randy3k/dotfiles/blob/master/.R/rstudio/themes/Wombat.rstheme) */ | |
| /* "Tomorrow night" color scheme adapted from chriskempson's Base16 (https://github.com/chriskempson/base16). */ | |
| .body { | |
| background: #ffffff; | |
| } |
| # triangle.py | |
| import math | |
| def triangle_perimeter(a, b, c): | |
| return a + b + c | |
| def triangle_area(a, b, c): | |
| p = triangle_perimeter(a, b, c) / 2 | |
| x = math.sqrt(p * (p - a) * (p - b) * (p - c)) | |
| return round(x, 2) |
| # package dependency | |
| library(haven) | |
| path_in = "new_EB with more var_2004-2016.dta" | |
| out_tsv = "new_EB.tsv" | |
| out_sav = "new_EB.sav" | |
| # name repair shows the issue with _merge | |
| x = haven::read_dta(p, .name_repair = "universal") | |
| names(x)[ names(x) == "._merge" ] = "MERGEVAR" |
| # change of PRNG in R >= 3.6.0 | |
| # use former PRNG | |
| RNGkind(sample.kind = "Rounding") | |
| # use new/fixed PRNG | |
| RNGkind(sample.kind = "default") | |
| # see ?Random for details |
| library(cartography) | |
| library(sp) | |
| # Load data | |
| data(nuts2006) | |
| # Get a SpatialLinesDataFrame of countries borders | |
| nuts0.contig.spdf <- getBorders(nuts0.spdf) | |
| # Get the GDP per capita | |
| nuts0.df$gdpcap <- nuts0.df$gdppps2008/nuts0.df$pop2008*1000000 |
| ## | |
| ## download all `ergm` sources from v3.0-1 to today | |
| ## | |
| library(rvest) | |
| d <- "ergm-sources" | |
| dir.create(d, showWarnings = FALSE) | |
| # current source |
| R to python useful data wrangling snippets | |
| The dplyr package in R makes data wrangling significantly easier. | |
| The beauty of dplyr is that, by design, the options available are limited. | |
| Specifically, a set of key verbs form the core of the package. | |
| Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. | |
| Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. | |
| The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package). | |
| dplyr is organised around six key verbs |
| library(tidyverse) | |
| library(patchwork) | |
| plot_pair <- function(data, x, y) { | |
| ggplot(data, aes_string(x = x, y = y, color = "Species", shape = "Species")) + | |
| geom_point() + | |
| scale_color_brewer(palette = "Dark2") + | |
| theme(legend.position = "none", plot.title = element_text(size = 7)) + | |
| labs(x = NULL, y = NULL, title = paste0(y, " ~ ", x)) |