cellstocks <- read.csv("cellstocks.csv", header=T)
class(cellstocks)[1] "data.frame"Data and file structure
Data and File organization
Data and file structure
Data modes in R
All objects have a data mode, generally one of the modes below:
Numeric: a number value with decimal places.
Integer: a number value without decimal places
Complex: a number value that is considered to be an imaginary number (not commonly used). Character: a variable with values other than numbers or a single “.” value present.
Logical: a Boolean value of true or false.
Recursive objects: objects that have multiple pieces or perform actions, they have modes such as ‘list’ or ‘function’ or a few others.
Data classes in R
In R the object “class” determines which functions work with it. If code does not work, and you have checked all typos, you may need to check the object class. Maybe you are using the incorrect code for the class.
R can provide information on the type of object:
Vector: a 1-dimensional object (e.g. column) of data with more than 1 cell. Can be any format (character, numeric, integer, factor, Boolean, date, etc.). If the vector only has one value, it is called a scaler.
Matrix: a 2-dimensional object (e.g. rows & columns) that only includes integer or numeric data and does not have row or column names.
Data frame: a simple 2-dimensional object (e.g. rows & columns) that can include any data format (character, numeric, integer, factor, Boolean, date, etc.) and has column and row names. R base can handle the manipulation of data frames
Data table: a more complex data frame, it is usually handled by different code than data frames.
List: a holder of multiple other objects (e.g. basket). Missing values
Data manipulation in R
Missing values
Missing values are a common issue in large data sets. Missing values are sometimes indicated by a blank, but they can also be indicated by N/A, NA or sometimes a zero or -9999. In the previous section na = c(“N/A”, “NA”, ““) was used to represent all missing values as NA. Alternatively, na.strings() can be used to achieve the same. If the na.strings argument is not defined, the default in R is to consider blank spaces as NA.
data1 <- read.csv("cellstocks.csv", header=T, na.strings =c("",".", "N/A","NA"), skip=5)To identify the location of missing values within vectors, lists, matrices and data frames use the is.na() function which will return a logical vector with TRUE in the element locations that contain missing values represented by NA.
is.na(data1) SampleID CellLine Origin vials DT rack Cell.Type...Source
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[8,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[10,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[11,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[14,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[15,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[16,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[17,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[19,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[20,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[21,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[22,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[23,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[24,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[25,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[26,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[27,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[28,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[29,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[30,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[31,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[32,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[33,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[34,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[35,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[36,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[38,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[39,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[40,] FALSE FALSE FALSE FALSE FALSE FALSE FALSEIf I wanted to know in which rows the doubling time (DT) is missing, the which function can be used. It should return two row numbers.
which (is.na(data1$DT))[1] 11 18We can exclude missing values from mathematical calculations by using the na.rm=TRUE argument.
mean(data1$DT)[1] NAmean(data1$DT, na.rm=TRUE)[1] 32.84211Note that the NAs are not removed from the table, they are just skipped over, to calculate the mean.
To remove the NAs, a subset can be created. To sub-set data by omitting NA values the na.omit() function can be used. For our data tables this will remove all rows where a NA is present in any column.
data2 <- na.omit(data1) You might want to be more selective, and only remove rows with NAs in the DT column:
data3 <- data1[!is.na(data1$DT), ]In data2, 8 rows were removed, in data3 only 2 rows were removed.
The ! in [!is.na(data1$DT) stands for the logical NOT.
The trailing comma inside
Data1[!is.na(Data1$DT), ]is required because standard Rdata.frames require two dimensions to index:[rows, columns].
It is good pracitce to get into the habit of looking at your data modes and ensuring everything is as intended prior to executing an analysis. Rstudio will provide information on object structure int he top right panel. Alternatively, you can use the str function
str(data1)'data.frame': 40 obs. of 7 variables:
$ SampleID : int 1 2 3 4 5 6 7 8 9 10 ...
$ CellLine : chr "HeLa" "HEK293" "A549" "MCF-7" ...
$ Origin : chr "Human" "Human" "Human" "Human" ...
$ vials : int 4 5 7 NA 5 6 12 24 1 2 ...
$ DT : int 24 30 24 40 34 60 40 30 22 40 ...
$ rack : int 1 1 1 1 1 1 1 1 1 1 ...
$ Cell.Type...Source: chr "Cervical adenocarcinoma" "Embryonic kidney" "Lung carcinoma (alveolar type II)" "Breast adenocarcinoma ($ER^+$)" ...str(data3)'data.frame': 38 obs. of 7 variables:
$ SampleID : int 1 2 3 4 5 6 7 8 9 10 ...
$ CellLine : chr "HeLa" "HEK293" "A549" "MCF-7" ...
$ Origin : chr "Human" "Human" "Human" "Human" ...
$ vials : int 4 5 7 NA 5 6 12 24 1 2 ...
$ DT : int 24 30 24 40 34 60 40 30 22 40 ...
$ rack : int 1 1 1 1 1 1 1 1 1 1 ...
$ Cell.Type...Source: chr "Cervical adenocarcinoma" "Embryonic kidney" "Lung carcinoma (alveolar type II)" "Breast adenocarcinoma ($ER^+$)" ...If there is an error in a row or column, data modes can be changed. A few example:
data1$DT=as.numeric(data1$DT)
data1DT=as.character(data1$DT)
Re-coding values
Sometimes it is better to recode missing values to zero, in this case only in column “vials”. This can be done as follows:
data3$vials [is.na(data3$vials)]<-0
data3 SampleID CellLine Origin vials DT rack
1 1 HeLa Human 4 24 1
2 2 HEK293 Human 5 30 1
3 3 A549 Human 7 24 1
4 4 MCF-7 Human 0 40 1
5 5 MDA-MB-231 Human 5 34 1
6 6 HepG2 Human 6 60 1
7 7 Caco-2 Human 12 40 1
8 8 HT-29 Human 24 30 1
9 9 HCT116 Human 1 22 1
10 10 PC-3 Human 2 40 1
12 12 U-87 MG Human 8 36 2
13 13 SH-SY5Y Human 94 70 2
14 14 U2OS Human 6 32 2
15 15 A431 Human 2 36 2
16 16 PANC-1 Human 7 28 2
17 17 SK-OV-3 Human 8 40 2
19 19 Jurkat Human 4 26 2
20 20 K562 Human 7 30 3
21 21 THP-1 Human 9 45 3
22 22 HL-60 Human 0 36 3
23 23 Raji Human 0 24 3
24 24 U937 Human 0 32 3
25 25 Daudi Human 0 36 3
26 26 NIH/3T3 Mouse 6 26 3
27 27 CHO Chinese Hamster 0 24 3
28 28 Vero African Green Monkey 0 30 3
29 29 COS-7 African Green Monkey 1 48 3
30 30 MDCK Dog 3 24 4
31 31 BHK-21 Baby Hamster 10 18 4
32 32 RAW 264.7 Mouse 6 15 4
33 33 MC3T3-E1 Mouse 6 36 4
34 34 C2C12 Mouse 5 20 4
35 35 3T3-L1 Mouse 2 24 4
36 36 PC12 Rat 8 48 5
37 37 HaCaT Human 7 30 5
38 38 HUVEC Human 4 36 5
39 39 Sf9 Insect (S. frugiperda) 3 30 5
40 40 High Five Insect (T. ni) 10 24 5
Cell.Type...Source
1 Cervical adenocarcinoma
2 Embryonic kidney
3 Lung carcinoma (alveolar type II)
4 Breast adenocarcinoma ($ER^+$)
5 Triple-negative breast cancer
6 Hepatocellular carcinoma
7 Colorectal adenocarcinoma
8 Colorectal adenocarcinoma
9 Colorectal carcinoma
10 Prostate adenocarcinoma (metastatic)
12 Glioblastoma multiforme
13 Neuroblastoma
14 Osteosarcoma
15 Epidermoid carcinoma
16 Pancreatic ductal adenocarcinoma
17 Ovarian adenocarcinoma
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 cells