Summary Of The December

本篇博客总结了作者2017年12月的英语学习经历,包括跟随AJ老师的迷你故事进行深度模仿及团队合作学习的经验。通过有组织的学习计划,不仅提升了个人英语水平,还激发了团队成员之间的创意交流。

     今天是2017年的最后一天,感觉很有纪念意义啊!

     所以今天呢,小咸儿来干一件有意义的事情,那就是将自己这一个月的英语学习来总结一下。

     首先来分享一个令人兴奋的消息,就是在孤军奋战这么久后,终于又一次的迎来了各位战斗人员,topic小组。

     这次在有组织有预谋的情况下,开始了小咸儿十二月份的英语学习,首先来显示一下领导的政策:

  • 政策
       关注点:
            如何更好的引导一个团队高效学习mini故事
            如何深度学习
            如何应用到生活中

     在了解了基本政策后,组长又进行了如下的安排:

  • 安排
        时间

             1、第1-2天:(周一、周二) 自己进行跟读,了解故事的大体内容(跟读建议:深度模仿语音语调、心情、肢体动作、将自己融入到场景当中)

             2、第3-4天:(周三、周四)自己跟读,小组进行Topic.(Topic 建议:问的问题简单易懂、回答:最大声、最快速、最精简)

             3、第5-6天:(周六、周日)自己跟读,小组进行Retell(Retell建议:还原场景+展开想象添加新元素)

            内容

            1、AJ老师的mini story(从后向前学习,第30个开始)

            2、Main Text(在学习mini story 的时候,也要听相应的main text,在每天的topic完成之后,讨论一下main text)
            3、学习Power English的七个规则。(当感到枯燥的时候,学习一下此项内容)

     在团队的带领下,自己已经参与过两个mini story的topic了,感觉很有意思,而且自己感觉无趣的时候,大家还会一起讨论如何让我们的topic更有意思。经过大家的讨论,发现各种奇思妙想,等待接下来的实践。

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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