IanColdwater

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vankesteren

library(tidyverse)

lights_dat <- read_csv("https://ckan.dataplatform.nl/dataset/83402c68-1c05-4aa5-ab28-2e99d2bc2261/resource/dc10e0ac-351a-49b6-b3db-d0152c29dc02/download/paal-20180906.csv")

pp <- 
  lights_dat %>% 
  filter(latitude > 50) %>% 
  ggplot(aes(x = longitude, y = latitude)) + 
  geom_point(alpha = 0.03, fill = "#FAFAAB", stroke = 0, pch = 21, size = 1.6) +
  geom_point(alpha = 0.8,  fill = "#FAFAAB", stroke = 0, pch = 21, size = 0.2) +

halhen

# Download elevation tif from eg http://www.eea.europa.eu/data-and-maps/data/digital-elevation-model-of-europe
# First, convert elevation tif to a space delimited xyz (lng lat elevation) file
#  $ gdal_translate -of XYZ elevation3x3.tif /tmp/file.xyz

df <- read_delim('/tmp/file.xyz', delim=' ', col_names=FALSE)

df %>%
  mutate(X3 = na_if(X3, 0)) %>%
  ggplot(aes(X1, -X2 + 20 * X3/max(X3, na.rm=TRUE), group=X2)) +
    geom_line(size=0.05) +

mick001

# Set a seed
set.seed(500)

library(MASS)
data <- Boston

# Check that no data is missing
apply(data,2,function(x) sum(is.na(x)))

# Train-test random splitting for linear model