Date time manipulation howtos

本文介绍了Erlang中calendar模块的功能,包括获取本地及UTC时间、验证日期有效性、判断闰年、查找每月最后一天等实用操作。通过具体示例展示了如何使用这些函数进行日期时间的转换与计算。

Erlang offers calendar module which provides computation of date, time and number of date time conversion functions. For more details, please refer to documentation for this module
Here are some examples:

Data types


Date = {Year, Month, Day}
Time = {Hour, Minute, Second}

where

Year = an integer and cannot be abbreviated. E.g.: 93 denotes year 93, not 1993
Month = 1..12
Day = 1..31
Hour = 0..23
Minute = 0..59
Second = 0..59

How to obtain local date time

{Date, Time} = calendar:local_time()


How to obtain current UTC time

{Date, Time} = calendar:universal_time()

How to convert time to seconds since midnight and via versa

Seconds = calendar:time_to_seconds(Time)
Time = calendar:seconds_to_time(Seconds)

How to verify if a year is leap year

Bool = calendar:is_leap_year(Year)

How to check if a date is valid

Bool = calendar:valid_date(Date)
Bool = calendar:valid_date(Year, Month, Day)

How to find out day of the week

DayNumber = calendar:day_of_the_week(Date)
DayNumber = calendar:day_of_the_week(Year, Month, Day)
1 = Monday, 2 = Tuesday, ….and 7 = Sunday

How to find out last day of a month

LastDay = calendar:last_day_of_the_month(Year, Month)

How to calculate date time difference

{Days, Time} = calendar:time_difference(DT1, DT2)
DT1 = {Date1, Time1}
DT2 = {Date2, Time2}
Logically equivalent to DT2 – DT1


The "flights" data frame in "nycflights 13" contains one row per commercial flight departure from New York's three major airports in 2013, and captures both planned schedules and actual performance. The features of this dataset are summarized as follows: • "year": Year of departure • "month": Month of departure (1-12) • "day": Day of month • "dep_time": Actual departure time; NA if cancelled • "sched_ dep _time": Scheduled departure time • "dep_delay": Departure delay (minutes) • "arr _time": Actual arrival time; NA if cancelled • "sched _arr_time": Scheduled arrival time • "arr_delay": Arrival delay (minutes; negative = early) • "carrier": Two-letter carrier code • "flight": Flight number • "talinum": Plane tail number • "origin": Origin airport code • "dest": Destination airport code • "air time": Time spent in the air (minutes) • "distance": Distance flown (miles) • "hour": Scheduled departure hour (0-23) • "minute": Scheduled departure minute (0-59) • "time hour": Scheduled departure timestamp that combines the scheduled departure date with its hour perform the following data manipulation operations in R, mainly using "ggplot2" and "dply" • 1. Count the number of "NA" entries for each column. • 2. Drop rows with "NA" entries to ensure clean analysis. • 3. Use "summarise" to get descriptive statistics on departure delay, arrival delay, and distance (e.g., mean, standard deviation, min, and max). • 4. Use "filter)" to select only flights whose departure delay is between -20 and 200 minutes. Then create a histogram with "binwidth = 10" to show the distribution of departure time. • 5. Use "summarise" to calculate the on-time rate for each carrier. You can use "on_time_rate = mean(arr_delay <= 0)". • 6. Create a box plot to visualize the distribution of the distance across carriers. You may facet by month. • 7. Group the cleaned dataframe by month, and count number of flights (how many rows) for each month. Then create a line curve to visualize how the total number of flights varies from January to December. • 8. Filter the cleaned dataframe to only include flights whose carrier is Virginia America(carrier = "VX"). Then create a scatter plot of "distance" (x-axis) versus "air_time" (y-axis) using a transparency level of 0.3. Also, add a smooth trend line to the scatter plot. You may use the 'Im' method). • 9. Create a new column "distance cat" with: less than 500 miles <= "short" 500-1,000 miles => "medium", and greater than 1,000 miles => "long". Then display the counts per category. You can use "cut" or "case when" • 10. Group the dataframe in Problem 9 by "distance_cat" and create a bar chart showing the average arrival delay for each "distance_cat" category. You can use geom_bar(stat = "identity").
06-17
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