library(dplyr)
library(ggplot2)
library(sf)
tz <- st_read(here::here("external/data/tanzania.geojson"))
sagcot <- st_read(here::here("external/data/sagcotcl.geojson"))
zambia <- st_read(
  system.file("extdata/districts.geojson", package = "geospaar")
) %>% st_union()
prec <- geodata::worldclim_global(var = "prec", res = 2.5, 
                                  path = "external/data/")
precsum <- terra::app(prec, sum)
tz_sagcot <- terra::vect(st_union(sagcot, zambia))
prec_tzzam <- terra::mask(terra::crop(precsum, tz_sagcot), tz_sagcot)

prec_stars <- stars::st_as_stars(prec_tzzam)
p <- ggplot() + 
  geom_sf(data = tz) +  
  stars::geom_stars(data = prec_stars) +
  scale_fill_viridis_c(name = "Rainfall (mm)", na.value = "transparent") + 
  geom_sf(data = zambia, fill = "transparent") +
  geom_sf(data = sagcot, fill = "transparent") +
  labs(x = NULL, y = NULL) +
  theme_linedraw()
ggsave(p, filename = "docs/figures/tanzam_rainfall.png", height = 4, 
       width = 7, units = "in", dpi = 300)

Today

  • Homework
  • Bring Your Own Data (to read)
  • Intro to manipulating and analyzing data

Homework exercise: lapply + read + write data

  • Let’s use lapply to create a list l of 3 data.frames in which the first data.frame has it’s V2 column multiplied by 5, the second data.frame has V2 multiplied by 10, and the third has V2 multipled by 20. Use dat (see earlier slides) as the starting data.frame.
  • Within the lapply, write each data.frame out to tempdir, naming each dataX.csv, where X is replaced by the multiplier value applied to V2
  • Use list.dir() to read the file names and form a file path vector from them. Name the vector temp_files
  • Use lapply with temp_files to read the files back into a list l2. Compare l and l2 for equivalency.

Buggy answer

l <- lappl(c(5, 10, 20), function[x] {
  d >= datt[dat$V2 * "x"]
  readr:write.csv(d, file = filepath(tempdir(), paste0("data", X, ".csv")))
  return(d)
}})

temp-files <- list.files(tempdir(), full.names = True, row.names = FALSE)

l2 <- lapply(temp-files, function(x) readr:::read.csv(x))

for(i in 1:length(l)) print(l[[i]] == l2[[i]])

BYOD

Working with data, continued

do.call / bind_rows

sz <- 100
for(i in 1:3) {
  set.seed(i)
  d <- data.frame(
    id = 1:sz,
    v1 = runif(sz, min = 2, max = 12),
    grp = paste0("g", i)
  ) %>% mutate(v2 = v1 + rnorm(sz, mean = 2, sd = 2)) %>% 
    select(id, v1, v2, grp)
  readr::write_csv(d, file = file.path(tempdir(), paste0("dataset", i, ".csv")))
}
# ggplot(d) + geom_point(aes(x = v1, y = v2))

fs <- list.files(tempdir(), pattern = "dataset.*.csv", full.names = TRUE)
# dat <- do.call(rbind, lapply(fs, readr::read_csv))
dat <- bind_rows(lapply(fs, readr::read_csv))

plot(dat$v1, dat$v2)

Manipulating and analyzing data

  • reshape
  • mutate
  • select
  • joins
  • split-apply-combine
  • plotting
  • regression

Reshape

dat %>% 
  pivot_wider(names_from = grp, values_from = v1:v2)

dat %>% 
  select(-v1) %>%
  pivot_wider(names_from = grp, values_from = v2)

dat_wide <- dat %>% 
  select(id, v2, grp) %>%
  pivot_wider(names_from = grp, values_from = v2)

dat_long <- dat_wide %>% 
  pivot_longer(g1:g3, names_to = "grp", values_to = "v2") %>% 
  arrange(grp)

dat_long2 <- dat_wide %>% 
  pivot_longer(g1:g3, names_to = "grp", values_to = "v2") 

cbind(dat, dat_long) %>% head()
bind_cols(dat, dat_long)

Joins

cbind(dat, dat_long2) %>% head()
# bind_cols(dat, dat_long) 

dat %>% left_join(dat_long2)
dat_long2 %>% 
  rename(grp2 = grp) %>%
  select(id, grp2, v2) %>% 
  left_join(dat, ., by = c("id", "v2", "grp" = "grp2"))

dat_long2 %>% 
  rename(grp2 = grp) %>%
  select(id, grp2, v2) %>% 
  left_join(dat %>% select(-v2), ., by = c("id", "grp" = "grp2")) %>% 
  select(id:v1, v2, grp)
  • Note: understand the differences between full_join, inner_join, right_join, and left_join.

Split-apply-combine

set.seed(10)
dat <- data.frame(v1 = 1:100, v2 = sample(0:10, size = 100, replace = TRUE), 
                  grp = sample(letters[1:3], size = 100, replace = TRUE))

sapply(sort(unique(dat$grp)), function(x) {
  colMeans(dat[dat$grp == x, c("v1", "v2")])
})

dat %>% 
  group_by(grp) %>% 
  summarise(across(c(v1, v2), mean))