The tidyextreme package provides a streamlined,
tidyverse-compliant toolkit for calculating essential climate extreme
indices as defined by the Expert Team on Climate Change Detection and
Indices (ETCCDI). Designed for seamless integration into modern data
analysis workflows, it allows researchers to compute key metrics—such as
maximum 1-day precipitation (Rx1day), consecutive dry days (CDD), warm
spell duration (WSDI), and monthly temperature extremes (TXx,
TNn)—directly from daily or hourly data using a consistent
calculate_*() syntax. By returning tidy data frames and
leveraging dplyr and ggplot2 compatibility,
tidyextreme simplifies the entire process of extreme event analysis,
from data preparation to visualization, making climate research more
efficient and reproducible.
Let’s create 10 years of realistic daily climate data:
set.seed(123)
library(tibble)
library(lubridate)
# Create 10 years of daily data (2000-2009)
n_years <- 10
n_days <- n_years * 365
climate_data <- tibble::tibble(
date = seq(as.Date("2000-01-01"), by = "day", length.out = n_days),
# Precipitation: seasonal pattern with extreme events
prcp = pmax(0, rgamma(n_days, shape = 1.2, rate = 0.4) +
sin(yday(date) * 2 * pi / 365) * 5),
# Maximum temperature: seasonal with warming trend
tmax = 20 + 10 * sin(yday(date) * 2 * pi / 365 - pi/2) +
rnorm(n_days, 0, 3) +
(year(date) - 2000) * 0.1,
# Minimum temperature: seasonal
tmin = 10 + 8 * sin(yday(date) * 2 * pi / 365 - pi/2) +
rnorm(n_days, 0, 2)
)
# Add extreme events
climate_data$prcp[100] <- 150
climate_data$tmax[500:505] <- 42
climate_data$tmin[800:803] <- -8
head(climate_data)## # A tibble: 6 × 4
## date prcp tmax tmin
## <date> <dbl> <dbl> <dbl>
## 1 2000-01-01 0.860 11.3 2.25
## 2 2000-01-02 5.30 5.77 1.95
## 3 2000-01-03 5.77 9.80 3.13
## 4 2000-01-04 7.52 9.06 0.351
## 5 2000-01-05 3.28 11.3 5.38
## 6 2000-01-06 9.03 10.5 -1.27Calculates the annual maximum 1-day precipitation amount, following ETCCDI definition Rx1day.
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
min_valid_years: Minimum years with valid data (default: 1)Returns: A data.frame with columns: year, Rx1day
library(tidyextreme)
rx1day_result <- calculate_Rx1day(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(rx1day_result)## # A tibble: 6 × 2
## year Rx1day
## <dbl> <dbl>
## 1 2000 150
## 2 2001 20.4
## 3 2002 21.2
## 4 2003 21.3
## 5 2004 18.4
## 6 2005 20.0Calculates the annual maximum precipitation amount accumulated over 5 consecutive days, following ETCCDI definition Rx5day.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)Returns: A data.frame with columns: year, Rx5day
rx5day_result <- calculate_Rx5day(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(rx5day_result)## # A tibble: 6 × 2
## year Rx5day
## <dbl> <dbl>
## 1 2000 185.
## 2 2001 57.8
## 3 2002 53.1
## 4 2003 63.7
## 5 2004 53.7
## 6 2005 52.2Counts the number of days per year when precipitation >= 10 mm, following ETCCDI definition R10mm.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string) (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
threshold: Precipitation threshold in mm (default: 10)Returns: A data.frame with columns: year, R10mm
r10mm_result <- calculate_R10mm(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(r10mm_result)## # A tibble: 6 × 2
## year R10mm
## <dbl> <int>
## 1 2000 17
## 2 2001 22
## 3 2002 18
## 4 2003 27
## 5 2004 21
## 6 2005 18Counts the number of days per year when precipitation >= 20 mm, following ETCCDI definition R20mm.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
threshold: Precipitation threshold in mm (default: 20)Returns: A data.frame with columns: year, R20mm
r20mm_result <- calculate_R20mm(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(r20mm_result)## # A tibble: 6 × 2
## year R20mm
## <dbl> <int>
## 1 2000 1
## 2 2001 1
## 3 2002 1
## 4 2003 2
## 5 2004 0
## 6 2005 0Counts the number of days per year when precipitation >= 1 mm, representing wet days.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
threshold: Precipitation threshold in mm (default: 1)Returns: A data.frame with columns: year, R1mm
r1mm_result <- calculate_R1mm(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(r1mm_result)## # A tibble: 6 × 2
## year R1mm
## <dbl> <int>
## 1 2000 234
## 2 2001 232
## 3 2002 223
## 4 2003 221
## 5 2004 236
## 6 2005 235Calculates statistics for dry spells (consecutive days with precipitation < 1 mm), following ETCCDI definition CDD.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
dry_threshold: Threshold for dry day in mm (default: 1)Returns: A data.frame with columns: year, CDD_max, CDD_mean, CDD_median, n_dry_spells
cdd_result <- calculate_CDD(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(cdd_result)## # A tibble: 6 × 5
## year CDD_max CDD_mean CDD_median n_dry_spells
## <dbl> <int> <dbl> <dbl> <int>
## 1 2000 22 3.77 2 35
## 2 2001 18 3.91 2 34
## 3 2002 21 4.73 3 30
## 4 2003 36 4.97 3 29
## 5 2004 23 3.71 2 35
## 6 2005 13 3.94 3 33Calculates statistics for wet spells (consecutive days with precipitation >= 1 mm), following ETCCDI definition CWD.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
wet_threshold: Threshold for wet day in mm (default: 1)Returns: A data.frame with columns: year, CWD_max, CWD_mean, CWD_median, n_wet_spells
cwd_result <- calculate_CWD(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(cwd_result)## # A tibble: 6 × 5
## year CWD_max CWD_mean CWD_median n_wet_spells
## <dbl> <int> <dbl> <dbl> <int>
## 1 2000 172 6.69 1 35
## 2 2001 164 6.63 2 35
## 3 2002 177 7.43 1 30
## 4 2003 171 7.37 1 30
## 5 2004 177 6.74 1 35
## 6 2005 179 6.91 1 34Calculates the mean precipitation amount on wet days (>= 1 mm), following ETCCDI definition SDII.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
wet_threshold: Threshold for wet day in mm (default: 1)Returns: A data.frame with columns: year, SDII, wet_days, total_prcp
sdii_result <- calculate_SDII(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(sdii_result)## # A tibble: 6 × 4
## year SDII wet_days total_prcp
## <dbl> <dbl> <int> <dbl>
## 1 2000 5.92 234 1398.
## 2 2001 5.88 232 1376.
## 3 2002 5.70 223 1285.
## 4 2003 6.15 221 1376.
## 5 2004 5.58 236 1329.
## 6 2005 5.54 235 1312.Calculates comprehensive annual precipitation statistics including total precipitation, number of wet days, mean daily precipitation, and maximum daily precipitation.
Parameters:
df: Data frame with precipitation data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
prcp_col: Name of precipitation column (daily data) (string)
precip_col: Name of precipitation column (hourly data) (string)
wet_threshold: Threshold for wet day in mm (default: 1)Returns: A data.frame with columns: year, PRCP_total, PRCP_days, PRCP_mean, PRCP_max
prcpstats_result <- calculate_PRCPstats(
df = climate_data,
frequency = "daily",
time_col = "date",
prcp_col = "prcp"
)
head(prcpstats_result)## # A tibble: 6 × 5
## year PRCP_total PRCP_days PRCP_mean PRCP_max
## <dbl> <dbl> <int> <dbl> <dbl>
## 1 2000 1398. 234 3.82 150
## 2 2001 1376. 232 3.77 20.4
## 3 2002 1285. 223 3.52 21.2
## 4 2003 1376. 221 3.77 21.3
## 5 2004 1329. 236 3.63 18.4
## 6 2005 1312. 235 3.59 20.0Counts the number of days per year when daily maximum temperature exceeds 25°C, following ETCCDI definition SU25.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmax_col: Name of maximum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)
threshold: Temperature threshold in °C (default: 25)Returns: A tibble with columns: year, TX25
tx25_result <- calculate_TX25(
df = climate_data,
frequency = "daily",
time_col = "date",
tmax_col = "tmax"
)
head(tx25_result)## # A tibble: 6 × 2
## year TX25
## <dbl> <int>
## 1 2000 115
## 2 2001 119
## 3 2002 122
## 4 2003 118
## 5 2004 117
## 6 2005 122Counts the number of days per year when daily minimum temperature exceeds 20 °C, following ETCCDI definition TR20.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmin_col: Name of minimum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)
threshold: Temperature threshold in °C (default: 20)Returns: A tibble with columns: year, TR20
tr20_result <- calculate_TR20(
df = climate_data,
frequency = "daily",
time_col = "date",
tmin_col = "tmin"
)
head(tr20_result)## # A tibble: 6 × 2
## year TR20
## <dbl> <int>
## 1 2000 6
## 2 2001 4
## 3 2002 10
## 4 2003 15
## 5 2004 8
## 6 2005 10Calculates the highest daily maximum temperature for each month, following ETCCDI definition TXx.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmax_col: Name of maximum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)
min_days: Minimum days per month for valid calculation (default: 20)Returns: A tibble with columns: year, month, TXx
txx_result <- calculate_TXx(
df = climate_data,
frequency = "daily",
time_col = "date",
tmax_col = "tmax"
)
head(txx_result)## # A tibble: 6 × 3
## year month TXx
## <dbl> <dbl> <dbl>
## 1 2000 1 14.4
## 2 2000 2 19.6
## 3 2000 3 22.7
## 4 2000 4 27.2
## 5 2000 5 33.8
## 6 2000 6 36.2Calculates the lowest daily minimum temperature for each month, following ETCCDI definition TNn.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmin_col: Name of minimum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)
min_days: Minimum days per month for valid calculation (default: 20)Returns: A tibble with columns: year, month, TNn
tnn_result <- calculate_TNn(
df = climate_data,
frequency = "daily",
time_col = "date",
tmin_col = "tmin"
)
head(tnn_result)## # A tibble: 6 × 3
## year month TNn
## <dbl> <dbl> <dbl>
## 1 2000 1 -1.27
## 2 2000 2 -4.54
## 3 2000 3 4.55
## 4 2000 4 8.25
## 5 2000 5 11.7
## 6 2000 6 13.6Counts the number of days per year when daily temperature is greater than or equal to 30 °C.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmax_col: Name of maximum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)Returns: A tibble with columns: year, TX30
tx30_result <- calculate_TX30(
df = climate_data,
frequency = "daily",
time_col = "date",
tmax_col = "tmax"
)
head(tx30_result)## # A tibble: 6 × 2
## year TX30
## <dbl> <int>
## 1 2000 38
## 2 2001 39
## 3 2002 43
## 4 2003 37
## 5 2004 34
## 6 2005 50Counts the number of days per year when daily temperature is greater than or equal to 35 °C.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmax_col: Name of maximum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)Returns: A tibble with columns: year, TX35
tx35_result <- calculate_TX35(
df = climate_data,
frequency = "daily",
time_col = "date",
tmax_col = "tmax"
)
head(tx35_result)## # A tibble: 6 × 2
## year TX35
## <dbl> <int>
## 1 2000 2
## 2 2001 8
## 3 2002 2
## 4 2003 3
## 5 2004 4
## 6 2005 3Counts the number of days per year when daily temperature is less than 0 °C.
Parameters:
df: Data frame with climate data
frequency Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmin_col: Name of minimum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)Returns: A tibble with columns: year, TN0
tn0_result <- calculate_TN0(
df = climate_data,
frequency = "daily",
time_col = "date",
tmin_col = "tmin"
)
head(tn0_result)## # A tibble: 6 × 2
## year TN0
## <dbl> <int>
## 1 2000 9
## 2 2001 9
## 3 2002 13
## 4 2003 8
## 5 2004 6
## 6 2005 7Calculates the mean and standard deviation of daily temperature range (difference between maximum and minimum temperature) per year.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmax_col: Name of maximum temperature column (daily data) (string)
tmin_col: Name of minimum temperature column (daily data) (string)
temp_col: Name of temperature column (hourly data)Returns: A tibble with columns: year, DTR_mean, DTR_sd, n_days
dtr_result <- calculate_DTR(
df = climate_data,
frequency = "daily",
time_col = "date",
tmax_col = "tmax",
tmin_col = "tmin"
)
head(dtr_result)## # A tibble: 6 × 4
## year DTR_mean DTR_sd n_days
## <dbl> <dbl> <dbl> <int>
## 1 2000 10.2 3.68 366
## 2 2001 10.5 4.23 365
## 3 2002 10.4 4.16 365
## 4 2003 9.96 3.67 365
## 5 2004 10.0 3.67 366
## 6 2005 10.4 3.78 365Calculates the 90th percentile of daily temperature per year, used as threshold for extreme warm days.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmax_col: Name of maximum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)Returns: A tibble with columns: year, TX90p
tx90p_result <- calculate_TX90p(
df = climate_data,
frequency = "daily",
time_col = "date",
tmax_col = "tmax"
)
head(tx90p_result)## # A tibble: 6 × 2
## year TX90p
## <dbl> <dbl>
## 1 2000 30.1
## 2 2001 30.0
## 3 2002 30.6
## 4 2003 30.0
## 5 2004 29.8
## 6 2005 30.9Calculates the 10th percentile of daily temperature per year, used as threshold for extreme cold nights.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmin_col: Name of minimum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)Returns: A tibble with columns: year, TN10p
tn10p_result <- calculate_TN10p(
df = climate_data,
frequency = "daily",
time_col = "date",
tmin_col = "tmin"
)
head(tn10p_result)## # A tibble: 6 × 2
## year TN10p
## <dbl> <dbl>
## 1 2000 2.24
## 2 2001 1.72
## 3 2002 2.17
## 4 2003 2.22
## 5 2004 2.34
## 6 2005 2.38Calculates the number of days with at least 6 consecutive days where temperature exceeds the 90th percentile, following ETCCDI definition WSDI.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmax_col: Name of maximum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)
window_days: Window size for percentile calculation (default: 30)
min_consecutive: Minimum consecutive days for warm spell (default: 6)Returns: A tibble with columns: year, WSDI, n_spells, mean_spell_length
wsdi_result <- calculate_WSDI(
df = climate_data,
frequency = "daily",
time_col = "date",
tmax_col = "tmax",
window_days = 30,
min_consecutive = 6
)
head(wsdi_result)## # A tibble: 0 × 4
## # ℹ 4 variables: year <dbl>, WSDI <int>, n_spells <int>,
## # mean_spell_length <dbl>Calculates the number of days with at least 6 consecutive days where temperature is below the 10th percentile, following ETCCDI definition CSDI.
Parameters:
df: Data frame with climate data
frequency: Temporal frequency "daily" or "hourly" (string)
time_col: Name of the time column (string). For daily frequency, the column should be of class Date or a string in the format YYYY-MM-DD. For hourly frequency, the column should be of class POSIXct or a string in the format YYYY-MM-DD HH:MM:SS.
tmin_col: Name of minimum temperature column (daily data) (string)
temp_col: Name of temperature column (for single temp or hourly) (string)
window_days: Window size for percentile calculation (default: 30)
min_consecutive: Minimum consecutive days for cold spell (default: 6)Returns: A tibble with columns: year, CSDI, n_spells, mean_spell_length
csdi_result <- calculate_CSDI(
df = climate_data,
frequency = "daily",
time_col = "date",
tmin_col = "tmin",
window_days = 30,
min_consecutive = 6
)
head(csdi_result)## # A tibble: 0 × 4
## # ℹ 4 variables: year <dbl>, CSDI <int>, n_spells <int>,
## # mean_spell_length <dbl>Let’s visualize some of the calculated indices to see trends over time:
library(ggplot2)
library(tidyr)
library(dplyr)
# Prepare data for visualization
precip_data <- rx1day_result |>
left_join(r10mm_result, by = "year") |>
left_join(sdii_result |> select(year, SDII), by = "year")
# Convert to long format for plotting
precip_long <- precip_data |>
pivot_longer(cols = -year, names_to = "index", values_to = "value")
# Plot precipitation indices
ggplot(precip_long, aes(x = year, y = value, color = index)) +
geom_line(size = 1) +
geom_point(size = 2) +
facet_wrap(~index, scales = "free_y", ncol = 1) +
labs(title = "Precipitation Indices Over Time",
x = "Year",
y = "Value") +
theme_minimal() +
theme(legend.position = "none")
The tidyextreme package provides a comprehensive set of functions for calculating climate extreme indices. All functions follow ETCCDI definitions and return tidy data frames for easy analysis and visualization.
This vignette demonstrates the basic usage of all functions in the tidyextreme package. For more detailed information about each function, run in R.
The tidyextreme package is under active development.
Feedback and contributions are welcome!
📋 Reporting Bugs & Feature Requests:
The preferred way to report a bug or suggest an enhancement is to
open an issue on our GitHub repository:
https://github.com/Mauritia-flexuosa/tidyextreme/issues.
📧 Contact the Maintainer:
For other questions or direct contact, you can email the package
maintainer:
marciobcure@gmail.com.
🌐 General Questions:
For general how-to questions, consider posting on community forums like
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Overflow using the relevant tags (e.g., [r] and
[tidyextreme]).
Thank you for using tidyextreme!
All indices in the tidyextreme package follow the
definitions from the Expert Team on Climate Change Detection and Indices
(ETCCDI).
For more information about the ETCCDI and the official definitions of these indices, please refer to:
ETCCDI Official Website: https://etccdi.pacificclimate.org/
Climate Change Indices: Definitions and methods available at https://etccdi.pacificclimate.org/list_27_indices.shtml
Key References:
Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B., and Zwiers, F.W. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Climate Change, 2(6), 851-870. https://doi.org/10.1002/wcc.147
Klein Tank, A.M.G., Zwiers, F.W., and Zhang, X. (2009). Guidelines on Analysis of extremes in a changing climate in support of informed decisions for adaptation. World Meteorological Organization, WMO-TD No. 1500, 56 pp.
The R package climdex.pcic which also implements ETCCDI indices: https://cran.r-project.org/package=climdex.pcic (not available anymore on CRAN)
## tidyextreme version: 1.0.0