3  Import Data

Author

Perry S

3.1 Load Packages

Before we can work with our data, we need to load the relevant packages. One of the most important ones for this task is tidyverse, which we learned about yesterday. We’ll import it here:

3.2 Read in Data

Next, we must import our data.

The common function for this is read_csv from readr (which is nested in tidyverse). For our demonstrations, we will use the WQ_P8D7.csv file housed in the data folder.

When importing data, we must specify the filepath where it’s housed. There are multiple ways to do this. We could hard-code in the path, which is called the absolute path:

df_wq <- read_csv('C:/R/IntrotoR/data/WQ_P8D7.csv')
Error: 'C:/R/IntrotoR/data/WQ_P8D7.csv' does not exist.

However, this code is very specific and will break when used on other computers.

If we instead house data in a Project, we can make use of relative filepaths. These are ideal because anyone who uses the Project can run the code:

df_wq <- read_csv('data/WQ_P8D7.csv')

If you received an error here, this is because your Rmd files are not being evaluated at the Project level.

One way to fix this is to change your options in Tools > Global Options > R Markdown > evaluate chunks in directory to Project.

You can also use the here package instead, which automatically evaluates at the Project level:

library(here)

df_wq <- read_csv(here('data/WQ_P8D7.csv'))
Data File Extensions and Delimiters

Here, we used the read_csv function, which takes .csv files by default. But what is a csv?

“csv” stands for “comma separated values”, where the comma is called a delimiter; it tells the code where to separate the data cells. If you want to use a different delimiter, you can use the read_delim function (also from the readr package):

read_delim('data/delim_ex.txt', delim = '|') # data separated by |
# A tibble: 2 × 4
  col   headers are       first    
  <chr> <chr>   <chr>     <chr>    
1 here  is      an        example  
2 of    a       different delimiter

for tab delimited data (a fairly frequent format), there’s read_tsv:

read_tsv('data/tab_ex.tsv')
# A tibble: 2 × 4
  col   headers are       first    
  <chr> <chr>   <chr>     <chr>    
1 here  is      an        example  
2 of    a       different delimiter

Excel files (.xlsx) are unique because they’re not solely defined by their delimiters, which allows for more complicated file formatting. To import these, we use read_excel from the readxl package:

library(readxl)

read_excel('data/excel_ex.xlsx', sheet = 'Sheet1') # read the first sheet (by name)
# A tibble: 2 × 4
  col   headers are   first  
  <chr> <chr>   <chr> <chr>  
1 here  is      an    example
2 of    an      excel file   
read_excel('data/excel_ex.xlsx', sheet = 2) # read the second sheet (by number)
# A tibble: 2 × 4
  col   headers are   first  
  <chr> <chr>   <chr> <chr>  
1 here  is      an    example
2 of    excel   sheet 2

We now have a data frame object called df_wq. We can use head to see what the first few rows of the data frame look like:

head(df_wq)
# A tibble: 6 × 20
  Station Date        Chla Pheophytin TotAlkalinity DissAmmonia
  <chr>   <date>     <dbl>      <dbl>         <dbl>       <dbl>
1 P8      2020-01-16  0.64       0.5             98        0.15
2 D7      2020-01-22  0.67       0.87            82        0.21
3 P8      2020-02-14  1.46       0.69            81        0.25
4 D7      2020-02-20  2.15       0.5             86        0.14
5 P8      2020-03-03  1.4        0.56            80        0.11
6 D7      2020-03-06  1.89       1.13            93        0.22
# ℹ 14 more variables: DissNitrateNitrite <dbl>, DOC <dbl>, TOC <dbl>,
#   DON <dbl>, TotPhos <dbl>, DissOrthophos <dbl>, TDS <dbl>, TSS <dbl>,
#   TKN <dbl>, Depth <dbl>, Secchi <dbl>, Microcystis <dbl>,
#   SpCndSurface <dbl>, WTSurface <dbl>

And glimpse to see information about the columns:

glimpse(df_wq)
Rows: 62
Columns: 20
$ Station            <chr> "P8", "D7", "P8", "D7", "P8", "D7", "P8", "D7", "P8…
$ Date               <date> 2020-01-16, 2020-01-22, 2020-02-14, 2020-02-20, 20…
$ Chla               <dbl> 0.64, 0.67, 1.46, 2.15, 1.40, 1.89, 4.73, 1.74, 6.4…
$ Pheophytin         <dbl> 0.50, 0.87, 0.69, 0.50, 0.56, 1.13, 1.25, 0.89, 0.8…
$ TotAlkalinity      <dbl> 98.0, 82.0, 81.0, 86.0, 80.0, 93.0, 59.0, 78.0, 63.…
$ DissAmmonia        <dbl> 0.150, 0.210, 0.250, 0.140, 0.110, 0.220, 0.050, 0.…
$ DissNitrateNitrite <dbl> 2.800, 0.490, 1.700, 0.480, 1.600, 0.380, 1.070, 0.…
$ DOC                <dbl> 3.90, 0.27, 2.80, 0.39, 2.00, 0.19, 2.80, 1.20, 3.1…
$ TOC                <dbl> 4.10, 0.32, 2.50, 0.41, 2.10, 0.20, 2.80, 1.20, 3.1…
$ DON                <dbl> NA, NA, NA, NA, NA, NA, 0.30, 0.20, 0.30, 0.10, 0.5…
$ TotPhos            <dbl> 0.310, 0.082, 0.130, 0.130, 0.190, 0.100, 0.188, 0.…
$ DissOrthophos      <dbl> 0.200, 0.071, 0.130, 0.065, 0.140, 0.082, 0.177, 0.…
$ TDS                <dbl> 380, 9500, 340, 5800, 290, 8700, 280, 7760, 227, 11…
$ TSS                <dbl> 8.9, 38.0, 2.2, 18.0, 1.4, 28.0, 6.6, 35.6, 5.3, 23…
$ TKN                <dbl> 0.520, 0.480, 0.430, 0.250, 0.400, 0.200, 0.400, 0.…
$ Depth              <dbl> 28.9, 18.8, 39.0, 7.1, 39.0, 7.2, 37.1, 5.2, 36.7, …
$ Secchi             <dbl> 116, 30, 212, 52, 340, 48, 100, 40, 160, 44, 120, 6…
$ Microcystis        <dbl> 1, 1, 1, 1, 1, 1, 3, 2, 3, 2, 4, 2, 3, 2, 2, 1, 1, …
$ SpCndSurface       <dbl> 667, 15532, 647, 11369, 530, 16257, 503, 12946, 404…
$ WTSurface          <dbl> 9.67, 9.97, 11.09, 12.51, 13.97, 13.81, 23.46, 21.1…