At the end of this exercise, you will be able to:
1. Import .csv files as data frames using read_csv().
2. Understand the importance of paths and working directories to import
data.
2. Use summary functions to explore the dimensions, structure, and
contents of a data frame.
library("tidyverse")In part 1, you learned how to work with vectors and data frames. For
the remainder of the course, we will work exclusively with data frames.
Recall that data frames store multiple classes of data. Last time, you
were shown how to build data frames by combining multiple vectors using
tibble().
We only use tibble() when working in the tidyverse. The
equivalent command in base R is data.frame().
Scientists often make their data available as supplementary material associated with a publication. This is excellent scientific practice as it insures repeatability by showing exactly how analyses were performed. As data scientists, we capitalize on this by importing data directly into R.
R allows us to import a wide variety of data types. The most common type of file is a .csv file which stands for comma separated values. Spreadsheets are often developed in Excel then saved as .csv files for use in R. There are packages that allow you to open excel files and many other formats but .csv is the most common.
To import a file, first make sure that you are in the correct working directory. If you are not in the correct directory, R will not “see” the file.
getwd()## [1] "/Users/switters/Desktop/datascibiol/lab3"Here we import a .csv file. Since we are using the tidyverse, we
import the file using read_csv(). readr is
included in the tidyverse set of packages.
hot_springs <- read_csv("hsprings.csv")## Rows: 9 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): scientist, spring
## dbl (2): temp, depth_ft
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Notice that when the data are imported, you are presented with a message that tells you how R interpreted the column classes. This is also where error messages will appear if there are problems.
Use the str() function to get an idea of the data
structure of hot_springs.
str(hot_springs)## spc_tbl_ [9 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ temp : num [1:9] 36.2 35.4 35.3 35.1 35.4 ...
## $ scientist: chr [1:9] "Jill" "Susan" "Steve" "Jill" ...
## $ spring : chr [1:9] "Buckeye" "Buckeye" "Buckeye" "Benton" ...
## $ depth_ft : num [1:9] 4.15 4.13 4.12 3.21 3.23 3.2 5.67 5.65 5.66
## - attr(*, "spec")=
## .. cols(
## .. temp = col_double(),
## .. scientist = col_character(),
## .. spring = col_character(),
## .. depth_ft = col_double()
## .. )
## - attr(*, "problems")=<externalptr>When making plots or performing statistical modeling, it may be important to convert column classes. For example, if a column contains categorical data (e.g., scientist names, treatment groups, etc.) it may be best to convert that column to a factor class.
Recall that factors are categorical variables that have a fixed number of possible values called levels.
What is the class of the scientist column? Let’s change it to factor and then show the levels of that factor.
names(hot_springs)## [1] "temp" "scientist" "spring" "depth_ft"class(hot_springs$scientist)## [1] "character"Did our change work? We can also check the levels of each column.
hot_springs$scientist <- as.factor(hot_springs$scientist)class(hot_springs$scientist)## [1] "factor"hot_springs$spring <- as.factor(hot_springs$spring)levels(hot_springs$spring)## [1] "Benton" "Buckeye" "Travertine"table(hot_springs$spring)##
## Benton Buckeye Travertine
## 3 3 3In your lab 3 folder there is another folder titled
data. Inside the data folder there is a
.csv titled Gaeta_etal_CLC_data.csv. Open this
data and store them as an object called fish.
The data are from: Gaeta J., G. Sass, S. Carpenter. 2012. Biocomplexity at North Temperate Lakes LTER: Coordinated Field Studies: Large Mouth Bass Growth 2006. Environmental Data Initiative. link
fish <- read_csv("data/Gaeta_etal_CLC_data.csv")## Rows: 4033 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): lakeid, annnumber
## dbl (4): fish_id, length, radii_length_mm, scalelength
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Once data have been loaded, you need to get an idea of its structure, contents, and dimensions. I routinely run one or more of these commands when data are first imported.
names(fish)## [1] "lakeid" "fish_id" "annnumber" "length"
## [5] "radii_length_mm" "scalelength"str(fish)## spc_tbl_ [4,033 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ lakeid : chr [1:4033] "AL" "AL" "AL" "AL" ...
## $ fish_id : num [1:4033] 299 299 299 300 300 300 300 301 301 301 ...
## $ annnumber : chr [1:4033] "EDGE" "2" "1" "EDGE" ...
## $ length : num [1:4033] 167 167 167 175 175 175 175 194 194 194 ...
## $ radii_length_mm: num [1:4033] 2.7 2.04 1.31 3.02 2.67 ...
## $ scalelength : num [1:4033] 2.7 2.7 2.7 3.02 3.02 ...
## - attr(*, "spec")=
## .. cols(
## .. lakeid = col_character(),
## .. fish_id = col_double(),
## .. annnumber = col_character(),
## .. length = col_double(),
## .. radii_length_mm = col_double(),
## .. scalelength = col_double()
## .. )
## - attr(*, "problems")=<externalptr>We can summarize our data frame with thesummary()
function.
summary(fish)## lakeid fish_id annnumber length
## Length:4033 Min. : 1.0 Length:4033 Min. : 58.0
## Class :character 1st Qu.:156.0 Class :character 1st Qu.:253.0
## Mode :character Median :267.0 Mode :character Median :299.0
## Mean :258.3 Mean :293.3
## 3rd Qu.:376.0 3rd Qu.:342.0
## Max. :478.0 Max. :420.0
## radii_length_mm scalelength
## Min. : 0.4569 Min. : 0.6282
## 1st Qu.: 2.3252 1st Qu.: 4.2596
## Median : 3.5380 Median : 5.4062
## Mean : 3.6589 Mean : 5.3821
## 3rd Qu.: 4.8229 3rd Qu.: 6.4145
## Max. :11.0258 Max. :11.0258glimpse() is another useful summary function.
glimpse(fish)## Rows: 4,033
## Columns: 6
## $ lakeid <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", …
## $ fish_id <dbl> 299, 299, 299, 300, 300, 300, 300, 301, 301, 301, 301,…
## $ annnumber <chr> "EDGE", "2", "1", "EDGE", "3", "2", "1", "EDGE", "3", …
## $ length <dbl> 167, 167, 167, 175, 175, 175, 175, 194, 194, 194, 194,…
## $ radii_length_mm <dbl> 2.697443, 2.037518, 1.311795, 3.015477, 2.670733, 2.13…
## $ scalelength <dbl> 2.697443, 2.697443, 2.697443, 3.015477, 3.015477, 3.01…nrow() gives the numbers of rows.
nrow(fish)## [1] 4033ncol gives the number of columns.
ncol(fish)## [1] 6dim() gives the dimensions.
dim(fish)## [1] 4033 6names gives the column names.
names(fish)## [1] "lakeid" "fish_id" "annnumber" "length"
## [5] "radii_length_mm" "scalelength"head() prints the first n rows of the data frame.
head(fish)## # A tibble: 6 × 6
## lakeid fish_id annnumber length radii_length_mm scalelength
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 AL 299 EDGE 167 2.70 2.70
## 2 AL 299 2 167 2.04 2.70
## 3 AL 299 1 167 1.31 2.70
## 4 AL 300 EDGE 175 3.02 3.02
## 5 AL 300 3 175 2.67 3.02
## 6 AL 300 2 175 2.14 3.02tail() prinst the last n rows of the data frame.
tail(fish)## # A tibble: 6 × 6
## lakeid fish_id annnumber length radii_length_mm scalelength
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 WS 180 6 403 5.41 11.0
## 2 WS 180 5 403 4.98 11.0
## 3 WS 180 4 403 4.22 11.0
## 4 WS 180 3 403 3.04 11.0
## 5 WS 180 2 403 2.03 11.0
## 6 WS 180 1 403 1.19 11.0table() is useful when you have a limited number of
categorical variables. It produces fast counts of the number of
observations in a variable.
table(fish$fish_id)##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 12 11 13 4 14 12 10 9 8 12 4 4 2 12 3 12 4 11 10 6
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 14 15 11 10 14 4 12 11 13 14 13 12 11 11 11 8 7 4 4 4
## 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 3 4 3 4 4 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2
## 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 14 11 11 10 10 10 9 10 8 9 6 8 7 6 6 6 6 5 5 4
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
## 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3
## 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 3 3 3 3 4 4 4 4 4 4 4 5 5 4 5 5 8 11 11 15
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## 2 2 2 2 3 4 6 7 7 7 7 7 7 7 7 9 9 10 10 10
## 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 10 10 11 11 11 11 12 12 12 13 1 1 1 3 3 3 3 3 4 5
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 5 6 6 7 7 9 10 11 11 11 12 13 14 14 15 14 15 15 15 17
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
## 2 3 4 9 8 8 7 7 10 10 10 10 10 11 10 11 11 13 13 13
## 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220
## 14 13 13 14 14 15 15 15 3 7 8 8 8 8 8 9 9 9 9 9
## 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 9 10 8 9 9 9 11 10 12 11 11 14 11 15 14 13 14 17 9 9
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
## 3 4 4 4 3 4 4 5 5 5 5 5 5 6 6 7 7 7 7 7
## 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280
## 7 8 7 7 8 8 7 8 2 4 6 7 7 7 9 9 9 9 9 9
## 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300
## 9 7 10 7 9 9 9 8 10 9 11 9 11 12 15 14 7 9 3 4
## 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
## 4 10 11 13 13 11 14 12 14 13 13 14 14 14 14 14 14 14 14 15
## 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340
## 14 15 16 15 14 14 16 17 4 4 4 4 5 4 4 4 4 4 4 4
## 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360
## 4 4 4 4 5 6 6 5 5 6 6 8 8 9 10 10 12 12 3 7
## 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380
## 7 10 10 11 8 12 10 10 10 12 13 12 11 11 13 14 12 12 15 14
## 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 13 13 16 14 15 17 15 15 2 2 2 3 3 3 3 4 4 5 5 5
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420
## 6 6 7 7 7 7 7 7 8 8 9 9 9 11 12 13 13 10 12 8
## 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440
## 14 6 10 7 9 13 10 6 11 13 17 9 9 9 10 7 10 10 8 8
## 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460
## 10 9 8 10 14 9 13 3 14 13 13 11 12 9 11 12 9 13 12 9
## 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478
## 7 14 14 10 9 9 12 12 11 13 13 11 9 14 8 13 11 15mammal_lifehistories_v2.csv and place it
into a new object called mammals.mammals <- read_csv("data/mammal_lifehistories_v2.csv")## Rows: 1440 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): order, family, Genus, species
## dbl (9): mass, gestation, newborn, weaning, wean mass, AFR, max. life, litte...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.dim(mammals)## [1] 1440 13names(mammals)## [1] "order" "family" "Genus" "species" "mass"
## [6] "gestation" "newborn" "weaning" "wean mass" "AFR"
## [11] "max. life" "litter size" "litters/year"str() to show the structure of the data frame and
its individual columns; compare this to glimpse().str(mammals)## spc_tbl_ [1,440 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ order : chr [1:1440] "Artiodactyla" "Artiodactyla" "Artiodactyla" "Artiodactyla" ...
## $ family : chr [1:1440] "Antilocapridae" "Bovidae" "Bovidae" "Bovidae" ...
## $ Genus : chr [1:1440] "Antilocapra" "Addax" "Aepyceros" "Alcelaphus" ...
## $ species : chr [1:1440] "americana" "nasomaculatus" "melampus" "buselaphus" ...
## $ mass : num [1:1440] 45375 182375 41480 150000 28500 ...
## $ gestation : num [1:1440] 8.13 9.39 6.35 7.9 6.8 5.08 5.72 5.5 8.93 9.14 ...
## $ newborn : num [1:1440] 3246 5480 5093 10167 -999 ...
## $ weaning : num [1:1440] 3 6.5 5.63 6.5 -999 ...
## $ wean mass : num [1:1440] 8900 -999 15900 -999 -999 ...
## $ AFR : num [1:1440] 13.5 27.3 16.7 23 -999 ...
## $ max. life : num [1:1440] 142 308 213 240 -999 251 228 255 300 324 ...
## $ litter size : num [1:1440] 1.85 1 1 1 1 1.37 1 1 1 1 ...
## $ litters/year: num [1:1440] 1 0.99 0.95 -999 -999 2 -999 1.89 1 1 ...
## - attr(*, "spec")=
## .. cols(
## .. order = col_character(),
## .. family = col_character(),
## .. Genus = col_character(),
## .. species = col_character(),
## .. mass = col_double(),
## .. gestation = col_double(),
## .. newborn = col_double(),
## .. weaning = col_double(),
## .. `wean mass` = col_double(),
## .. AFR = col_double(),
## .. `max. life` = col_double(),
## .. `litter size` = col_double(),
## .. `litters/year` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>summary(mammals)## order family Genus species
## Length:1440 Length:1440 Length:1440 Length:1440
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## mass gestation newborn weaning
## Min. : -999 Min. :-999.00 Min. : -999.00 Min. :-999.00
## 1st Qu.: 50 1st Qu.:-999.00 1st Qu.: -999.00 1st Qu.:-999.00
## Median : 403 Median : 1.05 Median : 2.65 Median : 0.73
## Mean : 383577 Mean :-287.25 Mean : 6703.15 Mean :-427.17
## 3rd Qu.: 7009 3rd Qu.: 4.50 3rd Qu.: 98.00 3rd Qu.: 2.00
## Max. :149000000 Max. : 21.46 Max. :2250000.00 Max. : 48.00
## wean mass AFR max. life litter size
## Min. : -999 Min. :-999.00 Min. :-999.0 Min. :-999.000
## 1st Qu.: -999 1st Qu.:-999.00 1st Qu.:-999.0 1st Qu.: 1.000
## Median : -999 Median : 2.50 Median :-999.0 Median : 2.270
## Mean : 16049 Mean :-408.12 Mean :-490.3 Mean : -55.634
## 3rd Qu.: 10 3rd Qu.: 15.61 3rd Qu.: 147.2 3rd Qu.: 3.835
## Max. :19075000 Max. : 210.00 Max. :1368.0 Max. : 14.180
## litters/year
## Min. :-999.000
## 1st Qu.:-999.000
## Median : 0.375
## Mean :-477.141
## 3rd Qu.: 1.155
## Max. : 7.500table() command to produce counts of mammal
order.names(mammals)## [1] "order" "family" "Genus" "species" "mass"
## [6] "gestation" "newborn" "weaning" "wean mass" "AFR"
## [11] "max. life" "litter size" "litters/year"table(mammals$order)##
## Artiodactyla Carnivora Cetacea Dermoptera Hyracoidea
## 161 197 55 2 4
## Insectivora Lagomorpha Macroscelidea Perissodactyla Pholidota
## 91 42 10 15 7
## Primates Proboscidea Rodentia Scandentia Sirenia
## 156 2 665 7 5
## Tubulidentata Xenarthra
## 1 20