VIGS with Annie
A pipeline for evaluating the RGB and PS2 data
Load the RGB data
list.files()## [1] "20240614_Annie_VIGS_1x1.xlsx - Sheet1.csv"
## [2] "20240708_Annie_VIGS.html"
## [3] "20240708_Annie_VIGS.nb.html"
## [4] "20240708_Annie_VIGS.Rmd"
## [5] "Annie_VIGS_Suvita_20240621.csv"
## [6] "Annie_VIGS_Suvita_20240621.TXT"
## [7] "consolidated_data.csv"
## [8] "rsconnect"RGB <- read.csv("consolidated_data.csv")
RGB$measurement_value <- as.numeric(as.character(RGB$measurement_value))
RGBLet’s separate the file_name into individual sections - including timestamp, trayID and so on.
strsplit(RGB$file_name[1], "_")## [[1]]
## [1] "20240614-110819" "36838114852864" "00034913" "010104"
## [5] "040284" "Side01.xml"1st element is timestamp (YYYMMDD-HHMMSS), then some unknown ID, then tray ID, experiment ID, some other unknown ID and side.xml file
We will need timestamp and trayID for this experiment:
timestamp <- strsplit(RGB$file_name[1], "_")[[1]][1]
tray.ID <- strsplit(RGB$file_name[1], "_")[[1]][3]Now let’s loop it into all of the rows of our dataset:
for(i in 1:nrow(RGB)){
RGB$timestamp[i] <- strsplit(RGB$file_name[i], "_")[[1]][1]
RGB$tray.ID[i] <- strsplit(RGB$file_name[i], "_")[[1]][3]
}
RGB$tray.ID <- as.numeric(as.character(RGB$tray.ID))
unique(RGB$tray.ID)## [1] 34913 34914 34915 34916 34917 34918Let’s keep only the trays that we know we have:
decode <- read.csv("20240614_Annie_VIGS_1x1.xlsx - Sheet1.csv")
decode <- decode[,c(2:3,6,7)]
colnames(decode)[1] <- "tray.ID"
decodeunique(RGB$tray_id)## NULLRGB2 <- merge(RGB, decode, by= "tray.ID", all=T)
RGB2unique(RGB2$measurement_name)## [1] "Rfid" "Side01_00_row1"
## [3] "Side01_00_area" "Side01_00_height"
## [5] "Side01_00_width" "Side01_00_circularity"
## [7] "Side01_00_compactness" "Side01_00_contlength"
## [9] "Side01_00_convexity" "Side01_00_rectangularity"
## [11] "Side01_00_anisometry" "Side01_00_bulkiness"
## [13] "Side01_00_struct_factor" "Side01_00_outer_radius"
## [15] "Side01_00_inner_radius" "Side01_00_dist_mean"
## [17] "Side01_00_dist_deviation" "Side01_00_roundness"
## [19] "Side01_00_holes_num" "Side01_00_areaInMm2"
## [21] "Side01_00_heightInMm" "Side01_00_widthInMm"
## [23] "Side01_00_Convex_Hull_Area" "Side01_00_AverageColor_H"
## [25] "Side01_00_AverageColor_S" "Side01_00_AverageColor_V"
## [27] "Side01_00_CenterOfMassX" "Side01_00_CenterOfMassY"
## [29] "Side01_00_CenterOfMassDistance" "Side01_00_H_Mean"
## [31] "Side01_00_H_Median" "Side01_00_H_StDev"
## [33] "Side01_00_H_Q1" "Side01_00_H_Q3"
## [35] "Side01_00_brown(H_14_27)" "Side01_00_brown-yellow(H_28_35)"
## [37] "Side01_00_yellow(H_36_43)" "Side01_00_yellow-green(H_44_57)"
## [39] "Side01_00_green(H_58_99)" "Side01_00_green-cyan(H_100_120)"
## [41] "Side01_00_cyan-blue(H_121_142)" "Side01_00_blue(H_143_156)"
## [43] "Side01_00_HistoH_0_5" "Side01_00_HistoH_5_10"
## [45] "Side01_00_HistoH_10_15" "Side01_00_HistoH_15_20"
## [47] "Side01_00_HistoH_20_25" "Side01_00_HistoH_25_30"
## [49] "Side01_00_HistoH_30_35" "Side01_00_HistoH_35_40"
## [51] "Side01_00_HistoH_40_45" "Side01_00_HistoH_45_50"
## [53] "Side01_00_HistoH_50_55" "Side01_00_HistoH_55_60"
## [55] "Side01_00_HistoH_60_65" "Side01_00_HistoH_65_70"
## [57] "Side01_00_HistoH_70_75" "Side01_00_HistoH_75_80"
## [59] "Side01_00_HistoH_80_85" "Side01_00_HistoH_85_90"
## [61] "Side01_00_HistoH_90_95" "Side01_00_HistoH_95_100"
## [63] "Side01_00_HistoH_100_105" "Side01_00_HistoH_105_110"
## [65] "Side01_00_HistoH_110_115" "Side01_00_HistoH_115_120"
## [67] "Side01_00_HistoH_120_125" "Side01_00_HistoH_125_130"
## [69] "Side01_00_HistoH_130_135" "Side01_00_HistoH_135_140"
## [71] "Side01_00_HistoH_140_145" "Side01_00_HistoH_145_150"
## [73] "Side01_00_HistoH_150_155" "Side01_00_HistoH_155_160"
## [75] "Side01_00_HistoH_160_165" "Side01_00_HistoH_165_170"
## [77] "Side01_00_HistoH_170_175" "Side01_00_HistoH_175_180"
## [79] "Side01_00_HistoH_180_185" "Side01_00_HistoH_185_190"
## [81] "Side01_00_HistoH_190_195" "Side01_00_HistoH_195_200"
## [83] "Side01_00_HistoH_200_205" "Side01_00_HistoH_205_210"
## [85] "Side01_00_HistoH_210_215" "Side01_00_HistoH_215_220"
## [87] "Side01_00_HistoH_220_225" "Side01_00_HistoH_225_230"
## [89] "Side01_00_HistoH_230_235" "Side01_00_HistoH_235_240"
## [91] "Side01_00_HistoH_240_245" "Side01_00_HistoH_245_250"
## [93] "Side01_00_HistoH_250_255" "Side01_00_Greennes_Mean"
## [95] "Side01_00_Greennes_Deviation" "Side01_00_BarcodeEnabled"
## [97] "Side01_00_BarcodeFound" "BarcodeResult"
## [99] "Side01_01_row1" "Side01_01_area"
## [101] "Side01_01_height" "Side01_01_width"
## [103] "Side01_01_circularity" "Side01_01_compactness"
## [105] "Side01_01_contlength" "Side01_01_convexity"
## [107] "Side01_01_rectangularity" "Side01_01_anisometry"
## [109] "Side01_01_bulkiness" "Side01_01_struct_factor"
## [111] "Side01_01_outer_radius" "Side01_01_inner_radius"
## [113] "Side01_01_dist_mean" "Side01_01_dist_deviation"
## [115] "Side01_01_roundness" "Side01_01_holes_num"
## [117] "Side01_01_areaInMm2" "Side01_01_heightInMm"
## [119] "Side01_01_widthInMm" "Side01_01_Convex_Hull_Area"
## [121] "Side01_01_AverageColor_H" "Side01_01_AverageColor_S"
## [123] "Side01_01_AverageColor_V" "Side01_01_CenterOfMassX"
## [125] "Side01_01_CenterOfMassY" "Side01_01_CenterOfMassDistance"
## [127] "Side01_01_H_Mean" "Side01_01_H_Median"
## [129] "Side01_01_H_StDev" "Side01_01_H_Q1"
## [131] "Side01_01_H_Q3" "Side01_01_brown(H_14_27)"
## [133] "Side01_01_brown-yellow(H_28_35)" "Side01_01_yellow(H_36_43)"
## [135] "Side01_01_yellow-green(H_44_57)" "Side01_01_green(H_58_99)"
## [137] "Side01_01_green-cyan(H_100_120)" "Side01_01_cyan-blue(H_121_142)"
## [139] "Side01_01_blue(H_143_156)" "Side01_01_HistoH_0_5"
## [141] "Side01_01_HistoH_5_10" "Side01_01_HistoH_10_15"
## [143] "Side01_01_HistoH_15_20" "Side01_01_HistoH_20_25"
## [145] "Side01_01_HistoH_25_30" "Side01_01_HistoH_30_35"
## [147] "Side01_01_HistoH_35_40" "Side01_01_HistoH_40_45"
## [149] "Side01_01_HistoH_45_50" "Side01_01_HistoH_50_55"
## [151] "Side01_01_HistoH_55_60" "Side01_01_HistoH_60_65"
## [153] "Side01_01_HistoH_65_70" "Side01_01_HistoH_70_75"
## [155] "Side01_01_HistoH_75_80" "Side01_01_HistoH_80_85"
## [157] "Side01_01_HistoH_85_90" "Side01_01_HistoH_90_95"
## [159] "Side01_01_HistoH_95_100" "Side01_01_HistoH_100_105"
## [161] "Side01_01_HistoH_105_110" "Side01_01_HistoH_110_115"
## [163] "Side01_01_HistoH_115_120" "Side01_01_HistoH_120_125"
## [165] "Side01_01_HistoH_125_130" "Side01_01_HistoH_130_135"
## [167] "Side01_01_HistoH_135_140" "Side01_01_HistoH_140_145"
## [169] "Side01_01_HistoH_145_150" "Side01_01_HistoH_150_155"
## [171] "Side01_01_HistoH_155_160" "Side01_01_HistoH_160_165"
## [173] "Side01_01_HistoH_165_170" "Side01_01_HistoH_170_175"
## [175] "Side01_01_HistoH_175_180" "Side01_01_HistoH_180_185"
## [177] "Side01_01_HistoH_185_190" "Side01_01_HistoH_190_195"
## [179] "Side01_01_HistoH_195_200" "Side01_01_HistoH_200_205"
## [181] "Side01_01_HistoH_205_210" "Side01_01_HistoH_210_215"
## [183] "Side01_01_HistoH_215_220" "Side01_01_HistoH_220_225"
## [185] "Side01_01_HistoH_225_230" "Side01_01_HistoH_230_235"
## [187] "Side01_01_HistoH_235_240" "Side01_01_HistoH_240_245"
## [189] "Side01_01_HistoH_245_250" "Side01_01_HistoH_250_255"
## [191] "Side01_01_Greennes_Mean" "Side01_01_Greennes_Deviation"
## [193] "Side01_01_BarcodeEnabled" "Side01_01_BarcodeFound"
## [195] "Side01_02_row1" "Side01_02_area"
## [197] "Side01_02_height" "Side01_02_width"
## [199] "Side01_02_circularity" "Side01_02_compactness"
## [201] "Side01_02_contlength" "Side01_02_convexity"
## [203] "Side01_02_rectangularity" "Side01_02_anisometry"
## [205] "Side01_02_bulkiness" "Side01_02_struct_factor"
## [207] "Side01_02_outer_radius" "Side01_02_inner_radius"
## [209] "Side01_02_dist_mean" "Side01_02_dist_deviation"
## [211] "Side01_02_roundness" "Side01_02_holes_num"
## [213] "Side01_02_areaInMm2" "Side01_02_heightInMm"
## [215] "Side01_02_widthInMm" "Side01_02_Convex_Hull_Area"
## [217] "Side01_02_AverageColor_H" "Side01_02_AverageColor_S"
## [219] "Side01_02_AverageColor_V" "Side01_02_CenterOfMassX"
## [221] "Side01_02_CenterOfMassY" "Side01_02_CenterOfMassDistance"
## [223] "Side01_02_H_Mean" "Side01_02_H_Median"
## [225] "Side01_02_H_StDev" "Side01_02_H_Q1"
## [227] "Side01_02_H_Q3" "Side01_02_brown(H_14_27)"
## [229] "Side01_02_brown-yellow(H_28_35)" "Side01_02_yellow(H_36_43)"
## [231] "Side01_02_yellow-green(H_44_57)" "Side01_02_green(H_58_99)"
## [233] "Side01_02_green-cyan(H_100_120)" "Side01_02_cyan-blue(H_121_142)"
## [235] "Side01_02_blue(H_143_156)" "Side01_02_HistoH_0_5"
## [237] "Side01_02_HistoH_5_10" "Side01_02_HistoH_10_15"
## [239] "Side01_02_HistoH_15_20" "Side01_02_HistoH_20_25"
## [241] "Side01_02_HistoH_25_30" "Side01_02_HistoH_30_35"
## [243] "Side01_02_HistoH_35_40" "Side01_02_HistoH_40_45"
## [245] "Side01_02_HistoH_45_50" "Side01_02_HistoH_50_55"
## [247] "Side01_02_HistoH_55_60" "Side01_02_HistoH_60_65"
## [249] "Side01_02_HistoH_65_70" "Side01_02_HistoH_70_75"
## [251] "Side01_02_HistoH_75_80" "Side01_02_HistoH_80_85"
## [253] "Side01_02_HistoH_85_90" "Side01_02_HistoH_90_95"
## [255] "Side01_02_HistoH_95_100" "Side01_02_HistoH_100_105"
## [257] "Side01_02_HistoH_105_110" "Side01_02_HistoH_110_115"
## [259] "Side01_02_HistoH_115_120" "Side01_02_HistoH_120_125"
## [261] "Side01_02_HistoH_125_130" "Side01_02_HistoH_130_135"
## [263] "Side01_02_HistoH_135_140" "Side01_02_HistoH_140_145"
## [265] "Side01_02_HistoH_145_150" "Side01_02_HistoH_150_155"
## [267] "Side01_02_HistoH_155_160" "Side01_02_HistoH_160_165"
## [269] "Side01_02_HistoH_165_170" "Side01_02_HistoH_170_175"
## [271] "Side01_02_HistoH_175_180" "Side01_02_HistoH_180_185"
## [273] "Side01_02_HistoH_185_190" "Side01_02_HistoH_190_195"
## [275] "Side01_02_HistoH_195_200" "Side01_02_HistoH_200_205"
## [277] "Side01_02_HistoH_205_210" "Side01_02_HistoH_210_215"
## [279] "Side01_02_HistoH_215_220" "Side01_02_HistoH_220_225"
## [281] "Side01_02_HistoH_225_230" "Side01_02_HistoH_230_235"
## [283] "Side01_02_HistoH_235_240" "Side01_02_HistoH_240_245"
## [285] "Side01_02_HistoH_245_250" "Side01_02_HistoH_250_255"
## [287] "Side01_02_Greennes_Mean" "Side01_02_Greennes_Deviation"
## [289] "Side01_02_BarcodeEnabled" "Side01_02_BarcodeFound"
## [291] "Side01_03_row1" "Side01_03_area"
## [293] "Side01_03_height" "Side01_03_width"
## [295] "Side01_03_circularity" "Side01_03_compactness"
## [297] "Side01_03_contlength" "Side01_03_convexity"
## [299] "Side01_03_rectangularity" "Side01_03_anisometry"
## [301] "Side01_03_bulkiness" "Side01_03_struct_factor"
## [303] "Side01_03_outer_radius" "Side01_03_inner_radius"
## [305] "Side01_03_dist_mean" "Side01_03_dist_deviation"
## [307] "Side01_03_roundness" "Side01_03_holes_num"
## [309] "Side01_03_areaInMm2" "Side01_03_heightInMm"
## [311] "Side01_03_widthInMm" "Side01_03_Convex_Hull_Area"
## [313] "Side01_03_AverageColor_H" "Side01_03_AverageColor_S"
## [315] "Side01_03_AverageColor_V" "Side01_03_CenterOfMassX"
## [317] "Side01_03_CenterOfMassY" "Side01_03_CenterOfMassDistance"
## [319] "Side01_03_H_Mean" "Side01_03_H_Median"
## [321] "Side01_03_H_StDev" "Side01_03_H_Q1"
## [323] "Side01_03_H_Q3" "Side01_03_brown(H_14_27)"
## [325] "Side01_03_brown-yellow(H_28_35)" "Side01_03_yellow(H_36_43)"
## [327] "Side01_03_yellow-green(H_44_57)" "Side01_03_green(H_58_99)"
## [329] "Side01_03_green-cyan(H_100_120)" "Side01_03_cyan-blue(H_121_142)"
## [331] "Side01_03_blue(H_143_156)" "Side01_03_HistoH_0_5"
## [333] "Side01_03_HistoH_5_10" "Side01_03_HistoH_10_15"
## [335] "Side01_03_HistoH_15_20" "Side01_03_HistoH_20_25"
## [337] "Side01_03_HistoH_25_30" "Side01_03_HistoH_30_35"
## [339] "Side01_03_HistoH_35_40" "Side01_03_HistoH_40_45"
## [341] "Side01_03_HistoH_45_50" "Side01_03_HistoH_50_55"
## [343] "Side01_03_HistoH_55_60" "Side01_03_HistoH_60_65"
## [345] "Side01_03_HistoH_65_70" "Side01_03_HistoH_70_75"
## [347] "Side01_03_HistoH_75_80" "Side01_03_HistoH_80_85"
## [349] "Side01_03_HistoH_85_90" "Side01_03_HistoH_90_95"
## [351] "Side01_03_HistoH_95_100" "Side01_03_HistoH_100_105"
## [353] "Side01_03_HistoH_105_110" "Side01_03_HistoH_110_115"
## [355] "Side01_03_HistoH_115_120" "Side01_03_HistoH_120_125"
## [357] "Side01_03_HistoH_125_130" "Side01_03_HistoH_130_135"
## [359] "Side01_03_HistoH_135_140" "Side01_03_HistoH_140_145"
## [361] "Side01_03_HistoH_145_150" "Side01_03_HistoH_150_155"
## [363] "Side01_03_HistoH_155_160" "Side01_03_HistoH_160_165"
## [365] "Side01_03_HistoH_165_170" "Side01_03_HistoH_170_175"
## [367] "Side01_03_HistoH_175_180" "Side01_03_HistoH_180_185"
## [369] "Side01_03_HistoH_185_190" "Side01_03_HistoH_190_195"
## [371] "Side01_03_HistoH_195_200" "Side01_03_HistoH_200_205"
## [373] "Side01_03_HistoH_205_210" "Side01_03_HistoH_210_215"
## [375] "Side01_03_HistoH_215_220" "Side01_03_HistoH_220_225"
## [377] "Side01_03_HistoH_225_230" "Side01_03_HistoH_230_235"
## [379] "Side01_03_HistoH_235_240" "Side01_03_HistoH_240_245"
## [381] "Side01_03_HistoH_245_250" "Side01_03_HistoH_250_255"
## [383] "Side01_03_Greennes_Mean" "Side01_03_Greennes_Deviation"
## [385] "Side01_03_BarcodeEnabled" "Side01_03_BarcodeFound"
## [387] "Side01_04_row1" "Side01_04_area"
## [389] "Side01_04_height" "Side01_04_width"
## [391] "Side01_04_circularity" "Side01_04_compactness"
## [393] "Side01_04_contlength" "Side01_04_convexity"
## [395] "Side01_04_rectangularity" "Side01_04_anisometry"
## [397] "Side01_04_bulkiness" "Side01_04_struct_factor"
## [399] "Side01_04_outer_radius" "Side01_04_inner_radius"
## [401] "Side01_04_dist_mean" "Side01_04_dist_deviation"
## [403] "Side01_04_roundness" "Side01_04_holes_num"
## [405] "Side01_04_areaInMm2" "Side01_04_heightInMm"
## [407] "Side01_04_widthInMm" "Side01_04_Convex_Hull_Area"
## [409] "Side01_04_AverageColor_H" "Side01_04_AverageColor_S"
## [411] "Side01_04_AverageColor_V" "Side01_04_CenterOfMassX"
## [413] "Side01_04_CenterOfMassY" "Side01_04_CenterOfMassDistance"
## [415] "Side01_04_H_Mean" "Side01_04_H_Median"
## [417] "Side01_04_H_StDev" "Side01_04_H_Q1"
## [419] "Side01_04_H_Q3" "Side01_04_brown(H_14_27)"
## [421] "Side01_04_brown-yellow(H_28_35)" "Side01_04_yellow(H_36_43)"
## [423] "Side01_04_yellow-green(H_44_57)" "Side01_04_green(H_58_99)"
## [425] "Side01_04_green-cyan(H_100_120)" "Side01_04_cyan-blue(H_121_142)"
## [427] "Side01_04_blue(H_143_156)" "Side01_04_HistoH_0_5"
## [429] "Side01_04_HistoH_5_10" "Side01_04_HistoH_10_15"
## [431] "Side01_04_HistoH_15_20" "Side01_04_HistoH_20_25"
## [433] "Side01_04_HistoH_25_30" "Side01_04_HistoH_30_35"
## [435] "Side01_04_HistoH_35_40" "Side01_04_HistoH_40_45"
## [437] "Side01_04_HistoH_45_50" "Side01_04_HistoH_50_55"
## [439] "Side01_04_HistoH_55_60" "Side01_04_HistoH_60_65"
## [441] "Side01_04_HistoH_65_70" "Side01_04_HistoH_70_75"
## [443] "Side01_04_HistoH_75_80" "Side01_04_HistoH_80_85"
## [445] "Side01_04_HistoH_85_90" "Side01_04_HistoH_90_95"
## [447] "Side01_04_HistoH_95_100" "Side01_04_HistoH_100_105"
## [449] "Side01_04_HistoH_105_110" "Side01_04_HistoH_110_115"
## [451] "Side01_04_HistoH_115_120" "Side01_04_HistoH_120_125"
## [453] "Side01_04_HistoH_125_130" "Side01_04_HistoH_130_135"
## [455] "Side01_04_HistoH_135_140" "Side01_04_HistoH_140_145"
## [457] "Side01_04_HistoH_145_150" "Side01_04_HistoH_150_155"
## [459] "Side01_04_HistoH_155_160" "Side01_04_HistoH_160_165"
## [461] "Side01_04_HistoH_165_170" "Side01_04_HistoH_170_175"
## [463] "Side01_04_HistoH_175_180" "Side01_04_HistoH_180_185"
## [465] "Side01_04_HistoH_185_190" "Side01_04_HistoH_190_195"
## [467] "Side01_04_HistoH_195_200" "Side01_04_HistoH_200_205"
## [469] "Side01_04_HistoH_205_210" "Side01_04_HistoH_210_215"
## [471] "Side01_04_HistoH_215_220" "Side01_04_HistoH_220_225"
## [473] "Side01_04_HistoH_225_230" "Side01_04_HistoH_230_235"
## [475] "Side01_04_HistoH_235_240" "Side01_04_HistoH_240_245"
## [477] "Side01_04_HistoH_245_250" "Side01_04_HistoH_250_255"
## [479] "Side01_04_Greennes_Mean" "Side01_04_Greennes_Deviation"
## [481] "Side01_04_BarcodeEnabled" "Side01_04_BarcodeFound"
## [483] "Side01_05_row1" "Side01_05_area"
## [485] "Side01_05_height" "Side01_05_width"
## [487] "Side01_05_circularity" "Side01_05_compactness"
## [489] "Side01_05_contlength" "Side01_05_convexity"
## [491] "Side01_05_rectangularity" "Side01_05_anisometry"
## [493] "Side01_05_bulkiness" "Side01_05_struct_factor"
## [495] "Side01_05_outer_radius" "Side01_05_inner_radius"
## [497] "Side01_05_dist_mean" "Side01_05_dist_deviation"
## [499] "Side01_05_roundness" "Side01_05_holes_num"
## [501] "Side01_05_areaInMm2" "Side01_05_heightInMm"
## [503] "Side01_05_widthInMm" "Side01_05_Convex_Hull_Area"
## [505] "Side01_05_AverageColor_H" "Side01_05_AverageColor_S"
## [507] "Side01_05_AverageColor_V" "Side01_05_CenterOfMassX"
## [509] "Side01_05_CenterOfMassY" "Side01_05_CenterOfMassDistance"
## [511] "Side01_05_H_Mean" "Side01_05_H_Median"
## [513] "Side01_05_H_StDev" "Side01_05_H_Q1"
## [515] "Side01_05_H_Q3" "Side01_05_brown(H_14_27)"
## [517] "Side01_05_brown-yellow(H_28_35)" "Side01_05_yellow(H_36_43)"
## [519] "Side01_05_yellow-green(H_44_57)" "Side01_05_green(H_58_99)"
## [521] "Side01_05_green-cyan(H_100_120)" "Side01_05_cyan-blue(H_121_142)"
## [523] "Side01_05_blue(H_143_156)" "Side01_05_HistoH_0_5"
## [525] "Side01_05_HistoH_5_10" "Side01_05_HistoH_10_15"
## [527] "Side01_05_HistoH_15_20" "Side01_05_HistoH_20_25"
## [529] "Side01_05_HistoH_25_30" "Side01_05_HistoH_30_35"
## [531] "Side01_05_HistoH_35_40" "Side01_05_HistoH_40_45"
## [533] "Side01_05_HistoH_45_50" "Side01_05_HistoH_50_55"
## [535] "Side01_05_HistoH_55_60" "Side01_05_HistoH_60_65"
## [537] "Side01_05_HistoH_65_70" "Side01_05_HistoH_70_75"
## [539] "Side01_05_HistoH_75_80" "Side01_05_HistoH_80_85"
## [541] "Side01_05_HistoH_85_90" "Side01_05_HistoH_90_95"
## [543] "Side01_05_HistoH_95_100" "Side01_05_HistoH_100_105"
## [545] "Side01_05_HistoH_105_110" "Side01_05_HistoH_110_115"
## [547] "Side01_05_HistoH_115_120" "Side01_05_HistoH_120_125"
## [549] "Side01_05_HistoH_125_130" "Side01_05_HistoH_130_135"
## [551] "Side01_05_HistoH_135_140" "Side01_05_HistoH_140_145"
## [553] "Side01_05_HistoH_145_150" "Side01_05_HistoH_150_155"
## [555] "Side01_05_HistoH_155_160" "Side01_05_HistoH_160_165"
## [557] "Side01_05_HistoH_165_170" "Side01_05_HistoH_170_175"
## [559] "Side01_05_HistoH_175_180" "Side01_05_HistoH_180_185"
## [561] "Side01_05_HistoH_185_190" "Side01_05_HistoH_190_195"
## [563] "Side01_05_HistoH_195_200" "Side01_05_HistoH_200_205"
## [565] "Side01_05_HistoH_205_210" "Side01_05_HistoH_210_215"
## [567] "Side01_05_HistoH_215_220" "Side01_05_HistoH_220_225"
## [569] "Side01_05_HistoH_225_230" "Side01_05_HistoH_230_235"
## [571] "Side01_05_HistoH_235_240" "Side01_05_HistoH_240_245"
## [573] "Side01_05_HistoH_245_250" "Side01_05_HistoH_250_255"
## [575] "Side01_05_Greennes_Mean" "Side01_05_Greennes_Deviation"
## [577] "Side01_05_BarcodeEnabled" "Side01_05_BarcodeFound"
## [579] "row1" "area"
## [581] "height" "width"
## [583] "circularity" "compactness"
## [585] "contlength" "convexity"
## [587] "rectangularity" "anisometry"
## [589] "bulkiness" "struct_factor"
## [591] "outer_radius" "inner_radius"
## [593] "dist_mean" "dist_deviation"
## [595] "roundness" "holes_num"
## [597] "areaInMm2" "heightInMm"
## [599] "widthInMm" "Convex_Hull_Area"
## [601] "AverageColor_H" "AverageColor_S"
## [603] "AverageColor_V" "CenterOfMassX"
## [605] "CenterOfMassY" "CenterOfMassDistance"
## [607] "H_Mean" "H_Median"
## [609] "H_StDev" "H_Q1"
## [611] "H_Q3" "brown(H_14_27)"
## [613] "brown-yellow(H_28_35)" "yellow(H_36_43)"
## [615] "yellow-green(H_44_57)" "green(H_58_99)"
## [617] "green-cyan(H_100_120)" "cyan-blue(H_121_142)"
## [619] "blue(H_143_156)" "HistoH_0_5"
## [621] "HistoH_5_10" "HistoH_10_15"
## [623] "HistoH_15_20" "HistoH_20_25"
## [625] "HistoH_25_30" "HistoH_30_35"
## [627] "HistoH_35_40" "HistoH_40_45"
## [629] "HistoH_45_50" "HistoH_50_55"
## [631] "HistoH_55_60" "HistoH_60_65"
## [633] "HistoH_65_70" "HistoH_70_75"
## [635] "HistoH_75_80" "HistoH_80_85"
## [637] "HistoH_85_90" "HistoH_90_95"
## [639] "HistoH_95_100" "HistoH_100_105"
## [641] "HistoH_105_110" "HistoH_110_115"
## [643] "HistoH_115_120" "HistoH_120_125"
## [645] "HistoH_125_130" "HistoH_130_135"
## [647] "HistoH_135_140" "HistoH_140_145"
## [649] "HistoH_145_150" "HistoH_150_155"
## [651] "HistoH_155_160" "HistoH_160_165"
## [653] "HistoH_165_170" "HistoH_170_175"
## [655] "HistoH_175_180" "HistoH_180_185"
## [657] "HistoH_185_190" "HistoH_190_195"
## [659] "HistoH_195_200" "HistoH_200_205"
## [661] "HistoH_205_210" "HistoH_210_215"
## [663] "HistoH_215_220" "HistoH_220_225"
## [665] "HistoH_225_230" "HistoH_230_235"
## [667] "HistoH_235_240" "HistoH_240_245"
## [669] "HistoH_245_250" "HistoH_250_255"
## [671] "Greennes_Mean" "Greennes_Deviation"
## [673] "BarcodeEnabled" "BarcodeFound"Let’s isolate only the area data for now - to see what we get from this:
unique(RGB$measurement_name)## [1] "Rfid" "Side01_00_row1"
## [3] "Side01_00_area" "Side01_00_height"
## [5] "Side01_00_width" "Side01_00_circularity"
## [7] "Side01_00_compactness" "Side01_00_contlength"
## [9] "Side01_00_convexity" "Side01_00_rectangularity"
## [11] "Side01_00_anisometry" "Side01_00_bulkiness"
## [13] "Side01_00_struct_factor" "Side01_00_outer_radius"
## [15] "Side01_00_inner_radius" "Side01_00_dist_mean"
## [17] "Side01_00_dist_deviation" "Side01_00_roundness"
## [19] "Side01_00_holes_num" "Side01_00_areaInMm2"
## [21] "Side01_00_heightInMm" "Side01_00_widthInMm"
## [23] "Side01_00_Convex_Hull_Area" "Side01_00_AverageColor_H"
## [25] "Side01_00_AverageColor_S" "Side01_00_AverageColor_V"
## [27] "Side01_00_CenterOfMassX" "Side01_00_CenterOfMassY"
## [29] "Side01_00_CenterOfMassDistance" "Side01_00_H_Mean"
## [31] "Side01_00_H_Median" "Side01_00_H_StDev"
## [33] "Side01_00_H_Q1" "Side01_00_H_Q3"
## [35] "Side01_00_brown(H_14_27)" "Side01_00_brown-yellow(H_28_35)"
## [37] "Side01_00_yellow(H_36_43)" "Side01_00_yellow-green(H_44_57)"
## [39] "Side01_00_green(H_58_99)" "Side01_00_green-cyan(H_100_120)"
## [41] "Side01_00_cyan-blue(H_121_142)" "Side01_00_blue(H_143_156)"
## [43] "Side01_00_HistoH_0_5" "Side01_00_HistoH_5_10"
## [45] "Side01_00_HistoH_10_15" "Side01_00_HistoH_15_20"
## [47] "Side01_00_HistoH_20_25" "Side01_00_HistoH_25_30"
## [49] "Side01_00_HistoH_30_35" "Side01_00_HistoH_35_40"
## [51] "Side01_00_HistoH_40_45" "Side01_00_HistoH_45_50"
## [53] "Side01_00_HistoH_50_55" "Side01_00_HistoH_55_60"
## [55] "Side01_00_HistoH_60_65" "Side01_00_HistoH_65_70"
## [57] "Side01_00_HistoH_70_75" "Side01_00_HistoH_75_80"
## [59] "Side01_00_HistoH_80_85" "Side01_00_HistoH_85_90"
## [61] "Side01_00_HistoH_90_95" "Side01_00_HistoH_95_100"
## [63] "Side01_00_HistoH_100_105" "Side01_00_HistoH_105_110"
## [65] "Side01_00_HistoH_110_115" "Side01_00_HistoH_115_120"
## [67] "Side01_00_HistoH_120_125" "Side01_00_HistoH_125_130"
## [69] "Side01_00_HistoH_130_135" "Side01_00_HistoH_135_140"
## [71] "Side01_00_HistoH_140_145" "Side01_00_HistoH_145_150"
## [73] "Side01_00_HistoH_150_155" "Side01_00_HistoH_155_160"
## [75] "Side01_00_HistoH_160_165" "Side01_00_HistoH_165_170"
## [77] "Side01_00_HistoH_170_175" "Side01_00_HistoH_175_180"
## [79] "Side01_00_HistoH_180_185" "Side01_00_HistoH_185_190"
## [81] "Side01_00_HistoH_190_195" "Side01_00_HistoH_195_200"
## [83] "Side01_00_HistoH_200_205" "Side01_00_HistoH_205_210"
## [85] "Side01_00_HistoH_210_215" "Side01_00_HistoH_215_220"
## [87] "Side01_00_HistoH_220_225" "Side01_00_HistoH_225_230"
## [89] "Side01_00_HistoH_230_235" "Side01_00_HistoH_235_240"
## [91] "Side01_00_HistoH_240_245" "Side01_00_HistoH_245_250"
## [93] "Side01_00_HistoH_250_255" "Side01_00_Greennes_Mean"
## [95] "Side01_00_Greennes_Deviation" "Side01_00_BarcodeEnabled"
## [97] "Side01_00_BarcodeFound" "BarcodeResult"
## [99] "Side01_01_row1" "Side01_01_area"
## [101] "Side01_01_height" "Side01_01_width"
## [103] "Side01_01_circularity" "Side01_01_compactness"
## [105] "Side01_01_contlength" "Side01_01_convexity"
## [107] "Side01_01_rectangularity" "Side01_01_anisometry"
## [109] "Side01_01_bulkiness" "Side01_01_struct_factor"
## [111] "Side01_01_outer_radius" "Side01_01_inner_radius"
## [113] "Side01_01_dist_mean" "Side01_01_dist_deviation"
## [115] "Side01_01_roundness" "Side01_01_holes_num"
## [117] "Side01_01_areaInMm2" "Side01_01_heightInMm"
## [119] "Side01_01_widthInMm" "Side01_01_Convex_Hull_Area"
## [121] "Side01_01_AverageColor_H" "Side01_01_AverageColor_S"
## [123] "Side01_01_AverageColor_V" "Side01_01_CenterOfMassX"
## [125] "Side01_01_CenterOfMassY" "Side01_01_CenterOfMassDistance"
## [127] "Side01_01_H_Mean" "Side01_01_H_Median"
## [129] "Side01_01_H_StDev" "Side01_01_H_Q1"
## [131] "Side01_01_H_Q3" "Side01_01_brown(H_14_27)"
## [133] "Side01_01_brown-yellow(H_28_35)" "Side01_01_yellow(H_36_43)"
## [135] "Side01_01_yellow-green(H_44_57)" "Side01_01_green(H_58_99)"
## [137] "Side01_01_green-cyan(H_100_120)" "Side01_01_cyan-blue(H_121_142)"
## [139] "Side01_01_blue(H_143_156)" "Side01_01_HistoH_0_5"
## [141] "Side01_01_HistoH_5_10" "Side01_01_HistoH_10_15"
## [143] "Side01_01_HistoH_15_20" "Side01_01_HistoH_20_25"
## [145] "Side01_01_HistoH_25_30" "Side01_01_HistoH_30_35"
## [147] "Side01_01_HistoH_35_40" "Side01_01_HistoH_40_45"
## [149] "Side01_01_HistoH_45_50" "Side01_01_HistoH_50_55"
## [151] "Side01_01_HistoH_55_60" "Side01_01_HistoH_60_65"
## [153] "Side01_01_HistoH_65_70" "Side01_01_HistoH_70_75"
## [155] "Side01_01_HistoH_75_80" "Side01_01_HistoH_80_85"
## [157] "Side01_01_HistoH_85_90" "Side01_01_HistoH_90_95"
## [159] "Side01_01_HistoH_95_100" "Side01_01_HistoH_100_105"
## [161] "Side01_01_HistoH_105_110" "Side01_01_HistoH_110_115"
## [163] "Side01_01_HistoH_115_120" "Side01_01_HistoH_120_125"
## [165] "Side01_01_HistoH_125_130" "Side01_01_HistoH_130_135"
## [167] "Side01_01_HistoH_135_140" "Side01_01_HistoH_140_145"
## [169] "Side01_01_HistoH_145_150" "Side01_01_HistoH_150_155"
## [171] "Side01_01_HistoH_155_160" "Side01_01_HistoH_160_165"
## [173] "Side01_01_HistoH_165_170" "Side01_01_HistoH_170_175"
## [175] "Side01_01_HistoH_175_180" "Side01_01_HistoH_180_185"
## [177] "Side01_01_HistoH_185_190" "Side01_01_HistoH_190_195"
## [179] "Side01_01_HistoH_195_200" "Side01_01_HistoH_200_205"
## [181] "Side01_01_HistoH_205_210" "Side01_01_HistoH_210_215"
## [183] "Side01_01_HistoH_215_220" "Side01_01_HistoH_220_225"
## [185] "Side01_01_HistoH_225_230" "Side01_01_HistoH_230_235"
## [187] "Side01_01_HistoH_235_240" "Side01_01_HistoH_240_245"
## [189] "Side01_01_HistoH_245_250" "Side01_01_HistoH_250_255"
## [191] "Side01_01_Greennes_Mean" "Side01_01_Greennes_Deviation"
## [193] "Side01_01_BarcodeEnabled" "Side01_01_BarcodeFound"
## [195] "Side01_02_row1" "Side01_02_area"
## [197] "Side01_02_height" "Side01_02_width"
## [199] "Side01_02_circularity" "Side01_02_compactness"
## [201] "Side01_02_contlength" "Side01_02_convexity"
## [203] "Side01_02_rectangularity" "Side01_02_anisometry"
## [205] "Side01_02_bulkiness" "Side01_02_struct_factor"
## [207] "Side01_02_outer_radius" "Side01_02_inner_radius"
## [209] "Side01_02_dist_mean" "Side01_02_dist_deviation"
## [211] "Side01_02_roundness" "Side01_02_holes_num"
## [213] "Side01_02_areaInMm2" "Side01_02_heightInMm"
## [215] "Side01_02_widthInMm" "Side01_02_Convex_Hull_Area"
## [217] "Side01_02_AverageColor_H" "Side01_02_AverageColor_S"
## [219] "Side01_02_AverageColor_V" "Side01_02_CenterOfMassX"
## [221] "Side01_02_CenterOfMassY" "Side01_02_CenterOfMassDistance"
## [223] "Side01_02_H_Mean" "Side01_02_H_Median"
## [225] "Side01_02_H_StDev" "Side01_02_H_Q1"
## [227] "Side01_02_H_Q3" "Side01_02_brown(H_14_27)"
## [229] "Side01_02_brown-yellow(H_28_35)" "Side01_02_yellow(H_36_43)"
## [231] "Side01_02_yellow-green(H_44_57)" "Side01_02_green(H_58_99)"
## [233] "Side01_02_green-cyan(H_100_120)" "Side01_02_cyan-blue(H_121_142)"
## [235] "Side01_02_blue(H_143_156)" "Side01_02_HistoH_0_5"
## [237] "Side01_02_HistoH_5_10" "Side01_02_HistoH_10_15"
## [239] "Side01_02_HistoH_15_20" "Side01_02_HistoH_20_25"
## [241] "Side01_02_HistoH_25_30" "Side01_02_HistoH_30_35"
## [243] "Side01_02_HistoH_35_40" "Side01_02_HistoH_40_45"
## [245] "Side01_02_HistoH_45_50" "Side01_02_HistoH_50_55"
## [247] "Side01_02_HistoH_55_60" "Side01_02_HistoH_60_65"
## [249] "Side01_02_HistoH_65_70" "Side01_02_HistoH_70_75"
## [251] "Side01_02_HistoH_75_80" "Side01_02_HistoH_80_85"
## [253] "Side01_02_HistoH_85_90" "Side01_02_HistoH_90_95"
## [255] "Side01_02_HistoH_95_100" "Side01_02_HistoH_100_105"
## [257] "Side01_02_HistoH_105_110" "Side01_02_HistoH_110_115"
## [259] "Side01_02_HistoH_115_120" "Side01_02_HistoH_120_125"
## [261] "Side01_02_HistoH_125_130" "Side01_02_HistoH_130_135"
## [263] "Side01_02_HistoH_135_140" "Side01_02_HistoH_140_145"
## [265] "Side01_02_HistoH_145_150" "Side01_02_HistoH_150_155"
## [267] "Side01_02_HistoH_155_160" "Side01_02_HistoH_160_165"
## [269] "Side01_02_HistoH_165_170" "Side01_02_HistoH_170_175"
## [271] "Side01_02_HistoH_175_180" "Side01_02_HistoH_180_185"
## [273] "Side01_02_HistoH_185_190" "Side01_02_HistoH_190_195"
## [275] "Side01_02_HistoH_195_200" "Side01_02_HistoH_200_205"
## [277] "Side01_02_HistoH_205_210" "Side01_02_HistoH_210_215"
## [279] "Side01_02_HistoH_215_220" "Side01_02_HistoH_220_225"
## [281] "Side01_02_HistoH_225_230" "Side01_02_HistoH_230_235"
## [283] "Side01_02_HistoH_235_240" "Side01_02_HistoH_240_245"
## [285] "Side01_02_HistoH_245_250" "Side01_02_HistoH_250_255"
## [287] "Side01_02_Greennes_Mean" "Side01_02_Greennes_Deviation"
## [289] "Side01_02_BarcodeEnabled" "Side01_02_BarcodeFound"
## [291] "Side01_03_row1" "Side01_03_area"
## [293] "Side01_03_height" "Side01_03_width"
## [295] "Side01_03_circularity" "Side01_03_compactness"
## [297] "Side01_03_contlength" "Side01_03_convexity"
## [299] "Side01_03_rectangularity" "Side01_03_anisometry"
## [301] "Side01_03_bulkiness" "Side01_03_struct_factor"
## [303] "Side01_03_outer_radius" "Side01_03_inner_radius"
## [305] "Side01_03_dist_mean" "Side01_03_dist_deviation"
## [307] "Side01_03_roundness" "Side01_03_holes_num"
## [309] "Side01_03_areaInMm2" "Side01_03_heightInMm"
## [311] "Side01_03_widthInMm" "Side01_03_Convex_Hull_Area"
## [313] "Side01_03_AverageColor_H" "Side01_03_AverageColor_S"
## [315] "Side01_03_AverageColor_V" "Side01_03_CenterOfMassX"
## [317] "Side01_03_CenterOfMassY" "Side01_03_CenterOfMassDistance"
## [319] "Side01_03_H_Mean" "Side01_03_H_Median"
## [321] "Side01_03_H_StDev" "Side01_03_H_Q1"
## [323] "Side01_03_H_Q3" "Side01_03_brown(H_14_27)"
## [325] "Side01_03_brown-yellow(H_28_35)" "Side01_03_yellow(H_36_43)"
## [327] "Side01_03_yellow-green(H_44_57)" "Side01_03_green(H_58_99)"
## [329] "Side01_03_green-cyan(H_100_120)" "Side01_03_cyan-blue(H_121_142)"
## [331] "Side01_03_blue(H_143_156)" "Side01_03_HistoH_0_5"
## [333] "Side01_03_HistoH_5_10" "Side01_03_HistoH_10_15"
## [335] "Side01_03_HistoH_15_20" "Side01_03_HistoH_20_25"
## [337] "Side01_03_HistoH_25_30" "Side01_03_HistoH_30_35"
## [339] "Side01_03_HistoH_35_40" "Side01_03_HistoH_40_45"
## [341] "Side01_03_HistoH_45_50" "Side01_03_HistoH_50_55"
## [343] "Side01_03_HistoH_55_60" "Side01_03_HistoH_60_65"
## [345] "Side01_03_HistoH_65_70" "Side01_03_HistoH_70_75"
## [347] "Side01_03_HistoH_75_80" "Side01_03_HistoH_80_85"
## [349] "Side01_03_HistoH_85_90" "Side01_03_HistoH_90_95"
## [351] "Side01_03_HistoH_95_100" "Side01_03_HistoH_100_105"
## [353] "Side01_03_HistoH_105_110" "Side01_03_HistoH_110_115"
## [355] "Side01_03_HistoH_115_120" "Side01_03_HistoH_120_125"
## [357] "Side01_03_HistoH_125_130" "Side01_03_HistoH_130_135"
## [359] "Side01_03_HistoH_135_140" "Side01_03_HistoH_140_145"
## [361] "Side01_03_HistoH_145_150" "Side01_03_HistoH_150_155"
## [363] "Side01_03_HistoH_155_160" "Side01_03_HistoH_160_165"
## [365] "Side01_03_HistoH_165_170" "Side01_03_HistoH_170_175"
## [367] "Side01_03_HistoH_175_180" "Side01_03_HistoH_180_185"
## [369] "Side01_03_HistoH_185_190" "Side01_03_HistoH_190_195"
## [371] "Side01_03_HistoH_195_200" "Side01_03_HistoH_200_205"
## [373] "Side01_03_HistoH_205_210" "Side01_03_HistoH_210_215"
## [375] "Side01_03_HistoH_215_220" "Side01_03_HistoH_220_225"
## [377] "Side01_03_HistoH_225_230" "Side01_03_HistoH_230_235"
## [379] "Side01_03_HistoH_235_240" "Side01_03_HistoH_240_245"
## [381] "Side01_03_HistoH_245_250" "Side01_03_HistoH_250_255"
## [383] "Side01_03_Greennes_Mean" "Side01_03_Greennes_Deviation"
## [385] "Side01_03_BarcodeEnabled" "Side01_03_BarcodeFound"
## [387] "Side01_04_row1" "Side01_04_area"
## [389] "Side01_04_height" "Side01_04_width"
## [391] "Side01_04_circularity" "Side01_04_compactness"
## [393] "Side01_04_contlength" "Side01_04_convexity"
## [395] "Side01_04_rectangularity" "Side01_04_anisometry"
## [397] "Side01_04_bulkiness" "Side01_04_struct_factor"
## [399] "Side01_04_outer_radius" "Side01_04_inner_radius"
## [401] "Side01_04_dist_mean" "Side01_04_dist_deviation"
## [403] "Side01_04_roundness" "Side01_04_holes_num"
## [405] "Side01_04_areaInMm2" "Side01_04_heightInMm"
## [407] "Side01_04_widthInMm" "Side01_04_Convex_Hull_Area"
## [409] "Side01_04_AverageColor_H" "Side01_04_AverageColor_S"
## [411] "Side01_04_AverageColor_V" "Side01_04_CenterOfMassX"
## [413] "Side01_04_CenterOfMassY" "Side01_04_CenterOfMassDistance"
## [415] "Side01_04_H_Mean" "Side01_04_H_Median"
## [417] "Side01_04_H_StDev" "Side01_04_H_Q1"
## [419] "Side01_04_H_Q3" "Side01_04_brown(H_14_27)"
## [421] "Side01_04_brown-yellow(H_28_35)" "Side01_04_yellow(H_36_43)"
## [423] "Side01_04_yellow-green(H_44_57)" "Side01_04_green(H_58_99)"
## [425] "Side01_04_green-cyan(H_100_120)" "Side01_04_cyan-blue(H_121_142)"
## [427] "Side01_04_blue(H_143_156)" "Side01_04_HistoH_0_5"
## [429] "Side01_04_HistoH_5_10" "Side01_04_HistoH_10_15"
## [431] "Side01_04_HistoH_15_20" "Side01_04_HistoH_20_25"
## [433] "Side01_04_HistoH_25_30" "Side01_04_HistoH_30_35"
## [435] "Side01_04_HistoH_35_40" "Side01_04_HistoH_40_45"
## [437] "Side01_04_HistoH_45_50" "Side01_04_HistoH_50_55"
## [439] "Side01_04_HistoH_55_60" "Side01_04_HistoH_60_65"
## [441] "Side01_04_HistoH_65_70" "Side01_04_HistoH_70_75"
## [443] "Side01_04_HistoH_75_80" "Side01_04_HistoH_80_85"
## [445] "Side01_04_HistoH_85_90" "Side01_04_HistoH_90_95"
## [447] "Side01_04_HistoH_95_100" "Side01_04_HistoH_100_105"
## [449] "Side01_04_HistoH_105_110" "Side01_04_HistoH_110_115"
## [451] "Side01_04_HistoH_115_120" "Side01_04_HistoH_120_125"
## [453] "Side01_04_HistoH_125_130" "Side01_04_HistoH_130_135"
## [455] "Side01_04_HistoH_135_140" "Side01_04_HistoH_140_145"
## [457] "Side01_04_HistoH_145_150" "Side01_04_HistoH_150_155"
## [459] "Side01_04_HistoH_155_160" "Side01_04_HistoH_160_165"
## [461] "Side01_04_HistoH_165_170" "Side01_04_HistoH_170_175"
## [463] "Side01_04_HistoH_175_180" "Side01_04_HistoH_180_185"
## [465] "Side01_04_HistoH_185_190" "Side01_04_HistoH_190_195"
## [467] "Side01_04_HistoH_195_200" "Side01_04_HistoH_200_205"
## [469] "Side01_04_HistoH_205_210" "Side01_04_HistoH_210_215"
## [471] "Side01_04_HistoH_215_220" "Side01_04_HistoH_220_225"
## [473] "Side01_04_HistoH_225_230" "Side01_04_HistoH_230_235"
## [475] "Side01_04_HistoH_235_240" "Side01_04_HistoH_240_245"
## [477] "Side01_04_HistoH_245_250" "Side01_04_HistoH_250_255"
## [479] "Side01_04_Greennes_Mean" "Side01_04_Greennes_Deviation"
## [481] "Side01_04_BarcodeEnabled" "Side01_04_BarcodeFound"
## [483] "Side01_05_row1" "Side01_05_area"
## [485] "Side01_05_height" "Side01_05_width"
## [487] "Side01_05_circularity" "Side01_05_compactness"
## [489] "Side01_05_contlength" "Side01_05_convexity"
## [491] "Side01_05_rectangularity" "Side01_05_anisometry"
## [493] "Side01_05_bulkiness" "Side01_05_struct_factor"
## [495] "Side01_05_outer_radius" "Side01_05_inner_radius"
## [497] "Side01_05_dist_mean" "Side01_05_dist_deviation"
## [499] "Side01_05_roundness" "Side01_05_holes_num"
## [501] "Side01_05_areaInMm2" "Side01_05_heightInMm"
## [503] "Side01_05_widthInMm" "Side01_05_Convex_Hull_Area"
## [505] "Side01_05_AverageColor_H" "Side01_05_AverageColor_S"
## [507] "Side01_05_AverageColor_V" "Side01_05_CenterOfMassX"
## [509] "Side01_05_CenterOfMassY" "Side01_05_CenterOfMassDistance"
## [511] "Side01_05_H_Mean" "Side01_05_H_Median"
## [513] "Side01_05_H_StDev" "Side01_05_H_Q1"
## [515] "Side01_05_H_Q3" "Side01_05_brown(H_14_27)"
## [517] "Side01_05_brown-yellow(H_28_35)" "Side01_05_yellow(H_36_43)"
## [519] "Side01_05_yellow-green(H_44_57)" "Side01_05_green(H_58_99)"
## [521] "Side01_05_green-cyan(H_100_120)" "Side01_05_cyan-blue(H_121_142)"
## [523] "Side01_05_blue(H_143_156)" "Side01_05_HistoH_0_5"
## [525] "Side01_05_HistoH_5_10" "Side01_05_HistoH_10_15"
## [527] "Side01_05_HistoH_15_20" "Side01_05_HistoH_20_25"
## [529] "Side01_05_HistoH_25_30" "Side01_05_HistoH_30_35"
## [531] "Side01_05_HistoH_35_40" "Side01_05_HistoH_40_45"
## [533] "Side01_05_HistoH_45_50" "Side01_05_HistoH_50_55"
## [535] "Side01_05_HistoH_55_60" "Side01_05_HistoH_60_65"
## [537] "Side01_05_HistoH_65_70" "Side01_05_HistoH_70_75"
## [539] "Side01_05_HistoH_75_80" "Side01_05_HistoH_80_85"
## [541] "Side01_05_HistoH_85_90" "Side01_05_HistoH_90_95"
## [543] "Side01_05_HistoH_95_100" "Side01_05_HistoH_100_105"
## [545] "Side01_05_HistoH_105_110" "Side01_05_HistoH_110_115"
## [547] "Side01_05_HistoH_115_120" "Side01_05_HistoH_120_125"
## [549] "Side01_05_HistoH_125_130" "Side01_05_HistoH_130_135"
## [551] "Side01_05_HistoH_135_140" "Side01_05_HistoH_140_145"
## [553] "Side01_05_HistoH_145_150" "Side01_05_HistoH_150_155"
## [555] "Side01_05_HistoH_155_160" "Side01_05_HistoH_160_165"
## [557] "Side01_05_HistoH_165_170" "Side01_05_HistoH_170_175"
## [559] "Side01_05_HistoH_175_180" "Side01_05_HistoH_180_185"
## [561] "Side01_05_HistoH_185_190" "Side01_05_HistoH_190_195"
## [563] "Side01_05_HistoH_195_200" "Side01_05_HistoH_200_205"
## [565] "Side01_05_HistoH_205_210" "Side01_05_HistoH_210_215"
## [567] "Side01_05_HistoH_215_220" "Side01_05_HistoH_220_225"
## [569] "Side01_05_HistoH_225_230" "Side01_05_HistoH_230_235"
## [571] "Side01_05_HistoH_235_240" "Side01_05_HistoH_240_245"
## [573] "Side01_05_HistoH_245_250" "Side01_05_HistoH_250_255"
## [575] "Side01_05_Greennes_Mean" "Side01_05_Greennes_Deviation"
## [577] "Side01_05_BarcodeEnabled" "Side01_05_BarcodeFound"
## [579] "row1" "area"
## [581] "height" "width"
## [583] "circularity" "compactness"
## [585] "contlength" "convexity"
## [587] "rectangularity" "anisometry"
## [589] "bulkiness" "struct_factor"
## [591] "outer_radius" "inner_radius"
## [593] "dist_mean" "dist_deviation"
## [595] "roundness" "holes_num"
## [597] "areaInMm2" "heightInMm"
## [599] "widthInMm" "Convex_Hull_Area"
## [601] "AverageColor_H" "AverageColor_S"
## [603] "AverageColor_V" "CenterOfMassX"
## [605] "CenterOfMassY" "CenterOfMassDistance"
## [607] "H_Mean" "H_Median"
## [609] "H_StDev" "H_Q1"
## [611] "H_Q3" "brown(H_14_27)"
## [613] "brown-yellow(H_28_35)" "yellow(H_36_43)"
## [615] "yellow-green(H_44_57)" "green(H_58_99)"
## [617] "green-cyan(H_100_120)" "cyan-blue(H_121_142)"
## [619] "blue(H_143_156)" "HistoH_0_5"
## [621] "HistoH_5_10" "HistoH_10_15"
## [623] "HistoH_15_20" "HistoH_20_25"
## [625] "HistoH_25_30" "HistoH_30_35"
## [627] "HistoH_35_40" "HistoH_40_45"
## [629] "HistoH_45_50" "HistoH_50_55"
## [631] "HistoH_55_60" "HistoH_60_65"
## [633] "HistoH_65_70" "HistoH_70_75"
## [635] "HistoH_75_80" "HistoH_80_85"
## [637] "HistoH_85_90" "HistoH_90_95"
## [639] "HistoH_95_100" "HistoH_100_105"
## [641] "HistoH_105_110" "HistoH_110_115"
## [643] "HistoH_115_120" "HistoH_120_125"
## [645] "HistoH_125_130" "HistoH_130_135"
## [647] "HistoH_135_140" "HistoH_140_145"
## [649] "HistoH_145_150" "HistoH_150_155"
## [651] "HistoH_155_160" "HistoH_160_165"
## [653] "HistoH_165_170" "HistoH_170_175"
## [655] "HistoH_175_180" "HistoH_180_185"
## [657] "HistoH_185_190" "HistoH_190_195"
## [659] "HistoH_195_200" "HistoH_200_205"
## [661] "HistoH_205_210" "HistoH_210_215"
## [663] "HistoH_215_220" "HistoH_220_225"
## [665] "HistoH_225_230" "HistoH_230_235"
## [667] "HistoH_235_240" "HistoH_240_245"
## [669] "HistoH_245_250" "HistoH_250_255"
## [671] "Greennes_Mean" "Greennes_Deviation"
## [673] "BarcodeEnabled" "BarcodeFound"want <- c("Side01_00_area", "Side01_01_area", "Side01_02_area", "Side01_03_area", "Side01_04_area", "Side01_05_area", "area")
RGB_Area <- subset(RGB2, RGB2$measurement_name %in% want)
RGB_AreaRGB_Area <- RGB_Area[,c(1,4:6)]
RGB_Arealibrary(reshape2)
RGB_Area2 <- RGB_Area[,c(1:2,4,3)]
cRGB_Area <- dcast(RGB_Area2, timestamp + tray.ID ~ measurement_name)## Using measurement_value as value column: use value.var to override.cRGB_Areanot sure what all of the missing values are about - but lets remove them.
nona.RGB_Area <- na.omit(cRGB_Area)
nona.RGB_Areaunique(nona.RGB_Area$tray.ID)## [1] 34913 34914 34915 34916 34917 34918now let’s summarize the area from top view and 6 different angles into one value:
nona.RGB_Area$Area.all <- nona.RGB_Area$area + nona.RGB_Area$Side01_00_area + nona.RGB_Area$Side01_01_area + nona.RGB_Area$Side01_02_area + nona.RGB_Area$Side01_03_area + nona.RGB_Area$Side01_04_area + nona.RGB_Area$Side01_05_area
nona.RGB_AreaOne last remaining thing would be to resolve the timestamp into the “Day of experiment”. First let’s split it into a date and time:
# date
strsplit(nona.RGB_Area$timestamp[1], "-")[[1]][1]## [1] "20240614"# time
strsplit(nona.RGB_Area$timestamp[1], "-")[[1]][2]## [1] "110819"now for all of the rows:
for(i in 1:nrow(nona.RGB_Area)){
nona.RGB_Area$date[i] <- strsplit(nona.RGB_Area$timestamp[i], "-")[[1]][1]
nona.RGB_Area$time[i] <- strsplit(nona.RGB_Area$timestamp[i], "-")[[1]][2]
}
nona.RGB_Areaplot the RGB data
library(ggplot2)
nona.RGB_Area$date <- as.factor(nona.RGB_Area$date)unique(nona.RGB_Area$tray_id)## NULL#one_tray <- subset(nona.RGB_Area, nona.RGB_Area$tray_id == 40284)
lgraph <- ggplot(data=nona.RGB_Area, aes(x= date, y=Area.all, group = tray.ID))
lgraph <- lgraph + geom_line(alpha = 0.7)
lgraph <- lgraph + ylab("digital biomass") + xlab("days after stress")
lgraph
OK - we see that there is something funky going on the 17th- probably there are two measurements that were taken. Let’s see if we can calculate it into hours of experiment.
What are the unique timestamps?
unique(nona.RGB_Area$timestamp)## [1] "20240614-110819" "20240614-110859" "20240614-110938" "20240614-111059"
## [5] "20240614-111231" "20240614-111403" "20240615-181637" "20240615-182214"
## [9] "20240615-182754" "20240615-183330" "20240615-183908" "20240615-184446"
## [13] "20240617-002314" "20240617-002853" "20240617-003431" "20240617-004009"
## [17] "20240617-004547" "20240617-005125" "20240617-104359" "20240617-104939"
## [21] "20240617-105519" "20240617-110059" "20240617-110639" "20240617-111219"
## [25] "20240618-104938" "20240618-105519" "20240618-110059" "20240618-110640"
## [29] "20240618-111220" "20240618-111801" "20240619-154332" "20240619-154912"
## [33] "20240619-155454" "20240619-160034" "20240619-160616" "20240619-161156"
## [37] "20240620-162024" "20240620-162606" "20240620-163147" "20240620-163729"
## [41] "20240620-164311" "20240620-164853"Seems that the experiment started on 14th of June around 11 AM. Let’s use this as our time zero:
nona.RGB_Area$day <- substr(nona.RGB_Area$date, 7, 8)
nona.RGB_Area$hour <- substr(nona.RGB_Area$time, 1, 2)
nona.RGB_Areanow let’s calculate time of experiment in hours:
nona.RGB_Area$TOE <- (as.numeric(as.character(nona.RGB_Area$day)) - 14)*24 + (as.numeric(as.character(nona.RGB_Area$hour)) - 11)
nona.RGB_Arealgraph <- ggplot(data=nona.RGB_Area, aes(x= TOE, y=Area.all, group = tray.ID))
lgraph <- lgraph + geom_line(alpha = 0.7)
lgraph <- lgraph + ylab("digital biomass") + xlab("days after stress")
lgraph
now - let’s try to add more info - such as specific treatment from the decoding file:
nona.RGB_Area2 <- merge(nona.RGB_Area, decode, by = "tray.ID", all = T)
nona.RGB_Area2library(ggpubr)
nona.RGB_Area2$Area.all <- as.numeric(as.character(nona.RGB_Area2$Area.all))
nona.RGB_Area2$TOE <- as.numeric(as.character(nona.RGB_Area2$TOE))
nona.RGB_Area2$TrayInfo <- as.factor(nona.RGB_Area2$TrayInfo)
Area_graph <- ggplot(data=nona.RGB_Area2, aes(x= TOE, y=Area.all, group = tray.ID, color = TrayInfo))
Area_graph <- Area_graph + geom_line(alpha = 0.7)
#Area_graph <- Area_graph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= TrayInfo), alpha=0.3)
Area_graph <- Area_graph + stat_summary(fun=mean, aes(group= TrayInfo), size=0.7, geom="line", linetype = "dashed")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.#Area_graph <- Area_graph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = TRUE)
Area_graph <- Area_graph + ylab("Digital Biomass") + xlab("Time of imaging (h)")
Area_graph
The reason we have these odd lines - is that some of the trays got
imaged one hour later - which pulls average into totally different
direction (aka - one sample average). In order to make sure that we are
comparing like-with-like - we can implement splines - so we can
“predict” the values for all timepoints - however - we need to make sure
that we are not over-interpreting the data - as in - predicting on a
time scale of minutes or hours - as we have imaged approximately every
24h.
So - how do we do it? Splines - sounds complicated. But it is not. First - let’s take one tray and calculate predicted spline values based on one tray:
# Create a vector 'hours' containing values from 0 to 144 (6 days of imaging)
days <- seq(0, 144, by = 24)
days## [1] 0 24 48 72 96 120 144# Subset the data for one tray
temp <- subset(nona.RGB_Area2, nona.RGB_Area2$tray.ID == unique(nona.RGB_Area2$tray.ID)[1])
# Convert the 'TOE' column in 'temp' from a factor to numeric
temp$TOE <- as.numeric(as.character(temp$TOE))
# Display the 'days' column from the 'temp' data frame
temp$TOE## [1] 0 71 31 95 124 149 61# Fit a cubic smoothing spline to the 'TOE' and 'Area.all' data in the 'temp' data frame
plot.spl <- with(temp, smooth.spline(TOE, Area.all, df = 5))
# Create a scatterplot of 'Area.sum' against 'days' using data from 'temp'
plot(Area.all ~ TOE, data = temp)
# Add a blue line to the plot based on the cubic smoothing spline ('plot.spl')
lines(plot.spl, col = "blue")
# Add a red line to the plot by predicting values using the cubic smoothing spline ('plot.spl')
lines(predict(plot.spl, days), col = "red")
# Define a vector 'names' containing column names
names <- c(text = "tray.ID", "TrayInfo", "TOE", "Area.SUM")
# Create an empty data frame 'spline_data' to store the data
spline_data <- data.frame()
# Loop through each element 'k' in the 'names' vector
for (k in names) {
# Create a new column in 'spline_data' with the name specified by 'k' and initialize it as character type
spline_data[[k]] <- as.character()
}
spline_datapred_temp <- predict(plot.spl, days)
# Display the predictions stored in 'pred_temp'
pred_temp## $x
## [1] 0 24 48 72 96 120 144
##
## $y
## [1] 742347.8 775630.5 836667.0 885372.5 947793.0 1044476.1 1145080.1# Update the first 7 rows of the 'spline_data' data frame, setting the 4th column to 'pred_temp$x'
spline_data[1:7, 3] <- pred_temp$x
# Update the first 7 rows of the 'spline_data' data frame, setting the 5th column to 'pred_temp$y'
spline_data[1:7, 4] <- pred_temp$y
# Update the first 7 rows of the 'spline_data' data frame, setting the 1st column to 'temp$TrayID[1]'
spline_data[1:7, 1] <- temp$tray.ID[1]
# Update the first 7 rows of the 'spline_data' data frame, setting the 2nd column to 'temp$TrayInfo[1]'
spline_data[1:7, 2] <- as.character(temp$TrayInfo[1])
spline_data# Create a copy of 'spline_data' and store it in 'final_spline'
final_spline <- spline_data
# check how many unique plant IDs we have
all_plants <- unique(nona.RGB_Area2$tray.ID)
length(all_plants)## [1] 6now let’s do it for all plants:
for (i in 2:6) {
temp <- subset(nona.RGB_Area2, nona.RGB_Area2$tray.ID == unique(nona.RGB_Area2$tray.ID)[i])
# Check if 'temp' has more than 4 rows
if (dim(temp)[1] > 4) {
# Fit a cubic smoothing spline to 'days' and 'Area.sum' in 'temp'
plot.spl <- with(temp, smooth.spline(TOE, Area.all, df = 5))
# Generate predictions using the smoothing spline
pred_temp <- predict(plot.spl, days)
# Update specific columns in 'spline_data' with 'pred_temp' and 'temp' values
spline_data[1:7, 3] <- pred_temp$x
spline_data[1:7, 4] <- pred_temp$y
spline_data[1:7, 1] <- temp$tray.ID[1]
spline_data[1:7, 2] <- as.character(temp$TrayInfo[1])
# Append 'spline_data' to 'final_spline'
final_spline <- rbind(final_spline, spline_data)
} else {
# Set specific columns in 'spline_data' when 'temp' has <= 4 rows
spline_data[1:7, 3] <- days
spline_data[1:7, 4] <- 0
spline_data[1:7, 1] <- temp$tray.ID[1]
spline_data[1:7, 2] <- as.character(temp$TrayInfo[1])
}
}
final_spline# Convert the 'TOE' column in 'final_spline' to numeric type
final_spline$TOE <- as.numeric(as.character(final_spline$TOE))
# Convert the 'Area.SUM' column in 'final_spline' to numeric type
final_spline$Area.SUM <- as.numeric(as.character(final_spline$Area.SUM))
# Filter 'final_spline' to keep rows where 'Area.SUM' is greater than 1
final_spline <- final_spline[final_spline$Area.SUM > 1, ]
final_splinelibrary(ggsci)
# Convert 'Area.SUM' column to numeric type
final_spline$Area.SUM <- as.numeric(as.character(final_spline$Area.SUM))
# Convert 'day' column to a factor with numeric levels
final_spline$TOE <- as.factor(as.numeric(as.character(final_spline$TOE)))
# Create a line graph (Area_lgraph) using ggplot2
Area_lgraph <- ggplot(data = final_spline, aes(x = TOE, y = Area.SUM, group = tray.ID, color = TrayInfo))
Area_lgraph <- Area_lgraph + geom_line(alpha = 0.1)
Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = TrayInfo), alpha = 0.3)
Area_lgraph <- Area_lgraph + stat_summary(fun = mean, aes(group = TrayInfo), size = 0.7, geom = "line", linetype = "dashed")
Area_lgraph <- Area_lgraph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = F)
Area_lgraph <- Area_lgraph + labs(x = "Hours of Imaging", y = "Digital Biomass (a.u.)") + scale_color_d3("category10") + theme(legend.position = "bottom")
Area_lgraph
Calculating growth rates for each plant:
Now that we have the clean data - let’s calculate the growth rates throughout the experiment:
First let’s isolate one plant from the experiment
temp <- subset(final_spline, final_spline$tray.ID == unique(final_spline$tray.ID)[1])
templet’s plot the change in plant size
temp$Area.SUM <- as.numeric(temp$Area.SUM)
temp$TOE <- as.numeric(as.character(temp$TOE))
plot(temp$Area.SUM ~ temp$TOE)
let’s add a linear regression model to this graph:
plot(temp$Area.SUM ~ temp$TOE) + abline(lm(temp$Area.SUM ~ temp$TOE))
## integer(0)awesome - now let’s extract the aspects of the linear regression into a table:
summary(lm(temp$Area.SUM ~ temp$TOE))##
## Call:
## lm(formula = temp$Area.SUM ~ temp$TOE)
##
## Residuals:
## 1 2 3 4 5 6 7
## 30261.2 -2778.1 -8063.5 -25679.9 -29581.4 779.8 35061.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 712086.6 18667.8 38.15 2.33e-07 ***
## temp$TOE 2763.4 215.7 12.81 5.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27400 on 5 degrees of freedom
## Multiple R-squared: 0.9704, Adjusted R-squared: 0.9645
## F-statistic: 164.1 on 1 and 5 DF, p-value: 5.16e-05model <- lm(temp$Area.SUM ~ temp$TOE)
model##
## Call:
## lm(formula = temp$Area.SUM ~ temp$TOE)
##
## Coefficients:
## (Intercept) temp$TOE
## 712087 2763from that model - I need to extract temp$TOE - as this is the “growth rate” with which my plants are growing (pixels / day). Which I get to by following command:
model$coefficients[[2]]## [1] 2763.414I would also like to save how well my model fits the observed data (R square):
summary(model)$adj.r.squared## [1] 0.9645151Now let’s make a table and save all of this data in the table:
names <- c(text = "tray.ID", "TrayInfo", "GR", "Adj.Rsq")
# Create an empty data frame 'spline_data' to store the data
growth_data <- data.frame()
# Loop through each element 'k' in the 'names' vector
for (k in names) {
# Create a new column in 'spline_data' with the name specified by 'k' and initialize it as character type
growth_data[[k]] <- as.character()
}
growth_data[1,1] <- temp$tray.ID[1]
growth_data[1,2] <- temp$TrayInfo[1]
growth_data[1,3] <- model$coefficients[[2]]
growth_data[1,4] <- summary(model)$adj.r.squared
growth_dataGreat - now we can loop it for all unique plants in our dataset:
for(i in 2:length(unique(final_spline$tray.ID))){
temp <- subset(final_spline, final_spline$tray.ID == unique(final_spline$tray.ID)[i])
temp$Area.SUM <- as.numeric(temp$Area.SUM)
temp$TOE <- as.numeric(as.character(temp$TOE))
model <- lm(temp$Area.SUM ~ temp$TOE)
growth_data[i,1] <- temp$tray.ID[1]
growth_data[i,2] <- temp$TrayInfo[1]
growth_data[i,3] <- model$coefficients[[2]]
growth_data[i,4] <- summary(model)$adj.r.squared
}
growth_dataAwesome - now let;s plot this:
growth_data$GR <- as.numeric(as.character(growth_data$GR))
GR_overall <- ggerrorplot(growth_data, x = "TrayInfo", y = "GR", color = "TrayInfo", fill = "TrayInfo",
desc_stat = "mean_sd", add = "jitter", ncol = 8,
xlab="", ylab= "Growth Rate (pixel / hour)", add.params = list(color = "darkgray"))
GR_overall <- GR_overall + stat_compare_means(aes(label = after_stat(p.signif)), method = "aov", hide.ns = T)
GR_overall <- GR_overall + rremove("legend")
GR_overall
OK - that looks great - now let’s move onto PS2 data:
PS2 data loading
PS2 <- read.csv("Annie_VIGS_Suvita_20240621.csv")
PS2remove “all” from my Object number analyzed results:
PS3 <- subset(PS2, PS2$Obj.No != "All")
PS3get only the columns that are informative biologically (for now):
colnames(PS3)## [1] "File" "Date" "Time" "Obj.No"
## [5] "nTmPam" "Obj.Size" "Obj.Xc" "Obj.Yc"
## [9] "dFv.Fm" "s.e." "F0" "s.e..1"
## [13] "Fj" "s.e..2" "Fi" "s.e..3"
## [17] "Fm" "s.e..4" "X1.Fj.Fm" "s.e..5"
## [21] "X1.Fi.Fm" "s.e..6" "Fv.Fm" "s.e..7"
## [25] "IC.Area" "s.e..8" "IC.Area.Fv" "s.e..9"
## [29] "PI" "s.e..10" "dFq.Fm" "s.e..11"
## [33] "Fs." "s.e..12" "Fm." "s.e..13"
## [37] "Fq..Fm." "s.e..14" "rETR" "s.e..15"
## [41] "dRfd" "s.e..16" "RfdFm" "s.e..17"
## [45] "RfdFt" "s.e..18" "Rfd" "s.e..19"
## [49] "Rfd.I..px." "Rfd.I...." "Rfd.II..px." "Rfd.II...."
## [53] "Rfd.III..px." "Rfd.III...." "Rfd.IV..px." "Rfd.IV...."
## [57] "Rfd.V..px." "Rfd.V...." "NPQ" "s.e..20"
## [61] "F0." "s.e..21" "qP" "s.e..22"
## [65] "qN" "s.e..23" "qL" "s.e..24"
## [69] "qE" "s.e..25" "qI" "s.e..26"
## [73] "X.Ñno" "s.e..27" "X.Ñnpq" "s.e..28"
## [77] "npq.t." "s.e..29" "Red" "s.e..30"
## [81] "Green" "s.e..31" "Blue" "s.e..32"
## [85] "Hue" "s.e..33" "Saturation" "s.e..34"
## [89] "Value" "s.e..35" "SpcGrn" "s.e..36"
## [93] "FarRed" "s.e..37" "Nir" "s.e..38"
## [97] "ChlIdx" "s.e..39" "AriIdx" "s.e..40"
## [101] "NDVI" "s.e..41" "dGfp" "s.e..42"
## [105] "Gfp" "s.e..43" "Auto" "s.e..44"
## [109] "cGfp" "s.e..45" "MaskGfp" "nObjGfp"
## [113] "nObjSize" "s.e..46" "Gfp.I..px." "Gfp.I...."
## [117] "Gfp.II..px." "Gfp.II...." "Gfp.III..px." "Gfp.III...."
## [121] "Gfp.IV..px." "Gfp.IV...." "Gfp.V..px." "Gfp.V...."
## [125] "dRfp" "s.e..47" "Rfp" "s.e..48"
## [129] "MaskRfp" "nObjRfp" "nObjSize.1" "s.e..49"
## [133] "Rfp.I..px." "Rfp.I...." "Rfp.II..px." "Rfp.II...."
## [137] "Rfp.III..px." "Rfp.III...." "Rfp.IV..px." "Rfp.IV...."
## [141] "Rfp.V..px." "Rfp.V...." "dChl" "s.e..50"
## [145] "Chl" "s.e..51" "aRed" "s.e..52"
## [149] "aFarRed" "s.e..53" "Alpha" "s.e..54"
## [153] "Border" "Mask.Border" "Points" "Area..CH."
## [157] "Mask.Area..CH." "X.Centre" "Y.Centre" "Radius"
## [161] "Area..MC." "Mask.Area..MC." "Size..SK." "Junction..SK."
## [165] "Endpoint..SK." "Path..SK."PS4 <- PS3[,c(1:3,23, 37, 59,77,97,99, 101)]
PS4PS4$Date <- as.factor(PS4$Date)
lgraph <- ggplot(data=PS4, aes(x= Date, y=ChlIdx))
lgraph <- lgraph + geom_point(alpha = 0.7)
lgraph <- lgraph + ylab("Chlorophylll Index") + xlab("days after stress")
lgraph
This is great start, but lets isolate the measurement per tray ID -
which is the third item in the File name
(HDR_ExpID_TrayID_roundID.INF)
How do we get there?
strsplit(PS4$File[1], "_")[[1]][3]## [1] "34913"Now - lets isolate it for the entire data sheet:
for(i in 1:nrow(PS4)){
PS4$tray.ID[i] <- strsplit(PS4$File[i], "_")[[1]][3]
}
PS4lgraph <- ggplot(data=PS4, aes(x= Date, y=ChlIdx, group = tray.ID))
lgraph <- lgraph + geom_line(alpha = 0.7)
lgraph <- lgraph + ylab("Chlorophylll Index") + xlab("days after stress")
lgraph
the straight lines at the date are signifying that there are more than
one measurement taken per day - so we maybe plot it per round ID
instead. But first we need to isolate it from File name again o_O’
gsub(".INF", "", strsplit(PS4$File[1], "_")[[1]][4])## [1] "10"Now - lets isolate it for the entire data sheet:
for(i in 1:nrow(PS4)){
PS4$Round.ID[i] <- gsub(".INF", "", strsplit(PS4$File[i], "_")[[1]][4])
}
PS4Let;s try to replot it:
lgraph <- ggplot(data=PS4, aes(x= Round.ID, y=ChlIdx, group = tray.ID))
lgraph <- lgraph + geom_line(alpha = 0.7)
lgraph <- lgraph + ylab("Chlorophylll Index") + xlab("rounds of imaging")
lgraph
Now - let’s try to figure out what are our treatments
What are our TrayIDs again here:
unique(PS4$tray.ID)## [1] "34913" "34914" "34915" "34916" "34917" "34918"Lets upload decoding file:
decode <- read.csv("20240614_Annie_VIGS_1x1.xlsx - Sheet1.csv")
decode <- decode[,c(2:3,6,7)]
decodecolnames(PS4)## [1] "File" "Date" "Time" "Fv.Fm" "Fq..Fm." "NPQ"
## [7] "npq.t." "ChlIdx" "AriIdx" "NDVI" "tray.ID" "Round.ID"colnames(decode)## [1] "Tray.ID" "TrayInfo" "PlantID" "PlantName"colnames(decode)[1] <- "tray.ID"
colnames(decode)## [1] "tray.ID" "TrayInfo" "PlantID" "PlantName"PS4deco <- merge(PS4, decode, by = "tray.ID", all = T)
PS4decolgraph <- ggplot(data=PS4deco, aes(x= Round.ID, y=ChlIdx, group = tray.ID, color = TrayInfo))
lgraph <- lgraph + geom_line(alpha = 0.7)
lgraph <- lgraph + ylab("Chlorophylll Index") + xlab("rounds of imaging")
lgraph
Wait - but this round numbering does not make sense!!!! Let’s change it to something that does.. Maybe numeric?
PS4deco$Round.ID <- as.numeric(PS4deco$Round.ID)
lgraph <- ggplot(data=PS4deco, aes(x= Round.ID, y=ChlIdx, group = tray.ID, color = TrayInfo))
lgraph <- lgraph + geom_line(alpha = 0.7)
lgraph <- lgraph + ylab("Chlorophylll Index") + xlab("rounds of imaging")
lgraph
This is all good - but let’s layer some more stats info onto this - like average trend line +/- SE;
library(ggpubr)
ChlIndex_graph <- ggplot(data=PS4deco, aes(x= Round.ID, y=ChlIdx, group = tray.ID, color = TrayInfo))
ChlIndex_graph <- ChlIndex_graph + geom_line(alpha = 0.7)
ChlIndex_graph <- ChlIndex_graph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= TrayInfo), alpha=0.3)
ChlIndex_graph <- ChlIndex_graph + stat_summary(fun=mean, aes(group= TrayInfo), size=0.7, geom="line", linetype = "dashed")
ChlIndex_graph <- ChlIndex_graph + stat_compare_means(aes(group = TrayInfo), label = "p.signif", method = "t.test", hide.ns = TRUE)
ChlIndex_graph <- ChlIndex_graph + ylab("Chlorophylll Index") + xlab("rounds of imaging")
ChlIndex_graph