Week_9_disease

library(readr)

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library(tidyverse)

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✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
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library(dplyr)
library(ggplot2)
disease <- 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)

# 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$income_group)

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

# unique function shows us the type of income group there is in the data-set (types)

n_distinct(disease$country)

[1] 168

# this function showed how many values are stored in country variable

qatar <- disease |>
  filter(country == "Qatar") |>
  pull(income_group)
qatar

[1] "High income: non-OECD"

names(postscriptFonts())

 [1] "serif"                "sans"                 "mono"                
 [4] "AvantGarde"           "Bookman"              "Courier"             
 [7] "Helvetica"            "Helvetica-Narrow"     "NewCenturySchoolbook"
[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_chart <- ggplot(disease, aes(x = infect_rate, y = democ_score)) +
  geom_point(size = 3, alpha =0.4,aes(color = income_group)) +
  # Here, alpha is changing the opacity of the point in the graph. Lower the value lower the opacity and vice-versa.
  geom_smooth(method = lm, se = FALSE, color = "blue", lty = 2, linewidth = 0.3) +
  labs(title = "Comparison of Disease Prevalance and Democratization Score", x = "Infectious Disease Prevalance", y = "Democratization Score") +
  theme_minimal(base_family = "mono")
disease_chart

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

#ggplotly(disease_chart)

library(plotly)

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Attaching package: 'plotly'

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p <- disease_chart +
  geom_point(size = 2.5, alpha = 0.5) +
  geom_smooth(method = lm, se = FALSE, color = "red")

p <- ggplotly(p)

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

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

p

ggplot(disease, 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 = "serif") + 
  geom_point(size = 3, alpha = 0.5) +
  geom_smooth(method=lm, se=FALSE, lty = 2, linewidth = 0.3)

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

library(dslabs)

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

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[10] "make-mice_weights.R"                 
[11] "make-mnist_127.R"                    
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[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"        

Using gapminder data-set

#library(gapminder)
p1 <- 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"
  )
p1

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library(gganimate) 

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#library(gifski)
anim <- p1 + transition_time(year) +
  ease_aes("linear")
anim

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Working with HTML Widgets and High charter

library(highcharter)

Warning: package 'highcharter' was built under R version 4.6.1

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 

Attaching package: 'highcharter'

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

    stars

library(RColorBrewer)

Warning: package 'RColorBrewer' was built under R version 4.6.1

nations <- read_csv("nations.csv")  |>
  mutate(gdp_tn = gdp_percap*population/10^12)

Rows: 5275 Columns: 10

── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): iso2c, iso3c, country, region, income
dbl (5): year, gdp_percap, population, birth_rate, neonat_mortal_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.

head(nations)

# A tibble: 6 × 11
  iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
  <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
1 AD    AND   Andorra  1996         NA      64291       10.9                2.8
2 AD    AND   Andorra  1994         NA      62707       10.9                3.2
3 AD    AND   Andorra  2003         NA      74783       10.3                2  
4 AD    AND   Andorra  1990         NA      54511       11.9                4.3
5 AD    AND   Andorra  2009         NA      85474        9.9                1.7
6 AD    AND   Andorra  2011         NA      82326       NA                  1.6
# ℹ 3 more variables: region <chr>, income <chr>, gdp_tn <dbl>

big4 <- nations |>
  filter(iso3c %in% c("CHN","DEU", "JPN", "USA")) |>
  arrange(year)

# basic symbol-and-line chart, default settings
highchart() |>
  hc_add_series(data = big4,
                   type = "line", 
                hcaes(x = year,
                      y = gdp_tn, 
                      group = country))

# define color palette
cols <- brewer.pal(4, "Set1")

highchart() |>
  hc_add_series(data = big4,
                   type = "line", hcaes(x = year,
                   y = gdp_tn, 
                   group = country)) |>
  hc_colors(cols)

highchart() |>
  hc_add_series(data = big4,
                   type = "line",
                   hcaes(x = year,
                   y = gdp_tn, 
                   group = country)) |>
  hc_colors(cols) |>
  hc_xAxis(title = list(text="Year")) |>
  hc_yAxis(title = list(text="GDP ($ trillion)"))

highchart() |>
  hc_add_series(data = big4,
                   type = "line",
                   hcaes(x = year,
                   y = gdp_tn, 
                   group = country)) |>
  hc_colors(cols) |>
  hc_xAxis(title = list(text="Year")) |>
  hc_yAxis(title = list(text="GDP ($ trillion)")) |>
  hc_plotOptions(series = list(marker = list(symbol = "circle"))) |>
  hc_legend(align = "right", 
            verticalAlign = "top")

# customize the tooltips

big4_chart <- highchart() |>
  hc_add_series(data = big4,
                   type = "line",
                   hcaes(x = year,
                   y = gdp_tn, 
                   group = country)) |>
  hc_colors(cols) |>
  hc_xAxis(title = list(text="Year")) |>
  hc_yAxis(title = list(text="GDP ($ trillion)")) |>
  hc_plotOptions(series = list(marker = list(symbol = "circle"))) |>
  hc_legend(align = "right", 
            verticalAlign = "top") |>
  hc_tooltip(shared = TRUE,
             borderColor = "black",
             pointFormat = "{point.country}: {point.gdp_tn:.2f}")
big4_chart

# prepare data
regions <- nations |>
  group_by(year,region) |>
  summarize(gdp_tn = sum(gdp_tn, na.rm = TRUE)) |>
  arrange(year,region)

`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by year and region.
ℹ Output is grouped by year.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(year, region))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.

# basic area chart, default options
highchart () |>
  hc_add_series(data = regions,
                   type = "area",
                   hcaes(x = year,
                   y = gdp_tn, 
                   group = region))

# set color palette
cols <- brewer.pal(7, "Set2")

# stacked area chart
highchart () |>
  hc_add_series(data = regions,
                   type = "area",
                   hcaes(x = year,
                   y = gdp_tn, 
                   group = region)) |>
  hc_colors(cols) |> 
  hc_chart(style = list(fontFamily = "Georgia",
                        fontWeight = "bold")) |>
  hc_plotOptions(series = list(stacking = "normal",
                               marker = list(enabled = FALSE,
                               states = list(hover = list(enabled = FALSE))),
                               lineWidth = 0.5,
                               lineColor = "white")) |>
  hc_xAxis(title = list(text="Year")) |>
  hc_yAxis(title = list(text="GDP ($ trillion)")) |>
  hc_legend(align = "right", verticalAlign = "top",
            layout = "vertical") 

food_stamps <- read_csv("food_stamps.csv")

Rows: 47 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (3): year, participants, costs

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.

cols <- c("dodgerblue","orange")  # set the color palette to dodger blue and orange

highchart() |>
  hc_yAxis_multiples(
    list(title = list(text = "Participants (millions)")),
    list(title = list(text = "Costs ($ billions)"),
         opposite = TRUE)
  ) |>
  hc_add_series(data = food_stamps$participants,
                name = "Participants (millions)",
                type = "column",
                yAxis = 0) |>
  hc_add_series(data = food_stamps$costs,
                name = "Costs ($ billions)",
                type = "line",
                yAxis = 1) |>
  hc_xAxis(categories = food_stamps$year,
           tickInterval = 5) |>
  hc_colors(cols) |>
  hc_chart(style = list(fontFamily = "AvantGarde",
                        fontWeight = "bold"))

In-class Activity, Week 9

Creating 2 graphs in ggplot

# First graph
ggplot(big4, aes(x = year, y = gdp_tn, color = country)) +
  geom_line() +
  geom_point() +
  labs(
    title = "China's Rise to Become the Largest Economy",
    x = "year",
    y = "GDP ($ trillion)"
  ) +
  scale_color_brewer(palette = "Set1") +
  theme_minimal()

# Second graph
ggplot(regions, aes(x = year, y = gdp_tn, fill = region)) +
  geom_area(color = "white") +
  labs(
    title = "GDP by World Bank Region",
    x = "year",
    y = "GDP ($ trillion)"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal()