class: center, middle, inverse, title-slide .title[ # Geospatial Analysis with R ] .subtitle[ ## Class 5 ] --- <img src="figures/sms-farmers.png" width="80%" style="display: block; margin: auto;" /> --- ```r library(sf) library(dplyr) f <- system.file("extdata", "farmer_spatial.csv", package = "geospaar") farmers <- readr::read_csv(f) farmers <- farmers %>% select(uuid, x, y) %>% distinct() %>% st_as_sf(., coords = c("x", "y")) f <- system.file("extdata", "districts.shp", package = "geospaar") dists <- read_sf(f) st_crs(farmers) <- st_crs(dists) # join farmers with districts (h/t https://mattherman.info/blog/point-in-poly/) farmers_in_dists <- st_join(farmers, dists, join = st_within) %>% tidyr::drop_na() farmer_count <- count(as_tibble(farmers_in_dists), distName) dists_w_farmers <- left_join(dists, farmer_count) %>% mutate(n = ifelse(is.na(n), 0, n)) png("figures/sms-farmers.png", height = 4, width = 5, res = 300, units = "in") plot(dists_w_farmers["n"], breaks = c(0, 5, 10, 15, 20, 50, 500), reset = FALSE, main = "Number of SMS Farmers by District") plot(farmers %>% filter(uuid %in% unique(farmers_in_dists$uuid)) %>% st_geometry(), add = TRUE, pch = "+", col = "cyan", cex = 0.5) dev.off() ``` --- # Today - Projects and package set-up - `git` / GitHub - syncing, commit, push, pull, branching, merging - data folder and lazy loads - inst folder and how to get at it - If time - RMarkdown - The `R` ecosystem - Building blocks of R: data types, functions, etc --- ## Git/GitHub <div class="figure" style="text-align: center"> <img src="https://kevintshoemaker.github.io/StatsChats/GIT1.png" alt="kevintshoemaker.github.io/StatsChats/GIT_tutorial" width="70%" /> <p class="caption">kevintshoemaker.github.io/StatsChats/GIT_tutorial</p> </div> --- <div class="figure" style="text-align: center"> <img src="https://kevintshoemaker.github.io/StatsChats/GIT2.png" alt="kevintshoemaker.github.io/StatsChats/GIT_tutorial" width="70%" /> <p class="caption">kevintshoemaker.github.io/StatsChats/GIT_tutorial</p> </div> --- <div class="figure" style="text-align: center"> <img src="https://i.stack.imgur.com/yof9f.png" alt="stackoverflow.com/questions/7212740/why-git-is-called-a-distributed-source-control-system" width="70%" /> <p class="caption">stackoverflow.com/questions/7212740/why-git-is-called-a-distributed-source-control-system</p> </div> --- ## Our Branching Model <img src="figures/class3_1.png" width="70%" style="display: block; margin: auto;" /> --- ## Merging <img src="figures/class4_gitmerge.png" width="50%" style="display: block; margin: auto;" /> --- ## Git exercises (1) - Open your test package from last class - Copy and paste the text in your Description file to a separate file. - Create a new branch, `dev/text_function`. You can create this branch in the Git tab, or in Terminal, using `git checkout -b dev/test_function` - With the new branch active, create a new file `test_function` within the R folder. Create a simple function that takes a number `x` as an input, and prints "`x` is my favorite number!". Save and commit this file in the new branch. - Switch back to the `main` branch. You should see a pop-up message. What happens to the `test_function.R` file? Can you see it in your file explorer? - merge the new file to the main branch using the command `git merge dev/test_function` in Terminal. What happens? - The `merge` command automatically merges and commits changes to the main branch --- ## Git exercises (2) - In the `dev/test_function` branch, open the Description file. Update the version to 0.0.2. Save and commit. - Switch to the `main` branch. Add a second author in the Author line. Save and commit. - Now test merging the change from `test_merge` to `main` using `git merge dev/test_function`. Does this work? - After you are done, delete the branch using `git branch -D dev/test_function` --- # Tips and Tricks - Tab completion and shortcuts - Reusing code - Code syntax - [Posting guide](https://www.r-project.org/posting-guide.html) --- ## Knowing how to get help as a skillset - Slack posting guide - Getting help via the search engine - (Eventually) posting to listserves --- ## Search Engine Science - Sometimes you just need the error message <img src="figures/class3_4.jpeg" width="90%" style="display: block; margin: auto;" /> --- ## Search Engine Science - Sometimes you need to search ``` fatal: unable to access 'https://github.com/agroimpacts/xyz346.git/': error setting certificate verify locations: CAfile: C:/Users/xyz/Desktop/ADP/RStudio/xyz346/Git/mingw64/ssl/ certs/ca-bundle.crt CApath: none ``` - How you search matters --- <img src="figures/class3_5.png" width="90%" style="display: block; margin: auto;" /> --- ## Listserves <img src="figures/class3_3.png" width="90%" style="display: block; margin: auto;" /> --- ## Package functions - `install.packages` : installs binary packages from CRAN - `devtools::install_github`: installs from Github - `devtools::install("C:/Users/micha/Documents/geospaar/")` to install locally --- ## Data in packages <img src="figures/class4_1.png" width="80%" style="display: block; margin: auto;" /> - packages often include example data - Lazy loading data only loads when used - Lazy loaded data in data/ folder - formats: .R, .rda, .RData, .tab, .txt, .csv - Non lazy loads (raw data) in inst/extdata --- ```r ls() ``` ``` ## [1] "a" "b" "colMax" "df" "DF" ## [6] "i" "l" "l1" "l2" "m1" ## [11] "m2" "myfun" "rowMax" "s" "s2" ## [16] "s2_unique" "v" "v1" "v2" "v3" ## [21] "v4" "x" ``` ```r data("farmers_env", package = "geospaar") ls() ``` ``` ## [1] "a" "b" "colMax" "df" ## [5] "DF" "farmers_env" "i" "l" ## [9] "l1" "l2" "m1" "m2" ## [13] "myfun" "rowMax" "s" "s2" ## [17] "s2_unique" "v" "v1" "v2" ## [21] "v3" "v4" "x" ``` ```r farmers_env ``` ``` ## uuid geometry rain district ## 1 009a8424 27.256, -16.926 66.93754 11 ## 2 00df166f 26.942, -16.504 72.00297 11 ## 3 02671a00 27.254, -16.914 66.93754 11 ## 4 03f4dcca 27.237, -16.733 69.78009 11 ## 5 042cf7b3 27.138, -16.807 75.14184 11 ## 6 05618404 26.875, -16.611 66.09323 11 ## 7 064beba0 26.752, -16.862 58.36053 20 ## 8 083a46a2 26.977, -16.765 70.38586 11 ## 9 08eb2224 26.912, -16.248 53.95671 59 ## 10 0ab761d6 27.113, -16.950 72.43802 11 ## 11 0bd19b6a 26.772, -16.860 58.36053 20 ## 12 0c0c6ed8 26.794, -16.843 57.79966 11 ## 13 0c18f625 26.862, -16.674 68.36250 11 ## 14 0cbc4225 26.859, -16.176 57.92710 59 ## 15 0eb65faa 27.056, -16.935 69.99150 11 ## 16 0ed7bf8d 27.294, -16.746 80.89200 11 ## 17 12d7333e 27.237, -16.733 69.78009 11 ## 18 130455fd 26.885, -16.589 73.05731 11 ## 19 146fd88d 27.237, -16.922 73.24045 11 ## 20 14e2047a 27.306, -16.742 78.35793 11 ## 21 14eb1bc1 26.913, -16.251 55.04950 11 ## 22 14f4d804 27.336, -16.834 72.22664 11 ## 23 16d25908 26.780, -16.851 58.36053 20 ## 24 186977a2 26.862, -16.674 68.36250 11 ## 25 19c2befd 26.933, -17.041 63.51126 11 ## 26 1a3ed58f 27.276, -16.878 67.82761 11 ## 27 1a4008c2 26.859, -16.178 57.92710 59 ## 28 1ae8ccff 27.094, -16.813 78.34108 11 ## 29 1c7690e0 27.261, -16.921 66.93754 11 ## 30 1cba393a 26.930, -16.508 72.00297 11 ## 31 261e38c9 26.974, -16.748 69.62228 11 ## 32 26b02eaa 27.114, -16.790 69.24673 11 ## 33 26ccd1ca 27.279, -16.926 66.93754 11 ## 34 26d99644 26.917, -16.532 72.00297 11 ## 35 28706e65 26.969, -16.723 69.62228 11 ## 36 28c79d87 26.857, -16.191 57.92710 59 ## 37 2abd46c3 27.294, -16.744 80.89200 11 ## 38 2b60914d 27.275, -16.884 67.82761 11 ## 39 2cf13d38 27.050, -17.003 69.43579 11 ## 40 2d70af82 26.863, -16.590 73.05731 11 ## 41 30c2e51a 27.313, -16.779 78.28657 11 ## 42 3112ec39 27.272, -16.873 67.82761 11 ## 43 32855ee6 26.975, -16.760 70.38586 11 ## 44 33361d1c 26.830, -16.817 63.43196 11 ## 45 36c0532e 26.953, -16.487 67.75720 11 ## 46 373bc918 27.050, -17.003 69.43579 11 ## 47 381e38ea 27.293, -16.744 80.89200 11 ## 48 38c83136 26.933, -16.463 69.48450 11 ## 49 3a042f06 27.076, -16.952 79.16811 11 ## 50 3a0b6560 27.285, -16.748 80.89200 11 ## 51 3a5c6f75 27.050, -17.004 69.43579 11 ## 52 3b3d1539 26.857, -16.193 57.92710 59 ## 53 3b6cbc47 27.059, -17.006 78.89503 11 ## 54 3e3c2a80 26.971, -16.753 70.38586 11 ## 55 3e8b66b7 27.118, -16.919 72.43802 11 ## 56 3fed4ec6 27.239, -16.741 69.78009 11 ## 57 4120e368 26.979, -16.762 70.38586 11 ## 58 4313155f 27.266, -16.921 66.93754 11 ## 59 44c8a2b6 27.063, -17.004 78.89503 11 ## 60 45a89b91 27.109, -16.980 70.20730 11 ## 61 46fa47e5 26.930, -17.054 52.02611 11 ## 62 47939706 27.275, -16.884 67.82761 11 ## 63 48635f6e 27.099, -16.964 79.16811 11 ## 64 48c828d0 27.265, -16.921 66.93754 11 ## 65 4932fac2 26.810, -16.607 59.16776 11 ## 66 496c557b 27.297, -16.935 66.93754 11 ## 67 499a72b8 26.831, -16.817 63.43196 11 ## 68 4a58fbbb 26.877, -16.585 73.05731 11 ## 69 4afc2d3e 27.067, -16.988 79.16811 11 ## 70 4b671e6b 27.294, -16.744 80.89200 11 ## 71 4ca666fd 27.089, -16.951 79.16811 11 ## 72 4cb11854 27.129, -16.790 69.24673 11 ## 73 4d524878 27.032, -16.961 75.39918 11 ## 74 4f75193d 26.961, -16.727 69.62228 11 ## 75 51d50f10 26.983, -16.765 70.38586 11 ## 76 54d0a15c 26.941, -16.485 69.48450 11 ## 77 55772276 26.864, -16.662 68.36250 11 ## 78 561a6a5d 27.080, -16.972 79.16811 11 ## 79 58d39a53 26.791, -16.841 57.79966 11 ## 80 5b732fcd 26.850, -16.216 53.30880 59 ## 81 5c093270 27.255, -16.908 66.93754 11 ## 82 5c13f0ee 27.255, -16.909 66.93754 11 ## 83 5e0473ca 26.821, -16.604 59.16776 11 ## 84 5e37a888 27.128, -16.772 69.24673 11 ## 85 5e65fc11 27.094, -16.890 73.09578 11 ## 86 5e911b62 27.061, -17.004 78.89503 11 ## 87 61228b69 26.802, -16.832 63.43196 11 ## 88 6132a9b3 27.316, -16.782 78.28657 11 ## 89 62642d7c 27.234, -16.733 69.78009 11 ## 90 646d6573 27.256, -16.926 66.93754 11 ## 91 64a63ad8 27.230, -16.717 69.78009 11 ## 92 664bdf41 26.781, -16.870 58.36053 20 ## 93 66e41828 26.804, -16.854 61.28291 11 ## 94 67cbcae6 26.896, -16.704 72.95570 11 ## 95 684d8902 26.966, -16.733 69.62228 11 ## 96 6b38721b 26.788, -16.876 58.36053 20 ## 97 6c223ecd 26.913, -16.519 72.00297 11 ## 98 6d05224a 26.853, -16.210 53.86879 59 ## 99 6d774598 26.913, -16.250 53.95671 59 ## 100 6dfd9cdf 27.301, -16.816 72.22664 11 ## 101 6ff740a4 27.299, -16.935 66.93754 11 ## 102 703825ba 26.788, -16.876 58.36053 20 ## 103 70b5f403 27.037, -16.954 75.39918 11 ## 104 718d866e 26.859, -16.178 57.92710 59 ## 105 730b727a 27.056, -16.934 69.99150 11 ## 106 732af424 26.833, -16.818 63.43196 11 ## 107 74ccfcac 26.899, -16.856 61.73315 11 ## 108 7572e663 27.199, -16.924 71.00794 11 ## 109 770da037 26.833, -16.817 63.43196 11 ## 110 78284b9d 26.995, -16.972 75.29367 11 ## 111 7af5af0a 27.301, -16.758 78.28657 11 ## 112 7d101d28 27.305, -16.803 72.22664 11 ## 113 7da072ff 27.066, -16.944 69.99150 11 ## 114 7eed2695 26.923, -17.049 63.51126 11 ## 115 7f49bc48 27.284, -16.749 80.89200 11 ## 116 7f9ab0f7 27.094, -16.976 79.16811 11 ## 117 814ca378 27.061, -17.004 78.89503 11 ## 118 85867e62 27.111, -16.933 72.43802 11 ## 119 85f03621 26.938, -16.513 72.00297 11 ## 120 88462901 26.881, -16.597 73.05731 11 ## 121 890ca2c8 27.107, -16.940 72.43802 11 ## 122 8b816f5f 27.255, -16.915 66.93754 11 ## 123 8d074cec 26.869, -16.596 73.05731 11 ## 124 8d2db211 26.934, -17.036 63.51126 11 ## 125 8d690816 26.936, -16.744 72.52380 11 ## 126 8da4a6d6 26.858, -16.594 73.05731 11 ## 127 92563785 27.234, -16.923 73.24045 11 ## 128 926caf97 26.869, -16.610 66.09323 11 ## 129 95374d3f 27.039, -16.842 70.56110 11 ## 130 96b6d097 26.995, -16.953 75.29367 11 ## 131 975ffe94 26.831, -16.818 63.43196 11 ## 132 981208ad 27.344, -16.776 78.28657 11 ## 133 991ece73 27.086, -16.967 79.16811 11 ## 134 99dc8db6 27.182, -16.917 71.00794 11 ## 135 9a8786eb 27.258, -16.929 66.93754 11 ## 136 9b42e80a 26.869, -16.614 66.09323 11 ## 137 9c0fe82f 26.974, -16.760 70.38586 11 ## 138 a006eab2 27.335, -16.798 78.28657 11 ## 139 a0f8b4ef 27.039, -16.830 70.56110 11 ## 140 a30b3bde 27.048, -16.926 71.20597 11 ## 141 a5ad178a 27.060, -17.005 78.89503 11 ## 142 a62be645 26.863, -16.595 73.05731 11 ## 143 a67f255e 26.815, -16.585 66.42364 11 ## 144 a84d2d02 26.831, -16.817 63.43196 11 ## 145 a914f6e1 26.788, -16.863 58.36053 20 ## 146 a9a99b3e 26.920, -17.052 52.02611 11 ## 147 a9d39da7 26.928, -16.520 72.00297 11 ## 148 aa5f0d29 26.951, -16.727 69.62228 11 ## 149 aac48cd5 27.050, -17.003 69.43579 11 ## 150 abd530e7 26.981, -16.762 70.38586 11 ## 151 ace80c2e 27.306, -16.931 66.28913 11 ## 152 ad783a0a 27.336, -16.797 78.28657 11 ## 153 afaecd2e 27.305, -16.793 78.28657 11 ## 154 affb013a 26.963, -16.920 70.25295 11 ## 155 b0e82f78 27.339, -16.834 72.22664 11 ## 156 b1e1a3f3 26.789, -16.878 58.36053 20 ## 157 b3d90826 26.922, -16.526 72.00297 11 ## 158 b5a7a1f4 26.935, -16.513 72.00297 11 ## 159 b66a5a55 26.974, -16.766 70.38586 11 ## 160 b825d94e 27.113, -16.773 69.24673 11 ## 161 b9d85ebc 27.234, -16.923 73.24045 11 ## 162 ba5973c2 26.835, -16.815 63.43196 11 ## 163 baea69b4 27.279, -16.877 67.82761 11 ## 164 bb876912 26.936, -16.505 72.00297 11 ## 165 bba2dfd0 27.339, -16.797 78.28657 11 ## 166 bc32b0b4 26.864, -16.599 73.05731 11 ## 167 bc8d8a74 27.275, -16.877 67.82761 11 ## 168 bf8b44c4 26.791, -16.874 58.36053 20 ## 169 c084cfc1 26.795, -16.879 58.36053 20 ## 170 c124742c 27.209, -16.917 73.24045 11 ## 171 c152455a 27.085, -16.934 69.99150 11 ## 172 c2668cdf 27.123, -16.769 69.24673 11 ## 173 c32d45ee 27.068, -16.939 69.99150 11 ## 174 c42827cf 27.096, -16.949 69.99150 11 ## 175 c6f737ea 27.304, -16.793 78.28657 11 ## 176 c784ee02 26.859, -16.194 57.92710 59 ## 177 c7d6f91c 26.831, -16.817 63.43196 11 ## 178 c7e2a514 26.810, -16.606 59.16776 11 ## 179 c946796f 27.004, -16.841 70.56110 11 ## 180 c9ee7136 27.039, -16.842 70.56110 11 ## 181 c9f5f663 27.128, -16.779 69.24673 11 ## 182 cc014f7d 26.778, -16.870 58.36053 20 ## 183 cc230be1 27.286, -16.788 76.04762 11 ## 184 cdcc9666 27.293, -16.747 80.89200 11 ## 185 ce041f38 26.961, -16.743 69.62228 11 ## 186 d027ee45 27.293, -16.747 80.89200 11 ## 187 d16f790f 27.016, -16.932 71.20597 11 ## 188 d1d9637e 27.136, -16.800 69.24673 11 ## 189 d1e20dc3 26.912, -16.254 55.04950 11 ## 190 d263ea45 26.827, -16.820 63.43196 11 ## 191 d362925c 26.859, -16.193 57.92710 59 ## 192 d6313bed 27.301, -16.758 78.28657 11 ## 193 d644ee96 26.867, -16.610 66.09323 11 ## 194 d6e18dd9 27.086, -16.961 79.16811 11 ## 195 d9ce012d 27.325, -16.757 78.28657 11 ## 196 de338705 27.293, -16.747 80.89200 11 ## 197 dea21f52 27.097, -16.899 73.09578 11 ## 198 df1604d4 27.271, -16.784 76.04762 11 ## 199 dfc6f992 26.823, -16.625 59.16776 11 ## 200 e2510e54 27.123, -16.922 72.43802 11 ## 201 e25b2b0f 26.789, -16.877 58.36053 20 ## 202 e27cd268 27.056, -16.935 69.99150 11 ## 203 e3407303 26.781, -16.856 58.36053 20 ## 204 e340de50 26.956, -16.738 69.62228 11 ## 205 e36fdd30 26.947, -16.746 72.52380 11 ## 206 e82a9751 26.790, -16.823 57.79966 11 ## 207 e91d6065 27.135, -16.770 69.24673 11 ## 208 e9f6b304 27.066, -16.944 69.99150 11 ## 209 ebd881fc 27.310, -16.803 72.22664 11 ## 210 ee2f89cf 27.090, -16.971 79.16811 11 ## 211 f0118efa 27.286, -16.932 66.93754 11 ## 212 f08a0aa3 27.032, -16.961 75.39918 11 ## 213 f0bcb418 27.107, -16.772 69.24673 11 ## 214 f6ebaa5b 27.300, -16.934 66.93754 11 ## 215 f752c993 27.300, -16.756 76.04762 11 ## 216 f825b96c 27.060, -16.986 79.16811 11 ## 217 fb3fc0e0 27.086, -16.783 71.25740 11 ## 218 fb666168 26.780, -16.851 58.36053 20 ## 219 fbdacd81 26.999, -16.956 75.29367 11 ## 220 fbef7ba1 27.227, -16.738 69.78009 11 ## 221 fc263f78 27.232, -16.720 69.78009 11 ## 222 fce31f54 26.826, -16.595 66.42364 11 ## 223 fe096545 27.260, -16.922 66.93754 11 ## 224 0649944b 27.336, -16.784 78.28657 11 ## 225 08683ecf 27.284, -16.749 80.89200 11 ## 226 0a2efd25 27.338, -16.794 78.28657 11 ## 227 0f095777 26.815, -16.599 66.42364 11 ## 228 116e1bcc 26.859, -16.595 73.05731 11 ## 229 16128e91 27.100, -16.894 73.09578 11 ## 230 27fb5351 27.094, -16.906 69.99150 11 ## 231 2a840b88 27.338, -16.794 78.28657 11 ## 232 3182c9bb 27.086, -16.961 79.16811 11 ## 233 3210d99e 26.939, -16.490 69.48450 11 ## 234 36883452 27.051, -16.840 78.34108 11 ## 235 36db3c21 26.769, -16.873 58.36053 20 ## 236 3c0a7255 27.280, -16.872 67.82761 11 ## 237 41b213e1 26.900, -16.852 61.73315 11 ## 238 4285ad8d 26.808, -16.835 63.43196 11 ## 239 46498dc8 26.975, -16.826 69.10195 11 ## 240 48b2c57f 26.781, -16.849 57.79966 11 ## 241 4ae722c5 27.298, -16.930 66.93754 11 ## 242 4bc9a120 27.183, -16.915 71.00794 11 ## 243 5769b555 27.336, -16.834 72.22664 11 ## 244 5ad27ec3 26.812, -16.599 66.42364 11 ## 245 60734a06 26.989, -16.830 69.10195 11 ## 246 646ea49f 27.053, -16.927 69.99150 11 ## 247 6be46cee 26.967, -16.821 69.10195 11 ## 248 6df8bb5e 26.977, -16.819 69.10195 11 ## 249 6e1aeb91 27.045, -16.962 75.39918 11 ## 250 73c31707 27.117, -16.920 72.43802 11 ## [ reached 'max' / getOption("max.print") -- omitted 543 rows ] ``` ```r rm(list = ls()) ls() ``` ``` ## character(0) ``` --- ```r library(geospaar) ls() ``` ``` ## character(0) ``` ```r farmers_env ``` ``` ## Simple feature collection with 793 features and 3 fields ## Geometry type: POINT ## Dimension: XY ## Bounding box: xmin: 24.777 ymin: -18.222 xmax: 33.332 ymax: -8.997 ## CRS: NA ## # A tibble: 793 × 4 ## uuid geometry rain district ## * <chr> <POINT> <dbl> <dbl> ## 1 009a8424 (27.256 -16.926) 66.9 11 ## 2 00df166f (26.942 -16.504) 72.0 11 ## 3 02671a00 (27.254 -16.914) 66.9 11 ## 4 03f4dcca (27.237 -16.733) 69.8 11 ## 5 042cf7b3 (27.138 -16.807) 75.1 11 ## 6 05618404 (26.875 -16.611) 66.1 11 ## 7 064beba0 (26.752 -16.862) 58.4 20 ## 8 083a46a2 (26.977 -16.765) 70.4 11 ## 9 08eb2224 (26.912 -16.248) 54.0 59 ## 10 0ab761d6 (27.113 -16.95) 72.4 11 ## # … with 783 more rows ``` ```r ls() ``` ``` ## character(0) ``` --- ## Raw data in inst/extdata <img src="figures/class4_2.png" width="80%" style="display: block; margin: auto;" /> --- ```r system.file("extdata", package = "geospaar") ``` ``` ## [1] "/Library/Frameworks/R.framework/Versions/4.2/Resources/library/geospaar/extdata" ``` ```r dir(system.file("extdata", package = "geospaar")) ``` ``` ## [1] "cdf_corn.csv" "chilanga_farmer" ## [3] "chirps.tif" "districts.geojson" ## [5] "EJ_POLY.cpg" "EJ_POLY.dbf" ## [7] "EJ_POLY.prj" "EJ_POLY.sbn" ## [9] "EJ_POLY.sbx" "EJ_POLY.shp" ## [11] "EJ_POLY.shp.xml" "EJ_POLY.shx" ## [13] "FAOSTAT_maize.csv" "FAOSTAT_sorghum.csv" ## [15] "FAOSTAT_wheat.csv" "farmer_spatial.csv" ## [17] "filt_data.rda" "roads.geojson" ## [19] "train_reference.csv" "westluse.rdc" ## [21] "westluse.rst" "westroad.vct" ## [23] "westroad.vdc" "whittier_down_d_h.rda" ## [25] "whittier_mid_d_h.rda" "whittier_up_d_h.rda" ``` ```r f <- system.file("extdata", "farmer_spatial.csv", package = "geospaar") head(read.csv(f)) ``` ``` ## uuid x y date season rained planted ## 1 009a8424 27.256 -16.926 2015-11-15 1 0 0 ## 2 00df166f 26.942 -16.504 2015-11-15 1 0 0 ## 3 02671a00 27.254 -16.914 2015-11-15 1 0 0 ## 4 03f4dcca 27.237 -16.733 2015-11-15 1 0 0 ## 5 042cf7b3 27.138 -16.807 2015-11-15 1 0 0 ## 6 05618404 26.875 -16.611 2015-11-15 1 0 0 ``` --- ## Other package folders - See [Chap 9 of "R Packages"](https://r-pkgs.org/misc.html) for more examples of R packages folders. --- --- # A look at RMarkdown Chunk options <img src="figures/class4_3.png" width="80%" style="display: block; margin: auto;" /> [Rmarkdown demo](rmarkdown_demo.html) by Lei Song [RMarkdown gallery](https://rmarkdown.rstudio.com/lesson-1.html) --- ## RMarkdown exercises - In your personal R project, Create new RMarkdown file using `usethis::use_vignette(name = "example_vignette")` - Create chunks for the following: - Load the `geospaar` package - Load the `chirps` data and print its structure using `str(chirps)` - Plot `chirps` data using `raster::plot(chirps)`. Above this chunk, include a text description for the CHIRPS data using different text formatting (bold, italics, etc). You can see information on the `CHIRPS` data in the `man` folder. - Include a chunk with an obvious error, like `5 + "string"` - Try knitting this RMD using different [chunk options](https://rmarkdown.rstudio.com/lesson-3.html) --- # The R Ecosystem <img src="figures/class5_u1m2.png" width="60%" style="display: block; margin: auto;" /> --- ## The R Ecosystem ### Common objects <div class="figure" style="text-align: center"> <img src="figures/class2_objects.png" alt="Credit: L. Song" width="80%" /> <p class="caption">Credit: L. Song</p> </div> --- ## The R Ecosystem ### Data types There are 6 atomic data types: - character e.g. `'hello world'`, `'abc'` - double (real or decimal) e.g. `10`, `3.14`, `1e10` - integer e.g. `1L` - logical e.g. `TRUE`, `FALSE`, `T`, `F` - complex e.g. `1 + 3i` (not commonly used by us) - *raw* (rarely used by us) --- ## The R Ecosystem ### Special data types - `NULL`: Does not exist - Missing data: `NA`. A special logical type that converts into the type it is associated with. - Infinity: `Inf` (e.g. `1 / 0`) and `-Inf` (e.g. `-1 / 0`. A special double type. - Undefined value: `NaN`. Also a special double type. (e.g. `0 / 0`) --- ## The R Ecosystem ### Special data types - Date: `as.Date('1970-1-5')` - Time: `as.POSIXct('1970-1-5')` - Factor: factors are integers with associated labels. --- ## The R Ecosystem ### Checking data type - `typeof(x)` - `is.xxx(x)`: * e.g. `is.double(x)`, `is.integer(x)`, `is.logical(x)`, `is.character(x)`, `is.complex(x)`, `is.raw(x)`, `is.factor` * `is.numeric(x)` * e.g. `is.na(y)`, `is.nan(y)`, `is.null(y)`, `is.infinite(y)`, `is.finite(y)`. --- ## The R Ecosystem ### Converting data type - `as.xxx(x)`: * e.g. `as.numeric(x)`, `as.double(x)`, `as.character(x)`, `as.integer(x)`, `as.logical(x)`, `as.complex(x)`, `as.raw(x)`, `as.factor(x)`, `as.Date(x)`, `as.POSIXct(x)`. - logical < integer < double < character --- ## The R Ecosystem ### Data structures - Atomic vectors (most commonly thought of kind): * A sequence of objects of the **same class**. * Arrays and matrices are vectors with more than one dimension. - Matrices have 2 dimensions. - Arrays could have higher dimensions. - Lists * Lists can contain objects of **different classes**. * Lists can be converted list-matrix or list-array by defining dimensions. * `data.frame` and `tibble` are S3 objects that are lists in tabular form --- ## The R Ecosystem ### Vectors <div class="figure" style="text-align: center"> <img src="figures/class2_vector.png" alt="Credit: L. Song" width="1921" /> <p class="caption">Credit: L. Song</p> </div> --- ## Let's create some data - `vector` - `matrix` - `data.frame` - `list` --- ## Creating a vector ```r v1 <- c(1,2,3,4,5) v2 <- 6:10 print(v1) ``` ``` ## [1] 1 2 3 4 5 ``` ```r print(v2) ``` ``` ## [1] 6 7 8 9 10 ``` --- ## Combining vectors ```r v1 <- c(1,2,3,4,5) v2 <- 6:10 v3 <- c(v1, v2) print(v3) ``` ``` ## [1] 1 2 3 4 5 6 7 8 9 10 ``` ```r print(class(v3)) ``` ``` ## [1] "numeric" ``` --- ## Combining vectors What happens when you combine data types in a vector? ```r v4 <- c(v1, "test") print(v4) ``` ``` ## [1] "1" "2" "3" "4" "5" "test" ``` ```r print(class(v4)) ``` ``` ## [1] "character" ``` --- ## Vectorized operations ```r v1 <- 1:9 print(v1) ``` ``` ## [1] 1 2 3 4 5 6 7 8 9 ``` ```r print(v1 + 5) ## addition applied to each element in vector ``` ``` ## [1] 6 7 8 9 10 11 12 13 14 ``` ```r print(v1 * 3) ## multiplication applied to each element in vector ``` ``` ## [1] 3 6 9 12 15 18 21 24 27 ``` --- ## Accessing elements in vector ```r v1 <- (1:9)*3 print(v1) ``` ``` ## [1] 3 6 9 12 15 18 21 24 27 ``` ```r print(v1[4]) ``` ``` ## [1] 12 ``` ```r v1 <- letters[1:7] print(paste0("This letter is ", v1)) # paste0 concatenates strings ``` ``` ## [1] "This letter is a" "This letter is b" "This letter is c" ## [4] "This letter is d" "This letter is e" "This letter is f" ## [7] "This letter is g" ``` Question: How would you say "Letter 1 is a", "Letter 2 is b", etc. --- ## Creating a matrix ```r v1 <- 1:9 m1 <- matrix(v1, nrow = 3, byrow = T) m2 <- cbind(v1 = 1:3, v2 = 1:3, v3 = 0:0) print(m1) ``` ``` ## [,1] [,2] [,3] ## [1,] 1 2 3 ## [2,] 4 5 6 ## [3,] 7 8 9 ``` ```r print(m2) ``` ``` ## v1 v2 v3 ## [1,] 1 1 0 ## [2,] 2 2 0 ## [3,] 3 3 0 ``` --- ## Accessing elements in a matrix ```r m2 <- cbind(v1 = 1:3, v2 = 1:3, v3 = 0:0) print(m2) ``` ``` ## v1 v2 v3 ## [1,] 1 1 0 ## [2,] 2 2 0 ## [3,] 3 3 0 ``` ```r print("first row:") ``` ``` ## [1] "first row:" ``` ```r print(m2[1, ]) ``` ``` ## v1 v2 v3 ## 1 1 0 ``` ```r print("second column") ``` ``` ## [1] "second column" ``` ```r print(m2[, 2]) ``` ``` ## [1] 1 2 3 ``` ```r print("access by column name") ``` ``` ## [1] "access by column name" ``` ```r print(m2[, "v2"]) ``` ``` ## [1] 1 2 3 ``` Question: Create a 3 row, 4 column matrix, where the first column is (1, 2, 3), second column is (4, 5, 6 ) etc. --- ## Lists Lists can mix object of different types. ```r l <- list("a", 1, 0.5, TRUE) print(l) ``` ``` ## [[1]] ## [1] "a" ## ## [[2]] ## [1] 1 ## ## [[3]] ## [1] 0.5 ## ## [[4]] ## [1] TRUE ``` ```r print(str(l)) # structure of l ``` ``` ## List of 4 ## $ : chr "a" ## $ : num 1 ## $ : num 0.5 ## $ : logi TRUE ## NULL ``` --- ## Lists Lists can also be nested. ```r l1 <- list("a", 1, 0.5, TRUE) l2 <- list(l1, "test", matrix(1:9, nrow = 3)) ## first element of l2 is a list. print(l2) ``` ``` ## [[1]] ## [[1]][[1]] ## [1] "a" ## ## [[1]][[2]] ## [1] 1 ## ## [[1]][[3]] ## [1] 0.5 ## ## [[1]][[4]] ## [1] TRUE ## ## ## [[2]] ## [1] "test" ## ## [[3]] ## [,1] [,2] [,3] ## [1,] 1 4 7 ## [2,] 2 5 8 ## [3,] 3 6 9 ``` Question: what is the length of l1? what is the length of each element of l1? (use `length` function) --- ## Accessing elements in lists Use double brackets `[[ ]]` to access elements in lists. ```r l1 <- list("a", 1, 0.5, TRUE) l2 <- list(l1, "test", matrix(1:9, nrow = 3)) ## first element of l2 is a list. print(l2[[1]]) ``` ``` ## [[1]] ## [1] "a" ## ## [[2]] ## [1] 1 ## ## [[3]] ## [1] 0.5 ## ## [[4]] ## [1] TRUE ``` ```r print(l2[[2]]) ``` ``` ## [1] "test" ``` ```r print(l2[[3]]) ``` ``` ## [,1] [,2] [,3] ## [1,] 1 4 7 ## [2,] 2 5 8 ## [3,] 3 6 9 ``` Question: The first element of l2 is a list. Access this first element from l2, then the 4th element from l1. --- ## Data frames We will use data frames A LOT. Data frames are really tables, but they are stored in R as lists, with the following conditions. - Each element in the list represents a column. - Each element in the list is an atomic vector. - The length of each vector is the same (the number of rows) ```r m1 <- cbind(1:3, letters[1:3]) print(m1) # a matrix cannot hold multiple data types. ``` ``` ## [,1] [,2] ## [1,] "1" "a" ## [2,] "2" "b" ## [3,] "3" "c" ``` ```r print(class(m1)) ``` ``` ## [1] "matrix" "array" ``` ```r df <- data.frame(numbers = 1:3, abc = letters[1:3]) print(df) # a data frame CAN hold multiple data types. ``` ``` ## numbers abc ## 1 1 a ## 2 2 b ## 3 3 c ``` ```r print(class(df)) ``` ``` ## [1] "data.frame" ``` --- ## Useful functions - `print` - You can print just about anything, but as a generic function, `print` works differently for different types of objects. ```r a <- "test" df <- data.frame(numbers = 1:3, abc = letters[1:3]) print(a) # a data frame CAN hold multiple data types. ``` ``` ## [1] "test" ``` ```r print(df) ``` ``` ## numbers abc ## 1 1 a ## 2 2 b ## 3 3 c ``` ```r methods(print) ## all the different ways to print... ``` ``` ## [1] print,ANY-method ## [2] print,Raster-method ## [3] print,Spatial-method ## [4] print.acf* ## [5] print.AES* ## [6] print.all_vars* ## [7] print.anova* ## [8] print.any_vars* ## [9] print.aov* ## [10] print.aovlist* ## [11] print.ar* ## [12] print.Arima* ## [13] print.arima0* ## [14] print.AsIs ## [15] print.aspell* ## [16] print.aspell_inspect_context* ## [17] print.bbox* ## [18] print.bibentry* ## [19] print.Bibtex* ## [20] print.bootstrap.lca* ## [21] print.browseVignettes* ## [22] print.bslib_fragment* ## [23] print.bslib_page* ## [24] print.by ## [25] print.bytes* ## [26] print.cache_info* ## [27] print.cachem* ## [28] print.cell_addr* ## [29] print.cell_limits* ## [30] print.changedFiles* ## [31] print.check_bogus_return* ## [32] print.check_code_usage_in_package* ## [33] print.check_compiled_code* ## [34] print.check_demo_index* ## [35] print.check_depdef* ## [36] print.check_details* ## [37] print.check_details_changes* ## [38] print.check_doi_db* ## [39] print.check_dotInternal* ## [40] print.check_make_vars* ## [41] print.check_nonAPI_calls* ## [42] print.check_package_code_assign_to_globalenv* ## [43] print.check_package_code_attach* ## [44] print.check_package_code_data_into_globalenv* ## [45] print.check_package_code_startup_functions* ## [46] print.check_package_code_syntax* ## [47] print.check_package_code_unload_functions* ## [48] print.check_package_compact_datasets* ## [49] print.check_package_CRAN_incoming* ## [50] print.check_package_datalist* ## [51] print.check_package_datasets* ## [52] print.check_package_depends* ## [53] print.check_package_description* ## [54] print.check_package_description_encoding* ## [55] print.check_package_license* ## [56] print.check_packages_in_dir* ## [57] print.check_packages_used* ## [58] print.check_po_files* ## [59] print.check_pragmas* ## [60] print.check_Rd_line_widths* ## [61] print.check_Rd_metadata* ## [62] print.check_Rd_xrefs* ## [63] print.check_RegSym_calls* ## [64] print.check_S3_methods_needing_delayed_registration* ## [65] print.check_so_symbols* ## [66] print.check_T_and_F* ## [67] print.check_url_db* ## [68] print.check_vignette_index* ## [69] print.checkDocFiles* ## [70] print.checkDocStyle* ## [71] print.checkFF* ## [72] print.checkRd* ## [73] print.checkRdContents* ## [74] print.checkReplaceFuns* ## [75] print.checkS3methods* ## [76] print.checkTnF* ## [77] print.checkVignettes* ## [78] print.citation* ## [79] print.classIntervals* ## [80] print.cli_ansi_html_style* ## [81] print.cli_ansi_string* ## [82] print.cli_ansi_style* ## [83] print.cli_boxx* ## [84] print.cli_diff_chr* ## [85] print.cli_doc* ## [86] print.cli_progress_demo* ## [87] print.cli_rule* ## [88] print.cli_sitrep* ## [89] print.cli_spark* ## [90] print.cli_spinner* ## [91] print.cli_tree* ## [92] print.codoc* ## [93] print.codocClasses* ## [94] print.codocData* ## [95] print.col_spec* ## [96] print.collector* ## [97] print.colorConverter* ## [98] print.compactPDF* ## [99] print.condition ## [100] print.connection ## [101] print.CRAN_package_reverse_dependencies_and_views* ## [102] print.crayon* ## [103] print.crossdist* ## [104] print.crs* ## [105] print.CRS* ## [106] print.css* ## [107] print.data.frame ## [108] print.Date ## [109] print.date_names* ## [110] print.dbplyr_catalog* ## [111] print.dbplyr_schema* ## [112] print.default ## [113] print.dendrogram* ## [114] print.density* ## [115] print.difftime ## [116] print.dist* ## [117] print.Dlist ## [118] print.DLLInfo ## [119] print.DLLInfoList ## [120] print.DLLRegisteredRoutines ## [121] print.DMS* ## [122] print.document_context* ## [123] print.document_position* ## [124] print.document_range* ## [125] print.document_selection* ## [126] print.dplyr_join_by* ## [127] print.dplyr_sel_vars* ## [128] print.drive_user* ## [129] print.dummy_coef* ## [130] print.dummy_coef_list* ## [131] print.ecdf* ## [132] print.eigen ## [133] print.element* ## [134] print.factanal* ## [135] print.factor ## [136] print.family* ## [137] print.fclust* ## [138] print.fileSnapshot* ## [139] print.findLineNumResult* ## [140] print.flatGridListing* ## [141] print.formula* ## [142] print.fs_bytes* ## [143] print.fs_path* ## [144] print.fs_perms* ## [145] print.fseq* ## [146] print.ftable* ## [147] print.fun_list* ## [148] print.function ## [149] print.gargle_oauth_dat* ## [150] print.getAnywhere* ## [151] print.ggplot* ## [152] print.ggplot2_bins* ## [153] print.ggproto* ## [154] print.ggproto_method* ## [155] print.gknn* ## [156] print.gList* ## [157] print.glm* ## [158] print.glue* ## [159] print.googlesheets4_spreadsheet* ## [160] print.gpar* ## [161] print.GridCoords* ## [162] print.GridGrobCoords* ## [163] print.GridGTreeCoords* ## [164] print.GridTopology* ## [165] print.grob* ## [166] print.gtable* ## [167] print.handle* ## [168] print.hashtab* ## [169] print.hcl_palettes* ## [170] print.hclust* ## [171] print.help_files_with_topic* ## [172] print.hexmode ## [173] print.hms* ## [174] print.HoltWinters* ## [175] print.hsearch* ## [176] print.hsearch_db* ## [177] print.htest* ## [178] print.html* ## [179] print.html_dependency* ## [180] print.htmltools.selector* ## [181] print.htmltools.selector.list* ## [182] print.ica* ## [183] print.ident* ## [184] print.infl* ## [185] print.integrate* ## [186] print.isoreg* ## [187] print.join_query* ## [188] print.json* ## [189] print.key_missing* ## [190] print.kmeans* ## [191] print.knitr_kable* ## [192] print.last_dplyr_warnings* ## [193] print.Latex* ## [194] print.LaTeX* ## [195] print.lazy_base_local_query* ## [196] print.lazy_base_remote_query* ## [197] print.lazy_join_query* ## [198] print.lazy_select_query* ## [199] print.lazy_semi_join_query* ## [200] print.lazy_set_op_query* ## [201] print.lca* ## [202] print.libraryIQR ## [203] print.lifecycle_warnings* ## [204] print.linterResults* ## [205] print.listof ## [206] print.lm* ## [207] print.loadings* ## [208] print.locale* ## [209] print.loess* ## [210] print.logLik* ## [211] print.ls_str* ## [212] print.m_range* ## [213] print.medpolish* ## [214] print.MethodsFunction* ## [215] print.mixed_units* ## [216] print.mtable* ## [217] print.naiveBayes* ## [218] print.NativeRoutineList ## [219] print.news_db* ## [220] print.nls* ## [221] print.noquote ## [222] print.numeric_version ## [223] print.oauth_app* ## [224] print.oauth_endpoint* ## [225] print.object_size* ## [226] print.octmode ## [227] print.opts_list* ## [228] print.packageDescription* ## [229] print.packageInfo ## [230] print.packageIQR* ## [231] print.packageStatus* ## [232] print.pairdist* ## [233] print.pairwise.htest* ## [234] print.path* ## [235] print.permutation* ## [236] print.person* ## [237] print.pillar* ## [238] print.pillar_1e* ## [239] print.pillar_colonnade* ## [240] print.pillar_ornament* ## [241] print.pillar_shaft* ## [242] print.pillar_squeezed_colonnade* ## [243] print.pillar_tbl_format_setup* ## [244] print.pillar_vctr* ## [245] print.pillar_vctr_attr* ## [246] print.POSIXct ## [247] print.POSIXlt ## [248] print.power.htest* ## [249] print.ppr* ## [250] print.prcomp* ## [251] print.princomp* ## [252] print.proc_time ## [253] print.proj_pipelines* ## [254] print.proxy_registry* ## [255] print.purrr_function_compose* ## [256] print.purrr_function_partial* ## [257] print.purrr_rate_backoff* ## [258] print.purrr_rate_delay* ## [259] print.quosure* ## [260] print.quosures* ## [261] print.R6* ## [262] print.R6ClassGenerator* ## [263] print.ra_ref* ## [264] print.range_spec* ## [265] print.raster* ## [266] print.Rcpp_stack_trace* ## [267] print.Rd* ## [268] print.recordedplot* ## [269] print.registry_entry* ## [270] print.registry_field* ## [271] print.rel* ## [272] print.request* ## [273] print.resample* ## [274] print.response* ## [275] print.restart ## [276] print.RGBcolorConverter* ## [277] print.rlang_box_done* ## [278] print.rlang_box_splice* ## [279] print.rlang_data_pronoun* ## [280] print.rlang_dict* ## [281] print.rlang_dyn_array* ## [282] print.rlang_envs* ## [283] print.rlang_error* ## [284] print.rlang_fake_data_pronoun* ## [285] print.rlang_lambda_function* ## [286] print.rlang_message* ## [287] print.rlang_trace* ## [288] print.rlang_warning* ## [289] print.rlang_zap* ## [290] print.rlang:::list_of_conditions* ## [291] print.rle ## [292] print.rlib_bytes* ## [293] print.rlib_error_3_0* ## [294] print.rlib_trace_3_0* ## [295] print.roman* ## [296] print.root_criterion* ## [297] print.rsconnect_secret* ## [298] print.rvest_field* ## [299] print.rvest_form* ## [300] print.rvest_session* ## [301] print.sass* ## [302] print.sass_bundle* ## [303] print.sass_layer* ## [304] print.scalar* ## [305] print.select_query* ## [306] print.semi_join_query* ## [307] print.sessionInfo* ## [308] print.set_op_query* ## [309] print.sf* ## [310] print.sf_layers* ## [311] print.sfc* ## [312] print.sfg* ## [313] print.sgbp* ## [314] print.sheets_id* ## [315] print.shingle* ## [316] print.shingleLevel* ## [317] print.shiny.tag* ## [318] print.shiny.tag.env* ## [319] print.shiny.tag.list* ## [320] print.shiny.tag.query* ## [321] print.simil* ## [322] print.simple.list ## [323] print.smooth.spline* ## [324] print.socket* ## [325] print.SpatialLines* ## [326] print.SpatialLinesDataFrame* ## [327] print.SpatialMultiPoints* ## [328] print.SpatialMultiPointsDataFrame* ## [329] print.SpatialPixels* ## [330] print.SpatialPixelsDataFrame* ## [331] print.SpatialPoints* ## [332] print.SpatialPointsDataFrame* ## [333] print.sql* ## [334] print.sql_variant* ## [335] print.src* ## [336] print.srcfile ## [337] print.srcref ## [338] print.stepfun* ## [339] print.stl* ## [340] print.stringr_view* ## [341] print.StructTS* ## [342] print.subdir_tests* ## [343] print.summarize_CRAN_check_status* ## [344] print.summary.aov* ## [345] print.summary.aovlist* ## [346] print.summary.ecdf* ## [347] print.summary.glm* ## [348] print.summary.GridTopology* ## [349] print.summary.lca* ## [350] print.summary.lm* ## [351] print.summary.loess* ## [352] print.summary.manova* ## [353] print.summary.nls* ## [354] print.summary.packageStatus* ## [355] print.summary.ppr* ## [356] print.summary.pr_DB* ## [357] print.summary.prcomp* ## [358] print.summary.princomp* ## [359] print.summary.Spatial* ## [360] print.summary.svm* ## [361] print.summary.table ## [362] print.summary.trellis* ## [363] print.summary.tune* ## [364] print.summary.warnings ## [365] print.summaryDefault ## [366] print.svm* ## [367] print.table ## [368] print.tables_aov* ## [369] print.tbl* ## [370] print.tbl_lazy* ## [371] print.tbl_sql* ## [372] print.terms* ## [373] print.theme* ## [374] print.tidyverse_conflicts* ## [375] print.tidyverse_logo* ## [376] print.trans* ## [377] print.trellis* ## [378] print.trunc_mat* ## [379] print.ts* ## [380] print.tskernel* ## [381] print.TukeyHSD* ## [382] print.tukeyline* ## [383] print.tukeysmooth* ## [384] print.tune* ## [385] print.undoc* ## [386] print.uneval* ## [387] print.unit* ## [388] print.units* ## [389] print.vctrs_bytes* ## [390] print.vctrs_sclr* ## [391] print.vctrs_unspecified* ## [392] print.vctrs_vctr* ## [393] print.viewport* ## [394] print.vignette* ## [395] print.warnings ## [396] print.xfun_raw_string* ## [397] print.xfun_rename_seq* ## [398] print.xfun_strict_list* ## [399] print.xgettext* ## [400] print.xml_document* ## [401] print.xml_missing* ## [402] print.xml_namespace* ## [403] print.xml_node* ## [404] print.xml_nodeset* ## [405] print.xngettext* ## [406] print.xtabs* ## [407] print.z_range* ## see '?methods' for accessing help and source code ``` --- ## Examining objects - `ls`, `class`, `str` ```r a <- "test" df <- data.frame(numbers = 1:3, abc = letters[1:3]) ls() ## list objects in global environemtn ``` ``` ## [1] "a" "df" "f" "l" "l1" "l2" "m1" "m2" "v1" "v2" "v3" "v4" ``` ```r class(df) ## class ``` ``` ## [1] "data.frame" ``` ```r str(df) ## structure ``` ``` ## 'data.frame': 3 obs. of 2 variables: ## $ numbers: int 1 2 3 ## $ abc : chr "a" "b" "c" ``` --- ## Sampling - Sample without replacement ```r set.seed(1234) ## set a random seed v <- 1:100 s <- sample(v, 50) print(s) ``` ``` ## [1] 28 80 22 9 5 38 16 4 86 90 70 79 78 14 56 62 93 84 21 40 92 ## [22] 67 96 66 47 81 48 3 41 32 42 43 2 54 49 99 51 6 77 29 71 85 ## [43] 57 8 26 17 58 91 60 76 ``` ```r print(sort(s)) ``` ``` ## [1] 2 3 4 5 6 8 9 14 16 17 21 22 26 28 29 32 38 40 41 42 43 ## [22] 47 48 49 51 54 56 57 58 60 62 66 67 70 71 76 77 78 79 80 81 84 ## [43] 85 86 90 91 92 93 96 99 ``` - Sample with replacement ```r s2 <- sample(v, 50, replace = T) print(sort(s2)) ``` ``` ## [1] 2 3 3 6 6 9 10 17 17 19 20 22 22 25 27 28 30 32 35 36 41 ## [22] 41 48 50 51 55 57 58 58 60 61 63 63 65 66 70 70 71 72 76 77 80 ## [43] 83 85 85 86 86 87 90 96 ``` --- ### Sampling --- ## Does S2 have duplicates? ```r s2_unique <- unique(s2) print(length(s2)) ``` ``` ## [1] 50 ``` ```r print(length(s2_unique)) ``` ``` ## [1] 40 ``` ```r print(length(s2) == length(s2_unique)) ``` ``` ## [1] FALSE ```