–metadata title=“R for Data Science”
R for Data Science(2e)
5 Data tidying
download the package and data
if(!require(tidyverse))
{install.packages("tidyverse")}## Loading required package: tidyverse## Warning: package 'tidyverse' was built under R version 4.5.2## Warning: package 'forcats' was built under R version 4.5.2## Warning: package 'lubridate' was built under R version 4.5.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0## ── 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 errorslibrary(tidyverse)5.1 Introduction
There are three interrelated rules that make a dataset tidy:
- Each variable is a column; each column is a variable.
- Each observation is a row; each row is an observation.
- Each value is a cell; each cell is a single value.
Table1
table1 <- tibble(
country = c("Afghanistan", "Afghanistan", "Brazil", "Brazil", "China", "China"),
year = c(1999, 2000, 1999, 2000, 1999, 2000),
cases = c(745, 2666, 37737, 80488, 212258, 213766),
population = c(19987071, 20595360, 172006362, 174504898, 1272915272, 1280428583)
)
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# 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# Compute total cases per year
table1 |>
group_by(year) |>
summarise(total_cases = sum(cases),
.groups = "drop")## # A tibble: 2 × 2
## year total_cases
## <dbl> <dbl>
## 1 1999 250740
## 2 2000 296920# Visualize changes over time
# a good example for having both points and lines
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
5.3 Lengthening data
glimpse(billboard)## Rows: 317
## Columns: 79
## $ artist <chr> "2 Pac", "2Ge+her", "3 Doors Down", "3 Doors Down", "504 …
## $ track <chr> "Baby Don't Cry (Keep...", "The Hardest Part Of ...", "Kr…
## $ date.entered <date> 2000-02-26, 2000-09-02, 2000-04-08, 2000-10-21, 2000-04-…
## $ wk1 <dbl> 87, 91, 81, 76, 57, 51, 97, 84, 59, 76, 84, 57, 50, 71, 7…
## $ wk2 <dbl> 82, 87, 70, 76, 34, 39, 97, 62, 53, 76, 84, 47, 39, 51, 6…
## $ wk3 <dbl> 72, 92, 68, 72, 25, 34, 96, 51, 38, 74, 75, 45, 30, 28, 5…
## $ wk4 <dbl> 77, NA, 67, 69, 17, 26, 95, 41, 28, 69, 73, 29, 28, 18, 4…
## $ wk5 <dbl> 87, NA, 66, 67, 17, 26, 100, 38, 21, 68, 73, 23, 21, 13, …
## $ wk6 <dbl> 94, NA, 57, 65, 31, 19, NA, 35, 18, 67, 69, 18, 19, 13, 3…
## $ wk7 <dbl> 99, NA, 54, 55, 36, 2, NA, 35, 16, 61, 68, 11, 20, 11, 34…
## $ wk8 <dbl> NA, NA, 53, 59, 49, 2, NA, 38, 14, 58, 65, 9, 17, 1, 29, …
## $ wk9 <dbl> NA, NA, 51, 62, 53, 3, NA, 38, 12, 57, 73, 9, 17, 1, 27, …
## $ wk10 <dbl> NA, NA, 51, 61, 57, 6, NA, 36, 10, 59, 83, 11, 17, 2, 30,…
## $ wk11 <dbl> NA, NA, 51, 61, 64, 7, NA, 37, 9, 66, 92, 1, 17, 2, 36, N…
## $ wk12 <dbl> NA, NA, 51, 59, 70, 22, NA, 37, 8, 68, NA, 1, 3, 3, 37, N…
## $ wk13 <dbl> NA, NA, 47, 61, 75, 29, NA, 38, 6, 61, NA, 1, 3, 3, 39, N…
## $ wk14 <dbl> NA, NA, 44, 66, 76, 36, NA, 49, 1, 67, NA, 1, 7, 4, 49, N…
## $ wk15 <dbl> NA, NA, 38, 72, 78, 47, NA, 61, 2, 59, NA, 4, 10, 12, 57,…
## $ wk16 <dbl> NA, NA, 28, 76, 85, 67, NA, 63, 2, 63, NA, 8, 17, 11, 63,…
## $ wk17 <dbl> NA, NA, 22, 75, 92, 66, NA, 62, 2, 67, NA, 12, 25, 13, 65…
## $ wk18 <dbl> NA, NA, 18, 67, 96, 84, NA, 67, 2, 71, NA, 22, 29, 15, 68…
## $ wk19 <dbl> NA, NA, 18, 73, NA, 93, NA, 83, 3, 79, NA, 23, 29, 18, 79…
## $ wk20 <dbl> NA, NA, 14, 70, NA, 94, NA, 86, 4, 89, NA, 43, 40, 20, 86…
## $ wk21 <dbl> NA, NA, 12, NA, NA, NA, NA, NA, 5, NA, NA, 44, 43, 30, NA…
## $ wk22 <dbl> NA, NA, 7, NA, NA, NA, NA, NA, 5, NA, NA, NA, 50, 40, NA,…
## $ wk23 <dbl> NA, NA, 6, NA, NA, NA, NA, NA, 6, NA, NA, NA, NA, 39, NA,…
## $ wk24 <dbl> NA, NA, 6, NA, NA, NA, NA, NA, 9, NA, NA, NA, NA, 44, NA,…
## $ wk25 <dbl> NA, NA, 6, NA, NA, NA, NA, NA, 13, NA, NA, NA, NA, NA, NA…
## $ wk26 <dbl> NA, NA, 5, NA, NA, NA, NA, NA, 14, NA, NA, NA, NA, NA, NA…
## $ wk27 <dbl> NA, NA, 5, NA, NA, NA, NA, NA, 16, NA, NA, NA, NA, NA, NA…
## $ wk28 <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 23, NA, NA, NA, NA, NA, NA…
## $ wk29 <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 22, NA, NA, NA, NA, NA, NA…
## $ wk30 <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 33, NA, NA, NA, NA, NA, NA…
## $ wk31 <dbl> NA, NA, 4, NA, NA, NA, NA, NA, 36, NA, NA, NA, NA, NA, NA…
## $ wk32 <dbl> NA, NA, 3, NA, NA, NA, NA, NA, 43, NA, NA, NA, NA, NA, NA…
## $ wk33 <dbl> NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wk34 <dbl> NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wk35 <dbl> NA, NA, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wk36 <dbl> NA, NA, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wk37 <dbl> NA, NA, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wk38 <dbl> NA, NA, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wk39 <dbl> NA, NA, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wk40 <dbl> NA, NA, 15, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk41 <dbl> NA, NA, 14, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk42 <dbl> NA, NA, 13, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk43 <dbl> NA, NA, 14, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk44 <dbl> NA, NA, 16, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk45 <dbl> NA, NA, 17, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk46 <dbl> NA, NA, 21, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk47 <dbl> NA, NA, 22, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk48 <dbl> NA, NA, 24, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk49 <dbl> NA, NA, 28, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk50 <dbl> NA, NA, 33, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk51 <dbl> NA, NA, 42, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk52 <dbl> NA, NA, 42, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk53 <dbl> NA, NA, 49, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk54 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk55 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk56 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk57 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk58 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk59 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk60 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk61 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk62 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk63 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk64 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk66 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk67 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk68 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk69 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk70 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk71 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk72 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk73 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk74 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk75 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wk76 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…billboard_long <- pivot_longer(billboard,
cols = starts_with("wk"), #or: !c(artist, track, date.entered)
names_to = "week",
values_to = "rank"
#Note that in the code "week" and "rank" are quoted because those are new variable
)
glimpse(billboard_long)## Rows: 24,092
## Columns: 5
## $ artist <chr> "2 Pac", "2 Pac", "2 Pac", "2 Pac", "2 Pac", "2 Pac", "2 …
## $ track <chr> "Baby Don't Cry (Keep...", "Baby Don't Cry (Keep...", "Ba…
## $ date.entered <date> 2000-02-26, 2000-02-26, 2000-02-26, 2000-02-26, 2000-02-…
## $ week <chr> "wk1", "wk2", "wk3", "wk4", "wk5", "wk6", "wk7", "wk8", "…
## $ rank <dbl> 87, 82, 72, 77, 87, 94, 99, NA, NA, NA, NA, NA, NA, NA, N…billboard_longer <- billboard |>
pivot_longer(
cols = starts_with("wk"), #or: !c(artist, track, date.entered)
names_to = "week",
values_to = "rank",
values_drop_na = TRUE #remove the NA values
) |>
mutate(week = parse_number(week))# extract the first number from a string, ignoring all other text
glimpse(billboard_longer)## Rows: 5,307
## Columns: 5
## $ artist <chr> "2 Pac", "2 Pac", "2 Pac", "2 Pac", "2 Pac", "2 Pac", "2 …
## $ track <chr> "Baby Don't Cry (Keep...", "Baby Don't Cry (Keep...", "Ba…
## $ date.entered <date> 2000-02-26, 2000-02-26, 2000-02-26, 2000-02-26, 2000-02-…
## $ week <dbl> 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 1, 2, 3, 4, 5, 6, 7, 8, 9, …
## $ rank <dbl> 87, 82, 72, 77, 87, 94, 99, 91, 87, 92, 81, 70, 68, 67, 6…billboard_longer |>
ggplot(aes(x = week, y = rank, group = track)) +
geom_line(alpha = 0.25) +
scale_y_reverse()
5.3.3 Many variables in column names
glimpse(who2)## Rows: 7,240
## Columns: 58
## $ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan",…
## $ year <dbl> 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989,…
## $ sp_m_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_m_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_m_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_m_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_m_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_m_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_m_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_f_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_f_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_f_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_f_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_f_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_f_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sp_f_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_m_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_m_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_m_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_m_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_m_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_m_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_m_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_f_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_f_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_f_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_f_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_f_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_f_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ sn_f_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_m_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_m_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_m_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_m_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_m_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_m_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_m_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_f_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_f_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_f_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_f_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_f_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_f_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ep_f_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_m_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_m_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_m_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_m_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_m_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_m_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_m_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_f_014 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_f_1524 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_f_2534 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_f_3544 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_f_4554 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_f_5564 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rel_f_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…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 rowswho2 |>
pivot_longer(
cols = !(country:year),
names_to = c("diagnosis", "gender", "age"),
names_sep = "_",
values_to = "count",
values_drop_na = TRUE
)## # A tibble: 76,046 × 6
## country year diagnosis gender age count
## <chr> <dbl> <chr> <chr> <chr> <dbl>
## 1 Afghanistan 1997 sp m 014 0
## 2 Afghanistan 1997 sp m 1524 10
## 3 Afghanistan 1997 sp m 2534 6
## 4 Afghanistan 1997 sp m 3544 3
## 5 Afghanistan 1997 sp m 4554 5
## 6 Afghanistan 1997 sp m 5564 2
## 7 Afghanistan 1997 sp m 65 0
## 8 Afghanistan 1997 sp f 014 5
## 9 Afghanistan 1997 sp f 1524 38
## 10 Afghanistan 1997 sp f 2534 36
## # ℹ 76,036 more rows5.3.4 Data and variable names in the column headers
household #> # A tibble: 5 × 5 #> family dob_child1 dob_child2
name_child1 name_child2 #>
#> 1 1 1998-11-26 2000-01-29 Susan Jose
#> 2 2 1996-06-22 NA Mark
#> 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
household |>
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# c(".value", "child"): splits the column names into two components:
# the first part determines the output column name (dob or name), and the second part determines the value of the child column5.4 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 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 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>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>5.4.1 How does pivot_wider() work?
df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"B", "bp1", 140,
"B", "bp2", 115,
"A", "bp2", 120,
"A", "bp3", 105
)
df |> pivot_wider(
id_cols = id,
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