Using several R packages (stationaRy, DiagrammeR, DiagrammeRsvg, magrittr, and dplyr), you can create a time-series plot of weather-related things. Here, I plotted daily rainfall during 2015.

yr_daily_rainfall_2015.R

# Load in several packages
devtools::install_github("rich-iannone/DiagrammeR")
library(DiagrammeR)
library(DiagrammeRsvg)
library(magrittr)
library(stationaRy)
library(dplyr)

# Get the daily rainfall values (in mm) during 2015 at
# the Vancouver Internation Airport (YVR)
yvr_rainfall_2015 <-
  get_isd_stations(startyear = 1970, endyear = 2015,
                   lower_lat = 49, upper_lat = 58,
                   lower_lon = -125, upper_lon = -120) %>%
  select_isd_station(name = "vancouver intl") %>%
  get_isd_station_data(startyear = 2015,
                       endyear = 2015,
                       select_additional_data = "AA1") %>%
  select(year, month, day, hour,
         period = aa1_1, rainfall_depth = aa1_2) %>%
  filter(period == 6) %>%
  filter(rainfall_depth != 999.9) %>%
  filter(year == 2015) %>%
  group_by(year, month, day) %>%
  summarise(daily_rainfall = mean(rainfall_depth,
                                  na.rm = TRUE)) %>%
  filter(daily_rainfall != 0) %>%
  mutate(date = paste0(year, "-",
                       sprintf("%02d", month), "-",
                       sprintf("%02d", day))) %>%
  bind_rows(
    data_frame(year = 2015, month = 1, day = 1,
               daily_rainfall = 0, date = "2015-01-01"),
    .
  )

# Create a series of x,y points for time series plotting
rainfall_pts <-
  create_xy_pts(
    series_label = "Daily Rainfall",
    x = yvr_rainfall_2015$date,
    y = yvr_rainfall_2015$daily_rainfall,
    line_width = 1.0,
    width = 0.05,
    height = 0.05,
    shape = "circle",
    line_color = "blue")

# Create an xy_graph
xy_graph <-
  create_xy_graph(
    rainfall_pts,
    x_name = "Date",
    y_name = "Daily Rainfall (mm)",
    heading = "#Daily Rainfall (YVR — 2015)",
    footer = c("#####Gist at: https://gist.github.com/rich-iannone/18bc60ff6350f0793cee",
               "#####DiagrammeR, stationaRy, dplyr, and magrittr were used."),
    xy_value_labels = c("date", "numeric"),
    include_xy_minima = c(TRUE, FALSE),
    bg_color = "white")

# Get vector of node ID values where precipitation values
# are in the top half of the y-scale
high_precip <-
  xy_graph %>% select_nodes("graph_component", "xy_pts") %>%
  select_nodes("y", ">5.0", "intersect") %>%
  get_selection %>% .$nodes

# Create a callout node stating some obvious stuff
high_precip_callout <-
  create_nodes(1, label = "High \nPrecipitation",
               shape = "plaintext",
               fontcolor = "grey50",
               x = 5, y = 9.5)

# Create the edges between the high precip points
# and that callout
high_precip_callout_edges <-
  create_edges(from = high_precip,
               to = rep("1", length(high_precip)),
               arrowhead = "none",
               headport = "s",
               color = "grey85")

# Create a small graph with the callout and the
# points with higher-than-usual precipitation
callout_graph <-
  create_graph(nodes_df = high_precip_callout,
               edges_df = high_precip_callout_edges)

# Combine these graphs into one
xy_graph <-
  combine_graphs(xy_graph, callouts)

# View the graph in the RStudio Viewer
xy_graph %>% render_graph

# Save the graph as a PDF
xy_graph %>% export_graph("yvr_daily_rainfall_2015.pdf")

# Save the graph as a PNG
xy_graph %>% export_graph("yvr_daily_rainfall_2015.png")

Here is the plot: