library(tidyverse)
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url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.csv"
df_crop <- read_csv(url)
## Rows: 13075 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Entity, Code
## dbl (12): Year, Wheat (tonnes per hectare), Rice (tonnes per hectare), Maize...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(df_crop)
## Rows: 13,075
## Columns: 14
## $ Entity                             <chr> "Afghanistan", "Afghanistan", "Afgh…
## $ Code                               <chr> "AFG", "AFG", "AFG", "AFG", "AFG", …
## $ Year                               <dbl> 1961, 1962, 1963, 1964, 1965, 1966,…
## $ `Wheat (tonnes per hectare)`       <dbl> 1.0220, 0.9735, 0.8317, 0.9510, 0.9…
## $ `Rice (tonnes per hectare)`        <dbl> 1.5190, 1.5190, 1.5190, 1.7273, 1.7…
## $ `Maize (tonnes per hectare)`       <dbl> 1.4000, 1.4000, 1.4260, 1.4257, 1.4…
## $ `Soybeans (tonnes per hectare)`    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Potatoes (tonnes per hectare)`    <dbl> 8.6667, 7.6667, 8.1333, 8.6000, 8.8…
## $ `Beans (tonnes per hectare)`       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Peas (tonnes per hectare)`        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Cassava (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Barley (tonnes per hectare)`      <dbl> 1.0800, 1.0800, 1.0800, 1.0857, 1.0…
## $ `Cocoa beans (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Bananas (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
df_crop$Entity <- as.factor(df_crop$Entity)
df_crop$Code <- as.factor(df_crop$Code)
glimpse(df_crop)
## Rows: 13,075
## Columns: 14
## $ Entity                             <fct> "Afghanistan", "Afghanistan", "Afgh…
## $ Code                               <fct> AFG, AFG, AFG, AFG, AFG, AFG, AFG, …
## $ Year                               <dbl> 1961, 1962, 1963, 1964, 1965, 1966,…
## $ `Wheat (tonnes per hectare)`       <dbl> 1.0220, 0.9735, 0.8317, 0.9510, 0.9…
## $ `Rice (tonnes per hectare)`        <dbl> 1.5190, 1.5190, 1.5190, 1.7273, 1.7…
## $ `Maize (tonnes per hectare)`       <dbl> 1.4000, 1.4000, 1.4260, 1.4257, 1.4…
## $ `Soybeans (tonnes per hectare)`    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Potatoes (tonnes per hectare)`    <dbl> 8.6667, 7.6667, 8.1333, 8.6000, 8.8…
## $ `Beans (tonnes per hectare)`       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Peas (tonnes per hectare)`        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Cassava (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Barley (tonnes per hectare)`      <dbl> 1.0800, 1.0800, 1.0800, 1.0857, 1.0…
## $ `Cocoa beans (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Bananas (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…

NOMOR 1

data_select <- select(df_crop, Entity, Year, `Potatoes (tonnes per hectare)`, `Cassava (tonnes per hectare)`)
data_select
## # A tibble: 13,075 × 4
##    Entity       Year `Potatoes (tonnes per hectare)` Cassava (tonnes per hecta…¹
##    <fct>       <dbl>                           <dbl>                       <dbl>
##  1 Afghanistan  1961                            8.67                          NA
##  2 Afghanistan  1962                            7.67                          NA
##  3 Afghanistan  1963                            8.13                          NA
##  4 Afghanistan  1964                            8.6                           NA
##  5 Afghanistan  1965                            8.8                           NA
##  6 Afghanistan  1966                            9.07                          NA
##  7 Afghanistan  1967                            9.8                           NA
##  8 Afghanistan  1968                           10                             NA
##  9 Afghanistan  1969                           10.2                           NA
## 10 Afghanistan  1970                            9.54                          NA
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹​`Cassava (tonnes per hectare)`

NOMOR 2

select(df_crop, -c(`Soybeans (tonnes per hectare)`, `Beans (tonnes per hectare)`, `Peas (tonnes per hectare)`))
## # A tibble: 13,075 × 11
##    Entity      Code   Year `Wheat (tonnes per hectare)` Rice (tonnes per hecta…¹
##    <fct>       <fct> <dbl>                        <dbl>                    <dbl>
##  1 Afghanistan AFG    1961                        1.02                      1.52
##  2 Afghanistan AFG    1962                        0.974                     1.52
##  3 Afghanistan AFG    1963                        0.832                     1.52
##  4 Afghanistan AFG    1964                        0.951                     1.73
##  5 Afghanistan AFG    1965                        0.972                     1.73
##  6 Afghanistan AFG    1966                        0.867                     1.52
##  7 Afghanistan AFG    1967                        1.12                      1.92
##  8 Afghanistan AFG    1968                        1.16                      1.95
##  9 Afghanistan AFG    1969                        1.19                      1.98
## 10 Afghanistan AFG    1970                        0.956                     1.81
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹​`Rice (tonnes per hectare)`
## # ℹ 6 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>,
## #   `Cassava (tonnes per hectare)` <dbl>, `Barley (tonnes per hectare)` <dbl>,
## #   `Cocoa beans (tonnes per hectare)` <dbl>,
## #   `Bananas (tonnes per hectare)` <dbl>

NOMOR 3

df_crop %>% filter(Entity == "Indonesia", `Rice (tonnes per hectare)` < 2) %>% select(Year)
## # A tibble: 7 × 1
##    Year
##   <dbl>
## 1  1961
## 2  1962
## 3  1963
## 4  1964
## 5  1965
## 6  1966
## 7  1967

NOMOR 4

df_crop %>% filter(`Wheat (tonnes per hectare)` > 5, Year >= 2000) %>% distinct(Entity)
## # A tibble: 36 × 1
##    Entity         
##    <fct>          
##  1 Austria        
##  2 Belgium        
##  3 Bulgaria       
##  4 Central America
##  5 Chile          
##  6 China          
##  7 Croatia        
##  8 Czech Republic 
##  9 Denmark        
## 10 Eastern Asia   
## # ℹ 26 more rows

NMOR 5

df_crop %>% filter(Entity %in% c("Indonesia", "Malaysia"), Year == 2015)
## # A tibble: 2 × 14
##   Entity    Code   Year `Wheat (tonnes per hectare)` `Rice (tonnes per hectare)`
##   <fct>     <fct> <dbl>                        <dbl>                       <dbl>
## 1 Indonesia IDN    2015                           NA                        5.34
## 2 Malaysia  MYS    2015                           NA                        4.02
## # ℹ 9 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Soybeans (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>, `Beans (tonnes per hectare)` <dbl>,
## #   `Peas (tonnes per hectare)` <dbl>, `Cassava (tonnes per hectare)` <dbl>,
## #   `Barley (tonnes per hectare)` <dbl>,
## #   `Cocoa beans (tonnes per hectare)` <dbl>,
## #   `Bananas (tonnes per hectare)` <dbl>

NOMOR 6

df_crop %>%
  filter(Year == 2020) %>%
  filter('Maize (tonnes per hectare)' == 
           min('Maize (tonnes per hectare)', na.rm = TRUE)) %>%
  select(Entity, 'Maize (tonnes per hectare)')
## # A tibble: 0 × 2
## # ℹ 2 variables: Entity <fct>, Maize (tonnes per hectare) <dbl>

NOMOR 7

df_crop %>% 
  filter(Entity == 'Indonesia') %>% 
  arrange(desc('Potatoes (tonnes per hectare)'))
## # A tibble: 58 × 14
##    Entity    Code   Year `Wheat (tonnes per hectare)` Rice (tonnes per hectare…¹
##    <fct>     <fct> <dbl>                        <dbl>                      <dbl>
##  1 Indonesia IDN    1961                           NA                       1.76
##  2 Indonesia IDN    1962                           NA                       1.79
##  3 Indonesia IDN    1963                           NA                       1.72
##  4 Indonesia IDN    1964                           NA                       1.76
##  5 Indonesia IDN    1965                           NA                       1.77
##  6 Indonesia IDN    1966                           NA                       1.77
##  7 Indonesia IDN    1967                           NA                       1.76
##  8 Indonesia IDN    1968                           NA                       2.14
##  9 Indonesia IDN    1969                           NA                       2.25
## 10 Indonesia IDN    1970                           NA                       2.38
## # ℹ 48 more rows
## # ℹ abbreviated name: ¹​`Rice (tonnes per hectare)`
## # ℹ 9 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Soybeans (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>, `Beans (tonnes per hectare)` <dbl>,
## #   `Peas (tonnes per hectare)` <dbl>, `Cassava (tonnes per hectare)` <dbl>,
## #   `Barley (tonnes per hectare)` <dbl>, …

NOMOR 8

df_crop %>% 
  mutate(Rice_Status = ifelse('Wheat (tonnes per hectare)' > 4, "Tinggi", "Rendah")) %>% select(Code,Year,Rice_Status)
## # A tibble: 13,075 × 3
##    Code   Year Rice_Status
##    <fct> <dbl> <chr>      
##  1 AFG    1961 Tinggi     
##  2 AFG    1962 Tinggi     
##  3 AFG    1963 Tinggi     
##  4 AFG    1964 Tinggi     
##  5 AFG    1965 Tinggi     
##  6 AFG    1966 Tinggi     
##  7 AFG    1967 Tinggi     
##  8 AFG    1968 Tinggi     
##  9 AFG    1969 Tinggi     
## 10 AFG    1970 Tinggi     
## # ℹ 13,065 more rows

NOMOR 9

df_crop %>% 
  filter(Entity == 'Indonesia') %>% 
  summarise('Bananas (tonnes per hectare)' = mean('Bananas (tonnes per hectare)', na.rm = TRUE))
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `Bananas (tonnes per hectare) = mean("Bananas (tonnes per
##   hectare)", na.rm = TRUE)`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA
## # A tibble: 1 × 1
##   `Bananas (tonnes per hectare)`
##                            <dbl>
## 1                             NA

NOMOR 10

df_crop %>% 
  filter(Year >= 2010) %>%
  group_by(Entity) %>% 
  summarise('Simpangan Baku Jagung' = sd('Maize (tonnes per hectare)', na.rm = TRUE)) %>%
  arrange(desc('Simpangan Baku Jagung'))
## Warning: There were 242 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `Simpangan Baku Jagung = sd("Maize (tonnes per hectare)", na.rm
##   = TRUE)`.
## ℹ In group 1: `Entity = "Afghanistan"`.
## Caused by warning in `var()`:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 241 remaining warnings.
## # A tibble: 242 × 2
##    Entity              `Simpangan Baku Jagung`
##    <fct>                                 <dbl>
##  1 Afghanistan                              NA
##  2 Africa                                   NA
##  3 Albania                                  NA
##  4 Algeria                                  NA
##  5 American Samoa                           NA
##  6 Americas                                 NA
##  7 Angola                                   NA
##  8 Antigua and Barbuda                      NA
##  9 Argentina                                NA
## 10 Armenia                                  NA
## # ℹ 232 more rows

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