Getting and Cleaning Data - Week3 Tidying Data with tidyr

tidy数据集特征:

• 每个变量形成一个列
• 每一个观察都形成一行
• 每一种观测单位都形成一个表

untidy数据集特征

• 列是值，而不是变量名 :
religion, income and frequency.

•多个变量存储在一个列中：
人口统计群体被sex(m, f)和age(0-14,15-25,25-34,35-44,45-54,55-64,55-64)划分

•变量存储在行和列中:
在各个列(id、年、月)中有变量，分布在列(day, d1-d31)和跨行(tmin, tmax)(最小和最高温度)。

•不同类型的观察单元存储在同一个表中:
billborad数据集实际上包含了对两种观察单元的观察:歌曲信息和它在每个星期的排名。艺术家artist，年year和时间time被重复了很多次。这个数据集需要细分为两个部分:一个歌曲数据集，它存储艺术家、歌曲名称和时间，以及一个排名数据集，每个星期都给出歌曲的排名。

•单个观察单元存储在多个表中:

PRACTICE

• data : sat.csv
• resource :The 2013 SAT Report on College & Career Readiness

tidy data

# 处理方案
# 1. select() all columns that do NOT contain the word "total",
# since if we have the male and female data, we can always
# recreate the total count in a separate column, if we want it.
# Hint: Use the contains() function, which you'll
# find detailed in 'Special functions' section of ?select.
#
# 2. gather() all columns EXCEPT score_range, using
# key = part_sex and value = count.
#
# 3. separate() part_sex into two separate variables (columns),
# called "part" and "sex", respectively. You may need to check
# the 'Examples' section of ?separate to remember how the 'into'
# argument should be phrased.
#

sat1 <- sat[2:11] %>%
  select(-contains("total")) %>%
  gather(part_sex, count, -score_range) %>%
    separate(part_sex, c("part", "sex")) %>%
    group_by(part, sex)%>%
    mutate(total = sum(count),
           prop = count / total
    ) %>% 
  print

cleaned data

处理后结果

Week 3 Quiz

程序代码