dplyr - 私は次のようなデータフレームを持つ2つの異なるデータフレームから条件に基づいて

Question

を引く：

quant <- structure(list(Name = structure(c(158L, 159L, 160L, 161L, 162L, 
163L, 164L, 165L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 98L, 
99L, 100L, 101L), .Label = c("abc_02_NEHC_025_100_A", "abc_02_NEHC_025_100_B", 
"abc_02_NEHC_025_100_C", "abc_02_NEHC_025_100_D", "abc_02_NEHC_025_100_E", 
"abc_02_NEHC_025_100_F", "abc_02_NEHC_025_100_G", "abc_02_NEHC_025_100_H", 
"abc_02_NEHC_05_100_A", "abc_02_NEHC_05_100_B", "abc_02_NEHC_05_100_C", 
"abc_02_NEHC_05_100_D", "abc_02_NEHC_05_100_E", "abc_02_NEHC_05_100_F", 
"abc_02_NEHC_05_100_G", "abc_02_NEHC_05_100_H", "abc_02_NEHC_100_1_A", 
"abc_02_NEHC_100_1_B", "abc_02_NEHC_100_1_C", "abc_02_NEHC_100_1_D", 
"abc_02_NEHC_100_1_E", "abc_02_NEHC_100_1_F", "abc_02_NEHC_100_1_G", 
"abc_02_NEHC_100_1_H", "abc_02_VL_025_100_A", "abc_02_VL_025_100_B", 
"abc_02_VL_025_100_C", "abc_02_VL_025_100_D", "abc_02_VL_025_100_E", 
"abc_02_VL_025_100_F", "abc_02_VL_025_100_G", "abc_02_VL_025_100_H", 
"abc_02_VL_05_100_A", "abc_02_VL_05_100_B", "abc_02_VL_05_100_C", 
"abc_02_VL_05_100_D", "abc_02_VL_05_100_E", "abc_02_VL_05_100_F", 
"abc_02_VL_05_100_G", "abc_02_VL_05_100_H", "abc_02_VL_1_100_A", 
"abc_02_VL_1_100_B", "abc_02_VL_1_100_C", "abc_02_VL_1_100_D", 
"abc_02_VL_1_100_E", "abc_02_VL_1_100_F", "abc_02_VL_1_100_G", 
"abc_02_VL_1_100_H", "BACKGROUND_NEHC_0125_100_A", "BACKGROUND_NEHC_0125_100_B", 
"BACKGROUND_NEHC_0125_100_C", "BACKGROUND_NEHC_0125_100_D", "BACKGROUND_NEHC_0125_100_E", 
"BACKGROUND_NEHC_0125_100_F", "BACKGROUND_NEHC_0125_100_G", "BACKGROUND_NEHC_025_100_A", 
"BACKGROUND_NEHC_025_100_B", "BACKGROUND_NEHC_025_100_C", "BACKGROUND_NEHC_025_100_D", 
"BACKGROUND_NEHC_025_100_F", "BACKGROUND_NEHC_025_100_G", "BACKGROUND_NEHC_05_100_A", 
"BACKGROUND_NEHC_05_100_B", "BACKGROUND_NEHC_05_100_C", "BACKGROUND_NEHC_05_100_D", 
"BACKGROUND_NEHC_05_100_F", "BACKGROUND_NEHC_05_100_G", "BACKGROUND_NEHC_05_100_H", 
"BACKGROUND_NEHC_1_100_A", "BACKGROUND_NEHC_1_100_B", "BACKGROUND_NEHC_1_100_C", 
"BACKGROUND_NEHC_1_100_D", "BACKGROUND_NEHC_1_100_E", "BACKGROUND_NEHC_1_100_F", 
"BACKGROUND_NEHC_1_100_G", "BACKGROUND_VL_0125_100_A", "BACKGROUND_VL_0125_100_B", 
"BACKGROUND_VL_0125_100_C", "BACKGROUND_VL_0125_100_D", "BACKGROUND_VL_0125_100_E", 
"BACKGROUND_VL_0125_100_F", "BACKGROUND_VL_025_100_A", "BACKGROUND_VL_025_100_B", 
"BACKGROUND_VL_025_100_C", "BACKGROUND_VL_025_100_D", "BACKGROUND_VL_025_100_E", 
"BACKGROUND_VL_025_100_F", "BACKGROUND_VL_025_100_G", "BACKGROUND_VL_025_100_H", 
"BACKGROUND_VL_05_100_A", "BACKGROUND_VL_05_100_B", "BACKGROUND_VL_05_100_C", 
"BACKGROUND_VL_05_100_D", "BACKGROUND_VL_05_100_E", "BACKGROUND_VL_05_100_F", 
"BACKGROUND_VL_05_100_G", "BACKGROUND_VL_05_100_H", "BACKGROUND_VL_1_100_A", 
"BACKGROUND_VL_1_100_B", "BACKGROUND_VL_1_100_C", "BACKGROUND_VL_1_100_D", 
"BACKGROUND_VL_1_100_E", "BACKGROUND_VL_1_100_F", "BACKGROUND_VL_1_100_G", 
"BACKGROUND_VL_1_100_H", "Epq_11_NEHC_0125_100_a", "Epq_11_NEHC_0125_100_B", 
"Epq_11_NEHC_0125_100_C", "Epq_11_NEHC_0125_100_D", "Epq_11_NEHC_0125_100_E", 
"Epq_11_NEHC_0125_100_F", "Epq_11_NEHC_0125_100_G", "Epq_11_NEHC_025_100_a", 
"Epq_11_NEHC_025_100_B", "Epq_11_NEHC_025_100_C", "Epq_11_NEHC_025_100_D", 
"Epq_11_NEHC_025_100_E", "Epq_11_NEHC_05_100_a", "Epq_11_NEHC_05_100_B", 
"Epq_11_NEHC_05_100_C", "Epq_11_NEHC_05_100_D", "Epq_11_NEHC_05_100_E", 
"Epq_11_NEHC_05_100_F", "Epq_11_NEHC_05_100_G", "Epq_11_NEHC_05_100_H", 
"Epq_11_NEHC_1_100_a", "Epq_11_NEHC_1_100_B", "Epq_11_NEHC_1_100_C", 
"Epq_11_NEHC_1_100_D", "Epq_11_NEHC_1_100_E", "Epq_11_NEHC_1_100_F", 
"Epq_11_NEHC_1_100_G", "Epq_11_NEHC_1_100_H", "Epq_11_VL_0125_100_A", 
"Epq_11_VL_0125_100_B", "Epq_11_VL_0125_100_C", "Epq_11_VL_0125_100_D", 
"Epq_11_VL_0125_100_E", "Epq_11_VL_0125_100_F", "Epq_11_VL_0125_100_G", 
"Epq_11_VL_0125_100_H", "Epq_11_VL_025_100_A", "Epq_11_VL_025_100_B", 
"Epq_11_VL_025_100_C", "Epq_11_VL_025_100_D", "Epq_11_VL_025_100_E", 
"Epq_11_VL_025_100_F", "Epq_11_VL_025_100_G", "Epq_11_VL_025_100_H", 
"Epq_11_VL_05_100_A", "Epq_11_VL_05_100_B", "Epq_11_VL_05_100_C", 
"Epq_11_VL_05_100_D", "Epq_11_VL_05_100_E", "Epq_11_VL_05_100_F", 
"Epq_11_VL_05_100_G", "Epq_11_VL_05_100_H", "Epq_11_VL_1_100_A", 
"Epq_11_VL_1_100_B", "Epq_11_VL_1_100_C", "Epq_11_VL_1_100_D", 
"Epq_11_VL_1_100_E", "Epq_11_VL_1_100_F", "Epq_11_VL_1_100_G", 
"Epq_11_VL_1_100_H"), class = "factor"), conc_factor = structure(c(4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L), .Label = c("pep_0.125", "pep_0.25", "pep_0.5", "pep_1.0" 
), class = "factor"), peptide_factor = structure(c(3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L), .Label = c("ABC", "Background", "EpQ_11"), class = "factor"), 
    serum_factor = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("NEHC", 
    "VL"), class = "factor"), mean_fluorescence = c(65535, 65535, 
    65534.93359, 65535, 65535, 65535, 65535, 65535, 21322.06055, 
    22704.08594, 22546.32617, 21801.30664, 21668.2168, 22054.40234, 
    21621.54688, 21516.33984, 17760.80273, 17886.12891, 18382.7832, 
    17531.80273)), class = "data.frame", row.names = c(NA, -20L 
), .Names = c("Name", "conc_factor", "peptide_factor", "serum_factor", 
"mean_fluorescence")) 

をこれは実際に私の完全なデータフレームのちょうどスライス（1:20）です。

summary_backgrounds <- quant %>% filter(peptide_factor=="Background") %>% group_by(conc_factor, serum_factor) %>% summarise(avg_fluorescence_grouped = mean(mean_fluorescence)) 

    conc_factor serum_factor avg_fluorescence_grouped 
     <fctr>  <fctr>     <dbl> 
1 pep_0.125   NEHC     18439.70 
2 pep_0.125   VL     16985.60 
3 pep_0.25   NEHC     18666.52 
4 pep_0.25   VL     17577.98 
5  pep_0.5   NEHC     18300.47 
6  pep_0.5   VL     18010.99 
7  pep_1.0   NEHC     16103.50 
8  pep_1.0   VL     17710.50 

私はmean_fluorescenceを得た：次のコマンドで

levels(quant$conc_factor) 
[1] "pep_0.125" "pep_0.25" "pep_0.5" "pep_1.0" 
levels(quant$peptide_factor) 
[1] "ABC"  "Background" "EpQ_11"  
levels(quant$serum_factor) 
[1] "NEHC" "VL" 

：ちょうど私の完全なデータフレームのより良いアイデアを持っているために、私は、変数conc_factor、peptide_factorとserum_factorのレベル以下貼り付けていますバックグラウンドの値は、それぞれconc_factorとserum_factorです。ここで私がしようとしていることは次のとおりです。データフレームquant（avg_fluorescence_minus_backgroundという名前）に新しい変数を追加して、背景値（summary_backgrounds$avg_fluorescence_grouped、conc_factorおよびserum_factorをそれぞれquant$mean_fluorescence 。

例えば、quant[1, ]のために、私はconc_factor=="pep_1.0"とserum_factor=="VL"を持っていることを考えると、私の結果は65535.00だろう - 。17710.50 = 47824.5などが参加して、あなたは彼らがこのタイプを作る見つける上に読む

Answer 1

。問題の非常に解決しやすい：

quant <- left_join(quant, summary_backgrounds, by = c("conc_factor", "serum_factor")) 
mutate(quant, avg_flourescence_minus_bg = mean_fluorescence - avg_fluorescence_grouped) 

Answer 2

以下を使用できます。 peptide_factor != 'Background'の行にavg_fluorescence_minus_backgroundの列にNAが追加され、目的の結果が得られます。

quant %>% group_by(conc_factor, serum_factor, peptide_factor) %>% 
    mutate(avg_fluorescence_grouped = mean(mean_fluorescence)) %>% 
    mutate(avg_fluorescence_minus_background = ifelse(peptide_factor == 
     "Background", avg_fluorescence_grouped - mean_fluorescence, 
     NA)) %>% select(-avg_fluorescence_grouped) 

## # A tibble: 20 x 6 
## # Groups: conc_factor, serum_factor, peptide_factor [3] 
## conc_factor peptide_factor serum_factor mean_fluorescence avg_fluorescence_minus_background 
##   <fctr>   <fctr>  <fctr>    <dbl>       <dbl> 
## 1  pep_1.0   EpQ_11   VL   65535.00        NA 
## 2  pep_1.0   EpQ_11   VL   65535.00        NA 
## 3  pep_1.0   EpQ_11   VL   65534.93        NA 
## 4  pep_1.0   EpQ_11   VL   65535.00        NA 
## 5  pep_1.0   EpQ_11   VL   65535.00        NA 
## 6  pep_1.0   EpQ_11   VL   65535.00        NA 
## 7  pep_1.0   EpQ_11   VL   65535.00        NA 
## 8  pep_1.0   EpQ_11   VL   65535.00        NA 
## 9  pep_1.0   ABC   VL   21322.06        NA 
## 10  pep_1.0   ABC   VL   22704.09        NA 
## 11  pep_1.0   ABC   VL   22546.33        NA 
## 12  pep_1.0   ABC   VL   21801.31        NA 
## 13  pep_1.0   ABC   VL   21668.22        NA 
## 14  pep_1.0   ABC   VL   22054.40        NA 
## 15  pep_1.0   ABC   VL   21621.55        NA 
## 16  pep_1.0   ABC   VL   21516.34        NA 
## 17  pep_1.0  Background   VL   17760.80      129.576662 
## 18  pep_1.0  Background   VL   17886.13      4.250482 
## 19  pep_1.0  Background   VL   18382.78     -492.403808 
## 20  pep_1.0  Background   VL   17531.80      358.576662 
# ... with 1 more variables: Name <fctr>