# Exploratory Data Analysis on Facebook

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In this blog, I want to perform Exploratory Data Analysis with Facebook dataset. This dataset contains almost 100,000 users and it varies from age, birthday,gender, to likes, mobile likes, etc..

Well this isn’t actually a real Facebook dataset. But this pseudo data is provided by Data Analysts at Facebook. So we can be assured it’s as good as the real one. This Exploratory Data Analysis ranging from my experience from Udacity Course, Exploratory Data Analysis with R, in which I acquired the dataset. You should check it, it’s really recommended course.

Here I generate the html using Knit HTML with Rstudio. the code is as given.

# Overview

Now, to get better at analyzing at the dataset, it’s good to have all summary that we need to do this analysiz. First, I will do some basic summary to get better understand at the dataset. Here the dataset contain the words ‘dob’, which means data of birth.

``````##      userid             age            dob_day         dob_year
##  Min.   :1000008   Min.   : 13.00   Min.   : 1.00   Min.   :1900
##  1st Qu.:1298806   1st Qu.: 20.00   1st Qu.: 7.00   1st Qu.:1963
##  Median :1596148   Median : 28.00   Median :14.00   Median :1985
##  Mean   :1597045   Mean   : 37.28   Mean   :14.53   Mean   :1976
##  3rd Qu.:1895744   3rd Qu.: 50.00   3rd Qu.:22.00   3rd Qu.:1993
##  Max.   :2193542   Max.   :113.00   Max.   :31.00   Max.   :2000
##
##    dob_month         gender          tenure        friend_count
##  Min.   : 1.000   female:40254   Min.   :   0.0   Min.   :   0.0
##  1st Qu.: 3.000   male  :58574   1st Qu.: 226.0   1st Qu.:  31.0
##  Median : 6.000   NA's  :  175   Median : 412.0   Median :  82.0
##  Mean   : 6.283                  Mean   : 537.9   Mean   : 196.4
##  3rd Qu.: 9.000                  3rd Qu.: 675.0   3rd Qu.: 206.0
##  Max.   :12.000                  Max.   :3139.0   Max.   :4923.0
##                                  NA's   :2
##  Min.   :   0.0        Min.   :    0.0   Min.   :     0.0
##  1st Qu.:  17.0        1st Qu.:    1.0   1st Qu.:     1.0
##  Median :  46.0        Median :   11.0   Median :     8.0
##  Mean   : 107.5        Mean   :  156.1   Mean   :   142.7
##  3rd Qu.: 117.0        3rd Qu.:   81.0   3rd Qu.:    59.0
##  Max.   :4144.0        Max.   :25111.0   Max.   :261197.0
##
##  Min.   :    0.0   Min.   :     0.00     Min.   :    0.00
##  1st Qu.:    0.0   1st Qu.:     0.00     1st Qu.:    0.00
##  Median :    4.0   Median :     4.00     Median :    0.00
##  Mean   :  106.1   Mean   :    84.12     Mean   :   49.96
##  3rd Qu.:   46.0   3rd Qu.:    33.00     3rd Qu.:    7.00
##  Max.   :25111.0   Max.   :138561.00     Max.   :14865.00
##
##  Min.   :     0.00
##  1st Qu.:     0.00
##  Median :     2.00
##  Mean   :    58.57
##  3rd Qu.:    20.00
##  Max.   :129953.00
## ``````
``````##    userid age dob_day dob_year dob_month gender tenure friend_count
## 1 2094382  14      19     1999        11   male    266            0
## 2 1192601  14       2     1999        11 female      6            0
## 3 2083884  14      16     1999        11   male     13            0
## 4 1203168  14      25     1999        12 female     93            0
## 5 1733186  14       4     1999        12   male     82            0
## 6 1524765  14       1     1999        12   male     15            0
## 1                     0     0              0            0
## 2                     0     0              0            0
## 3                     0     0              0            0
## 4                     0     0              0            0
## 5                     0     0              0            0
## 6                     0     0              0            0
## 1                     0         0                  0
## 2                     0         0                  0
## 3                     0         0                  0
## 4                     0         0                  0
## 5                     0         0                  0
## 6                     0         0                  0``````

Lastly, we may want to plot each other variables against another so we can get a better insight.

Now let’s disscuss some of these graph.

# Female vs Male

As this dataset also contain the gender, I want to know every analysis that differentiate the male from the female. First let’s take a look at each of the gender. Let’s see the friend count for both female and male.

``````## pf\$gender: female
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##       0      37      96     242     244    4923
## --------------------------------------------------------
## pf\$gender: male
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##       0      27      74     165     182    4917``````

The median of female is better than male, because the mean in female will drag the median lefwards of the graph. The median will resistance about the outliers(friend_count hight), because the average we can say that we at least try half of our data.