对数据集中的变量进行分组

Question

I have the following dataset:

Country/Region  1971    1972    1973    1974    1975    1976    1977    1978    1979    1980    1981    1982    1983    1984    1985    1986    1987    1988    1989    1990    1991    1992    1993    1994    1995    1996    1997    1998    1999    2000    2001    2002    2003    2004    2005    2006    2007    2008    2009    2010    GDP per Capita
Albania 3.9 4.5 3.9 4.2 4.5 4.9 5.2 6.2 7.5 7.6 6.4 6.7 7.3 7.6 7.2 7.2 7.5 7.6 7.2 6.3 4.4 2.8 2.3 2.3 1.9 1.9 1.4 1.7 3.0 3.1 3.3 3.8 4.0 4.3 4.1 4.0 4.0 3.9 3.5 3.8 5,626
Austria 48.7    50.5    54.0    51.3    50.2    54.3    51.8    54.5    57.2    55.7    52.8    51.0    51.1    52.9    54.3    53.2    54.2    52.1    52.5    56.4    60.6    55.7    56.0    56.2    59.4    63.1    62.4    62.9    61.4    61.7    65.9    67.4    72.6    73.7    74.6    72.5    70.0    70.6    63.5    69.3    56,259
Belarus 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 124.5   119.4   98.8    82.9    70.2    61.4    62.7    61.8    59.3    57.6    58.7    57.8    59.2    60.7    63.0    62.1    66.2    64.0    64.5    62.3    65.3    6,575
Belgium 116.8   126.7   132.7   130.6   115.6   124.5   123.5   129.0   132.3   125.7   115.5   109.3   100.6   102.6   101.9   102.6   102.8   104.6   105.9   107.9   113.3   112.3   109.8   115.5   115.2   121.3   118.5   120.9   117.4   118.6   119.1   111.9   119.5   116.5   112.6   109.6   105.6   111.0   100.7   106.4   51,237
Bosnia and Herzegovina  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23.7    21.2    15.6    13.1    3.0 3.2 4.1 8.3 10.5    10.2    13.5    13.3    14.0    14.3    15.0    15.6    17.2    18.2    19.9    19.4    19.9    6,140
Bulgaria    62.8    64.8    66.6    67.7    72.2    72.1    74.8    77.9    81.1    83.8    79.9    81.5    80.2    78.3    81.1    82.1    83.1    82.1    81.4    74.8    56.4    54.1    55.1    52.5    53.2    53.8    50.9    48.7    42.8    42.1    44.8    42.0    46.3    45.4    45.9    47.3    50.4    49.0    42.2    43.8    9,811
Croatia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21.6    15.7    15.2    15.8    15.0    15.8    15.6    17.3    18.4    18.3    17.7    18.6    19.6    21.0    20.4    20.8    20.8    22.1    21.0    19.8    19.0    15,533
Cyprus  1.8 2.2 2.3 1.8 1.7 2.0 2.1 2.3 2.5 2.6 2.5 2.6 2.7 2.8 2.8 3.1 3.6 3.6 3.8 3.8 4.4 4.7 4.9 5.3 5.2 5.5 5.7 5.8 6.0 6.3 6.2 6.3 7.0 6.9 7.0 7.1 7.3 7.6 7.5 7.2 30,521
Czech Republic  151.0   150.0   147.1   146.3   152.6   157.4   166.9   163.0   172.5   165.8   166.5   169.3   170.5   173.1   173.1   173.1   174.2   170.8   163.5   155.1   140.9   131.4   126.7   120.2   123.7   125.6   124.0   117.6   110.9   121.9   121.4   117.2   120.7   121.8   119.6   120.7   122.0   117.3   110.1   114.5   26,114
Denmark 55.0    57.1    56.0    49.8    52.5    58.1    59.7    59.2    62.7    62.5    52.5    54.6    51.3    52.9    60.5    61.1    59.3    55.5    49.8    50.4    60.5    54.8    57.1    61.0    58.0    71.2    61.6    57.7    54.6    50.6    52.2    51.9    57.1    51.6    48.3    56.0    51.4    48.4    46.7    47.0    66,196
Estonia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 36.1    32.1    23.5    18.0    17.8    16.1    17.0    16.5    16.0    14.9    14.6    15.1    14.6    16.6    16.7    16.9    15.5    19.3    17.7    14.7    18.5    25,260
Finland 39.8    43.7    48.0    44.5    44.4    50.5    50.2    54.7    54.4    55.2    46.0    44.5    43.2    44.4    48.6    49.5    53.8    53.1    52.9    54.4    55.9    53.7    54.8    61.4    56.0    62.2    60.1    56.8    56.1    55.1    60.3    63.0    70.8    67.2    55.2    66.8    65.0    57.0    55.0    62.9    54,869
France  431.9   448.6   484.8   464.6   430.6   469.3   455.3   474.7   481.8   461.4   414.1   396.7   381.0   369.5   360.3   347.8   342.3   340.5   355.9   352.3   379.6   368.0   348.9   344.4   353.8   368.6   361.7   385.3   377.7   376.9   383.8   375.9   385.2   385.4   388.4   379.6   373.1   370.2   351.4   357.8   46,493
Germany 978.6   1003.2  1053.1  1028.5  975.5   1032.2  1017.2  1055.9  1103.6  1055.6  1022.3  982.3   983.9   1006.1  1014.6  1016.3  1007.2  1001.2  976.8   949.7   924.8   886.5   879.9   868.5   867.8   896.5   865.8   858.9   826.9   825.0   843.3   830.7   839.8   840.8   809.0   820.9   796.3   800.1   747.1   761.6   53,276
Greece  25.2    29.2    34.1    32.6    34.5    39.1    40.4    42.8    45.1    45.3    44.9    46.3    49.3    51.0

(Sorry for the horrible formatting).

There are 41 countries and the years go from 1971-2010. The data for the years is CO2 emissions per capita.
However, due to the nature of the csv, I had to delete the first 2 rows of the dataset. I am not allowed to modify the csv, only manipulate the output in R.

I want to group the years together under a variable called "CO2 emissions per capita" so that it can be used in graphs, but still have individual columns for the years. I have managed to create the format using this code:

knitr::kable(europe.GDP) %>%
  kable_styling(bootstrap_options = c("striped", "condensed", "interactive", "bordered", "responsive"), 
                full_width = TRUE, font_size = 12, fixed_thead = TRUE) %>%
  add_header_above(c("", "CO2 Emissions per country" = 41), 
                   font_size = 14) %>% 
  column_spec(1, bold = TRUE) %>% 
  row_spec(row = 0, font_size = 14, bold = TRUE) %>%
  scroll_box(width = "100%", height = "800px")

but don't know how to make CO2 emissions a variable as opposed to every year being its own variable. I am very new to r, so I'm sorry if I'm not explaining what I'm trying to do very well.

Answer 1

I understand you are very new to R - perhaps I can help you out with a few ideas.

The table you created using kable may provide what you need in how the table looks. However, when plotting data, you will find it much easier and more flexible to have in a long format instead of wide .

Here's an example of how you can approach this. This requires the following libraries:

library(knitr)
library(kableExtra)
library(tidyverse)
library(ggplot2)

This is a simple data frame created for the example. Note you may need to do further manipulation depending on the structure of your data frame created from the csv file. If you use dput as @akrun suggested, it will help further.

df <- data.frame(
  Country = c("Albania", "Austria", "Belgium", "Bulgaria"),
  Emit_1971 = c(3.9, 48.7, 116.8, 62.8),
  Emit_1972 = c(4.5, 50.5, 126.7, 64.8),
  Emit_1973 = c(3.9, 54, 132.7, 66.6),
  Emit_1974 = c(4.2, 51.3, 130.6, 67.7)
)

So far, this can be used to provide a data table with kable as you currently have. Note you can define your column labels with col.names (reduced number of headers since did not provide as many years of data in add_header_above ).

knitr::kable(df, col.names = c("Country", seq(1971, 1974, 1))) %>%
  kable_styling(bootstrap_options = c("striped", "condensed", "interactive", "bordered", "responsive"), 
                full_width = TRUE, font_size = 12, fixed_thead = TRUE) %>%
  add_header_above(c("", "CO2 Emissions per country" = 4), 
                   font_size = 14) %>% 
  column_spec(1, bold = TRUE) %>% 
  row_spec(row = 0, font_size = 14, bold = TRUE) %>%
  scroll_box(width = "100%", height = "800px")

As suggested by @Gregor, you can convert your data from wide to long before plotting. I prefer to use tidyr in tidyverse for this. This assumes your column names have underscore and year (other options are also available).

long.df <- pivot_longer(df, cols = -Country, names_to = c(".value", "Year"), names_sep = "_", names_ptypes = list(Year = numeric())) 

# A tibble: 16 x 3
   Country   Year  Emit
   <fct>    <dbl> <dbl>
 1 Albania   1971   3.9
 2 Albania   1972   4.5
 3 Albania   1973   3.9
 4 Albania   1974   4.2
 5 Austria   1971  48.7
 6 Austria   1972  50.5
 7 Austria   1973  54  
 8 Austria   1974  51.3
 9 Belgium   1971 117. 
10 Belgium   1972 127. 
11 Belgium   1973 133. 
12 Belgium   1974 131. 
13 Bulgaria  1971  62.8
14 Bulgaria  1972  64.8
15 Bulgaria  1973  66.6
16 Bulgaria  1974  67.7

From this, you have options for further manipulation depending on plotting needs. For example, to plot countries emissions by year, you could do the following:

ggplot(long.df, aes(x = Year, y = Emit, col = Country)) +
  geom_line() +
  scale_x_continuous(breaks = seq(1971, 1974, 1)) +
  labs(title = "CO2 Emissions per country", x = "Year", y = "Emissions")

If you want to group countries by year (sum all country emissions in each year), you could do the following:

long.df.years <- long.df %>%
  group_by(Year) %>%
  summarise(Total = sum(Emit))

ggplot(long.df.years, aes(x = Year, y = Total)) +
  geom_line() +
  scale_x_continuous(breaks = seq(1971, 1974, 1)) +
  labs(title = "CO2 Emissions", x = "Year", y = "Emissions")

If you wanted to sum up the emissions across all years for each country, you could do the following:

long.df.europe <- long.df %>%
  group_by(Country) %>%
  summarise(Total = sum(Emit))

# A tibble: 4 x 2
  Country  Total
  <fct>    <dbl>
1 Albania   16.5
2 Austria  204. 
3 Belgium  507. 
4 Bulgaria 262.

Again, hope this is helpful. Please let me know if this is what you had in mind or what might require further clarification.