Getting an overview

# install.packages("tidyverse")
# install.packages("here")

library(tidyverse)
library(here)

athletes <- readRDS(file = here::here("raw-data", "athletes.rds"))
View(athletes)
head(athletes)
  NOC     ID                  Name Sex Age Height Weight        Team
1 AFG 132181           Najam Yahya   M  NA     NA     NA Afghanistan
2 AFG  87371 Ahmad Jahan Nuristani   M  NA     NA     NA Afghanistan
3 AFG  44977     Mohammad Halilula   M  28    163     57 Afghanistan
4 AFG    502     Ahmad Shah Abouwi   M  NA     NA     NA Afghanistan
5 AFG 109153    Shakar Khan Shakar   M  24     NA     74 Afghanistan
6 AFG  29626  Sultan Mohammad Dost   M  28    168     73 Afghanistan
        Games Year Season      City     Sport
1 1956 Summer 1956 Summer Melbourne    Hockey
2 1948 Summer 1948 Summer    London    Hockey
3 1980 Summer 1980 Summer    Moskva Wrestling
4 1956 Summer 1956 Summer Melbourne    Hockey
5 1964 Summer 1964 Summer     Tokyo Wrestling
6 1960 Summer 1960 Summer      Roma Wrestling
                                    Event Medal      Region
1                     Hockey Men's Hockey  <NA> Afghanistan
2                     Hockey Men's Hockey  <NA> Afghanistan
3 Wrestling Men's Bantamweight, Freestyle  <NA> Afghanistan
4                     Hockey Men's Hockey  <NA> Afghanistan
5 Wrestling Men's Welterweight, Freestyle  <NA> Afghanistan
6 Wrestling Men's Welterweight, Freestyle  <NA> Afghanistan
str(athletes)
'data.frame':   270767 obs. of  16 variables:
 $ NOC   : chr  "AFG" "AFG" "AFG" "AFG" ...
 $ ID    : int  132181 87371 44977 502 109153 29626 1076 121376 80210 87374 ...
 $ Name  : chr  "Najam Yahya" "Ahmad Jahan Nuristani" "Mohammad Halilula" "Ahmad Shah Abouwi" ...
 $ Sex   : chr  "M" "M" "M" "M" ...
 $ Age   : int  NA NA 28 NA 24 28 28 NA NA NA ...
 $ Height: int  NA NA 163 NA NA 168 NA NA NA NA ...
 $ Weight: num  NA NA 57 NA 74 73 NA NA 57 NA ...
 $ Team  : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ Games : chr  "1956 Summer" "1948 Summer" "1980 Summer" "1956 Summer" ...
 $ Year  : int  1956 1948 1980 1956 1964 1960 1936 1956 1972 1956 ...
 $ Season: chr  "Summer" "Summer" "Summer" "Summer" ...
 $ City  : chr  "Melbourne" "London" "Moskva" "Melbourne" ...
 $ Sport : chr  "Hockey" "Hockey" "Wrestling" "Hockey" ...
 $ Event : chr  "Hockey Men's Hockey" "Hockey Men's Hockey" "Wrestling Men's Bantamweight, Freestyle" "Hockey Men's Hockey" ...
 $ Medal : chr  NA NA NA NA ...
 $ Region: chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
summary(athletes)
        NOC               ID                Name               Sex        
 Length   :270767   Min.   :     1   Length   :270767   Length   :270767  
 N.unique :   229   1st Qu.: 34630   N.unique :134566   N.unique :     2  
 N.blank  :     0   Median : 68187   N.blank  :     0   N.blank  :     0  
 Min.nchar:     3   Mean   : 68229   Min.nchar:     2   Min.nchar:     1  
 Max.nchar:     3   3rd Qu.:102066   Max.nchar:   108   Max.nchar:     1  
                    Max.   :135571                                        

      Age            Height          Weight              Team       
 Min.   :10.00   Min.   :127.0   Min.   : 25.00   Length   :270767  
 1st Qu.:21.00   1st Qu.:168.0   1st Qu.: 60.00   N.unique :  1179  
 Median :24.00   Median :175.0   Median : 70.00   N.blank  :     0  
 Mean   :25.56   Mean   :175.3   Mean   : 70.71   Min.nchar:     2  
 3rd Qu.:28.00   3rd Qu.:183.0   3rd Qu.: 79.00   Max.nchar:    47  
 Max.   :97.00   Max.   :226.0   Max.   :214.00                     
 NAs    :9462    NAs    :60083   NAs    :62785                      
       Games             Year            Season              City       
 Length   :270767   Min.   :1896   Length   :270767   Length   :270767  
 N.unique :    51   1st Qu.:1960   N.unique :     2   N.unique :    42  
 N.blank  :     0   Median :1988   N.blank  :     0   N.blank  :     0  
 Min.nchar:    11   Mean   :1978   Min.nchar:     6   Min.nchar:     4  
 Max.nchar:    11   3rd Qu.:2002   Max.nchar:     6   Max.nchar:    22  
                    Max.   :2016                                        

       Sport              Event              Medal              Region      
 Length   :270767   Length   :270767   Length   :270767   Length   :270767  
 N.unique :    66   N.unique :   765   N.unique :     3   N.unique :   205  
 N.blank  :     0   N.blank  :     0   N.blank  :     0   N.blank  :     0  
 Min.nchar:     4   Min.nchar:    15   Min.nchar:     4   Min.nchar:     2  
 Max.nchar:    25   Max.nchar:    85   Max.nchar:     6   Max.nchar:    32  
                                       NAs      :230993   NAs      :    21  

---
title: "Getting an overview"
---

::: {.callout-caution icon="false" collapse="true"}
## Previous code

```{r message=FALSE}
# install.packages("tidyverse")
# install.packages("here")

library(tidyverse)
library(here)

athletes <- readRDS(file = here::here("raw-data", "athletes.rds"))
```
:::

Before starting to do something with your data, it is always a good idea to get an overview. Our goal is to answer questions in the line of:

1.  Which variables does our data have?
2.  How many rows/columns does our data frame have? If we have a list, how long is it, what is saved within?
3.  What types do our variables have (are they numeric, character ...)? Do we have to transform them before we can work with them?
4.  Do we have any [missing values](../missing/missing.qmd)?

To answer these questions, we have different tools at our disposal:

## `View()`

`View()` will open the data set Excel-style in a new window:

```{r, eval = FALSE}
View(athletes)
```

In this window we can sort and filter, which makes it a pretty useful tool.

## `head()`

Head helps you to get an overview of the data frame, as it prints the first six rows into your console:

```{r}
head(athletes)
```

## `str()`

This one is actually my favorite, as for bigger data sets it is often more feasible to only look at the structure and not the whole data set. It looks a bit different to what we are used to though:

```{r}
str(athletes)
```

Here, the column names are printed on the left side, followed by the [type](https://lmu-osc.github.io/introduction-to-R/qmd/data-structures/data-structures.html#data-types) of the column and then the first few values of each column. We can also see at the top that this object is a data frame with `r nrow(athletes)` rows and `r ncol(athletes)` columns.

## `summary()`

Finally, to get a more thourough overview of our variables, we can use `summary()`:

```{r}
summary(athletes)
```

For numeric columns we get their minimum and maximum, median and mean, as well as the first and third quantile. In case of [missing values](../missing/missing.qmd) (`NAs`) their number is printed at the bottom (e.g., look at the `Age` column). We will look at how to deal with missings soon, but first we have to talk about [subsetting data](../subsetting/subsetting.qmd).