setwd (".") Importing .csv files
Importing .csv files
Introduction
Working with data provided by R packages is a great way to learn data science tools, but you want to apply what you’ve learned to your own data at some point. In this section, you’ll learn the basics of reading data files into R.
R uses object-oriented language (aka object-based programming): R code builds “chains” of objects and functions to execute tasks.
Objects are holders of information, like a single value, word, data table, graph, etc.. Objects are in between the brackets.
Functions are special objects with a job. Functions require objects (e.g. data, parameters, etc.) to perform their tasks. Functions are before the brackets.
What can R read?
R does not read spaces. You can add spaces in you code for readability.
Keep object names short and informative. You can use “_” and “.” as well as numbers
you cannot use special symbols (%,&,*,#,!), “,”, nor can you start an object name with a number.
NOTE: If you repeat a file name in your workspace, the old file will be overwritten without a warning prompt. Keep track of your file names, so you know which files to use.
Loading data
In this chapter, you’ll learn how to load flat files in R with the readr package, which is part of the core tidyverse.
First, create a new Project, named Cell425 (or whatever you prefer)
Next create a new R Markdown file. File > New File > R Markdown
If you have not already done so, set your R working directory:
- Download the cellstocks file and cellstocksold file from the shared drive, and place them in your working directory
Next, activate tidyverse
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.2.1 ✔ readr 2.2.0
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.3 ✔ tibble 3.3.1
✔ lubridate 1.9.5 ✔ tidyr 1.3.2
✔ purrr 1.2.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsYou can also check the box in front in the panel at the left bottom (unless you changed the layout), make sure you select the Packages Tab.
We can read the test files into R using read.csv().
cellstocks <- read.csv("cellstocks.csv")
cellstocksold <- read.csv("cellstocksold.csv")The cellstocksold and cellstocks file should now appear in the ‘environment’ in the top right panel. You can access them in the markdown or console pane by using this code:
cellstocks Cell.culture.N2.stocks X X.1 X.2 X.3 X.4
1 Updated Jan 6 2026
2 Storage location MSB5-12
3 Dewar 2
4 racks 1-8
5 SampleID CellLine Origin vials DT rack
6 1 HeLa Human 4 24 1
7 2 HEK293 Human 5 30 1
8 3 A549 Human 7 24 1
9 4 MCF-7 Human N/A 40 1
10 5 MDA-MB-231 Human 5 34 1
11 6 HepG2 Human 6 60 1
12 7 Caco-2 Human 12 40 1
13 8 HT-29 Human 24 30 1
14 9 HCT116 Human 1 22 1
15 10 PC-3 Human 2 40 1
16 11 LNCaP Human 6 2
17 12 U-87 MG Human 8 36 2
18 13 SH-SY5Y Human 94 70 2
19 14 U2OS Human 6 32 2
20 15 A431 Human 2 36 2
21 16 PANC-1 Human 7 28 2
22 17 SK-OV-3 Human 8 40 2
23 18 DU145 Human 2 2
24 19 Jurkat Human 4 26 2
25 20 K562 Human 7 30 3
26 21 THP-1 Human 9 45 3
27 22 HL-60 Human N/A 36 3
28 23 Raji Human 24 3
29 24 U937 Human 32 3
30 25 Daudi Human 36 3
31 26 NIH/3T3 Mouse 6 26 3
32 27 CHO Chinese Hamster 24 3
33 28 Vero African Green Monkey 0 30 3
34 29 COS-7 African Green Monkey 1 48 3
35 30 MDCK Dog 3 24 4
36 31 BHK-21 Baby Hamster 10 18 4
37 32 RAW 264.7 Mouse 6 15 4
38 33 MC3T3-E1 Mouse 6 36 4
39 34 C2C12 Mouse 5 20 4
40 35 3T3-L1 Mouse 2 24 4
41 36 PC12 Rat 8 48 5
42 37 HaCaT Human 7 30 5
43 38 HUVEC Human 4 36 5
44 39 Sf9 Insect (S. frugiperda) 3 30 5
45 40 High Five Insect (T. ni) 10 24 5
X.5
1
2
3
4
5 Cell Type / Source
6 Cervical adenocarcinoma
7 Embryonic kidney
8 Lung carcinoma (alveolar type II)
9 Breast adenocarcinoma ($ER^+$)
10 Triple-negative breast cancer
11 Hepatocellular carcinoma
12 Colorectal adenocarcinoma
13 Colorectal adenocarcinoma
14 Colorectal carcinoma
15 Prostate adenocarcinoma (metastatic)
16 Prostate adenocarcinoma
17 Glioblastoma multiforme
18 Neuroblastoma
19 Osteosarcoma
20 Epidermoid carcinoma
21 Pancreatic ductal adenocarcinoma
22 Ovarian adenocarcinoma
23 Prostate carcinoma
24 T-lymphocyte leukemia
25 Chronic myelogenous leukemia (CML)
26 Acute monocytic leukemia
27 Promyelocytic leukemia
28 Burkitt's lymphoma B-lymphocyte
29 Histiocytic lymphoma
30 Burkitt's lymphoma B-lymphoblast
31 Embryonic fibroblast
32 Ovary epithelial
33 Kidney epithelial
34 Kidney fibroblast-like (SV40)
35 Kidney epithelial
36 Kidney fibroblast
37 Monocyte/macrophage
38 Pre-osteoblast
39 Myoblast
40 Pre-adipocyte
41 Pheochromocytoma
42 Immortalized keratinocyte
43 Umbilical vein endothelial cells
44 Ovarian cells
45 Ovarian cellsYou can also click on the file in the panel on the right to see what it contains. This will open a new tab. Close it after viewing.
You can list the files in the workspace by using the ls() function.
ls()[1] "cellstocks" "cellstocksold"You can remove files from the workspace with the rm() function
rm("cellstocksold")The file is not active anymore in your workspace, but it is still in your working directory.
when you look at the cellstocks file, there are some issues with it.
There is metadata in the first 5 rows of the data table. You need to tell R to skip rows 1-5 and start on row 6
The data set has a header row of columns names (header argument). R will default to assume that there are no column names (e.g. FALSE) so it adds its own unless you specify the columns are already named.
Blank cells (” “) in the dataset represent NA values ( the na.strings argument), but in this case N/A and NA are also blank values. We can indicate blanks with the na = c() function.
The resulting import function is the following:
data1 <- read.csv("cellstocks.csv", header=T, skip=5, na = c("N/A", "NA", ""))
data1 SampleID CellLine Origin vials DT rack
1 1 HeLa Human 4 24 1
2 2 HEK293 Human 5 30 1
3 3 A549 Human 7 24 1
4 4 MCF-7 Human NA 40 1
5 5 MDA-MB-231 Human 5 34 1
6 6 HepG2 Human 6 60 1
7 7 Caco-2 Human 12 40 1
8 8 HT-29 Human 24 30 1
9 9 HCT116 Human 1 22 1
10 10 PC-3 Human 2 40 1
11 11 LNCaP Human 6 NA 2
12 12 U-87 MG Human 8 36 2
13 13 SH-SY5Y Human 94 70 2
14 14 U2OS Human 6 32 2
15 15 A431 Human 2 36 2
16 16 PANC-1 Human 7 28 2
17 17 SK-OV-3 Human 8 40 2
18 18 DU145 Human 2 NA 2
19 19 Jurkat Human 4 26 2
20 20 K562 Human 7 30 3
21 21 THP-1 Human 9 45 3
22 22 HL-60 Human NA 36 3
23 23 Raji Human NA 24 3
24 24 U937 Human NA 32 3
25 25 Daudi Human NA 36 3
26 26 NIH/3T3 Mouse 6 26 3
27 27 CHO Chinese Hamster NA 24 3
28 28 Vero African Green Monkey 0 30 3
29 29 COS-7 African Green Monkey 1 48 3
30 30 MDCK Dog 3 24 4
31 31 BHK-21 Baby Hamster 10 18 4
32 32 RAW 264.7 Mouse 6 15 4
33 33 MC3T3-E1 Mouse 6 36 4
34 34 C2C12 Mouse 5 20 4
35 35 3T3-L1 Mouse 2 24 4
36 36 PC12 Rat 8 48 5
37 37 HaCaT Human 7 30 5
38 38 HUVEC Human 4 36 5
39 39 Sf9 Insect (S. frugiperda) 3 30 5
40 40 High Five Insect (T. ni) 10 24 5
Cell.Type...Source
1 Cervical adenocarcinoma
2 Embryonic kidney
3 Lung carcinoma (alveolar type II)
4 Breast adenocarcinoma ($ER^+$)
5 Triple-negative breast cancer
6 Hepatocellular carcinoma
7 Colorectal adenocarcinoma
8 Colorectal adenocarcinoma
9 Colorectal carcinoma
10 Prostate adenocarcinoma (metastatic)
11 Prostate adenocarcinoma
12 Glioblastoma multiforme
13 Neuroblastoma
14 Osteosarcoma
15 Epidermoid carcinoma
16 Pancreatic ductal adenocarcinoma
17 Ovarian adenocarcinoma
18 Prostate carcinoma
19 T-lymphocyte leukemia
20 Chronic myelogenous leukemia (CML)
21 Acute monocytic leukemia
22 Promyelocytic leukemia
23 Burkitt's lymphoma B-lymphocyte
24 Histiocytic lymphoma
25 Burkitt's lymphoma B-lymphoblast
26 Embryonic fibroblast
27 Ovary epithelial
28 Kidney epithelial
29 Kidney fibroblast-like (SV40)
30 Kidney epithelial
31 Kidney fibroblast
32 Monocyte/macrophage
33 Pre-osteoblast
34 Myoblast
35 Pre-adipocyte
36 Pheochromocytoma
37 Immortalized keratinocyte
38 Umbilical vein endothelial cells
39 Ovarian cells
40 Ovarian cellsThe metadata are removed, R recognizes the header and it should now say NA, instead of N/A. R can read the latter as a missing value, instead of the text N/A. This function is very useful when you work with large matrices; you will almost always have missing values.
Other ways to check the file structure are:
head(data1) – shows the top 6 rows
tail(data1) - shows the bottom 6 rows
Exporting data
To save the edited file to the working directory (or elsewhere is you define the path) you use:
write.csv(DataName, “FileName”, row.names=F)
DataName refers to the name of the data in the workspace
FileName refers to the name you want the saved file to have in the wd
row.names=F means R will not add and additional row number, before sample_ID
write.csv(data1, "cellstocksnew.csv", row.names=F)The file should now appear in your working directory.