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().
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.
class(hot_springs$scientist)## [1] "character"hot_springs$scientist <- as.factor(hot_springs$scientist)Did our change work? We can also check the levels of each column.
levels(hot_springs$scientist)## [1] "Jill" "Steve" "Susan"hot_springs$spring <- as.factor(hot_springs$spring)levels(hot_springs$spring)## [1] "Benton" "Buckeye" "Travertine"In 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.
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$lakeid)##
## AL AR BO BR CR DY FD JN LC LJ LR LSG MN RD UB WS
## 383 262 197 291 343 355 302 238 173 181 292 143 293 135 191 254mammal_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>glimpse(mammals)## Rows: 1,440
## Columns: 13
## $ order <chr> "Artiodactyla", "Artiodactyla", "Artiodactyla", "Artiod…
## $ family <chr> "Antilocapridae", "Bovidae", "Bovidae", "Bovidae", "Bov…
## $ Genus <chr> "Antilocapra", "Addax", "Aepyceros", "Alcelaphus", "Amm…
## $ species <chr> "americana", "nasomaculatus", "melampus", "buselaphus",…
## $ mass <dbl> 45375.0, 182375.0, 41480.0, 150000.0, 28500.0, 55500.0,…
## $ gestation <dbl> 8.13, 9.39, 6.35, 7.90, 6.80, 5.08, 5.72, 5.50, 8.93, 9…
## $ newborn <dbl> 3246.36, 5480.00, 5093.00, 10166.67, -999.00, 3810.00, …
## $ weaning <dbl> 3.00, 6.50, 5.63, 6.50, -999.00, 4.00, 4.04, 2.13, 10.7…
## $ `wean mass` <dbl> 8900, -999, 15900, -999, -999, -999, -999, -999, 157500…
## $ AFR <dbl> 13.53, 27.27, 16.66, 23.02, -999.00, 14.89, 10.23, 20.1…
## $ `max. life` <dbl> 142, 308, 213, 240, -999, 251, 228, 255, 300, 324, 300,…
## $ `litter size` <dbl> 1.85, 1.00, 1.00, 1.00, 1.00, 1.37, 1.00, 1.00, 1.00, 1…
## $ `litters/year` <dbl> 1.00, 0.99, 0.95, -999.00, -999.00, 2.00, -999.00, 1.89…table() command to produce counts of mammal
order.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