![[Stable]](figures/lifecycle-stable.svg)
Refer to the data available in the JMA Historical Weather Data Search. Executed by specifying the target location and date. Currently, not all types of data acquisition are supported.
Usage
jma_collect(
item = NULL,
block_no,
year,
month,
day,
cache = TRUE,
pack = TRUE,
quiet = FALSE
)Arguments
- item
Type of weather data to be acquired. Mainly specifies the interval between records (e.g.
dailyorhourly). See NOTE for details.- block_no
Block number of the location to be observed. It is assumed that block_no is input as a string consisting of a 4- or 5-digit number. If a numeric value is specified, it is processed as a string.
- year
select year
- month
select month
- day
select date (default
NULL)- cache
use cash and save to cache. (
TRUE, the default)- pack
Whether to packing common variables or not. (
TRUE, the default)- quiet
Whether to output information on variable and row combinations that were treated as missing values for some reason. (
TRUE, the default)
Note
The parameter item chooses one from these:
annually: Annual value. Please specify a location by
block_no.monthly: Monthly value. Please specify location and year.
3monthly: Value every 3 months. Please specify location and year.
10daily: Seasonal value. Please specify location and year.
mb5daily: Semi-seasonal value. Please specify location and year.
daily: Daily value. Please specify location, year and month.
hourly: Hourly value. Please specify location, year, month and day.
rank: Values of the largest in the history of observations.
nml_ym: Climatological normals for each year and month.
nml_3m: Climatological normals for each 3 months.
nml_10d: Climatological normals for each season (almost 10 days).
nml_mb5d: Climatological normals for each semi-season (almost 5 days).
nml_daily: Daily climatological normals for specific month. for each location.
Examples
# \donttest{
# Annually
jma_collect(item = "annually", "1284", year = 2017, month = 11, cache = FALSE)
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/annually_a.php?prec_no=11&block_no=1284&year=2017&month=11&day=&view=Treated as missing: lines 1, 32, 42 at precipitation_sum(mm)
#> Treated as missing: lines 1, 32, 42, 43 at precipitation_max_per_day(mm)
#> Treated as missing: lines 1, 18, 21, 29, 32, 42 at precipitation_max_1hour(mm)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 42 at precipitation_max_10minutes(mm)
#> Treated as missing: lines 1, 10, 17, 21, 32 at temperature_average(℃)
#> Treated as missing: lines 1, 7, 10, 17, 21, 32 at temperature_average_max(℃)
#> Treated as missing: lines 1, 7, 10, 17, 21, 32 at temperature_average_min(℃)
#> Treated as missing: lines 1, 3 at temperature_max(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 at temperature_min(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 at humidity_average(%)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 at humidity_min(%)
#> Treated as missing: lines 1, 32 at wind_average_speed(m/s)
#> Treated as missing: lines 1, 6, 32, 46 at wind_max_speed(m/s)
#> Treated as missing: lines 3, 7, 9, 13 at wind_max_speed_direction
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 at wind_max_instantaneous_speed(m/s)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at wind_max_instantaneous_direction
#> Treated as missing: lines 1, 10, 32, 33, 44 at daylight_(h)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 at snow_fall(cm)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 at snow_max_fall_day(cm)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 at snow_depth(cm)
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> .default = col_double(),
#> wind_max_speed_direction = col_character(),
#> wind_max_instantaneous_direction = col_character(),
#> `snow_fall(cm)` = col_logical(),
#> `snow_max_fall_day(cm)` = col_logical(),
#> `snow_depth(cm)` = col_logical()
#> )
#> ℹ Use `spec()` for the full column specifications.
#> # A tibble: 46 × 7
#> year precipitation$`sum(mm)` temperature$`average(℃)` humidity$`average(%)`
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1978 107 0.4 NA
#> 2 1979 687 5.8 NA
#> 3 1980 675 5.2 NA
#> 4 1981 904 5.1 NA
#> 5 1982 711 6 NA
#> 6 1983 799 4.9 NA
#> 7 1984 686 5.5 NA
#> 8 1985 913 5.2 NA
#> 9 1986 622 4.9 NA
#> 10 1987 917 5.7 NA
#> # ℹ 36 more rows
#> # ℹ 11 more variables: precipitation$`max_per_day(mm)` <dbl>,
#> # $`max_1hour(mm)` <dbl>, $`max_10minutes(mm)` <dbl>,
#> # temperature$`average_max(℃)` <dbl>, $`average_min(℃)` <dbl>,
#> # $`max(℃)` <dbl>, $`min(℃)` <dbl>, humidity$`min(%)` <dbl>,
#> # wind <tibble[,5]>, daylight <tibble[,1]>, snow <tibble[,3]>
# Daily
jma_collect(item = "daily", block_no = "0010", year = 2017, month = 11, cache = FALSE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/daily_a1.php?prec_no=12&block_no=0010&year=2017&month=11&day=&view=Treated as missing: lines 7 at precipitation_sum(mm)
#> Treated as missing: lines 7 at precipitation_max_1hour(mm)
#> Treated as missing: lines 7 at precipitation_max_10minutes(mm)
#> Treated as missing: lines 7, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 30 at temperature_average(℃)
#> Treated as missing: lines 7, 19, 20, 21, 24, 25, 26, 27, 30 at temperature_max(℃)
#> Treated as missing: lines 2, 3, 4, 5, 7, 10, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at temperature_min(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at humidity_average(%)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at humidity_min(%)
#> Treated as missing: lines 4, 7, 15, 16, 23, 24 at wind_average_speed(m/s)
#> Treated as missing: lines 4, 7, 15, 16, 23, 24 at wind_max_speed(m/s)
#> Treated as missing: lines 4, 7, 15, 16, 23, 24 at wind_max_speed_direction(m/s)
#> Treated as missing: lines 4, 7, 15, 16, 23, 24 at wind_max_instantaneous_speed(m/s)
#> Treated as missing: lines 4, 7, 15, 16, 23, 24 at wind_max_instantaneous_direction(m/s)
#> Treated as missing: lines 4, 7, 15, 16, 23, 24 at wind_direction_frequency(m/s)
#> Treated as missing: lines 7 at sunshine_duration_(h)
#> Treated as missing: lines 7 at snow_fall(cm)
#> Treated as missing: lines 7 at snow_depth(cm)
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> date = col_double(),
#> `precipitation_sum(mm)` = col_double(),
#> `precipitation_max_1hour(mm)` = col_double(),
#> `precipitation_max_10minutes(mm)` = col_double(),
#> `temperature_average(℃)` = col_double(),
#> `temperature_max(℃)` = col_double(),
#> `temperature_min(℃)` = col_double(),
#> `humidity_average(%)` = col_logical(),
#> `humidity_min(%)` = col_logical(),
#> `wind_average_speed(m/s)` = col_double(),
#> `wind_max_speed(m/s)` = col_double(),
#> `wind_max_speed_direction(m/s)` = col_character(),
#> `wind_max_instantaneous_speed(m/s)` = col_double(),
#> `wind_max_instantaneous_direction(m/s)` = col_character(),
#> `wind_direction_frequency(m/s)` = col_character(),
#> `sunshine_duration_(h)` = col_double(),
#> `snow_fall(cm)` = col_double(),
#> `snow_depth(cm)` = col_double()
#> )
#> # A tibble: 30 × 7
#> date precipitation$`sum(mm)` $`max_1hour(mm)` temperature$`average(℃)`
#> <date> <dbl> <dbl> <dbl>
#> 1 2017-11-01 14 5.5 9.1
#> 2 2017-11-02 1 2 5.8
#> 3 2017-11-03 3.5 1 3.1
#> 4 2017-11-04 7.5 2.5 1
#> 5 2017-11-05 1.5 1 2
#> 6 2017-11-06 0 0 10
#> 7 2017-11-07 0 0 8.7
#> 8 2017-11-08 8 7 9.5
#> 9 2017-11-09 2 1.5 3.3
#> 10 2017-11-10 2.5 1 2.9
#> # ℹ 20 more rows
#> # ℹ 7 more variables: precipitation$`max_10minutes(mm)` <dbl>,
#> # temperature$`max(℃)` <dbl>, $`min(℃)` <dbl>, humidity <tibble[,2]>,
#> # wind <tibble[,6]>, sunshine <tibble[,1]>, snow <tibble[,2]>
jma_collect(item = "daily", "0422", year = 2017, month = 11, cache = FALSE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/daily_a1.php?prec_no=48&block_no=0422&year=2017&month=11&day=&view=Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at temperature_average(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at temperature_max(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at temperature_min(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at humidity_average(%)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at humidity_min(%)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at wind_average_speed(m/s)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at wind_max_speed(m/s)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at wind_max_speed_direction(m/s)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at wind_max_instantaneous_speed(m/s)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at wind_max_instantaneous_direction(m/s)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at wind_direction_frequency(m/s)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at sunshine_duration_(h)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at snow_fall(cm)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 at snow_depth(cm)
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> date = col_double(),
#> `precipitation_sum(mm)` = col_double(),
#> `precipitation_max_1hour(mm)` = col_double(),
#> `precipitation_max_10minutes(mm)` = col_double(),
#> `temperature_average(℃)` = col_logical(),
#> `temperature_max(℃)` = col_logical(),
#> `temperature_min(℃)` = col_logical(),
#> `humidity_average(%)` = col_logical(),
#> `humidity_min(%)` = col_logical(),
#> `wind_average_speed(m/s)` = col_logical(),
#> `wind_max_speed(m/s)` = col_logical(),
#> `wind_max_speed_direction(m/s)` = col_logical(),
#> `wind_max_instantaneous_speed(m/s)` = col_logical(),
#> `wind_max_instantaneous_direction(m/s)` = col_logical(),
#> `wind_direction_frequency(m/s)` = col_logical(),
#> `sunshine_duration_(h)` = col_logical(),
#> `snow_fall(cm)` = col_logical(),
#> `snow_depth(cm)` = col_logical()
#> )
#> # A tibble: 30 × 7
#> date precipitation$`sum(mm)` $`max_1hour(mm)` temperature$`average(℃)`
#> <date> <dbl> <dbl> <lgl>
#> 1 2017-11-01 0 0 NA
#> 2 2017-11-02 0 0 NA
#> 3 2017-11-03 0 0 NA
#> 4 2017-11-04 0.5 0.5 NA
#> 5 2017-11-05 0 0 NA
#> 6 2017-11-06 0 0 NA
#> 7 2017-11-07 0 0 NA
#> 8 2017-11-08 4 1.5 NA
#> 9 2017-11-09 0 0 NA
#> 10 2017-11-10 0 0 NA
#> # ℹ 20 more rows
#> # ℹ 7 more variables: precipitation$`max_10minutes(mm)` <dbl>,
#> # temperature$`max(℃)` <lgl>, $`min(℃)` <lgl>, humidity <tibble[,2]>,
#> # wind <tibble[,6]>, sunshine <tibble[,1]>, snow <tibble[,2]>
# Hourly
jma_collect("hourly", "0010", 2018, 7, 30, cache = FALSE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/hourly_a1.php?prec_no=12&block_no=0010&year=2018&month=7&day=30&view=Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 at dew_point(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 at vapor(hPa)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 at humidity(%)
#> Treated as missing: lines 1, 2, 3, 4, 20, 21, 22, 23, 24 at daylight_(h)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 at snow_fall_moment(cm)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 at snow_fall_period(cm)
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> time = col_double(),
#> `precipitation(mm)` = col_double(),
#> `temperature(℃)` = col_double(),
#> `dew_point(℃)` = col_logical(),
#> `vapor(hPa)` = col_logical(),
#> `humidity(%)` = col_logical(),
#> `wind_speed(m/s)` = col_double(),
#> wind_direction = col_character(),
#> `daylight_(h)` = col_double(),
#> `snow_fall_moment(cm)` = col_logical(),
#> `snow_fall_period(cm)` = col_logical()
#> )
#> # A tibble: 24 × 12
#> date time `precipitation(mm)` `temperature(℃)` `dew_point(℃)`
#> <date> <dbl> <dbl> <dbl> <lgl>
#> 1 2018-07-30 1 0 22.4 NA
#> 2 2018-07-30 2 0 22.1 NA
#> 3 2018-07-30 3 0 21 NA
#> 4 2018-07-30 4 0 20.2 NA
#> 5 2018-07-30 5 0 20.4 NA
#> 6 2018-07-30 6 0 23.5 NA
#> 7 2018-07-30 7 0 27.3 NA
#> 8 2018-07-30 8 0 28.7 NA
#> 9 2018-07-30 9 0 30 NA
#> 10 2018-07-30 10 0 30.8 NA
#> # ℹ 14 more rows
#> # ℹ 7 more variables: `vapor(hPa)` <lgl>, `humidity(%)` <lgl>,
#> # `wind_speed(m/s)` <dbl>, wind_direction <chr>, `daylight_(h)` <dbl>,
#> # `snow_fall_moment(cm)` <lgl>, `snow_fall_period(cm)` <lgl>
# Historical Ranking
jma_collect("rank", block_no = "47646", year = 2020, cache = FALSE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/rank_s.php?prec_no=40&block_no=47646&year=2020&month=&day=&view=
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> element = col_character(),
#> period = col_character(),
#> rank = col_double(),
#> value = col_character(),
#> date = col_character()
#> )
#> # A tibble: 370 × 5
#> element period rank value date
#> <chr> <chr> <dbl> <chr> <chr>
#> 1 日最低海面気圧(hPa) 1921/1から1921/1 1 965.3 1922/8/24
#> 2 日最低海面気圧(hPa) 1921/1から1921/1 2 966.6 2002/10/1
#> 3 日最低海面気圧(hPa) 1921/1から1921/1 3 968.9 2017/10/23
#> 4 日最低海面気圧(hPa) 1921/1から1921/1 4 969.0 1928/10/8
#> 5 日最低海面気圧(hPa) 1921/1から1921/1 5 969.3 1932/11/15
#> 6 日最低海面気圧(hPa) 1921/1から1921/1 6 970.0 2019/10/12
#> 7 日最低海面気圧(hPa) 1921/1から1921/1 7 970.2 1943/10/3
#> 8 日最低海面気圧(hPa) 1921/1から1921/1 8 970.5 1936/10/3
#> 9 日最低海面気圧(hPa) 1921/1から1921/1 9 971.2 1998/9/16
#> 10 日最低海面気圧(hPa) 1921/1から1921/1 10 972.7 1994/2/21
#> # ℹ 360 more rows
# Climatological normals
jma_collect("nml_ym", block_no = "47646", cache = FALSE, pack = FALSE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/nml_sfc_ym.php?prec_no=40&block_no=47646&year=&month=&view=
#> The record is based on the statistical period from 1991 to 2020 (30 years of data).
#> Treated as missing: lines 1, 2, 12 at temperature_min(℃)
#> Treated as missing: lines 5, 6, 7, 8, 9, 10 at snow_fall(cm)
#> Treated as missing: lines 5, 6, 7, 8, 9, 10 at snow_max_fall_day(cm)
#> Treated as missing: lines 4, 5, 6, 7, 8, 9, 10 at snow_depth(cm)
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> .default = col_double(),
#> element = col_character(),
#> wind_most_frequent_direction = col_character(),
#> `snow_fall(cm)` = col_character(),
#> `snow_max_fall_day(cm)` = col_character(),
#> `snow_depth(cm)` = col_character(),
#> cloud_covering_mean = col_character(),
#> condition_snow_days = col_character(),
#> condition_fog_days = col_character(),
#> condition_thunder_days = col_character()
#> )
#> ℹ Use `spec()` for the full column specifications.
#> # A tibble: 13 × 20
#> element `atmosphere_land(hPa)` atmosphere_surface(hP…¹ precipitation_sum(mm…²
#> <chr> <dbl> <dbl> <dbl>
#> 1 1月 1012. 1016. 50.6
#> 2 2月 1013. 1016. 47.1
#> 3 3月 1012. 1015. 95.5
#> 4 4月 1011. 1014 110.
#> 5 5月 1009. 1012 130.
#> 6 6月 1006 1009. 132.
#> 7 7月 1006. 1009. 135.
#> 8 8月 1007. 1010. 118.
#> 9 9月 1010 1013. 188.
#> 10 10月 1014. 1017. 194.
#> 11 11月 1015. 1018. 79.1
#> 12 12月 1014. 1017 48.5
#> 13 年 1011. 1014. 1326
#> # ℹ abbreviated names: ¹`atmosphere_surface(hPa)`, ²`precipitation_sum(mm)`
#> # ℹ 16 more variables: `temperature_average(℃)` <dbl>,
#> # `temperature_max(℃)` <dbl>, `temperature_min(℃)` <dbl>, `vapor(hPa)` <dbl>,
#> # `relative_humidity(%)` <dbl>, `wind_average_speed(m/s)` <dbl>,
#> # wind_most_frequent_direction <chr>, `daylight_(h)` <dbl>,
#> # `solar_irradiance_average(MJ/m^2)` <dbl>, `snow_fall(cm)` <chr>,
#> # `snow_max_fall_day(cm)` <chr>, `snow_depth(cm)` <chr>, …
jma_collect("nml_3m", "47646", cache = FALSE, pack = FALSE, quiet = TRUE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/nml_sfc_3m.php?prec_no=40&block_no=47646&year=&month=&view=
#> The record is based on the statistical period from 1991 to 2020 (30 years of data).
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> element = col_character(),
#> `precipitation(mm)` = col_double(),
#> `temperature_average(℃)` = col_double(),
#> `temperature_min_num_days_lt_0.0(℃)` = col_double(),
#> `temperature_min_num_days_geq_35.0(℃)` = col_double(),
#> `temperature_max_num_days_lt_0.0(℃)` = col_double(),
#> `temperature_max_num_days_geq_25.0(℃)` = col_double(),
#> `temperature_max_num_days_geq_30.0(℃)` = col_double(),
#> `temperature_max_num_days_geq_35.0(℃)` = col_double(),
#> `daylight_(h)` = col_double(),
#> `snow_fall(cm)` = col_character(),
#> `snow_depth(cm)` = col_character()
#> )
#> # A tibble: 12 × 12
#> element `precipitation(mm)` temperature_average(℃…¹ temperature_min_num_…²
#> <chr> <dbl> <dbl> <dbl>
#> 1 11月～1月 184. 6.3 48.1
#> 2 12月～2月 147. 4.2 65.4
#> 3 1月～3月 193. 5 57.1
#> 4 2月～4月 252. 8.2 32.4
#> 5 3月～5月 335 12.7 12
#> 6 4月～6月 371. 17 1.2
#> 7 5月～7月 396. 20.9 0
#> 8 6月～8月 385. 23.8 0
#> 9 7月～9月 440. 24.3 0
#> 10 8月～10月 499. 21.6 0
#> 11 9月～11月 460. 16.5 3
#> 12 10月～12月 321. 10.8 22
#> # ℹ abbreviated names: ¹`temperature_average(℃)`,
#> # ²`temperature_min_num_days_lt_0.0(℃)`
#> # ℹ 8 more variables: `temperature_min_num_days_geq_35.0(℃)` <dbl>,
#> # `temperature_max_num_days_lt_0.0(℃)` <dbl>,
#> # `temperature_max_num_days_geq_25.0(℃)` <dbl>,
#> # `temperature_max_num_days_geq_30.0(℃)` <dbl>,
#> # `temperature_max_num_days_geq_35.0(℃)` <dbl>, `daylight_(h)` <dbl>, …
jma_collect("nml_10d", "0228", cache = FALSE, pack = FALSE, quiet = TRUE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/nml_amd_10d.php?prec_no=33&block_no=0228&year=&month=&view=
#> The record is based on the statistical period from 1991 to 2020 (30 years of data).
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> element = col_character(),
#> element2 = col_character(),
#> `precipitation(mm)` = col_double(),
#> `temperature_average(℃)` = col_double(),
#> `temperature_max(℃)` = col_double(),
#> `temperature_min(℃)` = col_double(),
#> `wind_average_speed(m/s)` = col_double(),
#> `daylight_(h)` = col_double(),
#> `snow_fall(cm)` = col_logical(),
#> `snow_depth(cm)` = col_logical()
#> )
#> # A tibble: 36 × 9
#> element `precipitation(mm)` `temperature_average(℃)` `temperature_max(℃)`
#> <chr> <dbl> <dbl> <dbl>
#> 1 1月上旬 19.4 -1.5 2.5
#> 2 1月中旬 12.3 -2.4 1.9
#> 3 1月下旬 14.8 -2.3 2
#> 4 2月上旬 12 -2.1 2.3
#> 5 2月中旬 16.7 -1.4 3.1
#> 6 2月下旬 11.1 -0.3 4.7
#> 7 3月上旬 26.9 0.8 5.7
#> 8 3月中旬 22.5 2.3 7.8
#> 9 3月下旬 27.6 3.7 9.5
#> 10 4月上旬 24.2 6.3 12.3
#> # ℹ 26 more rows
#> # ℹ 5 more variables: `temperature_min(℃)` <dbl>,
#> # `wind_average_speed(m/s)` <dbl>, `daylight_(h)` <dbl>,
#> # `snow_fall(cm)` <lgl>, `snow_depth(cm)` <lgl>
jma_collect("nml_mb5d", "0228", cache = FALSE, pack = FALSE, quiet = FALSE)
#> Retrying in 7 seconds.
#> Data from: https://www.data.jma.go.jp/stats/etrn/view/nml_amd_mb5d.php?prec_no=33&block_no=0228&year=&month=&view=
#> The record is based on the statistical period from 1991 to 2020 (30 years of data).
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 72, 73 at temperature_average(℃)
#> Treated as missing: lines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 66, 67, 68, 69, 70, 71, 72, 73 at temperature_min(℃)
#>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> element = col_character(),
#> element2 = col_character(),
#> element3 = col_character(),
#> `precipitation(mm)` = col_double(),
#> `temperature_average(℃)` = col_double(),
#> `temperature_max(℃)` = col_double(),
#> `temperature_min(℃)` = col_double(),
#> `daylight_(h)` = col_double()
#> )
#> # A tibble: 73 × 6
#> element `precipitation(mm)` temperature_average(…¹ `temperature_max(℃)`
#> <chr> <dbl> <dbl> <dbl>
#> 1 1月第1半旬1… 9.8 -1.4 2.6
#> 2 1月第2半旬6… 8.6 -1.8 2.2
#> 3 1月第3半旬11… 7.4 -2.2 2
#> 4 1月第4半旬16… 6.6 -2.3 2
#> 5 1月第5半旬21… 6.5 -2.3 2.1
#> 6 1月第6半旬26… 7.8 -2.3 2.1
#> 7 2月第1半旬1… 6.5 -2.2 2.2
#> 8 2月第2半旬6… 6.6 -1.9 2.5
#> 9 2月第3半旬11… 7.6 -1.5 2.9
#> 10 2月第4半旬16… 8 -1.1 3.5
#> # ℹ 63 more rows
#> # ℹ abbreviated name: ¹`temperature_average(℃)`
#> # ℹ 2 more variables: `temperature_min(℃)` <dbl>, `daylight_(h)` <dbl>
# }