r - Variable binning based on column value -


this difficult describe (thus vague title) i'll give example data want. have 2 dataframes

set.seed(5)     df = data.frame(id  = sort(rep(c("2006_01", "2006_02", "2006_03", "2006_04"), length.out = 100)),              increment = rep(seq(from = 1, = 25, = 1), length.out = 100),             var1 = rnorm(100))

and 

set.seed(23) df2 = data.frame(id  = sort(rep(c("2006_01", "2006_02", "2006_03", "2006_04"), length.out = 200)),               distance = rep(seq(from = 1, = 50, = 1), length.out = 200),               var2 = rnorm(200))

they both transects $increment , $distance being measures of how far along transect measurements taken. longer 1 has twice many measurements because higher resolution, representing same transect. join them based on proportion of distance through transect. calculated proportion column each following code:

df = ddply(df, "id", transform, proportion = increment/max(increment))

and

df2 = ddply(df2, "id", transform, proportion = distance/max(distance))

the output want is, join 2 dataframes (keeping var1 , var2). because df2 higher resolution, way join df bin it, real data isn't regular sample data, need dynamically bin based on proportion columns it's calculating means in df2 proportion values fall between each set of proportion values in df

to try , summarize, i'm trying join sets of data have different resolutions calculating mean values of higher resolution data set of points fall within resolution of lower resolution set.

-edit try , add desired output-

the first few lines of output dataframe following:

id        increment        var1           var2 2006_001         1  -0.84085548     -0.1207349 2006_001         2   1.38435934       1.353328 2006_001         3  -1.25549186       1.052048 2006_001         4   0.07014277      0.3705596

one method through tidyverse packages.

library(dplyr) library(tidyr) # nest, unnest library(purrr) # pmap

since 2 data.frames different dimensions, find it's nice nest data column.

df2 <- df2 %>%   group_by(id) %>%   mutate(     proportion = (distance - min(distance)) / diff(range(distance))   ) %>%   nest(.key = "dist") df2 # # tibble: 4 × 2 #        id              dist #    <fctr>            <list> # 1 2006_01 <tibble [50 × 3]> # 2 2006_02 <tibble [50 × 3]> # 3 2006_03 <tibble [50 × 3]> # 4 2006_04 <tibble [50 × 3]>  df3 <- df %>%   group_by(id) %>%   mutate(     proportion = (increment - min(increment)) / diff(range(increment))   ) %>%   nest(.key = "incr") %>%   left_join(df2, = "id") %>%   mutate(     incr = pmap(list(incr, dist),                 function(a, b) {                   zz <- tail(a$proportion, n = -1) -                     (tail(a$proportion, n = -1) - head(a$proportion, n = -1)) / 2                   a$var2 <- as.numeric(                     by(b$var2, cut(b$proportion, c(-1, zz, 2), labels = false), mean)                   )                                   })   )

now things set in df2, start doing same in df, , combine them side-by-side:

df3 <- df %>%   group_by(id) %>%   mutate(     proportion = (increment - min(increment)) / diff(range(increment))   ) %>%   nest(.key = "incr") %>%   left_join(df2, = "id") df3 # # tibble: 4 × 3 #        id              incr              dist #    <fctr>            <list>            <list> # 1 2006_01 <tibble [25 × 3]> <tibble [50 × 3]> # 2 2006_02 <tibble [25 × 3]> <tibble [50 × 3]> # 3 2006_03 <tibble [25 × 3]> <tibble [50 × 3]> # 4 2006_04 <tibble [25 × 3]> <tibble [50 × 3]>

note allows associate x rows of 1 data.frame y rows of another, when joined via id.

df3 %>%   mutate(     incr = pmap(list(incr, dist),                 function(a, b) {                   # offset between breaks, ...                   breaks <- tail(a$proportion, n = -1) -                     (tail(a$proportion, n = -1) - head(a$proportion, n = -1)) / 2                   # ... bookends ensure 100% membership                   breaks <- c(-1, breaks, 2)                   a$var2 <- as.numeric(                     by(b$var2, cut(b$proportion, breaks), mean)                   )                                   })   ) %>%   select(id, incr) %>%   unnest() %>%   select(-proportion) # # tibble: 100 × 4 #         id increment        var1        var2 #     <fctr>     <dbl>       <dbl>       <dbl> # 1  2006_01         1 -0.84085548 -0.12073489 # 2  2006_01         2  1.38435934  1.35332759 # 3  2006_01         3 -1.25549186  1.05204780 # 4  2006_01         4  0.07014277  0.37055960 # 5  2006_01         5  1.71144087  0.81060839 # 6  2006_01         6 -0.60290798 -0.41412345 # 7  2006_01         7 -0.47216639  0.09643082 # 8  2006_01         8 -0.63537131 -0.45411977 # 9  2006_01         9 -0.28577363 -0.48124606 # 10 2006_01        10  0.13810822  0.34763251 # # ... 90 more rows

Comments

Post a Comment

Popular posts from this blog

android - InAppBilling registering BroadcastReceiver in AndroidManifest -

i'm implementing inapp billing in android app. works well, however, i'm trying decouple broadcast receiver activity manifest. suggestion in android's trivialdrive sample: // important: dynamically register broadcast messages updated purchases. // register receiver here instead of <receiver> in manifest // because call getpurchases() @ startup, therefore can ignore // broadcasts sent while app isn't running. // note: registering listener in activity bad idea, done here // because sample. regardless, receiver must registered after // iabhelper setup, before first call getpurchases(). currently there's class extends broadcastreceiver : public class iabbroadcastreceiver extends broadcastreceiver { /** * intent action receiver should filter for. */ public static final string action = "com.android.vending.billing.purchases_updated"; private final iabbroadcastlistener mlistener; public iabbroadcastreceiver(iabbroadcastlistener listener) { m...
Read more

python Tkinter Capturing keyboard events save as one single string -

is possible store keyboard events 1 string? code below stores 1 char , prints it. card reader or bar code reader, contains collection of character/ string not 1 character @ time. goal save char pressed text variable. from tkinter import * root = tk() def key(event): text= event.char text+= event.char print ("pressed", text) def callback(event): frame.focus_set() print ("clicked at", event.x, event.y) frame = frame(root, width=100, height=100) frame.bind("<key>", key) frame.bind("<button-1>", callback) frame.pack() root.mainloop() currently, creating text variable , everytime key() function gets called, , text stores last character typed. you can define text module level variable , , use module level text inside key function - text = '' def key(event): global text text+= event.char print("pressed", text)
Read more

sql server - Why does Linq-to-SQL add unnecessary COUNT()? -

here tables: create table customer ( cid int primary key, name varchar(32) ) create table ord ( oid int primary key, cid int foreign key references customer, address varchar(20) ) here's linq-to-sql statement: var aaa = c in db.customer select new { c, o = c.ord.tolist() }; and here's inexplicable query generated linq-to-sql: select [t0].[cid] [cid], [t0].[name] [name], [t1].[oid] [oid], [t1].[cid] [cid2], [t1].[address] [address], (select count(*) [dbo].[ord] [t2] [t2].[cid] = [t0].[cid]) [value] [dbo].[customer] [t0] left outer join [dbo].[ord] [t1] on [t1].[cid] = [t0].[cid] order [t0].[cid], [t1].[oid] i want understand how rid of count(*) part. it's entirely uncalled for! linq needs count(*) in order determine number of entries returned o = c.ord.tolist() .
Read more