Untitled

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)
disease_democ <- read_csv("disease_democ.csv")

Rows: 168 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): country, income_group
dbl (2): democ_score, infect_rate

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

tail(disease_democ)

# A tibble: 6 × 4
  country       income_group        democ_score infect_rate
  <chr>         <chr>                     <dbl>       <dbl>
1 Turkey        Upper middle income        54.8          39
2 Panama        Upper middle income        46            40
3 Gabon         Upper middle income        34.6          41
4 Venezuela, RB Upper middle income        41.6          42
5 Colombia      Upper middle income        44.8          43
6 Brazil        Upper middle income        37.2          45

unique(disease_democ$income_group) 

[1] "High income: non-OECD" "High income: OECD"     "Low income"           
[4] "Lower middle income"   "Upper middle income"  

#lists all values
n_distinct(disease_democ$country)

[1] 168

#counts total number of countries

disease_democ |> filter(country == "Qatar")

# A tibble: 1 × 4
  country income_group          democ_score infect_rate
  <chr>   <chr>                       <dbl>       <dbl>
1 Qatar   High income: non-OECD        50.4          24

names(postscriptFonts())

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[10] "Palatino"             "Times"                "URWGothic"           
[13] "URWBookman"           "NimbusMon"            "NimbusSan"           
[16] "URWHelvetica"         "NimbusSanCond"        "CenturySch"          
[19] "URWPalladio"          "NimbusRom"            "URWTimes"            
[22] "URW2Helvetica"        "URW2HelveticaItalic"  "URW2Times"           
[25] "NimbusMonoPS"         "ArialMT"              "ComputerModern"      
[28] "ComputerModernItalic" "Japan1"               "Japan1HeiMin"        
[31] "Japan1GothicBBB"      "Japan1Ryumin"         "Korea1"              
[34] "Korea1deb"            "CNS1"                 "GB1"                 

disease_democ_chart <-ggplot(disease_democ, aes(x = infect_rate, y = democ_score)) + 
labs(x="Infectious Disease Prevalence Score",  y="Democratization Score",title="Comparison of Disease Prevalence and Democratization Score") 
theme_minimal(base_family = "mono")

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  [list output truncated]
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disease_democ_chart

disease_democ_chart +
  geom_point(size = 8, alpha = 0.25) +
  geom_smooth(method = lm, se=FALSE, color = "navy")

`geom_smooth()` using formula = 'y ~ x'

library(plotly) 

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

p<-disease_democ_chart +
geom_point(size = 1, alpha = 0.3) +
geom_smooth(method = lm, se=FALSE, color = "yellow")

p <- ggplotly(p)

`geom_smooth()` using formula = 'y ~ x'

p

disease_democ_chart <-ggplot(disease_democ, aes(x = infect_rate, y = democ_score, text= country))+ 
labs(x="Infectious Disease Prevalence Score",  y="Democratization Score",title="Comparison of Disease Prevalence and Democratization Score") 
p<-disease_democ_chart +
geom_point(size = 1.1, alpha = 0.3) +
geom_smooth(method = lm, se=FALSE, color = "navy")

p <- ggplotly(p)

`geom_smooth()` using formula = 'y ~ x'

p

disease_democ_chart + 
geom_point(size = 6, alpha = 0.3, aes(color = income_group)) +
geom_smooth(method = lm, se  =FALSE, color = "red", lty = 2, linewidth = 0.3) 

`geom_smooth()` using formula = 'y ~ x'

ggplot(disease_democ, aes(x = infect_rate, y = democ_score, color=income_group)) +
labs(x="Infectious Disease Prevalence Score",
     y="Democratization Score",
     title="Comparison of Disease Prevalence and Democratization Score") +
theme_minimal(base_size = 14, base_family = "mono") + 
geom_point(size = 2, alpha = 0.3) +
geom_smooth(method=lm, se=FALSE, lty = 4, linewidth = 0.7)

`geom_smooth()` using formula = 'y ~ x'

library("dslabs")
data(package="dslabs")
list.files(system.file("script", package = "dslabs"))

 [1] "make-admissions.R"                   
 [2] "make-brca.R"                         
 [3] "make-brexit_polls.R"                 
 [4] "make-calificaciones.R"               
 [5] "make-death_prob.R"                   
 [6] "make-divorce_margarine.R"            
 [7] "make-gapminder-rdas.R"               
 [8] "make-greenhouse_gases.R"             
 [9] "make-historic_co2.R"                 
[10] "make-mice_weights.R"                 
[11] "make-mnist_127.R"                    
[12] "make-mnist_27.R"                     
[13] "make-movielens.R"                    
[14] "make-murders-rda.R"                  
[15] "make-na_example-rda.R"               
[16] "make-nyc_regents_scores.R"           
[17] "make-olive.R"                        
[18] "make-outlier_example.R"              
[19] "make-polls_2008.R"                   
[20] "make-polls_us_election_2016.R"       
[21] "make-pr_death_counts.R"              
[22] "make-reported_heights-rda.R"         
[23] "make-research_funding_rates.R"       
[24] "make-results_us_election_2012.R"     
[25] "make-stars.R"                        
[26] "make-temp_carbon.R"                  
[27] "make-tissue-gene-expression.R"       
[28] "make-trump_tweets.R"                 
[29] "make-weekly_us_contagious_diseases.R"
[30] "save-gapminder-example-csv.R"        

library("dslabs")
library(gganimate)

p <- ggplot(gapminder, aes(gdp, life_expectancy, size = population, color = continent)) +
geom_point(alpha = 0.7) +
scale_color_viridis_d() +
scale_size(range = c(2, 12)) +
scale_x_log10() +
labs(
     title = "Year: {frame_time}",
     x = "GDP per capita",
     y = "Life expectancy"
)

anim <- p + transition_time(year) + ease_aes("linear")

animate(anim, nframes = 100, fps = 10, renderer = gifski_renderer("gapminder.gif"))

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Removed 29 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 27 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 27 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 14 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 14 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 13 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 10 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 10 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 6 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 6 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 9 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 17 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 17 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 185 rows containing missing values or values outside the scale range
(`geom_point()`).