A short description of the post.

There will be many instances in which you want to get an idea of what the people in your dataset look like. For instance, let’s take a look at the gender and levels of education in the CCES dataset. There are two variables that we will work with:

`gender`

= Gender`educ`

= Educational Attainment

```
cces_data %>% # I want to look at the cces_data
count(gender) # Show me a count of gender
```

```
gender n
1 Male 29531
2 Female 35069
```

Here’s another way to do it:

```
cces_data %>% # I want to look at the cces_data
group_by(gender) %>% # Group the data by gender
summarise(n = n()) # Give me a count
```

```
# A tibble: 2 x 2
gender n
* <ord> <int>
1 Male 29531
2 Female 35069
```

```
cces_data %>% # I want to look at the cces_data
count(educ) # Show me a count of education
```

```
educ n
1 No HS 1971
2 High school graduate 16381
3 Some college 15685
4 2-year 7169
5 4-year 14884
6 Post-grad 8510
```

```
cces_data %>% # I want to look at the cces_data
group_by(educ) %>% # Group the data by education
summarise(n = n()) # Give me a count
```

```
# A tibble: 6 x 2
educ n
* <ord> <int>
1 No HS 1971
2 High school graduate 16381
3 Some college 15685
4 2-year 7169
5 4-year 14884
6 Post-grad 8510
```

What if I want to see gender by education level? This will help us get an idea of the levels of education are consistent, or similar accross gender.

```
cces_data %>% # I want to look at the cces_data
count(gender, educ) # Show me a count of gender BY educational level
```

```
gender educ n
1 Male No HS 756
2 Male High school graduate 6642
3 Male Some college 7050
4 Male 2-year 2995
5 Male 4-year 7431
6 Male Post-grad 4657
7 Female No HS 1215
8 Female High school graduate 9739
9 Female Some college 8635
10 Female 2-year 4174
11 Female 4-year 7453
12 Female Post-grad 3853
```

```
cces_data %>% # I want to look at the cces_data
group_by(gender, educ) %>% # Group the data by gender then education
summarise(n = n()) # Show me a count of gender BY educational level
```

```
# A tibble: 12 x 3
# Groups: gender [2]
gender educ n
<ord> <ord> <int>
1 Male No HS 756
2 Male High school graduate 6642
3 Male Some college 7050
4 Male 2-year 2995
5 Male 4-year 7431
6 Male Post-grad 4657
7 Female No HS 1215
8 Female High school graduate 9739
9 Female Some college 8635
10 Female 2-year 4174
11 Female 4-year 7453
12 Female Post-grad 3853
```

This information is ok, but limited. Knowing that there are 756 males in the sample that have *No HS diploma*, is not that useful. What would be better is to know what **proportion** or **percentage** of males in the sample have no HS diploma. This is useful because the sample is nationally representative, meaning it is like a snapshot of the US population. So by using a proportion, we can get an idea of what the real US population looks like on this particular measure.

Look we can see that .22 or 22% of males in the CCES dataset have a HS diploma

```
cces_data %>% # I want to look at the cces_data
count(gender, educ) %>% # Show me a count of gender by educational level
group_by(gender) %>% # I want to group by Gender, since I'm interested in the Proportion (percentage) of MALES
mutate(proportion = n /sum(n)) # Create a variable that takes the raw count then divides it by the total for each gender.
```

```
# A tibble: 12 x 4
# Groups: gender [2]
gender educ n proportion
<ord> <ord> <int> <dbl>
1 Male No HS 756 0.0256
2 Male High school graduate 6642 0.225
3 Male Some college 7050 0.239
4 Male 2-year 2995 0.101
5 Male 4-year 7431 0.252
6 Male Post-grad 4657 0.158
7 Female No HS 1215 0.0346
8 Female High school graduate 9739 0.278
9 Female Some college 8635 0.246
10 Female 2-year 4174 0.119
11 Female 4-year 7453 0.213
12 Female Post-grad 3853 0.110
```

```
cces_data %>% # I want to look at the cces_data
group_by(gender,educ) %>% # I want to group by Gender, since I'm interested in the Proportion (percentage) of MALES
summarise( n = n()) %>% # Give me a count
mutate(proportion = n /sum(n)) # Create a variable that takes the raw count then divides it by the total for each gender.
```

```
# A tibble: 12 x 4
# Groups: gender [2]
gender educ n proportion
<ord> <ord> <int> <dbl>
1 Male No HS 756 0.0256
2 Male High school graduate 6642 0.225
3 Male Some college 7050 0.239
4 Male 2-year 2995 0.101
5 Male 4-year 7431 0.252
6 Male Post-grad 4657 0.158
7 Female No HS 1215 0.0346
8 Female High school graduate 9739 0.278
9 Female Some college 8635 0.246
10 Female 2-year 4174 0.119
11 Female 4-year 7453 0.213
12 Female Post-grad 3853 0.110
```

Somtimes you don’t need to know the full breakdown of education and you really just want to know for each gender, what level of education do **most** people in your data have? All we need to to get the level of education for each gender that has the largest proportion.

Sweet! This tells you that most of the males in the dataset have a 4-year degree, while most of the females in the dataset are high school graduates.

```
cces_data %>% # I want to look at the CCES dataset
group_by(gender, educ) %>% # Group by gender and education level
summarise(n = n()) %>% # give me a count
mutate(proportion = n /sum(n)) %>% # give me the proportion
top_n(1) # give me the top proportion for each gender
```

```
# A tibble: 2 x 4
# Groups: gender [2]
gender educ n proportion
<ord> <ord> <int> <dbl>
1 Male 4-year 7431 0.252
2 Female High school graduate 9739 0.278
```

```
cces_data %>% # I want to look at the CCES dataset
count(gender, educ) %>% # Group by gender and education level
group_by(gender) %>% # give me a count
mutate(proportion = n /sum(n)) %>% # give me the proportion
top_n(1) # give me the top proportion for each gender
```

```
# A tibble: 2 x 4
# Groups: gender [2]
gender educ n proportion
<ord> <ord> <int> <dbl>
1 Male 4-year 7431 0.252
2 Female High school graduate 9739 0.278
```

We can also do the smallest proportion too! Looks like for both males and females, those with NO HS diploma make up the smallest proportion of each group.

```
cces_data %>% # I want to look at the CCES dataset
count(gender, educ) %>% # Group by gender and education level
group_by(gender) %>% # give me a count
mutate(proportion = n /sum(n)) %>% # give me the proportion
top_n(-1) # give me the smallest proportion for each gender
```

```
# A tibble: 2 x 4
# Groups: gender [2]
gender educ n proportion
<ord> <ord> <int> <dbl>
1 Male No HS 756 0.0256
2 Female No HS 1215 0.0346
```

For attribution, please cite this work as

Shah (2021, April 7). Sono Shah: Using dplyr with the CCES. Retrieved from https://www.sonoshah.com/tutorials/2021-04-07-using-dplyr-with-the-cces/

BibTeX citation

@misc{shah2021using, author = {Shah, Sono}, title = {Sono Shah: Using dplyr with the CCES}, url = {https://www.sonoshah.com/tutorials/2021-04-07-using-dplyr-with-the-cces/}, year = {2021} }