tidyverse学习笔记——Data tidying篇

Data tidying

Rules that make a dataset clean

1. Each variable is a column; each column is a variable.
2. Each observation is a row; each row is an observation.
3. Each value is a cell; each cell is a single value.

pivot

pivot helps to tidy the datasets.

Lengthening data

pivot_longer

pivot_longer() “lengthens” data, increasing the number of rows and decreasing the number of columns.

Data in column names

artist	date.entered	wk1	wk2	wk3	wk4	wk5
2 Pac	2000-02-26	87	82	72	77	87
2Ge+her	2000-09-02	91	87	92	NA	NA
3 Doors Down	2000-04-08	81	70	68	67	66
3 Doors Down	2000-10-21	76	76	72	69	67
504 Boyz	2000-04-15	57	34	25	17	17
98^0	2000-08-19	51	39	34	26	26

billboard |> 
    pivot_longer(
        cols = starts_with("wk"), 
        names_to = "week", 
        values_to = "rank",
        values_drop_na = TRUE
    )

Variables in column names

country	year	sp_m_014	sp_m_1524	sp_f_2534	sp_m_3544	sp_m_4554
Afghanistan	1980	NA	NA	NA	NA	NA
Afghanistan	1981	NA	NA	NA	NA	NA
Afghanistan	1982	NA	NA	NA	NA	NA
Afghanistan	1983	NA	NA	NA	NA	NA
Afghanistan	1984	NA	NA	NA	NA	NA
Afghanistan	1985	NA	NA	NA	NA	NA

“sp” refers to diagnosis, “m” and “f” denote gender. “014” indicates age between 0 and 14.

who2 |> 
    pivot_longer(
        cols = !(country:year),
        names_to = c("diagnosis", "gender", "age"), 
        names_sep = "_",
        values_to = "count"
    )

Widening data

cms_patient_experience |> 
    pivot_wider(
        names_from = measure_cd,
        values_from =