数値変数、lm（）、抽出勾配でデータフレームをフィルタリングする

Question

異なるレベルの数値フィルタ（例：seq(10,80, by=2)）を適用し、別の変数との比較のためにこれらを1つのデータフレームにステッチバックします。私は現在これを行うことができますが、私はコードをコピーして貼り付けてからすべてを戻すので、より良い方法があることを期待しています。私が望む最終的な結果は、lm（）の勾配パラメータを持つ独自の列として各フィルタステップを抽出したことです。

Source: local data frame [23 x 17] 

          File FruitNum  est10 
         <fctr> <int>  <dbl> 
1 IMG_7888.JPGcolcorrected.jpg  2 -4.0000000 
2 IMG_7888.JPGcolcorrected.jpg  4 -2.0000000 
3 IMG_7889.JPGcolcorrected.jpg  1 -0.8178571 
4 IMG_7889.JPGcolcorrected.jpg  2 -2.1000000 
5 IMG_7890.JPGcolcorrected.jpg  1 -2.8000000 
6 IMG_7892.JPGcolcorrected.jpg  3 -2.3571429 
7 IMG_7895.JPGcolcorrected.jpg  1 -0.4000000 
8 IMG_7896.JPGcolcorrected.jpg  3 -6.5000000 
9 IMG_7898.JPGcolcorrected.jpg  1 -3.0000000 
10 IMG_7888.JPGcolcorrected.jpg  1   NA 
..       ...  ...  ... 
Variables not shown: est15 <dbl>, est20 <dbl>, est25 <dbl>, 
    est30 <dbl>, est35 <dbl>, est40 <dbl>, est45 <dbl>, est50 
    <dbl>, est55 <dbl>, est60 <dbl>, est65 <dbl>, est70 <dbl>, 
    est75 <dbl>, est80 <dbl>. 

私は現在hadleyverseでNSEパイプラインを使用していますし、そこに滞在したいと思いますが、ベース、data.tableまたは他の実装を見て満足しています。私はpurrrを見てきましたが、インライン変数にフィルタをマップする方法がわかりません。

library(dplyr) 
library(purrr) 
library(tidyr) 
library(broom) 

cukeDataDL <- read.delim("https://gist.githubusercontent.com/bhive01/e7508f552db0415fec1749d0a390c8e5/raw/a12386d43c936c2f73d550dfdaecb8e453d19cfe/widthtest.tsv") 

cukeDatatest <- 
    cukeDataDL %>% 
    mutate(ObjectWidth = strsplit(as.character(cukeDatatest$ObjectWidth), ',')) %>% # split ObjectWidth into a nested column containing a vector 
    unnest() %>% # unnest nested column, melting data to long form 
    mutate(ObjectWidth = as.integer(ObjectWidth)) %>% # convert data to integer 
    group_by(File, FruitNum) %>% 
    mutate(rownum = row_number()) #location within File x fruit 

estimate10 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.10 * max(ObjectWidth) & rownum > mean(rownum)) %>% # filtering for 10% of maxwidth and second half of fruit 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% #broom to clean up models and get coef()s 
    unnest() %>% #pull out nested information 
    filter(term == "rownum") %>% #only interested in slope value 
    select(File, FruitNum, est10 = estimate) #get rid of uninteresting columns and rename estimate for join 

estimate15 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.15 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est15 = estimate) 

estimate20 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.20 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est20 = estimate) 

estimate25 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.25 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est25 = estimate) 

estimate30 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.30 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est30 = estimate) 

estimate35 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.35 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est35 = estimate) 

estimate40 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.40 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est40 = estimate) 

estimate45 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.45 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est45 = estimate) 

estimate50 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.50 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est50 = estimate) 

estimate55 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.55 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est55 = estimate) 

estimate60 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.60 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est60 = estimate) 

estimate65 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.65 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est65 = estimate) 

estimate70 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.70 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est70 = estimate) 

estimate75 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.75 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est75 = estimate) 
estimate80 <- 
    cukeDatatest %>% 
    filter(ObjectWidth < 0.80 * max(ObjectWidth) & rownum > mean(rownum)) %>% 
    by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
    unnest() %>% 
    filter(term == "rownum") %>% 
    select(File, FruitNum, est80 = estimate) 

    # put everything together 
allEstimates <- 
    full_join(estimate10, estimate15) %>% 
    full_join(., estimate20) %>% 
    full_join(., estimate25) %>% 
    full_join(., estimate30) %>% 
    full_join(., estimate35) %>% 
    full_join(., estimate40) %>% 
    full_join(., estimate45) %>% 
    full_join(., estimate50) %>% 
    full_join(., estimate55) %>% 
    full_join(., estimate60) %>% 
    full_join(., estimate65) %>% 
    full_join(., estimate70) %>% 
    full_join(., estimate75) %>% 
    full_join(., estimate80) 
allEstimates #print it out 

Answer 1

非常に短い！ twitterで@NoamRossに感謝します。マップを使用して

1. 、あなたはそれは、列名、後
2. 使用bind_rowsに使用するnamesafeの列の記述を作成し、各反復
3. のためのデータフレームのシリーズを作成し
4. seq(10,80, by=2)を反復処理したいシリーズを供給します（）を使ってすべてを集める
5. spread（）を使用して、PCTwidthの各レベルを列にする
6. Profit ???

` `

library(dplyr) 
library(purrr) 
library(tidyr) 
library(broom) 

cukeDataDL <- read.delim("https://gist.githubusercontent.com/bhive01/e7508f552db0415fec1749d0a390c8e5/raw/a12386d43c936c2f73d550dfdaecb8e453d19cfe/widthtest.tsv") 
cukeDatatest <- 
    cukeDataDL %>% 
     select(File, FruitNum, ObjectWidth) %>% 
     # split ObjectWidth into a nested column containing a vector 
     mutate(ObjectWidth = strsplit(as.character(.$ObjectWidth), ',')) %>% 
     # unnest nested column, melting data to long form 
     unnest() %>% 
     # convert data to integer 
     mutate(ObjectWidth = as.integer(ObjectWidth)) %>% # convert data to integer 
     group_by(File, FruitNum) %>% 
     mutate(rownum = row_number()) 
allEstimates <- 
    map(seq(0.10,0.80, by=0.02), function(x) { 
     cukeDatatest %>% 
      filter(ObjectWidth < x * max(ObjectWidth) & rownum > mean(rownum)) %>% 
      by_slice(~tidy(lm(ObjectWidth ~ rownum, data = .))) %>% 
      unnest() %>% 
      filter(term == "rownum") %>% 
      select(File, FruitNum, estimate) %>% 
      mutate(PCTwidth = paste("est", round(x*100), sep="")) 
     } 
    ) %>% 
    bind_rows() %>% 
    spread(., PCTwidth, estimate) 

allEstimates #print everything out