select()At the end of this exercise, you will be able to:
1. Use summary functions to assess the structure of a data frame.
2. Us the select function of dplyr to build data frames
restricted to variable of interest.
3. Use the rename() function to provide new, consistent
names to variables in data frames.
For the remainder of the quarter, we will work within the
tidyverse. At the start of each lab, the library needs to
be loaded as shown below.
library("tidyverse")
library("janitor")These 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 uploaded, let’s get an idea of its structure, contents, and dimensions.
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…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. :11.0258The tidyverse is a collection of packages that make workflow in R easier. The packages operate more intuitively than base R commands and share a common organizational philosophy.
The first package that we will use that is part of the tidyverse is
dplyr. dplyr is used to transform data frames
by extracting, rearranging, and summarizing data such that they are
focused on a question of interest. This is very helpful, especially when
wrangling large data, and makes dplyr one of most frequently used
packages in the tidyverse. The two functions we will use most are
select() and filter().
select()Select allows you to pull out columns of interest from a dataframe.
To do this, just add the names of the columns to the
select() command. The order in which you add them, will
determine the order in which they appear in the output.
names(fish)## [1] "lakeid" "fish_id" "annnumber" "length"
## [5] "radii_length_mm" "scalelength"We are only interested in lakeid and scalelength.
select(fish, "lakeid", "scalelength")## # A tibble: 4,033 × 2
## lakeid scalelength
## <chr> <dbl>
## 1 AL 2.70
## 2 AL 2.70
## 3 AL 2.70
## 4 AL 3.02
## 5 AL 3.02
## 6 AL 3.02
## 7 AL 3.02
## 8 AL 3.34
## 9 AL 3.34
## 10 AL 3.34
## # ℹ 4,023 more rowsRemember that you can save the output to a new dataframe.
fish_subset <- select(fish, "lakeid", "scalelength")To add a range of columns use start_col:end_col.
select(fish, fish_id:length)## # A tibble: 4,033 × 3
## fish_id annnumber length
## <dbl> <chr> <dbl>
## 1 299 EDGE 167
## 2 299 2 167
## 3 299 1 167
## 4 300 EDGE 175
## 5 300 3 175
## 6 300 2 175
## 7 300 1 175
## 8 301 EDGE 194
## 9 301 3 194
## 10 301 2 194
## # ℹ 4,023 more rowsThe - operator is useful in select. It allows us to select everything except the specified variables.
select(fish, -"fish_id", -"annnumber", -"length", -"radii_length_mm")## # A tibble: 4,033 × 2
## lakeid scalelength
## <chr> <dbl>
## 1 AL 2.70
## 2 AL 2.70
## 3 AL 2.70
## 4 AL 3.02
## 5 AL 3.02
## 6 AL 3.02
## 7 AL 3.02
## 8 AL 3.34
## 9 AL 3.34
## 10 AL 3.34
## # ℹ 4,023 more rowsFor very large data frames with lots of variables,
select() utilizes lots of different operators to make
things easier. Let’s say we are only interested in the variables that
deal with length.
select(fish, contains("length"))## # A tibble: 4,033 × 3
## length radii_length_mm scalelength
## <dbl> <dbl> <dbl>
## 1 167 2.70 2.70
## 2 167 2.04 2.70
## 3 167 1.31 2.70
## 4 175 3.02 3.02
## 5 175 2.67 3.02
## 6 175 2.14 3.02
## 7 175 1.23 3.02
## 8 194 3.34 3.34
## 9 194 2.97 3.34
## 10 194 2.29 3.34
## # ℹ 4,023 more rowsWhen columns are sequentially named, starts_with() makes
selecting columns easier.
select(fish, starts_with("radii"))## # A tibble: 4,033 × 1
## radii_length_mm
## <dbl>
## 1 2.70
## 2 2.04
## 3 1.31
## 4 3.02
## 5 2.67
## 6 2.14
## 7 1.23
## 8 3.34
## 9 2.97
## 10 2.29
## # ℹ 4,023 more rowsOptions to select columns based on a specific criteria include:
1. ends_with() = Select columns that end with a character string
2. contains() = Select columns that contain a character string
3. matches() = Select columns that match a regular expression
4. one_of() = Select columns names that are from a group of names
We can use these functions to select columns that end with “id”.
select(fish, ends_with("id"))## # A tibble: 4,033 × 2
## lakeid fish_id
## <chr> <dbl>
## 1 AL 299
## 2 AL 299
## 3 AL 299
## 4 AL 300
## 5 AL 300
## 6 AL 300
## 7 AL 300
## 8 AL 301
## 9 AL 301
## 10 AL 301
## # ℹ 4,023 more rowsOr, we can select columns that contain “fish”.
select(fish, contains("fish"))## # A tibble: 4,033 × 1
## fish_id
## <dbl>
## 1 299
## 2 299
## 3 299
## 4 300
## 5 300
## 6 300
## 7 300
## 8 301
## 9 301
## 10 301
## # ℹ 4,023 more rowsYou can also select columns based on the class of data.
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…select_if(fish, is.numeric)## # A tibble: 4,033 × 4
## fish_id length radii_length_mm scalelength
## <dbl> <dbl> <dbl> <dbl>
## 1 299 167 2.70 2.70
## 2 299 167 2.04 2.70
## 3 299 167 1.31 2.70
## 4 300 175 3.02 3.02
## 5 300 175 2.67 3.02
## 6 300 175 2.14 3.02
## 7 300 175 1.23 3.02
## 8 301 194 3.34 3.34
## 9 301 194 2.97 3.34
## 10 301 194 2.29 3.34
## # ℹ 4,023 more rowsTo select all columns that are not a class of data, you need
to add a ~.
select_if(fish, ~!is.numeric(.)) #the ! means not## # A tibble: 4,033 × 2
## lakeid annnumber
## <chr> <chr>
## 1 AL EDGE
## 2 AL 2
## 3 AL 1
## 4 AL EDGE
## 5 AL 3
## 6 AL 2
## 7 AL 1
## 8 AL EDGE
## 9 AL 3
## 10 AL 2
## # ℹ 4,023 more rowsFor this exercise we will use life history data
mammal_lifehistories_v2.csv for mammals. The data are
from:
S. K. Morgan Ernest. 2003. Life history characteristics of
placental non-volant mammals. Ecology 84:3402.
Load the data.
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.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. : -999.0 Min. :-999.00
## 1st Qu.: 50 1st Qu.:-999.00 1st Qu.: -999.0 1st Qu.:-999.00
## Median : 403 Median : 1.05 Median : 2.6 Median : 0.73
## Mean : 383577 Mean :-287.25 Mean : 6703.1 Mean :-427.17
## 3rd Qu.: 7009 3rd Qu.: 4.50 3rd Qu.: 98.0 3rd Qu.: 2.00
## Max. :149000000 Max. : 21.46 Max. :2250000.0 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. : 7.500max. life as a test.anyNA(mammals)## [1] FALSEmammals
data?names(mammals)## [1] "order" "family" "Genus" "species" "mass"
## [6] "gestation" "newborn" "weaning" "wean mass" "AFR"
## [11] "max. life" "litter size" "litters/year"mammals <- clean_names(mammals)genus,
species, and mass. Use select()
to build a new dataframe mass focused on these
variables.select(mammals, "genus", "species", "mass")## # A tibble: 1,440 × 3
## genus species mass
## <chr> <chr> <dbl>
## 1 Antilocapra americana 45375
## 2 Addax nasomaculatus 182375
## 3 Aepyceros melampus 41480
## 4 Alcelaphus buselaphus 150000
## 5 Ammodorcas clarkei 28500
## 6 Ammotragus lervia 55500
## 7 Antidorcas marsupialis 30000
## 8 Antilope cervicapra 37500
## 9 Bison bison 497667.
## 10 Bison bonasus 500000
## # ℹ 1,430 more rowsorder and
family? Use the - operator to make the code
efficient.select(mammals, -"order", -"family")## # A tibble: 1,440 × 11
## genus species mass gestation newborn weaning wean_mass afr max_life
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Antilocap… americ… 4.54e4 8.13 3246. 3 8900 13.5 142
## 2 Addax nasoma… 1.82e5 9.39 5480 6.5 -999 27.3 308
## 3 Aepyceros melamp… 4.15e4 6.35 5093 5.63 15900 16.7 213
## 4 Alcelaphus busela… 1.5 e5 7.9 10167. 6.5 -999 23.0 240
## 5 Ammodorcas clarkei 2.85e4 6.8 -999 -999 -999 -999 -999
## 6 Ammotragus lervia 5.55e4 5.08 3810 4 -999 14.9 251
## 7 Antidorcas marsup… 3 e4 5.72 3910 4.04 -999 10.2 228
## 8 Antilope cervic… 3.75e4 5.5 3846 2.13 -999 20.1 255
## 9 Bison bison 4.98e5 8.93 20000 10.7 157500 29.4 300
## 10 Bison bonasus 5 e5 9.14 23000. 6.6 -999 30.0 324
## # ℹ 1,430 more rows
## # ℹ 2 more variables: litter_size <dbl>, litters_year <dbl>select(mammals, contains("mass"))## # A tibble: 1,440 × 2
## mass wean_mass
## <dbl> <dbl>
## 1 45375 8900
## 2 182375 -999
## 3 41480 15900
## 4 150000 -999
## 5 28500 -999
## 6 55500 -999
## 7 30000 -999
## 8 37500 -999
## 9 497667. 157500
## 10 500000 -999
## # ℹ 1,430 more rowscharacter.select_if(mammals, is.character)## # A tibble: 1,440 × 4
## order family genus species
## <chr> <chr> <chr> <chr>
## 1 Artiodactyla Antilocapridae Antilocapra americana
## 2 Artiodactyla Bovidae Addax nasomaculatus
## 3 Artiodactyla Bovidae Aepyceros melampus
## 4 Artiodactyla Bovidae Alcelaphus buselaphus
## 5 Artiodactyla Bovidae Ammodorcas clarkei
## 6 Artiodactyla Bovidae Ammotragus lervia
## 7 Artiodactyla Bovidae Antidorcas marsupialis
## 8 Artiodactyla Bovidae Antilope cervicapra
## 9 Artiodactyla Bovidae Bison bison
## 10 Artiodactyla Bovidae Bison bonasus
## # ℹ 1,430 more rowsAnother option is to use the janitor package. The janitor package has
a function called clean_names() that will make all column
names lowercase and replace spaces with underscores.
#install.packages("janitor")
library(janitor)names(mammals)## [1] "order" "family" "genus" "species" "mass"
## [6] "gestation" "newborn" "weaning" "wean_mass" "afr"
## [11] "max_life" "litter_size" "litters_year"clean_names(mammals)## # A tibble: 1,440 × 13
## order family genus species mass gestation newborn weaning wean_mass afr
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Artio… Antil… Anti… americ… 4.54e4 8.13 3246. 3 8900 13.5
## 2 Artio… Bovid… Addax nasoma… 1.82e5 9.39 5480 6.5 -999 27.3
## 3 Artio… Bovid… Aepy… melamp… 4.15e4 6.35 5093 5.63 15900 16.7
## 4 Artio… Bovid… Alce… busela… 1.5 e5 7.9 10167. 6.5 -999 23.0
## 5 Artio… Bovid… Ammo… clarkei 2.85e4 6.8 -999 -999 -999 -999
## 6 Artio… Bovid… Ammo… lervia 5.55e4 5.08 3810 4 -999 14.9
## 7 Artio… Bovid… Anti… marsup… 3 e4 5.72 3910 4.04 -999 10.2
## 8 Artio… Bovid… Anti… cervic… 3.75e4 5.5 3846 2.13 -999 20.1
## 9 Artio… Bovid… Bison bison 4.98e5 8.93 20000 10.7 157500 29.4
## 10 Artio… Bovid… Bison bonasus 5 e5 9.14 23000. 6.6 -999 30.0
## # ℹ 1,430 more rows
## # ℹ 3 more variables: max_life <dbl>, litter_size <dbl>, litters_year <dbl>–>Home