R scatterplot loop using two variables

Question

I'm trying to make a for loop to automatically generate ~50 scatterplots comparing two sets of data. It's a quality control analysis, so I'm looking at geochemical values that were analyzed twice (duplicates). So I have a list of 53 elements (periodic table elements) labeled Al1, Ag1, Au1..... and another list of 53 labeled Al2, Ag2....etc.

I've successfully gotten my loop to work for generating graphs that only need one variable, with the x axis being fixed, like below.

for(i in colNames){
  plt <- ggplot(YGS_Dupes, mapping = aes_string(x=Dup_Num, y = i)) +
geom_bar() + theme_calc() + ggtitle(paste(i, "Duplicate Comparison", sep=" - 
")) 
  print(plt)
  ggsave(paste0(i,".png"))
  Sys.sleep(2)
}

I set colNames to be the element columns, and the function runs through the different elements and generates a bar plot for each, where it's just showing Sample 1 or Sample 2 as the X axis (so it produces two bar plots side by side).

What I need to make now is a scatterplot where I compare the data from Al1 to Al2 or Fe1 to Fe2, so I need the for loop to run using two parallel sets of changing variables. I made the function for a single graph like so:

ggplot(YGS_Dup_Scatter, mapping = aes(x = Fe_pct1, y = 
Fe_pct2))+geom_point()

and it looks like this:

Fe vs Fe Scatterplot

So what I have done is made a similar set of colNames groups, like below:

colNames_scatter_dup <- names(YGS_Dup_Scatter)[4:56]
colNames_scatter_dup2 <- names(YGS_Dup_Scatter)[57:109]

Where 4-56 are all the element 1 set and 57-109 are the element 2 set. They are ordered the same so I want 4/57, 5/58....etc to be pairs.

How do I set up my for loop equation to do this?

Thank you for any help

Edit: Adding the dput data for people to try. I had too many observations and variables so I cut most of them out:

Edit 2: Ok, so I made a nested loop and it makes what I want, but it also makes way too many graphs, shown below:

for (j in colNames_scatter_dup2) {
  for(i in colNames_scatter_dup){
    plt <- ggplot(YGS_Dup_Scatter, mapping = aes_string(x=j, y = i)) +
      geom_point() 
    print(plt)
    ggsave(paste0(i,".png"))
    Sys.sleep(2)
  }
}

The issue I have now is that it does Al1 vs Al2, then Ag1 vs Al2, ......then gets to Al1 vs Ag2.....and make hundreds of graphs. I only want to make the actual 53 element pairs, and I can't figure out how to restrict it to just those.

thanks

structure(list(DUP_COMP_ID = structure(c(1L, 12L, 23L, 34L, 45L, 
56L, 67L, 78L, 89L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 24L, 25L, 26L, 
27L, 28L, 29L, 30L, 31L, 32L, 33L, 35L, 36L, 37L, 38L, 39L, 40L, 
41L, 42L, 43L, 44L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 
55L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 68L, 69L, 
70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 79L, 80L, 81L, 82L, 83L, 
84L, 85L, 86L, 87L, 88L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 
98L, 99L), .Label = c("DCI_1", "DCI_10", "DCI_11", "DCI_12", 
"DCI_13", "DCI_14", "DCI_15", "DCI_16", "DCI_17", "DCI_18", "DCI_19", 
"DCI_2", "DCI_20", "DCI_21", "DCI_22", "DCI_23", "DCI_24", "DCI_25", 
"DCI_26", "DCI_27", "DCI_28", "DCI_29", "DCI_3", "DCI_30", "DCI_31", 
"DCI_32", "DCI_33", "DCI_34", "DCI_35", "DCI_36", "DCI_37", "DCI_38", 
"DCI_39", "DCI_4", "DCI_40", "DCI_41", "DCI_42", "DCI_43", "DCI_44", 
"DCI_45", "DCI_46", "DCI_47", "DCI_48", "DCI_49", "DCI_5", "DCI_50", 
"DCI_51", "DCI_52", "DCI_53", "DCI_54", "DCI_55", "DCI_56", "DCI_57", 
"DCI_58", "DCI_59", "DCI_6", "DCI_60", "DCI_61", "DCI_62", "DCI_63", 
"DCI_64", "DCI_65", "DCI_66", "DCI_67", "DCI_68", "DCI_69", "DCI_7", 
"DCI_70", "DCI_71", "DCI_72", "DCI_73", "DCI_74", "DCI_75", "DCI_76", 
"DCI_77", "DCI_78", "DCI_79", "DCI_8", "DCI_80", "DCI_81", "DCI_82", 
"DCI_83", "DCI_84", "DCI_85", "DCI_86", "DCI_87", "DCI_88", "DCI_89", 
"DCI_9", "DCI_90", "DCI_91", "DCI_92", "DCI_93", "DCI_94", "DCI_95", 
"DCI_96", "DCI_97", "DCI_98", "DCI_99"), class = "factor"), Dup_Code = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = "Sample 1", class = "factor"), Dup_Code.1 = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = "Sample 2", class = "factor"), Ag_ppb1 = c(56L, 
58L, 52L, 59L, 68L, 318L, 50L, 70L, 398L, 114L, 38L, 52L, 63L, 
64L, 65L, 81L, 66L, 62L, 86L, 146L, 67L, 70L, 49L, 69L, 74L, 
55L, 55L, 47L, 109L, 41L, 78L, 115L, 65L, 373L, 59L, 47L, 85L, 
72L, 86L, 72L, 77L, 554L, 68L, 85L, 105L, 70L, 67L, 127L, 69L, 
67L, 38L, 59L, 284L, 94L, 57L, NA, 92L, 88L, 74L, 73L, 50L, NA, 
63L, 57L, 111L, 71L, 47L, 69L, 81L, 45L, 52L, 42L, 34L, 176L, 
73L, 140L, 87L, 41L, 36L, 204L, 272L, 52L, 37L, 45L, 187L, 180L, 
100L, 60L, 39L, 71L, 92L, 29L, 308L, 157L, 78L, 91L, NA, 60L, 
217L), As_ppm1 = c(4.3, 4.8, 4.6, 5, 1.9, 14.3, 3, 5.8, 49.7, 
9.2, 3.8, 3.1, 5.9, 5.4, 5, 4.3, 5.3, 4.2, 3.8, 35, 5.8, 6.6, 
3.3, 11.2, 3.5, 3.8, 3.8, 4.4, 8.8, 4.9, 3.6, 18.3, 3.6, 6.1, 
4.2, 4.4, 9, 7.3, 3.7, 3.4, 13.7, 21.9, 3.9, 5.8, 3.6, 4.4, 2.9, 
5.2, 4.9, 5.4, 4.4, 4.3, 5.5, 8.3, 3.4, NA, 6.2, 4.2, 3.5, 5.5, 
5, NA, 3.4, 4.2, 7.1, 5.1, 3.8, 6.9, 6.7, 3.2, 4.8, 4.3, 2.6, 
4.6, 4.8, 9.3, 7.5, 2.8, 4.2, 4.9, 17, 3.1, 3.9, 4.7, 9.7, 883.2, 
7.8, 5.1, 2.4, 10.4, 7.2, 2.9, 6.7, 9.3, 3.7, 7.3, NA, 4.8, 21.5
), Au_ppb1 = c(0.7, 4.6, 1.5, 0.6, 11.9, 2.4, 0.8, 0.8, 2.2, 
3.5, 0.4, 0.8, 0.9, 1.7, 1.2, 3.5, 1.4, 1.4, 2.2, 2.6, 3, 0.9, 
0.6, 1.5, 0.9, 0.7, 1.4, 3.5, 8.7, 0.4, 0.6, 2.4, 1.1, 1.7, 1.5, 
1.3, 0.1, 0.1, 4.5, 44.5, 0.8, 6.6, 48.7, 1.5, 0.7, 0.3, 0.8, 
1.1, 1.2, 5.5, 1.4, 1.4, 2.7, 1.9, 1, NA, 0.4, 1, 1.6, 0.3, 0.4, 
NA, 0.8, 1.8, 1.9, 0.1, 0.5, 1.4, 0.8, 0.2, 0.8, 0.6, 0.3, 1.1, 
1, 2.1, 0.8, 0.4, 0.9, 0.9, 1.2, 1.2, 1.2, 1.3, 1.2, 1.6, 1.8, 
0.5, 1.4, 1.3, 1.4, 0.1, 0.6, 1.9, 0.8, 1.5, NA, 0.6, 3.4), B_ppm1 = c(10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 21L, NA, 10L, 10L, 10L, 10L, 10L, NA, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, NA, 10L, 10L), Ba_ppm1 = c(141, 124.2, 171.9, 
171, 246.8, 359.3, 96, 205.4, 187.4, 195.3, 115.2, 134.9, 162.9, 
156.9, 186.7, 148.4, 164.9, 165.5, 329.1, 106.8, 137.3, 150.7, 
180.9, 123.4, 150.6, 122.7, 230.4, 176.1, 208.9, 154.5, 147.2, 
242.2, 184.2, 465.5, 217.2, 171.3, 286.6, 248, 243.1, 265.9, 
273.3, 317.4, 150.7, 272.7, 332.1, 293.1, 185.7, 262.9, 203.4, 
333, 185.2, 203.4, 300.8, 227.3, 193.2, NA, 328, 293.2, 225.7, 
286.9, 237.6, NA, 193.5, 293.8, 294.5, 252.2, 160.5, 277, 349.2, 
184.5, 231.3, 251.4, 150, 372.4, 237.7, 227.9, 271.8, 66.6, 92.8, 
53.4, 112.5, 172.6, 188.5, 177, 315.5, 193.8, 300.2, 132.9, 199.4, 
221.4, 375.6, 128.7, 82.7, 157.4, 175.5, 297.9, NA, 190.9, 206.4
), Be_ppm1 = c(0.3, 0.5, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.6, 0.3, 
0.4, 0.4, 0.3, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0.3, 0.9, 0.4, 0.6, 
0.3, 0.5, 0.3, 0.3, 0.2, 0.3, 0.3, 0.4, 0.6, 0.3, 0.2, 0.3, 0.3, 
0.3, 0.2, 0.6, 0.4, 0.4, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 
0.2, 0.3, 0.3, 0.2, 0.3, 0.3, NA, 0.3, 0.05, 0.3, 0.3, 0.2, NA, 
0.3, 0.5, 0.3, 0.5, 0.3, 0.3, 0.3, 0.3, 0.2, 0.3, 0.2, 0.4, 0.3, 
0.5, 0.4, 0.2, 0.1, 1.8, 1.8, 0.4, 0.2, 0.2, 0.8, 35.9, 0.3, 
0.4, 0.2, 0.4, 0.2, 0.4, 0.2, 0.4, 0.3, 0.4, NA, 0.4, 1.2), Bi_ppm1 = c(0.24, 
0.29, 0.21, 0.19, 0.13, 0.28, 0.15, 0.16, 0.73, 0.14, 0.12, 0.39, 
0.1, 0.12, 0.4, 0.42, 0.13, 0.13, 0.11, 6.67, 0.14, 0.22, 0.15, 
0.18, 0.09, 0.06, 0.09, 0.1, 0.18, 0.08, 0.08, 0.14, 0.06, 0.23, 
0.1, 0.09, 0.08, 0.14, 0.13, 0.06, 0.08, 0.13, 0.08, 0.15, 0.11, 
0.1, 0.07, 0.11, 0.1, 0.06, 0.11, 0.08, 0.11, 0.11, 0.08, NA, 
0.12, 0.22, 0.1, 0.13, 0.08, NA, 0.06, 0.18, 0.13, 0.1, 0.16, 
0.15, 0.13, 0.07, 0.09, 0.08, 0.06, 0.14, 0.07, 0.21, 0.17, 0.01, 
0.05, 2.07, 0.35, 0.13, 0.08, 0.09, 0.23, 0.55, 0.17, 1.1, 0.06, 
0.07, 0.14, 0.04, 0.06, 0.15, 0.08, 0.12, NA, 0.09, 0.97), Ca_pct1 = c(0.69, 
0.58, 0.46, 0.46, 0.42, 0.41, 0.51, 0.5, 0.6, 0.83, 0.42, 0.34, 
0.69, 0.98, 0.51, 0.43, 0.78, 0.44, 0.38, 0.56, 1.07, 0.46, 0.72, 
0.77, 1.08, 0.64, 0.46, 0.57, 0.5, 0.5, 0.88, 0.65, 0.67, 0.28, 
0.75, 0.59, 0.49, 0.72, 0.31, 0.42, 0.71, 0.14, 0.42, 0.69, 0.29, 
0.39, 0.31, 0.94, 0.7, 0.47, 0.71, 0.38, 0.31, 0.5, 0.47, NA, 
0.47, 0.37, 0.67, 0.68, 0.32, NA, 0.64, 0.31, 0.83, 0.52, 0.33, 
0.71, 0.91, 0.49, 0.58, 0.35, 0.34, 0.5, 0.54, 0.92, 0.4, 3.74, 
1.69, 0.21, 0.4, 0.45, 0.66, 0.49, 0.56, 0.88, 0.41, 0.41, 0.31, 
0.53, 0.96, 1.13, 0.35, 0.58, 0.33, 0.56, NA, 0.68, 0.32), Cd_ppm1 = c(0.13, 
0.22, 0.12, 0.15, 0.09, 0.99, 0.13, 0.19, 0.88, 0.34, 0.1, 0.15, 
0.17, 0.16, 0.14, 0.2, 0.14, 0.11, 0.15, 0.2, 0.14, 0.17, 0.1, 
0.17, 0.18, 0.13, 0.11, 0.13, 0.2, 0.12, 0.13, 0.27, 0.13, 0.37, 
0.21, 0.12, 0.18, 0.08, 0.14, 0.11, 0.15, 0.41, 0.19, 0.3, 0.23, 
0.15, 0.1, 0.34, 0.13, 0.13, 0.09, 0.15, 0.25, 0.17, 0.12, NA, 
0.17, 0.22, 0.14, 0.21, 0.11, NA, 0.1, 0.16, 0.27, 0.19, 0.13, 
0.22, 0.26, 0.05, 0.17, 0.15, 0.1, 0.39, 0.16, 0.47, 0.21, 0.17, 
0.14, 0.59, 1.11, 0.12, 0.13, 0.1, 0.63, 0.47, 0.33, 0.2, 0.11, 
0.26, 0.28, 0.11, 0.1, 0.55, 0.37, 0.29, NA, 0.18, 0.82), Ag_ppb2 = c(59L, 
73L, 69L, 75L, 85L, 319L, 43L, 73L, 405L, 121L, 33L, 45L, 71L, 
67L, 67L, 80L, 50L, 45L, 68L, 140L, 56L, 69L, 51L, 71L, 79L, 
51L, 36L, 52L, 93L, 31L, 98L, 134L, 67L, 386L, 47L, 46L, 90L, 
63L, 86L, 54L, 59L, 478L, 61L, 114L, 108L, 74L, 72L, 147L, 60L, 
74L, 40L, 56L, 256L, 112L, 62L, 87L, 71L, 104L, 109L, 55L, 45L, 
84L, 69L, 63L, 107L, 70L, 57L, 73L, 100L, 45L, 43L, 36L, 39L, 
161L, 108L, 100L, 93L, 32L, 45L, 187L, 267L, 68L, 37L, 57L, 228L, 
74L, 69L, 47L, 65L, 101L, 33L, 32L, 139L, 77L, 78L, NA, 59L, 
214L, 410L), As_ppm2 = c(3.9, 3.8, 4.4, 5.4, 1.7, 14.4, 3.1, 
5.9, 52.3, 9.7, 3.5, 2.7, 6.7, 5.2, 5, 4.3, 4.8, 4, 3.9, 31.9, 
5.3, 6.5, 3.6, 10.4, 3.5, 3.9, 3.6, 4.3, 8.9, 5.3, 3.8, 16.7, 
3.7, 6.1, 3.7, 4, 9.6, 6.4, 4, 3.1, 13.2, 22.1, 4.3, 6.9, 3.6, 
4.9, 3.4, 4.8, 4.1, 4.8, 4.2, 3.8, 5.3, 9.2, 3.3, 12.5, 5.3, 
4.4, 4.8, 5.7, 5, 5.5, 3.4, 4.4, 6.5, 4.8, 4, 6.5, 6.2, 3.4, 
4.5, 3.8, 2.6, 4.7, 8, 8.5, 7.6, 2.6, 4.7, 5.2, 15.8, 4, 3.1, 
5.3, 343.7, 7.4, 5.1, 3, 11, 7.3, 3, 6.8, 21.1, 4.1, 9.1, NA, 
4.4, 21, 122.1), Au_ppb2 = c(0.9, 1.6, 0.1, 1.3, 0.7, 1.8, 0.6, 
0.8, 1.6, 2.7, 0.4, 0.9, 0.9, 1.8, 1.5, 1.6, 1.5, 0.9, 2, 1.3, 
0.3, 3, 0.8, 2.5, 1.5, 0.4, 1.2, 1.4, 1, 1.1, 0.4, 113.3, 0.6, 
2.2, 1.9, 0.7, 0.5, 0.1, 1.8, 0.9, 1.4, 4.3, 1.6, 0.8, 0.7, 0.9, 
0.6, 2.4, 5.6, 1.2, 0.9, 1.1, 2.1, 1.1, 0.9, 0.8, 0.9, 1, 4, 
0.3, 1.5, 0.5, 1.2, 1, 1.5, 0.1, 1.2, 19.8, 32.8, 0.1, 0.7, 0.7, 
1, 0.5, 2.3, 1.6, 1.6, 0.6, 0.9, 1.7, 1.9, 1.3, 1.1, 1.1, 0.9, 
4.8, 0.5, 0.4, 1.6, 1, 0.1, 0.9, 1.3, 0.8, 2.7, NA, 0.8, 4, 3.6
), B_ppm2 = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 22L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 23L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, NA, 10L, 10L, 10L), Ba_ppm2 = c(137.5, 
128, 175, 205.6, 262.7, 356.1, 91.2, 212.8, 207, 217.4, 111, 
132.4, 179.4, 139.8, 188.9, 164.4, 136, 158.7, 348.9, 96.6, 141.3, 
143.7, 187, 121.2, 166.9, 131, 235.9, 189.5, 201.4, 158.7, 148.3, 
227, 190, 415.9, 197.2, 178, 268, 221.1, 251.5, 243.3, 260.4, 
310, 165.8, 308.2, 342.8, 317, 185, 241.7, 189.2, 291.4, 199.4, 
214.7, 312.2, 273, 197.8, 265, 255, 315.2, 281.7, 326, 236.5, 
229.7, 197.8, 308.4, 277.2, 258.7, 185.7, 261.2, 354.7, 177.7, 
213.2, 226.7, 159.2, 369.5, 359.1, 224.9, 275.4, 54, 106.7, 53.4, 
100.9, 194.7, 188.4, 187.4, 162.9, 237.7, 146.9, 189, 214.9, 
368.1, 134.8, 82.4, 130.4, 187.8, 291.2, NA, 171.9, 209.5, 318.5
), Be_ppm2 = c(0.2, 0.3, 0.4, 0.3, 0.3, 0.4, 0.1, 0.3, 0.6, 0.4, 
0.4, 0.5, 0.4, 0.3, 0.5, 0.7, 0.3, 0.3, 0.2, 0.4, 0.7, 0.4, 0.4, 
0.3, 0.4, 0.2, 0.3, 0.3, 0.5, 0.6, 0.5, 0.4, 0.3, 0.3, 0.3, 0.2, 
0.2, 0.2, 0.5, 0.2, 0.3, 0.3, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 
0.2, 0.3, 0.2, 0.3, 0.5, 0.3, 0.4, 0.3, 0.3, 0.2, 0.3, 0.1, 0.5, 
0.2, 0.6, 0.3, 0.4, 0.4, 0.2, 0.4, 0.3, 0.3, 0.2, 0.2, 0.3, 0.5, 
0.3, 0.3, 0.2, 0.2, 1.6, 1.8, 0.5, 0.2, 0.6, 33.1, 0.1, 0.6, 
0.05, 0.2, 0.3, 0.7, 0.2, 1.5, 0.3, 0.3, NA, 0.3, 1.2, 1.4), 
Bi_ppm2 = c(0.23, 0.28, 0.23, 0.21, 0.12, 0.26, 0.14, 0.16, 
0.69, 0.16, 0.12, 0.34, 0.11, 0.11, 0.41, 0.36, 0.12, 0.11, 
0.11, 2.86, 0.14, 0.23, 0.19, 0.18, 0.1, 0.05, 0.08, 0.11, 
0.15, 0.08, 0.09, 0.15, 0.06, 0.24, 0.08, 0.09, 0.09, 0.12, 
0.14, 0.07, 0.07, 0.12, 0.09, 0.18, 0.1, 0.1, 0.09, 0.09, 
0.11, 0.06, 0.1, 0.07, 0.1, 0.12, 0.08, 0.09, 0.1, 0.2, 0.09, 
0.1, 0.09, 0.17, 0.06, 0.15, 0.12, 0.1, 0.17, 0.13, 0.12, 
0.05, 0.08, 0.08, 0.07, 0.17, 0.12, 0.21, 0.17, 0.01, 0.05, 
1.93, 0.33, 0.15, 0.05, 0.08, 0.68, 0.12, 0.3, 0.06, 0.06, 
0.14, 0.05, 0.08, 0.4, 0.09, 0.12, NA, 0.07, 0.98, 2.21), 
Ca_pct2 = c(0.6, 0.56, 0.48, 0.53, 0.4, 0.41, 0.47, 0.51, 
0.58, 0.86, 0.41, 0.33, 0.7, 0.9, 0.51, 0.45, 0.67, 0.44, 
0.39, 0.56, 1.05, 0.48, 1.21, 0.83, 1.1, 0.66, 0.45, 0.62, 
0.5, 0.47, 1.04, 0.66, 0.64, 0.3, 0.74, 0.58, 0.49, 0.65, 
0.31, 0.42, 0.62, 0.13, 0.42, 0.84, 0.29, 0.4, 0.32, 1.01, 
0.6, 0.46, 0.71, 0.41, 0.3, 0.58, 0.5, 1.02, 0.4, 0.39, 0.87, 
0.79, 0.34, 0.44, 0.67, 0.31, 0.79, 0.47, 0.33, 0.67, 0.86, 
0.5, 0.49, 0.29, 0.35, 0.5, 0.87, 0.8, 0.39, 3.36, 1.78, 
0.22, 0.36, 0.5, 0.57, 0.53, 0.58, 0.37, 0.43, 0.3, 0.46, 
1.03, 1.12, 0.36, 0.48, 0.38, 0.52, NA, 0.52, 0.33, 1.21), 
Cd_ppm2 = c(0.13, 0.19, 0.12, 0.15, 0.1, 0.97, 0.1, 0.21, 
0.92, 0.35, 0.1, 0.09, 0.16, 0.18, 0.16, 0.17, 0.11, 0.11, 
0.2, 0.16, 0.11, 0.16, 0.13, 0.17, 0.2, 0.13, 0.14, 0.15, 
0.25, 0.05, 0.18, 0.28, 0.09, 0.3, 0.22, 0.09, 0.18, 0.12, 
0.1, 0.1, 0.15, 0.3, 0.17, 0.33, 0.2, 0.15, 0.1, 0.59, 0.16, 
0.16, 0.1, 0.13, 0.24, 0.21, 0.11, 0.46, 0.12, 0.24, 0.23, 
0.17, 0.11, 0.22, 0.13, 0.18, 0.24, 0.16, 0.17, 0.18, 0.23, 
0.09, 0.12, 0.1, 0.1, 0.35, 0.37, 0.43, 0.24, 0.16, 0.17, 
0.62, 1, 0.13, 0.12, 0.11, 0.56, 0.23, 0.22, 0.15, 0.23, 
0.28, 0.12, 0.1, 0.97, 0.36, 0.3, NA, 0.19, 0.89, 3.59)), class = "data.frame", row.names = c(NA, 
-99L))

When asking for help, you should include a simple [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input and desired output that can be used to test and verify possible solutions. Take out portions of the code not directly related to the problem (such as `theme()` stuff). — MrFlick, Sep 17 '18 at 19:43
I removed all of the theme info, but I can't seem to get the dput() to work. Is it possible to just attach the csv into the question? Or a small portion of it? — Avery_Tellig, Sep 17 '18 at 20:01
You just need to copy and paste the output from running `dput(YGS_Dup_Scatter)`. — JasonAizkalns, Sep 17 '18 at 20:08
@Avery_Tellig: these might be useful https://stackoverflow.com/a/50522928/ & https://stackoverflow.com/a/52045111/ — Tung, Sep 17 '18 at 20:13

Parfait · Answer 1 · 2018-09-18T14:11:05.180

Consider Map (wrapper to mapply) which is the iteration function to run elementwise between equal length lists and saves output into a list. Doing so, you avoid extraneous looping as seen with nested for loops approach.

# EXTRACT NEEDED NAMES
samples1 <- names(YGS_Dupes)[grep("1$", names(YGS_Dupes))][-1]  # -1 TO REMOVE Dupe_Code.1 
samples2 <- names(YGS_Dupes)[grep("2$", names(YGS_Dupes))]

# SET UP LOOPING FUNCTION
plot_fct <- function(s1, s2) {      
  s_title <- gsub("1", "", s1)

  p <- ggplot(YGS_Dupes, aes_string(x=s1, y=s2)) + geom_point(color="#0072B2") + 
    ggtitle(paste(s_title, "Duplicate Comparison", sep=" - ")) +
    theme(plot.title = element_text(hjust = 0.5), legend.position="top",
          axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5))

  ggsave(paste0(s_title,".png"))

  return(p)
}

# BUILD LIST LOOPING ELEMENTWISE
plot_list2 <- Map(plot_fct, samples1, samples2)

# OUTPUT PLOTS BY NAME
plot_list2$Ag_ppb1
plot_list2$As_ppm1
plot_list2$Au_ppb1

Output (first three plots)

Thank you for taking the time to make this, it's definitely an interesting way of visualizing the comparison data. Unfortunately the key requirement is making the graphs to be direct scatterplots against one another. However I was able to get it working with about 99% extra graphs, as shown in the edit. So now the trouble is getting it to only make the graphs I want. Thanks — Avery_Tellig, Sep 17 '18 at 22:31
See update using `Map` to iterate elementwise between both lists. There was still a `by` version I almost posted but did not to avoid confusion. — Parfait, Sep 18 '18 at 14:12

score 1 · Answer 2 · answered May 24 '20 at 10:44

As a general solution to plot scatterplots using for loop, you can use following flow.

Step 1: Create a plotting function

In the following code, I have explicitly provided my dataframe and the x-axis target variable. For the variable on the y-axis I pass the column number in the function so that it would be to run a for-loop later.

sct_plot_function <- function(dataset = car.c2.num, target_x = car.c2.num$price, target_y_num){

  ggplot(dataset, aes(x = target_x, y = car.c2.num[,target_y_num])) +
    geom_point() + 
    geom_smooth(level = 0.95) +
    theme_bw() +
    labs(title = paste("Scatter plot of Price Vs ", colnames(car.c2.num)[target_y_num]), y = colnames(car.c2.num)[target_y_num], x = "Price") +
    theme(plot.title = element_text(hjust = 0.5)) 
}

Step 2: Use a for loop to plot multiple scatter plots in one go.

Using dim(car.c2.num)[2] - 1 to extract the number of columns minus from the dataframe and loop it using i in 1:(dim(car.c2.num)[2] - 1)

The reason I have done is that the 14 variable for me is the target variable which is fixed for the x-axis.

for(i in 1:(dim(car.c2.num)[2] - 1) ){
        plot(sct_plot_function(target_y_num = i))
      }

you can use this as a basic structure to re-define for your multiple x and y axes. Can further use nested for loop if you are aiming to plot all variable combinations on x and y axes respectively.

Sample Image:

Scatter plot of Price Vs compression_ratio for Automobile dataset UCI

score 0 · Answer 3 · answered Sep 17 '18 at 20:13

Try this:

For (i in 1:length(colNames_scatter_dup)){ 
    print(ggplot(YGS_Dup_Scatter, mapping = aes(x = YGS_Dup_Scatter[,names(YGS_Dup_Scatter) %in% colNames_scatter_dup[i]], y = YGS_Dup_Scatter[,names(YGS_Dup_Scatter) %in% colNames_scatter_dup2[i]]))+geom_point())
}

score 0 · Answer 4 · answered Sep 18 '18 at 17:19

ok Parfait, thank you for helping, discussing your answer with a colleague got me to where I needed to be.

The final result was the following:

YGS_Dup_Scatter = read.csv(file.choose(), header=TRUE, sep=",")
colNames_scatter_dup <- names(YGS_Dup_Scatter)[4:56]
colNames_scatter_dup2 <- names(YGS_Dup_Scatter)[57:109]
for (j in 1:length(colNames_scatter_dup)) {
  plt <- ggplot(YGS_Dup_Scatter, mapping = aes_string(x=colNames_scatter_dup[j], y =colNames_scatter_dup2[j])) +
    geom_point() + theme_calc() + ggtitle(paste(colNames_scatter_dup[j], "Duplicate Comparison", sep=" - ")) + theme(plot.title = element_text(face = "bold", hjust = 0.5, size = 16), axis.text.x = element_text(face = "bold", size = "14"), axis.text.y = element_text(face = "bold", size = "12"), plot.margin = margin(10, 30, 2, 2), axis.title.y=element_text(face = "bold", size = "14"), plot.background = element_rect(fill = "lightskyblue2"))
  print(plt)
  ggsave(paste0(i,".png"))
  Sys.sleep(2)
}

The key was using the length function and structuring my columns so that it went A1, A2...A53, then B1, B2....etc.

Being the same length allowed the length function to keep them paired.

Thanks for the help everyone!

R scatterplot loop using two variables

4 Answers4