Data Frame

Let’s create a data frame using data.frame():

> df <- data.frame(person=c("Ripon","Mina","Meghla","Ridoy","Saad"),
+                  age=c(22,20,17,24,19),
+                  gender=factor(c("M","F","F","M","M")))
> df

  person age gender
1  Ripon  22      M
2   Mina  20      F
3 Meghla  17      F
4  Ridoy  24      M
5   Saad  19      M

Structure

To get the structure of data frame use str():

> str(df)

'data.frame':   5 obs. of  3 variables:
 $ person: chr  "Ripon" "Mina" "Meghla" "Ridoy" ...
 $ age   : num  22 20 17 24 19
 $ gender: Factor w/ 2 levels "F","M": 2 1 1 2 2

Summary Statistics

To know the summary statistics of the data frame use summary():

> summary(df)

    person               age       gender
 Length:5           Min.   :17.0   F:2   
 Class :character   1st Qu.:19.0   M:3   
 Mode  :character   Median :20.0         
                    Mean   :20.4         
                    3rd Qu.:22.0         
                    Max.   :24.0

Dimension, number of row and number of column

> dim(df)

[1] 5 3

> nrow(df)

[1] 5

> ncol(df)

[1] 3

Change Factor Values

To change the factor values of a colum-
Let’s change F to Female and M to Male. Here we can see gender has two levels:

> df$gender

[1] M F F M M
Levels: F M

> levels(df$gender)

[1] "F" "M"

Now simply replace the levels as desired:

> levels(df$gender) <- c("Female","Male")

Let’s see the result:

> df

  person age gender
1  Ripon  22   Male
2   Mina  20 Female
3 Meghla  17 Female
4  Ridoy  24   Male
5   Saad  19   Male

Column Selection

Select as vector

> df$age

[1] 22 20 17 24 19

> df[,'age']   # notice comma ( df[row,col] )

[1] 22 20 17 24 19

> df[,2]

[1] 22 20 17 24 19

Select as data frame

> df[2] # no comma for selecting 2nd column

> df['age']

Selecting multiple columns

> df[c('person','age')] # notice: no comma

  person age
1  Ripon  22
2   Mina  20
3 Meghla  17
4  Ridoy  24
5   Saad  19

> df[c(1,2)]

  person age
1  Ripon  22
2   Mina  20
3 Meghla  17
4  Ridoy  24
5   Saad  19

Row Selection

> df[2,]  # Selecting single row

  person age gender
2   Mina  20 Female

> df[c(3:5),] # notice: comma

  person age gender
3 Meghla  17 Female
4  Ridoy  24   Male
5   Saad  19   Male

Conditional Selection

Selecting observations with age greater than 18:

> df[df$age > 18,]  # notice comma

  person age gender
1  Ripon  22   Male
2   Mina  20 Female
4  Ridoy  24   Male
5   Saad  19   Male

Selecting only Males:

> df[df$gender == 'M',]

[1] person age    gender
<0 rows> (or 0-length row.names)

Selecting Males and age greater than 18:

> df[df$gender == 'M' & df$age > 18,]

[1] person age    gender
<0 rows> (or 0-length row.names)

Selecting Males and age greater than 18 and removing the gender column:

> df[df$gender == 'M' & df$age > 18,-3]

[1] person age   
<0 rows> (or 0-length row.names)

subset() function

Selecting Males and age greater than 18 and removing the gender column using subset():

> subset(x= df, gender=='M', select=-gender)

[1] person age   
<0 rows> (or 0-length row.names)

> subset(x= df, age>18 & gender=='M', select=-gender)

[1] person age   
<0 rows> (or 0-length row.names)

Another clever way to do this is using column names by names():

> names(df)

[1] "person" "age"    "gender"

> names(df) %in% 'gender' #checks which col. name matches 'gender'

[1] FALSE FALSE  TRUE

> !names(df) %in% 'gender'

[1]  TRUE  TRUE FALSE

> df[df$age > 18, !(names(df) %in% 'gender')] #selects only the TRUE columns

  person age
1  Ripon  22
2   Mina  20
4  Ridoy  24
5   Saad  19

Column Bind (cbind)

cbind() can be used to bind two different datasets by column.
Let’s make two data frames d1 and d2 with same number of rows:

> SL<-1:6
> name<-c('Abdul','Rafiq','Selim','Ataur','Moin','Lara')
> d1<-data.frame(SL,name)
> d1

  SL  name
1  1 Abdul
2  2 Rafiq
3  3 Selim
4  4 Ataur
5  5  Moin
6  6  Lara

> marks<-c(67,78,80,70,30,80)
> posi<-c(4,2,1,3,5,1)
> d2<-data.frame(marks,posi)
> d2

  marks posi
1    67    4
2    78    2
3    80    1
4    70    3
5    30    5
6    80    1

Now simply bind these two data frames using cbind():

> cb<-cbind(d1,d2)
> cb

  SL  name marks posi
1  1 Abdul    67    4
2  2 Rafiq    78    2
3  3 Selim    80    1
4  4 Ataur    70    3
5  5  Moin    30    5
6  6  Lara    80    1

Let’s see the class of cb:

> class(cb)

[1] "data.frame"

Again let’s say we have the following vectors

> person <- c("Ripon","Mina","Chris","Shad")
> age <- c(22,20,24,19)
> gender <- c("Male","Female","Male","Male")

In this case we can use cbind (column bind) to bind these vectors into a metrix and then converting them using as.data.frame() into a data frame:

> dfm<-cbind(person,age,gender)
> dfm

     person  age  gender  
[1,] "Ripon" "22" "Male"  
[2,] "Mina"  "20" "Female"
[3,] "Chris" "24" "Male"  
[4,] "Shad"  "19" "Male"

> class(dfm)

[1] "matrix" "array"

So the result comes out as a 2D array. Using as.data.frame():

> dfm<-as.data.frame(dfm)
> dfm

  person age gender
1  Ripon  22   Male
2   Mina  20 Female
3  Chris  24   Male
4   Shad  19   Male

> class(dfm)

[1] "data.frame"

Row Bind (Row Bind)

rbind can be used in binding different data frames with same number of columns.
Let’s make two data frame:

> SL<-1:3
> name<-c('Abdul','Rafiq','Selim')
> marks<-c(67,78,80)
> posi<-c(4,2,1)
> df_from_source_1<-data.frame(SL,name,marks,posi)
> df_from_source_1

  SL  name marks posi
1  1 Abdul    67    4
2  2 Rafiq    78    2
3  3 Selim    80    1

> SL<-4:6
> name<-c('Ataur','Moin','Lara')
> marks<-c(70,30,80)
> posi<-c(3,5,1)
> df_from_source_2<-data.frame(SL,name,marks,posi)
> df_from_source_2

  SL  name marks posi
1  4 Ataur    70    3
2  5  Moin    30    5
3  6  Lara    80    1

“Notice that the columns name must match and the number of columns must be equal”
Now bind these data frames by row using rbind():

> rdf<- rbind(df_from_source_1,df_from_source_2)
> rdf

  SL  name marks posi
1  1 Abdul    67    4
2  2 Rafiq    78    2
3  3 Selim    80    1
4  4 Ataur    70    3
5  5  Moin    30    5
6  6  Lara    80    1

Add column names to search path

Using the function attach() this can be done. For example take the data frame rdf that we created above -

> rdf

  SL  name marks posi
1  1 Abdul    67    4
2  2 Rafiq    78    2
3  3 Selim    80    1
4  4 Ataur    70    3
5  5  Moin    30    5
6  6  Lara    80    1

If we want to select a column as vector we would need to do it using dataframename$columnname -

> rdf$name

[1] "Abdul" "Rafiq" "Selim" "Ataur" "Moin"  "Lara"

If we run only name we will get an Error saying “Error: object ‘name’ not found”.

By attaching the data frame we can do the work easily-

> attach(rdf)
> name

[1] "Ataur" "Moin"  "Lara"

> marks

[1] 70 30 80

Using search we can see everything that is in the current search path of the global environment -

> search()

 [1] ".GlobalEnv"        "rdf"               "package:stats"    
 [4] "package:graphics"  "package:grDevices" "package:utils"    
 [7] "package:datasets"  "package:methods"   "Autoloads"        
[10] "package:base"

To detach anything from the search path, for example say the rdf data frame -

> detach("rdf", unload=T)

Now rdf is not in the search path -

> search()

[1] ".GlobalEnv"        "package:stats"     "package:graphics" 
[4] "package:grDevices" "package:utils"     "package:datasets" 
[7] "package:methods"   "Autoloads"         "package:base"

And we cannot call any column by the column name only. We have to use $ this method again.

Joining two data frames - merge

Let’s create two data frames:

> # data frame 1
> df1 = data.frame(CustomerId = c(1:6), 
+                  Product = c("Oven","Television","Mobile","WashingMachine","Lightings","Ipad"))
> df1

  CustomerId        Product
1          1           Oven
2          2     Television
3          3         Mobile
4          4 WashingMachine
5          5      Lightings
6          6           Ipad

> # data frame 2
> df2 = data.frame(CustomerId = c(1, 4, 6, 8), 
+                  State = c("California","Santiago","Texas","Indiana")) 
> df2

  CustomerId      State
1          1 California
2          4   Santiago
3          6      Texas
4          8    Indiana

Inner Join

Joins all the observations that match in both data frames by CustomerId

> merge(x=df1, y=df2, by="CustomerId")

  CustomerId        Product      State
1          1           Oven California
2          4 WashingMachine   Santiago
3          6           Ipad      Texas

Outer Join

Joins all the observations by CustomerId

> merge(x=df1, y=df2, by="CustomerId", all=TRUE)

  CustomerId        Product      State
1          1           Oven California
2          2     Television       <NA>
3          3         Mobile       <NA>
4          4 WashingMachine   Santiago
5          5      Lightings       <NA>
6          6           Ipad      Texas
7          8           <NA>    Indiana

Left Join

Joins all the observations of df1 to df2 by CustomerId

> merge(x=df1, y=df2, by="CustomerId", all.x=TRUE)

  CustomerId        Product      State
1          1           Oven California
2          2     Television       <NA>
3          3         Mobile       <NA>
4          4 WashingMachine   Santiago
5          5      Lightings       <NA>
6          6           Ipad      Texas

Right Join

Joins df1 to all the observations of df2 by CustomerId

> merge(x=df1,y=df2,by="CustomerId",all.y=TRUE)

  CustomerId        Product      State
1          1           Oven California
2          4 WashingMachine   Santiago
3          6           Ipad      Texas
4          8           <NA>    Indiana

Cross join in R

A Cross Join (also sometimes known as a Cartesian Join) results in every row of one table being joined to every row of another table

> merge(x = df1, y = df2, by=NULL)

   CustomerId.x        Product CustomerId.y      State
1             1           Oven            1 California
2             2     Television            1 California
3             3         Mobile            1 California
4             4 WashingMachine            1 California
5             5      Lightings            1 California
6             6           Ipad            1 California
7             1           Oven            4   Santiago
8             2     Television            4   Santiago
9             3         Mobile            4   Santiago
10            4 WashingMachine            4   Santiago
11            5      Lightings            4   Santiago
12            6           Ipad            4   Santiago
13            1           Oven            6      Texas
14            2     Television            6      Texas
15            3         Mobile            6      Texas
16            4 WashingMachine            6      Texas
17            5      Lightings            6      Texas
18            6           Ipad            6      Texas
19            1           Oven            8    Indiana
20            2     Television            8    Indiana
21            3         Mobile            8    Indiana
22            4 WashingMachine            8    Indiana
23            5      Lightings            8    Indiana
24            6           Ipad            8    Indiana

Know joining and merging in data frames using dplyr package from here

> df3 <- data.frame(SL = 1:5,
+                   CustomerId = 1:5,
+                   Product = c("Oven","Television","Mobile","WashingMachine","Lightings"))
> df4 <- data.frame(SL = 392:396,
+                   CustomerId = 1:5, 
+                   Location = c("California","Santiago","Texas","Indiana","New York"))
> 
> merge(x = df3 , y = df4, by = "CustomerId")

  CustomerId SL.x        Product SL.y   Location
1          1    1           Oven  392 California
2          2    2     Television  393   Santiago
3          3    3         Mobile  394      Texas
4          4    4 WashingMachine  395    Indiana
5          5    5      Lightings  396   New York

> merge(x = df3 , y = df4, by = "CustomerId", suffixes = c("df3","df4"))

  CustomerId SLdf3        Product SLdf4   Location
1          1     1           Oven   392 California
2          2     2     Television   393   Santiago
3          3     3         Mobile   394      Texas
4          4     4 WashingMachine   395    Indiana
5          5     5      Lightings   396   New York

Calculations in data frame

colMeans

Calculate mean column wise -

> set.seed(0)
> df1 <- data.frame(a = 1:10,
+                   b = round(rnorm(10, 5, 1),1))
> df1

> colMeans(df1)

   a    b 
5.50 5.37

Add the means to the data frame -

> temp <- df1
> temp["MeanGen",] <- colMeans(df1)  # adding a new column with column wise means 
> temp

           a    b
1        1.0 6.30
2        2.0 4.70
3        3.0 6.30
4        4.0 6.30
5        5.0 5.40
6        6.0 3.50
7        7.0 4.10
8        8.0 4.70
9        9.0 5.00
10      10.0 7.40
MeanGen  5.5 5.37

rowMeans

Calculate mean row wise -

> rowMeans(df1)

 [1] 3.65 3.35 4.65 5.15 5.20 4.75 5.55 6.35 7.00 8.70

Add the means to the data frame -

> temp <- df1
> temp["MeanGen"] <- rowMeans(df1)  # adding a new row with row wise means 
> temp

    a   b MeanGen
1   1 6.3    3.65
2   2 4.7    3.35
3   3 6.3    4.65
4   4 6.3    5.15
5   5 5.4    5.20
6   6 3.5    4.75
7   7 4.1    5.55
8   8 4.7    6.35
9   9 5.0    7.00
10 10 7.4    8.70

Creating a new column can be done using the mutate function from dplyr package -

> library(dplyr)
> df1 %>% 
+   mutate(a,b,MeanDpl = rowMeans(df1))

    a   b MeanDpl
1   1 6.3    3.65
2   2 4.7    3.35
3   3 6.3    4.65
4   4 6.3    5.15
5   5 5.4    5.20
6   6 3.5    4.75
7   7 4.1    5.55
8   8 4.7    6.35
9   9 5.0    7.00
10 10 7.4    8.70

apply

> apply(df1, 2, FUN=mean)  # applies function 'mean' to 2nd dimension (columns)

   a    b 
5.50 5.37

> apply(df1, 1, FUN=mean)  # applies function to 1st dimension (rows)

 [1] 3.65 3.35 4.65 5.15 5.20 4.75 5.55 6.35 7.00 8.70

> sapply(df1, FUN=mean)  # also takes mean of columns, treating data frame like list of vectors

   a    b 
5.50 5.37

> lapply(df1, FUN=mean)  # returns a list with column wise means

$a
[1] 5.5

$b
[1] 5.37

Complete rows

> df2 <- data.frame(
+   a = rep(10,5),
+   b = round(rnorm(5,10,3)),
+   c = c(2,4,NA,8,NA)
+ )
> df2

The above data frame has two NA/ uncomplete rows. To get the data frame with complete rows run the following command -

> df2[complete.cases(df2),]

Fill missing values

tidyr/fill

> df5 <- data.frame(SL = 1:10,
+                   vals = c("a",NA,NA,"b",NA,NA,NA,"c",NA,NA))
> df5

   SL vals
1   1    a
2   2 <NA>
3   3 <NA>
4   4    b
5   5 <NA>
6   6 <NA>
7   7 <NA>
8   8    c
9   9 <NA>
10 10 <NA>

> df5n <- tidyr::fill(data = df5, vals, .direction = "down")
> cbind(df5, vals_filled = df5n$vals)

   SL vals vals_filled
1   1    a           a
2   2 <NA>           a
3   3 <NA>           a
4   4    b           b
5   5 <NA>           b
6   6 <NA>           b
7   7 <NA>           b
8   8    c           c
9   9 <NA>           c
10 10 <NA>           c

Replacing with something

> df5[is.na(df5$vals),"vals"] <- 0
> df5

Multiple Response to Dummy Variable Creation

Let’s see the data -

> Data <- read.table("multiple reponse.txt", header = T, fill = T)
> Data

   ID Response
1   1      a,b
2   2         
3   3      b,c
4   4      a,d
5   5      a,c
6   6      a,b
7   7      a,b
8   8      b,d
9   9         
10 10        a
11 11      a,b
12 12      b,c

Creating dummy variables from this column -

> Data$Responses <- as.character(Data$Response)
> # splitting the responses
> resp.split <- strsplit(Data$Responses, split = ",")
> lev <- unique(unlist(resp.split))  # taking unique values for column
> # creating dummy
> resp.dummy <- t(sapply(resp.split, 
+                        FUN = function(x) table(factor(x,
+                                                       levels=lev))))
> # assigning it to a data frame
> Data2 <- with(Data, cbind(Responses, data.frame(resp.dummy)))
> Data2

   Responses a b c d
1        a,b 1 1 0 0
2            0 0 0 0
3        b,c 0 1 1 0
4        a,d 1 0 0 1
5        a,c 1 0 1 0
6        a,b 1 1 0 0
7        a,b 1 1 0 0
8        b,d 0 1 0 1
9            0 0 0 0
10         a 1 0 0 0
11       a,b 1 1 0 0
12       b,c 0 1 1 0

This solution of dummy variable creation from multiple responses is taken from here.

---
title: "Data Frame"
author: "MD AHSANUL ISLAM"
output:
  html_document:
    toc: yes
    toc_float: yes
    toc_depth: 4
    theme: readable
    code_download: yes
---
```{r, include=FALSE}
knitr::opts_chunk$set(
  comment = "", prompt = TRUE, message=F, warning=F
)
```

---

Let's create a data frame using `data.frame()`:
```{r}
df <- data.frame(person=c("Ripon","Mina","Meghla","Ridoy","Saad"),
                 age=c(22,20,17,24,19),
                 gender=factor(c("M","F","F","M","M")))
df
```   


---

### Structure
To get the structure of data frame use `str()`:
```{r}
str(df)
```

---

### Summary Statistics
To know the summary statistics of the data frame use `summary()`:
```{r}
summary(df)
```

---

### Dimension, number of row and number of column
```{r}
dim(df)
nrow(df)
ncol(df)
```

---

### Change Factor Values
To change the factor values of a colum-   
Let's change F to Female and M to Male. Here we can see gender has two levels:
```{r}
df$gender
levels(df$gender)
```

Now simply replace the levels as desired: 

```{r}
levels(df$gender) <- c("Female","Male")
```

Let's see the result:  

```{r}
df
```

---


### Column Selection

#### Select as vector

```{r}
df$age
df[,'age']   # notice comma ( df[row,col] )
df[,2]
```

#### Select as data frame

```{r}
df[2] # no comma for selecting 2nd column
df['age']
```


### Selecting multiple columns

```{r}
df[c('person','age')] # notice: no comma
df[c(1,2)]
```

---

### Row Selection

```{r}
df[2,]  # Selecting single row
df[c(3:5),] # notice: comma
```

---

### Conditional Selection

Selecting observations with age greater than 18:
```{r}
df[df$age > 18,]  # notice comma
```
Selecting only Males:

```{r}
df[df$gender == 'M',]
```

Selecting Males and age greater than 18:

```{r}
df[df$gender == 'M' & df$age > 18,]
```

Selecting Males and age greater than 18 and removing the `gender` column:

```{r}
df[df$gender == 'M' & df$age > 18,-3]
```

#### subset() function

Selecting Males and age greater than 18 and removing the `gender` column using `subset()`:
```{r}
subset(x= df, gender=='M', select=-gender)
subset(x= df, age>18 & gender=='M', select=-gender)
```

Another clever way to do this is using column names by `names()`:

```{r}
names(df)
names(df) %in% 'gender' #checks which col. name matches 'gender'
!names(df) %in% 'gender'
df[df$age > 18, !(names(df) %in% 'gender')] #selects only the TRUE columns
```

---

### Column Bind (cbind)

`cbind`() can be used to bind two different datasets by column.   
Let's make two data frames d1 and d2 with _same number of rows_:
```{r}
SL<-1:6
name<-c('Abdul','Rafiq','Selim','Ataur','Moin','Lara')
d1<-data.frame(SL,name)
d1

marks<-c(67,78,80,70,30,80)
posi<-c(4,2,1,3,5,1)
d2<-data.frame(marks,posi)
d2
```

Now simply bind these two data frames using `cbind()`:
```{r}
cb<-cbind(d1,d2)
cb
```

Let's see the class of cb:
```{r}
class(cb)
```

---

Again let's say we have the following vectors

```{r}
person <- c("Ripon","Mina","Chris","Shad")
age <- c(22,20,24,19)
gender <- c("Male","Female","Male","Male")
```

In this case we can use `cbind` (column bind) to bind these vectors into a metrix and then converting them using `as.data.frame()` into a data frame:

```{r}
dfm<-cbind(person,age,gender)
dfm
class(dfm)
```

So the result comes out as a 2D array. Using `as.data.frame()`:

```{r}
dfm<-as.data.frame(dfm)
dfm
class(dfm)
```

---

### Row Bind (Row Bind) 

`rbind` can be used in binding different data frames with _same number of columns_.   
Let's make two data frame:
```{r}
SL<-1:3
name<-c('Abdul','Rafiq','Selim')
marks<-c(67,78,80)
posi<-c(4,2,1)
df_from_source_1<-data.frame(SL,name,marks,posi)
df_from_source_1

SL<-4:6
name<-c('Ataur','Moin','Lara')
marks<-c(70,30,80)
posi<-c(3,5,1)
df_from_source_2<-data.frame(SL,name,marks,posi)
df_from_source_2
```
"Notice that the columns name must match and the number of columns must be equal"   
Now bind these data frames by row using `rbind()`:
```{r}
rdf<- rbind(df_from_source_1,df_from_source_2)
rdf
```


---

### Add column names to search path

Using the function `attach()` this can be done. For example take the data frame `rdf` that we created above - 
```{r}
rdf
```
If we want to select a column as vector we would need to do it using `dataframename$columnname` -
```{r}
rdf$name
```
If we run only `name` we will get an Error saying "Error: object 'name' not found".  

By attaching the data frame we can do the work easily-
```{r}
attach(rdf)
name
marks
```

Using search we can see everything that is in the current search path of the global environment -
```{r}
search()
```

To detach anything from the search path, for example say the rdf data frame - 
```{r}
detach("rdf", unload=T)
```

Now rdf is not in the search path - 
```{r}
search()
```
And we cannot call any column by the column name only. We have to use $ this method again.

---


### Joining two data frames - merge

Let's create two data frames:
```{r}
# data frame 1
df1 = data.frame(CustomerId = c(1:6), 
                 Product = c("Oven","Television","Mobile","WashingMachine","Lightings","Ipad"))
df1 

# data frame 2
df2 = data.frame(CustomerId = c(1, 4, 6, 8), 
                 State = c("California","Santiago","Texas","Indiana")) 
df2 
```

---

#### Inner Join
Joins all the observations that match in both data frames by `CustomerId`
```{r}
merge(x=df1, y=df2, by="CustomerId")
```

---

#### Outer Join
Joins all the observations by `CustomerId`
```{r}
merge(x=df1, y=df2, by="CustomerId", all=TRUE)
```

---

#### Left Join
Joins all the observations of `df1` to `df2` by `CustomerId` 
```{r}
merge(x=df1, y=df2, by="CustomerId", all.x=TRUE)
```

---

#### Right Join
Joins `df1` to all the observations of `df2` by `CustomerId` 
```{r}
merge(x=df1,y=df2,by="CustomerId",all.y=TRUE)
```

---

#### Cross join in R
A Cross Join (also sometimes known as a Cartesian Join) results in every row of one table being joined to every row of another table

```{r}
merge(x = df1, y = df2, by=NULL)
```


Know joining and merging in data frames using `dplyr` package from **[here](http://www.datasciencemadesimple.com/join-in-r-merge-in-r/)**   

```{r}
df3 <- data.frame(SL = 1:5,
                  CustomerId = 1:5,
                  Product = c("Oven","Television","Mobile","WashingMachine","Lightings"))
df4 <- data.frame(SL = 392:396,
                  CustomerId = 1:5, 
                  Location = c("California","Santiago","Texas","Indiana","New York"))

merge(x = df3 , y = df4, by = "CustomerId")
merge(x = df3 , y = df4, by = "CustomerId", suffixes = c("df3","df4"))
```


---

### Calculations in data frame

#### colMeans 

Calculate mean column wise - 
```{r}
set.seed(0)
df1 <- data.frame(a = 1:10,
                  b = round(rnorm(10, 5, 1),1))
df1
colMeans(df1) 
```

Add the means to the data frame - 
```{r}
temp <- df1
temp["MeanGen",] <- colMeans(df1)  # adding a new column with column wise means 
temp
```

#### rowMeans

Calculate mean row wise - 
```{r}
rowMeans(df1)
```

Add the means to the data frame - 
```{r}
temp <- df1
temp["MeanGen"] <- rowMeans(df1)  # adding a new row with row wise means 
temp
```

Creating a new column can be done using the mutate function from dplyr package - 
```{r}
library(dplyr)
df1 %>% 
  mutate(a,b,MeanDpl = rowMeans(df1))
```

#### apply 

```{r}
apply(df1, 2, FUN=mean)  # applies function 'mean' to 2nd dimension (columns)

apply(df1, 1, FUN=mean)  # applies function to 1st dimension (rows)

sapply(df1, FUN=mean)  # also takes mean of columns, treating data frame like list of vectors
 
lapply(df1, FUN=mean)  # returns a list with column wise means
```

### Complete rows

```{r}
df2 <- data.frame(
  a = rep(10,5),
  b = round(rnorm(5,10,3)),
  c = c(2,4,NA,8,NA)
)
df2
```

The above data frame has two NA/ uncomplete rows. To get the data frame with complete rows run the following command - 
```{r}
df2[complete.cases(df2),]
```

### Fill missing values

#### tidyr/fill
```{r}
df5 <- data.frame(SL = 1:10,
                  vals = c("a",NA,NA,"b",NA,NA,NA,"c",NA,NA))
df5
```

```{r}
df5n <- tidyr::fill(data = df5, vals, .direction = "down")
cbind(df5, vals_filled = df5n$vals)
```

#### Replacing with something

```{r}
df5[is.na(df5$vals),"vals"] <- 0
df5
```

### Multiple Response to Dummy Variable Creation

Let's see the data -
```{r}
Data <- read.table("multiple reponse.txt", header = T, fill = T)
Data
```

Creating dummy variables from this column - 
```{r}
Data$Responses <- as.character(Data$Response)
# splitting the responses
resp.split <- strsplit(Data$Responses, split = ",")
lev <- unique(unlist(resp.split))  # taking unique values for column
# creating dummy
resp.dummy <- t(sapply(resp.split, 
                       FUN = function(x) table(factor(x,
                                                      levels=lev))))
# assigning it to a data frame
Data2 <- with(Data, cbind(Responses, data.frame(resp.dummy)))
Data2
```

This solution of dummy variable creation from multiple responses is taken from [here](https://stackoverflow.com/a/56264964/13323413).



Data Frame

MD AHSANUL ISLAM

Structure

Summary Statistics

Dimension, number of row and number of column

Change Factor Values

Column Selection

Select as vector

Select as data frame

Selecting multiple columns

Row Selection

Conditional Selection

subset() function

Column Bind (cbind)

Row Bind (Row Bind)

Add column names to search path

Joining two data frames - merge

Inner Join

Outer Join

Left Join

Right Join

Cross join in R

Calculations in data frame

colMeans

rowMeans

apply

Complete rows

Fill missing values

tidyr/fill

Replacing with something

Multiple Response to Dummy Variable Creation