Importing Data Files using purrr and stringr
Updated:
Motivation
It’s a well-known [tenet](https://en.wikipedia.org/wiki/Rule_of_three_(computer_programming) of computer programming that if you have to copy and paste code more than twice, you should write a function.
In my phthalates exposure project, I imported 21 text files for tidying and analysis. This is clearly a task well-suited for a function, but instead, I tediously copied and pasted code 21 times. Why? Honestly, I just didn’t have much experience writing functions and I was too eager to get data into R to actually sit down and figure out how to automate it.
I knew, though, that one day, I’d sit down and figure out how to write that function. Today was that day.
A few things to note:
- I’m working with two file types here, and neither one is in a common .csv format. I downloaded these files from NHANES, which plays nicely with SAS, but less nicely with R. Some of the raw data they provide is in .txt format and some of it is in the SAS-ready .xport format. I’m using the
read.xportfunction in theforeignlibrary to import both types, but the function can easily be extended to more common file types. - The function below is specific to the situation, wherein the files are downloaded not only in .xport/.txt format, but they are named using the years first and survey type second. This led to error messages when I tried to save the imported dataframes using their original filenames (R doesn’t like objects named with numbers first), so I did some character re-shuffling using a few
stringrfunctions. If your files are named more appropriately, you won’t need this feature.
Code
As mentioned above, we’ll need the foreign library to read the xport files. We’ll also need stringr for some of the naming manipulations we’ll be doing within the function.
library(tidyverse)
library(foreign)
library(stringr)
First, we create a vector of filenames from the data folder and check its length.
files = list.files("../data/phthalates")
files
## [1] "2003_2004_DEMO_C.XPT" "2003_2004_PHTHTE_C.XPT.txt"
## [3] "2005_2006_DEMO_D.XPT" "2005_2006_PHTHTE_D.XPT.txt"
## [5] "2007_2008_DEMO_E.XPT" "2007_2008_PHTHTE_E.XPT.txt"
## [7] "2009_2010_DEMO_F.XPT" "2009_2010_PHTHTE_F.XPT.txt"
## [9] "2011_2012_DBQ_G.XPT.txt" "2011_2012_DEMO_G.XPT.txt"
## [11] "2011_2012_EPH_G.XPT.txt" "2011_2012_PHTHTE_G.XPT.txt"
## [13] "2013_2014_DBQ_H.XPT.txt" "2013_2014_DEMO_H.XPT.txt"
## [15] "2013_2014_EPHPP_H.XPT.txt" "2013_2014_PHTHTE_H.XPT.txt"
## [17] "2015_2016_ALB_CR_I.XPT.txt" "2015_2016_DBQ_I.XPT.txt"
## [19] "2015_2016_DEMO_I.XPT.txt" "2015_2016_EPHPP_I.XPT.txt"
## [21] "2015_2016_PHTHTE_I.XPT.txt"
length(files)
## [1] 21
Next, we write a function that does the following:
- Reads in a vector of names and concatenates it with the folder path, resulting in a vector of file path names called
y - Reads in the dataset represented by filepath
y - Removes the file extensions from y, since we’ll want these strings to name our imported dataframes
- Extracts the years, which originally come first in our filenames vector - otherwise, R will throw errors when we try to access the dataframes
- Rearranges the strings so that the years are at the end
- Stores the imported datasets, assigning a name using the resulting string from steps 3-5.
mult_import = function(x){
y = str_c("../data/phthalates/", x)
z = as.data.frame(read.xport(y))
name_1 = str_replace_all(x, ".txt|.XPT", "")
years = str_sub(x, 1, 9)
name_2 = str_sub(name_1, 11, 20)
new_name = str_c(name_2, years, sep = "_")
assign(new_name, z, envir = .GlobalEnv)
}
Next, we use purrr to apply mult_import to our datafiles. And voila! We have 21 neat dataframes to work with. I’m only going to print 3 of them here (just to prove that it worked).
data_sets = map(files, mult_import)
DEMO_C_2003_2004
## SEQN SDDSRVYR RIDSTATR RIDEXMON RIAGENDR RIDAGEYR RIDAGEMN RIDAGEEX RIDRETH1
## 1 21005 3 2 1 1 19 232 233 4
## 2 21006 3 2 2 2 16 203 205 4
## 3 21007 3 2 1 2 14 172 172 3
## 4 21008 3 2 2 1 17 208 209 4
## 5 21009 3 2 2 1 55 671 672 3
## 6 21010 3 2 2 2 52 633 634 3
## 7 21011 3 2 1 1 0 3 4 1
## 8 21012 3 2 2 1 63 765 766 4
## 9 21013 3 2 1 2 13 163 164 4
## 10 21014 3 2 2 1 3 42 42 4
## 11 21015 3 2 2 1 83 1006 1007 3
## 12 21016 3 2 1 1 13 165 166 4
## 13 21017 3 2 1 2 37 444 445 1
## 14 21018 3 2 2 2 33 404 405 4
## 15 21019 3 2 2 2 50 606 607 1
## 16 21020 3 2 1 2 55 666 667 4
## 17 21021 3 1 NA 2 72 864 NA 3
## 18 21022 3 2 1 2 2 32 32 1
## 19 21023 3 2 1 2 19 239 241 4
## 20 21024 3 2 2 1 14 178 179 3
## 21 21025 3 2 1 2 18 217 218 2
## 22 21026 3 2 1 1 16 195 196 4
## RIDRETH2 DMQMILIT DMDBORN DMDCITZN DMDYRSUS DMDEDUC3 DMDEDUC2 DMDEDUC DMDSCHOL
## 1 2 2 1 1 NA 11 NA 1 1
## 2 2 NA 1 1 NA 11 NA 1 2
## 3 1 NA 1 1 NA 8 NA 1 1
## 4 2 2 1 1 NA 10 NA 1 1
## 5 1 2 1 1 NA NA 3 2 NA
## 6 1 2 1 1 NA NA 4 3 NA
## 7 3 NA 1 1 NA NA NA NA NA
## 8 2 2 1 1 NA NA 3 2 NA
## 9 2 NA 1 1 NA 6 NA 1 1
## 10 2 NA 1 1 NA NA NA NA NA
## 11 1 1 1 1 NA NA 4 3 NA
## 12 2 NA 1 1 NA 7 NA 1 1
## 13 3 2 2 2 2 NA 2 1 NA
## 14 2 2 1 1 NA NA 3 2 NA
## 15 3 2 2 2 2 NA 1 1 NA
## 16 2 2 1 1 NA NA 2 1 NA
## 17 1 2 3 2 8 NA 3 2 NA
## 18 3 NA 1 1 NA NA NA NA NA
## 19 2 2 1 1 NA 11 NA 1 3
## 20 1 NA 1 1 NA 7 NA 1 1
## 21 5 2 3 1 5 11 NA 1 1
## 22 2 NA 1 1 NA 9 NA 1 1
## DMDMARTL DMDHHSIZ INDHHINC INDFMINC INDFMPIR RIDEXPRG DMDHRGND DMDHRAGE
## 1 5 2 6 6 2.44 NA 1 39
## 2 5 6 7 6 2.47 1 2 27
## 3 5 5 NA 6 1.60 2 1 39
## 4 5 7 11 7 2.75 NA 2 40
## 5 1 2 8 8 3.79 NA 1 55
## 6 6 3 6 4 1.24 2 1 54
## 7 NA 7 13 13 NA NA 2 22
## 8 6 2 2 2 0.89 NA 1 63
## 9 NA 3 2 2 0.59 2 2 30
## 10 NA 7 4 4 0.63 NA 2 31
## 11 1 2 4 4 1.20 NA 2 82
## 12 NA 6 10 10 2.58 NA 2 30
## 13 6 2 4 1 0.21 2 1 22
## 14 1 6 8 8 2.06 2 1 36
## 15 1 7 4 1 0.16 2 2 30
## 16 1 2 6 6 2.48 2 1 60
## 17 2 1 NA NA NA NA 2 72
## 18 NA 7 7 4 0.88 NA 1 32
## 19 5 5 4 1 0.00 2 2 48
## 20 5 3 9 9 3.51 NA 2 43
## 21 5 7 6 6 1.13 2 1 38
## 22 5 4 7 7 2.17 NA 2 32
## DMDHRBRN DMDHREDU DMDHRMAR DMDHSEDU SIALANG SIAPROXY SIAINTRP FIALANG FIAPROXY
## 1 1 5 3 NA 1 2 2 1 2
## 2 1 2 5 NA 1 2 2 1 2
## 3 1 4 NA NA 1 1 2 1 2
## 4 1 4 5 NA 1 2 2 1 2
## 5 1 3 1 4 1 2 2 1 2
## 6 1 1 6 NA 1 2 2 1 2
## 7 2 1 1 3 2 1 2 1 2
## 8 1 3 6 NA 1 2 2 1 2
## 9 1 4 3 NA 1 1 2 1 2
## 10 1 4 1 4 1 1 2 1 2
## 11 1 4 1 4 1 2 2 1 2
## 12 1 4 1 3 1 1 2 1 2
## 13 2 2 6 NA 2 2 2 1 2
## 14 1 1 1 3 1 2 2 1 2
## 15 2 2 1 1 2 2 2 2 2
## 16 1 4 1 2 1 2 2 1 2
## 17 3 3 2 NA 1 2 2 NA NA
## 18 2 2 1 2 2 1 2 1 2
## 19 3 1 1 2 1 2 2 1 2
## 20 1 5 3 NA 1 1 2 1 2
## 21 NA NA 1 4 1 2 2 2 2
## 22 1 3 1 3 1 2 2 1 2
## FIAINTRP MIALANG MIAPROXY MIAINTRP AIALANG WTINT2YR WTMEC2YR SDMVPSU SDMVSTRA
## 1 2 1 2 2 1 5512.321 5824.782 2 39
## 2 2 1 2 2 1 5422.140 5564.040 1 41
## 3 2 1 2 2 1 39764.177 40591.066 2 35
## 4 2 1 2 2 1 5599.499 5696.751 1 32
## 5 2 1 2 2 1 97593.679 97731.727 2 31
## 6 2 1 2 2 1 39599.363 43286.576 1 29
## 7 2 NA NA NA NA 1647.618 1692.168 2 40
## 8 2 NA NA NA NA 12629.440 12947.338 2 33
## 9 2 1 2 2 1 7174.371 7304.946 1 37
## 10 2 NA NA NA NA 7498.031 8385.246 1 33
## 11 2 1 2 2 NA 17625.876 19994.160 2 33
## 12 2 1 2 2 1 7689.833 7832.795 2 36
## 13 2 2 2 2 2 26619.834 26802.589 2 42
## 14 2 1 2 2 1 34998.530 36208.263 2 33
## 15 2 2 2 2 2 14746.454 15876.719 1 41
## 16 2 1 2 2 1 28232.100 29102.084 1 38
## 17 NA NA NA NA NA 46999.264 0.000 2 39
## 18 2 NA NA NA NA 6359.287 6329.696 1 39
## 19 2 1 2 2 1 5709.033 6114.820 1 35
## 20 2 1 2 2 1 38125.946 40061.772 2 33
## 21 2 1 2 2 1 13493.831 13772.570 2 43
## 22 2 1 2 2 1 5816.415 5924.548 1 37
## [ reached 'max' / getOption("max.print") -- omitted 10100 rows ]
DBQ_G_2011_2012
## SEQN DBQ010 DBD030 DBD041 DBD050 DBD055 DBD061 DBQ073A DBQ073B DBQ073C DBQ073D
## 1 62161 NA NA NA NA NA NA NA NA NA NA
## 2 62162 1 121 1 365 243 365 10 NA NA NA
## 3 62163 NA NA NA NA NA NA NA NA NA NA
## 4 62164 NA NA NA NA NA NA NA NA NA NA
## 5 62165 NA NA NA NA NA NA NA NA NA NA
## 6 62166 NA NA NA NA NA NA NA NA NA NA
## 7 62167 1 182 182 0 212 0 NA NA NA NA
## 8 62168 1 182 182 730 121 730 10 NA NA NA
## 9 62169 NA NA NA NA NA NA NA NA NA NA
## 10 62170 NA NA NA NA NA NA NA NA NA NA
## 11 62171 NA NA NA NA NA NA NA NA NA NA
## 12 62172 NA NA NA NA NA NA NA NA NA NA
## 13 62173 1 365 1 365 212 365 10 NA NA NA
## 14 62174 NA NA NA NA NA NA NA NA NA NA
## 15 62175 1 243 243 365 273 365 10 NA NA NA
## 16 62176 NA NA NA NA NA NA NA NA NA NA
## 17 62177 NA NA NA NA NA NA NA NA NA NA
## 18 62178 NA NA NA NA NA NA NA NA NA NA
## 19 62179 NA NA NA NA NA NA NA NA NA NA
## 20 62180 NA NA NA NA NA NA NA NA NA NA
## 21 62181 NA NA NA NA NA NA NA NA NA NA
## 22 62182 NA NA NA NA NA NA NA NA NA NA
## 23 62183 1 364 0 NA 182 365 NA NA NA NA
## DBQ073E DBQ073U DBQ700 DBQ197 DBQ223A DBQ223B DBQ223C DBQ223D DBQ223E DBQ223U
## 1 NA NA 2 3 NA NA NA 13 NA NA
## 2 NA NA NA 3 10 NA NA NA NA NA
## 3 NA NA NA 3 NA NA NA 13 NA NA
## 4 NA NA 1 0 NA NA NA NA NA NA
## 5 NA NA NA 3 10 NA NA NA NA NA
## 6 NA NA NA 3 NA NA 12 NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA 3 NA 11 NA NA NA NA
## 9 NA NA 3 1 NA 11 NA NA NA NA
## 10 NA NA NA 3 NA 11 NA NA NA NA
## 11 NA NA NA 3 10 NA NA NA NA NA
## 12 NA NA 3 3 NA 11 NA NA NA NA
## 13 NA NA NA 3 NA 11 NA NA NA NA
## 14 NA NA 2 2 NA 11 NA NA NA NA
## 15 NA NA NA 3 10 NA NA NA NA NA
## 16 NA NA 2 3 NA NA NA 13 NA NA
## 17 NA NA 3 2 NA 11 NA NA NA NA
## 18 NA NA 4 3 NA 11 NA NA NA NA
## 19 NA NA 3 0 NA NA NA NA NA NA
## 20 NA NA 4 3 NA NA NA NA NA 30
## 21 NA NA NA 3 10 11 NA NA NA NA
## 22 NA NA 2 1 NA NA NA 13 NA NA
## 23 14 NA NA 3 NA 11 NA NA NA NA
## DBQ229 DBQ235A DBQ235B DBQ235C DBQ301 DBQ330 DBQ360 DBQ370 DBD381 DBQ390 DBQ400
## 1 1 3 3 3 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA 1 1 5 3 1
## 4 2 NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA 1 1 5 1 1
## 6 NA NA NA NA NA NA 1 1 5 3 2
## 7 NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA 1 1 1 3 1
## 9 3 2 3 1 NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA 1 1 5 3 2
## 11 NA NA NA NA NA NA 1 1 4 2 1
## 12 1 3 3 3 NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA
## 14 1 3 3 3 2 2 NA NA NA NA NA
## 15 NA NA NA NA NA NA 1 1 5 1 1
## 16 1 3 3 3 NA NA NA NA NA NA NA
## 17 2 NA NA NA NA NA NA NA NA NA NA
## 18 1 3 3 3 1 2 NA NA NA NA NA
## 19 3 3 3 2 NA NA NA NA NA NA NA
## 20 1 3 3 3 NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA 1 1 5 1 1
## 22 3 3 3 1 2 2 NA NA NA NA NA
## 23 NA NA NA NA NA NA 1 1 5 3 1
## DBD411 DBQ421 DBQ424 DBD895 DBD900 DBD905 DBD910 CBQ595 CBQ600 CBQ605 CBQ610
## 1 NA NA NA 2 2 0 0 2 2 NA NA
## 2 NA NA NA 0 NA 0 1 NA NA NA NA
## 3 0 NA NA 0 NA 0 2 NA NA NA NA
## 4 NA NA NA 3 0 0 0 1 NA 2 2
## 5 5 1 3 2 2 5 3 NA NA NA NA
## 6 NA NA NA 1 0 2 2 NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA
## 8 0 NA NA 2 1 0 8 NA NA NA NA
## 9 NA NA NA 7 7 0 1 2 2 NA NA
## 10 NA NA NA 0 NA 0 0 NA NA NA NA
## 11 1 1 3 3 3 4 0 NA NA NA NA
## 12 NA NA NA 5 5 0 0 2 1 NA 2
## 13 NA NA NA 0 NA 0 0 NA NA NA NA
## 14 NA NA NA 0 NA 1 1 2 1 NA 1
## 15 0 NA 3 2 1 30 30 NA NA NA NA
## 16 NA NA NA 3 1 0 1 2 1 NA 2
## 17 NA NA NA 0 NA 0 0 2 2 NA NA
## 18 NA NA NA 1 0 6 17 2 2 NA NA
## 19 NA NA NA 3 0 2 0 2 1 NA 2
## 20 NA NA NA 8 4 0 4 1 NA 2 2
## 21 0 NA 3 1 1 0 0 NA NA NA NA
## 22 NA NA NA 2 0 1 1 1 NA 2 2
## 23 5 3 NA 1 1 5 0 NA NA NA NA
## [ reached 'max' / getOption("max.print") -- omitted 9733 rows ]
PHTHTE_C_2003_2004
## SEQN WTSB2YR URXMBP URDMBPLC URXMCP URDMCPLC URXMEP URDMEPLC URXMHP
## 1 21006 15983.728 375.6 0 0.2843 1 221.232 0 3.2
## 2 21013 20984.800 86.7 0 0.2843 1 306.702 0 33.8
## 3 21015 64191.400 12.6 0 0.2843 1 154.110 0 0.6
## 4 21016 23989.437 62.7 0 1.2060 0 132.528 0 10.6
## 5 21023 17565.945 206.1 0 0.2843 1 199.716 0 21.7
## 6 21026 18145.065 135.8 0 0.2843 1 108.966 0 2.5
## 7 21027 36738.941 28.4 0 0.2843 1 90.750 0 2.6
## 8 21031 263724.086 8.7 0 0.2843 1 16.434 0 0.6
## 9 21033 82316.528 32.9 0 0.2843 1 604.230 0 1.3
## 10 21036 71447.158 4.4 0 0.2843 1 7.524 0 0.6
## 11 21037 65800.789 39.9 0 0.2843 1 216.942 0 20.0
## 12 21038 21609.425 11.9 0 0.2843 1 30.558 0 1.0
## 13 21039 59483.964 17.5 0 0.2843 1 63.954 0 1.4
## 14 21041 9929.473 20.2 0 0.2843 1 142.494 0 15.6
## 15 21045 143827.869 21.1 0 0.2843 1 369.930 0 1.5
## 16 21047 19972.693 9.7 0 0.2843 1 57.420 0 2.3
## 17 21057 108477.791 22.6 0 0.2843 1 93.390 0 95.7
## 18 21064 34942.731 49.5 0 0.8040 0 64.350 0 7.5
## 19 21066 0.000 NA NA NA NA NA NA NA
## 20 21068 143113.714 10.0 0 0.2843 1 14.256 0 0.6
## 21 21083 16978.037 34.4 0 0.2843 1 83.094 0 1.5
## 22 21084 279483.714 13.0 0 0.2843 1 19.008 0 0.6
## 23 21086 85456.720 17.3 0 0.2843 1 12.936 0 1.3
## 24 21087 121948.671 11.9 0 0.2843 1 344.850 0 1.0
## 25 21090 55912.651 102.1 0 0.2843 1 380.094 0 4.0
## 26 21094 27317.087 71.4 0 0.2843 1 50.358 0 4.8
## 27 21095 14642.227 7.7 0 0.2843 1 124.146 0 0.6
## 28 21098 23448.273 27.8 0 0.2843 1 101.640 0 2.3
## 29 21100 91017.017 9.5 0 0.2843 1 152.724 0 0.6
## 30 21101 36738.941 46.4 0 0.2843 1 142.164 0 2.1
## 31 21104 41615.836 5.5 0 0.2843 1 1370.094 0 0.6
## 32 21107 197655.715 29.9 0 0.2843 1 93.720 0 0.6
## 33 21119 146985.242 34.1 0 1.2060 0 54.714 0 17.5
## 34 21124 66504.682 29.7 0 0.2843 1 26.994 0 1.7
## URDMHPLC URXMNP URDMNPLC URXMOP URDMOPLC URXMZP URDMZPLC URXMNM URDMNMLC URXMC1
## 1 0 1.0889 1 1.1879 1 17.784 0 5.4 0 4.1
## 2 0 1.0889 1 1.1879 1 40.464 0 23.8 0 7.9
## 3 1 1.0889 1 1.1879 1 2.880 0 3.6 0 2.0
## 4 0 1.0889 1 1.1879 1 49.392 0 8.0 0 6.1
## 5 0 1.0889 1 1.1879 1 15.984 0 3.2 0 7.6
## 6 0 1.0889 1 1.1879 1 9.720 0 5.6 0 4.6
## 7 0 1.0889 1 1.1879 1 23.976 0 0.7 1 9.0
## 8 1 1.0889 1 1.1879 1 1.728 0 1.3 0 1.0
## 9 0 1.0889 1 1.1879 1 5.976 0 3.9 0 2.4
## 10 1 1.0889 1 1.1879 1 0.504 0 0.7 1 0.3
## 11 0 1.0889 1 1.1879 1 22.248 0 2.6 0 4.2
## 12 0 1.0889 1 1.1879 1 9.648 0 0.7 1 1.9
## 13 0 1.0889 1 1.1879 1 2.736 0 4.1 0 3.7
## 14 0 1.0889 1 1.1879 1 17.352 0 0.7 1 5.1
## 15 0 1.0889 1 1.1879 1 13.824 0 1.4 0 3.2
## 16 0 1.0889 1 1.1879 1 5.184 0 0.7 1 2.4
## 17 0 1.0889 1 1.1879 1 12.600 0 3.9 0 2.3
## 18 0 1.0889 1 1.1879 1 71.352 0 1.1 0 17.2
## 19 NA NA NA NA NA NA NA NA NA NA
## 20 1 1.0889 1 1.1879 1 5.256 0 17.0 0 0.8
## 21 0 1.0889 1 1.1879 1 27.216 0 0.7 1 7.2
## 22 1 1.0889 1 1.1879 1 3.960 0 0.7 1 1.3
## 23 0 1.0889 1 1.1879 1 5.400 0 0.7 1 2.3
## 24 0 1.0889 1 1.1879 1 9.432 0 1.8 0 5.0
## 25 0 1.0889 1 1.1879 1 17.352 0 13.4 0 4.5
## 26 0 1.0889 1 1.1879 1 24.984 0 0.7 1 2.7
## 27 1 1.0889 1 1.1879 1 1.944 0 0.7 1 1.5
## 28 0 1.0889 1 1.1879 1 26.280 0 57.2 0 4.1
## 29 1 1.0889 1 1.1879 1 1.944 0 1.8 0 0.5
## 30 0 1.0889 1 1.1879 1 39.672 0 5.8 0 13.9
## 31 1 1.0889 1 1.1879 1 5.472 0 0.7 1 1.5
## 32 1 1.0889 1 1.1879 1 19.584 0 4.2 0 2.4
## 33 0 4.9280 0 1.1879 1 12.240 0 0.7 1 8.3
## 34 0 1.0889 1 1.1879 1 20.736 0 1.0 0 4.8
## URDMC1LC URXMHH URDMHHLC URXMOH URDMOHLC URXMIB URDMIBLC URXECP URDECPLC URXUCR
## 1 0 21.7 0 16.9 0 52.8 0 22.8 0 289
## 2 0 351.7 0 210.5 0 47.4 0 472.8 0 302
## 3 0 9.1 0 7.4 0 3.4 0 13.6 0 98
## 4 0 57.8 0 33.9 0 21.7 0 48.3 0 200
## 5 0 176.4 0 115.8 0 22.4 0 257.2 0 213
## 6 0 71.7 0 44.0 0 5.1 0 87.9 0 185
## 7 0 24.1 0 14.8 0 11.3 0 27.8 0 107
## 8 0 10.2 0 7.1 0 1.8 0 10.3 0 60
## 9 0 18.8 0 11.1 0 4.6 0 46.9 0 270
## 10 0 1.2 0 0.7 0 0.2 1 4.8 0 14
## 11 0 115.2 0 90.1 0 3.3 0 235.5 0 175
## 12 0 11.6 0 8.5 0 2.4 0 18.0 0 141
## 13 0 14.0 0 11.4 0 1.3 0 18.9 0 108
## 14 0 61.5 0 36.0 0 11.7 0 116.7 0 113
## 15 0 21.7 0 14.1 0 4.6 0 24.8 0 94
## 16 0 24.2 0 14.7 0 6.0 0 38.6 0 80
## 17 0 530.0 0 325.5 0 2.4 0 472.1 0 122
## 18 0 85.6 0 63.6 0 11.3 0 174.8 0 189
## 19 NA NA NA NA NA NA NA NA NA 28
## 20 0 6.1 0 4.2 0 2.9 0 6.6 0 103
## 21 0 21.8 0 13.3 0 2.5 0 48.4 0 88
## 22 0 5.2 0 2.9 0 1.0 0 15.0 0 41
## 23 0 20.7 0 12.0 0 5.5 0 29.5 0 119
## 24 0 20.6 0 13.8 0 2.8 0 38.8 0 157
## 25 0 50.8 0 30.7 0 6.8 0 56.6 0 259
## 26 0 67.2 0 39.1 0 8.0 0 87.5 0 137
## 27 0 3.5 0 2.7 0 1.3 0 10.8 0 72
## 28 0 32.4 0 23.6 0 5.3 0 64.4 0 112
## 29 0 37.4 0 29.6 0 0.2 1 62.0 0 70
## 30 0 25.3 0 18.8 0 16.3 0 45.7 0 101
## 31 0 6.1 0 3.4 0 3.6 0 8.6 0 72
## 32 0 13.0 0 8.3 0 5.5 0 22.7 0 155
## 33 0 286.2 0 214.7 0 11.3 0 397.2 0 135
## 34 0 24.7 0 16.6 0 38.9 0 27.1 0 328
## [ reached 'max' / getOption("max.print") -- omitted 2663 rows ]
Conclusion
Writing these 10 lines definitely took me about 5 hours and a lot of debugging. Quite frankly, it would have taken me about 10 minutes to just copy and paste slightly modified version of the same code 21 times. But then I wouldn’t have had the experience of the happy dance that resulted from the function running.
And I guess this is really a testament to the process of automation, which usually follows the so-called flywheel effect. It takes a lot of effort to get the thing spinning, but once it picks up enough momentum, you get tons of movement with minimal addition effort.