library(tidyverse)
library(lubridate)Lab 7 Data Tidying
Loading Libraries
Loading data from github repository
download.file("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
destfile = "data/time_series_covid19_confirmed_global.csv")
#|eval: falsetime_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")Data Tidying - Pivoting
time_series_confirmed_long <- time_series_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed") Dates and time
time_series_confirmed_long$Date <- mdy(time_series_confirmed_long$Date)Making Graphs from the time series data
time_series_confirmed_long|>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Confirmed)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Confirmed Cases")
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily <-time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed )))time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth(method = "gam", se = FALSE) +
ggtitle("COVID-19 Confirmed Cases")
Animated Graphs with gganimate
library(gganimate)
library(gifski)
theme_set(theme_bw())daily_counts <- time_series_confirmed_long_daily |>
filter (Country_Region == "US")
p <- ggplot(daily_counts, aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("Confirmed COVID-19 Cases") +
# gganimate lines
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
anim_save("daily_counts_US.gif", p)Animation of confirmed deaths
# This download may take about 5 minutes. You only need to do this once so set `#| eval: false` in your qmd file
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "data/time_series_covid19_deaths_global.csv")time_series_deaths_confirmed <- read_csv("data/time_series_covid19_deaths_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")
time_series_deaths_long <- time_series_deaths_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
time_series_deaths_long$Date <- mdy(time_series_deaths_long$Date)p <- time_series_deaths_long |>
filter (Country_Region %in% c("US","Canada", "Mexico","Brazil","Egypt","Ecuador","India", "Netherlands", "Germany", "China" )) |>
ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) +
geom_point(aes(size=Confirmed)) +
transition_time(Date) +
labs(title = "Cumulative Deaths: {frame_time}") +
ylab("Deaths") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
Exercises
Exercise 1 examples and exercises
library(tidyverse)table1# A tibble: 6 × 4
country year cases population
<chr> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071
2 Afghanistan 2000 2666 20595360
3 Brazil 1999 37737 172006362
4 Brazil 2000 80488 174504898
5 China 1999 212258 1272915272
6 China 2000 213766 1280428583#> # A tibble: 6 × 4
#> country year cases population
#> <chr> <dbl> <dbl> <dbl>
#> 1 Afghanistan 1999 745 19987071
#> 2 Afghanistan 2000 2666 20595360
#> 3 Brazil 1999 37737 172006362
#> 4 Brazil 2000 80488 174504898
#> 5 China 1999 212258 1272915272
#> 6 China 2000 213766 1280428583
table2# A tibble: 12 × 4
country year type count
<chr> <dbl> <chr> <dbl>
1 Afghanistan 1999 cases 745
2 Afghanistan 1999 population 19987071
3 Afghanistan 2000 cases 2666
4 Afghanistan 2000 population 20595360
5 Brazil 1999 cases 37737
6 Brazil 1999 population 172006362
7 Brazil 2000 cases 80488
8 Brazil 2000 population 174504898
9 China 1999 cases 212258
10 China 1999 population 1272915272
11 China 2000 cases 213766
12 China 2000 population 1280428583#> # A tibble: 12 × 4
#> country year type count
#> <chr> <dbl> <chr> <dbl>
#> 1 Afghanistan 1999 cases 745
#> 2 Afghanistan 1999 population 19987071
#> 3 Afghanistan 2000 cases 2666
#> 4 Afghanistan 2000 population 20595360
#> 5 Brazil 1999 cases 37737
#> 6 Brazil 1999 population 172006362
#> # ℹ 6 more rows
table3# A tibble: 6 × 3
country year rate
<chr> <dbl> <chr>
1 Afghanistan 1999 745/19987071
2 Afghanistan 2000 2666/20595360
3 Brazil 1999 37737/172006362
4 Brazil 2000 80488/174504898
5 China 1999 212258/1272915272
6 China 2000 213766/1280428583#> # A tibble: 6 × 3
#> country year rate
#> <chr> <dbl> <chr>
#> 1 Afghanistan 1999 745/19987071
#> 2 Afghanistan 2000 2666/20595360
#> 3 Brazil 1999 37737/172006362
#> 4 Brazil 2000 80488/174504898
#> 5 China 1999 212258/1272915272
#> 6 China 2000 213766/1280428583# Compute rate per 10,000
table1 |>
mutate(rate = cases / population * 10000)# A tibble: 6 × 5
country year cases population rate
<chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071 0.373
2 Afghanistan 2000 2666 20595360 1.29
3 Brazil 1999 37737 172006362 2.19
4 Brazil 2000 80488 174504898 4.61
5 China 1999 212258 1272915272 1.67
6 China 2000 213766 1280428583 1.67 #> # A tibble: 6 × 5
#> country year cases population rate
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Afghanistan 1999 745 19987071 0.373
#> 2 Afghanistan 2000 2666 20595360 1.29
#> 3 Brazil 1999 37737 172006362 2.19
#> 4 Brazil 2000 80488 174504898 4.61
#> 5 China 1999 212258 1272915272 1.67
#> 6 China 2000 213766 1280428583 1.67
# Compute total cases per year
table1 |>
group_by(year) |>
summarize(total_cases = sum(cases))# A tibble: 2 × 2
year total_cases
<dbl> <dbl>
1 1999 250740
2 2000 296920#> # A tibble: 2 × 2
#> year total_cases
#> <dbl> <dbl>
#> 1 1999 250740
#> 2 2000 296920
# Visualize changes over time
ggplot(table1, aes(x = year, y = cases)) +
geom_line(aes(group = country), color = "grey50") +
geom_point(aes(color = country, shape = country)) +
scale_x_continuous(breaks = c(1999, 2000)) # x-axis breaks at 1999 and 2000
5.2.1 exercise
1.
Table 1 shows the country and compares multiple factors relating to cases. The columns are the year, cases, population and rate. Table 2 just compares the year to total number of cases. The graphs gives more of a visual of how number of cases has increased in each country and they can be compared.
2.
library(dplyr)
library(tidyr)
tb_data <- table2 %>%
filter(type %in% c("cases", "population")) %>%
pivot_wider(names_from = type, values_from = count) %>%
mutate(rate = (cases / population) * 10000)5.3 Lengthening data
billboard# A tibble: 317 × 79
artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA
2 2Ge+her The … 2000-09-02 91 87 92 NA NA NA NA NA
3 3 Doors D… Kryp… 2000-04-08 81 70 68 67 66 57 54 53
4 3 Doors D… Loser 2000-10-21 76 76 72 69 67 65 55 59
5 504 Boyz Wobb… 2000-04-15 57 34 25 17 17 31 36 49
6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2
7 A*Teens Danc… 2000-07-08 97 97 96 95 100 NA NA NA
8 Aaliyah I Do… 2000-01-29 84 62 51 41 38 35 35 38
9 Aaliyah Try … 2000-03-18 59 53 38 28 21 18 16 14
10 Adams, Yo… Open… 2000-08-26 76 76 74 69 68 67 61 58
# ℹ 307 more rows
# ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,
# wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>,
# wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,
# wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>,
# wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>,
# wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, …#> # A tibble: 317 × 79
#> artist track date.entered wk1 wk2 wk3 wk4 wk5
#> <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 Pac Baby Don't Cry (Ke… 2000-02-26 87 82 72 77 87
#> 2 2Ge+her The Hardest Part O… 2000-09-02 91 87 92 NA NA
#> 3 3 Doors Down Kryptonite 2000-04-08 81 70 68 67 66
#> 4 3 Doors Down Loser 2000-10-21 76 76 72 69 67
#> 5 504 Boyz Wobble Wobble 2000-04-15 57 34 25 17 17
#> 6 98^0 Give Me Just One N… 2000-08-19 51 39 34 26 26
#> # ℹ 311 more rows
#> # ℹ 71 more variables: wk6 <dbl>, wk7 <dbl>, wk8 <dbl>, wk9 <dbl>, …billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank"
)# A tibble: 24,092 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk8 NA
9 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk9 NA
10 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk10 NA
# ℹ 24,082 more rows#> # A tibble: 24,092 × 5
#> artist track date.entered week rank
#> <chr> <chr> <date> <chr> <dbl>
#> 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
#> 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
#> 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
#> 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
#> 5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
#> 6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
#> 7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
#> 8 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk8 NA
#> 9 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk9 NA
#> 10 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk10 NA
#> # ℹ 24,082 more rowsbillboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
)# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 wk2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 wk3 92
# ℹ 5,297 more rows#> # A tibble: 5,307 × 5
#> artist track date.entered week rank
#> <chr> <chr> <date> <chr> <dbl>
#> 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
#> 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
#> 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
#> 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
#> 5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
#> 6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
#> # ℹ 5,301 more rowsbillboard_longer <- billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
) |>
mutate(
week = parse_number(week)
)
billboard_longer# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <dbl> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92
# ℹ 5,297 more rows#> # A tibble: 5,307 × 5
#> artist track date.entered week rank
#> <chr> <chr> <date> <dbl> <dbl>
#> 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
#> 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
#> 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
#> 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
#> 5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
#> 6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
#> # ℹ 5,301 more rowsbillboard_longer |>
ggplot(aes(x = week, y = rank, group = track)) +
geom_line(alpha = 0.25) +
scale_y_reverse()
5.3.2 code chunks
df <- tribble(
~id, ~bp1, ~bp2,
"A", 100, 120,
"B", 140, 115,
"C", 120, 125
)df |>
pivot_longer(
cols = bp1:bp2,
names_to = "measurement",
values_to = "value"
)# A tibble: 6 × 3
id measurement value
<chr> <chr> <dbl>
1 A bp1 100
2 A bp2 120
3 B bp1 140
4 B bp2 115
5 C bp1 120
6 C bp2 125#> # A tibble: 6 × 3
#> id measurement value
#> <chr> <chr> <dbl>
#> 1 A bp1 100
#> 2 A bp2 120
#> 3 B bp1 140
#> 4 B bp2 115
#> 5 C bp1 120
#> 6 C bp2 1255.3.3 code chunks
who2# A tibble: 7,240 × 58
country year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554 sp_m_5564
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1980 NA NA NA NA NA NA
2 Afghanistan 1981 NA NA NA NA NA NA
3 Afghanistan 1982 NA NA NA NA NA NA
4 Afghanistan 1983 NA NA NA NA NA NA
5 Afghanistan 1984 NA NA NA NA NA NA
6 Afghanistan 1985 NA NA NA NA NA NA
7 Afghanistan 1986 NA NA NA NA NA NA
8 Afghanistan 1987 NA NA NA NA NA NA
9 Afghanistan 1988 NA NA NA NA NA NA
10 Afghanistan 1989 NA NA NA NA NA NA
# ℹ 7,230 more rows
# ℹ 50 more variables: sp_m_65 <dbl>, sp_f_014 <dbl>, sp_f_1524 <dbl>,
# sp_f_2534 <dbl>, sp_f_3544 <dbl>, sp_f_4554 <dbl>, sp_f_5564 <dbl>,
# sp_f_65 <dbl>, sn_m_014 <dbl>, sn_m_1524 <dbl>, sn_m_2534 <dbl>,
# sn_m_3544 <dbl>, sn_m_4554 <dbl>, sn_m_5564 <dbl>, sn_m_65 <dbl>,
# sn_f_014 <dbl>, sn_f_1524 <dbl>, sn_f_2534 <dbl>, sn_f_3544 <dbl>,
# sn_f_4554 <dbl>, sn_f_5564 <dbl>, sn_f_65 <dbl>, ep_m_014 <dbl>, …#> # A tibble: 7,240 × 58
#> country year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Afghanistan 1980 NA NA NA NA NA
#> 2 Afghanistan 1981 NA NA NA NA NA
#> 3 Afghanistan 1982 NA NA NA NA NA
#> 4 Afghanistan 1983 NA NA NA NA NA
#> 5 Afghanistan 1984 NA NA NA NA NA
#> 6 Afghanistan 1985 NA NA NA NA NA
#> # ℹ 7,234 more rows
#> # ℹ 51 more variables: sp_m_5564 <dbl>, sp_m_65 <dbl>, sp_f_014 <dbl>, …who2 |>
pivot_longer(
cols = !(country:year),
names_to = c("diagnosis", "gender", "age"),
names_sep = "_",
values_to = "count"
)# A tibble: 405,440 × 6
country year diagnosis gender age count
<chr> <dbl> <chr> <chr> <chr> <dbl>
1 Afghanistan 1980 sp m 014 NA
2 Afghanistan 1980 sp m 1524 NA
3 Afghanistan 1980 sp m 2534 NA
4 Afghanistan 1980 sp m 3544 NA
5 Afghanistan 1980 sp m 4554 NA
6 Afghanistan 1980 sp m 5564 NA
7 Afghanistan 1980 sp m 65 NA
8 Afghanistan 1980 sp f 014 NA
9 Afghanistan 1980 sp f 1524 NA
10 Afghanistan 1980 sp f 2534 NA
# ℹ 405,430 more rows#> # A tibble: 405,440 × 6
#> country year diagnosis gender age count
#> <chr> <dbl> <chr> <chr> <chr> <dbl>
#> 1 Afghanistan 1980 sp m 014 NA
#> 2 Afghanistan 1980 sp m 1524 NA
#> 3 Afghanistan 1980 sp m 2534 NA
#> 4 Afghanistan 1980 sp m 3544 NA
#> 5 Afghanistan 1980 sp m 4554 NA
#> 6 Afghanistan 1980 sp m 5564 NA
#> # ℹ 405,434 more rows5.3.4 code chunks
household# A tibble: 5 × 5
family dob_child1 dob_child2 name_child1 name_child2
<int> <date> <date> <chr> <chr>
1 1 1998-11-26 2000-01-29 Susan Jose
2 2 1996-06-22 NA Mark <NA>
3 3 2002-07-11 2004-04-05 Sam Seth
4 4 2004-10-10 2009-08-27 Craig Khai
5 5 2000-12-05 2005-02-28 Parker Gracie #> # A tibble: 5 × 5
#> family dob_child1 dob_child2 name_child1 name_child2
#> <int> <date> <date> <chr> <chr>
#> 1 1 1998-11-26 2000-01-29 Susan Jose
#> 2 2 1996-06-22 NA Mark <NA>
#> 3 3 2002-07-11 2004-04-05 Sam Seth
#> 4 4 2004-10-10 2009-08-27 Craig Khai
#> 5 5 2000-12-05 2005-02-28 Parker Graciehousehold |>
pivot_longer(
cols = !family,
names_to = c(".value", "child"),
names_sep = "_",
values_drop_na = TRUE
)# A tibble: 9 × 4
family child dob name
<int> <chr> <date> <chr>
1 1 child1 1998-11-26 Susan
2 1 child2 2000-01-29 Jose
3 2 child1 1996-06-22 Mark
4 3 child1 2002-07-11 Sam
5 3 child2 2004-04-05 Seth
6 4 child1 2004-10-10 Craig
7 4 child2 2009-08-27 Khai
8 5 child1 2000-12-05 Parker
9 5 child2 2005-02-28 Gracie#> # A tibble: 9 × 4
#> family child dob name
#> <int> <chr> <date> <chr>
#> 1 1 child1 1998-11-26 Susan
#> 2 1 child2 2000-01-29 Jose
#> 3 2 child1 1996-06-22 Mark
#> 4 3 child1 2002-07-11 Sam
#> 5 3 child2 2004-04-05 Seth
#> 6 4 child1 2004-10-10 Craig
#> # ℹ 3 more rows5.4 Widening data
cms_patient_experience# A tibble: 500 × 5
org_pac_id org_nm measure_cd measure_title prf_rate
<chr> <chr> <chr> <chr> <dbl>
1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 63
2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 87
3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 86
4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 57
5 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 85
6 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 24
7 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 59
8 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 85
9 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 83
10 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 63
# ℹ 490 more rows#> # A tibble: 500 × 5
#> org_pac_id org_nm measure_cd measure_title prf_rate
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_1 CAHPS for MIPS… 63
#> 2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_2 CAHPS for MIPS… 87
#> 3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_3 CAHPS for MIPS… 86
#> 4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_5 CAHPS for MIPS… 57
#> 5 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_8 CAHPS for MIPS… 85
#> 6 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_12 CAHPS for MIPS… 24
#> # ℹ 494 more rowscms_patient_experience |>
distinct(measure_cd, measure_title)# A tibble: 6 × 2
measure_cd measure_title
<chr> <chr>
1 CAHPS_GRP_1 CAHPS for MIPS SSM: Getting Timely Care, Appointments, and Infor…
2 CAHPS_GRP_2 CAHPS for MIPS SSM: How Well Providers Communicate
3 CAHPS_GRP_3 CAHPS for MIPS SSM: Patient's Rating of Provider
4 CAHPS_GRP_5 CAHPS for MIPS SSM: Health Promotion and Education
5 CAHPS_GRP_8 CAHPS for MIPS SSM: Courteous and Helpful Office Staff
6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources #> # A tibble: 6 × 2
#> measure_cd measure_title
#> <chr> <chr>
#> 1 CAHPS_GRP_1 CAHPS for MIPS SSM: Getting Timely Care, Appointments, and In…
#> 2 CAHPS_GRP_2 CAHPS for MIPS SSM: How Well Providers Communicate
#> 3 CAHPS_GRP_3 CAHPS for MIPS SSM: Patient's Rating of Provider
#> 4 CAHPS_GRP_5 CAHPS for MIPS SSM: Health Promotion and Education
#> 5 CAHPS_GRP_8 CAHPS for MIPS SSM: Courteous and Helpful Office Staff
#> 6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resourcescms_patient_experience |>
pivot_wider(
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 500 × 9
org_pac_id org_nm measure_title CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 0446157747 USC CARE MEDICA… CAHPS for MI… 63 NA NA
2 0446157747 USC CARE MEDICA… CAHPS for MI… NA 87 NA
3 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA 86
4 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
5 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
6 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
7 0446162697 ASSOCIATION OF … CAHPS for MI… 59 NA NA
8 0446162697 ASSOCIATION OF … CAHPS for MI… NA 85 NA
9 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA 83
10 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA NA
# ℹ 490 more rows
# ℹ 3 more variables: CAHPS_GRP_5 <dbl>, CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>#> # A tibble: 500 × 9
#> org_pac_id org_nm measure_title CAHPS_GRP_1 CAHPS_GRP_2
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS… 63 NA
#> 2 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS… NA 87
#> 3 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS… NA NA
#> 4 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS… NA NA
#> 5 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS… NA NA
#> 6 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS… NA NA
#> # ℹ 494 more rows
#> # ℹ 4 more variables: CAHPS_GRP_3 <dbl>, CAHPS_GRP_5 <dbl>, …cms_patient_experience |>
pivot_wider(
id_cols = starts_with("org"),
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 95 × 8
org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0446157747 USC C… 63 87 86 57 85
2 0446162697 ASSOC… 59 85 83 63 88
3 0547164295 BEAVE… 49 NA 75 44 73
4 0749333730 CAPE … 67 84 85 65 82
5 0840104360 ALLIA… 66 87 87 64 87
6 0840109864 REX H… 73 87 84 67 91
7 0840513552 SCL H… 58 83 76 58 78
8 0941545784 GRITM… 46 86 81 54 NA
9 1052612785 COMMU… 65 84 80 58 87
10 1254237779 OUR L… 61 NA NA 65 NA
# ℹ 85 more rows
# ℹ 1 more variable: CAHPS_GRP_12 <dbl>#> # A tibble: 95 × 8
#> org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 0446157747 USC CARE MEDICA… 63 87 86 57
#> 2 0446162697 ASSOCIATION OF … 59 85 83 63
#> 3 0547164295 BEAVER MEDICAL … 49 NA 75 44
#> 4 0749333730 CAPE PHYSICIANS… 67 84 85 65
#> 5 0840104360 ALLIANCE PHYSIC… 66 87 87 64
#> 6 0840109864 REX HOSPITAL INC 73 87 84 67
#> # ℹ 89 more rows
#> # ℹ 2 more variables: CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>5.4.1
df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"B", "bp1", 140,
"B", "bp2", 115,
"A", "bp2", 120,
"A", "bp3", 105
)df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 4
id bp1 bp2 bp3
<chr> <dbl> <dbl> <dbl>
1 A 100 120 105
2 B 140 115 NA#> # A tibble: 2 × 4
#> id bp1 bp2 bp3
#> <chr> <dbl> <dbl> <dbl>
#> 1 A 100 120 105
#> 2 B 140 115 NAdf |>
distinct(measurement) |>
pull()[1] "bp1" "bp2" "bp3"#> [1] "bp1" "bp2" "bp3"df |>
select(-measurement, -value) |>
distinct()# A tibble: 2 × 1
id
<chr>
1 A
2 B #> # A tibble: 2 × 1
#> id
#> <chr>
#> 1 A
#> 2 Bdf |>
select(-measurement, -value) |>
distinct() |>
mutate(x = NA, y = NA, z = NA)# A tibble: 2 × 4
id x y z
<chr> <lgl> <lgl> <lgl>
1 A NA NA NA
2 B NA NA NA #> # A tibble: 2 × 4
#> id x y z
#> <chr> <lgl> <lgl> <lgl>
#> 1 A NA NA NA
#> 2 B NA NA NAdf <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"A", "bp1", 102,
"A", "bp2", 120,
"B", "bp1", 140,
"B", "bp2", 115
)df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 3
id bp1 bp2
<chr> <list> <list>
1 A <dbl [2]> <dbl [1]>
2 B <dbl [1]> <dbl [1]>#> Warning: Values from `value` are not uniquely identified; output will contain
#> list-cols.
#> • Use `values_fn = list` to suppress this warning.
#> • Use `values_fn = {summary_fun}` to summarise duplicates.
#> • Use the following dplyr code to identify duplicates.
#> {data} |>
#> dplyr::summarise(n = dplyr::n(), .by = c(id, measurement)) |>
#> dplyr::filter(n > 1L)
#> # A tibble: 2 × 3
#> id bp1 bp2
#> <chr> <list> <list>
#> 1 A <dbl [2]> <dbl [1]>
#> 2 B <dbl [1]> <dbl [1]>df |>
group_by(id, measurement) |>
summarize(n = n(), .groups = "drop") |>
filter(n > 1)# A tibble: 1 × 3
id measurement n
<chr> <chr> <int>
1 A bp1 2#> # A tibble: 1 × 3
#> id measurement n
#> <chr> <chr> <int>
#> 1 A bp1 2Exercise 2
# Load required libraries
library(dplyr)
library(tidyr)
library(ggplot2)
# Load the time series data for confirmed cases
url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
covid_data <- read.csv(url)
# Select countries of interest
countries <- c("US", "India", "Brazil", "United Kingdom", "South Africa")
# Prepare the data
daily_cases <- covid_data %>%
filter(Country.Region %in% countries) %>%
select(-Province.State, -Lat, -Long) %>%
group_by(Country.Region) %>%
summarise(across(where(is.numeric), sum)) %>%
pivot_longer(-Country.Region, names_to = "date", values_to = "cumulative") %>%
mutate(date = as.Date(gsub("X", "", date), format = "%m.%d.%y")) %>%
group_by(Country.Region) %>%
arrange(date) %>%
mutate(daily = cumulative - lag(cumulative, default = 0))
# Faceted plot
ggplot(daily_cases, aes(x = date, y = daily)) +
geom_line(color = "steelblue") +
facet_wrap(~ Country.Region, scales = "free_y") +
labs(title = "Daily Confirmed COVID-19 Cases by Country",
x = "Date", y = "Daily Cases") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5))
Exercise 3
# Load required libraries
library(dplyr)
library(tidyr)
library(ggplot2)
# Load the time series data for confirmed cases
url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
covid_data <- read.csv(url)
# Select countries of interest
countries <- c("US", "India", "Brazil", "United Kingdom", "South Africa")
# Prepare the data
daily_cases <- covid_data %>%
filter(Country.Region %in% countries) %>%
select(-Province.State, -Lat, -Long) %>%
group_by(Country.Region) %>%
summarise(across(where(is.numeric), sum)) %>%
pivot_longer(-Country.Region, names_to = "date", values_to = "cumulative") %>%
mutate(date = as.Date(gsub("X", "", date), format = "%m.%d.%y")) %>%
group_by(Country.Region) %>%
arrange(date) %>%
mutate(daily = cumulative - lag(cumulative, default = 0))
# Plot the data
ggplot(daily_cases, aes(x = date, y = daily, color = Country.Region)) +
geom_line(size = 1) +
labs(title = "Daily Confirmed COVID-19 Cases",
x = "Date", y = "Daily Cases",
color = "Country") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5))
Exercise 4
# Load required libraries
library(dplyr)
library(tidyr)
library(ggplot2)
# Load the time series data for deaths
url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
deaths_data <- read.csv(url)
# Select countries of interest
countries <- c("US", "Canada", "Mexico")
# Prepare the data
cumulative_deaths <- deaths_data %>%
filter(Country.Region %in% countries) %>%
select(-Province.State, -Lat, -Long) %>%
group_by(Country.Region) %>%
summarise(across(where(is.numeric), sum)) %>%
pivot_longer(-Country.Region, names_to = "date", values_to = "deaths") %>%
mutate(date = as.Date(gsub("X", "", date), format = "%m.%d.%y"))
# Plot the data
ggplot(cumulative_deaths, aes(x = date, y = deaths, color = Country.Region)) +
geom_line(size = 1) +
labs(title = "Cumulative COVID-19 Deaths",
x = "Date", y = "Total Deaths",
color = "Country") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5))
Exercise 5
# Load required libraries
library(dplyr)
library(tidyr)
library(ggplot2)
# Load the time series data for deaths
url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
deaths_data <- read.csv(url)
# Select countries of interest
countries <- c("Australia", "Costa Rica", "Egypt")
# Prepare the data
daily_deaths <- deaths_data %>%
filter(Country.Region %in% countries) %>%
select(-Province.State, -Lat, -Long) %>%
group_by(Country.Region) %>%
summarise(across(where(is.numeric), sum)) %>%
pivot_longer(-Country.Region, names_to = "date", values_to = "cumulative") %>%
mutate(date = as.Date(gsub("X", "", date), format = "%m.%d.%y")) %>%
group_by(Country.Region) %>%
arrange(date) %>%
mutate(daily = cumulative - lag(cumulative, default = 0))
# Plot the data
ggplot(daily_deaths, aes(x = date, y = daily, color = Country.Region)) +
geom_line(size = 1) +
labs(title = "Daily COVID-19 Deaths",
x = "Date", y = "Daily Deaths",
color = "Country") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5))
Exercise 6
library(ggplot2)
library(gganimate)
library(dplyr)
# Example data frame
data <- data.frame(
country = rep(c("United States", "China", "Germany", "Brazil", "Nigeria"), each = 10),
year = rep(2010:2019, times = 5),
gdp_per_capita = c(
seq(48000, 65000, length.out = 10), # US
seq(4000, 12000, length.out = 10), # China
seq(35000, 45000, length.out = 10), # Germany
seq(9000, 12000, length.out = 10), # Brazil
seq(2000, 3000, length.out = 10) # Nigeria
),
life_expectancy = c(
seq(78, 80, length.out = 10), # US
seq(74, 77, length.out = 10), # China
seq(80, 82, length.out = 10), # Germany
seq(72, 75, length.out = 10), # Brazil
seq(54, 60, length.out = 10) # Nigeria
)
)
# Animated scatter plot
ggplot(data, aes(x = gdp_per_capita, y = life_expectancy, color = country)) +
geom_point(size = 4, alpha = 0.8) +
labs(title = 'Year: {frame_time}', x = 'GDP per Capita', y = 'Life Expectancy') +
theme_minimal() +
transition_time(year) +
ease_aes('linear')