I was trying to use R to map with some data saved as 'csv' format. Date in my data is in YYYY-dd-mm format.I want to add some data information,like 'year''month',I used the following code:
effort_df <- effort_df %>%
mutate(year = year(date),
month = month(date))
after running the code, I get the following error:
Error in as.POSIXlt.default(x, tz = tz(x)) : do not know how to convert 'x' to class “POSIXlt”
And the complete codes are as followings:
#Load packages
library(tidyverse) # for general data wrangling and plotting
library(furrr) # for parallel operations on lists
library(lubridate) # for working with dates
library(sf) # for vector data
library(raster) # for working with rasters
library(maps) # additional helpful mapping packages
library(maptools)
library(rgeos)
# World polygons from the maps package
world_shp <- sf::st_as_sf(maps::map("world", plot = FALSE, fill = TRUE))
# Load EEZ polygons
eezs <- read_sf("F:/data/shapefiles/World_EEZ_v10_20180221", layer = 'eez_v10') %>%
filter(Pol_type == '200NM') # select the 200 nautical mile polygon layer
# Specify location of data directory containing daily csv files.
data_dir <- ("F:/yjs/data/fishing_effort/fishing_effort")
# Create dataframe of filenames dates and filter to date range of interest
effort_files <- tibble(
file = list.files(paste0(data_dir, 'fishing_effort_byflag'),
pattern ='.csv', recursive = T, full.names = T),
date =ymd(str_extract(file,
pattern = '[[:digit:]]{4}-[[:digit:]]{2}-[[:digit:]]{2}')))
# Generate a vector of dates of interest using ymd from lubridate
effort_dates <- seq(ymd('2016-01-01'), ymd('2016-12-31'), by='days')
# Filter to files within our date range of interest
effort_files <- filter(effort_files, date %in% effort_dates)
# Read in data (uncomment to read in parallel)
plan(multisession)
effort_df <- furrr::future_map_dfr(effort_files$file, .f = read_csv)
class(effort_df$date)
# Add date information
effort_df <- effort_df %>%
mutate(year = year(date),
month = month(date))
# Specify new (lower) resolution in degrees for aggregating data
res <- 0.25
# Transform data across all fleets and geartypes
effort_df <- effort_df %>%
mutate(
lat_bin = lat_bin / 100,
lon_bin = lon_bin / 100,
lat_bin = floor(lat_bin/res) * res + 0.5 * res,
lon_bin = floor(lon_bin/res) * res + 0.5 * res)
# Re-aggregate the data to 0.25 degrees
effort_df <- effort_df %>%
group_by(date, year, month, lon_bin, lat_bin, flag, geartype) %>%
summarize(vessel_hours = sum(vessel_hours, na.rm = T),
fishing_hours = sum(fishing_hours, na.rm = T),
mmsi_present = sum(mmsi_present, na.rm = T))
# Aggregate data across all fleets and geartypes
effort_all <- effort_df %>%
group_by(lon_bin,lat_bin) %>%
summarize(fishing_hours = sum(fishing_hours, na.rm = T),
log_fishing_hours = log10(sum(fishing_hours, na.rm = T))) %>%
ungroup() %>%
mutate(log_fishing_hours = ifelse(log_fishing_hours <= 1, 1, log_fishing_hours),
log_fishing_hours = ifelse(log_fishing_hours >= 5, 5, log_fishing_hours)) %>%
filter(fishing_hours >= 24)
# Linear green color palette function
effort_pal <- colorRampPalette(c('#0C276C', '#3B9088', '#EEFF00', '#ffffff'),
interpolate = 'linear')
# Map fishing effort
p1 <- effort_all %>%
ggplot() +
geom_sf(data = world_shp, fill = '#374a6d', color = '#0A1738',size = 0.1) +
geom_sf(data = eezs,color = '#374a6d',alpha = 0.2,fill = NA,size = 0.1) +
geom_raster(aes(x=lon_bin,y=lat_bin,fill=log_fishing_hours)) +
scale_fill_gradientn(
"Fishing Hours",
na.value = NA,limits <- c(1, 5),colours = effort_pal(5),
labels <- c("10","100","1,000","10,000","100,000+"),values = scales::rescale(c(0, 1))) +
labs(fill = "Fishing hours (log scale)",
title = "Global fishing effort in 2016") +
guides(fill = guide_colourbar(barwidth = 10)) +
gfw_theme
How can I solve this issue?
