.Rhistory

ggplot(aes(fertility, life_expectancy)) +
geom_point()
# add color as continent
filter(gapminder, year == 1962) %>%
ggplot(aes(fertility, life_expectancy, color = continent)) +
geom_point()
# facet by continent and year
filter(gapminder, year %in% c(1962, 2012)) %>%
ggplot(aes(fertility, life_expectancy, col = continent)) +
geom_point() +
facet_grid(continent ~ year)
# facet by year only
filter(gapminder, year %in% c(1962, 2012)) %>%
ggplot(aes(fertility, life_expectancy, col = continent)) +
geom_point() +
facet_grid(. ~ year)
# facet by year, plots wrapped onto multiple rows
years <- c(1962, 1980, 1990, 2000, 2012)
continents <- c("Europe", "Asia")
gapminder %>%
filter(year %in% years & continent %in% continents) %>%
ggplot(aes(fertility, life_expectancy, col = continent)) +
geom_point() +
facet_wrap(~year)
# line plot fertility time series for two countries - one line per country
gapminder %>% filter(country %in% countries) %>%
ggplot(aes(year, fertility, group = country)) +
geom_line()
rlang::last_error()
# line plot fertility time series for two countries- only one line (incorrect)
countries <- c("South Korea", "Germany")
gapminder %>% filter(country %in% countries) %>%
ggplot(aes(year, fertility)) +
geom_line()
# line plot fertility time series for two countries - one line per country
gapminder %>% filter(country %in% countries) %>%
ggplot(aes(year, fertility, group = country)) +
geom_line()
# fertility time series for two countries - lines colored by country
gapminder %>% filter(country %in% countries) %>%
ggplot(aes(year, fertility, col = country)) +
geom_line()
# life expectancy time series - lines colored by country and labeled, no legend
labels <- data.frame(country = countries, x = c(1975, 1965), y = c(60, 72))
gapminder %>% filter(country %in% countries) %>%
ggplot(aes(year, life_expectancy, col = country)) +
geom_line() +
geom_text(data = labels, aes(x, y, label = country), size = 5) +
theme(legend.position = "none")
gapminder <- gapminder %>%
mutate(dollars_per_day = gdp/population/365)
past_year <- 1970
gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
ggplot(aes(dollars_per_day)) +
geom_histogram(binwidth = 1, color = "black")
# repeat histogram with log2 scaled x-axis
gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
ggplot(aes(dollars_per_day)) +
geom_histogram(binwidth = 1, color = "black") +
scale_x_continuous(trans = "log2")
# add dollars per day variable
gapminder <- gapminder %>%
mutate(dollars_per_day = gdp/population/365)
# number of regions
length(levels(gapminder$region))
# boxplot of GDP by region in 1970
past_year <- 1970
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
ggplot(aes(region, dollars_per_day))
p + geom_boxplot()
# rotate names on x-axis
p + geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# reorder by median income and color by continent
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
mutate(region = reorder(region, dollars_per_day, FUN = median)) %>%    # reorder
ggplot(aes(region, dollars_per_day, fill = continent)) +    # color by continent
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("")
p
# log2 scale y-axis
p + scale_y_continuous(trans = "log2")
# add data points
p + scale_y_continuous(trans = "log2") + geom_point(show.legend = FALSE)
# add dollars per day variable
gapminder <- gapminder %>%
mutate(dollars_per_day = gdp/population/365)
# number of regions
length(levels(gapminder$region))
# boxplot of GDP by region in 1970
past_year <- 1970
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
ggplot(aes(region, dollars_per_day))
p + geom_boxplot()
# rotate names on x-axis
p + geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 2))
# reorder by median income and color by continent
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
mutate(region = reorder(region, dollars_per_day, FUN = median)) %>%    # reorder
ggplot(aes(region, dollars_per_day, fill = continent)) +    # color by continent
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("")
p
# log2 scale y-axis
p + scale_y_continuous(trans = "log2")
# add data points
p + scale_y_continuous(trans = "log2") + geom_point(show.legend = FALSE)
add dollars per day variable
gapminder <- gapminder %>%
mutate(dollars_per_day = gdp/population/365)
# number of regions
length(levels(gapminder$region))
# boxplot of GDP by region in 1970
past_year <- 1970
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
ggplot(aes(region, dollars_per_day))
p + geom_boxplot()
# rotate names on x-axis
p + geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 2))
# reorder by median income and color by continent
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
mutate(region = reorder(region, dollars_per_day, FUN = median)) %>%    # reorder
ggplot(aes(region, dollars_per_day, fill = continent)) +    # color by continent
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 2)) +
xlab("")
p
# log2 scale y-axis
p + scale_y_continuous(trans = "log2")
# add data points
p + scale_y_continuous(trans = "log2") + geom_point(show.legend = FALSE)
# add data points
p + scale_y_continuous(trans = "log2") + geom_point(show.legend = FALSE)
# boxplot of GDP by region in 1970
past_year <- 1970
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
ggplot(aes(region, dollars_per_day))
p + geom_boxplot()
# rotate names on x-axis
p + geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# reorder by median income and color by continent
p <- gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
mutate(region = reorder(region, dollars_per_day, FUN = median)) %>%    # reorder
ggplot(aes(region, dollars_per_day, fill = continent)) +    # color by continent
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("")
p
# log2 scale y-axis
p + scale_y_continuous(trans = "log2")
# add data points
p + scale_y_continuous(trans = "log2") + geom_point(show.legend = FALSE)
gapminder <- gapminder %>%
mutate(dollars_per_day = gdp/population/365)
past_year <- 1970
# define Western countries
west <- c("Western Europe", "Northern Europe", "Southern Europe", "Northern America", "Australia and New Zealand")
# facet by West vs devloping
gapminder %>%
filter(year == past_year & !is.na(gdp)) %>%
mutate(group = ifelse(region %in% west, "West", "Developing")) %>%
ggplot(aes(dollars_per_day)) +
geom_histogram(binwidth = 1, color = "black") +
scale_x_continuous(trans = "log2") +
facet_grid(. ~ group)
# facet by West/developing and year
present_year <- 2010
gapminder %>%
filter(year %in% c(past_year, present_year) & !is.na(gdp)) %>%
mutate(group = ifelse(region %in% west, "West", "Developing")) %>%
ggplot(aes(dollars_per_day)) +
geom_histogram(binwidth = 1, color = "black") +
scale_x_continuous(trans = "log2") +
facet_grid(year ~ group)
# Code: Income distribution of West versus developing world, only countries with data
# define countries that have data available in both years
country_list_1 <- gapminder %>%
filter(year == past_year & !is.na(dollars_per_day)) %>% .$country
country_list_2 <- gapminder %>%
filter(year == present_year & !is.na(dollars_per_day)) %>% .$country
country_list <- intersect(country_list_1, country_list_2)
# make histogram including only countries with data available in both years
gapminder %>%
filter(year %in% c(past_year, present_year) & country %in% country_list) %>%    # keep only selected countries
mutate(group = ifelse(region %in% west, "West", "Developing")) %>%
ggplot(aes(dollars_per_day)) +
geom_histogram(binwidth = 1, color = "black") +
scale_x_continuous(trans = "log2") +
facet_grid(year ~ group)
#Code: Boxplots of income in West versus developing world, 1970 and 2010
p <- gapminder %>%
filter(year %in% c(past_year, present_year) & country %in% country_list) %>%
mutate(region = reorder(region, dollars_per_day, FUN = median)) %>%
ggplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("") + scale_y_continuous(trans = "log2")
p + geom_boxplot(aes(region, dollars_per_day, fill = continent)) +
facet_grid(year ~ .)
# arrange matching boxplots next to each other, colored by year
p + geom_boxplot(aes(region, dollars_per_day, fill = factor(year)))
# smooth density plots - area under each curve adds to 1
gapminder %>%
filter(year == past_year & country %in% country_list) %>%
mutate(group = ifelse(region %in% west, "West", "Developing")) %>% group_by(group) %>%
summarize(n = n()) %>% knitr::kable()
#Code: Weighted stacked density plot
# weighted stacked density plot
gapminder %>%
filter(year %in% c(past_year, present_year) & country %in% country_list) %>%
group_by(year) %>%
mutate(weight = population/sum(population*2)) %>%
ungroup() %>%
ggplot(aes(dollars_per_day, fill = group, weight = weight)) +
scale_x_continuous(trans = "log2") +
geom_density(alpha = 0.2, bw = 0.75, position = "stack") + facet_grid(year ~ .)
# Density plots
#Code: Faceted smooth density plots
# see the code below the previous video for variable definitions
# smooth density plots - area under each curve adds to 1
gapminder %>%
filter(year == past_year & country %in% country_list) %>%
mutate(group = ifelse(region %in% west, "West", "Developing")) %>% group_by(group) %>%
summarize(n = n()) %>% knitr::kable()
# smooth density plots - variable counts on y-axis
p <- gapminder %>%
filter(year == past_year & country %in% country_list) %>%
mutate(group = ifelse(region %in% west, "West", "Developing")) %>%
ggplot(aes(dollars_per_day, y = ..count.., fill = group)) +
scale_x_continuous(trans = "log2")
p + geom_density(alpha = 0.2, bw = 0.75) + facet_grid(year ~ .)
#Code: Add new region groups with case_when
# add group as a factor, grouping regions
gapminder <- gapminder %>%
mutate(group = case_when(
.$region %in% west ~ "West",
.$region %in% c("Eastern Asia", "South-Eastern Asia") ~ "East Asia",
.$region %in% c("Caribbean", "Central America", "South America") ~ "Latin America",
.$continent == "Africa" & .$region != "Northern Africa" ~ "Sub-Saharan Africa",
TRUE ~ "Others"))
# reorder factor levels
gapminder <- gapminder %>%
mutate(group = factor(group, levels = c("Others", "Latin America", "East Asia", "Sub-Saharan Africa", "West")))
#Code: Stacked density plot
# note you must redefine p with the new gapminder object first
p <- gapminder %>%
filter(year %in% c(past_year, present_year) & country %in% country_list) %>%
ggplot(aes(dollars_per_day, fill = group)) +
scale_x_continuous(trans = "log2")
# stacked density plot
p + geom_density(alpha = 0.2, bw = 0.75, position = "stack") +
facet_grid(year ~ .)
#Code: Weighted stacked density plot
# weighted stacked density plot
gapminder %>%
filter(year %in% c(past_year, present_year) & country %in% country_list) %>%
group_by(year) %>%
mutate(weight = population/sum(population*2)) %>%
ungroup() %>%
ggplot(aes(dollars_per_day, fill = group, weight = weight)) +
scale_x_continuous(trans = "log2") +
geom_density(alpha = 0.2, bw = 0.75, position = "stack") + facet_grid(year ~ .)
# define gapminder
library(tidyverse)
library(dslabs)
data(gapminder)
# add additional cases
gapminder <- gapminder %>%
mutate(group = case_when(
.$region %in% west ~ "The West",
.$region %in% "Northern Africa" ~ "Northern Africa",
.$region %in% c("Eastern Asia", "South-Eastern Asia") ~ "East Asia",
.$region == "Southern Asia" ~ "Southern Asia",
.$region %in% c("Central America", "South America", "Caribbean") ~ "Latin America",
.$continent == "Africa" & .$region != "Northern Africa" ~ "Sub-Saharan Africa",
.$region %in% c("Melanesia", "Micronesia", "Polynesia") ~ "Pacific Islands"))
# define a data frame with group average income and average infant survival rate
surv_income <- gapminder %>%
filter(year %in% present_year & !is.na(gdp) & !is.na(infant_mortality) & !is.na(group)) %>%
group_by(group) %>%
summarize(income = sum(gdp)/sum(population)/365,
infant_survival_rate = 1 - sum(infant_mortality/1000*population)/sum(population))
surv_income %>% arrange(income)
# plot infant survival versus income, with transformed axes
surv_income %>% ggplot(aes(income, infant_survival_rate, label = group, color = group)) +
scale_x_continuous(trans = "log2", limit = c(0.25, 150)) +
scale_y_continuous(trans = "logit", limit = c(0.875, .9981),
breaks = c(.85, .90, .95, .99, .995, .998)) +
geom_label(size = 3, show.legend = FALSE)
dat %>%
mutate(location = ifelse(year == 2010, 1, 2),
location = ifelse(year == 2015 & country %in% c("United Kingdom", "Portugal"),
location + 0.22, location),
hjust = ifelse(year == 2010, 1, 0)) %>%
mutate(year = as.factor(year)) %>%
ggplot(aes(year, life_expectancy, group = country)) +
geom_line(aes(color = country), show.legend = FALSE) +
geom_text(aes(x = location, label = country, hjust = hjust), show.legend = FALSE) +
xlab("") +
ylab("Life Expectancy")
library(tidyverse)
library(dslabs)
data(gapminder)
west <- c("Western Europe", "Northern Europe", "Southern Europe", "Northern America", "Australia and New Zealand")
dat <- gapminder %>%
filter(year %in% c(2010, 2015) & region %in% west & !is.na(life_expectancy) & population > 10^7)
dat %>%
mutate(location = ifelse(year == 2010, 1, 2),
location = ifelse(year == 2015 & country %in% c("United Kingdom", "Portugal"),
location + 0.22, location),
hjust = ifelse(year == 2010, 1, 0)) %>%
mutate(year = as.factor(year)) %>%
ggplot(aes(year, life_expectancy, group = country)) +
geom_line(aes(color = country), show.legend = FALSE) +
geom_text(aes(x = location, label = country, hjust = hjust), show.legend = FALSE) +
xlab("") +
ylab("Life Expectancy")
library(ggrepel)
dat %>%
mutate(year = paste0("life_expectancy_", year)) %>%
select(country, year, life_expectancy) %>% spread(year, life_expectancy) %>%
mutate(average = (life_expectancy_2015 + life_expectancy_2010)/2,
difference = life_expectancy_2015 - life_expectancy_2010) %>%
ggplot(aes(average, difference, label = country)) +
geom_point() +
geom_text_repel() +
geom_abline(lty = 2) +
xlab("Average of 2010 and 2015") +
ylab("Difference between 2015 and 2010"
library(ggrepel)
library(ggrepel)
dat %>%
mutate(year = paste0("life_expectancy_", year)) %>%
select(country, year, life_expectancy) %>% spread(year, life_expectancy) %>%
mutate(average = (life_expectancy_2015 + life_expectancy_2010)/2,
difference = life_expectancy_2015 - life_expectancy_2010) %>%
ggplot(aes(average, difference, label = country)) +
geom_point() +
geom_text_repel() +
geom_abline(lty = 2) +
xlab("Average of 2010 and 2015") +
ylab("Difference between 2015 and 2010")
#three variable encoding using vaccine case study
# Tile plot of measles rate by year and state
# import data and inspect
library(tidyverse)
library(dslabs)
data(us_contagious_diseases)
str(us_contagious_diseases)
# assign dat to the per 10,000 rate of measles, removing Alaska and Hawaii and adjusting for weeks reporting
the_disease <- "Measles"
dat <- us_contagious_diseases %>%
filter(!state %in% c("Hawaii", "Alaska") & disease == the_disease) %>%
mutate(rate = count / population * 10000 * 52/weeks_reporting) %>%
mutate(state = reorder(state, rate))
# plot disease rates per year in California
dat %>% filter(state == "California" & !is.na(rate)) %>%
ggplot(aes(year, rate)) +
geom_line() +
ylab("Cases per 10,000") +
geom_vline(xintercept=1963, col = "blue")
# tile plot of disease rate by state and year
dat %>% ggplot(aes(year, state, fill=rate)) +
geom_tile(color = "grey50") +
scale_x_continuous(expand = c(0,0)) +
scale_fill_gradientn(colors = RColorBrewer::brewer.pal(9, "Reds"), trans = "sqrt") +
geom_vline(xintercept = 1963, col = "blue") +
theme_minimal() + theme(panel.grid = element_blank()) +
ggtitle(the_disease) +
ylab("") +
xlab("")
# compute US average measles rate by year
avg <- us_contagious_diseases %>%
filter(disease == the_disease) %>% group_by(year) %>%
summarize(us_rate = sum(count, na.rm = TRUE)/sum(population, na.rm = TRUE)*10000)
# make line plot of measles rate by year by state
dat %>%
filter(!is.na(rate)) %>%
ggplot() +
geom_line(aes(year, rate, group = state), color = "grey50",
show.legend = FALSE, alpha = 0.2, size = 1) +
geom_line(mapping = aes(year, us_rate), data = avg, size = 1, col = "black") +
scale_y_continuous(trans = "sqrt", breaks = c(5, 25, 125, 300)) +
ggtitle("Cases per 10,000 by state") +
xlab("") +
ylab("") +
geom_text(data = data.frame(x = 1955, y = 50),
mapping = aes(x, y, label = "US average"), color = "black") +
geom_vline(xintercept = 1963, col = "blue")
# make line plot of measles rate by year by state
dat %>%
filter(!is.na(rate)) %>%
ggplot() +
geom_line(aes(year, rate, group = state), color = "grey50",
show.legend = FALSE, alpha = 0.2, size = 1) +
geom_line(mapping = aes(year, us_rate), data = avg, linewidth = 1, col = "black") +
scale_y_continuous(trans = "sqrt", breaks = c(5, 25, 125, 300)) +
ggtitle("Cases per 10,000 by state") +
xlab("") +
ylab("") +
geom_text(data = data.frame(x = 1955, y = 50),
mapping = aes(x, y, label = "US average"), color = "black") +
geom_vline(xintercept = 1963, col = "blue") # development of measle vaccine
titanic <- titanic_train %>%
select(Survived, Pclass, Sex, Age, SibSp, Parch, Fare) %>%
mutate(Survived = factor(Survived),
Pclass = factor(Pclass),
Sex = factor(Sex))
options(digits = 3)    # report 3 significant digits
library(tidyverse)
library(titanic)
install.packages("titanic")
options(digits = 3)    # report 3 significant digits
library(tidyverse)
library(titanic)
titanic <- titanic_train %>%
select(Survived, Pclass, Sex, Age, SibSp, Parch, Fare) %>%
mutate(Survived = factor(Survived),
Pclass = factor(Pclass),
Sex = factor(Sex))
head(titanic)
_titanic_train
?titanic_train
titanic %>%
ggplot(aes(Age, y = ..count.., fill = Survived)) +
geom_density(alpha = 0.2)
library(tidyverse)
library(dslabs)
data(stars)
options(digits = 3)   # report 3 significant digits
head(stars)
mean(stars$magnitude)
sd(stars$magnitude)
stars %>%
ggplot(aes(magnitude)) +
geom_density()
stars %>%
ggplot(aes(x=log10(temp), magnitude)) +
scale_x_reverse() +
scale_y_reverse() +
geom_point()
stars %>%
ggplot(aes(log10(temp), magnitude)) +
geom_point() +
geom_text(aes(label = star)) +
scale_x_reverse() +
scale_y_reverse()
stars %>%
ggplot(aes((temp, magnitude)) +
stars %>%
ggplot(aes(temp, magnitude) +
geom_point() +
geom_text(aes(label = star)) +
scale_x_reverse() +
scale_y_reverse()
stars %>%
library(tidyverse)
library(dslabs)
data(temp_carbon)
data(greenhouse_gases)
data(historic_co2)
temp_carbon %>%
filter(!is.na(carbon_emissions)) %>%
.$year %>%
min()
temp_carbon %>%
filter(!is.na(carbon_emissions)) %>%
.$year %>%
max()
carbon1 <- temp_carbon %>%
filter(year == 1751) %>%
.$carbon_emissions
carbon2 <- temp_carbon %>%
filter(year == 2014) %>%
.$carbon_emissions
carbon2/carbon1
temp_carbon %>%
filter(!is.na(temp_anomaly)) %>%
.$year %>%
min()
temp_carbon %>%
filter(!is.na(temp_anomaly)) %>%
.$year %>%
max()
temp1 <- temp_carbon %>%
filter(year == "1880") %>%
.$temp_anomaly
temp2 <- temp_carbon %>%
filter(year == "2018") %>%
.$temp_anomaly
temp2 - temp1
setwd("~/Library/CloudStorage/OneDrive-Personal/Desktop/pyt-iba")
file.rename(Untitled2,Distributions_in_R)
file.rename(Untitled*,Distributions_in_R)
getwd()
new_path <- file.path("/Users/salmanmalik/Library/CloudStorage/OneDrive-Personal/Desktop/Visualisation_in_R")
setwd(new_path)
getwd()