From c5a81b92ba07d349f5a3b3faf4142708cf7dd1a7 Mon Sep 17 00:00:00 2001 From: Hadley Wickham Date: Sat, 17 Sep 2022 10:58:18 -0500 Subject: [PATCH] Fleshing out each file section --- DESCRIPTION | 1 + data/gapminder.R | 20 +++++++ data/gapminder/1952.xlsx | Bin 0 -> 11169 bytes data/gapminder/1957.xlsx | Bin 0 -> 11201 bytes data/gapminder/1962.xlsx | Bin 0 -> 11206 bytes data/gapminder/1967.xlsx | Bin 0 -> 11232 bytes data/gapminder/1972.xlsx | Bin 0 -> 11193 bytes data/gapminder/1977.xlsx | Bin 0 -> 11226 bytes data/gapminder/1982.xlsx | Bin 0 -> 11225 bytes data/gapminder/1987.xlsx | Bin 0 -> 11253 bytes data/gapminder/1992.xlsx | Bin 0 -> 11267 bytes data/gapminder/1997.xlsx | Bin 0 -> 11274 bytes data/gapminder/2002.xlsx | Bin 0 -> 11276 bytes data/gapminder/2007.xlsx | Bin 0 -> 11291 bytes iteration.qmd | 123 +++++++++++++++++++++++++++++++++------ 15 files changed, 125 insertions(+), 19 deletions(-) create mode 100644 data/gapminder.R create mode 100644 data/gapminder/1952.xlsx create mode 100644 data/gapminder/1957.xlsx create mode 100644 data/gapminder/1962.xlsx create mode 100644 data/gapminder/1967.xlsx create mode 100644 data/gapminder/1972.xlsx create mode 100644 data/gapminder/1977.xlsx create mode 100644 data/gapminder/1982.xlsx create mode 100644 data/gapminder/1987.xlsx create mode 100644 data/gapminder/1992.xlsx create mode 100644 data/gapminder/1997.xlsx create mode 100644 data/gapminder/2002.xlsx create mode 100644 data/gapminder/2007.xlsx diff --git a/DESCRIPTION b/DESCRIPTION index 8b2ab43..6219bad 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -54,6 +54,7 @@ Remotes: tidyverse/dbplyr, tidyverse/stringr, tidyverse/tidyr, + tidyverse/purrr jennybc/repurrrsive Encoding: UTF-8 License: CC NC ND 3.0 diff --git a/data/gapminder.R b/data/gapminder.R new file mode 100644 index 0000000..c2c2979 --- /dev/null +++ b/data/gapminder.R @@ -0,0 +1,20 @@ + + +repurrrsive::gap_simple |> + count(year) + +by_year <- repurrrsive::gap_simple |> + group_by(year) +paths <- by_year |> + group_keys() |> + mutate(path = str_glue("data/gapminder/{year}.xlsx")) |> + pull() +paths + +years <- by_year |> + group_split() |> + map(\(df) select(df, -year)) + +dir.create("data/gapminder") + +walk2(years, paths, writexl::write_xlsx) diff --git a/data/gapminder/1952.xlsx b/data/gapminder/1952.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..7ce82a5b346d5d40cf3edf24587e986c2ea19db2 GIT binary patch literal 11169 zcmZ{K1yCHzzcrBHPH>mS-QC^YEx0=bcXxMphv2RoT!RK#TtjdV_HqCBz3;x8dT+OS zx^`;LsoC@E>7K50lw?1AL=9;Bt{Xxe}Vhf$^y_=Q^%l;7D%(hJcd@n9-+cUVU9s-5FY zv5%Qt?>IyX`fdv1Y7GkEct}DX3Shn9!9F=8f6M5>M9v|hc1VFIl<5WEOTL`4Y>f<{ z4QY#uXvE9XL-8BQK z%N1MY>aUg@$`Hb-0k3c>;^Y@D^T{uGL#jV;=b#KQB%v1&)q1}FdP;+9_JZ@Q5Ywnz zm;c)$q34Yu{MW|i1D?{qDFf{=Y+{vt;vQ#sZXuZYm%yM%76h;ctZ}kCTTK+T z?paa4U^Q_r((`QyA-0JA_VrdsZtzzRFkNMAui_h)Pgfqhp$jWE8ArT8FPqMdB; z_9V2kmC6$rwlp&bb_{<;m}AzVOUxUN(gyQl*&GU|I=D3_lvn8KNoUi|@#(#g0%1p`O6obUuedN{Y+~yb 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You could do it with copy and paste: [^iteration-2]: If you instead had a directory of csv files with the same format, you can use `read_csv()` directly: `read_csv(c("data/y2019.xls", "data/y2020.xls", "data/y2021.xls", "data/y2020.xls").` @@ -314,9 +312,8 @@ data <- bind_rows(data2019, data2020, data2021, data2022) But you can imagine that this would get tedious quickly, since often you won't have four files, but more like 400. In this section you'll first learn a little bit about the base `dir()` function which allows you to list all the files in a directory. -And then about `map()` which lets you repeatedly apply a function to each element of a vector, allowing you to read many files in one step. - -`map()` is similar to `across()`, but instead of doing something to each column in a data frame, it does something to each element of a vector. +And then about `purrr::map()` which lets you repeatedly apply a function to each element of a vector, allowing you to read many files in one step. +And then we'll finish up with `purrr::list_rbind()` which takes a list of data frames and combines them all together. ### Listing files in a directory @@ -324,40 +321,128 @@ And then about `map()` which lets you repeatedly apply a function to each elemen Use `pattern`, a regular expression, to filter files. Always use `full.name`. +Let's make this problem real with a folder of 12 excel spreadsheets that contain data from the gapminder package that contains some information about multiple countries over time: + ```{r} -#| eval: false -paths <- dir("data", pattern = "\\.xls$", full.names = TRUE) +paths <- dir("data/gapminder", pattern = "\\.xlsx$", full.names = TRUE) +paths ``` ### Basic pattern -Two steps --- read every file into a list. -Then join the pieces back into a data frame. -Overall this framework is sometimes called split-apply-combine. -You split the problem up into pieces (here paths), apply a function to each piece (read_csv), and then combine the pieces back together. +Now that we have the paths, we want to call `read_excel()` with each path. +Since in general we won't know how many elements there are, instead of putting each individual data frame in its own variable, we'll save them all into a list: ```{r} #| eval: false +list( + readxl::read_excel("data/gapminder/1952.xls"), + readxl::read_excel("data/gapminder/1957.xls"), + readxl::read_excel("data/gapminder/1962.xls"), + ..., + readxl::read_excel("data/gapminder/2007.xls") +) +``` + +The shortcut for this is the `map()` function. +`map(x, f)` is short hand for: + +```{r} +#| eval: false +list( + f(x[[1]]), + f(x[[2]]), + ..., + f(x[[n]]) +) +``` + +`map()` is similar to `across()`, but instead of doing something to each column in a data frame, it does something to each element of a vector. + +We can use `map()` get a list of data frames in one step with: + +```{r} +files <- map(paths, readxl::read_excel) +length(files) + +files[[1]] +``` + +(This is another data structure that doesn't display particularly compactly with `str()` so you might want to load into RStudio and inspecting with `` View()` ``). + +Now we can to use `purrr::list_rbind()` to combine that list of data frames into a single data frame: + +```{r} +list_rbind(files) +``` + +Or we could combine in a single pipeline like this: + +```{r} +#| results: false paths |> - map(\(path) readxl::read_excel(path)) |> + map(readxl::read_excel) |> list_rbind() ``` +What if we want to pass in extra arguments to `read_excel()`? +We use the same trick that we used with across. +For example, it's often useful to peak at just the first few rows of the data: + +```{r} +paths |> + map(\(path) readxl::read_excel(path, n_max = 1)) |> + list_rbind() +``` + +This really hammers in something that you might've noticed earlier: each individual sheet doesn't contain the year. +That's only recorded in the path. + ### Data in the path -If the file name itself contains data, try: +Sometimes the name of the file is itself data. +In this example, the file name contains the year, which is not otherwise recorded in the individual data frames. +To get that column into the final data frame, we need to do two things. + +Firstly, we give the path vector names. +The easiest way to do this is with the `set_names()` function, which can optionally take a function. +Here we use `basename` to extract just the file name from the full path: + +```{r} +paths <- paths |> set_names(basename) +paths +``` + +Those paths are automatically carried along by all the map functions, so the list of data frames will have those same names: ```{r} #| eval: false paths |> - set_names(basename) |> - map(\(path) readxl::read_excel) |> - list_rbind(.id = "path") + map(readxl::read_excel) |> + names() ``` -You can then use `tidyr::separate_by()` and friends to turn into useful columns. +Then we use the `names_to` argument `list_rbind()` to tell it which column to save the names to: -You can use `set_names(basename)` to just use the file name. +```{r} +paths |> + set_names(basename) |> + map(readxl::read_excel) |> + list_rbind(names_to = "year") |> + mutate(year = parse_number(year)) +``` + +Here I used `readr::parse_number()` to turn year into a proper number. + +If the path contains more data, do `paths <- paths |> set_names()` to set the names to the full path, and then use `tidyr::separate_by()` and friends to turn them into useful columns. + +```{r} +paths |> + set_names() |> + map(readxl::read_excel) |> + list_rbind(names_to = "year") |> + separate(year, into = c(NA, "directory", "file", "ext"), sep = "[/.]") +``` ### Get to a single data frame as quickly as possible