Statistical Investigations Lab (SIL)

class: center, middle, inverse, title-slide

# Statistical Investigations Lab (SIL)

---

count: false

.panel1-my_cars-auto[

```r
*library(tidyverse)
```
]
 
.panel2-my_cars-auto[

]

---
count: false

.panel1-my_cars-auto[

```r
library(tidyverse)
*library(janitor)
```
]
 
.panel2-my_cars-auto[

]

---
count: false

.panel1-my_cars-auto[

```r
library(tidyverse)
library(janitor)

*url <- "https://raw.githubusercontent.com/acammack1234/ma206/main/commander_community_ties.csv"
```
]
 
.panel2-my_cars-auto[

]

---
count: false

.panel1-my_cars-auto[

```r
library(tidyverse)
library(janitor)

url <- "https://raw.githubusercontent.com/acammack1234/ma206/main/commander_community_ties.csv"

*read_csv(file = url,
*        guess_max = 10000)
```
]
 
.panel2-my_cars-auto[

```
# A tibble: 95 × 137
   DEPARTMENT NAME          POPULATION LEG2011ID LEG2011TUR RDRLEG2011 PDCILEG11
   <chr>      <chr>              <dbl>     <dbl>      <dbl>      <dbl>     <dbl>
 1 Danané     "Danan\xe9"       104672       193       45.4       26.2      3.97
 2 Man        "Podiagouin\…      21694       196       41.5       14.2     10.8 
 3 Danané     "Mahapleu"         44368       194       39.8       11.5     NA   
 4 Vavoua     "K\xe9tro-Ba…      24934       105       34.1       51.2     45.6 
 5 Vavoua     "Vavoua"          134651       105       34.1       51.2     45.6 
 6 Man        "Fagnampleu"        2967       196       41.5       14.2     10.8 
 7 Facobly    "Facobly"          22407        91        0          0        0   
 8 Man        "Man"             188704       197       51.1       53        3.18
 9 Botro      "Botro"            20337        58       64.2       NA       11.2 
10 Séguéla    "S\xe9gu\xe9…      63774       203       45.3       93.7      6.27
# … with 85 more rows, and 130 more variables: LEG2011WIN <chr>,
#   N2011WINPC <dbl>, MUN2013ID <dbl>, MUN2013TUR <dbl>, RDRMUN2013 <dbl>,
#   PDCIMUN13 <dbl>, MUN2013WIN <chr>, N2013WINPC <dbl>,
#   Ouattara2010votes <dbl>, total2010votes <dbl>, Ouattara2010vs <dbl>,
#   Primary.Ethnicity <chr>, Mining.EconActivity <chr>, PavedRoad.Access <dbl>,
#   Prefecture <chr>, Mairie <chr>, Gendarmerie.Police.Poste <chr>,
#   HealthClinic <chr>, Secondary.School <chr>, Bank <chr>, …
```
]

---
count: false

.panel1-my_cars-auto[

```r
library(tidyverse)
library(janitor)

url <- "https://raw.githubusercontent.com/acammack1234/ma206/main/commander_community_ties.csv"

read_csv(file = url,
         guess_max = 10000) %>%
* clean_names()
```
]
 
.panel2-my_cars-auto[

```
# A tibble: 95 × 137
   department name          population leg2011id leg2011tur rdrleg2011 pdcileg11
   <chr>      <chr>              <dbl>     <dbl>      <dbl>      <dbl>     <dbl>
 1 Danané     "Danan\xe9"       104672       193       45.4       26.2      3.97
 2 Man        "Podiagouin\…      21694       196       41.5       14.2     10.8 
 3 Danané     "Mahapleu"         44368       194       39.8       11.5     NA   
 4 Vavoua     "K\xe9tro-Ba…      24934       105       34.1       51.2     45.6 
 5 Vavoua     "Vavoua"          134651       105       34.1       51.2     45.6 
 6 Man        "Fagnampleu"        2967       196       41.5       14.2     10.8 
 7 Facobly    "Facobly"          22407        91        0          0        0   
 8 Man        "Man"             188704       197       51.1       53        3.18
 9 Botro      "Botro"            20337        58       64.2       NA       11.2 
10 Séguéla    "S\xe9gu\xe9…      63774       203       45.3       93.7      6.27
# … with 85 more rows, and 130 more variables: leg2011win <chr>,
#   n2011winpc <dbl>, mun2013id <dbl>, mun2013tur <dbl>, rdrmun2013 <dbl>,
#   pdcimun13 <dbl>, mun2013win <chr>, n2013winpc <dbl>,
#   ouattara2010votes <dbl>, total2010votes <dbl>, ouattara2010vs <dbl>,
#   primary_ethnicity <chr>, mining_econ_activity <chr>,
#   paved_road_access <dbl>, prefecture <chr>, mairie <chr>,
#   gendarmerie_police_poste <chr>, health_clinic <chr>, …
```
]

---
count: false

.panel1-my_cars-auto[

```r
library(tidyverse)
library(janitor)

url <- "https://raw.githubusercontent.com/acammack1234/ma206/main/commander_community_ties.csv"

read_csv(file = url,
         guess_max = 10000) %>%
  clean_names() %>%
* drop_na(postwarinfluence_index2,
*         fn_governance_index2,
*         commanderlocal_dummy)
```
]
 
.panel2-my_cars-auto[

```
# A tibble: 93 × 137
   department name          population leg2011id leg2011tur rdrleg2011 pdcileg11
   <chr>      <chr>              <dbl>     <dbl>      <dbl>      <dbl>     <dbl>
 1 Danané     "Danan\xe9"       104672       193       45.4       26.2      3.97
 2 Man        "Podiagouin\…      21694       196       41.5       14.2     10.8 
 3 Danané     "Mahapleu"         44368       194       39.8       11.5     NA   
 4 Vavoua     "K\xe9tro-Ba…      24934       105       34.1       51.2     45.6 
 5 Vavoua     "Vavoua"          134651       105       34.1       51.2     45.6 
 6 Man        "Fagnampleu"        2967       196       41.5       14.2     10.8 
 7 Facobly    "Facobly"          22407        91        0          0        0   
 8 Man        "Man"             188704       197       51.1       53        3.18
 9 Botro      "Botro"            20337        58       64.2       NA       11.2 
10 Séguéla    "S\xe9gu\xe9…      63774       203       45.3       93.7      6.27
# … with 83 more rows, and 130 more variables: leg2011win <chr>,
#   n2011winpc <dbl>, mun2013id <dbl>, mun2013tur <dbl>, rdrmun2013 <dbl>,
#   pdcimun13 <dbl>, mun2013win <chr>, n2013winpc <dbl>,
#   ouattara2010votes <dbl>, total2010votes <dbl>, ouattara2010vs <dbl>,
#   primary_ethnicity <chr>, mining_econ_activity <chr>,
#   paved_road_access <dbl>, prefecture <chr>, mairie <chr>,
#   gendarmerie_police_poste <chr>, health_clinic <chr>, …
```
]

---
count: false

.panel1-my_cars-auto[

```r
library(tidyverse)
library(janitor)

url <- "https://raw.githubusercontent.com/acammack1234/ma206/main/commander_community_ties.csv"

read_csv(file = url,
         guess_max = 10000) %>%
  clean_names() %>%
  drop_na(postwarinfluence_index2,
          fn_governance_index2,
          commanderlocal_dummy) %>%
* select(department,
*        name,
*        postwarinfluence_index2,
*        fn_governance_index2,
*        commanderlocal_dummy,
*        fn_organizedmeetings_dummy,
*        postwarinfluence_index2,
*        ethnicity_northern)
```
]
 
.panel2-my_cars-auto[

```
# A tibble: 93 × 7
   department name            postwarinfluenc… fn_governance_i… commanderlocal_…
   <chr>      <chr>                      <dbl>            <dbl>            <dbl>
 1 Danané     "Danan\xe9"                    4                2                0
 2 Man        "Podiagouin\xe…                0                2                0
 3 Danané     "Mahapleu"                     2                2                0
 4 Vavoua     "K\xe9tro-Bass…                1                2                0
 5 Vavoua     "Vavoua"                       4                5                0
 6 Man        "Fagnampleu"                   0                2                0
 7 Facobly    "Facobly"                      2                2                1
 8 Man        "Man"                          4                6                0
 9 Botro      "Botro"                        1                4                0
10 Séguéla    "S\xe9gu\xe9la"                4                4                0
# … with 83 more rows, and 2 more variables: fn_organizedmeetings_dummy <dbl>,
#   ethnicity_northern <dbl>
```
]

---
count: false

.panel1-my_cars-auto[

```r
library(tidyverse)
library(janitor)

url <- "https://raw.githubusercontent.com/acammack1234/ma206/main/commander_community_ties.csv"

read_csv(file = url,
         guess_max = 10000) %>%
  clean_names() %>%
  drop_na(postwarinfluence_index2,
          fn_governance_index2,
          commanderlocal_dummy) %>%
  select(department,
         name,
         postwarinfluence_index2,
         fn_governance_index2,
         commanderlocal_dummy,
         fn_organizedmeetings_dummy,
         postwarinfluence_index2,
         ethnicity_northern) ->
*df
```
]
 
.panel2-my_cars-auto[

]

---

# preview

```r
df %>% skimr::skim()
```

```
Skim summary statistics
 n obs: 93 
 n variables: 7 
 group variables:

── Variable type:character ─────────────────────────────────────────────────────
   variable missing complete  n min max empty n_unique
 department       0       93 93   3  15     0       32
       name       0       93 93   3  26     0       93

── Variable type:numeric ───────────────────────────────────────────────────────
                   variable missing complete  n mean   sd p0 p25 p50 p75 p100
       commanderlocal_dummy       0       93 93 0.12 0.32  0   0   0   0    1
         ethnicity_northern       0       93 93 0.58 0.5   0   0   1   1    1
       fn_governance_index2       0       93 93 2.23 1.23  0   2   2   3    6
 fn_organizedmeetings_dummy       0       93 93 0.59 0.49  0   0   1   1    1
    postwarinfluence_index2       0       93 93 1.57 1.35  0   0   1   3    4
     hist
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```

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