R语言扩展包dplyr——数据清洗和整理

xiaoxiao2021-03-25 16

<div id="article_content" class="article_content"> 该包主要用于数据清洗和整理，coursera课程链接：<a target="_blank" target="_blank" href="https://class.coursera.org/getdata-017" style="font-family:SimSun">Getting and Cleaning Data</a> 也可以载入swirl包，加载课Getting and Cleaning Data跟着学习。 如下： <pre name="code" class="html">library(swirl) install_from_swirl("Getting and Cleaning Data") swirl()</pre> 此文主要是参考R自带的简介：<a target="_blank" target="_blank" href="http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html">Introduce to dplyr</a> 1、示范数据 <pre name="code" class="html">> library(nycflights13) > dim(flights) [1] 336776 16 > head(flights, 3) Source: local data frame [3 x 16] year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time 1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227 3 2013 1 1 542 2 923 33 AA N619AA 1141 JFK MIA 160 Variables not shown: distance (dbl), hour (dbl), minute (dbl)</pre> 2、将过长的数据整理成友好的tbl_df数据 <pre name="code" class="html">> flights_df <- tbl_df(flights) > flights_df</pre> 3、筛选filter() <pre name="code" class="html">> filter(flights_df, month == 1, day == 1) Source: local data frame [842 x 16] year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time 1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227 2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227</pre>筛选出month=1和day=1的数据 同样效果的， <pre name="code" class="html">flights_df[flights_df$month == 1 & flights_df$day == 1, ]</pre> 4、选出几行数据slice() <pre name="code" class="html">slice(flights_df, 1:10)</pre> 5、排列arrange() <pre name="code" class="html">>arrange(flights_df, year, month, day)</pre>将flights_df数据按照year,month,day的升序排列。 降序 <pre name="code" class="html">>arrange(flights_df, year, desc(month), day)</pre>R语言当中的自带函数 <pre name="code" class="html">flights_df[order(flights$year, flights_df$month, flights_df$day), ] flights_df[order(desc(flights_df$arr_delay)), ]</pre> 6、选择select() 通过列名来选择所要的数据 <pre name="code" class="html">select(flights_df, year, month, day)</pre>选出三列数据 使用：符号 <pre name="code" class="html">select(flights_df, year:day)</pre>使用-来删除不要的列表 <pre name="code" class="html">select(flights_df, -(year:day))</pre> 7、变形mutate() 产生新的列 <pre name="code" class="html">> mutate(flights_df, + gain = arr_delay - dep_delay, + speed = distance / air_time * 60)</pre> 8、汇总summarize() <pre name="code" class="html"><pre name="code" class="html">> summarise(flights, + delay = mean(dep_delay, na.rm = TRUE)</pre> <pre></pre> 求dep_delay的均值 9、随机选出样本 <pre name="code" class="html">sample_n(flights_df, 10)</pre>随机选出10个样本 <pre name="code" class="html">sample_frac(flights_df, 0.01)</pre>随机选出1%个样本 10、分组group_py() <pre name="code" class="html">by_tailnum <- group_by(flights, tailnum) #确定组别为tailnum，赋值为by_tailnum delay <- summarise(by_tailnum, count = n(), dist = mean(distance, na.rm = TRUE), delay = mean(arr_delay, na.rm = TRUE)) #汇总flights里地tailnum组的分类数量，及其组别对应的distance和arr_delay的均值 delay <- filter(delay, count > 20, dist < 2000) ggplot(delay, aes(dist, delay)) + geom_point(aes(size = count), alpha = 1/2) + geom_smooth() + scale_size_area() </pre> <img src="https://img-blog.csdn.net/20150122175820824?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvdTAxMTI1Mzg3NA==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" alt=""> 结果都需要通过赋值存储 <pre name="code" class="html">a1 <- group_by(flights, year, month, day) a2 <- select(a1, arr_delay, dep_delay) a3 <- summarise(a2, arr = mean(arr_delay, na.rm = TRUE), dep = mean(dep_delay, na.rm = TRUE)) a4 <- filter(a3, arr > 30 | dep > 30)</pre> 11、引入链接符%>% 使用时把数据名作为开头，然后依次对数据进行多步操作： <pre name="code" class="html">flights %>% group_by(year, month, day) %>% select(arr_delay, dep_delay) %>% summarise( arr = mean(arr_delay, na.rm = TRUE), dep = mean(dep_delay, na.rm = TRUE) ) %>% filter(arr > 30 | dep > 30) </pre>前面都免去了数据名 若想要进行更多地了解这个包，可以参考其自带的说明书（60页）：<a target="_blank" target="_blank" href="http://cran.rstudio.com/web/packages/dplyr/dplyr.pdf">dplyr</a> </div>

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