Name: Hazel Here

ID: 2019221022 Here

Date: June 12, 2022

library(tidyverse)
library(here)
library(visdat)
library(plotly)

Data

Below, load your data and offer one sentence (or two) description of each data set (excepting our map data).

## Selection

load(here::here("data", "dashboard_data", "5_nations_df.rda" ))

## Map data
load(here::here("data", "dashboard_data", "world_map2_project.rda" ))
load(here::here("data", "dashboard_data", "gapminder_dat.rda" ))

## Gapminder One
load(here::here("data", "dashboard_data", "basic_tidy.rda" ))

Gapminder Case Study

I chose five countries in the data set: Brazil, Nigeria, China, United Kingdom, and Australia. They are from different continent and develop differently. By this way, I can be eaasier to find out the rule.

five_df %>% glimpse()
## Rows: 285
## Columns: 11
## $ country          <chr> "Australia", "Brazil", "China", "Nigeria", "United Ki…
## $ continent        <fct> Oceania, Americas, Asia, Africa, Europe, Oceania, Ame…
## $ region           <fct> Australia and New Zealand, South America, Eastern Asi…
## $ decade           <fct> 1960s, 1960s, 1960s, 1960s, 1960s, 1960s, 1960s, 1960…
## $ year             <int> 1960, 1960, 1960, 1960, 1960, 1961, 1961, 1961, 1961,…
## $ infant_mortality <dbl> 20.3, 129.4, 190.0, 165.0, 22.9, 20.0, 126.1, 161.0, …
## $ life_expectancy  <dbl> 70.87, 55.27, 30.53, 40.39, 71.02, 71.14, 55.78, 32.9…
## $ fertility        <dbl> 3.45, 6.21, 3.99, 6.35, 2.69, 3.55, 6.19, 3.28, 6.35,…
## $ population       <dbl> 10292328, 72493585, 644450173, 45211614, 52410496, 10…
## $ gdp              <dbl> 96677859364, 105343379555, 70348527260, 12836410903, …
## $ gdp_cap_ppp      <dbl> 16100, 3910, 1220, 1500, 15800, 15900, 4150, 986, 151…

Gapminder Download One

This data set is basic welfare index based on 7 indicators, infant mortality rate, life expectancy, kilocalories per person per day, literacy, mean years of schooling, educational equality and health equality and downloaded from Gapminder.org/data.

basic_tidy %>% glimpse()
## Rows: 7,200
## Columns: 3
## $ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "A…
## $ Year    <int> 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 19…
## $ Score   <dbl> 16.2, 16.2, 16.1, 16.1, 15.9, 15.8, 15.8, 15.8, 15.8, 18.7, 18…

Research Questions

Nowadays, everything is progressing, so I am interesting with the current status of basic welfare index(BWI) and want to figure out what factors influence it except its indicators. So I raise the questions like these: (1) what is the global BWI in the past and now? How does it change? And differences among different continents? (2) In these five countries, how is everything going? (3) What other factors influence BWI?

EDA Work

Requirements are a minimum of two (2) Exploratory Data Analysis graphs; and a minimum of two (2) sets of Summary Statistics which generally should directly complement, support, or relate to the EDA visualizations.

Question 1—global trend

This graph is to show the current status of BWI in the world.

BWI_global <- gapminder_dat  %>%
  left_join(basic_tidy, by = c("year" = "Year", "country")) %>%
  tibble() %>%
  select(country, continent, region, decade, year, gdp_cap_ppp, life_expectancy, Score)

BWI_dat_1975 <- basic_tidy %>%
  filter(Year == 1975) %>% 
  complete(country = world_map2$country, 
           fill = (list(number = NA )) ) %>%
  left_join(world_map2, by = "country") %>%
  replace_na(list(Year = 1975))

BWI_dat_1975
## # A tibble: 99,442 × 11
##    country   Year Score  long   lat group order code_2 code_3 code_num form_name
##    <chr>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  <chr>     <dbl> <chr>    
##  1 Afghani…  1975  16.2  74.9  37.2     2    12 AF     AFG           4 Islamic …
##  2 Afghani…  1975  16.2  74.8  37.2     2    13 AF     AFG           4 Islamic …
##  3 Afghani…  1975  16.2  74.8  37.2     2    14 AF     AFG           4 Islamic …
##  4 Afghani…  1975  16.2  74.7  37.3     2    15 AF     AFG           4 Islamic …
##  5 Afghani…  1975  16.2  74.7  37.3     2    16 AF     AFG           4 Islamic …
##  6 Afghani…  1975  16.2  74.7  37.3     2    17 AF     AFG           4 Islamic …
##  7 Afghani…  1975  16.2  74.6  37.2     2    18 AF     AFG           4 Islamic …
##  8 Afghani…  1975  16.2  74.4  37.2     2    19 AF     AFG           4 Islamic …
##  9 Afghani…  1975  16.2  74.4  37.1     2    20 AF     AFG           4 Islamic …
## 10 Afghani…  1975  16.2  74.5  37.1     2    21 AF     AFG           4 Islamic …
## # … with 99,432 more rows
BWI_map_1975 <- BWI_dat_1975 %>%
  filter(code_3 != "ATA") %>%
   ggplot(aes(x = long, 
             y = lat, 
             group = group, 
             label = country)) +
  geom_polygon(aes(fill = Score) )+
  scale_fill_viridis_c(option = "C") +
  labs(fill = "Index",
       title = "Basic Welfare Index for 1975") +
  theme_void()


plotly::ggplotly(BWI_map_1975)
BWI_map_1975

BWI_dat_2019 <- basic_tidy %>%
  filter(Year == 2019) %>% 
  complete(country = world_map2$country, 
           fill = (list(number = NA )) ) %>%
  left_join(world_map2, by = "country") %>%
  replace_na(list(Year = 2019))

BWI_dat_2019
## # A tibble: 99,442 × 11
##    country   Year Score  long   lat group order code_2 code_3 code_num form_name
##    <chr>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  <chr>     <dbl> <chr>    
##  1 Afghani…  2019    42  74.9  37.2     2    12 AF     AFG           4 Islamic …
##  2 Afghani…  2019    42  74.8  37.2     2    13 AF     AFG           4 Islamic …
##  3 Afghani…  2019    42  74.8  37.2     2    14 AF     AFG           4 Islamic …
##  4 Afghani…  2019    42  74.7  37.3     2    15 AF     AFG           4 Islamic …
##  5 Afghani…  2019    42  74.7  37.3     2    16 AF     AFG           4 Islamic …
##  6 Afghani…  2019    42  74.7  37.3     2    17 AF     AFG           4 Islamic …
##  7 Afghani…  2019    42  74.6  37.2     2    18 AF     AFG           4 Islamic …
##  8 Afghani…  2019    42  74.4  37.2     2    19 AF     AFG           4 Islamic …
##  9 Afghani…  2019    42  74.4  37.1     2    20 AF     AFG           4 Islamic …
## 10 Afghani…  2019    42  74.5  37.1     2    21 AF     AFG           4 Islamic …
## # … with 99,432 more rows
BWI_map_2019 <- BWI_dat_2019 %>%
  filter(code_3 != "ATA") %>%
   ggplot(aes(x = long, 
             y = lat, 
             group = group, 
             label = country)) +
  geom_polygon(aes(fill = Score) )+
  scale_fill_viridis_c(option = "C") +
  labs(fill = "Index",
       title = "Basic Welfare Index for 1975") +
  theme_void()


plotly::ggplotly(BWI_map_2019)
BWI_map_2019

BWI_global %>%
  group_by(continent) %>%
  ggplot( aes(x = continent, y = Score, color = continent)) +
  geom_boxplot() +
  stat_summary(fun.y = mean, shape = 8) +
  guides(color = "none") +
  facet_wrap(~decade) +
  labs(x = "", 
       y = "Index",
       title = "Basic Welfare Index by decades")

BWI_global
## # A tibble: 10,545 × 8
##    country       continent region decade  year gdp_cap_ppp life_expectancy Score
##    <chr>         <fct>     <fct>  <fct>  <int>       <dbl>           <dbl> <dbl>
##  1 Albania       Europe    South… 1960s   1960        2660            62.9    NA
##  2 Algeria       Africa    North… 1960s   1960        6480            47.5    NA
##  3 Angola        Africa    Middl… 1960s   1960        2300            36.0    NA
##  4 Antigua and … Americas  Carib… 1960s   1960        3630            63.0    NA
##  5 Argentina     Americas  South… 1960s   1960       10200            65.4    NA
##  6 Armenia       Asia      Weste… 1960s   1960        6670            66.9    NA
##  7 Aruba         Americas  Carib… 1960s   1960          NA            65.7    NA
##  8 Australia     Oceania   Austr… 1960s   1960       16100            70.9    NA
##  9 Austria       Europe    Weste… 1960s   1960       12000            68.8    NA
## 10 Azerbaijan    Asia      Weste… 1960s   1960        5500            61.3    NA
## # … with 10,535 more rows

Summary Stats

Should relate to your plot.

these_3 <- c("1970s", "1980s", "1990s", "2000s", "2010s")

BWI_global %>%
  group_by(year, country) %>%
  filter(decade%in%these_3) %>%
  dplyr::summarize(Avg_Ind = mean(Score, na.rm = TRUE),
                   SD_Ind = sd(Score, na.rm = TRUE),
                   Min_Ind = min(Score, na.rm = TRUE),
                   Max_Ind = max(Score, na.rm = TRUE))
## # A tibble: 8,695 × 6
## # Groups:   year [47]
##     year country             Avg_Ind SD_Ind Min_Ind Max_Ind
##    <int> <chr>                 <dbl>  <dbl>   <dbl>   <dbl>
##  1  1970 Albania                 NaN     NA     Inf    -Inf
##  2  1970 Algeria                 NaN     NA     Inf    -Inf
##  3  1970 Angola                  NaN     NA     Inf    -Inf
##  4  1970 Antigua and Barbuda     NaN     NA     Inf    -Inf
##  5  1970 Argentina               NaN     NA     Inf    -Inf
##  6  1970 Armenia                 NaN     NA     Inf    -Inf
##  7  1970 Aruba                   NaN     NA     Inf    -Inf
##  8  1970 Australia               NaN     NA     Inf    -Inf
##  9  1970 Austria                 NaN     NA     Inf    -Inf
## 10  1970 Azerbaijan              NaN     NA     Inf    -Inf
## # … with 8,685 more rows

EDA notes

My third plot shows me that (1) Americas and Asia are statistically similar. The mean and median of the two continents are close but Asia’s data is scattered compared with Americas. (2) The index of Africa is the lowest and that of Europe keeps highest. (3) Oceania ranks second but owns the most scattered data. Moreover, all of them keep progressing.

Question 2—case study in five countries

In this section, we have a case study in 5 countries.

these_nation <- c("Australia", "China", "Nigeria", "Brazil", "United Kingdom")

five_2000s <- BWI_global%>%
  filter(decade == "2000s", country%in%these_nation)


anova_one  <- aov(Score ~ country,  five_2000s )


results_one  <- TukeyHSD(anova_one) %>% 
  broom::tidy() %>% 
  dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))


results_one$decade <- "2000s"



five_1980s <- BWI_global %>%
  filter(decade == "1980s", country%in%these_nation)


anova_two <- aov(Score ~ country,  five_1980s  )


results_two <- TukeyHSD(anova_two ) %>% 
  broom::tidy() %>% 
  dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))


results_two$decade <- "1980s"


results_five <- results_one %>% 
  bind_rows(results_two) %>% 
  select(decade, everything())


tuk_gg <- results_five %>% 
  ggplot(aes(estimate, contrast , 
           color = contrast) ) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  facet_wrap(~decade) +
  labs(title = "Basic Welfare Index Means: 1980s & 2000s",
       subtitle = "ANOVA Results for Five Countries. 95% CI.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))

tuk_gg <- plotly::ggplotly(tuk_gg , tooltip = c("text" ))

tuk_gg %>% layout(showlegend = FALSE)

Output

results_five
## # A tibble: 20 × 8
##    decade term    contrast    null.value estimate conf.low conf.high adj.p.value
##    <chr>  <chr>   <chr>            <dbl>    <dbl>    <dbl>     <dbl>       <dbl>
##  1 2000s  country Nigeria-Au…          0   -55.5    -58.8    -52.2         0    
##  2 2000s  country United Kin…          0    53.2     49.9     56.5         0    
##  3 2000s  country Nigeria-Ch…          0   -30.2    -33.6    -26.9         0    
##  4 2000s  country Nigeria-Br…          0   -28.7    -32.1    -25.4         0    
##  5 2000s  country Brazil-Aus…          0   -26.8    -30.1    -23.4         0    
##  6 2000s  country China-Aust…          0   -25.3    -28.6    -21.9         0    
##  7 2000s  country United Kin…          0    24.5     21.1     27.8         0    
##  8 2000s  country United Kin…          0    23.0     19.6     26.3         0    
##  9 2000s  country United Kin…          0    -2.3     -5.62     1.02        0.298
## 10 2000s  country China-Braz…          0     1.51    -1.81     4.83        0.697
## 11 1980s  country Nigeria-Au…          0   -48.5    -50.9    -46.1         0    
## 12 1980s  country United Kin…          0    46.8     44.4     49.2         0    
## 13 1980s  country Brazil-Aus…          0   -32.3    -34.7    -29.9         0    
## 14 1980s  country United Kin…          0    30.6     28.2     33.0         0    
## 15 1980s  country China-Aust…          0   -25.3    -27.7    -22.9         0    
## 16 1980s  country United Kin…          0    23.6     21.3     26.0         0    
## 17 1980s  country Nigeria-Ch…          0   -23.2    -25.6    -20.8         0    
## 18 1980s  country Nigeria-Br…          0   -16.2    -18.6    -13.8         0    
## 19 1980s  country China-Braz…          0     6.99     4.60     9.38        0    
## 20 1980s  country United Kin…          0    -1.65    -4.04     0.736       0.300

The relationship between economy and BWI seems very close. We will prove it by this plot.

lm_5B <- BWI_global %>% 
  filter(country%in%these_nation, between(year, 1975, 2019))%>%
  mutate(across(where(is.numeric), round, 2))%>%
  ggplot( aes(x = year, 
              y = Score,
              size = gdp_cap_ppp,
              color = country)) + 
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", show.legend = FALSE) +
  labs(title = "Five Countries",
       y ="Index", 
       x = "Year + Per Capita GDP (PPP) + Country",
       color = "Country",
       text = "Per Capita (PPP)",
       subtitle = "Basic Welfare Index ~ gdp_cap_ppp + country + year") 



lm_5B

Summary Stats

Should relate to your plot.

my_fm <- as.formula("Score ~ year + gdp_cap_ppp +country")


mod_5B <- lm(formula = my_fm , data = BWI_global)




clean__mod_5B <- broom::tidy(mod_5B, conf.int = TRUE) %>%
  mutate(across(where(is.numeric), round, 4)) 


clean__mod_5B
## # A tibble: 156 × 7
##    term                estimate std.error statistic p.value   conf.low conf.high
##    <chr>                  <dbl>     <dbl>     <dbl>   <dbl>      <dbl>     <dbl>
##  1 (Intercept)       -1131.        7.62     -149.     0     -1146.      -1.12e+3
##  2 year                  0.596     0.0038    156.     0         0.588    6.03e-1
##  3 gdp_cap_ppp           0.0001    0          12.8    0         0.0001   1   e-4
##  4 countryAlgeria      -12.5       0.662     -18.9    0       -13.8     -1.12e+1
##  5 countryAngola       -35.9       0.662     -54.3    0       -37.2     -3.46e+1
##  6 countryArgentina      0.913     0.668       1.37   0.172    -0.396    2.22e+0
##  7 countryArmenia       -3.26      0.758      -4.31   0        -4.75    -1.78e+0
##  8 countryAustralia     17.1       0.695      24.6    0        15.8      1.85e+1
##  9 countryAustria       16.4       0.709      23.2    0        15.0      1.78e+1
## 10 countryAzerbaijan   -16.3       0.758     -21.5    0       -17.8     -1.48e+1
## # … with 146 more rows
mod_stats <- broom::glance(mod_5B)
mod_stats
## # A tibble: 1 × 12
##   r.squared adj.r.squared sigma statistic p.value    df  logLik    AIC    BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>   <dbl>  <dbl>  <dbl>
## 1     0.978         0.977  3.03     1703.       0   155 -15350. 31013. 32068.
## # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

EDA notes

The result shows that all of the 5 countries keep progressing which match the global trend. The differences are that the index of Australia and United Kingdom that are developed countries is the highest, that of China and Brazil that are developing countries is in the middle, and that of Nigeria that is less developed countries is the lowest. The adjusted R-squared value for the linear model: 0.977401 which shows that the relationship between economy and BWI is close. It is a positive correlation. However, from 1970s to 1980s, this correlation does not happen between China and Nigeria, which is that China’s Per Capita GDP (PPP) is lower than Nigeria, but the index of China is higher than Nigeria. We conjecture that one of the reasons is that at that time China was experiencing disasters both from human and the nature, leading to economic depression.

Question 3—other factors

In this section, we will take life expectancy as an example to see whether it will influence BWI.

five_2000s <- BWI_global%>%
  filter(decade == "2000s", country%in%these_nation)


anova_one  <- aov(life_expectancy ~ country,  five_2000s )


results_one  <- TukeyHSD(anova_one) %>% 
  broom::tidy() %>% 
  dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))


results_one$decade <- "2000s"



five_1980s <- BWI_global %>%
  filter(decade == "1980s", country%in%these_nation)


anova_two <- aov(life_expectancy ~ country,  five_1980s  )


results_two <- TukeyHSD(anova_two ) %>% 
  broom::tidy() %>% 
  dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))


results_two$decade <- "1980s"


results_five_2 <- results_one %>% 
  bind_rows(results_two) %>% 
  select(decade, everything())


tuk_g <- results_five_2 %>% 
  ggplot(aes(estimate, contrast , 
           color = contrast) ) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  facet_wrap(~decade) +
  labs(title = "Life Expectancy Means: 1980s & 2000s",
       subtitle = "ANOVA Results for Five Countries. 95% CI.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))

tuk_g

### Output

results_five_2
## # A tibble: 20 × 8
##    decade term    contrast    null.value estimate conf.low conf.high adj.p.value
##    <chr>  <chr>   <chr>            <dbl>    <dbl>    <dbl>     <dbl>       <dbl>
##  1 2000s  country Nigeria-Au…          0   -24.0    -25.5   -22.4         0     
##  2 2000s  country United Kin…          0    21.9     20.3    23.4         0     
##  3 2000s  country Nigeria-Br…          0   -15.2    -16.7   -13.7         0     
##  4 2000s  country Nigeria-Ch…          0   -14.8    -16.3   -13.2         0     
##  5 2000s  country China-Aust…          0    -9.2    -10.7    -7.66        0     
##  6 2000s  country Brazil-Aus…          0    -8.76   -10.3    -7.22        0     
##  7 2000s  country United Kin…          0     7.13     5.59    8.67        0     
##  8 2000s  country United Kin…          0     6.69     5.15    8.23        0     
##  9 2000s  country United Kin…          0    -2.07    -3.61   -0.529       0.0036
## 10 2000s  country China-Braz…          0    -0.44    -1.98    1.10        0.926 
## 11 1980s  country Nigeria-Au…          0   -22.4    -23.4   -21.3         0     
## 12 1980s  country United Kin…          0    21.3     20.2    22.4         0     
## 13 1980s  country Nigeria-Br…          0   -12.3    -13.4   -11.3         0     
## 14 1980s  country Nigeria-Ch…          0   -12.3    -13.4   -11.2         0     
## 15 1980s  country China-Aust…          0   -10.0    -11.1    -8.97        0     
## 16 1980s  country Brazil-Aus…          0   -10.0    -11.1    -8.95        0     
## 17 1980s  country United Kin…          0     8.98     7.90   10.1         0     
## 18 1980s  country United Kin…          0     8.96     7.88   10.0         0     
## 19 1980s  country United Kin…          0    -1.07    -2.15    0.0118      0.0538
## 20 1980s  country China-Braz…          0    -0.02    -1.10    1.06        1
lm_5B_lif <- BWI_global %>% 
  filter(between(year, 1975, 2019), country%in%these_nation)%>%
  ggplot( aes(x = year, 
              y = Score,
              size = life_expectancy,
              color = country) ) + 
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", show.legend = FALSE) +
  labs(title = "Five Nations",
       y ="Index", 
       x = "Year + Life Expectancy + Countries",
       color = "Nation",
       text = "Per Capita (PPP)",
       size = "PPP",
       subtitle = "Basic Welfare Index ~ life expectancy + country + year") 



lm_5B_lif

### Output

my_fm_lif <- as.formula("Score ~ year + life_expectancy +country")


mod_5B_lif <- lm(formula = my_fm_lif , data = BWI_global)

clean_mod_5B_lif <- broom::tidy(mod_5B_lif, conf.int = TRUE) %>%
  mutate(across(where(is.numeric), round, 4)) 

clean_mod_5B_lif
## # A tibble: 156 × 7
##    term              estimate std.error statistic p.value conf.low conf.high
##    <chr>                <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
##  1 (Intercept)       -882.       7.38    -120.      0     -897.     -868.   
##  2 year                 0.447    0.004    112.      0        0.439     0.455
##  3 life_expectancy      0.653    0.0112    58.4     0        0.631     0.674
##  4 countryAlgeria      -9.48     0.537    -17.7     0      -10.5      -8.43 
##  5 countryAngola      -21.4      0.593    -36.0     0      -22.5     -20.2  
##  6 countryArgentina     2.71     0.535      5.06    0        1.66      3.76 
##  7 countryArmenia      -0.340    0.615     -0.554   0.580   -1.55      0.864
##  8 countryAustralia    17.1      0.537     31.9     0       16.1      18.2  
##  9 countryAustria      17.9      0.536     33.5     0       16.9      19.0  
## 10 countryAzerbaijan  -10.8      0.619    -17.5     0      -12.0      -9.60 
## # … with 146 more rows
mod_stats_lif <- broom::glance(mod_5B_lif)
mod_stats_lif
## # A tibble: 1 × 12
##   r.squared adj.r.squared sigma statistic p.value    df  logLik    AIC    BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>   <dbl>  <dbl>  <dbl>
## 1     0.986         0.985  2.45     2628.       0   155 -14050. 28414. 29469.
## # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

Notes

The first plot tells us that both in 1980s and 2000s, meaningful differences exist between the countries being compared. Thus, according to this result, difference life expectancy exists between different countries we chose. The second plot tells us that a linear positive correlation exists in these five countries. With the increasing life expectancy, BWI also keeps increasing. Similar to the result between per capita GDP(ppp) and BWI, the five countries divide into three, Australia and United Kingdom, China and Brazil, and Nigeria. Thus, life expectancy is also the factor that influence BWI.

Main data set

BWI_global %>% 
  filter(country%in%these_nation, between(year, 1975, 2019))%>%
  DT::datatable(., class = "compact")

Save your results

dash_results <- c("anova_one", "anova_two", "basic_map_1997", "basic_map_2016", "basic_tidy", "BWI_dat_1975", "BWI_dat_2019", "BWI_global", "BWI_map_1975", "BWI_map_2019", "clean__mod_5B", "clean_mod_5B_lif", "country_ISO_codes2", "five_1980s", "five_2000s", "five_df", "gapminder_dat", "lm_5B", "lm_5B_lif", "mod_5B", "mod_5B_lif", "mod_stats", "mod_stats_lif",  "my_fm", "my_fm_lif", "results_five", "results_five_2", "results_one", "results_two", "these_3", "these_nation", "tuk_g", "tuk_gg", "world_map2"  )
save(list = dash_results, file = here::here("data", "dashboard_data", "dash_results.rda") )




save.image( file = here::here("data", "dashboard_data","picture.RData"))