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| #some libraries | |
| library(dplyr) | |
| library(tidyr) | |
| library(broom) | |
| library(purrr) | |
| #let's use the npk data | |
| head(npk) | |
| #but make up a few extra columns |
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| #'------------------------------ | |
| #' | |
| #' Script to make sure everything works | |
| #' for you for the 2019-01-22 Geospatial | |
| #' Data Carpentry workshop at UMB | |
| #'------------------------------ | |
| # Make sure we have rnaturalearth installed | |
| # If you do, comment out the install.packages line | |
| #or just don't run it. |
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| library(sf) | |
| library(fasterize) | |
| library(spex) | |
| library(dplyr) | |
| library(purrr) | |
| library(ggplot2) | |
| library(lwgeom) | |
| test_set <- devtools::source_gist("c95701bd444cda3e342838fd9a090fb3", |
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| structure(list(classification_id = c(82289537L, 82386012L, 84461998L, | |
| 85718077L, 91018956L, 102059723L, 102656990L, 103374144L, 105820504L, | |
| 108510525L, 113891667L, 120531542L, 133372372L, 133644897L, 134776350L | |
| ), geometry = structure(list(structure(list(structure(list(structure(c(314658.101094576, | |
| 314658.101094576, 314637.103677284, 314616.106259993, 314616.106259993, | |
| 314595.108842701, 314595.108842701, 314574.111425409, 314553.114008117, | |
| 314553.114008117, 314532.116590825, 314532.116590825, 314511.119173533, | |
| 314511.119173533, 314511.119173533, 314511.119173533, 314511.119173533, | |
| 314511.119173533, 314511.119173533, 314532.116590825, 314532.116590825, | |
| 314553.114008117, 314574.111425409, 314595.108842701, 314616.106259993, |
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| tidy_residuals <- function(model){ | |
| UseMethod("tidy_residuals") | |
| } | |
| tidy_residuals.brmsfit <- function(model){ | |
| res <- residuals(model, summary=FALSE) | |
| props <- summary(model) | |
| nchains <- props$chains | |
| iter <- props$iter - props$warmup |
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| brainGene <- structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control", "schizo", "bipolar" | |
| ), class = "factor"), PLP1.expression = c(-0.02, -0.27, -0.11, | |
| 0.09, 0.25, -0.02, 0.48, -0.24, 0.06, 0.07, -0.3, -0.18, 0.04, | |
| -0.16, 0.25, -0.1, -0.31, -0.05, 0.11, -0.38, 0.23, -0.23, -0.28, | |
| -0.36, -0.22, -0.4, -0.19, -0.34, -0.29, -0.12, -0.34, -0.39, | |
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| library(tidyverse) | |
| library(emmeans) | |
| library(brms) | |
| library(tidybayes) | |
| warp.brms <- brm(breaks ~ wool * tension, data = warpbreaks) | |
| #get the adjusted means | |
| warp_em <- emmeans (warp.brms, ~ tension | wool) | |
| warp_em |
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| library(raster) | |
| #devtools::install_github("jebyrnes/hadsstr") | |
| library(hadsstr) #from jebyrnes/hadsstr | |
| library(ggplot2) | |
| library(sf) | |
| library(rnaturalearth) | |
| #get the world in the lambert projection | |
| countries_50_sf <- ne_download(scale = 50, category = 'cultural', type = 'countries', returnclass="sf") | |
| countries_lambert <- st_transform(oceans, 2154) |
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| library(ggplot2) | |
| #A glm from the glm helpfile | |
| counts <- c(18,17,15,20,10,20,25,13,12) | |
| outcome <- gl(3,1,9) | |
| treatment <- gl(3,3) | |
| print(d.AD <- data.frame(treatment, outcome, counts)) | |
| glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) | |
| #The old way... |
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| library(sf) | |
| library(rnaturalearth) | |
| library(rnaturalearthdata) | |
| library(ggplot2) | |
| oceans <- ne_download(type = "ocean", category = "physical", returnclass="sf") | |
| ocean_antarctic <- st_transform(oceans, 2154) | |
| ggplot(ocean_antarctic) + | |
| geom_sf(fill = "lightblue", color=NA) |