library(tidyverse)
library(lubridate)
library(readxl)
library(esquisse)
library(janitor)
library(ggthemes)
library(ggrepel)
library(scales)
library(naniar)
library(viridis)
library(vtable)
library(gplots)
#install.packages("naniar")
emissions <- read.csv("emissions_dataset.csv")
# stop R from defaulting to exponents for large values
options(scipen = 1000000)Session2
Missing Values
When dealing with large data sets it can be helpful to get an idea of how many values are absent. There are many ways to do this in R, but one way I like is utilizing the below code from the “naniar” package.
install the package and load the library:
install.package(“naniar”)
library(naniar)
gg_miss_var(emissions, show_pct = TRUE)Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
use `guide = "none"` instead.
Basic Charting
Creating useful visualizations of our data is both art and science. To start, we’ll go over how to make basic versions of charts and follow with what the standard looks like for charts you will submit. For the homework this week we will accept the bare minimum as you’re still getting used to the tech. Later on we will expect higher quality outputs like appropriate labeling and titles.
This topic is incredibly broad and deep, and there is a massive amount of resources online dedicated to making some truly outrageous visualizations. You’re welcome to pursue this as far as your time and patience allows, but let’s first get the basics down.
This website is a great reference for how to make a large variety of charts and spruce them up. As you do the homework, we recommend referencing this site.
Bar charts
# open emissions dataset we already imported
emissions %>%
# filter for three countries we'd like to see populations for
filter(country_name %in% c("Angola","Argentina","Australia")) %>%
# filter for the year we'd like to view
filter(year == 2010) %>%
ggplot(aes(x = country_name, y = population)) +
geom_col()
emissions %>%
group_by(em_dm) %>%
na.omit() %>%
summarize(avg = mean(population)) %>%
ggplot(aes(x = em_dm, y = avg)) +
geom_col()
###Line charts
emissions %>%
filter(year == 2010:2019) %>%
group_by(year) %>%
summarize(avg = mean(territorial_co2)) %>%
ggplot(aes(x = year, y = avg)) +
geom_line()
Scatter plots
emissions %>%
na.omit() %>%
ggplot(aes(x = gdp_usd_current_prices, y = territorial_co2)) +
geom_point()
Bubble chart
emissions %>%
# filter(em_dm == "Emerging Markets") %>%
filter(year == 2010:2011) %>%
na.omit() %>%
# size tells it to scale the size of the bubbles based on the size of the population
# color tells it to provide a color pattern based on the unique variables in the em_dm column
ggplot(aes(x = gdp_usd_current_prices, y = territorial_co2, size = population, color = em_dm)) +
# alpha tells it to provide a level of transparency for each bubble
geom_point(alpha = .5)
Histograms
emissions %>%
filter(em_dm == "Emerging Markets") %>%
filter(year == 2019) %>%
filter(territorial_co2 <1000) %>%
ggplot(aes(x = territorial_co2)) +
# binwith tells it how wide to make each column
# fill tells it what color to make the column
# color tells it what color to make the edges of the column
geom_histogram(binwidth = 10, fill = "#69b3a2", color = "black")
Boxplot
emissions %>%
filter(year == 2010:2019) %>%
ggplot(aes(x = em_dm, y = govt_expenditure_pct_gdp)) +
geom_boxplot()
This version shows the distribution across the box plot through the addiont of the geom_jitter command.
emissions %>%
filter(year == 2010:2019) %>%
ggplot(aes(x = em_dm, y = govt_expenditure_pct_gdp, fill = em_dm)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) 
Violin plots
Allow you to compare the distribution of several groups by displaying their densities.
emissions %>%
filter(year == 2010:2019) %>%
ggplot(aes(x = em_dm, y = govt_expenditure_pct_gdp, fill = em_dm)) +
geom_violin()
Heatmaps
We can use a heatmap to display correlations between different variables.
emissions2 <- emissions %>%
na.omit() %>%
select(-c(1:4)) %>%
select(c(1:5))
heatmap.2(cor(emissions2), Rowv = FALSE, Colv = FALSE, dendrogram = "none",
cellnote = round(cor(emissions2),2),
notecol = "black", key = TRUE, trace = 'none', margins = c(10,10), cexRow = .7, cexCol = .7,
main = "Emissions Comparison")
Making a nice graph
This will be a brief intro to some basic functions you’ll end up using continuously to make decent looking graphs.
I’m going to change the emissions data to long format for this one. See the next section for an explanation of how what this is and how it works.
emissions_long <- emissions %>%
pivot_longer(cols = gdp_usd_current_prices:cumulative_co2_per_capita, names_to = "variable", values_to = "value")We’re going to take the below graph and turn it into something a little nicer looking.
emissions_long %>%
filter(year == 2019) %>%
filter(country_name %in% c("Angola","Argentina","Australia")) %>%
filter(variable %in% c("govt_expenditure_pct_gdp", "debt_pct_gdp")) %>%
drop_na() %>%
#group_by(em_dm) %>%
#summarize(avg = mean(value)) %>%
ggplot(aes(x = country_name, y = value, fill = variable)) +
geom_col(position = "dodge")
emissions_long %>%
filter(year == 2019) %>%
filter(country_name %in% c("Angola","Argentina","Australia")) %>%
filter(variable %in% c("govt_expenditure_pct_gdp", "debt_pct_gdp")) %>%
drop_na() %>%
#group_by(em_dm) %>%
#summarize(avg = mean(value)) %>%
ggplot(aes(x = country_name, y = (value/100), fill = variable)) +
# position = "dodge" will place the two variables we're measuring for each country next to each other.
geom_col(position = "dodge") +
# manually fill the columns with your desired colors
# change the label names to something easier to read
scale_fill_manual(values = c("steelblue4",
"steelblue1"), labels = c("Debt","Govt Expenditure")) +
# Alternatively if you don't feel like manually selecting but want a nicer color pattern, the below works nicely as well.
#scale_fill_viridis(discrete = TRUE, name = "", labels = c("Debt","Govt Expenditure")) +
labs(y = "Percent of GDP",
title = "Debt vs goverment expenditure levels",
subtitle = "Percentage of GDP",
caption = "Source: I don't remember") +
# ggplot and ggthemes have a nice selection of themes to help make the overall graph nicer looking. You can even make it look like the Economist or FT. Play around with it and see what you like best. Note that theme_x needs to come before this next part where we use the theme() function to clean some stuff.
theme_clean() +
# drop the X axis as we can clearly see they're countries.
# drop legend title
# move the caption at the bottom to the left side of the chart
theme(axis.title.x = element_blank(),
legend.title = element_blank(),
plot.caption = element_text(hjust=-.1, face="italic")) +
# the first part labels tells the function to convert the values to percentages (hence why I divide the value by 100 up above). Note that percent_format comes from the scales package.
# breaks and seq() tells it to start at zero and go to 1.10 (again because it's in percentages) and to show in increments of 20%.
# expand drops the bar down to the axis line, rather than hovering an inch above it. Not sure why this is used on scale_y_continuous rather than scale_x_continuous.
scale_y_continuous(labels = percent_format(accuracy = 1),
breaks = seq(0,1.10, by = .20 ), expand = c(0,0)) 
Pivot Wider vs Pivot Longer
Transforming a table into long format means condensing columns down into a single variable column and shifting the values under a separate column. This makes the dataframe “longer” by creating many more rows. This is useful for building graphs, particularly comparing variables. Below shows how to pivot a dataframe to long format. Note that when looking up how to do this or looking at other code, you might see the author use “gather.” Gather is an older version of this and is no longer maintained. It is recommended that you use pivot_longer and pivot_wider, both of which come from the tidyverse.
As a general rule, time series and plotting multiple variables is easier in long format, while analyzing data is typically easier in wide format.
First let’s take a look at our current dataset. We see that we have 27 indicators (columns) with various values. Let’s turn this into long form.
head(emissions) country_name iso3c em_dm year gdp_usd_current_prices
1 Angola AGO Emerging Markets 1990 12.571
2 Angola AGO Emerging Markets 1991 12.186
3 Angola AGO Emerging Markets 1992 9.395
4 Angola AGO Emerging Markets 1993 6.819
5 Angola AGO Emerging Markets 1994 4.965
6 Angola AGO Emerging Markets 1995 6.197
gdp_ppp_current_prices gdp_pc_usd_current_prices gdp_pc_ppp_current_prices
1 19.681 986.639 1544.61
2 22.805 928.506 1737.67
3 25.984 695.059 1922.26
4 29.518 489.755 2120.07
5 33.317 346.239 2323.25
6 37.541 419.529 2541.54
population govt_expenditure_pct_gdp debt_pct_gdp territorial_co2 trade_co2
1 12.742 NA NA 5.090 NA
2 13.124 NA NA 5.064 NA
3 13.518 NA NA 5.164 NA
4 13.923 NA NA 5.748 NA
5 14.341 NA NA 3.865 NA
6 14.771 NA NA 10.949 NA
consumption_co2 cumulative_co2 debt_usd govt_expenditure_usd
1 NA 115.663 NA NA
2 NA 120.727 NA NA
3 NA 125.891 NA NA
4 NA 131.640 NA NA
5 NA 135.504 NA NA
6 NA 146.453 NA NA
territorial_co2_per_capita trade_co2_per_capita consumption_co2_per_capita
1 0.3994663 NA NA
2 0.3858580 NA NA
3 0.3820092 NA NA
4 0.4128421 NA NA
5 0.2695070 NA NA
6 0.7412497 NA NA
territorial_co2_per_gdp trade_co2_per_gdp consumption_co2_per_gdp
1 0.2586251 NA NA
2 0.2220566 NA NA
3 0.1987377 NA NA
4 0.1947286 NA NA
5 0.1160068 NA NA
6 0.2916545 NA NA
territorial_co2_per_debt trade_co2_per_debt consumption_co2_per_debt
1 NA NA NA
2 NA NA NA
3 NA NA NA
4 NA NA NA
5 NA NA NA
6 NA NA NA
territorial_co2_per_govt_expenditure trade_co2_per_govt_expenditure
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
consumption_co2_govt_expenditure trade_pct_of_consumption_co2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
cumulative_co2_per_capita
1 9.077303
2 9.198948
3 9.312842
4 9.454859
5 9.448713
6 9.914901emissions_long <- emissions %>%
# cols tells it to grab the columns from gdp_us_current_prices all the way to cumulative_co2_per_capita
# names_to tells it to create a new column called "variable" and put all the column headers under it
# values_to tells it to take all the data contained in the columns and put it under a new column called "value"
pivot_longer(cols = gdp_usd_current_prices:cumulative_co2_per_capita, names_to = "variable", values_to = "value")We can see that we’ve gone from 31 total columns to 6 columns. But what can we do with this format?
head(emissions_long)# A tibble: 6 × 6
country_name iso3c em_dm year variable value
<chr> <chr> <chr> <int> <chr> <dbl>
1 Angola AGO Emerging Markets 1990 gdp_usd_current_prices 12.6
2 Angola AGO Emerging Markets 1990 gdp_ppp_current_prices 19.7
3 Angola AGO Emerging Markets 1990 gdp_pc_usd_current_prices 987.
4 Angola AGO Emerging Markets 1990 gdp_pc_ppp_current_prices 1545.
5 Angola AGO Emerging Markets 1990 population 12.7
6 Angola AGO Emerging Markets 1990 govt_expenditure_pct_gdp NA We can see in the graph below we’re able to easily compare multiple variables, which is the main point for pivoting long
emissions_long %>%
filter(year == 2019) %>%
filter(country_name %in% c("Angola","Argentina","Australia")) %>%
filter(variable %in% c("govt_expenditure_pct_gdp", "debt_pct_gdp")) %>%
drop_na() %>%
#group_by(em_dm) %>%
#summarize(avg = mean(value)) %>%
ggplot(aes(x = country_name, y = value, fill = variable)) +
geom_col(position = "dodge")
Let’s look at another example where having data in long format will make our lives easier. We are going to briefly look at the World Bank’s International Debt Statistics (IDS) dataset
IDS <- read.csv("IDS_All.csv")
head(IDS) Country.Name Country.Code Counterpart.Area.Name Counterpart.Area.Code
1 Afghanistan AFG World WLD
2 Afghanistan AFG World WLD
3 Afghanistan AFG World WLD
4 Afghanistan AFG World WLD
5 Afghanistan AFG World WLD
6 Afghanistan AFG World WLD
Series.Name
1 Average grace period on new external debt commitments (years)
2 Average grace period on new external debt commitments, official (years)
3 Average grace period on new external debt commitments, private (years)
4 Average grant element on new external debt commitments (%)
5 Average grant element on new external debt commitments, official (%)
6 Average grant element on new external debt commitments, private (%)
Series.Code X1970 X1971 X1972 X1973 X1974 X1975 X1976 X1977 X1978 X1979 X1980
1 DT.GPA.DPPG NA NA NA NA NA NA NA NA NA NA NA
2 DT.GPA.OFFT NA NA NA NA NA NA NA NA NA NA NA
3 DT.GPA.PRVT NA NA NA NA NA NA NA NA NA NA NA
4 DT.GRE.DPPG NA NA NA NA NA NA NA NA NA NA NA
5 DT.GRE.OFFT NA NA NA NA NA NA NA NA NA NA NA
6 DT.GRE.PRVT NA NA NA NA NA NA NA NA NA NA NA
X1981 X1982 X1983 X1984 X1985 X1986 X1987 X1988 X1989 X1990 X1991 X1992 X1993
1 NA NA NA NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA NA NA NA
X1994 X1995 X1996 X1997 X1998 X1999 X2000 X2001 X2002 X2003 X2004 X2005 X2006
1 NA NA NA NA NA NA NA NA NA NA NA NA 10.25
2 NA NA NA NA NA NA NA NA NA NA NA NA 10.25
3 NA NA NA NA NA NA NA NA NA NA NA NA 0.00
4 NA NA NA NA NA NA NA NA NA NA NA NA 50.62
5 NA NA NA NA NA NA NA NA NA NA NA NA 50.62
6 NA NA NA NA NA NA NA NA NA NA NA NA 0.00
X2007 X2008 X2009 X2010 X2011 X2012 X2013 X2014 X2015 X2016 X2017 X2018 X2019
1 8.08 9.96 0 7.83 0 0 0 29.83 0 0 19.84 0 17.90
2 8.08 9.96 0 7.83 0 0 0 29.83 0 0 19.84 0 17.90
3 0.00 0.00 0 0.00 0 0 0 0.00 0 0 0.00 0 0.00
4 43.02 58.01 0 21.21 0 0 0 81.93 0 0 64.26 0 73.08
5 43.02 58.01 0 21.21 0 0 0 81.93 0 0 64.26 0 73.08
6 0.00 0.00 0 0.00 0 0 0 0.00 0 0 0.00 0 0.00
X2020 X2021 X2022 X2023 X2024 X2025 X2026 X2027 X2028
1 0 NA NA NA NA NA NA NA NA
2 0 NA NA NA NA NA NA NA NA
3 0 NA NA NA NA NA NA NA NA
4 0 NA NA NA NA NA NA NA NA
5 0 NA NA NA NA NA NA NA NA
6 0 NA NA NA NA NA NA NA NA# For some reason R reads the years in with X's on the front so let's get rid of that
colnames(IDS)<-gsub("X","", colnames(IDS))
head(IDS) Country.Name Country.Code Counterpart.Area.Name Counterpart.Area.Code
1 Afghanistan AFG World WLD
2 Afghanistan AFG World WLD
3 Afghanistan AFG World WLD
4 Afghanistan AFG World WLD
5 Afghanistan AFG World WLD
6 Afghanistan AFG World WLD
Series.Name
1 Average grace period on new external debt commitments (years)
2 Average grace period on new external debt commitments, official (years)
3 Average grace period on new external debt commitments, private (years)
4 Average grant element on new external debt commitments (%)
5 Average grant element on new external debt commitments, official (%)
6 Average grant element on new external debt commitments, private (%)
Series.Code 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
1 DT.GPA.DPPG NA NA NA NA NA NA NA NA NA NA NA NA NA
2 DT.GPA.OFFT NA NA NA NA NA NA NA NA NA NA NA NA NA
3 DT.GPA.PRVT NA NA NA NA NA NA NA NA NA NA NA NA NA
4 DT.GRE.DPPG NA NA NA NA NA NA NA NA NA NA NA NA NA
5 DT.GRE.OFFT NA NA NA NA NA NA NA NA NA NA NA NA NA
6 DT.GRE.PRVT NA NA NA NA NA NA NA NA NA NA NA NA NA
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
1 NA NA NA NA NA NA NA NA 10.25 8.08 9.96 0 7.83 0
2 NA NA NA NA NA NA NA NA 10.25 8.08 9.96 0 7.83 0
3 NA NA NA NA NA NA NA NA 0.00 0.00 0.00 0 0.00 0
4 NA NA NA NA NA NA NA NA 50.62 43.02 58.01 0 21.21 0
5 NA NA NA NA NA NA NA NA 50.62 43.02 58.01 0 21.21 0
6 NA NA NA NA NA NA NA NA 0.00 0.00 0.00 0 0.00 0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
1 0 0 29.83 0 0 19.84 0 17.90 0 NA NA NA NA NA NA
2 0 0 29.83 0 0 19.84 0 17.90 0 NA NA NA NA NA NA
3 0 0 0.00 0 0 0.00 0 0.00 0 NA NA NA NA NA NA
4 0 0 81.93 0 0 64.26 0 73.08 0 NA NA NA NA NA NA
5 0 0 81.93 0 0 64.26 0 73.08 0 NA NA NA NA NA NA
6 0 0 0.00 0 0 0.00 0 0.00 0 NA NA NA NA NA NA
2027 2028
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA# figure out column numbers
colnames(IDS) [1] "Country.Name" "Country.Code" "Counterpart.Area.Name"
[4] "Counterpart.Area.Code" "Series.Name" "Series.Code"
[7] "1970" "1971" "1972"
[10] "1973" "1974" "1975"
[13] "1976" "1977" "1978"
[16] "1979" "1980" "1981"
[19] "1982" "1983" "1984"
[22] "1985" "1986" "1987"
[25] "1988" "1989" "1990"
[28] "1991" "1992" "1993"
[31] "1994" "1995" "1996"
[34] "1997" "1998" "1999"
[37] "2000" "2001" "2002"
[40] "2003" "2004" "2005"
[43] "2006" "2007" "2008"
[46] "2009" "2010" "2011"
[49] "2012" "2013" "2014"
[52] "2015" "2016" "2017"
[55] "2018" "2019" "2020"
[58] "2021" "2022" "2023"
[61] "2024" "2025" "2026"
[64] "2027" "2028" # with data like this there's often not a lot of good record keeping the further back you go, so we'll hack off some of these year columns. I'm also not interested in things past
IDS <- IDS %>%
select(-c(7:36))
# I'm doing this in two parts to validate the column numbers
IDS <- IDS %>%
select(-c(28:35))
head(IDS) Country.Name Country.Code Counterpart.Area.Name Counterpart.Area.Code
1 Afghanistan AFG World WLD
2 Afghanistan AFG World WLD
3 Afghanistan AFG World WLD
4 Afghanistan AFG World WLD
5 Afghanistan AFG World WLD
6 Afghanistan AFG World WLD
Series.Name
1 Average grace period on new external debt commitments (years)
2 Average grace period on new external debt commitments, official (years)
3 Average grace period on new external debt commitments, private (years)
4 Average grant element on new external debt commitments (%)
5 Average grant element on new external debt commitments, official (%)
6 Average grant element on new external debt commitments, private (%)
Series.Code 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
1 DT.GPA.DPPG NA NA NA NA NA NA 10.25 8.08 9.96 0 7.83 0
2 DT.GPA.OFFT NA NA NA NA NA NA 10.25 8.08 9.96 0 7.83 0
3 DT.GPA.PRVT NA NA NA NA NA NA 0.00 0.00 0.00 0 0.00 0
4 DT.GRE.DPPG NA NA NA NA NA NA 50.62 43.02 58.01 0 21.21 0
5 DT.GRE.OFFT NA NA NA NA NA NA 50.62 43.02 58.01 0 21.21 0
6 DT.GRE.PRVT NA NA NA NA NA NA 0.00 0.00 0.00 0 0.00 0
2012 2013 2014 2015 2016 2017 2018 2019 2020
1 0 0 29.83 0 0 19.84 0 17.90 0
2 0 0 29.83 0 0 19.84 0 17.90 0
3 0 0 0.00 0 0 0.00 0 0.00 0
4 0 0 81.93 0 0 64.26 0 73.08 0
5 0 0 81.93 0 0 64.26 0 73.08 0
6 0 0 0.00 0 0 0.00 0 0.00 0Now let’s pivot this into long format so we can work some time series.
When we do this, year gets changed to the character data type. Something you’ll see often is when you try to use as.numeric to fix this, it will give you an error message that “list object cannot be coerced to double.” Try running the functon below as I do, which you can also find here.
IDS_long <- IDS %>%
pivot_longer(cols = `2000`:`2020`, names_to = "year", values_to = "value")
# Teal's boutique function to deal with columns labled as characters that tell you they can't be coerced to numeric when trying change datatypes. Time series needs to be put in date or numeric for x-axis
character_num_to_numeric <- function(character_num) {
character_num %>%
# take out the commas
str_remove_all(pattern = ",") %>%
# take out any blank spaces before or after the number
str_trim() %>%
# coerce to numeric
as.numeric()
}
# run the function with mutate
IDS_long %>%
mutate(year = character_num_to_numeric(year))# A tibble: 1,625,169 × 8
Country.Name Country.Code Counterpart.A…¹ Count…² Serie…³ Serie…⁴ year value
<chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 Afghanistan AFG World WLD Averag… DT.GPA… 2000 NA
2 Afghanistan AFG World WLD Averag… DT.GPA… 2001 NA
3 Afghanistan AFG World WLD Averag… DT.GPA… 2002 NA
4 Afghanistan AFG World WLD Averag… DT.GPA… 2003 NA
5 Afghanistan AFG World WLD Averag… DT.GPA… 2004 NA
6 Afghanistan AFG World WLD Averag… DT.GPA… 2005 NA
7 Afghanistan AFG World WLD Averag… DT.GPA… 2006 10.2
8 Afghanistan AFG World WLD Averag… DT.GPA… 2007 8.08
9 Afghanistan AFG World WLD Averag… DT.GPA… 2008 9.96
10 Afghanistan AFG World WLD Averag… DT.GPA… 2009 0
# … with 1,625,159 more rows, and abbreviated variable names
# ¹Counterpart.Area.Name, ²Counterpart.Area.Code, ³Series.Name, ⁴Series.CodeFinally I can show you a time series graph. In later sessions we will go more in depth on how to make the graphs look much nicer.
IDS_long %>%
filter(Country.Name %in% c("Ghana", "Angola","Mozambique")) %>%
filter(year > 2010) %>%
#see why we want to change variable names? I made the mistake of not shortening and it was a pain for the entire project.
filter(Series.Name == "Average grace period on new external debt commitments (years)") %>%
ggplot(aes(x = year, y = value, group = Country.Name, color = Country.Name)) +
geom_line()
What if we want to pivot wider and break out all the series names? I maxxed out my computer attempting this (it tried to create over 500 columns. Not advised) so we’ll show it with the emissions dataset instead.
emissions_wide <- emissions_long %>%
pivot_wider(names_from = variable, values_from = value)
head(emissions_wide)# A tibble: 6 × 31
country_name iso3c em_dm year gdp_u…¹ gdp_p…² gdp_p…³ gdp_p…⁴ popul…⁵ govt_…⁶
<chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Angola AGO Emer… 1990 12.6 19.7 987. 1545. 12.7 NA
2 Angola AGO Emer… 1991 12.2 22.8 929. 1738. 13.1 NA
3 Angola AGO Emer… 1992 9.40 26.0 695. 1922. 13.5 NA
4 Angola AGO Emer… 1993 6.82 29.5 490. 2120. 13.9 NA
5 Angola AGO Emer… 1994 4.96 33.3 346. 2323. 14.3 NA
6 Angola AGO Emer… 1995 6.20 37.5 420. 2542. 14.8 NA
# … with 21 more variables: debt_pct_gdp <dbl>, territorial_co2 <dbl>,
# trade_co2 <dbl>, consumption_co2 <dbl>, cumulative_co2 <dbl>,
# debt_usd <dbl>, govt_expenditure_usd <dbl>,
# territorial_co2_per_capita <dbl>, trade_co2_per_capita <dbl>,
# consumption_co2_per_capita <dbl>, territorial_co2_per_gdp <dbl>,
# trade_co2_per_gdp <dbl>, consumption_co2_per_gdp <dbl>,
# territorial_co2_per_debt <dbl>, trade_co2_per_debt <dbl>, …