问题描述
我正在尝试对以下数据集进行时间序列分析：
Year 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780
Number 101 82 66 35 31 7 20 92 154 125
Year 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790
Number 85 68 38 23 10 24 83 132 131 118
Year 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800
Number 90 67 60 47 41 21 16 6 4 7
Year 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810
Number 14 34 45 43 48 42 28 10 8 2
Year 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820
Number 0 1 5 12 14 35 46 41 30 24
Year 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830
Number 16 7 4 2 8 17 36 50 62 67
Year 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
Number 71 48 28 8 13 57 122 138 103 86
Year 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850
Number 63 37 24 11 15 40 62 98 124 96
Year 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860
Number 66 64 54 39 21 7 4 23 55 94
Year 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870
Number 96 77 59 44 47 30 16 7 37 74
我的问题是数据放置在多行中。我正在尝试从数据中创建两列。一年代表数字，一个代表数字，因此在R中易于阅读。
> library(tidyverse)
> sun.df = data.frame(sunspots)
> Year = filter(sun.df,sunspots == "Year")
可以隔离Year数据，并且可以使用，但是我不确定如何将其放在列中。
有什么建议吗？
解决方法
尝试一下：
library(tidyverse)
df <- read_csv("test.csv",col_names = FALSE)
df
# A tibble: 6 x 4
# X1 X2 X3 X4
# <chr> <dbl> <dbl> <dbl>
# 1 Year 123 124 125
# 2 Number 1 2 3
# 3 Year 126 127 128
# 4 Number 4 5 6
# 5 Year 129 130 131
# 6 Number 7 8 9
# Removing first column and transpose it to get a dataframe of numbers
df_number <- as.data.frame(as.matrix(t(df[,-1])),row.names = FALSE)
df_number
# V1 V2 V3 V4 V5 V6
# 1 123 1 126 4 129 7
# 2 124 2 127 5 130 8
# 3 125 3 128 6 131 9
# Keep the first two column (V1,V2) and assign column names
df_new <- df_number[1:2]
colnames(df_new) <- c("Year","Number")
# Iterate and rbind with subsequent columns (2 by 2) to df_new
for(i in 1:((ncol(df_number) - 2 )/2)) {
df_mini <- df_number[(i*2+1):(i*2+2)]
colnames(df_mini) <- c("Year","Number")
df_new <- rbind(df_new,df_mini)
}
df_new
# Year Number
# 1 123 1
# 2 124 2
# 3 125 3
# 4 126 4
# 5 127 5
# 6 128 6
# 7 129 7
# 8 130 8
# 9 131 9