R Notebook

library(readr)
MRSA <- read_csv("~/Desktop/MRSA.csv")

Rows: 5190 Columns: 21
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): image, root_name, root, root_ontology, parent_name, parent, first_child, last_child
dbl (13): length, vector_length, surface, volume, direction, diameter, root_order, insertion_position, insertion_angle, n_child, c...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

MRSA

#MRSA <- na.omit(MRSA)
#MRSA

MRSA$root_order

   [1] 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1
  [65] 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1
 [129] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1
 [193] 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0
 [257] 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
 [321] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1
 [385] 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1
 [449] 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1
 [513] 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 1
 [577] 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 0
 [641] 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1
 [705] 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
 [769] 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 0 1 0 0 1 1 1 1 1 1
 [833] 0 1 0 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1 0 0 0 0
 [897] 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
 [961] 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1
 [ reached getOption("max.print") -- omitted 4190 entries ]

MRSA_MR <- subset(MRSA, MRSA$root_order == 0)
MRSA_LR <- subset(MRSA, MRSA$root_order != 0)
dim(MRSA_MR)

[1] 1342   21

dim(MRSA_LR)

[1] 3848   21

#subsetting MR between ones with lateral roots and without
MRSA_MR_noLR <- subset(MRSA_MR, MRSA_MR$n_child == 0)
MRSA_MR_LR <- subset(MRSA_MR, MRSA_MR$n_child != 0)

MRSA_MR_LR$root[1]

[1] "8cae30f1-14ab-4479-9106-cfe92381948a"

MRSA_LR$parent[1]

[1] "8cae30f1-14ab-4479-9106-cfe92381948a"

MRSA_LR$parent[2]

[1] "574b2ce2-8705-4285-9a99-6a184b49c424"

MRSA_LR$parent %in% MRSA_MR_LR$root

   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [26] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [51] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [101] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [126] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [176] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [201] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [251] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [276] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [326] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [351] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [401] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [426] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [476] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [501] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [551] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [576] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [601] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [626] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [651] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [676] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [726] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [751] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [776] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [801] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [826] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [851] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [876] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [901] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [926] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [951] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [976] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [ reached getOption("max.print") -- omitted 2848 entries ]

MRSA_LR

temp <- subset(MRSA_LR, MRSA_LR$parent == MRSA_MR_LR$root[1])
temp

LRL <- sum(temp$length)
temp$LRno <- 1
sum(temp$LRno)

[1] 3

LRL <- sum(temp$length)
temp$LRno <- 1
sum(temp$LRno)

[1] 3

MRSA

LRL

[1] 0.755542

dim(temp)

[1]  3 22

LRno <- dim(temp)[1]
LRno

[1] 3

MRSA_MR_LR$LRL <- 0
MRSA_MR_LR$LRno <- 0
MRSA_MR_LR

MRSA_MR_LR$LRL[1] <- LRL
MRSA_MR_LR$LRno[1] <- LRno
MRSA_MR_LR

for(i in 1:nrow(MRSA_MR_LR)){
  temp <- subset(MRSA_LR, MRSA_LR$parent == MRSA_MR_LR$root[i])
  MRSA_MR_LR$LRL[i]  <- sum(temp$length)
  MRSA_MR_LR$LRno[i]  <- dim(temp)[1]
}

MRSA_MR_LR

colnames(MRSA_MR_LR)

 [1] "image"                 "root_name"             "root"                  "length"                "vector_length"        
 [6] "surface"               "volume"                "direction"             "diameter"              "root_order"           
[11] "root_ontology"         "parent_name"           "parent"                "insertion_position"    "insertion_angle"      
[16] "n_child"               "child_density"         "first_child"           "insertion_first_child" "last_child"           
[21] "insertion_last_child"  "LRL"                   "LRno"                 

MRSA_MR_LR2 <- MRSA_MR_LR[,c(1:2,4,19,21,22:23)]
MRSA_MR_LR2

MRSA_MR_noLR$LRL <- 0
MRSA_MR_noLR$LRno <- 0
MRSA_MR_noLR2 <- MRSA_MR_noLR[,c(1:2,4,19,21,22:23)]
MRSA_MR_noLR2$insertion_first_child <- 0
MRSA_MR_noLR2$insertion_last_child <- 0
MRSA_MR_noLR2

MRSA_MR <- rbind(MRSA_MR_noLR2, MRSA_MR_LR2)
dim(MRSA_MR_noLR2)

[1] 530   7

dim(MRSA_MR_LR2)

[1] 812   7

dim(MRSA_MR)

[1] 1342    7

MRSA_MR

there are some MR where we have a suspicion that they are lost LR:

lets remove all that are smaller than 0.5 of MR length

MRSA_MR2 <- subset(MRSA_MR, MRSA_MR$length > 0.5)
MRSA_MR2

there are still some random roots that we have to remove:

unique(MRSA_MR3$root_name)

   [1] "11_S_m4_2"    "11_S_m4_1"    "11_S_m3_1"    "46_S_m16_4"  
   [5] "46_S_m16_1"   "85_S_m32_4"   "85_S_m31_3"   "85_S_m31_1"  
   [9] "85_S_m30_3"   "85_S_m30_1"   "85_S_m29_3"   "m21; 1"      
  [13] "79_C_m40_2"   "79_C_m40_1"   "79_C_m39_4"   "79_C_m39_2"  
  [17] "38_C_m11_3"   "38_C_m11_2"   "38_C_m11_1"   "38_C_m9_2"   
  [21] "43_C_m5_3"    "43_C_m5_2"    "43_C_m5_1"    "13_S_m36_3"  
  [25] "13_S_m36_2"   "13_S_m36_1"   "13_S_m34_3"   "13_S_m33_4"  
  [29] "13_S_m33_2"   "13_S_m33_1"   "55_C_m45_4"   "55_C_m45_1"  
  [33] "55_C_m45_3"   "55_C_m47_1"   "55_C_m46_1"   "67_S_m28_4"  
  [37] "67_S_m28_1"   "67_S_m27_3"   "67_S_m27_4"   "67_S_m26_4"  
  [41] "67_S_m26_1"   "67_S_m25_4"   "67_S_m25_2"   "88_S_m31_3"  
  [45] "88_S_m31_1"   "88_S_m30_3"   "88_S_m30_2"   "88_S_m30_1"  
  [49] "88_S_m29_4"   "88_S_m29_3"   "30_S_m39_4"   "30_S_m39_2"  
  [53] "30_S_m37_2"   "30_S_m37_1"   "3_C_m36_3"    "3_C_m36_2"   
  [57] "3_C_m35_2"    "3_C_m35_1"    "3_C_m34_2"    "3_C_m34_1"   
  [61] "3_C_m33_4"    "15_C_m16_1"   "15_C_m15_1"   "15_C_m13_3"  
  [65] "71_S_m12_4"   "71_S_m12_3"   "71_S_m12_2"   "71_S_m12_1"  
  [69] "71_S_m10_2"   "42_C_m3_3"    "1_S_m24_3"    "1_S_m21_1"   
  [73] "1_S_m22_4"    "58_C_m48_4"   "58_C_m48_2"   "58_C_m47_1"  
  [77] "58_C_m46_1"   "19_C_m12_4"   "19_C_m12_3"   "19_C_m10_2"  
  [81] "92_S_m12_4"   "92_S_m12_3"   "92_S_m12_2"   "92_S_m12_1"  
  [85] "92_S_m11_2"   "92_S_m10_1"   "92_S_m9_3"    "92_S_m9_2"   
  [89] "35_C_m16_2"   "35_C_m14_1"   "62_C_m31_4"   "62_C_m31_2"  
  [93] "62_C_m31_1"   "62_C_m30_3"   "62_C_m30_2"   "62_C_m30_1"  
  [97] "74_C_m30_3"   "74_C_m30_2"   "47_S_m5_2"    "47_S_m5_1"   
 [101] "23_C_m44_4"   "23_C_m43_4"   "23_C_m43_2"   "23_C_m41_4"  
 [105] "28_C_m4_4"    "28_C_m3_3"    "28_C_m3_1"    "28_C_m2_1"   
 [109] "90_C_m31_4"   "90_C_m31_3"   "90_C_m31_2"   "90_C_m31_1"  
 [113] "90_C_m30_4"   "90_C_m30_3"   "90_C_m30_2"   "90_C_m30_1"  
 [117] "76_S_m28_4"   "76_S_m28_2"   "76_S_m28_1"   "76_S_m27_4"  
 [121] "76_S_m26_1"   "76_S_m25_3"   "76_S_m25_2"   "76_S_m25_1"  
 [125] "12_C_m20_1"   "m3; 1"        "m2; 3"        "21_S_m40_1"  
 [129] "4_C_m18_4"    "4_C_m18_1"    "17_S_m23_2"   "17_S_m23_1"  
 [133] "17_S_m22_4"   "24_C_m2_4"    "24_C_m2_1"    "24_C_m1_1"   
 [137] "40_S_m16_2"   "40_S_m16_1"   "40_S_m15_4"   "40_S_m15_2"  
 [141] "40_S_m13_4"   "40_S_m13_3"   "40_S_m13_2"   "73_C_m27_4"  
 [145] "73_C_m27_3"   "73_C_m26_4"   "73_C_m26_3"   "73_C_m26_1"  
 [149] "65_C_m48_4"   "65_C_m48_3"   "65_C_m48_1"   "65_C_m47_4"  
 [153] "65_C_m47_3"   "65_C_m47_1"   "65_C_m46_1"   "32_C_m19_1"  
 [157] "32_C_m18_4"   "32_C_m17_2"   "49_C_m44_4"   "49_C_m44_3"  
 [161] "49_C_m44_2"   "49_C_m44_1"   "49_C_m42_3"   "49_C_m42_2"  
 [165] "49_C_m42_1"   "49_C_m41_4"   "49_C_m41_1"   "8_C_m12_4"   
 [169] "8_C_m12_1"    "8_C_m11_3"    "8_C_m10_1"    "8_C_m9_2"    
 [173] "82_S_m27_4"   "82_S_27_3"    "82_S_m26_3"   "m23; 2"      
 [177] "m23; 1"       "m22; 4"       "52_C_m22_3"   "52_C_m21_4"  
 [181] "m5_4"         "m5_3"         "m5_2"         "m5_1"        
 [185] "44_C_m3_1"    "5_S_m4_4"     "5_S_m4_1"     "5_S_m2_3"    
 [189] "5_S_m2_2"     "51_C_m28_2"   "51_C_m27_4"   "51_C_m27_3"  
 [193] "51_C_m27_1"   "51_C_m25_4"   "51_C_m25_1"   "6_C_m16_2"   
 [197] "6_C_m16_1"    "6_C_m15_4"    "6_C_m15_2"    "6_C_m15_1"   
 [201] "6_C_m14_3"    "6_C_m14_1"    "6_C_m13_1"    "10_C_m7_2"   
 [205] "10_C_m5_3"    "10_C_m5_2"    "10_C_m5_1"    "81_S_m32_2"  
 [209] "81_S_m30_3"   "81_S_m30_2"   "81_S_m29_2"   "39_S_m44_2"  
 [213] "39_S_m44_1"   "39_S_m43_1"   "m42; 4"       "m41; 1"      
 [217] "78_S_m12_3"   "54_S_m47_2"   "54_S_m46_3"   "54_S_m46_2"  
 [221] "26_C_m14_4"   "26_C_m14_3"   "26_C_m14_2"   "26_C_m13_4"  
 [225] "26_C_m13_3"   "26_C_m13_2"   "26_C_m13_1"   "27_C_m7_4"   
 [229] "27_C_m7_3"    "27_C_m6_3"    "27_C_m5_2"    "70_C_m39_4"  
 [233] "70_C_m39_3"   "70_C_m39_1"   "70_C_m37_1"   "66_C_m22_3"  
 [237] "66_C_m22_2"   "66_C_m22_1"   "66_C_m21_1"   "89_C_m28_1"  
 [241] "89_C_m27_3"   "89_C_m27_2"   "89_C_m27_1"   "89_C_m26_4"  
 [245] "89_C_m25_4"   "89_C_m25_3"   "89_C_m25_2"   "89_C_m25_1"  
 [249] "31_C_m18_1"   "93_C_m22_2"   "59_S_m47_1"   "75_S_m28_4"  
 [253] "75_S_m28_3"   "75_S_m28_2"   "75_S_m28_1"   "75_S_m27_4"  
 [257] "75_S_m27_3"   "75_S_m27_2"   "75_S_m27_1"   "75_S_m25_3"  
 [261] "75_S_m25_2"   "m39; 1"       "2_S_m22_1"    "34_S_m44_4"  
 [265] "34_S_m43_3"   "34_S_m42_2"   "34_S_m41_3"   "50_C_m38_3"  
 [269] "3_C_m36_1"    "63_S_m8_1"    "63_S_m7_1"    "68_S_m22_4"  
 [273] "68_S_m22_2"   "29_S_m39_4"   "29_S_m39_2"   "29_S_m39_1"  
 [277] "m38; 1"       "29_S_m37_4"   "29_S_m37_3"   "91_S_m12_4"  
 [281] "91_S_m12_3"   "91_S_m11_4"   "91_S_m11_1"   "91_S_m9_2"   
 [285] "61_C_m28_4"   "61_C_m28_3"   "61_C_m28_1"   "61_C_m27_4"  
 [289] "61_C_m27_3"   "61_C_m27_2"   "61_C_m27_1"   "61_C_m26_3"  
 [293] "61_C_m25_3"   "61_C_m25_2"   "61_C_m25_"    "20_C_m46_3"  
 [297] "13_S_m8_3"    "13_S_m7_1"    "13_S_m6_4"    "13_S_m6_1"   
 [301] "13_S_m5_4"    "13_S_m5_2"    "13_S_m5_1"    "64_S_m16_2"  
 [305] "64_S_m15_1"   "64_S_m13_4"   "64_S_m13_1"   "57_C_m44_3"  
 [309] "57_C_m44_2"   "57_C_m42_1"   "57_C_m41_3"   "57_C_m41_2"  
 [313] "57_C_m41_1"   "33_S_m17_4"   "33_S_m17_3"   "25_S_m8_1"   
 [317] "25_S_m7_4"    "25_S_m7_1"    "25_S_m5_4"    "25_S_m5_3"   
 [321] "25_S_m5_2"    "16_C_m40_2"   "16_C_m40_1"   "16_C_m39_4"  
 [325] "16_C_m39_1"   "16_C_m38_4"   "16_C_m38_3"   "16_C_m38_1"  
 [329] "16_C_m37_4"   "72_S_m18_2"   "72_S_m17_4"   "72_S_m17_3"  
 [333] "72_S_m17_1"   "41_C_m5_4"    "41_C_m5_2"    "41_C_m5_1"   
 [337] "9_S_m4_3"     "48_S_m20_2"   "48_S_m17_3"   "48_S_m17_2"  
 [341] "48_S_m17_1"   "94_C_m30_3"   "94_C_m30_2"   "94_C_m30_1"  
 [345] "94_C_m29_3"   "94_C_m29_1"   "83_C_m36_1"   "83_C_m35_2"  
 [349] "83_C_m35_1"   "83_C_m33_4"   "83_C_m33_2"   "55_C_m44_3"  
 [353] "55_C_m44_2"   "56_C_m44_1"   "55_C_m43_4"   "55_C_m43_3"  
 [357] "55_C_m46_4"   "56_C_m45_3"   "45_S_m44_3"   "45_S_m44_2"  
 [361] "45_S_m44_1"   "45_S_m43_4"   "m17; 3"       "m17; 2"      
 [365] "37_C_m12_4"   "37_C_m12_3"   "37_C_m12_2"   "37_C_m12_1"  
 [369] "37_C_m11_4"   "37_C_m11_2"   "86_S_m31_2"   "86_S_m31_1"  
 [373] "86_S_m30_4"   "86_S_m30_1"   "11_S_m3_4"    "11_S_m3_3"   
 [377] "11_S_m3_2"    "46_S_m16_3"   "46_S_m16_2"   "46_S_m15_4"  
 [381] "46_S_m15_3"   "46_S_m15_2"   "46_S_m15_1"   "46_S_m14_4"  
 [385] "46_S_m14_3"   "46_S_m14_2"   "46_S_m14_1"   "46_S_m13_4"  
 [389] "46_S_m13_3"   "46_S_m13_2"   "46_S_m13_1"   "7_S_m4_4"    
 [393] "7_S_m4_3"     "7_S_m4_2"     "7_S_m4_1"     "7_S_m3_4"    
 [397] "7_S_m3_3"     "7_S_m3_2"     "7_S_m3_1"     "7_S_m2_4"    
 [401] "7_S_m2_3"     "7_S_m2_2"     "7_S_m2_1"     "7_S_m1_1"    
 [405] "85_S_m32_3"   "85_S_m32_2"   "85_S_m32_1"   "85_S_m31_4"  
 [409] "85_S_m31_2"   "85_S_m30_4"   "85_S_m30_2"   "85_S_m29_4"  
 [413] "85_S_m29_2"   "85_S_m29_1"   "m21; 4"       "m21; 3"      
 [417] "m21; 2"       "79_C_m39_3"   "79_C_m39_1"   "79_C_m38_4"  
 [421] "79_C_m38_3"   "79_C_m38_2"   "79_C_m38_1"   "79_C_m37_4"  
 [425] "79_C_m37_3"   "79_C_m37_2"   "79_C_m37_1"   "80_C_m24_2"  
 [429] "80_C_m24_1"   "80_C_m21_4"   "80_C_m21_3"   "80_C_m21_2"  
 [433] "80_C_m21_1"   "38_C_m9_1"    "43_C_m8_4"    "43_C_m8_3"   
 [437] "43_C_m8_2"    "43_C_m8_1"    "13_S_m35_2"   "13_S_m35_1"  
 [441] "13_S_m34_4"   "13_S_m34_2"   "13_S_m34_1"   "55_C_m45_2"  
 [445] "67_S_m28_3"   "67_S_m28_2"   "67_S_m27_2"   "67_S_m27_1"  
 [449] "67_S_m26_3"   "67_S_m26_2"   "67_S_m25_3"   "67_S_m25_1"  
 [453] "88_S_m32_4"   "88_S_m32_3"   "88_S_m32_2"   "88_S_m32_1"  
 [457] "88_S_m31_4"   "88_S_m31_2"   "88_S_m30_4"   "88_S_m29_2"  
 [461] "88_S_m29_1"   "30_S_m39_3"   "30_S_m39_1"   "30_S_m38_4"  
 [465] "30_S_m38_3"   "30_S_m38_2"   "30_S_m38_1"   "30_S_m37_4"  
 [469] "30_S_m37_3"   "3_C_m35_3"    "3_C_m34_4"    "3_C_m34_3"   
 [473] "3_C_m33_3"    "3_C_m33_2"    "3_C_m33_1"    "15_C_m16_4"  
 [477] "15_C_m16_3"   "15_C_m15_4"   "15_C_m15_3"   "15_C_m14_4"  
 [481] "15_C_m14_3"   "15_C_m14_2"   "15_C_m14_1"   "15_C_m13_4"  
 [485] "15_C_m13_2"   "15_C_m13_1"   "71_S_m11_4"   "71_S_m11_3"  
 [489] "71_S_m11_2"   "71_S_m11_"    "71_S_m10_4"   "71_S_m10_1"  
 [493] "71_S_m9_4"    "71_S_m9_3"    "71_S_m9_2"    "71_S_m9_1"   
 [497] "42_C_m4_1"    "42_C_m3_4"    "42_C_m3_2"    "42_C_m3_1"   
 [501] "1_S_m24_4"    "1_S_m24_2"    "1_S_m24_1"    "1_S_m23_4"   
 [505] "1_S_m23_3"    "1_S_m23_2"    "1_S_m23_1"    "1_S_m22_3"   
 [509] "1_S_m22_2"    "1_S_m22_1"    "1_S_m21_4"    "1_S_m21_3"   
 [513] "1_S_m21_2"    "58_C_m48_3"   "58_C_m48_1"   "58_C_m47_2"  
 [517] "58_C_m45_3"   "58_C_m45_1"   "19_C_m12_2"   "19_C_m12_1"  
 [521] "19_C_m11_4"   "19_C_m11_3"   "19_C_m11_2"   "19_C_m11_1"  
 [525] "19_C_m10_4"   "19_C_m10_3"   "19_C_m10_1"   "19_C_m9_4"   
 [529] "19_C_m9_3"    "19_C_m9_2"    "19_C_m9_1"    "92_S_m11_4"  
 [533] "92_S_m11_3"   "92_S_m11_1"   "92_S_m9_4"    "92_S_m9_1"   
 [537] "35_C_m16_1"   "35_C_m14_4"   "35_C_m14_3"   "35_C_m14_2"  
 [541] "62_C_m32_4"   "62_C_m32_3"   "62_C_m32_2"   "62_C_m31_3"  
 [545] "62_C_m30_4"   "62_C_m29_4"   "62_C_m29_3"   "62_C_m29_2"  
 [549] "62_C_m29_1"   "74_C_m32_4"   "74_C_m32_3"   "74_C_m32_2"  
 [553] "74_C_m32_1"   "74_C_m30_4"   "74_C_m30_1"   "74_C_m29_4"  
 [557] "74_C_m29_3"   "74_C_m29_2"   "74_C_m29_1"   "47_S_m8_4"   
 [561] "47_S_m8_3"    "47_S_m8_2"    "47_S_m8_1"    "47_S_m7_4"   
 [565] "47_S_m7_3"    "47_S_m7_2"    "47_S_m7_1"    "47_S_m5_4"   
 [569] "47_S_m5_3"    "23_C_m44_3"   "23_C_m44_1"   "23_C_m43_3"  
 [573] "23_C_m43_1"   "23_C_m42_4"   "23_C_m42_3"   "23_C_m42_2"  
 [577] "23_C_m42_1"   "23_C_m41_3"   "23_C_m41_2"   "23_C_m41_1"  
 [581] "28_C_m4_3"    "28_C_m4_2"    "28_C_m4_1"    "28_C_m3_4"   
 [585] "28_C_m3_2"    "28_C_m2_2"    "90_C_m32_4"   "90_C_m32_3"  
 [589] "90_C_m32_2"   "90_C_m32_1"   "90_C_m29_4"   "90_C_m29_3"  
 [593] "90_C_m29_2"   "90_C_m29_1"   "69_C_m34_4"   "69_C_m34_3"  
 [597] "69_C_m34_2"   "69_C_m34_1"   "76_S_m28_3"   "76_S_m27_3"  
 [601] "76_S_m27_2"   "76_S_m27_1"   "76_S_m26_4"   "76_S_m26_2"  
 [605] "76_S_m25_4"   "12_C_m20_2"   "12_C_m19_4"   "12_C_m19_2"  
 [609] "12_C_m19_1"   "12_C_m18_4"   "12_C_m18_3"   "12_C_m18_2"  
 [613] "12_C_m18_1"   "12_C_m17_4"   "12_C_m17_3"   "12_C_m17_2"  
 [617] "12_C_m17_1"   "m4; 4"        "m4; 3"        "m4; 2"       
 [621] "m4; 1"        "m3; 4"        "m3; 3"        "m3; 2"       
 [625] "m2; 4"        "m2; 2"        "m2; 1"        "m1; 1"       
 [629] "21_S_m40_3"   "21_S_m40_2"   "21_S_m39_4"   "21_S_m39_3"  
 [633] "21_S_m39_2"   "21_S_m39_1"   "21_S_m38_4"   "21_S_m38_3"  
 [637] "21_S_m38_2"   "21_S_m38_1"   "21_S_m37_4"   "21_S_m37_3"  
 [641] "21_S_m37_2"   "21_S_m37_1"   "4_C_m19_4"    "4_C_m19_3"   
 [645] "4_C_m19_2"    "4_C_m19_1"    "4_C_m18_3"    "4_C_m18_2"   
 [649] "4_C_m17_4"    "4_C_m17_3"    "4_C_m17_2"    "4_C_m17_1"   
 [653] "17_S_m24_1"   "17_S_m24_4"   "17_S_m24_3"   "17_S_m24_2"  
 [657] "17_S_m23_4"   "17_S_m23_3"   "17_S_m22_3"   "17_S_m22_2"  
 [661] "17_S_m22_1"   "17_S_m21_4"   "17_S_m21_3"   "17_S_m21_2"  
 [665] "17_S_m21_1"   "24_C_m4_3"    "24_C_m4_2"    "24_C_m4_1"   
 [669] "24_C_m3_4"    "24_C_m3_3"    "24_C_m3_2"    "24_C_m3_1"   
 [673] "24_C_m2_3"    "24_C_m2_2"    "24_C_m1_2"    "40_S_m16_4"  
 [677] "40_S_m16_3"   "40_S_m15_3"   "40_S_m15_1"   "40_S_m14_4"  
 [681] "40_S_m14_3"   "40_S_m14_2"   "40_S_m14_1"   "40_S_m13_1"  
 [685] "73_C_m27_2"   "73_C_m26_2"   "65_C_m48_2"   "65_C_m47_2"  
 [689] "65_C_m46_3"   "65_C_m46_2"   "65_C_m45_4"   "65_C_m45_2"  
 [693] "65_C_m45_1"   "32_C_m19_4"   "32_C_m19_3"   "32_C_m18_3"  
 [697] "32_C_m18_2"   "32_C_m18_1"   "32_C_m17_4"   "32_C_m17_3"  
 [701] "32_C_m17_1"   "49_C_m43_4"   "49_C_m43_3"   "49_C_m43_2"  
 [705] "49_C_m43_1"   "49_C_m42_4"   "49_C_m41_3"   "49_C_m41_2"  
 [709] "8_C_m12_3"    "8_C_m12_2"    "8_C_m11_4"    "8_C_m11_2"   
 [713] "8_C_m11_1"    "8_C_m10_2"    "8_C_m9_4"     "8_C_m9_3"    
 [717] "8_C_m9_1"     "82_S_m27_2"   "82_S_m27_1"   "82_S_m26_4"  
 [721] "82_S_m26_2"   "82_S_m26_1"   "m24; 4"       "m24; 3"      
 [725] "m24; 2"       "m23; 4"       "m23; 3"       "m22; 3"      
 [729] "m22; 2"       "m22; 1"       "52_C_m24_4"   "52_C_m24_3"  
 [733] "52_C_m24_2"   "52_C_m24_1"   "52_C_m23_4"   "52_C_m23_3"  
 [737] "52_C_m23_2"   "52_C_m23_1"   "52_C_m22_4"   "52_C_m22_2"  
 [741] "52_C_m22_1"   "52_C_m21_3"   "52_C_m21_2"   "52_C_m21_1"  
 [745] "m6_2"         "m6_1"         "77_S_m34_4"   "77_S_m34_3"  
 [749] "77_S_m34_2"   "77_S_m34_1"   "44_C_m3_4"    "44_C_m3_3"   
 [753] "44_C_m3_2"    "5_S_m4_3"     "5_S_m4_2"     "5_S_m3_4"    
 [757] "5_S_m3_2"     "5_S_m3_1"     "5_S_m2_1"     "5_S_m3_3"    
 [761] "51_C_m28_3"   "51_C_m28_1"   "51_C_m27_2"   "51_C_m26_4"  
 [765] "51_C_m26_3"   "51_C_m26_2"   "51_C_m26_1"   "51_C_m25_3"  
 [769] "51_C_m25_2"   "6_C_m16_4"    "6_C_m16_3"    "6_C_m15_3"   
 [773] "6_C_m14_4"    "6_C_m14_2"    "6_C_m13_4"    "6_C_m13_3"   
 [777] "6_C_m13_2"    "10_C_m8_4"    "10_C_m8_3"    "10_C_m8_2"   
 [781] "10_C_m8_1"    "10_C_m7_4"    "10_C_m7_3"    "10_C_m7_1"   
 [785] "10_C_m6_1"    "81_S_m32_4"   "81_S_m32_3"   "81_S_m32_1"  
 [789] "81_S_m31_4"   "81_S_m31_3"   "81_S_m31_2"   "81_S_m31_1"  
 [793] "81_S_m30_1"   "81_S_m29_4"   "81_S_m29_3"   "81_S_m29_1"  
 [797] "39_S_m44_4"   "39_S_m44_3"   "39_S_m43_4"   "39_S_m43_3"  
 [801] "39_S_m43_2"   "39_S_m42_3"   "m42; 2"       "m42; 1"      
 [805] "m41; 4"       "m41; 3"       "m41; 2"       "78_S_m12_2"  
 [809] "78_S_m12_1"   "78_S_m11_4"   "78_S_m11_3"   "78_S_m11_2"  
 [813] "78_S_m11_1"   "78_S_m10_4"   "78_S_m10_3"   "78_S_m10_2"  
 [817] "78_S_m10_1"   "78_S_m9_4"    "78_S_m9_3"    "78_S_m9_2"   
 [821] "78_S_m9_1"    "54_S_m48_4"   "54_S_m48_3"   "54_S_m48_2"  
 [825] "54_S_m48_1"   "54_S_m47_4"   "54_S_m47_1"   "54_S_m46_4"  
 [829] "54_S_m46_1"   "54_S_m45_4"   "54_S_m45_3"   "54_S_m45_2"  
 [833] "54_S_m45_1"   "26_C_m14_1"   "27_C_m8_4"    "27_C_m8_3"   
 [837] "27_C_m8_2"    "27_C_m8_1"    "27_C_m7_2"    "27_C_m7_1"   
 [841] "27_C_m6_4"    "27_C_m6_2"    "27_C_m6_1"    "27_C_m5_4"   
 [845] "27_C_m5_1"    "70_C_m39_2\\" "70_C_m38_4"   "70_C_m38_3"  
 [849] "70_C_m38_2"   "70_C_m38_1"   "70_C_m37_4"   "70_C_m37_3"  
 [853] "70_C_m37_2"   "66_C_m24_4\\" "66_C_m24_3"   "66_C_m24_2"  
 [857] "66_C_m24_1"   "66_C_m23_4"   "66_C_m23_3"   "66_C_m23_2"  
 [861] "66_C_m23_1"   "66_C_m21_4"   "66_C_m21_3"   "66_C_m21_2"  
 [865] "89_C_m27_4"   "89_C_m26_3"   "89_C_m26_2"   "89_C_m26_1"  
 [869] "31_C_m19_2"   "31_C_m19_1"   "31_C_m18_4"   "31_C_m18_3"  
 [873] "31_C_m18_2"   "31_C_m17_4"   "31_C_m17_3"   "31_C_m17_2"  
 [877] "31_C_m17_1"   "18_S_m34_4"   "18_S_m34_3"   "18_S_m34_2"  
 [881] "18_S_m34_1"   "93_C_m24_4"   "93_C_m24_3"   "93_C_m24_1"  
 [885] "93_C_m23_4"   "93_C_m23_3"   "93_C_m23_2"   "93_C_m23_1"  
 [889] "93_C_m22_4"   "93_C_m22_3"   "93_C_m22_1"   "93_C_m21_4"  
 [893] "93_C_m21_3"   "93_C_m21_2"   "93_C_m21_1"   "59_S_m48_4"  
 [897] "59_S_m48_3"   "59_S_m48_2"   "59_S_m48_1"   "59_S_m45_4"  
 [901] "59_S_m45_3"   "59_S_m45_2"   "59_S_m45_1"   "75_S_m26_4"  
 [905] "75_S_m26_3"   "75_S_m26_2"   "75_S_m26_1"   "75_S_m25_4"  
 [909] "75_S_m25_1"   "22_S_m39_4"   "m39; 3"       "m39; 2"      
 [913] "22_S_m39_3"   "m38; 3"       "m38; 2"       "m37; 4"      
 [917] "m37; 3"       "m37; 2"       "m37; 1"       "2_S_m24_4"   
 [921] "2_S_m24_3"    "2_S_m24_2"    "2_S_m24_1"    "2_S_m23_3"   
 [925] "2_S_m23_2"    "2_S_m23_1"    "2_S_m22_4"    "2_S_m22_3"   
 [929] "2_S_m22_2"    "2_S_m21_4"    "2_S_m21_3"    "2_S_m21_2"   
 [933] "2_S_m21_1"    "2_S_m23_4"    "34_S_m44_3"   "34_S_m44_2"  
 [937] "34_S_m44_1"   "34_S_m43_4"   "34_S_m43_2"   "34_S_m43_1"  
 [941] "34_S_m42_4"   "34_S_m42_3"   "34_S_m42_1"   "34_S_m41_4"  
 [945] "34_S_m41_2"   "34_S_m41_1"   "50_C_m39_4"   "50_C_m39_3"  
 [949] "50_C_m39_"    "50_C_m39_1"   "50_C_m38_4"   "50_C_m38_2"  
 [953] "50_C_m38_1"   "50_C_m37_4"   "50_C_m37_3"   "50_C_m37_2"  
 [957] "50_C_m37_1"   "3_C_m35_4"    "63_S_m8_4"    "63_S_m8_3"   
 [961] "63_S_m8_2"    "63_S_m7_3"    "63_S_m7_2"    "87_S_m34_4"  
 [965] "87_S_m34_3"   "87_S_m34_2"   "87_S_m34_1"   "68_S_m24_4"  
 [969] "68_S_m24_3"   "68_S_m24_2"   "68_S_m24_1"   "68_S_m22_3"  
 [973] "68_S_m22_1"   "68_S_m21_4"   "68_S_m21_3"   "68_S_m21_2"  
 [977] "68_S_m21_1"   "29_S_m39_3"   "29_S_m38_4"   "29_S_m38_3"  
 [981] "29_S_m38_2"   "29_S_m38_1"   "29_S_m37_2"   "29_S_m37_1"  
 [985] "91_S_m12_2"   "91_S_m12_1"   "91_S_m11_3"   "91_S_m11_2"  
 [989] "91_S_m9_1"    "61_C_m28_2"   "61_C_m26_4"   "61_C_m26_2"  
 [993] "61_C_m26_1"   "61_C_m25_4"   "20_C_m48_4"   "20_C_m48_3"  
 [997] "20_C_m48_2"   "20_C_m48_1"   "20_C_m47_4"   "20_C_m47_3"  
 [ reached getOption("max.print") -- omitted 130 entries ]

Check - everything is fine now - lets move on to calculate remaining traits:

TRS

Error: object 'TRS' not found

OK - now let’s decode the information into genotype, conditions and so on:

# plant number
strsplit(MRSA_MR3$root_name[1], "_")[[1]][4]

[1] "2"

Lets loop the above for all of the data into the entire dataset:

OK there are still some mistakes here that we need to fix

corrected <- read.csv("~/Desktop/corrected.csv")
MRSA_MR4 <- na.omit(MRSA_MR3, MRSA_MR3$Plant.no != NA)
all_data <- rbind(MRSA_MR4, corrected)
all_data

#corrected data for mislabeling of roots and removal of some repeat roots
MRSA_MR3

unique(MRSA_MR4$Genotype)

 [1] "m4"  "m3"  "m16" "m32" "m31" "m30" "m29" "m40" "m39" "m11"
[11] "m9"  "m5"  "m36" "m34" "m33" "m45" "m47" "m46" "m28" "m27"
[21] "m26" "m25" "m37" "m35" "m15" "m13" "m12" "m10" "m24" "m21"
[31] "m22" "m48" "m14" "m44" "m43" "m41" "m2"  "m20" "m18" "m23"
[41] "m1"  "m19" "m17" "m42" "27"  "m7"  "m6"  "m38" "m8" 

unique(MRSA_MR4$Plate.no)

 [1] "11" "46" "85" "79" "38" "43" "13" "55" "67" "88" "30" "3" 
[13] "15" "71" "42" "1"  "58" "19" "92" "35" "62" "74" "47" "23"
[25] "28" "90" "76" "12" "21" "4"  "17" "24" "40" "73" "65" "32"
[37] "49" "8"  "82" "52" "44" "5"  "51" "6"  "10" "81" "39" "78"
[49] "54" "26" "27" "70" "66" "89" "31" "93" "59" "75" "2"  "34"
[61] "50" "63" "68" "29" "91" "61" "20" "64" "57" "33" "25" "16"
[73] "72" "41" "9"  "48" "94" "83" "56" "45" "37" "86" "7"  "80"
[85] "69" "77" "18" "22" "87"

unique(MRSA_MR4$Condition)

[1] "S" "C"

unique(MRSA_MR4$Plant.no)

[1] "2"   "1"   "4"   "3"   "2\\" "4\\"

MRSA_MR4$Plate.no <- gsub("p", "P", MRSA_MR4$Plate.no)
MRSA_MR4$Plate.no <- gsub("Pl", "", MRSA_MR4$Plate.no)
MRSA_MR4$Plate.no <- as.numeric(as.character(MRSA_MR4$Plate.no))

# same for genotype
MRSA_MR4$Genotype <- gsub("m10", "PC_2.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m11", "Sweet_12.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m12", "Sweet_12.3", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m13", "PC_11.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m14", "PC_7.5", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m15", "PC_7.3", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m16", "PC_6.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m17", "PC_1.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m18", "PC_3.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m19", "PC_10.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m20", "PC_11.3", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m21", "PC_4.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m22", "PC_7.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m23", "PC_16.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m24", "PC_10.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m25", "PC_6.3", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m26", "PC_7.4", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m27", "PC_14.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m28", "PC_15.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m29", "Sweet_12.4", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m31", "PC_5.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m32", "PC_16.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m33", "PC_6.4", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m34", "PC_9.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m35", "PC_8.4", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m36", "PC_12.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m37", "PC_8.3", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m38", "PC_4.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m39", "PC_7.6", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m40", "PC_15.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m41", "PC_13.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m42", "PC_2.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m43", "PC_3.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m44", "Sweet_12.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m45", "PC_9.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m46", "PC_14.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m47", "PC_6.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m48", "PC_4.3", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m30", "Col", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m1", "PC_8.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m2", "PC_4.4", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m3", "PC_8.5", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m4", "PC_8.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m5", "PC_5.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m6", "PC_11.1", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m7", "PC_7.2", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m8", "PC_12.3", MRSA_MR4$Genotype)
MRSA_MR4$Genotype <- gsub("m9", "PC_14.3", MRSA_MR4$Genotype)
unique(MRSA_MR4$Genotype)

 [1] "PC_8.1"     "PC_8.5"     "PC_6.2"     "PC_16.1"   
 [5] "PC_5.2"     "Col"        "Sweet_12.4" "PC_15.1"   
 [9] "PC_7.6"     "Sweet_12.2" "PC_14.3"    "PC_5.1"    
[13] "PC_12.1"    "PC_9.1"     "PC_6.4"     "PC_9.2"    
[17] "PC_6.1"     "PC_14.2"    "PC_15.2"    "PC_14.1"   
[21] "PC_7.4"     "PC_6.3"     "PC_8.3"     "PC_8.4"    
[25] "PC_7.3"     "PC_11.2"    "Sweet_12.3" "PC_2.1"    
[29] "PC_10.2"    "PC_4.1"     "PC_7.1"     "PC_4.3"    
[33] "PC_7.5"     "Sweet_12.1" "PC_3.1"     "PC_13.1"   
[37] "PC_4.4"     "PC_11.3"    "PC_3.2"     "PC_16.2"   
[41] "PC_8.2"     "PC_10.1"    "PC_1.1"     "PC_2.2"    
[45] "27"         "PC_7.2"     "PC_11.1"    "PC_4.2"    
[49] "PC_12.3"   

there is still one genotype that I would like to remove:

bye <- c("27")
MRSA_MR5 <- subset(MRSA_MR4, !(MRSA_MR4$Genotype %in% bye))
unique(MRSA_MR5$Genotype)

 [1] "PC_8.1"     "PC_8.5"     "PC_6.2"     "PC_16.1"    "PC_5.2"     "Col"        "Sweet_12.4" "PC_15.1"    "PC_7.6"     "Sweet_12.2"
[11] "PC_14.3"    "PC_5.1"     "PC_12.1"    "PC_9.1"     "PC_6.4"     "PC_9.2"     "PC_6.1"     "PC_14.2"    "PC_15.2"    "PC_14.1"   
[21] "PC_7.4"     "PC_6.3"     "PC_8.3"     "PC_8.4"     "PC_7.3"     "PC_11.2"    "Sweet_12.3" "PC_2.1"     "PC_10.2"    "PC_4.1"    
[31] "PC_7.1"     "PC_4.3"     "PC_7.5"     "Sweet_12.1" "PC_3.1"     "PC_13.1"    "PC_4.4"     "PC_11.3"    "PC_3.2"     "PC_16.2"   
[41] "PC_8.2"     "PC_10.1"    "PC_1.1"     "PC_2.2"     "PC_7.2"     "PC_11.1"    "PC_4.2"     "PC_12.3"   

MRSA_MR5

unique(MRSA_MR5$Condition)

[1] NA

# something wrong with condition - lets re-extract it from the root_name again:
for(i in 1:nrow(MRSA_MR5)){
  MRSA_MR5$Condition[i] <- strsplit(MRSA_MR5$root_name[i], "_")[[1]][2]
}
unique(MRSA_MR5$Condition)

[1] "S" "C"

MRSA_MR5

# same for condition
MRSA_MR5$Condition <- gsub("C", "0", MRSA_MR5$Condition)
MRSA_MR5$Condition <- gsub("S", "75", MRSA_MR5$Condition)
MRSA_MR5$Condition

   [1] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75"
  [26] "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
  [51] "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "0"  "75" "75" "75" "0"  "0"  "0" 
  [76] "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "0" 
 [101] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "0"  "75"
 [126] "0"  "0"  "75" "75" "75" "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [151] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "0"  "0"  "0"  "75"
 [176] "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75"
 [201] "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [226] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [251] "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [276] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0" 
 [301] "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "0"  "0" 
 [326] "0"  "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75"
 [351] "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [376] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [401] "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75"
 [426] "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [451] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [476] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "75" "75" "75"
 [501] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [526] "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [551] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [576] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75"
 [601] "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75"
 [626] "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [651] "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "0" 
 [676] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [701] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [726] "0"  "0"  "75" "75" "75" "75" "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [751] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75"
 [776] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [801] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [826] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [851] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0" 
 [876] "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [901] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0" 
 [926] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [951] "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "0"  "0"  "0"  "0"  "0"  "0" 
 [976] "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "0"  "75" "75" "75" "75" "75" "75" "75" "75" "75" "75" "75"
 [ reached getOption("max.print") -- omitted 113 entries ]

MRSA_MR5

lets clean up

MRSA_MR5$insertion_first_child <- as.numeric(as.character(MRSA_MR5$insertion_first_child))
MRSA_MR5$insertion_last_child <- as.numeric(as.character(MRSA_MR5$insertion_last_child))

MRSA_MR5$Branched <- MRSA_MR5$insertion_last_child - MRSA_MR5$insertion_first_child
MRSA_MR5$Basal <- MRSA_MR5$length - MRSA_MR5$insertion_last_child
MRSA_MR5$Apical <- MRSA_MR5$insertion_first_child
MRSA_MR5

#same for plant
MRSA_MR5$plant <- gsub("p.", "P.", MRSA_MR5$Plant.no)
MRSA_MR5$plant <- gsub("P.", "", MRSA_MR5$Plant.no)

unique(MRSA_MR5$Plate.no)

 [1] 11 46 85 79 38 43 13 55 67 88 30  3 15 71 42  1 58 19 92 35 62 74 47 23 28 90 76 12 21  4 17 24 40 73 65 32 49  8 82 52 44  5 51
[44]  6 10 81 39 78 54 26 27 70 66 89 31 93 59 75  2 34 50 63 68 29 91 61 20 64 57 33 25 16 72 41  9 48 94 83 56 45 37 86  7 80 69 77
[87] 18 22 87

unique(MRSA_MR5$Plant.no)

[1] "2"   "1"   "4"   "3"   "2\\" "4\\"

unique(MRSA_MR5$Condition)

[1] "75" "0" 

Lets Analyze!

install.packages("ggplot2")

Error in install.packages : Updating loaded packages

install.packages("ggpubr")

Error in install.packages : Updating loaded packages

library(ggplot2)
library(ggpubr)
TRS <- ggplot(MRSA_MR5, aes(x = Genotype, y = TRS))
TRS <- TRS + geom_boxplot() + theme(axis.text.x = element_text(angle = 90))
TRS <- TRS + facet_grid(~ Condition)
TRS

TRS <- ggplot(MRSA_MR5, aes(x = Genotype, y = TRS, fill = Genotype))
TRS <- TRS + geom_boxplot() + theme(axis.text.x = element_text(angle = 90)) + rremove("legend")
TRS <- TRS + facet_grid(~ Condition)
TRS

MRSA_MR5 <- MRSA_MR5 %>% 
  rename("length" = "MRL")

Error in rename(., length = "MRL") : could not find function "rename"

MRSA_MR5$Condition <- as.factor(MRSA_MR5$Condition)
library(ggpubr)

bye <- c("PC_11.3", "PC_12.1", "PC_8.2")

MRSA_MR6 <- subset(MRSA_MR5, !(MRSA_MR5$Genotype %in% bye))


LRL_d0_errorplot <- ggerrorplot(MRSA_MR6, y="LRL", x="Genotype", fill="Genotype", ncol = 3, facet.by = "Condition",
                                desc_stat = "mean_sd", add="jitter", add.params = list(color = "darkgray"),
                                xlab="", ylab="Lateral Root Length (cm)", main = "") + theme(axis.text.x = element_text(angle = 90))
LRL_d0_errorplot <- LRL_d0_errorplot + rremove("legend") + stat_compare_means(method = "t.test", ref = "Col", label = "p.signif")
LRL_d0_errorplot

MRSA_C <- subset(MRSA_MR6, MRSA_MR6$Condition == "0")
MRSA_S <- subset(MRSA_MR6, MRSA_MR6$Condition == "75")

TRS_C <- ggerrorplot(MRSA_C, y="TRS", x="Genotype", fill="Genotype", ncol = 3, 
                                desc_stat = "mean_sd", add="jitter", add.params = list(color = "darkgray"),
                                xlab="", ylab="Total Root Size (cm)", main = "") + theme(axis.text.x = element_text(angle = 90))
TRS_C <- TRS_C + rremove("legend") + stat_compare_means(method = "t.test", ref.group = "Col", label = "p.signif")
TRS_C

TRS_S <- ggerrorplot(MRSA_S, y="TRS", x="Genotype", fill="Genotype", ncol = 3, 
                                desc_stat = "mean_sd", add="jitter", add.params = list(color = "darkgray"),
                                xlab="", ylab="Total Root Size (cm)", main = "") + theme(axis.text.x = element_text(angle = 90))
TRS_S <- TRS_S + rremove("legend") + stat_compare_means(method = "t.test", ref.group = "Col", label = "p.signif")
TRS_S

The above is great - but messssyyyyy - lets try to unmess it:

First - lets order the mutants in some kind of logical order:

library(cowplot)
plot_grid(TRS_C, TRS_S, labels = "AUTO", ncol = 1)

pdf("TotalRootSize_Graph.pdf", width = 15, heigh = 15)

plot_grid(TRS_C, TRS_S, labels = "AUTO", ncol = 1)
dev.off()

quartz_off_screen 
                2 

getwd()

[1] "/Users/katiecarson/rootanalysis"

LRL_d0_errorplot <- ggerrorplot(MRSA_MR6, y="LRL", x="Genotype", fill="Genotype", ncol = 3, facet.by = "Condition",
                                desc_stat = "mean_sd", add="jitter", add.params = list(color = "darkgray"),
                                xlab="", ylab="Lateral Root Length (cm)", main = "") + theme(axis.text.x = element_text(angle = 90))
LRL_d0_errorplot <- LRL_d0_errorplot + rremove("legend") + stat_compare_means(method = "t.test", ref = "Col-0", label = "p.signif")
LRL_d0_errorplot

Warning: Computation failed in `stat_compare_means()`.
Caused by error in `if (ref.group == ".all.") ...`:
! missing value where TRUE/FALSE needed
Warning: Computation failed in `stat_compare_means()`.
Caused by error in `if (ref.group == ".all.") ...`:
! missing value where TRUE/FALSE needed

LRL_C <- ggerrorplot(MRSA_C, y="LRL", x="Genotype", fill="Genotype", ncol = 3, 
                                desc_stat = "mean_sd", add="jitter", add.params = list(color = "darkgray"),
                                xlab="", ylab="Lateral Root Length (cm)", main = "") + theme(axis.text.x = element_text(angle = 90))
LRL_C <- LRL_C + rremove("legend") + stat_compare_means(method = "t.test", ref.group = "Col", label = "p.signif")
LRL_C

LRL_S <- ggerrorplot(MRSA_S, y="LRL", x="Genotype", fill="Genotype", ncol = 3, 
                                desc_stat = "mean_sd", add="jitter", add.params = list(color = "darkgray"),
                                xlab="", ylab="Lateral Root Length (cm)", main = "") + theme(axis.text.x = element_text(angle = 90))
LRL_S <- LRL_S + rremove("legend") + stat_compare_means(method = "t.test", ref.group = "Col", label = "p.signif")
LRL_S

library(cowplot)
plot_grid(LRL_C, LRL_S, labels = "AUTO", ncol = 1)

pdf("LateralRootLength_Graph.pdf", width = 15, heigh = 15)
plot_grid(LRL_C, LRL_S, labels = "AUTO", ncol = 1)
dev.off()

quartz_off_screen 
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library(cowplot)
plot_grid(MRL_C, MRL_S, labels = "AUTO", ncol = 1)

pdf("MainRootLength_Graph.pdf", width = 15, heigh = 15)
plot_grid(MRL_C, MRL_S, labels = "AUTO", ncol = 1)
dev.off()

quartz_off_screen 
                2 

library(cowplot)
plot_grid(LRno_C, LRno_S, labels = "AUTO", ncol = 1)

pdf("LateralRootNumber_Graph.pdf", width = 15, heigh = 15)
plot_grid(LRno_C, LRno_S, labels = "AUTO", ncol = 1)
dev.off()

quartz_off_screen 
                2