如何使用dplyr

-1

我有兩個數據幀計算相關的所有針對-全部兩個數據幀列：如何使用dplyr

setA_df <- structure(list(gene_symbol = c("Wif1", "Wisp1", "Wnt11", "Wnt6", 
"Wnt9a", "Xxylt1", "Zbp1", "Zbtb7c", "Zc2hc1c", "Zfp300", "Zfp36", 
"Zfp367", "Zfp54", "Zfp612", "Zfp748", "Zfp783", "Zfp791", "Zic1", 
"Zic3", "Zic4"), Sample1 = c(6, 1420, 7, 2, 47, 290, 13, 34, 
4, 16, 165, 545, 100, 9, 160, 1, 15, 0, 0, 0), Sample2 = c(0, 
2617, 25, 1, 105, 206, 4, 83, 25, 4, 187, 159, 63, 34, 147, 6, 
4, 4, 0, 1)), row.names = c(NA, -20L), class = c("tbl_df", "tbl", 
"data.frame"), .Names = c("gene_symbol", "Sample1", "Sample2" 
)) 

setA_df 
#> gene_symbol Sample1 Sample2 
#> 1   Wif1  6  0 
#> 2  Wisp1 1420 2617 
#> 3  Wnt11  7  25 
#> 4   Wnt6  2  1 
#> 5  Wnt9a  47  105 
#> 6  Xxylt1  290  206 
#> 7   Zbp1  13  4 
#> 8  Zbtb7c  34  83 
#> 9  Zc2hc1c  4  25 
#> 10  Zfp300  16  4 
#> 11  Zfp36  165  187 
#> 12  Zfp367  545  159 
#> 13  Zfp54  100  63 
#> 14  Zfp612  9  34 
#> 15  Zfp748  160  147 
#> 16  Zfp783  1  6 
#> 17  Zfp791  15  4 
#> 18  Zic1  0  4 
#> 19  Zic3  0  0 
#> 20  Zic4  0  1 

setB_df <- structure(list(gene_symbol = c("Wif1", "Wisp1", "Wnt11", "Wnt6", 
"Wnt9a", "Xxylt1", "Zbp1", "Zbtb7c", "Zc2hc1c", "Zfp300", "Zfp36", 
"Zfp367", "Zfp54", "Zfp612", "Zfp748", "Zfp783", "Zfp791", "Zic1", 
"Zic3", "Zic4"), Blood = c(991.833, 104.167, 52.5, 4.833, 33.333, 
163.667, 131.333, 7, 77.667, 52.5, 222.333, 10.833, 63.5, 0.167, 
184.922, 20.167, 17.333, 0.833, 20.833, 0), Bone = c(4282.167, 
642.333, 5.667, 66.667, 16.667, 143.167, 112.667, 61.667, 45.833, 
24.833, 1070.333, 41.833, 140.402, 13.667, 156.167, 84.613, 5.333, 
44.667, 3.167, 18.167)), row.names = c(NA, -20L), class = c("tbl_df", 
"tbl", "data.frame"), .Names = c("gene_symbol", "Blood", "Bone" 
)) 


setB_df 
#> gene_symbol Blood  Bone 
#> 1   Wif1 991.833 4282.167 
#> 2  Wisp1 104.167 642.333 
#> 3  Wnt11 52.500 5.667 
#> 4   Wnt6 4.833 66.667 
#> 5  Wnt9a 33.333 16.667 
#> 6  Xxylt1 163.667 143.167 
#> 7   Zbp1 131.333 112.667 
#> 8  Zbtb7c 7.000 61.667 
#> 9  Zc2hc1c 77.667 45.833 
#> 10  Zfp300 52.500 24.833 
#> 11  Zfp36 222.333 1070.333 
#> 12  Zfp367 10.833 41.833 
#> 13  Zfp54 63.500 140.402 
#> 14  Zfp612 0.167 13.667 
#> 15  Zfp748 184.922 156.167 
#> 16  Zfp783 20.167 84.613 
#> 17  Zfp791 17.333 5.333 
#> 18  Zic1 0.833 44.667 
#> 19  Zic3 20.833 3.167 
#> 20  Zic4 0.000 18.167

我想要做的就是計算皮爾遜相關聯的所有兩兩列（不含gene_symbol最終的DF如下：？

Sample1 Blood -0.01524773 #cor(setA$Sample1,setB_df$Blood) 
Sample2 Blood -0.003247626 
Sample1 Bone 0.03889127 
Sample2 Bone 0.06420447

如何將其與dplyr實現實際上，每組有更多列。

來源

2017-05-09 neversaint

只是做'肺心病（setA_df [-1]，setB_df [-1]）' – Cath

你爲什麼需要dplyr？ 'cor（setA_df [， - 1]，setB_df [， - 1]）'應該給你想要的相關值的矩陣 – timfaber

你可以嘗試'setA_df％>％select（-gene_symbol）％>％cor（。，select （setB_df，-gene_symbol））' – akrun

這給出了定義的輸出，但如上所述，dplyr在這裏不會增加太多。

library(reshape2) 
library(dplyr) 

setA_df %>% 
    select(-gene_symbol) %>% 
    cor(setB_df %>% select(-gene_symbol)) %>% 
    melt() 

#  Var1 Var2  value 
# 1 Sample1 Blood -0.015247732 
# 2 Sample2 Blood -0.003247626 
# 3 Sample1 Bone 0.038891268 
# 4 Sample2 Bone 0.064204473

來源

2017-05-09 10:13:47 epi99

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